# coding: utf-8

# In[1]:

import numpy as np

import pandas as pd

# In[2]:

print 'Read Data!'

Train = pd.read_csv('train.csv')

Test = pd.read_csv('test.csv')

Store = pd.read_csv('store.csv')

print len(Train)

print len(Test)

# In[3]:

Train = pd.merge(Train, Store)

Test = pd.merge(Test, Store)

print len(Train)

Train = Train[Train.Sales != 0]

print len(Train)

# In[4]:

from datetime import datetime

def Parse_Time (x):

return Year, Month, Day

# In[5]:

Train["Year"], Train["Month"], Train["Day"] = zip(*Train["Date"].apply(Parse_Time))

# In[6]:

Test["Year"], Test["Month"], Test["Day"] = zip(*Test["Date"].apply(Parse_Time))

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Train_Target = Train.Sales

# print Train

# In[8]:

Test_ID = Test.Id

Train = Train.drop(['Date', 'Sales', 'Customers', 'PromoInterval'], axis = 1)

Test = Test.drop(['Date', 'Id', 'PromoInterval'], axis = 1)

# print Train.head()

# print Test.head()

# print len(Train)

# In[9]:

Store_Type_List = ['a', 'b', 'c', 'd']

Assortment_List = ['a', 'b', 'c']

Holiday_List = [0L, '0', 'a', 'b', 'c']

def Store_Type(x):

return Store_Type_List.index(x)

def Assortment(x):

return Assortment_List.index(x)

def Holiday(x):

return Holiday_List.index(x)

Train.StoreType = Train.StoreType.apply(Store_Type)

Train.Assortment = Train.Assortment.apply(Assortment)

Test.StoreType = Test.StoreType.apply(Store_Type)

Test.Assortment = Test.Assortment.apply(Assortment)

Train.StateHoliday = Train.StateHoliday.apply(Holiday)

Test.StateHoliday = Test.StateHoliday.apply(Holiday)

# In[10]:

Train = Train.fillna(0)

Test = Test.fillna(0)

# print Train.head()

# print Test.head()

# In[11]:

print 'Train Random Forests!'

from sklearn.ensemble.forest import RandomForestRegressor

RF = RandomForestRegressor(n_estimators = 500, random_state = 0)

# In[12]:

Rows = np.random.choice(Train.index.values, 400000)

Sampled_Train = Train.ix[Rows]

Sample_Train_Target = Train_Target.ix[Rows]

# RF.fit(Sampled_Train, Sample_Train_Target)

RF.fit(Train, Train_Target)

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print 'Predict!'

Test_Predict = RF.predict(Test.as_matrix())

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print Test_Predict.shape

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from collections import OrderedDict

Submission = pd.DataFrame(data = OrderedDict([('Id', Test_ID), ('Sales', Test_Predict)]))

Submission.to_csv('Submission_RF.csv', index = False)

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Test_Predict.shape

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