def test_oob_score_classification():
# Check that oob prediction is a good estimation of the generalization
# error.
rng = check_random_state(0)
X_train, X_test, y_train, y_test = train_test_split(iris.data,
iris.target,
random_state=rng)
for base_estimator in [DecisionTreeClassifier(), SVC()]:
clf = BaggingClassifier(base_estimator=base_estimator,
n_estimators=100,
bootstrap=True,
oob_score=True,
random_state=rng).fit(X_train, y_train)
test_score = clf.score(X_test, y_test)
assert abs(test_score - clf.oob_score_) < 0.1
# Test with few estimators
assert_warns(
UserWarning,
BaggingClassifier(base_estimator=base_estimator,
n_estimators=1,
bootstrap=True,
oob_score=True,
random_state=rng).fit, X_train, y_train)
def test_estimators_samples_deterministic():
# This test is a regression test to check that with a random step
# (e.g. SparseRandomProjection) and a given random state, the results
# generated at fit time can be identically reproduced at a later time using
# data saved in object attributes. Check issue #9524 for full discussion.
iris = load_iris()
X, y = iris.data, iris.target
base_pipeline = make_pipeline(SparseRandomProjection(n_components=2),
LogisticRegression())
clf = BaggingClassifier(base_estimator=base_pipeline,
max_samples=0.5,
random_state=0)
clf.fit(X, y)
pipeline_estimator_coef = clf.estimators_[0].steps[-1][1].coef_.copy()
estimator = clf.estimators_[0]
estimator_sample = clf.estimators_samples_[0]
estimator_feature = clf.estimators_features_[0]
X_train = (X[estimator_sample])[:, estimator_feature]
y_train = y[estimator_sample]
estimator.fit(X_train, y_train)
assert_array_equal(estimator.steps[-1][1].coef_, pipeline_estimator_coef)
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
ensemble = BaggingClassifier(base_estimator=DecisionTreeClassifier(),
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)))
# Degenerate case, where some classes are missing
ensemble = BaggingClassifier(base_estimator=LogisticRegression(),
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_oob_score_consistency():
# Make sure OOB scores are identical when random_state, estimator, and
# training data are fixed and fitting is done twice
X, y = make_hastie_10_2(n_samples=200, random_state=1)
bagging = BaggingClassifier(KNeighborsClassifier(),
max_samples=0.5,
max_features=0.5,
oob_score=True,
random_state=1)
assert bagging.fit(X, y).oob_score_ == bagging.fit(X, y).oob_score_
def test_bagging_sample_weight_unsupported_but_passed():
estimator = BaggingClassifier(DummyZeroEstimator())
rng = check_random_state(0)
estimator.fit(iris.data, iris.target).predict(iris.data)
assert_raises(ValueError,
estimator.fit,
iris.data,
iris.target,
sample_weight=rng.randint(10, size=(iris.data.shape[0])))
def test_bagging_small_max_features():
# Check that Bagging estimator can accept low fractional max_features
X = np.array([[1, 2], [3, 4]])
y = np.array([1, 0])
bagging = BaggingClassifier(LogisticRegression(),
max_features=0.3,
random_state=1)
bagging.fit(X, y)
def test_max_samples_consistency():
# Make sure validated max_samples and original max_samples are identical
# when valid integer max_samples supplied by user
max_samples = 100
X, y = make_hastie_10_2(n_samples=2 * max_samples, random_state=1)
bagging = BaggingClassifier(KNeighborsClassifier(),
max_samples=max_samples,
max_features=0.5,
random_state=1)
bagging.fit(X, y)
assert bagging._max_samples == max_samples
def test_base_not_int_n_estimators():
