Python sklearn.base 模块，is_classifier() 实例源码
我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用sklearn.base.is_classifier()。
def _grid_search(self, train_X, train_y):
if callable(self.inner_cv):
# inner_cv = self.inner_cv(train_X,train_y)
inner_cv = self.inner_cv.split(train_X, train_y)
else:
# inner_cv = _check_cv(self.inner_cv,train_X,train_y,
# classifier=is_classifier(self.estimator))
inner_cv = _check_cv(self.inner_cv, train_y,
classifier=is_classifier(
self.estimator)).split(train_X, train_y)
master = MPIGridSearchCVMaster(self.param_grid, inner_cv,
self.estimator, self.scorer_,
self.fit_params)
return master.run(train_X, train_y)
def fit(self, X, y):
"""Fit the model to the training data."""
X, y = check_X_y(X, y, force_all_finite=False,
multi_output=self.multi_output)
_check_param_grid(self.param_grid)
# cv = _check_cv(self.cv,X,y,classifier=is_classifier(self.estimator))
cv = _check_cv(self.cv, classifier=is_classifier(self.estimator))
self.scorer_ = check_scoring(self.estimator, scoring=self.scoring)
if comm_rank == 0:
self._fit_master(X, cv)
else:
self._fit_slave()
return self
def cross_val_score(estimator, y=None, groups=None, scoring=None, cv=None,
n_jobs=1, verbose=0, fit_params=None,
pre_dispatch='2*n_jobs'):
"""
Evaluate a score by cross-validation
"""
if not isinstance(scoring, (list, tuple)):
scoring = [scoring]
X, groups = indexable(X, groups)
cv = check_cv(cv, classifier=is_classifier(estimator))
splits = list(cv.split(X, groups))
scorer = [check_scoring(estimator, scoring=s) for s in scoring]
# We clone the estimator to make sure that all the folds are
# independent,and that it is pickle-able.
parallel = Parallel(n_jobs=n_jobs, verbose=verbose,
pre_dispatch=pre_dispatch)
scores = parallel(delayed(_fit_and_score)(clone(estimator), scorer,
train, test, verbose, None,
fit_params)
for train, test in splits)
group_order = []
if hasattr(cv, 'groups'):
group_order = [np.array(cv.groups)[test].tolist()[0] for _, test in splits]
return np.squeeze(np.array(scores)), group_order
def permutation_test_score(estimator,
n_permutations=100, n_jobs=1, random_state=0,
verbose=0, scoring=None):
"""
Evaluate the significance of a cross-validated score with permutations,
as in test 1 of [Ojala2010]_.
A modification of original sklearn's permutation test score function
to evaluate p-value outside this function,so that the score can be
reused from outside.
.. [Ojala2010] Ojala and Garriga. Permutation Tests for Studying Classifier
Performance. The Journal of Machine Learning Research (2010)
vol. 11
"""
X, classifier=is_classifier(estimator))
scorer = check_scoring(estimator, scoring=scoring)
random_state = check_random_state(random_state)
# We clone the estimator to make sure that all the folds are
# independent,and that it is pickle-able.
permutation_scores = Parallel(n_jobs=n_jobs, verbose=verbose)(
delayed(_permutation_test_score)(
clone(estimator), _shuffle(y, groups, random_state),
groups, cv, scorer)
for _ in range(n_permutations))
permutation_scores = np.array(permutation_scores)
return permutation_scores
def _set_cv(cv, clf=None, X=None, y=None):
from sklearn.base import is_classifier
# Set the default cross-validation depending on whether clf is classifier
# or regressor.
if check_version('sklearn', '0.18'):
from sklearn.model_selection import (check_cv, StratifiedKFold, KFold)
if isinstance(cv, (int, np.int)):
XFold = StratifiedKFold if is_classifier(clf) else KFold
cv = XFold(n_folds=cv)
cv = check_cv(cv=cv, y=y, classifier=is_classifier(clf))
else:
from sklearn.cross_validation import (check_cv, np.int)):
if is_classifier(clf):
cv = StratifiedKFold(y=y, n_folds=cv)
else:
cv = KFold(n=len(y), n_folds=cv)
cv = check_cv(cv=cv, X=X, classifier=is_classifier(clf))
# Extract train and test set to retrieve them at predict time
if hasattr(cv, 'split'):
cv_splits = [(train, test) for train, test in
cv.split(X=np.zeros_like(y), y=y)]
else:
# XXX support sklearn.cross_validation cv
cv_splits = [(train, test in cv]
if not np.all([len(train) for train, _ in cv_splits]):
raise ValueError('Some folds do not have any train epochs.')
return cv, cv_splits
def test_is_classifier():
svc = SVC()
assert_true(is_classifier(svc))
assert_true(is_classifier(GridSearchCV(svc, {'C': [0.1, 1]})))
assert_true(is_classifier(Pipeline([('svc', svc)])))
assert_true(is_classifier(Pipeline([('svc_cv',
GridSearchCV(svc, 1]}))])))
def fit(self, y):
"""Actual fitting,performing the search over parameters."""
parameter_iterable = ParameterGrid(self.param_grid)
estimator = self.estimator
cv = self.cv
n_samples = _num_samples(X)
X, y = indexable(X, y)
if y is not None:
if len(y) != n_samples:
raise ValueError('Target variable (y) has a different number '
'of samples (%i) than data (X: %i samples)'
% (len(y), n_samples))
cv = check_cv(cv, classifier=is_classifier(estimator))
if self.verbose > 0:
if isinstance(parameter_iterable, Sized):
n_candidates = len(parameter_iterable)
print("Fitting {0} folds for each of {1} candidates,totalling"
" {2} fits".format(len(cv), n_candidates,
n_candidates * len(cv)))
base_estimator = clone(self.estimator)
pre_dispatch = self.pre_dispatch
out = Parallel(
n_jobs=self.n_jobs, verbose=self.verbose,
pre_dispatch=pre_dispatch
)(delayed(cv_fit_and_score)(clone(base_estimator), self.scoring,
parameters, cv=cv)
for parameters in parameter_iterable)
best = sorted(out, key=lambda x: x[0])[-1]
self.best_params_ = best[1]
self.best_score_ = best[0]
if self.refit:
# fit the best estimator using the entire dataset
# clone first to work around broken estimators
best_estimator = clone(base_estimator).set_params(
**best[1])
if y is not None:
best_estimator.fit(X, **self.fit_params)
else:
best_estimator.fit(X, **self.fit_params)
self.best_estimator_ = best_estimator
return self
def fit(self,performing the search over parameters."""
parameter_iterable = ParameterSampler(self.param_distributions,
self.n_iter,
random_state=self.random_state)
estimator = self.estimator
cv = self.cv
n_samples = _num_samples(X)
X,
pre_dispatch=pre_dispatch
)(
delayed(cv_fit_and_score)(clone(base_estimator), reverse=True)[0]
self.best_params_ = best[1]
self.best_score_ = best[0]
if self.refit:
# fit the best estimator using the entire dataset
# clone first to work around broken estimators
best_estimator = clone(base_estimator).set_params(
**best[1])
if y is not None:
best_estimator.fit(X, **self.fit_params)
self.best_estimator_ = best_estimator
return self
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