#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer
from sklearn.metrics import accuracy_score


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unxz train/in.tsv.xz


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f = open("train/in.tsv", "r", encoding='utf-8')
lines = f.readlines()
x_train = pd.DataFrame(lines)
x_train.rename(columns = {0 : 'text'}, inplace = True)


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y_train = pd.read_csv('expected.tsv', sep='\t', names=['male'], encoding='utf-8')


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tfidf_vectorizer=TfidfVectorizer(use_idf=True, max_df=0.95)
tfidf_vectorizer.fit_transform(x_train['text'].values)
x_train_prepared = tfidf_vectorizer.transform(x_train['text'].values)


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model = LogisticRegression()
model.fit(x_train_prepared, y_train)


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f = open("dev-0/in.tsv", "r", encoding='utf-8')
lines_dev_0 = f.readlines()
x_test_dev_0 = pd.DataFrame(lines_dev_0)


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x_test_dev_0.rename(columns = {0 : 'text'}, inplace = True)
x_test_dev_0_prepared = tfidf_vectorizer.transform(x_test_dev_0['text'].values)


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y_dev_0_expected = pd.read_csv('expected.tsv', sep='\t', names=['male'], encoding='utf-8')


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y_dev_0_predicted = model.predict(x_test_dev_0_prepared)

file = open('out.tsv', 'w')
for y in y_dev_0_predicted:
    file.write(f'{y}\n')
file.close()

accuracy_dev_0 = accuracy_score(y_expected, y_predicted)
accuracy_dev_0


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f = open("dev-1/in.tsv", "r", encoding='utf-8')
lines_dev_1 = f.readlines()
f.close()


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x_test_dev_1 = pd.DataFrame(lines_dev_1)
x_test_dev_1.rename(columns = {0 : 'text'}, inplace = True)
x_test_dev_1_prepared = tfidf_vectorizer.transform(x_test_dev_1['text'].values)


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y_dev_1_expected = pd.read_csv('expected.tsv', sep='\t', names=['male'], encoding='utf-8')


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y_dev_1_predicted = model.predict(x_test_dev_1_prepared)

file = open('out.tsv', 'w')
for y in y_dev_1_predicted:
    file.write(f'{y}\n')
file.close()

accuracy_dev_1 = accuracy_score(y_dev_1_expected, y_dev_1_predicted)
accuracy_dev_1


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f = open("test-A/in.tsv", "r", encoding='utf-8')
lines_testing = f.readlines()
f.close()


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x_testing = pd.DataFrame(lines_testing)
x_testing.rename(columns = {0 : 'text'}, inplace = True)
x_testing_prepared = tfidf_vectorizer.transform(x_testing['text'].values)


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y_testing_predicted = model.predict(x_testing_prepared)


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file = open('out.tsv', 'w')
for y in y_testing_predicted:
    file.write(f'{y}\n')
file.close()