本文整理汇总了Python中sklearn.ensemble.VotingClassifier方法的典型用法代码示例。如果您正苦于以下问题：Python ensemble.VotingClassifier方法的具体用法？Python ensemble.VotingClassifier怎么用？Python ensemble.VotingClassifier使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.ensemble的用法示例。
在下文中一共展示了ensemble.VotingClassifier方法的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_notfitted
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
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 method.")
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) 示例2: test_parallel_fit
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
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)) 示例3: __init__
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
def __init__(self, num_features, **kwargs):
super(VotingClassifier, self).__init__()
kwargs = {**constants.VOTING_CLASSIFIER_PARAMS, **kwargs}
voting = kwargs.pop('voting')
self.num_features = num_features
estimators = []
for clf in constants.CLASSIFIERS_FOR_ENSEMBLE:
model = utils.init_model(clf, num_features=num_features, **kwargs)
estimators.append((clf, model.kernel))
# use as kernel the VotingClassifier coming from sklearn
self.kernel = SKVotingClassifier(
estimators=estimators, voting=voting, n_jobs=None
) 示例4: __init__
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
def __init__(self, api, lobes=False):
"""
lobes = a dict of classifiers to use in the VotingClassifier
defaults to RandomForestClassifier and DecisionTreeClassifier
"""
self.api = api
if not lobes:
lobes = {'rf': RandomForestClassifier(n_estimators=7,
random_state=666),
'dt': DecisionTreeClassifier()
}
self.lobe = VotingClassifier(
estimators=[(lobe, lobes[lobe]) for lobe in lobes],
voting='hard',
n_jobs=-1)
self._trained = False
self.split = splitTrainTestData
self.prep = prepDataframe 示例5: test_voting_hard_binary
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
def test_voting_hard_binary(self):
model = VotingClassifier(
voting="hard",
flatten_transform=False,
estimators=[
("lr", LogisticRegression()),
("lr2", LogisticRegression(fit_intercept=False)),
],
)
# predict_proba is not defined when voting is hard.
dump_binary_classification(
model,
suffix="Hard",
comparable_outputs=[0],
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.5.0')",
target_opset=TARGET_OPSET
) 示例6: test_voting_hard_binary_weights
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
def test_voting_hard_binary_weights(self):
model = VotingClassifier(
voting="hard",
flatten_transform=False,
weights=numpy.array([1000, 1]),
estimators=[
("lr", LogisticRegression()),
("lr2", LogisticRegression(fit_intercept=False)),
],
)
# predict_proba is not defined when voting is hard.
dump_binary_classification(
model,
suffix="WeightsHard",
comparable_outputs=[0],
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.5.0')",
target_opset=TARGET_OPSET
) 示例7: test_voting_soft_binary
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
def test_voting_soft_binary(self):
model = VotingClassifier(
voting="soft",
flatten_transform=False,
estimators=[
("lr", LogisticRegression()),
("lr2", LogisticRegression(fit_intercept=False)),
],
)
dump_binary_classification(
model,
suffix="Soft",
comparable_outputs=[0, 1],
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
target_opset=TARGET_OPSET
) 示例8: test_voting_soft_binary_weighted
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
def test_voting_soft_binary_weighted(self):
model = VotingClassifier(
voting="soft",
flatten_transform=False,
weights=numpy.array([1.8, 0.2]),
estimators=[
("lr", LogisticRegression()),
("lr2", LogisticRegression(fit_intercept=False)),
],
)
dump_binary_classification(
model,
suffix="WeightedSoft",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
target_opset=TARGET_OPSET
) 示例9: test_voting_hard_multi
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
def test_voting_hard_multi(self):
# predict_proba is not defined when voting is hard.
model = VotingClassifier(
voting="hard",
flatten_transform=False,
estimators=[
("lr", LogisticRegression()),
("lr2", DecisionTreeClassifier()),
],
)
dump_multiple_classification(
model,
suffix="Hard",
comparable_outputs=[0],
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.5.0')",
target_opset=TARGET_OPSET
) 示例10: test_voting_soft_multi
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
def test_voting_soft_multi(self):
model = VotingClassifier(
voting="soft",
flatten_transform=False,
estimators=[
("lr", LogisticRegression()),
("lr2", LogisticRegression()),
],
)
dump_multiple_classification(
model,
suffix="Soft",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
target_opset=TARGET_OPSET
) 示例11: test_voting_soft_multi_string
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
def test_voting_soft_multi_string(self):
model = VotingClassifier(
voting="soft",
flatten_transform=False,
estimators=[
("lr", LogisticRegression()),
("lr2", LogisticRegression()),
],
)
dump_multiple_classification(
model, label_string=True,
suffix="Soft",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
target_opset=TARGET_OPSET
) 示例12: test_voting_soft_multi_weighted
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
def test_voting_soft_multi_weighted(self):
model = VotingClassifier(
voting="soft",
flatten_transform=False,
weights=numpy.array([1.8, 0.2]),
estimators=[
("lr", LogisticRegression()),
("lr2", LogisticRegression()),
],
)
dump_multiple_classification(
model,
suffix="WeightedSoft",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
target_opset=TARGET_OPSET
) 示例13: test_voting_soft_multi_weighted4
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
def test_voting_soft_multi_weighted4(self):
model = VotingClassifier(
voting="soft",
flatten_transform=False,
weights=numpy.array([2.7, 0.3, 0.5, 0.5]),
estimators=[
("lr", LogisticRegression()),
("lra", LogisticRegression()),
("lrb", LogisticRegression()),
("lr2", LogisticRegression()),
],
)
dump_multiple_classification(
model,
suffix="Weighted4Soft",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
target_opset=TARGET_OPSET
) 示例14: test_voting_soft_multi_weighted42
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
def test_voting_soft_multi_weighted42(self):
model = VotingClassifier(
voting="soft",
flatten_transform=False,
weights=numpy.array([27, 0.3, 0.5, 0.5]),
estimators=[
("lr", LogisticRegression()),
("lra", LogisticRegression()),
("lrb", LogisticRegression()),
("lr2", LogisticRegression()),
],
)
dump_multiple_classification(
model,
suffix="Weighted42Soft",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
target_opset=TARGET_OPSET
) 示例15: test_parallel_fit
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import VotingClassifier [as 别名]
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_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))