Re-enabled one missing test, reworked some code for coverage.

sknn/backend/__init__.py

@@ -21,5 +21,5 @@ def __init__(self, _):
 # Automatically import the recommended backend if none was manually imported.
 def setup():
     if name == None:
-        from . import lasagne 
+        from . import pylearn2 
     assert name is not None, "No backend for module sknn was imported."

sknn/backend/pylearn2/__init__.py

@@ -1,6 +1,18 @@
 # -*- coding: utf-8 -*-
 from __future__ import (absolute_import, unicode_literals, print_function)
 
+from ...nn import ansi
+
+
+import warnings
+warnings.warn(ansi.YELLOW + """\n
+The PyLearn2 backend is deprecated; the next release will switch to Lasagne by default.
+
+Test the change using the following at the top of your script:
+> from sknn.backend import lasagne
+""" + ansi.ENDC, category=UserWarning)
+
+
 from ... import backend
 from .mlp import MultiLayerPerceptronBackend
 from .ae import AutoEncoderBackend

sknn/backend/pylearn2/dataset.py

@@ -103,7 +103,7 @@ def _mutate_fn(self, array):
         array = self._conv_fn(array)
         if self.mutator is not None:
             for i in range(array.shape[0]):
-                self.mutator(array[i])
+                array[i] = self.mutator(array[i])
         return array
 
     @functools.wraps(dataset.Dataset.iterator)
@@ -160,17 +160,3 @@ def iterator(self, **kwargs):
         if self.mutator is not None:
             bit._convert[0] = self._conv_fn
         return bit
-
-"""
-OriginalDatasetIterator = iteration.FiniteDatasetIterator
-
-def create_finite_iterator(*args, **kwargs):
-    print('create_finite_iterator', kwargs['convert'])
-    def conv_fn(x):
-        return x + 0.01
-    kwargs['convert'] = [conv_fn, None]
-    return OriginalDatasetIterator(*args, **kwargs)
-    # convert=convert)
-
-datasets.dense_design_matrix.FiniteDatasetIterator = create_finite_iterator
-"""

sknn/backend/pylearn2/mlp.py

@@ -270,10 +270,10 @@ def _train_impl(self, X, y):
             X = self.ds.view_converter.topo_view_to_design_mat(X)
         self.ds.X, self.ds.y = X, y
 
-        self._train_layer(self.trainer, self.mlp, self.ds)
+        return self._train_layer(self.trainer, self.mlp, self.ds)
 
     def _valid_impl(self, X, y):
-        self._valid_layer(self.mlp)
+        return self._valid_layer(self.mlp)
 
     @property
     def is_initialized(self):

sknn/backend/pylearn2/nn.py

@@ -87,10 +87,7 @@ def _train_layer(self, trainer, layer, dataset):
     def _valid_layer(self, layer):
         layer.monitor.report_epoch()
         layer.monitor()
-
+
+        # 'objective' channel is only defined with validation set.        
         objective = layer.monitor.channels.get('objective', None)
-        if objective:
-            return objective.val_shared.get_value()
-        else:
-            # 'objective' channel is only defined with validation set.
-            return None
+        return objective.val_shared.get_value() if objective else None

sknn/tests/test_types.py

@@ -41,10 +41,10 @@ def test_FitHybrid(self):
             y = numpy.zeros((8, 4), dtype=numpy.float32)
             self.nn._fit(X, y)
 
-    def __test_FitMutator(self):
+    def test_FitMutator(self):
         def mutate(x):
-            x -= 0.5
             self.count += 1
+            return x - 0.5
         self.nn.mutator = mutate
 
         for t in SPARSE_TYPES: