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_theanets_regression():
check_regression(TheanetsRegressor(layers=[3],
trainers=[dict(algo='rmsprop', **impatient)]),
**regressor_params)
check_regression(TheanetsRegressor(scaler=StandardScaler(),
trainers=[dict(algo='rmsprop', **impatient)]),
**regressor_params)
def test_sklearn_regression():
# supports weights
check_regression(SklearnRegressor(clf=AdaBoostRegressor(n_estimators=50)))
# doesn't support weights
check_regression(
SklearnRegressor(clf=GradientBoostingRegressor(n_estimators=50)),
supports_weight=False)
def test_tmva():
# check classifier
check_classifier(TMVAClassifier(),
check_instance=True,
has_staged_pp=False,
has_importances=False)
cl = TMVAClassifier(method='kSVM',
Gamma=0.25,
Tol=0.001,
sigmoid_function='identity')
check_classifier(cl,
check_instance=True,
has_staged_pp=False,
has_importances=False)
cl = TMVAClassifier(method='kCuts',
FitMethod='GA',
EffMethod='EffSel',
sigmoid_function='sig_eff=0.9')
check_classifier(cl,
check_instance=True,
has_staged_pp=False,
has_importances=False)
# check regressor, need to run twice to check for memory leak.
for i in range(2):
check_regression(TMVARegressor(),
check_instance=True,
has_staged_predictions=False,
has_importances=False)
def test_models():
for _ in range(3):
clf = CacheClassifier('clf', SGDClassifier(loss='log'))
check_classifier(clf, has_staged_pp=False, has_importances=False)
reg = CacheRegressor('reg', SGDRegressor())
check_regression(reg, has_staged_predictions=False, has_importances=False)
cache_helper.clear_cache()
def test_theanets_multi_regression():
check_regression(TheanetsRegressor(layers=[13],
trainers=[{
'algo': 'rmsprop',
'min_improvement': 0.1
}]),
n_targets=3,
**regressor_params)
def test_theanets_regression():
check_regression(
TheanetsRegressor(layers=[3],
trainers=[{
'algo': 'rmsprop',
'learning_rate': 0.1
}]), **regressor_params)
check_regression(TheanetsRegressor(scaler=StandardScaler()),
**regressor_params)
def test_theanets_regression():
check_regression(
TheanetsRegressor(layers=[20],
trainers=[{
'optimize': 'rmsprop',
'min_improvement': 0.1
}]), **regressor_params)
check_regression(TheanetsRegressor(scaler=StandardScaler()),
**regressor_params)
def test_theanets_regression():
check_regression(
TheanetsRegressor(layers=[3],
trainers=[dict(algo='rmsprop', **impatient)]),
**regressor_params)
check_regression(
TheanetsRegressor(scaler=StandardScaler(),
trainers=[dict(algo='rmsprop', **impatient)]),
**regressor_params)
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_models():
for _ in range(3):
clf = CacheClassifier('clf', SGDClassifier(loss='log'))
check_classifier(clf, has_staged_pp=False, has_importances=False)
reg = CacheRegressor('reg', SGDRegressor())
check_regression(reg,
has_staged_predictions=False,
has_importances=False)
cache_helper.clear_cache()
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_theanets_regression():
check_regression(TheanetsRegressor(layers=[20],
trainers=[{
'optimize': 'rmsprop'
}]),
supports_weight=False,
has_staged_predictions=False,
has_importances=False)
check_regression(TheanetsRegressor(),
supports_weight=False,
has_staged_predictions=False,
has_importances=False)
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_tmva():
# check classifier
check_classifier(TMVAClassifier(), check_instance=True, has_staged_pp=False, has_importances=False)
cl = TMVAClassifier(method='kSVM', Gamma=0.25, Tol=0.001, sigmoid_function='identity')
check_classifier(cl, check_instance=True, has_staged_pp=False, has_importances=False)
cl = TMVAClassifier(method='kCuts', FitMethod='GA', EffMethod='EffSel', sigmoid_function='sig_eff=0.9')
check_classifier(cl, check_instance=True, has_staged_pp=False, has_importances=False)
# check regressor, need to run twice to check for memory leak.
for i in range(2):
check_regression(TMVARegressor(), check_instance=True, has_staged_predictions=False, has_importances=False)
def test_neurolab_regression():
check_regression(NeurolabRegressor(layers=[],
show=0,
epochs=N_EPOCHS_REGR,
trainf=nl.train.train_rprop),
supports_weight=False,
has_importances=False,
has_staged_predictions=False)
check_regression(NeurolabRegressor(net_type='single-layer',
cn='auto',
show=0,
epochs=N_EPOCHS_REGR,
trainf=nl.train.train_delta),
supports_weight=False,
has_importances=False,
has_staged_predictions=False)
def test_tmva():
# check classifier
factory_options = "Silent=True:V=False:DrawProgressBar=False"
cl = TMVAClassifier(factory_options=factory_options, method='kBDT', NTrees=10)
check_classifier(cl, check_instance=True, has_staged_pp=False, has_importances=False)
cl = TMVAClassifier(factory_options=factory_options, method='kSVM', Gamma=0.25, Tol=0.001,
sigmoid_function='identity')
check_classifier(cl, check_instance=True, has_staged_pp=False, has_importances=False)
cl = TMVAClassifier(factory_options=factory_options, method='kCuts',
FitMethod='GA', EffMethod='EffSel', sigmoid_function='sig_eff=0.9')
check_classifier(cl, check_instance=True, has_staged_pp=False, has_importances=False)
