544 lines
21 KiB
Python
544 lines
21 KiB
Python
"""Testing for the VotingClassifier and VotingRegressor"""
|
|
|
|
import warnings
|
|
import pytest
|
|
import re
|
|
import numpy as np
|
|
|
|
from sklearn.utils._testing import assert_almost_equal, assert_array_equal
|
|
from sklearn.utils._testing import assert_array_almost_equal
|
|
from sklearn.utils._testing import assert_raise_message
|
|
from sklearn.exceptions import NotFittedError
|
|
from sklearn.linear_model import LinearRegression
|
|
from sklearn.linear_model import LogisticRegression
|
|
from sklearn.naive_bayes import GaussianNB
|
|
from sklearn.ensemble import RandomForestClassifier
|
|
from sklearn.ensemble import RandomForestRegressor
|
|
from sklearn.ensemble import VotingClassifier, VotingRegressor
|
|
from sklearn.tree import DecisionTreeClassifier
|
|
from sklearn.tree import DecisionTreeRegressor
|
|
from sklearn.model_selection import GridSearchCV
|
|
from sklearn import datasets
|
|
from sklearn.model_selection import cross_val_score, train_test_split
|
|
from sklearn.datasets import make_multilabel_classification
|
|
from sklearn.svm import SVC
|
|
from sklearn.multiclass import OneVsRestClassifier
|
|
from sklearn.neighbors import KNeighborsClassifier
|
|
from sklearn.base import BaseEstimator, ClassifierMixin, clone
|
|
from sklearn.dummy import DummyRegressor
|
|
|
|
|
|
# Load datasets
|
|
iris = datasets.load_iris()
|
|
X, y = iris.data[:, 1:3], iris.target
|
|
|
|
X_r, y_r = datasets.load_diabetes(return_X_y=True)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"params, err_msg",
|
|
[({'estimators': []},
|
|
"Invalid 'estimators' attribute, 'estimators' should be a list of"),
|
|
({'estimators': [('lr', LogisticRegression())], 'voting': 'error'},
|
|
r"Voting must be 'soft' or 'hard'; got \(voting='error'\)"),
|
|
({'estimators': [('lr', LogisticRegression())], 'weights': [1, 2]},
|
|
"Number of `estimators` and weights must be equal")]
|
|
)
|
|
def test_voting_classifier_estimator_init(params, err_msg):
|
|
ensemble = VotingClassifier(**params)
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
ensemble.fit(X, y)
|
|
|
|
|
|
def test_predictproba_hardvoting():
|
|
eclf = VotingClassifier(estimators=[('lr1', LogisticRegression()),
|
|
('lr2', LogisticRegression())],
|
|
voting='hard')
|
|
msg = "predict_proba is not available when voting='hard'"
|
|
with pytest.raises(AttributeError, match=msg):
|
|
eclf.predict_proba
|
|
|
|
assert not hasattr(eclf, "predict_proba")
|
|
eclf.fit(X, y)
|
|
assert not hasattr(eclf, "predict_proba")
|
|
|
|
|
|
def test_notfitted():
|
|
eclf = VotingClassifier(estimators=[('lr1', LogisticRegression()),
|
|
('lr2', LogisticRegression())],
|
|
voting='soft')
|
|
ereg = VotingRegressor([('dr', DummyRegressor())])
|
|
msg = ("This %s instance is not fitted yet. Call \'fit\'"
|
|
" with appropriate arguments before using this estimator.")
|
|
assert_raise_message(NotFittedError, msg % 'VotingClassifier',
|
|
eclf.predict, X)
|
|
assert_raise_message(NotFittedError, msg % 'VotingClassifier',
|
|
eclf.predict_proba, X)
|
|
assert_raise_message(NotFittedError, msg % 'VotingClassifier',
|
|
eclf.transform, X)
|
|
assert_raise_message(NotFittedError, msg % 'VotingRegressor',
|
|
ereg.predict, X_r)
|
|
assert_raise_message(NotFittedError, msg % 'VotingRegressor',
|
|
ereg.transform, X_r)
|
|
|
|
|
|
def test_majority_label_iris():
|
|
"""Check classification by majority label on dataset iris."""
