751 lines
29 KiB
Python
751 lines
29 KiB
Python
|
"""
|
||
|
|
Testing for the bagging ensemble module (sklearn.ensemble.bagging).
|
||
|
|
"""
|
||
|
|
|
||
|
|
# Author: Gilles Louppe
|
||
|
|
# License: BSD 3 clause
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from sklearn.base import BaseEstimator
|
||
|
|
|
||
|
|
from sklearn.utils.testing import assert_array_equal
|
||
|
|
from sklearn.utils.testing import assert_array_almost_equal
|
||
|
|
from sklearn.utils.testing import assert_equal
|
||
|
|
from sklearn.utils.testing import assert_raises
|
||
|
|
from sklearn.utils.testing import assert_greater
|
||
|
|
from sklearn.utils.testing import assert_less
|
||
|
|
from sklearn.utils.testing import assert_true
|
||
|
|
from sklearn.utils.testing import assert_false
|
||
|
|
from sklearn.utils.testing import assert_warns
|
||
|
|
from sklearn.utils.testing import assert_warns_message
|
||
|
|
from sklearn.utils.testing import assert_raise_message
|
||
|
|
|
||
|
|
from sklearn.dummy import DummyClassifier, DummyRegressor
|
||
|
|
from sklearn.model_selection import GridSearchCV, ParameterGrid
|
||
|
|
from sklearn.ensemble import BaggingClassifier, BaggingRegressor
|
||
|
|
from sklearn.linear_model import Perceptron, LogisticRegression
|
||
|
|
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
|
||
|
|
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
|
||
|
|
from sklearn.svm import SVC, SVR
|
||
|
|
from sklearn.pipeline import make_pipeline
|
||
|
|
from sklearn.feature_selection import SelectKBest
|
||
|
|
from sklearn.model_selection import train_test_split
|
||
|
|
from sklearn.datasets import load_boston, load_iris, make_hastie_10_2
|
||
|
|
from sklearn.utils import check_random_state
|
||
|
|
|
||
|
|
from scipy.sparse import csc_matrix, csr_matrix
|
||
|
|
|
||
|
|
rng = check_random_state(0)
|
||
|
|
|
||
|
|
# also load the iris dataset
|
||
|
|
# and randomly permute it
|
||
|
|
iris = load_iris()
|
||
|
|
perm = rng.permutation(iris.target.size)
|
||
|
|
iris.data = iris.data[perm]
|
||
|
|
iris.target = iris.target[perm]
|
||
|
|
|
||
|
|
# also load the boston dataset
|
||
|
|
# and randomly permute it
|
||
|
|
boston = load_boston()
|
||
|
|
perm = rng.permutation(boston.target.size)
|
||
|
|
boston.data = boston.data[perm]
|
||
|
|
boston.target = boston.target[perm]
|
||
|
|
|
||
|
|
|
||
|
|
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(tol=1e-3),
|
||
|
|
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_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(CustomSVC, self).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_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_regression():
|
||
|
|
# Check regression for various parameter settings.
|
||
|
|
rng = check_random_state(0)
|
||
|
|
X_train, X_test, y_train, y_test = train_test_split(boston.data[:50],
|
||
|
|
boston.target[:50],
|
||
|
|
random_state=rng)
|
||
|
|
grid = ParameterGrid({"max_samples": [0.5, 1.0],
|
||
|
|
"max_features": [0.5, 1.0],
|
||
|
|
"bootstrap": [True, False],
|
||
|
|
"bootstrap_features": [True, False]})
|
||
|
|
|
||
|
|
for base_estimator in [None,
|
||
|
|
DummyRegressor(),
|
||
|
|
DecisionTreeRegressor(),
|
||
|
|
KNeighborsRegressor(),
|
||
|
|
SVR()]:
|
||
|
|
for params in grid:
|
||
|
|
BaggingRegressor(base_estimator=base_estimator,
|
||
|
|
random_state=rng,
|
||
|
|
**params).fit(X_train, y_train).predict(X_test)
|
||
|
|
|
||
|
|
|
||
|
|
def test_sparse_regression():
|
||
|
|
# Check regression for various parameter settings on sparse input.
|
||
|
|
rng = check_random_state(0)
|
||
|
|
X_train, X_test, y_train, y_test = train_test_split(boston.data[:50],
|
||
|
|
boston.target[:50],
|
||
|
|
random_state=rng)
|
||
|
|
|
||
|
|
class CustomSVR(SVR):
|
||
|
|
"""SVC variant that records the nature of the training set"""
|
||
|
|
|
||
|
|
def fit(self, X, y):
|
||
|
|
super(CustomSVR, self).fit(X, y)
|
||
|
|
self.data_type_ = type(X)
|
||
|
|
return self
|
||
|
|
|
||
|
|
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:
|
||
|
|
|
||
|
|
# Trained on sparse format
|
||
|
|
sparse_classifier = BaggingRegressor(
|
||
|
|
base_estimator=CustomSVR(),
|
||
|
|
random_state=1,
|
||
|
|
**params
|
||
|
|
).fit(X_train_sparse, y_train)
|
||
|
|
sparse_results = sparse_classifier.predict(X_test_sparse)
|
||
|
|
|
||
|
|
# Trained on dense format
|
||
|
|
dense_results = BaggingRegressor(
|
||
|
|
base_estimator=CustomSVR(),
|
||
|
|
random_state=1,
|
||
|
|
**params
|
||
|
|
).fit(X_train, y_train).predict(X_test)
|
||
|
|
|
||
|
|
sparse_type = type(X_train_sparse)
|
||
|
|
types = [i.data_type_ for i in sparse_classifier.estimators_]
|
||
|
|
|
||
|
|
assert_array_equal(sparse_results, dense_results)
|
||
|
|
assert all([t == sparse_type for t in types])
|
||
|
|
assert_array_equal(sparse_results, dense_results)
|
||
|
|
|
||
|
|
|
||
|
|
def test_bootstrap_samples():
