# -*- coding: utf-8 -*- 
 
import numpy 
from sklearn import metrics 
from sklearn.svm import LinearSVC 
from sklearn.naive_bayes import MultinomialNB 
from sklearn import linear_model 
from sklearn.datasets import load_iris 
from sklearn.cross_validation import train_test_split 
from sklearn.preprocessing import OneHotEncoder, StandardScaler 
from sklearn import cross_validation 
from sklearn import preprocessing 
import scipy as sp
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import SelectKBest ,chi2
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
#import iris_data 
 
'''
creativeID,userID,positionID,clickTime,conversionTime,connectionType,
telecomsOperator,appPlatform,sitesetID,positionType,age,gender,
education,marriageStatus,haveBaby,hometown,residence,appID,appCategory,label
'''
 
 
def test():
 df = pd.read_table("/var/lib/mysql-files/data1.csv", sep=",")
 df1 = df[["connectionType","telecomsOperator","appPlatform","sitesetID",
    "positionType","age","gender","education","marriageStatus",
    "haveBaby","hometown","residence","appCategory","label"]]
 print df1["label"].value_counts()
 N_data = df1[df1["label"]==0]
 P_data = df1[df1["label"]==1]
 N_data = N_data.sample(n=P_data.shape[0], frac=None, replace=False, weights=None, random_state=2, axis=0)
 #print df1.loc[:,"label"]==0
 print P_data.shape
 print N_data.shape
 
 data = pd.concat([N_data,P_data])
 print data.shape
 data = data.sample(frac=1).reset_index(drop=True) 
 print data[["label"]]
 return

import pandas as pd
#對(duì)dataframe隨機(jī)抽取2000個(gè)樣本
pd.sample(df, n=2000)

from sklearn.model_selection import train_test_split
#y是在X中的某一個(gè)屬性列
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, stratify=y)