Monkey-patching sklearn every use rather than upfront

sknn/mlp.py

@@ -9,6 +9,7 @@
 import time
 import logging
 import itertools
+import contextlib
 
 log = logging.getLogger('sknn')
 
@@ -318,17 +319,20 @@ def _setup(self):
         super(Classifier, self)._setup()
         self.label_binarizers = []
 
+    @contextlib.contextmanager
+    def _patch_sklearn(self):
         # WARNING: Unfortunately, sklearn's LabelBinarizer handles binary data
         # as a special case and encodes it very differently to multiclass cases.
         # In our case, we want to have 2D outputs when there are 2 classes, or
         # the predicted probabilities (e.g. Softmax) will be incorrect.
         # The LabelBinarizer is also implemented in a way that this cannot be
-        # customized without a providing a complete rewrite, so here we patch
-        # the `type_of_target` function for this to work correctly,
+        # customized without a providing a near-complete rewrite, so here we patch
+        # the `type_of_target` function for this to work correctly.
         import sklearn.preprocessing.label as spl
-        assert 'type_of_target' in dir(spl),\
-               "Could not setup sklearn.preprocessing.label.LabelBinarizer functionality."
+        backup = spl.type_of_target 
         spl.type_of_target = lambda _: "multiclass"
+        yield
+        spl.type_of_target = backup
 
     def fit(self, X, y, w=None):
         """Fit the neural network to symbolic labels as a classification problem.
@@ -368,15 +372,17 @@ def fit(self, X, y, w=None):
         # Deal deal with single- and multi-output classification problems.
         LB = sklearn.preprocessing.LabelBinarizer
         self.label_binarizers = [LB() for _ in range(y.shape[1])]
-        ys = [lb.fit_transform(y[:,i]) for i, lb in enumerate(self.label_binarizers)]
+        with self._patch_sklearn():
+            ys = [lb.fit_transform(y[:,i]) for i, lb in enumerate(self.label_binarizers)]
         yp = numpy.concatenate(ys, axis=1)
 
         # Also transform the validation set if it was explicitly specified.
         if self.valid_set is not None:
             X_v, y_v = self.valid_set
             if y_v.ndim == 1:
                 y_v = y_v.reshape((y_v.shape[0], 1))
-            ys = [lb.transform(y_v[:,i]) for i, lb in enumerate(self.label_binarizers)]
+            with self._patch_sklearn():
+                ys = [lb.transform(y_v[:,i]) for i, lb in enumerate(self.label_binarizers)]
             y_vp = numpy.concatenate(ys, axis=1)
             self.valid_set = (X_v, y_vp)
 

sknn/tests/test_conv.py

@@ -235,7 +235,7 @@ def _run(self, activation):
     def test_RectifierConv(self):
         self._run("Rectifier")
 
-    def test_RectifierConv(self):
+    def test_ExponentialLinear(self):
         self._run("ExpLin")
 
     def test_SigmoidConv(self):