|
|
clf1 = LogisticRegression(solver='liblinear', random_state=123)
|
|
clf2 = RandomForestClassifier(n_estimators=10, random_state=123)
|
|
clf3 = GaussianNB()
|
|
eclf = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('gnb', clf3)],
|
|
voting='hard')
|
|
scores = cross_val_score(eclf, X, y, scoring='accuracy')
|
|
assert_almost_equal(scores.mean(), 0.95, decimal=2)
|
|
|
|
|
|
def test_tie_situation():
|
|
"""Check voting classifier selects smaller class label in tie situation."""
|
|
clf1 = LogisticRegression(random_state=123, solver='liblinear')
|
|
clf2 = RandomForestClassifier(random_state=123)
|
|
eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2)],
|
|
voting='hard')
|
|
assert clf1.fit(X, y).predict(X)[73] == 2
|
|
assert clf2.fit(X, y).predict(X)[73] == 1
|
|
assert eclf.fit(X, y).predict(X)[73] == 1
|
|
|
|
|
|
def test_weights_iris():
|
|
"""Check classification by average probabilities on dataset iris."""
|
|
clf1 = LogisticRegression(random_state=123)
|
|
clf2 = RandomForestClassifier(random_state=123)
|
|
clf3 = GaussianNB()
|
|
eclf = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('gnb', clf3)],
|
|
voting='soft',
|
|
weights=[1, 2, 10])
|
|
scores = cross_val_score(eclf, X, y, scoring='accuracy')
|
|
assert_almost_equal(scores.mean(), 0.93, decimal=2)
|
|
|
|
|
|
def test_weights_regressor():
|
|
"""Check weighted average regression prediction on diabetes dataset."""
|
|
reg1 = DummyRegressor(strategy='mean')
|
|
reg2 = DummyRegressor(strategy='median')
|
|
reg3 = DummyRegressor(strategy='quantile', quantile=.2)
|
|
ereg = VotingRegressor([('mean', reg1), ('median', reg2),
|
|
('quantile', reg3)], weights=[1, 2, 10])
|
|
|
|
X_r_train, X_r_test, y_r_train, y_r_test = \
|
|
train_test_split(X_r, y_r, test_size=.25)
|
|
|
|
reg1_pred = reg1.fit(X_r_train, y_r_train).predict(X_r_test)
|
|
reg2_pred = reg2.fit(X_r_train, y_r_train).predict(X_r_test)
|
|
reg3_pred = reg3.fit(X_r_train, y_r_train).predict(X_r_test)
|
|
ereg_pred = ereg.fit(X_r_train, y_r_train).predict(X_r_test)
|
|
|
|
avg = np.average(np.asarray([reg1_pred, reg2_pred, reg3_pred]), axis=0,
|
|
weights=[1, 2, 10])
|
|
assert_almost_equal(ereg_pred, avg, decimal=2)
|
|
|
|
ereg_weights_none = VotingRegressor([('mean', reg1), ('median', reg2),
|
|
('quantile', reg3)], weights=None)
|
|
ereg_weights_equal = VotingRegressor([('mean', reg1), ('median', reg2),
|
|
('quantile', reg3)],
|
|
weights=[1, 1, 1])
|
|
ereg_weights_none.fit(X_r_train, y_r_train)
|
|
ereg_weights_equal.fit(X_r_train, y_r_train)
|
|
ereg_none_pred = ereg_weights_none.predict(X_r_test)
|
|
ereg_equal_pred = ereg_weights_equal.predict(X_r_test)
|
|
assert_almost_equal(ereg_none_pred, ereg_equal_pred, decimal=2)
|
|
|
|
|
|
def test_predict_on_toy_problem():
|
|
"""Manually check predicted class labels for toy dataset."""
|
|
clf1 = LogisticRegression(random_state=123)
|
|
clf2 = RandomForestClassifier(random_state=123)
|
|
clf3 = GaussianNB()
|
|
|
|
X = np.array([[-1.1, -1.5],
|
|
[-1.2, -1.4],
|
|
[-3.4, -2.2],
|
|
[1.1, 1.2],
|
|
[2.1, 1.4],
|
|
[3.1, 2.3]])
|
|
|
|
y = np.array([1, 1, 1, 2, 2, 2])
|
|
|
|
assert_array_equal(clf1.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
|
|
assert_array_equal(clf2.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
|
|
assert_array_equal(clf3.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
|
|
|
|
eclf = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('gnb', clf3)],
|
|
voting='hard',
|
|
weights=[1, 1, 1])
|
|
assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
|
|
|
|
eclf = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('gnb', clf3)],
|
|
voting='soft',
|
|
weights=[1, 1, 1])
|
|
assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
|
|
|
|
|
|
def test_predict_proba_on_toy_problem():
|
|
"""Calculate predicted probabilities on toy dataset."""
