Merge pull request #177 from Michael-Ainsworth/master

Add docstrings to transformers.py

proglearn/transformers.py

@@ -20,6 +20,63 @@
 
 
 class NeuralClassificationTransformer(BaseTransformer):
+    """
+    A class used to transform data from a category to a specialized representation.
+
+    Parameters
+    ----------
+    network : object
+        A neural network used in the classification transformer.
+    euclidean_layer_idx : int
+        An integer to represent the final layer of the transformer.
+    optimizer : str
+        An optimizer used when compiling the neural network.
+    loss : str, default="categorical_crossentropy"
+        A loss function used when compiling the neural network.
+    pretrained : bool, default=False
+        A boolean used to identify if the network is pretrained.
+    compile_kwargs : dict, default={"metrics": ["acc"]}
+        A dictionary containing metrics for judging network performance.
+    fit_kwargs : dict, default={
+                "epochs": 100,
+                "callbacks": [keras.callbacks.EarlyStopping(patience=5, monitor="val_acc")],
+                "verbose": False,
+                "validation_split": 0.33,
+            },
+        A dictionary to hold epochs, callbacks, verbose, and validation split for the network.
+
+    Attributes (class)
+    ----------
+    None
+
+    Attributes (object)
+    ----------
+    network : object
+        A Keras model cloned from the network parameter.
+    encoder : object
+        A Keras model with inputs and outputs based on the network attribute. Output layers
+        are determined by the euclidean_layer_idx parameter.
+    _is_fitted : bool
+        A boolean to identify if the network has already been fitted.
+    optimizer : str
+        A string to identify the optimizer used in the network.
+    loss : str
+        A string to identify the loss function used in the network.
+    compile_kwargs : dict
+        A dictionary containing metrics for judging network performance.
+    fit_kwargs : dict
+        A dictionary to hold epochs, callbacks, verbose, and validation split for the network.   
+
+    Methods
+    ----------
+    fit(X, y)
+        Fits the transformer to data X with labels y.
+    transform(X)
+        Performs inference using the transformer.
+    is_fitted()
+        Indicates whether the transformer is fitted.
+    """
+
     def __init__(
         self,
         network,
@@ -35,9 +92,6 @@ def __init__(
             "validation_split": 0.33,
         },
     ):
-        """
-        Doc strings here.
-        """
         self.network = keras.models.clone_model(network)
         self.encoder = keras.models.Model(
             inputs=self.network.inputs,
@@ -51,8 +105,16 @@ def __init__(
 
     def fit(self, X, y):
         """
-        Doc strings here.
+        Fits the transformer to data X with labels y.
+
+        Parameters
+        ----------
+        X : ndarray
+            Input data matrix.
+        y : ndarray
+            Output (i.e. response data matrix).
         """
+
         check_classification_targets(y)
         _, y = np.unique(y, return_inverse=True)
         self.num_classes = len(np.unique(y))
@@ -72,7 +134,12 @@ def fit(self, X, y):
 
     def transform(self, X):
         """
-        Doc strings here.
+        Performs inference using the transformer.
+
+        Parameters
+        ----------
+        X : ndarray
+            Input data matrix.
         """
 
         if not self.is_fitted():
@@ -87,25 +154,53 @@ def transform(self, X):
 
     def is_fitted(self):
         """
-        Doc strings here.
+        Indicates whether the transformer is fitted.
+
+        Parameters
+        ----------
+        None
         """
 
         return self._is_fitted
 
 
 class TreeClassificationTransformer(BaseTransformer):
+    """
+    A class used to transform data from a category to a specialized representation.
+
+    Attributes (object)
+    ----------
+    kwargs : dict
+        A dictionary to contain parameters of the tree.
+    _is_fitted_ : bool
+        A boolean to identify if the model is currently fitted.
+
+    Methods
+    ----------
+    fit(X, y)
+        Fits the transformer to data X with labels y.
+    transform(X)
+        Performs inference using the transformer.
+    is_fitted()
+        Indicates whether the transformer is fitted.
+    """
+
     def __init__(self, kwargs={}):
-        """
-        Doc strings here.
-        """
 
         self.kwargs = kwargs
 
         self._is_fitted = False
 
     def fit(self, X, y):
         """
-        Doc strings here.
+        Fits the transformer to data X with labels y.
+
+        Parameters
+        ----------
+        X : ndarray
+            Input data matrix.
+        y : ndarray
+            Output (i.e. response data matrix).
         """
 
         X, y = check_X_y(X, y)
@@ -119,7 +214,12 @@ def fit(self, X, y):
 
     def transform(self, X):
         """
-        Doc strings here.
+        Performs inference using the transformer.
+
+        Parameters
+        ----------
+        X : ndarray
+            Input data matrix.
         """
 
         if not self.is_fitted():
@@ -134,7 +234,11 @@ def transform(self, X):
 
     def is_fitted(self):
         """
-        Doc strings here.
+        Indicates whether the transformer is fitted.
+
+        Parameters
+        ----------
+        None
         """
 
         return self._is_fitted