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Merged
lopuhin merged 5 commits into from
Dec 23, 2024
Merged

Upgrade to comply with scikitlearn >1.3 #44

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4fcd73d
Update requirements.txt
Richie94 Dec 4, 2024
1892dd3
Update permutation_importance.py
Richie94 Dec 4, 2024
a427ae8
Update permutation_importance.py
Richie94 Dec 4, 2024
ae1de47
Update permutation_importance.py
Richie94 Dec 4, 2024
def1dfd
Update requirements.txt
Richie94 Dec 5, 2024
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18 changes: 12 additions & 6 deletions eli5/sklearn/permutation_importance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@

import numpy as np
from sklearn.model_selection import check_cv
from sklearn.utils.metaestimators import if_delegate_has_method
from sklearn.utils.metaestimators import available_if
from sklearn.utils import check_array, check_random_state
from sklearn.base import (
BaseEstimator,
Expand All @@ -20,6 +20,12 @@
if pandas_available:
import pandas as pd

def _estimator_has(attr):
def check(self):
return hasattr(self.wrapped_estimator_, attr)

return check

CAVEATS_CV_NONE = """
Feature importances are computed on the same data as used for training,
i.e. feature importances don't reflect importance of features for
Expand Down Expand Up @@ -247,23 +253,23 @@ def caveats_(self):

# ============= Exposed methods of a wrapped estimator:

@if_delegate_has_method(delegate='wrapped_estimator_')
@available_if(_estimator_has('score'))
def score(self, X, y=None, *args, **kwargs):
return self.wrapped_estimator_.score(X, y, *args, **kwargs)

@if_delegate_has_method(delegate='wrapped_estimator_')
@available_if(_estimator_has('predict'))
def predict(self, X):
return self.wrapped_estimator_.predict(X)

@if_delegate_has_method(delegate='wrapped_estimator_')
@available_if(_estimator_has('predict_proba'))
def predict_proba(self, X):
return self.wrapped_estimator_.predict_proba(X)

@if_delegate_has_method(delegate='wrapped_estimator_')
@available_if(_estimator_has('predict_log_proba'))
def predict_log_proba(self, X):
return self.wrapped_estimator_.predict_log_proba(X)

@if_delegate_has_method(delegate='wrapped_estimator_')
@available_if(_estimator_has('decision_function'))
def decision_function(self, X):
return self.wrapped_estimator_.decision_function(X)

Expand Down
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