|
||
|
|
# Test that bootstrapping samples generate non-perfect base estimators.
|
||
|
|
rng = check_random_state(0)
|
||
|
|
X_train, X_test, y_train, y_test = train_test_split(boston.data,
|
||
|
|
boston.target,
|
||
|
|
random_state=rng)
|
||
|
|
|
||
|
|
base_estimator = DecisionTreeRegressor().fit(X_train, y_train)
|
||
|
|
|
||
|
|
# without bootstrap, all trees are perfect on the training set
|
||
|
|
ensemble = BaggingRegressor(base_estimator=DecisionTreeRegressor(),
|
||
|
|
max_samples=1.0,
|
||
|
|
bootstrap=False,
|
||
|
|
random_state=rng).fit(X_train, y_train)
|
||
|
|
|
||
|
|
assert_equal(base_estimator.score(X_train, y_train),
|
||
|
|
ensemble.score(X_train, y_train))
|
||
|
|
|
||
|
|
# with bootstrap, trees are no longer perfect on the training set
|
||
|
|
ensemble = BaggingRegressor(base_estimator=DecisionTreeRegressor(),
|
||
|
|
max_samples=1.0,
|
||
|
|
bootstrap=True,
|
||
|
|
random_state=rng).fit(X_train, y_train)
|
||
|
|
|
||
|
|
assert_greater(base_estimator.score(X_train, y_train),
|
||
|
|
ensemble.score(X_train, y_train))
|
||
|
|
|
||
|
|
|
||
|
|
def test_bootstrap_features():
|
||
|
|
# Test that bootstrapping features may generate duplicate features.
|
||
|
|
rng = check_random_state(0)
|
||
|
|
X_train, X_test, y_train, y_test = train_test_split(boston.data,
|
||
|
|
boston.target,
|
||
|
|
random_state=rng)
|
||
|
|
|
||
|
|
ensemble = BaggingRegressor(base_estimator=DecisionTreeRegressor(),
|
||
|
|
max_features=1.0,
|
||
|
|
bootstrap_features=False,
|
||
|
|
random_state=rng).fit(X_train, y_train)
|
||
|
|
|
||
|
|
for features in ensemble.estimators_features_:
|
||
|
|
assert_equal(boston.data.shape[1], np.unique(features).shape[0])
|
||
|
|
|
||
|
|
ensemble = BaggingRegressor(base_estimator=DecisionTreeRegressor(),
|
||
|
|
max_features=1.0,
|
||
|
|
bootstrap_features=True,
|
||
|
|
random_state=rng).fit(X_train, y_train)
|
||
|
|
|
||
|
|
for features in ensemble.estimators_features_:
|
||
|
|
assert_greater(boston.data.shape[1], np.unique(features).shape[0])
|
||
|
|
|
||
|
|
|
||
|
|
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_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_less(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_oob_score_regression():
|
||
|
|
# 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(boston.data,
|
||
|
|
boston.target,
|
||
|
|
random_state=rng)
|
||
|
|
|
||
|
|
clf = BaggingRegressor(base_estimator=DecisionTreeRegressor(),
|
||
|
|
n_estimators=50,
|
||
|
|
bootstrap=True,
|
||
|
|
oob_score=True,
|
||
|
|
random_state=rng).fit(X_train, y_train)
|
||
|
|
|
||
|
|
test_score = clf.score(X_test, y_test)
|
||
|
|
|
||
|
|
assert_less(abs(test_score - clf.oob_score_), 0.1)
|
||
|
|
|
||
|
|
# Test with few estimators
|
||
|
|
assert_warns(UserWarning,
|
||
|
|
BaggingRegressor(base_estimator=DecisionTreeRegressor(),
|
||
|
|
n_estimators=1,
|
||
|
|
bootstrap=True,
|
||
|
|
oob_score=True,
|
||
|
|
random_state=rng).fit,
|
||
|
|
X_train,
|
||
|
|
y_train)
|
||
|
|
|
||
|
|
|
||
|
|
def test_single_estimator():
