import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline
from sklearn.feature_extraction.text import TfidfVectorizer

with open("train/in.tsv") as f:
    x_train = f.readlines()

with open("train/expected.tsv") as f:
    y_train = f.readlines()

y_train = LabelEncoder().fit_transform(y_train)

pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())

model = pipeline.fit(x_train, y_train)

with open("dev-0/in.tsv") as f:
    x_dev = f.readlines()

prediction = model.predict(x_dev)
np.savetxt("dev-0/out.tsv", prediction, fmt='%d')

with open("test-A/in.tsv") as f:
    x_test = f.readlines()

prediction = model.predict(x_test)
np.savetxt("test-A/out.tsv", prediction, fmt='%d')