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Porting to scikit-learn 1.5.0 #3

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Porting to scikit-learn 1.5.0 #3

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1a5f7ce
Porting to scikit-learn 1.5.0
slimebob1975 Jun 18, 2024
c7bf1c0
Porting to scikit-learn 1.5.0
slimebob1975 Jun 18, 2024
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20 changes: 10 additions & 10 deletions skclean/handlers/example_weighting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,7 +85,7 @@ class WeightedBagging(BaggingClassifier):
Parameters
------------------
classifier: object
A classifier instance supporting sklearn API. Same as `base_estimator`
A classifier instance supporting sklearn API. Same as `estimator`
of scikit-learn's BaggingClassifier.

detector : `BaseDetector` or None, default=None
Expand Down Expand Up @@ -124,7 +124,7 @@ def __init__(self,
verbose=0):
BaggingClassifier.__init__(
self,
base_estimator=classifier,
estimator=classifier,
warm_start=False,
n_estimators=n_estimators,
n_jobs=n_jobs,
Expand All @@ -139,16 +139,16 @@ def __init__(self,

@property
def classifier(self):
return self.base_estimator
return self.estimator

@classifier.setter
def classifier(self, clf):
self.base_estimator = clf
self.estimator = clf


def _validate_estimator(self, default=DecisionTreeClassifier()):
super()._validate_estimator()
self.base_estimator_ = _WBBase(self.base_estimator_, self.replacement, self.sampling_ratio, self.conf_score_)
self.estimator_ = _WBBase(self.estimator_, self.replacement, self.sampling_ratio, self.conf_score_)

def fit(self, X, y, conf_score=None, **kwargs):
X, y, conf_score = _check_data_params(self, X, y, conf_score)
Expand Down Expand Up @@ -194,7 +194,7 @@ class Costing(BaggingClassifier):
Parameters
------------------
classifier: object
A classifier instance supporting sklearn API. Same as `base_estimator`
A classifier instance supporting sklearn API. Same as `estimator`
of scikit-learn's BaggingClassifier.

detector : `BaseDetector` or None, default=None
Expand Down Expand Up @@ -224,7 +224,7 @@ def __init__(self,
verbose=0):
BaggingClassifier.__init__(
self,
base_estimator=classifier,
estimator=classifier,
n_estimators=n_estimators,
warm_start=False,
n_jobs=n_jobs,
Expand All @@ -237,15 +237,15 @@ def __init__(self,

@property
def classifier(self):
return self.base_estimator
return self.estimator

@classifier.setter
def classifier(self, clf):
self.base_estimator = clf
self.estimator = clf

def _validate_estimator(self, default=DecisionTreeClassifier()):
super()._validate_estimator()
self.base_estimator_ = _RSBase(self.base_estimator_, self.conf_score_)
self.estimator_ = _RSBase(self.estimator_, self.conf_score_)

# Duplicate fit
def fit(self, X, y, conf_score=None, **kwargs):
Expand Down
12 changes: 9 additions & 3 deletions skclean/pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,8 +15,8 @@

from sklearn import pipeline as skpipeline
from sklearn.base import clone
from sklearn.utils import _print_elapsed_time
from sklearn.utils.metaestimators import if_delegate_has_method
from sklearn.utils._user_interface import _print_elapsed_time
from sklearn.utils.metaestimators import available_if
from sklearn.utils.validation import check_memory

__all__ = ["Pipeline", "make_pipeline"]
Expand Down Expand Up @@ -214,7 +214,13 @@ def fit_transform(self, X, y=None, **fit_params):
else:
return last_step.fit(Xt, yt, **fit_params_last).transform(Xt)

@if_delegate_has_method(delegate="_final_estimator")
def _estimator_has(attr):
def check(self):
return hasattr(self.estimator, attr)

return check

@available_if(_estimator_has("_final_estimator"))
def fit_predict(self, X, y=None, **fit_params):
"""Apply `fit_predict` of last step in pipeline after transforms.

Expand Down