# Check that instantiating a BaseEnsemble with a string as n_estimators
# raises a ValueError demanding n_estimators to be supplied as an integer.
string_ensemble = BaggingClassifier(base_estimator=Perceptron(),
n_estimators='3')
iris = load_iris()
assert_raise_message(ValueError,
"n_estimators must be an integer",
string_ensemble.fit, iris.data, iris.target)
float_ensemble = BaggingClassifier(base_estimator=Perceptron(),
n_estimators=3.0)
assert_raise_message(ValueError,
"n_estimators must be an integer",
float_ensemble.fit, iris.data, iris.target)
def test_set_oob_score_label_encoding():
# Make sure the oob_score doesn't change when the labels change
# See: https://github.com/scikit-learn/scikit-learn/issues/8933
random_state = 5
X = [[-1], [0], [1]] * 5
Y1 = ['A', 'B', 'C'] * 5
Y2 = [-1, 0, 1] * 5
Y3 = [0, 1, 2] * 5
x1 = BaggingClassifier(oob_score=True,
random_state=random_state).fit(X, Y1).oob_score_
x2 = BaggingClassifier(oob_score=True,
random_state=random_state).fit(X, Y2).oob_score_
x3 = BaggingClassifier(oob_score=True,
random_state=random_state).fit(X, Y3).oob_score_
assert [x1, x2] == [x3, x3]
def test_classification():
# Check classification for various parameter settings.
rng = check_random_state(0)
X_train, X_test, y_train, y_test = train_test_split(iris.data,
iris.target,
random_state=rng)
grid = ParameterGrid({
"max_samples": [0.5, 1.0],
"max_features": [1, 2, 4],
"bootstrap": [True, False],
"bootstrap_features": [True, False]
})
for base_estimator in [
None,
DummyClassifier(),
Perceptron(),
DecisionTreeClassifier(),
KNeighborsClassifier(),
SVC()
]:
for params in grid:
BaggingClassifier(base_estimator=base_estimator,
random_state=rng,
**params).fit(X_train, y_train).predict(X_test)
def test_oob_score_removed_on_warm_start():
X, y = make_hastie_10_2(n_samples=2000, random_state=1)
clf = BaggingClassifier(n_estimators=50, oob_score=True)
clf.fit(X, y)
clf.set_params(warm_start=True, oob_score=False, n_estimators=100)
clf.fit(X, y)
assert_raises(AttributeError, getattr, clf, "oob_score_")
def test_base_zero_n_estimators():
# Check that instantiating a BaseEnsemble with n_estimators<=0 raises
# a ValueError.
ensemble = BaggingClassifier(base_estimator=Perceptron(),
n_estimators=0)
iris = load_iris()
assert_raise_message(ValueError,
"n_estimators must be greater than zero, got 0.",
ensemble.fit, iris.data, iris.target)
def test_base_estimator():
# Check base_estimator and its default values.
rng = check_random_state(0)
# Classification
X_train, X_test, y_train, y_test = train_test_split(iris.data,
iris.target,
random_state=rng)
ensemble = BaggingClassifier(None, n_jobs=3,
random_state=0).fit(X_train, y_train)
assert isinstance(ensemble.base_estimator_, DecisionTreeClassifier)
ensemble = BaggingClassifier(DecisionTreeClassifier(),
n_jobs=3,
random_state=0).fit(X_train, y_train)
assert isinstance(ensemble.base_estimator_, DecisionTreeClassifier)
ensemble = BaggingClassifier(Perceptron(), n_jobs=3,
random_state=0).fit(X_train, y_train)
assert isinstance(ensemble.base_estimator_, Perceptron)
# Regression
X_train, X_test, y_train, y_test = train_test_split(boston.data,
boston.target,
random_state=rng)
ensemble = BaggingRegressor(None, n_jobs=3,
random_state=0).fit(X_train, y_train)
assert isinstance(ensemble.base_estimator_, DecisionTreeRegressor)
ensemble = BaggingRegressor(DecisionTreeRegressor(),
n_jobs=3,
random_state=0).fit(X_train, y_train)
assert isinstance(ensemble.base_estimator_, DecisionTreeRegressor)
ensemble = BaggingRegressor(SVR(), n_jobs=3,
random_state=0).fit(X_train, y_train)
assert isinstance(ensemble.base_estimator_, SVR)
def test_gridsearch():
