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31 lines
1.2 KiB
Python
31 lines
1.2 KiB
Python
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import numpy as np
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def conf_matrix(p,labels,names=['1','0'],threshold=.5,show=True):
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"""
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Returns true and false positives in a binary classification problem
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- Column names: true class of the examples
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- Row names: classification assigned by the model
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p: probabilities estimated for observation of belonging to class '1'
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labels: observations' class
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names: classes' names
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threshold: probability value at which the model allocate an element to each class
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show: whether the matrix should be shown or not
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"""
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p = p.flatten()
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labels = labels.flatten()
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N = p.size
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C = np.ones(N)
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C[p<threshold] = 0
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True_1 = float((labels - C)[labels-C==0].shape[0] )
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False_1 = float((labels - C)[labels-C==-2].shape[0] )
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True_0 = float((labels - C)[labels-C==-1].shape[0] )
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False_0 = float((labels - C)[labels-C==1].shape[0] )
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if show:
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print (True_1 + True_0 + 0.)/N * 100,'% instances correctly classified'
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print '%-10s| %-10s| %-10s| ' % ('',names[0],names[1])
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print '----------|------------|------------|'
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print '%-10s| %-10s| %-10s| ' % (names[0],True_1,False_0)
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print '%-10s| %-10s| %-10s| ' % (names[1],False_1,True_0)
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return True_1, False_1, True_0, False_0
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