automl.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Automated ML\n",
        "\n",
        "TODO: Import Dependencies. In the cell below, import all the dependencies that you will need to complete the project."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Azure Machine Learning and Pipeline SDK-specific imports"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import logging\n",
        "import os\n",
        "import csv\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "import pkg_resources\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.core.dataset import Dataset\n",
        "\n",
        "from azureml.pipeline.steps import AutoMLStep\n",
        "\n",
        "# Check core SDK version number\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "SDK version: 1.37.0\n"
        }
      ],
      "execution_count": 1,
      "metadata": {
        "gather": {
          "logged": 1643120039844
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Compute cluster"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "ws = Workspace.from_config()\r\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\r\n",
        "\r\n",
        "# choose a name for experiment\r\n",
        "experiment_name = 'palsy'\r\n",
        "project_folder = './palsy-project'\r\n",
        "\r\n",
        "\r\n",
        "experiment=Experiment(ws, experiment_name)\r\n",
        "print(experiment)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "quick-starts-ws-178503\naml-quickstarts-178503\nsouthcentralus\ncdbe0b43-92a0-4715-838a-f2648cc7ad21\nExperiment(Name: palsy,\nWorkspace: quick-starts-ws-178503)\n"
        }
      ],
      "execution_count": 2,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1643120040742
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core.compute import AmlCompute\n",
        "from azureml.core.compute import ComputeTarget\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# NOTE: update the cluster name to match the existing cluster\n",
        "# Choose a name for your CPU cluster\n",
        "amlcompute_cluster_name = \"cpucluster4#\"#auto-ml\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
        "    print('Found existing cluster, use it.')\n",
        "except ComputeTargetException:\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS3_V2',# for GPU, use \"STANDARD_NC6\"\n",
        "                                                           #vm_priority = 'lowpriority', # optional\n",
        "                                                           max_nodes=4)\n",
        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
        "\n",
        "compute_target.wait_for_completion(show_output=True) #min_node_count = 1, , timeout_in_minutes = 10\n",
        "# For a more detailed view of current AmlCompute status, use get_status()."
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "InProgress.\nSucceededProvisioning operation finished, operation \"Succeeded\"\nSucceeded\nAmlCompute wait for completion finished\n\nMinimum number of nodes requested have been provisioned\n"
        }
      ],
      "execution_count": 3,
      "metadata": {
        "gather": {
          "logged": 1643120046717
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset\n",
        "\n",
        "### Overview\n",
        "TODO: In this markdown cell, give an overview of the dataset you are using. Also mention the task you will be performing.\n",
        "\n",
        "............\n",
        "\n",
        "TODO: Get data. In the cell below, write code to access the data you will be using in this project. Remember that the dataset needs to be external."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# ws = Workspace.from_config()\n",
        "# print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n",
        "\n",
        "# # choose a name for experiment\n",
        "# experiment_name = 'palsy'\n",
        "# project_folder = './palsy-project'\n",
        "\n",
        "\n",
        "# experiment=Experiment(ws, experiment_name)\n",
        "# print(experiment)\n",
        "\n",
        "# Try to load the dataset from the Workspace. Otherwise, create it from the file\n",
        "# NOTE: update the key to match the dataset name\n",
        "found = False\n",
        "key = \"bellpalsy\"\n",
        "description_text = \"Datapoints of face\"\n",
        "\n",
        "if key in ws.datasets.keys(): \n",
        "        found = True\n",
        "        dataset = ws.datasets[key] \n",
        "\n",
        "if not found:\n",
        "        # Create AML Dataset and register it into Workspace\n",
        "#         datastore = Datastore.get(ws, datastore_name='palda')\n",
        "#         example_data=datastore\n",
        "        path=\"https://raw.githubusercontent.com/nsourlos/palda_azure/main/palda.csv\"\n",
        "#         example_data = 'https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv'\n",
        "        dataset = Dataset.Tabular.from_delimited_files(path=path)#path = [(datastore, 'palda.csv')])#example_data)        \n",
        "        #Register Dataset in Workspace\n",
        "        dataset = dataset.register(workspace=ws,\n",
        "                                   name=key,\n",
        "                                   description=description_text)\n",
        "\n",
        "df=dataset.to_pandas_dataframe()\n",
        "# print(df)\n",
        "df = df.iloc[: , :-1]\n",
        "\n",
        "# df = dataset.to_pandas_dataframe()\n",
        "df.describe()\n",
        "print(df)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "     x_1  y_1  x_2  y_2  x_3  y_3  x_4  y_4  x_5  y_5  ...  y_64  x_65  y_65  \\\n0    132  439  115  496  111  566  134  633  177  688  ...   581   588   583   \n1    194  402  186  444  182  486  199  543  230  591  ...   490   542   507   \n2     76  379   78  455   86  559  121  631  161  704  ...   628   654   629   \n3    201  366  203  369  217  415  230  444  249  467  ...   418   487   419   \n4    153  519  157  563  144  614  151  684  166  757  ...   672   520   686   \n..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   \n198  227  226  234  257  244  287  254  315  268  340  ...   303   402   303   \n199  131  168  128  184  125  200  123  217  125  233  ...   219   214   226   \n200  132  231  134  254  136  278  139  303  148  327  ...   312   269   312   \n201   62  103   63  116   64  127   66  139   69  151  ...   138   126   141   \n202   -2  378    5  465   20  556   37  650   63  745  ...   660   531   673   \n\n     x_66  y_66  x_67  y_67  x_68  y_68   diagnosis  \n0     539   629   470   624   407   612  peripheral  \n1     515   517   484   520   434   521  peripheral  \n2     628   688   558   711   484   712  peripheral  \n3     444   457   396   461   350   455  peripheral  \n4     460   698   427   703   362   706  peripheral  \n..    ...   ...   ...   ...   ...   ...         ...  \n198   385   318   372   321   359   321     Healthy  \n199   198   226   189   225   181   223     Healthy  \n200   242   325   230   327   216   326     Healthy  \n201   114   146   108   147   102   146     Healthy  \n202   431   723   375   728   318   722     Healthy  \n\n[203 rows x 137 columns]\n"
        }
      ],
      "execution_count": 4,
      "metadata": {
        "gather": {
          "logged": 1643120050354
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split df!!!!\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test=train_test_split(df,shuffle=True)#,df.iloc[:,-1],#x,y,test_size=0.2,random_state=42)\n",
        "#,y_train,y_test\n",
        "# print(x_train)\n",
        "# print(y_train)\n",
        "x_train.to_csv('x_train.csv',index=False)\n",
        "\n",
        "datastore=ws.get_default_datastore()\n",
        "datastore.upload_files(files=['./x_train.csv'])\n",
        "\n",
        "train=Dataset.Tabular.from_delimited_files([(datastore,'x_train.csv')])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": "\"datastore.upload_files\" is deprecated after version 1.0.69. Please use \"FileDatasetFactory.upload_directory\" instead. See Dataset API change notice at https://aka.ms/dataset-deprecation.\n"
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Uploading an estimated of 1 files\nTarget already exists. Skipping upload for x_train.csv\nUploaded 0 files\n"
        }
      ],
      "execution_count": 5,
      "metadata": {
        "gather": {
          "logged": 1643120053157
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## AutoML Configuration\n",
        "\n",
        "TODO: Explain why you chose the automl settings and cofiguration you used below."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can define outputs for the AutoMLStep using TrainingOutput"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# from azureml.pipeline.core import PipelineData, TrainingOutput\n",
        "\n",
