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+14
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@@ -1680,8 +1681,7 @@
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-46 | | def __init__(
self,
cat_cols: List[str],
target_col: str,
@@ -1774,7 +1774,8 @@
Source code in inference_model/training/trainer.py
- 48
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+ 48
49
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@@ -1841,8 +1842,7 @@
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-116 | | def train(
self,
df_train: pd.DataFrame,
params: Optional[Dict] = None,
@@ -1962,7 +1962,8 @@
Source code in inference_model/training/trainer.py
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@@ -2005,8 +2006,7 @@
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-161
-162 | | def fit(
self,
df_train: pd.DataFrame,
df_valid: pd.DataFrame,
diff --git a/search/search_index.json b/search/search_index.json
index 27c9398..f079045 100755
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"index.html","text":"\u00b6 Basic Architecture Overview This repository is for the edge pod implementation, monitoring, and analysis in SUCCESS6G project. Solution overview \u00b6 Solution is deployed in Microk8s. Description of the components: Grafana - dashboards Ingress - expose services to the operator Prometheus - gather pod metrics InfluxDB - gather vehicular measurements and predictions MinIO - store models and training/testing data JupyterHub - develop new models MLflow - MLops, experiment and model tracking Kserve - serve inference models to predefined pods Istio - to ensure optimal traffic flow between microservices Knative - to ensure autoscaling of inference service pods Kepler - gather energy consumption data Redis - API for transfer of OBU measurements to Kubernetes TODO \u00b6 graph TB subgraph Cluster1 direction TB Worker1[PySyft Worker Node 1] end subgraph Cluster2 direction TB Worker2[PySyft Worker Node 2] end subgraph Cluster3 direction TB Worker3[PySyft Worker Node 3] end subgraph CentralCluster direction TB CentralServer[PySyft Central Server] end Worker1 <--> CentralServer Worker2 <--> CentralServer Worker3 <--> CentralServer federated learning/training in Kubernetes: PySyft [PREFERRED] focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data easy to deploy workers/clients Flower emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment more focused on iOS and Android deployment KubeFATE - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques Additional ideas \u00b6 KubeEdge deployment - same as Microk8s except with KubeEdge and Kubeflow/Kserver is swapped for Sedna implement multimodel pods e.g. by ModelMesh , or alpha feature of Kserve use Rancher to manage multi cluster Kubernetes","title":"home"},{"location":"index.html#_1","text":"Basic Architecture Overview This repository is for the edge pod implementation, monitoring, and analysis in SUCCESS6G project.","title":""},{"location":"index.html#solution-overview","text":"Solution is deployed in Microk8s. Description of the components: Grafana - dashboards Ingress - expose services to the operator Prometheus - gather pod metrics InfluxDB - gather vehicular measurements and predictions MinIO - store models and training/testing data JupyterHub - develop new models MLflow - MLops, experiment and model tracking Kserve - serve inference models to predefined pods Istio - to ensure optimal traffic flow between microservices Knative - to ensure autoscaling of inference service pods Kepler - gather energy consumption data Redis - API for transfer of OBU measurements to Kubernetes","title":"Solution overview"},{"location":"index.html#todo","text":"graph TB subgraph Cluster1 direction TB Worker1[PySyft Worker Node 1] end subgraph Cluster2 direction TB Worker2[PySyft Worker Node 2] end subgraph Cluster3 direction TB Worker3[PySyft Worker Node 3] end subgraph CentralCluster direction TB CentralServer[PySyft Central Server] end Worker1 <--> CentralServer Worker2 <--> CentralServer Worker3 <--> CentralServer federated learning/training in Kubernetes: PySyft [PREFERRED] focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data easy to deploy workers/clients Flower emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment more focused on iOS and Android deployment KubeFATE - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques","title":"TODO"},{"location":"index.html#additional-ideas","text":"KubeEdge deployment - same as Microk8s except with KubeEdge and Kubeflow/Kserver is swapped for Sedna implement multimodel pods e.g. by ModelMesh , or alpha feature of Kserve use Rancher to manage multi cluster Kubernetes","title":"Additional ideas"},{"location":"architecture_overview.html","text":"Architecture \u00b6 Basic Architecture \u00b6 Basic Architecture Single cluster architecture \u00b6 Single cluster architecture Advantages: Simplified Management: Easier to manage and maintain as there's only one cluster. Simplified network configuration and service discovery within the cluster. Resource Efficiency: Better utilization of resources since there is no need to allocate separate resources for multiple clusters. Cost-Effective: Lower operational costs due to reduced overhead in managing multiple clusters. Reduced complexity in monitoring and logging, leading to potential cost savings. Unified Security and Policy Management: Simplified implementation of security policies, access controls, and compliance measures. Consistent Environment: Ensures a consistent environment across all nodes, simplifying development and deployment processes. Disadvantages: Scalability Limitations: May face scalability issues as the number of edge devices or workloads increases. Single point of failure; if the cluster goes down, all connected devices and services are affected. Performance Bottlenecks: Potential for performance bottlenecks if the cluster becomes overloaded. Limited fault isolation; issues in one part of the cluster can impact the entire system. Geographical Constraints: Less optimal for geographically distributed edge nodes as latency might increase. May not efficiently handle diverse network conditions across different locations. Single cluster architecture (advanced) \u00b6 Single cluster architecture(advanced) Architecture improved by involving service mesh for optimal micro service traffic routing. For more info see istio documentation Multi cluster architecture \u00b6 Multi cluster architecture Multi cluster deployment that can utilize dUPF metrics (e.g. number of connected users) to decide if the application should be deployed in a neighboring micro cluster Advantages: Scalability and Fault Isolation: Better scalability by distributing workloads across multiple clusters. Improved fault isolation; problems in one cluster do not affect others. Geographical Distribution: Optimized for geographically distributed deployments, reducing latency by placing clusters closer to the edge devices. Can handle diverse network conditions more effectively. Performance Optimization: Easier to optimize performance by dedicating clusters to specific workloads or regions. Enhanced load balancing and resource distribution. Resilience and High Availability: Higher resilience and availability; failure in one cluster can be mitigated by other clusters. Enhanced disaster recovery capabilities. Disadvantages: Increased Complexity: More complex to manage and maintain multiple clusters. Higher administrative overhead in coordinating and synchronizing clusters. Resource Overhead: Requires more resources for cluster management (e.g., control planes for each cluster). Potentially higher infrastructure costs due to multiple clusters. Security and Policy Management: More complex to implement and manage security policies and compliance across multiple clusters. Potential challenges in maintaining consistent security postures across clusters. Inter-Cluster Communication: Requires robust inter-cluster communication mechanisms, which can add to the complexity. More complicated service discovery and networking between clusters. Conclusion \u00b6 The choice between single-cluster and multi-cluster Kubernetes deployments for IoT on the edge depends on factors such as scale, geographic distribution, resource availability, and management complexity. Single-cluster deployments are simpler and more cost-effective for smaller, localized environments, whereas multi-cluster deployments offer better scalability, fault isolation, and performance optimization for larger, distributed environments.","title":"architecture overview"},{"location":"architecture_overview.html#architecture","text":"","title":"Architecture"},{"location":"architecture_overview.html#basic-architecture","text":"Basic Architecture","title":"Basic Architecture"},{"location":"architecture_overview.html#single-cluster-architecture","text":"Single cluster architecture Advantages: Simplified Management: Easier to manage and maintain as there's only one cluster. Simplified network configuration and service discovery within the cluster. Resource Efficiency: Better utilization of resources since there is no need to allocate separate resources for multiple clusters. Cost-Effective: Lower operational costs due to reduced overhead in managing multiple clusters. Reduced complexity in monitoring and logging, leading to potential cost savings. Unified Security and Policy Management: Simplified implementation of security policies, access controls, and compliance measures. Consistent Environment: Ensures a consistent environment across all nodes, simplifying development and deployment processes. Disadvantages: Scalability Limitations: May face scalability issues as the number of edge devices or workloads increases. Single point of failure; if the cluster goes down, all connected devices and services are affected. Performance Bottlenecks: Potential for performance bottlenecks if the cluster becomes overloaded. Limited fault isolation; issues in one part of the cluster can impact the entire system. Geographical Constraints: Less optimal for geographically distributed edge nodes as latency might increase. May not efficiently handle diverse network conditions across different locations.","title":"Single cluster architecture"},{"location":"architecture_overview.html#single-cluster-architecture-advanced","text":"Single cluster architecture(advanced) Architecture improved by involving service mesh for optimal micro service traffic routing. For more info see istio documentation","title":"Single cluster architecture (advanced)"},{"location":"architecture_overview.html#multi-cluster-architecture","text":"Multi cluster architecture Multi cluster deployment that can utilize dUPF metrics (e.g. number of connected users) to decide if the application should be deployed in a neighboring micro cluster Advantages: Scalability and Fault Isolation: Better scalability by distributing workloads across multiple clusters. Improved fault isolation; problems in one cluster do not affect others. Geographical Distribution: Optimized for geographically distributed deployments, reducing latency by placing clusters closer to the edge devices. Can handle diverse network conditions more effectively. Performance Optimization: Easier to optimize performance by dedicating clusters to specific workloads or regions. Enhanced load balancing and resource distribution. Resilience and High Availability: Higher resilience and availability; failure in one cluster can be mitigated by other clusters. Enhanced disaster recovery capabilities. Disadvantages: Increased Complexity: More complex to manage and maintain multiple clusters. Higher administrative overhead in coordinating and synchronizing clusters. Resource Overhead: Requires more resources for cluster management (e.g., control planes for each cluster). Potentially higher infrastructure costs due to multiple clusters. Security and Policy Management: More complex to implement and manage security policies and compliance across multiple clusters. Potential challenges in maintaining consistent security postures across clusters. Inter-Cluster Communication: Requires robust inter-cluster communication mechanisms, which can add to the complexity. More complicated service discovery and networking between clusters.","title":"Multi cluster architecture"},{"location":"code_profiling.html","text":"Code profiling [5 min read] \u00b6 JupyterHub(any Kubernetes) pod \u00b6 Max RAM/CPU \u00b6 Using PromQL to query Prometheus metrics of the pod. Max RAM/CPU/GPU from prometheus_api_client import PrometheusConnect from math import ceil def get_pod_max_cpu_memory_in_prometheus( prometheus_url: str = \"http://prometheus-kube-prometheus-prometheus.monitoring:9090\", container_namespace: str = \"jupyterhub\", pod_name: str = \"jupyter-5uperpalo\", ) -> dict: \"\"\" Function to query max CPU and memory consumption of a specific pod in Kubernetes in the past 10 days. container_memory_working_set_bytes metric returns value per spawned pod id (different pods might have been spawned with the same name in the past 10 days) - only the last is checked For the specific query details see the link below: https://stackoverflow.com/questions/58747562/how-to-get-max-cpu-useage-of-a-pod-in-kubernetes-over-a-time-interval-say-30-da https://stackoverflow.com/a/66778814 Args: prometheus_url (str): Prometheus service URL container_namespace (str): Kubernetes pod namespace name pod_name (str): Kubernetes namespace name Returns: metrics (dict): dictionary with values \"\"\" def round_up(n, decimals=0): multiplier = 10**decimals return math.ceil(n * multiplier) / multiplier prom = PrometheusConnect(url=prometheus_url, disable_ssl=True) pod_name = f\"'{pod_name}'\" container_namespace = f\"'{container_namespace}'\" container_cpu_max_query = f\"max_over_time(sum(rate(container_cpu_usage_seconds_total{{namespace={container_namespace}, pod={pod_name}, container!=''}}[1m]))[10d:1m])\" container_memory_max_query = f\"max_over_time(container_memory_working_set_bytes{{namespace={container_namespace}, pod={pod_name}, container!=''}}[10d])\" cpu_max = prom.custom_query(query=container_cpu_max_query) memory_max = prom.custom_query(query=container_memory_max_query) metrics = { \"cpu\": round_up(float(cpu_max[-1][\"value\"][1]), 2), \"memory\": round_up(float(memory_max[-1][\"value\"][1])/1024**3, 2), } return metrics besteffort_pods_list = [ (\"kepler-7dfh2\", \"kepler\"), (\"nginx-ingress-microk8s-controller-h5kfj\", \"ingress\"), ] for pod, namespace in besteffort_pods_list: metrics = get_pod_max_cpu_memory_in_prometheus( container_namespace=namespace, pod_name=pod, ) print(f\"{pod} ({namespace}), {metrics}\") >>> kepler-7dfh2 (kepler), {'cpu': 0.19, 'memory': 0.07} >>> nginx-ingress-microk8s-controller-h5kfj (ingress), {'cpu': 0.12, 'memory': 0.45} Max GPU \u00b6 In case it is necessary a the Prometheus metrics can be extended by including NVIDIA DCGM exporter in exported metrics and then querying the metrics of interest using similar function to get_pod_max_cpu_memory_in_prometheus . Energy measurements \u00b6 Using Kubernetes Efficient Power Level Exporter (Kepler) . Core/Uncore measurements might not be available(only PKG) depending on Intel architecture. See RAPL section in Kepler metrics explanation. Kepler metrics from datetime import datetime from prometheus_api_client import PrometheusConnect def get_kepler_pod_stats( to_timestamp: float, from_timestamp: float, prometheus_url: str = \"http://prometheus-kube-prometheus-prometheus.monitoring:9090\", container_namespace: str = \"jupyterhub\", pod_name: str = \"jupyter-5uperpalo\", ) -> dict: \"\"\"Function to query Kepler power consumption data of specific pod in Kubernetes. # https://sustainable-computing.io/design/kepler-energy-sources/ # https://github.com/sustainable-computing-io/kepler/blob/1c397ff00b72b5cb1585d0de2cd495c73d88f07a/grafana-dashboards/Kepler-Exporter.json#L299 # https://prometheus.io/docs/prometheus/latest/querying/basics/#time-durations # [metric for metric in prom.all_metrics() if \"kepler\" in metric] Args: to_timestamp (list): 'to' timestamp from_timestamp (list): 'from' timestamp prometheus_url (str): Prometheus service url container_namespace (str): Kubernetes pod namespace name pod_name (str): Kubernetes namespace name Returns: metrics (dict): Kepler metrics of the power consumption of pod in Kubernetes \"\"\" prom = PrometheusConnect(url=prometheus_url, disable_ssl=True) pod_name = f\"'{pod_name}'\" container_namespace = f\"'{container_namespace}'\" time_range_sec = str(int(to_timestamp - from_timestamp)) container_sum_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_core_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_core_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_uncore_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_uncore_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_pkg_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_package_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_dram_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_dram_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_other_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_other_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_gpu_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_gpu_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" sum_data = prom.custom_query(query=container_sum_query) core_data = prom.custom_query(query=container_core_query) uncore_data = prom.custom_query(query=container_uncore_query) pkg_data = prom.custom_query(query=container_pkg_query) dram_data = prom.custom_query(query=container_dram_query) other_data = prom.custom_query(query=container_other_query) gpu_data = prom.custom_query(query=container_gpu_query) metrics = { \"from\": datetime.fromtimestamp(from_timestamp).strftime(\"%m/%d/%Y, %H:%M:%S\"), \"to\": datetime.fromtimestamp(to_timestamp).strftime(\"%m/%d/%Y, %H:%M:%S\"), \"sum\": float(sum_data[0][\"value\"][1]), \"core\": float(core_data[0][\"value\"][1]), \"uncore\": float(uncore_data[0][\"value\"][1]), \"pkg\": float(pkg_data[0][\"value\"][1]), \"dram\": float(dram_data[0][\"value\"][1]), \"other\": float(other_data[0][\"value\"][1]), \"gpu\": float(gpu_data[0][\"value\"][1]), } return metrics >>> # prometheus has UTC 00:00 as opposed to Barcelona UTC +02:00 >>> to_timestamp = datetime(2024, 6, 11, 8, 31).timestamp() >>> from_timestamp = datetime(2024, 6, 11, 8, 25).timestamp() >>> >>> get_kepler_pod_stats(to_timestamp=to_timestamp, from_timestamp=from_timestamp) >>> >>> {'from': '06/11/2024, 08:25:00', >>> 'to': '06/11/2024, 08:31:00', >>> 'sum': 45.333333333333336, >>> 'core': 0.0, >>> 'uncore': 0.0, >>> 'pkg': 44.48569999999987, >>> 'dram': 0.8289999999998447, >>> 'other': 0.0, >>> 'gpu': 0.0} Carbon emission monitoring \u00b6 Grafana dashboard provided by Kepler uses predefined natural gas/coal/petroleum conversions from US . See simple implemented formula here with coefficient defined here . The Energy measurements can be easily transformed to Carbon emissions using CodeCarbon methodology . Code \u00b6 RAM/CPU/GPU \u00b6 All-in-one solution by Scalene: https://github.com/plasma-umass/scalene Carbon emission monitoring: \u00b6 https://mlco2.github.io/codecarbon/usage.html# https://github.com/sb-ai-lab/Eco2AI https://github.com/lfwa/carbontracker Code Execution timing \u00b6 Timer decorator Import and prepend the time decorator to log time(into specified log file) that it takes to execute analyzed function/class, e.g.: import logging from functools import wraps from time import time def timer(func): \"\"\"Wrapper to time and log the function execution. Parameters: func: function \"\"\" @wraps(func) def timer_func(*args, **kwargs): start_time = time() value = func(*args, **kwargs) end_time = time() logging.info( f\"Finished {func.__name__} in {(end_time - start_time):.4f} seconds.\" # noqa ) return value return timer_func @timer def training(config: TrainingConfig, custom_params: CustomParameters): [Legacy] RAM \u00b6 Max RAM To get max RAM used during executition of the function import the decorator and put it above it function to log max RAM used into specified logging file, e.g.: NOTE : this applies only to functions and NOT classes or class methods. import logging from functools import wraps from memory_profiler import memory_usage def ram_usage(func): \"\"\"Wrapper to monitor and log RAM usage during the function execution. NOTE: can be applied to function but not to method of a class. Parameters: func: function \"\"\" @wraps(func) def ram_usage_func(*args, **kwargs): ram, value = memory_usage( (func, args, *kwargs), interval=1.0, retval=True, max_usage=True ) logging.info( f\"Finished {func.__name__,}. Max RAM used {(ram / 1000):.4f} GB.\" ) # noqa return value return ram_usage_func from churn_pred.code_profiling import ram_usage @ram_usage def training(config: TrainingConfig, custom_params: CustomParameters): Per line RAM usage analysis To analyze RAM usage per line of the code import profile decorator, put it above the function you want to analyze: from memory_profiler import profile @profile def training(config: TrainingConfig, custom_params: CustomParameters): , run the code using memory_profile command line util, and analyze the RAM usage, e.g.: $ python -m memory_profiler local_run.py Filename: /XXX/main.py Line # Mem usage Increment Occurrences Line Contents ============================================================= 53 230.8 MiB 230.8 MiB 1 @ram_usage 54 @profile 55 def training(config: TrainingConfig, custom_params: CustomParameters): 56 230.9 MiB 0.1 MiB 1 data_config = DataConfig.load(config.data_config) 57 230.9 MiB 0.0 MiB 1 hyperparameters = Hyperparameters.parse_obj(config.hyperparameters) 58 59 687.5 MiB 456.6 MiB 1 queries, df_data = get_data(config, custom_params) 60 687.5 MiB 0.0 MiB 1 (","title":"code profiling"},{"location":"code_profiling.html#code-profiling-5-min-read","text":"","title":"Code profiling [5 min read]"},{"location":"code_profiling.html#jupyterhubany-kubernetes-pod","text":"","title":"JupyterHub(any Kubernetes) pod"},{"location":"code_profiling.html#code","text":"","title":"Code"},{"location":"contributing.html","text":"Code \u00b6 Before making a pushing a code and making a pull request please run codestyle checks and tests ./code_style.sh pytest --doctest-modules churn_pred --cov-report xml --cov-report term --disable-pytest-warnings --cov = eda tests/ Documentation \u00b6 Documentation is created using github pages and mkdocs, see # to build locally cd docs pip install -r requirements.txt mkdocs build # to push to github pages mkdocs gh-deploy # if you want to run webserver locally mkdocs serve","title":"contributing"},{"location":"contributing.html#code","text":"Before making a pushing a code and making a pull request please run codestyle checks and tests ./code_style.sh pytest --doctest-modules churn_pred --cov-report xml --cov-report term --disable-pytest-warnings --cov = eda tests/","title":"Code"},{"location":"contributing.html#documentation","text":"Documentation is created using github pages and mkdocs, see # to build locally cd docs pip install -r requirements.txt mkdocs build # to push to github pages mkdocs gh-deploy # if you want to run webserver locally mkdocs serve","title":"Documentation"},{"location":"data_management.html","text":"Data management \u00b6 The choice of data storage and management options was influenced by the following factors: * time series data (measurements and predictions) -> time series database * easy to administer, popular solution with helm chart available by developers and Python API support -> InfluxDB * responsibility ownership of communication between vehicle and endpoint (v2x communication) on v2x measurement provider, i.e. Idneo -> Idneo choice - Redis * monitoring of energy consumption(carbonn footprint) + CPU/GPU/RAM utilization in Kubernetes -> Prometheus + Kepler + Grafana Local/edge data management \u00b6 Both the vehicle On Board Unit (OBU) and endpoint API are administered by Idneo . V2X vehicle measurements refer to the data that the vehicle uses to communicate its presence on the road. This data is essential in Intelligent Transportation Systems (ITS) to complement and provide the Cooperative Awareness (CA) service, which generates Cooperative Awareness Messages (CAM) according to the European standard ETSI, equivalent to the Basic Safety Message (BSM) of the American standard (SAE). The data shared through V2X in ITS services represents only a small portion of the data available in the vehicle's internal network Notice that generating simple V2X messages with their mandatory data, only requires the public vehicle data. However, a V2X stack has an interface where car manufacturers can insert some private data to enrich the V2X messages (these are optional fields) The data is sampled through the OBDII diagnostic port by constant polling. This data is uploaded to a transactional database Each variable has its operating range and units. For more details, see On-board diagnostics parameter definitions . For a detailed communication description, i.e. how the communication is enforced to stay in an edge node, please see networking section single cluster Kubernetes the Redis database is deployed on every edge node by use of Kubernetes pod assigning options and defining DaemonSets that will run a pod on every selected node multi-cluster Kubernetes multi-cluster environment suggests the use of a small cluster per edge location, in this case, a separate Redis database deployment in each cluster is sufficient Predictions \u00b6 If there is no responsibility ownership division an Inference Service provided by Kserve would be an endpoint for OBU. Compatibility with this scenario is assured by model predict methods . As the Redis database is an endpoint for OBU we use the Redis client channel subscription method to get the latest vehicle measurements and forward the predictions to the central Influx database. Additional notes to \"Local/edge data management\" and \"Predictions\" \u00b6 channel subscription scenario is not \"usual\" as inference service is used indirectly keeping the v2x data on the edge reduces communication overhead and if necessary for analysis it can be (i) queried from the Redis database or (ii) aggregated to InfluxDB by use of Telegram keeping the v2x data on the edge also reduces the possible security/privacy concerns caused by transferring all the data to the central location we do not use Prometheus for predictions storing as Prometheus is designed for scraping metrics and not storing data; storing functionality can improved by Thanos , but the solution is primarily designed for metrics not measurements and predictions we do not use Grafana for visualization of predictions as InfluxDB by default includes User Interface(UI) for visualization and forwarding data to Grafana would cause additional unnecessary data communication System Metrics \u00b6 We use combination of Prometheus + Kepler + Grafana to monitor resource utilization inside the cluster. This data can be later used for downscaling the solution and reducing the carbon footprint in production.","title":"data management"},{"location":"data_management.html#data-management","text":"The choice of data storage and management options was influenced by the following factors: * time series data (measurements and predictions) -> time series database * easy to administer, popular solution with helm chart available by developers and Python API support -> InfluxDB * responsibility ownership of communication between vehicle and endpoint (v2x communication) on v2x measurement provider, i.e. Idneo -> Idneo choice - Redis * monitoring of energy consumption(carbonn footprint) + CPU/GPU/RAM utilization in Kubernetes -> Prometheus + Kepler + Grafana","title":"Data management"},{"location":"data_management.html#localedge-data-management","text":"Both the vehicle On Board Unit (OBU) and endpoint API are administered by Idneo . V2X vehicle measurements refer to the data that the vehicle uses to communicate its presence on the road. This data is essential in Intelligent Transportation Systems (ITS) to complement and provide the Cooperative Awareness (CA) service, which generates Cooperative Awareness Messages (CAM) according to the European standard ETSI, equivalent to the Basic Safety Message (BSM) of the American standard (SAE). The data shared through V2X in ITS services represents only a small portion of the data available in the vehicle's internal network Notice that generating simple V2X messages with their mandatory data, only requires the public vehicle data. However, a V2X stack has an interface where car manufacturers can insert some private data to enrich the V2X messages (these are optional fields) The data is sampled through the OBDII diagnostic port by constant polling. This data is uploaded to a transactional database Each variable has its operating range and units. For more details, see On-board diagnostics parameter definitions . For a detailed communication description, i.e. how the communication is enforced to stay in an edge node, please see networking section single cluster Kubernetes the Redis database is deployed on every edge node by use of Kubernetes pod assigning options and defining DaemonSets that will run a pod on every selected node multi-cluster Kubernetes multi-cluster environment suggests the use of a small cluster per edge location, in this case, a separate Redis database deployment in each cluster is sufficient","title":"Local/edge data management"},{"location":"data_management.html#predictions","text":"If there is no responsibility ownership division an Inference Service provided by Kserve would be an endpoint for OBU. Compatibility with this scenario is assured by model predict methods . As the Redis database is an endpoint for OBU we use the Redis client channel subscription method to get the latest vehicle measurements and forward the predictions to the central Influx database.","title":"Predictions"},{"location":"data_management.html#additional-notes-to-localedge-data-management-and-predictions","text":"channel subscription scenario is not \"usual\" as inference service is used indirectly keeping the v2x data on the edge reduces communication overhead and if necessary for analysis it can be (i) queried from the Redis database or (ii) aggregated to InfluxDB by use of Telegram keeping the v2x data on the edge also reduces the possible security/privacy concerns caused by transferring all the data to the central location we do not use Prometheus for predictions storing as Prometheus is designed for scraping metrics and not storing data; storing functionality can improved by Thanos , but the solution is primarily designed for metrics not measurements and predictions we do not use Grafana for visualization of predictions as InfluxDB by default includes User Interface(UI) for visualization and forwarding data to Grafana would cause additional unnecessary data communication","title":"Additional notes to \"Local/edge data management\" and \"Predictions\""},{"location":"data_management.html#system-metrics","text":"We use combination of Prometheus + Kepler + Grafana to monitor resource utilization inside the cluster. This data can be later used for downscaling the solution and reducing the carbon footprint in production.","title":"System Metrics"},{"location":"development.html","text":"\u00b6 SUstainable and high PErformance COMputing platform Research & Development testbed. R&D Architecture steps: setup full testbed use RaspberryPis to emulate edge nodes send gathered data using Python script and target specific edge nodes (emulation of vehicle moving between base stations) test centralized vs edge computation vs federated learning query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, Testbed environment make pictures of used HW (RaspberyPis, workstation, SUPERCOM servers rack?)","title":"development"},{"location":"development.html#_1","text":"SUstainable and high PErformance COMputing platform Research & Development testbed. R&D Architecture steps: setup full testbed use RaspberryPis to emulate edge nodes send gathered data using Python script and target specific edge nodes (emulation of vehicle moving between base stations) test centralized vs edge computation vs federated learning query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, Testbed environment make pictures of used HW (RaspberyPis, workstation, SUPERCOM servers rack?)","title":""},{"location":"ml_model_development.html","text":"Machine learning model development \u00b6 Centralized training \u00b6 The project is focused on developing a production-ready solution. At the time of writing the documentation, we do not have vehicular measurements that would include labels with vehicle faults. We focus on exploring the best possible overall/engineering solutions as opposed to finding the best-performing model. Centralized training is therefore first choice for implementation. advantages: simplified data management - having all data in one place can simplify data management, preprocessing, and cleaning disadvantages: privacy concerns - ggregating data from multiple sources can lead to significant privacy issues, especially with sensitive information Online/stream vs batch \u00b6 V2X measurements form a stream of data from a vehicle to AI/ML on the edge. Unfortunately, this causes issues with the possibility of a high number(100-10,000?) streams per edge location. Until such a system is implemented we do not know the probable number of streams per edge location and we are unable to define requirements of the system that would be able to process X streams with Y data points per second. Such a system has to be able to process streams either by multi-threading or parallel processing. Also, methods like ADWIN as the window size is reset to the initial value each time vehicle changes edge device as the state of the ML model can't be migrated between locations per vehicle as of the number of vehicles and their traveling speed VS migration time of the model to a new location. For now, we pick the \"easier\" option of batch processing where (i) every sample is treated individually, or (ii) samples from the same vehicle are processed in fixed batch sizes Deep vs \"Traditional\" \u00b6 As we are dealing with strictly tabular data we focus on the tree-based model solution implementation. For detailed answer, we refer a reader to e.g. paper \"Why do tree-based models still outperform deep learning on tabular data?\" [TODO] Federated learning/training \u00b6 We implement a federated learning model to address concerns of privacy and data management in case of multi-cluster solution. advantages: enhanced privacy - raw data never leaves the local devices, reducing privacy risks reduced data transfer - only model updates (rather than raw data) are transmitted, which can be more bandwidth-efficient compliance - easier to comply with data sovereignty and privacy regulations since data remains local fault tolerance - the system can be more resilient to individual server failures since the data and computation are distributed disadvantages: complexity - implementing federated learning is more complex due to the need to manage distributed training, aggregation, and synchronization heterogeneity - variability in data quality and distribution across devices can impact model performance limited computational resources - Local devices may have limited computational power compared to centralized data centers communication overhead - while raw data isn't transferred, frequent model updates can still generate significant communication overhead, especially in large-scale systems graph TB subgraph Cluster1 direction TB Worker1[PySyft Worker Node 1] end subgraph Cluster2 direction TB Worker2[PySyft Worker Node 2] end subgraph Cluster3 direction TB Worker3[PySyft Worker Node 3] end subgraph CentralCluster direction TB CentralServer[PySyft Central Server] end Worker1 <--> CentralServer Worker2 <--> CentralServer Worker3 <--> CentralServer PySyft [PREFERRED] focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data easy to deploy workers/clients Flower emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment more focused on iOS and Android deployment KubeFATE - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques","title":"ML model dev"},{"location":"ml_model_development.html#machine-learning-model-development","text":"","title":"Machine learning model development"},{"location":"ml_model_development.html#centralized-training","text":"The project is focused on developing a production-ready solution. At the time of writing the documentation, we do not have vehicular measurements that would include labels with vehicle faults. We focus on exploring the best possible overall/engineering solutions as opposed to finding the best-performing model. Centralized training is therefore first choice for implementation. advantages: simplified data management - having all data in one place can simplify data management, preprocessing, and cleaning disadvantages: privacy concerns - ggregating data from multiple sources can lead to significant privacy issues, especially with sensitive information","title":"Centralized training"},{"location":"ml_model_development.html#todo-federated-learningtraining","text":"We implement a federated learning model to address concerns of privacy and data management in case of multi-cluster solution. advantages: enhanced privacy - raw data never leaves the local devices, reducing privacy risks reduced data transfer - only model updates (rather than raw data) are transmitted, which can be more bandwidth-efficient compliance - easier to comply with data sovereignty and privacy regulations since data remains local fault tolerance - the system can be more resilient to individual server failures since the data and computation are distributed disadvantages: complexity - implementing federated learning is more complex due to the need to manage distributed training, aggregation, and synchronization heterogeneity - variability in data quality and distribution across devices can impact model performance limited computational resources - Local devices may have limited computational power compared to centralized data centers communication overhead - while raw data isn't transferred, frequent model updates can still generate significant communication overhead, especially in large-scale systems graph TB subgraph Cluster1 direction TB Worker1[PySyft Worker Node 1] end subgraph Cluster2 direction TB Worker2[PySyft Worker Node 2] end subgraph Cluster3 direction TB Worker3[PySyft Worker Node 3] end subgraph CentralCluster direction TB CentralServer[PySyft Central Server] end Worker1 <--> CentralServer Worker2 <--> CentralServer Worker3 <--> CentralServer PySyft [PREFERRED] focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data easy to deploy workers/clients Flower emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment more focused on iOS and Android deployment KubeFATE - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques","title":"[TODO] Federated learning/training"},{"location":"networking.html","text":"Networking \u00b6 Description of traffic flow for simple(no Istio) Single cluster traffic flow. Vehicle data processing \u00b6 Vehicle data processing Traffic flow: Vehicle sends measurements to Vehicle API service (provided by Idneo \u2013 data preprocessing +Redis DB) transmission is received by antenna Transmission enters data(IPv4/IPv6) network Data is forwarded to the closest Kubernetes Edge node using (i) Anycast IP address, shared by all Edge Nodes, and (ii) nodePort defined in NodePort service Edge Node receives the data, checks the IP table in kube-proxy, and forwards it to local Pod A as the service is configured with externalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the packet is dropped Vehicle API service forwards the data to the ML prediction service(Kserve inference) Data is forwarded to Pod B as Kserve inference service is configured with internalTrafficPolicy: Local If there is no local Pod for Kserve inference service, the packet is dropped or ML prediction service pod periodically queries local Redis DB in Vehicle API service \u2013 as Vehicle API service is also configured with internalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the queries/requests are dropped Kserve inference service pushes the data into InfluxDB Grafana periodically queries data from InfluxDB Energy consumption monitoring \u00b6 Energy consumption monitoring Traffic flow: Kepler exporter uses eBPF to probe CPU, GPU, RAM performance counters and exposes them over time via HTTP Prometheus periodically queries Kepler exporter for a new data Grafana periodically queries Prometheus for anew data Vehicle data and energy consumption monitoring \u00b6 Vehicle data and energy consumption monitoring Traffic flow: User enters Grafana FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it Grafana GUI comes up in the Users web browser ML model development/update \u00b6 ML model development/update Traffic flow: User enters MLflow FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it to Mlflow GUI where user can check current models User updates the model locally or in cloud and uploads it to MLflow API Mlflow API forwards the model to the backend database - Minio ML model deployment \u00b6 ML model deployment Traffic flow: Admin user instructs Kserve to deploy a model Kserve queries the model from Minio storage Kserve updates/\u201dserves\u201d the model in all Kserver inference Pods","title":"networking"},{"location":"networking.html#networking","text":"Description of traffic flow for simple(no Istio) Single cluster traffic flow.","title":"Networking"},{"location":"networking.html#vehicle-data-processing","text":"Vehicle data processing Traffic flow: Vehicle sends measurements to Vehicle API service (provided by Idneo \u2013 data preprocessing +Redis DB) transmission is received by antenna Transmission enters data(IPv4/IPv6) network Data is forwarded to the closest Kubernetes Edge node using (i) Anycast IP address, shared by all Edge Nodes, and (ii) nodePort defined in NodePort service Edge Node receives the data, checks the IP table in kube-proxy, and forwards it to local Pod A as the service is configured with externalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the packet is dropped Vehicle API service forwards the data to the ML prediction service(Kserve inference) Data is forwarded to Pod B as Kserve inference service is configured with internalTrafficPolicy: Local If there is no local Pod for Kserve inference service, the packet is dropped or ML prediction service pod periodically queries local Redis DB in Vehicle API service \u2013 as Vehicle API service is also configured with internalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the queries/requests are dropped Kserve inference service pushes the data into InfluxDB Grafana periodically queries data from InfluxDB","title":"Vehicle data processing"},{"location":"networking.html#energy-consumption-monitoring","text":"Energy consumption monitoring Traffic flow: Kepler exporter uses eBPF to probe CPU, GPU, RAM performance counters and exposes them over time via HTTP Prometheus periodically queries Kepler exporter for a new data Grafana periodically queries Prometheus for anew data","title":"Energy consumption monitoring"},{"location":"networking.html#vehicle-data-and-energy-consumption-monitoring","text":"Vehicle data and energy consumption monitoring Traffic flow: User enters Grafana FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it Grafana GUI comes up in the Users web browser","title":"Vehicle data and energy consumption monitoring"},{"location":"networking.html#ml-model-developmentupdate","text":"ML model development/update Traffic flow: User enters MLflow FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it to Mlflow GUI where user can check current models User updates the model locally or in cloud and uploads it to MLflow API Mlflow API forwards the model to the backend database - Minio","title":"ML model development/update"},{"location":"networking.html#ml-model-deployment","text":"ML model deployment Traffic flow: Admin user instructs Kserve to deploy a model Kserve queries the model from Minio storage Kserve updates/\u201dserves\u201d the model in all Kserver inference Pods","title":"ML model deployment"},{"location":"testing.html","text":"Real-world testing and data gathering - Parcmotor Castellol\u00ed administered by Cellnex \u00b6 Castellol\u00ed testing architecture Castellol\u00ed testing architecture legend: white - mandatory grey pattern - optional steps: prepare the Kserve model and helm charts make testing requests before the day of testing run the real-world test with vehicles in Castelloli query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, NBC environment make vehicle pictures","title":"testing"},{"location":"testing.html#real-world-testing-and-data-gathering-parcmotor-castelloli-administered-by-cellnex","text":"Castellol\u00ed testing architecture Castellol\u00ed testing architecture legend: white - mandatory grey pattern - optional steps: prepare the Kserve model and helm charts make testing requests before the day of testing run the real-world test with vehicles in Castelloli query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, NBC environment make vehicle pictures","title":"Real-world testing and data gathering - Parcmotor Castellol\u00ed administered by Cellnex"},{"location":"use_cases.html","text":"Vehicular condition monitoring with robust V2X connectivity - EXTEND \u00b6 Use Case Extend Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. However, wireless transmission of in-vehicle monitoring data comes inadvertently with challenges which involve distortions and missing data owing to the inherently shared wireless medium. Incomplete vehicular data attributed to either increased interference levels, channel attenuation phenomena or sensor malfunctioning may lead to incorrect decision-making and failure to predict maintenance actions. Addressing the degradation of transmitted V2X data is thus essential for high-fidelity estimation of the system network state and informed actions. In this context, machine learning (ML)-empowered signal reconstruction and imputation techniques are expected to obtain accurate estimates of incomplete sensor trajectories by leveraging the intrinsic spatiotemporal synergies of captured information. By deeply investigating the interplay between vehicle dynamics and fault provisioning, faulty vehicular equipment will be timely detected/predicted, improving road safety by preventing critical failures. Vehicular condition monitoring with robust V2X connectivity - DEVISE \u00b6 Use Case Devise Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. Wireless transmission of in-vehicle monitoring data is susceptible to various security threats which may destabilize system operation, degrade network performance, and potentially lead to incorrect decision-making regarding the vehicle condition status. At the same time, the pervasive integration of data-driven techniques expands the attack surface, giving rise to finely targeted, stealthier, and scalable attacks targeting both phases (training, inference) of ML techniques. The mitigation of such threats becomes imperative for the correct estimation of vehicle condition. Condition monitoring data used for subsequent predictive diagnostics should be properly secured such that they do not contain falsified information, while abnormal traffic should be detected and isolated in its entirety. In this context, appropriate security countermeasures are expected to accurately detect malicious content in the transmitted information and ensure the semantic correctness of the aggregated data for trustworthy decision-making. Vehicular condition monitoring with robust V2X connectivity - VERIFY \u00b6 Use Case Extend 01 Use Case Extend 02 Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. The limited computational capabilities of edge nodes pose significant challenges for the realization of computationally efficient V2X communication aimed for condition monitoring. Aiming to achieve dramatically reduced data processing times, over-the-air computing methods and decentralized deployments of AI models aim at rapid knowledge extraction from the vehicular measurement streams and reduction of computational overhead. In this context, over-the-air computing methods will be explored to address the computational limitations and stringent latency requirements in V2X systems by means of over-the-air aggregation. In addition, decentralized methods for predictive diagnosis of vehicles condition will be investigated to leverage the benefits of edge-computing capabilities for efficient inference, aiming at minimizing the vehicle downtime incidents. The integration of both approaches holds the promise of distributed learning parallelization over the wireless compute medium and the realization of data intensive V2X services with stringent performance requirements.","title":"use cases"},{"location":"use_cases.html#vehicular-condition-monitoring-with-robust-v2x-connectivity-extend","text":"Use Case Extend Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. However, wireless transmission of in-vehicle monitoring data comes inadvertently with challenges which involve distortions and missing data owing to the inherently shared wireless medium. Incomplete vehicular data attributed to either increased interference levels, channel attenuation phenomena or sensor malfunctioning may lead to incorrect decision-making and failure to predict maintenance actions. Addressing the degradation of transmitted V2X data is thus essential for high-fidelity estimation of the system network state and informed actions. In this context, machine learning (ML)-empowered signal reconstruction and imputation techniques are expected to obtain accurate estimates of incomplete sensor trajectories by leveraging the intrinsic spatiotemporal synergies of captured information. By deeply investigating the interplay between vehicle dynamics and fault provisioning, faulty vehicular equipment will be timely detected/predicted, improving road safety by preventing critical failures.","title":"Vehicular condition monitoring with robust V2X connectivity - EXTEND"},{"location":"use_cases.html#vehicular-condition-monitoring-with-robust-v2x-connectivity-devise","text":"Use Case Devise Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. Wireless transmission of in-vehicle monitoring data is susceptible to various security threats which may destabilize system operation, degrade network performance, and potentially lead to incorrect decision-making regarding the vehicle condition status. At the same time, the pervasive integration of data-driven techniques expands the attack surface, giving rise to finely targeted, stealthier, and scalable attacks targeting both phases (training, inference) of ML techniques. The mitigation of such threats becomes imperative for the correct estimation of vehicle condition. Condition monitoring data used for subsequent predictive diagnostics should be properly secured such that they do not contain falsified information, while abnormal traffic should be detected and isolated in its entirety. In this context, appropriate security countermeasures are expected to accurately detect malicious content in the transmitted information and ensure the semantic correctness of the aggregated data for trustworthy decision-making.","title":"Vehicular condition monitoring with robust V2X connectivity - DEVISE"},{"location":"use_cases.html#vehicular-condition-monitoring-with-robust-v2x-connectivity-verify","text":"Use Case Extend 01 Use Case Extend 02 Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. The limited computational capabilities of edge nodes pose significant challenges for the realization of computationally efficient V2X communication aimed for condition monitoring. Aiming to achieve dramatically reduced data processing times, over-the-air computing methods and decentralized deployments of AI models aim at rapid knowledge extraction from the vehicular measurement streams and reduction of computational overhead. In this context, over-the-air computing methods will be explored to address the computational limitations and stringent latency requirements in V2X systems by means of over-the-air aggregation. In addition, decentralized methods for predictive diagnosis of vehicles condition will be investigated to leverage the benefits of edge-computing capabilities for efficient inference, aiming at minimizing the vehicle downtime incidents. The integration of both approaches holds the promise of distributed learning parallelization over the wireless compute medium and the realization of data intensive V2X services with stringent performance requirements.","title":"Vehicular condition monitoring with robust V2X connectivity - VERIFY"},{"location":"idneo/datasets.html","text":"Datasets \u00b6 DS1 : Ignition on but stopped DS2 : national road. ~ 90km/h DS3 : highway. ~ 120km/h Data structure \u00b6 STAMP : Time delay in seconds after starting the log. STAMP Sensor 1.02 value 2.48 value 3.95 value","title":"datasets"},{"location":"idneo/datasets.html#datasets","text":"DS1 : Ignition on but stopped DS2 : national road. ~ 90km/h DS3 : highway. ~ 120km/h","title":"Datasets"},{"location":"idneo/datasets.html#data-structure","text":"STAMP : Time delay in seconds after starting the log. STAMP Sensor 1.02 value 2.48 value 3.95 value","title":"Data structure"},{"location":"idneo/vehicle_injector_tool.html","text":"Vehicle Injector Tool \u00b6 This tool facilitates the incorporation of real data from a vehicle's sensors, stored in csv Datasets. Its purpose is to facilitate the development of algorithms responsible for processing the information that is collected and stored in this REDIS database within the Success6G project. graph LR Client(VEHICLE
INJECTOR) -- TCP/IP
(63790 / 127.0.0.1)--> Database(REDIS
DATABASE); click Client \"https://github.com/5uperpalo/success6g-edge/blob/main/tools/vehicle/vehicle_injector.py\" click Database \"https://github.com/5uperpalo/success6g-edge/blob/main/configs/edge_redis.yaml\" Dataset[Dataset File] --> Client classDef orange fill:#f96,stroke:#333,stroke-width:2px class Dataset orange Usage \u00b6 Install the Redis database in your local development setup. docker_compose -d configs/edge_redis.yaml Verify docker container is running docker ps -a Run \u201cVehicle Injector\u201c script with dataset and station-id script: python3 vehicle_injector.py -f","title":"vehicle injector tool"},{"location":"idneo/vehicle_injector_tool.html#vehicle-injector-tool","text":"This tool facilitates the incorporation of real data from a vehicle's sensors, stored in csv Datasets. Its purpose is to facilitate the development of algorithms responsible for processing the information that is collected and stored in this REDIS database within the Success6G project. graph LR Client(VEHICLE
INJECTOR) -- TCP/IP
(63790 / 127.0.0.1)--> Database(REDIS
DATABASE); click Client \"https://github.com/5uperpalo/success6g-edge/blob/main/tools/vehicle/vehicle_injector.py\" click Database \"https://github.com/5uperpalo/success6g-edge/blob/main/configs/edge_redis.yaml\" Dataset[Dataset File] --> Client classDef orange fill:#f96,stroke:#333,stroke-width:2px class Dataset orange","title":"Vehicle Injector Tool"},{"location":"idneo/vehicle_injector_tool.html#usage","text":"Install the Redis database in your local development setup. docker_compose -d configs/edge_redis.yaml Verify docker container is running docker ps -a Run \u201cVehicle Injector\u201c script with dataset and station-id script: python3 vehicle_injector.py -f","title":"Usage"},{"location":"inference_model/eda.html","text":"Preprocess data \u00b6 analysis \u00b6 init_check \u00b6 init_check ( df , identifier = None , cat_cols = None , cont_cols = None , verbose = False , ) Procedure to check: * duplicated rows in teh dataset * general stats of numerical features * general stats of categorical features Parameters: df ( DataFrame ) \u2013 pandas dataframe identifier ( str ) \u2013 column which identifies unique user IDs cat_cols ( list ) \u2013 categorical features in the dataset cont_cols ( list ) \u2013 numerical features in the dataset Returns: duplicated_ids ( int ) \u2013 cont_cols_desc ( pd . DataFrame ) \u2013 cat_cols_desc ( pd . DataFrame ) \u2013 Source code in inference_model/eda/features/analysis.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 def init_check ( df : pd . DataFrame , identifier : Optional [ str ] = None , cat_cols : Optional [ list ] = None , cont_cols : Optional [ list ] = None , verbose : bool = False , ) -> Tuple [ int , pd . DataFrame , pd . DataFrame ]: \"\"\"Procedure to check: * duplicated rows in teh dataset * general stats of numerical features * general stats of categorical features Args: df (DataFrame): pandas dataframe identifier (str): column which identifies unique user IDs cat_cols (list): categorical features in the dataset cont_cols (list): numerical features in the dataset Returns: duplicated_ids (int): cont_cols_desc (pd.DataFrame): cat_cols_desc (pd.DataFrame): \"\"\" data = df . copy () if identifier : duplicated_ids = data [ identifier ] . duplicated () . sum () if verbose : print ( \"[CHECK] Number of duplicated ids: {} \" . format ( duplicated_ids )) else : duplicated_ids = None if cont_cols : cont_cols_f = intsec ( data . columns . values , cont_cols ) cont_cols_desc = data [ cont_cols_f ] . describe () . transpose () if verbose : print ( \"[CHECK] Numerical columns\" ) with pd . option_context ( \"display.precision\" , 2 ): print ( cont_cols_desc ) else : cont_cols_desc = None if cat_cols : cat_cols_f = intsec ( data . columns . values , cat_cols ) cat_cols_desc = data [ cat_cols_f ] . describe () . transpose () if verbose : print ( \"[CHECK] Categorical columns\" ) with pd . option_context ( \"display.precision\" , 2 ): print ( cat_cols_desc ) else : cat_cols_desc = None return duplicated_ids , cont_cols_desc , cat_cols_desc missing \u00b6 missing ( df , scale = 'linear' , plot = False ) Procedure to check fraction of missing values in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: missing_val_frac ( DataFrame ) \u2013 sorted dataframe with fraction of missing values per feature fig ( Figure ) \u2013 plot with sorted fractions of missing values in each column Source code in inference_model/eda/features/analysis.py 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 def missing ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to check fraction of missing values in the dataset. Args: df (DataFrame): pandas dataframe scale (str): y scale of the plot plot (bool): whether to output the plot Returns: missing_val_frac (DataFrame): sorted dataframe with fraction of missing values per feature fig (Figure): plot with sorted fractions of missing values in each column \"\"\" data = df . copy () missing_val_frac = data . isna () . sum () / len ( data ) missing_val_frac . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( missing_val_frac , ax , title = \"Fraction of missing values in the dataset\" , ) else : fig = None ax . set_yscale ( scale ) return missing_val_frac , fig zero \u00b6 zero ( df , scale = 'linear' , plot = False ) Procedure to check fraction of zero values in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: zero_val_frac ( DataFrame ) \u2013 sorted dataframe with fraction of '0' values per feature fig ( Figure ) \u2013 plot with sorted fractions of '0' values in each column Source code in inference_model/eda/features/analysis.py 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 def zero ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to check fraction of zero values in the dataset. Args: df (DataFrame): pandas dataframe scale (str): y scale of the plot plot (bool): whether to output the plot Returns: zero_val_frac (DataFrame): sorted dataframe with fraction of '0' values per feature fig (Figure): plot with sorted fractions of '0' values in each column \"\"\" data = df . copy () zero_val_frac = data . isin ([ 0 ]) . sum () / len ( data ) zero_val_frac . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( zero_val_frac , ax , title = \"Fraction of zero values in the dataset\" ) else : fig = None ax . set_yscale ( scale ) return zero_val_frac , fig nunique \u00b6 nunique ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their number of unique values. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted number of unique values in features Source code in inference_model/eda/features/analysis.py 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 def nunique ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their number of unique values. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted number of unique values in features \"\"\" data = df . copy () data_nunique = data . nunique () data_nunique . sort_values ( ascending = True , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( data_nunique . dropna (), ax , title = \"Features number of unique values (NA are dropped)\" , ) else : fig = None ax . set_yscale ( scale ) return data_nunique , fig std \u00b6 std ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their variance. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted standard deviation of continuous features Source code in inference_model/eda/features/analysis.py 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def std ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their variance. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted standard deviation of continuous features \"\"\" data = df . copy () data_std = data . std () data_std . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( data_std . dropna (), ax , title = \"Features variance (NA are dropped)\" ) else : fig = None ax . set_yscale ( scale ) return data_std , fig entropy \u00b6 entropy ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their entropy. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted entropy of the features Source code in inference_model/eda/features/analysis.py 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 def entropy ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their entropy. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted entropy of the features \"\"\" data = df . copy () data = data . astype ( str ) col_entropies = data . apply ( entropy_calc , axis = 0 ) col_entropies . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( col_entropies . dropna (), ax , title = \"Features entropies (NA are dropped)\" ) else : fig = None ax . set_yscale ( scale ) return col_entropies , fig plotting \u00b6 cross_correlation \u00b6 cross_correlation ( df , n = 10 , verbose = False ) Procedure to calculate and plot cross-correlation of features in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe verbose ( bool ) \u2013 show n most correlated features n ( int ) \u2013 number correlated features in verbose output Returns: fig ( Figure ) \u2013 heatmap of continuous features cross correlations Source code in inference_model/eda/features/plotting.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def cross_correlation ( df : pd . DataFrame , n : int = 10 , verbose : bool = False , ) -> Figure : \"\"\"Procedure to calculate and plot cross-correlation of features in the dataset. Args: df (DataFrame): pandas dataframe verbose (bool): show n most correlated features n (int): number correlated features in verbose output Returns: fig (Figure): heatmap of continuous features cross correlations \"\"\" data = df . copy () # corrwith uses numpy which does not like pandas Float dtypes corr_matrix = data . astype ( float ) . corr () upper_stacked_sorted = ( corr_matrix . abs () . where ( np . triu ( np . ones ( corr_matrix . shape ), k = 1 ) . astype ( \"bool\" )) . stack () . sort_values ( ascending = False ) ) fig , ax = plt . subplots ( figsize = ( 10 , 10 )) ax . set_title ( \"Cross-correlation matrix\" , size = 20 ) sns . set ( font_scale = 1.0 ) sns . heatmap ( corr_matrix , ax = ax , cbar = True , square = True , # linewidths=0.5, fmt = \".2f\" , ) if verbose : print ( \"Top {} absolute correlations\" . format ( n )) print ( upper_stacked_sorted [: n ]) return fig distributions \u00b6 distributions ( df , low_per_cut = 0 , high_per_cut = 1 , type = \"box\" ) Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Parameters: df ( DataFrame ) \u2013 pandas dataframe low_per_cut ( float ) \u2013 lower percentile where to cut the plot for better readability high_per_cut ( float ) \u2013 higher percentile where to cut the plot for better readability type ( str ) \u2013 type of distribution plot Returns: fig ( Figure ) \u2013 ditribution plot per each feature Source code in inference_model/eda/features/plotting.py 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 def distributions ( df : pd . DataFrame , low_per_cut : float = 0 , high_per_cut : float = 1 , type : Literal [ \"box\" , \"violin\" ] = \"box\" , ) -> Figure : \"\"\"Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Args: df (DataFrame): pandas dataframe low_per_cut (float): lower percentile where to cut the plot for better readability high_per_cut (float): higher percentile where to cut the plot for better readability type (str): type of distribution plot Returns: fig (Figure): ditribution plot per each feature \"\"\" data = df . copy () cols = data . columns . values data = data . fillna ( 0 ) . astype ( float ) fig , ax = plt . subplots ( len ( cols ), 1 , figsize = ( 16 , len ( cols ) * 2 )) fig . suptitle ( \"Distribution of feature values\" , size = 20 ) for col , i in zip ( cols , range ( len ( cols ))): if type == \"violin\" : sns . violinplot ( ax = ax [ i ], x = col , orient = \"h\" , # cut=0, # showextrem=False, data = data , ) if type == \"box\" : sns . boxplot ( ax = ax [ i ], x = col , orient = \"h\" , data = data , ) ax [ i ] . set_xlim ( data [ col ] . quantile ( low_per_cut ), data [ col ] . quantile ( high_per_cut ) ) ax [ i ] . get_yaxis () . set_visible ( False ) plt . subplots_adjust ( hspace = 0.9 ) return fig analysis \u00b6 correlation \u00b6 correlation ( df , target , scale = 'linear' , plot = False ) Procedure to plot most correlated numerical features with target column. Parameters: df ( pd . DataFrame ) \u2013 pandas dataframe target ( pd . Series ) \u2013 target values scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: sorted_corr_cols ( pd . DataFrame ) \u2013 sorted dataframe with feature and target value correlation fig ( Figure ) \u2013 sorted bar plot with feature and target value correlation Source code in inference_model/eda/target/analysis.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 def correlation ( df : pd . DataFrame , target : pd . Series , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot most correlated numerical features with target column. Args: df (pd.DataFrame): pandas dataframe target (pd.Series): target values scale (str): y scale of the plot plot (bool): whether to output the plot Returns: sorted_corr_cols (pd.DataFrame): sorted dataframe with feature and target value correlation fig (Figure): sorted bar plot with feature and target value correlation \"\"\" data = df . copy () target_col = target . name correlation_df = pd . concat ([ data , target ], axis = 1 ) . corr () sorted_corr_cols = correlation_df [ target_col ] . drop ( index = [ target_col ]) sorted_corr_cols . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( sorted_corr_cols , ax , title = f \"Sorted features by their correlation with { target_col } \" , ) else : fig = None ax . set_yscale ( scale ) return sorted_corr_cols , fig plotting \u00b6 prob_distrib_per_class \u00b6 prob_distrib_per_class ( predicted_probs , actual , task ) Procedure to plot probability density distributions per class from LightGBM predictions. Parameters: predicted_probs ( ndarray ) \u2013 predicted probs actual ( ndarray ) \u2013 ground truth classes task ( str ) \u2013 type of task Returns: fig ( Figure ) \u2013 probability density ditributions plot per each class Source code in inference_model/eda/target/plotting.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def prob_distrib_per_class ( predicted_probs : np . ndarray , actual : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ], ) -> Figure : \"\"\"Procedure to plot probability density distributions per class from LightGBM predictions. Args: predicted_probs (ndarray): predicted probs actual (ndarray): ground truth classes task (str): type of task Returns: fig (Figure): probability density ditributions plot per each class \"\"\" if task == \"binary\" : temp = pd . DataFrame ({ \"predicted_proba\" : predicted_probs , \"actual\" : actual }) fig , ax = plt . subplots ( figsize = ( 3 , 2 )) fig . suptitle ( \"Predicted probability density per class\" ) for label , alpha , color in zip ([ 0 , 1 ], [ 1 , 0.7 ], [ \"black\" , \"red\" ]): ax . hist ( temp [ temp [ \"actual\" ] == label ][ \"predicted_proba\" ], density = True , bins = np . linspace ( 0 , 1 , 10 ), alpha = alpha , color = color , ec = \"k\" , ) ax . set_xlim ([ 0 , 1 ]) ax . set_xlabel ( \"probability\" ) ax . set_ylabel ( \"probability_density\" ) elif task == \"multiclass\" : temp = pd . DataFrame ( { \"predicted_proba\" : np . take_along_axis ( predicted_probs , np . vstack ( actual ), axis = 1 # type: ignore ) . flatten (), \"actual\" : actual , } ) # Dictionary of color for each label color_d = dict ( zip_longest ( temp [ \"actual\" ] . unique (), plt . rcParams [ \"axes.prop_cycle\" ] . by_key ()[ \"color\" ], ) ) n_classes = temp [ \"actual\" ] . nunique () fig , ax = plt . subplots ( ncols = n_classes , figsize = ( n_classes * 3 , 2 ), sharex = True , sharey = True ) fig . suptitle ( \"Predicted probability density per class\" ) plt . subplots_adjust ( wspace = 0.3 , top = 0.75 ) for i , ( label , gp ) in enumerate ( temp . groupby ( \"actual\" )): ax [ i ] . hist ( gp [ \"predicted_proba\" ], density = True , bins = np . linspace ( 0 , 1 , 10 ), color = color_d [ label ], ec = \"k\" , ) ax [ i ] . set_title ( label ) ax [ i ] . set_xlim ([ 0 , 1 ]) ax [ i ] . set_xlabel ( \"probability\" ) ax [ i ] . set_ylabel ( \"probability_density\" ) return fig distributions_in_binary_cls \u00b6 distributions_in_binary_cls ( df , target , low_per_cut = 0 , high_per_cut = 1 ) Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Parameters: df ( DataFrame ) \u2013 pandas dataframe cont_cols ( list ) \u2013 numerical features in the dataset target ( pd . Series ) \u2013 target values, i.e. binary classes Returns: fig ( Figure ) \u2013 ditribution plot per each feature Source code in inference_model/eda/target/plotting.py 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 def distributions_in_binary_cls ( df : pd . DataFrame , target : pd . Series , low_per_cut : float = 0 , high_per_cut : float = 1 , ) -> Figure : \"\"\"Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Args: df (DataFrame): pandas dataframe cont_cols (list): numerical features in the dataset target (pd.Series): target values, i.e. binary classes Returns: fig (Figure): ditribution plot per each feature \"\"\" data = pd . concat ([ df , target ], axis = 1 ) cols = data . columns . values data [ \"dummy\" ] = 0 data = data . fillna ( 0 ) . astype ( float ) fig , ax = plt . subplots ( len ( cols ), 1 , figsize = ( 16 , len ( cols ) * 2 )) fig . suptitle ( f \"Distribution of feature values splitted by { target . name } \" , size = 20 ) for col , i in zip ( cols , range ( len ( cols ))): if col != target . name : sns . violinplot ( ax = ax [ i ], hue = target . name , x = col , y = \"dummy\" , orient = \"h\" , cut = 0 , showextrem = False , split = True , data = data , ) ax [ i ] . get_legend () . remove () ax [ i ] . set_xlim ( data [ col ] . quantile ( low_per_cut ), data [ col ] . quantile ( high_per_cut ) ) ax [ i ] . get_yaxis () . set_visible ( False ) plt . subplots_adjust ( hspace = 0.9 , top = 0.97 ) return fig plotting \u00b6 bar_plot \u00b6 bar_plot ( df , ax , title ) Helper method for unified bar plot Parameters: df ( DataFrame ) \u2013 dataframe to plot ax ( Axes ) \u2013 axes defining where to plot it Returns: adjusted_plot ( Axes ) \u2013 adjusted axes Source code in inference_model/eda/plotting.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 def bar_plot ( df : pd . DataFrame , ax : Axes , title : str ) -> Axes : \"\"\"Helper method for unified bar plot Args: df (DataFrame): dataframe to plot ax (Axes): axes defining where to plot it Returns: adjusted_plot (Axes): adjusted axes \"\"\" data = df . copy () ax . set_title ( title , size = 20 ) ax . tick_params ( labelsize = 8 ) ax . set_xlabel ( \"sorted features\" , size = 12 ) return data . plot . bar ( ax = ax ) general_utils \u00b6 intsec \u00b6 intsec ( list1 , list2 ) Simple intesection of two lists. Parameters: list1 ( list ) \u2013 list1 list2 ( list ) \u2013 list2 Returns: list ( list ) \u2013 intersection of lists Source code in inference_model/eda/general_utils.py 6 7 8 9 10 11 12 13 14 def intsec ( list1 : list , list2 : list ) -> list : \"\"\"Simple intesection of two lists. Args: list1 (list): list1 list2 (list): list2 Returns: list (list): intersection of lists \"\"\" return list ( set . intersection ( set ( list1 ), set ( list2 ))) entropy_calc \u00b6 entropy_calc ( labels , base = np . e ) Computes entropy of both continuous and categorical features. Shamelessly stolen from : https://stackoverflow.com/a/45091961 Parameters: labels ( list , ndarray , Series ) \u2013 list of values Returns: ent ( float ) \u2013 entropy of the list of values Source code in inference_model/eda/general_utils.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def entropy_calc ( labels : list , base : float = np . e ) -> float : \"\"\"Computes entropy of both continuous and categorical features. Shamelessly stolen from : https://stackoverflow.com/a/45091961 Args: labels (list, ndarray, Series): list of values Returns: ent (float): entropy of the list of values \"\"\" n_labels = len ( labels ) value , counts = np . unique ( labels , return_counts = True ) probs = counts / n_labels n_classes = np . count_nonzero ( probs ) ent = float ( 0 ) if n_classes > 1 : for i in probs : ent -= i * math . log ( i , base ) return ent","title":"eda"},{"location":"inference_model/eda.html#preprocess-data","text":"","title":"Preprocess data"},{"location":"inference_model/eda.html#inference_model.eda.features.analysis","text":"","title":"analysis"},{"location":"inference_model/eda.html#inference_model.eda.features.plotting","text":"","title":"plotting"},{"location":"inference_model/eda.html#inference_model.eda.target.analysis","text":"","title":"analysis"},{"location":"inference_model/eda.html#inference_model.eda.target.plotting","text":"","title":"plotting"},{"location":"inference_model/eda.html#inference_model.eda.plotting","text":"","title":"plotting"},{"location":"inference_model/eda.html#inference_model.eda.general_utils","text":"","title":"general_utils"},{"location":"inference_model/preprocessing.html","text":"Preprocess data \u00b6 label_encoder \u00b6 LabelEncoder \u00b6 LabelEncoder ( columns_to_encode = None ) Bases: object Label Encode categorical values for multiple columns at once NOTE : Shamlessly copied from https://github.com/jrzaurin/pytorch-widedeep NOTE : LabelEncoder reserves 0 for unseen new categories. This is convenient when defining the embedding layers, since we can just set padding idx to 0. Parameters: columns_to_encode ( list, Optional, default = None ) \u2013 List of strings containing the names of the columns to encode. If None all columns of type object in the dataframe will be label encoded. Attributes: encoding_dict ( Dict ) \u2013 Dictionary containing the encoding mappings in the format, e.g. : {'colname1': {'cat1': 1, 'cat2': 2, ...}, 'colname2': {'cat1': 1, 'cat2': 2, ...}, ...} # noqa inverse_encoding_dict(Dict) ( Dict ) \u2013 Dictionary containing the inverse encoding mappings in the format, e.g. : {'colname1': {1: 'cat1', 2: 'cat2', ...}, 'colname2': {1: 'cat1', 2: 'cat2', ...}, ...} # noqa Source code in inference_model/preprocessing/label_encoder.py 29 30 31 32 33 def __init__ ( self , columns_to_encode : Optional [ List [ str ]] = None , ): self . columns_to_encode = columns_to_encode fit \u00b6 fit ( df ) Creates encoding attributes Returns: LabelEncoder ( LabelEncoder ) \u2013 LabelEncoder fitted object Source code in inference_model/preprocessing/label_encoder.py 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 def fit ( self , df : pd . DataFrame ) -> \"LabelEncoder\" : \"\"\"Creates encoding attributes Returns: LabelEncoder: `LabelEncoder` fitted object \"\"\" df_inp = df . copy () if self . columns_to_encode is None : self . columns_to_encode = list ( df_inp . select_dtypes ( include = [ \"object\" ]) . columns ) else : # sanity check to make sure all categorical columns are in an adequate # format for col in self . columns_to_encode : df_inp [ col ] = df_inp [ col ] . astype ( \"O\" ) unique_column_vals = dict () for c in self . columns_to_encode : unique_column_vals [ c ] = df_inp [ c ] . unique () self . encoding_dict = dict () # leave 0 for padding/\"unseen\" categories idx = 1 for k , v in unique_column_vals . items (): self . encoding_dict [ k ] = { o : i + idx for i , o in enumerate ( unique_column_vals [ k ]) } idx = 1 self . inverse_encoding_dict = dict () for c in self . encoding_dict : self . inverse_encoding_dict [ c ] = { v : k for k , v in self . encoding_dict [ c ] . items () } self . inverse_encoding_dict [ c ][ 0 ] = \"unseen\" return self transform \u00b6 transform ( df ) Label Encoded the categories in columns_to_encode Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Source code in inference_model/preprocessing/label_encoder.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 def transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Label Encoded the categories in `columns_to_encode` Returns: pd.DataFrame: label-encoded dataframe \"\"\" try : self . encoding_dict except AttributeError : raise NotFittedError ( \"This LabelEncoder instance is not fitted yet. \" \"Call 'fit' with appropriate arguments before using this LabelEncoder.