def test_pybrain_classification(): clf = PyBrainClassifier(epochs=2) check_classifier(clf, **classifier_params) check_classifier( PyBrainClassifier(epochs=-1, continue_epochs=1, layers=[]), **classifier_params) check_classifier(PyBrainClassifier(epochs=2, layers=[5, 2]), **classifier_params)
def test_pybrain_classification(): check_classifier(PyBrainClassifier(), has_staged_pp=False, has_importances=False, supports_weight=False) check_classifier(PyBrainClassifier(layers=[10, 10]), has_staged_pp=False, has_importances=False, supports_weight=False)
def test_simple_stacking_pybrain(): base_pybrain = PyBrainClassifier() check_classifier(SklearnClassifier( clf=BaggingClassifier(base_estimator=base_pybrain, n_estimators=3)), has_staged_pp=False, has_importances=False, supports_weight=False)
def test_pybrain_reproducibility(): # This test fails. Because PyBrain can't reproduce training. X, y, _ = generate_classification_data() clf1 = PyBrainClassifier(layers=[4], epochs=2).fit(X, y) clf2 = PyBrainClassifier(layers=[4], epochs=2).fit(X, y) print(clf1.predict_proba(X) - clf2.predict_proba(X)) assert numpy.allclose(clf1.predict_proba(X), clf2.predict_proba(X)), 'different predicitons' check_classification_reproducibility(clf1, X, y)
def test_pybrain_Tanh(): check_classifier(PyBrainClassifier(layers=[10], hiddenclass=['TanhLayer']), has_staged_pp=False, has_importances=False, supports_weight=False) check_regression(PyBrainRegressor(layers=[10], hiddenclass=['TanhLayer']), has_staged_predictions=False, has_importances=False, supports_weight=False)
def test_pybrain_Linear_MDLSTM(): check_classifier( PyBrainClassifier(epochs=2, layers=[10, 2], hiddenclass=['LinearLayer', 'MDLSTMLayer']), **classifier_params) check_regression( PyBrainRegressor(epochs=3, layers=[10, 2], hiddenclass=['LinearLayer', 'MDLSTMLayer']), **regressor_params)
def test_pybrain_SoftMax_Tanh(): check_classifier( PyBrainClassifier(epochs=10, layers=[5, 2], hiddenclass=['TanhLayer', 'SoftmaxLayer'], use_rprop=True), **classifier_params) check_regression( PyBrainRegressor( epochs=2, layers=[10, 5, 2], hiddenclass=['TanhLayer', 'SoftmaxLayer', 'TanhLayer']), **regressor_params)
def test_pybrain_multi_classification(): check_classifier(PyBrainClassifier(), n_classes=4, **classifier_params)
def pybrain_test_partial_fit(): clf = PyBrainClassifier(layers=[4], epochs=2) X, y, _ = generate_classification_data() clf.partial_fit(X, y) clf.partial_fit(X[:2], y[:2])
def test_simple_stacking_pybrain(): base_pybrain = PyBrainClassifier(epochs=2) base_bagging = BaggingClassifier(base_estimator=base_pybrain, n_estimators=3) check_classifier(SklearnClassifier(clf=base_bagging), **classifier_params)
def test_pybrain_multiclassification(): check_classifier(PyBrainClassifier(), has_staged_pp=False, has_importances=False, supports_weight=False, n_classes=4)
def test_pybrain_rprop(): check_classifier(PyBrainClassifier(use_rprop=True), has_staged_pp=False, has_importances=False, supports_weight=False)