|
|
clf1 = LogisticRegression(random_state=123)
|
|
clf2 = RandomForestClassifier(random_state=123)
|
|
clf3 = GaussianNB()
|
|
X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
|
|
y = np.array([1, 1, 2, 2])
|
|
|
|
clf1_res = np.array([[0.59790391, 0.40209609],
|
|
[0.57622162, 0.42377838],
|
|
[0.50728456, 0.49271544],
|
|
[0.40241774, 0.59758226]])
|
|
|
|
clf2_res = np.array([[0.8, 0.2],
|
|
[0.8, 0.2],
|
|
[0.2, 0.8],
|
|
[0.3, 0.7]])
|
|
|
|
clf3_res = np.array([[0.9985082, 0.0014918],
|
|
[0.99845843, 0.00154157],
|
|
[0., 1.],
|
|
[0., 1.]])
|
|
|
|
t00 = (2*clf1_res[0][0] + clf2_res[0][0] + clf3_res[0][0]) / 4
|
|
t11 = (2*clf1_res[1][1] + clf2_res[1][1] + clf3_res[1][1]) / 4
|
|
t21 = (2*clf1_res[2][1] + clf2_res[2][1] + clf3_res[2][1]) / 4
|
|
t31 = (2*clf1_res[3][1] + clf2_res[3][1] + clf3_res[3][1]) / 4
|
|
|
|
eclf = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('gnb', clf3)],
|
|
voting='soft',
|
|
weights=[2, 1, 1])
|
|
eclf_res = eclf.fit(X, y).predict_proba(X)
|
|
|
|
assert_almost_equal(t00, eclf_res[0][0], decimal=1)
|
|
assert_almost_equal(t11, eclf_res[1][1], decimal=1)
|
|
assert_almost_equal(t21, eclf_res[2][1], decimal=1)
|
|
assert_almost_equal(t31, eclf_res[3][1], decimal=1)
|
|
|
|
with pytest.raises(
|
|
AttributeError,
|
|
match="predict_proba is not available when voting='hard'"):
|
|
eclf = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('gnb', clf3)],
|
|
voting='hard')
|
|
eclf.fit(X, y).predict_proba(X)
|
|
|
|
|
|
def test_multilabel():
|
|
"""Check if error is raised for multilabel classification."""
|
|
X, y = make_multilabel_classification(n_classes=2, n_labels=1,
|
|
allow_unlabeled=False,
|
|
random_state=123)
|
|
clf = OneVsRestClassifier(SVC(kernel='linear'))
|
|
|
|
eclf = VotingClassifier(estimators=[('ovr', clf)], voting='hard')
|
|
|
|
try:
|
|
eclf.fit(X, y)
|
|
except NotImplementedError:
|
|
return
|
|
|
|
|
|
def test_gridsearch():
|
|
"""Check GridSearch support."""
|
|
clf1 = LogisticRegression(random_state=1)
|
|
clf2 = RandomForestClassifier(random_state=1)
|
|
clf3 = GaussianNB()
|
|
eclf = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('gnb', clf3)],
|
|
voting='soft')
|
|
|
|
params = {'lr__C': [1.0, 100.0],
|
|
'voting': ['soft', 'hard'],
|
|
'weights': [[0.5, 0.5, 0.5], [1.0, 0.5, 0.5]]}
|
|
|
|
grid = GridSearchCV(estimator=eclf, param_grid=params)
|
|
grid.fit(iris.data, iris.target)
|
|
|
|
|
|
def test_parallel_fit():
|
|
"""Check parallel backend of VotingClassifier on toy dataset."""
