def test_complex_stacking_xgboost():
# Ada over kFold over xgboost
base_kfold = FoldingClassifier(base_estimator=XGBoostClassifier())
check_classifier(SklearnClassifier(
clf=AdaBoostClassifier(base_estimator=base_kfold, n_estimators=3)),
has_staged_pp=False,
has_importances=False)
def test_theanets_simple_stacking():
base_tnt = TheanetsClassifier()
check_classifier(SklearnClassifier(
clf=BaggingClassifier(base_estimator=base_tnt, n_estimators=3)),
supports_weight=False,
has_staged_pp=False,
has_importances=False)
def test_complex_stacking_tmva():
# Ada over kFold over TMVA
base_kfold = FoldingClassifier(base_estimator=TMVAClassifier(),
random_state=13)
check_classifier(SklearnClassifier(
clf=AdaBoostClassifier(base_estimator=base_kfold, n_estimators=3)),
has_staged_pp=False,
has_importances=False)
def test_simple_stacking_nolearn():
# AdaBoostClassifier fails because sample_weight is not supported in nolearn
base_nl = NolearnClassifier()
check_classifier(SklearnClassifier(
clf=BaggingClassifier(base_estimator=base_nl, n_estimators=3)),
has_staged_pp=False,
has_importances=False,
supports_weight=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_simple_stacking_tmva():
base_tmva = TMVAClassifier()
check_classifier(SklearnClassifier(clf=BaggingClassifier(
base_estimator=base_tmva, n_estimators=3, random_state=13)),
has_staged_pp=False,
has_importances=False)
def test_simple_stacking_sklearn():
base_sk = AdaBoostClassifier(n_estimators=30)
check_classifier(
SklearnClassifier(
clf=AdaBoostClassifier(base_estimator=base_sk, n_estimators=3)))
def test_simple_stacking_xgboost():
base_xgboost = XGBoostClassifier()
classifier = SklearnClassifier(
clf=AdaBoostClassifier(base_estimator=base_xgboost, n_estimators=3))
check_classifier(classifier, has_staged_pp=False)
def test_theanets_simple_stacking():
base_tnt = TheanetsClassifier(trainers=[{'min_improvement': 0.1}])
base_bagging = BaggingClassifier(base_estimator=base_tnt, n_estimators=3)
check_classifier(SklearnClassifier(clf=base_bagging), **classifier_params)
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_complex_stacking_tmva():
# Ada over kFold over TMVA
base_kfold = FoldingClassifier(base_estimator=TMVAClassifier(factory_options="Silent=True:V=False:DrawProgressBar=False",
method='kBDT', NTrees=10), random_state=13)
check_classifier(SklearnClassifier(clf=AdaBoostClassifier(base_estimator=base_kfold, n_estimators=3)),
has_staged_pp=False, has_importances=False)
def test_simple_stacking_tmva():
base_tmva = TMVAClassifier(factory_options="Silent=True:V=False:DrawProgressBar=False")
check_classifier(SklearnClassifier(clf=BaggingClassifier(base_estimator=base_tmva, n_estimators=3, random_state=13)),
has_staged_pp=False, has_importances=False)