8000 MAINT Parameters validation for sklearn.preprocessing.label_binarize … · scikit-learn/scikit-learn@d4d9801 · GitHub
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MAINT Parameters validation for sklearn.preprocessing.label_binarize (#26078)
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
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‎sklearn/preprocessing/_label.py‎

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from ..base import BaseEstimator, TransformerMixin
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from ..utils.sparsefuncs import min_max_axis
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from ..utils._param_validation import Interval, validate_params
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from ..utils import column_or_1d
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from ..utils.validation import _num_samples, check_array, check_is_fitted
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from ..utils.multiclass import unique_labels
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return {"X_types": ["1dlabels"]}
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@validate_params(
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{
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"y": ["array-like"],
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"classes": ["array-like"],
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"neg_label": [Interval(Integral, None, None, closed="neither")],
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"pos_label": [Interval(Integral, None, None, closed="neither")],
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"sparse_output": ["boolean"],
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}
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)
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def label_binarize(y, *, classes, neg_label=0, pos_label=1, sparse_output=False):
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"""Binarize labels in a one-vs-all fashion.
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‎sklearn/tests/test_public_functions.py‎

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"sklearn.model_selection.train_test_split",
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"sklearn.preprocessing.add_dummy_feature",
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"sklearn.preprocessing.binarize",
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"sklearn.preprocessing.label_binarize",
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"sklearn.preprocessing.maxabs_scale",
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"sklearn.preprocessing.scale",
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"sklearn.random_projection.johnson_lindenstrauss_min_dim",

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