        "# ds = ws.get_default_datastore()\n",
        "# metrics_output_name = 'metrics_output'\n",
        "# best_model_output_name = 'best_model_output'\n",
        "\n",
        "# metrics_data = PipelineData(name='metrics_data',\n",
        "#                            datastore=ds,\n",
        "#                            pipeline_output_name=metrics_output_name,\n",
        "#                            training_output=TrainingOutput(type='Metrics'))\n",
        "# model_data = PipelineData(name='model_data',\n",
        "#                            datastore=ds,\n",
        "#                            pipeline_output_name=best_model_output_name,\n",
        "#                            training_output=TrainingOutput(type='Model'))"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "gather": {
          "logged": 1643120053376
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# automl_step = AutoMLStep(\n",
        "#     name='automl_module',\n",
        "#     automl_config=automl_config,\n",
        "#     outputs=[metrics_data, model_data],\n",
        "#     allow_reuse=True)"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "gather": {
          "logged": 1643120053586
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from azureml.pipeline.core import Pipeline\n",
        "# pipeline = Pipeline(\n",
        "#     description=\"pipeline_with_automlstep\",\n",
        "#     workspace=ws,    \n",
        "#     steps=[automl_step])"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "gather": {
          "logged": 1643120053803
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: Put your automl settings here\n",
        "#taken from https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py\n",
        "automl_settings = {\n",
        "    \"experiment_timeout_minutes\": 20,\n",
        "    \"max_concurrent_iterations\": 5,\n",
        "    \"primary_metric\" : 'AUC_weighted',\n",
        "    \"enable_onnx_compatible_models\": True\n",
        "}\n",
        "\n",
        "# TODO: Put your automl config here\n",
        "automl_config = AutoMLConfig(compute_target=compute_target,\n",
        "                             task = \"classification\",\n",
        "                             training_data=train,#df\n",
        "                             label_column_name=\"diagnosis\",   \n",
        "                             path = project_folder,\n",
        "                             enable_early_stopping= True,\n",
        "                             featurization= 'auto',\n",
        "                             debug_log = \"automl_errors.log\",\n",
        "                             **automl_settings\n",
        "                            )"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "gather": {
          "logged": 1643120054012
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: Submit your experiment\n",
        "remote_run = experiment.submit(automl_config)#,show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Submitting remote run.\n"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>palsy</td><td>AutoML_161e8d7b-0ced-4643-b13c-d20a022de3db</td><td>automl</td><td>NotStarted</td><td><a href=\"https://ml.azure.com/runs/AutoML_161e8d7b-0ced-4643-b13c-d20a022de3db?wsid=/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourcegroups/aml-quickstarts-178503/workspaces/quick-starts-ws-178503&amp;tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "gather": {
          "logged": 1643120057363
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Run Details\n",
        "\n",
        "OPTIONAL: Write about the different models trained and their performance. Why do you think some models did better than others?\n",
        "\n",
        "TODO: In the cell below, use the `RunDetails` widget to show the different experiments."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(remote_run).show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "_AutoMLWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', 's…",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e0aca567168845b0ae22eefa8ad1f83d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/runs/AutoML_161e8d7b-0ced-4643-b13c-d20a022de3db?wsid=/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourcegroups/aml-quickstarts-178503/workspaces/quick-starts-ws-178503&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\", \"run_id\": \"AutoML_161e8d7b-0ced-4643-b13c-d20a022de3db\", \"run_properties\": {\"run_id\": \"AutoML_161e8d7b-0ced-4643-b13c-d20a022de3db\", \"created_utc\": \"2022-01-25T14:14:16.455278Z\", \"properties\": {\"num_iterations\": \"1000\", \"training_type\": \"TrainFull\", \"acquisition_function\": \"EI\", \"primary_metric\": \"AUC_weighted\", \"train_split\": \"0\", \"acquisition_parameter\": \"0\", \"num_cross_validation\": null, \"target\": \"cpucluster4\", \"AMLSettingsJsonString\": 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dataset.\", \"Generating individually featurized CV splits.\", \"Beginning model selection.\", \"Best run model explanations started\", \"Model explanations data setup completed\", \"Choosing LightGBM as the surrogate model for explanations\", \"Computation of engineered features started\", \"Computation of engineered features completed\", \"Computation of raw features started\", \"Computation of raw features completed\", \"Best run model explanations completed\"]}]}], \"run_logs\": \"\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.37.0\"}, \"loading\": false}"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Current provisioning state of AmlCompute is \"Deleting\"\n\nCurrent provisioning state of AmlCompute is \"Deleting\"\n\nCurrent provisioning state of AmlCompute is \"Deleting\"\n\nCurrent provisioning state of AmlCompute is \"Deleting\"\n\n"
        }
      ],
      "execution_count": 11,
      "metadata": {
        "gather": {
          "logged": 1643120057825
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "remote_run.wait_for_completion(show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>palsy</td><td>AutoML_161e8d7b-0ced-4643-b13c-d20a022de3db</td><td>automl</td><td>NotStarted</td><td><a href=\"https://ml.azure.com/runs/AutoML_161e8d7b-0ced-4643-b13c-d20a022de3db?wsid=/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourcegroups/aml-quickstarts-178503/workspaces/quick-starts-ws-178503&amp;tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
          },
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        },
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          "output_type": "stream",
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          "text": "\nCurrent status: FeaturesGeneration. Generating features for the dataset.\nCurrent status: DatasetCrossValidationSplit. Generating individually featurized CV splits.\nCurrent status: ModelSelection. Beginning model selection.\n\n********************************************************************************************\nDATA GUARDRAILS: \n\nTYPE:         Cross validation\nSTATUS:       DONE\nDESCRIPTION:  Each iteration of the trained model was validated through cross-validation.\n              \nDETAILS:      \n+------------------------------+\n|Number of folds               |\n+==============================+\n|10                            |\n+------------------------------+\n\n********************************************************************************************\n\nTYPE:         Class balancing detection\nSTATUS:       PASSED\nDESCRIPTION:  Your inputs were analyzed, and all classes are balanced in your training data.\n              Learn more about imbalanced data: https://aka.ms/AutomatedMLImbalancedData\n\n********************************************************************************************\n\nTYPE:         Missing feature values imputation\nSTATUS:       PASSED\nDESCRIPTION:  No feature missing values were detected in the training data.\n              Learn more about missing value imputation: https://aka.ms/AutomatedMLFeaturization\n\n********************************************************************************************\n\nTYPE:         High cardinality feature detection\nSTATUS:       PASSED\nDESCRIPTION:  Your inputs were analyzed, and no high cardinality features were detected.\n              Learn more about high cardinality feature handling: https://aka.ms/AutomatedMLFeaturization\n\n********************************************************************************************\n\n********************************************************************************************\nITER: The iteration being evaluated.\nPIPELINE: A summary description of the pipeline being evaluated.\nDURATION: Time taken for the current iteration.\nMETRIC: The result of computing score on the fitted pipeline.\nBEST: The best observed score thus far.