\" ) df_inp = df . copy () # sanity check to make sure all categorical columns are in an adequate # format for col in self . columns_to_encode : # type: ignore df_inp [ col ] = df_inp [ col ] . astype ( \"O\" ) for k , v in self . encoding_dict . items (): df_inp [ k ] = df_inp [ k ] . apply ( lambda x : v [ x ] if x in v . keys () else 0 ) return df_inp fit_transform \u00b6 fit_transform ( df ) Combines fit and transform Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd . DataFrame ({ 'col1' : [ 1 , 2 , 3 ], 'col2' : [ 'me' , 'you' , 'him' ]}) >>> columns_to_encode = [ 'col2' ] >>> encoder = LabelEncoder ( columns_to_encode ) >>> encoder . fit_transform ( df ) col1 col2 0 1 1 1 2 2 2 3 3 >>> encoder . encoding_dict {'col2': {'me': 1, 'you': 2, 'him': 3}} Source code in inference_model/preprocessing/label_encoder.py 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 def fit_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Combines `fit` and `transform` Returns: pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']}) >>> columns_to_encode = ['col2'] >>> encoder = LabelEncoder(columns_to_encode) >>> encoder.fit_transform(df) col1 col2 0 1 1 1 2 2 2 3 3 >>> encoder.encoding_dict {'col2': {'me': 1, 'you': 2, 'him': 3}} \"\"\" return self . fit ( df ) . transform ( df ) inverse_transform \u00b6 inverse_transform ( df ) Returns the original categories Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd . DataFrame ({ 'col1' : [ 1 , 2 , 3 ], 'col2' : [ 'me' , 'you' , 'him' ]}) >>> columns_to_encode = [ 'col2' ] >>> encoder = LabelEncoder ( columns_to_encode ) >>> df_enc = encoder . fit_transform ( df ) >>> encoder . inverse_transform ( df_enc ) col1 col2 0 1 me 1 2 you 2 3 him Source code in inference_model/preprocessing/label_encoder.py 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def inverse_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Returns the original categories Returns: pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']}) >>> columns_to_encode = ['col2'] >>> encoder = LabelEncoder(columns_to_encode) >>> df_enc = encoder.fit_transform(df) >>> encoder.inverse_transform(df_enc) col1 col2 0 1 me 1 2 you 2 3 him \"\"\" for k , v in self . inverse_encoding_dict . items (): df [ k ] = df [ k ] . apply ( lambda x : v [ x ]) return df preprocess_data \u00b6 drop_high_nan_cols \u00b6 drop_high_nan_cols ( df , threshold = 0.8 , verbose = False ) Returns dataframe without columns that have ratio of missingness above threshold. Parameters: df ( pd . DataFrame ) \u2013 input dataframe threshold ( float = 0.8 ) \u2013 ratio of missingness applied per column verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 def drop_high_nan_cols ( df : pd . DataFrame , threshold : float = 0.8 , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without columns that have ratio of missingness above threshold. Args: df (pd.DataFrame): input dataframe threshold (float = 0.8): ratio of missingness applied per column verbose (bool): whether the output should be verbose \"\"\" n_rows = df . shape [ 0 ] nan_fraction_per_col = df . apply ( lambda x : x . isna () . sum () / n_rows , axis = 0 ) high_nan_percentage_cols = nan_fraction_per_col [ nan_fraction_per_col > threshold ] . index . values df = df . drop ( high_nan_percentage_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} columns with fraction of NaN above threshold = {}. Affected columns: {} \"\"\" . format ( len ( high_nan_percentage_cols ), threshold , high_nan_percentage_cols ) ) return df drop_constant_cols \u00b6 drop_constant_cols ( df , verbose = False ) Returns dataframe without constant columns, i.e. those with just 1 unique value for all rows. Source code in inference_model/preprocessing/preprocess_data.py 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 def drop_constant_cols ( df : pd . DataFrame , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without constant columns, i.e. those with just 1 unique value for all rows.\"\"\" nunique_per_col = df . apply ( lambda x : x . nunique (), axis = 0 ) const_cols = nunique_per_col [ nunique_per_col == 1 ] . index . values df = df . drop ( const_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} constant columns. Affected columns: {} \"\"\" . format ( len ( const_cols ), const_cols ) ) return df drop_high_uq_cat_cols \u00b6 drop_high_uq_cat_cols ( df , cat_cols , uq_val_count , verbose = False ) Returns dataframe without categorical columns that have too many unique values Parameters: df ( pd . DataFrame ) \u2013 input dataframe cat_cols ( list ) \u2013 list of categorical columns uq_val_count ( int ) \u2013 unique value count verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 def drop_high_uq_cat_cols ( df : pd . DataFrame , cat_cols : list , uq_val_count : int , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without categorical columns that have too many unique values Args: df (pd.DataFrame): input dataframe cat_cols (list): list of categorical columns uq_val_count (int): unique value count verbose (bool): whether the output should be verbose \"\"\" nunique_per_col = df [ cat_cols ] . apply ( lambda x : x . nunique (), axis = 0 ) high_uq_value_cat_cols = nunique_per_col [ nunique_per_col > uq_val_count ] . index . values df = df . drop ( high_uq_value_cat_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} high unique value columns. Affected columns: {} \"\"\" . format ( len ( high_uq_value_cat_cols ), high_uq_value_cat_cols ) ) return df drop_highly_correlated_columns \u00b6 drop_highly_correlated_columns ( df , cont_cols , crosscorr_val = 0.95 , verbose = False ) Returns dataframe without highly correlated columns, cross correlation is evaluated with crosscorr_val. Parameters: df ( pd . DataFrame ) \u2013 input dataframe cont_cols ( list ) \u2013 list of columns to evaluate correlation for crosscorr_val ( float = 0.95 ) \u2013 threshold value of correlation verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 def drop_highly_correlated_columns ( df : pd . DataFrame , cont_cols : list , crosscorr_val : float = 0.95 , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Returns dataframe without highly correlated columns, cross correlation is evaluated with crosscorr_val. Args: df (pd.DataFrame): input dataframe cont_cols (list): list of columns to evaluate correlation for crosscorr_val (float = 0.95): threshold value of correlation verbose (bool): whether the output should be verbose \"\"\" corr_matrix = df [ cont_cols ] . corr () . abs () upper = corr_matrix . where ( np . triu ( np . ones ( corr_matrix . shape ), k = 1 ) . astype ( \"bool\" )) upper_above = upper . apply ( lambda x : any ( x > crosscorr_val ), axis = 1 ) to_drop = upper_above [ upper_above ] . index . values df = df . drop ( to_drop , axis = 1 ) if verbose : print ( \"\"\" Dropped {} highly correlated columns. Affected columns: {} \"\"\" . format ( len ( to_drop ), to_drop ) ) return df nan_with_unknown_imputer \u00b6 nan_with_unknown_imputer ( df , columns , fill_token = \"unknown\" , verbose = False ) Fills NAs with surrogate string, 'unknown' is default value used, it can be customized. Parameters: df ( pd . DataFrame ) \u2013 input dataframe columns ( List [ str ] ) \u2013 ist of columns that will be filled fillna_token ( str = \"unknown\" ) \u2013 string used to replace NAs verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 def nan_with_unknown_imputer ( df : pd . DataFrame , columns : List [ str ], fill_token : str = \"unknown\" , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Fills NAs with surrogate string, 'unknown' is default value used, it can be customized. Args: df (pd.DataFrame): input dataframe columns (List[str]): ist of columns that will be filled fillna_token (str = \"unknown\"): string used to replace NAs verbose (bool): whether the output should be verbose \"\"\" df = df . copy () if verbose : sum_nan_vals = df [ columns ] . isna () . sum () nans_cols = [] for c in columns : nans_cols . append (( c , df [ c ] . isna () . sum ())) for c in columns : df [ c ] = df [ c ] . astype ( object ) . fillna ( fill_token ) dfc = df . copy () dfc [ columns ] = dfc [ columns ] . apply ( lambda x : x . astype ( object ) . fillna ( fill_token )) if verbose : print ( \"\"\" Imputed {} NaN values with {}. Affected columns (col, num_NaNs): {} \"\"\" . format ( sum_nan_vals , fill_token , nans_cols ) ) return df nan_with_number_imputer \u00b6 nan_with_number_imputer ( df , columns , fill_number =- 1.0 , verbose = False ) Fills NAs with surrogate float, -1 is default value used, it can be customized. Parameters: df ( pd . DataFrame ) \u2013 input dataframe columns ( List [ str ] ) \u2013 list of columns that will be filled fill_number ( float = -1 ) \u2013 number used to replace NAs. Defaults to verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 def nan_with_number_imputer ( df : pd . DataFrame , columns : List [ str ], fill_number : float = - 1.0 , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Fills NAs with surrogate float, -1 is default value used, it can be customized. Args: df (pd.DataFrame): input dataframe columns (List[str]): list of columns that will be filled fill_number (float = -1): number used to replace NAs. Defaults to verbose (bool): whether the output should be verbose \"\"\" dfc = df . copy () if verbose : sum_nan_vals = df [ columns ] . isna () . sum () nans_cols = [] for c in columns : nans_cols . append (( c , df [ c ] . isna () . sum ())) dfc = df . copy () dfc [ columns ] = dfc [ columns ] . apply ( lambda x : x . astype ( float ) . fillna ( fill_number )) if verbose : print ( \"\"\" Imputed {} NaN values with {}. Affected columns (col, num_NaNs): {} \"\"\" . format ( sum_nan_vals , fill_number , nans_cols ) ) return dfc nuq_in_list_col \u00b6 nuq_in_list_col ( dfs ) Returns pd.Series with the number of unique values in the lists. Parameters: dfs ( pd . Series ) \u2013 input pandas series containing string list values Source code in inference_model/preprocessing/preprocess_data.py 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 def nuq_in_list_col ( dfs : pd . Series ): \"\"\"Returns pd.Series with the number of unique values in the lists. Args: dfs (pd.Series): input pandas series containing string list values \"\"\" dfsc = dfs . copy () dfsc . apply ( lambda string : _nunique ( list ( string . replace ( \"[\" , \"\" ) . replace ( \"]\" , \"\" ) . split ( \", \" )) ) if isinstance ( string , str ) else string ) return dfsc most_frequent_in_list_col \u00b6 most_frequent_in_list_col ( dfs ) Returns pd.Series with the most frequent values in the lists. Parameters: dfs ( pd . Series ) \u2013 input pandas series containing string list values Source code in inference_model/preprocessing/preprocess_data.py 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 def most_frequent_in_list_col ( dfs : pd . Series ): \"\"\"Returns pd.Series with the most frequent values in the lists. Args: dfs (pd.Series): input pandas series containing string list values \"\"\" dfsc = dfs . copy () dfsc . apply ( lambda string : _most_frequent ( list ( string . replace ( \"[\" , \"\" ) . replace ( \"]\" , \"\" ) . split ( \", \" )) ) if isinstance ( string , str ) else string ) return dfsc replace_rare_categories_with_str_other \u00b6 replace_rare_categories_with_str_other ( df , categorical_cols , quantile = 0.05 , surrogate_value = \"other\" , verbose = False , ) Replaces rare category value with surrogate string. Parameters: df ( pd . DataFrame ) \u2013 input dataframe categorical_cols ( List [ str ] ) \u2013 list of columns in dataframe to process. quantile ( float = 0.05 ) \u2013 determines what values are considered as rare surrogate_value ( str = \"other\" ) \u2013 string used to replace rare values Returns: Tuple [ pd . DataFrame , Dict ] \u2013 Tuple[pd.DataFrame, Dict]: New dataframe and a dict. with mapping between orig. and surrogate values. Source code in inference_model/preprocessing/preprocess_data.py 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 def replace_rare_categories_with_str_other ( df : pd . DataFrame , categorical_cols : List [ str ], quantile : float = 0.05 , surrogate_value : str = \"other\" , verbose : bool = False , ) -> Tuple [ pd . DataFrame , Dict ]: \"\"\"Replaces rare category value with surrogate string. Args: df (pd.DataFrame): input dataframe categorical_cols (List[str]): list of columns in dataframe to process. quantile (float = 0.05): determines what values are considered as rare surrogate_value (str = \"other\"): string used to replace rare values Returns: Tuple[pd.DataFrame, Dict]: New dataframe and a dict. with mapping between orig. and surrogate values. \"\"\" if surrogate_value in df [ categorical_cols ] . values : raise ValueError ( \"Surrogate string - \" + surrogate_value + \" - is already present, choose another one.\" ) dfc = df . copy () replace_dict_per_col = ( df [ categorical_cols ] . apply ( _replace_rare_dict , args = [ surrogate_value , quantile ]) . to_dict () ) dfc = dfc . replace ( replace_dict_per_col ) if verbose : sum_rare_cats = sum ( [ len ( replace_dict_per_col [ col ]) for col in replace_dict_per_col ] ) rare_cats_per_col = [ ( col , list ( replace_dict_per_col [ col ] . keys ())) for col in replace_dict_per_col ] print ( \"\"\" Replaced {} rare categories (val_count < {}) with {}. Affected columns (List[(col, rare_cats)]): {} \"\"\" . format ( sum_rare_cats , quantile , surrogate_value , rare_cats_per_col , ) ) return dfc , replace_dict_per_col preprocess \u00b6 PreprocessData \u00b6 PreprocessData ( target_col , id_cols , cat_cols = None , cont_cols = None ) Object to preprocess the dataset. Parameters: target_col ( str ) \u2013 target column name id_cols ( List [ str ] ) \u2013 id columns cat_cols ( Optional [ List [ str ]] ) \u2013 list of categorical column names cont_cols ( Optional [ List [ str ]] ) \u2013 list of continuous column names Source code in inference_model/preprocessing/preprocess.py 28 29 30 31 32 33 34 35 36 37 38 39 40 def __init__ ( self , target_col : str , id_cols : str , cat_cols : Optional [ List [ str ]] = None , cont_cols : Optional [ List [ str ]] = None , ): self . target_col = target_col self . id_cols = id_cols self . cat_cols = cat_cols self . cont_cols = cont_cols self . is_fitted = False fit_transform \u00b6 fit_transform ( df ) Fit peprocessor and transform dataset in training step. Source code in inference_model/preprocessing/preprocess.py 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 def fit_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Fit peprocessor and transform dataset in training step.\"\"\" dfc = df . drop ( columns = self . id_cols ) . copy () dfc = dfc . pipe ( drop_constant_cols ) self . cat_cols = intsec ( list ( dfc ), self . cat_cols ) if self . cat_cols is None : self . cat_cols = self . _infere_cat_cols ( dfc ) dfc_cat_cols = drop_high_nan_cols ( dfc [ self . cat_cols ]) dfc = dfc . drop ( columns = self . cat_cols ) dfc = pd . concat ([ dfc , dfc_cat_cols ], axis = 1 ) self . cat_cols = dfc_cat_cols . columns . values . tolist () if self . cont_cols is None : self . init_cont_cols = self . _infere_cont_cols ( dfc , self . cat_cols ) dfc_init_cont_cols = drop_high_nan_cols ( dfc [ self . init_cont_cols ]) dfc = dfc . drop ( columns = self . init_cont_cols ) dfc = pd . concat ([ dfc , dfc_init_cont_cols ], axis = 1 ) self . init_cont_cols = dfc_init_cont_cols . columns . values . tolist () dfc = nan_with_number_imputer ( dfc , self . init_cont_cols , - 9999.0 ) ( dfc , self . final_cont_cols , ) = self . _drop_highly_corr_cols_and_get_final_cont_cols ( dfc , self . init_cont_cols ) else : self . final_cont_cols = list ( set ( self . cont_cols ) . intersection ( set ( dfc . columns . values )) ) dfc = nan_with_number_imputer ( dfc , self . final_cont_cols , - 9999.0 ) dfc = nan_with_unknown_imputer ( dfc , self . cat_cols ) dfc = dfc [ self . cat_cols + self . final_cont_cols + [ self . target_col ]] self . label_encoder = LabelEncoder ( self . cat_cols ) dfc_le = self . label_encoder . fit_transform ( dfc ) dfc_le = self . _change_int_float_types ( dfc_le ) dfc_le [ self . id_cols ] = df [ self . id_cols ] self . is_fitted = True return dfc_le transform \u00b6 transform ( df ) Transform dataset in inference step. Source code in inference_model/preprocessing/preprocess.py 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 def transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Transform dataset in inference step.\"\"\" if not self . is_fitted : raise NotFittedError ( \"\"\"This instance of 'PreprocessData' has not been fitted yet. Please, run 'fit' first\"\"\" ) dfc = df . drop ( columns = self . id_cols ) . copy () # added as mlflow inference is receiving all the data as objects dfc [ self . cat_cols ] = dfc [ self . cat_cols ] . astype ( str ) dfc [ self . final_cont_cols ] = dfc [ self . final_cont_cols ] . astype ( float ) try : dfc = dfc [ self . cat_cols + self . final_cont_cols + [ self . target_col ]] except KeyError : dfc = dfc [ self . cat_cols + self . final_cont_cols ] # dfc = dfc.replace(self.replace_rare_categories_dict) dfc = nan_with_unknown_imputer ( dfc , self . cat_cols ) dfc = nan_with_number_imputer ( dfc , list ( set ( self . final_cont_cols )), - 9999.0 , # noqa ) dfc_le = self . label_encoder . transform ( dfc ) dfc_le = self . _change_int_float_types ( dfc_le ) dfc_le [ self . id_cols ] = df [ self . id_cols ] return dfc_le fit \u00b6 fit ( df ) Just to keep familiar naming convention with sklearn. Source code in inference_model/preprocessing/preprocess.py 134 135 136 def fit ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Just to keep familiar naming convention with sklearn.\"\"\" return self . fit_transform ( df ) scaler \u00b6 scaler_mapper \u00b6 scaler_mapper ( cont_cols , cat_cols , id_cols , scaler_mapper_def = None ) Function that maps scaler functions to appropriate columns. By default does not assign any scaler to continuous, categorical or identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers. Only columns defined in mapper object will be present in the transformed dataset. Parameters: cont_cols ( list ) \u2013 list of continuous feature columns in the dataset cat_cols ( list ) \u2013 list of categorical feature columns in the dataset id_cols ( list ) \u2013 identifier columns scaler_mapper_def ( dict ) \u2013 optional dictionary that contains keys ['cont_cols', 'cat_cols', 'id_cols'] with their corresponding scalers (defined by names, not instantiated) from sklearn library Returns: scaler ( DataFrameMapper ) \u2013 scaler object mapping sklearn scalers to columns in pandas dataframe Source code in inference_model/preprocessing/scaler.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 def scaler_mapper ( cont_cols : List [ str ], cat_cols : List [ str ], id_cols : List [ str ], scaler_mapper_def : Optional [ dict ] = None , ) -> DataFrameMapper : \"\"\"Function that maps scaler functions to appropriate columns. By default does not assign any scaler to continuous, categorical or identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers. Only columns defined in mapper object will be present in the transformed dataset. Args: cont_cols (list): list of continuous feature columns in the dataset cat_cols (list): list of categorical feature columns in the dataset id_cols (list): identifier columns scaler_mapper_def (dict): optional dictionary that contains keys ['cont_cols', 'cat_cols', 'id_cols'] with their corresponding scalers (defined by names, not instantiated) from sklearn library Returns: scaler (DataFrameMapper): scaler object mapping sklearn scalers to columns in pandas dataframe \"\"\" if scaler_mapper_def : cont_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cont_cols )), classes = [ scaler_mapper_def [ \"cont_cols\" ]], ) cat_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cat_cols )), classes = [ scaler_mapper_def [ \"cat_cols\" ]], ) id_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], id_cols )), classes = [ scaler_mapper_def [ \"id_cols\" ]], ) else : cont_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cont_cols )), classes = [ None ] ) cat_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cat_cols )), classes = [ None ] ) id_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], id_cols )), classes = [ None ] ) scaler = DataFrameMapper ( cont_cols_def + cat_cols_def + id_cols_def , df_out = True ) return scaler","title":"preprocessing"},{"location":"inference_model/preprocessing.html#preprocess-data","text":"","title":"Preprocess data"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.label_encoder","text":"","title":"label_encoder"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.preprocess_data","text":"","title":"preprocess_data"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.preprocess","text":"","title":"preprocess"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.scaler","text":"","title":"scaler"},{"location":"inference_model/training.html","text":"Training \u00b6 metrics \u00b6 smape \u00b6 smape ( actual , predicted ) Symmetric Mean Absolute Percentage Error https://vedexcel.com/how-to-calculate-smape-in-python/ Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Returns: smape ( float ) \u2013 symmetric mean absolute percentage error Source code in inference_model/training/metrics.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 def smape ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Symmetric Mean Absolute Percentage Error https://vedexcel.com/how-to-calculate-smape-in-python/ Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Returns: smape (float): symmetric mean absolute percentage error \"\"\" return ( 100 / len ( actual ) * np . sum ( 2 * np . abs ( predicted - actual ) / ( np . abs ( actual ) + np . abs ( predicted ))) ) ndcg \u00b6 ndcg ( actual , predicted ) Normalized Discounted Cumulative Gain Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Return ndcg (float): normalized discounted cumulative gain Source code in inference_model/training/metrics.py 32 33 34 35 36 37 38 39 40 41 42 43 44 def ndcg ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Normalized Discounted Cumulative Gain Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Return: ndcg (float): normalized discounted cumulative gain \"\"\" return ndcg_score ( [ rankdata ( actual , method = \"ordinal\" )], [ rankdata ( predicted , method = \"ordinal\" )], ) rmse \u00b6 rmse ( actual , predicted ) Root Mean Squared Error Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Returns: rmse ( float ) \u2013 root mean square error Source code in inference_model/training/metrics.py 47 48 49 50 51 52 53 54 55 56 def rmse ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Root Mean Squared Error Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Returns: rmse (float): root mean square error \"\"\" return mean_squared_error ( actual , predicted , squared = False ) nacil \u00b6 nacil ( actual , high_quantile_predicted , low_quantile_predicted ) Normalized Average Confidence Interval Length for Quantile Regression. The value is normalized to actual(expected) value. Parameters: actual ( np . ndarray ) \u2013 actual values high_quantile_predicted ( np . ndarray ) \u2013 high quantile predicted values low_quantile_predicted ( np . ndarray ) \u2013 low quantile predicted values Returns: nacil ( float ) \u2013 normalized average confidence interval length Source code in inference_model/training/metrics.py 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def nacil ( actual : np . ndarray , high_quantile_predicted : np . ndarray , low_quantile_predicted : np . ndarray , ): \"\"\"Normalized Average Confidence Interval Length for Quantile Regression. The value is normalized to actual(expected) value. Args: actual (np.ndarray): actual values high_quantile_predicted (np.ndarray): high quantile predicted values low_quantile_predicted (np.ndarray): low quantile predicted values Returns: nacil (float): normalized average confidence interval length \"\"\" return np . mean (( high_quantile_predicted - low_quantile_predicted ) / actual ) aiqc \u00b6 aiqc ( actual , high_quantile , low_quantile ) Average InterQuantile Coverage for Quantile Regression. Check if the interquantile coverage is close to the coverage of in the test dataset. Parameters: actual ( np . ndarray ) \u2013 actual values high_quantile_predicted ( np . ndarray ) \u2013 high quantile predicted values low_quantile_predicted ( np . ndarray ) \u2013 low quantile predicted values Returns: aiqc ( float ) \u2013 average interquantile coverage Source code in inference_model/training/metrics.py 77 78 79 80 81 82 83 84 85 86 87 88 def aiqc ( actual : np . ndarray , high_quantile : np . ndarray , low_quantile : np . ndarray ): \"\"\"Average InterQuantile Coverage for Quantile Regression. Check if the interquantile coverage is close to the coverage of in the test dataset. Args: actual (np.ndarray): actual values high_quantile_predicted (np.ndarray): high quantile predicted values low_quantile_predicted (np.ndarray): low quantile predicted values Returns: aiqc (float): average interquantile coverage \"\"\" return np . sum (( actual < high_quantile ) & ( actual > low_quantile )) / len ( actual ) optuna_optimizer \u00b6 LGBOptunaOptimizer \u00b6 LGBOptunaOptimizer ( objective , n_class = None ) Bases: BaseOptimizer Fallback/backup Optuna optimizer. Development is focused on Raytune. Kepping this code as backup. Parameters: objective ( str ) \u2013 objective of the model n_class ( int ) \u2013 number of classes in the dataset Source code in inference_model/training/optuna_optimizer.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def __init__ ( self , objective : Literal [ \"binary\" , \"multiclass\" , \"regression\" ], n_class : Optional [ int ] = None , ): \"\"\"Fallback/backup Optuna optimizer. Development is focused on Raytune. Kepping this code as backup. Args: objective (str): objective of the model n_class (int): number of classes in the dataset \"\"\" # Optuna does not support original lighgbm implemenattion and with a workaroud it # is possible to get the binarry classfier with focal_loss(any custom loss) # running but the multiclass requires changes to Optuna code, eg. this post: # https://lightrun.com/answers/optuna-optuna-error-when-using-custom-metrics-in-optunaintegrationlightgbm # noqa # loss = None super ( LGBOptunaOptimizer , self ) . __init__ ( objective , n_class ) self . params = self . base_params optimize \u00b6 optimize ( dtrain , deval ) Optimize LGBM model on provided datasets. Parameters: dtrain ( lgbDataset ) \u2013 training lgb dataset deval ( lgbDataset ) \u2013 evaluation lgb dataset Source code in inference_model/training/optuna_optimizer.py 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 def optimize ( self , dtrain : lgbDataset , deval : lgbDataset ): \"\"\"Optimize LGBM model on provided datasets. Args: dtrain (lgbDataset): training lgb dataset deval (lgbDataset): evaluation lgb dataset \"\"\" dtrain_copy = deepcopy ( dtrain ) deval_copy = deepcopy ( deval ) if deval is not None else None study = create_study ( study_name = \"LightGBMTuner\" ) tuner = LightGBMTuner ( params = self . params , train_set = dtrain_copy , valid_sets = deval_copy , num_boost_round = 1000 , callbacks = [ lgb . early_stopping ( stopping_rounds = 50 )], feval = self . feval , study = study , # fobj=self.fobj, ) tuner . run () self . study = study self . best = tuner . best_params # since n_estimators is not among the params that Optuna optimizes we # need to add it manually. We add a high value since it will be used # with early_stopping_rounds self . best [ \"n_estimators\" ] = 1000 # type: ignore trainer \u00b6 Trainer \u00b6 Trainer ( cat_cols , target_col , id_cols , objective , optimizer = None , n_class = None , preprocessors = None , ) Bases: BaseTrainer Objects that governs training and parameter optimization of the lgbm model. Parameters: cat_cols ( list ) \u2013 list of categorical feature column names target_col ( str ) \u2013 column name that represents target id_cols ( list ) \u2013 identification column names objective ( str ) \u2013 type of the task/objective optimizer ( BaseOptimizer ) \u2013 parameter optimizer object Source code in inference_model/training/trainer.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 def __init__ ( self , cat_cols : List [ str ], target_col : str , id_cols : List [ str ], objective : Literal [ \"binary\" ], optimizer : Union [ Any , BaseOptimizer , None ] = None , n_class : Optional [ int ] = None , preprocessors : Optional [ List [ Union [ Any , PreprocessData ]]] = None , ): \"\"\"Objects that governs training and parameter optimization of the lgbm model. Args: cat_cols (list): list of categorical feature column names target_col (str): column name that represents target id_cols (list): identification column names objective (str): type of the task/objective optimizer (BaseOptimizer): parameter optimizer object \"\"\" super ( Trainer , self ) . __init__ ( cat_cols = cat_cols , target_col = target_col , id_cols = id_cols , objective = objective , n_class = n_class , preprocessors = preprocessors , ) if optimizer is not None : if not hasattr ( optimizer , \"optimize\" ): raise AttributeError ( \" {} optimizer must have {} method\" . format ( optimizer , \"optimize\" ) ) self . optimizer = optimizer train \u00b6 train ( df_train , params = None , df_valid = None ) Train the model with the parameters. Parameters: df_train ( pd . DataFrame ) \u2013 training dataset params ( dict ) \u2013 model paramaters df_valid ( pd . DataFrame ) \u2013 optional validation dataset Returns: model ( lgb . basic . Booster ) \u2013 trained model Source code in inference_model/training/trainer.py 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def train ( self , df_train : pd . DataFrame , params : Optional [ Dict ] = None , df_valid : Optional [ pd . DataFrame ] = None , ): \"\"\"Train the model with the parameters. Args: df_train (pd.DataFrame): training dataset params (dict): model paramaters df_valid (pd.DataFrame): optional validation dataset Returns: model (lgb.basic.Booster): trained model \"\"\" if self . preprocessors : for prep in self . preprocessors : df_train_prep = prep . transform ( df_train . drop ( columns = [ self . target_col ])) df_valid_prep = ( prep . transform ( df_valid . drop ( columns = [ self . target_col ])) if df_valid is not None else None ) if self . objective in [ \"binary\" ]: df_train_prep [ self . target_col ] = df_train [ self . target_col ] . astype ( int ) if df_valid is not None : df_valid_prep [ self . target_col ] = df_valid [ self . target_col ] . astype ( int ) else : raise NotImplementedError else : df_train_prep = df_train . copy () df_valid_prep = df_valid . copy () if df_valid is not None else None cat_cols = self . cat_cols if params : config = params elif ( params is None ) and ( self . optimizer ): config = self . optimizer . best if hasattr ( self . optimizer , \"best_to_drop\" ): df_train_prep . drop ( columns = self . optimizer . best_to_drop , # type: ignore inplace = True , ) if df_valid is not None : df_valid_prep . drop ( columns = self . optimizer . best_to_drop , # type: ignore inplace = True , ) cat_cols = intsec ( self . cat_cols , df_train_prep . columns . values ) elif ( params is None ) and ( self . optimizer is None ): config = self . base_params lgb_train , lgb_valid = to_lgbdataset ( train = df_train_prep , cat_cols = cat_cols , target_col = self . target_col , id_cols = self . id_cols , valid = df_valid_prep , ) self . model = lgb_train_function ( config = config , lgbtrain = lgb_train , lgbeval = lgb_valid , feval = self . feval , ) fit \u00b6 fit ( df_train , df_valid , df_test , random_state = 1 ) Train the model and optimize the parameters. Parameters: df_train ( pd . DataFrame ) \u2013 training dataset df_valid ( pd . DataFrame ) \u2013 validation dataset df_test ( pd . DataFrame ) \u2013 testing dataset Returns: model ( lgb . basic . Booster ) \u2013 trained mdoel Source code in inference_model/training/trainer.py 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 def fit ( self , df_train : pd . DataFrame , df_valid : pd . DataFrame , df_test : pd . DataFrame , random_state : int = 1 , ) -> pd . DataFrame : \"\"\"Train the model and optimize the parameters. Args: df_train (pd.DataFrame): training dataset df_valid (pd.DataFrame): validation dataset df_test (pd.DataFrame): testing dataset Returns: model (lgb.basic.Booster): trained mdoel \"\"\" if self . preprocessors : # this should be # df_train_prep = prep.transform(df_train_prep.drop(columns=[self.target_col])) for prep in self . preprocessors : df_train_prep = prep . transform ( df_train . drop ( columns = [ self . target_col ])) df_valid_prep = prep . transform ( df_valid . drop ( columns = [ self . target_col ])) if self . objective in [ \"binary\" ]: df_train_prep [ self . target_col ] = df_train [ self . target_col ] . astype ( int ) df_valid_prep [ self . target_col ] = df_valid [ self . target_col ] . astype ( int ) else : raise NotImplementedError else : df_train_prep = df_train . copy () df_valid_prep = df_valid . copy () lgb_train , lgb_valid = to_lgbdataset ( train = df_train_prep , cat_cols = self . cat_cols , target_col = self . target_col , id_cols = self . id_cols , valid = df_valid_prep , ) self . optimizer . optimize ( dtrain = lgb_train , deval = lgb_valid ) df_train_valid = pd . concat ([ df_train , df_valid ], ignore_index = True ) df_train_valid = df_train_valid . reset_index ( drop = True ) self . train ( df_train = df_train_valid ) metrics_dict = self . compute_metrics ( df_test ) return metrics_dict utils \u00b6 get_or_create_experiment \u00b6 get_or_create_experiment ( experiment_name ) Retrieve the ID of an existing MLflow experiment or create a new one if it doesn't exist. This function checks if an experiment with the given name exists within MLflow. If it does, the function returns its ID. If not, it creates a new experiment with the provided name and returns its ID. experiment_name (str): Name of the MLflow experiment. str: ID of the existing or newly created MLflow experiment. Source code in inference_model/training/utils.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 def get_or_create_experiment ( experiment_name ): \"\"\" Retrieve the ID of an existing MLflow experiment or create a new one if it doesn't exist. This function checks if an experiment with the given name exists within MLflow. If it does, the function returns its ID. If not, it creates a new experiment with the provided name and returns its ID. Parameters: - experiment_name (str): Name of the MLflow experiment. Returns: - str: ID of the existing or newly created MLflow experiment. \"\"\" if experiment := mlflow . get_experiment_by_name ( experiment_name ): return experiment . experiment_id else : return mlflow . create_experiment ( experiment_name ) to_lgbdataset \u00b6 to_lgbdataset ( train , cat_cols , target_col , id_cols = [], valid = None ) Transform pandas dataframe to lgbm dataset, or datasets(eval). Parameters: train ( pd . DataFrame ) \u2013 training dataset cat_cols ( list ) \u2013 list of categorical columns target_col ( str ) \u2013 target column in the dataset id_cols ( list ) \u2013 list of identifier columns valid ( pd . DataFrame ) \u2013 validation dataset Returns: lgb_train ( lgbDataset ) \u2013 lgbm training dataset lgb_valid ( lgbDataset ) \u2013 lgbm valid dataset Source code in inference_model/training/utils.py 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 def to_lgbdataset ( train : pd . DataFrame , cat_cols : List [ str ], target_col : str , id_cols : List [ str ] = [], valid : Optional [ pd . DataFrame ] = None , ) -> Tuple [ lgbDataset , Optional [ lgbDataset ]]: \"\"\"Transform pandas dataframe to lgbm dataset, or datasets(eval). Args: train (pd.DataFrame): training dataset cat_cols (list): list of categorical columns target_col (str): target column in the dataset id_cols (list): list of identifier columns valid (pd.DataFrame): validation dataset Returns: lgb_train (lgbDataset): lgbm training dataset lgb_valid (lgbDataset): lgbm valid dataset \"\"\" lgb_train = lgbDataset ( train . drop ( columns = [ target_col ] + id_cols ), train [ target_col ], categorical_feature = cat_cols , free_raw_data = False , ) if valid is not None : lgb_valid = lgbDataset ( valid . drop ( columns = [ target_col ] + id_cols ), valid [ target_col ], reference = lgb_train , free_raw_data = False , ) else : lgb_valid = None return lgb_train , lgb_valid predict_proba_lgbm_from_raw \u00b6 predict_proba_lgbm_from_raw ( preds_raw , task , binary2d = False ) Apply softmax to array of arrays that is an output of lightgbm.predct(). This replaces predict_proba(). Parameters: predicted_raw ( ndarray ) \u2013 1D numpy array of arrays task ( str ) \u2013 type of task/objective binary2d ( boolean ) \u2013 wether the output of binary classification should be 1 or 2d vector Returns: predicted_probs ( ndarray ) \u2013 array with predicted probabilities Source code in inference_model/training/utils.py 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 def predict_proba_lgbm_from_raw ( preds_raw : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ], binary2d : Optional [ bool ] = False , ) -> np . ndarray : \"\"\"Apply softmax to array of arrays that is an output of lightgbm.predct(). This replaces predict_proba(). Args: predicted_raw (ndarray): 1D numpy array of arrays task (str): type of task/objective binary2d (boolean): wether the output of binary classification should be 1 or 2d vector Returns: predicted_probs (ndarray): array with predicted probabilities \"\"\" if task == \"binary\" : predicted_probs = _sigmoid ( preds_raw ) if binary2d : predicted_probs = np . apply_along_axis ( _binary_margin_to_prob , 1 , np . vstack ( tuple ( predicted_probs )) ) elif task == \"multiclass\" : predicted_probs = np . apply_along_axis ( _softmax , 1 , np . stack ( tuple ( preds_raw ))) return predicted_probs predict_cls_lgbm_from_raw \u00b6 predict_cls_lgbm_from_raw ( preds_raw , task ) Helper function to convert raw margin predictions through a sigmoid to represent a probability. Parameters: preds_raw ( ndarray ) \u2013 predictions lgbDataset ( lightgbm . Dataset ) \u2013 dataset, containing labels, used for prediction Returns: y_true , preds \u2013 tuple containg labels and predictions for further evaluation Source code in inference_model/training/utils.py 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 def predict_cls_lgbm_from_raw ( preds_raw : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ] ) -> np . ndarray : \"\"\"Helper function to convert raw margin predictions through a sigmoid to represent a probability. Args: preds_raw (ndarray): predictions lgbDataset (lightgbm.Dataset): dataset, containing labels, used for prediction Returns: (y_true, preds): tuple containg labels and predictions for further evaluation \"\"\" predicted_probs = predict_proba_lgbm_from_raw ( preds_raw = preds_raw , task = task ) if task == \"binary\" : pred_cls = np . array ([ int ( p > 0.5 ) for p in predicted_probs ]) elif task == \"multiclass\" : pred_cls = predicted_probs . argmax ( axis = 1 ) return pred_cls get_feature_importance \u00b6 get_feature_importance ( model ) Extract model feature importances and return sorted dataframe. Parameters: model ( lgb . basic . Booster ) \u2013 LightGBM model Returns: feature_imp ( pd . DataFrame ) \u2013 sorted dataframe with features and their importances Source code in inference_model/training/utils.py 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 def get_feature_importance ( model : lgb . basic . Booster ) -> pd . DataFrame : \"\"\"Extract model feature importances and return sorted dataframe. Args: model (lgb.basic.Booster): LightGBM model Returns: feature_imp (pd.DataFrame): sorted dataframe with features and their importances \"\"\" feature_imp = pd . DataFrame ( { \"value\" : model . feature_importance (), \"feature\" : model . feature_name ()} ) feature_imp . sort_values ( by = \"value\" , inplace = True ) feature_imp . reset_index ( drop = True , inplace = True ) return feature_imp flatten_dict \u00b6 flatten_dict ( d , parent_key = '' , sep = '_' ) fastest according to https://www.freecodecamp.org/news/how-to-flatten-a-dictionary-in-python-in-4-different-ways/ Source code in inference_model/training/utils.py 184 185 186 187 188 189 190 191 192 193 194 195 196 197 def flatten_dict ( d : MutableMapping , parent_key : str = \"\" , sep : str = \"_\" ) -> MutableMapping : \"\"\" fastest according to https://www.freecodecamp.org/news/how-to-flatten-a-dictionary-in-python-in-4-different-ways/ \"\"\" items : List [ Any ] = [] for k , v in d . items (): new_key = parent_key + sep + k if parent_key else k if isinstance ( v , MutableMapping ): items . extend ( flatten_dict ( v , new_key , sep = sep ) . items ()) else : items . append (( new_key , v )) return dict ( items )","title":"training"},{"location":"inference_model/training.html#training","text":"","title":"Training"},{"location":"inference_model/training.html#inference_model.training.metrics","text":"","title":"metrics"},{"location":"inference_model/training.html#inference_model.training.optuna_optimizer","text":"","title":"optuna_optimizer"},{"location":"inference_model/training.html#inference_model.training.trainer","text":"","title":"trainer"},{"location":"inference_model/training.html#inference_model.training.utils","text":"","title":"utils"},{"location":"inference_model/utils.html","text":"Utils \u00b6 utils \u00b6 intsec \u00b6 intsec ( list1 , list2 ) Simple intesection of two lists. Parameters: list1 ( list ) \u2013 list1 list2 ( list ) \u2013 list2 Returns: list ( list ) \u2013 intersection of lists Source code in inference_model/utils.py 7 8 9 10 11 12 13 14 15 def intsec ( list1 : list , list2 : list ) -> list : \"\"\"Simple intesection of two lists. Args: list1 (list): list1 list2 (list): list2 Returns: list (list): intersection of lists \"\"\" return list ( set . intersection ( set ( list1 ), set ( list2 ))) dill_load \u00b6 dill_load ( file_loc ) Helper function to open/close dill file, otherwise the python outputs warning that the file remains opened Parameters: file_loc ( str ) \u2013 location of the file Returns: content ( dict ) \u2013 content of dill file, usually dictionary Source code in inference_model/utils.py 18 19 20 21 22 23 24 25 26 27 28 29 def dill_load ( file_loc : Union [ str , Path ]) -> Any : \"\"\"Helper function to open/close dill file, otherwise the python outputs warning that the file remains opened Args: file_loc (str): location of the file Returns: content (dict): content of dill file, usually dictionary \"\"\" with open ( file_loc , \"rb\" ) as f : content = dill . load ( f ) return content dill_dump \u00b6 dill_dump ( file_loc , content ) Helper function to open/close dill file and dump content into it, otherwise the python outputs warning that the file remains opened Parameters: file_loc ( str ) \u2013 location of the file content ( object ) \u2013 data that will be saved to dill, usually dictionary Source code in inference_model/utils.py 32 33 34 35 36 37 38 39 40 41 def dill_dump ( file_loc : Union [ str , Path ], content : object ): \"\"\"Helper function to open/close dill file and dump content into it, otherwise the python outputs warning that the file remains opened Args: file_loc (str): location of the file content (object): data that will be saved to dill, usually dictionary \"\"\" with open ( file_loc , \"wb\" ) as f : dill . dump ( content , f )","title":"utils"},{"location":"inference_model/utils.html#utils","text":"","title":"Utils"},{"location":"inference_model/utils.html#inference_model.utils","text":"","title":"utils"},{"location":"notebooks/01_feature_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Feature analysis \u00b6 Purpose : analyse dataset features without inclusion of target value if it looks healthy duplicate values NA values zero/0 values if the values are \"too constant\", ie. most [robably bad features for classifcation/regression small std values entropy is too high/too low(not sure right now?) - \"Entropy is a measure of disorder or uncertainty\" number of unique values in categorical features is too high - most probably they are unable to identify any group of targets drop highly correlated values to improve performance of the model training to decrease dataset size for further exploration Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing , zero , nunique , init_check , std , entropy , nunique , ) pd . set_option ( \"display.max_columns\" , 100 ) pd . set_option ( \"display.max_rows\" , 300 ) from inference_model.eda.features.plotting import cross_correlation , distributions import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing, zero, nunique, init_check, std, entropy, nunique, ) pd.set_option(\"display.max_columns\", 100) pd.set_option(\"display.max_rows\", 300) from inference_model.eda.features.plotting import cross_correlation, distributions import ast Dataset \u00b6 In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None Features \u00b6 Analysis \u00b6 In [4]: Copied! duplicated_ids , cont_cols_desc , cat_cols_desc = init_check ( df = df_pd , identifier = id_cols , cat_cols = cat_cols , cont_cols = cont_cols , verbose = True , ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() duplicated_ids, cont_cols_desc, cat_cols_desc = init_check( df=df_pd, identifier=id_cols, cat_cols=cat_cols, cont_cols=cont_cols, verbose=True, ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() [CHECK] Number of duplicated ids: 1034 [CHECK] Numerical columns count mean std min 25% 50% \\ maf 1035.0 11.00 3.40 0.03 9.77 10.19 vehicle_speed 1035.0 0.95 3.70 0.00 0.00 0.00 engine_load 1035.0 46.29 21.59 30.59 35.69 37.25 barometric_pressure 1035.0 101.00 0.00 101.00 101.00 101.00 intake_air_temp 1035.0 28.56 18.16 -6.00 20.00 26.00 throttle_position 1035.0 83.21 0.15 83.14 83.14 83.14 engine_coolant_temp 1035.0 76.66 21.89 17.00 74.50 88.00 engine_speed 1035.0 929.39 270.78 3.20 893.00 896.50 fuel_rg_pressure 1035.0 35967.15 14377.15 110.00 31220.00 34050.00 control_voltage 1035.0 14.32 2.14 0.05 14.76 14.78 75% max maf 11.99 31.83 vehicle_speed 0.00 20.00 engine_load 38.82 100.00 barometric_pressure 101.00 101.00 intake_air_temp 32.00 145.00 throttle_position 83.14 83.53 engine_coolant_temp 89.00 91.00 engine_speed 899.50 2282.50 fuel_rg_pressure 35390.00 108770.00 control_voltage 14.80 14.84 In [5]: Copied! missing_val_frac , fig = missing ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) missing_val_frac . head () missing_val_frac, fig = missing( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) missing_val_frac.head() Out[5]: engine_load 0.0 engine_coolant_temp 0.0 engine_speed 0.0 vehicle_speed 0.0 intake_air_temp 0.0 dtype: float64 In [6]: Copied! zero_val_frac , fig = zero ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) zero_val_frac . head () zero_val_frac, fig = zero( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) zero_val_frac.head() Out[6]: vehicle_speed 0.927536 class 0.902415 engine_load 0.000000 engine_coolant_temp 0.000000 engine_speed 0.000000 dtype: float64 In [7]: Copied! data_nunique , fig = nunique ( df = df_pd . drop ( columns = id_cols ), scale = \"log\" , plot = True , ) data_nunique . head () data_nunique, fig = nunique( df=df_pd.drop(columns=id_cols), scale=\"log\", plot=True, ) data_nunique.head() Out[7]: barometric_pressure 1 throttle_position 2 class 2 vehicle_speed 21 control_voltage 48 dtype: int64 In [8]: Copied! data_std , fig = std ( df = df_pd [ cont_cols ], scale = \"log\" , plot = True , ) data_std . head () data_std, fig = std( df=df_pd[cont_cols], scale=\"log\", plot=True, ) data_std.head() Out[8]: fuel_rg_pressure 14377.152211 engine_speed 270.781548 engine_coolant_temp 21.888726 engine_load 21.587427 intake_air_temp 18.163539 dtype: float64 In [9]: Copied! col_entropies , fig = entropy ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) col_entropies . head () col_entropies, fig = entropy( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) col_entropies.head() Out[9]: maf 4.800777 engine_speed 3.981783 fuel_rg_pressure 3.797527 intake_air_temp 3.612889 engine_load 3.253640 dtype: float64 Plotting \u00b6 In [10]: Copied! fig = cross_correlation ( df = df_pd [ cont_cols ], n = 10 , verbose = True ) fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True) Top 10 absolute correlations vehicle_speed fuel_rg_pressure 0.648543 maf 0.544904 engine_load engine_coolant_temp 0.531108 engine_speed vehicle_speed 0.525832 engine_coolant_temp throttle_position 0.491290 engine_speed maf 0.398281 maf fuel_rg_pressure 0.331396 engine_speed fuel_rg_pressure 0.329811 engine_coolant_temp intake_air_temp 0.305559 engine_load intake_air_temp 0.207530 dtype: float64 In [11]: Copied! fig = distributions ( df = df_pd [ cont_cols ], low_per_cut = 0 , high_per_cut = 1 , type = \"box\" , ) fig = distributions( df=df_pd[cont_cols], low_per_cut=0, high_per_cut=1, type=\"box\", ) /home/jovyan/success6g-edge/inference_model/eda/features/plotting.py:98: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding. ax[i].set_xlim(data[col].quantile(low_per_cut), data[col].quantile(high_per_cut))","title":"01_feature_analysis"},{"location":"notebooks/01_feature_analysis.html#feature-analysis","text":"Purpose : analyse dataset features without inclusion of target value if it looks healthy duplicate values NA values zero/0 values if the values are \"too constant\", ie. most [robably bad features for classifcation/regression small std values entropy is too high/too low(not sure right now?) - \"Entropy is a measure of disorder or uncertainty\" number of unique values in categorical features is too high - most probably they are unable to identify any group of targets drop highly correlated values to improve performance of the model training to decrease dataset size for further exploration","title":"Feature analysis"},{"location":"notebooks/01_feature_analysis.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing , zero , nunique , init_check , std , entropy , nunique , ) pd . set_option ( \"display.max_columns\" , 100 ) pd . set_option ( \"display.max_rows\" , 300 ) from inference_model.eda.features.plotting import cross_correlation , distributions import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing, zero, nunique, init_check, std, entropy, nunique, ) pd.set_option(\"display.max_columns\", 100) pd.set_option(\"display.max_rows\", 300) from inference_model.eda.features.plotting import cross_correlation, distributions import ast","title":"Imports"},{"location":"notebooks/01_feature_analysis.html#dataset","text":"In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None","title":"Dataset"},{"location":"notebooks/01_feature_analysis.html#features","text":"","title":"Features"},{"location":"notebooks/01_feature_analysis.html#analysis","text":"In [4]: Copied! duplicated_ids , cont_cols_desc , cat_cols_desc = init_check ( df = df_pd , identifier = id_cols , cat_cols = cat_cols , cont_cols = cont_cols , verbose = True , ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() duplicated_ids, cont_cols_desc, cat_cols_desc = init_check( df=df_pd, identifier=id_cols, cat_cols=cat_cols, cont_cols=cont_cols, verbose=True, ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() [CHECK] Number of duplicated ids: 1034 [CHECK] Numerical columns count mean std min 25% 50% \\ maf 1035.0 11.00 3.40 0.03 9.77 10.19 vehicle_speed 1035.0 0.95 3.70 0.00 0.00 0.00 engine_load 1035.0 46.29 21.59 30.59 35.69 37.25 barometric_pressure 1035.0 101.00 0.00 101.00 101.00 101.00 intake_air_temp 1035.0 28.56 18.16 -6.00 20.00 26.00 throttle_position 1035.0 83.21 0.15 83.14 83.14 83.14 engine_coolant_temp 1035.0 76.66 21.89 17.00 74.50 88.00 engine_speed 1035.0 929.39 270.78 3.20 893.00 896.50 fuel_rg_pressure 1035.0 35967.15 14377.15 110.00 31220.00 34050.00 control_voltage 1035.0 14.32 2.14 0.05 14.76 14.78 75% max maf 11.99 31.83 vehicle_speed 0.00 20.00 engine_load 38.82 100.00 barometric_pressure 101.00 101.00 intake_air_temp 32.00 145.00 throttle_position 83.14 83.53 engine_coolant_temp 89.00 91.00 engine_speed 899.50 2282.50 fuel_rg_pressure 35390.00 108770.00 control_voltage 14.80 14.84 In [5]: Copied! missing_val_frac , fig = missing ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) missing_val_frac . head () missing_val_frac, fig = missing( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) missing_val_frac.head() Out[5]: engine_load 0.0 engine_coolant_temp 0.0 engine_speed 0.0 vehicle_speed 0.0 intake_air_temp 0.0 dtype: float64 In [6]: Copied! zero_val_frac , fig = zero ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) zero_val_frac . head () zero_val_frac, fig = zero( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) zero_val_frac.head() Out[6]: vehicle_speed 0.927536 class 0.902415 engine_load 0.000000 engine_coolant_temp 0.000000 engine_speed 0.000000 dtype: float64 In [7]: Copied! data_nunique , fig = nunique ( df = df_pd . drop ( columns = id_cols ), scale = \"log\" , plot = True , ) data_nunique . head () data_nunique, fig = nunique( df=df_pd.drop(columns=id_cols), scale=\"log\", plot=True, ) data_nunique.head() Out[7]: barometric_pressure 1 throttle_position 2 class 2 vehicle_speed 21 control_voltage 48 dtype: int64 In [8]: Copied! data_std , fig = std ( df = df_pd [ cont_cols ], scale = \"log\" , plot = True , ) data_std . head () data_std, fig = std( df=df_pd[cont_cols], scale=\"log\", plot=True, ) data_std.head() Out[8]: fuel_rg_pressure 14377.152211 engine_speed 270.781548 engine_coolant_temp 21.888726 engine_load 21.587427 intake_air_temp 18.163539 dtype: float64 In [9]: Copied! col_entropies , fig = entropy ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) col_entropies . head () col_entropies, fig = entropy( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) col_entropies.head() Out[9]: maf 4.800777 engine_speed 3.981783 fuel_rg_pressure 3.797527 intake_air_temp 3.612889 engine_load 3.253640 dtype: float64","title":"Analysis"},{"location":"notebooks/01_feature_analysis.html#plotting","text":"In [10]: Copied! fig = cross_correlation ( df = df_pd [ cont_cols ], n = 10 , verbose = True ) fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True) Top 10 absolute correlations vehicle_speed fuel_rg_pressure 0.648543 maf 0.544904 engine_load engine_coolant_temp 0.531108 engine_speed vehicle_speed 0.525832 engine_coolant_temp throttle_position 0.491290 engine_speed maf 0.398281 maf fuel_rg_pressure 0.331396 engine_speed fuel_rg_pressure 0.329811 engine_coolant_temp intake_air_temp 0.305559 engine_load intake_air_temp 0.207530 dtype: float64 In [11]: Copied! fig = distributions ( df = df_pd [ cont_cols ], low_per_cut = 0 , high_per_cut = 1 , type = \"box\" , ) fig = distributions( df=df_pd[cont_cols], low_per_cut=0, high_per_cut=1, type=\"box\", ) /home/jovyan/success6g-edge/inference_model/eda/features/plotting.py:98: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding. ax[i].set_xlim(data[col].quantile(low_per_cut), data[col].quantile(high_per_cut))","title":"Plotting"},{"location":"notebooks/02_target_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? 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var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Target analysis \u00b6 Purpose : put features into context with predicted or actual target values analyse feature distribution per group in binary classification check correlation of features with target value check 'certainty' of the model predictions by plotting prob_distrib_per_class to see how certain the model is in predicting each class visually check predicted vs actual(ground truth values) by simple scatter plot by 'improved scatter plot' ( joint_dist ) - this can be usefull if the actual values form a cluster (eg. regression applied to clusters of users) and we want check if the model predictions also form a nice cluster with similar distribution as actual values Intended use : check how your model is performing check the possible connection between feature and target values (eg. if the distributions of features are different for each class) Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls , ) import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls, ) import ast Dataset \u00b6 In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None Analysis \u00b6 In [4]: Copied! sorted_corr_cols , fig = correlation ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], scale = \"linear\" , plot = True , ) sorted_corr_cols . head () sorted_corr_cols, fig = correlation( df=df_pd[cont_cols], target=df_pd[target_col], scale=\"linear\", plot=True, ) sorted_corr_cols.head() Out[4]: engine_load 0.818614 throttle_position 0.245668 maf 0.087101 control_voltage 0.042763 engine_speed -0.062052 Name: class, dtype: float64 Plotting \u00b6 Predicted and actual/ground_truth values were gathered using LightGBM with Optuna optimizer on the dataset In [5]: Copied! fig = distributions_in_binary_cls ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], low_per_cut = 0 , high_per_cut = 1 , ) fig = distributions_in_binary_cls( df=df_pd[cont_cols], target=df_pd[target_col], low_per_cut=0, high_per_cut=1, ) /home/jovyan/success6g-edge/inference_model/eda/target/plotting.py:123: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding. ax[i].set_xlim(","title":"02_target_analysis"},{"location":"notebooks/02_target_analysis.html#target-analysis","text":"Purpose : put features into context with predicted or actual target values analyse feature distribution per group in binary classification check correlation of features with target value check 'certainty' of the model predictions by plotting prob_distrib_per_class to see how certain the model is in predicting each class visually check predicted vs actual(ground truth values) by simple scatter plot by 'improved scatter plot' ( joint_dist ) - this can be usefull if the actual values form a cluster (eg. regression applied to clusters of users) and we want check if the model predictions also form a nice cluster with similar distribution as actual values Intended use : check how your model is performing check the possible connection between feature and target values (eg. if the distributions of features are different for each class)","title":"Target analysis"},{"location":"notebooks/02_target_analysis.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls , ) import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls, ) import ast","title":"Imports"},{"location":"notebooks/02_target_analysis.html#dataset","text":"In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None","title":"Dataset"},{"location":"notebooks/02_target_analysis.html#analysis","text":"In [4]: Copied! sorted_corr_cols , fig = correlation ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], scale = \"linear\" , plot = True , ) sorted_corr_cols . head () sorted_corr_cols, fig = correlation( df=df_pd[cont_cols], target=df_pd[target_col], scale=\"linear\", plot=True, ) sorted_corr_cols.head() Out[4]: engine_load 0.818614 throttle_position 0.245668 maf 0.087101 control_voltage 0.042763 engine_speed -0.062052 Name: class, dtype: float64","title":"Analysis"},{"location":"notebooks/02_target_analysis.html#plotting","text":"Predicted and actual/ground_truth values were gathered using LightGBM with Optuna optimizer on the dataset In [5]: Copied! fig = distributions_in_binary_cls ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], low_per_cut = 0 , high_per_cut = 1 , ) fig = distributions_in_binary_cls( df=df_pd[cont_cols], target=df_pd[target_col], low_per_cut=0, high_per_cut=1, ) /home/jovyan/success6g-edge/inference_model/eda/target/plotting.py:123: UserWarning: Attempting to set identical low and high xlims makes transformation singular; 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Models \u00b6 Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report , RocCurveDisplay , roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw , predict_proba_lgbm_from_raw , ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, RocCurveDisplay, roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw, predict_proba_lgbm_from_raw, ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() Dataset \u00b6 In [3]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True In [4]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [8]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) check possible class imbalance In [9]: Copied! df_pd [ target_col ] . value_counts () df_pd[target_col].value_counts() Out[9]: class 0 934 1 101 Name: count, dtype: int64 Preprocessing \u00b6 divide dataset into train, test, valid scale continuous columns by standard scaler(not needed for LightGBM but for other mdoels...) In [10]: Copied! df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) df_train . reset_index ( inplace = True , drop = True ) df_valid . reset_index ( inplace = True , drop = True ) df_test . reset_index ( inplace = True , drop = True ) scaler_mapper_def = { \"cont_cols\" : StandardScaler , \"cat_cols\" : None , \"id_cols\" : None , } scaler = scaler_mapper ( cont_cols = cont_cols , cat_cols = cat_cols , id_cols = [ target_col ] + id_cols , scaler_mapper_def = scaler_mapper_def , ) df_train_scaled = scaler . fit_transform ( df_train ) df_test_scaled = scaler . transform ( df_test ) df_valid_scaled = scaler . transform ( df_valid ) df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) df_train.reset_index(inplace=True, drop=True) df_valid.reset_index(inplace=True, drop=True) df_test.reset_index(inplace=True, drop=True) scaler_mapper_def = { \"cont_cols\": StandardScaler, \"cat_cols\": None, \"id_cols\": None, } scaler = scaler_mapper( cont_cols=cont_cols, cat_cols=cat_cols, id_cols=[target_col] + id_cols, scaler_mapper_def=scaler_mapper_def, ) df_train_scaled = scaler.fit_transform(df_train) df_test_scaled = scaler.transform(df_test) df_valid_scaled = scaler.transform(df_valid) Logistic Regression \u00b6 In [11]: Copied! LR_clf = LogisticRegression ( class_weight = \"balanced\" ) LR_clf . fit ( df_train_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ), df_train_scaled [ target_col ], ) LR_predicted = LR_clf . predict ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) print ( \"LR classification report : \\n \" + str ( classification_report ( df_test_scaled [ target_col ], LR_predicted )) ) LR_clf = LogisticRegression(class_weight=\"balanced\") LR_clf.fit( df_train_scaled.drop(columns=[target_col] + id_cols + cat_cols), df_train_scaled[target_col], ) LR_predicted = LR_clf.predict( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) print( \"LR classification report :\\n\" + str(classification_report(df_test_scaled[target_col], LR_predicted)) ) LR classification report : precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [12]: Copied! # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np . arange ( 0.2 , 0.8 , 0.01 ): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) )[:, 1 ] >= t ) . astype ( int ) threshold_score . append ( ( t , criterion ( df_test_scaled [ target_col ], preds_bin , average = \"weighted\" )) ) threshold_score = sorted ( threshold_score , key = lambda x : x [ 1 ], reverse = True ) best_threshold , best_score = threshold_score [ 0 ][ 0 ], threshold_score [ 0 ][ 1 ] print ( f \"The best threshold \\n { best_threshold } \\n , with score: \\n { best_score } \" ) # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np.arange(0.2, 0.8, 0.01): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) )[:, 1] >= t ).astype(int) threshold_score.append( (t, criterion(df_test_scaled[target_col], preds_bin, average=\"weighted\")) ) threshold_score = sorted(threshold_score, key=lambda x: x[1], reverse=True) best_threshold, best_score = threshold_score[0][0], threshold_score[0][1] print(f\"The best threshold\\n{best_threshold}\\n, with score:\\n{best_score}\") The best threshold 0.2 , with score: 1.0 In [13]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) In [14]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0 H2O AutoML \u00b6 NOTE : java is required : sudo apt install openjdk-17-jdk In [16]: Copied! # initialize H2O h2o . init ( log_dir = \"h2o_logs\" , log_level = \"WARN\" ) # read as h2o file h2o_train = h2o . H2OFrame ( df_train_scaled . drop ( columns = id_cols )) h2o_valid = h2o . H2OFrame ( df_valid_scaled . drop ( columns = id_cols )) h2o_test = h2o . H2OFrame ( df_test_scaled . drop ( columns = id_cols )) # For binary classification, response should be a factor h2o_train [ target_col ] = h2o_train [ target_col ] . asfactor () h2o_valid [ target_col ] = h2o_valid [ target_col ] . asfactor () h2o_test [ target_col ] = h2o_test [ target_col ] . asfactor () # Define AML task aml = H2OAutoML ( seed = random_state , max_runtime_secs = 180 ) # over/under sample for classification tasks aml . balance_classes = True # Run it _ = aml . train ( x = list ( cont_cols + cat_cols ), y = target_col , training_frame = h2o_train , leaderboard_frame = h2o_valid , ) m = aml . get_best_model () # Leaderboard, show and save lb = h2o . automl . get_leaderboard ( aml , extra_columns = \"ALL\" ) print ( lb ) print ( m . model_performance ( h2o_valid )) predictions = m . predict ( h2o_test ) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") # initialize H2O h2o.init(log_dir=\"h2o_logs\", log_level=\"WARN\") # read as h2o file h2o_train = h2o.H2OFrame(df_train_scaled.drop(columns=id_cols)) h2o_valid = h2o.H2OFrame(df_valid_scaled.drop(columns=id_cols)) h2o_test = h2o.H2OFrame(df_test_scaled.drop(columns=id_cols)) # For binary classification, response should be a factor h2o_train[target_col] = h2o_train[target_col].asfactor() h2o_valid[target_col] = h2o_valid[target_col].asfactor() h2o_test[target_col] = h2o_test[target_col].asfactor() # Define AML task aml = H2OAutoML(seed=random_state, max_runtime_secs=180) # over/under sample for classification tasks aml.balance_classes = True # Run it _ = aml.train( x=list(cont_cols + cat_cols), y=target_col, training_frame=h2o_train, leaderboard_frame=h2o_valid, ) m = aml.get_best_model() # Leaderboard, show and save lb = h2o.automl.get_leaderboard(aml, extra_columns=\"ALL\") print(lb) print(m.model_performance(h2o_valid)) predictions = m.predict(h2o_test) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") Checking whether there is an H2O instance running at http://localhost:54321..... not found. Attempting to start a local H2O server... Java Version: openjdk version \"17.0.10\" 2024-01-16; OpenJDK Runtime Environment (build 17.0.10+7-Ubuntu-122.04.1); OpenJDK 64-Bit Server VM (build 17.0.10+7-Ubuntu-122.04.1, mixed mode, sharing) Starting server from /opt/conda/lib/python3.10/site-packages/h2o/backend/bin/h2o.jar Ice root: /tmp/tmp0jm6weez JVM stdout: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.out JVM stderr: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.err Server is running at http://127.0.0.1:54321 Connecting to H2O server at http://127.0.0.1:54321 ... successful. #h2o-table-1.h2o-container { overflow-x: auto; } #h2o-table-1 .h2o-table { /* width: 100%; */ margin-top: 1em; margin-bottom: 1em; } #h2o-table-1 .h2o-table caption { white-space: nowrap; caption-side: top; text-align: left; /* margin-left: 1em; */ margin: 0; font-size: larger; } #h2o-table-1 .h2o-table thead { white-space: nowrap; position: sticky; top: 0; box-shadow: 0 -1px inset; } #h2o-table-1 .h2o-table tbody { overflow: auto; } #h2o-table-1 .h2o-table th, #h2o-table-1 .h2o-table td { text-align: right; /* border: 1px solid; */ } #h2o-table-1 .h2o-table tr:nth-child(even) { /* background: #F5F5F5 */ } H2O_cluster_uptime: 01 secs H2O_cluster_timezone: Etc/UTC H2O_data_parsing_timezone: UTC H2O_cluster_version: 3.46.0.2 H2O_cluster_version_age: 8 days H2O_cluster_name: H2O_from_python_unknownUser_maoqtf H2O_cluster_total_nodes: 1 H2O_cluster_free_memory: 16 Gb H2O_cluster_total_cores: 12 H2O_cluster_allowed_cores: 12 H2O_cluster_status: locked, healthy H2O_connection_url: http://127.0.0.1:54321 H2O_connection_proxy: {\"http\": null, \"https\": null} H2O_internal_security: False Python_version: 3.10.11 final Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% AutoML progress: | 13:16:41.801: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:44.939: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:46.154: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:16:59.224: _train param, Dropping unused columns: [barometric_pressure] 13:16:59.913: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:03.240: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:05.128: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:16.407: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588 13:17:27.855: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:17:40.196: _train param, Dropping unused columns: [barometric_pressure] 13:17:40.764: _train param, Dropping unused columns: [barometric_pressure] 13:17:41.338: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:44.575: _train param, Dropping bad and constant columns: [barometric_pressure] 13:17:45.413: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:53.937: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:55.712: _train param, Dropping unused columns: [barometric_pressure] 13:17:56.367: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 13:19:35.448: _train param, Dropping unused columns: [barometric_pressure] \u2588 13:19:36.22: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588| (done) 100% model_id auc logloss aucpr mean_per_class_error rmse mse training_time_ms predict_time_per_row_ms algo DeepLearning_grid_1_AutoML_1_20240522_131641_model_8 1 0.0177159 1 0 0.0582198 0.00338955 628 0.127786 DeepLearning DeepLearning_grid_1_AutoML_1_20240522_131641_model_4 1 0.0155053 1 0 0.0481989 0.00232314 117 0.1101 DeepLearning XGBoost_1_AutoML_1_20240522_131641 1 0.0347672 1 0 0.0789026 0.00622562 464 0.078661 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_12 1 0.0651884 1 0 0.106454 0.0113325 328 0.072441 XGBoost DeepLearning_grid_1_AutoML_1_20240522_131641_model_1 1 0.0235103 1 0 0.0600679 0.00360815 99 0.098096 DeepLearning XGBoost_grid_1_AutoML_1_20240522_131641_model_11 1 0.00556141 1 0 0.0320985 0.00103031 996 0.105708 XGBoost GBM_4_AutoML_1_20240522_131641 1 9.26741e-06 1 0 9.39779e-05 8.83184e-09 4293 0.280377 GBM XGBoost_grid_1_AutoML_1_20240522_131641_model_6 1 0.0141646 1 0 0.0244465 0.000597631 630 0.057813 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_13 1 0.0318374 1 0 0.0810283 0.00656559 745 0.069021 XGBoost GBM_3_AutoML_1_20240522_131641 1 2.55592e-05 1 0 0.000259039 6.7101e-08 4037 0.153118 GBM [47 rows x 10 columns] ModelMetricsBinomial: deeplearning ** Reported on test data. ** MSE: 0.0033895466349161047 RMSE: 0.05821981307867713 LogLoss: 0.017715874610611436 Mean Per-Class Error: 0.0 AUC: 1.0 AUCPR: 1.0 Gini: 1.0 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.9979014979275557 0 1 Error Rate ----- --- --- ------- ----------- 0 93 0 0 (0.0/93.0) 1 0 10 0 (0.0/10.0) Total 93 10 0 (0.0/103.0) Maximum Metrics: Maximum metrics at their respective thresholds metric threshold value idx --------------------------- ----------- ------- ----- max f1 0.997901 1 9 max f2 0.997901 1 9 max f0point5 0.997901 1 9 max accuracy 0.997901 1 9 max precision 0.999961 1 0 max recall 0.997901 1 9 max specificity 0.999961 1 0 max absolute_mcc 0.997901 1 9 max min_per_class_accuracy 0.997901 1 9 max mean_per_class_accuracy 0.997901 1 9 max tns 0.999961 93 0 max fns 0.999961 9 0 max fps 3.49717e-09 93 102 max tps 0.997901 10 9 max tnr 0.999961 1 0 max fnr 0.999961 0.9 0 max fpr 3.49717e-09 1 102 max tpr 0.997901 1 9 Gains/Lift Table: Avg response rate: 9.71 %, avg score: 11.26 % group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain kolmogorov_smirnov ------- -------------------------- ----------------- ------ ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------ ----------------- -------------------- 1 0.0194175 0.999958 10.3 10.3 1 0.999959 1 0.999959 0.2 0.2 930 930 0.2 2 0.0291262 0.999947 10.3 10.3 1 0.999947 1 0.999955 0.1 0.3 930 930 0.3 3 0.038835 0.999937 10.3 10.3 1 0.999938 1 0.999951 0.1 0.4 930 930 0.4 4 0.0485437 0.999927 10.3 10.3 1 0.99993 1 0.999947 0.1 0.5 930 930 0.5 5 0.0582524 0.999888 10.3 10.3 1 0.999889 1 0.999937 0.1 0.6 930 930 0.6 6 0.106796 0.372647 8.24 9.36364 0.8 0.879862 0.909091 0.945358 0.4 1 724 836.364 0.989247 7 0.15534 0.10825 0 6.4375 0 0.168191 0.625 0.702493 0 1 -100 543.75 0.935484 8 0.203883 0.00316095 0 4.90476 0 0.0682301 0.47619 0.551478 0 1 -100 390.476 0.88172 9 0.300971 0.000343083 0 3.32258 0 0.00100323 0.322581 0.373906 0 1 -100 232.258 0.774194 10 0.398058 0.000119786 0 2.5122 0 0.000202804 0.243902 0.282759 0 1 -100 151.22 0.666667 11 0.504854 8.52118e-05 0 1.98077 0 0.000102754 0.192308 0.222966 0 1 -100 98.0769 0.548387 12 0.601942 6.24023e-05 0 1.66129 0 7.12997e-05 0.16129 0.187015 0 1 -100 66.129 0.44086 13 0.699029 4.02512e-05 0 1.43056 0 5.12346e-05 0.138889 0.161048 0 1 -100 43.0556 0.333333 14 0.796117 2.31994e-05 0 1.2561 0 3.1514e-05 0.121951 0.141412 0 1 -100 25.6098 0.225806 15 0.893204 1.36306e-05 0 1.11957 0 1.91168e-05 0.108696 0.126043 0 1 -100 11.9565 0.11828 16 1 3.49717e-09 0 1 0 5.41845e-06 0.0970874 0.112583 0 1 -100 0 0 deeplearning prediction progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% In [17]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = h2o . as_list ( predictions . drop ([ \"predict\" ])) . values micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = h2o.as_list(predictions.drop([\"predict\"])).values micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" In [18]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0 In [19]: Copied! fig , ax = plt . subplots ( figsize = ( 6 , 6 )) n_classes = 2 for class_id in range ( n_classes ): RocCurveDisplay . from_predictions ( y_onehot_test [:, class_id ], y_score [:, class_id ], name = f \"ROC curve for class { class_id } \" , ax = ax , # plot_chance_level=(class_id == 2), ) fig, ax = plt.subplots(figsize=(6, 6)) n_classes = 2 for class_id in range(n_classes): RocCurveDisplay.from_predictions( y_onehot_test[:, class_id], y_score[:, class_id], name=f\"ROC curve for class {class_id}\", ax=ax, # plot_chance_level=(class_id == 2), ) In [20]: Copied! print ( \"Classification report: \\n {} \" . format ( classification_report ( df_test [ target_col ], h2o . as_list ( predictions [ \"predict\" ])) ) ) print( \"Classification report:\\n{}\".format( classification_report(df_test[target_col], h2o.as_list(predictions[\"predict\"])) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" LightGBM \u00b6 In [21]: Copied! config = {} config [ \"objective\" ] = \"binary\" df_train_scaled_enc = df_train_scaled . copy () df_valid_scaled_enc = df_valid_scaled . copy () df_test_scaled_enc = df_test_scaled . copy () lgb_cat_cols = cat_cols label_encoder = LabelEncoder ( lgb_cat_cols ) df_train_scaled_enc = label_encoder . fit_transform ( df_train_scaled_enc ) df_valid_scaled_enc = label_encoder . transform ( df_valid_scaled_enc ) df_test_scaled_enc = label_encoder . transform ( df_test_scaled_enc ) lgbtrain = lgbm . Dataset ( df_train_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_train_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbvalid = lgbm . Dataset ( df_valid_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_valid_scaled_enc [ target_col ], reference = lgbtrain , free_raw_data = False , ) # Final TRAIN/TEST ftrain = pd . concat ([ df_train_scaled_enc , df_valid_scaled_enc ]) . reset_index ( drop = True ) flgbtrain = lgbm . Dataset ( ftrain . drop ( columns = [ target_col ] + id_cols ), ftrain [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbtest = lgbm . Dataset ( df_test_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_test_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , reference = flgbtrain , free_raw_data = False , ) config = {} config[\"objective\"] = \"binary\" df_train_scaled_enc = df_train_scaled.copy() df_valid_scaled_enc = df_valid_scaled.copy() df_test_scaled_enc = df_test_scaled.copy() lgb_cat_cols = cat_cols label_encoder = LabelEncoder(lgb_cat_cols) df_train_scaled_enc = label_encoder.fit_transform(df_train_scaled_enc) df_valid_scaled_enc = label_encoder.transform(df_valid_scaled_enc) df_test_scaled_enc = label_encoder.transform(df_test_scaled_enc) lgbtrain = lgbm.Dataset( df_train_scaled_enc.drop(columns=[target_col] + id_cols), df_train_scaled_enc[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbvalid = lgbm.Dataset( df_valid_scaled_enc.drop(columns=[target_col] + id_cols), df_valid_scaled_enc[target_col], reference=lgbtrain, free_raw_data=False, ) # Final TRAIN/TEST ftrain = pd.concat([df_train_scaled_enc, df_valid_scaled_enc]).reset_index(drop=True) flgbtrain = lgbm.Dataset( ftrain.drop(columns=[target_col] + id_cols), ftrain[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbtest = lgbm.Dataset( df_test_scaled_enc.drop(columns=[target_col] + id_cols), df_test_scaled_enc[target_col], categorical_feature=lgb_cat_cols, reference=flgbtrain, free_raw_data=False, ) In [22]: Copied! model = lgbm . train ( config , lgbtrain , valid_sets = [ lgbvalid ], valid_names = [ \"\" ], feval = None , ) model = lgbm.train( config, lgbtrain, valid_sets=[lgbvalid], valid_names=[\"\"], feval=None, ) [LightGBM] [Info] Number of positive: 81, number of negative: 747 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002491 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 467 [LightGBM] [Info] Number of data points in the train set: 828, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097826 -> initscore=-2.221616 [LightGBM] [Info] Start training from score -2.221616 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf In [23]: Copied! res = predict_cls_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data , raw_score = True ), task = \"binary\" , ) result = pd . DataFrame ( { \"predicted\" : res , \"ground_truth\" : df_test [ target_col ] . values , } ) print ( \"Classification report: \\n {} \" . format ( classification_report ( result [ \"ground_truth\" ], result [ \"predicted\" ]) ) ) res = predict_cls_lgbm_from_raw( preds_raw=model.predict(lgbtest.data, raw_score=True), task=\"binary\", ) result = pd.DataFrame( { \"predicted\": res, \"ground_truth\": df_test[target_col].values, } ) print( \"Classification report:\\n{}\".format( classification_report(result[\"ground_truth\"], result[\"predicted\"]) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [24]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = predict_proba_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data ), task = \"binary\" , binary2d = True , ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = predict_proba_lgbm_from_raw( preds_raw=model.predict(lgbtest.data), task=\"binary\", binary2d=True, ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0","title":"03_models"},{"location":"notebooks/03_models.html#models","text":"","title":"Models"},{"location":"notebooks/03_models.