55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
# Authors: Andreas Mueller
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# License: Simplified BSD
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import numpy as np
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def compute_class_weight(class_weight, classes, y):
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"""Estimate class weights for unbalanced datasets.
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Parameters
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----------
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class_weight : dict, 'auto' or None
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If 'auto', class weights will be given inverse proportional
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to the frequency of the class in the data.
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If a dictionary is given, keys are classes and values
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are corresponding class weights.
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If None is given, the class weights will be uniform.
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classes : list
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List of the classes occuring in the data, as given by
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``np.unique(y_org)`` with ``y_org`` the original class labels.
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y : array-like, shape=(n_samples,), dtype=int
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Array of class indices per sample;
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0 <= y[i] < n_classes for i in range(n_samples).
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Returns
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-------
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class_weight_vect : ndarray, shape=(n_classes,)
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Array with class_weight_vect[i] the weight for i-th class
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(as determined by sorting).
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"""
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if class_weight is None or len(class_weight) == 0:
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# uniform class weights
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weight = np.ones(classes.shape[0], dtype=np.float64, order='C')
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elif class_weight == 'auto':
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# anti-proportional to the number of samples in the class
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weight = np.array([1.0 / np.sum(y == i) for i in classes],
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dtype=np.float64, order='C')
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weight *= classes.shape[0] / np.sum(weight)
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else:
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# user-defined dictionary
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weight = np.ones(classes.shape[0], dtype=np.float64, order='C')
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if not isinstance(class_weight, dict):
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raise ValueError("class_weight must be dict, 'auto', or None,"
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" got: %r" % class_weight)
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for c in class_weight:
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i = np.searchsorted(classes, c)
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if classes[i] != c:
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raise ValueError("Class label %d not present." % c)
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else:
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weight[i] = class_weight[c]
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return weight
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