# Check that bagging ensembles can be grid-searched.
# Transform iris into a binary classification task
X, y = iris.data, iris.target
y[y == 2] = 1
# Grid search with scoring based on decision_function
parameters = {'n_estimators': (1, 2), 'base_estimator__C': (1, 2)}
GridSearchCV(BaggingClassifier(SVC()), parameters,
scoring="roc_auc").fit(X, y)
def test_warm_start_smaller_n_estimators():
# Test if warm start'ed second fit with smaller n_estimators raises error.
X, y = make_hastie_10_2(n_samples=20, random_state=1)
clf = BaggingClassifier(n_estimators=5, warm_start=True)
clf.fit(X, y)
clf.set_params(n_estimators=4)
assert_raises(ValueError, clf.fit, X, y)
def test_warm_start(random_state=42):
# Test if fitting incrementally with warm start gives a forest of the
# right size and the same results as a normal fit.
X, y = make_hastie_10_2(n_samples=20, random_state=1)
clf_ws = None
for n_estimators in [5, 10]:
if clf_ws is None:
clf_ws = BaggingClassifier(n_estimators=n_estimators,
random_state=random_state,
warm_start=True)
else:
clf_ws.set_params(n_estimators=n_estimators)
clf_ws.fit(X, y)
assert len(clf_ws) == n_estimators
clf_no_ws = BaggingClassifier(n_estimators=10,
random_state=random_state,
warm_start=False)
clf_no_ws.fit(X, y)
assert (set([tree.random_state for tree in clf_ws
]) == set([tree.random_state for tree in clf_no_ws]))
def test_estimators_samples():
# Check that format of estimators_samples_ is correct and that results
# generated at fit time can be identically reproduced at a later time
# using data saved in object attributes.
X, y = make_hastie_10_2(n_samples=200, random_state=1)
bagging = BaggingClassifier(LogisticRegression(),
max_samples=0.5,
max_features=0.5,
random_state=1,
bootstrap=False)
bagging.fit(X, y)
# Get relevant attributes
estimators_samples = bagging.estimators_samples_
estimators_features = bagging.estimators_features_
estimators = bagging.estimators_
# Test for correct formatting
assert len(estimators_samples) == len(estimators)
assert len(estimators_samples[0]) == len(X) // 2
assert estimators_samples[0].dtype.kind == 'i'
# Re-fit single estimator to test for consistent sampling
estimator_index = 0
estimator_samples = estimators_samples[estimator_index]
estimator_features = estimators_features[estimator_index]
estimator = estimators[estimator_index]
X_train = (X[estimator_samples])[:, estimator_features]
y_train = y[estimator_samples]
orig_coefs = estimator.coef_
estimator.fit(X_train, y_train)
new_coefs = estimator.coef_
assert_array_almost_equal(orig_coefs, new_coefs)
def test_warm_start_equal_n_estimators():
# Test that nothing happens when fitting without increasing n_estimators
X, y = make_hastie_10_2(n_samples=20, random_state=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=43)
clf = BaggingClassifier(n_estimators=5, warm_start=True, random_state=83)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
# modify X to nonsense values, this should not change anything
X_train += 1.
assert_warns_message(
UserWarning,
"Warm-start fitting without increasing n_estimators does not", clf.fit,
X_train, y_train)
assert_array_equal(y_pred, clf.predict(X_test))
def test_base():
