def test_neurolab_single_classification():
check_classifier(
NeurolabClassifier(layers=[], epochs=N_EPOCHS2, trainf=None),
**classifier_params)
check_classifier(NeurolabClassifier(layers=[2], epochs=N_EPOCHS2),
**classifier_params)
check_classifier(NeurolabClassifier(layers=[1, 1], epochs=N_EPOCHS2),
**classifier_params)
def test_neurolab_classification_types():
import pandas as pd
for net_type in rep.estimators.neurolab.NET_TYPES.keys():
try:
clf = NeurolabClassifier(net_type=net_type, epochs=2)
ds = pd.DataFrame()
ds['feature1'] = [0, 1, 2, 3, 4, 5]
ds['feature2'] = [5, 7, 2, 4, 7, 9]
ds['y'] = [0, 0, 0, 1, 1, 1]
clf.fit(ds[['feature1', 'feature2']] / 10., ds['y'])
_ = clf.predict_proba(ds[['feature1', 'feature2']] / 10.)
print(net_type, 'is ok')
except Exception as e:
print(net_type, 'FAILED', e)
def test_neurolab_classification_types():
import pandas as pd
for net_type in rep.estimators.neurolab.NET_TYPES.keys():
try:
clf = NeurolabClassifier(net_type=net_type, epochs=2)
ds = pd.DataFrame()
ds['feature1'] = [0, 1, 2, 3, 4, 5]
ds['feature2'] = [5, 7, 2, 4, 7, 9]
ds['y'] = [0, 0, 0, 1, 1, 1]
clf.fit(ds[['feature1', 'feature2']] / 10., ds['y'])
_ = clf.predict_proba(ds[['feature1', 'feature2']] / 10.)
print(net_type, 'is ok')
except Exception as e:
print(net_type, 'FAILED', e)
def test_neurolab_single_classification():
check_classifier(NeurolabClassifier(show=0,
layers=[],
epochs=N_EPOCHS2,
trainf=nl.train.train_rprop),
supports_weight=False,
has_staged_pp=False,
has_importances=False)
check_classifier(NeurolabClassifier(net_type='single-layer',
cn='auto',
show=0,
epochs=N_EPOCHS2,
trainf=nl.train.train_delta),
supports_weight=False,
has_staged_pp=False,
has_importances=False)
def test_neurolab_stacking():
base_nlab = NeurolabClassifier(show=0,
layers=[],
epochs=N_EPOCHS2,
trainf=nl.train.train_rprop)
check_classifier(SklearnClassifier(
clf=BaggingClassifier(base_estimator=base_nlab, n_estimators=3)),
supports_weight=False,
has_staged_pp=False,
has_importances=False)
def test_neurolab_stacking():
base_nlab = NeurolabClassifier(layers=[],
epochs=N_EPOCHS2 * 2,
trainf=nl.train.train_rprop)
base_bagging = BaggingClassifier(base_estimator=base_nlab, n_estimators=3)
check_classifier(SklearnClassifier(clf=base_bagging), **classifier_params)
def test_neurolab_multiclassification():
check_classifier(NeurolabClassifier(layers=[10],
epochs=N_EPOCHS4,
trainf=nl.train.train_rprop),
n_classes=4,
**classifier_params)
def test_neurolab_reproducibility():
clf = NeurolabClassifier(layers=[4, 5], epochs=2, trainf=nl.train.train_gd)
X, y, _ = generate_classification_data()
check_classification_reproducibility(clf, X, y)
def test_partial_fit():
clf = NeurolabClassifier(layers=[4, 5], epochs=2, trainf=nl.train.train_gd)
X, y, _ = generate_classification_data()
clf.fit(X, y)
clf.partial_fit(X[:2], y[:2])
def test_partial_fit():
clf = NeurolabClassifier(layers=[4, 5], epochs=2, trainf=nl.train.train_gd)
X, y, _ = generate_classification_data()
clf.fit(X, y)
clf.partial_fit(X[:2], y[:2])