\n********************************************************************************************\n\n ITER   PIPELINE                                       DURATION            METRIC      BEST\n    4   StandardScalerWrapper LightGBM                 0:00:12             0.8030    0.8030\n    5   StandardScalerWrapper KNN                      0:00:12             0.7922    0.8030\n    6   SparseNormalizer XGBoostClassifier             0:00:11             0.7813    0.8030\n    7   SparseNormalizer RandomForest                  0:00:15             0.8396    0.8396\n    0   MaxAbsScaler LightGBM                          0:00:13             0.7983    0.8396\n    1   MaxAbsScaler XGBoostClassifier                 0:00:15             0.8112    0.8396\n    2   MaxAbsScaler ExtremeRandomTrees                0:00:11             0.8226    0.8396\n    8   RobustScaler KNN                               0:00:11             0.7985    0.8396\n   12   StandardScalerWrapper XGBoostClassifier        0:00:11             0.7959    0.8396\n    3   MaxAbsScaler RandomForest                      0:00:11             0.8050    0.8396\n    9   MinMaxScaler RandomForest                      0:00:13             0.8018    0.8396\n   10   StandardScalerWrapper LogisticRegression       0:00:12             0.8824    0.8824\n   11   StandardScalerWrapper SVM                      0:00:12             0.8962    0.8962\n   13   SparseNormalizer KNN                           0:00:11             0.7948    0.8962\n   14   RobustScaler ExtremeRandomTrees                0:00:12             0.7908    0.8962\n   15   SparseNormalizer XGBoostClassifier             0:00:12             0.7676    0.8962\n   16   MinMaxScaler ExtremeRandomTrees                0:00:12             0.8011    0.8962\n   17   MinMaxScaler ExtremeRandomTrees                0:00:11             0.8427    0.8962\n   18   SparseNormalizer LightGBM                      0:00:11             0.8008    0.8962\n   19   MaxAbsScaler ExtremeRandomTrees                0:00:18             0.8182    0.8962\n   20   StandardScalerWrapper XGBoostClassifier        0:00:42             0.8199    0.8962\n   23   MaxAbsScaler LogisticRegression                0:00:44             0.8590    0.8962\n   22   MaxAbsScaler LightGBM                          0:00:46             0.8001    0.8962\n   21   StandardScalerWrapper RandomForest             0:01:34             0.8067    0.8962\n   24   SparseNormalizer SVM                           0:00:43             0.9114    0.9114\n   25   StandardScalerWrapper ExtremeRandomTrees       0:00:50             0.8208    0.9114\n   26   MinMaxScaler LogisticRegression                0:00:45             0.7987    0.9114\n   27   StandardScalerWrapper LogisticRegression       0:00:43             0.7293    0.9114\n   28   StandardScalerWrapper KNN                      0:00:42             0.8054    0.9114\n   29   StandardScalerWrapper SVM                      0:00:44             0.8138    0.9114\n   32   RobustScaler SVM                               0:00:42             0.8364    0.9114\n   31   RobustScaler SVM                               0:01:13             0.8987    0.9114\n   33   MinMaxScaler RandomForest                      0:00:54             0.7871    0.9114\n   34   RobustScaler SVM                               0:00:44             0.8914    0.9114\n   35   SparseNormalizer SVM                           0:00:42             0.9384    0.9384\n   36   RobustScaler SVM                               0:00:43             0.8275    0.9384\n   30   PCA SVM                                        0:02:46             0.7280    0.9384\n   37   StandardScalerWrapper SVM                      0:00:54             0.8904    0.9384\n   38   RobustScaler SVM                               0:00:45             0.8939    0.9384\n   39   StandardScalerWrapper SVM                      0:00:46             0.8914    0.9384\n   40   MaxAbsScaler SVM                               0:00:45             0.7874    0.9384\n   41   RobustScaler SVM                               0:00:43             0.9000    0.9384\n   42   RobustScaler SVM                               0:00:42             0.8844    0.9384\n   43   RobustScaler SVM                               0:00:48             0.8679    0.9384\n   44   StandardScalerWrapper XGBoostClassifier        0:01:03             0.8309    0.9384\n   45   SparseNormalizer SVM                           0:00:42             0.7955    0.9384\n   46   StandardScalerWrapper SVM                      0:00:44             0.8963    0.9384\n   47   RobustScaler SVM                               0:00:44             0.8774    0.9384\n   48   StandardScalerWrapper SVM                      0:00:43             0.8934    0.9384\n   49   SparseNormalizer KNN                           0:00:27             0.7757    0.9384\n   50   SparseNormalizer SVM                           0:00:38             0.9362    0.9384\n   51   StandardScalerWrapper SVM                      0:00:34             0.8952    0.9384\n   52                                                  0:00:29                nan    0.9384\n   53    VotingEnsemble                                0:01:35             0.9374    0.9384\n"
        },
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "{'runId': 'AutoML_161e8d7b-0ced-4643-b13c-d20a022de3db',\n 'target': 'cpucluster4',\n 'status': 'Completed',\n 'startTimeUtc': '2022-01-25T14:14:30.75411Z',\n 'endTimeUtc': '2022-01-25T14:33:11.26297Z',\n 'services': {},\n 'warnings': [{'source': 'JasmineService',\n   'message': 'No scores improved over last 20 iterations, so experiment stopped early. This early stopping behavior can be disabled by setting enable_early_stopping = False in AutoMLConfig for notebook/python SDK runs.'}],\n 'properties': {'num_iterations': '1000',\n  'training_type': 'TrainFull',\n  'acquisition_function': 'EI',\n  'primary_metric': 'AUC_weighted',\n  'train_split': '0',\n  'acquisition_parameter': '0',\n  'num_cross_validation': None,\n  'target': 'cpucluster4',\n  'AMLSettingsJsonString': '{\"path\":null,\"name\":\"palsy\",\"subscription_id\":\"cdbe0b43-92a0-4715-838a-f2648cc7ad21\",\"resource_group\":\"aml-quickstarts-178503\",\"workspace_name\":\"quick-starts-ws-178503\",\"region\":\"southcentralus\",\"compute_target\":\"cpucluster4\",\"spark_service\":null,\"azure_service\":\"remote\",\"many_models\":false,\"pipeline_fetch_max_batch_size\":1,\"enable_batch_run\":true,\"enable_run_restructure\":false,\"start_auxiliary_runs_before_parent_complete\":false,\"enable_code_generation\":false,\"iterations\":1000,\"primary_metric\":\"AUC_weighted\",\"task_type\":\"classification\",\"positive_label\":null,\"data_script\":null,\"test_size\":0.0,\"test_include_predictions_only\":false,\"validation_size\":0.0,\"n_cross_validations\":null,\"y_min\":null,\"y_max\":null,\"num_classes\":null,\"featurization\":\"auto\",\"_ignore_package_version_incompatibilities\":false,\"is_timeseries\":false,\"max_cores_per_iteration\":1,\"max_concurrent_iterations\":5,\"iteration_timeout_minutes\":null,\"mem_in_mb\":null,\"enforce_time_on_windows\":false,\"experiment_timeout_minutes\":20,\"experiment_exit_score\":null,\"whitelist_models\":null,\"blacklist_algos\":[\"TensorFlowLinearClassifier\",\"TensorFlowDNN\"],\"supported_models\":[\"MultinomialNaiveBayes\",\"DecisionTree\",\"RandomForest\",\"AveragedPerceptronClassifier\",\"SGD\",\"TensorFlowLinearClassifier\",\"GradientBoosting\",\"SVM\",\"BernoulliNaiveBayes\",\"ExtremeRandomTrees\",\"LogisticRegression\",\"LightGBM\",\"XGBoostClassifier\",\"LinearSVM\",\"KNN\",\"TensorFlowDNN\"],\"private_models\":[\"TabnetClassifier\"],\"auto_blacklist\":true,\"blacklist_samples_reached\":false,\"exclude_nan_labels\":true,\"verbosity\":20,\"_debug_log\":\"azureml_automl.log\",\"show_warnings\":false,\"model_explainability\":true,\"service_url\":null,\"sdk_url\":null,\"sdk_packages\":null,\"enable_onnx_compatible_models\":true,\"enable_split_onnx_featurizer_estimator_models\":false,\"vm_type\":\"STANDARD_DS3_V2\",\"telemetry_verbosity\":20,\"send_telemetry\":true,\"enable_dnn\":false,\"scenario\":\"SDK-1.13.0\",\"environment_label\":null,\"save_mlflow\":false,\"enable_categorical_indicators\":false,\"force_text_dnn\":false,\"enable_feature_sweeping\":false,\"enable_early_stopping\":true,\"early_stopping_n_iters\":10,\"arguments\":null,\"dataset_id\":null,\"hyperdrive_config\":null,\"validation_dataset_id\":null,\"run_source\":null,\"metrics\":null,\"enable_metric_confidence\":false,\"enable_ensembling\":true,\"enable_stack_ensembling\":false,\"ensemble_iterations\":15,\"enable_tf\":false,\"enable_subsampling\":null,\"subsample_seed\":null,\"enable_nimbusml\":false,\"enable_streaming\":false,\"force_streaming\":false,\"track_child_runs\":true,\"allowed_private_models\":[],\"label_column_name\":\"diagnosis\",\"weight_column_name\":null,\"cv_split_column_names\":null,\"enable_local_managed\":false,\"_local_managed_run_id\":null,\"cost_mode\":1,\"lag_length\":0,\"metric_operation\":\"maximize\",\"preprocess\":true}',\n  