# check regressor, need to run twice to check for memory leak.
for i in range(2):
check_regression(TMVARegressor(factory_options=factory_options, method='kBDT', NTrees=10), check_instance=True,
has_staged_predictions=False, has_importances=False)
def test_tmva():
# check classifier
factory_options = "Silent=True:V=False:DrawProgressBar=False"
cl = TMVAClassifier(factory_options=factory_options,
method='kBDT',
NTrees=10)
check_classifier(cl,
check_instance=True,
has_staged_pp=False,
has_importances=False)
cl = TMVAClassifier(factory_options=factory_options,
method='kSVM',
Gamma=0.25,
Tol=0.001,
sigmoid_function='identity')
check_classifier(cl,
check_instance=True,
has_staged_pp=False,
has_importances=False)
cl = TMVAClassifier(factory_options=factory_options,
method='kCuts',
FitMethod='GA',
EffMethod='EffSel',
sigmoid_function='sig_eff=0.9')
check_classifier(cl,
check_instance=True,
has_staged_pp=False,
has_importances=False)
# check regressor, need to run twice to check for memory leak.
for i in range(2):
check_regression(TMVARegressor(factory_options=factory_options,
method='kBDT',
NTrees=10),
check_instance=True,
has_staged_predictions=False,
has_importances=False)
def test_xgboost():
check_classifier(XGBoostClassifier(n_estimators=20), n_classes=2)
check_classifier(XGBoostClassifier(n_estimators=20), n_classes=4)
check_regression(XGBoostRegressor(n_estimators=20))
def test_theanets_regression():
check_regression(TheanetsRegressor(layers=[20], trainers=[{'optimize': 'rmsprop'}]),
supports_weight=False, has_staged_predictions=False, has_importances=False)
check_regression(TheanetsRegressor(),
supports_weight=False, has_staged_predictions=False, has_importances=False)
def test_sklearn_regression():
# supports weights
check_regression(SklearnRegressor(clf=AdaBoostRegressor(n_estimators=50)))
# doesn't support weights
check_regression(SklearnRegressor(clf=GradientBoostingRegressor(n_estimators=50)),
supports_weight=False)
def test_folding_regressor_with_check_model():
base_clf = SklearnRegressor(GradientBoostingRegressor(n_estimators=4))
folding_str = FoldingRegressor(base_clf, n_folds=2)
check_regression(folding_str, True, True, True)
def test_folding_regressor_with_check_model():
base_clf = SklearnRegressor(GradientBoostingRegressor(n_estimators=4))
folding_str = FoldingRegressor(base_clf, n_folds=2)
check_regression(folding_str, True, True, True)
def test_neurolab_regression():
check_regression(NeurolabRegressor(layers=[1], epochs=N_EPOCHS_REGR),
**regressor_params)
def test_mn_regression():
clf = MatrixNetRegressor()
check_regression(clf)
assert {'effect', 'information', 'efficiency'} == set(clf.get_feature_importances().columns)
def test_neurolab_multi_regression():
check_regression(NeurolabRegressor(layers=[10], epochs=N_EPOCHS_REGR),
n_targets=3, **regressor_params)
def test_pybrain_regression():
check_regression(PyBrainRegressor(), has_staged_predictions=False, has_importances=False, supports_weight=False)
def test_xgboost():
check_classifier(XGBoostClassifier(), n_classes=2)
check_classifier(XGBoostClassifier(), n_classes=4)
check_regression(XGBoostRegressor())
def test_theanets_multi_regression():
check_regression(TheanetsRegressor(layers=[20], trainers=[{'optimize': 'rmsprop', 'min_improvement': 0.1}]),
n_targets=3, **regressor_params)
def test_xgboost():
check_classifier(XGBoostClassifier(n_estimators=20), n_classes=2)
check_classifier(XGBoostClassifier(n_estimators=20), n_classes=4)
check_regression(XGBoostRegressor(n_estimators=20))
def test_mn_regression():
clf = MatrixNetRegressor()
check_regression(clf)
assert {'effect', 'information',
'efficiency'} == set(clf.get_feature_importances().columns)
def test_neurolab_multi_regression():
check_regression(NeurolabRegressor(layers=[10], epochs=N_EPOCHS_REGR),
n_targets=3,
**regressor_params)
def test_pybrain_regression():
check_regression(PyBrainRegressor(),
has_staged_predictions=False,
has_importances=False,
supports_weight=False)
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_neurolab_regression():
check_regression(NeurolabRegressor(layers=[], show=0, epochs=N_EPOCHS_REGR, trainf=nl.train.train_rprop),
supports_weight=False, has_importances=False, has_staged_predictions=False)
check_regression(NeurolabRegressor(net_type='single-layer', cn='auto', show=0, epochs=N_EPOCHS_REGR, trainf=nl.train.train_delta),
supports_weight=False, has_importances=False, has_staged_predictions=False)
def test_sklearn_regression():
check_regression(SklearnRegressor(clf=AdaBoostRegressor(n_estimators=50)))
check_regression(
SklearnRegressor(clf=GradientBoostingRegressor(n_estimators=50)))
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_theanets_regression():
check_regression(TheanetsRegressor(layers=[20], trainers=[{'optimize': 'rmsprop', 'min_improvement': 0.1}]),
**regressor_params)
check_regression(TheanetsRegressor(scaler=StandardScaler()), **regressor_params)
def test_pybrain_multi_regression():
check_regression(PyBrainRegressor(), n_targets=4, **regressor_params)
def test_theanets_regression():
check_regression(TheanetsRegressor(layers=[11],
trainers=[{'algo': 'rmsprop', 'learning_rate': 0.1}]),
**regressor_params)
check_regression(TheanetsRegressor(scaler=StandardScaler()), **regressor_params)
def test_pybrain_regression():
check_regression(PyBrainRegressor(), **regressor_params)
def test_neurolab_regression():
check_regression(NeurolabRegressor(layers=[1], epochs=N_EPOCHS_REGR), **regressor_params)