def test_probability():
"""Predict probabilities."""
rng = check_random_state(0)
X_train, X_test, y_train, y_test = train_test_split(iris.data,
iris.target,
random_state=rng)
with np.errstate(divide="ignore", invalid="ignore"):
# Normal case
print("start")
ensemble = LazyBaggingClassifier(random_state=rng).fit(X_train, y_train)
assert_array_almost_equal(np.sum(ensemble.predict_proba(X_test),
axis=1),
np.ones(len(X_test)))
assert_array_almost_equal(ensemble.predict_proba(X_test),
np.exp(ensemble.predict_log_proba(X_test)))
print("stop")
# Degenerate case, where some classes are missing
ensemble = LazyBaggingClassifier(base_estimator=DecisionTreeClassifier(),
random_state=rng,
max_samples=5).fit(X_train, y_train)
assert_array_almost_equal(np.sum(ensemble.predict_proba(X_test),
axis=1),
np.ones(len(X_test)))
assert_array_almost_equal(ensemble.predict_proba(X_test),
np.exp(ensemble.predict_log_proba(X_test)))
def test_multioutput():
X, y = make_multilabel_classification(n_samples=100, n_labels=1,
n_classes=5, random_state=0,
return_indicator=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
est = LazyBaggingClassifier(random_state=0, n_estimators=10,
bootstrap=False)
est.fit(X_train, y_train)
assert_almost_equal(est.score(X_train, y_train), 1.)
y_proba = est.predict_proba(X_test)
y_log_proba = est.predict_log_proba(X_test)
for p, log_p in zip(y_proba, y_log_proba):
assert_array_almost_equal(p, np.exp(log_p))
est = LazyBaggingRegressor(random_state=0, n_estimators=10,
bootstrap=False)
est.fit(X_train, y_train)
assert_almost_equal(est.score(X_train, y_train), 1.)
