70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
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# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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#
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# A function for generating a confusion matrix.
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# Author: Sebastian Raschka <sebastianraschka.com>
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#
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# License: BSD 3 clause
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from itertools import product
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import numpy as np
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def confusion_matrix(y_target, y_predicted, binary=False, positive_label=1):
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"""Compute a confusion matrix/contingency table.
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Parameters
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-----------
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y_target : array-like, shape=[n_samples]
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True class labels.
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y_predicted : array-like, shape=[n_samples]
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Predicted class labels.
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binary : bool (default: False)
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Maps a multi-class problem onto a
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binary confusion matrix, where
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the positive class is 1 and
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all other classes are 0.
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positive_label : int (default: 1)
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Class label of the positive class.
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Returns
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----------
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mat : array-like, shape=[n_classes, n_classes]
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Examples
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-----------
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For usage examples, please see
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http://rasbt.github.io/mlxtend/user_guide/evaluate/confusion_matrix/
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"""
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if not isinstance(y_target, np.ndarray):
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targ_tmp = np.asarray(y_target)
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else:
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targ_tmp = y_target
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if not isinstance(y_predicted, np.ndarray):
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pred_tmp = np.asarray(y_predicted)
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else:
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pred_tmp = y_predicted
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if len(y_target) != len(y_predicted):
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raise AttributeError('`y_target` and `y_predicted`'
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' don\'t have the same number of elements.')
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if binary:
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targ_tmp = np.where(targ_tmp != positive_label, 0, 1)
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pred_tmp = np.where(pred_tmp != positive_label, 0, 1)
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class_labels = np.unique(np.concatenate((targ_tmp, pred_tmp)))
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if class_labels.shape[0] == 1:
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if class_labels[0] != 0:
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class_labels = np.array([0, class_labels[0]])
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else:
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class_labels = np.array([class_labels[0], 1])
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n_labels = class_labels.shape[0]
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lst = []
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z = list(zip(targ_tmp, pred_tmp))
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for combi in product(class_labels, repeat=2):
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lst.append(z.count(combi))
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mat = np.asarray(lst)[:, None].reshape(n_labels, n_labels)
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return mat
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