#!/usr/bin/env python
import numpy as np
import sys
import csv
from sklearn.feature_extraction.text import CountVectorizer
from numpy import loadtxt
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
tsv_file = open("train/in.tsv")
tsv_file3 = open("dev-0/in.tsv")
tsv_file2 = open("train/expected.tsv")
read_tsv = csv.reader(tsv_file)
read_tsv2 = csv.reader(tsv_file2)
listatesting = []
listatesting2 = []
listatesting = list(tsv_file)
listatesting3 = []
listatesting3 = list(tsv_file3)
for line2 in read_tsv2:
	listatesting2.append(line2)

lista = []


vectorizer = CountVectorizer()
seed = 7
X = vectorizer.fit_transform(listatesting)
Y = np.ravel(listatesting2)
X_train, y_train, x_test, y_test = train_test_split(X,Y, test_size=0.33,random_state=seed)
seed = 7
param = {
'objective':'binary:logistic'}
model = XGBClassifier()
model.fit(X_train, x_test)
y_pred = model.predict_proba(y_train)
Z_train = vectorizer.transform(listatesting3)
y_pred2 = model.predict_proba(Z_train)
predictions = [value for value in y_pred2]
for a in predictions:
	print(1-a[0])