75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
# 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 per class accuracy
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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 _compute_metric(target_temp, predicted_temp, normalize=True):
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if normalize:
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return (target_temp == predicted_temp).mean()
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else:
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return (target_temp == predicted_temp).sum()
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def accuracy_score(y_target, y_predicted, method="standard",
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pos_label=1, normalize=True):
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"""General accuracy function for supervised learning.
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Parameters
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------------
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y_target : array-like, shape=[n_values]
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True class labels or target values.
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y_predicted : array-like, shape=[n_values]
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Predicted class labels or target values.
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method : str, 'standard' by default.
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The chosen method for accuracy computation.
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If set to 'standard', computes overall accuracy.
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If set to 'binary', computes accuracy for class pos_label.
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If set to 'average', computes average per class accuracy.
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pos_label : str or int, 1 by default.
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The class whose accuracy score is to be reported.
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Used only when `method` is set to 'binary'
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normalize : bool, True by default.
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If True, returns fraction of correctly classified samples.
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If False, returns number of correctly classified samples.
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Returns
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------------
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score: float
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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/accuracy_score/
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"""
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target_temp = np.asarray(y_target)
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predicted_temp = np.asarray(y_predicted)
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unique_labels = np.unique(target_temp)
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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 method == "standard":
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return _compute_metric(target_temp, predicted_temp, normalize)
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elif method == "binary":
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if pos_label not in unique_labels:
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raise AttributeError("Chosen value of pos_label doesn't exist")
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target_temp = np.where(target_temp == pos_label, 1, 0)
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predicted_temp = np.where(predicted_temp == pos_label, 1, 0)
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return _compute_metric(target_temp, predicted_temp, normalize)
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elif method == "average":
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return sum([_compute_metric(np.where(target_temp != l, 1, 0),
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np.where(predicted_temp != l, 1, 0))
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for l in unique_labels]) / float(unique_labels.shape[0])
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else:
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raise ValueError('`method` must be "standard", "average"'
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'or "binary". Got "%s".' % method)
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