sharifamit / semi-supervised-phishing-detection-gan Goto Github PK

from src.model import *
import numpy as np
from sklearn.metrics import classification_report

discriminator = define_discriminator()
#create the generator
discriminator.load_weights('PhishGan_copy\dmodel_000200.h5')

data = np.load('phishing.npz')
#result = discriminator.evaluate(data['X_test'])

y_pred = discriminator.predict(data['X_test'], batch_size=64, verbose=1)
y_pred_bool = np.argmax(y_pred[1], axis=1)
y_test_arg=np.argmax(data['y_test'],axis=1)
print(classification_report(y_test_arg, y_pred_bool))

              precision    recall  f1-score   support

           0       0.50      1.00      0.67      5000
           1       1.00      0.00      0.00      5000

    accuracy                           0.50     10000
   macro avg       0.75      0.50      0.33     10000
weighted avg       0.75      0.50      0.33     10000