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report , RocCurveDisplay , roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw , predict_proba_lgbm_from_raw , ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, RocCurveDisplay, roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw, predict_proba_lgbm_from_raw, ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start()","title":"Imports"},{"location":"notebooks/03_models.html#dataset","text":"In [3]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True In [4]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [8]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) check possible class imbalance In [9]: Copied! df_pd [ target_col ] . value_counts () df_pd[target_col].value_counts() Out[9]: class 0 934 1 101 Name: count, dtype: int64","title":"Dataset"},{"location":"notebooks/03_models.html#preprocessing","text":"divide dataset into train, test, valid scale continuous columns by standard scaler(not needed for LightGBM but for other mdoels...) In [10]: Copied! df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) df_train . reset_index ( inplace = True , drop = True ) df_valid . reset_index ( inplace = True , drop = True ) df_test . reset_index ( inplace = True , drop = True ) scaler_mapper_def = { \"cont_cols\" : StandardScaler , \"cat_cols\" : None , \"id_cols\" : None , } scaler = scaler_mapper ( cont_cols = cont_cols , cat_cols = cat_cols , id_cols = [ target_col ] + id_cols , scaler_mapper_def = scaler_mapper_def , ) df_train_scaled = scaler . fit_transform ( df_train ) df_test_scaled = scaler . transform ( df_test ) df_valid_scaled = scaler . transform ( df_valid ) df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) df_train.reset_index(inplace=True, drop=True) df_valid.reset_index(inplace=True, drop=True) df_test.reset_index(inplace=True, drop=True) scaler_mapper_def = { \"cont_cols\": StandardScaler, \"cat_cols\": None, \"id_cols\": None, } scaler = scaler_mapper( cont_cols=cont_cols, cat_cols=cat_cols, id_cols=[target_col] + id_cols, scaler_mapper_def=scaler_mapper_def, ) df_train_scaled = scaler.fit_transform(df_train) df_test_scaled = scaler.transform(df_test) df_valid_scaled = scaler.transform(df_valid)","title":"Preprocessing"},{"location":"notebooks/03_models.html#logistic-regression","text":"In [11]: Copied! LR_clf = LogisticRegression ( class_weight = \"balanced\" ) LR_clf . fit ( df_train_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ), df_train_scaled [ target_col ], ) LR_predicted = LR_clf . predict ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) print ( \"LR classification report : \\n \" + str ( classification_report ( df_test_scaled [ target_col ], LR_predicted )) ) LR_clf = LogisticRegression(class_weight=\"balanced\") LR_clf.fit( df_train_scaled.drop(columns=[target_col] + id_cols + cat_cols), df_train_scaled[target_col], ) LR_predicted = LR_clf.predict( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) print( \"LR classification report :\\n\" + str(classification_report(df_test_scaled[target_col], LR_predicted)) ) LR classification report : precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [12]: Copied! # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np . arange ( 0.2 , 0.8 , 0.01 ): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) )[:, 1 ] >= t ) . astype ( int ) threshold_score . append ( ( t , criterion ( df_test_scaled [ target_col ], preds_bin , average = \"weighted\" )) ) threshold_score = sorted ( threshold_score , key = lambda x : x [ 1 ], reverse = True ) best_threshold , best_score = threshold_score [ 0 ][ 0 ], threshold_score [ 0 ][ 1 ] print ( f \"The best threshold \\n { best_threshold } \\n , with score: \\n { best_score } \" ) # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np.arange(0.2, 0.8, 0.01): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) )[:, 1] >= t ).astype(int) threshold_score.append( (t, criterion(df_test_scaled[target_col], preds_bin, average=\"weighted\")) ) threshold_score = sorted(threshold_score, key=lambda x: x[1], reverse=True) best_threshold, best_score = threshold_score[0][0], threshold_score[0][1] print(f\"The best threshold\\n{best_threshold}\\n, with score:\\n{best_score}\") The best threshold 0.2 , with score: 1.0 In [13]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) In [14]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0","title":"Logistic Regression"},{"location":"notebooks/03_models.html#h2o-automl","text":"NOTE : java is required : sudo apt install openjdk-17-jdk In [16]: Copied! # initialize H2O h2o . init ( log_dir = \"h2o_logs\" , log_level = \"WARN\" ) # read as h2o file h2o_train = h2o . H2OFrame ( df_train_scaled . drop ( columns = id_cols )) h2o_valid = h2o . H2OFrame ( df_valid_scaled . drop ( columns = id_cols )) h2o_test = h2o . H2OFrame ( df_test_scaled . drop ( columns = id_cols )) # For binary classification, response should be a factor h2o_train [ target_col ] = h2o_train [ target_col ] . asfactor () h2o_valid [ target_col ] = h2o_valid [ target_col ] . asfactor () h2o_test [ target_col ] = h2o_test [ target_col ] . asfactor () # Define AML task aml = H2OAutoML ( seed = random_state , max_runtime_secs = 180 ) # over/under sample for classification tasks aml . balance_classes = True # Run it _ = aml . train ( x = list ( cont_cols + cat_cols ), y = target_col , training_frame = h2o_train , leaderboard_frame = h2o_valid , ) m = aml . get_best_model () # Leaderboard, show and save lb = h2o . automl . get_leaderboard ( aml , extra_columns = \"ALL\" ) print ( lb ) print ( m . model_performance ( h2o_valid )) predictions = m . predict ( h2o_test ) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") # initialize H2O h2o.init(log_dir=\"h2o_logs\", log_level=\"WARN\") # read as h2o file h2o_train = h2o.H2OFrame(df_train_scaled.drop(columns=id_cols)) h2o_valid = h2o.H2OFrame(df_valid_scaled.drop(columns=id_cols)) h2o_test = h2o.H2OFrame(df_test_scaled.drop(columns=id_cols)) # For binary classification, response should be a factor h2o_train[target_col] = h2o_train[target_col].asfactor() h2o_valid[target_col] = h2o_valid[target_col].asfactor() h2o_test[target_col] = h2o_test[target_col].asfactor() # Define AML task aml = H2OAutoML(seed=random_state, max_runtime_secs=180) # over/under sample for classification tasks aml.balance_classes = True # Run it _ = aml.train( x=list(cont_cols + cat_cols), y=target_col, training_frame=h2o_train, leaderboard_frame=h2o_valid, ) m = aml.get_best_model() # Leaderboard, show and save lb = h2o.automl.get_leaderboard(aml, extra_columns=\"ALL\") print(lb) print(m.model_performance(h2o_valid)) predictions = m.predict(h2o_test) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") Checking whether there is an H2O instance running at http://localhost:54321..... not found. Attempting to start a local H2O server... Java Version: openjdk version \"17.0.10\" 2024-01-16; OpenJDK Runtime Environment (build 17.0.10+7-Ubuntu-122.04.1); OpenJDK 64-Bit Server VM (build 17.0.10+7-Ubuntu-122.04.1, mixed mode, sharing) Starting server from /opt/conda/lib/python3.10/site-packages/h2o/backend/bin/h2o.jar Ice root: /tmp/tmp0jm6weez JVM stdout: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.out JVM stderr: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.err Server is running at http://127.0.0.1:54321 Connecting to H2O server at http://127.0.0.1:54321 ... successful. #h2o-table-1.h2o-container { overflow-x: auto; } #h2o-table-1 .h2o-table { /* width: 100%; */ margin-top: 1em; margin-bottom: 1em; } #h2o-table-1 .h2o-table caption { white-space: nowrap; caption-side: top; text-align: left; /* margin-left: 1em; */ margin: 0; font-size: larger; } #h2o-table-1 .h2o-table thead { white-space: nowrap; position: sticky; top: 0; box-shadow: 0 -1px inset; } #h2o-table-1 .h2o-table tbody { overflow: auto; } #h2o-table-1 .h2o-table th, #h2o-table-1 .h2o-table td { text-align: right; /* border: 1px solid; */ } #h2o-table-1 .h2o-table tr:nth-child(even) { /* background: #F5F5F5 */ } H2O_cluster_uptime: 01 secs H2O_cluster_timezone: Etc/UTC H2O_data_parsing_timezone: UTC H2O_cluster_version: 3.46.0.2 H2O_cluster_version_age: 8 days H2O_cluster_name: H2O_from_python_unknownUser_maoqtf H2O_cluster_total_nodes: 1 H2O_cluster_free_memory: 16 Gb H2O_cluster_total_cores: 12 H2O_cluster_allowed_cores: 12 H2O_cluster_status: locked, healthy H2O_connection_url: http://127.0.0.1:54321 H2O_connection_proxy: {\"http\": null, \"https\": null} H2O_internal_security: False Python_version: 3.10.11 final Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% AutoML progress: | 13:16:41.801: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:44.939: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:46.154: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:16:59.224: _train param, Dropping unused columns: [barometric_pressure] 13:16:59.913: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:03.240: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:05.128: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:16.407: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588 13:17:27.855: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:17:40.196: _train param, Dropping unused columns: [barometric_pressure] 13:17:40.764: _train param, Dropping unused columns: [barometric_pressure] 13:17:41.338: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:44.575: _train param, Dropping bad and constant columns: [barometric_pressure] 13:17:45.413: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:53.937: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:55.712: _train param, Dropping unused columns: [barometric_pressure] 13:17:56.367: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 13:19:35.448: _train param, Dropping unused columns: [barometric_pressure] \u2588 13:19:36.22: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588| (done) 100% model_id auc logloss aucpr mean_per_class_error rmse mse training_time_ms predict_time_per_row_ms algo DeepLearning_grid_1_AutoML_1_20240522_131641_model_8 1 0.0177159 1 0 0.0582198 0.00338955 628 0.127786 DeepLearning DeepLearning_grid_1_AutoML_1_20240522_131641_model_4 1 0.0155053 1 0 0.0481989 0.00232314 117 0.1101 DeepLearning XGBoost_1_AutoML_1_20240522_131641 1 0.0347672 1 0 0.0789026 0.00622562 464 0.078661 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_12 1 0.0651884 1 0 0.106454 0.0113325 328 0.072441 XGBoost DeepLearning_grid_1_AutoML_1_20240522_131641_model_1 1 0.0235103 1 0 0.0600679 0.00360815 99 0.098096 DeepLearning XGBoost_grid_1_AutoML_1_20240522_131641_model_11 1 0.00556141 1 0 0.0320985 0.00103031 996 0.105708 XGBoost GBM_4_AutoML_1_20240522_131641 1 9.26741e-06 1 0 9.39779e-05 8.83184e-09 4293 0.280377 GBM XGBoost_grid_1_AutoML_1_20240522_131641_model_6 1 0.0141646 1 0 0.0244465 0.000597631 630 0.057813 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_13 1 0.0318374 1 0 0.0810283 0.00656559 745 0.069021 XGBoost GBM_3_AutoML_1_20240522_131641 1 2.55592e-05 1 0 0.000259039 6.7101e-08 4037 0.153118 GBM [47 rows x 10 columns] ModelMetricsBinomial: deeplearning ** Reported on test data. ** MSE: 0.0033895466349161047 RMSE: 0.05821981307867713 LogLoss: 0.017715874610611436 Mean Per-Class Error: 0.0 AUC: 1.0 AUCPR: 1.0 Gini: 1.0 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.9979014979275557 0 1 Error Rate ----- --- --- ------- ----------- 0 93 0 0 (0.0/93.0) 1 0 10 0 (0.0/10.0) Total 93 10 0 (0.0/103.0) Maximum Metrics: Maximum metrics at their respective thresholds metric threshold value idx --------------------------- ----------- ------- ----- max f1 0.997901 1 9 max f2 0.997901 1 9 max f0point5 0.997901 1 9 max accuracy 0.997901 1 9 max precision 0.999961 1 0 max recall 0.997901 1 9 max specificity 0.999961 1 0 max absolute_mcc 0.997901 1 9 max min_per_class_accuracy 0.997901 1 9 max mean_per_class_accuracy 0.997901 1 9 max tns 0.999961 93 0 max fns 0.999961 9 0 max fps 3.49717e-09 93 102 max tps 0.997901 10 9 max tnr 0.999961 1 0 max fnr 0.999961 0.9 0 max fpr 3.49717e-09 1 102 max tpr 0.997901 1 9 Gains/Lift Table: Avg response rate: 9.71 %, avg score: 11.26 % group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain kolmogorov_smirnov ------- -------------------------- ----------------- ------ ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------ ----------------- -------------------- 1 0.0194175 0.999958 10.3 10.3 1 0.999959 1 0.999959 0.2 0.2 930 930 0.2 2 0.0291262 0.999947 10.3 10.3 1 0.999947 1 0.999955 0.1 0.3 930 930 0.3 3 0.038835 0.999937 10.3 10.3 1 0.999938 1 0.999951 0.1 0.4 930 930 0.4 4 0.0485437 0.999927 10.3 10.3 1 0.99993 1 0.999947 0.1 0.5 930 930 0.5 5 0.0582524 0.999888 10.3 10.3 1 0.999889 1 0.999937 0.1 0.6 930 930 0.6 6 0.106796 0.372647 8.24 9.36364 0.8 0.879862 0.909091 0.945358 0.4 1 724 836.364 0.989247 7 0.15534 0.10825 0 6.4375 0 0.168191 0.625 0.702493 0 1 -100 543.75 0.935484 8 0.203883 0.00316095 0 4.90476 0 0.0682301 0.47619 0.551478 0 1 -100 390.476 0.88172 9 0.300971 0.000343083 0 3.32258 0 0.00100323 0.322581 0.373906 0 1 -100 232.258 0.774194 10 0.398058 0.000119786 0 2.5122 0 0.000202804 0.243902 0.282759 0 1 -100 151.22 0.666667 11 0.504854 8.52118e-05 0 1.98077 0 0.000102754 0.192308 0.222966 0 1 -100 98.0769 0.548387 12 0.601942 6.24023e-05 0 1.66129 0 7.12997e-05 0.16129 0.187015 0 1 -100 66.129 0.44086 13 0.699029 4.02512e-05 0 1.43056 0 5.12346e-05 0.138889 0.161048 0 1 -100 43.0556 0.333333 14 0.796117 2.31994e-05 0 1.2561 0 3.1514e-05 0.121951 0.141412 0 1 -100 25.6098 0.225806 15 0.893204 1.36306e-05 0 1.11957 0 1.91168e-05 0.108696 0.126043 0 1 -100 11.9565 0.11828 16 1 3.49717e-09 0 1 0 5.41845e-06 0.0970874 0.112583 0 1 -100 0 0 deeplearning prediction progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% In [17]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = h2o . as_list ( predictions . drop ([ \"predict\" ])) . values micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = h2o.as_list(predictions.drop([\"predict\"])).values micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" In [18]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0 In [19]: Copied! fig , ax = plt . subplots ( figsize = ( 6 , 6 )) n_classes = 2 for class_id in range ( n_classes ): RocCurveDisplay . from_predictions ( y_onehot_test [:, class_id ], y_score [:, class_id ], name = f \"ROC curve for class { class_id } \" , ax = ax , # plot_chance_level=(class_id == 2), ) fig, ax = plt.subplots(figsize=(6, 6)) n_classes = 2 for class_id in range(n_classes): RocCurveDisplay.from_predictions( y_onehot_test[:, class_id], y_score[:, class_id], name=f\"ROC curve for class {class_id}\", ax=ax, # plot_chance_level=(class_id == 2), ) In [20]: Copied! print ( \"Classification report: \\n {} \" . format ( classification_report ( df_test [ target_col ], h2o . as_list ( predictions [ \"predict\" ])) ) ) print( \"Classification report:\\n{}\".format( classification_report(df_test[target_col], h2o.as_list(predictions[\"predict\"])) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\"","title":"H2O AutoML"},{"location":"notebooks/03_models.html#lightgbm","text":"In [21]: Copied! config = {} config [ \"objective\" ] = \"binary\" df_train_scaled_enc = df_train_scaled . copy () df_valid_scaled_enc = df_valid_scaled . copy () df_test_scaled_enc = df_test_scaled . copy () lgb_cat_cols = cat_cols label_encoder = LabelEncoder ( lgb_cat_cols ) df_train_scaled_enc = label_encoder . fit_transform ( df_train_scaled_enc ) df_valid_scaled_enc = label_encoder . transform ( df_valid_scaled_enc ) df_test_scaled_enc = label_encoder . transform ( df_test_scaled_enc ) lgbtrain = lgbm . Dataset ( df_train_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_train_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbvalid = lgbm . Dataset ( df_valid_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_valid_scaled_enc [ target_col ], reference = lgbtrain , free_raw_data = False , ) # Final TRAIN/TEST ftrain = pd . concat ([ df_train_scaled_enc , df_valid_scaled_enc ]) . reset_index ( drop = True ) flgbtrain = lgbm . Dataset ( ftrain . drop ( columns = [ target_col ] + id_cols ), ftrain [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbtest = lgbm . Dataset ( df_test_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_test_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , reference = flgbtrain , free_raw_data = False , ) config = {} config[\"objective\"] = \"binary\" df_train_scaled_enc = df_train_scaled.copy() df_valid_scaled_enc = df_valid_scaled.copy() df_test_scaled_enc = df_test_scaled.copy() lgb_cat_cols = cat_cols label_encoder = LabelEncoder(lgb_cat_cols) df_train_scaled_enc = label_encoder.fit_transform(df_train_scaled_enc) df_valid_scaled_enc = label_encoder.transform(df_valid_scaled_enc) df_test_scaled_enc = label_encoder.transform(df_test_scaled_enc) lgbtrain = lgbm.Dataset( df_train_scaled_enc.drop(columns=[target_col] + id_cols), df_train_scaled_enc[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbvalid = lgbm.Dataset( df_valid_scaled_enc.drop(columns=[target_col] + id_cols), df_valid_scaled_enc[target_col], reference=lgbtrain, free_raw_data=False, ) # Final TRAIN/TEST ftrain = pd.concat([df_train_scaled_enc, df_valid_scaled_enc]).reset_index(drop=True) flgbtrain = lgbm.Dataset( ftrain.drop(columns=[target_col] + id_cols), ftrain[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbtest = lgbm.Dataset( df_test_scaled_enc.drop(columns=[target_col] + id_cols), df_test_scaled_enc[target_col], categorical_feature=lgb_cat_cols, reference=flgbtrain, free_raw_data=False, ) In [22]: Copied! model = lgbm . train ( config , lgbtrain , valid_sets = [ lgbvalid ], valid_names = [ \"\" ], feval = None , ) model = lgbm.train( config, lgbtrain, valid_sets=[lgbvalid], valid_names=[\"\"], feval=None, ) [LightGBM] [Info] Number of positive: 81, number of negative: 747 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002491 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 467 [LightGBM] [Info] Number of data points in the train set: 828, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097826 -> initscore=-2.221616 [LightGBM] [Info] Start training from score -2.221616 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best 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positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf In [23]: Copied! res = predict_cls_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data , raw_score = True ), task = \"binary\" , ) result = pd . DataFrame ( { \"predicted\" : res , \"ground_truth\" : df_test [ target_col ] . values , } ) print ( \"Classification report: \\n {} \" . format ( classification_report ( result [ \"ground_truth\" ], result [ \"predicted\" ]) ) ) res = predict_cls_lgbm_from_raw( preds_raw=model.predict(lgbtest.data, raw_score=True), task=\"binary\", ) result = pd.DataFrame( { \"predicted\": res, \"ground_truth\": df_test[target_col].values, } ) print( \"Classification report:\\n{}\".format( classification_report(result[\"ground_truth\"], result[\"predicted\"]) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [24]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = predict_proba_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data ), task = \"binary\" , binary2d = True , ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = predict_proba_lgbm_from_raw( preds_raw=model.predict(lgbtest.data), task=\"binary\", binary2d=True, ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0","title":"LightGBM"},{"location":"notebooks/04_model_training_pipeline.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Example usage notebook \u00b6 Objects and functions in this notebook are listed with all paramaters to ilustrate their capabillities. Most of the paramaters have default values in the implementation Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd import mlflow pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd import mlflow pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" In [2]: Copied! import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump , dill_load from inference_model.training.utils import flatten_dict , get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump, dill_load from inference_model.training.utils import flatten_dict, get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast Train a testing model \u00b6 In [3]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [4]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) In [6]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) In [7]: Copied! prepare_data = PreprocessData ( id_cols = id_cols , target_col = target_col , cat_cols = cat_cols , cont_cols = cont_cols , ) # this should be fitted only on training data _ = prepare_data . fit ( df = df_pd ) prepare_data = PreprocessData( id_cols=id_cols, target_col=target_col, cat_cols=cat_cols, cont_cols=cont_cols, ) # this should be fitted only on training data _ = prepare_data.fit(df=df_pd) In [8]: Copied! optimizer = LGBOptunaOptimizer ( objective = \"binary\" , n_class = 2 , ) trainer = Trainer ( cat_cols = prepare_data . cat_cols , target_col = prepare_data . target_col , id_cols = id_cols , objective = \"binary\" , n_class = 2 , optimizer = optimizer , preprocessors = [ prepare_data ], ) metrics_dict = trainer . fit ( df_train = df_train , df_valid = df_valid , df_test = df_test , ) optimizer = LGBOptunaOptimizer( objective=\"binary\", n_class=2, ) trainer = Trainer( cat_cols=prepare_data.cat_cols, target_col=prepare_data.target_col, id_cols=id_cols, objective=\"binary\", n_class=2, optimizer=optimizer, preprocessors=[prepare_data], ) metrics_dict = trainer.fit( df_train=df_train, df_valid=df_valid, df_test=df_test, ) [I 2024-05-22 13:01:18,589] A new study created in memory with name: LightGBMTuner feature_fraction, val_score: inf: 0%| | 0/7 [00:00 In [27]: Copied! metrics_dict_flattened = flatten_dict ( metrics_dict ) # mlflow metrics can be only int, float not list del metrics_dict_flattened [ \"cm\" ] del metrics_dict_flattened [ \"prec_rec_curve\" ] metrics_dict_flattened = flatten_dict(metrics_dict) # mlflow metrics can be only int, float not list del metrics_dict_flattened[\"cm\"] del metrics_dict_flattened[\"prec_rec_curve\"] In [29]: Copied! run_name = \"test\" with mlflow . start_run ( experiment_id = experiment_id , run_name = run_name , nested = True ) as run : mlflow . log_params ( trainer . optimizer . best ) mlflow . log_metrics ( metrics_dict_flattened ) # Log tags mlflow . set_tags ( tags = { \"project\" : \"SUCCESS6G\" , \"optimizer_engine\" : \"optuna\" , \"model_family\" : \"ligtgbm\" , \"feature_set_version\" : 1 , } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow . pyfunc . log_model ( python_model = trainer , artifact_path = artifact_path , registered_model_name = registered_model_name , ) model_uri = mlflow . get_artifact_uri ( artifact_path ) print ( f \"Run ID: \\n { run . info . run_id } \\n Model uri: \\n { model_uri } \" ) run_name = \"test\" with mlflow.start_run( experiment_id=experiment_id, run_name=run_name, nested=True ) as run: mlflow.log_params(trainer.optimizer.best) mlflow.log_metrics(metrics_dict_flattened) # Log tags mlflow.set_tags( tags={ \"project\": \"SUCCESS6G\", \"optimizer_engine\": \"optuna\", \"model_family\": \"ligtgbm\", \"feature_set_version\": 1, } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow.pyfunc.log_model( python_model=trainer, artifact_path=artifact_path, registered_model_name=registered_model_name, ) model_uri = mlflow.get_artifact_uri(artifact_path) print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: typing.Union[pandas.core.frame.DataFrame, dict], skipping schema inference 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: , skipping schema inference Successfully registered model 'success6g_model'. 2024/05/22 14:44:22 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: success6g_model, version 1 Run ID: 24fedb96c5004767800fa32a3220c8dd Model uri: s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model Created version '1' of model 'success6g_model'. Testing of predictions \u00b6 Example data \u00b6 In [30]: Copied! df_pd . iloc [: 2 ] df_pd.iloc[:2] Out[30]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc In [31]: Copied! # for testing using mlflow inference service request_dict = { \"engine_load\" : [ 100.0 , 100.0 ], \"engine_coolant_temp\" : [ 17.0 , 17.0 ], \"engine_speed\" : [ 904.5 , 906.0 ], \"vehicle_speed\" : [ 0.0 , 0.0 ], \"intake_air_temp\" : [ 10.0 , 11.0 ], \"maf\" : [ 12.55 , 12.36 ], \"throttle_position\" : [ 83.14 , 83.14 ], \"fuel_rg_pressure\" : [ 37270.0 , 37800.0 ], \"barometric_pressure\" : [ 101.0 , 101.0 ], \"control_voltage\" : [ 0.06 , 14.56 ], \"class\" : [ 1 , 1 ], \"car_id\" : [ \"123abc\" , \"123abc\" ], } # for testing using mlflow inference service request_dict = { \"engine_load\": [100.0, 100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"class\": [1, 1], \"car_id\": [\"123abc\", \"123abc\"], } Trainer \u00b6 In [32]: Copied! trainer . predict ( df = df_pd . iloc [: 2 ] . drop ( columns = [ \"class\" ]), context = {}) trainer.predict(df=df_pd.iloc[:2].drop(columns=[\"class\"]), context={}) Out[32]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 0 14.269467 1 14.269467 Downloaded Trainer \u00b6 In [33]: Copied! model_uri model_uri Out[33]: 's3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model' In [34]: Copied! loaded_trainer = mlflow . pyfunc . load_model ( model_uri ) loaded_trainer = mlflow.pyfunc.load_model(model_uri) Downloading artifacts: 0%| | 0/9 [00:00=0.3.7 (from virtualenv) #10 1.197 Downloading distlib-0.3.8-py2.py3-none-any.whl.metadata (5.1 kB) #10 1.276 Collecting filelock < 4,>=3.12.2 (from virtualenv) #10 1.291 Downloading filelock-3.14.0-py3-none-any.whl.metadata (2.8 kB) #10 1.368 Collecting platformdirs < 5,>=3.9.1 (from virtualenv) #10 1.383 Downloading platformdirs-4.2.2-py3-none-any.whl.metadata (11 kB) #10 1.439 Downloading virtualenv-20.26.2-py3-none-any.whl (3.9 MB) #10 1.656 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.9/3.9 MB 18.3 MB/s eta 0:00:00 #10 1.673 Downloading distlib-0.3.8-py2.py3-none-any.whl (468 kB) #10 1.706 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 468.9/468.9 kB 17.0 MB/s eta 0:00:00 #10 1.724 Downloading filelock-3.14.0-py3-none-any.whl (12 kB) #10 1.752 Downloading platformdirs-4.2.2-py3-none-any.whl (18 kB) #10 1.871 Installing collected packages: distlib, platformdirs, filelock, virtualenv #10 2.123 Successfully installed distlib-0.3.8 filelock-3.14.0 platformdirs-4.2.2 virtualenv-20.26.2 #10 2.123 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #10 DONE 2.3s #11 [ 7/17] RUN apt-get install -y --no-install-recommends openjdk-11-jdk maven #11 0.605 Reading package lists... #11 1.699 Building dependency tree... #11 1.870 Reading state information... #11 2.070 The following additional packages will be installed: #11 2.070 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.070 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.070 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.070 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.070 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.070 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.070 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.070 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.070 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libplexus-utils2-java-doc junit4 testng libcommons-logging-java #11 2.072 liblog4j1.2-java openjdk-11-demo openjdk-11-source visualvm libnss-mdns #11 2.072 fonts-dejavu-extra fonts-ipafont-gothic fonts-ipafont-mincho #11 2.072 fonts-wqy-microhei | fonts-wqy-zenhei fonts-indic #11 2.072 Recommended packages: #11 2.072 alsa-ucm-conf alsa-topology-conf dbus libatk-wrapper-java-jni #11 2.072 fonts-dejavu-extra #11 2.267 The following NEW packages will be installed: #11 2.268 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.268 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.268 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.268 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.268 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.268 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.268 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.268 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.268 libmaven-resolver-java libmaven-shared-utils-java libmaven3-core-java #11 2.268 libpcsclite1 libplexus-cipher-java libplexus-classworlds-java #11 2.268 libplexus-component-annotations-java libplexus-interpolation-java #11 2.268 libplexus-sec-dispatcher-java libplexus-utils2-java libsisu-inject-java #11 2.268 libsisu-plexus-java libslf4j-java libwagon-file-java #11 2.269 libwagon-http-shaded-java libwagon-provider-api-java maven openjdk-11-jdk #11 2.269 openjdk-11-jdk-headless openjdk-11-jre openjdk-11-jre-headless #11 2.386 0 upgraded, 51 newly installed, 0 to remove and 0 not upgraded. #11 2.386 Need to get 125 MB of archives. #11 2.386 After this operation, 277 MB of additional disk space will be used. #11 2.386 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdbus-1-3 amd64 1.12.16-2ubuntu2.3 [179 kB] #11 2.602 Get:2 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previously unselected package libasound2-data. #11 26.91 Preparing to unpack .../07-libasound2-data_1.2.2-2.1ubuntu2.5_all.deb ... #11 26.91 Unpacking libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 27.01 Selecting previously unselected package libasound2:amd64. #11 27.01 Preparing to unpack .../08-libasound2_1.2.2-2.1ubuntu2.5_amd64.deb ... #11 27.02 Unpacking libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 27.14 Selecting previously unselected package libgraphite2-3:amd64. #11 27.14 Preparing to unpack .../09-libgraphite2-3_1.3.13-11build1_amd64.deb ... #11 27.15 Unpacking libgraphite2-3:amd64 (1.3.13-11build1) ... #11 27.24 Selecting previously unselected package libharfbuzz0b:amd64. #11 27.24 Preparing to unpack .../10-libharfbuzz0b_2.6.4-1ubuntu4.2_amd64.deb ... #11 27.25 Unpacking libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 27.37 Selecting previously unselected package libpcsclite1:amd64. #11 27.38 Preparing to unpack .../11-libpcsclite1_1.8.26-3_amd64.deb ... #11 27.38 Unpacking libpcsclite1:amd64 (1.8.26-3) ... #11 27.47 Selecting previously unselected package openjdk-11-jre-headless:amd64. #11 27.47 Preparing to unpack .../12-openjdk-11-jre-headless_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 27.48 Unpacking openjdk-11-jre-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 31.00 Selecting previously unselected package ca-certificates-java. #11 31.00 Preparing to unpack .../13-ca-certificates-java_20190405ubuntu1.1_all.deb ... #11 31.02 Unpacking ca-certificates-java (20190405ubuntu1.1) ... #11 31.08 Selecting previously unselected package libapache-pom-java. #11 31.09 Preparing to unpack .../14-libapache-pom-java_18-1_all.deb ... #11 31.10 Unpacking libapache-pom-java (18-1) ... #11 31.16 Selecting previously unselected package libatinject-jsr330-api-java. #11 31.16 Preparing to unpack .../15-libatinject-jsr330-api-java_1.0+ds1-5_all.deb ... #11 31.17 Unpacking libatinject-jsr330-api-java (1.0+ds1-5) ... #11 31.23 Selecting previously unselected package libgeronimo-interceptor-3.0-spec-java. #11 31.24 Preparing to unpack .../16-libgeronimo-interceptor-3.0-spec-java_1.0.1-4fakesync_all.deb ... #11 31.25 Unpacking libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 31.31 Selecting previously unselected package libcdi-api-java. #11 31.31 Preparing to unpack .../17-libcdi-api-java_1.2-2_all.deb ... #11 31.32 Unpacking libcdi-api-java (1.2-2) ... #11 31.39 Selecting previously unselected package libcommons-cli-java. #11 31.39 Preparing to unpack .../18-libcommons-cli-java_1.4-1_all.deb ... #11 31.40 Unpacking libcommons-cli-java (1.4-1) ... #11 31.47 Selecting previously unselected package libcommons-parent-java. #11 31.48 Preparing to unpack .../19-libcommons-parent-java_43-1_all.deb ... #11 31.49 Unpacking libcommons-parent-java (43-1) ... #11 31.55 Selecting previously unselected package libcommons-io-java. #11 31.56 Preparing to unpack .../20-libcommons-io-java_2.6-2ubuntu0.20.04.1_all.deb ... #11 31.56 Unpacking libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 31.66 Selecting previously unselected package libcommons-lang3-java. #11 31.66 Preparing to unpack .../21-libcommons-lang3-java_3.8-2_all.deb ... #11 31.67 Unpacking libcommons-lang3-java (3.8-2) ... #11 31.78 Selecting previously unselected package libgeronimo-annotation-1.3-spec-java. #11 31.78 Preparing to unpack .../22-libgeronimo-annotation-1.3-spec-java_1.0-1_all.deb ... #11 31.79 Unpacking libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 31.86 Selecting previously unselected package libgif7:amd64. #11 31.87 Preparing to unpack .../23-libgif7_5.1.9-1_amd64.deb ... #11 31.88 Unpacking libgif7:amd64 (5.1.9-1) ... #11 31.95 Selecting previously unselected package libjsr305-java. #11 31.95 Preparing to unpack .../24-libjsr305-java_0.1~+svn49-11_all.deb ... #11 31.96 Unpacking libjsr305-java (0.1~+svn49-11) ... #11 32.03 Selecting previously unselected package libguava-java. #11 32.03 Preparing to unpack .../25-libguava-java_19.0-1_all.deb ... #11 32.04 Unpacking libguava-java (19.0-1) ... #11 32.29 Selecting previously unselected package libaopalliance-java. #11 32.29 Preparing to unpack .../26-libaopalliance-java_20070526-6_all.deb ... #11 32.30 Unpacking libaopalliance-java (20070526-6) ... #11 32.36 Selecting previously unselected package libguice-java. #11 32.37 Preparing to unpack .../27-libguice-java_4.2.1-1ubuntu0.2_all.deb ... #11 32.37 Unpacking libguice-java (4.2.1-1ubuntu0.2) ... #11 32.59 Selecting previously unselected package libhawtjni-runtime-java. #11 32.60 Preparing to unpack .../28-libhawtjni-runtime-java_1.17-1_all.deb ... #11 32.60 Unpacking libhawtjni-runtime-java (1.17-1) ... #11 32.67 Selecting previously unselected package libjansi-native-java. #11 32.68 Preparing to unpack .../29-libjansi-native-java_1.8-1_all.deb ... #11 32.68 Unpacking libjansi-native-java (1.8-1) ... #11 32.75 Selecting previously unselected package libjansi-java. #11 32.75 Preparing to unpack .../30-libjansi-java_1.18-1_all.deb ... #11 32.76 Unpacking libjansi-java (1.18-1) ... #11 32.83 Selecting previously unselected package libmaven-parent-java. #11 32.84 Preparing to unpack .../31-libmaven-parent-java_31-2_all.deb ... #11 32.85 Unpacking libmaven-parent-java (31-2) ... #11 32.91 Selecting previously unselected package libplexus-utils2-java. #11 32.91 Preparing to unpack .../32-libplexus-utils2-java_3.3.0-1_all.deb ... #11 32.92 Unpacking libplexus-utils2-java (3.3.0-1) ... #11 33.02 Selecting previously unselected package libwagon-provider-api-java. #11 33.02 Preparing to unpack .../33-libwagon-provider-api-java_3.3.4-1_all.deb ... #11 33.03 Unpacking libwagon-provider-api-java (3.3.4-1) ... #11 33.10 Selecting previously unselected package libmaven-resolver-java. #11 33.11 Preparing to unpack .../34-libmaven-resolver-java_1.4.1-1_all.deb ... #11 33.12 Unpacking libmaven-resolver-java (1.4.1-1) ... #11 33.26 Selecting previously unselected package libmaven-shared-utils-java. #11 33.27 Preparing to unpack .../35-libmaven-shared-utils-java_3.3.0-1ubuntu0.20.04.1_all.deb ... #11 33.27 Unpacking libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 33.36 Selecting previously unselected package libplexus-cipher-java. #11 33.37 Preparing to unpack .../36-libplexus-cipher-java_1.7-3_all.deb ... #11 33.37 Unpacking libplexus-cipher-java (1.7-3) ... #11 33.44 Selecting previously unselected package libplexus-classworlds-java. #11 33.45 Preparing to unpack .../37-libplexus-classworlds-java_2.6.0-1_all.deb ... #11 33.45 Unpacking libplexus-classworlds-java (2.6.0-1) ... #11 33.53 Selecting previously unselected package libplexus-component-annotations-java. #11 33.53 Preparing to unpack .../38-libplexus-component-annotations-java_2.1.0-1_all.deb ... #11 33.54 Unpacking libplexus-component-annotations-java (2.1.0-1) ... #11 33.60 Selecting previously unselected package libplexus-interpolation-java. #11 33.60 Preparing to unpack .../39-libplexus-interpolation-java_1.26-1_all.deb ... #11 33.61 Unpacking libplexus-interpolation-java (1.26-1) ... #11 33.69 Selecting previously unselected package libplexus-sec-dispatcher-java. #11 33.70 Preparing to unpack .../40-libplexus-sec-dispatcher-java_1.4-4_all.deb ... #11 33.70 Unpacking libplexus-sec-dispatcher-java (1.4-4) ... #11 33.77 Selecting previously unselected package libslf4j-java. #11 33.77 Preparing to unpack .../41-libslf4j-java_1.7.25-3_all.deb ... #11 33.78 Unpacking libslf4j-java (1.7.25-3) ... #11 33.89 Selecting previously unselected package libsisu-inject-java. #11 33.89 Preparing to unpack .../42-libsisu-inject-java_0.3.3-1_all.deb ... #11 33.90 Unpacking libsisu-inject-java (0.3.3-1) ... #11 34.01 Selecting previously unselected package libsisu-plexus-java. #11 34.02 Preparing to unpack .../43-libsisu-plexus-java_0.3.3-3_all.deb ... #11 34.02 Unpacking libsisu-plexus-java (0.3.3-3) ... #11 34.12 Selecting previously unselected package libmaven3-core-java. #11 34.12 Preparing to unpack .../44-libmaven3-core-java_3.6.3-1ubuntu0.1_all.deb ... #11 34.13 Unpacking libmaven3-core-java (3.6.3-1ubuntu0.1) ... #11 34.35 Selecting previously unselected package libwagon-file-java. #11 34.36 Preparing to unpack .../45-libwagon-file-java_3.3.4-1_all.deb ... #11 34.36 Unpacking libwagon-file-java (3.3.4-1) ... #11 34.42 Selecting previously unselected package libwagon-http-shaded-java. #11 34.43 Preparing to unpack .../46-libwagon-http-shaded-java_3.3.4-1_all.deb ... #11 34.43 Unpacking libwagon-http-shaded-java (3.3.4-1) ... #11 34.68 Selecting previously unselected package maven. #11 34.68 Preparing to unpack .../47-maven_3.6.3-1ubuntu0.1_all.deb ... #11 34.69 Unpacking maven (3.6.3-1ubuntu0.1) ... #11 34.76 Selecting previously unselected package openjdk-11-jre:amd64. #11 34.76 Preparing to unpack .../48-openjdk-11-jre_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 34.77 Unpacking openjdk-11-jre:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 34.88 Selecting previously unselected package openjdk-11-jdk-headless:amd64. #11 34.89 Preparing to unpack .../49-openjdk-11-jdk-headless_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 34.89 Unpacking openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 39.97 Selecting previously unselected package openjdk-11-jdk:amd64. #11 39.97 Preparing to unpack .../50-openjdk-11-jdk_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 39.98 Unpacking openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 40.14 Setting up libgraphite2-3:amd64 (1.3.13-11build1) ... #11 40.16 Setting up liblcms2-2:amd64 (2.9-4) ... #11 40.18 Setting up libslf4j-java (1.7.25-3) ... #11 40.20 Setting up libplexus-utils2-java (3.3.0-1) ... #11 40.22 Setting up libplexus-classworlds-java (2.6.0-1) ... #11 40.24 Setting up libjsr305-java (0.1~+svn49-11) ... #11 40.26 Setting up java-common (0.72) ... #11 40.30 Setting up libaopalliance-java (20070526-6) ... #11 40.32 Setting up libcommons-cli-java (1.4-1) ... #11 40.34 Setting up libplexus-component-annotations-java (2.1.0-1) ... #11 40.38 Setting up libplexus-cipher-java (1.7-3) ... #11 40.40 Setting up libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 40.42 Setting up libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 40.44 Setting up libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 40.46 Setting up libapache-pom-java (18-1) ... #11 40.48 Setting up libavahi-common-data:amd64 (0.7-4ubuntu7.3) ... #11 40.50 Setting up libatinject-jsr330-api-java (1.0+ds1-5) ... #11 40.53 Setting up libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 40.55 Setting up libplexus-interpolation-java (1.26-1) ... #11 40.57 Setting up libpcsclite1:amd64 (1.8.26-3) ... #11 40.59 Setting up libgif7:amd64 (5.1.9-1) ... #11 40.61 Setting up libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 40.63 Setting up libplexus-sec-dispatcher-java (1.4-4) ... #11 40.65 Setting up libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 40.67 Setting up libwagon-http-shaded-java (3.3.4-1) ... #11 40.69 Setting up libcdi-api-java (1.2-2) ... #11 40.71 Setting up libhawtjni-runtime-java (1.17-1) ... #11 40.73 Setting up libwagon-provider-api-java (3.3.4-1) ... #11 40.75 Setting up libmaven-parent-java (31-2) ... #11 40.77 Setting up libcommons-parent-java (43-1) ... #11 40.79 Setting up libavahi-common3:amd64 (0.7-4ubuntu7.3) ... #11 40.81 Setting up libmaven-resolver-java (1.4.1-1) ... #11 40.84 Setting up libguava-java (19.0-1) ... #11 40.86 Setting up libcommons-lang3-java (3.8-2) ... #11 40.88 Setting up libjansi-native-java (1.8-1) ... #11 40.90 Setting up libwagon-file-java (3.3.4-1) ... #11 40.92 Setting up libavahi-client3:amd64 (0.7-4ubuntu7.3) ... #11 40.94 Setting up libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 40.96 Setting up libguice-java (4.2.1-1ubuntu0.2) ... #11 40.98 Setting up libjansi-java (1.18-1) ... #11 41.00 Setting up libcups2:amd64 (2.3.1-9ubuntu1.6) ... #11 41.02 Setting up libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 41.04 Setting up libsisu-inject-java (0.3.3-1) ... #11 41.06 Setting up libsisu-plexus-java (0.3.3-3) ... #11 41.08 Setting up libmaven3-core-java (3.6.3-1ubuntu0.1) ... #11 41.11 Setting up openjdk-11-jre-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.31 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/java to provide /usr/bin/java (java) in auto mode #11 41.32 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jjs to provide /usr/bin/jjs (jjs) in auto mode #11 41.33 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/keytool to provide /usr/bin/keytool (keytool) in auto mode #11 41.34 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmid to provide /usr/bin/rmid (rmid) in auto mode #11 41.34 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmiregistry to provide /usr/bin/rmiregistry (rmiregistry) in auto mode #11 41.35 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/pack200 to provide /usr/bin/pack200 (pack200) in auto mode #11 41.36 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/unpack200 to provide /usr/bin/unpack200 (unpack200) in auto mode #11 41.37 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/lib/jexec to provide /usr/bin/jexec (jexec) in auto mode #11 41.92 Setting up openjdk-11-jre:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.95 Setting up openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.98 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jar to provide /usr/bin/jar (jar) in auto mode #11 41.99 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jarsigner to provide /usr/bin/jarsigner (jarsigner) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javac to provide /usr/bin/javac (javac) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javadoc to provide /usr/bin/javadoc (javadoc) in auto mode #11 42.01 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javap to provide /usr/bin/javap (javap) in auto mode #11 42.02 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jcmd to provide /usr/bin/jcmd (jcmd) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdb to provide /usr/bin/jdb (jdb) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeprscan to provide /usr/bin/jdeprscan (jdeprscan) in auto mode #11 42.04 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeps to provide /usr/bin/jdeps (jdeps) in auto mode #11 42.05 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jfr to provide /usr/bin/jfr (jfr) in auto mode #11 42.06 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jimage to provide /usr/bin/jimage (jimage) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jinfo to provide /usr/bin/jinfo (jinfo) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jlink to provide /usr/bin/jlink (jlink) in auto mode #11 42.08 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmap to provide /usr/bin/jmap (jmap) in auto mode #11 42.09 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmod to provide /usr/bin/jmod (jmod) in auto mode #11 42.10 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jps to provide /usr/bin/jps (jps) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jrunscript to provide /usr/bin/jrunscript (jrunscript) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jshell to provide /usr/bin/jshell (jshell) in auto mode #11 42.12 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstack to provide /usr/bin/jstack (jstack) in auto mode #11 42.13 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstat to provide /usr/bin/jstat (jstat) in auto mode #11 42.14 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstatd to provide /usr/bin/jstatd (jstatd) in auto mode #11 42.15 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmic to provide /usr/bin/rmic (rmic) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/serialver to provide /usr/bin/serialver (serialver) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jaotc to provide /usr/bin/jaotc (jaotc) in auto mode #11 42.17 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jhsdb to provide /usr/bin/jhsdb (jhsdb) in auto mode #11 42.19 Setting up maven (3.6.3-1ubuntu0.1) ... #11 42.23 update-alternatives: using /usr/share/maven/bin/mvn to provide /usr/bin/mvn (mvn) in auto mode #11 42.25 Setting up openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 42.28 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jconsole to provide /usr/bin/jconsole (jconsole) in auto mode #11 42.29 Setting up ca-certificates-java (20190405ubuntu1.1) ... #11 42.35 head: cannot open '/etc/ssl/certs/java/cacerts' for reading: No such file or directory #11 42.65 Adding debian:COMODO_RSA_Certification_Authority.pem #11 42.66 Adding debian:Hongkong_Post_Root_CA_1.pem #11 42.67 Adding debian:Entrust_Root_Certification_Authority_-_EC1.pem #11 42.67 Adding debian:Certainly_Root_R1.pem #11 42.68 Adding debian:Amazon_Root_CA_2.pem #11 42.68 Adding debian:Certainly_Root_E1.pem #11 42.68 Adding debian:AffirmTrust_Premium_ECC.pem #11 42.68 Adding debian:ANF_Secure_Server_Root_CA.pem #11 42.68 Adding debian:GlobalSign_Root_R46.pem #11 42.69 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068.pem #11 42.69 Adding debian:Certum_Trusted_Root_CA.pem #11 42.69 Adding debian:Go_Daddy_Root_Certificate_Authority_-_G2.pem #11 42.69 Adding debian:SecureSign_RootCA11.pem #11 42.69 Adding debian:Amazon_Root_CA_4.pem #11 42.70 Adding debian:GDCA_TrustAUTH_R5_ROOT.pem #11 42.70 Adding debian:T-TeleSec_GlobalRoot_Class_2.pem #11 42.70 Adding debian:Trustwave_Global_ECC_P256_Certification_Authority.pem #11 42.70 Adding debian:GTS_Root_R1.pem #11 42.71 Adding debian:Izenpe.com.pem #11 42.71 Adding debian:CFCA_EV_ROOT.pem #11 42.71 Adding debian:DigiCert_Global_Root_G2.pem #11 42.71 Adding debian:XRamp_Global_CA_Root.pem #11 42.72 Adding debian:SSL.com_EV_Root_Certification_Authority_RSA_R2.pem #11 42.72 Adding debian:Security_Communication_RootCA3.pem #11 42.72 Adding debian:Starfield_Services_Root_Certificate_Authority_-_G2.pem #11 42.72 Adding debian:Amazon_Root_CA_1.pem #11 42.72 Adding debian:DigiCert_Assured_ID_Root_CA.pem #11 42.72 Adding debian:SSL.com_Root_Certification_Authority_RSA.pem #11 42.73 Adding debian:Telia_Root_CA_v2.pem #11 42.73 Adding debian:SwissSign_Gold_CA_-_G2.pem #11 42.73 Adding debian:GLOBALTRUST_2020.pem #11 42.73 Adding debian:emSign_ECC_Root_CA_-_G3.pem #11 42.73 Adding debian:Microsoft_RSA_Root_Certificate_Authority_2017.pem #11 42.74 Adding debian:TunTrust_Root_CA.pem #11 42.74 Adding debian:Security_Communication_ECC_RootCA1.pem #11 42.74 Adding debian:DigiCert_TLS_ECC_P384_Root_G5.pem #11 42.74 Adding debian:vTrus_Root_CA.pem #11 42.75 Adding debian:Certum_EC-384_CA.pem #11 42.76 Adding debian:CA_Disig_Root_R2.pem #11 42.76 Adding debian:emSign_Root_CA_-_C1.pem #11 42.77 Adding debian:AffirmTrust_Commercial.pem #11 42.77 Adding debian:Entrust_Root_Certification_Authority_-_G4.pem #11 42.78 Adding debian:Starfield_Class_2_CA.pem #11 42.78 Adding debian:Baltimore_CyberTrust_Root.pem #11 42.79 Adding debian:GTS_Root_R4.pem #11 42.79 Adding debian:Starfield_Root_Certificate_Authority_-_G2.pem #11 42.80 Adding debian:QuoVadis_Root_CA_1_G3.pem #11 42.81 Adding debian:DigiCert_Trusted_Root_G4.pem #11 42.81 Adding debian:DigiCert_High_Assurance_EV_Root_CA.pem #11 42.82 Adding debian:AC_RAIZ_FNMT-RCM.pem #11 42.82 Adding debian:Comodo_AAA_Services_root.pem #11 42.82 Adding debian:emSign_ECC_Root_CA_-_C3.pem #11 42.83 Adding debian:Entrust_Root_Certification_Authority.pem #11 42.84 Adding debian:D-TRUST_EV_Root_CA_1_2020.pem #11 42.85 Adding debian:Certigna_Root_CA.pem #11 42.85 Adding debian:TeliaSonera_Root_CA_v1.pem #11 42.86 Adding debian:Certum_Trusted_Network_CA.pem #11 42.86 Adding debian:HiPKI_Root_CA_-_G1.pem #11 42.87 Adding debian:SZAFIR_ROOT_CA2.pem #11 42.87 Adding debian:USERTrust_RSA_Certification_Authority.pem #11 42.88 Adding debian:OISTE_WISeKey_Global_Root_GC_CA.pem #11 42.88 Adding debian:Buypass_Class_2_Root_CA.pem #11 42.89 Adding debian:AC_RAIZ_FNMT-RCM_SERVIDORES_SEGUROS.pem #11 42.89 Adding debian:e-Szigno_Root_CA_2017.pem #11 42.90 Adding debian:Microsoft_ECC_Root_Certificate_Authority_2017.pem #11 42.91 Adding debian:Security_Communication_Root_CA.pem #11 42.91 Adding debian:GlobalSign_Root_CA.pem #11 42.92 Adding debian:E-Tugra_Certification_Authority.pem #11 42.93 Adding debian:Trustwave_Global_Certification_Authority.pem #11 42.93 Adding debian:QuoVadis_Root_CA_2_G3.pem #11 42.93 Adding debian:SSL.com_EV_Root_Certification_Authority_ECC.pem #11 42.94 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068_2.pem #11 42.95 Adding debian:E-Tugra_Global_Root_CA_ECC_v3.pem #11 42.95 Adding debian:Entrust.net_Premium_2048_Secure_Server_CA.pem #11 42.96 Adding debian:AffirmTrust_Premium.pem #11 42.96 Adding debian:Amazon_Root_CA_3.pem #11 42.97 Adding debian:AffirmTrust_Networking.pem #11 42.98 Adding debian:IdenTrust_Public_Sector_Root_CA_1.pem #11 42.98 Adding debian:GlobalSign_ECC_Root_CA_-_R4.pem #11 42.99 Adding debian:QuoVadis_Root_CA_3_G3.pem #11 42.99 Adding debian:certSIGN_ROOT_CA.pem #11 43.00 Adding debian:IdenTrust_Commercial_Root_CA_1.pem #11 43.00 Adding debian:SecureTrust_CA.pem #11 43.01 Adding debian:DigiCert_Global_Root_G3.pem #11 43.01 Adding debian:GlobalSign_Root_E46.pem #11 43.02 Adding debian:SwissSign_Silver_CA_-_G2.pem #11 43.02 Adding debian:vTrus_ECC_Root_CA.pem #11 43.03 Adding debian:GlobalSign_ECC_Root_CA_-_R5.pem #11 43.04 Adding debian:OISTE_WISeKey_Global_Root_GB_CA.pem #11 43.04 Adding debian:DigiCert_Assured_ID_Root_G3.pem #11 43.05 Adding debian:emSign_Root_CA_-_G1.pem #11 43.05 Adding debian:DigiCert_TLS_RSA4096_Root_G5.pem #11 43.06 Adding debian:COMODO_Certification_Authority.pem #11 43.06 Adding debian:DigiCert_Global_Root_CA.pem #11 43.07 Adding debian:QuoVadis_Root_CA_2.pem #11 43.08 Adding debian:TWCA_Global_Root_CA.pem #11 43.09 Adding debian:ACCVRAIZ1.pem #11 43.09 Adding debian:GTS_Root_R2.pem #11 43.10 Adding debian:HARICA_TLS_RSA_Root_CA_2021.pem #11 43.10 Adding debian:Actalis_Authentication_Root_CA.pem #11 43.11 Adding debian:Certum_Trusted_Network_CA_2.pem #11 43.11 Adding debian:Atos_TrustedRoot_2011.pem #11 43.11 Adding debian:T-TeleSec_GlobalRoot_Class_3.pem #11 43.13 Adding debian:NetLock_Arany_=Class_Gold=_F\u0151tan\u00fas\u00edtv\u00e1ny.pem #11 43.13 Adding debian:Certigna.pem #11 43.14 Adding debian:Microsec_e-Szigno_Root_CA_2009.pem #11 43.14 Adding debian:UCA_Extended_Validation_Root.pem #11 43.14 Adding debian:DigiCert_Assured_ID_Root_G2.pem #11 43.15 Adding debian:QuoVadis_Root_CA_3.pem #11 43.15 Adding debian:NAVER_Global_Root_Certification_Authority.pem #11 43.16 Adding debian:GlobalSign_Root_CA_-_R6.pem #11 43.17 Adding debian:TUBITAK_Kamu_SM_SSL_Kok_Sertifikasi_-_Surum_1.pem #11 43.17 Adding debian:Trustwave_Global_ECC_P384_Certification_Authority.pem #11 43.18 Adding debian:Go_Daddy_Class_2_CA.pem #11 43.18 Adding debian:TWCA_Root_Certification_Authority.pem #11 43.19 Adding debian:certSIGN_Root_CA_G2.pem #11 43.19 Adding debian:COMODO_ECC_Certification_Authority.pem #11 43.20 Adding debian:ISRG_Root_X2.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_EV_2009.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_2009.pem #11 43.22 Adding debian:UCA_Global_G2_Root.pem #11 43.22 Adding debian:D-TRUST_BR_Root_CA_1_2020.pem #11 43.23 Adding debian:Secure_Global_CA.pem #11 43.24 Adding debian:Hongkong_Post_Root_CA_3.pem #11 43.24 Adding debian:Buypass_Class_3_Root_CA.pem #11 43.25 Adding debian:Entrust_Root_Certification_Authority_-_G2.pem #11 43.25 Adding debian:SSL.com_Root_Certification_Authority_ECC.pem #11 43.26 Adding debian:Hellenic_Academic_and_Research_Institutions_RootCA_2015.pem #11 43.27 Adding debian:E-Tugra_Global_Root_CA_RSA_v3.pem #11 43.27 Adding debian:USERTrust_ECC_Certification_Authority.pem #11 43.28 Adding debian:ePKI_Root_Certification_Authority.pem #11 43.28 Adding debian:ISRG_Root_X1.pem #11 43.29 Adding debian:HARICA_TLS_ECC_Root_CA_2021.pem #11 43.29 Adding debian:GTS_Root_R3.pem #11 43.30 Adding debian:Hellenic_Academic_and_Research_Institutions_ECC_RootCA_2015.pem #11 43.30 Adding debian:Security_Communication_RootCA2.pem #11 43.31 Adding debian:GlobalSign_Root_CA_-_R3.pem #11 43.35 done. #11 43.36 Processing triggers for mime-support (3.64ubuntu1) ... #11 43.40 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #11 43.45 Processing triggers for ca-certificates (20230311ubuntu0.20.04.1) ... #11 43.47 Updating certificates in /etc/ssl/certs... #11 44.82 0 added, 0 removed; done. #11 44.82 Running hooks in /etc/ca-certificates/update.d... #11 44.83 #11 45.16 done. #11 45.16 done. #11 DONE 45.9s #12 [ 8/17] WORKDIR /opt/mlflow #12 DONE 0.1s #13 [ 9/17] RUN pip install mlflow==2.12.2 #13 1.009 Collecting mlflow==2.12.2 #13 1.075 Downloading mlflow-2.12.2-py3-none-any.whl.metadata (29 kB) #13 1.207 Collecting Flask < 4 (from mlflow==2.12.2) #13 1.222 Downloading flask-3.0.3-py3-none-any.whl.metadata (3.2 kB) #13 1.309 Collecting alembic!=1.10.0, < 2 (from mlflow==2.12.2) #13 1.324 Downloading alembic-1.13.1-py3-none-any.whl.metadata (7.4 kB) #13 1.395 Collecting click < 9,>=7.0 (from mlflow==2.12.2) #13 1.409 Downloading click-8.1.7-py3-none-any.whl.metadata (3.0 kB) #13 1.467 Collecting cloudpickle < 4 (from mlflow==2.12.2) #13 1.483 Downloading cloudpickle-3.0.0-py3-none-any.whl.metadata (7.0 kB) #13 1.564 Collecting docker < 8,>=4.0.0 (from mlflow==2.12.2) #13 1.579 Downloading docker-7.0.0-py3-none-any.whl.metadata (3.5 kB) #13 1.635 Collecting entrypoints < 1 (from mlflow==2.12.2) #13 1.651 Downloading entrypoints-0.4-py3-none-any.whl.metadata (2.6 kB) #13 1.742 Collecting gitpython < 4,>=3.1.9 (from mlflow==2.12.2) #13 1.757 Downloading GitPython-3.1.43-py3-none-any.whl.metadata (13 kB) #13 1.822 Collecting graphene < 4 (from mlflow==2.12.2) #13 1.837 Downloading graphene-3.3-py2.py3-none-any.whl.metadata (7.7 kB) #13 1.946 Collecting importlib-metadata!=4.7.0, < 8,>=3.7.0 (from mlflow==2.12.2) #13 1.960 Downloading importlib_metadata-7.1.0-py3-none-any.whl.metadata (4.7 kB) #13 2.027 Collecting markdown < 4,>=3.3 (from mlflow==2.12.2) #13 2.079 Downloading Markdown-3.6-py3-none-any.whl.metadata (7.0 kB) #13 2.299 Collecting matplotlib < 4 (from mlflow==2.12.2) #13 2.314 Downloading matplotlib-3.7.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (5.7 kB) #13 2.593 Collecting numpy < 2 (from mlflow==2.12.2) #13 2.612 Downloading numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB) #13 2.685 Collecting packaging < 25 (from mlflow==2.12.2) #13 2.698 Downloading packaging-24.0-py3-none-any.whl.metadata (3.2 kB) #13 2.902 Collecting pandas < 3 (from mlflow==2.12.2) #13 2.916 Downloading pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB) #13 3.172 Collecting protobuf < 5,>=3.12.0 (from mlflow==2.12.2) #13 3.187 Downloading protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes) #13 3.323 Collecting pyarrow < 16,>=4.0.0 (from mlflow==2.12.2) #13 3.338 Downloading pyarrow-15.0.2-cp38-cp38-manylinux_2_28_x86_64.whl.metadata (3.0 kB) #13 3.456 Collecting pytz < 2025 (from mlflow==2.12.2) #13 3.471 Downloading pytz-2024.1-py2.py3-none-any.whl.metadata (22 kB) #13 3.482 Requirement already satisfied: pyyaml < 7,>=5.1 in /usr/lib/python3/dist-packages (from mlflow==2.12.2) (5.3.1) #13 3.672 Collecting querystring-parser < 2 (from mlflow==2.12.2) #13 3.689 Downloading querystring_parser-1.2.4-py2.py3-none-any.whl.metadata (559 bytes) #13 3.780 Collecting requests < 3,>=2.17.3 (from mlflow==2.12.2) #13 3.794 Downloading requests-2.31.0-py3-none-any.whl.metadata (4.6 kB) #13 3.969 Collecting scikit-learn < 2 (from mlflow==2.12.2) #13 3.985 Downloading scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) #13 4.159 Collecting scipy < 2 (from mlflow==2.12.2) #13 4.175 Downloading scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (58 kB) #13 4.199 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 58.9/58.9 kB 2.7 MB/s eta 0:00:00 #13 4.633 Collecting sqlalchemy < 3,>=1.4.0 (from mlflow==2.12.2) #13 4.648 Downloading SQLAlchemy-2.0.30-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.6 kB) #13 4.709 Collecting sqlparse < 1,>=0.4.0 (from mlflow==2.12.2) #13 4.724 Downloading sqlparse-0.5.0-py3-none-any.whl.metadata (3.9 kB) #13 4.795 Collecting Jinja2 < 4,>=2.11 (from mlflow==2.12.2) #13 4.809 Downloading jinja2-3.1.4-py3-none-any.whl.metadata (2.6 kB) #13 4.929 Collecting gunicorn < 23 (from mlflow==2.12.2) #13 4.944 Downloading gunicorn-22.0.0-py3-none-any.whl.metadata (4.4 kB) #13 5.043 Collecting Mako (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.058 Downloading Mako-1.3.5-py3-none-any.whl.metadata (2.9 kB) #13 5.122 Collecting typing-extensions>=4 (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.136 Downloading typing_extensions-4.11.0-py3-none-any.whl.metadata (3.0 kB) #13 5.225 Collecting importlib-resources (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.241 Downloading importlib_resources-6.4.0-py3-none-any.whl.metadata (3.9 kB) #13 5.348 Collecting urllib3>=1.26.0 (from docker < 8,>=4.0.0->mlflow==2.12.2) #13 5.362 Downloading urllib3-2.2.1-py3-none-any.whl.metadata (6.4 kB) #13 5.445 Collecting Werkzeug>=3.0.0 (from Flask < 4->mlflow==2.12.2) #13 5.460 Downloading werkzeug-3.0.3-py3-none-any.whl.metadata (3.7 kB) #13 5.519 Collecting itsdangerous>=2.1.2 (from Flask < 4->mlflow==2.12.2) #13 5.537 Downloading itsdangerous-2.2.0-py3-none-any.whl.metadata (1.9 kB) #13 5.606 Collecting blinker>=1.6.2 (from Flask < 4->mlflow==2.12.2) #13 5.623 Downloading blinker-1.8.2-py3-none-any.whl.metadata (1.6 kB) #13 5.716 Collecting gitdb < 5,>=4.0.1 (from gitpython < 4,>=3.1.9->mlflow==2.12.2) #13 5.732 Downloading gitdb-4.0.11-py3-none-any.whl.metadata (1.2 kB) #13 5.847 Collecting graphql-core < 3.3,>=3.1 (from graphene < 4->mlflow==2.12.2) #13 5.862 Downloading graphql_core-3.2.3-py3-none-any.whl.metadata (10 kB) #13 5.920 Collecting graphql-relay < 3.3,>=3.1 (from graphene < 4->mlflow==2.12.2) #13 5.938 Downloading graphql_relay-3.2.0-py3-none-any.whl.metadata (12 kB) #13 6.001 Collecting aniso8601 < 10,>=8 (from graphene < 4->mlflow==2.12.2) #13 6.017 Downloading aniso8601-9.0.1-py2.py3-none-any.whl.metadata (23 kB) #13 6.122 Collecting zipp>=0.5 (from importlib-metadata!=4.7.0, < 8,>=3.7.0->mlflow==2.12.2) #13 6.136 Downloading zipp-3.18.2-py3-none-any.whl.metadata (3.5 kB) #13 6.235 Collecting MarkupSafe>=2.0 (from Jinja2 < 4,>=2.11->mlflow==2.12.2) #13 6.250 Downloading MarkupSafe-2.1.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.0 kB) #13 6.406 Collecting contourpy>=1.0.1 (from matplotlib < 4->mlflow==2.12.2) #13 6.420 Downloading contourpy-1.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.9 kB) #13 6.468 Collecting cycler>=0.10 (from matplotlib < 4->mlflow==2.12.2) #13 6.485 Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB) #13 6.657 Collecting fonttools>=4.22.0 (from matplotlib < 4->mlflow==2.12.2) #13 6.672 Downloading fonttools-4.51.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (159 kB) #13 6.694 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 159.5/159.5 kB 7.7 MB/s eta 0:00:00 #13 6.787 Collecting kiwisolver>=1.0.1 (from matplotlib < 4->mlflow==2.12.2) #13 6.826 Downloading kiwisolver-1.4.5-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (6.4 kB) #13 7.174 Collecting pillow>=6.2.0 (from matplotlib < 4->mlflow==2.12.2) #13 7.191 Downloading 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fonttools, entrypoints, cycler, cloudpickle, click, charset-normalizer, certifi, blinker, Werkzeug, sqlalchemy, scipy, requests, querystring-parser, python-dateutil, pyarrow, Mako, Jinja2, importlib-resources, importlib-metadata, gunicorn, graphql-relay, gitdb, contourpy, scikit-learn, pandas, matplotlib, markdown, graphene, gitpython, Flask, docker, alembic, mlflow #13 34.72 Successfully installed Flask-3.0.3 Jinja2-3.1.4 Mako-1.3.5 MarkupSafe-2.1.5 Werkzeug-3.0.3 alembic-1.13.1 aniso8601-9.0.1 blinker-1.8.2 certifi-2024.2.2 charset-normalizer-3.3.2 click-8.1.7 cloudpickle-3.0.0 contourpy-1.1.1 cycler-0.12.1 docker-7.0.0 entrypoints-0.4 fonttools-4.51.0 gitdb-4.0.11 gitpython-3.1.43 graphene-3.3 graphql-core-3.2.3 graphql-relay-3.2.0 greenlet-3.0.3 gunicorn-22.0.0 idna-3.7 importlib-metadata-7.1.0 importlib-resources-6.4.0 itsdangerous-2.2.0 joblib-1.4.2 kiwisolver-1.4.5 markdown-3.6 matplotlib-3.7.5 mlflow-2.12.2 numpy-1.24.4 packaging-24.0 pandas-2.0.3 pillow-10.3.0 protobuf-4.25.3 pyarrow-15.0.2 pyparsing-3.1.2 python-dateutil-2.9.0.post0 pytz-2024.1 querystring-parser-1.2.4 requests-2.31.0 scikit-learn-1.3.2 scipy-1.10.1 six-1.16.0 smmap-5.0.1 sqlalchemy-2.0.30 sqlparse-0.5.0 threadpoolctl-3.5.0 typing-extensions-4.11.0 tzdata-2024.1 urllib3-2.2.1 zipp-3.18.2 #13 34.72 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #13 DONE 36.4s #14 [10/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:pom -DoutputDirectory=/opt/java #14 1.558 [INFO] Scanning for projects... #14 1.845 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-clean-plugin/2.5/maven-clean-plugin-2.5.pom #14 2.423 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-clean-plugin/2.5/maven-clean-plugin-2.5.pom (3.9 kB at 6.7 kB/s) #14 2.448 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-plugins/22/maven-plugins-22.pom #14 2.499 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-plugins/22/maven-plugins-22.pom (13 kB at 256 kB/s) #14 2.511 [INFO] Downloading from central: 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12.86 [INFO] Finished at: 2024-05-16T20:25:32Z #14 12.86 [INFO] ------------------------------------------------------------------------ #14 DONE 13.0s #15 [11/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:jar -DoutputDirectory=/opt/java/jars #15 1.696 [INFO] Scanning for projects... #15 1.897 [INFO] #15 1.897 [INFO] ------------------ < org.apache.maven:standalone-pom >------------------- #15 1.898 [INFO] Building Maven Stub Project (No POM) 1 #15 1.898 [INFO] --------------------------------[ pom ]--------------------------------- #15 1.905 [INFO] #15 1.905 [INFO] --- maven-dependency-plugin:2.8:copy (default-cli) @ standalone-pom --- #15 2.594 [INFO] Configured Artifact: org.mlflow:mlflow-scoring:2.12.2:jar #15 2.804 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/mlflow/mlflow-scoring/2.12.2/mlflow-scoring-2.12.2.jar #15 3.207 [INFO] Downloaded from central: 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Downloaded from google-maven-central: https://maven-central.storage-download.googleapis.com/repos/central/data/org/apache/httpcomponents/httpclient/4.5.6/httpclient-4.5.6.jar (767 kB at 83 kB/s) #17 133.8 [INFO] Downloaded from google-maven-central: https://maven-central.storage-download.googleapis.com/repos/central/data/commons-io/commons-io/2.5/commons-io-2.5.jar (209 kB at 22 kB/s) #17 133.8 [INFO] Downloaded from google-maven-central: https://maven-central.storage-download.googleapis.com/repos/central/data/commons-codec/commons-codec/1.10/commons-codec-1.10.jar (284 kB at 29 kB/s) #17 133.9 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/com/google/protobuf/protobuf-java/3.19.4/protobuf-java-3.19.4.jar #17 134.0 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/com/google/protobuf/protobuf-java/3.19.4/protobuf-java-3.19.4.jar (1.7 MB at 13 MB/s) #17 134.1 [INFO] #17 134.1 [INFO] --- maven-dependency-plugin:2.8:copy-dependencies (default-cli) @ mlflow-scoring --- #17 134.4 [INFO] Copying wagon-provider-api-1.0-beta-6.jar to /opt/java/jars/wagon-provider-api-1.0-beta-6.jar #17 134.4 [INFO] Copying mleap-tensor_2.11-0.10.3.jar to /opt/java/jars/mleap-tensor_2.11-0.10.3.jar #17 134.4 [INFO] Copying maven-error-diagnostics-2.2.1.jar to /opt/java/jars/maven-error-diagnostics-2.2.1.jar #17 134.4 [INFO] Copying breeze_2.11-0.13.2.jar to /opt/java/jars/breeze_2.11-0.13.2.jar #17 134.5 [INFO] Copying plexus-java-0.9.8.jar to /opt/java/jars/plexus-java-0.9.8.jar #17 134.5 [INFO] Copying scala-library-2.11.8.jar to /opt/java/jars/scala-library-2.11.8.jar #17 134.5 [INFO] Copying maven-settings-2.2.1.jar to /opt/java/jars/maven-settings-2.2.1.jar #17 134.5 [INFO] Copying sourcecode_2.11-0.1.3.jar to /opt/java/jars/sourcecode_2.11-0.1.3.jar #17 134.5 [INFO] Copying httpcore-4.4.10.jar to /opt/java/jars/httpcore-4.4.10.jar #17 134.5 [INFO] Copying maven-surefire-common-2.22.0.jar to /opt/java/jars/maven-surefire-common-2.22.0.jar #17 134.5 [INFO] Copying macro-compat_2.11-1.1.1.jar to /opt/java/jars/macro-compat_2.11-1.1.1.jar #17 134.5 [INFO] Copying bundle-ml_2.11-0.10.3.jar to /opt/java/jars/bundle-ml_2.11-0.10.3.jar #17 134.5 [INFO] Copying fastparse-utils_2.11-0.4.2.jar to /opt/java/jars/fastparse-utils_2.11-0.4.2.jar #17 134.5 [INFO] Copying maven-artifact-manager-2.2.1.jar to /opt/java/jars/maven-artifact-manager-2.2.1.jar #17 134.5 [INFO] Copying jackrabbit-webdav-1.5.0.jar to /opt/java/jars/jackrabbit-webdav-1.5.0.jar #17 134.5 [INFO] Copying lenses_2.11-0.4.12.jar to /opt/java/jars/lenses_2.11-0.4.12.jar #17 134.5 [INFO] Copying asm-6.1.1.jar to /opt/java/jars/asm-6.1.1.jar #17 134.5 [INFO] Copying jackson-annotations-2.10.0.jar to /opt/java/jars/jackson-annotations-2.10.0.jar #17 134.5 [INFO] Copying snakeyaml-1.24.jar to /opt/java/jars/snakeyaml-1.24.jar #17 134.5 [INFO] Copying machinist_2.11-0.6.1.jar to /opt/java/jars/machinist_2.11-0.6.1.jar #17 134.5 [INFO] Copying maven-toolchain-2.2.1.jar to /opt/java/jars/maven-toolchain-2.2.1.jar #17 134.5 [INFO] Copying spire_2.11-0.13.0.jar to /opt/java/jars/spire_2.11-0.13.0.jar #17 134.5 [INFO] Copying maven-plugin-api-2.2.1.jar to /opt/java/jars/maven-plugin-api-2.2.1.jar #17 134.5 [INFO] Copying maven-project-2.2.1.jar to /opt/java/jars/maven-project-2.2.1.jar #17 134.5 [INFO] Copying qdox-2.0-M7.jar to /opt/java/jars/qdox-2.0-M7.jar #17 134.5 [INFO] Copying jetty-server-9.4.11.v20180605.jar to /opt/java/jars/jetty-server-9.4.11.v20180605.jar #17 134.5 [INFO] Copying jackson-core-2.10.0.jar to /opt/java/jars/jackson-core-2.10.0.jar #17 134.5 [INFO] Copying core-1.1.2.jar to /opt/java/jars/core-1.1.2.jar #17 134.5 [INFO] Copying jetty-security-9.4.11.v20180605.jar to /opt/java/jars/jetty-security-9.4.11.v20180605.jar #17 134.5 [INFO] Copying config-1.3.0.jar to /opt/java/jars/config-1.3.0.jar #17 134.5 [INFO] Copying maven-monitor-2.2.1.jar to /opt/java/jars/maven-monitor-2.2.1.jar #17 134.5 [INFO] Copying wagon-http-1.0-beta-6.jar to /opt/java/jars/wagon-http-1.0-beta-6.jar #17 134.5 [INFO] Copying mleap-core_2.11-0.10.3.jar to /opt/java/jars/mleap-core_2.11-0.10.3.jar #17 134.5 [INFO] Copying hamcrest-core-1.3.jar to /opt/java/jars/hamcrest-core-1.3.jar #17 134.5 [INFO] Copying breeze-macros_2.11-0.13.2.jar to /opt/java/jars/breeze-macros_2.11-0.13.2.jar #17 134.5 [INFO] Copying xercesMinimal-1.9.6.2.jar to /opt/java/jars/xercesMinimal-1.9.6.2.jar #17 134.5 [INFO] Copying jetty-http-9.4.11.v20180605.jar to /opt/java/jars/jetty-http-9.4.11.v20180605.jar #17 134.5 [INFO] Copying fastparse_2.11-0.4.2.jar to /opt/java/jars/fastparse_2.11-0.4.2.jar #17 134.5 [INFO] Copying protobuf-java-3.19.4.jar to /opt/java/jars/protobuf-java-3.19.4.jar #17 134.5 [INFO] Copying scala-arm_2.11-2.0.jar to /opt/java/jars/scala-arm_2.11-2.0.jar #17 134.5 [INFO] Copying arpack_combined_all-0.1.jar to /opt/java/jars/arpack_combined_all-0.1.jar #17 134.5 [INFO] Copying shapeless_2.11-2.3.2.jar to /opt/java/jars/shapeless_2.11-2.3.2.jar #17 134.5 [INFO] Copying mleap-runtime_2.11-0.10.3.jar to /opt/java/jars/mleap-runtime_2.11-0.10.3.jar #17 134.5 [INFO] Copying scala-reflect-2.11.8.jar to /opt/java/jars/scala-reflect-2.11.8.jar #17 134.5 [INFO] Copying commons-logging-1.2.jar to /opt/java/jars/commons-logging-1.2.jar #17 134.5 [INFO] Copying jetty-util-9.4.11.v20180605.jar to /opt/java/jars/jetty-util-9.4.11.v20180605.jar #17 134.5 [INFO] Copying jackson-databind-2.10.0.jar to /opt/java/jars/jackson-databind-2.10.0.jar #17 134.5 [INFO] Copying mleap-spark_2.11-0.10.3.jar to /opt/java/jars/mleap-spark_2.11-0.10.3.jar #17 134.5 [INFO] Copying plexus-utils-1.5.15.jar to /opt/java/jars/plexus-utils-1.5.15.jar #17 134.5 [INFO] Copying spire-macros_2.11-0.13.0.jar to /opt/java/jars/spire-macros_2.11-0.13.0.jar #17 134.5 [INFO] Copying plexus-interpolation-1.11.jar to /opt/java/jars/plexus-interpolation-1.11.jar #17 134.5 [INFO] Copying plexus-cipher-1.4.jar to /opt/java/jars/plexus-cipher-1.4.jar #17 134.5 [INFO] Copying spark-mllib-local_2.11-2.3.0.jar to /opt/java/jars/spark-mllib-local_2.11-2.3.0.jar #17 134.5 [INFO] Copying jtransforms-2.4.0.jar to /opt/java/jars/jtransforms-2.4.0.jar #17 134.5 [INFO] Copying plexus-container-default-1.0-alpha-9-stable-1.jar to /opt/java/jars/plexus-container-default-1.0-alpha-9-stable-1.jar #17 134.5 [INFO] Copying spray-json_2.11-1.3.2.jar to /opt/java/jars/spray-json_2.11-1.3.2.jar #17 134.5 [INFO] Copying jetty-servlet-9.4.11.v20180605.jar to /opt/java/jars/jetty-servlet-9.4.11.v20180605.jar #17 134.5 [INFO] Copying commons-io-2.5.jar to /opt/java/jars/commons-io-2.5.jar #17 134.5 [INFO] Copying surefire-logger-api-2.22.0.jar to /opt/java/jars/surefire-logger-api-2.22.0.jar #17 134.5 [INFO] Copying mleap-base_2.11-0.10.3.jar to /opt/java/jars/mleap-base_2.11-0.10.3.jar #17 134.5 [INFO] Copying surefire-booter-2.22.0.jar to /opt/java/jars/surefire-booter-2.22.0.jar #17 134.5 [INFO] Copying opencsv-2.3.jar to /opt/java/jars/opencsv-2.3.jar #17 134.5 [INFO] Copying spark-tags_2.11-2.3.0.jar to /opt/java/jars/spark-tags_2.11-2.3.0.jar #17 134.5 [INFO] Copying jackrabbit-jcr-commons-1.5.0.jar to /opt/java/jars/jackrabbit-jcr-commons-1.5.0.jar #17 134.5 [INFO] Copying classworlds-1.1.jar to /opt/java/jars/classworlds-1.1.jar #17 134.5 [INFO] Copying nekohtml-1.9.6.2.jar to /opt/java/jars/nekohtml-1.9.6.2.jar #17 134.5 [INFO] Copying maven-core-2.2.1.jar to /opt/java/jars/maven-core-2.2.1.jar #17 134.5 [INFO] Copying maven-surefire-plugin-2.22.0.jar to /opt/java/jars/maven-surefire-plugin-2.22.0.jar #17 134.5 [INFO] Copying mleap-spark-base_2.11-0.10.3.jar to /opt/java/jars/mleap-spark-base_2.11-0.10.3.jar #17 134.5 [INFO] Copying maven-repository-metadata-2.2.1.jar to /opt/java/jars/maven-repository-metadata-2.2.1.jar #17 134.5 [INFO] Copying wagon-webdav-jackrabbit-1.0-beta-6.jar to /opt/java/jars/wagon-webdav-jackrabbit-1.0-beta-6.jar #17 134.5 [INFO] Copying maven-artifact-2.2.1.jar to /opt/java/jars/maven-artifact-2.2.1.jar #17 134.5 [INFO] Copying commons-codec-1.10.jar to /opt/java/jars/commons-codec-1.10.jar #17 134.5 [INFO] Copying maven-plugin-descriptor-2.2.1.jar to /opt/java/jars/maven-plugin-descriptor-2.2.1.jar #17 134.5 [INFO] Copying unused-1.0.0.jar to /opt/java/jars/unused-1.0.0.jar #17 134.5 [INFO] Copying scalapb-runtime_2.11-0.6.0.jar to /opt/java/jars/scalapb-runtime_2.11-0.6.0.jar #17 134.5 [INFO] Copying maven-profile-2.2.1.jar to /opt/java/jars/maven-profile-2.2.1.jar #17 134.5 [INFO] Copying plexus-sec-dispatcher-1.3.jar to /opt/java/jars/plexus-sec-dispatcher-1.3.jar #17 134.5 [INFO] Copying maven-plugin-annotations-3.5.jar to /opt/java/jars/maven-plugin-annotations-3.5.jar #17 134.5 [INFO] Copying maven-reporting-api-2.2.1.jar to /opt/java/jars/maven-reporting-api-2.2.1.jar #17 134.5 [INFO] Copying javax.servlet-api-3.1.0.jar to /opt/java/jars/javax.servlet-api-3.1.0.jar #17 134.5 [INFO] Copying maven-plugin-parameter-documenter-2.2.1.jar to /opt/java/jars/maven-plugin-parameter-documenter-2.2.1.jar #17 134.5 [INFO] Copying commons-math3-3.4.1.jar to /opt/java/jars/commons-math3-3.4.1.jar #17 134.5 [INFO] Copying junit-4.12.jar to /opt/java/jars/junit-4.12.jar #17 134.5 [INFO] Copying slf4j-jdk14-1.7.25.jar to /opt/java/jars/slf4j-jdk14-1.7.25.jar #17 134.5 [INFO] Copying slf4j-api-1.7.25.jar to /opt/java/jars/slf4j-api-1.7.25.jar #17 134.5 [INFO] Copying jetty-io-9.4.11.v20180605.jar to /opt/java/jars/jetty-io-9.4.11.v20180605.jar #17 134.5 [INFO] Copying httpclient-4.5.6.jar to /opt/java/jars/httpclient-4.5.6.jar #17 134.5 [INFO] Copying maven-model-2.2.1.jar to /opt/java/jars/maven-model-2.2.1.jar #17 134.5 [INFO] Copying jackson-dataformat-yaml-2.10.0.jar to /opt/java/jars/jackson-dataformat-yaml-2.10.0.jar #17 134.5 [INFO] Copying doxia-logging-api-1.1.jar to /opt/java/jars/doxia-logging-api-1.1.jar #17 134.5 [INFO] Copying surefire-api-2.22.0.jar to /opt/java/jars/surefire-api-2.22.0.jar #17 134.5 [INFO] Copying maven-plugin-registry-2.2.1.jar to /opt/java/jars/maven-plugin-registry-2.2.1.jar #17 134.5 [INFO] Copying backport-util-concurrent-3.1.jar to /opt/java/jars/backport-util-concurrent-3.1.jar #17 134.5 [INFO] Copying wagon-http-shared-1.0-beta-6.jar to /opt/java/jars/wagon-http-shared-1.0-beta-6.jar #17 134.5 [INFO] Copying commons-httpclient-3.1.jar to /opt/java/jars/commons-httpclient-3.1.jar #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] BUILD SUCCESS #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] Total time: 02:12 min #17 134.5 [INFO] Finished at: 2024-05-16T20:27:50Z #17 134.5 [INFO] ------------------------------------------------------------------------ #17 DONE 134.8s #18 [14/17] COPY model_dir/success6g_model /opt/ml/model #18 DONE 0.1s #19 [15/17] RUN python -c \"from mlflow.models import container as C; C._install_pyfunc_deps('/opt/ml/model', install_mlflow=False, enable_mlserver=True, env_manager='virtualenv');\" #19 1.285 2024/05/16 20:27:52 INFO mlflow.models.container: creating and activating custom environment #19 1.289 2024/05/16 20:27:52 INFO mlflow.utils.virtualenv: Installing python 3.10.11 if it does not exist #19 1.673 Downloading Python-3.10.11.tar.xz... #19 1.673 -> https://www.python.org/ftp/python/3.10.11/Python-3.10.11.tar.xz #19 4.792 Installing Python-3.10.11... #19 108.0 Installed Python-3.10.11 to /root/.pyenv/versions/3.10.11 #19 108.4 2024/05/16 20:29:39 INFO mlflow.utils.virtualenv: Creating a new environment in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b with /root/.pyenv/versions/3.10.11/bin/python #19 109.3 created virtual environment CPython3.10.11.final.0-64 in 719ms #19 109.3 creator CPython3Posix(dest=/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b, clear=False, no_vcs_ignore=False, global=False) #19 109.3 seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/.local/share/virtualenv) #19 109.3 added seed packages: pip==24.0, setuptools==69.5.1, wheel==0.43.0 #19 109.3 activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator #19 109.4 2024/05/16 20:29:40 INFO mlflow.utils.virtualenv: Installing dependencies #19 168.4 WARNING: There was an error checking the latest version of pip. #19 169.3 Collecting mlserver!=1.3.1, < 1.4.0,>=1.2.0 #19 169.4 Downloading mlserver-1.3.5-py3-none-any.whl (113 kB) #19 169.4 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 113.2/113.2 kB 3.4 MB/s eta 0:00:00 #19 169.6 Collecting mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0 #19 169.6 Downloading mlserver_mlflow-1.3.5-py3-none-any.whl (10 kB) #19 169.7 Requirement already satisfied: click in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) (8.1.3) #19 169.8 Collecting fastapi!=0.89.0, < =0.89.1,>=0.88.0 (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.8 Downloading fastapi-0.89.1-py3-none-any.whl (55 kB) #19 169.8 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 55.8/55.8 kB 7.4 MB/s eta 0:00:00 #19 169.9 Collecting python-dotenv (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.9 Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB) #19 171.0 Collecting grpcio (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.0 Downloading grpcio-1.63.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB) #19 171.3 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1.4.0,>=1.2.0) (4.25.3) #19 171.5 Collecting uvicorn (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.5 Downloading uvicorn-0.29.0-py3-none-any.whl (60 kB) #19 171.6 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 60.8/60.8 kB 8.7 MB/s eta 0:00:00 #19 171.7 Collecting starlette-exporter (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.7 Downloading starlette_exporter-0.21.0-py3-none-any.whl (14 kB) #19 171.9 Collecting py-grpc-prometheus (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.9 Downloading py_grpc_prometheus-0.8.0-py3-none-any.whl (12 kB) #19 172.1 Collecting aiokafka (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 172.1 Downloading aiokafka-0.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB) #19 172.2 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anyio, aiosignal, starlette, geventhttpclient, aiohttp, starlette-exporter, fastapi, mlserver, mlserver-mlflow #19 184.4 Successfully installed aiofiles-23.2.1 aiohttp-3.9.5 aiokafka-0.10.0 aiosignal-1.3.1 anyio-4.3.0 attrs-23.2.0 brotli-1.1.0 exceptiongroup-1.2.1 fastapi-0.89.1 frozenlist-1.4.1 gevent-24.2.1 geventhttpclient-2.0.2 grpcio-1.63.0 h11-0.14.0 importlib-resources-6.4.0 mlserver-1.3.5 mlserver-mlflow-1.3.5 multidict-6.0.5 orjson-3.10.3 prometheus-client-0.20.0 py-grpc-prometheus-0.8.0 pydantic-1.10.15 python-dotenv-1.0.1 python-rapidjson-1.16 sniffio-1.3.1 starlette-0.22.0 starlette-exporter-0.17.1 tritonclient-2.45.0 uvicorn-0.29.0 uvloop-0.19.0 yarl-1.9.4 zope.event-5.0 zope.interface-6.4 #19 184.4 WARNING: There was an error checking the latest version of pip. #19 DONE 186.7s #20 [16/17] RUN chmod o+rwX /opt/mlflow/ #20 DONE 0.4s #21 [17/17] RUN rm -rf /var/lib/apt/lists/* #21 DONE 0.6s #22 exporting to image #22 exporting layers #22 exporting layers 12.0s done #22 writing image sha256:29875270957bd037ee2b833cf9201a001d353e4fef34b1d8c4923b278ef8ac02 done #22 naming to docker.io/5uperpalo/mlflow-success6g done #22 DONE 12.1s ```","title":"04_model_training_pipeline"},{"location":"notebooks/04_model_training_pipeline.html#example-usage-notebook","text":"Objects and functions in this notebook are listed with all paramaters to ilustrate their capabillities. Most of the paramaters have default values in the implementation","title":"Example usage notebook"},{"location":"notebooks/04_model_training_pipeline.