def evaluate(self):
if self.X_development:
self.Y_development_predicted = self.classifier.predict(self.X_development)
if self.X_test:
self.Y_test_predicted = self.classifier.predict(self.X_test)
self.accuracy, self.precision, self.recall, self.f1score = metrics(self.Y_development, self.Y_development_predicted, self.labels)
def classEvaluation(self, Y_test, Y_predicted, labels):
print("\n~~~ Class Evaluation ~~~ \n")
print("Class \t Precision \t Recall \t F-score")
for label in labels:
accuracy, precision, recall, f1score = metrics(
Y_test, Y_predicted, [label])
print('{} \t {} \t\t {} \t\t {}'.format(label, round(precision, 3),
round(recall, 3),
round(f1score, 3)))
def evaluate(self):
self.Y_development_predicted = self.model.predict(self.X_development)
self.Y_development_predicted = np.argmax(self.Y_development_predicted,
axis=1)
self.Y_development_predicted = [
self.labels_dict_rev[int(i)]
for i in list(self.Y_development_predicted)
]
self.Y_development = np.argmax(self.Y_development, axis=1)
self.Y_development = [
self.labels_dict_rev[int(i)] for i in list(self.Y_development)
]
self.accuracy, self.precision, self.recall, self.f1score = metrics(
self.Y_development, self.Y_development_predicted, self.labels)