sklearn/ensemble/_bootstrap.py - main - githubmirror/scikit-learn

sklearn/ensemble/_bootstrap.py
69 строк3 KB
antoinebaker
FIX Draw indices using sample_weight in Random Forests (#31529)
16 янв 2026, 17:13
16 янв 2026, 17:13ce1b377
"""Utility function to get the number of bootstrap samples."""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

from numbers import Integral
from warnings import warn


def _get_n_samples_bootstrap(n_samples, max_samples, sample_weight):
    """
    Get the number of samples in a bootstrap sample.

    Notes
    -----
    The frequency semantics of :term:`sample_weight` is guaranteed when
    `max_samples` is a float or integer, but not when `max_samples` is None. The
    returned `n_samples_bootstrap` will be the same between a weighted dataset
    with integer `sample_weights` and a dataset with as many rows repeated when
    `max_samples` is a float or integer. They will differ when `max_samples` is
    None (the weighted and repeated datasets do not have the same number of rows).

    Parameters
    ----------
    n_samples : int
        Number of samples in the dataset.

    max_samples : None, int or float
        The maximum number of samples to draw.

        - If None, then draw `n_samples` samples.
        - If int, then draw `max_samples` samples.
        - If float, then draw `max_samples * n_samples` unweighted samples or
          `max_samples * sample_weight.sum()` weighted samples.

    sample_weight : array of shape (n_samples,) or None
        Sample weights.

    Returns
    -------
    n_samples_bootstrap : int
        The total number of samples to draw for the bootstrap sample.
    """
    if max_samples is None:
        return n_samples
    elif isinstance(max_samples, Integral):
        return max_samples

    if sample_weight is None:
        weighted_n_samples = n_samples
        weighted_n_samples_msg = f"the number of samples is {weighted_n_samples} "
    else:
        weighted_n_samples = sample_weight.sum()
        weighted_n_samples_msg = (
            f"the total sum of sample weights is {weighted_n_samples} "
        )

    # max_samples Real fractional value relative to weighted_n_samples
    n_samples_bootstrap = max(int(max_samples * weighted_n_samples), 1)
    # Warn when number of bootstrap samples is suspiciously small.
    # This heuristic for "suspiciously small" might be adapted if found
    # unsuitable in practice.
    if n_samples_bootstrap < max(10, n_samples ** (1 / 3)):
        warn(
            f"Using the fractional value {max_samples=} when {weighted_n_samples_msg}"
            f"results in a low number ({n_samples_bootstrap}) of bootstrap samples. "
            "We recommend passing `max_samples` as an integer instead."
        )
    return n_samples_bootstrap