def cross_val_score(estimator,
X,
y=None,
score_func=None,
cv=None,
n_jobs=-1,
verbose=0,
as_dvalues=False):
"""Evaluate a score by cross-validation.
Replacement of :func:`sklearn.cross_validation.cross_val_score`, used to
support computation of decision values.
"""
X, y = check_arrays(X, y, sparse_format='csr')
cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
if score_func is None:
if not hasattr(estimator, 'score'):
raise TypeError(
"If no score_func is specified, the estimator passed "
"should have a 'score' method. The estimator %s "
"does not." % estimator)
# We clone the estimator to make sure that all the folds are
# independent, and that it is pickle-able.
scores = Parallel(n_jobs=n_jobs, verbose=verbose)(
delayed(_cross_val_score)(clone(estimator), X, y, score_func, train,
test, verbose, as_dvalues)
for train, test in cv)
return np.array(scores)
def Bootstrap_cv(estimator1, estimator2, X, y, score_func, cv=None, n_jobs=1,
verbose=0, ratio=.5):
X, y = cross_validation.check_arrays(X, y, sparse_format='csr')
cv = cross_validation.check_cv(cv, X, y,
classifier=
cross_validation.is_classifier(estimator1))
if score_func is None:
if not hasattr(estimator1, 'score') or \
not hasattr(estimator2, 'score'):
raise TypeError(
"If no score_func is specified, the estimator passed "
"should have a 'score' method. The estimator %s "
"does not." % estimator1)
# We clone the estimator to make sure that all the folds are
# independent, and that it is pickle-able.
scores = \
cross_validation.Parallel(
n_jobs=n_jobs, verbose=verbose)(
cross_validation.delayed(
dual_cross_val_score)
(cross_validation.clone(estimator1),
cross_validation.clone(estimator2),
X, y, score_func, train, test, verbose, ratio)
for train, test in cv)
return np.array(scores)
def cross_val_score(estimator, X, y=None, score_func=None, cv=None, n_jobs=-1,
verbose=0, as_dvalues=False):
"""Evaluate a score by cross-validation.
Replacement of :func:`sklearn.cross_validation.cross_val_score`, used to
support computation of decision values.
"""
X, y = check_arrays(X, y, sparse_format='csr')
cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
if score_func is None:
if not hasattr(estimator, 'score'):
raise TypeError(
"If no score_func is specified, the estimator passed "
"should have a 'score' method. The estimator %s "
"does not." % estimator)
# We clone the estimator to make sure that all the folds are
# independent, and that it is pickle-able.
scores = Parallel(n_jobs=n_jobs, verbose=verbose)(
delayed(_cross_val_score)(clone(estimator), X, y, score_func, train, test,
verbose, as_dvalues)
for train, test in cv)
return np.array(scores)