class Wordk2VecSA:
def __init__(self):
print("Start Word2Vec - Sentiment Analysis")
self.sp = Support()
file_model = ROOT_DIR_DATA_EMBEDDING + "SBW-vectors-300-min5.txt"
model_tmp = self.sp.load_vectors_from_csv(file_model)
self.model = Word2Vec.load(model_tmp)
self.data, self.label = self.sp.process_data(filename='bank_message', size_msg=3, clean=True, replace_text=True,
stemmed=None, lemmatize=None, spelling=None)
def baseline(self, num_features=300, fold=5, iteration=3):
for model_type, classifier in wor2vec_model.items():
sum_recall = 0.0
sum_precision = 0.0
sum_f1 = 0.0
sum_accuracy = 0.0
for i in range(0, iteration):
x_train, x_test, y_train, y_test = train_test_split(self.data, self.label, test_size=0.25, random_state=1000)
x_train, y_train = self.sp.balanced_data(x_train, y_train)
x_test, y_test = self.sp.balanced_data(x_test, y_test)
trainDataVecs = self.sp.getAvgFeatureVecs(x_train, self.model, num_features)
testDataVecs = self.sp.getAvgFeatureVecs(x_test, self.model, num_features)
classifier.fit(trainDataVecs, y_train)
predict = classifier.predict(testDataVecs)
# Recall Scores
recall_scores = cross_val_score(classifier, trainDataVecs, y_train, cv=fold, scoring='recall_macro')
sum_recall += recall_scores
# Precision Score
precision_score = cross_val_score(classifier, trainDataVecs, y_train, cv=fold, scoring='precision_weighted')
sum_precision += precision_score
# F1 Score
f1_score = cross_val_score(classifier, trainDataVecs, y_train, cv=fold, scoring='f1_weighted')
sum_f1 += f1_score
# Accuracy Score
accuracy_score = cross_val_score(classifier, trainDataVecs, y_train, cv=fold, scoring='balanced_accuracy')
sum_accuracy += accuracy_score
recall = sum_recall / iteration
precision = sum_precision / iteration
f1 = sum_f1 / iteration
accuracy = sum_accuracy / iteration
self.sp.print_score(model_type, predicted_classes=predict, recall=recall, precision=precision, f1=f1,
accuracy=accuracy, test=y_test)
def baseline(self, model_name, list_classifier, analyzer='word', ngram= None, min_df=3,
max_features=None, fold=5, stop_words=None, iteration=3):
try:
start_time = time.time()
result = []
ngram = (1, 2) if ngram is None else ngram
stopw = set(stopwords.words("spanish")) if stop_words is not None else None
models = ClassifierModels(list_classifier).classifiers
for classifier_name, classifier in models.items():
dict_data = {}
x_train, x_test, y_train, y_test = train_test_split(self.data['msg'], self.data['label'], test_size=0.25,
random_state=1000)
# Se balancean las instancias del training y el sa_test
# training and sa_test are balanced
df_train = pd.DataFrame({'msg': x_train, 'label': y_train})
df_test = pd.DataFrame({'msg': x_test, 'label': y_test})
train = Support.balanced_data(df_train)
x_train = train['msg']
y_train = train['label']
test = Support.balanced_data(df_test)
x_test = test['msg']
y_test = test['label']
vectorizer = CountVectorizer(analyzer=analyzer, lowercase=True, encoding='utf-8', min_df=min_df,
ngram_range=ngram, max_features=max_features, stop_words=stopw)
vectorizer.fit(x_train)
x_train = vectorizer.transform(x_train)
x_test = vectorizer.transform(x_test)
classifier.fit(x_train, y_train)
predict = classifier.predict(x_test)
sum_recall = 0.0
sum_precision = 0.0
sum_f1 = 0.0
sum_accuracy = 0.0
for i in range(0, iteration):
# Recall Scores
recall_scores = cross_val_score(classifier, x_train, y_train, cv=fold, scoring='recall_macro')
sum_recall += recall_scores
# Precision Score
precision_score = cross_val_score(classifier, x_train, y_train, cv=fold, scoring='precision_weighted')
sum_precision += precision_score
# F1 Score
f1_score = cross_val_score(classifier, x_train, y_train, cv=fold, scoring='f1_weighted')
sum_f1 += f1_score
# Accuracy Score
accuracy_score = cross_val_score(classifier, x_train, y_train, cv=fold, scoring='balanced_accuracy')
sum_accuracy += accuracy_score
#Calculated Scores
dict_data['classifier_name'] = classifier_name
recall = sum_recall/iteration
dict_data['recall'] = round(np.mean(recall) * 100, 2)
precision = sum_precision/iteration
dict_data['precision'] = round(np.mean(precision) * 100, 2)
