def predict_function():
x_list = []
line_list = []
line_dict = {}
predict_doc = joblib.load('logreg.pkl')
feature_doc = joblib.load("word_vec.pkl")
y_train, x_train = get_feature()
line = "bad bad good good"
line_list = line.split()
for line in x_train:
for key in line:
line_dict[key] = 0
line_dict.update(dict(Counter(line_list)))
for a in sorted(line_dict.items(), key = lambda x:x[1]):
print(a)
x_list.append(line_dict)
print(x_list)
exit()
X = DictVectorizer().fit_transform(x_list)
pred = predict_doc.predict(X)
prob = predict_doc.predict_proba(X)
for pred, prob in zip(pred,prob):
print(pred, prob)
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