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import numpy as np
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
def evaluate_model(model, test_gen, test_triples, batch_size):
# model_name = os.path.basename(model_file)
# model = load_model(model_file)
# print("=== Evaluating model: {:s} ===".format(model_name))
print("=== Evaluating model")
ytrue, ypred = [], []
num_test_steps = len(test_triples) // batch_size
for i in range(num_test_steps):
# (X1, X2), Y = test_gen.next()
(X1, X2), Y = next(test_gen)
Y_ = model.predict([X1, X2])
ytrue.extend(np.argmax(Y, axis=1).tolist())
ypred.extend(np.argmax(Y_, axis=1).tolist())
accuracy = accuracy_score(ytrue, ypred)
print("\nAccuracy: {:.3f}".format(accuracy))
print("\nConfusion Matrix")
print(confusion_matrix(ytrue, ypred))
print("\nClassification Report")
print(classification_report(ytrue, ypred))
return accuracy
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