|
|
clf1 = LogisticRegression(random_state=123)
|
|
clf2 = RandomForestClassifier(random_state=123)
|
|
clf3 = GaussianNB()
|
|
X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
|
|
y = np.array([1, 1, 2, 2])
|
|
|
|
eclf1 = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('gnb', clf3)],
|
|
voting='soft',
|
|
n_jobs=1).fit(X, y)
|
|
eclf2 = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('gnb', clf3)],
|
|
voting='soft',
|
|
n_jobs=2).fit(X, y)
|
|
|
|
assert_array_equal(eclf1.predict(X), eclf2.predict(X))
|
|
assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
|
|
|
|
|
|
def test_sample_weight():
|
|
"""Tests sample_weight parameter of VotingClassifier"""
|
|
clf1 = LogisticRegression(random_state=123)
|
|
clf2 = RandomForestClassifier(random_state=123)
|
|
clf3 = SVC(probability=True, random_state=123)
|
|
eclf1 = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('svc', clf3)],
|
|
voting='soft').fit(X, y, sample_weight=np.ones((len(y),)))
|
|
eclf2 = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('svc', clf3)],
|
|
voting='soft').fit(X, y)
|
|
assert_array_equal(eclf1.predict(X), eclf2.predict(X))
|
|
assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
|
|
|
|
sample_weight = np.random.RandomState(123).uniform(size=(len(y),))
|
|
eclf3 = VotingClassifier(estimators=[('lr', clf1)], voting='soft')
|
|
eclf3.fit(X, y, sample_weight)
|
|
clf1.fit(X, y, sample_weight)
|
|
assert_array_equal(eclf3.predict(X), clf1.predict(X))
|
|
assert_array_almost_equal(eclf3.predict_proba(X), clf1.predict_proba(X))
|
|
|
|
# check that an error is raised and indicative if sample_weight is not
|
|
# supported.
|
|
clf4 = KNeighborsClassifier()
|
|
eclf3 = VotingClassifier(estimators=[
|
|
('lr', clf1), ('svc', clf3), ('knn', clf4)],
|
|
voting='soft')
|
|
msg = ('Underlying estimator KNeighborsClassifier does not support '
|
|
'sample weights.')
|
|
with pytest.raises(TypeError, match=msg):
|
|
eclf3.fit(X, y, sample_weight)
|
|
|
|
# check that _fit_single_estimator will raise the right error
|
|
# it should raise the original error if this is not linked to sample_weight
|
|
class ClassifierErrorFit(ClassifierMixin, BaseEstimator):
|
|
def fit(self, X, y, sample_weight):
|
|
raise TypeError('Error unrelated to sample_weight.')
|
|
clf = ClassifierErrorFit()
|
|
with pytest.raises(TypeError, match='Error unrelated to sample_weight'):
|
|
clf.fit(X, y, sample_weight=sample_weight)
|
|
|
|
|
|
def test_sample_weight_kwargs():
|
|
"""Check that VotingClassifier passes sample_weight as kwargs"""
|
|
class MockClassifier(ClassifierMixin, BaseEstimator):
|
|
"""Mock Classifier to check that sample_weight is received as kwargs"""
|
|
def fit(self, X, y, *args, **sample_weight):
|
|
assert 'sample_weight' in sample_weight
|
|
|
|
clf = MockClassifier()
|
|
eclf = VotingClassifier(estimators=[('mock', clf)], voting='soft')