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd import mlflow pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd import mlflow pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" In [2]: Copied! import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump , dill_load from inference_model.training.utils import flatten_dict , get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump, dill_load from inference_model.training.utils import flatten_dict, get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast","title":"Imports"},{"location":"notebooks/04_model_training_pipeline.html#train-a-testing-model","text":"In [3]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [4]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) In [6]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) In [7]: Copied! prepare_data = PreprocessData ( id_cols = id_cols , target_col = target_col , cat_cols = cat_cols , cont_cols = cont_cols , ) # this should be fitted only on training data _ = prepare_data . fit ( df = df_pd ) prepare_data = PreprocessData( id_cols=id_cols, target_col=target_col, cat_cols=cat_cols, cont_cols=cont_cols, ) # this should be fitted only on training data _ = prepare_data.fit(df=df_pd) In [8]: Copied! optimizer = LGBOptunaOptimizer ( objective = \"binary\" , n_class = 2 , ) trainer = Trainer ( cat_cols = prepare_data . cat_cols , target_col = prepare_data . target_col , id_cols = id_cols , objective = \"binary\" , n_class = 2 , optimizer = optimizer , preprocessors = [ prepare_data ], ) metrics_dict = trainer . fit ( df_train = df_train , df_valid = df_valid , df_test = df_test , ) optimizer = LGBOptunaOptimizer( objective=\"binary\", n_class=2, ) trainer = Trainer( cat_cols=prepare_data.cat_cols, target_col=prepare_data.target_col, id_cols=id_cols, objective=\"binary\", n_class=2, optimizer=optimizer, preprocessors=[prepare_data], ) metrics_dict = trainer.fit( df_train=df_train, df_valid=df_valid, df_test=df_test, ) [I 2024-05-22 13:01:18,589] A new study created in memory with name: LightGBMTuner feature_fraction, val_score: inf: 0%| | 0/7 [00:00 In [27]: Copied! metrics_dict_flattened = flatten_dict ( metrics_dict ) # mlflow metrics can be only int, float not list del metrics_dict_flattened [ \"cm\" ] del metrics_dict_flattened [ \"prec_rec_curve\" ] metrics_dict_flattened = flatten_dict(metrics_dict) # mlflow metrics can be only int, float not list del metrics_dict_flattened[\"cm\"] del metrics_dict_flattened[\"prec_rec_curve\"] In [29]: Copied! run_name = \"test\" with mlflow . start_run ( experiment_id = experiment_id , run_name = run_name , nested = True ) as run : mlflow . log_params ( trainer . optimizer . best ) mlflow . log_metrics ( metrics_dict_flattened ) # Log tags mlflow . set_tags ( tags = { \"project\" : \"SUCCESS6G\" , \"optimizer_engine\" : \"optuna\" , \"model_family\" : \"ligtgbm\" , \"feature_set_version\" : 1 , } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow . pyfunc . log_model ( python_model = trainer , artifact_path = artifact_path , registered_model_name = registered_model_name , ) model_uri = mlflow . get_artifact_uri ( artifact_path ) print ( f \"Run ID: \\n { run . info . run_id } \\n Model uri: \\n { model_uri } \" ) run_name = \"test\" with mlflow.start_run( experiment_id=experiment_id, run_name=run_name, nested=True ) as run: mlflow.log_params(trainer.optimizer.best) mlflow.log_metrics(metrics_dict_flattened) # Log tags mlflow.set_tags( tags={ \"project\": \"SUCCESS6G\", \"optimizer_engine\": \"optuna\", \"model_family\": \"ligtgbm\", \"feature_set_version\": 1, } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow.pyfunc.log_model( python_model=trainer, artifact_path=artifact_path, registered_model_name=registered_model_name, ) model_uri = mlflow.get_artifact_uri(artifact_path) print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: typing.Union[pandas.core.frame.DataFrame, dict], skipping schema inference 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: , skipping schema inference Successfully registered model 'success6g_model'. 2024/05/22 14:44:22 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: success6g_model, version 1 Run ID: 24fedb96c5004767800fa32a3220c8dd Model uri: s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model Created version '1' of model 'success6g_model'.","title":"Mlflow"},{"location":"notebooks/04_model_training_pipeline.html#testing-of-predictions","text":"","title":"Testing of predictions"},{"location":"notebooks/04_model_training_pipeline.html#example-data","text":"In [30]: Copied! df_pd . iloc [: 2 ] df_pd.iloc[:2] Out[30]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc In [31]: Copied! # for testing using mlflow inference service request_dict = { \"engine_load\" : [ 100.0 , 100.0 ], \"engine_coolant_temp\" : [ 17.0 , 17.0 ], \"engine_speed\" : [ 904.5 , 906.0 ], \"vehicle_speed\" : [ 0.0 , 0.0 ], \"intake_air_temp\" : [ 10.0 , 11.0 ], \"maf\" : [ 12.55 , 12.36 ], \"throttle_position\" : [ 83.14 , 83.14 ], \"fuel_rg_pressure\" : [ 37270.0 , 37800.0 ], \"barometric_pressure\" : [ 101.0 , 101.0 ], \"control_voltage\" : [ 0.06 , 14.56 ], \"class\" : [ 1 , 1 ], \"car_id\" : [ \"123abc\" , \"123abc\" ], } # for testing using mlflow inference service request_dict = { \"engine_load\": [100.0, 100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"class\": [1, 1], \"car_id\": [\"123abc\", \"123abc\"], }","title":"Example data"},{"location":"notebooks/04_model_training_pipeline.html#trainer","text":"In [32]: Copied! trainer . predict ( df = df_pd . iloc [: 2 ] . drop ( columns = [ \"class\" ]), context = {}) trainer.predict(df=df_pd.iloc[:2].drop(columns=[\"class\"]), context={}) Out[32]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 0 14.269467 1 14.269467","title":"Trainer"},{"location":"notebooks/04_model_training_pipeline.html#downloaded-trainer","text":"In [33]: Copied! model_uri model_uri Out[33]: 's3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model' In [34]: Copied! loaded_trainer = mlflow . pyfunc . load_model ( model_uri ) loaded_trainer = mlflow.pyfunc.load_model(model_uri) Downloading artifacts: 0%| | 0/9 [00:00=0.3.7 (from virtualenv) #10 1.197 Downloading distlib-0.3.8-py2.py3-none-any.whl.metadata (5.1 kB) #10 1.276 Collecting filelock < 4,>=3.12.2 (from virtualenv) #10 1.291 Downloading filelock-3.14.0-py3-none-any.whl.metadata (2.8 kB) #10 1.368 Collecting platformdirs < 5,>=3.9.1 (from virtualenv) #10 1.383 Downloading platformdirs-4.2.2-py3-none-any.whl.metadata (11 kB) #10 1.439 Downloading virtualenv-20.26.2-py3-none-any.whl (3.9 MB) #10 1.656 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.9/3.9 MB 18.3 MB/s eta 0:00:00 #10 1.673 Downloading distlib-0.3.8-py2.py3-none-any.whl (468 kB) #10 1.706 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 468.9/468.9 kB 17.0 MB/s eta 0:00:00 #10 1.724 Downloading filelock-3.14.0-py3-none-any.whl (12 kB) #10 1.752 Downloading platformdirs-4.2.2-py3-none-any.whl (18 kB) #10 1.871 Installing collected packages: distlib, platformdirs, filelock, virtualenv #10 2.123 Successfully installed distlib-0.3.8 filelock-3.14.0 platformdirs-4.2.2 virtualenv-20.26.2 #10 2.123 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #10 DONE 2.3s #11 [ 7/17] RUN apt-get install -y --no-install-recommends openjdk-11-jdk maven #11 0.605 Reading package lists... #11 1.699 Building dependency tree... #11 1.870 Reading state information... #11 2.070 The following additional packages will be installed: #11 2.070 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.070 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.070 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.070 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.070 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.070 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.070 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.070 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.070 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libplexus-utils2-java-doc junit4 testng libcommons-logging-java #11 2.072 liblog4j1.2-java openjdk-11-demo openjdk-11-source visualvm libnss-mdns #11 2.072 fonts-dejavu-extra fonts-ipafont-gothic fonts-ipafont-mincho #11 2.072 fonts-wqy-microhei | fonts-wqy-zenhei fonts-indic #11 2.072 Recommended packages: #11 2.072 alsa-ucm-conf alsa-topology-conf dbus libatk-wrapper-java-jni #11 2.072 fonts-dejavu-extra #11 2.267 The following NEW packages will be installed: #11 2.268 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.268 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.268 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.268 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.268 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.268 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.268 libguice-java libharfbuzz0b 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(Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 24567 files and directories currently installed.) #11 26.21 Preparing to unpack .../00-libdbus-1-3_1.12.16-2ubuntu2.3_amd64.deb ... #11 26.22 Unpacking libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 26.33 Selecting previously unselected package java-common. #11 26.33 Preparing to unpack .../01-java-common_0.72_all.deb ... #11 26.34 Unpacking java-common (0.72) ... #11 26.40 Selecting previously unselected package libavahi-common-data:amd64. #11 26.41 Preparing to unpack .../02-libavahi-common-data_0.7-4ubuntu7.3_amd64.deb ... #11 26.42 Unpacking libavahi-common-data:amd64 (0.7-4ubuntu7.3) ... #11 26.49 Selecting previously unselected package libavahi-common3:amd64. #11 26.50 Preparing to unpack .../03-libavahi-common3_0.7-4ubuntu7.3_amd64.deb ... #11 26.50 Unpacking libavahi-common3:amd64 (0.7-4ubuntu7.3) ... #11 26.58 Selecting previously unselected package libavahi-client3:amd64. #11 26.59 Preparing to unpack .../04-libavahi-client3_0.7-4ubuntu7.3_amd64.deb ... #11 26.59 Unpacking libavahi-client3:amd64 (0.7-4ubuntu7.3) ... #11 26.68 Selecting previously unselected package libcups2:amd64. #11 26.68 Preparing to unpack .../05-libcups2_2.3.1-9ubuntu1.6_amd64.deb ... #11 26.69 Unpacking libcups2:amd64 (2.3.1-9ubuntu1.6) ... #11 26.80 Selecting previously unselected package liblcms2-2:amd64. #11 26.80 Preparing to unpack .../06-liblcms2-2_2.9-4_amd64.deb ... #11 26.81 Unpacking liblcms2-2:amd64 (2.9-4) ... #11 26.90 Selecting previously unselected package libasound2-data. #11 26.91 Preparing to unpack .../07-libasound2-data_1.2.2-2.1ubuntu2.5_all.deb ... #11 26.91 Unpacking libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 27.01 Selecting previously unselected package libasound2:amd64. #11 27.01 Preparing to unpack .../08-libasound2_1.2.2-2.1ubuntu2.5_amd64.deb ... #11 27.02 Unpacking libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 27.14 Selecting previously unselected package libgraphite2-3:amd64. #11 27.14 Preparing to unpack .../09-libgraphite2-3_1.3.13-11build1_amd64.deb ... #11 27.15 Unpacking libgraphite2-3:amd64 (1.3.13-11build1) ... #11 27.24 Selecting previously unselected package libharfbuzz0b:amd64. #11 27.24 Preparing to unpack .../10-libharfbuzz0b_2.6.4-1ubuntu4.2_amd64.deb ... #11 27.25 Unpacking libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 27.37 Selecting previously unselected package libpcsclite1:amd64. #11 27.38 Preparing to unpack .../11-libpcsclite1_1.8.26-3_amd64.deb ... #11 27.38 Unpacking libpcsclite1:amd64 (1.8.26-3) ... #11 27.47 Selecting previously unselected package openjdk-11-jre-headless:amd64. #11 27.47 Preparing to unpack .../12-openjdk-11-jre-headless_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 27.48 Unpacking openjdk-11-jre-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 31.00 Selecting previously unselected package ca-certificates-java. #11 31.00 Preparing to unpack .../13-ca-certificates-java_20190405ubuntu1.1_all.deb ... #11 31.02 Unpacking ca-certificates-java (20190405ubuntu1.1) ... #11 31.08 Selecting previously unselected package libapache-pom-java. #11 31.09 Preparing to unpack .../14-libapache-pom-java_18-1_all.deb ... #11 31.10 Unpacking libapache-pom-java (18-1) ... #11 31.16 Selecting previously unselected package libatinject-jsr330-api-java. #11 31.16 Preparing to unpack .../15-libatinject-jsr330-api-java_1.0+ds1-5_all.deb ... #11 31.17 Unpacking libatinject-jsr330-api-java (1.0+ds1-5) ... #11 31.23 Selecting previously unselected package libgeronimo-interceptor-3.0-spec-java. #11 31.24 Preparing to unpack .../16-libgeronimo-interceptor-3.0-spec-java_1.0.1-4fakesync_all.deb ... #11 31.25 Unpacking libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 31.31 Selecting previously unselected package libcdi-api-java. #11 31.31 Preparing to unpack .../17-libcdi-api-java_1.2-2_all.deb ... #11 31.32 Unpacking libcdi-api-java (1.2-2) ... #11 31.39 Selecting previously unselected package libcommons-cli-java. #11 31.39 Preparing to unpack .../18-libcommons-cli-java_1.4-1_all.deb ... #11 31.40 Unpacking libcommons-cli-java (1.4-1) ... #11 31.47 Selecting previously unselected package libcommons-parent-java. #11 31.48 Preparing to unpack .../19-libcommons-parent-java_43-1_all.deb ... #11 31.49 Unpacking libcommons-parent-java (43-1) ... #11 31.55 Selecting previously unselected package libcommons-io-java. #11 31.56 Preparing to unpack .../20-libcommons-io-java_2.6-2ubuntu0.20.04.1_all.deb ... #11 31.56 Unpacking libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 31.66 Selecting previously unselected package libcommons-lang3-java. #11 31.66 Preparing to unpack .../21-libcommons-lang3-java_3.8-2_all.deb ... #11 31.67 Unpacking libcommons-lang3-java (3.8-2) ... #11 31.78 Selecting previously unselected package libgeronimo-annotation-1.3-spec-java. #11 31.78 Preparing to unpack .../22-libgeronimo-annotation-1.3-spec-java_1.0-1_all.deb ... #11 31.79 Unpacking libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 31.86 Selecting previously unselected package libgif7:amd64. #11 31.87 Preparing to unpack .../23-libgif7_5.1.9-1_amd64.deb ... #11 31.88 Unpacking libgif7:amd64 (5.1.9-1) ... #11 31.95 Selecting previously unselected package libjsr305-java. #11 31.95 Preparing to unpack .../24-libjsr305-java_0.1~+svn49-11_all.deb ... #11 31.96 Unpacking libjsr305-java (0.1~+svn49-11) ... #11 32.03 Selecting previously unselected package libguava-java. #11 32.03 Preparing to unpack .../25-libguava-java_19.0-1_all.deb ... #11 32.04 Unpacking libguava-java (19.0-1) ... #11 32.29 Selecting previously unselected package libaopalliance-java. #11 32.29 Preparing to unpack .../26-libaopalliance-java_20070526-6_all.deb ... #11 32.30 Unpacking libaopalliance-java (20070526-6) ... #11 32.36 Selecting previously unselected package libguice-java. #11 32.37 Preparing to unpack .../27-libguice-java_4.2.1-1ubuntu0.2_all.deb ... #11 32.37 Unpacking libguice-java (4.2.1-1ubuntu0.2) ... #11 32.59 Selecting previously unselected package libhawtjni-runtime-java. #11 32.60 Preparing to unpack .../28-libhawtjni-runtime-java_1.17-1_all.deb ... #11 32.60 Unpacking libhawtjni-runtime-java (1.17-1) ... #11 32.67 Selecting previously unselected package libjansi-native-java. #11 32.68 Preparing to unpack .../29-libjansi-native-java_1.8-1_all.deb ... #11 32.68 Unpacking libjansi-native-java (1.8-1) ... #11 32.75 Selecting previously unselected package libjansi-java. #11 32.75 Preparing to unpack .../30-libjansi-java_1.18-1_all.deb ... #11 32.76 Unpacking libjansi-java (1.18-1) ... #11 32.83 Selecting previously unselected package libmaven-parent-java. #11 32.84 Preparing to unpack .../31-libmaven-parent-java_31-2_all.deb ... #11 32.85 Unpacking libmaven-parent-java (31-2) ... #11 32.91 Selecting previously unselected package libplexus-utils2-java. #11 32.91 Preparing to unpack .../32-libplexus-utils2-java_3.3.0-1_all.deb ... #11 32.92 Unpacking libplexus-utils2-java (3.3.0-1) ... #11 33.02 Selecting previously unselected package libwagon-provider-api-java. #11 33.02 Preparing to unpack .../33-libwagon-provider-api-java_3.3.4-1_all.deb ... #11 33.03 Unpacking libwagon-provider-api-java (3.3.4-1) ... #11 33.10 Selecting previously unselected package libmaven-resolver-java. #11 33.11 Preparing to unpack .../34-libmaven-resolver-java_1.4.1-1_all.deb ... #11 33.12 Unpacking libmaven-resolver-java (1.4.1-1) ... #11 33.26 Selecting previously unselected package libmaven-shared-utils-java. #11 33.27 Preparing to unpack .../35-libmaven-shared-utils-java_3.3.0-1ubuntu0.20.04.1_all.deb ... #11 33.27 Unpacking libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 33.36 Selecting previously unselected package libplexus-cipher-java. #11 33.37 Preparing to unpack .../36-libplexus-cipher-java_1.7-3_all.deb ... #11 33.37 Unpacking libplexus-cipher-java (1.7-3) ... #11 33.44 Selecting previously unselected package libplexus-classworlds-java. #11 33.45 Preparing to unpack .../37-libplexus-classworlds-java_2.6.0-1_all.deb ... #11 33.45 Unpacking libplexus-classworlds-java (2.6.0-1) ... #11 33.53 Selecting previously unselected package libplexus-component-annotations-java. #11 33.53 Preparing to unpack .../38-libplexus-component-annotations-java_2.1.0-1_all.deb ... #11 33.54 Unpacking libplexus-component-annotations-java (2.1.0-1) ... #11 33.60 Selecting previously unselected package libplexus-interpolation-java. #11 33.60 Preparing to unpack .../39-libplexus-interpolation-java_1.26-1_all.deb ... #11 33.61 Unpacking libplexus-interpolation-java (1.26-1) ... #11 33.69 Selecting previously unselected package libplexus-sec-dispatcher-java. #11 33.70 Preparing to unpack .../40-libplexus-sec-dispatcher-java_1.4-4_all.deb ... #11 33.70 Unpacking libplexus-sec-dispatcher-java (1.4-4) ... #11 33.77 Selecting previously unselected package libslf4j-java. #11 33.77 Preparing to unpack .../41-libslf4j-java_1.7.25-3_all.deb ... #11 33.78 Unpacking libslf4j-java (1.7.25-3) ... #11 33.89 Selecting previously unselected package libsisu-inject-java. #11 33.89 Preparing to unpack .../42-libsisu-inject-java_0.3.3-1_all.deb ... #11 33.90 Unpacking libsisu-inject-java (0.3.3-1) ... #11 34.01 Selecting previously unselected package libsisu-plexus-java. #11 34.02 Preparing to unpack .../43-libsisu-plexus-java_0.3.3-3_all.deb ... #11 34.02 Unpacking libsisu-plexus-java (0.3.3-3) ... #11 34.12 Selecting previously unselected package libmaven3-core-java. #11 34.12 Preparing to unpack .../44-libmaven3-core-java_3.6.3-1ubuntu0.1_all.deb ... #11 34.13 Unpacking libmaven3-core-java (3.6.3-1ubuntu0.1) ... #11 34.35 Selecting previously unselected package libwagon-file-java. #11 34.36 Preparing to unpack .../45-libwagon-file-java_3.3.4-1_all.deb ... #11 34.36 Unpacking libwagon-file-java (3.3.4-1) ... #11 34.42 Selecting previously unselected package libwagon-http-shaded-java. #11 34.43 Preparing to unpack .../46-libwagon-http-shaded-java_3.3.4-1_all.deb ... #11 34.43 Unpacking libwagon-http-shaded-java (3.3.4-1) ... #11 34.68 Selecting previously unselected package maven. #11 34.68 Preparing to unpack .../47-maven_3.6.3-1ubuntu0.1_all.deb ... #11 34.69 Unpacking maven (3.6.3-1ubuntu0.1) ... #11 34.76 Selecting previously unselected package openjdk-11-jre:amd64. #11 34.76 Preparing to unpack .../48-openjdk-11-jre_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 34.77 Unpacking openjdk-11-jre:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 34.88 Selecting previously unselected package openjdk-11-jdk-headless:amd64. #11 34.89 Preparing to unpack .../49-openjdk-11-jdk-headless_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 34.89 Unpacking openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 39.97 Selecting previously unselected package openjdk-11-jdk:amd64. #11 39.97 Preparing to unpack .../50-openjdk-11-jdk_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 39.98 Unpacking openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 40.14 Setting up libgraphite2-3:amd64 (1.3.13-11build1) ... #11 40.16 Setting up liblcms2-2:amd64 (2.9-4) ... #11 40.18 Setting up libslf4j-java (1.7.25-3) ... #11 40.20 Setting up libplexus-utils2-java (3.3.0-1) ... #11 40.22 Setting up libplexus-classworlds-java (2.6.0-1) ... #11 40.24 Setting up libjsr305-java (0.1~+svn49-11) ... #11 40.26 Setting up java-common (0.72) ... #11 40.30 Setting up libaopalliance-java (20070526-6) ... #11 40.32 Setting up libcommons-cli-java (1.4-1) ... #11 40.34 Setting up libplexus-component-annotations-java (2.1.0-1) ... #11 40.38 Setting up libplexus-cipher-java (1.7-3) ... #11 40.40 Setting up libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 40.42 Setting up libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 40.44 Setting up libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 40.46 Setting up libapache-pom-java (18-1) ... #11 40.48 Setting up libavahi-common-data:amd64 (0.7-4ubuntu7.3) ... #11 40.50 Setting up libatinject-jsr330-api-java (1.0+ds1-5) ... #11 40.53 Setting up libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 40.55 Setting up libplexus-interpolation-java (1.26-1) ... #11 40.57 Setting up libpcsclite1:amd64 (1.8.26-3) ... #11 40.59 Setting up libgif7:amd64 (5.1.9-1) ... #11 40.61 Setting up libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 40.63 Setting up libplexus-sec-dispatcher-java (1.4-4) ... #11 40.65 Setting up libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 40.67 Setting up libwagon-http-shaded-java (3.3.4-1) ... #11 40.69 Setting up libcdi-api-java (1.2-2) ... #11 40.71 Setting up libhawtjni-runtime-java (1.17-1) ... #11 40.73 Setting up libwagon-provider-api-java (3.3.4-1) ... #11 40.75 Setting up libmaven-parent-java (31-2) ... #11 40.77 Setting up libcommons-parent-java (43-1) ... #11 40.79 Setting up libavahi-common3:amd64 (0.7-4ubuntu7.3) ... #11 40.81 Setting up libmaven-resolver-java (1.4.1-1) ... #11 40.84 Setting up libguava-java (19.0-1) ... #11 40.86 Setting up libcommons-lang3-java (3.8-2) ... #11 40.88 Setting up libjansi-native-java (1.8-1) ... #11 40.90 Setting up libwagon-file-java (3.3.4-1) ... #11 40.92 Setting up libavahi-client3:amd64 (0.7-4ubuntu7.3) ... #11 40.94 Setting up libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 40.96 Setting up libguice-java (4.2.1-1ubuntu0.2) ... #11 40.98 Setting up libjansi-java (1.18-1) ... #11 41.00 Setting up libcups2:amd64 (2.3.1-9ubuntu1.6) ... #11 41.02 Setting up libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 41.04 Setting up libsisu-inject-java (0.3.3-1) ... #11 41.06 Setting up libsisu-plexus-java (0.3.3-3) ... #11 41.08 Setting up libmaven3-core-java (3.6.3-1ubuntu0.1) ... #11 41.11 Setting up openjdk-11-jre-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.31 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/java to provide /usr/bin/java (java) in auto mode #11 41.32 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jjs to provide /usr/bin/jjs (jjs) in auto mode #11 41.33 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/keytool to provide /usr/bin/keytool (keytool) in auto mode #11 41.34 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmid to provide /usr/bin/rmid (rmid) in auto mode #11 41.34 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmiregistry to provide /usr/bin/rmiregistry (rmiregistry) in auto mode #11 41.35 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/pack200 to provide /usr/bin/pack200 (pack200) in auto mode #11 41.36 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/unpack200 to provide /usr/bin/unpack200 (unpack200) in auto mode #11 41.37 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/lib/jexec to provide /usr/bin/jexec (jexec) in auto mode #11 41.92 Setting up openjdk-11-jre:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.95 Setting up openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.98 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jar to provide /usr/bin/jar (jar) in auto mode #11 41.99 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jarsigner to provide /usr/bin/jarsigner (jarsigner) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javac to provide /usr/bin/javac (javac) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javadoc to provide /usr/bin/javadoc (javadoc) in auto mode #11 42.01 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javap to provide /usr/bin/javap (javap) in auto mode #11 42.02 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jcmd to provide /usr/bin/jcmd (jcmd) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdb to provide /usr/bin/jdb (jdb) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeprscan to provide /usr/bin/jdeprscan (jdeprscan) in auto mode #11 42.04 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeps to provide /usr/bin/jdeps (jdeps) in auto mode #11 42.05 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jfr to provide /usr/bin/jfr (jfr) in auto mode #11 42.06 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jimage to provide /usr/bin/jimage (jimage) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jinfo to provide /usr/bin/jinfo (jinfo) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jlink to provide /usr/bin/jlink (jlink) in auto mode #11 42.08 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmap to provide /usr/bin/jmap (jmap) in auto mode #11 42.09 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmod to provide /usr/bin/jmod (jmod) in auto mode #11 42.10 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jps to provide /usr/bin/jps (jps) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jrunscript to provide /usr/bin/jrunscript (jrunscript) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jshell to provide /usr/bin/jshell (jshell) in auto mode #11 42.12 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstack to provide /usr/bin/jstack (jstack) in auto mode #11 42.13 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstat to provide /usr/bin/jstat (jstat) in auto mode #11 42.14 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstatd to provide /usr/bin/jstatd (jstatd) in auto mode #11 42.15 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmic to provide /usr/bin/rmic (rmic) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/serialver to provide /usr/bin/serialver (serialver) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jaotc to provide /usr/bin/jaotc (jaotc) in auto mode #11 42.17 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jhsdb to provide /usr/bin/jhsdb (jhsdb) in auto mode #11 42.19 Setting up maven (3.6.3-1ubuntu0.1) ... #11 42.23 update-alternatives: using /usr/share/maven/bin/mvn to provide /usr/bin/mvn (mvn) in auto mode #11 42.25 Setting up openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 42.28 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jconsole to provide /usr/bin/jconsole (jconsole) in auto mode #11 42.29 Setting up ca-certificates-java (20190405ubuntu1.1) ... #11 42.35 head: cannot open '/etc/ssl/certs/java/cacerts' for reading: No such file or directory #11 42.65 Adding debian:COMODO_RSA_Certification_Authority.pem #11 42.66 Adding debian:Hongkong_Post_Root_CA_1.pem #11 42.67 Adding debian:Entrust_Root_Certification_Authority_-_EC1.pem #11 42.67 Adding debian:Certainly_Root_R1.pem #11 42.68 Adding debian:Amazon_Root_CA_2.pem #11 42.68 Adding debian:Certainly_Root_E1.pem #11 42.68 Adding debian:AffirmTrust_Premium_ECC.pem #11 42.68 Adding debian:ANF_Secure_Server_Root_CA.pem #11 42.68 Adding debian:GlobalSign_Root_R46.pem #11 42.69 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068.pem #11 42.69 Adding debian:Certum_Trusted_Root_CA.pem #11 42.69 Adding debian:Go_Daddy_Root_Certificate_Authority_-_G2.pem #11 42.69 Adding debian:SecureSign_RootCA11.pem #11 42.69 Adding debian:Amazon_Root_CA_4.pem #11 42.70 Adding debian:GDCA_TrustAUTH_R5_ROOT.pem #11 42.70 Adding debian:T-TeleSec_GlobalRoot_Class_2.pem #11 42.70 Adding debian:Trustwave_Global_ECC_P256_Certification_Authority.pem #11 42.70 Adding debian:GTS_Root_R1.pem #11 42.71 Adding debian:Izenpe.com.pem #11 42.71 Adding debian:CFCA_EV_ROOT.pem #11 42.71 Adding debian:DigiCert_Global_Root_G2.pem #11 42.71 Adding debian:XRamp_Global_CA_Root.pem #11 42.72 Adding debian:SSL.com_EV_Root_Certification_Authority_RSA_R2.pem #11 42.72 Adding debian:Security_Communication_RootCA3.pem #11 42.72 Adding debian:Starfield_Services_Root_Certificate_Authority_-_G2.pem #11 42.72 Adding debian:Amazon_Root_CA_1.pem #11 42.72 Adding debian:DigiCert_Assured_ID_Root_CA.pem #11 42.72 Adding debian:SSL.com_Root_Certification_Authority_RSA.pem #11 42.73 Adding debian:Telia_Root_CA_v2.pem #11 42.73 Adding debian:SwissSign_Gold_CA_-_G2.pem #11 42.73 Adding debian:GLOBALTRUST_2020.pem #11 42.73 Adding debian:emSign_ECC_Root_CA_-_G3.pem #11 42.73 Adding debian:Microsoft_RSA_Root_Certificate_Authority_2017.pem #11 42.74 Adding debian:TunTrust_Root_CA.pem #11 42.74 Adding debian:Security_Communication_ECC_RootCA1.pem #11 42.74 Adding debian:DigiCert_TLS_ECC_P384_Root_G5.pem #11 42.74 Adding debian:vTrus_Root_CA.pem #11 42.75 Adding debian:Certum_EC-384_CA.pem #11 42.76 Adding debian:CA_Disig_Root_R2.pem #11 42.76 Adding debian:emSign_Root_CA_-_C1.pem #11 42.77 Adding debian:AffirmTrust_Commercial.pem #11 42.77 Adding debian:Entrust_Root_Certification_Authority_-_G4.pem #11 42.78 Adding debian:Starfield_Class_2_CA.pem #11 42.78 Adding debian:Baltimore_CyberTrust_Root.pem #11 42.79 Adding debian:GTS_Root_R4.pem #11 42.79 Adding debian:Starfield_Root_Certificate_Authority_-_G2.pem #11 42.80 Adding debian:QuoVadis_Root_CA_1_G3.pem #11 42.81 Adding debian:DigiCert_Trusted_Root_G4.pem #11 42.81 Adding debian:DigiCert_High_Assurance_EV_Root_CA.pem #11 42.82 Adding debian:AC_RAIZ_FNMT-RCM.pem #11 42.82 Adding debian:Comodo_AAA_Services_root.pem #11 42.82 Adding debian:emSign_ECC_Root_CA_-_C3.pem #11 42.83 Adding debian:Entrust_Root_Certification_Authority.pem #11 42.84 Adding debian:D-TRUST_EV_Root_CA_1_2020.pem #11 42.85 Adding debian:Certigna_Root_CA.pem #11 42.85 Adding debian:TeliaSonera_Root_CA_v1.pem #11 42.86 Adding debian:Certum_Trusted_Network_CA.pem #11 42.86 Adding debian:HiPKI_Root_CA_-_G1.pem #11 42.87 Adding debian:SZAFIR_ROOT_CA2.pem #11 42.87 Adding debian:USERTrust_RSA_Certification_Authority.pem #11 42.88 Adding debian:OISTE_WISeKey_Global_Root_GC_CA.pem #11 42.88 Adding debian:Buypass_Class_2_Root_CA.pem #11 42.89 Adding debian:AC_RAIZ_FNMT-RCM_SERVIDORES_SEGUROS.pem #11 42.89 Adding debian:e-Szigno_Root_CA_2017.pem #11 42.90 Adding debian:Microsoft_ECC_Root_Certificate_Authority_2017.pem #11 42.91 Adding debian:Security_Communication_Root_CA.pem #11 42.91 Adding debian:GlobalSign_Root_CA.pem #11 42.92 Adding debian:E-Tugra_Certification_Authority.pem #11 42.93 Adding debian:Trustwave_Global_Certification_Authority.pem #11 42.93 Adding debian:QuoVadis_Root_CA_2_G3.pem #11 42.93 Adding debian:SSL.com_EV_Root_Certification_Authority_ECC.pem #11 42.94 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068_2.pem #11 42.95 Adding debian:E-Tugra_Global_Root_CA_ECC_v3.pem #11 42.95 Adding debian:Entrust.net_Premium_2048_Secure_Server_CA.pem #11 42.96 Adding debian:AffirmTrust_Premium.pem #11 42.96 Adding debian:Amazon_Root_CA_3.pem #11 42.97 Adding debian:AffirmTrust_Networking.pem #11 42.98 Adding debian:IdenTrust_Public_Sector_Root_CA_1.pem #11 42.98 Adding debian:GlobalSign_ECC_Root_CA_-_R4.pem #11 42.99 Adding debian:QuoVadis_Root_CA_3_G3.pem #11 42.99 Adding debian:certSIGN_ROOT_CA.pem #11 43.00 Adding debian:IdenTrust_Commercial_Root_CA_1.pem #11 43.00 Adding debian:SecureTrust_CA.pem #11 43.01 Adding debian:DigiCert_Global_Root_G3.pem #11 43.01 Adding debian:GlobalSign_Root_E46.pem #11 43.02 Adding debian:SwissSign_Silver_CA_-_G2.pem #11 43.02 Adding debian:vTrus_ECC_Root_CA.pem #11 43.03 Adding debian:GlobalSign_ECC_Root_CA_-_R5.pem #11 43.04 Adding debian:OISTE_WISeKey_Global_Root_GB_CA.pem #11 43.04 Adding debian:DigiCert_Assured_ID_Root_G3.pem #11 43.05 Adding debian:emSign_Root_CA_-_G1.pem #11 43.05 Adding debian:DigiCert_TLS_RSA4096_Root_G5.pem #11 43.06 Adding debian:COMODO_Certification_Authority.pem #11 43.06 Adding debian:DigiCert_Global_Root_CA.pem #11 43.07 Adding debian:QuoVadis_Root_CA_2.pem #11 43.08 Adding debian:TWCA_Global_Root_CA.pem #11 43.09 Adding debian:ACCVRAIZ1.pem #11 43.09 Adding debian:GTS_Root_R2.pem #11 43.10 Adding debian:HARICA_TLS_RSA_Root_CA_2021.pem #11 43.10 Adding debian:Actalis_Authentication_Root_CA.pem #11 43.11 Adding debian:Certum_Trusted_Network_CA_2.pem #11 43.11 Adding debian:Atos_TrustedRoot_2011.pem #11 43.11 Adding debian:T-TeleSec_GlobalRoot_Class_3.pem #11 43.13 Adding debian:NetLock_Arany_=Class_Gold=_F\u0151tan\u00fas\u00edtv\u00e1ny.pem #11 43.13 Adding debian:Certigna.pem #11 43.14 Adding debian:Microsec_e-Szigno_Root_CA_2009.pem #11 43.14 Adding debian:UCA_Extended_Validation_Root.pem #11 43.14 Adding debian:DigiCert_Assured_ID_Root_G2.pem #11 43.15 Adding debian:QuoVadis_Root_CA_3.pem #11 43.15 Adding debian:NAVER_Global_Root_Certification_Authority.pem #11 43.16 Adding debian:GlobalSign_Root_CA_-_R6.pem #11 43.17 Adding debian:TUBITAK_Kamu_SM_SSL_Kok_Sertifikasi_-_Surum_1.pem #11 43.17 Adding debian:Trustwave_Global_ECC_P384_Certification_Authority.pem #11 43.18 Adding debian:Go_Daddy_Class_2_CA.pem #11 43.18 Adding debian:TWCA_Root_Certification_Authority.pem #11 43.19 Adding debian:certSIGN_Root_CA_G2.pem #11 43.19 Adding debian:COMODO_ECC_Certification_Authority.pem #11 43.20 Adding debian:ISRG_Root_X2.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_EV_2009.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_2009.pem #11 43.22 Adding debian:UCA_Global_G2_Root.pem #11 43.22 Adding debian:D-TRUST_BR_Root_CA_1_2020.pem #11 43.23 Adding debian:Secure_Global_CA.pem #11 43.24 Adding debian:Hongkong_Post_Root_CA_3.pem #11 43.24 Adding debian:Buypass_Class_3_Root_CA.pem #11 43.25 Adding debian:Entrust_Root_Certification_Authority_-_G2.pem #11 43.25 Adding debian:SSL.com_Root_Certification_Authority_ECC.pem #11 43.26 Adding debian:Hellenic_Academic_and_Research_Institutions_RootCA_2015.pem #11 43.27 Adding debian:E-Tugra_Global_Root_CA_RSA_v3.pem #11 43.27 Adding debian:USERTrust_ECC_Certification_Authority.pem #11 43.28 Adding debian:ePKI_Root_Certification_Authority.pem #11 43.28 Adding debian:ISRG_Root_X1.pem #11 43.29 Adding debian:HARICA_TLS_ECC_Root_CA_2021.pem #11 43.29 Adding debian:GTS_Root_R3.pem #11 43.30 Adding debian:Hellenic_Academic_and_Research_Institutions_ECC_RootCA_2015.pem #11 43.30 Adding debian:Security_Communication_RootCA2.pem #11 43.31 Adding debian:GlobalSign_Root_CA_-_R3.pem #11 43.35 done. #11 43.36 Processing triggers for mime-support (3.64ubuntu1) ... #11 43.40 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #11 43.45 Processing triggers for ca-certificates (20230311ubuntu0.20.04.1) ... #11 43.47 Updating certificates in /etc/ssl/certs... #11 44.82 0 added, 0 removed; done. #11 44.82 Running hooks in /etc/ca-certificates/update.d... #11 44.83 #11 45.16 done. #11 45.16 done. #11 DONE 45.9s #12 [ 8/17] WORKDIR /opt/mlflow #12 DONE 0.1s #13 [ 9/17] RUN pip install mlflow==2.12.2 #13 1.009 Collecting mlflow==2.12.2 #13 1.075 Downloading mlflow-2.12.2-py3-none-any.whl.metadata (29 kB) #13 1.207 Collecting Flask < 4 (from mlflow==2.12.2) #13 1.222 Downloading flask-3.0.3-py3-none-any.whl.metadata (3.2 kB) #13 1.309 Collecting alembic!=1.10.0, < 2 (from mlflow==2.12.2) #13 1.324 Downloading alembic-1.13.1-py3-none-any.whl.metadata (7.4 kB) #13 1.395 Collecting click < 9,>=7.0 (from mlflow==2.12.2) #13 1.409 Downloading click-8.1.7-py3-none-any.whl.metadata (3.0 kB) #13 1.467 Collecting cloudpickle < 4 (from mlflow==2.12.2) #13 1.483 Downloading cloudpickle-3.0.0-py3-none-any.whl.metadata (7.0 kB) #13 1.564 Collecting docker < 8,>=4.0.0 (from mlflow==2.12.2) #13 1.579 Downloading docker-7.0.0-py3-none-any.whl.metadata (3.5 kB) #13 1.635 Collecting entrypoints < 1 (from mlflow==2.12.2) #13 1.651 Downloading entrypoints-0.4-py3-none-any.whl.metadata (2.6 kB) #13 1.742 Collecting gitpython < 4,>=3.1.9 (from mlflow==2.12.2) #13 1.757 Downloading GitPython-3.1.43-py3-none-any.whl.metadata (13 kB) #13 1.822 Collecting graphene < 4 (from mlflow==2.12.2) #13 1.837 Downloading graphene-3.3-py2.py3-none-any.whl.metadata (7.7 kB) #13 1.946 Collecting importlib-metadata!=4.7.0, < 8,>=3.7.0 (from mlflow==2.12.2) #13 1.960 Downloading importlib_metadata-7.1.0-py3-none-any.whl.metadata (4.7 kB) #13 2.027 Collecting markdown < 4,>=3.3 (from mlflow==2.12.2) #13 2.079 Downloading Markdown-3.6-py3-none-any.whl.metadata (7.0 kB) #13 2.299 Collecting matplotlib < 4 (from mlflow==2.12.2) #13 2.314 Downloading matplotlib-3.7.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (5.7 kB) #13 2.593 Collecting numpy < 2 (from mlflow==2.12.2) #13 2.612 Downloading numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB) #13 2.685 Collecting packaging < 25 (from mlflow==2.12.2) #13 2.698 Downloading packaging-24.0-py3-none-any.whl.metadata (3.2 kB) #13 2.902 Collecting pandas < 3 (from mlflow==2.12.2) #13 2.916 Downloading pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB) #13 3.172 Collecting protobuf < 5,>=3.12.0 (from mlflow==2.12.2) #13 3.187 Downloading protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes) #13 3.323 Collecting pyarrow < 16,>=4.0.0 (from mlflow==2.12.2) #13 3.338 Downloading pyarrow-15.0.2-cp38-cp38-manylinux_2_28_x86_64.whl.metadata (3.0 kB) #13 3.456 Collecting pytz < 2025 (from mlflow==2.12.2) #13 3.471 Downloading pytz-2024.1-py2.py3-none-any.whl.metadata (22 kB) #13 3.482 Requirement already satisfied: pyyaml < 7,>=5.1 in /usr/lib/python3/dist-packages (from mlflow==2.12.2) (5.3.1) #13 3.672 Collecting querystring-parser < 2 (from mlflow==2.12.2) #13 3.689 Downloading querystring_parser-1.2.4-py2.py3-none-any.whl.metadata (559 bytes) #13 3.780 Collecting requests < 3,>=2.17.3 (from mlflow==2.12.2) #13 3.794 Downloading requests-2.31.0-py3-none-any.whl.metadata (4.6 kB) #13 3.969 Collecting scikit-learn < 2 (from mlflow==2.12.2) #13 3.985 Downloading scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) #13 4.159 Collecting scipy < 2 (from mlflow==2.12.2) #13 4.175 Downloading scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (58 kB) #13 4.199 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 58.9/58.9 kB 2.7 MB/s eta 0:00:00 #13 4.633 Collecting sqlalchemy < 3,>=1.4.0 (from mlflow==2.12.2) #13 4.648 Downloading SQLAlchemy-2.0.30-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.6 kB) #13 4.709 Collecting sqlparse < 1,>=0.4.0 (from mlflow==2.12.2) #13 4.724 Downloading sqlparse-0.5.0-py3-none-any.whl.metadata (3.9 kB) #13 4.795 Collecting Jinja2 < 4,>=2.11 (from mlflow==2.12.2) #13 4.809 Downloading jinja2-3.1.4-py3-none-any.whl.metadata (2.6 kB) #13 4.929 Collecting gunicorn < 23 (from mlflow==2.12.2) #13 4.944 Downloading gunicorn-22.0.0-py3-none-any.whl.metadata (4.4 kB) #13 5.043 Collecting Mako (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.058 Downloading Mako-1.3.5-py3-none-any.whl.metadata (2.9 kB) #13 5.122 Collecting typing-extensions>=4 (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.136 Downloading typing_extensions-4.11.0-py3-none-any.whl.metadata (3.0 kB) #13 5.225 Collecting importlib-resources (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.241 Downloading importlib_resources-6.4.0-py3-none-any.whl.metadata (3.9 kB) #13 5.348 Collecting urllib3>=1.26.0 (from docker < 8,>=4.0.0->mlflow==2.12.2) #13 5.362 Downloading urllib3-2.2.1-py3-none-any.whl.metadata (6.4 kB) #13 5.445 Collecting Werkzeug>=3.0.0 (from Flask < 4->mlflow==2.12.2) #13 5.460 Downloading werkzeug-3.0.3-py3-none-any.whl.metadata (3.7 kB) #13 5.519 Collecting itsdangerous>=2.1.2 (from Flask < 4->mlflow==2.12.2) #13 5.537 Downloading itsdangerous-2.2.0-py3-none-any.whl.metadata (1.9 kB) #13 5.606 Collecting blinker>=1.6.2 (from Flask < 4->mlflow==2.12.2) #13 5.623 Downloading blinker-1.8.2-py3-none-any.whl.metadata (1.6 kB) #13 5.716 Collecting gitdb < 5,>=4.0.1 (from gitpython < 4,>=3.1.9->mlflow==2.12.2) #13 5.732 Downloading gitdb-4.0.11-py3-none-any.whl.metadata (1.2 kB) #13 5.847 Collecting graphql-core < 3.3,>=3.1 (from graphene < 4->mlflow==2.12.2) #13 5.862 Downloading graphql_core-3.2.3-py3-none-any.whl.metadata (10 kB) #13 5.920 Collecting graphql-relay < 3.3,>=3.1 (from graphene < 4->mlflow==2.12.2) #13 5.938 Downloading graphql_relay-3.2.0-py3-none-any.whl.metadata (12 kB) #13 6.001 Collecting aniso8601 < 10,>=8 (from graphene < 4->mlflow==2.12.2) #13 6.017 Downloading aniso8601-9.0.1-py2.py3-none-any.whl.metadata (23 kB) #13 6.122 Collecting zipp>=0.5 (from importlib-metadata!=4.7.0, < 8,>=3.7.0->mlflow==2.12.2) #13 6.136 Downloading zipp-3.18.2-py3-none-any.whl.metadata (3.5 kB) #13 6.235 Collecting MarkupSafe>=2.0 (from Jinja2 < 4,>=2.11->mlflow==2.12.2) #13 6.250 Downloading MarkupSafe-2.1.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.0 kB) #13 6.406 Collecting contourpy>=1.0.1 (from matplotlib < 4->mlflow==2.12.2) #13 6.420 Downloading contourpy-1.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.9 kB) #13 6.468 Collecting cycler>=0.10 (from matplotlib < 4->mlflow==2.12.2) #13 6.485 Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB) #13 6.657 Collecting fonttools>=4.22.0 (from matplotlib < 4->mlflow==2.12.2) #13 6.672 Downloading fonttools-4.51.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (159 kB) #13 6.694 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 159.5/159.5 kB 7.7 MB/s eta 0:00:00 #13 6.787 Collecting kiwisolver>=1.0.1 (from matplotlib < 4->mlflow==2.12.2) #13 6.826 Downloading kiwisolver-1.4.5-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (6.4 kB) #13 7.174 Collecting pillow>=6.2.0 (from matplotlib < 4->mlflow==2.12.2) #13 7.191 Downloading pillow-10.3.0-cp38-cp38-manylinux_2_28_x86_64.whl.metadata (9.2 kB) #13 7.275 Collecting pyparsing>=2.3.1 (from matplotlib < 4->mlflow==2.12.2) #13 7.290 Downloading pyparsing-3.1.2-py3-none-any.whl.metadata (5.1 kB) #13 7.351 Collecting python-dateutil>=2.7 (from matplotlib < 4->mlflow==2.12.2) #13 7.365 Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB) #13 7.525 Collecting tzdata>=2022.1 (from pandas < 3->mlflow==2.12.2) #13 7.538 Downloading tzdata-2024.1-py2.py3-none-any.whl.metadata (1.4 kB) #13 7.620 Collecting six (from querystring-parser < 2->mlflow==2.12.2) #13 7.633 Downloading six-1.16.0-py2.py3-none-any.whl.metadata (1.8 kB) #13 7.758 Collecting charset-normalizer < 4,>=2 (from requests < 3,>=2.17.3->mlflow==2.12.2) #13 7.772 Downloading charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (33 kB) #13 7.834 Collecting idna < 4,>=2.5 (from requests < 3,>=2.17.3->mlflow==2.12.2) #13 7.848 Downloading 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fonttools, entrypoints, cycler, cloudpickle, click, charset-normalizer, certifi, blinker, Werkzeug, sqlalchemy, scipy, requests, querystring-parser, python-dateutil, pyarrow, Mako, Jinja2, importlib-resources, importlib-metadata, gunicorn, graphql-relay, gitdb, contourpy, scikit-learn, pandas, matplotlib, markdown, graphene, gitpython, Flask, docker, alembic, mlflow #13 34.72 Successfully installed Flask-3.0.3 Jinja2-3.1.4 Mako-1.3.5 MarkupSafe-2.1.5 Werkzeug-3.0.3 alembic-1.13.1 aniso8601-9.0.1 blinker-1.8.2 certifi-2024.2.2 charset-normalizer-3.3.2 click-8.1.7 cloudpickle-3.0.0 contourpy-1.1.1 cycler-0.12.1 docker-7.0.0 entrypoints-0.4 fonttools-4.51.0 gitdb-4.0.11 gitpython-3.1.43 graphene-3.3 graphql-core-3.2.3 graphql-relay-3.2.0 greenlet-3.0.3 gunicorn-22.0.0 idna-3.7 importlib-metadata-7.1.0 importlib-resources-6.4.0 itsdangerous-2.2.0 joblib-1.4.2 kiwisolver-1.4.5 markdown-3.6 matplotlib-3.7.5 mlflow-2.12.2 numpy-1.24.4 packaging-24.0 pandas-2.0.3 pillow-10.3.0 protobuf-4.25.3 pyarrow-15.0.2 pyparsing-3.1.2 python-dateutil-2.9.0.post0 pytz-2024.1 querystring-parser-1.2.4 requests-2.31.0 scikit-learn-1.3.2 scipy-1.10.1 six-1.16.0 smmap-5.0.1 sqlalchemy-2.0.30 sqlparse-0.5.0 threadpoolctl-3.5.0 typing-extensions-4.11.0 tzdata-2024.1 urllib3-2.2.1 zipp-3.18.2 #13 34.72 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #13 DONE 36.4s #14 [10/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:pom -DoutputDirectory=/opt/java #14 1.558 [INFO] Scanning for projects... #14 1.845 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-clean-plugin/2.5/maven-clean-plugin-2.5.pom #14 2.423 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-clean-plugin/2.5/maven-clean-plugin-2.5.pom (3.9 kB at 6.7 kB/s) #14 2.448 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-plugins/22/maven-plugins-22.pom #14 2.499 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-plugins/22/maven-plugins-22.pom (13 kB at 256 kB/s) #14 2.511 [INFO] Downloading from central: 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12.86 [INFO] Finished at: 2024-05-16T20:25:32Z #14 12.86 [INFO] ------------------------------------------------------------------------ #14 DONE 13.0s #15 [11/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:jar -DoutputDirectory=/opt/java/jars #15 1.696 [INFO] Scanning for projects... #15 1.897 [INFO] #15 1.897 [INFO] ------------------ < org.apache.maven:standalone-pom >------------------- #15 1.898 [INFO] Building Maven Stub Project (No POM) 1 #15 1.898 [INFO] --------------------------------[ pom ]--------------------------------- #15 1.905 [INFO] #15 1.905 [INFO] --- maven-dependency-plugin:2.8:copy (default-cli) @ standalone-pom --- #15 2.594 [INFO] Configured Artifact: org.mlflow:mlflow-scoring:2.12.2:jar #15 2.804 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/mlflow/mlflow-scoring/2.12.2/mlflow-scoring-2.12.2.jar #15 3.207 [INFO] Downloaded from central: 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Downloaded from google-maven-central: https://maven-central.storage-download.googleapis.com/repos/central/data/org/apache/httpcomponents/httpclient/4.5.6/httpclient-4.5.6.jar (767 kB at 83 kB/s) #17 133.8 [INFO] Downloaded from google-maven-central: https://maven-central.storage-download.googleapis.com/repos/central/data/commons-io/commons-io/2.5/commons-io-2.5.jar (209 kB at 22 kB/s) #17 133.8 [INFO] Downloaded from google-maven-central: https://maven-central.storage-download.googleapis.com/repos/central/data/commons-codec/commons-codec/1.10/commons-codec-1.10.jar (284 kB at 29 kB/s) #17 133.9 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/com/google/protobuf/protobuf-java/3.19.4/protobuf-java-3.19.4.jar #17 134.0 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/com/google/protobuf/protobuf-java/3.19.4/protobuf-java-3.19.4.jar (1.7 MB at 13 MB/s) #17 134.1 [INFO] #17 134.1 [INFO] --- maven-dependency-plugin:2.8:copy-dependencies (default-cli) @ mlflow-scoring --- #17 134.4 [INFO] Copying wagon-provider-api-1.0-beta-6.jar to /opt/java/jars/wagon-provider-api-1.0-beta-6.jar #17 134.4 [INFO] Copying mleap-tensor_2.11-0.10.3.jar to /opt/java/jars/mleap-tensor_2.11-0.10.3.jar #17 134.4 [INFO] Copying maven-error-diagnostics-2.2.1.jar to /opt/java/jars/maven-error-diagnostics-2.2.1.jar #17 134.4 [INFO] Copying breeze_2.11-0.13.2.jar to /opt/java/jars/breeze_2.11-0.13.2.jar #17 134.5 [INFO] Copying plexus-java-0.9.8.jar to /opt/java/jars/plexus-java-0.9.8.jar #17 134.5 [INFO] Copying scala-library-2.11.8.jar to /opt/java/jars/scala-library-2.11.8.jar #17 134.5 [INFO] Copying maven-settings-2.2.1.jar to /opt/java/jars/maven-settings-2.2.1.jar #17 134.5 [INFO] Copying sourcecode_2.11-0.1.3.jar to /opt/java/jars/sourcecode_2.11-0.1.3.jar #17 134.5 [INFO] Copying httpcore-4.4.10.jar to /opt/java/jars/httpcore-4.4.10.jar #17 134.5 [INFO] Copying maven-surefire-common-2.22.0.jar to /opt/java/jars/maven-surefire-common-2.22.0.jar #17 134.5 [INFO] Copying macro-compat_2.11-1.1.1.jar to /opt/java/jars/macro-compat_2.11-1.1.1.jar #17 134.5 [INFO] Copying bundle-ml_2.11-0.10.3.jar to /opt/java/jars/bundle-ml_2.11-0.10.3.jar #17 134.5 [INFO] Copying fastparse-utils_2.11-0.4.2.jar to /opt/java/jars/fastparse-utils_2.11-0.4.2.jar #17 134.5 [INFO] Copying maven-artifact-manager-2.2.1.jar to /opt/java/jars/maven-artifact-manager-2.2.1.jar #17 134.5 [INFO] Copying jackrabbit-webdav-1.5.0.jar to /opt/java/jars/jackrabbit-webdav-1.5.0.jar #17 134.5 [INFO] Copying lenses_2.11-0.4.12.jar to /opt/java/jars/lenses_2.11-0.4.12.jar #17 134.5 [INFO] Copying asm-6.1.1.jar to /opt/java/jars/asm-6.1.1.jar #17 134.5 [INFO] Copying jackson-annotations-2.10.0.jar to /opt/java/jars/jackson-annotations-2.10.0.jar #17 134.5 [INFO] Copying snakeyaml-1.24.jar to /opt/java/jars/snakeyaml-1.24.jar #17 134.5 [INFO] Copying machinist_2.11-0.6.1.jar to /opt/java/jars/machinist_2.11-0.6.1.jar #17 134.5 [INFO] Copying maven-toolchain-2.2.1.jar to /opt/java/jars/maven-toolchain-2.2.1.jar #17 134.5 [INFO] Copying spire_2.11-0.13.0.jar to /opt/java/jars/spire_2.11-0.13.0.jar #17 134.5 [INFO] Copying maven-plugin-api-2.2.1.jar to /opt/java/jars/maven-plugin-api-2.2.1.jar #17 134.5 [INFO] Copying maven-project-2.2.1.jar to /opt/java/jars/maven-project-2.2.1.jar #17 134.5 [INFO] Copying qdox-2.0-M7.jar to /opt/java/jars/qdox-2.0-M7.jar #17 134.5 [INFO] Copying jetty-server-9.4.11.v20180605.jar to /opt/java/jars/jetty-server-9.4.11.v20180605.jar #17 134.5 [INFO] Copying jackson-core-2.10.0.jar to /opt/java/jars/jackson-core-2.10.0.jar #17 134.5 [INFO] Copying core-1.1.2.jar to /opt/java/jars/core-1.1.2.jar #17 134.5 [INFO] Copying jetty-security-9.4.11.v20180605.jar to /opt/java/jars/jetty-security-9.4.11.v20180605.jar #17 134.5 [INFO] Copying config-1.3.0.jar to /opt/java/jars/config-1.3.0.jar #17 134.5 [INFO] Copying maven-monitor-2.2.1.jar to /opt/java/jars/maven-monitor-2.2.1.jar #17 134.5 [INFO] Copying wagon-http-1.0-beta-6.jar to /opt/java/jars/wagon-http-1.0-beta-6.jar #17 134.5 [INFO] Copying mleap-core_2.11-0.10.3.jar to /opt/java/jars/mleap-core_2.11-0.10.3.jar #17 134.5 [INFO] Copying hamcrest-core-1.3.jar to /opt/java/jars/hamcrest-core-1.3.jar #17 134.5 [INFO] Copying breeze-macros_2.11-0.13.2.jar to /opt/java/jars/breeze-macros_2.11-0.13.2.jar #17 134.5 [INFO] Copying xercesMinimal-1.9.6.2.jar to /opt/java/jars/xercesMinimal-1.9.6.2.jar #17 134.5 [INFO] Copying jetty-http-9.4.11.v20180605.jar to /opt/java/jars/jetty-http-9.4.11.v20180605.jar #17 134.5 [INFO] Copying fastparse_2.11-0.4.2.jar to /opt/java/jars/fastparse_2.11-0.4.2.jar #17 134.5 [INFO] Copying protobuf-java-3.19.4.jar to /opt/java/jars/protobuf-java-3.19.4.jar #17 134.5 [INFO] Copying scala-arm_2.11-2.0.jar to /opt/java/jars/scala-arm_2.11-2.0.jar #17 134.5 [INFO] Copying arpack_combined_all-0.1.jar to /opt/java/jars/arpack_combined_all-0.1.jar #17 134.5 [INFO] Copying shapeless_2.11-2.3.2.jar to /opt/java/jars/shapeless_2.11-2.3.2.jar #17 134.5 [INFO] Copying mleap-runtime_2.11-0.10.3.jar to /opt/java/jars/mleap-runtime_2.11-0.10.3.jar #17 134.5 [INFO] Copying scala-reflect-2.11.8.jar to /opt/java/jars/scala-reflect-2.11.8.jar #17 134.5 [INFO] Copying commons-logging-1.2.jar to /opt/java/jars/commons-logging-1.2.jar #17 134.5 [INFO] Copying jetty-util-9.4.11.v20180605.jar to /opt/java/jars/jetty-util-9.4.11.v20180605.jar #17 134.5 [INFO] Copying jackson-databind-2.10.0.jar to /opt/java/jars/jackson-databind-2.10.0.jar #17 134.5 [INFO] Copying mleap-spark_2.11-0.10.3.jar to /opt/java/jars/mleap-spark_2.11-0.10.3.jar #17 134.5 [INFO] Copying plexus-utils-1.5.15.jar to /opt/java/jars/plexus-utils-1.5.15.jar #17 134.5 [INFO] Copying spire-macros_2.11-0.13.0.jar to /opt/java/jars/spire-macros_2.11-0.13.0.jar #17 134.5 [INFO] Copying plexus-interpolation-1.11.jar to /opt/java/jars/plexus-interpolation-1.11.jar #17 134.5 [INFO] Copying plexus-cipher-1.4.jar to /opt/java/jars/plexus-cipher-1.4.jar #17 134.5 [INFO] Copying spark-mllib-local_2.11-2.3.0.jar to /opt/java/jars/spark-mllib-local_2.11-2.3.0.jar #17 134.5 [INFO] Copying jtransforms-2.4.0.jar to /opt/java/jars/jtransforms-2.4.0.jar #17 134.5 [INFO] Copying plexus-container-default-1.0-alpha-9-stable-1.jar to /opt/java/jars/plexus-container-default-1.0-alpha-9-stable-1.jar #17 134.5 [INFO] Copying spray-json_2.11-1.3.2.jar to /opt/java/jars/spray-json_2.11-1.3.2.jar #17 134.5 [INFO] Copying jetty-servlet-9.4.11.v20180605.jar to /opt/java/jars/jetty-servlet-9.4.11.v20180605.jar #17 134.5 [INFO] Copying commons-io-2.5.jar to /opt/java/jars/commons-io-2.5.jar #17 134.5 [INFO] Copying surefire-logger-api-2.22.0.jar to /opt/java/jars/surefire-logger-api-2.22.0.jar #17 134.5 [INFO] Copying mleap-base_2.11-0.10.3.jar to /opt/java/jars/mleap-base_2.11-0.10.3.jar #17 134.5 [INFO] Copying surefire-booter-2.22.0.jar to /opt/java/jars/surefire-booter-2.22.0.jar #17 134.5 [INFO] Copying opencsv-2.3.jar to /opt/java/jars/opencsv-2.3.jar #17 134.5 [INFO] Copying spark-tags_2.11-2.3.0.jar to /opt/java/jars/spark-tags_2.11-2.3.0.jar #17 134.5 [INFO] Copying jackrabbit-jcr-commons-1.5.0.jar to /opt/java/jars/jackrabbit-jcr-commons-1.5.0.jar #17 134.5 [INFO] Copying classworlds-1.1.jar to /opt/java/jars/classworlds-1.1.jar #17 134.5 [INFO] Copying nekohtml-1.9.6.2.jar to /opt/java/jars/nekohtml-1.9.6.2.jar #17 134.5 [INFO] Copying maven-core-2.2.1.jar to /opt/java/jars/maven-core-2.2.1.jar #17 134.5 [INFO] Copying maven-surefire-plugin-2.22.0.jar to /opt/java/jars/maven-surefire-plugin-2.22.0.jar #17 134.5 [INFO] Copying mleap-spark-base_2.11-0.10.3.jar to /opt/java/jars/mleap-spark-base_2.11-0.10.3.jar #17 134.5 [INFO] Copying maven-repository-metadata-2.2.1.jar to /opt/java/jars/maven-repository-metadata-2.2.1.jar #17 134.5 [INFO] Copying wagon-webdav-jackrabbit-1.0-beta-6.jar to /opt/java/jars/wagon-webdav-jackrabbit-1.0-beta-6.jar #17 134.5 [INFO] Copying maven-artifact-2.2.1.jar to /opt/java/jars/maven-artifact-2.2.1.jar #17 134.5 [INFO] Copying commons-codec-1.10.jar to /opt/java/jars/commons-codec-1.10.jar #17 134.5 [INFO] Copying maven-plugin-descriptor-2.2.1.jar to /opt/java/jars/maven-plugin-descriptor-2.2.1.jar #17 134.5 [INFO] Copying unused-1.0.0.jar to /opt/java/jars/unused-1.0.0.jar #17 134.5 [INFO] Copying scalapb-runtime_2.11-0.6.0.jar to /opt/java/jars/scalapb-runtime_2.11-0.6.0.jar #17 134.5 [INFO] Copying maven-profile-2.2.1.jar to /opt/java/jars/maven-profile-2.2.1.jar #17 134.5 [INFO] Copying plexus-sec-dispatcher-1.3.jar to /opt/java/jars/plexus-sec-dispatcher-1.3.jar #17 134.5 [INFO] Copying maven-plugin-annotations-3.5.jar to /opt/java/jars/maven-plugin-annotations-3.5.jar #17 134.5 [INFO] Copying maven-reporting-api-2.2.1.jar to /opt/java/jars/maven-reporting-api-2.2.1.jar #17 134.5 [INFO] Copying javax.servlet-api-3.1.0.jar to /opt/java/jars/javax.servlet-api-3.1.0.jar #17 134.5 [INFO] Copying maven-plugin-parameter-documenter-2.2.1.jar to /opt/java/jars/maven-plugin-parameter-documenter-2.2.1.jar #17 134.5 [INFO] Copying commons-math3-3.4.1.jar to /opt/java/jars/commons-math3-3.4.1.jar #17 134.5 [INFO] Copying junit-4.12.jar to /opt/java/jars/junit-4.12.jar #17 134.5 [INFO] Copying slf4j-jdk14-1.7.25.jar to /opt/java/jars/slf4j-jdk14-1.7.25.jar #17 134.5 [INFO] Copying slf4j-api-1.7.25.jar to /opt/java/jars/slf4j-api-1.7.25.jar #17 134.5 [INFO] Copying jetty-io-9.4.11.v20180605.jar to /opt/java/jars/jetty-io-9.4.11.v20180605.jar #17 134.5 [INFO] Copying httpclient-4.5.6.jar to /opt/java/jars/httpclient-4.5.6.jar #17 134.5 [INFO] Copying maven-model-2.2.1.jar to /opt/java/jars/maven-model-2.2.1.jar #17 134.5 [INFO] Copying jackson-dataformat-yaml-2.10.0.jar to /opt/java/jars/jackson-dataformat-yaml-2.10.0.jar #17 134.5 [INFO] Copying doxia-logging-api-1.1.jar to /opt/java/jars/doxia-logging-api-1.1.jar #17 134.5 [INFO] Copying surefire-api-2.22.0.jar to /opt/java/jars/surefire-api-2.22.0.jar #17 134.5 [INFO] Copying maven-plugin-registry-2.2.1.jar to /opt/java/jars/maven-plugin-registry-2.2.1.jar #17 134.5 [INFO] Copying backport-util-concurrent-3.1.jar to /opt/java/jars/backport-util-concurrent-3.1.jar #17 134.5 [INFO] Copying wagon-http-shared-1.0-beta-6.jar to /opt/java/jars/wagon-http-shared-1.0-beta-6.jar #17 134.5 [INFO] Copying commons-httpclient-3.1.jar to /opt/java/jars/commons-httpclient-3.1.jar #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] BUILD SUCCESS #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] Total time: 02:12 min #17 134.5 [INFO] Finished at: 2024-05-16T20:27:50Z #17 134.5 [INFO] ------------------------------------------------------------------------ #17 DONE 134.8s #18 [14/17] COPY model_dir/success6g_model /opt/ml/model #18 DONE 0.1s #19 [15/17] RUN python -c \"from mlflow.models import container as C; C._install_pyfunc_deps('/opt/ml/model', install_mlflow=False, enable_mlserver=True, env_manager='virtualenv');\" #19 1.285 2024/05/16 20:27:52 INFO mlflow.models.container: creating and activating custom environment #19 1.289 2024/05/16 20:27:52 INFO mlflow.utils.virtualenv: Installing python 3.10.11 if it does not exist #19 1.673 Downloading Python-3.10.11.tar.xz... #19 1.673 -> https://www.python.org/ftp/python/3.10.11/Python-3.10.11.tar.xz #19 4.792 Installing Python-3.10.11... #19 108.0 Installed Python-3.10.11 to /root/.pyenv/versions/3.10.11 #19 108.4 2024/05/16 20:29:39 INFO mlflow.utils.virtualenv: Creating a new environment in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b with /root/.pyenv/versions/3.10.11/bin/python #19 109.3 created virtual environment CPython3.10.11.final.0-64 in 719ms #19 109.3 creator CPython3Posix(dest=/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b, clear=False, no_vcs_ignore=False, global=False) #19 109.3 seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/.local/share/virtualenv) #19 109.3 added seed packages: pip==24.0, setuptools==69.5.1, wheel==0.43.0 #19 109.3 activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator #19 109.4 2024/05/16 20:29:40 INFO mlflow.utils.virtualenv: Installing dependencies #19 168.4 WARNING: There was an error checking the latest version of pip. #19 169.3 Collecting mlserver!=1.3.1, < 1.4.0,>=1.2.0 #19 169.4 Downloading mlserver-1.3.5-py3-none-any.whl (113 kB) #19 169.4 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 113.2/113.2 kB 3.4 MB/s eta 0:00:00 #19 169.6 Collecting mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0 #19 169.6 Downloading mlserver_mlflow-1.3.5-py3-none-any.whl (10 kB) #19 169.7 Requirement already satisfied: click in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) (8.1.3) #19 169.8 Collecting fastapi!=0.89.0, < =0.89.1,>=0.88.0 (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.8 Downloading fastapi-0.89.1-py3-none-any.whl (55 kB) #19 169.8 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 55.8/55.8 kB 7.4 MB/s eta 0:00:00 #19 169.9 Collecting python-dotenv (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.9 Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB) #19 171.0 Collecting grpcio (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.0 Downloading grpcio-1.63.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB) #19 171.3 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1.4.0,>=1.2.0) (4.25.3) #19 171.5 Collecting uvicorn (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.5 Downloading uvicorn-0.29.0-py3-none-any.whl (60 kB) #19 171.6 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 60.8/60.8 kB 8.7 MB/s eta 0:00:00 #19 171.7 Collecting starlette-exporter (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.7 Downloading starlette_exporter-0.21.0-py3-none-any.whl (14 kB) #19 171.9 Collecting py-grpc-prometheus (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.9 Downloading py_grpc_prometheus-0.8.0-py3-none-any.whl (12 kB) #19 172.1 Collecting aiokafka (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 172.1 Downloading aiokafka-0.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB) #19 172.2 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requirements. 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uvicorn->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (4.11.0) #19 176.9 Collecting aiosignal>=1.1.2 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.9 Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB) #19 177.0 Collecting attrs>=17.3.0 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 177.0 Downloading attrs-23.2.0-py3-none-any.whl (60 kB) #19 177.0 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 60.8/60.8 kB 8.7 MB/s eta 0:00:00 #19 177.2 Collecting frozenlist>=1.1.1 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 177.2 Downloading frozenlist-1.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (239 kB) #19 177.2 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 239.5/239.5 kB 29.8 MB/s eta 0:00:00 #19 177.6 Collecting multidict < 7.0,>=4.5 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 177.7 Downloading multidict-6.0.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (124 kB) #19 177.7 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 124.3/124.3 kB 17.1 MB/s eta 0:00:00 #19 178.0 Collecting yarl < 2.0,>=1.0 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 178.0 Downloading yarl-1.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (301 kB) #19 178.0 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 301.6/301.6 kB 16.3 MB/s eta 0:00:00 #19 178.0 Requirement already satisfied: Mako in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from alembic!=1.10.0, < 2->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (1.3.5) #19 178.1 Requirement already satisfied: Werkzeug>=3.0.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from Flask < 4->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.0.3) #19 178.1 Requirement already satisfied: itsdangerous>=2.1.2 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from Flask < 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/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from sqlalchemy < 3,>=1.4.0->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.0.3) #19 179.6 Collecting sniffio>=1.1 (from anyio < 5,>=3.4.0->starlette==0.22.0->fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 179.6 Downloading sniffio-1.3.1-py3-none-any.whl (10 kB) #19 179.7 Collecting exceptiongroup>=1.0.2 (from anyio < 5,>=3.4.0->starlette==0.22.0->fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 179.7 Downloading exceptiongroup-1.2.1-py3-none-any.whl (16 kB) #19 179.9 Collecting zope.event (from gevent>=0.13->geventhttpclient < =2.0.2,>=1.4.4->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 180.0 Downloading zope.event-5.0-py3-none-any.whl (6.8 kB) #19 180.2 Collecting zope.interface (from gevent>=0.13->geventhttpclient < =2.0.2,>=1.4.4->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 180.2 Downloading zope.interface-6.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (247 kB) #19 180.2 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 247.5/247.5 kB 31.3 MB/s eta 0:00:00 #19 180.2 Requirement already satisfied: smmap < 6,>=3.0.1 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from gitdb < 5,>=4.0.1->gitpython < 4,>=3.1.9->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (5.0.1) #19 181.2 Installing collected packages: brotli, zope.interface, zope.event, uvloop, sniffio, python-rapidjson, python-dotenv, pydantic, prometheus-client, orjson, multidict, importlib-resources, h11, grpcio, frozenlist, exceptiongroup, attrs, aiokafka, aiofiles, yarl, uvicorn, tritonclient, py-grpc-prometheus, gevent, anyio, aiosignal, starlette, geventhttpclient, aiohttp, starlette-exporter, fastapi, mlserver, mlserver-mlflow #19 184.4 Successfully installed aiofiles-23.2.1 aiohttp-3.9.5 aiokafka-0.10.0 aiosignal-1.3.1 anyio-4.3.0 attrs-23.2.0 brotli-1.1.0 exceptiongroup-1.2.1 fastapi-0.89.1 frozenlist-1.4.1 gevent-24.2.1 geventhttpclient-2.0.2 grpcio-1.63.0 h11-0.14.0 importlib-resources-6.4.0 mlserver-1.3.5 mlserver-mlflow-1.3.5 multidict-6.0.5 orjson-3.10.3 prometheus-client-0.20.0 py-grpc-prometheus-0.8.0 pydantic-1.10.15 python-dotenv-1.0.1 python-rapidjson-1.16 sniffio-1.3.1 starlette-0.22.0 starlette-exporter-0.17.1 tritonclient-2.45.0 uvicorn-0.29.0 uvloop-0.19.0 yarl-1.9.4 zope.event-5.0 zope.interface-6.4 #19 184.4 WARNING: There was an error checking the latest version of pip. #19 DONE 186.7s #20 [16/17] RUN chmod o+rwX /opt/mlflow/ #20 DONE 0.4s #21 [17/17] RUN rm -rf /var/lib/apt/lists/* #21 DONE 0.6s #22 exporting to image #22 exporting layers #22 exporting layers 12.0s done #22 writing image sha256:29875270957bd037ee2b833cf9201a001d353e4fef34b1d8c4923b278ef8ac02 done #22 naming to docker.io/5uperpalo/mlflow-success6g done #22 DONE 12.1s ```","title":"Appendix"},{"location":"notebooks/05_model_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = 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event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Model analysis \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [3]: Copied! from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast In [4]: Copied! trainer = dill_load ( \"lgbm_trainer.dill\" ) trainer = dill_load(\"lgbm_trainer.dill\") In [5]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ trainer . target_col ], random_state = random_state , ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ trainer . target_col ], random_state = random_state , ) # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[trainer.target_col], random_state=random_state, ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[trainer.target_col], random_state=random_state, ) In [6]: Copied! df_predicted_proba = trainer . predict_proba ( df = df_test . drop ( columns = trainer . target_col ), binary2d = False ) df_predicted_cls = trainer . predict_cls ( df = df_test . drop ( columns = trainer . target_col )) df_predicted_proba = trainer.predict_proba( df=df_test.drop(columns=trainer.target_col), binary2d=False ) df_predicted_cls = trainer.predict_cls(df=df_test.drop(columns=trainer.target_col)) Model feature importance and \"certainty\" of the model in predicting classes - probability density per class \u00b6 In [7]: Copied! lgb . plot_importance ( trainer . model , importance_type = \"split\" ) # lgb.plot_importance(trainer.model, importance_type=\"gain\") lgb.plot_importance(trainer.model, importance_type=\"split\") # lgb.plot_importance(trainer.model, importance_type=\"gain\") Out[7]: In [8]: Copied! fig = prob_distrib_per_class ( predicted_probs = df_predicted_proba . values . flatten (), actual = df_test [ trainer . target_col ] . values . flatten (), task = \"binary\" , ) fig = prob_distrib_per_class( predicted_probs=df_predicted_proba.values.flatten(), actual=df_test[trainer.target_col].values.flatten(), task=\"binary\", ) SHAP analysis \u00b6 In [9]: Copied! df_test_prep = trainer . preprocessors [ 0 ] . transform ( df_test . drop ( columns = [ trainer . target_col ]) ) df_test_prep [ trainer . target_col ] = df_test [ trainer . target_col ] . astype ( int ) lgb_test , _ = to_lgbdataset ( train = df_test_prep , cat_cols = trainer . cat_cols , target_col = trainer . target_col , id_cols = trainer . id_cols , ) df_test_prep = trainer.preprocessors[0].transform( df_test.drop(columns=[trainer.target_col]) ) df_test_prep[trainer.target_col] = df_test[trainer.target_col].astype(int) lgb_test, _ = to_lgbdataset( train=df_test_prep, cat_cols=trainer.cat_cols, target_col=trainer.target_col, id_cols=trainer.id_cols, ) In [10]: Copied! base_value = trainer . model . predict ( data = lgb_test . data , pred_contrib = True ) shap_values = base_value [:, : - 1 ] base_value = np . mean ( base_value [:, - 1 ]) base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True) shap_values = base_value[:, :-1] base_value = np.mean(base_value[:, -1]) In [11]: Copied! shap . summary_plot ( shap_values , features = lgb_test . data , plot_type = \"dot\" , sort = False , show = True ) shap.summary_plot( shap_values, features=lgb_test.data, plot_type=\"dot\", sort=False, show=True ) In [19]: Copied! df_predicted_cls [ 10 : 12 ] df_predicted_cls[10:12] Out[19]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 10 0 11 1 In [20]: Copied! df_test . iloc [ 10 : 12 ] df_test.iloc[10:12] Out[20]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 579 38.04 89.0 895.5 0.0 22.0 9.77 83.14 34050.0 101.0 14.76 0 123abc 23 100.00 22.0 926.0 0.0 11.0 12.66 83.53 32160.0 101.0 14.72 1 123abc In [21]: Copied! shap_decision_plot = shap . decision_plot ( base_value = base_value , shap_values = shap_values [ 10 : 12 ], features = trainer . model . feature_name (), feature_names = trainer . model . feature_name (), # legend_labels=legend_labels, link = \"logit\" , # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), ) shap_decision_plot = shap.decision_plot( base_value=base_value, shap_values=shap_values[10:12], features=trainer.model.feature_name(), feature_names=trainer.model.feature_name(), # legend_labels=legend_labels, link=\"logit\", # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), )","title":"05_model_analysis"},{"location":"notebooks/05_model_analysis.html#model-analysis","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [3]: Copied! from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast In [4]: Copied! trainer = dill_load ( \"lgbm_trainer.dill\" ) trainer = dill_load(\"lgbm_trainer.dill\") In [5]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ trainer . target_col ], random_state = random_state , ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ trainer . target_col ], random_state = random_state , ) # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[trainer.target_col], random_state=random_state, ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[trainer.target_col], random_state=random_state, ) In [6]: Copied! df_predicted_proba = trainer . predict_proba ( df = df_test . drop ( columns = trainer . target_col ), binary2d = False ) df_predicted_cls = trainer . predict_cls ( df = df_test . drop ( columns = trainer . target_col )) df_predicted_proba = trainer.predict_proba( df=df_test.drop(columns=trainer.target_col), binary2d=False ) df_predicted_cls = trainer.predict_cls(df=df_test.drop(columns=trainer.target_col))","title":"Model analysis"},{"location":"notebooks/05_model_analysis.html#model-feature-importance-and-certainty-of-the-model-in-predicting-classes-probability-density-per-class","text":"In [7]: Copied! lgb . plot_importance ( trainer . model , importance_type = \"split\" ) # lgb.plot_importance(trainer.model, importance_type=\"gain\") lgb.plot_importance(trainer.model, importance_type=\"split\") # lgb.plot_importance(trainer.model, importance_type=\"gain\") Out[7]: In [8]: Copied! fig = prob_distrib_per_class ( predicted_probs = df_predicted_proba . values . flatten (), actual = df_test [ trainer . target_col ] . values . flatten (), task = \"binary\" , ) fig = prob_distrib_per_class( predicted_probs=df_predicted_proba.values.flatten(), actual=df_test[trainer.target_col].values.flatten(), task=\"binary\", )","title":"Model feature importance and \"certainty\" of the model in predicting classes - probability density per class"},{"location":"notebooks/05_model_analysis.html#shap-analysis","text":"In [9]: Copied! df_test_prep = trainer . preprocessors [ 0 ] . transform ( df_test . drop ( columns = [ trainer . target_col ]) ) df_test_prep [ trainer . target_col ] = df_test [ trainer . target_col ] . astype ( int ) lgb_test , _ = to_lgbdataset ( train = df_test_prep , cat_cols = trainer . cat_cols , target_col = trainer . target_col , id_cols = trainer . id_cols , ) df_test_prep = trainer.preprocessors[0].transform( df_test.drop(columns=[trainer.target_col]) ) df_test_prep[trainer.target_col] = df_test[trainer.target_col].astype(int) lgb_test, _ = to_lgbdataset( train=df_test_prep, cat_cols=trainer.cat_cols, target_col=trainer.target_col, id_cols=trainer.id_cols, ) In [10]: Copied! base_value = trainer . model . predict ( data = lgb_test . data , pred_contrib = True ) shap_values = base_value [:, : - 1 ] base_value = np . mean ( base_value [:, - 1 ]) base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True) shap_values = base_value[:, :-1] base_value = np.mean(base_value[:, -1]) In [11]: Copied! shap . summary_plot ( shap_values , features = lgb_test . data , plot_type = \"dot\" , sort = False , show = True ) shap.summary_plot( shap_values, features=lgb_test.data, plot_type=\"dot\", sort=False, show=True ) In [19]: Copied! df_predicted_cls [ 10 : 12 ] df_predicted_cls[10:12] Out[19]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 10 0 11 1 In [20]: Copied! df_test . iloc [ 10 : 12 ] df_test.iloc[10:12] Out[20]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 579 38.04 89.0 895.5 0.0 22.0 9.77 83.14 34050.0 101.0 14.76 0 123abc 23 100.00 22.0 926.0 0.0 11.0 12.66 83.53 32160.0 101.0 14.72 1 123abc In [21]: Copied! shap_decision_plot = shap . decision_plot ( base_value = base_value , shap_values = shap_values [ 10 : 12 ], features = trainer . model . feature_name (), feature_names = trainer . model . feature_name (), # legend_labels=legend_labels, link = \"logit\" , # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), ) shap_decision_plot = shap.decision_plot( base_value=base_value, shap_values=shap_values[10:12], features=trainer.model.feature_name(), feature_names=trainer.model.feature_name(), # legend_labels=legend_labels, link=\"logit\", # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), )","title":"SHAP analysis"},{"location":"notebooks/success6g-edge.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); In [1]: Copied! import pandas as pd import ast import os import redis os . chdir ( \"..\" ) import pandas as pd import ast import os import redis os.chdir(\"..\") Get the data into pandas \u00b6 In [2]: Copied! with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df = pd . concat ([ df , temp_df ], axis = 1 ) with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df = pd.concat([df, temp_df], axis=1) In [3]: Copied! df df Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage 0 37.25 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 37.25 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 2 36.86 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 3 36.86 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 4 36.86 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 ... ... ... ... ... ... ... ... ... ... ... 32740 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32741 NaN NaN NaN NaN NaN 9.44 NaN NaN NaN NaN 32742 NaN NaN NaN NaN NaN 9.49 NaN NaN NaN NaN 32743 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32744 NaN NaN NaN NaN NaN 9.52 NaN NaN NaN NaN 32745 rows \u00d7 10 columns In [4]: Copied! df . dropna ( inplace = True ) df.dropna(inplace=True) In [5]: Copied! df [ \"class\" ] = 0 df . loc [: 100 , [ \"class\" ]] = 1 df . loc [: 100 , [ \"engine_load\" ]] = 100 df[\"class\"] = 0 df.loc[:100, [\"class\"]] = 1 df.loc[:100, [\"engine_load\"]] = 100 In [6]: Copied! from lightgbm import LGBMClassifier model = LGBMClassifier () model . fit ( df . drop ( columns = [ \"class\" ]), df [ \"class\" ]) from lightgbm import LGBMClassifier model = LGBMClassifier() model.fit(df.drop(columns=[\"class\"]), df[\"class\"]) [LightGBM] [Info] Number of positive: 101, number of negative: 934 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 508 [LightGBM] [Info] Number of data points in the train set: 1035, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097585 -> initscore=-2.224356 [LightGBM] [Info] Start training from score -2.224356 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf Out[6]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} LGBMClassifier() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. LGBMClassifier LGBMClassifier() Redis \u00b6 Install and start the redis database \u00b6 pip install prometheus-client sudo apt install lsb-release curl gpg curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list sudo apt-get update sudo apt-get install redis sudo service redis-server start # sudo service redis-server stop sudo service redis-server status redis-cli ping Fill the Redis database \u00b6 send data to redis redis-cli -h localhost -p 6379 redis-cli -h 10.152.183.169 -a redis -p 6379 XADD v2x * car_id 123 engine_load 37.25 engine_coolant_temp 17.00 engine_speed 904.50 vehicle_speed 0.00 intake_air_temp 10.00 maf 12.55 throttle_position 83.14 fuel_rg_pressure 37270.00 barometric_pressure 101.00 control_voltage 0.0666 In [7]: Copied! # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis . Redis ( host = redis_host , port = 6379 , password = \"redis\" ) # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis.Redis(host=redis_host, port=6379, password=\"redis\") # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) In [8]: Copied! entry_data = { \"field1\" : \"value1\" , \"field2\" : \"value2\" } for index , row in df . iterrows (): redis_conn . xadd ( \"v2x\" , dict ( row )) entry_data = {\"field1\": \"value1\", \"field2\": \"value2\"} for index, row in df.iterrows(): redis_conn.xadd(\"v2x\", dict(row)) Retrieve data from redis \u00b6 In [13]: Copied! while True : # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[ stream , [[ number , parts ]]]] = redis_conn . xread ( streams = { \"v2x\" : \"$\" }, block = 0 ) keys = [ p . decode ( \"utf-8\" ) for p in parts . keys ()] vals = [ float ( p . decode ( \"utf-8\" )) for p in parts . values ()] d = pd . DataFrame ( dict ( zip ( keys , vals )), index = [ 0 ]) d [ \"predicted_class\" ] = model . predict ( d . drop ( columns = [ \"car_id\" ]))[ 0 ] print ( d ) while True: # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[stream, [[number, parts]]]] = redis_conn.xread(streams={\"v2x\": \"$\"}, block=0) keys = [p.decode(\"utf-8\") for p in parts.keys()] vals = [float(p.decode(\"utf-8\")) for p in parts.values()] d = pd.DataFrame(dict(zip(keys, vals)), index=[0]) d[\"predicted_class\"] = model.predict(d.drop(columns=[\"car_id\"]))[0] print(d)","title":"success6g-edge"},{"location":"notebooks/success6g-edge.html#get-the-data-into-pandas","text":"In [2]: Copied! with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df = pd . concat ([ df , temp_df ], axis = 1 ) with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df = pd.concat([df, temp_df], axis=1) In [3]: Copied! df df Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage 0 37.25 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 37.25 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 2 36.86 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 3 36.86 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 4 36.86 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 ... ... ... ... ... ... ... ... ... ... ... 32740 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32741 NaN NaN NaN NaN NaN 9.44 NaN NaN NaN NaN 32742 NaN NaN NaN NaN NaN 9.49 NaN NaN NaN NaN 32743 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32744 NaN NaN NaN NaN NaN 9.52 NaN NaN NaN NaN 32745 rows \u00d7 10 columns In [4]: Copied! df . dropna ( inplace = True ) df.dropna(inplace=True) In [5]: Copied! df [ \"class\" ] = 0 df . loc [: 100 , [ \"class\" ]] = 1 df . loc [: 100 , [ \"engine_load\" ]] = 100 df[\"class\"] = 0 df.loc[:100, [\"class\"]] = 1 df.loc[:100, [\"engine_load\"]] = 100 In [6]: Copied! from lightgbm import LGBMClassifier model = LGBMClassifier () model . fit ( df . drop ( columns = [ \"class\" ]), df [ \"class\" ]) from lightgbm import LGBMClassifier model = LGBMClassifier() model.fit(df.drop(columns=[\"class\"]), df[\"class\"]) [LightGBM] [Info] Number of positive: 101, number of negative: 934 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 508 [LightGBM] [Info] Number of data points in the train set: 1035, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097585 -> initscore=-2.224356 [LightGBM] [Info] Start training from score -2.224356 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf Out[6]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: 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div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} LGBMClassifier() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. LGBMClassifier LGBMClassifier()","title":"Get the data into pandas"},{"location":"notebooks/success6g-edge.html#redis","text":"","title":"Redis"},{"location":"notebooks/success6g-edge.html#install-and-start-the-redis-database","text":"pip install prometheus-client sudo apt install lsb-release curl gpg curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list sudo apt-get update sudo apt-get install redis sudo service redis-server start # sudo service redis-server stop sudo service redis-server status redis-cli ping","title":"Install and start the redis database"},{"location":"notebooks/success6g-edge.html#fill-the-redis-database","text":"send data to redis redis-cli -h localhost -p 6379 redis-cli -h 10.152.183.169 -a redis -p 6379 XADD v2x * car_id 123 engine_load 37.25 engine_coolant_temp 17.00 engine_speed 904.50 vehicle_speed 0.00 intake_air_temp 10.00 maf 12.55 throttle_position 83.14 fuel_rg_pressure 37270.00 barometric_pressure 101.00 control_voltage 0.0666 In [7]: Copied! # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis . Redis ( host = redis_host , port = 6379 , password = \"redis\" ) # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis.Redis(host=redis_host, port=6379, password=\"redis\") # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) In [8]: Copied! entry_data = { \"field1\" : \"value1\" , \"field2\" : \"value2\" } for index , row in df . iterrows (): redis_conn . xadd ( \"v2x\" , dict ( row )) entry_data = {\"field1\": \"value1\", \"field2\": \"value2\"} for index, row in df.iterrows(): redis_conn.xadd(\"v2x\", dict(row))","title":"Fill the Redis database"},{"location":"notebooks/success6g-edge.html#retrieve-data-from-redis","text":"In [13]: Copied! while True : # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[ stream , [[ number , parts ]]]] = redis_conn . xread ( streams = { \"v2x\" : \"$\" }, block = 0 ) keys = [ p . decode ( \"utf-8\" ) for p in parts . keys ()] vals = [ float ( p . decode ( \"utf-8\" )) for p in parts . values ()] d = pd . DataFrame ( dict ( zip ( keys , vals )), index = [ 0 ]) d [ \"predicted_class\" ] = model . predict ( d . drop ( columns = [ \"car_id\" ]))[ 0 ] print ( d ) while True: # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[stream, [[number, parts]]]] = redis_conn.xread(streams={\"v2x\": \"$\"}, block=0) keys = [p.decode(\"utf-8\") for p in parts.keys()] vals = [float(p.decode(\"utf-8\")) for p in parts.values()] d = pd.DataFrame(dict(zip(keys, vals)), index=[0]) d[\"predicted_class\"] = model.predict(d.drop(columns=[\"car_id\"]))[0] print(d)","title":"Retrieve data from redis"}]}
