def test_BayesianClassifier():
"""
Trains Bayesian Classifier on test data and then tests it
"""
train_X, train_y = process_train_data("train.csv")
classifier = BayesianClassifier()
classifier.fit(train_X, train_y)
test_data = pd \
.read_csv("test.csv", encoding="utf8") \
.drop(labels=["id", "Unnamed: 0"], axis=1)
test_X = test_data.drop("label", axis=1)
test_y = test_data.drop("tweet", axis=1)
print("model score: ", classifier.score(test_X, test_y) * 100, "%")
for index, i in data[['text']].iterrows():
t = i['text'].lower().translate(
str.maketrans('', '', string.punctuation)).strip().split()
for j in t:
if j not in stop_words:
lst_for_rep.append(j)
data.at[index, 'text'] = lst_for_rep
lst_for_rep = []
return data
def read_stop_words():
"""
Reads file and returns list with words from file.
"""
lst = []
with open('authors/stop_words.txt', 'r') as file:
reader = csv.reader(file)
for row in reader:
lst += row
return lst
if __name__ == "__main__":
train = process_data("authors/train.csv")
test = process_data("authors/test.csv")
classifier = BayesianClassifier()
classifier.fit(train)
print(f"Model score: {classifier.score(test)}%")