|
||
|
|
# Check singleton ensembles.
|
||
|
|
rng = check_random_state(0)
|
||
|
|
X_train, X_test, y_train, y_test = train_test_split(boston.data,
|
||
|
|
boston.target,
|
||
|
|
random_state=rng)
|
||
|
|
|
||
|
|
clf1 = BaggingRegressor(base_estimator=KNeighborsRegressor(),
|
||
|
|
n_estimators=1,
|
||
|
|
bootstrap=False,
|
||
|
|
bootstrap_features=False,
|
||
|
|
random_state=rng).fit(X_train, y_train)
|
||
|
|
|
||
|
|
clf2 = KNeighborsRegressor().fit(X_train, y_train)
|
||
|
|
|
||
|
|
assert_array_equal(clf1.predict(X_test), clf2.predict(X_test))
|
||
|
|
|
||
|
|
|
||
|
|
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_false(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_parallel_regression():
|
||
|
|
# Check parallel regression.
|
||
|
|
rng = check_random_state(0)
|
||
|
|
|
||
|
|
X_train, X_test, y_train, y_test = train_test_split(boston.data,
|
||
|
|
boston.target,
|
||
|
|
random_state=rng)
|
||
|
|
|
||
|
|
ensemble = BaggingRegressor(DecisionTreeRegressor(),
|
||
|
|
n_jobs=3,
|
||
|
|
random_state=0).fit(X_train, y_train)
|
||
|
|
|
||
|
|
ensemble.set_params(n_jobs=1)
|
||
|
|
y1 = ensemble.predict(X_test)
|
||
|
|
ensemble.set_params(n_jobs=2)
|
||
|
|
y2 = ensemble.predict(X_test)
|
||
|
|
assert_array_almost_equal(y1, y2)
|
||
|
|
|
||
|
|
ensemble = BaggingRegressor(DecisionTreeRegressor(),
|
||
|
|
n_jobs=1,
|
||
|
|
random_state=0).fit(X_train, y_train)
|
||
|
|
|
||
|
|
y3 = ensemble.predict(X_test)
|
||
|
|
assert_array_almost_equal(y1, y3)
|
||
|
|
|
||
|
|
|
||
|
|
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_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_true(isinstance(ensemble.base_estimator_, DecisionTreeClassifier))
|
||
|
|
|
||
|
|
ensemble = BaggingClassifier(DecisionTreeClassifier(),
|
||
|
|
n_jobs=3,
|
||
|
|
random_state=0).fit(X_train, y_train)
|
||
|
|
|
||
|
|
assert_true(isinstance(ensemble.base_estimator_, DecisionTreeClassifier))
|
||
|
|
|
||
|
|
ensemble = BaggingClassifier(Perceptron(tol=1e-3),
|
||
|
|
n_jobs=3,
|
||
|
|
random_state=0).fit(X_train, y_train)
|
||
|
|
|
||
|
|
assert_true(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_true(isinstance(ensemble.base_estimator_, DecisionTreeRegressor))
|
||
|
|
|
||
|
|
ensemble = BaggingRegressor(DecisionTreeRegressor(),
|
||
|
|
n_jobs=3,
|
||
|
|
random_state=0).fit(X_train, y_train)
|
||
|
|
|
||
|
|
assert_true(isinstance(ensemble.base_estimator_, DecisionTreeRegressor))
|
||
|
|
|
||
|
|
ensemble = BaggingRegressor(SVR(),
|
||
|
|
n_jobs=3,
|
||
|
|
random_state=0).fit(X_train, y_train)
|
||
|
|
assert_true(isinstance(ensemble.base_estimator_, SVR))
|
||
|
|
|
||
|
|
|
||
|
|
def test_bagging_with_pipeline():
|
||
|
|
estimator = BaggingClassifier(make_pipeline(SelectKBest(k=1),
|
||
|
|
DecisionTreeClassifier()),
|
||
|
|
max_features=2)
|
||
|
|
estimator.fit(iris.data, iris.target)
|
||
|
|
assert_true(isinstance(estimator[0].steps[-1][1].random_state,
|
||
|
|
int))
|
||
|
|
|
||
|
|
|
||
|
|
class DummyZeroEstimator(BaseEstimator):
|
||
|
|
|
||
|
|
def fit(self, X, y):
|
||
|
|
self.classes_ = np.unique(y)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def predict(self, X):
|
||
|
|
return self.classes_[np.zeros(X.shape[0], dtype=int)]
|
||
|
|
|
||
|
|
|
||
|
|
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_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_equal(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_equal(set([tree.random_state for tree in clf_ws]),
|
||
|
|
set([tree.random_state for tree in clf_no_ws]))
|
||
|
|
|
||
|
|
|
||
|
|
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_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_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)
|
||
|
|
|
||
|
|
|
||
|
|
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_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_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_equal(bagging.fit(X, y).oob_score_, bagging.fit(X, y).oob_score_)
|
||
|
|
|
||
|
|
|
||
|
|
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_equal(len(estimators_samples), len(estimators))
|
||
|
|
assert_equal(len(estimators_samples[0]), len(X))
|
||
|
|
assert_equal(estimators_samples[0].dtype.kind, 'b')
|
||
|
|
|
||
|
|
# 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_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_equal(bagging._max_samples, max_samples)
|
||
|
|
|
||
|
|
|
||
|
|
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_equal([x1, x2], [x3, x3])
|