101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
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# Sebastian Raschka 2014-2016
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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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import numpy as np
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def lift_score(y_target, y_predicted, binary=True, positive_label=1):
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"""Lift measures the degree to which the predictions of a
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classification model are better than randomly-generated predictions.
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The in terms of True Positives (TP), True Negatives (TN),
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False Positives (FP), and False Negatives (FN), the lift score is
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computed as:
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[ TP / (TP+FP) ] / [ (TP+FN) / (TP+TN+FP+FN) ]
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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: True)
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Maps a multi-class problem onto a
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binary, 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: 0)
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Class label of the positive class.
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Returns
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----------
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score : float
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Lift score in the range [0, $\infty$]
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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/lift_score/
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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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pred_tmp = pred_tmp.T
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targ_tmp = targ_tmp.T
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if len(pred_tmp) != len(targ_tmp):
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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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binary_check_targ_tmp = np.extract(targ_tmp > 1, targ_tmp)
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binary_check_pred_tmp = np.extract(pred_tmp > 1, pred_tmp)
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if len(binary_check_targ_tmp) or len(binary_check_pred_tmp):
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raise AttributeError('`y_target` and `y_predicted`'
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' have different elements from 0 and 1.')
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return (support(targ_tmp, pred_tmp) /
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(support(targ_tmp) * support(pred_tmp)))
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def support(y_target, y_predicted=None):
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"""Support is the fraction of the true value
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in predictions and target values.
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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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Returns
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----------
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score : float
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Support score in the range [0, 1]
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"""
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if y_predicted is None:
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if y_target.ndim == 1:
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return (y_target == 1).sum() / float(y_target.shape[0])
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return (y_target == 1).all(axis=1).sum() / float(y_target.shape[0])
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else:
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all_prod = np.column_stack([y_target, y_predicted])
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return (all_prod == 1).all(axis=1).sum() / float(all_prod.shape[0])
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