Merge pull request #308 from alexbrillant/issue-236

Fix Issue #236

neuraxle/base.py

@@ -3035,6 +3035,13 @@ class TransformHandlerOnlyMixin(NonFittableMixin):
 
     @abstractmethod
     def _transform_data_container(self, data_container: DataContainer, context: ExecutionContext) -> DataContainer:
+        """
+        Transform data container with the given execution context.
+
+        :param data_container: data container
+        :param context: execution context
+        :return: transformed data container
+        """
         raise NotImplementedError('Must implement _transform_data_container in {0}'.format(self.name))
 
     def transform(self, data_inputs) -> 'HandleOnlyMixin':
@@ -3111,7 +3118,13 @@ def __init__(self, cache_folder=None):
             cache_folder = DEFAULT_CACHE_FOLDER
         self.cache_folder = cache_folder
 
-    def transform(self, data_inputs):
+    def transform(self, data_inputs) -> Iterable:
+        """
+        Using :func:`~neuraxle.base.BaseStep.handle_transform`, transform data inputs.
+
+        :param data_inputs: data inputs
+        :return: outputs
+        """
         execution_context = ExecutionContext(self.cache_folder, execution_mode=ExecutionMode.TRANSFORM)
         context, data_container = self._encapsulate_data(
             data_inputs, expected_outputs=None, execution_mode=ExecutionMode.TRANSFORM)
@@ -3121,18 +3134,38 @@ def transform(self, data_inputs):
         return data_container.data_inputs
 
     def fit(self, data_inputs, expected_outputs=None) -> 'HandleOnlyMixin':
+        """
+        Using :func:`~neuraxle.base.BaseStep.handle_fit`, fit step with the given data inputs, and expected outputs.
+
+        :param data_inputs: data inputs
+        :return: fitted self
+        """
         context, data_container = self._encapsulate_data(data_inputs, expected_outputs, ExecutionMode.FIT)
         new_self = self.handle_fit(data_container, context)
 
         return new_self
 
     def fit_transform(self, data_inputs, expected_outputs=None) -> Tuple['HandleOnlyMixin', Iterable]:
+        """
+        Using :func:`~neuraxle.base.BaseStep.handle_fit_transform`, fit and transform step with the given data inputs, and expected outputs.
+
+        :param data_inputs: data inputs
+        :return: fitted self, outputs
+        """
         context, data_container = self._encapsulate_data(data_inputs, expected_outputs, ExecutionMode.FIT_TRANSFORM)
         new_self, data_container = self.handle_fit_transform(data_container, context)
 
         return new_self, data_container.data_inputs
 
-    def _encapsulate_data(self, data_inputs, expected_outputs=None, execution_mode=None):
+    def _encapsulate_data(self, data_inputs, expected_outputs=None, execution_mode=None) -> Tuple[ExecutionContext, DataContainer]:
+        """
+        Encapsulate data with :class:`~neuraxle.data_container.DataContainer`.
+
+        :param data_inputs: data inputs
+        :param expected_outputs: expected outputs
+        :param execution_mode: execution mode
+        :return: execution context, data container
+        """
         data_container = DataContainer(data_inputs=data_inputs, expected_outputs=expected_outputs)
         context = ExecutionContext(root=self.cache_folder, execution_mode=execution_mode)
 
@@ -3179,6 +3212,16 @@ class FullDumpLoader(Identity):
     Identity step that can load the full dump of a pipeline step.
     Used by :func:`~neuraxle.base.BaseStep.load`.
 
+    Usage example:
+
+    .. code-block:: python
+
+        saved_step = FullDumpLoader(
+            name=path,
+            stripped_saver=self.stripped_saver
+        ).load(context_for_loading, True)
+
+
     .. seealso::
         :class:`ExecutionContext`
         :class:`BaseStep`,