#import numpy as np
import gzip
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn import metrics

#df = pd.read_csv('sport-text-classification-ball-ISI-public/train/train.tsv.gz', compression='gzip', header=None, sep='\t', error_bad_lines=False)
train_X = [] 
train_y = []
with gzip.open('train/train.tsv.gz','r') as fin:        
    for line in fin:
        sline = line.decode('UTF-8').replace("\n", "").split("\t")
        train_y.append(sline[0])
        train_X.append(''.join(sline[1:]))

test_X = []
with open('dev-0/in.tsv','r') as test_in_file:
    for line in test_in_file:
        test_X.append(line.rstrip('\n'))

test_y = []
with open('dev-0/expected.tsv','r') as test_expected_file:
    for line in test_expected_file:
        test_y.append(line.rstrip('\n'))

vectorizer = TfidfVectorizer(lowercase = True)
X_train_tf = vectorizer.fit_transform(train_X)
print("n_samples: %d, n_features: %d" % X_train_tf.shape)

X_test_tf = vectorizer.transform(test_X)
print("n_samples: %d, n_features: %d" % X_test_tf.shape)

naive_bayes_classifier = MultinomialNB()
naive_bayes_classifier.fit(X_train_tf, train_y)

y_pred = naive_bayes_classifier.predict(X_test_tf)

score1 = metrics.accuracy_score(test_y, y_pred)
print("accuracy:   %0.3f" % score1)

print(metrics.classification_report(test_y, y_pred,
                                            target_names=['1', '0']))

print("confusion matrix:")
print(metrics.confusion_matrix(test_y, y_pred))

print('------------------------------')

file = open('dev-0/out.tsv',"w") 
for i in y_pred:
    file.writelines("{}\n".format(i))    
file.close() 

val_X = []
with open('test-A/in.tsv','r') as test_in_file:
    for line in test_in_file:
        val_X.append(line.rstrip('\n'))
        
X_val_tf = vectorizer.transform(val_X)
print("n_samples: %d, n_features: %d" % X_val_tf.shape)

val_y_pred = naive_bayes_classifier.predict(X_val_tf)

file = open('test-A/out.tsv',"w") 
for i in val_y_pred:
    file.writelines("{}\n".format(i))    
file.close()