\ No newline at end of file
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Description of the components: Grafana - dashboards Ingress - expose services to the operator Prometheus - gather pod metrics InfluxDB - gather vehicular measurements and predictions MinIO - store models and training/testing data JupyterHub - develop new models MLflow - MLops, experiment and model tracking Kserve - serve inference models to predefined pods Istio - to ensure optimal traffic flow between microservices Knative - to ensure autoscaling of inference service pods Kepler - gather energy consumption data Redis - API for transfer of OBU measurements to Kubernetes Additional ideas \u00b6 KubeEdge deployment - same as Microk8s except with KubeEdge and Kubeflow/Kserver is swapped for Sedna implement multimodel pods e.g. by ModelMesh , or alpha feature of Kserve use Rancher to manage multi cluster Kubernetes","title":"home"},{"location":"index.html#_1","text":"Basic Architecture Overview This repository is for the edge pod implementation, monitoring, and analysis in SUCCESS6G project.","title":""},{"location":"index.html#solution-overview","text":"Solution is deployed in Microk8s. Description of the components: Grafana - dashboards Ingress - expose services to the operator Prometheus - gather pod metrics InfluxDB - gather vehicular measurements and predictions MinIO - store models and training/testing data JupyterHub - develop new models MLflow - MLops, experiment and model tracking Kserve - serve inference models to predefined pods Istio - to ensure optimal traffic flow between microservices Knative - to ensure autoscaling of inference service pods Kepler - gather energy consumption data Redis - API for transfer of OBU measurements to Kubernetes","title":"Solution overview"},{"location":"index.html#additional-ideas","text":"KubeEdge deployment - same as Microk8s except with KubeEdge and Kubeflow/Kserver is swapped for Sedna implement multimodel pods e.g. by ModelMesh , or alpha feature of Kserve use Rancher to manage multi cluster Kubernetes","title":"Additional ideas"},{"location":"architecture_overview.html","text":"Architecture \u00b6 Basic Architecture \u00b6 Basic Architecture Single cluster architecture \u00b6 Single cluster architecture Advantages: Simplified Management: Easier to manage and maintain as there's only one cluster. Simplified network configuration and service discovery within the cluster. Resource Efficiency: Better utilization of resources since there is no need to allocate separate resources for multiple clusters. Cost-Effective: Lower operational costs due to reduced overhead in managing multiple clusters. Reduced complexity in monitoring and logging, leading to potential cost savings. Unified Security and Policy Management: Simplified implementation of security policies, access controls, and compliance measures. Consistent Environment: Ensures a consistent environment across all nodes, simplifying development and deployment processes. Disadvantages: Scalability Limitations: May face scalability issues as the number of edge devices or workloads increases. Single point of failure; if the cluster goes down, all connected devices and services are affected. Performance Bottlenecks: Potential for performance bottlenecks if the cluster becomes overloaded. Limited fault isolation; issues in one part of the cluster can impact the entire system. Geographical Constraints: Less optimal for geographically distributed edge nodes as latency might increase. May not efficiently handle diverse network conditions across different locations. Single cluster architecture (advanced) \u00b6 Single cluster architecture(advanced) Architecture improved by involving service mesh for optimal micro service traffic routing. For more info see istio documentation Multi cluster architecture \u00b6 Multi cluster architecture Multi cluster deployment that can utilize dUPF metrics (e.g. number of connected users) to decide if the application should be deployed in a neighboring micro cluster Advantages: Scalability and Fault Isolation: Better scalability by distributing workloads across multiple clusters. Improved fault isolation; problems in one cluster do not affect others. Geographical Distribution: Optimized for geographically distributed deployments, reducing latency by placing clusters closer to the edge devices. Can handle diverse network conditions more effectively. Performance Optimization: Easier to optimize performance by dedicating clusters to specific workloads or regions. Enhanced load balancing and resource distribution. Resilience and High Availability: Higher resilience and availability; failure in one cluster can be mitigated by other clusters. Enhanced disaster recovery capabilities. Disadvantages: Increased Complexity: More complex to manage and maintain multiple clusters. Higher administrative overhead in coordinating and synchronizing clusters. Resource Overhead: Requires more resources for cluster management (e.g., control planes for each cluster). Potentially higher infrastructure costs due to multiple clusters. Security and Policy Management: More complex to implement and manage security policies and compliance across multiple clusters. Potential challenges in maintaining consistent security postures across clusters. Inter-Cluster Communication: Requires robust inter-cluster communication mechanisms, which can add to the complexity. More complicated service discovery and networking between clusters. Conclusion \u00b6 The choice between single-cluster and multi-cluster Kubernetes deployments for IoT on the edge depends on factors such as scale, geographic distribution, resource availability, and management complexity. Single-cluster deployments are simpler and more cost-effective for smaller, localized environments, whereas multi-cluster deployments offer better scalability, fault isolation, and performance optimization for larger, distributed environments.","title":"architecture overview"},{"location":"architecture_overview.html#architecture","text":"","title":"Architecture"},{"location":"architecture_overview.html#basic-architecture","text":"Basic Architecture","title":"Basic Architecture"},{"location":"architecture_overview.html#single-cluster-architecture","text":"Single cluster architecture Advantages: Simplified Management: Easier to manage and maintain as there's only one cluster. Simplified network configuration and service discovery within the cluster. Resource Efficiency: Better utilization of resources since there is no need to allocate separate resources for multiple clusters. Cost-Effective: Lower operational costs due to reduced overhead in managing multiple clusters. Reduced complexity in monitoring and logging, leading to potential cost savings. Unified Security and Policy Management: Simplified implementation of security policies, access controls, and compliance measures. Consistent Environment: Ensures a consistent environment across all nodes, simplifying development and deployment processes. Disadvantages: Scalability Limitations: May face scalability issues as the number of edge devices or workloads increases. Single point of failure; if the cluster goes down, all connected devices and services are affected. Performance Bottlenecks: Potential for performance bottlenecks if the cluster becomes overloaded. Limited fault isolation; issues in one part of the cluster can impact the entire system. Geographical Constraints: Less optimal for geographically distributed edge nodes as latency might increase. May not efficiently handle diverse network conditions across different locations.","title":"Single cluster architecture"},{"location":"architecture_overview.html#single-cluster-architecture-advanced","text":"Single cluster architecture(advanced) Architecture improved by involving service mesh for optimal micro service traffic routing. For more info see istio documentation","title":"Single cluster architecture (advanced)"},{"location":"architecture_overview.html#multi-cluster-architecture","text":"Multi cluster architecture Multi cluster deployment that can utilize dUPF metrics (e.g. number of connected users) to decide if the application should be deployed in a neighboring micro cluster Advantages: Scalability and Fault Isolation: Better scalability by distributing workloads across multiple clusters. Improved fault isolation; problems in one cluster do not affect others. Geographical Distribution: Optimized for geographically distributed deployments, reducing latency by placing clusters closer to the edge devices. Can handle diverse network conditions more effectively. Performance Optimization: Easier to optimize performance by dedicating clusters to specific workloads or regions. Enhanced load balancing and resource distribution. Resilience and High Availability: Higher resilience and availability; failure in one cluster can be mitigated by other clusters. Enhanced disaster recovery capabilities. Disadvantages: Increased Complexity: More complex to manage and maintain multiple clusters. Higher administrative overhead in coordinating and synchronizing clusters. Resource Overhead: Requires more resources for cluster management (e.g., control planes for each cluster). Potentially higher infrastructure costs due to multiple clusters. Security and Policy Management: More complex to implement and manage security policies and compliance across multiple clusters. Potential challenges in maintaining consistent security postures across clusters. Inter-Cluster Communication: Requires robust inter-cluster communication mechanisms, which can add to the complexity. More complicated service discovery and networking between clusters.","title":"Multi cluster architecture"},{"location":"code_profiling.html","text":"Code profiling [5 min read] \u00b6 JupyterHub(any Kubernetes) pod \u00b6 Max RAM/CPU \u00b6 Using PromQL to query Prometheus metrics of the pod. Max RAM/CPU/GPU from prometheus_api_client import PrometheusConnect from math import ceil def get_pod_max_cpu_memory_in_prometheus( prometheus_url: str = \"http://prometheus-kube-prometheus-prometheus.monitoring:9090\", container_namespace: str = \"jupyterhub\", pod_name: str = \"jupyter-5uperpalo\", ) -> dict: \"\"\" Function to query max CPU and memory consumption of a specific pod in Kubernetes in the past 10 days. container_memory_working_set_bytes metric returns value per spawned pod id (different pods might have been spawned with the same name in the past 10 days) - only the last is checked For the specific query details see the link below: https://stackoverflow.com/questions/58747562/how-to-get-max-cpu-useage-of-a-pod-in-kubernetes-over-a-time-interval-say-30-da https://stackoverflow.com/a/66778814 Args: prometheus_url (str): Prometheus service URL container_namespace (str): Kubernetes pod namespace name pod_name (str): Kubernetes namespace name Returns: metrics (dict): dictionary with values \"\"\" def round_up(n, decimals=0): multiplier = 10**decimals return math.ceil(n * multiplier) / multiplier prom = PrometheusConnect(url=prometheus_url, disable_ssl=True) pod_name = f\"'{pod_name}'\" container_namespace = f\"'{container_namespace}'\" container_cpu_max_query = f\"max_over_time(sum(rate(container_cpu_usage_seconds_total{{namespace={container_namespace}, pod={pod_name}, container!=''}}[1m]))[10d:1m])\" container_memory_max_query = f\"max_over_time(container_memory_working_set_bytes{{namespace={container_namespace}, pod={pod_name}, container!=''}}[10d])\" cpu_max = prom.custom_query(query=container_cpu_max_query) memory_max = prom.custom_query(query=container_memory_max_query) metrics = { \"cpu\": round_up(float(cpu_max[-1][\"value\"][1]), 2), \"memory\": round_up(float(memory_max[-1][\"value\"][1])/1024**3, 2), } return metrics besteffort_pods_list = [ (\"kepler-7dfh2\", \"kepler\"), (\"nginx-ingress-microk8s-controller-h5kfj\", \"ingress\"), ] for pod, namespace in besteffort_pods_list: metrics = get_pod_max_cpu_memory_in_prometheus( container_namespace=namespace, pod_name=pod, ) print(f\"{pod} ({namespace}), {metrics}\") >>> kepler-7dfh2 (kepler), {'cpu': 0.19, 'memory': 0.07} >>> nginx-ingress-microk8s-controller-h5kfj (ingress), {'cpu': 0.12, 'memory': 0.45} Max GPU \u00b6 In case it is necessary a the Prometheus metrics can be extended by including NVIDIA DCGM exporter in exported metrics and then querying the metrics of interest using similar function to get_pod_max_cpu_memory_in_prometheus . Energy measurements \u00b6 Using Kubernetes Efficient Power Level Exporter (Kepler) . Core/Uncore measurements might not be available(only PKG) depending on Intel architecture. See RAPL section in Kepler metrics explanation. Kepler metrics from datetime import datetime from prometheus_api_client import PrometheusConnect def get_kepler_pod_stats( to_timestamp: float, from_timestamp: float, prometheus_url: str = \"http://prometheus-kube-prometheus-prometheus.monitoring:9090\", container_namespace: str = \"jupyterhub\", pod_name: str = \"jupyter-5uperpalo\", ) -> dict: \"\"\"Function to query Kepler power consumption data of specific pod in Kubernetes. # https://sustainable-computing.io/design/kepler-energy-sources/ # https://github.com/sustainable-computing-io/kepler/blob/1c397ff00b72b5cb1585d0de2cd495c73d88f07a/grafana-dashboards/Kepler-Exporter.json#L299 # https://prometheus.io/docs/prometheus/latest/querying/basics/#time-durations # [metric for metric in prom.all_metrics() if \"kepler\" in metric] Args: to_timestamp (list): 'to' timestamp from_timestamp (list): 'from' timestamp prometheus_url (str): Prometheus service url container_namespace (str): Kubernetes pod namespace name pod_name (str): Kubernetes namespace name Returns: metrics (dict): Kepler metrics of the power consumption of pod in Kubernetes \"\"\" prom = PrometheusConnect(url=prometheus_url, disable_ssl=True) pod_name = f\"'{pod_name}'\" container_namespace = f\"'{container_namespace}'\" time_range_sec = str(int(to_timestamp - from_timestamp)) container_sum_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_core_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_core_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_uncore_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_uncore_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_pkg_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_package_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_dram_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_dram_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_other_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_other_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_gpu_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_gpu_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" sum_data = prom.custom_query(query=container_sum_query) core_data = prom.custom_query(query=container_core_query) uncore_data = prom.custom_query(query=container_uncore_query) pkg_data = prom.custom_query(query=container_pkg_query) dram_data = prom.custom_query(query=container_dram_query) other_data = prom.custom_query(query=container_other_query) gpu_data = prom.custom_query(query=container_gpu_query) metrics = { \"from\": datetime.fromtimestamp(from_timestamp).strftime(\"%m/%d/%Y, %H:%M:%S\"), \"to\": datetime.fromtimestamp(to_timestamp).strftime(\"%m/%d/%Y, %H:%M:%S\"), \"sum\": float(sum_data[0][\"value\"][1]), \"core\": float(core_data[0][\"value\"][1]), \"uncore\": float(uncore_data[0][\"value\"][1]), \"pkg\": float(pkg_data[0][\"value\"][1]), \"dram\": float(dram_data[0][\"value\"][1]), \"other\": float(other_data[0][\"value\"][1]), \"gpu\": float(gpu_data[0][\"value\"][1]), } return metrics >>> # prometheus has UTC 00:00 as opposed to Barcelona UTC +02:00 >>> to_timestamp = datetime(2024, 6, 11, 8, 31).timestamp() >>> from_timestamp = datetime(2024, 6, 11, 8, 25).timestamp() >>> >>> get_kepler_pod_stats(to_timestamp=to_timestamp, from_timestamp=from_timestamp) >>> >>> {'from': '06/11/2024, 08:25:00', >>> 'to': '06/11/2024, 08:31:00', >>> 'sum': 45.333333333333336, >>> 'core': 0.0, >>> 'uncore': 0.0, >>> 'pkg': 44.48569999999987, >>> 'dram': 0.8289999999998447, >>> 'other': 0.0, >>> 'gpu': 0.0} Carbon emission monitoring \u00b6 Grafana dashboard provided by Kepler uses predefined natural gas/coal/petroleum conversions from US . See simple implemented formula here with coefficient defined here . The Energy measurements can be easily transformed to Carbon emissions using CodeCarbon methodology . Code \u00b6 RAM/CPU/GPU \u00b6 All-in-one solution by Scalene: https://github.com/plasma-umass/scalene Carbon emission monitoring: \u00b6 https://mlco2.github.io/codecarbon/usage.html# https://github.com/sb-ai-lab/Eco2AI https://github.com/lfwa/carbontracker Code Execution timing \u00b6 Timer decorator Import and prepend the time decorator to log time(into specified log file) that it takes to execute analyzed function/class, e.g.: import logging from functools import wraps from time import time def timer(func): \"\"\"Wrapper to time and log the function execution. Parameters: func: function \"\"\" @wraps(func) def timer_func(*args, **kwargs): start_time = time() value = func(*args, **kwargs) end_time = time() logging.info( f\"Finished {func.__name__} in {(end_time - start_time):.4f} seconds.\" # noqa ) return value return timer_func @timer def training(config: TrainingConfig, custom_params: CustomParameters): [Legacy] RAM \u00b6 Max RAM To get max RAM used during executition of the function import the decorator and put it above it function to log max RAM used into specified logging file, e.g.: NOTE : this applies only to functions and NOT classes or class methods. import logging from functools import wraps from memory_profiler import memory_usage def ram_usage(func): \"\"\"Wrapper to monitor and log RAM usage during the function execution. NOTE: can be applied to function but not to method of a class. Parameters: func: function \"\"\" @wraps(func) def ram_usage_func(*args, **kwargs): ram, value = memory_usage( (func, args, *kwargs), interval=1.0, retval=True, max_usage=True ) logging.info( f\"Finished {func.__name__,}. Max RAM used {(ram / 1000):.4f} GB.\" ) # noqa return value return ram_usage_func from churn_pred.code_profiling import ram_usage @ram_usage def training(config: TrainingConfig, custom_params: CustomParameters): Per line RAM usage analysis To analyze RAM usage per line of the code import profile decorator, put it above the function you want to analyze: from memory_profiler import profile @profile def training(config: TrainingConfig, custom_params: CustomParameters): , run the code using memory_profile command line util, and analyze the RAM usage, e.g.: $ python -m memory_profiler local_run.py Filename: /XXX/main.py Line # Mem usage Increment Occurrences Line Contents ============================================================= 53 230.8 MiB 230.8 MiB 1 @ram_usage 54 @profile 55 def training(config: TrainingConfig, custom_params: CustomParameters): 56 230.9 MiB 0.1 MiB 1 data_config = DataConfig.load(config.data_config) 57 230.9 MiB 0.0 MiB 1 hyperparameters = Hyperparameters.parse_obj(config.hyperparameters) 58 59 687.5 MiB 456.6 MiB 1 queries, df_data = get_data(config, custom_params) 60 687.5 MiB 0.0 MiB 1 (","title":"code profiling"},{"location":"code_profiling.html#code-profiling-5-min-read","text":"","title":"Code profiling [5 min read]"},{"location":"code_profiling.html#jupyterhubany-kubernetes-pod","text":"","title":"JupyterHub(any Kubernetes) pod"},{"location":"code_profiling.html#code","text":"","title":"Code"},{"location":"contributing.html","text":"Code \u00b6 Before making a pushing a code and making a pull request please run codestyle checks and tests ./code_style.sh pytest --doctest-modules churn_pred --cov-report xml --cov-report term --disable-pytest-warnings --cov = eda tests/ Documentation \u00b6 Documentation is created using github pages and mkdocs, see # to build locally cd docs pip install -r requirements.txt mkdocs build # to push to github pages mkdocs gh-deploy # if you want to run webserver locally mkdocs serve","title":"contributing"},{"location":"contributing.html#code","text":"Before making a pushing a code and making a pull request please run codestyle checks and tests ./code_style.sh pytest --doctest-modules churn_pred --cov-report xml --cov-report term --disable-pytest-warnings --cov = eda tests/","title":"Code"},{"location":"contributing.html#documentation","text":"Documentation is created using github pages and mkdocs, see # to build locally cd docs pip install -r requirements.txt mkdocs build # to push to github pages mkdocs gh-deploy # if you want to run webserver locally mkdocs serve","title":"Documentation"},{"location":"data_management.html","text":"Data management \u00b6 The choice of data storage and management options was influenced by the following factors: * time series data (measurements and predictions) -> time series database * easy to administer, popular solution with helm chart available by developers and Python API support -> InfluxDB * responsibility ownership of communication between vehicle and endpoint (v2x communication) on v2x measurement provider, i.e. Idneo -> Idneo choice - Redis * monitoring of energy consumption(carbonn footprint) + CPU/GPU/RAM utilization in Kubernetes -> Prometheus + Kepler + Grafana Local/edge data management \u00b6 Both the vehicle On Board Unit (OBU) and endpoint API are administered by Idneo . V2X vehicle measurements refer to the data that the vehicle uses to communicate its presence on the road. This data is essential in Intelligent Transportation Systems (ITS) to complement and provide the Cooperative Awareness (CA) service, which generates Cooperative Awareness Messages (CAM) according to the European standard ETSI, equivalent to the Basic Safety Message (BSM) of the American standard (SAE). The data shared through V2X in ITS services represents only a small portion of the data available in the vehicle's internal network Notice that generating simple V2X messages with their mandatory data, only requires the public vehicle data. However, a V2X stack has an interface where car manufacturers can insert some private data to enrich the V2X messages (these are optional fields) The data is sampled through the OBDII diagnostic port by constant polling. This data is uploaded to a transactional database Each variable has its operating range and units. For more details, see On-board diagnostics parameter definitions . For a detailed communication description, i.e. how the communication is enforced to stay in an edge node, please see networking section single cluster Kubernetes the Redis database is deployed on every edge node by use of Kubernetes pod assigning options and defining DaemonSets that will run a pod on every selected node multi-cluster Kubernetes multi-cluster environment suggests the use of a small cluster per edge location, in this case, a separate Redis database deployment in each cluster is sufficient Predictions \u00b6 If there is no responsibility ownership division an Inference Service provided by Kserve would be an endpoint for OBU. Compatibility with this scenario is assured by model predict methods . As the Redis database is an endpoint for OBU we use the Redis client channel subscription method to get the latest vehicle measurements and forward the predictions to the central Influx database. Additional notes to \"Local/edge data management\" and \"Predictions\" \u00b6 channel subscription scenario is not \"usual\" as inference service is used indirectly keeping the v2x data on the edge reduces communication overhead and if necessary for analysis it can be (i) queried from the Redis database or (ii) aggregated to InfluxDB by use of Telegram keeping the v2x data on the edge also reduces the possible security/privacy concerns caused by transferring all the data to the central location we do not use Prometheus for predictions storing as Prometheus is designed for scraping metrics and not storing data; storing functionality can improved by Thanos , but the solution is primarily designed for metrics not measurements and predictions we do not use Grafana for visualization of predictions as InfluxDB by default includes User Interface(UI) for visualization and forwarding data to Grafana would cause additional unnecessary data communication System Metrics \u00b6 We use combination of Prometheus + Kepler + Grafana to monitor resource utilization inside the cluster. This data can be later used for downscaling the solution and reducing the carbon footprint in production.","title":"data management"},{"location":"data_management.html#data-management","text":"The choice of data storage and management options was influenced by the following factors: * time series data (measurements and predictions) -> time series database * easy to administer, popular solution with helm chart available by developers and Python API support -> InfluxDB * responsibility ownership of communication between vehicle and endpoint (v2x communication) on v2x measurement provider, i.e. Idneo -> Idneo choice - Redis * monitoring of energy consumption(carbonn footprint) + CPU/GPU/RAM utilization in Kubernetes -> Prometheus + Kepler + Grafana","title":"Data management"},{"location":"data_management.html#localedge-data-management","text":"Both the vehicle On Board Unit (OBU) and endpoint API are administered by Idneo . V2X vehicle measurements refer to the data that the vehicle uses to communicate its presence on the road. This data is essential in Intelligent Transportation Systems (ITS) to complement and provide the Cooperative Awareness (CA) service, which generates Cooperative Awareness Messages (CAM) according to the European standard ETSI, equivalent to the Basic Safety Message (BSM) of the American standard (SAE). The data shared through V2X in ITS services represents only a small portion of the data available in the vehicle's internal network Notice that generating simple V2X messages with their mandatory data, only requires the public vehicle data. However, a V2X stack has an interface where car manufacturers can insert some private data to enrich the V2X messages (these are optional fields) The data is sampled through the OBDII diagnostic port by constant polling. This data is uploaded to a transactional database Each variable has its operating range and units. For more details, see On-board diagnostics parameter definitions . For a detailed communication description, i.e. how the communication is enforced to stay in an edge node, please see networking section single cluster Kubernetes the Redis database is deployed on every edge node by use of Kubernetes pod assigning options and defining DaemonSets that will run a pod on every selected node multi-cluster Kubernetes multi-cluster environment suggests the use of a small cluster per edge location, in this case, a separate Redis database deployment in each cluster is sufficient","title":"Local/edge data management"},{"location":"data_management.html#predictions","text":"If there is no responsibility ownership division an Inference Service provided by Kserve would be an endpoint for OBU. Compatibility with this scenario is assured by model predict methods . As the Redis database is an endpoint for OBU we use the Redis client channel subscription method to get the latest vehicle measurements and forward the predictions to the central Influx database.","title":"Predictions"},{"location":"data_management.html#additional-notes-to-localedge-data-management-and-predictions","text":"channel subscription scenario is not \"usual\" as inference service is used indirectly keeping the v2x data on the edge reduces communication overhead and if necessary for analysis it can be (i) queried from the Redis database or (ii) aggregated to InfluxDB by use of Telegram keeping the v2x data on the edge also reduces the possible security/privacy concerns caused by transferring all the data to the central location we do not use Prometheus for predictions storing as Prometheus is designed for scraping metrics and not storing data; storing functionality can improved by Thanos , but the solution is primarily designed for metrics not measurements and predictions we do not use Grafana for visualization of predictions as InfluxDB by default includes User Interface(UI) for visualization and forwarding data to Grafana would cause additional unnecessary data communication","title":"Additional notes to \"Local/edge data management\" and \"Predictions\""},{"location":"data_management.html#system-metrics","text":"We use combination of Prometheus + Kepler + Grafana to monitor resource utilization inside the cluster. This data can be later used for downscaling the solution and reducing the carbon footprint in production.","title":"System Metrics"},{"location":"development.html","text":"\u00b6 SUstainable and high PErformance COMputing platform Research & Development testbed. R&D Architecture steps: setup full testbed use RaspberryPis to emulate edge nodes send gathered data using Python script and target specific edge nodes (emulation of vehicle moving between base stations) test centralized vs edge computation vs federated learning query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, Testbed environment make pictures of used HW (RaspberyPis, workstation, SUPERCOM servers rack?)","title":"development"},{"location":"development.html#_1","text":"SUstainable and high PErformance COMputing platform Research & Development testbed. R&D Architecture steps: setup full testbed use RaspberryPis to emulate edge nodes send gathered data using Python script and target specific edge nodes (emulation of vehicle moving between base stations) test centralized vs edge computation vs federated learning query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, Testbed environment make pictures of used HW (RaspberyPis, workstation, SUPERCOM servers rack?)","title":""},{"location":"ml_model_development.html","text":"Machine learning model development \u00b6 Centralized training \u00b6 The project is focused on developing a production-ready solution. At the time of writing the documentation, we do not have vehicular measurements that would include labels with vehicle faults. We focus on exploring the best possible overall/engineering solutions as opposed to finding the best-performing model. Centralized training is therefore first choice for implementation. advantages: simplified data management - having all data in one place can simplify data management, preprocessing, and cleaning disadvantages: privacy concerns - ggregating data from multiple sources can lead to significant privacy issues, especially with sensitive information Online/stream vs batch \u00b6 V2X measurements form a stream of data from a vehicle to AI/ML on the edge. Unfortunately, this causes issues with the possibility of a high number(100-10,000?) streams per edge location. Until such a system is implemented we do not know the probable number of streams per edge location and we are unable to define requirements of the system that would be able to process X streams with Y data points per second. Such a system has to be able to process streams either by multi-threading or parallel processing. Also, methods like ADWIN as the window size is reset to the initial value each time vehicle changes edge device as the state of the ML model can't be migrated between locations per vehicle as of the number of vehicles and their traveling speed VS migration time of the model to a new location. For now, we pick the \"easier\" option of batch processing where (i) every sample is treated individually, or (ii) samples from the same vehicle are processed in fixed batch sizes Deep vs \"Traditional\" \u00b6 As we are dealing with strictly tabular data we focus on the tree-based model solution implementation. For detailed answer, we refer a reader to e.g. paper \"Why do tree-based models still outperform deep learning on tabular data?\" [TODO] Federated learning/training \u00b6 We implement a federated learning model to address concerns of privacy and data management in case of multi-cluster solution. advantages: enhanced privacy - raw data never leaves the local devices, reducing privacy risks reduced data transfer - only model updates (rather than raw data) are transmitted, which can be more bandwidth-efficient compliance - easier to comply with data sovereignty and privacy regulations since data remains local fault tolerance - the system can be more resilient to individual server failures since the data and computation are distributed disadvantages: complexity - implementing federated learning is more complex due to the need to manage distributed training, aggregation, and synchronization heterogeneity - variability in data quality and distribution across devices can impact model performance limited computational resources - Local devices may have limited computational power compared to centralized data centers communication overhead - while raw data isn't transferred, frequent model updates can still generate significant communication overhead, especially in large-scale systems graph TB subgraph Cluster1 direction TB Worker1[PySyft Worker Node 1] end subgraph Cluster2 direction TB Worker2[PySyft Worker Node 2] end subgraph Cluster3 direction TB Worker3[PySyft Worker Node 3] end subgraph CentralCluster direction TB CentralServer[PySyft Central Server] end Worker1 <--> CentralServer Worker2 <--> CentralServer Worker3 <--> CentralServer PySyft [PREFERRED] focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data easy to deploy workers/clients Flower emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment more focused on iOS and Android deployment KubeFATE - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques","title":"ML model dev"},{"location":"ml_model_development.html#machine-learning-model-development","text":"","title":"Machine learning model development"},{"location":"ml_model_development.html#centralized-training","text":"The project is focused on developing a production-ready solution. At the time of writing the documentation, we do not have vehicular measurements that would include labels with vehicle faults. We focus on exploring the best possible overall/engineering solutions as opposed to finding the best-performing model. Centralized training is therefore first choice for implementation. advantages: simplified data management - having all data in one place can simplify data management, preprocessing, and cleaning disadvantages: privacy concerns - ggregating data from multiple sources can lead to significant privacy issues, especially with sensitive information","title":"Centralized training"},{"location":"ml_model_development.html#todo-federated-learningtraining","text":"We implement a federated learning model to address concerns of privacy and data management in case of multi-cluster solution. advantages: enhanced privacy - raw data never leaves the local devices, reducing privacy risks reduced data transfer - only model updates (rather than raw data) are transmitted, which can be more bandwidth-efficient compliance - easier to comply with data sovereignty and privacy regulations since data remains local fault tolerance - the system can be more resilient to individual server failures since the data and computation are distributed disadvantages: complexity - implementing federated learning is more complex due to the need to manage distributed training, aggregation, and synchronization heterogeneity - variability in data quality and distribution across devices can impact model performance limited computational resources - Local devices may have limited computational power compared to centralized data centers communication overhead - while raw data isn't transferred, frequent model updates can still generate significant communication overhead, especially in large-scale systems graph TB subgraph Cluster1 direction TB Worker1[PySyft Worker Node 1] end subgraph Cluster2 direction TB Worker2[PySyft Worker Node 2] end subgraph Cluster3 direction TB Worker3[PySyft Worker Node 3] end subgraph CentralCluster direction TB CentralServer[PySyft Central Server] end Worker1 <--> CentralServer Worker2 <--> CentralServer Worker3 <--> CentralServer PySyft [PREFERRED] focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data easy to deploy workers/clients Flower emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment more focused on iOS and Android deployment KubeFATE - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques","title":"[TODO] Federated learning/training"},{"location":"networking.html","text":"Networking \u00b6 Description of traffic flow for simple(no Istio) Single cluster traffic flow. Vehicle data processing \u00b6 Vehicle data processing Traffic flow: Vehicle sends measurements to Vehicle API service (provided by Idneo \u2013 data preprocessing +Redis DB) transmission is received by antenna Transmission enters data(IPv4/IPv6) network Data is forwarded to the closest Kubernetes Edge node using (i) Anycast IP address, shared by all Edge Nodes, and (ii) nodePort defined in NodePort service Edge Node receives the data, checks the IP table in kube-proxy, and forwards it to local Pod A as the service is configured with externalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the packet is dropped Vehicle API service forwards the data to the ML prediction service(Kserve inference) Data is forwarded to Pod B as Kserve inference service is configured with internalTrafficPolicy: Local If there is no local Pod for Kserve inference service, the packet is dropped or ML prediction service pod periodically queries local Redis DB in Vehicle API service \u2013 as Vehicle API service is also configured with internalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the queries/requests are dropped Kserve inference service pushes the data into InfluxDB Grafana periodically queries data from InfluxDB Energy consumption monitoring \u00b6 Energy consumption monitoring Traffic flow: Kepler exporter uses eBPF to probe CPU, GPU, RAM performance counters and exposes them over time via HTTP Prometheus periodically queries Kepler exporter for a new data Grafana periodically queries Prometheus for anew data Vehicle data and energy consumption monitoring \u00b6 Vehicle data and energy consumption monitoring Traffic flow: User enters Grafana FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it Grafana GUI comes up in the Users web browser ML model development/update \u00b6 ML model development/update Traffic flow: User enters MLflow FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it to Mlflow GUI where user can check current models User updates the model locally or in cloud and uploads it to MLflow API Mlflow API forwards the model to the backend database - Minio ML model deployment \u00b6 ML model deployment Traffic flow: Admin user instructs Kserve to deploy a model Kserve queries the model from Minio storage Kserve updates/\u201dserves\u201d the model in all Kserver inference Pods","title":"networking"},{"location":"networking.html#networking","text":"Description of traffic flow for simple(no Istio) Single cluster traffic flow.","title":"Networking"},{"location":"networking.html#vehicle-data-processing","text":"Vehicle data processing Traffic flow: Vehicle sends measurements to Vehicle API service (provided by Idneo \u2013 data preprocessing +Redis DB) transmission is received by antenna Transmission enters data(IPv4/IPv6) network Data is forwarded to the closest Kubernetes Edge node using (i) Anycast IP address, shared by all Edge Nodes, and (ii) nodePort defined in NodePort service Edge Node receives the data, checks the IP table in kube-proxy, and forwards it to local Pod A as the service is configured with externalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the packet is dropped Vehicle API service forwards the data to the ML prediction service(Kserve inference) Data is forwarded to Pod B as Kserve inference service is configured with internalTrafficPolicy: Local If there is no local Pod for Kserve inference service, the packet is dropped or ML prediction service pod periodically queries local Redis DB in Vehicle API service \u2013 as Vehicle API service is also configured with internalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the queries/requests are dropped Kserve inference service pushes the data into InfluxDB Grafana periodically queries data from InfluxDB","title":"Vehicle data processing"},{"location":"networking.html#energy-consumption-monitoring","text":"Energy consumption monitoring Traffic flow: Kepler exporter uses eBPF to probe CPU, GPU, RAM performance counters and exposes them over time via HTTP Prometheus periodically queries Kepler exporter for a new data Grafana periodically queries Prometheus for anew data","title":"Energy consumption monitoring"},{"location":"networking.html#vehicle-data-and-energy-consumption-monitoring","text":"Vehicle data and energy consumption monitoring Traffic flow: User enters Grafana FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it Grafana GUI comes up in the Users web browser","title":"Vehicle data and energy consumption monitoring"},{"location":"networking.html#ml-model-developmentupdate","text":"ML model development/update Traffic flow: User enters MLflow FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it to Mlflow GUI where user can check current models User updates the model locally or in cloud and uploads it to MLflow API Mlflow API forwards the model to the backend database - Minio","title":"ML model development/update"},{"location":"networking.html#ml-model-deployment","text":"ML model deployment Traffic flow: Admin user instructs Kserve to deploy a model Kserve queries the model from Minio storage Kserve updates/\u201dserves\u201d the model in all Kserver inference Pods","title":"ML model deployment"},{"location":"testing.html","text":"Real-world testing and data gathering - Parcmotor Castellol\u00ed administered by Cellnex \u00b6 Castellol\u00ed testing architecture Castellol\u00ed testing architecture legend: white - mandatory grey pattern - optional steps: prepare the Kserve model and helm charts make testing requests before the day of testing run the real-world test with vehicles in Castelloli query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, NBC environment make vehicle pictures","title":"testing"},{"location":"testing.html#real-world-testing-and-data-gathering-parcmotor-castelloli-administered-by-cellnex","text":"Castellol\u00ed testing architecture Castellol\u00ed testing architecture legend: white - mandatory grey pattern - optional steps: prepare the Kserve model and helm charts make testing requests before the day of testing run the real-world test with vehicles in Castelloli query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, NBC environment make vehicle pictures","title":"Real-world testing and data gathering - Parcmotor Castellol\u00ed administered by Cellnex"},{"location":"use_cases.html","text":"Vehicular condition monitoring with robust V2X connectivity - EXTEND \u00b6 Use Case Extend Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. However, wireless transmission of in-vehicle monitoring data comes inadvertently with challenges which involve distortions and missing data owing to the inherently shared wireless medium. Incomplete vehicular data attributed to either increased interference levels, channel attenuation phenomena or sensor malfunctioning may lead to incorrect decision-making and failure to predict maintenance actions. Addressing the degradation of transmitted V2X data is thus essential for high-fidelity estimation of the system network state and informed actions. In this context, machine learning (ML)-empowered signal reconstruction and imputation techniques are expected to obtain accurate estimates of incomplete sensor trajectories by leveraging the intrinsic spatiotemporal synergies of captured information. By deeply investigating the interplay between vehicle dynamics and fault provisioning, faulty vehicular equipment will be timely detected/predicted, improving road safety by preventing critical failures. Vehicular condition monitoring with robust V2X connectivity - DEVISE \u00b6 Use Case Devise Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. Wireless transmission of in-vehicle monitoring data is susceptible to various security threats which may destabilize system operation, degrade network performance, and potentially lead to incorrect decision-making regarding the vehicle condition status. At the same time, the pervasive integration of data-driven techniques expands the attack surface, giving rise to finely targeted, stealthier, and scalable attacks targeting both phases (training, inference) of ML techniques. The mitigation of such threats becomes imperative for the correct estimation of vehicle condition. Condition monitoring data used for subsequent predictive diagnostics should be properly secured such that they do not contain falsified information, while abnormal traffic should be detected and isolated in its entirety. In this context, appropriate security countermeasures are expected to accurately detect malicious content in the transmitted information and ensure the semantic correctness of the aggregated data for trustworthy decision-making. Vehicular condition monitoring with robust V2X connectivity - VERIFY \u00b6 Use Case Extend 01 Use Case Extend 02 Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. The limited computational capabilities of edge nodes pose significant challenges for the realization of computationally efficient V2X communication aimed for condition monitoring. Aiming to achieve dramatically reduced data processing times, over-the-air computing methods and decentralized deployments of AI models aim at rapid knowledge extraction from the vehicular measurement streams and reduction of computational overhead. In this context, over-the-air computing methods will be explored to address the computational limitations and stringent latency requirements in V2X systems by means of over-the-air aggregation. In addition, decentralized methods for predictive diagnosis of vehicles condition will be investigated to leverage the benefits of edge-computing capabilities for efficient inference, aiming at minimizing the vehicle downtime incidents. The integration of both approaches holds the promise of distributed learning parallelization over the wireless compute medium and the realization of data intensive V2X services with stringent performance requirements.","title":"use cases"},{"location":"use_cases.html#vehicular-condition-monitoring-with-robust-v2x-connectivity-extend","text":"Use Case Extend Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. However, wireless transmission of in-vehicle monitoring data comes inadvertently with challenges which involve distortions and missing data owing to the inherently shared wireless medium. Incomplete vehicular data attributed to either increased interference levels, channel attenuation phenomena or sensor malfunctioning may lead to incorrect decision-making and failure to predict maintenance actions. Addressing the degradation of transmitted V2X data is thus essential for high-fidelity estimation of the system network state and informed actions. In this context, machine learning (ML)-empowered signal reconstruction and imputation techniques are expected to obtain accurate estimates of incomplete sensor trajectories by leveraging the intrinsic spatiotemporal synergies of captured information. By deeply investigating the interplay between vehicle dynamics and fault provisioning, faulty vehicular equipment will be timely detected/predicted, improving road safety by preventing critical failures.","title":"Vehicular condition monitoring with robust V2X connectivity - EXTEND"},{"location":"use_cases.html#vehicular-condition-monitoring-with-robust-v2x-connectivity-devise","text":"Use Case Devise Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. Wireless transmission of in-vehicle monitoring data is susceptible to various security threats which may destabilize system operation, degrade network performance, and potentially lead to incorrect decision-making regarding the vehicle condition status. At the same time, the pervasive integration of data-driven techniques expands the attack surface, giving rise to finely targeted, stealthier, and scalable attacks targeting both phases (training, inference) of ML techniques. The mitigation of such threats becomes imperative for the correct estimation of vehicle condition. Condition monitoring data used for subsequent predictive diagnostics should be properly secured such that they do not contain falsified information, while abnormal traffic should be detected and isolated in its entirety. In this context, appropriate security countermeasures are expected to accurately detect malicious content in the transmitted information and ensure the semantic correctness of the aggregated data for trustworthy decision-making.","title":"Vehicular condition monitoring with robust V2X connectivity - DEVISE"},{"location":"use_cases.html#vehicular-condition-monitoring-with-robust-v2x-connectivity-verify","text":"Use Case Extend 01 Use Case Extend 02 Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. The limited computational capabilities of edge nodes pose significant challenges for the realization of computationally efficient V2X communication aimed for condition monitoring. Aiming to achieve dramatically reduced data processing times, over-the-air computing methods and decentralized deployments of AI models aim at rapid knowledge extraction from the vehicular measurement streams and reduction of computational overhead. In this context, over-the-air computing methods will be explored to address the computational limitations and stringent latency requirements in V2X systems by means of over-the-air aggregation. In addition, decentralized methods for predictive diagnosis of vehicles condition will be investigated to leverage the benefits of edge-computing capabilities for efficient inference, aiming at minimizing the vehicle downtime incidents. The integration of both approaches holds the promise of distributed learning parallelization over the wireless compute medium and the realization of data intensive V2X services with stringent performance requirements.","title":"Vehicular condition monitoring with robust V2X connectivity - VERIFY"},{"location":"idneo/datasets.html","text":"Datasets \u00b6 DS1 : Ignition on but stopped DS2 : national road. ~ 90km/h DS3 : highway. ~ 120km/h Data structure \u00b6 STAMP : Time delay in seconds after starting the log. STAMP Sensor 1.02 value 2.48 value 3.95 value","title":"datasets"},{"location":"idneo/datasets.html#datasets","text":"DS1 : Ignition on but stopped DS2 : national road. ~ 90km/h DS3 : highway. ~ 120km/h","title":"Datasets"},{"location":"idneo/datasets.html#data-structure","text":"STAMP : Time delay in seconds after starting the log. STAMP Sensor 1.02 value 2.48 value 3.95 value","title":"Data structure"},{"location":"idneo/vehicle_injector_tool.html","text":"Vehicle Injector Tool \u00b6 This tool facilitates the incorporation of real data from a vehicle's sensors, stored in csv Datasets. Its purpose is to facilitate the development of algorithms responsible for processing the information that is collected and stored in this REDIS database within the Success6G project. graph LR Client(VEHICLE
INJECTOR) -- TCP/IP
(63790 / 127.0.0.1)--> Database(REDIS
DATABASE); click Client \"https://github.com/5uperpalo/success6g-edge/blob/main/tools/vehicle/vehicle_injector.py\" click Database \"https://github.com/5uperpalo/success6g-edge/blob/main/configs/edge_redis.yaml\" Dataset[Dataset File] --> Client classDef orange fill:#f96,stroke:#333,stroke-width:2px class Dataset orange Usage \u00b6 Install the Redis database in your local development setup. docker_compose -d configs/edge_redis.yaml Verify docker container is running docker ps -a Run \u201cVehicle Injector\u201c script with dataset and station-id script: python3 vehicle_injector.py -f","title":"vehicle injector tool"},{"location":"idneo/vehicle_injector_tool.html#vehicle-injector-tool","text":"This tool facilitates the incorporation of real data from a vehicle's sensors, stored in csv Datasets. Its purpose is to facilitate the development of algorithms responsible for processing the information that is collected and stored in this REDIS database within the Success6G project. graph LR Client(VEHICLE
INJECTOR) -- TCP/IP
(63790 / 127.0.0.1)--> Database(REDIS
DATABASE); click Client \"https://github.com/5uperpalo/success6g-edge/blob/main/tools/vehicle/vehicle_injector.py\" click Database \"https://github.com/5uperpalo/success6g-edge/blob/main/configs/edge_redis.yaml\" Dataset[Dataset File] --> Client classDef orange fill:#f96,stroke:#333,stroke-width:2px class Dataset orange","title":"Vehicle Injector Tool"},{"location":"idneo/vehicle_injector_tool.html#usage","text":"Install the Redis database in your local development setup. docker_compose -d configs/edge_redis.yaml Verify docker container is running docker ps -a Run \u201cVehicle Injector\u201c script with dataset and station-id script: python3 vehicle_injector.py -f","title":"Usage"},{"location":"inference_model/eda.html","text":"Preprocess data \u00b6 analysis \u00b6 init_check \u00b6 init_check ( df , identifier = None , cat_cols = None , cont_cols = None , verbose = False , ) Procedure to check: * duplicated rows in teh dataset * general stats of numerical features * general stats of categorical features Parameters: df ( DataFrame ) \u2013 pandas dataframe identifier ( str ) \u2013 column which identifies unique user IDs cat_cols ( list ) \u2013 categorical features in the dataset cont_cols ( list ) \u2013 numerical features in the dataset Returns: duplicated_ids ( int ) \u2013 cont_cols_desc ( pd . DataFrame ) \u2013 cat_cols_desc ( pd . DataFrame ) \u2013 Source code in inference_model/eda/features/analysis.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 def init_check ( df : pd . DataFrame , identifier : Optional [ str ] = None , cat_cols : Optional [ list ] = None , cont_cols : Optional [ list ] = None , verbose : bool = False , ) -> Tuple [ int , pd . DataFrame , pd . DataFrame ]: \"\"\"Procedure to check: * duplicated rows in teh dataset * general stats of numerical features * general stats of categorical features Args: df (DataFrame): pandas dataframe identifier (str): column which identifies unique user IDs cat_cols (list): categorical features in the dataset cont_cols (list): numerical features in the dataset Returns: duplicated_ids (int): cont_cols_desc (pd.DataFrame): cat_cols_desc (pd.DataFrame): \"\"\" data = df . copy () if identifier : duplicated_ids = data [ identifier ] . duplicated () . sum () if verbose : print ( \"[CHECK] Number of duplicated ids: {} \" . format ( duplicated_ids )) else : duplicated_ids = None if cont_cols : cont_cols_f = intsec ( data . columns . values , cont_cols ) cont_cols_desc = data [ cont_cols_f ] . describe () . transpose () if verbose : print ( \"[CHECK] Numerical columns\" ) with pd . option_context ( \"display.precision\" , 2 ): print ( cont_cols_desc ) else : cont_cols_desc = None if cat_cols : cat_cols_f = intsec ( data . columns . values , cat_cols ) cat_cols_desc = data [ cat_cols_f ] . describe () . transpose () if verbose : print ( \"[CHECK] Categorical columns\" ) with pd . option_context ( \"display.precision\" , 2 ): print ( cat_cols_desc ) else : cat_cols_desc = None return duplicated_ids , cont_cols_desc , cat_cols_desc missing \u00b6 missing ( df , scale = 'linear' , plot = False ) Procedure to check fraction of missing values in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: missing_val_frac ( DataFrame ) \u2013 sorted dataframe with fraction of missing values per feature fig ( Figure ) \u2013 plot with sorted fractions of missing values in each column Source code in inference_model/eda/features/analysis.py 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 def missing ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to check fraction of missing values in the dataset. Args: df (DataFrame): pandas dataframe scale (str): y scale of the plot plot (bool): whether to output the plot Returns: missing_val_frac (DataFrame): sorted dataframe with fraction of missing values per feature fig (Figure): plot with sorted fractions of missing values in each column \"\"\" data = df . copy () missing_val_frac = data . isna () . sum () / len ( data ) missing_val_frac . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( missing_val_frac , ax , title = \"Fraction of missing values in the dataset\" , ) else : fig = None ax . set_yscale ( scale ) return missing_val_frac , fig zero \u00b6 zero ( df , scale = 'linear' , plot = False ) Procedure to check fraction of zero values in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: zero_val_frac ( DataFrame ) \u2013 sorted dataframe with fraction of '0' values per feature fig ( Figure ) \u2013 plot with sorted fractions of '0' values in each column Source code in inference_model/eda/features/analysis.py 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 def zero ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to check fraction of zero values in the dataset. Args: df (DataFrame): pandas dataframe scale (str): y scale of the plot plot (bool): whether to output the plot Returns: zero_val_frac (DataFrame): sorted dataframe with fraction of '0' values per feature fig (Figure): plot with sorted fractions of '0' values in each column \"\"\" data = df . copy () zero_val_frac = data . isin ([ 0 ]) . sum () / len ( data ) zero_val_frac . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( zero_val_frac , ax , title = \"Fraction of zero values in the dataset\" ) else : fig = None ax . set_yscale ( scale ) return zero_val_frac , fig nunique \u00b6 nunique ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their number of unique values. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted number of unique values in features Source code in inference_model/eda/features/analysis.py 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 def nunique ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their number of unique values. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted number of unique values in features \"\"\" data = df . copy () data_nunique = data . nunique () data_nunique . sort_values ( ascending = True , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( data_nunique . dropna (), ax , title = \"Features number of unique values (NA are dropped)\" , ) else : fig = None ax . set_yscale ( scale ) return data_nunique , fig std \u00b6 std ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their variance. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted standard deviation of continuous features Source code in inference_model/eda/features/analysis.py 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def std ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their variance. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted standard deviation of continuous features \"\"\" data = df . copy () data_std = data . std () data_std . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( data_std . dropna (), ax , title = \"Features variance (NA are dropped)\" ) else : fig = None ax . set_yscale ( scale ) return data_std , fig entropy \u00b6 entropy ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their entropy. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted entropy of the features Source code in inference_model/eda/features/analysis.py 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 def entropy ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their entropy. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted entropy of the features \"\"\" data = df . copy () data = data . astype ( str ) col_entropies = data . apply ( entropy_calc , axis = 0 ) col_entropies . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( col_entropies . dropna (), ax , title = \"Features entropies (NA are dropped)\" ) else : fig = None ax . set_yscale ( scale ) return col_entropies , fig plotting \u00b6 cross_correlation \u00b6 cross_correlation ( df , n = 10 , verbose = False ) Procedure to calculate and plot cross-correlation of features in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe verbose ( bool ) \u2013 show n most correlated features n ( int ) \u2013 number correlated features in verbose output Returns: fig ( Figure ) \u2013 heatmap of continuous features cross correlations Source code in inference_model/eda/features/plotting.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def cross_correlation ( df : pd . DataFrame , n : int = 10 , verbose : bool = False , ) -> Figure : \"\"\"Procedure to calculate and plot cross-correlation of features in the dataset. Args: df (DataFrame): pandas dataframe verbose (bool): show n most correlated features n (int): number correlated features in verbose output Returns: fig (Figure): heatmap of continuous features cross correlations \"\"\" data = df . copy () # corrwith uses numpy which does not like pandas Float dtypes corr_matrix = data . astype ( float ) . corr () upper_stacked_sorted = ( corr_matrix . abs () . where ( np . triu ( np . ones ( corr_matrix . shape ), k = 1 ) . astype ( \"bool\" )) . stack () . sort_values ( ascending = False ) ) fig , ax = plt . subplots ( figsize = ( 10 , 10 )) ax . set_title ( \"Cross-correlation matrix\" , size = 20 ) sns . set ( font_scale = 1.0 ) sns . heatmap ( corr_matrix , ax = ax , cbar = True , square = True , # linewidths=0.5, fmt = \".2f\" , ) if verbose : print ( \"Top {} absolute correlations\" . format ( n )) print ( upper_stacked_sorted [: n ]) return fig distributions \u00b6 distributions ( df , low_per_cut = 0 , high_per_cut = 1 , type = \"box\" ) Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Parameters: df ( DataFrame ) \u2013 pandas dataframe low_per_cut ( float ) \u2013 lower percentile where to cut the plot for better readability high_per_cut ( float ) \u2013 higher percentile where to cut the plot for better readability type ( str ) \u2013 type of distribution plot Returns: fig ( Figure ) \u2013 ditribution plot per each feature Source code in inference_model/eda/features/plotting.py 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 def distributions ( df : pd . DataFrame , low_per_cut : float = 0 , high_per_cut : float = 1 , type : Literal [ \"box\" , \"violin\" ] = \"box\" , ) -> Figure : \"\"\"Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Args: df (DataFrame): pandas dataframe low_per_cut (float): lower percentile where to cut the plot for better readability high_per_cut (float): higher percentile where to cut the plot for better readability type (str): type of distribution plot Returns: fig (Figure): ditribution plot per each feature \"\"\" data = df . copy () cols = data . columns . values data = data . fillna ( 0 ) . astype ( float ) fig , ax = plt . subplots ( len ( cols ), 1 , figsize = ( 16 , len ( cols ) * 2 )) fig . suptitle ( \"Distribution of feature values\" , size = 20 ) for col , i in zip ( cols , range ( len ( cols ))): if type == \"violin\" : sns . violinplot ( ax = ax [ i ], x = col , orient = \"h\" , # cut=0, # showextrem=False, data = data , ) if type == \"box\" : sns . boxplot ( ax = ax [ i ], x = col , orient = \"h\" , data = data , ) ax [ i ] . set_xlim ( data [ col ] . quantile ( low_per_cut ), data [ col ] . quantile ( high_per_cut ) ) ax [ i ] . get_yaxis () . set_visible ( False ) plt . subplots_adjust ( hspace = 0.9 ) return fig analysis \u00b6 correlation \u00b6 correlation ( df , target , scale = 'linear' , plot = False ) Procedure to plot most correlated numerical features with target column. Parameters: df ( pd . DataFrame ) \u2013 pandas dataframe target ( pd . Series ) \u2013 target values scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: sorted_corr_cols ( pd . DataFrame ) \u2013 sorted dataframe with feature and target value correlation fig ( Figure ) \u2013 sorted bar plot with feature and target value correlation Source code in inference_model/eda/target/analysis.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 def correlation ( df : pd . DataFrame , target : pd . Series , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot most correlated numerical features with target column. Args: df (pd.DataFrame): pandas dataframe target (pd.Series): target values scale (str): y scale of the plot plot (bool): whether to output the plot Returns: sorted_corr_cols (pd.DataFrame): sorted dataframe with feature and target value correlation fig (Figure): sorted bar plot with feature and target value correlation \"\"\" data = df . copy () target_col = target . name correlation_df = pd . concat ([ data , target ], axis = 1 ) . corr () sorted_corr_cols = correlation_df [ target_col ] . drop ( index = [ target_col ]) sorted_corr_cols . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( sorted_corr_cols , ax , title = f \"Sorted features by their correlation with { target_col } \" , ) else : fig = None ax . set_yscale ( scale ) return sorted_corr_cols , fig plotting \u00b6 prob_distrib_per_class \u00b6 prob_distrib_per_class ( predicted_probs , actual , task ) Procedure to plot probability density distributions per class from LightGBM predictions. Parameters: predicted_probs ( ndarray ) \u2013 predicted probs actual ( ndarray ) \u2013 ground truth classes task ( str ) \u2013 type of task Returns: fig ( Figure ) \u2013 probability density ditributions plot per each class Source code in inference_model/eda/target/plotting.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def prob_distrib_per_class ( predicted_probs : np . ndarray , actual : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ], ) -> Figure : \"\"\"Procedure to plot probability density distributions per class from LightGBM predictions. Args: predicted_probs (ndarray): predicted probs actual (ndarray): ground truth classes task (str): type of task Returns: fig (Figure): probability density ditributions plot per each class \"\"\" if task == \"binary\" : temp = pd . DataFrame ({ \"predicted_proba\" : predicted_probs , \"actual\" : actual }) fig , ax = plt . subplots ( figsize = ( 3 , 2 )) fig . suptitle ( \"Predicted probability density per class\" ) for label , alpha , color in zip ([ 0 , 1 ], [ 1 , 0.7 ], [ \"black\" , \"red\" ]): ax . hist ( temp [ temp [ \"actual\" ] == label ][ \"predicted_proba\" ], density = True , bins = np . linspace ( 0 , 1 , 10 ), alpha = alpha , color = color , ec = \"k\" , ) ax . set_xlim ([ 0 , 1 ]) ax . set_xlabel ( \"probability\" ) ax . set_ylabel ( \"probability_density\" ) elif task == \"multiclass\" : temp = pd . DataFrame ( { \"predicted_proba\" : np . take_along_axis ( predicted_probs , np . vstack ( actual ), axis = 1 # type: ignore ) . flatten (), \"actual\" : actual , } ) # Dictionary of color for each label color_d = dict ( zip_longest ( temp [ \"actual\" ] . unique (), plt . rcParams [ \"axes.prop_cycle\" ] . by_key ()[ \"color\" ], ) ) n_classes = temp [ \"actual\" ] . nunique () fig , ax = plt . subplots ( ncols = n_classes , figsize = ( n_classes * 3 , 2 ), sharex = True , sharey = True ) fig . suptitle ( \"Predicted probability density per class\" ) plt . subplots_adjust ( wspace = 0.3 , top = 0.75 ) for i , ( label , gp ) in enumerate ( temp . groupby ( \"actual\" )): ax [ i ] . hist ( gp [ \"predicted_proba\" ], density = True , bins = np . linspace ( 0 , 1 , 10 ), color = color_d [ label ], ec = \"k\" , ) ax [ i ] . set_title ( label ) ax [ i ] . set_xlim ([ 0 , 1 ]) ax [ i ] . set_xlabel ( \"probability\" ) ax [ i ] . set_ylabel ( \"probability_density\" ) return fig distributions_in_binary_cls \u00b6 distributions_in_binary_cls ( df , target , low_per_cut = 0 , high_per_cut = 1 ) Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Parameters: df ( DataFrame ) \u2013 pandas dataframe cont_cols ( list ) \u2013 numerical features in the dataset target ( pd . Series ) \u2013 target values, i.e. binary classes Returns: fig ( Figure ) \u2013 ditribution plot per each feature Source code in inference_model/eda/target/plotting.py 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 def distributions_in_binary_cls ( df : pd . DataFrame , target : pd . Series , low_per_cut : float = 0 , high_per_cut : float = 1 , ) -> Figure : \"\"\"Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Args: df (DataFrame): pandas dataframe cont_cols (list): numerical features in the dataset target (pd.Series): target values, i.e. binary classes Returns: fig (Figure): ditribution plot per each feature \"\"\" data = pd . concat ([ df , target ], axis = 1 ) cols = data . columns . values data [ \"dummy\" ] = 0 data = data . fillna ( 0 ) . astype ( float ) fig , ax = plt . subplots ( len ( cols ), 1 , figsize = ( 16 , len ( cols ) * 2 )) fig . suptitle ( f \"Distribution of feature values splitted by { target . name } \" , size = 20 ) for col , i in zip ( cols , range ( len ( cols ))): if col != target . name : sns . violinplot ( ax = ax [ i ], hue = target . name , x = col , y = \"dummy\" , orient = \"h\" , cut = 0 , showextrem = False , split = True , data = data , ) ax [ i ] . get_legend () . remove () ax [ i ] . set_xlim ( data [ col ] . quantile ( low_per_cut ), data [ col ] . quantile ( high_per_cut ) ) ax [ i ] . get_yaxis () . set_visible ( False ) plt . subplots_adjust ( hspace = 0.9 , top = 0.97 ) return fig plotting \u00b6 bar_plot \u00b6 bar_plot ( df , ax , title ) Helper method for unified bar plot Parameters: df ( DataFrame ) \u2013 dataframe to plot ax ( Axes ) \u2013 axes defining where to plot it Returns: adjusted_plot ( Axes ) \u2013 adjusted axes Source code in inference_model/eda/plotting.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 def bar_plot ( df : pd . DataFrame , ax : Axes , title : str ) -> Axes : \"\"\"Helper method for unified bar plot Args: df (DataFrame): dataframe to plot ax (Axes): axes defining where to plot it Returns: adjusted_plot (Axes): adjusted axes \"\"\" data = df . copy () ax . set_title ( title , size = 20 ) ax . tick_params ( labelsize = 8 ) ax . set_xlabel ( \"sorted features\" , size = 12 ) return data . plot . bar ( ax = ax ) general_utils \u00b6 intsec \u00b6 intsec ( list1 , list2 ) Simple intesection of two lists. Parameters: list1 ( list ) \u2013 list1 list2 ( list ) \u2013 list2 Returns: list ( list ) \u2013 intersection of lists Source code in inference_model/eda/general_utils.py 6 7 8 9 10 11 12 13 14 def intsec ( list1 : list , list2 : list ) -> list : \"\"\"Simple intesection of two lists. Args: list1 (list): list1 list2 (list): list2 Returns: list (list): intersection of lists \"\"\" return list ( set . intersection ( set ( list1 ), set ( list2 ))) entropy_calc \u00b6 entropy_calc ( labels , base = np . e ) Computes entropy of both continuous and categorical features. Shamelessly stolen from : https://stackoverflow.com/a/45091961 Parameters: labels ( list , ndarray , Series ) \u2013 list of values Returns: ent ( float ) \u2013 entropy of the list of values Source code in inference_model/eda/general_utils.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def entropy_calc ( labels : list , base : float = np . e ) -> float : \"\"\"Computes entropy of both continuous and categorical features. Shamelessly stolen from : https://stackoverflow.com/a/45091961 Args: labels (list, ndarray, Series): list of values Returns: ent (float): entropy of the list of values \"\"\" n_labels = len ( labels ) value , counts = np . unique ( labels , return_counts = True ) probs = counts / n_labels n_classes = np . count_nonzero ( probs ) ent = float ( 0 ) if n_classes > 1 : for i in probs : ent -= i * math . log ( i , base ) return ent","title":"eda"},{"location":"inference_model/eda.html#preprocess-data","text":"","title":"Preprocess data"},{"location":"inference_model/eda.html#inference_model.eda.features.analysis","text":"","title":"analysis"},{"location":"inference_model/eda.html#inference_model.eda.features.plotting","text":"","title":"plotting"},{"location":"inference_model/eda.html#inference_model.eda.target.analysis","text":"","title":"analysis"},{"location":"inference_model/eda.html#inference_model.eda.target.plotting","text":"","title":"plotting"},{"location":"inference_model/eda.html#inference_model.eda.plotting","text":"","title":"plotting"},{"location":"inference_model/eda.html#inference_model.eda.general_utils","text":"","title":"general_utils"},{"location":"inference_model/preprocessing.html","text":"Preprocess data \u00b6 label_encoder \u00b6 LabelEncoder \u00b6 LabelEncoder ( columns_to_encode = None ) Bases: object Label Encode categorical values for multiple columns at once NOTE : Shamlessly copied from https://github.com/jrzaurin/pytorch-widedeep NOTE : LabelEncoder reserves 0 for unseen new categories. This is convenient when defining the embedding layers, since we can just set padding idx to 0. Parameters: columns_to_encode ( list, Optional, default = None ) \u2013 List of strings containing the names of the columns to encode. If None all columns of type object in the dataframe will be label encoded. Attributes: encoding_dict ( Dict ) \u2013 Dictionary containing the encoding mappings in the format, e.g. : {'colname1': {'cat1': 1, 'cat2': 2, ...}, 'colname2': {'cat1': 1, 'cat2': 2, ...}, ...} # noqa inverse_encoding_dict(Dict) ( Dict ) \u2013 Dictionary containing the inverse encoding mappings in the format, e.g. : {'colname1': {1: 'cat1', 2: 'cat2', ...}, 'colname2': {1: 'cat1', 2: 'cat2', ...}, ...} # noqa Source code in inference_model/preprocessing/label_encoder.py 29 30 31 32 33 def __init__ ( self , columns_to_encode : Optional [ List [ str ]] = None , ): self . columns_to_encode = columns_to_encode fit \u00b6 fit ( df ) Creates encoding attributes Returns: LabelEncoder ( LabelEncoder ) \u2013 LabelEncoder fitted object Source code in inference_model/preprocessing/label_encoder.py 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 def fit ( self , df : pd . DataFrame ) -> \"LabelEncoder\" : \"\"\"Creates encoding attributes Returns: LabelEncoder: `LabelEncoder` fitted object \"\"\" df_inp = df . copy () if self . columns_to_encode is None : self . columns_to_encode = list ( df_inp . select_dtypes ( include = [ \"object\" ]) . columns ) else : # sanity check to make sure all categorical columns are in an adequate # format for col in self . columns_to_encode : df_inp [ col ] = df_inp [ col ] . astype ( \"O\" ) unique_column_vals = dict () for c in self . columns_to_encode : unique_column_vals [ c ] = df_inp [ c ] . unique () self . encoding_dict = dict () # leave 0 for padding/\"unseen\" categories idx = 1 for k , v in unique_column_vals . items (): self . encoding_dict [ k ] = { o : i + idx for i , o in enumerate ( unique_column_vals [ k ]) } idx = 1 self . inverse_encoding_dict = dict () for c in self . encoding_dict : self . inverse_encoding_dict [ c ] = { v : k for k , v in self . encoding_dict [ c ] . items () } self . inverse_encoding_dict [ c ][ 0 ] = \"unseen\" return self transform \u00b6 transform ( df ) Label Encoded the categories in columns_to_encode Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Source code in inference_model/preprocessing/label_encoder.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 def transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Label Encoded the categories in `columns_to_encode` Returns: pd.DataFrame: label-encoded dataframe \"\"\" try : self . encoding_dict except AttributeError : raise NotFittedError ( \"This LabelEncoder instance is not fitted yet. \" \"Call 'fit' with appropriate arguments before using this LabelEncoder.\" ) df_inp = df . copy () # sanity check to make sure all categorical columns are in an adequate # format for col in self . columns_to_encode : # type: ignore df_inp [ col ] = df_inp [ col ] . astype ( \"O\" ) for k , v in self . encoding_dict . items (): df_inp [ k ] = df_inp [ k ] . apply ( lambda x : v [ x ] if x in v . keys () else 0 ) return df_inp fit_transform \u00b6 fit_transform ( df ) Combines fit and transform Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd . DataFrame ({ 'col1' : [ 1 , 2 , 3 ], 'col2' : [ 'me' , 'you' , 'him' ]}) >>> columns_to_encode = [ 'col2' ] >>> encoder = LabelEncoder ( columns_to_encode ) >>> encoder . fit_transform ( df ) col1 col2 0 1 1 1 2 2 2 3 3 >>> encoder . encoding_dict {'col2': {'me': 1, 'you': 2, 'him': 3}} Source code in inference_model/preprocessing/label_encoder.py 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 def fit_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Combines `fit` and `transform` Returns: pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']}) >>> columns_to_encode = ['col2'] >>> encoder = LabelEncoder(columns_to_encode) >>> encoder.fit_transform(df) col1 col2 0 1 1 1 2 2 2 3 3 >>> encoder.encoding_dict {'col2': {'me': 1, 'you': 2, 'him': 3}} \"\"\" return self . fit ( df ) . transform ( df ) inverse_transform \u00b6 inverse_transform ( df ) Returns the original categories Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd . DataFrame ({ 'col1' : [ 1 , 2 , 3 ], 'col2' : [ 'me' , 'you' , 'him' ]}) >>> columns_to_encode = [ 'col2' ] >>> encoder = LabelEncoder ( columns_to_encode ) >>> df_enc = encoder . fit_transform ( df ) >>> encoder . inverse_transform ( df_enc ) col1 col2 0 1 me 1 2 you 2 3 him Source code in inference_model/preprocessing/label_encoder.py 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def inverse_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Returns the original categories Returns: pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']}) >>> columns_to_encode = ['col2'] >>> encoder = LabelEncoder(columns_to_encode) >>> df_enc = encoder.fit_transform(df) >>> encoder.inverse_transform(df_enc) col1 col2 0 1 me 1 2 you 2 3 him \"\"\" for k , v in self . inverse_encoding_dict . items (): df [ k ] = df [ k ] . apply ( lambda x : v [ x ]) return df preprocess_data \u00b6 drop_high_nan_cols \u00b6 drop_high_nan_cols ( df , threshold = 0.8 , verbose = False ) Returns dataframe without columns that have ratio of missingness above threshold. Parameters: df ( pd . DataFrame ) \u2013 input dataframe threshold ( float = 0.8 ) \u2013 ratio of missingness applied per column verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 def drop_high_nan_cols ( df : pd . DataFrame , threshold : float = 0.8 , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without columns that have ratio of missingness above threshold. Args: df (pd.DataFrame): input dataframe threshold (float = 0.8): ratio of missingness applied per column verbose (bool): whether the output should be verbose \"\"\" n_rows = df . shape [ 0 ] nan_fraction_per_col = df . apply ( lambda x : x . isna () . sum () / n_rows , axis = 0 ) high_nan_percentage_cols = nan_fraction_per_col [ nan_fraction_per_col > threshold ] . index . values df = df . drop ( high_nan_percentage_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} columns with fraction of NaN above threshold = {}. Affected columns: {} \"\"\" . format ( len ( high_nan_percentage_cols ), threshold , high_nan_percentage_cols ) ) return df drop_constant_cols \u00b6 drop_constant_cols ( df , verbose = False ) Returns dataframe without constant columns, i.e. those with just 1 unique value for all rows. Source code in inference_model/preprocessing/preprocess_data.py 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 def drop_constant_cols ( df : pd . DataFrame , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without constant columns, i.e. those with just 1 unique value for all rows.