'DataPrepJsonString': '{\\\\\"training_data\\\\\": {\\\\\"datasetId\\\\\": \\\\\"edddd79b-739a-4448-96c0-614b9e14a0f9\\\\\"}, \\\\\"datasets\\\\\": 0}',\n  'EnableSubsampling': None,\n  'runTemplate': 'AutoML',\n  'azureml.runsource': 'automl',\n  'display_task_type': 'classification',\n  'dependencies_versions': '{\"azureml-widgets\": \"1.37.0\", \"azureml-train\": \"1.37.0\", \"azureml-train-restclients-hyperdrive\": \"1.37.0\", \"azureml-train-core\": \"1.37.0\", \"azureml-train-automl\": \"1.37.0\", \"azureml-train-automl-runtime\": \"1.37.0\", \"azureml-train-automl-client\": \"1.37.0\", \"azureml-tensorboard\": \"1.37.0\", \"azureml-telemetry\": \"1.37.0\", \"azureml-sdk\": \"1.37.0\", \"azureml-samples\": \"0+unknown\", \"azureml-responsibleai\": \"1.37.0\", \"azureml-pipeline\": \"1.37.0\", \"azureml-pipeline-steps\": \"1.37.0\", \"azureml-pipeline-core\": \"1.37.0\", \"azureml-opendatasets\": \"1.37.0\", \"azureml-mlflow\": \"1.37.0\", \"azureml-interpret\": \"1.37.0\", \"azureml-inference-server-http\": \"0.4.2\", \"azureml-explain-model\": \"1.37.0\", \"azureml-defaults\": \"1.37.0\", \"azureml-dataset-runtime\": \"1.37.0\", \"azureml-dataprep\": \"2.25.0\", \"azureml-dataprep-rslex\": \"2.1.0\", \"azureml-dataprep-native\": \"38.0.0\", \"azureml-datadrift\": \"1.37.0\", \"azureml-core\": \"1.37.0\", \"azureml-contrib-services\": \"1.37.0\", \"azureml-contrib-server\": \"1.37.0\", \"azureml-contrib-reinforcementlearning\": \"1.37.0\", \"azureml-contrib-pipeline-steps\": \"1.37.0\", \"azureml-contrib-notebook\": \"1.37.0\", \"azureml-contrib-fairness\": \"1.37.0\", \"azureml-contrib-dataset\": \"1.37.0\", \"azureml-contrib-automl-pipeline-steps\": \"1.37.0\", \"azureml-cli-common\": \"1.37.0\", \"azureml-automl-runtime\": \"1.37.0\", \"azureml-automl-dnn-nlp\": \"1.37.0\", \"azureml-automl-core\": \"1.37.0\", \"azureml-accel-models\": \"1.37.0\"}',\n  '_aml_system_scenario_identification': 'Remote.Parent',\n  'ClientType': 'SDK',\n  'environment_cpu_name': 'AzureML-AutoML',\n  'environment_cpu_label': 'prod',\n  'environment_gpu_name': 'AzureML-AutoML-GPU',\n  'environment_gpu_label': 'prod',\n  'root_attribution': 'automl',\n  'attribution': 'AutoML',\n  'Orchestrator': 'AutoML',\n  'CancelUri': 'https://southcentralus.api.azureml.ms/jasmine/v1.0/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourceGroups/aml-quickstarts-178503/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-178503/experimentids/d706c9a1-d495-4d9a-a309-4c1780e469ca/cancel/AutoML_161e8d7b-0ced-4643-b13c-d20a022de3db',\n  'ClientSdkVersion': '1.37.0',\n  'snapshotId': '00000000-0000-0000-0000-000000000000',\n  'SetupRunId': 'AutoML_161e8d7b-0ced-4643-b13c-d20a022de3db_setup',\n  'SetupRunContainerId': 'dcid.AutoML_161e8d7b-0ced-4643-b13c-d20a022de3db_setup',\n  'FeaturizationRunJsonPath': 'featurizer_container.json',\n  'FeaturizationRunId': 'AutoML_161e8d7b-0ced-4643-b13c-d20a022de3db_featurize',\n  'ProblemInfoJsonString': '{\"dataset_num_categorical\": 0, \"is_sparse\": false, \"subsampling\": false, \"has_extra_col\": true, \"dataset_classes\": 3, \"dataset_features\": 136, \"dataset_samples\": 152, \"single_frequency_class_detected\": false}',\n  'ModelExplainRunId': 'AutoML_161e8d7b-0ced-4643-b13c-d20a022de3db_ModelExplain'},\n 'inputDatasets': [{'dataset': {'id': 'edddd79b-739a-4448-96c0-614b9e14a0f9'}, 'consumptionDetails': {'type': 'RunInput', 'inputName': 'training_data', 'mechanism': 'Direct'}}],\n 'outputDatasets': [],\n 'logFiles': {},\n 'submittedBy': 'ODL_User 178503'}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "gather": {
          "logged": 1643121204415
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Best Model\n",
        "\n",
        "TODO: In the cell below, get the best model from the automl experiments and display all the properties of the model.\n",
        "\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve and save your best automl model.\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "remote_run.get_metrics()\n",
        "\n",
        "import onnxmltools\n",
        "best_run, fitted_model = remote_run.get_output(return_onnx_model=True)\n",
        "print(best_run.get_metrics())\n",
        "print(best_run)\n",
        "print(fitted_model)\n",
        "# Taken from https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-auto-train-models\n",
        "\n",
        "onnxmltools.utils.save_model(fitted_model, './best_automl.onnx')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "{'accuracy': 0.8475000000000001, 'norm_macro_recall': 0.6282738095238094, 'precision_score_weighted': 0.8006212121212121, 'AUC_macro': 0.9331466558133223, 'balanced_accuracy': 0.7521825396825397, 'average_precision_score_micro': 0.9265458493936292, 'precision_score_micro': 0.8475000000000001, 'recall_score_macro': 0.7521825396825397, 'weighted_accuracy': 0.9042746544311688, 'AUC_weighted': 0.9384439356939357, 'recall_score_weighted': 0.8475000000000001, 'log_loss': 0.39703535429064296, 'average_precision_score_macro': 0.8733472909365766, 'AUC_micro': 0.9578715277777776, 'recall_score_micro': 0.8475000000000001, 'f1_score_macro': 0.7362077840173816, 'matthews_correlation': 0.7599297274029568, 'f1_score_weighted': 0.8130769800410862, 'f1_score_micro': 0.8475000000000001, 'average_precision_score_weighted': 0.9102310906685906, 'precision_score_macro': 0.7435016835016834, 'accuracy_table': 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      elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_29\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_30\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_30\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_31\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_31\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_32\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_32\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_33\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_33\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_34\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_34\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_35\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_35\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_36\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_36\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_37\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_37\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_38\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_38\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_39\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_39\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_40\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_40\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_41\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_41\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_42\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_42\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_43\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_43\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_44\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_44\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_45\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_45\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_46\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_46\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_47\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_47\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_48\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_48\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_49\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_49\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_50\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_50\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_51\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_51\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_52\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_52\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_53\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_53\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_54\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_54\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_55\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_55\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_56\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_56\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_57\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_57\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_58\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_58\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_59\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_59\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_60\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_60\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_61\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_61\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_62\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_62\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_63\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_63\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_64\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_64\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_65\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_65\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_66\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_66\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_67\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_67\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"x_68\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  