|
|
|
|
# Should not raise an error.
|
|
eclf.fit(X, y, sample_weight=np.ones((len(y),)))
|
|
|
|
|
|
def test_voting_classifier_set_params():
|
|
# check equivalence in the output when setting underlying estimators
|
|
clf1 = LogisticRegression(random_state=123, C=1.0)
|
|
clf2 = RandomForestClassifier(random_state=123, max_depth=None)
|
|
clf3 = GaussianNB()
|
|
|
|
eclf1 = VotingClassifier([('lr', clf1), ('rf', clf2)], voting='soft',
|
|
weights=[1, 2]).fit(X, y)
|
|
eclf2 = VotingClassifier([('lr', clf1), ('nb', clf3)], voting='soft',
|
|
weights=[1, 2])
|
|
eclf2.set_params(nb=clf2).fit(X, y)
|
|
|
|
assert_array_equal(eclf1.predict(X), eclf2.predict(X))
|
|
assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
|
|
assert eclf2.estimators[0][1].get_params() == clf1.get_params()
|
|
assert eclf2.estimators[1][1].get_params() == clf2.get_params()
|
|
|
|
|
|
def test_set_estimator_drop():
|
|
# VotingClassifier set_params should be able to set estimators as drop
|
|
# Test predict
|
|
clf1 = LogisticRegression(random_state=123)
|
|
clf2 = RandomForestClassifier(n_estimators=10, random_state=123)
|
|
clf3 = GaussianNB()
|
|
eclf1 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
|
|
('nb', clf3)],
|
|
voting='hard', weights=[1, 0, 0.5]).fit(X, y)
|
|
|
|
eclf2 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
|
|
('nb', clf3)],
|
|
voting='hard', weights=[1, 1, 0.5])
|
|
with pytest.warns(None) as record:
|
|
with warnings.catch_warnings():
|
|
# scipy 1.3.0 uses tostring which is deprecated in numpy
|
|
warnings.filterwarnings("ignore", "tostring", DeprecationWarning)
|
|
eclf2.set_params(rf='drop').fit(X, y)
|
|
|
|
assert not record
|
|
assert_array_equal(eclf1.predict(X), eclf2.predict(X))
|
|
|
|
assert dict(eclf2.estimators)["rf"] == 'drop'
|
|
assert len(eclf2.estimators_) == 2
|
|
assert all(isinstance(est, (LogisticRegression, GaussianNB))
|
|
for est in eclf2.estimators_)
|
|
assert eclf2.get_params()["rf"] == 'drop'
|
|
|
|
eclf1.set_params(voting='soft').fit(X, y)
|
|
with pytest.warns(None) as record:
|
|
with warnings.catch_warnings():
|
|
# scipy 1.3.0 uses tostring which is deprecated in numpy
|
|
warnings.filterwarnings("ignore", "tostring", DeprecationWarning)
|
|
eclf2.set_params(voting='soft').fit(X, y)
|
|
|
|
assert not record
|
|
assert_array_equal(eclf1.predict(X), eclf2.predict(X))
|
|
assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
|
|
msg = 'All estimators are dropped. At least one is required'
|
|
with pytest.warns(None) as record:
|
|
with pytest.raises(ValueError, match=msg):
|
|
eclf2.set_params(lr='drop', rf='drop', nb='drop').fit(X, y)
|
|
assert not record
|
|
|
|
# Test soft voting transform
|
|
X1 = np.array([[1], [2]])
|
|
y1 = np.array([1, 2])
|
|
eclf1 = VotingClassifier(estimators=[('rf', clf2), ('nb', clf3)],
|
|
voting='soft', weights=[0, 0.5],
|
|
flatten_transform=False).fit(X1, y1)
|
|
|
|
eclf2 = VotingClassifier(estimators=[('rf', clf2), ('nb', clf3)],
|
|
voting='soft', weights=[1, 0.5],
|
|
flatten_transform=False)
|
|
with pytest.warns(None) as record:
|
|
with warnings.catch_warnings():
|
|
# scipy 1.3.0 uses tostring which is deprecated in numpy
|
|
warnings.filterwarnings("ignore", "tostring", DeprecationWarning)
|
|
eclf2.set_params(rf='drop').fit(X1, y1)
|
|
assert not record
|
|
assert_array_almost_equal(eclf1.transform(X1),
|
|
np.array([[[0.7, 0.3], [0.3, 0.7]],
|
|
[[1., 0.], [0., 1.]]]))
|
|
assert_array_almost_equal(eclf2.transform(X1),
|
|
np.array([[[1., 0.],
|
|
[0., 1.]]]))
|
|
eclf1.set_params(voting='hard')
|
|
eclf2.set_params(voting='hard')
|
|
assert_array_equal(eclf1.transform(X1), np.array([[0, 0], [1, 1]]))
|
|
assert_array_equal(eclf2.transform(X1), np.array([[0], [1]]))
|
|
|
|
|
|
def test_estimator_weights_format():
|
|
# Test estimator weights inputs as list and array
|
|
clf1 = LogisticRegression(random_state=123)
|
|
clf2 = RandomForestClassifier(random_state=123)
|
|
eclf1 = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2)],
|
|
weights=[1, 2],
|
|
voting='soft')
|
|
eclf2 = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2)],
|
|
weights=np.array((1, 2)),
|
|
voting='soft')
|
|
eclf1.fit(X, y)
|
|
eclf2.fit(X, y)
|
|
assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
|
|
|
|
|
|
def test_transform():
|
|
"""Check transform method of VotingClassifier on toy dataset."""
