CrfLoss(optimizer_crf))
model_crf.eval()
model_trans.eval()
labels_predict, transitions = predict_crf(data_batch(
idxs_test, labels_test, posind_test, 1),
model_crf,
entity=True,
rel=False)
labels_test_map = [label_map[item] for sub in labels_test for item in sub]
labels_predict_map = [
label_map[t.item()] for sub in labels_predict for t in sub
]
print("Training epoch: %d" % epoch)
print(eva.f1_score(labels_test_map, labels_predict_map))
report = eva.classification_report(labels_test_map, labels_predict_map)
print(report)
print(loss)
print(labels_test_map)
print(labels_predict_map)
print(transitions)
f_out.write("Training epoch: " + str(epoch) + "\n")
f_out.write("performance on entity extraction:" + "\n")
f_out.write("precision: " +
str(eva.precision_score(labels_test_map, labels_predict_map)))
f_out.write("\t" + "recall: " +
str(eva.recall_score(labels_test_map, labels_predict_map)))
f_out.write("\t" + "f1: " +
str(eva.f1_score(labels_test_map, labels_predict_map)))
f_out.write("\n" + report)
def reviewsAttributeData(reviews):
# reviews_withoutStop = extract.reviewsWithoutStopWords(reviews)
train_reviews = reviews[0:175]
test_reviews = reviews[175:len(reviews)]
# test_reviews = [('best laptop ever', 'LAPTOP#GENERAL')]
X_train = [extract.review_features_romanian(text, labelType.Label.attribute) for (text, label) in train_reviews]
Y_train = []
for i in range(len(train_reviews)):
features_length = len(X_train[i])
(text, label) = train_reviews[i]
y_train = []
for i in range(features_length):
y_train.append(label)
Y_train.append(y_train)
# print(len(X_train[0]))
# print(X_train[0])
# print(X_train[1])
# print(X_train[2])
# print(len(Y_train[0]))
# print(Y_train[0])
X_test = [extract.review_features_romanian(text, labelType.Label.attribute) for (text, label) in test_reviews]
Y_test = []
for i in range(len(test_reviews)):
features_length = len(X_test[i])
(text, label) = test_reviews[i]
y_test = []
for i in range(features_length):
y_test.append(label)
Y_test.append(y_test)
trainer = pycrfsuite.Trainer(verbose=False)
for xseq, yseq in zip(X_train, Y_train):
trainer.append(xseq, yseq)
trainer.set_params({
'c1': 1.0, # coefficient for L1 penalty
'c2': 1e-3, # coefficient for L2 penalty
'max_iterations': 50, # stop earlier
# include transitions that are possible, but not observed
'feature.possible_transitions': True
})
trainer.train('Laptops.crfsuite')
tagger = pycrfsuite.Tagger()
tagger.open('Laptops.crfsuite')
# print("Predicted:", ' '.join(tagger.tag(extract.review_features_romanian('nice keyboard', labelType.Label.attribute))))
# print("Correct1: ", ' '.join(extract.review_labels(('este usor', 'Laptop#General'))))
Y_pred = [tagger.tag(xseq) for xseq in X_test]
Y_test_final = []
for y_test in Y_test:
Y_test_final.append(y_test[0])
Y_pred_final = []
for y_pred in Y_pred:
Y_pred_final.append(y_pred[0])
print(Y_test_final)
print(Y_pred_final)
# print(len(set(Y_test_final)))
# print(len(set(Y_pred_final)))
print(evaluate.classification_report(Y_test_final, Y_pred_final))