MNT remove pre-dispatch and verbose from CalibratedClassifierCV
@@ -127,30 +127,6 @@ class CalibratedClassifierCV(ClassifierMixin,
.. versionadded:: 0.24
- pre_dispatch : int or str, default='2*n_jobs'
- Controls the number of jobs that get dispatched during parallel
- execution. Reducing this number can be useful to avoid an
- explosion of memory consumption when more jobs get dispatched
- than CPUs can process. This parameter can be:
-
- - None, in which case all the jobs are immediately
- created and spawned. Use this for lightweight and
- fast-running jobs, to avoid delays due to on-demand
- spawning of the jobs
-
- - An int, giving the exact number of total jobs that are
- spawned
-
- - A str, giving an expression as a function of n_jobs,
- as in '2*n_jobs'
-
- .. versionadded:: 0.24
-
- verbose : int, default=0
- Controls the verbosity: the higher, the more messages.
-
- .. versionadded:: 0.24
-
Attributes
----------
classes_ : ndarray of shape (n_classes,)
@@ -217,14 +193,11 @@ class CalibratedClassifierCV(ClassifierMixin,
"""
@_deprecate_positional_args
def __init__(self, base_estimator=None, *, method='sigmoid',
- cv=None, n_jobs=None, pre_dispatch='2*n_jobs',
- verbose=0):
+ cv=None, n_jobs=None):
self.base_estimator = base_estimator
self.method = method
self.cv = cv
self.n_jobs = n_jobs
- self.verbose = verbose
- self.pre_dispatch = pre_dispatch
def fit(self, X, y, sample_weight=None):
"""Fit the calibrated model
@@ -304,8 +277,7 @@ class CalibratedClassifierCV(ClassifierMixin,
"sample weights will only be used for the "
"calibration itself." % estimator_name)
- parallel = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
- pre_dispatch=self.pre_dispatch)
+ parallel = Parallel(n_jobs=self.n_jobs)
self.calibrated_classifiers_ = parallel(delayed(
_fit_calibrated_classifer)(clone(base_estimator),
