Cleaning up the code, adding tests for coverage.

sknn/ae.py

@@ -33,7 +33,7 @@ def __init__(self,
         if type not in ['denoising', 'autoencoder']:
             raise NotImplementedError("AutoEncoder layer type `%s` is not implemented." % type)
         if cost not in ['msre', 'mbce']:
-            raise NotImplementedError("Error type '%s' is not implemented." % self.cost)
+            raise NotImplementedError("Error type '%s' is not implemented." % cost)
 
         self.name = name
         self.type = type

sknn/mlp.py

@@ -302,12 +302,6 @@ def is_initialized(self):
         """
         return not (self.mlp is None or self.f is None)
 
-    @property
-    def is_convolution(self):
-        """Check whether this neural network includes convolution layers.
-        """
-        return isinstance(self.layers[0], Convolution)
-
     def __getstate__(self):
         assert self.mlp is not None,\
             "The neural network has not been initialized."

sknn/nn.py

@@ -410,20 +410,21 @@ def __init__(
         self._setup()
 
     def _setup(self):
-        raise NotImplementedError("MultiLayerPerceptron is an abstract class; "
-                                  "use the Classifier or Regressor instead.")
+        raise NotImplementedError("NeuralNetwork is an abstract class; "
+                                  "use the mlp.Classifier or mlp.Regressor instead.")
 
     @property
     def is_initialized(self):
         """Check if the neural network was setup already.
         """
-        return None # not (self.mlp is None or self.f is None)
+        raise NotImplementedError("NeuralNetwork is an abstract class; "
+                                  "use the mlp.Classifier or mlp.Regressor instead.")
 
     @property
     def is_convolution(self):
         """Check whether this neural network includes convolution layers.
         """
-        return False
+        return isinstance(self.layers[0], Convolution)
 
     def _create_matrix_input(self, X, y=None):
         if self.is_convolution:

sknn/tests/test_ae.py

@@ -1,6 +1,5 @@
 import unittest
-from nose.tools import (assert_false, assert_raises, assert_true,
-                        assert_equal, assert_in)
+from nose.tools import (assert_raises)
 
 import numpy
 
@@ -17,3 +16,20 @@ def test_FitData(self):
         X = numpy.zeros((8,4))
         ae = AE(layers=[L("Sigmoid", units=8)], n_iter=1)
         ae.fit(X)
+
+
+class TestParameters(unittest.TestCase):
+
+    def test_CostFunctions(self):
+        for t in ['msre', 'mbce']:
+            AE(layers=[L("Sigmoid", units=8, cost=t)])
+
+    def test_LayerTypes(self):
+        for l in ['autoencoder', 'denoising']:
+            AE(layers=[L("Sigmoid", type=l, units=8)])
+
+    def test_UnknownCostFunction(self):
+        assert_raises(NotImplementedError, L, "Sigmoid", cost="unknown")
+
+    def test_UnknownType(self):
+        assert_raises(NotImplementedError, L, "Sigmoid", type="unknown")

sknn/tests/test_nn.py

@@ -0,0 +1,13 @@
+import unittest
+from nose.tools import (assert_raises)
+
+from sknn.nn import NeuralNetwork as NN
+
+
+class TestAbstractNeuralNetwork(unittest.TestCase):
+
+    def test_SetupRaisesException(self):
+        assert_raises(NotImplementedError, NN, layers=[])
+
+    def test_IsInitializedRaisesExecption(self):
+        assert_raises(NotImplementedError, NN.is_initialized.fget, object())