\"\"\" nunique_per_col = df . apply ( lambda x : x . nunique (), axis = 0 ) const_cols = nunique_per_col [ nunique_per_col == 1 ] . index . values df = df . drop ( const_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} constant columns. Affected columns: {} \"\"\" . format ( len ( const_cols ), const_cols ) ) return df drop_high_uq_cat_cols \u00b6 drop_high_uq_cat_cols ( df , cat_cols , uq_val_count , verbose = False ) Returns dataframe without categorical columns that have too many unique values Parameters: df ( pd . DataFrame ) \u2013 input dataframe cat_cols ( list ) \u2013 list of categorical columns uq_val_count ( int ) \u2013 unique value count verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 def drop_high_uq_cat_cols ( df : pd . DataFrame , cat_cols : list , uq_val_count : int , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without categorical columns that have too many unique values Args: df (pd.DataFrame): input dataframe cat_cols (list): list of categorical columns uq_val_count (int): unique value count verbose (bool): whether the output should be verbose \"\"\" nunique_per_col = df [ cat_cols ] . apply ( lambda x : x . nunique (), axis = 0 ) high_uq_value_cat_cols = nunique_per_col [ nunique_per_col > uq_val_count ] . index . values df = df . drop ( high_uq_value_cat_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} high unique value columns. Affected columns: {} \"\"\" . format ( len ( high_uq_value_cat_cols ), high_uq_value_cat_cols ) ) return df drop_highly_correlated_columns \u00b6 drop_highly_correlated_columns ( df , cont_cols , crosscorr_val = 0.95 , verbose = False ) Returns dataframe without highly correlated columns, cross correlation is evaluated with crosscorr_val. Parameters: df ( pd . DataFrame ) \u2013 input dataframe cont_cols ( list ) \u2013 list of columns to evaluate correlation for crosscorr_val ( float = 0.95 ) \u2013 threshold value of correlation verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 def drop_highly_correlated_columns ( df : pd . DataFrame , cont_cols : list , crosscorr_val : float = 0.95 , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Returns dataframe without highly correlated columns, cross correlation is evaluated with crosscorr_val. Args: df (pd.DataFrame): input dataframe cont_cols (list): list of columns to evaluate correlation for crosscorr_val (float = 0.95): threshold value of correlation verbose (bool): whether the output should be verbose \"\"\" corr_matrix = df [ cont_cols ] . corr () . abs () upper = corr_matrix . where ( np . triu ( np . ones ( corr_matrix . shape ), k = 1 ) . astype ( \"bool\" )) upper_above = upper . apply ( lambda x : any ( x > crosscorr_val ), axis = 1 ) to_drop = upper_above [ upper_above ] . index . values df = df . drop ( to_drop , axis = 1 ) if verbose : print ( \"\"\" Dropped {} highly correlated columns. Affected columns: {} \"\"\" . format ( len ( to_drop ), to_drop ) ) return df nan_with_unknown_imputer \u00b6 nan_with_unknown_imputer ( df , columns , fill_token = \"unknown\" , verbose = False ) Fills NAs with surrogate string, 'unknown' is default value used, it can be customized. Parameters: df ( pd . DataFrame ) \u2013 input dataframe columns ( List [ str ] ) \u2013 ist of columns that will be filled fillna_token ( str = \"unknown\" ) \u2013 string used to replace NAs verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 def nan_with_unknown_imputer ( df : pd . DataFrame , columns : List [ str ], fill_token : str = \"unknown\" , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Fills NAs with surrogate string, 'unknown' is default value used, it can be customized. Args: df (pd.DataFrame): input dataframe columns (List[str]): ist of columns that will be filled fillna_token (str = \"unknown\"): string used to replace NAs verbose (bool): whether the output should be verbose \"\"\" df = df . copy () if verbose : sum_nan_vals = df [ columns ] . isna () . sum () nans_cols = [] for c in columns : nans_cols . append (( c , df [ c ] . isna () . sum ())) for c in columns : df [ c ] = df [ c ] . astype ( object ) . fillna ( fill_token ) dfc = df . copy () dfc [ columns ] = dfc [ columns ] . apply ( lambda x : x . astype ( object ) . fillna ( fill_token )) if verbose : print ( \"\"\" Imputed {} NaN values with {}. Affected columns (col, num_NaNs): {} \"\"\" . format ( sum_nan_vals , fill_token , nans_cols ) ) return df nan_with_number_imputer \u00b6 nan_with_number_imputer ( df , columns , fill_number =- 1.0 , verbose = False ) Fills NAs with surrogate float, -1 is default value used, it can be customized. Parameters: df ( pd . DataFrame ) \u2013 input dataframe columns ( List [ str ] ) \u2013 list of columns that will be filled fill_number ( float = -1 ) \u2013 number used to replace NAs. Defaults to verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 def nan_with_number_imputer ( df : pd . DataFrame , columns : List [ str ], fill_number : float = - 1.0 , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Fills NAs with surrogate float, -1 is default value used, it can be customized. Args: df (pd.DataFrame): input dataframe columns (List[str]): list of columns that will be filled fill_number (float = -1): number used to replace NAs. Defaults to verbose (bool): whether the output should be verbose \"\"\" dfc = df . copy () if verbose : sum_nan_vals = df [ columns ] . isna () . sum () nans_cols = [] for c in columns : nans_cols . append (( c , df [ c ] . isna () . sum ())) dfc = df . copy () dfc [ columns ] = dfc [ columns ] . apply ( lambda x : x . astype ( float ) . fillna ( fill_number )) if verbose : print ( \"\"\" Imputed {} NaN values with {}. Affected columns (col, num_NaNs): {} \"\"\" . format ( sum_nan_vals , fill_number , nans_cols ) ) return dfc nuq_in_list_col \u00b6 nuq_in_list_col ( dfs ) Returns pd.Series with the number of unique values in the lists. Parameters: dfs ( pd . Series ) \u2013 input pandas series containing string list values Source code in inference_model/preprocessing/preprocess_data.py 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 def nuq_in_list_col ( dfs : pd . Series ): \"\"\"Returns pd.Series with the number of unique values in the lists. Args: dfs (pd.Series): input pandas series containing string list values \"\"\" dfsc = dfs . copy () dfsc . apply ( lambda string : _nunique ( list ( string . replace ( \"[\" , \"\" ) . replace ( \"]\" , \"\" ) . split ( \", \" )) ) if isinstance ( string , str ) else string ) return dfsc most_frequent_in_list_col \u00b6 most_frequent_in_list_col ( dfs ) Returns pd.Series with the most frequent values in the lists. Parameters: dfs ( pd . Series ) \u2013 input pandas series containing string list values Source code in inference_model/preprocessing/preprocess_data.py 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 def most_frequent_in_list_col ( dfs : pd . Series ): \"\"\"Returns pd.Series with the most frequent values in the lists. Args: dfs (pd.Series): input pandas series containing string list values \"\"\" dfsc = dfs . copy () dfsc . apply ( lambda string : _most_frequent ( list ( string . replace ( \"[\" , \"\" ) . replace ( \"]\" , \"\" ) . split ( \", \" )) ) if isinstance ( string , str ) else string ) return dfsc replace_rare_categories_with_str_other \u00b6 replace_rare_categories_with_str_other ( df , categorical_cols , quantile = 0.05 , surrogate_value = \"other\" , verbose = False , ) Replaces rare category value with surrogate string. Parameters: df ( pd . DataFrame ) \u2013 input dataframe categorical_cols ( List [ str ] ) \u2013 list of columns in dataframe to process. quantile ( float = 0.05 ) \u2013 determines what values are considered as rare surrogate_value ( str = \"other\" ) \u2013 string used to replace rare values Returns: Tuple [ pd . DataFrame , Dict ] \u2013 Tuple[pd.DataFrame, Dict]: New dataframe and a dict. with mapping between orig. and surrogate values. Source code in inference_model/preprocessing/preprocess_data.py 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 def replace_rare_categories_with_str_other ( df : pd . DataFrame , categorical_cols : List [ str ], quantile : float = 0.05 , surrogate_value : str = \"other\" , verbose : bool = False , ) -> Tuple [ pd . DataFrame , Dict ]: \"\"\"Replaces rare category value with surrogate string. Args: df (pd.DataFrame): input dataframe categorical_cols (List[str]): list of columns in dataframe to process. quantile (float = 0.05): determines what values are considered as rare surrogate_value (str = \"other\"): string used to replace rare values Returns: Tuple[pd.DataFrame, Dict]: New dataframe and a dict. with mapping between orig. and surrogate values. \"\"\" if surrogate_value in df [ categorical_cols ] . values : raise ValueError ( \"Surrogate string - \" + surrogate_value + \" - is already present, choose another one.\" ) dfc = df . copy () replace_dict_per_col = ( df [ categorical_cols ] . apply ( _replace_rare_dict , args = [ surrogate_value , quantile ]) . to_dict () ) dfc = dfc . replace ( replace_dict_per_col ) if verbose : sum_rare_cats = sum ( [ len ( replace_dict_per_col [ col ]) for col in replace_dict_per_col ] ) rare_cats_per_col = [ ( col , list ( replace_dict_per_col [ col ] . keys ())) for col in replace_dict_per_col ] print ( \"\"\" Replaced {} rare categories (val_count < {}) with {}. Affected columns (List[(col, rare_cats)]): {} \"\"\" . format ( sum_rare_cats , quantile , surrogate_value , rare_cats_per_col , ) ) return dfc , replace_dict_per_col preprocess \u00b6 PreprocessData \u00b6 PreprocessData ( target_col , id_cols , cat_cols = None , cont_cols = None ) Object to preprocess the dataset. Parameters: target_col ( str ) \u2013 target column name id_cols ( List [ str ] ) \u2013 id columns cat_cols ( Optional [ List [ str ]] ) \u2013 list of categorical column names cont_cols ( Optional [ List [ str ]] ) \u2013 list of continuous column names Source code in inference_model/preprocessing/preprocess.py 28 29 30 31 32 33 34 35 36 37 38 39 40 def __init__ ( self , target_col : str , id_cols : str , cat_cols : Optional [ List [ str ]] = None , cont_cols : Optional [ List [ str ]] = None , ): self . target_col = target_col self . id_cols = id_cols self . cat_cols = cat_cols self . cont_cols = cont_cols self . is_fitted = False fit_transform \u00b6 fit_transform ( df ) Fit peprocessor and transform dataset in training step. Source code in inference_model/preprocessing/preprocess.py 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 def fit_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Fit peprocessor and transform dataset in training step.\"\"\" dfc = df . drop ( columns = self . id_cols ) . copy () dfc = dfc . pipe ( drop_constant_cols ) self . cat_cols = intsec ( list ( dfc ), self . cat_cols ) if self . cat_cols is None : self . cat_cols = self . _infere_cat_cols ( dfc ) dfc_cat_cols = drop_high_nan_cols ( dfc [ self . cat_cols ]) dfc = dfc . drop ( columns = self . cat_cols ) dfc = pd . concat ([ dfc , dfc_cat_cols ], axis = 1 ) self . cat_cols = dfc_cat_cols . columns . values . tolist () if self . cont_cols is None : self . init_cont_cols = self . _infere_cont_cols ( dfc , self . cat_cols ) dfc_init_cont_cols = drop_high_nan_cols ( dfc [ self . init_cont_cols ]) dfc = dfc . drop ( columns = self . init_cont_cols ) dfc = pd . concat ([ dfc , dfc_init_cont_cols ], axis = 1 ) self . init_cont_cols = dfc_init_cont_cols . columns . values . tolist () dfc = nan_with_number_imputer ( dfc , self . init_cont_cols , - 9999.0 ) ( dfc , self . final_cont_cols , ) = self . _drop_highly_corr_cols_and_get_final_cont_cols ( dfc , self . init_cont_cols ) else : self . final_cont_cols = list ( set ( self . cont_cols ) . intersection ( set ( dfc . columns . values )) ) dfc = nan_with_number_imputer ( dfc , self . final_cont_cols , - 9999.0 ) dfc = nan_with_unknown_imputer ( dfc , self . cat_cols ) dfc = dfc [ self . cat_cols + self . final_cont_cols + [ self . target_col ]] self . label_encoder = LabelEncoder ( self . cat_cols ) dfc_le = self . label_encoder . fit_transform ( dfc ) dfc_le = self . _change_int_float_types ( dfc_le ) dfc_le [ self . id_cols ] = df [ self . id_cols ] self . is_fitted = True return dfc_le transform \u00b6 transform ( df ) Transform dataset in inference step. Source code in inference_model/preprocessing/preprocess.py 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 def transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Transform dataset in inference step.\"\"\" if not self . is_fitted : raise NotFittedError ( \"\"\"This instance of 'PreprocessData' has not been fitted yet. Please, run 'fit' first\"\"\" ) dfc = df . drop ( columns = self . id_cols ) . copy () # added as mlflow inference is receiving all the data as objects dfc [ self . cat_cols ] = dfc [ self . cat_cols ] . astype ( str ) dfc [ self . final_cont_cols ] = dfc [ self . final_cont_cols ] . astype ( float ) try : dfc = dfc [ self . cat_cols + self . final_cont_cols + [ self . target_col ]] except KeyError : dfc = dfc [ self . cat_cols + self . final_cont_cols ] # dfc = dfc.replace(self.replace_rare_categories_dict) dfc = nan_with_unknown_imputer ( dfc , self . cat_cols ) dfc = nan_with_number_imputer ( dfc , list ( set ( self . final_cont_cols )), - 9999.0 , # noqa ) dfc_le = self . label_encoder . transform ( dfc ) dfc_le = self . _change_int_float_types ( dfc_le ) dfc_le [ self . id_cols ] = df [ self . id_cols ] return dfc_le fit \u00b6 fit ( df ) Just to keep familiar naming convention with sklearn. Source code in inference_model/preprocessing/preprocess.py 134 135 136 def fit ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Just to keep familiar naming convention with sklearn.\"\"\" return self . fit_transform ( df ) scaler \u00b6 scaler_mapper \u00b6 scaler_mapper ( cont_cols , cat_cols , id_cols , scaler_mapper_def = None ) Function that maps scaler functions to appropriate columns. By default does not assign any scaler to continuous, categorical or identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers. Only columns defined in mapper object will be present in the transformed dataset. Parameters: cont_cols ( list ) \u2013 list of continuous feature columns in the dataset cat_cols ( list ) \u2013 list of categorical feature columns in the dataset id_cols ( list ) \u2013 identifier columns scaler_mapper_def ( dict ) \u2013 optional dictionary that contains keys ['cont_cols', 'cat_cols', 'id_cols'] with their corresponding scalers (defined by names, not instantiated) from sklearn library Returns: scaler ( DataFrameMapper ) \u2013 scaler object mapping sklearn scalers to columns in pandas dataframe Source code in inference_model/preprocessing/scaler.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 def scaler_mapper ( cont_cols : List [ str ], cat_cols : List [ str ], id_cols : List [ str ], scaler_mapper_def : Optional [ dict ] = None , ) -> DataFrameMapper : \"\"\"Function that maps scaler functions to appropriate columns. By default does not assign any scaler to continuous, categorical or identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers. Only columns defined in mapper object will be present in the transformed dataset. Args: cont_cols (list): list of continuous feature columns in the dataset cat_cols (list): list of categorical feature columns in the dataset id_cols (list): identifier columns scaler_mapper_def (dict): optional dictionary that contains keys ['cont_cols', 'cat_cols', 'id_cols'] with their corresponding scalers (defined by names, not instantiated) from sklearn library Returns: scaler (DataFrameMapper): scaler object mapping sklearn scalers to columns in pandas dataframe \"\"\" if scaler_mapper_def : cont_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cont_cols )), classes = [ scaler_mapper_def [ \"cont_cols\" ]], ) cat_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cat_cols )), classes = [ scaler_mapper_def [ \"cat_cols\" ]], ) id_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], id_cols )), classes = [ scaler_mapper_def [ \"id_cols\" ]], ) else : cont_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cont_cols )), classes = [ None ] ) cat_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cat_cols )), classes = [ None ] ) id_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], id_cols )), classes = [ None ] ) scaler = DataFrameMapper ( cont_cols_def + cat_cols_def + id_cols_def , df_out = True ) return scaler","title":"preprocessing"},{"location":"inference_model/preprocessing.html#preprocess-data","text":"","title":"Preprocess data"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.label_encoder","text":"","title":"label_encoder"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.preprocess_data","text":"","title":"preprocess_data"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.preprocess","text":"","title":"preprocess"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.scaler","text":"","title":"scaler"},{"location":"inference_model/training.html","text":"Training \u00b6 metrics \u00b6 smape \u00b6 smape ( actual , predicted ) Symmetric Mean Absolute Percentage Error https://vedexcel.com/how-to-calculate-smape-in-python/ Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Returns: smape ( float ) \u2013 symmetric mean absolute percentage error Source code in inference_model/training/metrics.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 def smape ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Symmetric Mean Absolute Percentage Error https://vedexcel.com/how-to-calculate-smape-in-python/ Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Returns: smape (float): symmetric mean absolute percentage error \"\"\" return ( 100 / len ( actual ) * np . sum ( 2 * np . abs ( predicted - actual ) / ( np . abs ( actual ) + np . abs ( predicted ))) ) ndcg \u00b6 ndcg ( actual , predicted ) Normalized Discounted Cumulative Gain Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Return ndcg (float): normalized discounted cumulative gain Source code in inference_model/training/metrics.py 32 33 34 35 36 37 38 39 40 41 42 43 44 def ndcg ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Normalized Discounted Cumulative Gain Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Return: ndcg (float): normalized discounted cumulative gain \"\"\" return ndcg_score ( [ rankdata ( actual , method = \"ordinal\" )], [ rankdata ( predicted , method = \"ordinal\" )], ) rmse \u00b6 rmse ( actual , predicted ) Root Mean Squared Error Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Returns: rmse ( float ) \u2013 root mean square error Source code in inference_model/training/metrics.py 47 48 49 50 51 52 53 54 55 56 def rmse ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Root Mean Squared Error Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Returns: rmse (float): root mean square error \"\"\" return mean_squared_error ( actual , predicted , squared = False ) nacil \u00b6 nacil ( actual , high_quantile_predicted , low_quantile_predicted ) Normalized Average Confidence Interval Length for Quantile Regression. The value is normalized to actual(expected) value. Parameters: actual ( np . ndarray ) \u2013 actual values high_quantile_predicted ( np . ndarray ) \u2013 high quantile predicted values low_quantile_predicted ( np . ndarray ) \u2013 low quantile predicted values Returns: nacil ( float ) \u2013 normalized average confidence interval length Source code in inference_model/training/metrics.py 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def nacil ( actual : np . ndarray , high_quantile_predicted : np . ndarray , low_quantile_predicted : np . ndarray , ): \"\"\"Normalized Average Confidence Interval Length for Quantile Regression. The value is normalized to actual(expected) value. Args: actual (np.ndarray): actual values high_quantile_predicted (np.ndarray): high quantile predicted values low_quantile_predicted (np.ndarray): low quantile predicted values Returns: nacil (float): normalized average confidence interval length \"\"\" return np . mean (( high_quantile_predicted - low_quantile_predicted ) / actual ) aiqc \u00b6 aiqc ( actual , high_quantile , low_quantile ) Average InterQuantile Coverage for Quantile Regression. Check if the interquantile coverage is close to the coverage of in the test dataset. Parameters: actual ( np . ndarray ) \u2013 actual values high_quantile_predicted ( np . ndarray ) \u2013 high quantile predicted values low_quantile_predicted ( np . ndarray ) \u2013 low quantile predicted values Returns: aiqc ( float ) \u2013 average interquantile coverage Source code in inference_model/training/metrics.py 77 78 79 80 81 82 83 84 85 86 87 88 def aiqc ( actual : np . ndarray , high_quantile : np . ndarray , low_quantile : np . ndarray ): \"\"\"Average InterQuantile Coverage for Quantile Regression. Check if the interquantile coverage is close to the coverage of in the test dataset. Args: actual (np.ndarray): actual values high_quantile_predicted (np.ndarray): high quantile predicted values low_quantile_predicted (np.ndarray): low quantile predicted values Returns: aiqc (float): average interquantile coverage \"\"\" return np . sum (( actual < high_quantile ) & ( actual > low_quantile )) / len ( actual ) optuna_optimizer \u00b6 LGBOptunaOptimizer \u00b6 LGBOptunaOptimizer ( objective , n_class = None ) Bases: BaseOptimizer Fallback/backup Optuna optimizer. Development is focused on Raytune. Kepping this code as backup. Parameters: objective ( str ) \u2013 objective of the model n_class ( int ) \u2013 number of classes in the dataset Source code in inference_model/training/optuna_optimizer.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def __init__ ( self , objective : Literal [ \"binary\" , \"multiclass\" , \"regression\" ], n_class : Optional [ int ] = None , ): \"\"\"Fallback/backup Optuna optimizer. Development is focused on Raytune. Kepping this code as backup. Args: objective (str): objective of the model n_class (int): number of classes in the dataset \"\"\" # Optuna does not support original lighgbm implemenattion and with a workaroud it # is possible to get the binarry classfier with focal_loss(any custom loss) # running but the multiclass requires changes to Optuna code, eg. this post: # https://lightrun.com/answers/optuna-optuna-error-when-using-custom-metrics-in-optunaintegrationlightgbm # noqa # loss = None super ( LGBOptunaOptimizer , self ) . __init__ ( objective , n_class ) self . params = self . base_params optimize \u00b6 optimize ( dtrain , deval ) Optimize LGBM model on provided datasets. Parameters: dtrain ( lgbDataset ) \u2013 training lgb dataset deval ( lgbDataset ) \u2013 evaluation lgb dataset Source code in inference_model/training/optuna_optimizer.py 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 def optimize ( self , dtrain : lgbDataset , deval : lgbDataset ): \"\"\"Optimize LGBM model on provided datasets. Args: dtrain (lgbDataset): training lgb dataset deval (lgbDataset): evaluation lgb dataset \"\"\" dtrain_copy = deepcopy ( dtrain ) deval_copy = deepcopy ( deval ) if deval is not None else None study = create_study ( study_name = \"LightGBMTuner\" ) tuner = LightGBMTuner ( params = self . params , train_set = dtrain_copy , valid_sets = deval_copy , num_boost_round = 1000 , callbacks = [ lgb . early_stopping ( stopping_rounds = 50 )], feval = self . feval , study = study , # fobj=self.fobj, ) tuner . run () self . study = study self . best = tuner . best_params # since n_estimators is not among the params that Optuna optimizes we # need to add it manually. We add a high value since it will be used # with early_stopping_rounds self . best [ \"n_estimators\" ] = 1000 # type: ignore trainer \u00b6 Trainer \u00b6 Trainer ( cat_cols , target_col , id_cols , objective , optimizer = None , n_class = None , preprocessors = None , ) Bases: BaseTrainer Objects that governs training and parameter optimization of the lgbm model. Parameters: cat_cols ( list ) \u2013 list of categorical feature column names target_col ( str ) \u2013 column name that represents target id_cols ( list ) \u2013 identification column names objective ( str ) \u2013 type of the task/objective optimizer ( BaseOptimizer ) \u2013 parameter optimizer object Source code in inference_model/training/trainer.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 def __init__ ( self , cat_cols : List [ str ], target_col : str , id_cols : List [ str ], objective : Literal [ \"binary\" ], optimizer : Union [ Any , BaseOptimizer , None ] = None , n_class : Optional [ int ] = None , preprocessors : Optional [ List [ Union [ Any , PreprocessData ]]] = None , ): \"\"\"Objects that governs training and parameter optimization of the lgbm model. Args: cat_cols (list): list of categorical feature column names target_col (str): column name that represents target id_cols (list): identification column names objective (str): type of the task/objective optimizer (BaseOptimizer): parameter optimizer object \"\"\" super ( Trainer , self ) . __init__ ( cat_cols = cat_cols , target_col = target_col , id_cols = id_cols , objective = objective , n_class = n_class , preprocessors = preprocessors , ) if optimizer is not None : if not hasattr ( optimizer , \"optimize\" ): raise AttributeError ( \" {} optimizer must have {} method\" . format ( optimizer , \"optimize\" ) ) self . optimizer = optimizer train \u00b6 train ( df_train , params = None , df_valid = None ) Train the model with the parameters. Parameters: df_train ( pd . DataFrame ) \u2013 training dataset params ( dict ) \u2013 model paramaters df_valid ( pd . DataFrame ) \u2013 optional validation dataset Returns: model ( lgb . basic . Booster ) \u2013 trained model Source code in inference_model/training/trainer.py 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 def train ( self , df_train : pd . DataFrame , params : Optional [ Dict ] = None , df_valid : Optional [ pd . DataFrame ] = None , ): \"\"\"Train the model with the parameters. Args: df_train (pd.DataFrame): training dataset params (dict): model paramaters df_valid (pd.DataFrame): optional validation dataset Returns: model (lgb.basic.Booster): trained model \"\"\" if self . preprocessors : for prep in self . preprocessors : df_train_prep = prep . transform ( df_train . drop ( columns = [ self . target_col ])) df_valid_prep = ( prep . transform ( df_valid . drop ( columns = [ self . target_col ])) if df_valid is not None else None ) if self . objective in [ \"binary\" ]: df_train_prep [ self . target_col ] = df_train [ self . target_col ] . astype ( int ) if df_valid is not None : df_valid_prep [ self . target_col ] = df_valid [ self . target_col ] . astype ( int ) else : raise NotImplementedError else : df_train_prep = df_train . copy () df_valid_prep = df_valid . copy () if df_valid is not None else None cat_cols = self . cat_cols if params : config = params elif ( params is None ) and ( self . optimizer ): config = self . optimizer . best if hasattr ( self . optimizer , \"best_to_drop\" ): df_train_prep . drop ( columns = self . optimizer . best_to_drop , # type: ignore inplace = True , ) if df_valid is not None : df_valid_prep . drop ( columns = self . optimizer . best_to_drop , # type: ignore inplace = True , ) cat_cols = intsec ( self . cat_cols , df_train_prep . columns . values ) elif ( params is None ) and ( self . optimizer is None ): config = self . base_params lgb_train , lgb_valid = to_lgbdataset ( train = df_train_prep , cat_cols = cat_cols , target_col = self . target_col , id_cols = self . id_cols , valid = df_valid_prep , ) self . model = lgb_train_function ( config = config , lgbtrain = lgb_train , lgbeval = lgb_valid , feval = self . feval , ) fit \u00b6 fit ( df_train , df_valid , df_test , random_state = 1 ) Train the model and optimize the parameters. Parameters: df_train ( pd . DataFrame ) \u2013 training dataset df_valid ( pd . DataFrame ) \u2013 validation dataset df_test ( pd . DataFrame ) \u2013 testing dataset Returns: model ( lgb . basic . Booster ) \u2013 trained mdoel Source code in inference_model/training/trainer.py 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 def fit ( self , df_train : pd . DataFrame , df_valid : pd . DataFrame , df_test : pd . DataFrame , random_state : int = 1 , ) -> pd . DataFrame : \"\"\"Train the model and optimize the parameters. Args: df_train (pd.DataFrame): training dataset df_valid (pd.DataFrame): validation dataset df_test (pd.DataFrame): testing dataset Returns: model (lgb.basic.Booster): trained mdoel \"\"\" if self . preprocessors : # this should be # df_train_prep = prep.transform(df_train_prep.drop(columns=[self.target_col])) for prep in self . preprocessors : df_train_prep = prep . transform ( df_train . drop ( columns = [ self . target_col ])) df_valid_prep = prep . transform ( df_valid . drop ( columns = [ self . target_col ])) if self . objective in [ \"binary\" ]: df_train_prep [ self . target_col ] = df_train [ self . target_col ] . astype ( int ) df_valid_prep [ self . target_col ] = df_valid [ self . target_col ] . astype ( int ) else : raise NotImplementedError else : df_train_prep = df_train . copy () df_valid_prep = df_valid . copy () lgb_train , lgb_valid = to_lgbdataset ( train = df_train_prep , cat_cols = self . cat_cols , target_col = self . target_col , id_cols = self . id_cols , valid = df_valid_prep , ) self . optimizer . optimize ( dtrain = lgb_train , deval = lgb_valid ) df_train_valid = pd . concat ([ df_train , df_valid ], ignore_index = True ) df_train_valid = df_train_valid . reset_index ( drop = True ) self . train ( df_train = df_train_valid ) metrics_dict = self . compute_metrics ( df_test ) return metrics_dict utils \u00b6 get_or_create_experiment \u00b6 get_or_create_experiment ( experiment_name ) Retrieve the ID of an existing MLflow experiment or create a new one if it doesn't exist. This function checks if an experiment with the given name exists within MLflow. If it does, the function returns its ID. If not, it creates a new experiment with the provided name and returns its ID. experiment_name (str): Name of the MLflow experiment. str: ID of the existing or newly created MLflow experiment. Source code in inference_model/training/utils.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 def get_or_create_experiment ( experiment_name ): \"\"\" Retrieve the ID of an existing MLflow experiment or create a new one if it doesn't exist. This function checks if an experiment with the given name exists within MLflow. If it does, the function returns its ID. If not, it creates a new experiment with the provided name and returns its ID. Parameters: - experiment_name (str): Name of the MLflow experiment. Returns: - str: ID of the existing or newly created MLflow experiment. \"\"\" if experiment := mlflow . get_experiment_by_name ( experiment_name ): return experiment . experiment_id else : return mlflow . create_experiment ( experiment_name ) to_lgbdataset \u00b6 to_lgbdataset ( train , cat_cols , target_col , id_cols = [], valid = None ) Transform pandas dataframe to lgbm dataset, or datasets(eval). Parameters: train ( pd . DataFrame ) \u2013 training dataset cat_cols ( list ) \u2013 list of categorical columns target_col ( str ) \u2013 target column in the dataset id_cols ( list ) \u2013 list of identifier columns valid ( pd . DataFrame ) \u2013 validation dataset Returns: lgb_train ( lgbDataset ) \u2013 lgbm training dataset lgb_valid ( lgbDataset ) \u2013 lgbm valid dataset Source code in inference_model/training/utils.py 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 def to_lgbdataset ( train : pd . DataFrame , cat_cols : List [ str ], target_col : str , id_cols : List [ str ] = [], valid : Optional [ pd . DataFrame ] = None , ) -> Tuple [ lgbDataset , Optional [ lgbDataset ]]: \"\"\"Transform pandas dataframe to lgbm dataset, or datasets(eval). Args: train (pd.DataFrame): training dataset cat_cols (list): list of categorical columns target_col (str): target column in the dataset id_cols (list): list of identifier columns valid (pd.DataFrame): validation dataset Returns: lgb_train (lgbDataset): lgbm training dataset lgb_valid (lgbDataset): lgbm valid dataset \"\"\" lgb_train = lgbDataset ( train . drop ( columns = [ target_col ] + id_cols ), train [ target_col ], categorical_feature = cat_cols , free_raw_data = False , ) if valid is not None : lgb_valid = lgbDataset ( valid . drop ( columns = [ target_col ] + id_cols ), valid [ target_col ], reference = lgb_train , free_raw_data = False , ) else : lgb_valid = None return lgb_train , lgb_valid predict_proba_lgbm_from_raw \u00b6 predict_proba_lgbm_from_raw ( preds_raw , task , binary2d = False ) Apply softmax to array of arrays that is an output of lightgbm.predct(). This replaces predict_proba(). Parameters: predicted_raw ( ndarray ) \u2013 1D numpy array of arrays task ( str ) \u2013 type of task/objective binary2d ( boolean ) \u2013 wether the output of binary classification should be 1 or 2d vector Returns: predicted_probs ( ndarray ) \u2013 array with predicted probabilities Source code in inference_model/training/utils.py 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 def predict_proba_lgbm_from_raw ( preds_raw : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ], binary2d : Optional [ bool ] = False , ) -> np . ndarray : \"\"\"Apply softmax to array of arrays that is an output of lightgbm.predct(). This replaces predict_proba(). Args: predicted_raw (ndarray): 1D numpy array of arrays task (str): type of task/objective binary2d (boolean): wether the output of binary classification should be 1 or 2d vector Returns: predicted_probs (ndarray): array with predicted probabilities \"\"\" if task == \"binary\" : predicted_probs = _sigmoid ( preds_raw ) if binary2d : predicted_probs = np . apply_along_axis ( _binary_margin_to_prob , 1 , np . vstack ( tuple ( predicted_probs )) ) elif task == \"multiclass\" : predicted_probs = np . apply_along_axis ( _softmax , 1 , np . stack ( tuple ( preds_raw ))) return predicted_probs predict_cls_lgbm_from_raw \u00b6 predict_cls_lgbm_from_raw ( preds_raw , task ) Helper function to convert raw margin predictions through a sigmoid to represent a probability. Parameters: preds_raw ( ndarray ) \u2013 predictions lgbDataset ( lightgbm . Dataset ) \u2013 dataset, containing labels, used for prediction Returns: y_true , preds \u2013 tuple containg labels and predictions for further evaluation Source code in inference_model/training/utils.py 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 def predict_cls_lgbm_from_raw ( preds_raw : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ] ) -> np . ndarray : \"\"\"Helper function to convert raw margin predictions through a sigmoid to represent a probability. Args: preds_raw (ndarray): predictions lgbDataset (lightgbm.Dataset): dataset, containing labels, used for prediction Returns: (y_true, preds): tuple containg labels and predictions for further evaluation \"\"\" predicted_probs = predict_proba_lgbm_from_raw ( preds_raw = preds_raw , task = task ) if task == \"binary\" : pred_cls = np . array ([ int ( p > 0.5 ) for p in predicted_probs ]) elif task == \"multiclass\" : pred_cls = predicted_probs . argmax ( axis = 1 ) return pred_cls get_feature_importance \u00b6 get_feature_importance ( model ) Extract model feature importances and return sorted dataframe. Parameters: model ( lgb . basic . Booster ) \u2013 LightGBM model Returns: feature_imp ( pd . DataFrame ) \u2013 sorted dataframe with features and their importances Source code in inference_model/training/utils.py 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 def get_feature_importance ( model : lgb . basic . Booster ) -> pd . DataFrame : \"\"\"Extract model feature importances and return sorted dataframe. Args: model (lgb.basic.Booster): LightGBM model Returns: feature_imp (pd.DataFrame): sorted dataframe with features and their importances \"\"\" feature_imp = pd . DataFrame ( { \"value\" : model . feature_importance (), \"feature\" : model . feature_name ()} ) feature_imp . sort_values ( by = \"value\" , inplace = True ) feature_imp . reset_index ( drop = True , inplace = True ) return feature_imp flatten_dict \u00b6 flatten_dict ( d , parent_key = '' , sep = '_' ) fastest according to https://www.freecodecamp.org/news/how-to-flatten-a-dictionary-in-python-in-4-different-ways/ Source code in inference_model/training/utils.py 184 185 186 187 188 189 190 191 192 193 194 195 196 197 def flatten_dict ( d : MutableMapping , parent_key : str = \"\" , sep : str = \"_\" ) -> MutableMapping : \"\"\" fastest according to https://www.freecodecamp.org/news/how-to-flatten-a-dictionary-in-python-in-4-different-ways/ \"\"\" items : List [ Any ] = [] for k , v in d . items (): new_key = parent_key + sep + k if parent_key else k if isinstance ( v , MutableMapping ): items . extend ( flatten_dict ( v , new_key , sep = sep ) . items ()) else : items . append (( new_key , v )) return dict ( items )","title":"training"},{"location":"inference_model/training.html#training","text":"","title":"Training"},{"location":"inference_model/training.html#inference_model.training.metrics","text":"","title":"metrics"},{"location":"inference_model/training.html#inference_model.training.optuna_optimizer","text":"","title":"optuna_optimizer"},{"location":"inference_model/training.html#inference_model.training.trainer","text":"","title":"trainer"},{"location":"inference_model/training.html#inference_model.training.utils","text":"","title":"utils"},{"location":"inference_model/utils.html","text":"Utils \u00b6 utils \u00b6 intsec \u00b6 intsec ( list1 , list2 ) Simple intesection of two lists. Parameters: list1 ( list ) \u2013 list1 list2 ( list ) \u2013 list2 Returns: list ( list ) \u2013 intersection of lists Source code in inference_model/utils.py 7 8 9 10 11 12 13 14 15 def intsec ( list1 : list , list2 : list ) -> list : \"\"\"Simple intesection of two lists. Args: list1 (list): list1 list2 (list): list2 Returns: list (list): intersection of lists \"\"\" return list ( set . intersection ( set ( list1 ), set ( list2 ))) dill_load \u00b6 dill_load ( file_loc ) Helper function to open/close dill file, otherwise the python outputs warning that the file remains opened Parameters: file_loc ( str ) \u2013 location of the file Returns: content ( dict ) \u2013 content of dill file, usually dictionary Source code in inference_model/utils.py 18 19 20 21 22 23 24 25 26 27 28 29 def dill_load ( file_loc : Union [ str , Path ]) -> Any : \"\"\"Helper function to open/close dill file, otherwise the python outputs warning that the file remains opened Args: file_loc (str): location of the file Returns: content (dict): content of dill file, usually dictionary \"\"\" with open ( file_loc , \"rb\" ) as f : content = dill . load ( f ) return content dill_dump \u00b6 dill_dump ( file_loc , content ) Helper function to open/close dill file and dump content into it, otherwise the python outputs warning that the file remains opened Parameters: file_loc ( str ) \u2013 location of the file content ( object ) \u2013 data that will be saved to dill, usually dictionary Source code in inference_model/utils.py 32 33 34 35 36 37 38 39 40 41 def dill_dump ( file_loc : Union [ str , Path ], content : object ): \"\"\"Helper function to open/close dill file and dump content into it, otherwise the python outputs warning that the file remains opened Args: file_loc (str): location of the file content (object): data that will be saved to dill, usually dictionary \"\"\" with open ( file_loc , \"wb\" ) as f : dill . dump ( content , f )","title":"utils"},{"location":"inference_model/utils.html#utils","text":"","title":"Utils"},{"location":"inference_model/utils.html#inference_model.utils","text":"","title":"utils"},{"location":"notebooks/01_feature_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Feature analysis \u00b6 Purpose : analyse dataset features without inclusion of target value if it looks healthy duplicate values NA values zero/0 values if the values are \"too constant\", ie. most [robably bad features for classifcation/regression small std values entropy is too high/too low(not sure right now?) - \"Entropy is a measure of disorder or uncertainty\" number of unique values in categorical features is too high - most probably they are unable to identify any group of targets drop highly correlated values to improve performance of the model training to decrease dataset size for further exploration Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing , zero , nunique , init_check , std , entropy , nunique , ) pd . set_option ( \"display.max_columns\" , 100 ) pd . set_option ( \"display.max_rows\" , 300 ) from inference_model.eda.features.plotting import cross_correlation , distributions import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing, zero, nunique, init_check, std, entropy, nunique, ) pd.set_option(\"display.max_columns\", 100) pd.set_option(\"display.max_rows\", 300) from inference_model.eda.features.plotting import cross_correlation, distributions import ast Dataset \u00b6 In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None Features \u00b6 Analysis \u00b6 In [4]: Copied! duplicated_ids , cont_cols_desc , cat_cols_desc = init_check ( df = df_pd , identifier = id_cols , cat_cols = cat_cols , cont_cols = cont_cols , verbose = True , ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() duplicated_ids, cont_cols_desc, cat_cols_desc = init_check( df=df_pd, identifier=id_cols, cat_cols=cat_cols, cont_cols=cont_cols, verbose=True, ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() [CHECK] Number of duplicated ids: 1034 [CHECK] Numerical columns count mean std min 25% 50% \\ maf 1035.0 11.00 3.40 0.03 9.77 10.19 vehicle_speed 1035.0 0.95 3.70 0.00 0.00 0.00 engine_load 1035.0 46.29 21.59 30.59 35.69 37.25 barometric_pressure 1035.0 101.00 0.00 101.00 101.00 101.00 intake_air_temp 1035.0 28.56 18.16 -6.00 20.00 26.00 throttle_position 1035.0 83.21 0.15 83.14 83.14 83.14 engine_coolant_temp 1035.0 76.66 21.89 17.00 74.50 88.00 engine_speed 1035.0 929.39 270.78 3.20 893.00 896.50 fuel_rg_pressure 1035.0 35967.15 14377.15 110.00 31220.00 34050.00 control_voltage 1035.0 14.32 2.14 0.05 14.76 14.78 75% max maf 11.99 31.83 vehicle_speed 0.00 20.00 engine_load 38.82 100.00 barometric_pressure 101.00 101.00 intake_air_temp 32.00 145.00 throttle_position 83.14 83.53 engine_coolant_temp 89.00 91.00 engine_speed 899.50 2282.50 fuel_rg_pressure 35390.00 108770.00 control_voltage 14.80 14.84 In [5]: Copied! missing_val_frac , fig = missing ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) missing_val_frac . head () missing_val_frac, fig = missing( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) missing_val_frac.head() Out[5]: engine_load 0.0 engine_coolant_temp 0.0 engine_speed 0.0 vehicle_speed 0.0 intake_air_temp 0.0 dtype: float64 In [6]: Copied! zero_val_frac , fig = zero ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) zero_val_frac . head () zero_val_frac, fig = zero( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) zero_val_frac.head() Out[6]: vehicle_speed 0.927536 class 0.902415 engine_load 0.000000 engine_coolant_temp 0.000000 engine_speed 0.000000 dtype: float64 In [7]: Copied! data_nunique , fig = nunique ( df = df_pd . drop ( columns = id_cols ), scale = \"log\" , plot = True , ) data_nunique . head () data_nunique, fig = nunique( df=df_pd.drop(columns=id_cols), scale=\"log\", plot=True, ) data_nunique.head() Out[7]: barometric_pressure 1 throttle_position 2 class 2 vehicle_speed 21 control_voltage 48 dtype: int64 In [8]: Copied! data_std , fig = std ( df = df_pd [ cont_cols ], scale = \"log\" , plot = True , ) data_std . head () data_std, fig = std( df=df_pd[cont_cols], scale=\"log\", plot=True, ) data_std.head() Out[8]: fuel_rg_pressure 14377.152211 engine_speed 270.781548 engine_coolant_temp 21.888726 engine_load 21.587427 intake_air_temp 18.163539 dtype: float64 In [9]: Copied! col_entropies , fig = entropy ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) col_entropies . head () col_entropies, fig = entropy( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) col_entropies.head() Out[9]: maf 4.800777 engine_speed 3.981783 fuel_rg_pressure 3.797527 intake_air_temp 3.612889 engine_load 3.253640 dtype: float64 Plotting \u00b6 In [10]: Copied! fig = cross_correlation ( df = df_pd [ cont_cols ], n = 10 , verbose = True ) fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True) Top 10 absolute correlations vehicle_speed fuel_rg_pressure 0.648543 maf 0.544904 engine_load engine_coolant_temp 0.531108 engine_speed vehicle_speed 0.525832 engine_coolant_temp throttle_position 0.491290 engine_speed maf 0.398281 maf fuel_rg_pressure 0.331396 engine_speed fuel_rg_pressure 0.329811 engine_coolant_temp intake_air_temp 0.305559 engine_load intake_air_temp 0.207530 dtype: float64 In [11]: Copied! fig = distributions ( df = df_pd [ cont_cols ], low_per_cut = 0 , high_per_cut = 1 , type = \"box\" , ) fig = distributions( df=df_pd[cont_cols], low_per_cut=0, high_per_cut=1, type=\"box\", ) /home/jovyan/success6g-edge/inference_model/eda/features/plotting.py:98: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding. ax[i].set_xlim(data[col].quantile(low_per_cut), data[col].quantile(high_per_cut))","title":"01_feature_analysis"},{"location":"notebooks/01_feature_analysis.html#feature-analysis","text":"Purpose : analyse dataset features without inclusion of target value if it looks healthy duplicate values NA values zero/0 values if the values are \"too constant\", ie. most [robably bad features for classifcation/regression small std values entropy is too high/too low(not sure right now?) - \"Entropy is a measure of disorder or uncertainty\" number of unique values in categorical features is too high - most probably they are unable to identify any group of targets drop highly correlated values to improve performance of the model training to decrease dataset size for further exploration","title":"Feature analysis"},{"location":"notebooks/01_feature_analysis.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing , zero , nunique , init_check , std , entropy , nunique , ) pd . set_option ( \"display.max_columns\" , 100 ) pd . set_option ( \"display.max_rows\" , 300 ) from inference_model.eda.features.plotting import cross_correlation , distributions import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing, zero, nunique, init_check, std, entropy, nunique, ) pd.set_option(\"display.max_columns\", 100) pd.set_option(\"display.max_rows\", 300) from inference_model.eda.features.plotting import cross_correlation, distributions import ast","title":"Imports"},{"location":"notebooks/01_feature_analysis.html#dataset","text":"In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None","title":"Dataset"},{"location":"notebooks/01_feature_analysis.html#features","text":"","title":"Features"},{"location":"notebooks/01_feature_analysis.html#analysis","text":"In [4]: Copied! duplicated_ids , cont_cols_desc , cat_cols_desc = init_check ( df = df_pd , identifier = id_cols , cat_cols = cat_cols , cont_cols = cont_cols , verbose = True , ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() duplicated_ids, cont_cols_desc, cat_cols_desc = init_check( df=df_pd, identifier=id_cols, cat_cols=cat_cols, cont_cols=cont_cols, verbose=True, ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() [CHECK] Number of duplicated ids: 1034 [CHECK] Numerical columns count mean std min 25% 50% \\ maf 1035.0 11.00 3.40 0.03 9.77 10.19 vehicle_speed 1035.0 0.95 3.70 0.00 0.00 0.00 engine_load 1035.0 46.29 21.59 30.59 35.69 37.25 barometric_pressure 1035.0 101.00 0.00 101.00 101.00 101.00 intake_air_temp 1035.0 28.56 18.16 -6.00 20.00 26.00 throttle_position 1035.0 83.21 0.15 83.14 83.14 83.14 engine_coolant_temp 1035.0 76.66 21.89 17.00 74.50 88.00 engine_speed 1035.0 929.39 270.78 3.20 893.00 896.50 fuel_rg_pressure 1035.0 35967.15 14377.15 110.00 31220.00 34050.00 control_voltage 1035.0 14.32 2.14 0.05 14.76 14.78 75% max maf 11.99 31.83 vehicle_speed 0.00 20.00 engine_load 38.82 100.00 barometric_pressure 101.00 101.00 intake_air_temp 32.00 145.00 throttle_position 83.14 83.53 engine_coolant_temp 89.00 91.00 engine_speed 899.50 2282.50 fuel_rg_pressure 35390.00 108770.00 control_voltage 14.80 14.84 In [5]: Copied! missing_val_frac , fig = missing ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) missing_val_frac . head () missing_val_frac, fig = missing( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) missing_val_frac.head() Out[5]: engine_load 0.0 engine_coolant_temp 0.0 engine_speed 0.0 vehicle_speed 0.0 intake_air_temp 0.0 dtype: float64 In [6]: Copied! zero_val_frac , fig = zero ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) zero_val_frac . head () zero_val_frac, fig = zero( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) zero_val_frac.head() Out[6]: vehicle_speed 0.927536 class 0.902415 engine_load 0.000000 engine_coolant_temp 0.000000 engine_speed 0.000000 dtype: float64 In [7]: Copied! data_nunique , fig = nunique ( df = df_pd . drop ( columns = id_cols ), scale = \"log\" , plot = True , ) data_nunique . head () data_nunique, fig = nunique( df=df_pd.drop(columns=id_cols), scale=\"log\", plot=True, ) data_nunique.head() Out[7]: barometric_pressure 1 throttle_position 2 class 2 vehicle_speed 21 control_voltage 48 dtype: int64 In [8]: Copied! data_std , fig = std ( df = df_pd [ cont_cols ], scale = \"log\" , plot = True , ) data_std . head () data_std, fig = std( df=df_pd[cont_cols], scale=\"log\", plot=True, ) data_std.head() Out[8]: fuel_rg_pressure 14377.152211 engine_speed 270.781548 engine_coolant_temp 21.888726 engine_load 21.587427 intake_air_temp 18.163539 dtype: float64 In [9]: Copied! col_entropies , fig = entropy ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) col_entropies . head () col_entropies, fig = entropy( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) col_entropies.head() Out[9]: maf 4.800777 engine_speed 3.981783 fuel_rg_pressure 3.797527 intake_air_temp 3.612889 engine_load 3.253640 dtype: float64","title":"Analysis"},{"location":"notebooks/01_feature_analysis.html#plotting","text":"In [10]: Copied! fig = cross_correlation ( df = df_pd [ cont_cols ], n = 10 , verbose = True ) fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True) Top 10 absolute correlations vehicle_speed fuel_rg_pressure 0.648543 maf 0.544904 engine_load engine_coolant_temp 0.531108 engine_speed vehicle_speed 0.525832 engine_coolant_temp throttle_position 0.491290 engine_speed maf 0.398281 maf fuel_rg_pressure 0.331396 engine_speed fuel_rg_pressure 0.329811 engine_coolant_temp intake_air_temp 0.305559 engine_load intake_air_temp 0.207530 dtype: float64 In [11]: Copied! fig = distributions ( df = df_pd [ cont_cols ], low_per_cut = 0 , high_per_cut = 1 , type = \"box\" , ) fig = distributions( df=df_pd[cont_cols], low_per_cut=0, high_per_cut=1, type=\"box\", ) /home/jovyan/success6g-edge/inference_model/eda/features/plotting.py:98: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding. ax[i].set_xlim(data[col].quantile(low_per_cut), data[col].quantile(high_per_cut))","title":"Plotting"},{"location":"notebooks/02_target_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? 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var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Target analysis \u00b6 Purpose : put features into context with predicted or actual target values analyse feature distribution per group in binary classification check correlation of features with target value check 'certainty' of the model predictions by plotting prob_distrib_per_class to see how certain the model is in predicting each class visually check predicted vs actual(ground truth values) by simple scatter plot by 'improved scatter plot' ( joint_dist ) - this can be usefull if the actual values form a cluster (eg. regression applied to clusters of users) and we want check if the model predictions also form a nice cluster with similar distribution as actual values Intended use : check how your model is performing check the possible connection between feature and target values (eg. if the distributions of features are different for each class) Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls , ) import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls, ) import ast Dataset \u00b6 In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None Analysis \u00b6 In [4]: Copied! sorted_corr_cols , fig = correlation ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], scale = \"linear\" , plot = True , ) sorted_corr_cols . head () sorted_corr_cols, fig = correlation( df=df_pd[cont_cols], target=df_pd[target_col], scale=\"linear\", plot=True, ) sorted_corr_cols.head() Out[4]: engine_load 0.818614 throttle_position 0.245668 maf 0.087101 control_voltage 0.042763 engine_speed -0.062052 Name: class, dtype: float64 Plotting \u00b6 Predicted and actual/ground_truth values were gathered using LightGBM with Optuna optimizer on the dataset In [5]: Copied! fig = distributions_in_binary_cls ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], low_per_cut = 0 , high_per_cut = 1 , ) fig = distributions_in_binary_cls( df=df_pd[cont_cols], target=df_pd[target_col], low_per_cut=0, high_per_cut=1, ) /home/jovyan/success6g-edge/inference_model/eda/target/plotting.py:123: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding. ax[i].set_xlim(","title":"02_target_analysis"},{"location":"notebooks/02_target_analysis.html#target-analysis","text":"Purpose : put features into context with predicted or actual target values analyse feature distribution per group in binary classification check correlation of features with target value check 'certainty' of the model predictions by plotting prob_distrib_per_class to see how certain the model is in predicting each class visually check predicted vs actual(ground truth values) by simple scatter plot by 'improved scatter plot' ( joint_dist ) - this can be usefull if the actual values form a cluster (eg. regression applied to clusters of users) and we want check if the model predictions also form a nice cluster with similar distribution as actual values Intended use : check how your model is performing check the possible connection between feature and target values (eg. if the distributions of features are different for each class)","title":"Target analysis"},{"location":"notebooks/02_target_analysis.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls , ) import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls, ) import ast","title":"Imports"},{"location":"notebooks/02_target_analysis.html#dataset","text":"In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None","title":"Dataset"},{"location":"notebooks/02_target_analysis.html#analysis","text":"In [4]: Copied! sorted_corr_cols , fig = correlation ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], scale = \"linear\" , plot = True , ) sorted_corr_cols . head () sorted_corr_cols, fig = correlation( df=df_pd[cont_cols], target=df_pd[target_col], scale=\"linear\", plot=True, ) sorted_corr_cols.head() Out[4]: engine_load 0.818614 throttle_position 0.245668 maf 0.087101 control_voltage 0.042763 engine_speed -0.062052 Name: class, dtype: float64","title":"Analysis"},{"location":"notebooks/02_target_analysis.html#plotting","text":"Predicted and actual/ground_truth values were gathered using LightGBM with Optuna optimizer on the dataset In [5]: Copied! fig = distributions_in_binary_cls ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], low_per_cut = 0 , high_per_cut = 1 , ) fig = distributions_in_binary_cls( df=df_pd[cont_cols], target=df_pd[target_col], low_per_cut=0, high_per_cut=1, ) /home/jovyan/success6g-edge/inference_model/eda/target/plotting.py:123: UserWarning: Attempting to set identical low and high xlims makes transformation singular; 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Models \u00b6 Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report , RocCurveDisplay , roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw , predict_proba_lgbm_from_raw , ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, RocCurveDisplay, roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw, predict_proba_lgbm_from_raw, ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() Dataset \u00b6 In [3]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True In [4]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [8]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) check possible class imbalance In [9]: Copied! df_pd [ target_col ] . value_counts () df_pd[target_col].value_counts() Out[9]: class 0 934 1 101 Name: count, dtype: int64 Preprocessing \u00b6 divide dataset into train, test, valid scale continuous columns by standard scaler(not needed for LightGBM but for other mdoels...) In [10]: Copied! df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) df_train . reset_index ( inplace = True , drop = True ) df_valid . reset_index ( inplace = True , drop = True ) df_test . reset_index ( inplace = True , drop = True ) scaler_mapper_def = { \"cont_cols\" : StandardScaler , \"cat_cols\" : None , \"id_cols\" : None , } scaler = scaler_mapper ( cont_cols = cont_cols , cat_cols = cat_cols , id_cols = [ target_col ] + id_cols , scaler_mapper_def = scaler_mapper_def , ) df_train_scaled = scaler . fit_transform ( df_train ) df_test_scaled = scaler . transform ( df_test ) df_valid_scaled = scaler . transform ( df_valid ) df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) df_train.reset_index(inplace=True, drop=True) df_valid.reset_index(inplace=True, drop=True) df_test.reset_index(inplace=True, drop=True) scaler_mapper_def = { \"cont_cols\": StandardScaler, \"cat_cols\": None, \"id_cols\": None, } scaler = scaler_mapper( cont_cols=cont_cols, cat_cols=cat_cols, id_cols=[target_col] + id_cols, scaler_mapper_def=scaler_mapper_def, ) df_train_scaled = scaler.fit_transform(df_train) df_test_scaled = scaler.transform(df_test) df_valid_scaled = scaler.transform(df_valid) Logistic Regression \u00b6 In [11]: Copied! LR_clf = LogisticRegression ( class_weight = \"balanced\" ) LR_clf . fit ( df_train_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ), df_train_scaled [ target_col ], ) LR_predicted = LR_clf . predict ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) print ( \"LR classification report : \\n \" + str ( classification_report ( df_test_scaled [ target_col ], LR_predicted )) ) LR_clf = LogisticRegression(class_weight=\"balanced\") LR_clf.fit( df_train_scaled.drop(columns=[target_col] + id_cols + cat_cols), df_train_scaled[target_col], ) LR_predicted = LR_clf.predict( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) print( \"LR classification report :\\n\" + str(classification_report(df_test_scaled[target_col], LR_predicted)) ) LR classification report : precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [12]: Copied! # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np . arange ( 0.2 , 0.8 , 0.01 ): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) )[:, 1 ] >= t ) . astype ( int ) threshold_score . append ( ( t , criterion ( df_test_scaled [ target_col ], preds_bin , average = \"weighted\" )) ) threshold_score = sorted ( threshold_score , key = lambda x : x [ 1 ], reverse = True ) best_threshold , best_score = threshold_score [ 0 ][ 0 ], threshold_score [ 0 ][ 1 ] print ( f \"The best threshold \\n { best_threshold } \\n , with score: \\n { best_score } \" ) # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np.arange(0.2, 0.8, 0.01): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) )[:, 1] >= t ).astype(int) threshold_score.append( (t, criterion(df_test_scaled[target_col], preds_bin, average=\"weighted\")) ) threshold_score = sorted(threshold_score, key=lambda x: x[1], reverse=True) best_threshold, best_score = threshold_score[0][0], threshold_score[0][1] print(f\"The best threshold\\n{best_threshold}\\n, with score:\\n{best_score}\") The best threshold 0.2 , with score: 1.0 In [13]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) In [14]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0 H2O AutoML \u00b6 NOTE : java is required : sudo apt install openjdk-17-jdk In [16]: Copied! # initialize H2O h2o . init ( log_dir = \"h2o_logs\" , log_level = \"WARN\" ) # read as h2o file h2o_train = h2o . H2OFrame ( df_train_scaled . drop ( columns = id_cols )) h2o_valid = h2o . H2OFrame ( df_valid_scaled . drop ( columns = id_cols )) h2o_test = h2o . H2OFrame ( df_test_scaled . drop ( columns = id_cols )) # For binary classification, response should be a factor h2o_train [ target_col ] = h2o_train [ target_col ] . asfactor () h2o_valid [ target_col ] = h2o_valid [ target_col ] . asfactor () h2o_test [ target_col ] = h2o_test [ target_col ] . asfactor () # Define AML task aml = H2OAutoML ( seed = random_state , max_runtime_secs = 180 ) # over/under sample for classification tasks aml . balance_classes = True # Run it _ = aml . train ( x = list ( cont_cols + cat_cols ), y = target_col , training_frame = h2o_train , leaderboard_frame = h2o_valid , ) m = aml . get_best_model () # Leaderboard, show and save lb = h2o . automl . get_leaderboard ( aml , extra_columns = \"ALL\" ) print ( lb ) print ( m . model_performance ( h2o_valid )) predictions = m . predict ( h2o_test ) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") # initialize H2O h2o.init(log_dir=\"h2o_logs\", log_level=\"WARN\") # read as h2o file h2o_train = h2o.H2OFrame(df_train_scaled.drop(columns=id_cols)) h2o_valid = h2o.H2OFrame(df_valid_scaled.drop(columns=id_cols)) h2o_test = h2o.H2OFrame(df_test_scaled.drop(columns=id_cols)) # For binary classification, response should be a factor h2o_train[target_col] = h2o_train[target_col].asfactor() h2o_valid[target_col] = h2o_valid[target_col].asfactor() h2o_test[target_col] = h2o_test[target_col].asfactor() # Define AML task aml = H2OAutoML(seed=random_state, max_runtime_secs=180) # over/under sample for classification tasks aml.balance_classes = True # Run it _ = aml.train( x=list(cont_cols + cat_cols), y=target_col, training_frame=h2o_train, leaderboard_frame=h2o_valid, ) m = aml.get_best_model() # Leaderboard, show and save lb = h2o.automl.get_leaderboard(aml, extra_columns=\"ALL\") print(lb) print(m.model_performance(h2o_valid)) predictions = m.predict(h2o_test) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") Checking whether there is an H2O instance running at http://localhost:54321..... not found. Attempting to start a local H2O server... Java Version: openjdk version \"17.0.10\" 2024-01-16; OpenJDK Runtime Environment (build 17.0.10+7-Ubuntu-122.04.1); OpenJDK 64-Bit Server VM (build 17.0.10+7-Ubuntu-122.04.1, mixed mode, sharing) Starting server from /opt/conda/lib/python3.10/site-packages/h2o/backend/bin/h2o.jar Ice root: /tmp/tmp0jm6weez JVM stdout: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.out JVM stderr: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.err Server is running at http://127.0.0.1:54321 Connecting to H2O server at http://127.0.0.1:54321 ... successful. #h2o-table-1.h2o-container { overflow-x: auto; } #h2o-table-1 .h2o-table { /* width: 100%; */ margin-top: 1em; margin-bottom: 1em; } #h2o-table-1 .h2o-table caption { white-space: nowrap; caption-side: top; text-align: left; /* margin-left: 1em; */ margin: 0; font-size: larger; } #h2o-table-1 .h2o-table thead { white-space: nowrap; position: sticky; top: 0; box-shadow: 0 -1px inset; } #h2o-table-1 .h2o-table tbody { overflow: auto; } #h2o-table-1 .h2o-table th, #h2o-table-1 .h2o-table td { text-align: right; /* border: 1px solid; */ } #h2o-table-1 .h2o-table tr:nth-child(even) { /* background: #F5F5F5 */ } H2O_cluster_uptime: 01 secs H2O_cluster_timezone: Etc/UTC H2O_data_parsing_timezone: UTC H2O_cluster_version: 3.46.0.2 H2O_cluster_version_age: 8 days H2O_cluster_name: H2O_from_python_unknownUser_maoqtf H2O_cluster_total_nodes: 1 H2O_cluster_free_memory: 16 Gb H2O_cluster_total_cores: 12 H2O_cluster_allowed_cores: 12 H2O_cluster_status: locked, healthy H2O_connection_url: http://127.0.0.1:54321 H2O_connection_proxy: {\"http\": null, \"https\": null} H2O_internal_security: False Python_version: 3.10.11 final Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% AutoML progress: | 13:16:41.801: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:44.939: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:46.154: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:16:59.224: _train param, Dropping unused columns: [barometric_pressure] 13:16:59.913: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:03.240: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:05.128: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:16.407: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588 13:17:27.855: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:17:40.196: _train param, Dropping unused columns: [barometric_pressure] 13:17:40.764: _train param, Dropping unused columns: [barometric_pressure] 13:17:41.338: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:44.575: _train param, Dropping bad and constant columns: [barometric_pressure] 13:17:45.413: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:53.937: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:55.712: _train param, Dropping unused columns: [barometric_pressure] 13:17:56.367: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 13:19:35.448: _train param, Dropping unused columns: [barometric_pressure] \u2588 13:19:36.22: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588| (done) 100% model_id auc logloss aucpr mean_per_class_error rmse mse training_time_ms predict_time_per_row_ms algo DeepLearning_grid_1_AutoML_1_20240522_131641_model_8 1 0.0177159 1 0 0.0582198 0.00338955 628 0.127786 DeepLearning DeepLearning_grid_1_AutoML_1_20240522_131641_model_4 1 0.0155053 1 0 0.0481989 0.00232314 117 0.1101 DeepLearning XGBoost_1_AutoML_1_20240522_131641 1 0.0347672 1 0 0.0789026 0.00622562 464 0.078661 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_12 1 0.0651884 1 0 0.106454 0.0113325 328 0.072441 XGBoost DeepLearning_grid_1_AutoML_1_20240522_131641_model_1 1 0.0235103 1 0 0.0600679 0.00360815 99 0.098096 DeepLearning XGBoost_grid_1_AutoML_1_20240522_131641_model_11 1 0.00556141 1 0 0.0320985 0.00103031 996 0.105708 XGBoost GBM_4_AutoML_1_20240522_131641 1 9.26741e-06 1 0 9.39779e-05 8.83184e-09 4293 0.280377 GBM XGBoost_grid_1_AutoML_1_20240522_131641_model_6 1 0.0141646 1 0 0.0244465 0.000597631 630 0.057813 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_13 1 0.0318374 1 0 0.0810283 0.00656559 745 0.069021 XGBoost GBM_3_AutoML_1_20240522_131641 1 2.55592e-05 1 0 0.000259039 6.7101e-08 4037 0.153118 GBM [47 rows x 10 columns] ModelMetricsBinomial: deeplearning ** Reported on test data. ** MSE: 0.0033895466349161047 RMSE: 0.05821981307867713 LogLoss: 0.017715874610611436 Mean Per-Class Error: 0.0 AUC: 1.0 AUCPR: 1.0 Gini: 1.0 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.9979014979275557 0 1 Error Rate ----- --- --- ------- ----------- 0 93 0 0 (0.0/93.0) 1 0 10 0 (0.0/10.0) Total 93 10 0 (0.0/103.0) Maximum Metrics: Maximum metrics at their respective thresholds metric threshold value idx --------------------------- ----------- ------- ----- max f1 0.997901 1 9 max f2 0.997901 1 9 max f0point5 0.997901 1 9 max accuracy 0.997901 1 9 max precision 0.999961 1 0 max recall 0.997901 1 9 max specificity 0.999961 1 0 max absolute_mcc 0.997901 1 9 max min_per_class_accuracy 0.997901 1 9 max mean_per_class_accuracy 0.997901 1 9 max tns 0.999961 93 0 max fns 0.999961 9 0 max fps 3.49717e-09 93 102 max tps 0.997901 10 9 max tnr 0.999961 1 0 max fnr 0.999961 0.9 0 max fpr 3.49717e-09 1 102 max tpr 0.997901 1 9 Gains/Lift Table: Avg response rate: 9.71 %, avg score: 11.26 % group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain kolmogorov_smirnov ------- -------------------------- ----------------- ------ ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------ ----------------- -------------------- 1 0.0194175 0.999958 10.3 10.3 1 0.999959 1 0.999959 0.2 0.2 930 930 0.2 2 0.0291262 0.999947 10.3 10.3 1 0.999947 1 0.999955 0.1 0.3 930 930 0.3 3 0.038835 0.999937 10.3 10.3 1 0.999938 1 0.999951 0.1 0.4 930 930 0.4 4 0.0485437 0.999927 10.3 10.3 1 0.99993 1 0.999947 0.1 0.5 930 930 0.5 5 0.0582524 0.999888 10.3 10.3 1 0.999889 1 0.999937 0.1 0.6 930 930 0.6 6 0.106796 0.372647 8.24 9.36364 0.8 0.879862 0.909091 0.945358 0.4 1 724 836.364 0.989247 7 0.15534 0.10825 0 6.4375 0 0.168191 0.625 0.702493 0 1 -100 543.75 0.935484 8 0.203883 0.00316095 0 4.90476 0 0.0682301 0.47619 0.551478 0 1 -100 390.476 0.88172 9 0.300971 0.000343083 0 3.32258 0 0.00100323 0.322581 0.373906 0 1 -100 232.258 0.774194 10 0.398058 0.000119786 0 2.5122 0 0.000202804 0.243902 0.282759 0 1 -100 151.22 0.666667 11 0.504854 8.52118e-05 0 1.98077 0 0.000102754 0.192308 0.222966 0 1 -100 98.0769 0.548387 12 0.601942 6.24023e-05 0 1.66129 0 7.12997e-05 0.16129 0.187015 0 1 -100 66.129 0.44086 13 0.699029 4.02512e-05 0 1.43056 0 5.12346e-05 0.138889 0.161048 0 1 -100 43.0556 0.333333 14 0.796117 2.31994e-05 0 1.2561 0 3.1514e-05 0.121951 0.141412 0 1 -100 25.6098 0.225806 15 0.893204 1.36306e-05 0 1.11957 0 1.91168e-05 0.108696 0.126043 0 1 -100 11.9565 0.11828 16 1 3.49717e-09 0 1 0 5.41845e-06 0.0970874 0.112583 0 1 -100 0 0 deeplearning prediction progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% In [17]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = h2o . as_list ( predictions . drop ([ \"predict\" ])) . values micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = h2o.as_list(predictions.drop([\"predict\"])).values micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" In [18]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0 In [19]: Copied! fig , ax = plt . subplots ( figsize = ( 6 , 6 )) n_classes = 2 for class_id in range ( n_classes ): RocCurveDisplay . from_predictions ( y_onehot_test [:, class_id ], y_score [:, class_id ], name = f \"ROC curve for class { class_id } \" , ax = ax , # plot_chance_level=(class_id == 2), ) fig, ax = plt.subplots(figsize=(6, 6)) n_classes = 2 for class_id in range(n_classes): RocCurveDisplay.from_predictions( y_onehot_test[:, class_id], y_score[:, class_id], name=f\"ROC curve for class {class_id}\", ax=ax, # plot_chance_level=(class_id == 2), ) In [20]: Copied! print ( \"Classification report: \\n {} \" . format ( classification_report ( df_test [ target_col ], h2o . as_list ( predictions [ \"predict\" ])) ) ) print( \"Classification report:\\n{}\".format( classification_report(df_test[target_col], h2o.as_list(predictions[\"predict\"])) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" LightGBM \u00b6 In [21]: Copied! config = {} config [ \"objective\" ] = \"binary\" df_train_scaled_enc = df_train_scaled . copy () df_valid_scaled_enc = df_valid_scaled . copy () df_test_scaled_enc = df_test_scaled . copy () lgb_cat_cols = cat_cols label_encoder = LabelEncoder ( lgb_cat_cols ) df_train_scaled_enc = label_encoder . fit_transform ( df_train_scaled_enc ) df_valid_scaled_enc = label_encoder . transform ( df_valid_scaled_enc ) df_test_scaled_enc = label_encoder . transform ( df_test_scaled_enc ) lgbtrain = lgbm . Dataset ( df_train_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_train_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbvalid = lgbm . Dataset ( df_valid_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_valid_scaled_enc [ target_col ], reference = lgbtrain , free_raw_data = False , ) # Final TRAIN/TEST ftrain = pd . concat ([ df_train_scaled_enc , df_valid_scaled_enc ]) . reset_index ( drop = True ) flgbtrain = lgbm . Dataset ( ftrain . drop ( columns = [ target_col ] + id_cols ), ftrain [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbtest = lgbm . Dataset ( df_test_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_test_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , reference = flgbtrain , free_raw_data = False , ) config = {} config[\"objective\"] = \"binary\" df_train_scaled_enc = df_train_scaled.copy() df_valid_scaled_enc = df_valid_scaled.copy() df_test_scaled_enc = df_test_scaled.copy() lgb_cat_cols = cat_cols label_encoder = LabelEncoder(lgb_cat_cols) df_train_scaled_enc = label_encoder.fit_transform(df_train_scaled_enc) df_valid_scaled_enc = label_encoder.transform(df_valid_scaled_enc) df_test_scaled_enc = label_encoder.transform(df_test_scaled_enc) lgbtrain = lgbm.Dataset( df_train_scaled_enc.drop(columns=[target_col] + id_cols), df_train_scaled_enc[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbvalid = lgbm.Dataset( df_valid_scaled_enc.drop(columns=[target_col] + id_cols), df_valid_scaled_enc[target_col], reference=lgbtrain, free_raw_data=False, ) # Final TRAIN/TEST ftrain = pd.concat([df_train_scaled_enc, df_valid_scaled_enc]).reset_index(drop=True) flgbtrain = lgbm.Dataset( ftrain.drop(columns=[target_col] + id_cols), ftrain[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbtest = lgbm.Dataset( df_test_scaled_enc.drop(columns=[target_col] + id_cols), df_test_scaled_enc[target_col], categorical_feature=lgb_cat_cols, reference=flgbtrain, free_raw_data=False, ) In [22]: Copied! model = lgbm . train ( config , lgbtrain , valid_sets = [ lgbvalid ], valid_names = [ \"\" ], feval = None , ) model = lgbm.train( config, lgbtrain, valid_sets=[lgbvalid], valid_names=[\"\"], feval=None, ) [LightGBM] [Info] Number of positive: 81, number of negative: 747 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002491 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 467 [LightGBM] [Info] Number of data points in the train set: 828, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097826 -> initscore=-2.221616 [LightGBM] [Info] Start training from score -2.221616 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf In [23]: Copied! res = predict_cls_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data , raw_score = True ), task = \"binary\" , ) result = pd . DataFrame ( { \"predicted\" : res , \"ground_truth\" : df_test [ target_col ] . values , } ) print ( \"Classification report: \\n {} \" . format ( classification_report ( result [ \"ground_truth\" ], result [ \"predicted\" ]) ) ) res = predict_cls_lgbm_from_raw( preds_raw=model.predict(lgbtest.data, raw_score=True), task=\"binary\", ) result = pd.DataFrame( { \"predicted\": res, \"ground_truth\": df_test[target_col].values, } ) print( \"Classification report:\\n{}\".format( classification_report(result[\"ground_truth\"], result[\"predicted\"]) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [24]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = predict_proba_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data ), task = \"binary\" , binary2d = True , ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = predict_proba_lgbm_from_raw( preds_raw=model.predict(lgbtest.data), task=\"binary\", binary2d=True, ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0","title":"03_models"},{"location":"notebooks/03_models.html#models","text":"","title":"Models"},{"location":"notebooks/03_models.