input {\n    name: \"y_68\"\n    type {\n      tensor_type {\n        elem_type: 7\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 1\n          }\n        }\n      }\n    }\n  }\n  output {\n    name: \"output_label1\"\n    type {\n      tensor_type {\n        elem_type: 8\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n        }\n      }\n    }\n  }\n  output {\n    name: \"output_probabity1\"\n    type {\n      tensor_type {\n        elem_type: 1\n        shape {\n          dim {\n            dim_param: \"n\"\n          }\n          dim {\n            dim_value: 3\n          }\n        }\n      }\n    }\n  }\n}\nopset_import {\n  domain: \"ai.onnx\"\n  version: 1\n}\nopset_import {\n  domain: \"ai.onnx.ml\"\n  version: 1\n}\n\n"
        }
      ],
      "execution_count": 31,
      "metadata": {
        "gather": {
          "logged": 1643131749757
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#TODO: Save the best model"
      ],
      "outputs": [],
      "execution_count": 14,
      "metadata": {
        "gather": {
          "logged": 1643121364173
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Deployment\n",
        "\n",
        "Remember you have to deploy only one of the two models you trained but you still need to register both the models. Perform the steps in the rest of this notebook only if you wish to deploy this model.\n",
        "\n",
        "TODO: In the cell below, register the model, create an inference config and deploy the model as a web service."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core.model import Model\n",
        "\n",
        "model = Model.register(workspace = ws,\n",
        "                        model_name = \"palda_dataset\",\n",
        "                        model_path = \"./best_automl.onnx\",\n",
        "                        description = \"Best AutoML Model\",)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Registering model palda_dataset\n"
        }
      ],
      "execution_count": 15,
      "metadata": {
        "gather": {
          "logged": 1643121364975
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=remote_run.register_model(model_name = \"palda_dataset\",\r\n",
        "                        description = \"Best AutoML Model\",)\r\n",
        "                        #taken from jwood803 github"
      ],
      "outputs": [],
      "execution_count": 16,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1643121365903
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#taken from https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py\n",
        "#and from https://stackoverflow.com/questions/66437607/how-to-create-azure-ml-inference-config-and-deployment-config-class-objects-from\n",
        "\n",
        "from azureml.core.environment import Environment\n",
        "from azureml.automl.core.shared import constants\n",
        "best_run.download_file(constants.CONDA_ENV_FILE_PATH, 'myenv.yml')\n",
        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\") #environment instance creation\n",
        "#we need these otherwise we will get error on non-known environment to run\n",
        "\n",
        "script_file_name = 'inference/score.py'\n",
        "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py') #this exists before we run the commands in the cells above\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "from azureml.core.model import InferenceConfig\n",
        "from azureml.core.webservice import AciWebservice\n",
        "from azureml.core.webservice import Webservice\n",
        "from azureml.core.model import Model\n",
        "from azureml.core.environment import Environment\n",
        "\n",
        "inference_config = InferenceConfig(entry_script=script_file_name, environment=myenv)\n",
        "\n",
        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
        "                                               memory_gb = 1, \n",
        "                                               description = 'palsy classification service')\n",
        "\n",
        "aci_service_name = 'palsy-classification2'\n",
        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
        "aci_service.wait_for_deployment(show_output=True)\n",
        "\n",
        "\n",
        "\n",
        "# InferenceConfig(entry_script=)\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Tips: You can try get_logs(): https://aka.ms/debugimage#dockerlog or local deployment: https://aka.ms/debugimage#debug-locally to debug if deployment takes longer than 10 minutes.\nRunning\n2022-01-25 14:36:08+00:00 Creating Container Registry if not exists.\n2022-01-25 14:36:09+00:00 Registering the environment.\n2022-01-25 14:36:10+00:00 Use the existing image.\n2022-01-25 14:36:11+00:00 Generating deployment configuration.\n2022-01-25 14:36:12+00:00 Submitting deployment to compute.\n2022-01-25 14:36:14+00:00 Checking the status of deployment palsy-classification2..\n2022-01-25 14:41:43+00:00 Checking the status of inference endpoint palsy-classification2.\nSucceeded\nACI service creation operation finished, operation \"Succeeded\"\n"
        }
      ],
      "execution_count": 17,
      "metadata": {
        "gather": {
          "logged": 1643121709279
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# published_pipeline = remote_run.publish_pipeline(\n",
        "#     name=\"palda\", description=\"palsy dataset\", version=\"1.0\")\n",
        "\n",
        "# published_pipeline"
      ],
      "outputs": [],
      "execution_count": 18,
      "metadata": {
        "gather": {
          "logged": 1643121709514
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from azureml.core.authentication import InteractiveLoginAuthentication\n",
        "\n",
        "# interactive_auth = InteractiveLoginAuthentication()\n",
        "# auth_header = interactive_auth.get_authentication_header()\n",
        "\n"
      ],
      "outputs": [],
      "execution_count": 19,
      "metadata": {
        "gather": {
          "logged": 1643121709742
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import requests\n",
        "\n",
        "# rest_endpoint = published_pipeline.endpoint\n",
        "# response = requests.post(rest_endpoint, \n",
        "#                          headers=auth_header, \n",
        "#                          json={\"ExperimentName\": \"pipeline-rest-endpoint\"}\n",
        "#                         )"
      ],
      "outputs": [],
      "execution_count": 20,
      "metadata": {
        "gather": {
          "logged": 1643121709952
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# try:\n",
        "#     response.raise_for_status()\n",
        "# except Exception:    \n",
        "#     raise Exception(\"Received bad response from the endpoint: {}\\n\"\n",
        "#                     \"Response Code: {}\\n\"\n",
        "#                     \"Headers: {}\\n\"\n",
        "#                     \"Content: {}\".format(rest_endpoint, response.status_code, response.headers, response.content))\n",
        "\n",
        "# run_id = response.json().get('Id')\n",
        "# print('Submitted pipeline run: ', run_id)"
      ],
      "outputs": [],
      "execution_count": 21,
      "metadata": {
        "gather": {
          "logged": 1643121710229
        }
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "TODO: In the cell below, send a request to the web service you deployed to test it."
      ],
      "metadata": {
        "collapsed": true,
        "gather": {
          "logged": 1598431657736
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print(\"Key: \" + aci_service.get_keys()[0]) #[0]\n"
      ],
      "outputs": [],
      "execution_count": 22,
      "metadata": {
