def test(self):
corpus: CategorizedCorpus = DataFetcher.load_corpus(
NLPData.AIVIVN2019_SA_SAMPLE)
# corpus: CategorizedCorpus = DataFetcher.load_corpus(NLPData.AIVIVN2019_SA)
params = {
"vectorizer": CountVectorizer(ngram_range=(1, 2),
max_features=4000),
"svc": SVC(kernel='linear', C=0.3)
}
classifier = TextClassifier(estimator=TEXT_CLASSIFIER_ESTIMATOR.SVC,
**params)
model_trainer = ModelTrainer(classifier, corpus)
tmp_model_folder = mkdtemp()
def negative_f1_score(y_true, y_pred):
score_class_0, score_class_1 = f1_score(y_true,
y_pred,
average=None)
return score_class_1
def macro_f1_score(y_true, y_pred):
return f1_score(y_true, y_pred, average='macro')
score = model_trainer.train(tmp_model_folder,
scoring=negative_f1_score)
print(score)
classifier = TextClassifier.load(tmp_model_folder)
sentence = Sentence('tuyệt vời')
classifier.predict(sentence)
shutil.rmtree(tmp_model_folder)
print(sentence)
def test_fasttext(self):
corpus: CategorizedCorpus = DataFetcher.load_corpus(
NLPData.AIVIVN2019_SA_SAMPLE)
params = {"lr": 0.01, "epoch": 20, "wordNgrams": 3, "dim": 20}
classifier = TextClassifier(
estimator=TEXT_CLASSIFIER_ESTIMATOR.FAST_TEXT, **params)
model_trainer = ModelTrainer(classifier, corpus)
tmp_model_folder = mkdtemp()
def macro_f1_score(y_true, y_pred):
return f1_score(y_true, y_pred, average='macro')
score = model_trainer.train(tmp_model_folder, scoring=macro_f1_score)
print(score)
classifier = TextClassifier.load(tmp_model_folder)
sentence = Sentence('tuyệt vời')
classifier.predict(sentence)
shutil.rmtree(tmp_model_folder)
print(sentence)
def my_run(estimator__C, features__lower_pipe__tfidf__ngram_range,
features__with_tone_char__ngram_range,
features__remove_tone__tfidf__ngram_range):
params = locals().copy()
start = time.time()
print(params)
corpus: CategorizedCorpus = DataFetcher.load_corpus(NLPData.VLSP2016_SA)
pipeline = Pipeline(
steps=[(
'features',
FeatureUnion([(
'lower_pipe',
Pipeline([('lower', Lowercase()),
('tfidf',
TfidfVectorizer(
ngram_range=(1, 4), norm='l2', min_df=2))])),
('with_tone_char',
TfidfVectorizer(ngram_range=(1, 6),
norm='l2',
min_df=2,
analyzer='char')),
('remove_tone',
Pipeline([('remove_tone',
RemoveTone()), ('lower', Lowercase()),
('tfidf',
TfidfVectorizer(ngram_range=(1, 4),
norm='l2',
min_df=2))])
), ('emoticons', CountEmoticons())])),
('estimator',
SVC(kernel='linear', C=0.2175, class_weight=None, verbose=True)
)])
pipeline.set_params(**params)
classifier = TextClassifier(estimator=TEXT_CLASSIFIER_ESTIMATOR.PIPELINE,
pipeline=pipeline)
model_trainer = ModelTrainer(classifier, corpus)
tmp_model_folder = mkdtemp()
def negative_f1_score(y_true, y_pred):
score_class_0, score_class_1, score_class_2 = f1_score(y_true,
y_pred,
average=None)
return score_class_1
def macro_f1_score(y_true, y_pred):
return f1_score(y_true, y_pred, average='macro')
score = model_trainer.train(tmp_model_folder, scoring=negative_f1_score)
ex.log_scalar('dev_score', score['dev_score'])
ex.log_scalar('test_score', score['test_score'])
print(time.time() - start)
return score['dev_score']
def test(self):
corpus: CategorizedCorpus = DataFetcher.load_corpus(NLPData.UTS2017_BANK_SA_SAMPLE)
# corpus: CategorizedCorpus = DataFetcher.load_corpus(NLPData.AIVIVN2019_SA)
pipeline = Pipeline(
steps=[('features', CountVectorizer(ngram_range=(1, 2), max_features=4000)),
('estimator', OneVsRestClassifier(SVC(kernel='linear', C=0.3)))]
)
classifier = TextClassifier(estimator=TEXT_CLASSIFIER_ESTIMATOR.PIPELINE, pipeline=pipeline, multilabel=True)
model_trainer = ModelTrainer(classifier, corpus)
tmp_model_folder = mkdtemp()
def macro_f1_score(y_true, y_pred):
return f1_score(y_true, y_pred, average='macro')
score = model_trainer.train(tmp_model_folder, scoring=macro_f1_score)
print(score)
classifier = TextClassifier.load(tmp_model_folder)
sentence = Sentence('Dịch vụ tiện dụng quá')
classifier.predict(sentence)
print(sentence)
shutil.rmtree(tmp_model_folder)
def sentiment(text):
global classifier
if not classifier:
if os.path.exists(model_path):
classifier = TextClassifier.load(model_path)
else:
logger.error(
f"Could not load model at {model_path}.\n"
f"Download model with \"underthesea download {UTSModel.sa_bank.value}\"."
