Added first commit to generate documents

Author: prithagupta

.github/workflows/docs.yml

@@ -0,0 +1,71 @@
+name: docs
+
+# on:
+#   # Trigger manually
+#   workflow_dispatch:
+#
+#   # Trigger on any push to the main
+#   push:
+#     branches:
+#       - main
+#       - development
+#
+#   # Trigger on any push to a PR that targets main
+#   pull_request:
+#     branches:
+#       - main
+#       - development
+
+permissions:
+  contents: write
+
+env:
+  name: AutoMLQuantILDetect
+
+jobs:
+  build-and-deploy:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v3
+
+    - name: Setup Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: "3.10"
+
+    - name: Install dependencies
+      run: |
+        pip install ".[dev]"
+
+    - name: Make docs
+      run: |
+        make clean
+        make docs
+
+    - name: Pull latest gh-pages
+      if: (contains(github.ref, 'development') || contains(github.ref, 'main'))
+      run: |
+        cd ..
+        git clone https://github.com/${{ github.repository }}.git --branch gh-pages --single-branch gh-pages
+
+    - name: Copy new docs into gh-pages
+      if: (contains(github.ref, 'development') || contains(github.ref, 'main'))
+      run: |
+        branch_name=${GITHUB_REF##*/}
+        cd ../gh-pages
+        rm -rf $branch_name
+        cp -r ../${{ env.name }}/docs/build/html $branch_name
+
+    - name: Push to gh-pages
+      if: (contains(github.ref, 'development') || contains(github.ref, 'main'))
+      run: |
+        last_commit=$(git log --pretty=format:"%an: %s")
+        cd ../gh-pages
+        branch_name=${GITHUB_REF##*/}
+        git add $branch_name/
+        git config --global user.name 'Github Actions'
+        git config --global user.email 'not@mail.com'
+        git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/${{ github.repository }}
+        git commit -am "$last_commit"
+        git push

.readthedocs.yaml

@@ -0,0 +1,27 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the version of Python and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.10"
+  apt_packages:
+    - pandoc
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+   configuration: docs/source/conf.py
+
+# If using Sphinx, optionally build your docs in additional formats such as PDF
+# formats:
+#    - pdf
+
+# Optionally declare the Python requirements required to build your docs
+python:
+  install:
+    - requirements: docs/requirements.txt

autoqild/classifiers/layers.py

@@ -1,32 +1,67 @@
 from keras.layers import Layer, Dense, Activation, BatchNormalization
 
 class NormalizedDense(Layer):
-    """Stop training when a monitored quantity has stopped improving.
-    # Arguments
-    units: Positive integer, dimensionality of the output space.
-    activation: Activation function to use
-        (see [activations](../activations.md)).
-        If you don't specify anything, no activation is applied
-        (ie. "relu:).
-    normalize_before_activation: True if normalize the inputs before applying the activation.
-    False if activation is applied before Bach Normalization
-    """
 
     def __init__(self, units, activation="relu", **kwd):
+        """
+            A custom dense layer with batch normalization and optional pre-activation normalization.
+
+            Parameters
+            ----------
+            units : int
+                Positive integer, dimensionality of the output space.
+            activation : str, optional
+                Activation function to use (default is 'relu').
+                If not specified, no activation is applied (i.e., "linear").
+            normalize_before_activation : bool, optional
+                If True, normalizes the inputs before applying the activation function.
+                If False, applies activation before batch normalization.
+            **kwd : dict
+                Additional keyword arguments to pass to the `Dense` layer.
+        """
         self.dense = Dense(units, activation="linear", **kwd)
         self.activation = Activation(activation=activation)
         self.batchnorm = BatchNormalization()
         self.norm_layer = None
 
     def __call__(self, x):
+        """
+            Applies the layer to the input tensor `x`.
+
+            Parameters
+            ----------
+            x : tensor
+                Input tensor to the layer.
+
+            Returns
+            -------
+            tensor
+                Output tensor after applying dense, activation, and batch normalization.
+        """
         return self.batchnorm(self.activation(self.dense(x)))
 
     def get_weights(self):
+        """
+            Returns the weights of the batch normalization and dense layers.
+
+            Returns
+            -------
+            tuple
+                A tuple containing the weights of the batch normalization and dense layers.
+        """
         w_b = self.batchnorm.get_weights()
         w_d = self.dense.get_weights()
         return w_b, w_d
 
     def set_weights(self, weights):
+        """
+            Sets the weights of the batch normalization and dense layers.
+
+            Parameters
+            ----------
+            weights : tuple
+                A tuple containing the weights for the batch normalization and dense layers.
+        """
         w_b, w_d = weights
         self.batchnorm.set_weights(w_b)
         self.dense.set_weights(w_d)

autoqild/classifiers/multi_layer_perceptron.py

@@ -184,8 +184,7 @@ def fit(self, X, y, epochs=50, batch_size=32, callbacks=None, validation_split=0
             epochs : int, optional, default=50
                 Number of training epochs.
 
-            batch_size : int, optional, default=32
-                Number of samples per gradient update.
+            batch_size : 
 
             callbacks : list of keras.callbacks.Callback, optional
                 List of callback instances to apply during training.

autoqild/detectors/ild_base_class.py

@@ -24,6 +24,7 @@
 class InformationLeakageDetector(metaclass=ABCMeta):
     def __init__(self, padding_name, learner_params, fit_params, hash_value, cv_iterations, n_hypothesis,
                  base_directory, detection_method, random_state, **kwargs):
+
         self.logger = logging.getLogger(InformationLeakageDetector.__name__)
         self.padding_name, self.padding_code = self.format_name(padding_name)
         self.fit_params = fit_params

autoqild/utilities/dimensionality_reduction_techniques.py

@@ -7,6 +7,34 @@
 
 # Create a dictionary to store the techniques and their options
 def create_dimensionality_reduction_model(reduction_technique, n_reduced=20):
+    """
+        Creates a dimensionality reduction model based on the specified technique.
+
+        Parameters
+        ----------
+        reduction_technique : str
+            The technique to use for dimensionality reduction. Options include:
+            - 'recursive_feature_elimination_et': Recursive Feature Elimination with ExtraTreesClassifier.
+            - 'recursive_feature_elimination_rf': Recursive Feature Elimination with RandomForestClassifier.
+            - 'select_from_model_et': SelectFromModel with ExtraTreesClassifier.
+            - 'select_from_model_rf': SelectFromModel with RandomForestClassifier.
+            - 'pca': Principal Component Analysis.
+            - 'lda': Linear Discriminant Analysis.
+            - 'tsne': t-Distributed Stochastic Neighbor Embedding.
+            - 'nmf': Non-negative Matrix Factorization.
+        n_reduced : int, optional
+            The number of components or features to reduce to (default is 20).
+
+        Returns
+        -------
+        object
+            A dimensionality reduction model corresponding to the specified technique.
+
+        Raises
+        ------
+        ValueError
+            If the specified reduction technique is not defined.
+    """
     reduction_techniques = {
         'recursive_feature_elimination_et': RFE(ExtraTreesClassifier(), n_features_to_select=n_reduced),
         'recursive_feature_elimination_rf': RFE(RandomForestClassifier(), n_features_to_select=n_reduced),