        "gather": {
          "logged": 1643121710446
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.iloc[16].to_dict() #0,2,16 - helathy peripheral central"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 23,
          "data": {
            "text/plain": "{'x_1': 309,\n 'y_1': 326,\n 'x_2': 312,\n 'y_2': 390,\n 'x_3': 318,\n 'y_3': 463,\n 'x_4': 328,\n 'y_4': 524,\n 'x_5': 341,\n 'y_5': 575,\n 'x_6': 365,\n 'y_6': 616,\n 'x_7': 395,\n 'y_7': 653,\n 'x_8': 427,\n 'y_8': 680,\n 'x_9': 452,\n 'y_9': 682,\n 'x_10': 491,\n 'y_10': 672,\n 'x_11': 528,\n 'y_11': 648,\n 'x_12': 566,\n 'y_12': 608,\n 'x_13': 586,\n 'y_13': 551,\n 'x_14': 595,\n 'y_14': 489,\n 'x_15': 604,\n 'y_15': 421,\n 'x_16': 602,\n 'y_16': 360,\n 'x_17': 606,\n 'y_17': 318,\n 'x_18': 332,\n 'y_18': 240,\n 'x_19': 348,\n 'y_19': 212,\n 'x_20': 371,\n 'y_20': 196,\n 'x_21': 401,\n 'y_21': 205,\n 'x_22': 422,\n 'y_22': 223,\n 'x_23': 493,\n 'y_23': 270,\n 'x_24': 510,\n 'y_24': 247,\n 'x_25': 541,\n 'y_25': 247,\n 'x_26': 569,\n 'y_26': 254,\n 'x_27': 582,\n 'y_27': 268,\n 'x_28': 462,\n 'y_28': 291,\n 'x_29': 462,\n 'y_29': 330,\n 'x_30': 462,\n 'y_30': 366,\n 'x_31': 458,\n 'y_31': 396,\n 'x_32': 409,\n 'y_32': 419,\n 'x_33': 433,\n 'y_33': 424,\n 'x_34': 460,\n 'y_34': 432,\n 'x_35': 486,\n 'y_35': 431,\n 'x_36': 509,\n 'y_36': 428,\n 'x_37': 358,\n 'y_37': 312,\n 'x_38': 382,\n 'y_38': 300,\n 'x_39': 406,\n 'y_39': 305,\n 'x_40': 418,\n 'y_40': 320,\n 'x_41': 402,\n 'y_41': 330,\n 'x_42': 379,\n 'y_42': 326,\n 'x_43': 507,\n 'y_43': 327,\n 'x_44': 527,\n 'y_44': 311,\n 'x_45': 554,\n 'y_45': 317,\n 'x_46': 570,\n 'y_46': 330,\n 'x_47': 545,\n 'y_47': 343,\n 'x_48': 524,\n 'y_48': 340,\n 'x_49': 389,\n 'y_49': 512,\n 'x_50': 406,\n 'y_50': 495,\n 'x_51': 434,\n 'y_51': 490,\n 'x_52': 458,\n 'y_52': 494,\n 'x_53': 485,\n 'y_53': 507,\n 'x_54': 506,\n 'y_54': 527,\n 'x_55': 523,\n 'y_55': 559,\n 'x_56': 492,\n 'y_56': 572,\n 'x_57': 469,\n 'y_57': 576,\n 'x_58': 440,\n 'y_58': 575,\n 'x_59': 416,\n 'y_59': 564,\n 'x_60': 404,\n 'y_60': 546,\n 'x_61': 400,\n 'y_61': 518,\n 'x_62': 435,\n 'y_62': 507,\n 'x_63': 457,\n 'y_63': 512,\n 'x_64': 486,\n 'y_64': 528,\n 'x_65': 509,\n 'y_65': 548,\n 'x_66': 475,\n 'y_66': 552,\n 'x_67': 450,\n 'y_67': 548,\n 'x_68': 422,\n 'y_68': 536,\n 'diagnosis': 'peripheral'}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 23,
      "metadata": {
        "gather": {
          "logged": 1643121710741
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# aci_service.get_keys()"
      ],
      "outputs": [],
      "execution_count": 24,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1643121711191
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#endpoint.py file\n",
        "import requests\n",
        "import json\n",
        "\n",
        "# URL for the web service, should be similar to:\n",
        "# 'http://8530a665-66f3-49c8-a953-b82a2d312917.eastus.azurecontainer.io/score'\n",
        "# scoring_uri = 'http://7bbc8877-700c-4035-8097-c35c896039a2.southcentralus.azurecontainer.io/score'\n",
        "scoring_uri=aci_service.scoring_uri\n",
        "# If the service is authenticated, set the key or token\n",
        "# key = 'Y9bgMCp5O0geM74pEThdQVLiaRMHBjrB'\n",
        "# key=aci_service.get_keys()[0]\n",
        "\n",
        "# Two sets of data to score, so we get two results back\n",
        "data = {\"data\":\n",
        "        [\n",
        "          {'x_1': 38,\n",
        "            'y_1': 88,\n",
        "            'x_2': 42,\n",
        "            'y_2': 125,\n",
        "            'x_3': 50,\n",
        "            'y_3': 161,\n",
        "            'x_4': 57,\n",
        "            'y_4': 195,\n",
        "            'x_5': 69,\n",
        "            'y_5': 228,\n",
        "            'x_6': 89,\n",
        "            'y_6': 259,\n",
        "            'x_7': 113,\n",
        "            'y_7': 285,\n",
        "            'x_8': 144,\n",
        "            'y_8': 306,\n",
        "            'x_9': 177,\n",
        "            'y_9': 311,\n",
        "            'x_10': 209,\n",
        "            'y_10': 303,\n",
        "            'x_11': 238,\n",
        "            'y_11': 281,\n",
        "            'x_12': 262,\n",
        "            'y_12': 253,\n",
        "            'x_13': 281,\n",
        "            'y_13': 222,\n",
        "            'x_14': 292,\n",
        "            'y_14': 188,\n",
        "            'x_15': 297,\n",
        "            'y_15': 152,\n",
        "            'x_16': 301,\n",
        "            'y_16': 117,\n",
        "            'x_17': 301,\n",
        "            'y_17': 81,\n",
        "            'x_18': 55,\n",
        "            'y_18': 60,\n",
        "            'x_19': 69,\n",
        "            'y_19': 42,\n",
        "            'x_20': 93,\n",
        "            'y_20': 32,\n",
        "            'x_21': 117,\n",
        "            'y_21': 35,\n",
        "            'x_22': 139,\n",
        "            'y_22': 46,\n",
        "            'x_23': 197,\n",
        "            'y_23': 43,\n",
        "            'x_24': 218,\n",
        "            'y_24': 31,\n",
        "            'x_25': 241,\n",
        "            'y_25': 26,\n",
        "            'x_26': 266,\n",
        "            'y_26': 30,\n",
        "            'x_27': 283,\n",
        "            'y_27': 46,\n",
        "            'x_28': 169,\n",
        "            'y_28': 78,\n",
        "            'x_29': 171,\n",
        "            'y_29': 100,\n",
        "            'x_30': 172,\n",
        "            'y_30': 123,\n",
        "            'x_31': 173,\n",
        "            'y_31': 146,\n",
        "            'x_32': 147,\n",
        "            'y_32': 164,\n",
        "            'x_33': 160,\n",
        "            'y_33': 168,\n",
        "            'x_34': 173,\n",
        "            'y_34': 172,\n",
        "            'x_35': 187,\n",
        "            'y_35': 167,\n",
        "            'x_36': 199,\n",
        "            'y_36': 163,\n",
        "            'x_37': 81,\n",
        "            'y_37': 89,\n",
        "            'x_38': 96,\n",
        "            'y_38': 81,\n",
        "            'x_39': 114,\n",
        "            'y_39': 82,\n",
        "            'x_40': 131,\n",
        "            'y_40': 92,\n",
        "            'x_41': 113,\n",
        "            'y_41': 93,\n",
        "            'x_42': 96,\n",
        "            'y_42': 93,\n",
        "            'x_43': 207,\n",
        "            'y_43': 89,\n",
        "            'x_44': 224,\n",
        "            'y_44': 76,\n",
        "            'x_45': 241,\n",
        "            'y_45': 73,\n",
        "            'x_46': 258,\n",
        "            'y_46': 81,\n",
        "            'x_47': 244,\n",
        "            'y_47': 86,\n",
        "            'x_48': 226,\n",
        "            'y_48': 88,\n",
        "            'x_49': 105,\n",
        "            'y_49': 201,\n",
        "            'x_50': 128,\n",
        "            'y_50': 190,\n",
        "            'x_51': 154,\n",
        "            'y_51': 186,\n",
        "            'x_52': 173,\n",
        "            'y_52': 188,\n",
        "            'x_53': 192,\n",
        "            'y_53': 185,\n",
        "            'x_54': 216,\n",
        "            'y_54': 187,\n",
        "            'x_55': 239,\n",
        "            'y_55': 197,\n",
        "            'x_56': 220,\n",
        "            'y_56': 237,\n",
        "            'x_57': 195,\n",
        "            'y_57': 254,\n",
        "            'x_58': 173,\n",
        "            'y_58': 257,\n",
        "            'x_59': 152,\n",
        "            'y_59': 256,\n",
        "            'x_60': 126,\n",
        "            'y_60': 240,\n",
        "            'x_61': 114,\n",
        "            'y_61': 204,\n",
        "            'x_62': 154,\n",
        "            'y_62': 197,\n",
        "            'x_63': 173,\n",
        "            'y_63': 198,\n",
        "            'x_64': 192,\n",
        "            'y_64': 196,\n",
        "            'x_65': 231,\n",
        "            'y_65': 200,\n",
        "            'x_66': 193,\n",
        "            'y_66': 235,\n",
        "            'x_67': 173,\n",
        "            'y_67': 238,\n",
        "            'x_68': 153,\n",
        "            'y_68': 237     \n",
        "          },\n",
        "          {\n",
        "            'x_1': 209,\n",
        "            'y_1': 238,\n",
        "            'x_2': 208,\n",
        "            'y_2': 300,\n",
        "            'x_3': 208,\n",
        "            'y_3': 354,\n",
        "            'x_4': 211,\n",
        "            'y_4': 403,\n",
        "            'x_5': 218,\n",
