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WSVM_metrics.py
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WSVM_metrics.py
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import seaborn as sn
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
'''
############################################################################################################
METRICS
############################################################################################################
'''
def perf_measure(y_actual, y_pred):
'''
Returns Recognition Accuracy, F-Measure, Precision, Recall and Confusion Matrix
Confusion Matrix genereated below is by considering all target/known
classes as positive samples and Unknown classes as Negative samples.
'''
import numpy as np
TP = 0
FP = 0
TN = 0
FN = 0
for i in range(len(y_pred)):
if y_actual[i]==99 and y_actual[i]==y_pred[i]:
TN += 1
elif y_actual[i]==99 and y_actual[i]!=y_pred[i]:
FN +=1
elif y_actual[i]!=99 and y_actual[i]==y_pred[i]:
TP +=1
elif y_actual[i]!=99 and y_actual[i]!=y_pred[i]:
FP += 1
cm=np.zeros((2,2))
cm[0][0] = TP
cm[0][1] = FP
cm[1][0] = FN
cm[1][1] = TN
recognition_accuracy = (TP+TN)/(TP+TN+FP+FN)
np.set_printoptions(suppress=True)
precision = TP/(TP+FP)
recall = TP/(TP+FN)
fmeasure = 2*precision*recall/(precision+recall)
return(recognition_accuracy,precision,recall,fmeasure,cm)
df = pd.read_csv('output.csv',header= None)
ytest = df.iloc[:,0]
pred = df.iloc[:,1]
recognition_accuracy,precision,recall,fmeasure,cm = perf_measure(ytest, pred)
print(f'Recognition Accuracy: {recognition_accuracy}, F-Measure: {fmeasure}, Precision: {precision}, Recall: {precision}')
"""
###########################################################################################################################
CONFUSION MATRIX
###########################################################################################################################
"""
cm = cm.astype(int)
plt.figure(figsize = (10,10))
sn.set(font_scale=1.4)
mat_names = ['True Positive', 'False Positive', 'False Negative', 'True Negative']
mat_vals = ['{0:0.0f}'.format(value) for value in cm.flatten()]
labels = ["{}\n{}".format(v1,v2) for (v1,v2) in zip(mat_names, mat_vals)]
labels = np.asarray(labels).reshape(2,2)
sn.heatmap(cm,cmap ='Blues', annot=labels, xticklabels = ['Positive','Negative'],
yticklabels = ['Positive','Negative'], square = True,fmt='')
plt.ylabel("Predicted")
plt.xlabel("Actual")
plt.savefig('WSVM_confusion_matrix.png', dpi= 300)