8000 FIX Sets max_samples=1 when it is a float and too low in RandomForest… · thomasjpfan/scikit-learn@01c8e0b · GitHub
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FIX Sets max_samples=1 when it is a float and too low in RandomForestClassifier (scikit-learn#25601)
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
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‎doc/whats_new/v1.3.rst‎

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@@ -204,6 +204,10 @@ Changelog
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:class:`ensemble.BaggingRegressor` exp 8000 ose the `allow_nan` tag from the
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underlying estimator. :pr:`25506` by `Thomas Fan`_.
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- |Fix| :meth:`ensemble.RandomForestClassifier.fit` sets `max_samples = 1`
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when `max_samples` is a float and `round(n_samples * max_samples) < 1`.
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:pr:`25601` by :user:`Jan Fidor <JanFidor>`.
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:mod:`sklearn.exception`
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........................
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- |Feature| Added :class:`exception.InconsistentVersionWarning` which is raised

‎sklearn/ensemble/_forest.py‎

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@@ -117,7 +117,7 @@ def _get_n_samples_bootstrap(n_samples, max_samples):
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return max_samples
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if isinstance(max_samples, Real):
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return round(n_samples * max_samples)
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return max(round(n_samples * max_samples), 1)
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def _generate_sample_indices(random_state, n_samples, n_samples_bootstrap):
@@ -1283,7 +1283,7 @@ class RandomForestClassifier(ForestClassifier):
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- If None (default), then draw `X.shape[0]` samples.
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- If int, then draw `max_samples` samples.
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- If float, then draw `max_samples * X.shape[0]` samples. Thus,
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- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,
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`max_samples` should be in the interval `(0.0, 1.0]`.
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.. versionadded:: 0.22
@@ -1636,7 +1636,7 @@ class RandomForestRegressor(ForestRegressor):
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- If None (default), then draw `X.shape[0]` samples.
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- If int, then draw `max_samples` samples.
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- If float, then draw `max_samples * X.shape[0]` samples. Thus,
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- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,
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`max_samples` should be in the interval `(0.0, 1.0]`.
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.. versionadded:: 0.22

‎sklearn/ensemble/tests/test_forest.py‎

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@@ -1807,3 +1807,16 @@ def test_read_only_buffer(monkeypatch):
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clf = RandomForestClassifier(n_jobs=2, random_state=rng)
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cross_val_score(clf, X, y, cv=2)
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@pytest.mark.parametrize("class_weight", ["balanced_subsample", None])
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def test_round_samples_to_one_when_samples_too_low(class_weight):
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"""Check low max_samples works and is rounded to one.
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Non-regression test for gh-24037.
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"""
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X, y = datasets.load_wine(return_X_y=True)
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forest = RandomForestClassifier(
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n_estimators=10, max_samples=1e-4, class_weight=class_weight, random_state=0
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)
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forest.fit(X, y)

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