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_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_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 grid_tmva(score_function):
grid_param = OrderedDict({"MaxDepth": [4, 5], "NTrees": [10, 20]})
generator = SubgridParameterOptimizer(grid_param)
scorer = FoldingScorer(score_function)
from rep.estimators import TMVAClassifier
grid = GridOptimalSearchCV(TMVAClassifier(features=['column0', 'column1']),
generator, scorer)
cl = check_grid(grid, False, False, False)
assert 1 <= len(cl.features) <= 3
params = cl.get_params()
for key in grid_param:
assert params[key] == grid.generator.best_params_[key]
def test_gridsearch_on_tmva():
metric = numpy.random.choice([OptimalAMS(), RocAuc()])
scorer = FoldingScorer(metric)
grid_param = OrderedDict({"MaxDepth": [4, 5], "NTrees": [10, 20]})
generator = SubgridParameterOptimizer(grid_param)
try:
from rep.estimators import TMVAClassifier
grid = GridOptimalSearchCV(
TMVAClassifier(features=['column0', 'column1']), generator, scorer)
classifier = check_grid(grid, False, False, False)
# checking parameters
assert len(classifier.features) == 2
params = classifier.get_params()
for key in grid_param:
assert params[key] == grid.generator.best_params_[key]
except ImportError:
pass
def test_gridsearch_on_tmva():
metric = numpy.random.choice([OptimalAMS(), RocAuc()])
scorer = FoldingScorer(metric)
grid_param = OrderedDict({"MaxDepth": [4, 5], "NTrees": [10, 20]})
generator = SubgridParameterOptimizer(n_evaluations=5,
param_grid=grid_param)
try:
from rep.estimators import TMVAClassifier
base_tmva = TMVAClassifier(
factory_options="Silent=True:V=False:DrawProgressBar=False",
features=['column0', 'column1'],
method='kBDT')
grid = GridOptimalSearchCV(base_tmva, generator, scorer)
classifier = check_grid(grid, False, False, False)
# checking parameters
assert len(classifier.features) == 2
params = classifier.get_params()
for key in grid_param:
assert params[key] == grid.generator.best_params_[key]
except ImportError:
pass
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_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)