def evaluate(self, model, input_value, target_value):
target = MultiLabelBinarizer().fit_transform(target_value)
prediction = model.predict(input_value)
conf = confusion_matrix(target.argmax(axis=1),
prediction.argmax(axis=1))
acc = [accuracy_score(target[i], prediction[i]) for i in range(5)]
pre = precision_score(target, prediction, average=None)
rec = recall_score(target, prediction, average=None)
f1 = f1_score(target, prediction, average=None)
# print(classification_report(target, prediction, target_names=['#GENERAL', '#FEATURE', '#PRICE', '#CAMERA', '#DESIGN#SCREEN'], zero_division=1))
return conf, acc, pre, rec, f1
ground_truth_test = read_conll('test_sentences_groundtruth.txt')
length_ground_truth_test = len(ground_truth_test)
print("Length of all prediction: ", length_ground_truth_test)
words_groundtruth_test = [[c[0] for c in x] for x in ground_truth_test]
groundtruth_label_test = [[c[1] for c in x] for x in ground_truth_test]
ground_truth_train = read_conll('train_sentences_groundtruth.txt')
length_ground_truth_train = len(ground_truth_train)
print("Length of all prediction: ", length_ground_truth_train)
words_groundtruth_train = [[c[0] for c in x] for x in ground_truth_train]
groundtruth_label_train = [[c[1] for c in x] for x in ground_truth_train]
y_true_total = MultiLabelBinarizer().fit_transform(groundtruth_label)
y_pred_total = MultiLabelBinarizer().fit_transform(label_aggregation)
print(
metrics.confusion_matrix(y_true_total.argmax(axis=1),
y_pred_total.argmax(axis=1)))
print(
metrics.classification_report(y_true_total,
y_pred_total,
digits=3,
labels=[0, 1, 2, 3, 4, 5, 6, 7]))
pr_test = y_pred_total
yh = y_true_total
fyh, fpr = score(yh, pr_test)
print("Testing accuracy:" + str(accuracy_score(fyh, fpr)))
print("Testing confusion matrix:")
print(confusion_matrix(fyh, fpr))
