Classifications Metrics

Classification Metrics

"""
Classification Metrics
Author: Balamurali M
"""

import numpy as np
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.metrics import cohen_kappa_score, classification_report
from sklearn import svm

import warnings
warnings.filterwarnings('ignore')

#Generating matrix with random explanatory and response variables
matr = np.random.randint(2, size=(100, 20))
print (matr.shape)

train_exp = matr[:80, :19]
train_res = matr[:80, 19:]
test_exp = matr[80:, :19]
test_act = matr[80:, 19:]

class SVM1:
    def __init__(self, w1, x1, y1, z1):
        self.w1 = w1
        self.x1 = x1
        self.y1 = y1
        self.z1 = z1 
     
    def SVM_fit(self):
        a1 = svm.SVC()
        return a1.fit(self.w1, self.x1)
        
matr_exp = SVM1(train_exp, train_res, test_exp, test_act)

fit1 = matr_exp.SVM_fit()
predicted1 = fit1.predict(test_exp)
print ('Actual class')
print (test_act)

print ('Predicted class')
print (predicted1)

conf_1 = confusion_matrix(test_act, predicted1)  #confusion Matrix
print (conf_1)    

tneg, fpos, fneg, tpos = confusion_matrix(test_act, predicted1).ravel()
print(tneg, fpos, fneg, tpos) #true negative, false positive, false negative, true positive

acc_1 = accuracy_score(test_act, predicted1)
print (acc_1) #accuracy score