forked from 170010011/fr
182 lines
7.0 KiB
Python
182 lines
7.0 KiB
Python
|
# Authors: Andreas Mueller
|
||
|
|
# Manoj Kumar
|
||
|
|
# License: BSD 3 clause
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from .validation import _deprecate_positional_args
|
||
|
|
|
||
|
|
|
||
|
|
@_deprecate_positional_args
|
||
|
|
def compute_class_weight(class_weight, *, classes, y):
|
||
|
|
"""Estimate class weights for unbalanced datasets.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
class_weight : dict, 'balanced' or None
|
||
|
|
If 'balanced', class weights will be given by
|
||
|
|
``n_samples / (n_classes * np.bincount(y))``.
|
||
|
|
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 : array-like of shape (n_samples,)
|
||
|
|
Array of original class labels per sample.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
class_weight_vect : ndarray of shape (n_classes,)
|
||
|
|
Array with class_weight_vect[i] the weight for i-th class.
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
The "balanced" heuristic is inspired by
|
||
|
|
Logistic Regression in Rare Events Data, King, Zen, 2001.
|
||
|
|
"""
|
||
|
|
# Import error caused by circular imports.
|
||
|
|
from ..preprocessing import LabelEncoder
|
||
|
|
|
||
|
|
if set(y) - set(classes):
|
||
|
|
raise ValueError("classes should include all valid labels that can "
|
||
|
|
"be in y")
|
||
|
|
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 == 'balanced':
|
||
|
|
# Find the weight of each class as present in y.
|
||
|
|
le = LabelEncoder()
|
||
|
|
y_ind = le.fit_transform(y)
|
||
|
|
if not all(np.in1d(classes, le.classes_)):
|
||
|
|
raise ValueError("classes should have valid labels that are in y")
|
||
|
|
|
||
|
|
recip_freq = len(y) / (len(le.classes_) *
|
||
|
|
np.bincount(y_ind).astype(np.float64))
|
||
|
|
weight = recip_freq[le.transform(classes)]
|
||
|
|
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, 'balanced', or None,"
|
||
|
|
" got: %r" % class_weight)
|
||
|
|
for c in class_weight:
|
||
|
|
i = np.searchsorted(classes, c)
|
||
|
|
if i >= len(classes) or classes[i] != c:
|
||
|
|
raise ValueError("Class label {} not present.".format(c))
|
||
|
|
else:
|
||
|
|
weight[i] = class_weight[c]
|
||
|
|
|
||
|
|
return weight
|
||
|
|
|
||
|
|
|
||
|
|
@_deprecate_positional_args
|
||
|
|
def compute_sample_weight(class_weight, y, *, indices=None):
|
||
|
|
"""Estimate sample weights by class for unbalanced datasets.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
class_weight : dict, list of dicts, "balanced", or None
|
||
|
|
Weights associated with classes in the form ``{class_label: weight}``.
|
||
|
|
If not given, all classes are supposed to have weight one. For
|
||
|
|
multi-output problems, a list of dicts can be provided in the same
|
||
|
|
order as the columns of y.
|
||
|
|
|
||
|
|
Note that for multioutput (including multilabel) weights should be
|
||
|
|
defined for each class of every column in its own dict. For example,
|
||
|
|
for four-class multilabel classification weights should be
|
||
|
|
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
|
||
|
|
[{1:1}, {2:5}, {3:1}, {4:1}].
|
||
|
|
|
||
|
|
The "balanced" mode uses the values of y to automatically adjust
|
||
|
|
weights inversely proportional to class frequencies in the input data:
|
||
|
|
``n_samples / (n_classes * np.bincount(y))``.
|
||
|
|
|
||
|
|
For multi-output, the weights of each column of y will be multiplied.
|
||
|
|
|
||
|
|
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
|
||
|
|
Array of original class labels per sample.
