# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
[docs]def conf_matrix(p,labels,names=['1','0'],threshold=.5,show=True):
"""
Returns error rate and true/false positives in a binary classification problem
- Actual classes are displayed by column.
- Predicted classes are displayed by row.
:param p: array of class '1' probabilities.
:param labels: array of actual classes.
:param names: list of class names, defaults to ['1','0'].
:param threshold: probability value used to decide the class.
:param show: whether the matrix should be shown or not
:type show: False|True
"""
assert p.size == labels.size, "Arrays p and labels have different dimensions."
decision = np.ones((labels.size,1))
decision[p<threshold] = 0
diff = decision - labels
false_0 = diff[diff == -1].size
false_1 = diff[diff == 1].size
true_1 = np.sum(decision[diff ==0])
true_0 = labels.size - true_1 - false_0 - false_1
error = (false_1 + false_0)/np.float(labels.size)
if show:
print(100. - error * 100,'% instances correctly classified')
print('%-10s| %-10s| %-10s| ' % ('',names[0],names[1]))
print('----------|------------|------------|')
print('%-10s| %-10s| %-10s| ' % (names[0],true_1,false_0))
print('%-10s| %-10s| %-10s| ' % (names[1],false_1,true_0))
return error,true_1, false_1, true_0, false_0