Source code for sknetwork.classification.metrics

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in July 2020
@author: Nathan de Lara <nathan.delara@polytechnique.org>
@author: Thomas Bonald <thomas.bonald@telecom-paris.fr>
"""
from typing import Union, Tuple

import numpy as np
from scipy import sparse

from sknetwork.utils.check import check_vector_format


[docs]def get_accuracy_score(labels_true: np.ndarray, labels_pred: np.ndarray) -> float: """Return the proportion of correctly labeled samples. Negative labels ignored. Parameters ---------- labels_true : np.ndarray True labels. labels_pred : np.ndarray Predicted labels Returns ------- accuracy : float A score between 0 and 1. Examples -------- >>> import numpy as np >>> labels_true = np.array([0, 0, 1, 1]) >>> labels_pred = np.array([0, 0, 0, 1]) >>> get_accuracy_score(labels_true, labels_pred) 0.75 """ check_vector_format(labels_true, labels_pred) mask = (labels_true >= 0) & (labels_pred >= 0) if np.sum(mask): return np.mean(labels_true[mask] == labels_pred[mask]) else: raise ValueError('No sample with both true non-negative label and predicted non-negative label.')
[docs]def get_confusion_matrix(labels_true: np.ndarray, labels_pred: np.ndarray) -> sparse.csr_matrix: """Return the confusion matrix in sparse format (true labels on rows, predicted labels on columns). Negative labels ignored. Parameters ---------- labels_true : np.ndarray True labels. labels_pred : np.ndarray Predicted labels Returns ------- confusion matrix : sparse.csr_matrix Confusion matrix. Examples -------- >>> import numpy as np >>> labels_true = np.array([0, 0, 1, 1]) >>> labels_pred = np.array([0, 0, 0, 1]) >>> get_confusion_matrix(labels_true, labels_pred).toarray() array([[2, 0], [1, 1]]) """ check_vector_format(labels_true, labels_pred) mask = (labels_true >= 0) & (labels_pred >= 0) if np.sum(mask): n_labels = max(max(labels_true), max(labels_pred)) + 1 row = labels_true[mask] col = labels_pred[mask] data = np.ones(np.sum(mask), dtype=int) return sparse.csr_matrix((data, (row, col)), shape=(n_labels, n_labels)) else: raise ValueError('No sample with both true non-negative label and predicted non-negative label.')
[docs]def get_f1_score(labels_true: np.ndarray, labels_pred: np.ndarray, return_precision_recall: bool = False) \ -> Union[float, Tuple[float, float, float]]: """Return the f1 score of binary classification. Negative labels ignored. Parameters ---------- labels_true : np.ndarray True labels. labels_pred : np.ndarray Predicted labels return_precision_recall : bool If ``True``, also return precision and recall. Returns ------- score, [precision, recall] : np.ndarray F1 score (between 0 and 1). Optionally, also return precision and recall. Examples -------- >>> import numpy as np >>> labels_true = np.array([0, 0, 1, 1]) >>> labels_pred = np.array([0, 0, 0, 1]) >>> np.round(get_f1_score(labels_true, labels_pred), 2) 0.67 """ values = set(labels_true[labels_true >= 0]) | set(labels_pred[labels_pred >= 0]) if values != {0, 1}: raise ValueError('Labels must be binary. ' 'Check get_f1_scores or get_average_f1_score for multi-label classification.') if return_precision_recall: f1_scores, precisions, recalls = get_f1_scores(labels_true, labels_pred, True) return f1_scores[1], precisions[1], recalls[1] else: f1_scores = get_f1_scores(labels_true, labels_pred, False) return f1_scores[1]
[docs]def get_f1_scores(labels_true: np.ndarray, labels_pred: np.ndarray, return_precision_recall: bool = False) \ -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray, np.ndarray]]: """Return the f1 scores of multi-label classification (one per label). Negative labels ignored. Parameters ---------- labels_true : np.ndarray True labels. labels_pred : np.ndarray Predicted labels return_precision_recall : bool If ``True``, also return precisions and recalls. Returns ------- scores, [precisions, recalls] : np.ndarray F1 scores (between 0 and 1). Optionally, also return F1 precisions and recalls. Examples -------- >>> import numpy as np >>> labels_true = np.array([0, 0, 1, 1]) >>> labels_pred = np.array([0, 0, 0, 1]) >>> np.round(get_f1_scores(labels_true, labels_pred), 2) array([0.8 , 0.67]) """ confusion = get_confusion_matrix(labels_true, labels_pred) n_labels = confusion.shape[0] counts_correct = confusion.diagonal() counts_true = confusion.dot(np.ones(n_labels)) counts_pred = confusion.T.dot(np.ones(n_labels)) mask = counts_true > 0 recalls = np.zeros(n_labels) recalls[mask] = counts_correct[mask] / counts_true[mask] precisions = np.zeros(n_labels) mask = counts_pred > 0 precisions[mask] = counts_correct[mask] / counts_pred[mask] f1_scores = np.zeros(n_labels) mask = (counts_true > 0) & (counts_pred > 0) f1_scores[mask] = 2 / (1 / precisions[mask] + 1 / recalls[mask]) if return_precision_recall: return f1_scores, precisions, recalls else: return f1_scores
[docs]def get_average_f1_score(labels_true: np.ndarray, labels_pred: np.ndarray, average: str = 'macro') -> float: """Return the average f1 score of multi-label classification. Negative labels ignored. Parameters ---------- labels_true : np.ndarray True labels. labels_pred : np.ndarray Predicted labels average : str Averaging method. Can be either ``'macro'`` (default), ``'micro'`` or ``'weighted'``. Returns ------- score : float Average F1 score (between 0 and 1). Examples -------- >>> import numpy as np >>> labels_true = np.array([0, 0, 1, 1]) >>> labels_pred = np.array([0, 0, 0, 1]) >>> np.round(get_average_f1_score(labels_true, labels_pred), 2) 0.73 """ if average == 'micro': # micro averaging = accuracy return get_accuracy_score(labels_true, labels_pred) else: f1_scores = get_f1_scores(labels_true, labels_pred) if average == 'macro': return np.mean(f1_scores) elif average == 'weighted': labels_unique, counts = np.unique(labels_true[labels_true >= 0], return_counts=True) return np.sum(f1_scores[labels_unique] * counts) / np.sum(counts) else: raise ValueError('Check the ``average`` parameter.')