)
sys.exit(1)
sentence = Sentence(text)
classifier.predict(sentence)
labels = sentence.labels
return [label.value for label in labels]
def classify(X):
global classifier
if not classifier:
if os.path.exists(model_path):
classifier = TextClassifier.load(model_path)
else:
logger.error(
f"Could not load model at {model_path}.\n"
f"Download model with \"underthesea download {UTSModel.tc_general.value}\".")
sys.exit(1)
sentence = Sentence(X)
classifier.predict(sentence)
labels = sentence.labels
return labels
def sentiment(text):
global classifier
if not classifier:
if os.path.exists(model_path):
classifier = TextClassifier.load(model_path)
else:
logger.error(
f"Could not load model at {model_path}.\n"
f"Download model with \"underthesea download {UTSModel.sa_general.value}\"."
)
sys.exit(1)
sentence = Sentence(text)
classifier.predict(sentence)
label = sentence.labels[0]
if label == "1":
label = "negative"
if label == "0":
label = "positive"
return label
def run(estimator, features):
corpus: CategorizedCorpus = DataFetcher.load_corpus(NLPData.AIVIVN2019_SA)
pipeline = Pipeline(
steps=[(
'features',
FeatureUnion([(
'lower_tfidf',
Pipeline([('lower', Lowercase()),
('tfidf',
TfidfVectorizer(
ngram_range=(1, 4), norm='l2', min_df=2))])),
('with_tone_char',
TfidfVectorizer(ngram_range=(1, 6),
norm='l2',
min_df=2,
analyzer='char')),
('remove_tone',
Pipeline([('remove_tone',
RemoveTone()), ('lower', Lowercase()),
('tfidf',
TfidfVectorizer(ngram_range=(1, 4),
norm='l2',
min_df=2))]))])),
('estimator',
SVC(kernel='linear', C=0.2175, class_weight=None, verbose=True)
)])
classifier = TextClassifier(estimator=TEXT_CLASSIFIER_ESTIMATOR.PIPELINE,
pipeline=pipeline)
model_trainer = ModelTrainer(classifier, corpus)
tmp_model_folder = mkdtemp()
def negative_f1_score(y_true, y_pred):
score_class_0, score_class_1 = f1_score(y_true, y_pred, average=None)
return score_class_1
def macro_f1_score(y_true, y_pred):
return f1_score(y_true, y_pred, average='macro')
score = model_trainer.train(tmp_model_folder, scoring=negative_f1_score)
ex.log_scalar('dev_score', score['dev_score'])
ex.log_scalar('test_score', score['test_score'])
return score['test_score']
def my_run(estimator__C, features__lower_pipe__tfidf__max_features,
features__lower_pipe__tfidf__ngram_range,
features__with_tone_char__ngram_range,
features__remove_tone__tfidf__ngram_range):
params = locals().copy()
start = time.time()
print(params)
corpus: CategorizedCorpus = DataFetcher.load_corpus(
NLPData.UTS2017_BANK_TC)
pipeline = Pipeline(steps=[(
'features',
FeatureUnion([('lower_pipe',
Pipeline([('lower', Lowercase()),
('tfidf',
TfidfVectorizer(norm='l2', min_df=2))])),
('with_tone_char',
TfidfVectorizer(norm='l2', min_df=2, analyzer='char')),
('remove_tone',
Pipeline([('remove_tone',
RemoveTone()), ('lower', Lowercase()),
('tfidf', TfidfVectorizer(norm='l2', min_df=2)
)])), ('emoticons', CountEmoticons())])
), ('estimator', SVC(kernel='linear', class_weight=None, verbose=True))])
pipeline.set_params(**params)
classifier = TextClassifier(estimator=TEXT_CLASSIFIER_ESTIMATOR.PIPELINE,
pipeline=pipeline)
model_trainer = ModelTrainer(classifier, corpus)
