def _test_classification_report(n_classes=2):
classifiers = ClassifiersFactory()
classifiers.add_classifier('gb',
GradientBoostingClassifier(n_estimators=10))
classifiers.add_classifier('rf', RandomForestClassifier())
classifiers.add_classifier('ada', AdaBoostClassifier(n_estimators=10))
X, y = generate_classification_sample(1000, 5, n_classes=n_classes)
classifiers.fit(X, y)
X, y = generate_classification_sample(1000, 5, n_classes=n_classes)
test_lds = LabeledDataStorage(X, y, sample_weight=None)
report = classifiers.test_on_lds(test_lds)
val = numpy.mean(X['column0'])
labels_dict = None
if n_classes > 2:
labels_dict = {}
for i in range(n_classes):
labels_dict[i] = str(i)
_classification_mask_report(report, "column0 > %f" % val, X, labels_dict)
_classification_mask_report(report,
lambda x: numpy.array(x['column0']) < val, X,
labels_dict)
_classification_mask_report(report, None, X, labels_dict)
def test_factory():
factory = ClassifiersFactory()
try:
from rep.estimators.tmva import TMVAClassifier
factory.add_classifier('tmva', TMVAClassifier())
except ImportError:
pass
factory.add_classifier('rf', RandomForestClassifier(n_estimators=10))
factory.add_classifier('ada', AdaBoostClassifier(n_estimators=20))
X, y, sample_weight = generate_classification_data()
assert factory == factory.fit(X, y, sample_weight=sample_weight, features=list(X.columns),
parallel_profile='threads-4')
for cl in factory.values():
assert list(cl.features) == list(X.columns)
proba = factory.predict_proba(X, parallel_profile='threads-4')
labels = factory.predict(X, parallel_profile='threads-4')
for key, val in labels.items():
score = accuracy_score(y, val)
print(key, score)
assert score > 0.7, key
for key, val in proba.items():
assert numpy.allclose(val.sum(axis=1), 1), 'probabilities do not sum to 1'
assert numpy.all(val >= 0.), 'negative probabilities'
auc_score = roc_auc_score(y, val[:, 1])
print(auc_score)
assert auc_score > 0.8
for key, iterator in factory.staged_predict_proba(X).items():
assert key != 'tmva', 'tmva does not support staged pp'
for p in iterator:
assert p.shape == (len(X), 2)
# checking that last iteration coincides with previous
assert numpy.all(p == proba[key])
# testing picklability
dump_string = cPickle.dumps(factory)
clf_loaded = cPickle.loads(dump_string)
assert type(factory) == type(clf_loaded)
probs1 = factory.predict_proba(X)
probs2 = clf_loaded.predict_proba(X)
for key, val in probs1.items():
assert numpy.all(val == probs2[key]), 'something strange was loaded'
report = ClassificationReport({'rf': factory['rf']}, LabeledDataStorage(X, y, sample_weight))
report.feature_importance_shuffling(roc_auc_score_mod).plot(new_plot=True, figsize=(18, 3))
report = factory.test_on_lds(LabeledDataStorage(X, y, sample_weight))
report = factory.test_on(X, y, sample_weight=sample_weight)
val = numpy.mean(X['column0'])
yield check_report_with_mask, report, "column0 > %f" % (val / 2.), X
yield check_report_with_mask, report, lambda x: numpy.array(x['column0']) < val * 2., X
yield check_report_with_mask, report, None, X
def _check_classification_report(n_classes=2):
classifiers = ClassifiersFactory()
classifiers.add_classifier('gb', GradientBoostingClassifier(n_estimators=10))
classifiers.add_classifier('rf', RandomForestClassifier())
classifiers.add_classifier('ada', AdaBoostClassifier(n_estimators=10))
X, y = generate_classification_sample(1000, 5, n_classes=n_classes)
classifiers.fit(X, y)
X, y = generate_classification_sample(1000, 5, n_classes=n_classes)
test_lds = LabeledDataStorage(X, y, sample_weight=None)
report = classifiers.test_on_lds(test_lds)
val = numpy.mean(X['column0'])
labels_dict = None
if n_classes > 2:
labels_dict = {i: str(i) for i in range(n_classes)}
_classification_mask_report(report, "column0 > %f" % val, X, labels_dict)
_classification_mask_report(report, lambda x: numpy.array(x['column0']) < val, X, labels_dict)
_classification_mask_report(report, None, X, labels_dict)
check_classification_learning_curve_masks(report, n_classes=n_classes)