|
||
|
|
|
||
|
|
indices : array-like of shape (n_subsample,), default=None
|
||
|
|
Array of indices to be used in a subsample. Can be of length less than
|
||
|
|
n_samples in the case of a subsample, or equal to n_samples in the
|
||
|
|
case of a bootstrap subsample with repeated indices. If None, the
|
||
|
|
sample weight will be calculated over the full sample. Only "balanced"
|
||
|
|
is supported for class_weight if this is provided.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
sample_weight_vect : ndarray of shape (n_samples,)
|
||
|
|
Array with sample weights as applied to the original y.
|
||
|
|
"""
|
||
|
|
|
||
|
|
y = np.atleast_1d(y)
|
||
|
|
if y.ndim == 1:
|
||
|
|
y = np.reshape(y, (-1, 1))
|
||
|
|
n_outputs = y.shape[1]
|
||
|
|
|
||
|
|
if isinstance(class_weight, str):
|
||
|
|
if class_weight not in ['balanced']:
|
||
|
|
raise ValueError('The only valid preset for class_weight is '
|
||
|
|
'"balanced". Given "%s".' % class_weight)
|
||
|
|
elif (indices is not None and
|
||
|
|
not isinstance(class_weight, str)):
|
||
|
|
raise ValueError('The only valid class_weight for subsampling is '
|
||
|
|
'"balanced". Given "%s".' % class_weight)
|
||
|
|
elif n_outputs > 1:
|
||
|
|
if (not hasattr(class_weight, "__iter__") or
|
||
|
|
isinstance(class_weight, dict)):
|
||
|
|
raise ValueError("For multi-output, class_weight should be a "
|
||
|
|
"list of dicts, or a valid string.")
|
||
|
|
if len(class_weight) != n_outputs:
|
||
|
|
raise ValueError("For multi-output, number of elements in "
|
||
|
|
"class_weight should match number of outputs.")
|
||
|
|
|
||
|
|
expanded_class_weight = []
|
||
|
|
for k in range(n_outputs):
|
||
|
|
|
||
|
|
y_full = y[:, k]
|
||
|
|
classes_full = np.unique(y_full)
|
||
|
|
classes_missing = None
|
||
|
|
|
||
|
|
if class_weight == 'balanced' or n_outputs == 1:
|
||
|
|
class_weight_k = class_weight
|
||
|
|
else:
|
||
|
|
class_weight_k = class_weight[k]
|
||
|
|
|
||
|
|
if indices is not None:
|
||
|
|
# Get class weights for the subsample, covering all classes in
|
||
|
|
# case some labels that were present in the original data are
|
||
|
|
# missing from the sample.
|
||
|
|
y_subsample = y[indices, k]
|
||
|
|
classes_subsample = np.unique(y_subsample)
|
||
|
|
|
||
|
|
weight_k = np.take(compute_class_weight(class_weight_k,
|
||
|
|
classes=classes_subsample,
|
||
|
|
y=y_subsample),
|
||
|
|
np.searchsorted(classes_subsample,
|
||
|
|
classes_full),
|
||
|
|
mode='clip')
|
||
|
|
|
||
|
|
classes_missing = set(classes_full) - set(classes_subsample)
|
||
|
|
else:
|
||
|
|
weight_k = compute_class_weight(class_weight_k,
|
||
|
|
classes=classes_full,
|
||
|
|
y=y_full)
|
||
|
|
|
||
|
|
weight_k = weight_k[np.searchsorted(classes_full, y_full)]
|
||
|
|
|
||
|
|
if classes_missing:
|
||
|
|
# Make missing classes' weight zero
|
||
|
|
weight_k[np.in1d(y_full, list(classes_missing))] = 0.
|
||
|
|
|
||
|
|
expanded_class_weight.append(weight_k)
|
||
|
|
|
||
|
|
expanded_class_weight = np.prod(expanded_class_weight,
|
||
|
|
axis=0,
|
||
|
|
dtype=np.float64)
|
||
|
|
|
||
|
|
return expanded_class_weight
|