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GPy/util/classification.py

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