97 lines
3.7 KiB
Python
97 lines
3.7 KiB
Python
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# Author: Hamzeh Alsalhi <ha258@cornell.edu>
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#
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# License: BSD 3 clause
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import numpy as np
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import scipy.sparse as sp
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import array
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from . import check_random_state
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from ._random import sample_without_replacement
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__all__ = ['sample_without_replacement']
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def _random_choice_csc(n_samples, classes, class_probability=None,
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random_state=None):
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"""Generate a sparse random matrix given column class distributions
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Parameters
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----------
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n_samples : int,
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Number of samples to draw in each column.
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classes : list of size n_outputs of arrays of size (n_classes,)
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List of classes for each column.
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class_probability : list of size n_outputs of arrays of \
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shape (n_classes,), default=None
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Class distribution of each column. If None, uniform distribution is
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assumed.
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random_state : int, RandomState instance or None, default=None
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Controls the randomness of the sampled classes.
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See :term:`Glossary <random_state>`.
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Returns
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-------
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random_matrix : sparse csc matrix of size (n_samples, n_outputs)
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"""
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data = array.array('i')
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indices = array.array('i')
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indptr = array.array('i', [0])
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for j in range(len(classes)):
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classes[j] = np.asarray(classes[j])
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if classes[j].dtype.kind != 'i':
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raise ValueError("class dtype %s is not supported" %
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classes[j].dtype)
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classes[j] = classes[j].astype(np.int64, copy=False)
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# use uniform distribution if no class_probability is given
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if class_probability is None:
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class_prob_j = np.empty(shape=classes[j].shape[0])
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class_prob_j.fill(1 / classes[j].shape[0])
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else:
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class_prob_j = np.asarray(class_probability[j])
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if not np.isclose(np.sum(class_prob_j), 1.0):
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raise ValueError("Probability array at index {0} does not sum to "
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"one".format(j))
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if class_prob_j.shape[0] != classes[j].shape[0]:
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raise ValueError("classes[{0}] (length {1}) and "
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"class_probability[{0}] (length {2}) have "
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"different length.".format(j,
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classes[j].shape[0],
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class_prob_j.shape[0]))
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# If 0 is not present in the classes insert it with a probability 0.0
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if 0 not in classes[j]:
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classes[j] = np.insert(classes[j], 0, 0)
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class_prob_j = np.insert(class_prob_j, 0, 0.0)
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# If there are nonzero classes choose randomly using class_probability
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rng = check_random_state(random_state)
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if classes[j].shape[0] > 1:
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p_nonzero = 1 - class_prob_j[classes[j] == 0]
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nnz = int(n_samples * p_nonzero)
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ind_sample = sample_without_replacement(n_population=n_samples,
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n_samples=nnz,
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random_state=random_state)
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indices.extend(ind_sample)
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# Normalize probabilities for the nonzero elements
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classes_j_nonzero = classes[j] != 0
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class_probability_nz = class_prob_j[classes_j_nonzero]
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class_probability_nz_norm = (class_probability_nz /
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np.sum(class_probability_nz))
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classes_ind = np.searchsorted(class_probability_nz_norm.cumsum(),
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rng.rand(nnz))
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data.extend(classes[j][classes_j_nonzero][classes_ind])
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indptr.append(len(indices))
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return sp.csc_matrix((data, indices, indptr),
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(n_samples, len(classes)),
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dtype=int)
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