last-branch

skrypt.py

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+#!/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("test-A/in.tsv")
+read_tsv = csv.reader(tsv_file)
+listatesting = []
+for line in read_tsv:
+	listatesting.append(line[0])
+
+lista = []
+
+for line in sys.stdin:
+	lista.append(line)
+	
+vectorizer = CountVectorizer()
+X = vectorizer.fit_transform(lista)
+Y = loadtxt("train/expected.tsv")
+seed = 1
+param = {
+'objective':'binary:logistic'}
+
+X_train = X
+X_test = Y 
+Y_train =  vectorizer.fit_transform(listatesting)
+model = XGBClassifier()
+model.fit(X_train, X_test)
+y_pred = model.predict_proba(X_train)
+predictions = [value for value in y_pred]
+for a in predictions:
+	print(a[0])