|
|
clf1 = LogisticRegression(random_state=123)
|
|
clf2 = RandomForestClassifier(random_state=123)
|
|
clf3 = GaussianNB()
|
|
X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
|
|
y = np.array([1, 1, 2, 2])
|
|
|
|
eclf1 = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('gnb', clf3)],
|
|
voting='soft').fit(X, y)
|
|
eclf2 = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('gnb', clf3)],
|
|
voting='soft',
|
|
flatten_transform=True).fit(X, y)
|
|
eclf3 = VotingClassifier(estimators=[
|
|
('lr', clf1), ('rf', clf2), ('gnb', clf3)],
|
|
voting='soft',
|
|
flatten_transform=False).fit(X, y)
|
|
|
|
assert_array_equal(eclf1.transform(X).shape, (4, 6))
|
|
assert_array_equal(eclf2.transform(X).shape, (4, 6))
|
|
assert_array_equal(eclf3.transform(X).shape, (3, 4, 2))
|
|
assert_array_almost_equal(eclf1.transform(X),
|
|
eclf2.transform(X))
|
|
assert_array_almost_equal(
|
|
eclf3.transform(X).swapaxes(0, 1).reshape((4, 6)),
|
|
eclf2.transform(X)
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"X, y, voter",
|
|
[(X, y, VotingClassifier(
|
|
[('lr', LogisticRegression()),
|
|
('rf', RandomForestClassifier(n_estimators=5))])),
|
|
(X_r, y_r, VotingRegressor(
|
|
[('lr', LinearRegression()),
|
|
('rf', RandomForestRegressor(n_estimators=5))]))]
|
|
)
|
|
def test_none_estimator_with_weights(X, y, voter):
|
|
# check that an estimator can be set to 'drop' and passing some weight
|
|
# regression test for
|
|
# https://github.com/scikit-learn/scikit-learn/issues/13777
|
|
voter = clone(voter)
|
|
voter.fit(X, y, sample_weight=np.ones(y.shape))
|
|
voter.set_params(lr='drop')
|
|
with pytest.warns(None) as record:
|
|
voter.fit(X, y, sample_weight=np.ones(y.shape))
|
|
assert not record
|
|
y_pred = voter.predict(X)
|
|
assert y_pred.shape == y.shape
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"est",
|
|
[VotingRegressor(
|
|
estimators=[('lr', LinearRegression()),
|
|
('tree', DecisionTreeRegressor(random_state=0))]),
|
|
VotingClassifier(
|
|
estimators=[('lr', LogisticRegression(random_state=0)),
|
|
('tree', DecisionTreeClassifier(random_state=0))])],
|
|
ids=['VotingRegressor', 'VotingClassifier']
|
|
)
|
|
def test_n_features_in(est):
|
|
|
|
X = [[1, 2], [3, 4], [5, 6]]
|
|
y = [0, 1, 2]
|
|
|
|
assert not hasattr(est, 'n_features_in_')
|
|
est.fit(X, y)
|
|
assert est.n_features_in_ == 2
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"estimator",
|
|
[VotingRegressor(
|
|
estimators=[('lr', LinearRegression()),
|
|
('rf', RandomForestRegressor(random_state=123))],
|
|
verbose=True),
|
|
VotingClassifier(
|
|
estimators=[('lr', LogisticRegression(random_state=123)),
|
|
('rf', RandomForestClassifier(random_state=123))],
|
|
verbose=True)]
|
|
)
|
|
def test_voting_verbose(estimator, capsys):
|
|
|
|
X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
|
|
y = np.array([1, 1, 2, 2])
|
|
|
|
pattern = (r'\[Voting\].*\(1 of 2\) Processing lr, total=.*\n'
|
|
r'\[Voting\].*\(2 of 2\) Processing rf, total=.*\n$')
|
|
|
|
estimator.fit(X, y)
|
|
assert re.match(pattern, capsys.readouterr()[0])
|