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

import codecs
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.svm import SVC

def get_data(menu_file):
    data = []
    for i in xrange(2*len(menu_file)/3):
        line = menu_file[i].split("\t")
        if len(line) <= 3: continue
        num, kitchen, menu = line[0], line[1], line[2:]
        if len(num) > 6 or len(menu) <= 1 or kitchen == "" or len(kitchen.split("/")) > 1: continue
        data.append((kitchen, u' '.join(menu)))
    return data

def suggest(menu, clf, vectorizer):
    return clf.predict(vectorizer.transform([menu]).toarray())[0]

def test(menu_file, clf, vectorizer):
    for i in xrange(2*len(menu_file)/3, len(menu_file)):
        line = menu_file[i].split("\t")
        if len(line) <= 3: continue
        num, kitchen, menu = line[0], line[1], line[2:]
        if len(num) > 6 or len(menu) <= 1 or kitchen == "" or len(kitchen.split("/")) > 1: continue
        print u"Кухня №%s - %s (на самом деле - %s)" % (num, suggest(u' '.join(menu), clf, vectorizer), kitchen)
        

def main():
    menu_file = codecs.open('menu.txt', 'r', 'utf-8').readlines()
    vectorizer = CountVectorizer(min_df=1)

    corpus = get_data(menu_file)
    y = [a for a,b in corpus]
    X = vectorizer.fit_transform((b for a,b in corpus)).toarray()
    clf = SVC()
    clf.fit(X, y)
    test(menu_file, clf, vectorizer)

main()