# encoding=utf8
from gensim.models import Word2Vec
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score
import sys
from sklearn_pandas import DataFrameMapper
import util
import pandas as pd

reload(sys)
sys.setdefaultencoding('utf-8')

features = \
    [
        "id", "major", "age", "gender",
        "isenglish", "isjunior", "isbachelor", "ismaster", "isintern",
        "total_previous_job",
        "last_salary", "last_size", "last_position_name", "last_industry", "last_type", "last_type1", "last_department",
        "last_start_year", "last_start_month", "last_end_year", "last_end_month", "last_interval_month",
        "third_salary", "third_size", "third_position_name", "third_industry", "third_type", "third_type1",
        "third_department",
        "third_start_year", "third_start_month", "third_end_year", "third_end_month", "third_interval_month",