Checking if the super class has the method fit to avoid unecessary stack from the base class

bob/pipelines/mixins.py

@@ -12,7 +12,7 @@ from sklearn.pipeline import Pipeline
 from dask import delayed
 import dask.bag
 
-def dask_it(o, fit_tag=None, transform_tag=None):
+def dask_it(o, fit_tag=None, transform_tag=None, npartitions=None):
     """
     Mix up any :py:class:`sklearn.pipeline.Pipeline` or :py:class:`sklearn.estimator.Base` with
     :py:class`DaskEstimatorMixin`
@@ -76,7 +76,7 @@ def dask_it(o, fit_tag=None, transform_tag=None):
 
     if isinstance(o, Pipeline):
         #Adding a daskbag in the tail of the pipeline
-        o.steps.insert(0, ('0', DaskBagMixin()))
+        o.steps.insert(0, ('0', DaskBagMixin(npartitions=npartitions)))
 
     # Patching dask_resources
     dasked = mix_me_up(DaskEstimatorMixin, o)
@@ -184,9 +184,6 @@ class SampleMixin:
         Also implement ``predict``, ``predict_proba``, and ``score``. See:
         https://scikit-learn.org/stable/developers/develop.html#apis-of-scikit-learn-objects
 
-    .. todo::
-
-        Allow handling the targets given to the ``fit`` method.
     """
 
     def transform(self, samples):
@@ -206,7 +203,13 @@ class SampleMixin:
 
 
     def fit(self, samples, y=None):
-        return super().fit([s.data for s in samples], y=y)
+        
+        # IF THE SUPER METHOD IS NOT FITTABLE,
+        # THERE'S NO REASON TO STACK THOSE SAMPLES
+        if( hasattr(super(), "fit")):
+            return super().fit([s.data for s in samples], y=y)
+
+        return self
 
 
 class CheckpointMixin:
@@ -251,13 +254,21 @@ class CheckpointMixin:
 
 
     def fit(self, samples, y=None):
+
+        # IF THE SUPER METHOD IS NOT FITTABLE,
+        # THERE'S NO REASON TO STACK THOSE SAMPLES
+        if( not hasattr(super(), "fit")  ):
+            return self
+
         if self.model_path is not None and os.path.isfile(self.model_path):
             return self.load_model()
 
         super().fit(samples, y=y)
         return self.save_model()
 
+        
     def fit_transform(self, samples, y=None):
+
         return self.fit(samples, y=y).transform(samples)
 
     def make_path(self, sample):
@@ -403,7 +414,7 @@ class DaskEstimatorMixin:
         return self
 
     def transform(self, X):
-        def _transf(X_line, dask_state): 
+        def _transf(X_line, dask_state):
             return super(DaskEstimatorMixin, dask_state).transform(X_line)
 
         map_partitions = X.map_partitions(_transf, self._dask_state)