githubmirror/scikit-learn

sklearn/utils/random.py
103 строки4 KB
Dan Schult
Enable config setting `sparse_interface` to control sparray and spmatrix creation (#31177)
11 мар 2026, 09:04
11 мар 2026, 09:040169f83
"""Utilities for random sampling."""

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

import array

import numpy as np
import scipy.sparse as sp

from sklearn.utils import check_random_state
from sklearn.utils._random import sample_without_replacement
from sklearn.utils._sparse import _align_api_if_sparse

__all__ = ["sample_without_replacement"]


def _random_choice_csc(n_samples, classes, class_probability=None, random_state=None):
    """Generate a sparse random matrix given column class distributions

    Parameters
    ----------
    n_samples : int,
        Number of samples to draw in each column.

    classes : list of size n_outputs of arrays of size (n_classes,)
        List of classes for each column.

    class_probability : list of size n_outputs of arrays of \
        shape (n_classes,), default=None
        Class distribution of each column. If None, uniform distribution is
        assumed.

    random_state : int, RandomState instance or None, default=None
        Controls the randomness of the sampled classes.
        See :term:`Glossary <random_state>`.

    Returns
    -------
    random_matrix : sparse csc matrix of size (n_samples, n_outputs)

    """
    data = array.array("i")
    indices = array.array("i")
    indptr = array.array("i", [0])

    for j in range(len(classes)):
        classes[j] = np.asarray(classes[j])
        if classes[j].dtype.kind != "i":
            raise ValueError("class dtype %s is not supported" % classes[j].dtype)
        classes[j] = classes[j].astype(np.int64, copy=False)

        # use uniform distribution if no class_probability is given
        if class_probability is None:
            class_prob_j = np.empty(shape=classes[j].shape[0])
            class_prob_j.fill(1 / classes[j].shape[0])
        else:
            class_prob_j = np.asarray(class_probability[j])

        if not np.isclose(np.sum(class_prob_j), 1.0):
            raise ValueError(
                "Probability array at index {0} does not sum to one".format(j)
            )

        if class_prob_j.shape[0] != classes[j].shape[0]:
            raise ValueError(
                "classes[{0}] (length {1}) and "
                "class_probability[{0}] (length {2}) have "
                "different length.".format(
                    j, classes[j].shape[0], class_prob_j.shape[0]
                )
            )

        # If 0 is not present in the classes insert it with a probability 0.0
        if 0 not in classes[j]:
            classes[j] = np.insert(classes[j], 0, 0)
            class_prob_j = np.insert(class_prob_j, 0, 0.0)

        # If there are nonzero classes choose randomly using class_probability
        rng = check_random_state(random_state)
        if classes[j].shape[0] > 1:
            index_class_0 = np.flatnonzero(classes[j] == 0).item()
            p_nonzero = 1 - class_prob_j[index_class_0]
            nnz = int(n_samples * p_nonzero)
            ind_sample = sample_without_replacement(
                n_population=n_samples, n_samples=nnz, random_state=random_state
            )
            indices.extend(ind_sample)

            # Normalize probabilities for the nonzero elements
            classes_j_nonzero = classes[j] != 0
            class_probability_nz = class_prob_j[classes_j_nonzero]
            class_probability_nz_norm = class_probability_nz / np.sum(
                class_probability_nz
            )
            classes_ind = np.searchsorted(
                class_probability_nz_norm.cumsum(), rng.uniform(size=nnz)
            )
            data.extend(classes[j][classes_j_nonzero][classes_ind])
        indptr.append(len(indices))

    csc = sp.csc_array((data, indices, indptr), (n_samples, len(classes)), dtype=int)
    return _align_api_if_sparse(csc)