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report , RocCurveDisplay , roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw , predict_proba_lgbm_from_raw , ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, RocCurveDisplay, roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw, predict_proba_lgbm_from_raw, ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start()","title":"Imports"},{"location":"notebooks/03_models.html#dataset","text":"In [3]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True In [4]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [8]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) check possible class imbalance In [9]: Copied! df_pd [ target_col ] . value_counts () df_pd[target_col].value_counts() Out[9]: class 0 934 1 101 Name: count, dtype: int64","title":"Dataset"},{"location":"notebooks/03_models.html#preprocessing","text":"divide dataset into train, test, valid scale continuous columns by standard scaler(not needed for LightGBM but for other mdoels...) In [10]: Copied! df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) df_train . reset_index ( inplace = True , drop = True ) df_valid . reset_index ( inplace = True , drop = True ) df_test . reset_index ( inplace = True , drop = True ) scaler_mapper_def = { \"cont_cols\" : StandardScaler , \"cat_cols\" : None , \"id_cols\" : None , } scaler = scaler_mapper ( cont_cols = cont_cols , cat_cols = cat_cols , id_cols = [ target_col ] + id_cols , scaler_mapper_def = scaler_mapper_def , ) df_train_scaled = scaler . fit_transform ( df_train ) df_test_scaled = scaler . transform ( df_test ) df_valid_scaled = scaler . transform ( df_valid ) df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) df_train.reset_index(inplace=True, drop=True) df_valid.reset_index(inplace=True, drop=True) df_test.reset_index(inplace=True, drop=True) scaler_mapper_def = { \"cont_cols\": StandardScaler, \"cat_cols\": None, \"id_cols\": None, } scaler = scaler_mapper( cont_cols=cont_cols, cat_cols=cat_cols, id_cols=[target_col] + id_cols, scaler_mapper_def=scaler_mapper_def, ) df_train_scaled = scaler.fit_transform(df_train) df_test_scaled = scaler.transform(df_test) df_valid_scaled = scaler.transform(df_valid)","title":"Preprocessing"},{"location":"notebooks/03_models.html#logistic-regression","text":"In [11]: Copied! LR_clf = LogisticRegression ( class_weight = \"balanced\" ) LR_clf . fit ( df_train_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ), df_train_scaled [ target_col ], ) LR_predicted = LR_clf . predict ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) print ( \"LR classification report : \\n \" + str ( classification_report ( df_test_scaled [ target_col ], LR_predicted )) ) LR_clf = LogisticRegression(class_weight=\"balanced\") LR_clf.fit( df_train_scaled.drop(columns=[target_col] + id_cols + cat_cols), df_train_scaled[target_col], ) LR_predicted = LR_clf.predict( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) print( \"LR classification report :\\n\" + str(classification_report(df_test_scaled[target_col], LR_predicted)) ) LR classification report : precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [12]: Copied! # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np . arange ( 0.2 , 0.8 , 0.01 ): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) )[:, 1 ] >= t ) . astype ( int ) threshold_score . append ( ( t , criterion ( df_test_scaled [ target_col ], preds_bin , average = \"weighted\" )) ) threshold_score = sorted ( threshold_score , key = lambda x : x [ 1 ], reverse = True ) best_threshold , best_score = threshold_score [ 0 ][ 0 ], threshold_score [ 0 ][ 1 ] print ( f \"The best threshold \\n { best_threshold } \\n , with score: \\n { best_score } \" ) # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np.arange(0.2, 0.8, 0.01): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) )[:, 1] >= t ).astype(int) threshold_score.append( (t, criterion(df_test_scaled[target_col], preds_bin, average=\"weighted\")) ) threshold_score = sorted(threshold_score, key=lambda x: x[1], reverse=True) best_threshold, best_score = threshold_score[0][0], threshold_score[0][1] print(f\"The best threshold\\n{best_threshold}\\n, with score:\\n{best_score}\") The best threshold 0.2 , with score: 1.0 In [13]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) In [14]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0","title":"Logistic Regression"},{"location":"notebooks/03_models.html#h2o-automl","text":"NOTE : java is required : sudo apt install openjdk-17-jdk In [16]: Copied! # initialize H2O h2o . init ( log_dir = \"h2o_logs\" , log_level = \"WARN\" ) # read as h2o file h2o_train = h2o . H2OFrame ( df_train_scaled . drop ( columns = id_cols )) h2o_valid = h2o . H2OFrame ( df_valid_scaled . drop ( columns = id_cols )) h2o_test = h2o . H2OFrame ( df_test_scaled . drop ( columns = id_cols )) # For binary classification, response should be a factor h2o_train [ target_col ] = h2o_train [ target_col ] . asfactor () h2o_valid [ target_col ] = h2o_valid [ target_col ] . asfactor () h2o_test [ target_col ] = h2o_test [ target_col ] . asfactor () # Define AML task aml = H2OAutoML ( seed = random_state , max_runtime_secs = 180 ) # over/under sample for classification tasks aml . balance_classes = True # Run it _ = aml . train ( x = list ( cont_cols + cat_cols ), y = target_col , training_frame = h2o_train , leaderboard_frame = h2o_valid , ) m = aml . get_best_model () # Leaderboard, show and save lb = h2o . automl . get_leaderboard ( aml , extra_columns = \"ALL\" ) print ( lb ) print ( m . model_performance ( h2o_valid )) predictions = m . predict ( h2o_test ) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") # initialize H2O h2o.init(log_dir=\"h2o_logs\", log_level=\"WARN\") # read as h2o file h2o_train = h2o.H2OFrame(df_train_scaled.drop(columns=id_cols)) h2o_valid = h2o.H2OFrame(df_valid_scaled.drop(columns=id_cols)) h2o_test = h2o.H2OFrame(df_test_scaled.drop(columns=id_cols)) # For binary classification, response should be a factor h2o_train[target_col] = h2o_train[target_col].asfactor() h2o_valid[target_col] = h2o_valid[target_col].asfactor() h2o_test[target_col] = h2o_test[target_col].asfactor() # Define AML task aml = H2OAutoML(seed=random_state, max_runtime_secs=180) # over/under sample for classification tasks aml.balance_classes = True # Run it _ = aml.train( x=list(cont_cols + cat_cols), y=target_col, training_frame=h2o_train, leaderboard_frame=h2o_valid, ) m = aml.get_best_model() # Leaderboard, show and save lb = h2o.automl.get_leaderboard(aml, extra_columns=\"ALL\") print(lb) print(m.model_performance(h2o_valid)) predictions = m.predict(h2o_test) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") Checking whether there is an H2O instance running at http://localhost:54321..... not found. Attempting to start a local H2O server... Java Version: openjdk version \"17.0.10\" 2024-01-16; OpenJDK Runtime Environment (build 17.0.10+7-Ubuntu-122.04.1); OpenJDK 64-Bit Server VM (build 17.0.10+7-Ubuntu-122.04.1, mixed mode, sharing) Starting server from /opt/conda/lib/python3.10/site-packages/h2o/backend/bin/h2o.jar Ice root: /tmp/tmp0jm6weez JVM stdout: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.out JVM stderr: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.err Server is running at http://127.0.0.1:54321 Connecting to H2O server at http://127.0.0.1:54321 ... successful. #h2o-table-1.h2o-container { overflow-x: auto; } #h2o-table-1 .h2o-table { /* width: 100%; */ margin-top: 1em; margin-bottom: 1em; } #h2o-table-1 .h2o-table caption { white-space: nowrap; caption-side: top; text-align: left; /* margin-left: 1em; */ margin: 0; font-size: larger; } #h2o-table-1 .h2o-table thead { white-space: nowrap; position: sticky; top: 0; box-shadow: 0 -1px inset; } #h2o-table-1 .h2o-table tbody { overflow: auto; } #h2o-table-1 .h2o-table th, #h2o-table-1 .h2o-table td { text-align: right; /* border: 1px solid; */ } #h2o-table-1 .h2o-table tr:nth-child(even) { /* background: #F5F5F5 */ } H2O_cluster_uptime: 01 secs H2O_cluster_timezone: Etc/UTC H2O_data_parsing_timezone: UTC H2O_cluster_version: 3.46.0.2 H2O_cluster_version_age: 8 days H2O_cluster_name: H2O_from_python_unknownUser_maoqtf H2O_cluster_total_nodes: 1 H2O_cluster_free_memory: 16 Gb H2O_cluster_total_cores: 12 H2O_cluster_allowed_cores: 12 H2O_cluster_status: locked, healthy H2O_connection_url: http://127.0.0.1:54321 H2O_connection_proxy: {\"http\": null, \"https\": null} H2O_internal_security: False Python_version: 3.10.11 final Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% AutoML progress: | 13:16:41.801: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:44.939: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:46.154: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:16:59.224: _train param, Dropping unused columns: [barometric_pressure] 13:16:59.913: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:03.240: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:05.128: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:16.407: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588 13:17:27.855: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:17:40.196: _train param, Dropping unused columns: [barometric_pressure] 13:17:40.764: _train param, Dropping unused columns: [barometric_pressure] 13:17:41.338: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:44.575: _train param, Dropping bad and constant columns: [barometric_pressure] 13:17:45.413: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:53.937: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:55.712: _train param, Dropping unused columns: [barometric_pressure] 13:17:56.367: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 13:19:35.448: _train param, Dropping unused columns: [barometric_pressure] \u2588 13:19:36.22: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588| (done) 100% model_id auc logloss aucpr mean_per_class_error rmse mse training_time_ms predict_time_per_row_ms algo DeepLearning_grid_1_AutoML_1_20240522_131641_model_8 1 0.0177159 1 0 0.0582198 0.00338955 628 0.127786 DeepLearning DeepLearning_grid_1_AutoML_1_20240522_131641_model_4 1 0.0155053 1 0 0.0481989 0.00232314 117 0.1101 DeepLearning XGBoost_1_AutoML_1_20240522_131641 1 0.0347672 1 0 0.0789026 0.00622562 464 0.078661 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_12 1 0.0651884 1 0 0.106454 0.0113325 328 0.072441 XGBoost DeepLearning_grid_1_AutoML_1_20240522_131641_model_1 1 0.0235103 1 0 0.0600679 0.00360815 99 0.098096 DeepLearning XGBoost_grid_1_AutoML_1_20240522_131641_model_11 1 0.00556141 1 0 0.0320985 0.00103031 996 0.105708 XGBoost GBM_4_AutoML_1_20240522_131641 1 9.26741e-06 1 0 9.39779e-05 8.83184e-09 4293 0.280377 GBM XGBoost_grid_1_AutoML_1_20240522_131641_model_6 1 0.0141646 1 0 0.0244465 0.000597631 630 0.057813 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_13 1 0.0318374 1 0 0.0810283 0.00656559 745 0.069021 XGBoost GBM_3_AutoML_1_20240522_131641 1 2.55592e-05 1 0 0.000259039 6.7101e-08 4037 0.153118 GBM [47 rows x 10 columns] ModelMetricsBinomial: deeplearning ** Reported on test data. ** MSE: 0.0033895466349161047 RMSE: 0.05821981307867713 LogLoss: 0.017715874610611436 Mean Per-Class Error: 0.0 AUC: 1.0 AUCPR: 1.0 Gini: 1.0 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.9979014979275557 0 1 Error Rate ----- --- --- ------- ----------- 0 93 0 0 (0.0/93.0) 1 0 10 0 (0.0/10.0) Total 93 10 0 (0.0/103.0) Maximum Metrics: Maximum metrics at their respective thresholds metric threshold value idx --------------------------- ----------- ------- ----- max f1 0.997901 1 9 max f2 0.997901 1 9 max f0point5 0.997901 1 9 max accuracy 0.997901 1 9 max precision 0.999961 1 0 max recall 0.997901 1 9 max specificity 0.999961 1 0 max absolute_mcc 0.997901 1 9 max min_per_class_accuracy 0.997901 1 9 max mean_per_class_accuracy 0.997901 1 9 max tns 0.999961 93 0 max fns 0.999961 9 0 max fps 3.49717e-09 93 102 max tps 0.997901 10 9 max tnr 0.999961 1 0 max fnr 0.999961 0.9 0 max fpr 3.49717e-09 1 102 max tpr 0.997901 1 9 Gains/Lift Table: Avg response rate: 9.71 %, avg score: 11.26 % group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain kolmogorov_smirnov ------- -------------------------- ----------------- ------ ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------ ----------------- -------------------- 1 0.0194175 0.999958 10.3 10.3 1 0.999959 1 0.999959 0.2 0.2 930 930 0.2 2 0.0291262 0.999947 10.3 10.3 1 0.999947 1 0.999955 0.1 0.3 930 930 0.3 3 0.038835 0.999937 10.3 10.3 1 0.999938 1 0.999951 0.1 0.4 930 930 0.4 4 0.0485437 0.999927 10.3 10.3 1 0.99993 1 0.999947 0.1 0.5 930 930 0.5 5 0.0582524 0.999888 10.3 10.3 1 0.999889 1 0.999937 0.1 0.6 930 930 0.6 6 0.106796 0.372647 8.24 9.36364 0.8 0.879862 0.909091 0.945358 0.4 1 724 836.364 0.989247 7 0.15534 0.10825 0 6.4375 0 0.168191 0.625 0.702493 0 1 -100 543.75 0.935484 8 0.203883 0.00316095 0 4.90476 0 0.0682301 0.47619 0.551478 0 1 -100 390.476 0.88172 9 0.300971 0.000343083 0 3.32258 0 0.00100323 0.322581 0.373906 0 1 -100 232.258 0.774194 10 0.398058 0.000119786 0 2.5122 0 0.000202804 0.243902 0.282759 0 1 -100 151.22 0.666667 11 0.504854 8.52118e-05 0 1.98077 0 0.000102754 0.192308 0.222966 0 1 -100 98.0769 0.548387 12 0.601942 6.24023e-05 0 1.66129 0 7.12997e-05 0.16129 0.187015 0 1 -100 66.129 0.44086 13 0.699029 4.02512e-05 0 1.43056 0 5.12346e-05 0.138889 0.161048 0 1 -100 43.0556 0.333333 14 0.796117 2.31994e-05 0 1.2561 0 3.1514e-05 0.121951 0.141412 0 1 -100 25.6098 0.225806 15 0.893204 1.36306e-05 0 1.11957 0 1.91168e-05 0.108696 0.126043 0 1 -100 11.9565 0.11828 16 1 3.49717e-09 0 1 0 5.41845e-06 0.0970874 0.112583 0 1 -100 0 0 deeplearning prediction progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% In [17]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = h2o . as_list ( predictions . drop ([ \"predict\" ])) . values micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = h2o.as_list(predictions.drop([\"predict\"])).values micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" In [18]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0 In [19]: Copied! fig , ax = plt . subplots ( figsize = ( 6 , 6 )) n_classes = 2 for class_id in range ( n_classes ): RocCurveDisplay . from_predictions ( y_onehot_test [:, class_id ], y_score [:, class_id ], name = f \"ROC curve for class { class_id } \" , ax = ax , # plot_chance_level=(class_id == 2), ) fig, ax = plt.subplots(figsize=(6, 6)) n_classes = 2 for class_id in range(n_classes): RocCurveDisplay.from_predictions( y_onehot_test[:, class_id], y_score[:, class_id], name=f\"ROC curve for class {class_id}\", ax=ax, # plot_chance_level=(class_id == 2), ) In [20]: Copied! print ( \"Classification report: \\n {} \" . format ( classification_report ( df_test [ target_col ], h2o . as_list ( predictions [ \"predict\" ])) ) ) print( \"Classification report:\\n{}\".format( classification_report(df_test[target_col], h2o.as_list(predictions[\"predict\"])) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\"","title":"H2O AutoML"},{"location":"notebooks/03_models.html#lightgbm","text":"In [21]: Copied! config = {} config [ \"objective\" ] = \"binary\" df_train_scaled_enc = df_train_scaled . copy () df_valid_scaled_enc = df_valid_scaled . copy () df_test_scaled_enc = df_test_scaled . copy () lgb_cat_cols = cat_cols label_encoder = LabelEncoder ( lgb_cat_cols ) df_train_scaled_enc = label_encoder . fit_transform ( df_train_scaled_enc ) df_valid_scaled_enc = label_encoder . transform ( df_valid_scaled_enc ) df_test_scaled_enc = label_encoder . transform ( df_test_scaled_enc ) lgbtrain = lgbm . Dataset ( df_train_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_train_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbvalid = lgbm . Dataset ( df_valid_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_valid_scaled_enc [ target_col ], reference = lgbtrain , free_raw_data = False , ) # Final TRAIN/TEST ftrain = pd . concat ([ df_train_scaled_enc , df_valid_scaled_enc ]) . reset_index ( drop = True ) flgbtrain = lgbm . Dataset ( ftrain . drop ( columns = [ target_col ] + id_cols ), ftrain [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbtest = lgbm . Dataset ( df_test_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_test_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , reference = flgbtrain , free_raw_data = False , ) config = {} config[\"objective\"] = \"binary\" df_train_scaled_enc = df_train_scaled.copy() df_valid_scaled_enc = df_valid_scaled.copy() df_test_scaled_enc = df_test_scaled.copy() lgb_cat_cols = cat_cols label_encoder = LabelEncoder(lgb_cat_cols) df_train_scaled_enc = label_encoder.fit_transform(df_train_scaled_enc) df_valid_scaled_enc = label_encoder.transform(df_valid_scaled_enc) df_test_scaled_enc = label_encoder.transform(df_test_scaled_enc) lgbtrain = lgbm.Dataset( df_train_scaled_enc.drop(columns=[target_col] + id_cols), df_train_scaled_enc[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbvalid = lgbm.Dataset( df_valid_scaled_enc.drop(columns=[target_col] + id_cols), df_valid_scaled_enc[target_col], reference=lgbtrain, free_raw_data=False, ) # Final TRAIN/TEST ftrain = pd.concat([df_train_scaled_enc, df_valid_scaled_enc]).reset_index(drop=True) flgbtrain = lgbm.Dataset( ftrain.drop(columns=[target_col] + id_cols), ftrain[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbtest = lgbm.Dataset( df_test_scaled_enc.drop(columns=[target_col] + id_cols), df_test_scaled_enc[target_col], categorical_feature=lgb_cat_cols, reference=flgbtrain, free_raw_data=False, ) In [22]: Copied! model = lgbm . train ( config , lgbtrain , valid_sets = [ lgbvalid ], valid_names = [ \"\" ], feval = None , ) model = lgbm.train( config, lgbtrain, valid_sets=[lgbvalid], valid_names=[\"\"], feval=None, ) [LightGBM] [Info] Number of positive: 81, number of negative: 747 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002491 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 467 [LightGBM] [Info] Number of data points in the train set: 828, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097826 -> initscore=-2.221616 [LightGBM] [Info] Start training from score -2.221616 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best 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positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf In [23]: Copied! res = predict_cls_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data , raw_score = True ), task = \"binary\" , ) result = pd . DataFrame ( { \"predicted\" : res , \"ground_truth\" : df_test [ target_col ] . values , } ) print ( \"Classification report: \\n {} \" . format ( classification_report ( result [ \"ground_truth\" ], result [ \"predicted\" ]) ) ) res = predict_cls_lgbm_from_raw( preds_raw=model.predict(lgbtest.data, raw_score=True), task=\"binary\", ) result = pd.DataFrame( { \"predicted\": res, \"ground_truth\": df_test[target_col].values, } ) print( \"Classification report:\\n{}\".format( classification_report(result[\"ground_truth\"], result[\"predicted\"]) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [24]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = predict_proba_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data ), task = \"binary\" , binary2d = True , ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = predict_proba_lgbm_from_raw( preds_raw=model.predict(lgbtest.data), task=\"binary\", binary2d=True, ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0","title":"LightGBM"},{"location":"notebooks/04_model_training_pipeline.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Example usage notebook \u00b6 Objects and functions in this notebook are listed with all paramaters to ilustrate their capabillities. Most of the paramaters have default values in the implementation Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd import mlflow pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd import mlflow pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" In [2]: Copied! import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump , dill_load from inference_model.training.utils import flatten_dict , get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump, dill_load from inference_model.training.utils import flatten_dict, get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast Train a testing model \u00b6 In [3]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [4]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) In [6]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) In [7]: Copied! prepare_data = PreprocessData ( id_cols = id_cols , target_col = target_col , cat_cols = cat_cols , cont_cols = cont_cols , ) # this should be fitted only on training data _ = prepare_data . fit ( df = df_pd ) prepare_data = PreprocessData( id_cols=id_cols, target_col=target_col, cat_cols=cat_cols, cont_cols=cont_cols, ) # this should be fitted only on training data _ = prepare_data.fit(df=df_pd) In [8]: Copied! optimizer = LGBOptunaOptimizer ( objective = \"binary\" , n_class = 2 , ) trainer = Trainer ( cat_cols = prepare_data . cat_cols , target_col = prepare_data . target_col , id_cols = id_cols , objective = \"binary\" , n_class = 2 , optimizer = optimizer , preprocessors = [ prepare_data ], ) metrics_dict = trainer . fit ( df_train = df_train , df_valid = df_valid , df_test = df_test , ) optimizer = LGBOptunaOptimizer( objective=\"binary\", n_class=2, ) trainer = Trainer( cat_cols=prepare_data.cat_cols, target_col=prepare_data.target_col, id_cols=id_cols, objective=\"binary\", n_class=2, optimizer=optimizer, preprocessors=[prepare_data], ) metrics_dict = trainer.fit( df_train=df_train, df_valid=df_valid, df_test=df_test, ) [I 2024-05-22 13:01:18,589] A new study created in memory with name: LightGBMTuner feature_fraction, val_score: inf: 0%| | 0/7 [00:00 In [27]: Copied! metrics_dict_flattened = flatten_dict ( metrics_dict ) # mlflow metrics can be only int, float not list del metrics_dict_flattened [ \"cm\" ] del metrics_dict_flattened [ \"prec_rec_curve\" ] metrics_dict_flattened = flatten_dict(metrics_dict) # mlflow metrics can be only int, float not list del metrics_dict_flattened[\"cm\"] del metrics_dict_flattened[\"prec_rec_curve\"] In [29]: Copied! run_name = \"test\" with mlflow . start_run ( experiment_id = experiment_id , run_name = run_name , nested = True ) as run : mlflow . log_params ( trainer . optimizer . best ) mlflow . log_metrics ( metrics_dict_flattened ) # Log tags mlflow . set_tags ( tags = { \"project\" : \"SUCCESS6G\" , \"optimizer_engine\" : \"optuna\" , \"model_family\" : \"ligtgbm\" , \"feature_set_version\" : 1 , } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow . pyfunc . log_model ( python_model = trainer , artifact_path = artifact_path , registered_model_name = registered_model_name , ) model_uri = mlflow . get_artifact_uri ( artifact_path ) print ( f \"Run ID: \\n { run . info . run_id } \\n Model uri: \\n { model_uri } \" ) run_name = \"test\" with mlflow.start_run( experiment_id=experiment_id, run_name=run_name, nested=True ) as run: mlflow.log_params(trainer.optimizer.best) mlflow.log_metrics(metrics_dict_flattened) # Log tags mlflow.set_tags( tags={ \"project\": \"SUCCESS6G\", \"optimizer_engine\": \"optuna\", \"model_family\": \"ligtgbm\", \"feature_set_version\": 1, } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow.pyfunc.log_model( python_model=trainer, artifact_path=artifact_path, registered_model_name=registered_model_name, ) model_uri = mlflow.get_artifact_uri(artifact_path) print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: typing.Union[pandas.core.frame.DataFrame, dict], skipping schema inference 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: , skipping schema inference Successfully registered model 'success6g_model'. 2024/05/22 14:44:22 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: success6g_model, version 1 Run ID: 24fedb96c5004767800fa32a3220c8dd Model uri: s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model Created version '1' of model 'success6g_model'. Testing of predictions \u00b6 Example data \u00b6 In [30]: Copied! df_pd . iloc [: 2 ] df_pd.iloc[:2] Out[30]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc In [31]: Copied! # for testing using mlflow inference service request_dict = { \"engine_load\" : [ 100.0 , 100.0 ], \"engine_coolant_temp\" : [ 17.0 , 17.0 ], \"engine_speed\" : [ 904.5 , 906.0 ], \"vehicle_speed\" : [ 0.0 , 0.0 ], \"intake_air_temp\" : [ 10.0 , 11.0 ], \"maf\" : [ 12.55 , 12.36 ], \"throttle_position\" : [ 83.14 , 83.14 ], \"fuel_rg_pressure\" : [ 37270.0 , 37800.0 ], \"barometric_pressure\" : [ 101.0 , 101.0 ], \"control_voltage\" : [ 0.06 , 14.56 ], \"class\" : [ 1 , 1 ], \"car_id\" : [ \"123abc\" , \"123abc\" ], } # for testing using mlflow inference service request_dict = { \"engine_load\": [100.0, 100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"class\": [1, 1], \"car_id\": [\"123abc\", \"123abc\"], } Trainer \u00b6 In [32]: Copied! trainer . predict ( df = df_pd . iloc [: 2 ] . drop ( columns = [ \"class\" ]), context = {}) trainer.predict(df=df_pd.iloc[:2].drop(columns=[\"class\"]), context={}) Out[32]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 0 14.269467 1 14.269467 Downloaded Trainer \u00b6 In [33]: Copied! model_uri model_uri Out[33]: 's3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model' In [34]: Copied! loaded_trainer = mlflow . pyfunc . load_model ( model_uri ) loaded_trainer = mlflow.pyfunc.load_model(model_uri) Downloading artifacts: 0%| | 0/9 [00:00=0.3.7 (from virtualenv) #10 1.197 Downloading distlib-0.3.8-py2.py3-none-any.whl.metadata (5.1 kB) #10 1.276 Collecting filelock < 4,>=3.12.2 (from virtualenv) #10 1.291 Downloading filelock-3.14.0-py3-none-any.whl.metadata (2.8 kB) #10 1.368 Collecting platformdirs < 5,>=3.9.1 (from virtualenv) #10 1.383 Downloading platformdirs-4.2.2-py3-none-any.whl.metadata (11 kB) #10 1.439 Downloading virtualenv-20.26.2-py3-none-any.whl (3.9 MB) #10 1.656 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.9/3.9 MB 18.3 MB/s eta 0:00:00 #10 1.673 Downloading distlib-0.3.8-py2.py3-none-any.whl (468 kB) #10 1.706 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 468.9/468.9 kB 17.0 MB/s eta 0:00:00 #10 1.724 Downloading filelock-3.14.0-py3-none-any.whl (12 kB) #10 1.752 Downloading platformdirs-4.2.2-py3-none-any.whl (18 kB) #10 1.871 Installing collected packages: distlib, platformdirs, filelock, virtualenv #10 2.123 Successfully installed distlib-0.3.8 filelock-3.14.0 platformdirs-4.2.2 virtualenv-20.26.2 #10 2.123 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #10 DONE 2.3s #11 [ 7/17] RUN apt-get install -y --no-install-recommends openjdk-11-jdk maven #11 0.605 Reading package lists... #11 1.699 Building dependency tree... #11 1.870 Reading state information... #11 2.070 The following additional packages will be installed: #11 2.070 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.070 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.070 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.070 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.070 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.070 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.070 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.070 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.070 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libplexus-utils2-java-doc junit4 testng libcommons-logging-java #11 2.072 liblog4j1.2-java openjdk-11-demo openjdk-11-source visualvm libnss-mdns #11 2.072 fonts-dejavu-extra fonts-ipafont-gothic fonts-ipafont-mincho #11 2.072 fonts-wqy-microhei | fonts-wqy-zenhei fonts-indic #11 2.072 Recommended packages: #11 2.072 alsa-ucm-conf alsa-topology-conf dbus libatk-wrapper-java-jni #11 2.072 fonts-dejavu-extra #11 2.267 The following NEW packages will be installed: #11 2.268 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.268 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.268 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.268 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.268 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.268 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.268 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.268 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.268 libmaven-resolver-java libmaven-shared-utils-java libmaven3-core-java #11 2.268 libpcsclite1 libplexus-cipher-java libplexus-classworlds-java #11 2.268 libplexus-component-annotations-java libplexus-interpolation-java #11 2.268 libplexus-sec-dispatcher-java libplexus-utils2-java libsisu-inject-java #11 2.268 libsisu-plexus-java libslf4j-java libwagon-file-java #11 2.269 libwagon-http-shaded-java libwagon-provider-api-java maven openjdk-11-jdk #11 2.269 openjdk-11-jdk-headless openjdk-11-jre openjdk-11-jre-headless #11 2.386 0 upgraded, 51 newly installed, 0 to remove and 0 not upgraded. #11 2.386 Need to get 125 MB of archives. #11 2.386 After this operation, 277 MB of additional disk space will be used. #11 2.386 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdbus-1-3 amd64 1.12.16-2ubuntu2.3 [179 kB] #11 2.602 Get:2 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previously unselected package libasound2-data. #11 26.91 Preparing to unpack .../07-libasound2-data_1.2.2-2.1ubuntu2.5_all.deb ... #11 26.91 Unpacking libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 27.01 Selecting previously unselected package libasound2:amd64. #11 27.01 Preparing to unpack .../08-libasound2_1.2.2-2.1ubuntu2.5_amd64.deb ... #11 27.02 Unpacking libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 27.14 Selecting previously unselected package libgraphite2-3:amd64. #11 27.14 Preparing to unpack .../09-libgraphite2-3_1.3.13-11build1_amd64.deb ... #11 27.15 Unpacking libgraphite2-3:amd64 (1.3.13-11build1) ... #11 27.24 Selecting previously unselected package libharfbuzz0b:amd64. #11 27.24 Preparing to unpack .../10-libharfbuzz0b_2.6.4-1ubuntu4.2_amd64.deb ... #11 27.25 Unpacking libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 27.37 Selecting previously unselected package libpcsclite1:amd64. #11 27.38 Preparing to unpack .../11-libpcsclite1_1.8.26-3_amd64.deb ... #11 27.38 Unpacking libpcsclite1:amd64 (1.8.26-3) ... #11 27.47 Selecting previously unselected package openjdk-11-jre-headless:amd64. #11 27.47 Preparing to unpack .../12-openjdk-11-jre-headless_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 27.48 Unpacking openjdk-11-jre-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 31.00 Selecting previously unselected package ca-certificates-java. #11 31.00 Preparing to unpack .../13-ca-certificates-java_20190405ubuntu1.1_all.deb ... #11 31.02 Unpacking ca-certificates-java (20190405ubuntu1.1) ... #11 31.08 Selecting previously unselected package libapache-pom-java. #11 31.09 Preparing to unpack .../14-libapache-pom-java_18-1_all.deb ... #11 31.10 Unpacking libapache-pom-java (18-1) ... #11 31.16 Selecting previously unselected package libatinject-jsr330-api-java. #11 31.16 Preparing to unpack .../15-libatinject-jsr330-api-java_1.0+ds1-5_all.deb ... #11 31.17 Unpacking libatinject-jsr330-api-java (1.0+ds1-5) ... #11 31.23 Selecting previously unselected package libgeronimo-interceptor-3.0-spec-java. #11 31.24 Preparing to unpack .../16-libgeronimo-interceptor-3.0-spec-java_1.0.1-4fakesync_all.deb ... #11 31.25 Unpacking libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 31.31 Selecting previously unselected package libcdi-api-java. #11 31.31 Preparing to unpack .../17-libcdi-api-java_1.2-2_all.deb ... #11 31.32 Unpacking libcdi-api-java (1.2-2) ... #11 31.39 Selecting previously unselected package libcommons-cli-java. #11 31.39 Preparing to unpack .../18-libcommons-cli-java_1.4-1_all.deb ... #11 31.40 Unpacking libcommons-cli-java (1.4-1) ... #11 31.47 Selecting previously unselected package libcommons-parent-java. #11 31.48 Preparing to unpack .../19-libcommons-parent-java_43-1_all.deb ... #11 31.49 Unpacking libcommons-parent-java (43-1) ... #11 31.55 Selecting previously unselected package libcommons-io-java. #11 31.56 Preparing to unpack .../20-libcommons-io-java_2.6-2ubuntu0.20.04.1_all.deb ... #11 31.56 Unpacking libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 31.66 Selecting previously unselected package libcommons-lang3-java. #11 31.66 Preparing to unpack .../21-libcommons-lang3-java_3.8-2_all.deb ... #11 31.67 Unpacking libcommons-lang3-java (3.8-2) ... #11 31.78 Selecting previously unselected package libgeronimo-annotation-1.3-spec-java. #11 31.78 Preparing to unpack .../22-libgeronimo-annotation-1.3-spec-java_1.0-1_all.deb ... #11 31.79 Unpacking libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 31.86 Selecting previously unselected package libgif7:amd64. #11 31.87 Preparing to unpack .../23-libgif7_5.1.9-1_amd64.deb ... #11 31.88 Unpacking libgif7:amd64 (5.1.9-1) ... #11 31.95 Selecting previously unselected package libjsr305-java. #11 31.95 Preparing to unpack .../24-libjsr305-java_0.1~+svn49-11_all.deb ... #11 31.96 Unpacking libjsr305-java (0.1~+svn49-11) ... #11 32.03 Selecting previously unselected package libguava-java. #11 32.03 Preparing to unpack .../25-libguava-java_19.0-1_all.deb ... #11 32.04 Unpacking libguava-java (19.0-1) ... #11 32.29 Selecting previously unselected package libaopalliance-java. #11 32.29 Preparing to unpack .../26-libaopalliance-java_20070526-6_all.deb ... #11 32.30 Unpacking libaopalliance-java (20070526-6) ... #11 32.36 Selecting previously unselected package libguice-java. #11 32.37 Preparing to unpack .../27-libguice-java_4.2.1-1ubuntu0.2_all.deb ... #11 32.37 Unpacking libguice-java (4.2.1-1ubuntu0.2) ... #11 32.59 Selecting previously unselected package libhawtjni-runtime-java. #11 32.60 Preparing to unpack .../28-libhawtjni-runtime-java_1.17-1_all.deb ... #11 32.60 Unpacking libhawtjni-runtime-java (1.17-1) ... #11 32.67 Selecting previously unselected package libjansi-native-java. #11 32.68 Preparing to unpack .../29-libjansi-native-java_1.8-1_all.deb ... #11 32.68 Unpacking libjansi-native-java (1.8-1) ... #11 32.75 Selecting previously unselected package libjansi-java. #11 32.75 Preparing to unpack .../30-libjansi-java_1.18-1_all.deb ... #11 32.76 Unpacking libjansi-java (1.18-1) ... #11 32.83 Selecting previously unselected package libmaven-parent-java. #11 32.84 Preparing to unpack .../31-libmaven-parent-java_31-2_all.deb ... #11 32.85 Unpacking libmaven-parent-java (31-2) ... #11 32.91 Selecting previously unselected package libplexus-utils2-java. #11 32.91 Preparing to unpack .../32-libplexus-utils2-java_3.3.0-1_all.deb ... #11 32.92 Unpacking libplexus-utils2-java (3.3.0-1) ... #11 33.02 Selecting previously unselected package libwagon-provider-api-java. #11 33.02 Preparing to unpack .../33-libwagon-provider-api-java_3.3.4-1_all.deb ... #11 33.03 Unpacking libwagon-provider-api-java (3.3.4-1) ... #11 33.10 Selecting previously unselected package libmaven-resolver-java. #11 33.11 Preparing to unpack .../34-libmaven-resolver-java_1.4.1-1_all.deb ... #11 33.12 Unpacking libmaven-resolver-java (1.4.1-1) ... #11 33.26 Selecting previously unselected package libmaven-shared-utils-java. #11 33.27 Preparing to unpack .../35-libmaven-shared-utils-java_3.3.0-1ubuntu0.20.04.1_all.deb ... #11 33.27 Unpacking libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 33.36 Selecting previously unselected package libplexus-cipher-java. #11 33.37 Preparing to unpack .../36-libplexus-cipher-java_1.7-3_all.deb ... #11 33.37 Unpacking libplexus-cipher-java (1.7-3) ... #11 33.44 Selecting previously unselected package libplexus-classworlds-java. #11 33.45 Preparing to unpack .../37-libplexus-classworlds-java_2.6.0-1_all.deb ... #11 33.45 Unpacking libplexus-classworlds-java (2.6.0-1) ... #11 33.53 Selecting previously unselected package libplexus-component-annotations-java. #11 33.53 Preparing to unpack .../38-libplexus-component-annotations-java_2.1.0-1_all.deb ... #11 33.54 Unpacking libplexus-component-annotations-java (2.1.0-1) ... #11 33.60 Selecting previously unselected package libplexus-interpolation-java. #11 33.60 Preparing to unpack .../39-libplexus-interpolation-java_1.26-1_all.deb ... #11 33.61 Unpacking libplexus-interpolation-java (1.26-1) ... #11 33.69 Selecting previously unselected package libplexus-sec-dispatcher-java. #11 33.70 Preparing to unpack .../40-libplexus-sec-dispatcher-java_1.4-4_all.deb ... #11 33.70 Unpacking libplexus-sec-dispatcher-java (1.4-4) ... #11 33.77 Selecting previously unselected package libslf4j-java. #11 33.77 Preparing to unpack .../41-libslf4j-java_1.7.25-3_all.deb ... #11 33.78 Unpacking libslf4j-java (1.7.25-3) ... #11 33.89 Selecting previously unselected package libsisu-inject-java. #11 33.89 Preparing to unpack .../42-libsisu-inject-java_0.3.3-1_all.deb ... #11 33.90 Unpacking libsisu-inject-java (0.3.3-1) ... #11 34.01 Selecting previously unselected package libsisu-plexus-java. #11 34.02 Preparing to unpack .../43-libsisu-plexus-java_0.3.3-3_all.deb ... #11 34.02 Unpacking libsisu-plexus-java (0.3.3-3) ... #11 34.12 Selecting previously unselected package libmaven3-core-java. #11 34.12 Preparing to unpack .../44-libmaven3-core-java_3.6.3-1ubuntu0.1_all.deb ... #11 34.13 Unpacking libmaven3-core-java (3.6.3-1ubuntu0.1) ... #11 34.35 Selecting previously unselected package libwagon-file-java. #11 34.36 Preparing to unpack .../45-libwagon-file-java_3.3.4-1_all.deb ... #11 34.36 Unpacking libwagon-file-java (3.3.4-1) ... #11 34.42 Selecting previously unselected package libwagon-http-shaded-java. #11 34.43 Preparing to unpack .../46-libwagon-http-shaded-java_3.3.4-1_all.deb ... #11 34.43 Unpacking libwagon-http-shaded-java (3.3.4-1) ... #11 34.68 Selecting previously unselected package maven. #11 34.68 Preparing to unpack .../47-maven_3.6.3-1ubuntu0.1_all.deb ... #11 34.69 Unpacking maven (3.6.3-1ubuntu0.1) ... #11 34.76 Selecting previously unselected package openjdk-11-jre:amd64. #11 34.76 Preparing to unpack .../48-openjdk-11-jre_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 34.77 Unpacking openjdk-11-jre:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 34.88 Selecting previously unselected package openjdk-11-jdk-headless:amd64. #11 34.89 Preparing to unpack .../49-openjdk-11-jdk-headless_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 34.89 Unpacking openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 39.97 Selecting previously unselected package openjdk-11-jdk:amd64. #11 39.97 Preparing to unpack .../50-openjdk-11-jdk_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 39.98 Unpacking openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 40.14 Setting up libgraphite2-3:amd64 (1.3.13-11build1) ... #11 40.16 Setting up liblcms2-2:amd64 (2.9-4) ... #11 40.18 Setting up libslf4j-java (1.7.25-3) ... #11 40.20 Setting up libplexus-utils2-java (3.3.0-1) ... #11 40.22 Setting up libplexus-classworlds-java (2.6.0-1) ... #11 40.24 Setting up libjsr305-java (0.1~+svn49-11) ... #11 40.26 Setting up java-common (0.72) ... #11 40.30 Setting up libaopalliance-java (20070526-6) ... #11 40.32 Setting up libcommons-cli-java (1.4-1) ... #11 40.34 Setting up libplexus-component-annotations-java (2.1.0-1) ... #11 40.38 Setting up libplexus-cipher-java (1.7-3) ... #11 40.40 Setting up libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 40.42 Setting up libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 40.44 Setting up libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 40.46 Setting up libapache-pom-java (18-1) ... #11 40.48 Setting up libavahi-common-data:amd64 (0.7-4ubuntu7.3) ... #11 40.50 Setting up libatinject-jsr330-api-java (1.0+ds1-5) ... #11 40.53 Setting up libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 40.55 Setting up libplexus-interpolation-java (1.26-1) ... #11 40.57 Setting up libpcsclite1:amd64 (1.8.26-3) ... #11 40.59 Setting up libgif7:amd64 (5.1.9-1) ... #11 40.61 Setting up libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 40.63 Setting up libplexus-sec-dispatcher-java (1.4-4) ... #11 40.65 Setting up libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 40.67 Setting up libwagon-http-shaded-java (3.3.4-1) ... #11 40.69 Setting up libcdi-api-java (1.2-2) ... #11 40.71 Setting up libhawtjni-runtime-java (1.17-1) ... #11 40.73 Setting up libwagon-provider-api-java (3.3.4-1) ... #11 40.75 Setting up libmaven-parent-java (31-2) ... #11 40.77 Setting up libcommons-parent-java (43-1) ... #11 40.79 Setting up libavahi-common3:amd64 (0.7-4ubuntu7.3) ... #11 40.81 Setting up libmaven-resolver-java (1.4.1-1) ... #11 40.84 Setting up libguava-java (19.0-1) ... #11 40.86 Setting up libcommons-lang3-java (3.8-2) ... #11 40.88 Setting up libjansi-native-java (1.8-1) ... #11 40.90 Setting up libwagon-file-java (3.3.4-1) ... #11 40.92 Setting up libavahi-client3:amd64 (0.7-4ubuntu7.3) ... #11 40.94 Setting up libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 40.96 Setting up libguice-java (4.2.1-1ubuntu0.2) ... #11 40.98 Setting up libjansi-java (1.18-1) ... #11 41.00 Setting up libcups2:amd64 (2.3.1-9ubuntu1.6) ... #11 41.02 Setting up libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 41.04 Setting up libsisu-inject-java (0.3.3-1) ... #11 41.06 Setting up libsisu-plexus-java (0.3.3-3) ... #11 41.08 Setting up libmaven3-core-java (3.6.3-1ubuntu0.1) ... #11 41.11 Setting up openjdk-11-jre-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.31 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/java to provide /usr/bin/java (java) in auto mode #11 41.32 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jjs to provide /usr/bin/jjs (jjs) in auto mode #11 41.33 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/keytool to provide /usr/bin/keytool (keytool) in auto mode #11 41.34 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmid to provide /usr/bin/rmid (rmid) in auto mode #11 41.34 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmiregistry to provide /usr/bin/rmiregistry (rmiregistry) in auto mode #11 41.35 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/pack200 to provide /usr/bin/pack200 (pack200) in auto mode #11 41.36 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/unpack200 to provide /usr/bin/unpack200 (unpack200) in auto mode #11 41.37 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/lib/jexec to provide /usr/bin/jexec (jexec) in auto mode #11 41.92 Setting up openjdk-11-jre:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.95 Setting up openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.98 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jar to provide /usr/bin/jar (jar) in auto mode #11 41.99 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jarsigner to provide /usr/bin/jarsigner (jarsigner) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javac to provide /usr/bin/javac (javac) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javadoc to provide /usr/bin/javadoc (javadoc) in auto mode #11 42.01 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javap to provide /usr/bin/javap (javap) in auto mode #11 42.02 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jcmd to provide /usr/bin/jcmd (jcmd) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdb to provide /usr/bin/jdb (jdb) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeprscan to provide /usr/bin/jdeprscan (jdeprscan) in auto mode #11 42.04 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeps to provide /usr/bin/jdeps (jdeps) in auto mode #11 42.05 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jfr to provide /usr/bin/jfr (jfr) in auto mode #11 42.06 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jimage to provide /usr/bin/jimage (jimage) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jinfo to provide /usr/bin/jinfo (jinfo) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jlink to provide /usr/bin/jlink (jlink) in auto mode #11 42.08 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmap to provide /usr/bin/jmap (jmap) in auto mode #11 42.09 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmod to provide /usr/bin/jmod (jmod) in auto mode #11 42.10 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jps to provide /usr/bin/jps (jps) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jrunscript to provide /usr/bin/jrunscript (jrunscript) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jshell to provide /usr/bin/jshell (jshell) in auto mode #11 42.12 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstack to provide /usr/bin/jstack (jstack) in auto mode #11 42.13 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstat to provide /usr/bin/jstat (jstat) in auto mode #11 42.14 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstatd to provide /usr/bin/jstatd (jstatd) in auto mode #11 42.15 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmic to provide /usr/bin/rmic (rmic) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/serialver to provide /usr/bin/serialver (serialver) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jaotc to provide /usr/bin/jaotc (jaotc) in auto mode #11 42.17 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jhsdb to provide /usr/bin/jhsdb (jhsdb) in auto mode #11 42.19 Setting up maven (3.6.3-1ubuntu0.1) ... #11 42.23 update-alternatives: using /usr/share/maven/bin/mvn to provide /usr/bin/mvn (mvn) in auto mode #11 42.25 Setting up openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 42.28 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jconsole to provide /usr/bin/jconsole (jconsole) in auto mode #11 42.29 Setting up ca-certificates-java (20190405ubuntu1.1) ... #11 42.35 head: cannot open '/etc/ssl/certs/java/cacerts' for reading: No such file or directory #11 42.65 Adding debian:COMODO_RSA_Certification_Authority.pem #11 42.66 Adding debian:Hongkong_Post_Root_CA_1.pem #11 42.67 Adding debian:Entrust_Root_Certification_Authority_-_EC1.pem #11 42.67 Adding debian:Certainly_Root_R1.pem #11 42.68 Adding debian:Amazon_Root_CA_2.pem #11 42.68 Adding debian:Certainly_Root_E1.pem #11 42.68 Adding debian:AffirmTrust_Premium_ECC.pem #11 42.68 Adding debian:ANF_Secure_Server_Root_CA.pem #11 42.68 Adding debian:GlobalSign_Root_R46.pem #11 42.69 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068.pem #11 42.69 Adding debian:Certum_Trusted_Root_CA.pem #11 42.69 Adding debian:Go_Daddy_Root_Certificate_Authority_-_G2.pem #11 42.69 Adding debian:SecureSign_RootCA11.pem #11 42.69 Adding debian:Amazon_Root_CA_4.pem #11 42.70 Adding debian:GDCA_TrustAUTH_R5_ROOT.pem #11 42.70 Adding debian:T-TeleSec_GlobalRoot_Class_2.pem #11 42.70 Adding debian:Trustwave_Global_ECC_P256_Certification_Authority.pem #11 42.70 Adding debian:GTS_Root_R1.pem #11 42.71 Adding debian:Izenpe.com.pem #11 42.71 Adding debian:CFCA_EV_ROOT.pem #11 42.71 Adding debian:DigiCert_Global_Root_G2.pem #11 42.71 Adding debian:XRamp_Global_CA_Root.pem #11 42.72 Adding debian:SSL.com_EV_Root_Certification_Authority_RSA_R2.pem #11 42.72 Adding debian:Security_Communication_RootCA3.pem #11 42.72 Adding debian:Starfield_Services_Root_Certificate_Authority_-_G2.pem #11 42.72 Adding debian:Amazon_Root_CA_1.pem #11 42.72 Adding debian:DigiCert_Assured_ID_Root_CA.pem #11 42.72 Adding debian:SSL.com_Root_Certification_Authority_RSA.pem #11 42.73 Adding debian:Telia_Root_CA_v2.pem #11 42.73 Adding debian:SwissSign_Gold_CA_-_G2.pem #11 42.73 Adding debian:GLOBALTRUST_2020.pem #11 42.73 Adding debian:emSign_ECC_Root_CA_-_G3.pem #11 42.73 Adding debian:Microsoft_RSA_Root_Certificate_Authority_2017.pem #11 42.74 Adding debian:TunTrust_Root_CA.pem #11 42.74 Adding debian:Security_Communication_ECC_RootCA1.pem #11 42.74 Adding debian:DigiCert_TLS_ECC_P384_Root_G5.pem #11 42.74 Adding debian:vTrus_Root_CA.pem #11 42.75 Adding debian:Certum_EC-384_CA.pem #11 42.76 Adding debian:CA_Disig_Root_R2.pem #11 42.76 Adding debian:emSign_Root_CA_-_C1.pem #11 42.77 Adding debian:AffirmTrust_Commercial.pem #11 42.77 Adding debian:Entrust_Root_Certification_Authority_-_G4.pem #11 42.78 Adding debian:Starfield_Class_2_CA.pem #11 42.78 Adding debian:Baltimore_CyberTrust_Root.pem #11 42.79 Adding debian:GTS_Root_R4.pem #11 42.79 Adding debian:Starfield_Root_Certificate_Authority_-_G2.pem #11 42.80 Adding debian:QuoVadis_Root_CA_1_G3.pem #11 42.81 Adding debian:DigiCert_Trusted_Root_G4.pem #11 42.81 Adding debian:DigiCert_High_Assurance_EV_Root_CA.pem #11 42.82 Adding debian:AC_RAIZ_FNMT-RCM.pem #11 42.82 Adding debian:Comodo_AAA_Services_root.pem #11 42.82 Adding debian:emSign_ECC_Root_CA_-_C3.pem #11 42.83 Adding debian:Entrust_Root_Certification_Authority.pem #11 42.84 Adding debian:D-TRUST_EV_Root_CA_1_2020.pem #11 42.85 Adding debian:Certigna_Root_CA.pem #11 42.85 Adding debian:TeliaSonera_Root_CA_v1.pem #11 42.86 Adding debian:Certum_Trusted_Network_CA.pem #11 42.86 Adding debian:HiPKI_Root_CA_-_G1.pem #11 42.87 Adding debian:SZAFIR_ROOT_CA2.pem #11 42.87 Adding debian:USERTrust_RSA_Certification_Authority.pem #11 42.88 Adding debian:OISTE_WISeKey_Global_Root_GC_CA.pem #11 42.88 Adding debian:Buypass_Class_2_Root_CA.pem #11 42.89 Adding debian:AC_RAIZ_FNMT-RCM_SERVIDORES_SEGUROS.pem #11 42.89 Adding debian:e-Szigno_Root_CA_2017.pem #11 42.90 Adding debian:Microsoft_ECC_Root_Certificate_Authority_2017.pem #11 42.91 Adding debian:Security_Communication_Root_CA.pem #11 42.91 Adding debian:GlobalSign_Root_CA.pem #11 42.92 Adding debian:E-Tugra_Certification_Authority.pem #11 42.93 Adding debian:Trustwave_Global_Certification_Authority.pem #11 42.93 Adding debian:QuoVadis_Root_CA_2_G3.pem #11 42.93 Adding debian:SSL.com_EV_Root_Certification_Authority_ECC.pem #11 42.94 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068_2.pem #11 42.95 Adding debian:E-Tugra_Global_Root_CA_ECC_v3.pem #11 42.95 Adding debian:Entrust.net_Premium_2048_Secure_Server_CA.pem #11 42.96 Adding debian:AffirmTrust_Premium.pem #11 42.96 Adding debian:Amazon_Root_CA_3.pem #11 42.97 Adding debian:AffirmTrust_Networking.pem #11 42.98 Adding debian:IdenTrust_Public_Sector_Root_CA_1.pem #11 42.98 Adding debian:GlobalSign_ECC_Root_CA_-_R4.pem #11 42.99 Adding debian:QuoVadis_Root_CA_3_G3.pem #11 42.99 Adding debian:certSIGN_ROOT_CA.pem #11 43.00 Adding debian:IdenTrust_Commercial_Root_CA_1.pem #11 43.00 Adding debian:SecureTrust_CA.pem #11 43.01 Adding debian:DigiCert_Global_Root_G3.pem #11 43.01 Adding debian:GlobalSign_Root_E46.pem #11 43.02 Adding debian:SwissSign_Silver_CA_-_G2.pem #11 43.02 Adding debian:vTrus_ECC_Root_CA.pem #11 43.03 Adding debian:GlobalSign_ECC_Root_CA_-_R5.pem #11 43.04 Adding debian:OISTE_WISeKey_Global_Root_GB_CA.pem #11 43.04 Adding debian:DigiCert_Assured_ID_Root_G3.pem #11 43.05 Adding debian:emSign_Root_CA_-_G1.pem #11 43.05 Adding debian:DigiCert_TLS_RSA4096_Root_G5.pem #11 43.06 Adding debian:COMODO_Certification_Authority.pem #11 43.06 Adding debian:DigiCert_Global_Root_CA.pem #11 43.07 Adding debian:QuoVadis_Root_CA_2.pem #11 43.08 Adding debian:TWCA_Global_Root_CA.pem #11 43.09 Adding debian:ACCVRAIZ1.pem #11 43.09 Adding debian:GTS_Root_R2.pem #11 43.10 Adding debian:HARICA_TLS_RSA_Root_CA_2021.pem #11 43.10 Adding debian:Actalis_Authentication_Root_CA.pem #11 43.11 Adding debian:Certum_Trusted_Network_CA_2.pem #11 43.11 Adding debian:Atos_TrustedRoot_2011.pem #11 43.11 Adding debian:T-TeleSec_GlobalRoot_Class_3.pem #11 43.13 Adding debian:NetLock_Arany_=Class_Gold=_F\u0151tan\u00fas\u00edtv\u00e1ny.pem #11 43.13 Adding debian:Certigna.pem #11 43.14 Adding debian:Microsec_e-Szigno_Root_CA_2009.pem #11 43.14 Adding debian:UCA_Extended_Validation_Root.pem #11 43.14 Adding debian:DigiCert_Assured_ID_Root_G2.pem #11 43.15 Adding debian:QuoVadis_Root_CA_3.pem #11 43.15 Adding debian:NAVER_Global_Root_Certification_Authority.pem #11 43.16 Adding debian:GlobalSign_Root_CA_-_R6.pem #11 43.17 Adding debian:TUBITAK_Kamu_SM_SSL_Kok_Sertifikasi_-_Surum_1.pem #11 43.17 Adding debian:Trustwave_Global_ECC_P384_Certification_Authority.pem #11 43.18 Adding debian:Go_Daddy_Class_2_CA.pem #11 43.18 Adding debian:TWCA_Root_Certification_Authority.pem #11 43.19 Adding debian:certSIGN_Root_CA_G2.pem #11 43.19 Adding debian:COMODO_ECC_Certification_Authority.pem #11 43.20 Adding debian:ISRG_Root_X2.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_EV_2009.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_2009.pem #11 43.22 Adding debian:UCA_Global_G2_Root.pem #11 43.22 Adding debian:D-TRUST_BR_Root_CA_1_2020.pem #11 43.23 Adding debian:Secure_Global_CA.pem #11 43.24 Adding debian:Hongkong_Post_Root_CA_3.pem #11 43.24 Adding debian:Buypass_Class_3_Root_CA.pem #11 43.25 Adding debian:Entrust_Root_Certification_Authority_-_G2.pem #11 43.25 Adding debian:SSL.com_Root_Certification_Authority_ECC.pem #11 43.26 Adding debian:Hellenic_Academic_and_Research_Institutions_RootCA_2015.pem #11 43.27 Adding debian:E-Tugra_Global_Root_CA_RSA_v3.pem #11 43.27 Adding debian:USERTrust_ECC_Certification_Authority.pem #11 43.28 Adding debian:ePKI_Root_Certification_Authority.pem #11 43.28 Adding debian:ISRG_Root_X1.pem #11 43.29 Adding debian:HARICA_TLS_ECC_Root_CA_2021.pem #11 43.29 Adding debian:GTS_Root_R3.pem #11 43.30 Adding debian:Hellenic_Academic_and_Research_Institutions_ECC_RootCA_2015.pem #11 43.30 Adding debian:Security_Communication_RootCA2.pem #11 43.31 Adding debian:GlobalSign_Root_CA_-_R3.pem #11 43.35 done. #11 43.36 Processing triggers for mime-support (3.64ubuntu1) ... #11 43.40 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #11 43.45 Processing triggers for ca-certificates (20230311ubuntu0.20.04.1) ... #11 43.47 Updating certificates in /etc/ssl/certs... #11 44.82 0 added, 0 removed; done. #11 44.82 Running hooks in /etc/ca-certificates/update.d... #11 44.83 #11 45.16 done. #11 45.16 done. #11 DONE 45.9s #12 [ 8/17] WORKDIR /opt/mlflow #12 DONE 0.1s #13 [ 9/17] RUN pip install mlflow==2.12.2 #13 1.009 Collecting mlflow==2.12.2 #13 1.075 Downloading mlflow-2.12.2-py3-none-any.whl.metadata (29 kB) #13 1.207 Collecting Flask < 4 (from mlflow==2.12.2) #13 1.222 Downloading flask-3.0.3-py3-none-any.whl.metadata (3.2 kB) #13 1.309 Collecting alembic!=1.10.0, < 2 (from mlflow==2.12.2) #13 1.324 Downloading alembic-1.13.1-py3-none-any.whl.metadata (7.4 kB) #13 1.395 Collecting click < 9,>=7.0 (from mlflow==2.12.2) #13 1.409 Downloading click-8.1.7-py3-none-any.whl.metadata (3.0 kB) #13 1.467 Collecting cloudpickle < 4 (from mlflow==2.12.2) #13 1.483 Downloading cloudpickle-3.0.0-py3-none-any.whl.metadata (7.0 kB) #13 1.564 Collecting docker < 8,>=4.0.0 (from mlflow==2.12.2) #13 1.579 Downloading docker-7.0.0-py3-none-any.whl.metadata (3.5 kB) #13 1.635 Collecting entrypoints < 1 (from mlflow==2.12.2) #13 1.651 Downloading entrypoints-0.4-py3-none-any.whl.metadata (2.6 kB) #13 1.742 Collecting gitpython < 4,>=3.1.9 (from mlflow==2.12.2) #13 1.757 Downloading GitPython-3.1.43-py3-none-any.whl.metadata (13 kB) #13 1.822 Collecting graphene < 4 (from mlflow==2.12.2) #13 1.837 Downloading graphene-3.3-py2.py3-none-any.whl.metadata (7.7 kB) #13 1.946 Collecting importlib-metadata!=4.7.0, < 8,>=3.7.0 (from mlflow==2.12.2) #13 1.960 Downloading importlib_metadata-7.1.0-py3-none-any.whl.metadata (4.7 kB) #13 2.027 Collecting markdown < 4,>=3.3 (from mlflow==2.12.2) #13 2.079 Downloading Markdown-3.6-py3-none-any.whl.metadata (7.0 kB) #13 2.299 Collecting matplotlib < 4 (from mlflow==2.12.2) #13 2.314 Downloading matplotlib-3.7.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (5.7 kB) #13 2.593 Collecting numpy < 2 (from mlflow==2.12.2) #13 2.612 Downloading numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB) #13 2.685 Collecting packaging < 25 (from mlflow==2.12.2) #13 2.698 Downloading packaging-24.0-py3-none-any.whl.metadata (3.2 kB) #13 2.902 Collecting pandas < 3 (from mlflow==2.12.2) #13 2.916 Downloading pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB) #13 3.172 Collecting protobuf < 5,>=3.12.0 (from mlflow==2.12.2) #13 3.187 Downloading protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes) #13 3.323 Collecting pyarrow < 16,>=4.0.0 (from mlflow==2.12.2) #13 3.338 Downloading pyarrow-15.0.2-cp38-cp38-manylinux_2_28_x86_64.whl.metadata (3.0 kB) #13 3.456 Collecting pytz < 2025 (from mlflow==2.12.2) #13 3.471 Downloading pytz-2024.1-py2.py3-none-any.whl.metadata (22 kB) #13 3.482 Requirement already satisfied: pyyaml < 7,>=5.1 in /usr/lib/python3/dist-packages (from mlflow==2.12.2) (5.3.1) #13 3.672 Collecting querystring-parser < 2 (from mlflow==2.12.2) #13 3.689 Downloading querystring_parser-1.2.4-py2.py3-none-any.whl.metadata (559 bytes) #13 3.780 Collecting requests < 3,>=2.17.3 (from mlflow==2.12.2) #13 3.794 Downloading requests-2.31.0-py3-none-any.whl.metadata (4.6 kB) #13 3.969 Collecting scikit-learn < 2 (from mlflow==2.12.2) #13 3.985 Downloading scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) #13 4.159 Collecting scipy < 2 (from mlflow==2.12.2) #13 4.175 Downloading scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (58 kB) #13 4.199 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 58.9/58.9 kB 2.7 MB/s eta 0:00:00 #13 4.633 Collecting sqlalchemy < 3,>=1.4.0 (from mlflow==2.12.2) #13 4.648 Downloading SQLAlchemy-2.0.30-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.6 kB) #13 4.709 Collecting sqlparse < 1,>=0.4.0 (from mlflow==2.12.2) #13 4.724 Downloading sqlparse-0.5.0-py3-none-any.whl.metadata (3.9 kB) #13 4.795 Collecting Jinja2 < 4,>=2.11 (from mlflow==2.12.2) #13 4.809 Downloading jinja2-3.1.4-py3-none-any.whl.metadata (2.6 kB) #13 4.929 Collecting gunicorn < 23 (from mlflow==2.12.2) #13 4.944 Downloading gunicorn-22.0.0-py3-none-any.whl.metadata (4.4 kB) #13 5.043 Collecting Mako (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.058 Downloading Mako-1.3.5-py3-none-any.whl.metadata (2.9 kB) #13 5.122 Collecting typing-extensions>=4 (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.136 Downloading typing_extensions-4.11.0-py3-none-any.whl.metadata (3.0 kB) #13 5.225 Collecting importlib-resources (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.241 Downloading importlib_resources-6.4.0-py3-none-any.whl.metadata (3.9 kB) #13 5.348 Collecting urllib3>=1.26.0 (from docker < 8,>=4.0.0->mlflow==2.12.2) #13 5.362 Downloading urllib3-2.2.1-py3-none-any.whl.metadata (6.4 kB) #13 5.445 Collecting Werkzeug>=3.0.0 (from Flask < 4->mlflow==2.12.2) #13 5.460 Downloading werkzeug-3.0.3-py3-none-any.whl.metadata (3.7 kB) #13 5.519 Collecting itsdangerous>=2.1.2 (from Flask < 4->mlflow==2.12.2) #13 5.537 Downloading itsdangerous-2.2.0-py3-none-any.whl.metadata (1.9 kB) #13 5.606 Collecting blinker>=1.6.2 (from Flask < 4->mlflow==2.12.2) #13 5.623 Downloading blinker-1.8.2-py3-none-any.whl.metadata (1.6 kB) #13 5.716 Collecting gitdb < 5,>=4.0.1 (from gitpython < 4,>=3.1.9->mlflow==2.12.2) #13 5.732 Downloading gitdb-4.0.11-py3-none-any.whl.metadata (1.2 kB) #13 5.847 Collecting graphql-core < 3.3,>=3.1 (from graphene < 4->mlflow==2.12.2) #13 5.862 Downloading graphql_core-3.2.3-py3-none-any.whl.metadata (10 kB) #13 5.920 Collecting graphql-relay < 3.3,>=3.1 (from graphene < 4->mlflow==2.12.2) #13 5.938 Downloading graphql_relay-3.2.0-py3-none-any.whl.metadata (12 kB) #13 6.001 Collecting aniso8601 < 10,>=8 (from graphene < 4->mlflow==2.12.2) #13 6.017 Downloading aniso8601-9.0.1-py2.py3-none-any.whl.metadata (23 kB) #13 6.122 Collecting zipp>=0.5 (from importlib-metadata!=4.7.0, < 8,>=3.7.0->mlflow==2.12.2) #13 6.136 Downloading zipp-3.18.2-py3-none-any.whl.metadata (3.5 kB) #13 6.235 Collecting MarkupSafe>=2.0 (from Jinja2 < 4,>=2.11->mlflow==2.12.2) #13 6.250 Downloading MarkupSafe-2.1.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.0 kB) #13 6.406 Collecting contourpy>=1.0.1 (from matplotlib < 4->mlflow==2.12.2) #13 6.420 Downloading contourpy-1.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.9 kB) #13 6.468 Collecting cycler>=0.10 (from matplotlib < 4->mlflow==2.12.2) #13 6.485 Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB) #13 6.657 Collecting fonttools>=4.22.0 (from matplotlib < 4->mlflow==2.12.2) #13 6.672 Downloading fonttools-4.51.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (159 kB) #13 6.694 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 159.5/159.5 kB 7.7 MB/s eta 0:00:00 #13 6.787 Collecting kiwisolver>=1.0.1 (from matplotlib < 4->mlflow==2.12.2) #13 6.826 Downloading kiwisolver-1.4.5-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (6.4 kB) #13 7.174 Collecting pillow>=6.2.0 (from matplotlib < 4->mlflow==2.12.2) #13 7.191 Downloading 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fonttools, entrypoints, cycler, cloudpickle, click, charset-normalizer, certifi, blinker, Werkzeug, sqlalchemy, scipy, requests, querystring-parser, python-dateutil, pyarrow, Mako, Jinja2, importlib-resources, importlib-metadata, gunicorn, graphql-relay, gitdb, contourpy, scikit-learn, pandas, matplotlib, markdown, graphene, gitpython, Flask, docker, alembic, mlflow #13 34.72 Successfully installed Flask-3.0.3 Jinja2-3.1.4 Mako-1.3.5 MarkupSafe-2.1.5 Werkzeug-3.0.3 alembic-1.13.1 aniso8601-9.0.1 blinker-1.8.2 certifi-2024.2.2 charset-normalizer-3.3.2 click-8.1.7 cloudpickle-3.0.0 contourpy-1.1.1 cycler-0.12.1 docker-7.0.0 entrypoints-0.4 fonttools-4.51.0 gitdb-4.0.11 gitpython-3.1.43 graphene-3.3 graphql-core-3.2.3 graphql-relay-3.2.0 greenlet-3.0.3 gunicorn-22.0.0 idna-3.7 importlib-metadata-7.1.0 importlib-resources-6.4.0 itsdangerous-2.2.0 joblib-1.4.2 kiwisolver-1.4.5 markdown-3.6 matplotlib-3.7.5 mlflow-2.12.2 numpy-1.24.4 packaging-24.0 pandas-2.0.3 pillow-10.3.0 protobuf-4.25.3 pyarrow-15.0.2 pyparsing-3.1.2 python-dateutil-2.9.0.post0 pytz-2024.1 querystring-parser-1.2.4 requests-2.31.0 scikit-learn-1.3.2 scipy-1.10.1 six-1.16.0 smmap-5.0.1 sqlalchemy-2.0.30 sqlparse-0.5.0 threadpoolctl-3.5.0 typing-extensions-4.11.0 tzdata-2024.1 urllib3-2.2.1 zipp-3.18.2 #13 34.72 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #13 DONE 36.4s #14 [10/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:pom -DoutputDirectory=/opt/java #14 1.558 [INFO] Scanning for projects... #14 1.845 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-clean-plugin/2.5/maven-clean-plugin-2.5.pom #14 2.423 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-clean-plugin/2.5/maven-clean-plugin-2.5.pom (3.9 kB at 6.7 kB/s) #14 2.448 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-plugins/22/maven-plugins-22.pom #14 2.499 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-plugins/22/maven-plugins-22.pom (13 kB at 256 kB/s) #14 2.511 [INFO] Downloading from central: 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12.86 [INFO] Finished at: 2024-05-16T20:25:32Z #14 12.86 [INFO] ------------------------------------------------------------------------ #14 DONE 13.0s #15 [11/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:jar -DoutputDirectory=/opt/java/jars #15 1.696 [INFO] Scanning for projects... #15 1.897 [INFO] #15 1.897 [INFO] ------------------ < org.apache.maven:standalone-pom >------------------- #15 1.898 [INFO] Building Maven Stub Project (No POM) 1 #15 1.898 [INFO] --------------------------------[ pom ]--------------------------------- #15 1.905 [INFO] #15 1.905 [INFO] --- maven-dependency-plugin:2.8:copy (default-cli) @ standalone-pom --- #15 2.594 [INFO] Configured Artifact: org.mlflow:mlflow-scoring:2.12.2:jar #15 2.804 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/mlflow/mlflow-scoring/2.12.2/mlflow-scoring-2.12.2.jar #15 3.207 [INFO] Downloaded from central: 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Downloaded from google-maven-central: https://maven-central.storage-download.googleapis.com/repos/central/data/org/apache/httpcomponents/httpclient/4.5.6/httpclient-4.5.6.jar (767 kB at 83 kB/s) #17 133.8 [INFO] Downloaded from google-maven-central: https://maven-central.storage-download.googleapis.com/repos/central/data/commons-io/commons-io/2.5/commons-io-2.5.jar (209 kB at 22 kB/s) #17 133.8 [INFO] Downloaded from google-maven-central: https://maven-central.storage-download.googleapis.com/repos/central/data/commons-codec/commons-codec/1.10/commons-codec-1.10.jar (284 kB at 29 kB/s) #17 133.9 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/com/google/protobuf/protobuf-java/3.19.4/protobuf-java-3.19.4.jar #17 134.0 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/com/google/protobuf/protobuf-java/3.19.4/protobuf-java-3.19.4.jar (1.7 MB at 13 MB/s) #17 134.1 [INFO] #17 134.1 [INFO] --- maven-dependency-plugin:2.8:copy-dependencies (default-cli) @ mlflow-scoring --- #17 134.4 [INFO] Copying wagon-provider-api-1.0-beta-6.jar to /opt/java/jars/wagon-provider-api-1.0-beta-6.jar #17 134.4 [INFO] Copying mleap-tensor_2.11-0.10.3.jar to /opt/java/jars/mleap-tensor_2.11-0.10.3.jar #17 134.4 [INFO] Copying maven-error-diagnostics-2.2.1.jar to /opt/java/jars/maven-error-diagnostics-2.2.1.jar #17 134.4 [INFO] Copying breeze_2.11-0.13.2.jar to /opt/java/jars/breeze_2.11-0.13.2.jar #17 134.5 [INFO] Copying plexus-java-0.9.8.jar to /opt/java/jars/plexus-java-0.9.8.jar #17 134.5 [INFO] Copying scala-library-2.11.8.jar to /opt/java/jars/scala-library-2.11.8.jar #17 134.5 [INFO] Copying maven-settings-2.2.1.jar to /opt/java/jars/maven-settings-2.2.1.jar #17 134.5 [INFO] Copying sourcecode_2.11-0.1.3.jar to /opt/java/jars/sourcecode_2.11-0.1.3.jar #17 134.5 [INFO] Copying httpcore-4.4.10.jar to /opt/java/jars/httpcore-4.4.10.jar #17 134.5 [INFO] Copying maven-surefire-common-2.22.0.jar to /opt/java/jars/maven-surefire-common-2.22.0.jar #17 134.5 [INFO] Copying macro-compat_2.11-1.1.1.jar to /opt/java/jars/macro-compat_2.11-1.1.1.jar #17 134.5 [INFO] Copying bundle-ml_2.11-0.10.3.jar to /opt/java/jars/bundle-ml_2.11-0.10.3.jar #17 134.5 [INFO] Copying fastparse-utils_2.11-0.4.2.jar to /opt/java/jars/fastparse-utils_2.11-0.4.2.jar #17 134.5 [INFO] Copying maven-artifact-manager-2.2.1.jar to /opt/java/jars/maven-artifact-manager-2.2.1.jar #17 134.5 [INFO] Copying jackrabbit-webdav-1.5.0.jar to /opt/java/jars/jackrabbit-webdav-1.5.0.jar #17 134.5 [INFO] Copying lenses_2.11-0.4.12.jar to /opt/java/jars/lenses_2.11-0.4.12.jar #17 134.5 [INFO] Copying asm-6.1.1.jar to /opt/java/jars/asm-6.1.1.jar #17 134.5 [INFO] Copying jackson-annotations-2.10.0.jar to /opt/java/jars/jackson-annotations-2.10.0.jar #17 134.5 [INFO] Copying snakeyaml-1.24.jar to /opt/java/jars/snakeyaml-1.24.jar #17 134.5 [INFO] Copying machinist_2.11-0.6.1.jar to /opt/java/jars/machinist_2.11-0.6.1.jar #17 134.5 [INFO] Copying maven-toolchain-2.2.1.jar to /opt/java/jars/maven-toolchain-2.2.1.jar #17 134.5 [INFO] Copying spire_2.11-0.13.0.jar to /opt/java/jars/spire_2.11-0.13.0.jar #17 134.5 [INFO] Copying maven-plugin-api-2.2.1.jar to /opt/java/jars/maven-plugin-api-2.2.1.jar #17 134.5 [INFO] Copying maven-project-2.2.1.jar to /opt/java/jars/maven-project-2.2.1.jar #17 134.5 [INFO] Copying qdox-2.0-M7.jar to /opt/java/jars/qdox-2.0-M7.jar #17 134.5 [INFO] Copying jetty-server-9.4.11.v20180605.jar to /opt/java/jars/jetty-server-9.4.11.v20180605.jar #17 134.5 [INFO] Copying jackson-core-2.10.0.jar to /opt/java/jars/jackson-core-2.10.0.jar #17 134.5 [INFO] Copying core-1.1.2.jar to /opt/java/jars/core-1.1.2.jar #17 134.5 [INFO] Copying jetty-security-9.4.11.v20180605.jar to /opt/java/jars/jetty-security-9.4.11.v20180605.jar #17 134.5 [INFO] Copying config-1.3.0.jar to /opt/java/jars/config-1.3.0.jar #17 134.5 [INFO] Copying maven-monitor-2.2.1.jar to /opt/java/jars/maven-monitor-2.2.1.jar #17 134.5 [INFO] Copying wagon-http-1.0-beta-6.jar to /opt/java/jars/wagon-http-1.0-beta-6.jar #17 134.5 [INFO] Copying mleap-core_2.11-0.10.3.jar to /opt/java/jars/mleap-core_2.11-0.10.3.jar #17 134.5 [INFO] Copying hamcrest-core-1.3.jar to /opt/java/jars/hamcrest-core-1.3.jar #17 134.5 [INFO] Copying breeze-macros_2.11-0.13.2.jar to /opt/java/jars/breeze-macros_2.11-0.13.2.jar #17 134.5 [INFO] Copying xercesMinimal-1.9.6.2.jar to /opt/java/jars/xercesMinimal-1.9.6.2.jar #17 134.5 [INFO] Copying jetty-http-9.4.11.v20180605.jar to /opt/java/jars/jetty-http-9.4.11.v20180605.jar #17 134.5 [INFO] Copying fastparse_2.11-0.4.2.jar to /opt/java/jars/fastparse_2.11-0.4.2.jar #17 134.5 [INFO] Copying protobuf-java-3.19.4.jar to /opt/java/jars/protobuf-java-3.19.4.jar #17 134.5 [INFO] Copying scala-arm_2.11-2.0.jar to /opt/java/jars/scala-arm_2.11-2.0.jar #17 134.5 [INFO] Copying arpack_combined_all-0.1.jar to /opt/java/jars/arpack_combined_all-0.1.jar #17 134.5 [INFO] Copying shapeless_2.11-2.3.2.jar to /opt/java/jars/shapeless_2.11-2.3.2.jar #17 134.5 [INFO] Copying mleap-runtime_2.11-0.10.3.jar to /opt/java/jars/mleap-runtime_2.11-0.10.3.jar #17 134.5 [INFO] Copying scala-reflect-2.11.8.jar to /opt/java/jars/scala-reflect-2.11.8.jar #17 134.5 [INFO] Copying commons-logging-1.2.jar to /opt/java/jars/commons-logging-1.2.jar #17 134.5 [INFO] Copying jetty-util-9.4.11.v20180605.jar to /opt/java/jars/jetty-util-9.4.11.v20180605.jar #17 134.5 [INFO] Copying jackson-databind-2.10.0.jar to /opt/java/jars/jackson-databind-2.10.0.jar #17 134.5 [INFO] Copying mleap-spark_2.11-0.10.3.jar to /opt/java/jars/mleap-spark_2.11-0.10.3.jar #17 134.5 [INFO] Copying plexus-utils-1.5.15.jar to /opt/java/jars/plexus-utils-1.5.15.jar #17 134.5 [INFO] Copying spire-macros_2.11-0.13.0.jar to /opt/java/jars/spire-macros_2.11-0.13.0.jar #17 134.5 [INFO] Copying plexus-interpolation-1.11.jar to /opt/java/jars/plexus-interpolation-1.11.jar #17 134.5 [INFO] Copying plexus-cipher-1.4.jar to /opt/java/jars/plexus-cipher-1.4.jar #17 134.5 [INFO] Copying spark-mllib-local_2.11-2.3.0.jar to /opt/java/jars/spark-mllib-local_2.11-2.3.0.jar #17 134.5 [INFO] Copying jtransforms-2.4.0.jar to /opt/java/jars/jtransforms-2.4.0.jar #17 134.5 [INFO] Copying plexus-container-default-1.0-alpha-9-stable-1.jar to /opt/java/jars/plexus-container-default-1.0-alpha-9-stable-1.jar #17 134.5 [INFO] Copying spray-json_2.11-1.3.2.jar to /opt/java/jars/spray-json_2.11-1.3.2.jar #17 134.5 [INFO] Copying jetty-servlet-9.4.11.v20180605.jar to /opt/java/jars/jetty-servlet-9.4.11.v20180605.jar #17 134.5 [INFO] Copying commons-io-2.5.jar to /opt/java/jars/commons-io-2.5.jar #17 134.5 [INFO] Copying surefire-logger-api-2.22.0.jar to /opt/java/jars/surefire-logger-api-2.22.0.jar #17 134.5 [INFO] Copying mleap-base_2.11-0.10.3.jar to /opt/java/jars/mleap-base_2.11-0.10.3.jar #17 134.5 [INFO] Copying surefire-booter-2.22.0.jar to /opt/java/jars/surefire-booter-2.22.0.jar #17 134.5 [INFO] Copying opencsv-2.3.jar to /opt/java/jars/opencsv-2.3.jar #17 134.5 [INFO] Copying spark-tags_2.11-2.3.0.jar to /opt/java/jars/spark-tags_2.11-2.3.0.jar #17 134.5 [INFO] Copying jackrabbit-jcr-commons-1.5.0.jar to /opt/java/jars/jackrabbit-jcr-commons-1.5.0.jar #17 134.5 [INFO] Copying classworlds-1.1.jar to /opt/java/jars/classworlds-1.1.jar #17 134.5 [INFO] Copying nekohtml-1.9.6.2.jar to /opt/java/jars/nekohtml-1.9.6.2.jar #17 134.5 [INFO] Copying maven-core-2.2.1.jar to /opt/java/jars/maven-core-2.2.1.jar #17 134.5 [INFO] Copying maven-surefire-plugin-2.22.0.jar to /opt/java/jars/maven-surefire-plugin-2.22.0.jar #17 134.5 [INFO] Copying mleap-spark-base_2.11-0.10.3.jar to /opt/java/jars/mleap-spark-base_2.11-0.10.3.jar #17 134.5 [INFO] Copying maven-repository-metadata-2.2.1.jar to /opt/java/jars/maven-repository-metadata-2.2.1.jar #17 134.5 [INFO] Copying wagon-webdav-jackrabbit-1.0-beta-6.jar to /opt/java/jars/wagon-webdav-jackrabbit-1.0-beta-6.jar #17 134.5 [INFO] Copying maven-artifact-2.2.1.jar to /opt/java/jars/maven-artifact-2.2.1.jar #17 134.5 [INFO] Copying commons-codec-1.10.jar to /opt/java/jars/commons-codec-1.10.jar #17 134.5 [INFO] Copying maven-plugin-descriptor-2.2.1.jar to /opt/java/jars/maven-plugin-descriptor-2.2.1.jar #17 134.5 [INFO] Copying unused-1.0.0.jar to /opt/java/jars/unused-1.0.0.jar #17 134.5 [INFO] Copying scalapb-runtime_2.11-0.6.0.jar to /opt/java/jars/scalapb-runtime_2.11-0.6.0.jar #17 134.5 [INFO] Copying maven-profile-2.2.1.jar to /opt/java/jars/maven-profile-2.2.1.jar #17 134.5 [INFO] Copying plexus-sec-dispatcher-1.3.jar to /opt/java/jars/plexus-sec-dispatcher-1.3.jar #17 134.5 [INFO] Copying maven-plugin-annotations-3.5.jar to /opt/java/jars/maven-plugin-annotations-3.5.jar #17 134.5 [INFO] Copying maven-reporting-api-2.2.1.jar to /opt/java/jars/maven-reporting-api-2.2.1.jar #17 134.5 [INFO] Copying javax.servlet-api-3.1.0.jar to /opt/java/jars/javax.servlet-api-3.1.0.jar #17 134.5 [INFO] Copying maven-plugin-parameter-documenter-2.2.1.jar to /opt/java/jars/maven-plugin-parameter-documenter-2.2.1.jar #17 134.5 [INFO] Copying commons-math3-3.4.1.jar to /opt/java/jars/commons-math3-3.4.1.jar #17 134.5 [INFO] Copying junit-4.12.jar to /opt/java/jars/junit-4.12.jar #17 134.5 [INFO] Copying slf4j-jdk14-1.7.25.jar to /opt/java/jars/slf4j-jdk14-1.7.25.jar #17 134.5 [INFO] Copying slf4j-api-1.7.25.jar to /opt/java/jars/slf4j-api-1.7.25.jar #17 134.5 [INFO] Copying jetty-io-9.4.11.v20180605.jar to /opt/java/jars/jetty-io-9.4.11.v20180605.jar #17 134.5 [INFO] Copying httpclient-4.5.6.jar to /opt/java/jars/httpclient-4.5.6.jar #17 134.5 [INFO] Copying maven-model-2.2.1.jar to /opt/java/jars/maven-model-2.2.1.jar #17 134.5 [INFO] Copying jackson-dataformat-yaml-2.10.0.jar to /opt/java/jars/jackson-dataformat-yaml-2.10.0.jar #17 134.5 [INFO] Copying doxia-logging-api-1.1.jar to /opt/java/jars/doxia-logging-api-1.1.jar #17 134.5 [INFO] Copying surefire-api-2.22.0.jar to /opt/java/jars/surefire-api-2.22.0.jar #17 134.5 [INFO] Copying maven-plugin-registry-2.2.1.jar to /opt/java/jars/maven-plugin-registry-2.2.1.jar #17 134.5 [INFO] Copying backport-util-concurrent-3.1.jar to /opt/java/jars/backport-util-concurrent-3.1.jar #17 134.5 [INFO] Copying wagon-http-shared-1.0-beta-6.jar to /opt/java/jars/wagon-http-shared-1.0-beta-6.jar #17 134.5 [INFO] Copying commons-httpclient-3.1.jar to /opt/java/jars/commons-httpclient-3.1.jar #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] BUILD SUCCESS #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] Total time: 02:12 min #17 134.5 [INFO] Finished at: 2024-05-16T20:27:50Z #17 134.5 [INFO] ------------------------------------------------------------------------ #17 DONE 134.8s #18 [14/17] COPY model_dir/success6g_model /opt/ml/model #18 DONE 0.1s #19 [15/17] RUN python -c \"from mlflow.models import container as C; C._install_pyfunc_deps('/opt/ml/model', install_mlflow=False, enable_mlserver=True, env_manager='virtualenv');\" #19 1.285 2024/05/16 20:27:52 INFO mlflow.models.container: creating and activating custom environment #19 1.289 2024/05/16 20:27:52 INFO mlflow.utils.virtualenv: Installing python 3.10.11 if it does not exist #19 1.673 Downloading Python-3.10.11.tar.xz... #19 1.673 -> https://www.python.org/ftp/python/3.10.11/Python-3.10.11.tar.xz #19 4.792 Installing Python-3.10.11... #19 108.0 Installed Python-3.10.11 to /root/.pyenv/versions/3.10.11 #19 108.4 2024/05/16 20:29:39 INFO mlflow.utils.virtualenv: Creating a new environment in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b with /root/.pyenv/versions/3.10.11/bin/python #19 109.3 created virtual environment CPython3.10.11.final.0-64 in 719ms #19 109.3 creator CPython3Posix(dest=/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b, clear=False, no_vcs_ignore=False, global=False) #19 109.3 seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/.local/share/virtualenv) #19 109.3 added seed packages: pip==24.0, setuptools==69.5.1, wheel==0.43.0 #19 109.3 activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator #19 109.4 2024/05/16 20:29:40 INFO mlflow.utils.virtualenv: Installing dependencies #19 168.4 WARNING: There was an error checking the latest version of pip. #19 169.3 Collecting mlserver!=1.3.1, < 1.4.0,>=1.2.0 #19 169.4 Downloading mlserver-1.3.5-py3-none-any.whl (113 kB) #19 169.4 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 113.2/113.2 kB 3.4 MB/s eta 0:00:00 #19 169.6 Collecting mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0 #19 169.6 Downloading mlserver_mlflow-1.3.5-py3-none-any.whl (10 kB) #19 169.7 Requirement already satisfied: click in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) (8.1.3) #19 169.8 Collecting fastapi!=0.89.0, < =0.89.1,>=0.88.0 (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.8 Downloading fastapi-0.89.1-py3-none-any.whl (55 kB) #19 169.8 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 55.8/55.8 kB 7.4 MB/s eta 0:00:00 #19 169.9 Collecting python-dotenv (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.9 Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB) #19 171.0 Collecting grpcio (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.0 Downloading grpcio-1.63.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB) #19 171.3 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1.4.0,>=1.2.0) (4.25.3) #19 171.5 Collecting uvicorn (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.5 Downloading uvicorn-0.29.0-py3-none-any.whl (60 kB) #19 171.6 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 60.8/60.8 kB 8.7 MB/s eta 0:00:00 #19 171.7 Collecting starlette-exporter (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.7 Downloading starlette_exporter-0.21.0-py3-none-any.whl (14 kB) #19 171.9 Collecting py-grpc-prometheus (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.9 Downloading py_grpc_prometheus-0.8.0-py3-none-any.whl (12 kB) #19 172.1 Collecting aiokafka (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 172.1 Downloading aiokafka-0.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB) #19 172.2 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anyio, aiosignal, starlette, geventhttpclient, aiohttp, starlette-exporter, fastapi, mlserver, mlserver-mlflow #19 184.4 Successfully installed aiofiles-23.2.1 aiohttp-3.9.5 aiokafka-0.10.0 aiosignal-1.3.1 anyio-4.3.0 attrs-23.2.0 brotli-1.1.0 exceptiongroup-1.2.1 fastapi-0.89.1 frozenlist-1.4.1 gevent-24.2.1 geventhttpclient-2.0.2 grpcio-1.63.0 h11-0.14.0 importlib-resources-6.4.0 mlserver-1.3.5 mlserver-mlflow-1.3.5 multidict-6.0.5 orjson-3.10.3 prometheus-client-0.20.0 py-grpc-prometheus-0.8.0 pydantic-1.10.15 python-dotenv-1.0.1 python-rapidjson-1.16 sniffio-1.3.1 starlette-0.22.0 starlette-exporter-0.17.1 tritonclient-2.45.0 uvicorn-0.29.0 uvloop-0.19.0 yarl-1.9.4 zope.event-5.0 zope.interface-6.4 #19 184.4 WARNING: There was an error checking the latest version of pip. #19 DONE 186.7s #20 [16/17] RUN chmod o+rwX /opt/mlflow/ #20 DONE 0.4s #21 [17/17] RUN rm -rf /var/lib/apt/lists/* #21 DONE 0.6s #22 exporting to image #22 exporting layers #22 exporting layers 12.0s done #22 writing image sha256:29875270957bd037ee2b833cf9201a001d353e4fef34b1d8c4923b278ef8ac02 done #22 naming to docker.io/5uperpalo/mlflow-success6g done #22 DONE 12.1s ```","title":"04_model_training_pipeline"},{"location":"notebooks/04_model_training_pipeline.html#example-usage-notebook","text":"Objects and functions in this notebook are listed with all paramaters to ilustrate their capabillities. Most of the paramaters have default values in the implementation","title":"Example usage notebook"},{"location":"notebooks/04_model_training_pipeline.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd import mlflow pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd import mlflow pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" In [2]: Copied! import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump , dill_load from inference_model.training.utils import flatten_dict , get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump, dill_load from inference_model.training.utils import flatten_dict, get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast","title":"Imports"},{"location":"notebooks/04_model_training_pipeline.html#train-a-testing-model","text":"In [3]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [4]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) In [6]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) In [7]: Copied! prepare_data = PreprocessData ( id_cols = id_cols , target_col = target_col , cat_cols = cat_cols , cont_cols = cont_cols , ) # this should be fitted only on training data _ = prepare_data . fit ( df = df_pd ) prepare_data = PreprocessData( id_cols=id_cols, target_col=target_col, cat_cols=cat_cols, cont_cols=cont_cols, ) # this should be fitted only on training data _ = prepare_data.fit(df=df_pd) In [8]: Copied! optimizer = LGBOptunaOptimizer ( objective = \"binary\" , n_class = 2 , ) trainer = Trainer ( cat_cols = prepare_data . cat_cols , target_col = prepare_data . target_col , id_cols = id_cols , objective = \"binary\" , n_class = 2 , optimizer = optimizer , preprocessors = [ prepare_data ], ) metrics_dict = trainer . fit ( df_train = df_train , df_valid = df_valid , df_test = df_test , ) optimizer = LGBOptunaOptimizer( objective=\"binary\", n_class=2, ) trainer = Trainer( cat_cols=prepare_data.cat_cols, target_col=prepare_data.target_col, id_cols=id_cols, objective=\"binary\", n_class=2, optimizer=optimizer, preprocessors=[prepare_data], ) metrics_dict = trainer.fit( df_train=df_train, df_valid=df_valid, df_test=df_test, ) [I 2024-05-22 13:01:18,589] A new study created in memory with name: LightGBMTuner feature_fraction, val_score: inf: 0%| | 0/7 [00:00 In [27]: Copied! metrics_dict_flattened = flatten_dict ( metrics_dict ) # mlflow metrics can be only int, float not list del metrics_dict_flattened [ \"cm\" ] del metrics_dict_flattened [ \"prec_rec_curve\" ] metrics_dict_flattened = flatten_dict(metrics_dict) # mlflow metrics can be only int, float not list del metrics_dict_flattened[\"cm\"] del metrics_dict_flattened[\"prec_rec_curve\"] In [29]: Copied! run_name = \"test\" with mlflow . start_run ( experiment_id = experiment_id , run_name = run_name , nested = True ) as run : mlflow . log_params ( trainer . optimizer . best ) mlflow . log_metrics ( metrics_dict_flattened ) # Log tags mlflow . set_tags ( tags = { \"project\" : \"SUCCESS6G\" , \"optimizer_engine\" : \"optuna\" , \"model_family\" : \"ligtgbm\" , \"feature_set_version\" : 1 , } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow . pyfunc . log_model ( python_model = trainer , artifact_path = artifact_path , registered_model_name = registered_model_name , ) model_uri = mlflow . get_artifact_uri ( artifact_path ) print ( f \"Run ID: \\n { run . info . run_id } \\n Model uri: \\n { model_uri } \" ) run_name = \"test\" with mlflow.start_run( experiment_id=experiment_id, run_name=run_name, nested=True ) as run: mlflow.log_params(trainer.optimizer.best) mlflow.log_metrics(metrics_dict_flattened) # Log tags mlflow.set_tags( tags={ \"project\": \"SUCCESS6G\", \"optimizer_engine\": \"optuna\", \"model_family\": \"ligtgbm\", \"feature_set_version\": 1, } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow.pyfunc.log_model( python_model=trainer, artifact_path=artifact_path, registered_model_name=registered_model_name, ) model_uri = mlflow.get_artifact_uri(artifact_path) print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: typing.Union[pandas.core.frame.DataFrame, dict], skipping schema inference 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: , skipping schema inference Successfully registered model 'success6g_model'. 