81 lines
3.3 KiB
Diff
81 lines
3.3 KiB
Diff
From 1fb59eb42f4bef229b953de313c7e78f0857ea42 Mon Sep 17 00:00:00 2001
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From: Philip Wilk <p.wilk@student.reading.ac.uk>
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Date: Sun, 23 Mar 2025 16:14:51 +0000
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Subject: [PATCH] StackingCVClassifier/fit: ensure compatibility with
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*scikit-learn* versions 1.4 and above by dynamically selecting between
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`fit_params` and `params`
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---
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mlxtend/classifier/stacking_cv_classification.py | 5 ++++-
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mlxtend/regressor/stacking_cv_regression.py | 6 +++++-
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2 files changed, 9 insertions(+), 2 deletions(-)
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diff --git a/mlxtend/classifier/stacking_cv_classification.py b/mlxtend/classifier/stacking_cv_classification.py
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index 5bff6907..f4c45b8c 100644
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--- a/mlxtend/classifier/stacking_cv_classification.py
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+++ b/mlxtend/classifier/stacking_cv_classification.py
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@@ -15,6 +15,7 @@ from sklearn.base import TransformerMixin, clone
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from sklearn.model_selection import cross_val_predict
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from sklearn.model_selection._split import check_cv
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from sklearn.preprocessing import LabelEncoder
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+from sklearn import __version__ as sklearn_version
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from ..externals.estimator_checks import check_is_fitted
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from ..externals.name_estimators import _name_estimators
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@@ -266,6 +267,8 @@ class StackingCVClassifier(
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if self.verbose > 1:
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print(_name_estimators((model,))[0][1])
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+ param_name = "fit_params" if sklearn_version < "1.4" else "params"
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+
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prediction = cross_val_predict(
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model,
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X,
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@@ -273,10 +276,10 @@ class StackingCVClassifier(
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groups=groups,
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cv=final_cv,
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n_jobs=self.n_jobs,
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- fit_params=fit_params,
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verbose=self.verbose,
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pre_dispatch=self.pre_dispatch,
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method="predict_proba" if self.use_probas else "predict",
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+ **{param_name: fit_params},
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)
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if not self.use_probas:
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diff --git a/mlxtend/regressor/stacking_cv_regression.py b/mlxtend/regressor/stacking_cv_regression.py
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index a1faf2ff..d2fb1c49 100644
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--- a/mlxtend/regressor/stacking_cv_regression.py
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+++ b/mlxtend/regressor/stacking_cv_regression.py
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@@ -19,6 +19,7 @@ from sklearn.base import RegressorMixin, TransformerMixin, clone
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from sklearn.model_selection import cross_val_predict
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from sklearn.model_selection._split import check_cv
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from sklearn.utils import check_X_y
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+from sklearn import __version__ as sklearn_version
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from ..externals.estimator_checks import check_is_fitted
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from ..externals.name_estimators import _name_estimators
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@@ -211,6 +212,9 @@ class StackingCVRegressor(_BaseXComposition, RegressorMixin, TransformerMixin):
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fit_params = None
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else:
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fit_params = dict(sample_weight=sample_weight)
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+
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+ param_name = "fit_params" if sklearn_version < "1.4" else "params"
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+
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meta_features = np.column_stack(
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[
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cross_val_predict(
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@@ -221,8 +225,8 @@ class StackingCVRegressor(_BaseXComposition, RegressorMixin, TransformerMixin):
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cv=kfold,
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verbose=self.verbose,
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n_jobs=self.n_jobs,
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- fit_params=fit_params,
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pre_dispatch=self.pre_dispatch,
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+ **{param_name: fit_params},
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)
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for regr in self.regr_
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]
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--
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2.47.1
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