#!/usr/bin/python

#-*- coding:utf-8 -*-

import os

import pandas as pd

import numpy as np

from sklearn import preprocessing

from sklearn.cross_validation import train_test_split

from sklearn.cross_validation import cross_val_score

from sklearn.linear_model import LinearRegression

from sklearn import metrics

from sklearn.ensemble import RandomForestRegressor

import my_filter

import chardet

import ConfigParser

import types

import sys

reload(sys)

sys.setdefaultencoding('utf-8')

# import seaborn as sns

# import matplotlib as inline

prompt = {'choose' : 'Choose function:\

'index' : 'Enter the index you want to count: '}

# feature_cols = ['weekend', 'hour', 'holiday', 'weather']

feature_cols = ['weekend', 'weather', 'temp_high', 'temp_low', 'temp_mean', 'temp_predict']

algorithm = [1, 2]

def my_random_forests(file_path_read, df_test):

return result

def my_linear_regression(file_path_read, df_test):

return result

if __name__ == '__main__':

file_path_read = raw_input(prompt['file_path_read'])

line_num = int(raw_input('what the line number: '))

algorithm_type = int(raw_input('what kind of algorithm you want to use:\

\n1: linear regression\

\n2: random forest\n'))

df_test = pd.read_csv('./date-7-analysed.csv')

# X_test = df_test[feature_cols]

# X_test = preprocessing.normalize(X_test)

# print X_test.head()

if os.path.isdir(file_path_read):

file_path_read = my_filter.walk_dir(file_path_read, 'csv')

else:

file_path_read = [file_path_read]

while(algorithm_type not in algorithm):

print '\n\nwrong algorithm type!\n'

algorithm_type = int(raw_input('what kind of algorithm you want to use:\

\n1: linear regression\

\n2: random forests\n'))

if algorithm_type == 1:

result = my_linear_regression(file_path_read, df_test)

file_path_write = 'predict' + '-linear' + '-line' + str(line_num) + '.txt'

else:

result = my_random_forests(file_path_read, df_test)

file_path_write = 'predict' + '-randomforest' + '-line' + str(line_num) + '.txt'

# result = {}

# i = 6

# while i <= 21:

# result[i] = []

# i += 1

# for path_read in file_path_read:

# df = pd.read_csv(path_read)

# scaler = preprocessing.StandardScaler().fit(df[feature_cols])

# i = 6

# while i <= 21:

# df_hour = df[df.hour == i][df.date > 20141030]

# X_train = df_hour[feature_cols]

# X_train = scaler.transform(X_train)

# # print X_train.head()

# y_train = df_hour.num

# X_test = df_test[df_test.hour == i][feature_cols]

# X_test = scaler.transform(X_test)

# # print y_train.head()

# linreg = LinearRegression()

# linreg.fit(X_train, y_train)

# # print linreg.intercept_

# # print linreg.coef_

# y_pred = linreg.predict(X_test)

# y_pred_list = y_pred.tolist()

# if file_path_read.index(path_read) == 0:

# for y in y_pred_list:

# result[i].append(int(y))

# else:

# j = 0

# for y in y_pred_list:

# result[i][j] += int(y)

# j += 1

# print 'done'

# i += 1

# file_path_write = 'predict' + str(line_num) + '.txt'

fp_write = open(file_path_write, 'w')

my_date = df_test.date.tolist()

my_hour = df_test.hour.tolist()

# print zip(my_date, my_hour)

i = 0

for date in my_date:

value = '线路' + str(line_num) + ','

value += str(my_date[i]) + ','

value += str(my_hour[i]) + ','

num = int(result[int(my_hour[i])][i/16])

if my_hour[i] == 12:

if line_num == 10:

if my_date[i] <= 20150103:

num += 740

else:

num += 3000

if line_num == 15:

if my_date[i] <= 20150103:

num += 370

else:

num += 1500

value += str(num)

fp_write.write(str(value)+'\n')

i += 1

fp_write.close()

# df = pd.read_csv('../line10/line10/train_data_filtered-line10-student-date-analyse.csv')

# print df.head()

# df.info()

# print df.shape

# # %matplotlib inline

# # sns.pairplot(df, x_vars=['what_day', 'hour', 'holiday'], y_vars = 'num',

# # size = 7, aspect = 0.8)

# feature_cols = ['what_day', 'hour', 'holiday']

# X = df[feature_cols]

# print X.head()

# y = df.num

# print y.head()

# X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 1)

# print X_train.shape

# print y_train.shape

# print X_test.shape

# print y_test.shape

# linreg = LinearRegression()

# linreg.fit(X_train, y_train)

# print linreg.intercept_

# print linreg.coef_

# y_pred = linreg.predict(X_test)

# print y_pred

# print "MAE:",metrics.mean_absolute_error(y_test, y_pred)

# # s = cross_val_score(linreg, X_test, y_test, cv=5)

# # print s

# print 'lalala'