2024/05/22 14:44:22 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: success6g_model, version 1 Run ID: 24fedb96c5004767800fa32a3220c8dd Model uri: s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model Created version '1' of model 'success6g_model'.","title":"Mlflow"},{"location":"notebooks/04_model_training_pipeline.html#testing-of-predictions","text":"","title":"Testing of predictions"},{"location":"notebooks/04_model_training_pipeline.html#example-data","text":"In [30]: Copied! df_pd . iloc [: 2 ] df_pd.iloc[:2] Out[30]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc In [31]: Copied! # for testing using mlflow inference service request_dict = { \"engine_load\" : [ 100.0 , 100.0 ], \"engine_coolant_temp\" : [ 17.0 , 17.0 ], \"engine_speed\" : [ 904.5 , 906.0 ], \"vehicle_speed\" : [ 0.0 , 0.0 ], \"intake_air_temp\" : [ 10.0 , 11.0 ], \"maf\" : [ 12.55 , 12.36 ], \"throttle_position\" : [ 83.14 , 83.14 ], \"fuel_rg_pressure\" : [ 37270.0 , 37800.0 ], \"barometric_pressure\" : [ 101.0 , 101.0 ], \"control_voltage\" : [ 0.06 , 14.56 ], \"class\" : [ 1 , 1 ], \"car_id\" : [ \"123abc\" , \"123abc\" ], } # for testing using mlflow inference service request_dict = { \"engine_load\": [100.0, 100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"class\": [1, 1], \"car_id\": [\"123abc\", \"123abc\"], }","title":"Example data"},{"location":"notebooks/04_model_training_pipeline.html#trainer","text":"In [32]: Copied! trainer . predict ( df = df_pd . iloc [: 2 ] . drop ( columns = [ \"class\" ]), context = {}) trainer.predict(df=df_pd.iloc[:2].drop(columns=[\"class\"]), context={}) Out[32]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 0 14.269467 1 14.269467","title":"Trainer"},{"location":"notebooks/04_model_training_pipeline.html#downloaded-trainer","text":"In [33]: Copied! model_uri model_uri Out[33]: 's3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model' In [34]: Copied! loaded_trainer = mlflow . pyfunc . load_model ( model_uri ) loaded_trainer = mlflow.pyfunc.load_model(model_uri) Downloading artifacts: 0%| | 0/9 [00:00=0.3.7 (from virtualenv) #10 1.197 Downloading distlib-0.3.8-py2.py3-none-any.whl.metadata (5.1 kB) #10 1.276 Collecting filelock < 4,>=3.12.2 (from virtualenv) #10 1.291 Downloading filelock-3.14.0-py3-none-any.whl.metadata (2.8 kB) #10 1.368 Collecting platformdirs < 5,>=3.9.1 (from virtualenv) #10 1.383 Downloading platformdirs-4.2.2-py3-none-any.whl.metadata (11 kB) #10 1.439 Downloading virtualenv-20.26.2-py3-none-any.whl (3.9 MB) #10 1.656 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.9/3.9 MB 18.3 MB/s eta 0:00:00 #10 1.673 Downloading distlib-0.3.8-py2.py3-none-any.whl (468 kB) #10 1.706 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 468.9/468.9 kB 17.0 MB/s eta 0:00:00 #10 1.724 Downloading filelock-3.14.0-py3-none-any.whl (12 kB) #10 1.752 Downloading platformdirs-4.2.2-py3-none-any.whl (18 kB) #10 1.871 Installing collected packages: distlib, platformdirs, filelock, virtualenv #10 2.123 Successfully installed distlib-0.3.8 filelock-3.14.0 platformdirs-4.2.2 virtualenv-20.26.2 #10 2.123 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #10 DONE 2.3s #11 [ 7/17] RUN apt-get install -y --no-install-recommends openjdk-11-jdk maven #11 0.605 Reading package lists... #11 1.699 Building dependency tree... #11 1.870 Reading state information... #11 2.070 The following additional packages will be installed: #11 2.070 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.070 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.070 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.070 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.070 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.070 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.070 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.070 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.070 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libplexus-utils2-java-doc junit4 testng libcommons-logging-java #11 2.072 liblog4j1.2-java openjdk-11-demo openjdk-11-source visualvm libnss-mdns #11 2.072 fonts-dejavu-extra fonts-ipafont-gothic fonts-ipafont-mincho #11 2.072 fonts-wqy-microhei | fonts-wqy-zenhei fonts-indic #11 2.072 Recommended packages: #11 2.072 alsa-ucm-conf alsa-topology-conf dbus libatk-wrapper-java-jni #11 2.072 fonts-dejavu-extra #11 2.267 The following NEW packages will be installed: #11 2.268 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.268 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.268 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.268 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.268 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.268 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.268 libguice-java libharfbuzz0b 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(Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 24567 files and directories currently installed.) #11 26.21 Preparing to unpack .../00-libdbus-1-3_1.12.16-2ubuntu2.3_amd64.deb ... #11 26.22 Unpacking libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 26.33 Selecting previously unselected package java-common. #11 26.33 Preparing to unpack .../01-java-common_0.72_all.deb ... #11 26.34 Unpacking java-common (0.72) ... #11 26.40 Selecting previously unselected package libavahi-common-data:amd64. #11 26.41 Preparing to unpack .../02-libavahi-common-data_0.7-4ubuntu7.3_amd64.deb ... #11 26.42 Unpacking libavahi-common-data:amd64 (0.7-4ubuntu7.3) ... #11 26.49 Selecting previously unselected package libavahi-common3:amd64. #11 26.50 Preparing to unpack .../03-libavahi-common3_0.7-4ubuntu7.3_amd64.deb ... #11 26.50 Unpacking libavahi-common3:amd64 (0.7-4ubuntu7.3) ... #11 26.58 Selecting previously unselected package libavahi-client3:amd64. #11 26.59 Preparing to unpack .../04-libavahi-client3_0.7-4ubuntu7.3_amd64.deb ... #11 26.59 Unpacking libavahi-client3:amd64 (0.7-4ubuntu7.3) ... #11 26.68 Selecting previously unselected package libcups2:amd64. #11 26.68 Preparing to unpack .../05-libcups2_2.3.1-9ubuntu1.6_amd64.deb ... #11 26.69 Unpacking libcups2:amd64 (2.3.1-9ubuntu1.6) ... #11 26.80 Selecting previously unselected package liblcms2-2:amd64. #11 26.80 Preparing to unpack .../06-liblcms2-2_2.9-4_amd64.deb ... #11 26.81 Unpacking liblcms2-2:amd64 (2.9-4) ... #11 26.90 Selecting previously unselected package libasound2-data. #11 26.91 Preparing to unpack .../07-libasound2-data_1.2.2-2.1ubuntu2.5_all.deb ... #11 26.91 Unpacking libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 27.01 Selecting previously unselected package libasound2:amd64. #11 27.01 Preparing to unpack .../08-libasound2_1.2.2-2.1ubuntu2.5_amd64.deb ... #11 27.02 Unpacking libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 27.14 Selecting previously unselected package libgraphite2-3:amd64. #11 27.14 Preparing to unpack .../09-libgraphite2-3_1.3.13-11build1_amd64.deb ... #11 27.15 Unpacking libgraphite2-3:amd64 (1.3.13-11build1) ... #11 27.24 Selecting previously unselected package libharfbuzz0b:amd64. #11 27.24 Preparing to unpack .../10-libharfbuzz0b_2.6.4-1ubuntu4.2_amd64.deb ... #11 27.25 Unpacking libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 27.37 Selecting previously unselected package libpcsclite1:amd64. #11 27.38 Preparing to unpack .../11-libpcsclite1_1.8.26-3_amd64.deb ... #11 27.38 Unpacking libpcsclite1:amd64 (1.8.26-3) ... #11 27.47 Selecting previously unselected package openjdk-11-jre-headless:amd64. #11 27.47 Preparing to unpack .../12-openjdk-11-jre-headless_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 27.48 Unpacking openjdk-11-jre-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 31.00 Selecting previously unselected package ca-certificates-java. #11 31.00 Preparing to unpack .../13-ca-certificates-java_20190405ubuntu1.1_all.deb ... #11 31.02 Unpacking ca-certificates-java (20190405ubuntu1.1) ... #11 31.08 Selecting previously unselected package libapache-pom-java. #11 31.09 Preparing to unpack .../14-libapache-pom-java_18-1_all.deb ... #11 31.10 Unpacking libapache-pom-java (18-1) ... #11 31.16 Selecting previously unselected package libatinject-jsr330-api-java. #11 31.16 Preparing to unpack .../15-libatinject-jsr330-api-java_1.0+ds1-5_all.deb ... #11 31.17 Unpacking libatinject-jsr330-api-java (1.0+ds1-5) ... #11 31.23 Selecting previously unselected package libgeronimo-interceptor-3.0-spec-java. #11 31.24 Preparing to unpack .../16-libgeronimo-interceptor-3.0-spec-java_1.0.1-4fakesync_all.deb ... #11 31.25 Unpacking libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 31.31 Selecting previously unselected package libcdi-api-java. #11 31.31 Preparing to unpack .../17-libcdi-api-java_1.2-2_all.deb ... #11 31.32 Unpacking libcdi-api-java (1.2-2) ... #11 31.39 Selecting previously unselected package libcommons-cli-java. #11 31.39 Preparing to unpack .../18-libcommons-cli-java_1.4-1_all.deb ... #11 31.40 Unpacking libcommons-cli-java (1.4-1) ... #11 31.47 Selecting previously unselected package libcommons-parent-java. #11 31.48 Preparing to unpack .../19-libcommons-parent-java_43-1_all.deb ... #11 31.49 Unpacking libcommons-parent-java (43-1) ... #11 31.55 Selecting previously unselected package libcommons-io-java. #11 31.56 Preparing to unpack .../20-libcommons-io-java_2.6-2ubuntu0.20.04.1_all.deb ... #11 31.56 Unpacking libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 31.66 Selecting previously unselected package libcommons-lang3-java. #11 31.66 Preparing to unpack .../21-libcommons-lang3-java_3.8-2_all.deb ... #11 31.67 Unpacking libcommons-lang3-java (3.8-2) ... #11 31.78 Selecting previously unselected package libgeronimo-annotation-1.3-spec-java. #11 31.78 Preparing to unpack .../22-libgeronimo-annotation-1.3-spec-java_1.0-1_all.deb ... #11 31.79 Unpacking libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 31.86 Selecting previously unselected package libgif7:amd64. #11 31.87 Preparing to unpack .../23-libgif7_5.1.9-1_amd64.deb ... #11 31.88 Unpacking libgif7:amd64 (5.1.9-1) ... #11 31.95 Selecting previously unselected package libjsr305-java. #11 31.95 Preparing to unpack .../24-libjsr305-java_0.1~+svn49-11_all.deb ... #11 31.96 Unpacking libjsr305-java (0.1~+svn49-11) ... #11 32.03 Selecting previously unselected package libguava-java. #11 32.03 Preparing to unpack .../25-libguava-java_19.0-1_all.deb ... #11 32.04 Unpacking libguava-java (19.0-1) ... #11 32.29 Selecting previously unselected package libaopalliance-java. #11 32.29 Preparing to unpack .../26-libaopalliance-java_20070526-6_all.deb ... #11 32.30 Unpacking libaopalliance-java (20070526-6) ... #11 32.36 Selecting previously unselected package libguice-java. #11 32.37 Preparing to unpack .../27-libguice-java_4.2.1-1ubuntu0.2_all.deb ... #11 32.37 Unpacking libguice-java (4.2.1-1ubuntu0.2) ... #11 32.59 Selecting previously unselected package libhawtjni-runtime-java. #11 32.60 Preparing to unpack .../28-libhawtjni-runtime-java_1.17-1_all.deb ... #11 32.60 Unpacking libhawtjni-runtime-java (1.17-1) ... #11 32.67 Selecting previously unselected package libjansi-native-java. #11 32.68 Preparing to unpack .../29-libjansi-native-java_1.8-1_all.deb ... #11 32.68 Unpacking libjansi-native-java (1.8-1) ... #11 32.75 Selecting previously unselected package libjansi-java. #11 32.75 Preparing to unpack .../30-libjansi-java_1.18-1_all.deb ... #11 32.76 Unpacking libjansi-java (1.18-1) ... #11 32.83 Selecting previously unselected package libmaven-parent-java. #11 32.84 Preparing to unpack .../31-libmaven-parent-java_31-2_all.deb ... #11 32.85 Unpacking libmaven-parent-java (31-2) ... #11 32.91 Selecting previously unselected package libplexus-utils2-java. #11 32.91 Preparing to unpack .../32-libplexus-utils2-java_3.3.0-1_all.deb ... #11 32.92 Unpacking libplexus-utils2-java (3.3.0-1) ... #11 33.02 Selecting previously unselected package libwagon-provider-api-java. #11 33.02 Preparing to unpack .../33-libwagon-provider-api-java_3.3.4-1_all.deb ... #11 33.03 Unpacking libwagon-provider-api-java (3.3.4-1) ... #11 33.10 Selecting previously unselected package libmaven-resolver-java. #11 33.11 Preparing to unpack .../34-libmaven-resolver-java_1.4.1-1_all.deb ... #11 33.12 Unpacking libmaven-resolver-java (1.4.1-1) ... #11 33.26 Selecting previously unselected package libmaven-shared-utils-java. #11 33.27 Preparing to unpack .../35-libmaven-shared-utils-java_3.3.0-1ubuntu0.20.04.1_all.deb ... #11 33.27 Unpacking libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 33.36 Selecting previously unselected package libplexus-cipher-java. #11 33.37 Preparing to unpack .../36-libplexus-cipher-java_1.7-3_all.deb ... #11 33.37 Unpacking libplexus-cipher-java (1.7-3) ... #11 33.44 Selecting previously unselected package libplexus-classworlds-java. #11 33.45 Preparing to unpack .../37-libplexus-classworlds-java_2.6.0-1_all.deb ... #11 33.45 Unpacking libplexus-classworlds-java (2.6.0-1) ... #11 33.53 Selecting previously unselected package libplexus-component-annotations-java. #11 33.53 Preparing to unpack .../38-libplexus-component-annotations-java_2.1.0-1_all.deb ... #11 33.54 Unpacking libplexus-component-annotations-java (2.1.0-1) ... #11 33.60 Selecting previously unselected package libplexus-interpolation-java. #11 33.60 Preparing to unpack .../39-libplexus-interpolation-java_1.26-1_all.deb ... #11 33.61 Unpacking libplexus-interpolation-java (1.26-1) ... #11 33.69 Selecting previously unselected package libplexus-sec-dispatcher-java. #11 33.70 Preparing to unpack .../40-libplexus-sec-dispatcher-java_1.4-4_all.deb ... #11 33.70 Unpacking libplexus-sec-dispatcher-java (1.4-4) ... #11 33.77 Selecting previously unselected package libslf4j-java. #11 33.77 Preparing to unpack .../41-libslf4j-java_1.7.25-3_all.deb ... #11 33.78 Unpacking libslf4j-java (1.7.25-3) ... #11 33.89 Selecting previously unselected package libsisu-inject-java. #11 33.89 Preparing to unpack .../42-libsisu-inject-java_0.3.3-1_all.deb ... #11 33.90 Unpacking libsisu-inject-java (0.3.3-1) ... #11 34.01 Selecting previously unselected package libsisu-plexus-java. #11 34.02 Preparing to unpack .../43-libsisu-plexus-java_0.3.3-3_all.deb ... #11 34.02 Unpacking libsisu-plexus-java (0.3.3-3) ... #11 34.12 Selecting previously unselected package libmaven3-core-java. #11 34.12 Preparing to unpack .../44-libmaven3-core-java_3.6.3-1ubuntu0.1_all.deb ... #11 34.13 Unpacking libmaven3-core-java (3.6.3-1ubuntu0.1) ... #11 34.35 Selecting previously unselected package libwagon-file-java. #11 34.36 Preparing to unpack .../45-libwagon-file-java_3.3.4-1_all.deb ... #11 34.36 Unpacking libwagon-file-java (3.3.4-1) ... #11 34.42 Selecting previously unselected package libwagon-http-shaded-java. #11 34.43 Preparing to unpack .../46-libwagon-http-shaded-java_3.3.4-1_all.deb ... #11 34.43 Unpacking libwagon-http-shaded-java (3.3.4-1) ... #11 34.68 Selecting previously unselected package maven. #11 34.68 Preparing to unpack .../47-maven_3.6.3-1ubuntu0.1_all.deb ... #11 34.69 Unpacking maven (3.6.3-1ubuntu0.1) ... #11 34.76 Selecting previously unselected package openjdk-11-jre:amd64. #11 34.76 Preparing to unpack .../48-openjdk-11-jre_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 34.77 Unpacking openjdk-11-jre:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 34.88 Selecting previously unselected package openjdk-11-jdk-headless:amd64. #11 34.89 Preparing to unpack .../49-openjdk-11-jdk-headless_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 34.89 Unpacking openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 39.97 Selecting previously unselected package openjdk-11-jdk:amd64. #11 39.97 Preparing to unpack .../50-openjdk-11-jdk_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 39.98 Unpacking openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 40.14 Setting up libgraphite2-3:amd64 (1.3.13-11build1) ... #11 40.16 Setting up liblcms2-2:amd64 (2.9-4) ... #11 40.18 Setting up libslf4j-java (1.7.25-3) ... #11 40.20 Setting up libplexus-utils2-java (3.3.0-1) ... #11 40.22 Setting up libplexus-classworlds-java (2.6.0-1) ... #11 40.24 Setting up libjsr305-java (0.1~+svn49-11) ... #11 40.26 Setting up java-common (0.72) ... #11 40.30 Setting up libaopalliance-java (20070526-6) ... #11 40.32 Setting up libcommons-cli-java (1.4-1) ... #11 40.34 Setting up libplexus-component-annotations-java (2.1.0-1) ... #11 40.38 Setting up libplexus-cipher-java (1.7-3) ... #11 40.40 Setting up libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 40.42 Setting up libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 40.44 Setting up libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 40.46 Setting up libapache-pom-java (18-1) ... #11 40.48 Setting up libavahi-common-data:amd64 (0.7-4ubuntu7.3) ... #11 40.50 Setting up libatinject-jsr330-api-java (1.0+ds1-5) ... #11 40.53 Setting up libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 40.55 Setting up libplexus-interpolation-java (1.26-1) ... #11 40.57 Setting up libpcsclite1:amd64 (1.8.26-3) ... #11 40.59 Setting up libgif7:amd64 (5.1.9-1) ... #11 40.61 Setting up libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 40.63 Setting up libplexus-sec-dispatcher-java (1.4-4) ... #11 40.65 Setting up libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 40.67 Setting up libwagon-http-shaded-java (3.3.4-1) ... #11 40.69 Setting up libcdi-api-java (1.2-2) ... #11 40.71 Setting up libhawtjni-runtime-java (1.17-1) ... #11 40.73 Setting up libwagon-provider-api-java (3.3.4-1) ... #11 40.75 Setting up libmaven-parent-java (31-2) ... #11 40.77 Setting up libcommons-parent-java (43-1) ... #11 40.79 Setting up libavahi-common3:amd64 (0.7-4ubuntu7.3) ... #11 40.81 Setting up libmaven-resolver-java (1.4.1-1) ... #11 40.84 Setting up libguava-java (19.0-1) ... #11 40.86 Setting up libcommons-lang3-java (3.8-2) ... #11 40.88 Setting up libjansi-native-java (1.8-1) ... #11 40.90 Setting up libwagon-file-java (3.3.4-1) ... #11 40.92 Setting up libavahi-client3:amd64 (0.7-4ubuntu7.3) ... #11 40.94 Setting up libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 40.96 Setting up libguice-java (4.2.1-1ubuntu0.2) ... #11 40.98 Setting up libjansi-java (1.18-1) ... #11 41.00 Setting up libcups2:amd64 (2.3.1-9ubuntu1.6) ... #11 41.02 Setting up libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 41.04 Setting up libsisu-inject-java (0.3.3-1) ... #11 41.06 Setting up libsisu-plexus-java (0.3.3-3) ... #11 41.08 Setting up libmaven3-core-java (3.6.3-1ubuntu0.1) ... #11 41.11 Setting up openjdk-11-jre-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.31 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/java to provide /usr/bin/java (java) in auto mode #11 41.32 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jjs to provide /usr/bin/jjs (jjs) in auto mode #11 41.33 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/keytool to provide /usr/bin/keytool (keytool) in auto mode #11 41.34 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmid to provide /usr/bin/rmid (rmid) in auto mode #11 41.34 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmiregistry to provide /usr/bin/rmiregistry (rmiregistry) in auto mode #11 41.35 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/pack200 to provide /usr/bin/pack200 (pack200) in auto mode #11 41.36 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/unpack200 to provide /usr/bin/unpack200 (unpack200) in auto mode #11 41.37 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/lib/jexec to provide /usr/bin/jexec (jexec) in auto mode #11 41.92 Setting up openjdk-11-jre:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.95 Setting up openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.98 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jar to provide /usr/bin/jar (jar) in auto mode #11 41.99 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jarsigner to provide /usr/bin/jarsigner (jarsigner) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javac to provide /usr/bin/javac (javac) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javadoc to provide /usr/bin/javadoc (javadoc) in auto mode #11 42.01 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javap to provide /usr/bin/javap (javap) in auto mode #11 42.02 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jcmd to provide /usr/bin/jcmd (jcmd) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdb to provide /usr/bin/jdb (jdb) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeprscan to provide /usr/bin/jdeprscan (jdeprscan) in auto mode #11 42.04 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeps to provide /usr/bin/jdeps (jdeps) in auto mode #11 42.05 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jfr to provide /usr/bin/jfr (jfr) in auto mode #11 42.06 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jimage to provide /usr/bin/jimage (jimage) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jinfo to provide /usr/bin/jinfo (jinfo) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jlink to provide /usr/bin/jlink (jlink) in auto mode #11 42.08 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmap to provide /usr/bin/jmap (jmap) in auto mode #11 42.09 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmod to provide /usr/bin/jmod (jmod) in auto mode #11 42.10 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jps to provide /usr/bin/jps (jps) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jrunscript to provide /usr/bin/jrunscript (jrunscript) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jshell to provide /usr/bin/jshell (jshell) in auto mode #11 42.12 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstack to provide /usr/bin/jstack (jstack) in auto mode #11 42.13 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstat to provide /usr/bin/jstat (jstat) in auto mode #11 42.14 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstatd to provide /usr/bin/jstatd (jstatd) in auto mode #11 42.15 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmic to provide /usr/bin/rmic (rmic) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/serialver to provide /usr/bin/serialver (serialver) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jaotc to provide /usr/bin/jaotc (jaotc) in auto mode #11 42.17 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jhsdb to provide /usr/bin/jhsdb (jhsdb) in auto mode #11 42.19 Setting up maven (3.6.3-1ubuntu0.1) ... #11 42.23 update-alternatives: using /usr/share/maven/bin/mvn to provide /usr/bin/mvn (mvn) in auto mode #11 42.25 Setting up openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 42.28 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jconsole to provide /usr/bin/jconsole (jconsole) in auto mode #11 42.29 Setting up ca-certificates-java (20190405ubuntu1.1) ... #11 42.35 head: cannot open '/etc/ssl/certs/java/cacerts' for reading: No such file or directory #11 42.65 Adding debian:COMODO_RSA_Certification_Authority.pem #11 42.66 Adding debian:Hongkong_Post_Root_CA_1.pem #11 42.67 Adding debian:Entrust_Root_Certification_Authority_-_EC1.pem #11 42.67 Adding debian:Certainly_Root_R1.pem #11 42.68 Adding debian:Amazon_Root_CA_2.pem #11 42.68 Adding debian:Certainly_Root_E1.pem #11 42.68 Adding debian:AffirmTrust_Premium_ECC.pem #11 42.68 Adding debian:ANF_Secure_Server_Root_CA.pem #11 42.68 Adding debian:GlobalSign_Root_R46.pem #11 42.69 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068.pem #11 42.69 Adding debian:Certum_Trusted_Root_CA.pem #11 42.69 Adding debian:Go_Daddy_Root_Certificate_Authority_-_G2.pem #11 42.69 Adding debian:SecureSign_RootCA11.pem #11 42.69 Adding debian:Amazon_Root_CA_4.pem #11 42.70 Adding debian:GDCA_TrustAUTH_R5_ROOT.pem #11 42.70 Adding debian:T-TeleSec_GlobalRoot_Class_2.pem #11 42.70 Adding debian:Trustwave_Global_ECC_P256_Certification_Authority.pem #11 42.70 Adding debian:GTS_Root_R1.pem #11 42.71 Adding debian:Izenpe.com.pem #11 42.71 Adding debian:CFCA_EV_ROOT.pem #11 42.71 Adding debian:DigiCert_Global_Root_G2.pem #11 42.71 Adding debian:XRamp_Global_CA_Root.pem #11 42.72 Adding debian:SSL.com_EV_Root_Certification_Authority_RSA_R2.pem #11 42.72 Adding debian:Security_Communication_RootCA3.pem #11 42.72 Adding debian:Starfield_Services_Root_Certificate_Authority_-_G2.pem #11 42.72 Adding debian:Amazon_Root_CA_1.pem #11 42.72 Adding debian:DigiCert_Assured_ID_Root_CA.pem #11 42.72 Adding debian:SSL.com_Root_Certification_Authority_RSA.pem #11 42.73 Adding debian:Telia_Root_CA_v2.pem #11 42.73 Adding debian:SwissSign_Gold_CA_-_G2.pem #11 42.73 Adding debian:GLOBALTRUST_2020.pem #11 42.73 Adding debian:emSign_ECC_Root_CA_-_G3.pem #11 42.73 Adding debian:Microsoft_RSA_Root_Certificate_Authority_2017.pem #11 42.74 Adding debian:TunTrust_Root_CA.pem #11 42.74 Adding debian:Security_Communication_ECC_RootCA1.pem #11 42.74 Adding debian:DigiCert_TLS_ECC_P384_Root_G5.pem #11 42.74 Adding debian:vTrus_Root_CA.pem #11 42.75 Adding debian:Certum_EC-384_CA.pem #11 42.76 Adding debian:CA_Disig_Root_R2.pem #11 42.76 Adding debian:emSign_Root_CA_-_C1.pem #11 42.77 Adding debian:AffirmTrust_Commercial.pem #11 42.77 Adding debian:Entrust_Root_Certification_Authority_-_G4.pem #11 42.78 Adding debian:Starfield_Class_2_CA.pem #11 42.78 Adding debian:Baltimore_CyberTrust_Root.pem #11 42.79 Adding debian:GTS_Root_R4.pem #11 42.79 Adding debian:Starfield_Root_Certificate_Authority_-_G2.pem #11 42.80 Adding debian:QuoVadis_Root_CA_1_G3.pem #11 42.81 Adding debian:DigiCert_Trusted_Root_G4.pem #11 42.81 Adding debian:DigiCert_High_Assurance_EV_Root_CA.pem #11 42.82 Adding debian:AC_RAIZ_FNMT-RCM.pem #11 42.82 Adding debian:Comodo_AAA_Services_root.pem #11 42.82 Adding debian:emSign_ECC_Root_CA_-_C3.pem #11 42.83 Adding debian:Entrust_Root_Certification_Authority.pem #11 42.84 Adding debian:D-TRUST_EV_Root_CA_1_2020.pem #11 42.85 Adding debian:Certigna_Root_CA.pem #11 42.85 Adding debian:TeliaSonera_Root_CA_v1.pem #11 42.86 Adding debian:Certum_Trusted_Network_CA.pem #11 42.86 Adding debian:HiPKI_Root_CA_-_G1.pem #11 42.87 Adding debian:SZAFIR_ROOT_CA2.pem #11 42.87 Adding debian:USERTrust_RSA_Certification_Authority.pem #11 42.88 Adding debian:OISTE_WISeKey_Global_Root_GC_CA.pem #11 42.88 Adding debian:Buypass_Class_2_Root_CA.pem #11 42.89 Adding debian:AC_RAIZ_FNMT-RCM_SERVIDORES_SEGUROS.pem #11 42.89 Adding debian:e-Szigno_Root_CA_2017.pem #11 42.90 Adding debian:Microsoft_ECC_Root_Certificate_Authority_2017.pem #11 42.91 Adding debian:Security_Communication_Root_CA.pem #11 42.91 Adding debian:GlobalSign_Root_CA.pem #11 42.92 Adding debian:E-Tugra_Certification_Authority.pem #11 42.93 Adding debian:Trustwave_Global_Certification_Authority.pem #11 42.93 Adding debian:QuoVadis_Root_CA_2_G3.pem #11 42.93 Adding debian:SSL.com_EV_Root_Certification_Authority_ECC.pem #11 42.94 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068_2.pem #11 42.95 Adding debian:E-Tugra_Global_Root_CA_ECC_v3.pem #11 42.95 Adding debian:Entrust.net_Premium_2048_Secure_Server_CA.pem #11 42.96 Adding debian:AffirmTrust_Premium.pem #11 42.96 Adding debian:Amazon_Root_CA_3.pem #11 42.97 Adding debian:AffirmTrust_Networking.pem #11 42.98 Adding debian:IdenTrust_Public_Sector_Root_CA_1.pem #11 42.98 Adding debian:GlobalSign_ECC_Root_CA_-_R4.pem #11 42.99 Adding debian:QuoVadis_Root_CA_3_G3.pem #11 42.99 Adding debian:certSIGN_ROOT_CA.pem #11 43.00 Adding debian:IdenTrust_Commercial_Root_CA_1.pem #11 43.00 Adding debian:SecureTrust_CA.pem #11 43.01 Adding debian:DigiCert_Global_Root_G3.pem #11 43.01 Adding debian:GlobalSign_Root_E46.pem #11 43.02 Adding debian:SwissSign_Silver_CA_-_G2.pem #11 43.02 Adding debian:vTrus_ECC_Root_CA.pem #11 43.03 Adding debian:GlobalSign_ECC_Root_CA_-_R5.pem #11 43.04 Adding debian:OISTE_WISeKey_Global_Root_GB_CA.pem #11 43.04 Adding debian:DigiCert_Assured_ID_Root_G3.pem #11 43.05 Adding debian:emSign_Root_CA_-_G1.pem #11 43.05 Adding debian:DigiCert_TLS_RSA4096_Root_G5.pem #11 43.06 Adding debian:COMODO_Certification_Authority.pem #11 43.06 Adding debian:DigiCert_Global_Root_CA.pem #11 43.07 Adding debian:QuoVadis_Root_CA_2.pem #11 43.08 Adding debian:TWCA_Global_Root_CA.pem #11 43.09 Adding debian:ACCVRAIZ1.pem #11 43.09 Adding debian:GTS_Root_R2.pem #11 43.10 Adding debian:HARICA_TLS_RSA_Root_CA_2021.pem #11 43.10 Adding debian:Actalis_Authentication_Root_CA.pem #11 43.11 Adding debian:Certum_Trusted_Network_CA_2.pem #11 43.11 Adding debian:Atos_TrustedRoot_2011.pem #11 43.11 Adding debian:T-TeleSec_GlobalRoot_Class_3.pem #11 43.13 Adding debian:NetLock_Arany_=Class_Gold=_F\u0151tan\u00fas\u00edtv\u00e1ny.pem #11 43.13 Adding debian:Certigna.pem #11 43.14 Adding debian:Microsec_e-Szigno_Root_CA_2009.pem #11 43.14 Adding debian:UCA_Extended_Validation_Root.pem #11 43.14 Adding debian:DigiCert_Assured_ID_Root_G2.pem #11 43.15 Adding debian:QuoVadis_Root_CA_3.pem #11 43.15 Adding debian:NAVER_Global_Root_Certification_Authority.pem #11 43.16 Adding debian:GlobalSign_Root_CA_-_R6.pem #11 43.17 Adding debian:TUBITAK_Kamu_SM_SSL_Kok_Sertifikasi_-_Surum_1.pem #11 43.17 Adding debian:Trustwave_Global_ECC_P384_Certification_Authority.pem #11 43.18 Adding debian:Go_Daddy_Class_2_CA.pem #11 43.18 Adding debian:TWCA_Root_Certification_Authority.pem #11 43.19 Adding debian:certSIGN_Root_CA_G2.pem #11 43.19 Adding debian:COMODO_ECC_Certification_Authority.pem #11 43.20 Adding debian:ISRG_Root_X2.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_EV_2009.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_2009.pem #11 43.22 Adding debian:UCA_Global_G2_Root.pem #11 43.22 Adding debian:D-TRUST_BR_Root_CA_1_2020.pem #11 43.23 Adding debian:Secure_Global_CA.pem #11 43.24 Adding debian:Hongkong_Post_Root_CA_3.pem #11 43.24 Adding debian:Buypass_Class_3_Root_CA.pem #11 43.25 Adding debian:Entrust_Root_Certification_Authority_-_G2.pem #11 43.25 Adding debian:SSL.com_Root_Certification_Authority_ECC.pem #11 43.26 Adding debian:Hellenic_Academic_and_Research_Institutions_RootCA_2015.pem #11 43.27 Adding debian:E-Tugra_Global_Root_CA_RSA_v3.pem #11 43.27 Adding debian:USERTrust_ECC_Certification_Authority.pem #11 43.28 Adding debian:ePKI_Root_Certification_Authority.pem #11 43.28 Adding debian:ISRG_Root_X1.pem #11 43.29 Adding debian:HARICA_TLS_ECC_Root_CA_2021.pem #11 43.29 Adding debian:GTS_Root_R3.pem #11 43.30 Adding debian:Hellenic_Academic_and_Research_Institutions_ECC_RootCA_2015.pem #11 43.30 Adding debian:Security_Communication_RootCA2.pem #11 43.31 Adding debian:GlobalSign_Root_CA_-_R3.pem #11 43.35 done. #11 43.36 Processing triggers for mime-support (3.64ubuntu1) ... #11 43.40 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #11 43.45 Processing triggers for ca-certificates (20230311ubuntu0.20.04.1) ... #11 43.47 Updating certificates in /etc/ssl/certs... #11 44.82 0 added, 0 removed; done. #11 44.82 Running hooks in /etc/ca-certificates/update.d... #11 44.83 #11 45.16 done. #11 45.16 done. #11 DONE 45.9s #12 [ 8/17] WORKDIR /opt/mlflow #12 DONE 0.1s #13 [ 9/17] RUN pip install mlflow==2.12.2 #13 1.009 Collecting mlflow==2.12.2 #13 1.075 Downloading mlflow-2.12.2-py3-none-any.whl.metadata (29 kB) #13 1.207 Collecting Flask < 4 (from mlflow==2.12.2) #13 1.222 Downloading flask-3.0.3-py3-none-any.whl.metadata (3.2 kB) #13 1.309 Collecting alembic!=1.10.0, < 2 (from mlflow==2.12.2) #13 1.324 Downloading alembic-1.13.1-py3-none-any.whl.metadata (7.4 kB) #13 1.395 Collecting click < 9,>=7.0 (from mlflow==2.12.2) #13 1.409 Downloading click-8.1.7-py3-none-any.whl.metadata (3.0 kB) #13 1.467 Collecting cloudpickle < 4 (from mlflow==2.12.2) #13 1.483 Downloading cloudpickle-3.0.0-py3-none-any.whl.metadata (7.0 kB) #13 1.564 Collecting docker < 8,>=4.0.0 (from mlflow==2.12.2) #13 1.579 Downloading docker-7.0.0-py3-none-any.whl.metadata (3.5 kB) #13 1.635 Collecting entrypoints < 1 (from mlflow==2.12.2) #13 1.651 Downloading entrypoints-0.4-py3-none-any.whl.metadata (2.6 kB) #13 1.742 Collecting gitpython < 4,>=3.1.9 (from mlflow==2.12.2) #13 1.757 Downloading GitPython-3.1.43-py3-none-any.whl.metadata (13 kB) #13 1.822 Collecting graphene < 4 (from mlflow==2.12.2) #13 1.837 Downloading graphene-3.3-py2.py3-none-any.whl.metadata (7.7 kB) #13 1.946 Collecting importlib-metadata!=4.7.0, < 8,>=3.7.0 (from mlflow==2.12.2) #13 1.960 Downloading importlib_metadata-7.1.0-py3-none-any.whl.metadata (4.7 kB) #13 2.027 Collecting markdown < 4,>=3.3 (from mlflow==2.12.2) #13 2.079 Downloading Markdown-3.6-py3-none-any.whl.metadata (7.0 kB) #13 2.299 Collecting matplotlib < 4 (from mlflow==2.12.2) #13 2.314 Downloading matplotlib-3.7.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (5.7 kB) #13 2.593 Collecting numpy < 2 (from mlflow==2.12.2) #13 2.612 Downloading numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB) #13 2.685 Collecting packaging < 25 (from mlflow==2.12.2) #13 2.698 Downloading packaging-24.0-py3-none-any.whl.metadata (3.2 kB) #13 2.902 Collecting pandas < 3 (from mlflow==2.12.2) #13 2.916 Downloading pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB) #13 3.172 Collecting protobuf < 5,>=3.12.0 (from mlflow==2.12.2) #13 3.187 Downloading protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes) #13 3.323 Collecting pyarrow < 16,>=4.0.0 (from mlflow==2.12.2) #13 3.338 Downloading pyarrow-15.0.2-cp38-cp38-manylinux_2_28_x86_64.whl.metadata (3.0 kB) #13 3.456 Collecting pytz < 2025 (from mlflow==2.12.2) #13 3.471 Downloading pytz-2024.1-py2.py3-none-any.whl.metadata (22 kB) #13 3.482 Requirement already satisfied: pyyaml < 7,>=5.1 in /usr/lib/python3/dist-packages (from mlflow==2.12.2) (5.3.1) #13 3.672 Collecting querystring-parser < 2 (from mlflow==2.12.2) #13 3.689 Downloading querystring_parser-1.2.4-py2.py3-none-any.whl.metadata (559 bytes) #13 3.780 Collecting requests < 3,>=2.17.3 (from mlflow==2.12.2) #13 3.794 Downloading requests-2.31.0-py3-none-any.whl.metadata (4.6 kB) #13 3.969 Collecting scikit-learn < 2 (from mlflow==2.12.2) #13 3.985 Downloading scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) #13 4.159 Collecting scipy < 2 (from mlflow==2.12.2) #13 4.175 Downloading scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (58 kB) #13 4.199 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 58.9/58.9 kB 2.7 MB/s eta 0:00:00 #13 4.633 Collecting sqlalchemy < 3,>=1.4.0 (from mlflow==2.12.2) #13 4.648 Downloading SQLAlchemy-2.0.30-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.6 kB) #13 4.709 Collecting sqlparse < 1,>=0.4.0 (from mlflow==2.12.2) #13 4.724 Downloading sqlparse-0.5.0-py3-none-any.whl.metadata (3.9 kB) #13 4.795 Collecting Jinja2 < 4,>=2.11 (from mlflow==2.12.2) #13 4.809 Downloading jinja2-3.1.4-py3-none-any.whl.metadata (2.6 kB) #13 4.929 Collecting gunicorn < 23 (from mlflow==2.12.2) #13 4.944 Downloading gunicorn-22.0.0-py3-none-any.whl.metadata (4.4 kB) #13 5.043 Collecting Mako (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.058 Downloading Mako-1.3.5-py3-none-any.whl.metadata (2.9 kB) #13 5.122 Collecting typing-extensions>=4 (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.136 Downloading typing_extensions-4.11.0-py3-none-any.whl.metadata (3.0 kB) #13 5.225 Collecting importlib-resources (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.241 Downloading importlib_resources-6.4.0-py3-none-any.whl.metadata (3.9 kB) #13 5.348 Collecting urllib3>=1.26.0 (from docker < 8,>=4.0.0->mlflow==2.12.2) #13 5.362 Downloading urllib3-2.2.1-py3-none-any.whl.metadata (6.4 kB) #13 5.445 Collecting Werkzeug>=3.0.0 (from Flask < 4->mlflow==2.12.2) #13 5.460 Downloading werkzeug-3.0.3-py3-none-any.whl.metadata (3.7 kB) #13 5.519 Collecting itsdangerous>=2.1.2 (from Flask < 4->mlflow==2.12.2) #13 5.537 Downloading itsdangerous-2.2.0-py3-none-any.whl.metadata (1.9 kB) #13 5.606 Collecting blinker>=1.6.2 (from Flask < 4->mlflow==2.12.2) #13 5.623 Downloading blinker-1.8.2-py3-none-any.whl.metadata (1.6 kB) #13 5.716 Collecting gitdb < 5,>=4.0.1 (from gitpython < 4,>=3.1.9->mlflow==2.12.2) #13 5.732 Downloading gitdb-4.0.11-py3-none-any.whl.metadata (1.2 kB) #13 5.847 Collecting graphql-core < 3.3,>=3.1 (from graphene < 4->mlflow==2.12.2) #13 5.862 Downloading graphql_core-3.2.3-py3-none-any.whl.metadata (10 kB) #13 5.920 Collecting graphql-relay < 3.3,>=3.1 (from graphene < 4->mlflow==2.12.2) #13 5.938 Downloading graphql_relay-3.2.0-py3-none-any.whl.metadata (12 kB) #13 6.001 Collecting aniso8601 < 10,>=8 (from graphene < 4->mlflow==2.12.2) #13 6.017 Downloading aniso8601-9.0.1-py2.py3-none-any.whl.metadata (23 kB) #13 6.122 Collecting zipp>=0.5 (from importlib-metadata!=4.7.0, < 8,>=3.7.0->mlflow==2.12.2) #13 6.136 Downloading zipp-3.18.2-py3-none-any.whl.metadata (3.5 kB) #13 6.235 Collecting MarkupSafe>=2.0 (from Jinja2 < 4,>=2.11->mlflow==2.12.2) #13 6.250 Downloading MarkupSafe-2.1.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.0 kB) #13 6.406 Collecting contourpy>=1.0.1 (from matplotlib < 4->mlflow==2.12.2) #13 6.420 Downloading contourpy-1.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.9 kB) #13 6.468 Collecting cycler>=0.10 (from matplotlib < 4->mlflow==2.12.2) #13 6.485 Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB) #13 6.657 Collecting fonttools>=4.22.0 (from matplotlib < 4->mlflow==2.12.2) #13 6.672 Downloading fonttools-4.51.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (159 kB) #13 6.694 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 159.5/159.5 kB 7.7 MB/s eta 0:00:00 #13 6.787 Collecting kiwisolver>=1.0.1 (from matplotlib < 4->mlflow==2.12.2) #13 6.826 Downloading kiwisolver-1.4.5-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (6.4 kB) #13 7.174 Collecting pillow>=6.2.0 (from matplotlib < 4->mlflow==2.12.2) #13 7.191 Downloading pillow-10.3.0-cp38-cp38-manylinux_2_28_x86_64.whl.metadata (9.2 kB) #13 7.275 Collecting pyparsing>=2.3.1 (from matplotlib < 4->mlflow==2.12.2) #13 7.290 Downloading pyparsing-3.1.2-py3-none-any.whl.metadata (5.1 kB) #13 7.351 Collecting python-dateutil>=2.7 (from matplotlib < 4->mlflow==2.12.2) #13 7.365 Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB) #13 7.525 Collecting tzdata>=2022.1 (from pandas < 3->mlflow==2.12.2) #13 7.538 Downloading tzdata-2024.1-py2.py3-none-any.whl.metadata (1.4 kB) #13 7.620 Collecting six (from querystring-parser < 2->mlflow==2.12.2) #13 7.633 Downloading six-1.16.0-py2.py3-none-any.whl.metadata (1.8 kB) #13 7.758 Collecting charset-normalizer < 4,>=2 (from requests < 3,>=2.17.3->mlflow==2.12.2) #13 7.772 Downloading charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (33 kB) #13 7.834 Collecting idna < 4,>=2.5 (from requests < 3,>=2.17.3->mlflow==2.12.2) #13 7.848 Downloading 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fonttools, entrypoints, cycler, cloudpickle, click, charset-normalizer, certifi, blinker, Werkzeug, sqlalchemy, scipy, requests, querystring-parser, python-dateutil, pyarrow, Mako, Jinja2, importlib-resources, importlib-metadata, gunicorn, graphql-relay, gitdb, contourpy, scikit-learn, pandas, matplotlib, markdown, graphene, gitpython, Flask, docker, alembic, mlflow #13 34.72 Successfully installed Flask-3.0.3 Jinja2-3.1.4 Mako-1.3.5 MarkupSafe-2.1.5 Werkzeug-3.0.3 alembic-1.13.1 aniso8601-9.0.1 blinker-1.8.2 certifi-2024.2.2 charset-normalizer-3.3.2 click-8.1.7 cloudpickle-3.0.0 contourpy-1.1.1 cycler-0.12.1 docker-7.0.0 entrypoints-0.4 fonttools-4.51.0 gitdb-4.0.11 gitpython-3.1.43 graphene-3.3 graphql-core-3.2.3 graphql-relay-3.2.0 greenlet-3.0.3 gunicorn-22.0.0 idna-3.7 importlib-metadata-7.1.0 importlib-resources-6.4.0 itsdangerous-2.2.0 joblib-1.4.2 kiwisolver-1.4.5 markdown-3.6 matplotlib-3.7.5 mlflow-2.12.2 numpy-1.24.4 packaging-24.0 pandas-2.0.3 pillow-10.3.0 protobuf-4.25.3 pyarrow-15.0.2 pyparsing-3.1.2 python-dateutil-2.9.0.post0 pytz-2024.1 querystring-parser-1.2.4 requests-2.31.0 scikit-learn-1.3.2 scipy-1.10.1 six-1.16.0 smmap-5.0.1 sqlalchemy-2.0.30 sqlparse-0.5.0 threadpoolctl-3.5.0 typing-extensions-4.11.0 tzdata-2024.1 urllib3-2.2.1 zipp-3.18.2 #13 34.72 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #13 DONE 36.4s #14 [10/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:pom -DoutputDirectory=/opt/java #14 1.558 [INFO] Scanning for projects... #14 1.845 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-clean-plugin/2.5/maven-clean-plugin-2.5.pom #14 2.423 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-clean-plugin/2.5/maven-clean-plugin-2.5.pom (3.9 kB at 6.7 kB/s) #14 2.448 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-plugins/22/maven-plugins-22.pom #14 2.499 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-plugins/22/maven-plugins-22.pom (13 kB at 256 kB/s) #14 2.511 [INFO] Downloading from central: 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12.86 [INFO] Finished at: 2024-05-16T20:25:32Z #14 12.86 [INFO] ------------------------------------------------------------------------ #14 DONE 13.0s #15 [11/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:jar -DoutputDirectory=/opt/java/jars #15 1.696 [INFO] Scanning for projects... #15 1.897 [INFO] #15 1.897 [INFO] ------------------ < org.apache.maven:standalone-pom >------------------- #15 1.898 [INFO] Building Maven Stub Project (No POM) 1 #15 1.898 [INFO] --------------------------------[ pom ]--------------------------------- #15 1.905 [INFO] #15 1.905 [INFO] --- maven-dependency-plugin:2.8:copy (default-cli) @ standalone-pom --- #15 2.594 [INFO] Configured Artifact: org.mlflow:mlflow-scoring:2.12.2:jar #15 2.804 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/mlflow/mlflow-scoring/2.12.2/mlflow-scoring-2.12.2.jar #15 3.207 [INFO] Downloaded from central: 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Downloaded from google-maven-central: https://maven-central.storage-download.googleapis.com/repos/central/data/org/apache/httpcomponents/httpclient/4.5.6/httpclient-4.5.6.jar (767 kB at 83 kB/s) #17 133.8 [INFO] Downloaded from google-maven-central: https://maven-central.storage-download.googleapis.com/repos/central/data/commons-io/commons-io/2.5/commons-io-2.5.jar (209 kB at 22 kB/s) #17 133.8 [INFO] Downloaded from google-maven-central: https://maven-central.storage-download.googleapis.com/repos/central/data/commons-codec/commons-codec/1.10/commons-codec-1.10.jar (284 kB at 29 kB/s) #17 133.9 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/com/google/protobuf/protobuf-java/3.19.4/protobuf-java-3.19.4.jar #17 134.0 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/com/google/protobuf/protobuf-java/3.19.4/protobuf-java-3.19.4.jar (1.7 MB at 13 MB/s) #17 134.1 [INFO] #17 134.1 [INFO] --- maven-dependency-plugin:2.8:copy-dependencies (default-cli) @ mlflow-scoring --- #17 134.4 [INFO] Copying wagon-provider-api-1.0-beta-6.jar to /opt/java/jars/wagon-provider-api-1.0-beta-6.jar #17 134.4 [INFO] Copying mleap-tensor_2.11-0.10.3.jar to /opt/java/jars/mleap-tensor_2.11-0.10.3.jar #17 134.4 [INFO] Copying maven-error-diagnostics-2.2.1.jar to /opt/java/jars/maven-error-diagnostics-2.2.1.jar #17 134.4 [INFO] Copying breeze_2.11-0.13.2.jar to /opt/java/jars/breeze_2.11-0.13.2.jar #17 134.5 [INFO] Copying plexus-java-0.9.8.jar to /opt/java/jars/plexus-java-0.9.8.jar #17 134.5 [INFO] Copying scala-library-2.11.8.jar to /opt/java/jars/scala-library-2.11.8.jar #17 134.5 [INFO] Copying maven-settings-2.2.1.jar to /opt/java/jars/maven-settings-2.2.1.jar #17 134.5 [INFO] Copying sourcecode_2.11-0.1.3.jar to /opt/java/jars/sourcecode_2.11-0.1.3.jar #17 134.5 [INFO] Copying httpcore-4.4.10.jar to /opt/java/jars/httpcore-4.4.10.jar #17 134.5 [INFO] Copying maven-surefire-common-2.22.0.jar to /opt/java/jars/maven-surefire-common-2.22.0.jar #17 134.5 [INFO] Copying macro-compat_2.11-1.1.1.jar to /opt/java/jars/macro-compat_2.11-1.1.1.jar #17 134.5 [INFO] Copying bundle-ml_2.11-0.10.3.jar to /opt/java/jars/bundle-ml_2.11-0.10.3.jar #17 134.5 [INFO] Copying fastparse-utils_2.11-0.4.2.jar to /opt/java/jars/fastparse-utils_2.11-0.4.2.jar #17 134.5 [INFO] Copying maven-artifact-manager-2.2.1.jar to /opt/java/jars/maven-artifact-manager-2.2.1.jar #17 134.5 [INFO] Copying jackrabbit-webdav-1.5.0.jar to /opt/java/jars/jackrabbit-webdav-1.5.0.jar #17 134.5 [INFO] Copying lenses_2.11-0.4.12.jar to /opt/java/jars/lenses_2.11-0.4.12.jar #17 134.5 [INFO] Copying asm-6.1.1.jar to /opt/java/jars/asm-6.1.1.jar #17 134.5 [INFO] Copying jackson-annotations-2.10.0.jar to /opt/java/jars/jackson-annotations-2.10.0.jar #17 134.5 [INFO] Copying snakeyaml-1.24.jar to /opt/java/jars/snakeyaml-1.24.jar #17 134.5 [INFO] Copying machinist_2.11-0.6.1.jar to /opt/java/jars/machinist_2.11-0.6.1.jar #17 134.5 [INFO] Copying maven-toolchain-2.2.1.jar to /opt/java/jars/maven-toolchain-2.2.1.jar #17 134.5 [INFO] Copying spire_2.11-0.13.0.jar to /opt/java/jars/spire_2.11-0.13.0.jar #17 134.5 [INFO] Copying maven-plugin-api-2.2.1.jar to /opt/java/jars/maven-plugin-api-2.2.1.jar #17 134.5 [INFO] Copying maven-project-2.2.1.jar to /opt/java/jars/maven-project-2.2.1.jar #17 134.5 [INFO] Copying qdox-2.0-M7.jar to /opt/java/jars/qdox-2.0-M7.jar #17 134.5 [INFO] Copying jetty-server-9.4.11.v20180605.jar to /opt/java/jars/jetty-server-9.4.11.v20180605.jar #17 134.5 [INFO] Copying jackson-core-2.10.0.jar to /opt/java/jars/jackson-core-2.10.0.jar #17 134.5 [INFO] Copying core-1.1.2.jar to /opt/java/jars/core-1.1.2.jar #17 134.5 [INFO] Copying jetty-security-9.4.11.v20180605.jar to /opt/java/jars/jetty-security-9.4.11.v20180605.jar #17 134.5 [INFO] Copying config-1.3.0.jar to /opt/java/jars/config-1.3.0.jar #17 134.5 [INFO] Copying maven-monitor-2.2.1.jar to /opt/java/jars/maven-monitor-2.2.1.jar #17 134.5 [INFO] Copying wagon-http-1.0-beta-6.jar to /opt/java/jars/wagon-http-1.0-beta-6.jar #17 134.5 [INFO] Copying mleap-core_2.11-0.10.3.jar to /opt/java/jars/mleap-core_2.11-0.10.3.jar #17 134.5 [INFO] Copying hamcrest-core-1.3.jar to /opt/java/jars/hamcrest-core-1.3.jar #17 134.5 [INFO] Copying breeze-macros_2.11-0.13.2.jar to /opt/java/jars/breeze-macros_2.11-0.13.2.jar #17 134.5 [INFO] Copying xercesMinimal-1.9.6.2.jar to /opt/java/jars/xercesMinimal-1.9.6.2.jar #17 134.5 [INFO] Copying jetty-http-9.4.11.v20180605.jar to /opt/java/jars/jetty-http-9.4.11.v20180605.jar #17 134.5 [INFO] Copying fastparse_2.11-0.4.2.jar to /opt/java/jars/fastparse_2.11-0.4.2.jar #17 134.5 [INFO] Copying protobuf-java-3.19.4.jar to /opt/java/jars/protobuf-java-3.19.4.jar #17 134.5 [INFO] Copying scala-arm_2.11-2.0.jar to /opt/java/jars/scala-arm_2.11-2.0.jar #17 134.5 [INFO] Copying arpack_combined_all-0.1.jar to /opt/java/jars/arpack_combined_all-0.1.jar #17 134.5 [INFO] Copying shapeless_2.11-2.3.2.jar to /opt/java/jars/shapeless_2.11-2.3.2.jar #17 134.5 [INFO] Copying mleap-runtime_2.11-0.10.3.jar to /opt/java/jars/mleap-runtime_2.11-0.10.3.jar #17 134.5 [INFO] Copying scala-reflect-2.11.8.jar to /opt/java/jars/scala-reflect-2.11.8.jar #17 134.5 [INFO] Copying commons-logging-1.2.jar to /opt/java/jars/commons-logging-1.2.jar #17 134.5 [INFO] Copying jetty-util-9.4.11.v20180605.jar to /opt/java/jars/jetty-util-9.4.11.v20180605.jar #17 134.5 [INFO] Copying jackson-databind-2.10.0.jar to /opt/java/jars/jackson-databind-2.10.0.jar #17 134.5 [INFO] Copying mleap-spark_2.11-0.10.3.jar to /opt/java/jars/mleap-spark_2.11-0.10.3.jar #17 134.5 [INFO] Copying plexus-utils-1.5.15.jar to /opt/java/jars/plexus-utils-1.5.15.jar #17 134.5 [INFO] Copying spire-macros_2.11-0.13.0.jar to /opt/java/jars/spire-macros_2.11-0.13.0.jar #17 134.5 [INFO] Copying plexus-interpolation-1.11.jar to /opt/java/jars/plexus-interpolation-1.11.jar #17 134.5 [INFO] Copying plexus-cipher-1.4.jar to /opt/java/jars/plexus-cipher-1.4.jar #17 134.5 [INFO] Copying spark-mllib-local_2.11-2.3.0.jar to /opt/java/jars/spark-mllib-local_2.11-2.3.0.jar #17 134.5 [INFO] Copying jtransforms-2.4.0.jar to /opt/java/jars/jtransforms-2.4.0.jar #17 134.5 [INFO] Copying plexus-container-default-1.0-alpha-9-stable-1.jar to /opt/java/jars/plexus-container-default-1.0-alpha-9-stable-1.jar #17 134.5 [INFO] Copying spray-json_2.11-1.3.2.jar to /opt/java/jars/spray-json_2.11-1.3.2.jar #17 134.5 [INFO] Copying jetty-servlet-9.4.11.v20180605.jar to /opt/java/jars/jetty-servlet-9.4.11.v20180605.jar #17 134.5 [INFO] Copying commons-io-2.5.jar to /opt/java/jars/commons-io-2.5.jar #17 134.5 [INFO] Copying surefire-logger-api-2.22.0.jar to /opt/java/jars/surefire-logger-api-2.22.0.jar #17 134.5 [INFO] Copying mleap-base_2.11-0.10.3.jar to /opt/java/jars/mleap-base_2.11-0.10.3.jar #17 134.5 [INFO] Copying surefire-booter-2.22.0.jar to /opt/java/jars/surefire-booter-2.22.0.jar #17 134.5 [INFO] Copying opencsv-2.3.jar to /opt/java/jars/opencsv-2.3.jar #17 134.5 [INFO] Copying spark-tags_2.11-2.3.0.jar to /opt/java/jars/spark-tags_2.11-2.3.0.jar #17 134.5 [INFO] Copying jackrabbit-jcr-commons-1.5.0.jar to /opt/java/jars/jackrabbit-jcr-commons-1.5.0.jar #17 134.5 [INFO] Copying classworlds-1.1.jar to /opt/java/jars/classworlds-1.1.jar #17 134.5 [INFO] Copying nekohtml-1.9.6.2.jar to /opt/java/jars/nekohtml-1.9.6.2.jar #17 134.5 [INFO] Copying maven-core-2.2.1.jar to /opt/java/jars/maven-core-2.2.1.jar #17 134.5 [INFO] Copying maven-surefire-plugin-2.22.0.jar to /opt/java/jars/maven-surefire-plugin-2.22.0.jar #17 134.5 [INFO] Copying mleap-spark-base_2.11-0.10.3.jar to /opt/java/jars/mleap-spark-base_2.11-0.10.3.jar #17 134.5 [INFO] Copying maven-repository-metadata-2.2.1.jar to /opt/java/jars/maven-repository-metadata-2.2.1.jar #17 134.5 [INFO] Copying wagon-webdav-jackrabbit-1.0-beta-6.jar to /opt/java/jars/wagon-webdav-jackrabbit-1.0-beta-6.jar #17 134.5 [INFO] Copying maven-artifact-2.2.1.jar to /opt/java/jars/maven-artifact-2.2.1.jar #17 134.5 [INFO] Copying commons-codec-1.10.jar to /opt/java/jars/commons-codec-1.10.jar #17 134.5 [INFO] Copying maven-plugin-descriptor-2.2.1.jar to /opt/java/jars/maven-plugin-descriptor-2.2.1.jar #17 134.5 [INFO] Copying unused-1.0.0.jar to /opt/java/jars/unused-1.0.0.jar #17 134.5 [INFO] Copying scalapb-runtime_2.11-0.6.0.jar to /opt/java/jars/scalapb-runtime_2.11-0.6.0.jar #17 134.5 [INFO] Copying maven-profile-2.2.1.jar to /opt/java/jars/maven-profile-2.2.1.jar #17 134.5 [INFO] Copying plexus-sec-dispatcher-1.3.jar to /opt/java/jars/plexus-sec-dispatcher-1.3.jar #17 134.5 [INFO] Copying maven-plugin-annotations-3.5.jar to /opt/java/jars/maven-plugin-annotations-3.5.jar #17 134.5 [INFO] Copying maven-reporting-api-2.2.1.jar to /opt/java/jars/maven-reporting-api-2.2.1.jar #17 134.5 [INFO] Copying javax.servlet-api-3.1.0.jar to /opt/java/jars/javax.servlet-api-3.1.0.jar #17 134.5 [INFO] Copying maven-plugin-parameter-documenter-2.2.1.jar to /opt/java/jars/maven-plugin-parameter-documenter-2.2.1.jar #17 134.5 [INFO] Copying commons-math3-3.4.1.jar to /opt/java/jars/commons-math3-3.4.1.jar #17 134.5 [INFO] Copying junit-4.12.jar to /opt/java/jars/junit-4.12.jar #17 134.5 [INFO] Copying slf4j-jdk14-1.7.25.jar to /opt/java/jars/slf4j-jdk14-1.7.25.jar #17 134.5 [INFO] Copying slf4j-api-1.7.25.jar to /opt/java/jars/slf4j-api-1.7.25.jar #17 134.5 [INFO] Copying jetty-io-9.4.11.v20180605.jar to /opt/java/jars/jetty-io-9.4.11.v20180605.jar #17 134.5 [INFO] Copying httpclient-4.5.6.jar to /opt/java/jars/httpclient-4.5.6.jar #17 134.5 [INFO] Copying maven-model-2.2.1.jar to /opt/java/jars/maven-model-2.2.1.jar #17 134.5 [INFO] Copying jackson-dataformat-yaml-2.10.0.jar to /opt/java/jars/jackson-dataformat-yaml-2.10.0.jar #17 134.5 [INFO] Copying doxia-logging-api-1.1.jar to /opt/java/jars/doxia-logging-api-1.1.jar #17 134.5 [INFO] Copying surefire-api-2.22.0.jar to /opt/java/jars/surefire-api-2.22.0.jar #17 134.5 [INFO] Copying maven-plugin-registry-2.2.1.jar to /opt/java/jars/maven-plugin-registry-2.2.1.jar #17 134.5 [INFO] Copying backport-util-concurrent-3.1.jar to /opt/java/jars/backport-util-concurrent-3.1.jar #17 134.5 [INFO] Copying wagon-http-shared-1.0-beta-6.jar to /opt/java/jars/wagon-http-shared-1.0-beta-6.jar #17 134.5 [INFO] Copying commons-httpclient-3.1.jar to /opt/java/jars/commons-httpclient-3.1.jar #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] BUILD SUCCESS #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] Total time: 02:12 min #17 134.5 [INFO] Finished at: 2024-05-16T20:27:50Z #17 134.5 [INFO] ------------------------------------------------------------------------ #17 DONE 134.8s #18 [14/17] COPY model_dir/success6g_model /opt/ml/model #18 DONE 0.1s #19 [15/17] RUN python -c \"from mlflow.models import container as C; C._install_pyfunc_deps('/opt/ml/model', install_mlflow=False, enable_mlserver=True, env_manager='virtualenv');\" #19 1.285 2024/05/16 20:27:52 INFO mlflow.models.container: creating and activating custom environment #19 1.289 2024/05/16 20:27:52 INFO mlflow.utils.virtualenv: Installing python 3.10.11 if it does not exist #19 1.673 Downloading Python-3.10.11.tar.xz... #19 1.673 -> https://www.python.org/ftp/python/3.10.11/Python-3.10.11.tar.xz #19 4.792 Installing Python-3.10.11... #19 108.0 Installed Python-3.10.11 to /root/.pyenv/versions/3.10.11 #19 108.4 2024/05/16 20:29:39 INFO mlflow.utils.virtualenv: Creating a new environment in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b with /root/.pyenv/versions/3.10.11/bin/python #19 109.3 created virtual environment CPython3.10.11.final.0-64 in 719ms #19 109.3 creator CPython3Posix(dest=/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b, clear=False, no_vcs_ignore=False, global=False) #19 109.3 seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/.local/share/virtualenv) #19 109.3 added seed packages: pip==24.0, setuptools==69.5.1, wheel==0.43.0 #19 109.3 activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator #19 109.4 2024/05/16 20:29:40 INFO mlflow.utils.virtualenv: Installing dependencies #19 168.4 WARNING: There was an error checking the latest version of pip. #19 169.3 Collecting mlserver!=1.3.1, < 1.4.0,>=1.2.0 #19 169.4 Downloading mlserver-1.3.5-py3-none-any.whl (113 kB) #19 169.4 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 113.2/113.2 kB 3.4 MB/s eta 0:00:00 #19 169.6 Collecting mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0 #19 169.6 Downloading mlserver_mlflow-1.3.5-py3-none-any.whl (10 kB) #19 169.7 Requirement already satisfied: click in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) (8.1.3) #19 169.8 Collecting fastapi!=0.89.0, < =0.89.1,>=0.88.0 (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.8 Downloading fastapi-0.89.1-py3-none-any.whl (55 kB) #19 169.8 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 55.8/55.8 kB 7.4 MB/s eta 0:00:00 #19 169.9 Collecting python-dotenv (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.9 Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB) #19 171.0 Collecting grpcio (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.0 Downloading grpcio-1.63.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB) #19 171.3 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1.4.0,>=1.2.0) (4.25.3) #19 171.5 Collecting uvicorn (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.5 Downloading uvicorn-0.29.0-py3-none-any.whl (60 kB) #19 171.6 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 60.8/60.8 kB 8.7 MB/s eta 0:00:00 #19 171.7 Collecting starlette-exporter (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.7 Downloading starlette_exporter-0.21.0-py3-none-any.whl (14 kB) #19 171.9 Collecting py-grpc-prometheus (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.9 Downloading py_grpc_prometheus-0.8.0-py3-none-any.whl (12 kB) #19 172.1 Collecting aiokafka (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 172.1 Downloading aiokafka-0.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB) #19 172.2 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requirements. 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uvicorn->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (4.11.0) #19 176.9 Collecting aiosignal>=1.1.2 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.9 Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB) #19 177.0 Collecting attrs>=17.3.0 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 177.0 Downloading attrs-23.2.0-py3-none-any.whl (60 kB) #19 177.0 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 60.8/60.8 kB 8.7 MB/s eta 0:00:00 #19 177.2 Collecting frozenlist>=1.1.1 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 177.2 Downloading frozenlist-1.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (239 kB) #19 177.2 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 239.5/239.5 kB 29.8 MB/s eta 0:00:00 #19 177.6 Collecting multidict < 7.0,>=4.5 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 177.7 Downloading multidict-6.0.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (124 kB) #19 177.7 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 124.3/124.3 kB 17.1 MB/s eta 0:00:00 #19 178.0 Collecting yarl < 2.0,>=1.0 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 178.0 Downloading yarl-1.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (301 kB) #19 178.0 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 301.6/301.6 kB 16.3 MB/s eta 0:00:00 #19 178.0 Requirement already satisfied: Mako in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from alembic!=1.10.0, < 2->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (1.3.5) #19 178.1 Requirement already satisfied: Werkzeug>=3.0.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from Flask < 4->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.0.3) #19 178.1 Requirement already satisfied: itsdangerous>=2.1.2 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from Flask < 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/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from sqlalchemy < 3,>=1.4.0->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.0.3) #19 179.6 Collecting sniffio>=1.1 (from anyio < 5,>=3.4.0->starlette==0.22.0->fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 179.6 Downloading sniffio-1.3.1-py3-none-any.whl (10 kB) #19 179.7 Collecting exceptiongroup>=1.0.2 (from anyio < 5,>=3.4.0->starlette==0.22.0->fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 179.7 Downloading exceptiongroup-1.2.1-py3-none-any.whl (16 kB) #19 179.9 Collecting zope.event (from gevent>=0.13->geventhttpclient < =2.0.2,>=1.4.4->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 180.0 Downloading zope.event-5.0-py3-none-any.whl (6.8 kB) #19 180.2 Collecting zope.interface (from gevent>=0.13->geventhttpclient < =2.0.2,>=1.4.4->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 180.2 Downloading zope.interface-6.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (247 kB) #19 180.2 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 247.5/247.5 kB 31.3 MB/s eta 0:00:00 #19 180.2 Requirement already satisfied: smmap < 6,>=3.0.1 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from gitdb < 5,>=4.0.1->gitpython < 4,>=3.1.9->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (5.0.1) #19 181.2 Installing collected packages: brotli, zope.interface, zope.event, uvloop, sniffio, python-rapidjson, python-dotenv, pydantic, prometheus-client, orjson, multidict, importlib-resources, h11, grpcio, frozenlist, exceptiongroup, attrs, aiokafka, aiofiles, yarl, uvicorn, tritonclient, py-grpc-prometheus, gevent, anyio, aiosignal, starlette, geventhttpclient, aiohttp, starlette-exporter, fastapi, mlserver, mlserver-mlflow #19 184.4 Successfully installed aiofiles-23.2.1 aiohttp-3.9.5 aiokafka-0.10.0 aiosignal-1.3.1 anyio-4.3.0 attrs-23.2.0 brotli-1.1.0 exceptiongroup-1.2.1 fastapi-0.89.1 frozenlist-1.4.1 gevent-24.2.1 geventhttpclient-2.0.2 grpcio-1.63.0 h11-0.14.0 importlib-resources-6.4.0 mlserver-1.3.5 mlserver-mlflow-1.3.5 multidict-6.0.5 orjson-3.10.3 prometheus-client-0.20.0 py-grpc-prometheus-0.8.0 pydantic-1.10.15 python-dotenv-1.0.1 python-rapidjson-1.16 sniffio-1.3.1 starlette-0.22.0 starlette-exporter-0.17.1 tritonclient-2.45.0 uvicorn-0.29.0 uvloop-0.19.0 yarl-1.9.4 zope.event-5.0 zope.interface-6.4 #19 184.4 WARNING: There was an error checking the latest version of pip. #19 DONE 186.7s #20 [16/17] RUN chmod o+rwX /opt/mlflow/ #20 DONE 0.4s #21 [17/17] RUN rm -rf /var/lib/apt/lists/* #21 DONE 0.6s #22 exporting to image #22 exporting layers #22 exporting layers 12.0s done #22 writing image sha256:29875270957bd037ee2b833cf9201a001d353e4fef34b1d8c4923b278ef8ac02 done #22 naming to docker.io/5uperpalo/mlflow-success6g done #22 DONE 12.1s ```","title":"Appendix"},{"location":"notebooks/05_model_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = 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event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Model analysis \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [3]: Copied! from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast In [4]: Copied! trainer = dill_load ( \"lgbm_trainer.dill\" ) trainer = dill_load(\"lgbm_trainer.dill\") In [5]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ trainer . target_col ], random_state = random_state , ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ trainer . target_col ], random_state = random_state , ) # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[trainer.target_col], random_state=random_state, ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[trainer.target_col], random_state=random_state, ) In [6]: Copied! df_predicted_proba = trainer . predict_proba ( df = df_test . drop ( columns = trainer . target_col ), binary2d = False ) df_predicted_cls = trainer . predict_cls ( df = df_test . drop ( columns = trainer . target_col )) df_predicted_proba = trainer.predict_proba( df=df_test.drop(columns=trainer.target_col), binary2d=False ) df_predicted_cls = trainer.predict_cls(df=df_test.drop(columns=trainer.target_col)) Model feature importance and \"certainty\" of the model in predicting classes - probability density per class \u00b6 In [7]: Copied! lgb . plot_importance ( trainer . model , importance_type = \"split\" ) # lgb.plot_importance(trainer.model, importance_type=\"gain\") lgb.plot_importance(trainer.model, importance_type=\"split\") # lgb.plot_importance(trainer.model, importance_type=\"gain\") Out[7]: In [8]: Copied! fig = prob_distrib_per_class ( predicted_probs = df_predicted_proba . values . flatten (), actual = df_test [ trainer . target_col ] . values . flatten (), task = \"binary\" , ) fig = prob_distrib_per_class( predicted_probs=df_predicted_proba.values.flatten(), actual=df_test[trainer.target_col].values.flatten(), task=\"binary\", ) SHAP analysis \u00b6 In [9]: Copied! df_test_prep = trainer . preprocessors [ 0 ] . transform ( df_test . drop ( columns = [ trainer . target_col ]) ) df_test_prep [ trainer . target_col ] = df_test [ trainer . target_col ] . astype ( int ) lgb_test , _ = to_lgbdataset ( train = df_test_prep , cat_cols = trainer . cat_cols , target_col = trainer . target_col , id_cols = trainer . id_cols , ) df_test_prep = trainer.preprocessors[0].transform( df_test.drop(columns=[trainer.target_col]) ) df_test_prep[trainer.target_col] = df_test[trainer.target_col].astype(int) lgb_test, _ = to_lgbdataset( train=df_test_prep, cat_cols=trainer.cat_cols, target_col=trainer.target_col, id_cols=trainer.id_cols, ) In [10]: Copied! base_value = trainer . model . predict ( data = lgb_test . data , pred_contrib = True ) shap_values = base_value [:, : - 1 ] base_value = np . mean ( base_value [:, - 1 ]) base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True) shap_values = base_value[:, :-1] base_value = np.mean(base_value[:, -1]) In [11]: Copied! shap . summary_plot ( shap_values , features = lgb_test . data , plot_type = \"dot\" , sort = False , show = True ) shap.summary_plot( shap_values, features=lgb_test.data, plot_type=\"dot\", sort=False, show=True ) In [19]: Copied! df_predicted_cls [ 10 : 12 ] df_predicted_cls[10:12] Out[19]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 10 0 11 1 In [20]: Copied! df_test . iloc [ 10 : 12 ] df_test.iloc[10:12] Out[20]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 579 38.04 89.0 895.5 0.0 22.0 9.77 83.14 34050.0 101.0 14.76 0 123abc 23 100.00 22.0 926.0 0.0 11.0 12.66 83.53 32160.0 101.0 14.72 1 123abc In [21]: Copied! shap_decision_plot = shap . decision_plot ( base_value = base_value , shap_values = shap_values [ 10 : 12 ], features = trainer . model . feature_name (), feature_names = trainer . model . feature_name (), # legend_labels=legend_labels, link = \"logit\" , # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), ) shap_decision_plot = shap.decision_plot( base_value=base_value, shap_values=shap_values[10:12], features=trainer.model.feature_name(), feature_names=trainer.model.feature_name(), # legend_labels=legend_labels, link=\"logit\", # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), )","title":"05_model_analysis"},{"location":"notebooks/05_model_analysis.html#model-analysis","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [3]: Copied! from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast In [4]: Copied! trainer = dill_load ( \"lgbm_trainer.dill\" ) trainer = dill_load(\"lgbm_trainer.dill\") In [5]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ trainer . target_col ], random_state = random_state , ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ trainer . target_col ], random_state = random_state , ) # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[trainer.target_col], random_state=random_state, ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[trainer.target_col], random_state=random_state, ) In [6]: Copied! df_predicted_proba = trainer . predict_proba ( df = df_test . drop ( columns = trainer . target_col ), binary2d = False ) df_predicted_cls = trainer . predict_cls ( df = df_test . drop ( columns = trainer . target_col )) df_predicted_proba = trainer.predict_proba( df=df_test.drop(columns=trainer.target_col), binary2d=False ) df_predicted_cls = trainer.predict_cls(df=df_test.drop(columns=trainer.target_col))","title":"Model analysis"},{"location":"notebooks/05_model_analysis.html#model-feature-importance-and-certainty-of-the-model-in-predicting-classes-probability-density-per-class","text":"In [7]: Copied! lgb . plot_importance ( trainer . model , importance_type = \"split\" ) # lgb.plot_importance(trainer.model, importance_type=\"gain\") lgb.plot_importance(trainer.model, importance_type=\"split\") # lgb.plot_importance(trainer.model, importance_type=\"gain\") Out[7]: In [8]: Copied! fig = prob_distrib_per_class ( predicted_probs = df_predicted_proba . values . flatten (), actual = df_test [ trainer . target_col ] . values . flatten (), task = \"binary\" , ) fig = prob_distrib_per_class( predicted_probs=df_predicted_proba.values.flatten(), actual=df_test[trainer.target_col].values.flatten(), task=\"binary\", )","title":"Model feature importance and \"certainty\" of the model in predicting classes - probability density per class"},{"location":"notebooks/05_model_analysis.html#shap-analysis","text":"In [9]: Copied! df_test_prep = trainer . preprocessors [ 0 ] . transform ( df_test . drop ( columns = [ trainer . target_col ]) ) df_test_prep [ trainer . target_col ] = df_test [ trainer . target_col ] . astype ( int ) lgb_test , _ = to_lgbdataset ( train = df_test_prep , cat_cols = trainer . cat_cols , target_col = trainer . target_col , id_cols = trainer . id_cols , ) df_test_prep = trainer.preprocessors[0].transform( df_test.drop(columns=[trainer.target_col]) ) df_test_prep[trainer.target_col] = df_test[trainer.target_col].astype(int) lgb_test, _ = to_lgbdataset( train=df_test_prep, cat_cols=trainer.cat_cols, target_col=trainer.target_col, id_cols=trainer.id_cols, ) In [10]: Copied! base_value = trainer . model . predict ( data = lgb_test . data , pred_contrib = True ) shap_values = base_value [:, : - 1 ] base_value = np . mean ( base_value [:, - 1 ]) base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True) shap_values = base_value[:, :-1] base_value = np.mean(base_value[:, -1]) In [11]: Copied! shap . summary_plot ( shap_values , features = lgb_test . data , plot_type = \"dot\" , sort = False , show = True ) shap.summary_plot( shap_values, features=lgb_test.data, plot_type=\"dot\", sort=False, show=True ) In [19]: Copied! df_predicted_cls [ 10 : 12 ] df_predicted_cls[10:12] Out[19]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 10 0 11 1 In [20]: Copied! df_test . iloc [ 10 : 12 ] df_test.iloc[10:12] Out[20]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 579 38.04 89.0 895.5 0.0 22.0 9.77 83.14 34050.0 101.0 14.76 0 123abc 23 100.00 22.0 926.0 0.0 11.0 12.66 83.53 32160.0 101.0 14.72 1 123abc In [21]: Copied! shap_decision_plot = shap . decision_plot ( base_value = base_value , shap_values = shap_values [ 10 : 12 ], features = trainer . model . feature_name (), feature_names = trainer . model . feature_name (), # legend_labels=legend_labels, link = \"logit\" , # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), ) shap_decision_plot = shap.decision_plot( base_value=base_value, shap_values=shap_values[10:12], features=trainer.model.feature_name(), feature_names=trainer.model.feature_name(), # legend_labels=legend_labels, link=\"logit\", # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), )","title":"SHAP analysis"},{"location":"notebooks/success6g-edge.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); In [1]: Copied! import pandas as pd import ast import os import redis os . chdir ( \"..\" ) import pandas as pd import ast import os import redis os.chdir(\"..\") Get the data into pandas \u00b6 In [2]: Copied! with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df = pd . concat ([ df , temp_df ], axis = 1 ) with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df = pd.concat([df, temp_df], axis=1) In [3]: Copied! df df Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage 0 37.25 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 37.25 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 2 36.86 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 3 36.86 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 4 36.86 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 ... ... ... ... ... ... ... ... ... ... ... 32740 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32741 NaN NaN NaN NaN NaN 9.44 NaN NaN NaN NaN 32742 NaN NaN NaN NaN NaN 9.49 NaN NaN NaN NaN 32743 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32744 NaN NaN NaN NaN NaN 9.52 NaN NaN NaN NaN 32745 rows \u00d7 10 columns In [4]: Copied! df . dropna ( inplace = True ) df.dropna(inplace=True) In [5]: Copied! df [ \"class\" ] = 0 df . loc [: 100 , [ \"class\" ]] = 1 df . loc [: 100 , [ \"engine_load\" ]] = 100 df[\"class\"] = 0 df.loc[:100, [\"class\"]] = 1 df.loc[:100, [\"engine_load\"]] = 100 In [6]: Copied! from lightgbm import LGBMClassifier model = LGBMClassifier () model . fit ( df . drop ( columns = [ \"class\" ]), df [ \"class\" ]) from lightgbm import LGBMClassifier model = LGBMClassifier() model.fit(df.drop(columns=[\"class\"]), df[\"class\"]) [LightGBM] [Info] Number of positive: 101, number of negative: 934 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 508 [LightGBM] [Info] Number of data points in the train set: 1035, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097585 -> initscore=-2.224356 [LightGBM] [Info] Start training from score -2.224356 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf Out[6]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} LGBMClassifier() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. LGBMClassifier LGBMClassifier() Redis \u00b6 Install and start the redis database \u00b6 pip install prometheus-client sudo apt install lsb-release curl gpg curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list sudo apt-get update sudo apt-get install redis sudo service redis-server start # sudo service redis-server stop sudo service redis-server status redis-cli ping Fill the Redis database \u00b6 send data to redis redis-cli -h localhost -p 6379 redis-cli -h 10.152.183.169 -a redis -p 6379 XADD v2x * car_id 123 engine_load 37.25 engine_coolant_temp 17.00 engine_speed 904.50 vehicle_speed 0.00 intake_air_temp 10.00 maf 12.55 throttle_position 83.14 fuel_rg_pressure 37270.00 barometric_pressure 101.00 control_voltage 0.0666 In [7]: Copied! # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis . Redis ( host = redis_host , port = 6379 , password = \"redis\" ) # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis.Redis(host=redis_host, port=6379, password=\"redis\") # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) In [8]: Copied! entry_data = { \"field1\" : \"value1\" , \"field2\" : \"value2\" } for index , row in df . iterrows (): redis_conn . xadd ( \"v2x\" , dict ( row )) entry_data = {\"field1\": \"value1\", \"field2\": \"value2\"} for index, row in df.iterrows(): redis_conn.xadd(\"v2x\", dict(row)) Retrieve data from redis \u00b6 In [13]: Copied! while True : # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[ stream , [[ number , parts ]]]] = redis_conn . xread ( streams = { \"v2x\" : \"$\" }, block = 0 ) keys = [ p . decode ( \"utf-8\" ) for p in parts . keys ()] vals = [ float ( p . decode ( \"utf-8\" )) for p in parts . values ()] d = pd . DataFrame ( dict ( zip ( keys , vals )), index = [ 0 ]) d [ \"predicted_class\" ] = model . predict ( d . drop ( columns = [ \"car_id\" ]))[ 0 ] print ( d ) while True: # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[stream, [[number, parts]]]] = redis_conn.xread(streams={\"v2x\": \"$\"}, block=0) keys = [p.decode(\"utf-8\") for p in parts.keys()] vals = [float(p.decode(\"utf-8\")) for p in parts.values()] d = pd.DataFrame(dict(zip(keys, vals)), index=[0]) d[\"predicted_class\"] = model.predict(d.drop(columns=[\"car_id\"]))[0] print(d)","title":"success6g-edge"},{"location":"notebooks/success6g-edge.html#get-the-data-into-pandas","text":"In [2]: Copied! with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df = pd . concat ([ df , temp_df ], axis = 1 ) with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df = pd.concat([df, temp_df], axis=1) In [3]: Copied! df df Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage 0 37.25 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 37.25 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 2 36.86 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 3 36.86 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 4 36.86 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 ... ... ... ... ... ... ... ... ... ... ... 32740 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32741 NaN NaN NaN NaN NaN 9.44 NaN NaN NaN NaN 32742 NaN NaN NaN NaN NaN 9.49 NaN NaN NaN NaN 32743 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32744 NaN NaN NaN NaN NaN 9.52 NaN NaN NaN NaN 32745 rows \u00d7 10 columns In [4]: Copied! df . dropna ( inplace = True ) df.dropna(inplace=True) In [5]: Copied! df [ \"class\" ] = 0 df . loc [: 100 , [ \"class\" ]] = 1 df . loc [: 100 , [ \"engine_load\" ]] = 100 df[\"class\"] = 0 df.loc[:100, [\"class\"]] = 1 df.loc[:100, [\"engine_load\"]] = 100 In [6]: Copied! from lightgbm import LGBMClassifier model = LGBMClassifier () model . fit ( df . drop ( columns = [ \"class\" ]), df [ \"class\" ]) from lightgbm import LGBMClassifier model = LGBMClassifier() model.fit(df.drop(columns=[\"class\"]), df[\"class\"]) [LightGBM] [Info] Number of positive: 101, number of negative: 934 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 508 [LightGBM] [Info] Number of data points in the train set: 1035, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097585 -> initscore=-2.224356 [LightGBM] [Info] Start training from score -2.224356 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf Out[6]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} LGBMClassifier() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. LGBMClassifier LGBMClassifier()","title":"Get the data into pandas"},{"location":"notebooks/success6g-edge.html#redis","text":"","title":"Redis"},{"location":"notebooks/success6g-edge.html#install-and-start-the-redis-database","text":"pip install prometheus-client sudo apt install lsb-release curl gpg curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list sudo apt-get update sudo apt-get install redis sudo service redis-server start # sudo service redis-server stop sudo service redis-server status redis-cli ping","title":"Install and start the redis database"},{"location":"notebooks/success6g-edge.html#fill-the-redis-database","text":"send data to redis redis-cli -h localhost -p 6379 redis-cli -h 10.152.183.169 -a redis -p 6379 XADD v2x * car_id 123 engine_load 37.25 engine_coolant_temp 17.00 engine_speed 904.50 vehicle_speed 0.00 intake_air_temp 10.00 maf 12.55 throttle_position 83.14 fuel_rg_pressure 37270.00 barometric_pressure 101.00 control_voltage 0.0666 In [7]: Copied! # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis . Redis ( host = redis_host , port = 6379 , password = \"redis\" ) # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis.Redis(host=redis_host, port=6379, password=\"redis\") # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) In [8]: Copied! entry_data = { \"field1\" : \"value1\" , \"field2\" : \"value2\" } for index , row in df . iterrows (): redis_conn . xadd ( \"v2x\" , dict ( row )) entry_data = {\"field1\": \"value1\", \"field2\": \"value2\"} for index, row in df.iterrows(): redis_conn.xadd(\"v2x\", dict(row))","title":"Fill the Redis database"},{"location":"notebooks/success6g-edge.html#retrieve-data-from-redis","text":"In [13]: Copied! while True : # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[ stream , [[ number , parts ]]]] = redis_conn . xread ( streams = { \"v2x\" : \"$\" }, block = 0 ) keys = [ p . decode ( \"utf-8\" ) for p in parts . keys ()] vals = [ float ( p . decode ( \"utf-8\" )) for p in parts . values ()] d = pd . DataFrame ( dict ( zip ( keys , vals )), index = [ 0 ]) d [ \"predicted_class\" ] = model . predict ( d . drop ( columns = [ \"car_id\" ]))[ 0 ] print ( d ) while True: # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[stream, [[number, parts]]]] = redis_conn.xread(streams={\"v2x\": \"$\"}, block=0) keys = [p.decode(\"utf-8\") for p in parts.keys()] vals = [float(p.decode(\"utf-8\")) for p in parts.values()] d = pd.DataFrame(dict(zip(keys, vals)), index=[0]) d[\"predicted_class\"] = model.predict(d.drop(columns=[\"car_id\"]))[0] print(d)","title":"Retrieve data from redis"}]}
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