Tabnet training

clustering/tabnet-classifier-tools/.bumpversion.cfg

@@ -0,0 +1,29 @@
+[bumpversion]
+current_version = 0.1.0-dev0
+commit = True
+tag = False
+parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<dev>\d+))?
+serialize = 
+	{major}.{minor}.{patch}-{release}{dev}
+	{major}.{minor}.{patch}
+
+[bumpversion:part:release]
+optional_value = _
+first_value = dev
+values = 
+	dev
+	_
+
+[bumpversion:part:dev]
+
+[bumpversion:file:pyproject.toml]
+search = version = "{current_version}"
+replace = version = "{new_version}"
+
+[bumpversion:file:plugin.json]
+[bumpversion:file:PytorchTabnet.cwl]
+[bumpversion:file:ict.yaml]
+
+[bumpversion:file:VERSION]
+
+[bumpversion:file:src/polus/tabular/clustering/pytorch_tabnet/__init__.py]

clustering/tabnet-classifier-tools/CHANGELOG.md

@@ -0,0 +1 @@
+# PyTorch TabNet tool(0.1.0-dev0)

clustering/tabnet-classifier-tools/Dockerfile

@@ -0,0 +1,20 @@
+FROM polusai/bfio:2.3.6
+
+# environment variables defined in polusai/bfio
+ENV EXEC_DIR="/opt/executables"
+ENV POLUS_IMG_EXT=".ome.tif"
+ENV POLUS_TAB_EXT=".arrow"
+ENV POLUS_LOG="INFO"
+
+# Work directory defined in the base container
+WORKDIR ${EXEC_DIR}
+
+COPY pyproject.toml ${EXEC_DIR}
+COPY VERSION ${EXEC_DIR}
+COPY README.md ${EXEC_DIR}
+COPY src ${EXEC_DIR}/src
+
+RUN pip3 install ${EXEC_DIR} --no-cache-dir
+
+ENTRYPOINT ["python3", "-m", "polus.tabular.clustering.pytorch_tabnet"]
+CMD ["--help"]