tmp_model_folder = "tmp/tmp_model"
def micro_f1_score(y_true, y_pred):
return f1_score(y_true, y_pred, average='micro')
score = model_trainer.train(tmp_model_folder, scoring=micro_f1_score)
tmp_files = listdir(tmp_model_folder)
for file in tmp_files:
if "gitignore" in file:
continue
os.remove(f"{tmp_model_folder}/{file}")
ex.log_scalar('dev_score', score['dev_score'])
ex.log_scalar('test_score', score['test_score'])
print(f"Time: {round(time.time() - start, 2)} s")
return score['dev_score']
def my_run(features__max_df, features__ngram_range):
params = locals().copy()
start = time.time()
print(params)
from languageflow.data_fetcher import DataFetcher
corpus: CategorizedCorpus = DataFetcher.load_corpus(NLPData.VNTC)
pipeline = Pipeline(steps=[('features',
TfidfVectorizer()), ('estimator',
LinearSVC())])
pipeline.set_params(**params)
classifier = TextClassifier(estimator=TEXT_CLASSIFIER_ESTIMATOR.PIPELINE,
pipeline=pipeline)
model_trainer = ModelTrainer(classifier, corpus)
tmp_model_folder = mkdtemp()
def micro_f1_score(y_true, y_pred):
return f1_score(y_true, y_pred, average='micro')
score = model_trainer.train(tmp_model_folder, scoring=micro_f1_score)
ex.log_scalar('dev_score', score['dev_score'])
ex.log_scalar('test_score', score['test_score'])
print(time.time() - start)
return score['dev_score']
from languageflow.data import CategorizedCorpus
from languageflow.data_fetcher import DataFetcher, NLPData
from languageflow.models.text_classifier import TextClassifier, TEXT_CLASSIFIER_ESTIMATOR
from languageflow.trainers.model_trainer import ModelTrainer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import f1_score
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
corpus: CategorizedCorpus = DataFetcher.load_corpus(NLPData.AIVIVN2019_SA)
pipeline = Pipeline(
steps=[('features', CountVectorizer(ngram_range=(
1, 2), max_features=4000)), ('estimator',
SVC(kernel='linear', C=0.3))])
classifier = TextClassifier(estimator=TEXT_CLASSIFIER_ESTIMATOR.PIPELINE,
pipeline=pipeline)
model_trainer = ModelTrainer(classifier, corpus)
tmp_model_folder = mkdtemp()
def negative_f1_score(y_true, y_pred):
score_class_0, score_class_1 = f1_score(y_true, y_pred, average=None)
return score_class_1
def macro_f1_score(y_true, y_pred):
return f1_score(y_true, y_pred, average='macro')
score = model_trainer.train(tmp_model_folder, scoring=negative_f1_score)
print(score)
from languageflow.data import Sentence
from languageflow.data_fetcher import NLPData, DataFetcher
from languageflow.models.text_classifier import TextClassifier
model_folder = "tmp/sentiment_svm_uts2017_bank_sa"
print(f"Load model from {model_folder}")
classifier = TextClassifier.load(model_folder)
print(f"Model is loaded.")
def predict(text):
print(f"\nText: {text}")
sentence = Sentence(text)
classifier.predict(sentence)
labels = sentence.labels
print(f"Labels: {labels}")
corpus = DataFetcher.load_corpus(NLPData.UTS2017_BANK_SA)
predict(
'Bạn nên làm thẻ credit, đừng làm debit. Mình dùng thẻ debit của vcb, tết vừa rồi bị hack mất 28 triệu trong tài khoản mà đến giờ vcb đã giải quyết cho mình đâu. Bực mình!'
)