        "            'y_5': 463,\n",
        "            'x_6': 236,\n",
        "            'y_6': 558,\n",
        "            'x_7': 286,\n",
        "            'y_7': 623,\n",
        "            'x_8': 348,\n",
        "            'y_8': 667,\n",
        "            'x_9': 429,\n",
        "            'y_9': 689,\n",
        "            'x_10': 502,\n",
        "            'y_10': 691,\n",
        "            'x_11': 574,\n",
        "            'y_11': 662,\n",
        "            'x_12': 626,\n",
        "            'y_12': 599,\n",
        "            'x_13': 672,\n",
        "            'y_13': 556,\n",
        "            'x_14': 696,\n",
        "            'y_14': 513,\n",
        "            'x_15': 696,\n",
        "            'y_15': 463,\n",
        "            'x_16': 709,\n",
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        "            'x_18': 258,\n",
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        "            'x_19': 300,\n",
        "            'y_19': 101,\n",
        "            'x_20': 342,\n",
        "            'y_20': 90,\n",
        "            'x_21': 387,\n",
        "            'y_21': 90,\n",
        "            'x_22': 424,\n",
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        "            'x_24': 569,\n",
        "            'y_24': 93,\n",
        "            'x_25': 614,\n",
        "            'y_25': 91,\n",
        "            'x_26': 668,\n",
        "            'y_26': 104,\n",
        "            'x_27': 702,\n",
        "            'y_27': 144,\n",
        "            'x_28': 476,\n",
        "            'y_28': 159,\n",
        "            'x_29': 476,\n",
        "            'y_29': 215,\n",
        "            'x_30': 473,\n",
        "            'y_30': 269,\n",
        "            'x_31': 459,\n",
        "            'y_31': 342,\n",
        "            'x_32': 389,\n",
        "            'y_32': 373,\n",
        "            'x_33': 415,\n",
        "            'y_33': 386,\n",
        "            'x_34': 450,\n",
        "            'y_34': 392,\n",
        "            'x_35': 493,\n",
        "            'y_35': 392,\n",
        "            'x_36': 527,\n",
        "            'y_36': 390,\n",
        "            'x_37': 291,\n",
        "            'y_37': 197,\n",
        "            'x_38': 324,\n",
        "            'y_38': 176,\n",
        "            'x_39': 379,\n",
        "            'y_39': 171,\n",
        "            'x_40': 411,\n",
        "            'y_40': 196,\n",
        "            'x_41': 364,\n",
        "            'y_41': 211,\n",
        "            'x_42': 321,\n",
        "            'y_42': 210,\n",
        "            'x_43': 543,\n",
        "            'y_43': 212,\n",
        "            'x_44': 577,\n",
        "            'y_44': 181,\n",
        "            'x_45': 635,\n",
        "            'y_45': 193,\n",
        "            'x_46': 661,\n",
        "            'y_46': 221,\n",
        "            'x_47': 616,\n",
        "            'y_47': 233,\n",
        "            'x_48': 568,\n",
        "            'y_48': 228,\n",
        "            'x_49': 341,\n",
        "            'y_49': 471,\n",
        "            'x_50': 364,\n",
        "            'y_50': 451,\n",
        "            'x_51': 403,\n",
        "            'y_51': 445,\n",
        "            'x_52': 440,\n",
        "            'y_52': 453,\n",
        "            'x_53': 471,\n",
        "            'y_53': 469,\n",
        "            'x_54': 506,\n",
        "            'y_54': 489,\n",
        "            'x_55': 535,\n",
        "            'y_55': 523,\n",
        "            'x_56': 506,\n",
        "            'y_56': 544,\n",
        "            'x_57': 467,\n",
        "            'y_57': 541,\n",
        "            'x_58': 428,\n",
        "            'y_58': 529,\n",
        "            'x_59': 400,\n",
        "            'y_59': 516,\n",
        "            'x_60': 366,\n",
        "            'y_60': 498,\n",
        "            'x_61': 356,\n",
        "            'y_61': 473,\n",
        "            'x_62': 395,\n",
        "            'y_62': 475,\n",
        "            'x_63': 429,\n",
        "            'y_63': 486,\n",
        "            'x_64': 470,\n",
        "            'y_64': 499,\n",
        "            'x_65': 511,\n",
        "            'y_65': 513,\n",
        "            'x_66': 469,\n",
        "            'y_66': 508,\n",
        "            'x_67': 430,\n",
        "            'y_67': 501,\n",
        "            'x_68': 377,\n",
        "            'y_68': 482,   \n",
        "          },\n",
        "           \n",
        "           {'x_1': 124,\n",
        "            'y_1': 348,\n",
        "            'x_2': 134,\n",
        "            'y_2': 415,\n",
        "            'x_3': 154,\n",
        "            'y_3': 494,\n",
        "            'x_4': 179,\n",
        "            'y_4': 568,\n",
        "            'x_5': 219,\n",
        "            'y_5': 617,\n",
        "            'x_6': 263,\n",
        "            'y_6': 663,\n",
        "            'x_7': 323,\n",
        "            'y_7': 696,\n",
        "            'x_8': 385,\n",
        "            'y_8': 711,\n",
        "            'x_9': 466,\n",
        "            'y_9': 713,\n",
        "            'x_10': 550,\n",
        "            'y_10': 710,\n",
        "            'x_11': 609,\n",
        "            'y_11': 681,\n",
        "            'x_12': 674,\n",
        "            'y_12': 631,\n",
        "            'x_13': 724,\n",
        "            'y_13': 584,\n",
        "            'x_14': 756,\n",
        "            'y_14': 538,\n",
        "            'x_15': 778,\n",
        "            'y_15': 489,\n",
        "            'x_16': 789,\n",
        "            'y_16': 426,\n",
        "            'x_17': 786,\n",
        "            'y_17': 356,\n",
        "            'x_18': 172,\n",
        "            'y_18': 257,\n",
        "            'x_19': 221,\n",
        "            'y_19': 224,\n",
        "            'x_20': 284,\n",
        "            'y_20': 220,\n",
        "            'x_21': 341,\n",
        "            'y_21': 230,\n",
        "            'x_22': 392,\n",
        "            'y_22': 265,\n",
        "            'x_23': 533,\n",
        "            'y_23': 246,\n",
        "            'x_24': 593,\n",
        "            'y_24': 217,\n",
        "            'x_25': 634,\n",
        "            'y_25': 218,\n",
        "            'x_26': 685,\n",
        "            'y_26': 224,\n",
        "            'x_27': 729,\n",
        "            'y_27': 245,\n",
        "            'x_28': 453,\n",
        "            'y_28': 283,\n",
        "            'x_29': 455,\n",
        "            'y_29': 336,\n",
        "            'x_30': 459,\n",
        "            'y_30': 387,\n",
        "            'x_31': 463,\n",
        "            'y_31': 454,\n",
        "            'x_32': 367,\n",
        "            'y_32': 457,\n",
        "            'x_33': 413,\n",
        "            'y_33': 466,\n",
        "            'x_34': 470,\n",
        "            'y_34': 485,\n",
        "            'x_35': 522,\n",
        "            'y_35': 466,\n",
        "            'x_36': 568,\n",
        "            'y_36': 444,\n",
        "            'x_37': 228,\n",
        "            'y_37': 313,\n",
        "            'x_38': 271,\n",
        "            'y_38': 291,\n",
        "            'x_39': 323,\n",
        "            'y_39': 288,\n",
        "            'x_40': 361,\n",
        "            'y_40': 308,\n",
        "            'x_41': 315,\n",
        "            'y_41': 327,\n",
        "            'x_42': 265,\n",
        "            'y_42': 322,\n",
        "            'x_43': 550,\n",
        "            'y_43': 304,\n",
        "            'x_44': 605,\n",
        "            'y_44': 277,\n",
        "            'x_45': 663,\n",
        "            'y_45': 280,\n",
        "            'x_46': 703,\n",
        "            'y_46': 297,\n",
        "            'x_47': 655,\n",
        "            'y_47': 312,\n",
        "            'x_48': 594,\n",
        "            'y_48': 312,\n",
        "            'x_49': 326,\n",
        "            'y_49': 547,\n",
        "            'x_50': 377,\n",
        "            'y_50': 529,\n",
        "            'x_51': 425,\n",
        "            'y_51': 525,\n",
        "            'x_52': 478,\n",
        "            'y_52': 524,\n",
        "            'x_53': 535,\n",
        "            'y_53': 517,\n",
        "            'x_54': 585,\n",
        "            'y_54': 512,\n",
        "            'x_55': 644,\n",
        "            'y_55': 518,\n",
        "            'x_56': 609,\n",