clustering/tabnet-classifier-tools/PytorchTabnet.cwl

@@ -0,0 +1,172 @@
+class: CommandLineTool
+cwlVersion: v1.2
+inputs:
+  batchSize:
+    inputBinding:
+      prefix: --batchSize
+    type: string?
+  catEmbDim:
+    inputBinding:
+      prefix: --catEmbDim
+    type: string?
+  classifier:
+    inputBinding:
+      prefix: --classifier
+    type: string
+  clipValue:
+    inputBinding:
+      prefix: --clipValue
+    type: double?
+  computeImportance:
+    inputBinding:
+      prefix: --computeImportance
+    type: boolean?
+  deviceName:
+    inputBinding:
+      prefix: --deviceName
+    type: string
+  dropLast:
+    inputBinding:
+      prefix: --dropLast
+    type: boolean?
+  epsilon:
+    inputBinding:
+      prefix: --epsilon
+    type: double?
+  evalMetric:
+    inputBinding:
+      prefix: --evalMetric
+    type: string
+  filePattern:
+    inputBinding:
+      prefix: --filePattern
+    type: string?
+  gamma:
+    inputBinding:
+      prefix: --gamma
+    type: double?
+  groupedFeatures:
+    inputBinding:
+      prefix: --groupedFeatures
+    type: string?
+  inpDir:
+    inputBinding:
+      prefix: --inpDir
+    type: Directory
+  lambdaSparse:
+    inputBinding:
+      prefix: --lambdaSparse
+    type: double?
+  lossFn:
+    inputBinding:
+      prefix: --lossFn
+    type: string
+  lr:
+    inputBinding:
+      prefix: --lr
+    type: double?
+  maskType:
+    inputBinding:
+      prefix: --maskType
+    type: string
+  maxEpochs:
+    inputBinding:
+      prefix: --maxEpochs
+    type: string?
+  momentum:
+    inputBinding:
+      prefix: --momentum
+    type: double?
+  nA:
+    inputBinding:
+      prefix: --nA
+    type: string?
+  nD:
+    inputBinding:
+      prefix: --nD
+    type: string?
+  nIndepDecoder:
+    inputBinding:
+      prefix: --nIndepDecoder
+    type: string?
+  nIndependent:
+    inputBinding:
+      prefix: --nIndependent
+    type: string?
+  nShared:
+    inputBinding:
+      prefix: --nShared
+    type: string?
+  nSharedDecoder:
+    inputBinding:
+      prefix: --nSharedDecoder
+    type: string?
+  nSteps:
+    inputBinding:
+      prefix: --nSteps
+    type: string?
+  numWorkers:
+    inputBinding:
+      prefix: --numWorkers
+    type: string?
+  optimizerFn:
+    inputBinding:
+      prefix: --optimizerFn
+    type: string
+  outDir:
+    inputBinding:
+      prefix: --outDir
+    type: Directory
+  patience:
+    inputBinding:
+      prefix: --patience
+    type: string?
+  preview:
+    inputBinding:
+      prefix: --preview
+    type: boolean?
+  schedulerFn:
+    inputBinding:
+      prefix: --schedulerFn
+    type: string
+  seed:
+    inputBinding:
+      prefix: --seed
+    type: string?
+  stepSize:
+    inputBinding:
+      prefix: --stepSize
+    type: string?
+  targetVar:
+    inputBinding:
+      prefix: --targetVar
+    type: string
+  testSize:
+    inputBinding:
+      prefix: --testSize
+    type: double?
+  virtualBatchSize:
+    inputBinding:
+      prefix: --virtualBatchSize
+    type: string?
+  warmStart:
+    inputBinding:
+      prefix: --warmStart
+    type: boolean?
+  weights:
+    inputBinding:
+      prefix: --weights
+    type: string?
+outputs:
+  outDir:
+    outputBinding:
+      glob: $(inputs.outDir.basename)
+    type: Directory
+requirements:
+  DockerRequirement:
+    dockerPull: polusai/pytorch-tabnet-tool:0.1.0-dev0
+  InitialWorkDirRequirement:
+    listing:
+    - entry: $(inputs.outDir)
+      writable: true
+  InlineJavascriptRequirement: {}

