def test_handle_errors(self):
data, target = datasets.make_classification(300,
n_features=4,
n_classes=2)
x_train, x_test, y_train, y_test = model_selection.train_test_split(
data, target, test_size=0.3)
with self.assertRaises(ValueError):
# First network has two output layers and the second
# just one.
algorithms.DynamicallyAveragedNetwork([
algorithms.RPROP((4, 10, 2), step=0.1),
algorithms.GradientDescent((4, 10, 1), step=0.1)
])
with self.assertRaises(ValueError):
# Use ensemble with less than one network
algorithms.DynamicallyAveragedNetwork(
[algorithms.GradientDescent((4, 10, 1), step=0.1)])
with self.assertRaises(ValueError):
# Output greater than 1
dan = algorithms.DynamicallyAveragedNetwork([
algorithms.GradientDescent([
Input(4),
Sigmoid(10),
Relu(1, weight=init.Uniform(), bias=init.Uniform()),
],
step=0.01),
algorithms.RPROP((4, 10, 1), step=0.01),
])
dan.train(x_train, y_train, epochs=10)
dan.predict(x_test)
def test_dan_repr(self):
dan = algorithms.DynamicallyAveragedNetwork([
algorithms.Momentum((3, 2, 1)),
algorithms.GradientDescent((3, 2, 1)),
])
dan_repr = str(dan)
self.assertIn('DynamicallyAveragedNetwork', dan_repr)
self.assertIn('Momentum', dan_repr)
self.assertIn('GradientDescent', dan_repr)
def test_handle_errors(self):
data, target = datasets.make_classification(300, n_features=4,
n_classes=2)
x_train, x_test, y_train, y_test = cross_validation.train_test_split(
data, target, train_size=0.7
)
with self.assertRaises(ValueError):
# First network has two output layers and the second
# just one.
algorithms.DynamicallyAveragedNetwork([
algorithms.RPROP((4, 10, 2), step=0.1),
algorithms.GradientDescent((4, 10, 1), step=0.1)
])
with self.assertRaises(ValueError):
# Use ensemble with less than one network
algorithms.DynamicallyAveragedNetwork([
algorithms.GradientDescent((4, 10, 1), step=0.1)
])
with self.assertRaises(ValueError):
# Output greater than 1
dan = algorithms.DynamicallyAveragedNetwork([
algorithms.GradientDescent(
[
Sigmoid(4),
Relu(10, init_method='bounded', bounds=(0, 1)),
Output(1),
],
step=0.01
),
algorithms.RPROP((4, 10, 1), step=0.01),
])
dan.train(x_train, y_train, epochs=10)
dan.predict(x_test)
def test_dan(self):
data, target = datasets.make_classification(300, n_features=4,
n_classes=2)
x_train, x_test, y_train, y_test = cross_validation.train_test_split(
data, target, train_size=0.7
)
dan = algorithms.DynamicallyAveragedNetwork([
algorithms.RPROP((4, 10, 1), step=0.1, maxstep=1),
algorithms.GradientDescent((4, 5, 1), step=0.1),
algorithms.ConjugateGradient((4, 5, 1), step=0.01),
])
dan.train(x_train, y_train, epochs=500)
result = dan.predict(x_test)
ensemble_result = metrics.accuracy_score(y_test, result)
self.assertAlmostEqual(0.9222, ensemble_result, places=4)
def test_ensemble(self):
data, target = datasets.make_classification(300,
n_features=4,
n_classes=2)
x_train, x_test, y_train, y_test = train_test_split(data,
target,
train_size=0.7)
dan = algorithms.DynamicallyAveragedNetwork([
algorithms.RPROP((4, 5, 1), step=0.1, maxstep=1),
algorithms.GradientDescent((4, 5, 1), step=0.1),
algorithms.ConjugateGradient((4, 5, 1), step=0.01),
])
pipeline = Pipeline([
('min_max_scaler', preprocessing.StandardScaler()),
('dan', dan),
])
pipeline.fit(x_train, y_train, dan__epochs=100)
result = pipeline.predict(x_test)
ensemble_result = metrics.accuracy_score(y_test, result)
self.assertAlmostEqual(0.9222, ensemble_result, places=4)