        "            'y_56': 551,\n",
        "            'x_57': 557,\n",
        "            'y_57': 579,\n",
        "            'x_58': 483,\n",
        "            'y_58': 594,\n",
        "            'x_59': 424,\n",
        "            'y_59': 596,\n",
        "            'x_60': 377,\n",
        "            'y_60': 588,\n",
        "            'x_61': 342,\n",
        "            'y_61': 551,\n",
        "            'x_62': 427,\n",
        "            'y_62': 542,\n",
        "            'x_63': 484,\n",
        "            'y_63': 540,\n",
        "            'x_64': 558,\n",
        "            'y_64': 527,\n",
        "            'x_65': 624,\n",
        "            'y_65': 527,\n",
        "            'x_66': 545,\n",
        "            'y_66': 566,\n",
        "            'x_67': 478,\n",
        "            'y_67': 577,\n",
        "            'x_68': 400,\n",
        "            'y_68': 578,  \n",
        "          },\n",
        "      ]\n",
        "    }\n",
        "# Convert to JSON string\n",
        "input_data = json.dumps(data)\n",
        "with open(\"data.json\", \"w\") as _f:\n",
        "    _f.write(input_data)\n",
        "\n",
        "# Set the content type\n",
        "headers = {'Content-Type': 'application/json'}\n",
        "# If authentication is enabled, set the authorization header\n",
        "headers['Authorization'] = f'Bearer {key}'\n",
        "\n",
        "# Make the request and display the response\n",
        "resp = requests.post(scoring_uri, input_data, headers=headers)\n",
        "#print(resp)\n",
        "print(resp.json())\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "{\"result\": [\"Healthy\", \"peripheral\", \"central\"]}\n"
        }
      ],
      "execution_count": 25,
      "metadata": {
        "gather": {
          "logged": 1643121711495
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "TODO: In the cell below, print the logs of the web service and delete the service"
      ],
      "metadata": {
        "collapsed": true,
        "gather": {
          "logged": 1598432765711
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aci_service.get_logs()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": "'2022-01-25T14:41:09,626206800+00:00 - rsyslog/run \\n2022-01-25T14:41:09,635537100+00:00 - iot-server/run \\n2022-01-25T14:41:09,641743000+00:00 - gunicorn/run \\nDynamic Python package installation is disabled.\\nStarting HTTP server\\n2022-01-25T14:41:09,652004200+00:00 - nginx/run \\nrsyslogd: /azureml-envs/azureml_0f7f18983f8fadd52db7ab2a1965cfbb/lib/libuuid.so.1: no version information available (required by rsyslogd)\\nEdgeHubConnectionString and IOTEDGE_IOTHUBHOSTNAME are not set. Exiting...\\n2022-01-25T14:41:10,260522100+00:00 - iot-server/finish 1 0\\n2022-01-25T14:41:10,265130400+00:00 - Exit code 1 is normal. Not restarting iot-server.\\nStarting gunicorn 20.1.0\\nListening at: http://127.0.0.1:31311 (74)\\nUsing worker: sync\\nworker timeout is set to 300\\nBooting worker with pid: 103\\nSPARK_HOME not set. Skipping PySpark Initialization.\\nGenerating new fontManager, this may take some time...\\nInitializing logger\\n2022-01-25 14:41:12,999 | root | INFO | Starting up app insights client\\nlogging socket was found. logging is available.\\nlogging socket was found. logging is available.\\n2022-01-25 14:41:13,000 | root | INFO | Starting up request id generator\\n2022-01-25 14:41:13,000 | root | INFO | Starting up app insight hooks\\n2022-01-25 14:41:13,000 | root | INFO | Invoking user\\'s init function\\n2022-01-25 14:41:17,208 | azureml.core | WARNING | Failure while loading azureml_run_type_providers. Failed to load entrypoint hyperdrive = azureml.train.hyperdrive:HyperDriveRun._from_run_dto with exception (cryptography 36.0.1 (/azureml-envs/azureml_0f7f18983f8fadd52db7ab2a1965cfbb/lib/python3.6/site-packages), Requirement.parse(\\'cryptography!=1.9,!=2.0.*,!=2.1.*,!=2.2.*,<4.0.0\\'), {\\'azureml-core\\'}).\\nFailure while loading azureml_run_type_providers. Failed to load entrypoint hyperdrive = azureml.train.hyperdrive:HyperDriveRun._from_run_dto with exception (cryptography 36.0.1 (/azureml-envs/azureml_0f7f18983f8fadd52db7ab2a1965cfbb/lib/python3.6/site-packages), Requirement.parse(\\'cryptography!=1.9,!=2.0.*,!=2.1.*,!=2.2.*,<4.0.0\\'), {\\'azureml-core\\'}).\\n2022-01-25 14:41:17,245 | azureml.core | WARNING | Failure while loading azureml_run_type_providers. Failed to load entrypoint automl = azureml.train.automl.run:AutoMLRun._from_run_dto with exception (cryptography 36.0.1 (/azureml-envs/azureml_0f7f18983f8fadd52db7ab2a1965cfbb/lib/python3.6/site-packages), Requirement.parse(\\'cryptography!=1.9,!=2.0.*,!=2.1.*,!=2.2.*,<4.0.0\\'), {\\'azureml-core\\'}).\\nFailure while loading azureml_run_type_providers. Failed to load entrypoint automl = azureml.train.automl.run:AutoMLRun._from_run_dto with exception (cryptography 36.0.1 (/azureml-envs/azureml_0f7f18983f8fadd52db7ab2a1965cfbb/lib/python3.6/site-packages), Requirement.parse(\\'cryptography!=1.9,!=2.0.*,!=2.1.*,!=2.2.*,<4.0.0\\'), {\\'azureml-core\\'}).\\n2022-01-25 14:41:17,248 | azureml.core | WARNING | Failure while loading azureml_run_type_providers. Failed to load entrypoint azureml.scriptrun = azureml.core.script_run:ScriptRun._from_run_dto with exception (cryptography 36.0.1 (/azureml-envs/azureml_0f7f18983f8fadd52db7ab2a1965cfbb/lib/python3.6/site-packages), Requirement.parse(\\'cryptography!=1.9,!=2.0.*,!=2.1.*,!=2.2.*,<4.0.0\\')).\\nFailure while loading azureml_run_type_providers. Failed to load entrypoint azureml.scriptrun = azureml.core.script_run:ScriptRun._from_run_dto with exception (cryptography 36.0.1 (/azureml-envs/azureml_0f7f18983f8fadd52db7ab2a1965cfbb/lib/python3.6/site-packages), Requirement.parse(\\'cryptography!=1.9,!=2.0.*,!=2.1.*,!=2.2.*,<4.0.0\\')).\\n2022-01-25 14:41:18,048 | root | INFO | Users\\'s init has completed successfully\\n2022-01-25 14:41:18,052 | root | INFO | Skipping middleware: dbg_model_info as it\\'s not enabled.\\n2022-01-25 14:41:18,052 | root | INFO | Skipping middleware: dbg_resource_usage as it\\'s not enabled.\\nGenerating swagger file: /tmp/tmph9azii7f\\n2022-01-25 14:41:18,063 | root | INFO | Scoring timeout is found from os.environ: 60000 ms\\n2022-01-25 14:41:43,992 | root | INFO | 200\\n127.0.0.1 - - [25/Jan/2022:14:41:43 +0000] \"GET /swagger.json HTTP/1.0\" 200 11160 \"-\" \"Go-http-client/1.1\"\\n2022-01-25 14:41:49,004 | root | INFO | 200\\n127.0.0.1 - - [25/Jan/2022:14:41:48 +0000] \"GET /swagger.json HTTP/1.0\" 200 11160 \"-\" \"Go-http-client/1.1\"\\n2022-01-25 14:41:49,447 | root | INFO | Validation Request Content-Type\\n2022-01-25 14:41:49,448 | root | INFO | Scoring Timer is set to 60.0 seconds\\n2022-01-25 14:41:50,251 | root | INFO | 200\\n127.0.0.1 - - [25/Jan/2022:14:41:50 +0000] \"POST /score HTTP/1.0\" 200 58 \"-\" \"python-requests/2.26.0\"\\n'"
          },
          "metadata": {}
        }
      ],
      "execution_count": 26,
      "metadata": {
        "gather": {
          "logged": 1643121711945
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#logs.py file\n",
        "# from azureml.core.webservice import Webservice\n",
        "\n",
        "# # Set with the deployment name\n",
        "# name = \"palsy-classification2\"\n",
        "\n",
        "# # load existing web service\n",
        "# service = Webservice(name=name, workspace=ws)\n",
        "# logs = service.get_logs()\n",
        "\n",
        "# for line in logs.split('\\n'):\n",
        "#     print(line)\n",
        "\n",
        "# service.update(enable_app_insights=True)"
      ],
      "outputs": [],
      "execution_count": 27,
      "metadata": {
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1643121712179
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Submission Checklist**\n",
        "- I have registered the model.\n",
        "- I have deployed the model with the best accuracy as a webservice.\n",
        "- I have tested the webservice by sending a request to the model endpoint.\n",
        "- I have deleted the webservice and shutdown all the computes that I have used.\n",
        "- I have taken a screenshot showing the model endpoint as active.\n",
        "- The project includes a file containing the environment details.\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "aci_service.delete()\n",
        "\n",
        "# service.delete()\n",
        "\n",
        "compute_target.delete()\n"
      ],
      "outputs": [],
      "execution_count": 29,
      "metadata": {
        "gather": {
          "logged": 1643121991688
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.9",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    },
    "microsoft": {
      "host": {
        "AzureML": {
          "notebookHasBeenCompleted": true
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}