203 lines
7.2 KiB
Python
203 lines
7.2 KiB
Python
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"""
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Common code for all metrics
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"""
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# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# Mathieu Blondel <mathieu@mblondel.org>
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# Olivier Grisel <olivier.grisel@ensta.org>
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# Arnaud Joly <a.joly@ulg.ac.be>
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# Jochen Wersdorfer <jochen@wersdoerfer.de>
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# Lars Buitinck
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# Joel Nothman <joel.nothman@gmail.com>
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# Noel Dawe <noel@dawe.me>
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# License: BSD 3 clause
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from itertools import combinations
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import numpy as np
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from ..utils import check_array, check_consistent_length
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from ..utils.multiclass import type_of_target
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def _average_binary_score(binary_metric, y_true, y_score, average,
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sample_weight=None):
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"""Average a binary metric for multilabel classification
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Parameters
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----------
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y_true : array, shape = [n_samples] or [n_samples, n_classes]
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True binary labels in binary label indicators.
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y_score : array, shape = [n_samples] or [n_samples, n_classes]
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Target scores, can either be probability estimates of the positive
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class, confidence values, or binary decisions.
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average : string, [None, 'micro', 'macro' (default), 'samples', 'weighted']
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If ``None``, the scores for each class are returned. Otherwise,
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this determines the type of averaging performed on the data:
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``'micro'``:
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Calculate metrics globally by considering each element of the label
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indicator matrix as a label.
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``'macro'``:
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Calculate metrics for each label, and find their unweighted
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mean. This does not take label imbalance into account.
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``'weighted'``:
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Calculate metrics for each label, and find their average, weighted
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by support (the number of true instances for each label).
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``'samples'``:
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Calculate metrics for each instance, and find their average.
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Will be ignored when ``y_true`` is binary.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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binary_metric : callable, returns shape [n_classes]
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The binary metric function to use.
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Returns
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-------
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score : float or array of shape [n_classes]
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If not ``None``, average the score, else return the score for each
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classes.
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"""
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average_options = (None, 'micro', 'macro', 'weighted', 'samples')
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if average not in average_options:
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raise ValueError('average has to be one of {0}'
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''.format(average_options))
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y_type = type_of_target(y_true)
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if y_type not in ("binary", "multilabel-indicator"):
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raise ValueError("{0} format is not supported".format(y_type))
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if y_type == "binary":
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return binary_metric(y_true, y_score, sample_weight=sample_weight)
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check_consistent_length(y_true, y_score, sample_weight)
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y_true = check_array(y_true)
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y_score = check_array(y_score)
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not_average_axis = 1
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score_weight = sample_weight
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average_weight = None
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if average == "micro":
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if score_weight is not None:
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score_weight = np.repeat(score_weight, y_true.shape[1])
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y_true = y_true.ravel()
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y_score = y_score.ravel()
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elif average == 'weighted':
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if score_weight is not None:
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average_weight = np.sum(np.multiply(
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y_true, np.reshape(score_weight, (-1, 1))), axis=0)
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else:
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average_weight = np.sum(y_true, axis=0)
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if np.isclose(average_weight.sum(), 0.0):
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return 0
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elif average == 'samples':
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# swap average_weight <-> score_weight
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average_weight = score_weight
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score_weight = None
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not_average_axis = 0
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if y_true.ndim == 1:
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y_true = y_true.reshape((-1, 1))
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if y_score.ndim == 1:
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y_score = y_score.reshape((-1, 1))
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n_classes = y_score.shape[not_average_axis]
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score = np.zeros((n_classes,))
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for c in range(n_classes):
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y_true_c = y_true.take([c], axis=not_average_axis).ravel()
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y_score_c = y_score.take([c], axis=not_average_axis).ravel()
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score[c] = binary_metric(y_true_c, y_score_c,
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sample_weight=score_weight)
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# Average the results
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if average is not None:
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if average_weight is not None:
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# Scores with 0 weights are forced to be 0, preventing the average
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# score from being affected by 0-weighted NaN elements.
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average_weight = np.asarray(average_weight)
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score[average_weight == 0] = 0
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return np.average(score, weights=average_weight)
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else:
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return score
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def _average_multiclass_ovo_score(binary_metric, y_true, y_score,
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average='macro'):
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"""Average one-versus-one scores for multiclass classification.
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Uses the binary metric for one-vs-one multiclass classification,
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where the score is computed according to the Hand & Till (2001) algorithm.
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Parameters
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----------
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binary_metric : callable
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The binary metric function to use that accepts the following as input
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y_true_target : array, shape = [n_samples_target]
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Some sub-array of y_true for a pair of classes designated
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positive and negative in the one-vs-one scheme.
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y_score_target : array, shape = [n_samples_target]
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Scores corresponding to the probability estimates
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of a sample belonging to the designated positive class label
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y_true : array-like of shape (n_samples,)
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True multiclass labels.
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y_score : array-like of shape (n_samples, n_classes)
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Target scores corresponding to probability estimates of a sample
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belonging to a particular class
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average : 'macro' or 'weighted', optional (default='macro')
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Determines the type of averaging performed on the pairwise binary
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metric scores
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``'macro'``:
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Calculate metrics for each label, and find their unweighted
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mean. This does not take label imbalance into account. Classes
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are assumed to be uniformly distributed.
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``'weighted'``:
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Calculate metrics for each label, taking into account the
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prevalence of the classes.
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Returns
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-------
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score : float
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Average of the pairwise binary metric scores
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"""
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check_consistent_length(y_true, y_score)
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y_true_unique = np.unique(y_true)
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n_classes = y_true_unique.shape[0]
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n_pairs = n_classes * (n_classes - 1) // 2
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pair_scores = np.empty(n_pairs)
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is_weighted = average == "weighted"
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prevalence = np.empty(n_pairs) if is_weighted else None
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# Compute scores treating a as positive class and b as negative class,
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# then b as positive class and a as negative class
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for ix, (a, b) in enumerate(combinations(y_true_unique, 2)):
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a_mask = y_true == a
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b_mask = y_true == b
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ab_mask = np.logical_or(a_mask, b_mask)
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if is_weighted:
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prevalence[ix] = np.average(ab_mask)
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a_true = a_mask[ab_mask]
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b_true = b_mask[ab_mask]
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a_true_score = binary_metric(a_true, y_score[ab_mask, a])
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b_true_score = binary_metric(b_true, y_score[ab_mask, b])
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pair_scores[ix] = (a_true_score + b_true_score) / 2
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return np.average(pair_scores, weights=prevalence)
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