bai33_evaluation_Precision_and_Recall.py

# -*- coding: utf-8 -*-
"""
Created on Mon May  4 22:24:56 2020

@author: phamk
"""


from __future__ import print_function
import numpy as np 
# confusion matrix to precision + recall
def cm2pr_binary(cm):
    p = cm[0,0]/np.sum(cm[:,0])
    r = cm[0,0]/np.sum(cm[0])
    return (p, r)

# example of a confusion matrix for binary classification problem 
cm = np.array([[100., 10], [20, 70]])
p,r = cm2pr_binary(cm)
print("precition = {0:.2f}, recall = {1:.2f}".format(p, r))



tp1, fp1, fn1 = 10, 5, 3
tp2, fp2, fn2 = 17, 7, 10
tp3, fp3, fn3 = 25, 2, 4 

def PR(tp, fp, fn):
    P = float(tp)/(tp + fp)
    R = float(tp)/(tp + fn)
    return (P, R)

(P1, R1) = PR(tp1, fp1, fn1)
(P2, R2) = PR(tp2, fp2, fn2)
(P3, R3) = PR(tp3, fp3, fn2)

print('(P1, R1) = (%.2f, %.2f)'%(P1, R1))
print('(P2, R2) = (%.2f, %.2f)'%(P2, R2))
print('(P3, R3) = (%.2f, %.2f)'%(P3, R3))

total_tp = tp1 + tp2 + tp3
total_fp = fp1 + fp2 + fp3 
total_fn = fn1 + fn2 + fn3 
micro_ap = float(total_tp)/(total_tp + total_fp)
micro_ar = float(total_tp)/(total_tp + total_fn)
print('(micro_ap, micro_ar) = (%.2f, %.2f)' % (micro_ap, micro_ar))

macro_ap = (P1 + P2 + P3)/3
macro_ar = (R1 + R2 + R3)/3
print('(micro_ap, micro_ar) = (%.2f, %.2f)' % (macro_ap, macro_ar))