# Check BaseEnsemble methods.
ensemble = BaggingClassifier(
base_estimator=Perceptron(random_state=None), n_estimators=3)
iris = load_iris()
ensemble.fit(iris.data, iris.target)
ensemble.estimators_ = [] # empty the list and create estimators manually
ensemble._make_estimator()
random_state = np.random.RandomState(3)
ensemble._make_estimator(random_state=random_state)
ensemble._make_estimator(random_state=random_state)
ensemble._make_estimator(append=False)
assert 3 == len(ensemble)
assert 3 == len(ensemble.estimators_)
assert isinstance(ensemble[0], Perceptron)
assert ensemble[0].random_state is None
assert isinstance(ensemble[1].random_state, int)
assert isinstance(ensemble[2].random_state, int)
assert ensemble[1].random_state != ensemble[2].random_state
np_int_ensemble = BaggingClassifier(base_estimator=Perceptron(),
n_estimators=np.int32(3))
np_int_ensemble.fit(iris.data, iris.target)
def test_bagging_classifier_with_missing_inputs():
# Check that BaggingClassifier can accept X with missing/infinite data
X = np.array([
[1, 3, 5],
[2, None, 6],
[2, np.nan, 6],
[2, np.inf, 6],
[2, np.NINF, 6],
])
y = np.array([3, 6, 6, 6, 6])
classifier = DecisionTreeClassifier()
pipeline = make_pipeline(FunctionTransformer(replace), classifier)
pipeline.fit(X, y).predict(X)
bagging_classifier = BaggingClassifier(pipeline)
bagging_classifier.fit(X, y)
y_hat = bagging_classifier.predict(X)
assert y.shape == y_hat.shape
bagging_classifier.predict_log_proba(X)
bagging_classifier.predict_proba(X)
# Verify that exceptions can be raised by wrapper classifier
classifier = DecisionTreeClassifier()
pipeline = make_pipeline(classifier)
assert_raises(ValueError, pipeline.fit, X, y)
bagging_classifier = BaggingClassifier(pipeline)
assert_raises(ValueError, bagging_classifier.fit, X, y)
def test_sparse_classification():
# Check classification for various parameter settings on sparse input.
class CustomSVC(SVC):
"""SVC variant that records the nature of the training set"""
def fit(self, X, y):
super().fit(X, y)
self.data_type_ = type(X)
return self
rng = check_random_state(0)
X_train, X_test, y_train, y_test = train_test_split(iris.data,
iris.target,
random_state=rng)
parameter_sets = [
{
"max_samples": 0.5,
"max_features": 2,
"bootstrap": True,
"bootstrap_features": True
},
{
"max_samples": 1.0,
"max_features": 4,
"bootstrap": True,
"bootstrap_features": True
},
{
"max_features": 2,
"bootstrap": False,
"bootstrap_features": True
},
{
"max_samples": 0.5,
"bootstrap": True,
"bootstrap_features": False
},
]
for sparse_format in [csc_matrix, csr_matrix]:
X_train_sparse = sparse_format(X_train)
X_test_sparse = sparse_format(X_test)
for params in parameter_sets:
for f in [
'predict', 'predict_proba', 'predict_log_proba',
'decision_function'
]:
# Trained on sparse format
sparse_classifier = BaggingClassifier(
base_estimator=CustomSVC(decision_function_shape='ovr'),
random_state=1,
**params).fit(X_train_sparse, y_train)
sparse_results = getattr(sparse_classifier, f)(X_test_sparse)
# Trained on dense format
dense_classifier = BaggingClassifier(
base_estimator=CustomSVC(decision_function_shape='ovr'),
random_state=1,
**params).fit(X_train, y_train)
dense_results = getattr(dense_classifier, f)(X_test)
assert_array_almost_equal(sparse_results, dense_results)
sparse_type = type(X_train_sparse)
types = [i.data_type_ for i in sparse_classifier.estimators_]
assert all([t == sparse_type for t in types])
def test_error():
