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DOC Improve docs of BaseLabelPropagation.transduction_ #24985

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Merged
merged 6 commits into from
Dec 1, 2022
Merged

DOC Improve docs of BaseLabelPropagation.transduction_ #24985

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1caf2ad
Improve docs of BaseLabelPropagation.transduction_
scottgigante-immunai Nov 18, 2022
56bab93
Document lack of fit_predict
scottgigante-immunai Nov 18, 2022
087f7ea
`transduction_` is not a `:term:`
scottgigante-immunai Nov 18, 2022
85343cb
Remove attributes from private class
scottgigante-immunai Nov 21, 2022
069e78d
Merge branch 'main' into patch-2
scottgigante-immunai Nov 21, 2022
838060a
Trailing whitespace
scottgigante-immunai Nov 21, 2022
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12 changes: 8 additions & 4 deletions sklearn/semi_supervised/_label_propagation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -184,6 +184,10 @@ def predict(self, X):
y : ndarray of shape (n_samples,)
Predictions for input data.
"""
# Note: since `predict` does not accept semi-supervised labels as input,
# `fit(X, y).predict(X) != fit(X, y).transduction_`.
# Hence, `fit_predict` is not implemented.
# See https://github.com/scikit-learn/scikit-learn/pull/24898
probas = self.predict_proba(X)
return self.classes_[np.argmax(probas, axis=1)].ravel()

Expand Down Expand Up @@ -244,7 +248,7 @@ def fit(self, X, y):
y : array-like of shape (n_samples,)
Target class values with unlabeled points marked as -1.
All unlabeled samples will be transductively assigned labels
internally.
internally, which are stored in `transduction_`.

Returns
-------
Expand Down Expand Up @@ -371,7 +375,7 @@ class LabelPropagation(BaseLabelPropagation):
Categorical distribution for each item.

transduction_ : ndarray of shape (n_samples)
Label assigned to each item via the transduction.
Label assigned to each item during :term:`fit`.

n_features_in_ : int
Number of features seen during :term:`fit`.
Expand Down Expand Up @@ -466,7 +470,7 @@ def fit(self, X, y):
y : array-like of shape (n_samples,)
Target class values with unlabeled points marked as -1.
All unlabeled samples will be transductively assigned labels
internally.
internally, which are stored in `transduction_`.

Returns
-------
Expand Down Expand Up @@ -531,7 +535,7 @@ class LabelSpreading(BaseLabelPropagation):
Categorical distribution for each item.

transduction_ : ndarray of shape (n_samples,)
Label assigned to each item via the transduction.
Label assigned to each item during :term:`fit`.

n_features_in_ : int
Number of features seen during :term:`fit`.
Expand Down