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Avaliacao_Performance_Clasificador.py
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Avaliacao_Performance_Clasificador.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 24 20:29:33 2018
@author: abinaya
"""
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import classification_report
def avaliacao_PerformanceC(df_train_class, predicted_train, predicted_prob_train, df_test_class, predicted_test, predicted_prob_test, roc_y_n):
### Confusion Matrix
confusion_matrix_train = confusion_matrix(df_train_class, predicted_train)
confusion_matrix_test = confusion_matrix(df_test_class, predicted_test)
print ("\nTraining Confusion Matrix:\n ", confusion_matrix_train)
print ("\nTesting Confusion Matrix:\n ", confusion_matrix_test)
### Accuracy score
score_train = accuracy_score(df_train_class, predicted_train)
score_test = accuracy_score(df_test_class, predicted_test)
print ("\nTraining Accuracy Score: ", score_train)
print ("\nTesting Accuracy Score: ", score_test)
### Precision, Recall
precision_train = precision_score(df_train_class, predicted_train)
precision_test = precision_score(df_test_class, predicted_test)
print ("\nTraining Precision: ", precision_train)
print ("\nTesting Precision: ", precision_test)
recall_train = recall_score(df_train_class, predicted_train)
recall_test = recall_score(df_test_class, predicted_test)
print ("\nTraining Recall: ", recall_train)
print ("\nTesting Recall: ", recall_test)
### Classification Report
print ("\nTrain Classification Report: \n",classification_report(df_train_class, predicted_train))
print ("\nTest Classification Report: \n",classification_report(df_test_class, predicted_test))
### F1 Score
f1score_train = f1_score(df_train_class, predicted_train)#, average='weighted')
f1score_test = f1_score(df_test_class, predicted_test)#, average='weighted')
print ("\nTraining F1score: ", f1score_train)
print ("\nTesting F1score: ", f1score_test)
f1score_train = f1_score(df_train_class, predicted_train, average='weighted')
f1score_test = f1_score(df_test_class, predicted_test, average='weighted')
print ("\nTraining Weigted F1score: ", f1score_train)
print ("\nTesting Weighted F1score: ", f1score_test)
### ROC-AUC
if roc_y_n == 'y':
fpr, tpr, threshold = roc_curve(df_train_class, predicted_prob_train[:,1])
roc_auc_train = auc(fpr, tpr)
print ("\nTraining AUC for ROC: ",roc_auc_train)
plt.figure()
plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc_train)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.legend(loc = 'lower right')
plt.title('Training - Receiver Operating Characteristic')
fpr, tpr, threshold = roc_curve(df_test_class, predicted_prob_test[:,1])
roc_auc_test = auc(fpr, tpr)
print ("\nTesting AUC for ROC: ",roc_auc_test)
plt.figure()
plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc_test)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.legend(loc = 'lower right')
plt.title('Testing - Receiver Operating Characteristic')