f1 = sum_f1/iteration
dict_data['f1'] = round(np.mean(f1) * 100, 2)
accuracy = sum_accuracy/iteration
dict_data['accuracy'] = round(np.mean(accuracy) * 100, 2)
#Calculated Time processing
t_sec = round(time.time() - start_time)
(t_min, t_sec) = divmod(t_sec, 60)
(t_hour, t_min) = divmod(t_min, 60)
time_processing = '{} hour:{} min:{} sec'.format(t_hour, t_min, t_sec)
dict_data['time_processing'] = time_processing
Support.print_score(dict_data=dict_data)
dict_data['model_name'] = model_name
return result
except Exception as e:
print("\t ERROR baseline BoW: ", e)
class DomainOfWord:
def __init__(self, clean_data=True):
self.sp = Support()
self.data = self.sp.process_data(filename='bank_message',
size_msg=3,
clean_data=clean_data,
replace_text=True,
stemmed=None,
lemmatize=None,
spelling=None)
def baseline(self,
model_name,
list_classifier,
ngram=None,
min_df=3,
max_features=5000,
fold=5,
stop_words=None,
iteration=3):
try:
start_time = time.time()
result = []
ngram = (1, 2) if ngram is None else ngram
stopw = set(
stopwords.words("spanish")) if stop_words is not None else None
models = ClassifierModels(list_classifier).classifiers
for classifier_name, classifier in models.items():
dict_data = {}
x_train, x_test, y_train, y_test = train_test_split(
self.data['msg'],
self.data['label'],
test_size=0.25,
random_state=1000)
# training and sa_test are balanced
df_train = pd.DataFrame({'msg': x_train, 'label': y_train})
df_test = pd.DataFrame({'msg': x_test, 'label': y_test})
train = self.sp.balanced_data(df_train)
x_train = train['msg']
y_train = train['label']
test = self.sp.balanced_data(df_test)
x_test = test['msg']
y_test = test['label']
vec_bow = CountVectorizer(analyzer='word',
lowercase=True,
encoding='utf-8',
ngram_range=ngram,
min_df=min_df,
max_features=max_features,
stop_words=stopw)
vec_tfidf = TfidfVectorizer(analyzer='word',
lowercase=True,
encoding='utf-8',
ngram_range=ngram,
min_df=min_df,
max_features=max_features,
stop_words=stopw)
lexicon = LexiconExtractor()
terminology = TerminologyExtractor()
vec_bow.fit(x_train)
vec_tfidf.fit(x_train)
lexicon.fit(x_train)
terminology.fit(x_train)
pipeline = Pipeline([
(
'feats',
FeatureUnion(
[('bow', vec_bow), ('tfidf', vec_tfidf),
('lexicon', lexicon),
('terminology', terminology)],
# weight components in FeatureUnion
transformer_weights={
'bow': 0.2,
'tfidf': 0.2,
'lexicon': 0.3,
'terminology': 0.3
})),
(classifier_name, classifier) # classifier
])
pipeline.fit(x_train, y_train)
predict = pipeline.predict(x_test)
sum_recall = 0.0
sum_precision = 0.0
sum_f1 = 0.0
sum_accuracy = 0.0
for i in range(0, iteration):
print('Iteration {0} for Cross-Validation'.format(i + 1))
#Recall
recall_scores = cross_val_score(pipeline,
x_train,
y_train,
cv=fold,
scoring='recall_macro',
n_jobs=-1)
sum_recall += recall_scores
#Precision
precision_score = cross_val_score(
pipeline,
x_train,
y_train,
cv=fold,
scoring='precision_weighted',
n_jobs=-1)
sum_precision += precision_score
#F1
f1_score = cross_val_score(pipeline,
x_train,
y_train,
cv=fold,
scoring='f1_weighted',
n_jobs=-1)
sum_f1 += f1_score
#Accuracy
accuracy_score = cross_val_score(
pipeline,
x_train,
y_train,
cv=fold,
scoring='balanced_accuracy',
n_jobs=-1)
sum_accuracy += accuracy_score
# Calculated Scores
dict_data['classifier_name'] = classifier_name
recall = sum_recall / iteration
dict_data['recall'] = round(np.mean(recall) * 100, 2)
precision = sum_precision / iteration
dict_data['precision'] = round(np.mean(precision) * 100, 2)
f1 = sum_f1 / iteration
dict_data['f1'] = round(np.mean(f1) * 100, 2)
accuracy = sum_accuracy / iteration
dict_data['accuracy'] = round(np.mean(accuracy) * 100, 2)
# Calculated Time processing
t_sec = round(time.time() - start_time)
(t_min, t_sec) = divmod(t_sec, 60)
(t_hour, t_min) = divmod(t_min, 60)
time_processing = '{} hour:{} min:{} sec'.format(
t_hour, t_min, t_sec)
dict_data['time_processing'] = time_processing
Support.print_score(dict_data=dict_data)
dict_data['model_name'] = model_name
result.append(dict_data)
return result
except Exception as e:
print("\t ERROR baseline DomainOfWord: ", e)