import pandas as pd
import numpy as np
import gzip
from sklearn.pipeline import make_pipeline
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import metrics

# Read data
dev = pd.read_table('dev-0/in.tsv', error_bad_lines=False, header=None)
test = pd.read_table('test-A/in.tsv', error_bad_lines=False, header=None)
test_expected = pd.read_table(
    'dev-0/expected.tsv', error_bad_lines=False, header=None)

X_train = []
y_train = []

with gzip.open('train/train.tsv.gz', 'r') as f:
    for l in f:
        line = l.decode('UTF-8').replace("\n", "").split("\t")
        y_train.append(int(line[0]))
        X_train.append(str(line[1:]))

# Convert to unified types
X_train = np.asarray(X_train)
y_train = np.asarray(y_train)

X_dev = dev[0].values
X_test = test[0].values

# Create model
model = make_pipeline(TfidfVectorizer(), MultinomialNB())
model.fit(X_train, y_train)

# Predict
dev_p = model.predict(X_dev)
test_p = model.predict(X_test)

# Accuracy
score = metrics.accuracy_score(test_expected, dev_p)
print("Accuracy:   %0.3f" % score)

# Save to files
dev_p.tofile('./dev-0/out.tsv', sep='\n')
test_p.tofile('./test-A/out.tsv', sep='\n')