58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
# Authors: Andreas Mueller
|
|
# License: BSD 3 clause
|
|
|
|
import numpy as np
|
|
|
|
from .fixes import bincount
|
|
|
|
|
|
def compute_class_weight(class_weight, classes, y_ind):
|
|
"""Estimate class weights for unbalanced datasets.
|
|
|
|
Parameters
|
|
----------
|
|
class_weight : dict, 'auto' or None
|
|
If 'auto', class weights will be given inverse proportional
|
|
to the frequency of the class in the data.
|
|
If a dictionary is given, keys are classes and values
|
|
are corresponding class weights.
|
|
If None is given, the class weights will be uniform.
|
|
|
|
classes : ndarray
|
|
Array of the classes occurring in the data, as given by
|
|
``np.unique(y_org)`` with ``y_org`` the original class labels.
|
|
|
|
y_ind : array-like, shape=(n_samples,), dtype=int
|
|
Array of class indices per sample;
|
|
0 <= y_ind[i] < n_classes for i in range(n_samples).
|
|
|
|
Returns
|
|
-------
|
|
class_weight_vect : ndarray, shape=(n_classes,)
|
|
Array with class_weight_vect[i] the weight for i-th class
|
|
(as determined by sorting).
|
|
"""
|
|
if class_weight is None or len(class_weight) == 0:
|
|
# uniform class weights
|
|
weight = np.ones(classes.shape[0], dtype=np.float64, order='C')
|
|
elif class_weight == 'auto':
|
|
# inversely proportional to the number of samples in the class
|
|
counts = bincount(y_ind, minlength=len(classes))
|
|
counts = np.maximum(counts, 1)
|
|
weight = 1. / counts
|
|
weight *= classes.shape[0] / np.sum(weight)
|
|
else:
|
|
# user-defined dictionary
|
|
weight = np.ones(classes.shape[0], dtype=np.float64, order='C')
|
|
if not isinstance(class_weight, dict):
|
|
raise ValueError("class_weight must be dict, 'auto', or None,"
|
|
" got: %r" % class_weight)
|
|
for c in class_weight:
|
|
i = np.searchsorted(classes, c)
|
|
if classes[i] != c:
|
|
raise ValueError("Class label %d not present." % c)
|
|
else:
|
|
weight[i] = class_weight[c]
|
|
|
|
return weight
|