# Test that it gives proper exception on deficient input.
X, y = iris.data, iris.target
base = DecisionTreeClassifier()
# Test max_samples
assert_raises(ValueError,
BaggingClassifier(base, max_samples=-1).fit, X, y)
assert_raises(ValueError,
BaggingClassifier(base, max_samples=0.0).fit, X, y)
assert_raises(ValueError,
BaggingClassifier(base, max_samples=2.0).fit, X, y)
assert_raises(ValueError,
BaggingClassifier(base, max_samples=1000).fit, X, y)
assert_raises(ValueError,
BaggingClassifier(base, max_samples="foobar").fit, X, y)
# Test max_features
assert_raises(ValueError,
BaggingClassifier(base, max_features=-1).fit, X, y)
assert_raises(ValueError,
BaggingClassifier(base, max_features=0.0).fit, X, y)
assert_raises(ValueError,
BaggingClassifier(base, max_features=2.0).fit, X, y)
assert_raises(ValueError,
BaggingClassifier(base, max_features=5).fit, X, y)
assert_raises(ValueError,
BaggingClassifier(base, max_features="foobar").fit, X, y)
# Test support of decision_function
assert not hasattr(BaggingClassifier(base).fit(X, y), 'decision_function')
def test_parallel_classification():
# Check parallel classification.
rng = check_random_state(0)
# Classification
X_train, X_test, y_train, y_test = train_test_split(iris.data,
iris.target,
random_state=rng)
ensemble = BaggingClassifier(DecisionTreeClassifier(),
n_jobs=3,
random_state=0).fit(X_train, y_train)
# predict_proba
ensemble.set_params(n_jobs=1)
y1 = ensemble.predict_proba(X_test)
ensemble.set_params(n_jobs=2)
y2 = ensemble.predict_proba(X_test)
assert_array_almost_equal(y1, y2)
ensemble = BaggingClassifier(DecisionTreeClassifier(),
n_jobs=1,
random_state=0).fit(X_train, y_train)
y3 = ensemble.predict_proba(X_test)
assert_array_almost_equal(y1, y3)
# decision_function
ensemble = BaggingClassifier(SVC(decision_function_shape='ovr'),
n_jobs=3,
random_state=0).fit(X_train, y_train)
ensemble.set_params(n_jobs=1)
decisions1 = ensemble.decision_function(X_test)
ensemble.set_params(n_jobs=2)
decisions2 = ensemble.decision_function(X_test)
assert_array_almost_equal(decisions1, decisions2)
X_err = np.hstack((X_test, np.zeros((X_test.shape[0], 1))))
assert_raise_message(
ValueError, "Number of features of the model "
"must match the input. Model n_features is {0} "
"and input n_features is {1} "
"".format(X_test.shape[1], X_err.shape[1]), ensemble.decision_function,
X_err)
ensemble = BaggingClassifier(SVC(decision_function_shape='ovr'),
n_jobs=1,
random_state=0).fit(X_train, y_train)
decisions3 = ensemble.decision_function(X_test)
assert_array_almost_equal(decisions1, decisions3)
def test_bagging_with_pipeline():
estimator = BaggingClassifier(make_pipeline(SelectKBest(k=1),
DecisionTreeClassifier()),
max_features=2)
estimator.fit(iris.data, iris.target)
assert isinstance(estimator[0].steps[-1][1].random_state, int)
def test_warm_start_with_oob_score_fails():
# Check using oob_score and warm_start simultaneously fails
X, y = make_hastie_10_2(n_samples=20, random_state=1)
clf = BaggingClassifier(n_estimators=5, warm_start=True, oob_score=True)
assert_raises(ValueError, clf.fit, X, y)
def test_warm_start_equivalence():
# warm started classifier with 5+5 estimators should be equivalent to
# one classifier with 10 estimators
X, y = make_hastie_10_2(n_samples=20, random_state=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=43)
clf_ws = BaggingClassifier(n_estimators=5,
warm_start=True,
random_state=3141)
clf_ws.fit(X_train, y_train)
clf_ws.set_params(n_estimators=10)
clf_ws.fit(X_train, y_train)
y1 = clf_ws.predict(X_test)
clf = BaggingClassifier(n_estimators=10,
warm_start=False,
random_state=3141)
clf.fit(X_train, y_train)
y2 = clf.predict(X_test)
assert_array_almost_equal(y1, y2)