clustering/tabnet-classifier-tools/README.md

@@ -0,0 +1,73 @@
+# PyTorch TabNet tool(v0.1.0-dev0)
+
+This tool uses [tabnet](https://github.com/dreamquark-ai/tabnet/tree/develop), a deep learning model designed for tabular data structured in rows and columns. TabNet is suitable for classification, regression, and multi-task learning.
+
+## Inputs:
+
+### Input data:
+The input tabular data that need to be trained. This plugin supports `.csv`, `.feather`and `.arrow` file formats
+
+### Details:
+
+PyTorch-TabNet can be employed for:.
+
+1. TabNetClassifier: For binary and multi-class classification problems
+2. TabNetRegressor: For simple and multi-task regression problems
+3. TabNetMultiTaskClassifier: multi-task multi-classification problems
+
+
+## Building
+
+To build the Docker image for the conversion plugin, run
+`./build-docker.sh`.
+
+## Install WIPP Plugin
+
+If WIPP is running, navigate to the plugins page and add a new plugin. Paste the contents of `plugin.json` into the pop-up window and submit.
+For more information on WIPP, visit the [official WIPP page](https://isg.nist.gov/deepzoomweb/software/wipp).
+
+## Options
+
+This plugin takes 38 input arguments and one output argument:
+
+| Name             | Description                                                                 | I/O    | Type          |
+| ---------------- | --------------------------------------------------------------------------- | ------ | ------------- |
+| `--inpdir`       | Input tabular data                                                          | Input  | genericData   |
+| `--filePattern`  | Pattern to parse tabular files                                              | Input  | string        |
+| `--testSize`     | Proportion of the dataset to include in the test set                        | Input  | number        |
+| `--nD`           | Width of the decision prediction layer                          | Input  | integer       |
+| `--nA`           | Width of the attention embedding for each mask                          | Input  | integer       |
+| `--nSteps`       | Number of steps in the architecture                         | Input  | integer       |
+| `--gamma`        | Coefficient for feature reuse in the masks                         | Input  | number       |
+| `--catEmbDim`    | List of embedding sizes for each categorical feature                          | Input  | integer       |
+| `--nIndependent`    | Number of independent Gated Linear Unit layers at each step          | Input  | integer       |
+| `--nShared`    | Number of shared Gated Linear Unit layers at each step          | Input  | integer       |
+| `--epsilon`     | Constant value                        | Input  | number        |
+| `--seed`    | Random seed for reproducibility          | Input  | integer       |
+| `--momentum`     | Momentum for batch normalization                        | Input  | number        |
+| `--clipValue`     | Clipping of the gradient value                        | Input  | number        |
+| `--lambdaSparse`     | Extra sparsity loss coefficient                        | Input  | number        |
+| `--optimizerFn`     | Pytorch optimizer function                        | Input  | enum        |
+| `--lr`     | learning rate for the optimizer                        | Input  | number         |
+| `--schedulerFn`     | Parameters used initialize the optimizer                        | Input  | enum         |
+| `--stepSize`     | Parameter to apply to the scheduler_fn                        | Input  | integer         |
+| `--deviceName`     | Platform used for training                        | Input  | enum         |
+| `--maskType`     | A masking function for feature selection                        | Input  | enum         |
+| `--groupedFeatures`     | Allow the model to share attention across features within the same group | Input  | integer         |
+| `--nSharedDecoder`     | Number of shared GLU block in decoder | Input  | integer         |
+| `--nIndepDecoder`     | Number of independent GLU block in decoder | Input  | integer         |
+| `--evalMetric`     | Metrics utilized for early stopping evaluation                        | Input  | enum         |
+| `--maxEpochs`     | Maximum number of epochs for training | Input  | integer         |
+| `--patience`     | Consecutive epochs without improvement before early stopping | Input  | integer         |
+| `--weights`     | Sampling parameter only for TabNetClassifier | Input  | integer         |
+| `--lossFn`     | Loss function                        | Input  | enum         |
+| `--batchSize`     | Batch size                       | Input  | integer         |
+| `--virtualBatchSize`     | Size of mini-batches for Ghost Batch Normalization    | Input  | integer         |
+| `--numWorkers`     | Number or workers used in torch.utils.data.Dataloader    | Input  | integer         |
+| `--dropLast`     | Option to drop incomplete last batch during training    | Input  | boolean         |
+| `--warmStart`     | For scikit-learn compatibility, enabling fitting the same model twice    | Input  | boolean         |
+| `--targetVar`     | Target feature containing classification labels   | Input  | string         |
+| `--computeImportance`     | Compute feature importance    | Input  | boolean         |
+| `--classifier`     | Pytorch tabnet Classifier for training   | Input  | enum         |
+| `--preview`      | Generate JSON file of sample outputs                       | Input | boolean         |
+| `--outdir`       | Output collection                            | Output | genericData   |

clustering/tabnet-classifier-tools/VERSION

@@ -0,0 +1 @@
+0.1.0-dev0

clustering/tabnet-classifier-tools/build-docker.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+
+version=$(<VERSION)
+docker build . -t polusai/pytorch-tabnet-tool:${version}

clustering/tabnet-classifier-tools/examples/feature_importances.json

@@ -0,0 +1,16 @@
+{
+  "capital-gain": 0.2208,
+  "marital-status": 0.1949,
+  "educational-num": 0.1724,
+  "hours-per-week": 0.1041,
+  "age": 0.0789,
+  "gender": 0.0756,
+  "occupation": 0.0635,
+  "relationship": 0.0264,
+  "capital-loss": 0.0225,
+  "native-country": 0.0223,
+  "race": 0.0102,
+  "fnlwgt": 0.0061,
+  "education": 0.0016,
+  "workclass": 0.001
+}