classifier_evaluation_demo.py

# -*- coding: utf-8 -*-
"""
Created on Fri Sep 02 12:36:55 2016

@author: DIP
"""

from sklearn import metrics
import numpy as np
import pandas as pd
from collections import Counter

actual_labels = ['spam', 'ham', 'spam', 'spam', 'spam',
               'ham', 'ham', 'spam', 'ham', 'spam',
               'spam', 'ham', 'ham', 'ham', 'spam',
               'ham', 'ham', 'spam', 'spam', 'ham']
              
predicted_labels = ['spam', 'spam', 'spam', 'ham', 'spam',
                    'spam', 'ham', 'ham', 'spam', 'spam',
                    'ham', 'ham', 'spam', 'ham', 'ham',
                    'ham', 'spam', 'ham', 'spam', 'spam']
                    
ac = Counter(actual_labels)                     
pc = Counter(predicted_labels)  

print 'Actual counts:', ac.most_common()
print 'Predicted counts:', pc.most_common()          
        
cm = metrics.confusion_matrix(y_true=actual_labels,
                         y_pred=predicted_labels,
                         labels=['spam','ham'])
print pd.DataFrame(data=cm, 
                   columns=pd.MultiIndex(levels=[['Predicted:'],
                                                 ['spam','ham']], 
                                         labels=[[0,0],[0,1]]), 
                   index=pd.MultiIndex(levels=[['Actual:'],
                                               ['spam','ham']], 
                                       labels=[[0,0],[0,1]]))
                                       
positive_class = 'spam'

true_positive = 5.
false_positive = 6.
false_negative = 5.
true_negative = 4.

accuracy = np.round(
                metrics.accuracy_score(y_true=actual_labels,
                                       y_pred=predicted_labels),2)
accuracy_manual = np.round(
                    (true_positive + true_negative) /
                      (true_positive + true_negative +
                       false_negative + false_positive),2)
print 'Accuracy:', accuracy
print 'Manually computed accuracy:', accuracy_manual                                       


precision = np.round(
                metrics.precision_score(y_true=actual_labels,
                                        y_pred=predicted_labels,
                                        pos_label=positive_class),2)
precision_manual = np.round(
                        (true_positive) /
                        (true_positive + false_positive),2)
print 'Precision:', precision
print 'Manually computed precision:', precision_manual


recall = np.round(
            metrics.recall_score(y_true=actual_labels,
                                 y_pred=predicted_labels,
                                 pos_label=positive_class),2)
recall_manual = np.round(
                    (true_positive) /
                    (true_positive + false_negative),2)
print 'Recall:', recall
print 'Manually computed recall:', recall_manual


f1_score = np.round(
                metrics.f1_score(y_true=actual_labels,
                                 y_pred=predicted_labels,
                                 pos_label=positive_class),2) 
f1_score_manual = np.round(
                    (2 * precision * recall) /
                    (precision + recall),2)
print 'F1 score:', f1_score
print 'Manually computed F1 score:', f1_score_manual