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walmart_sales_utils.py
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from __future__ import print_function
import csv
import pandas as pd
import re
import os
import numpy as np
import math
import pandas as pd
import tensorflow as tf
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import pickle
from collections import OrderedDict
def createPickleFromRawData(params, writeToFile=False):
df = pd.read_csv('Walmart/train.csv')
dfStores = pd.read_csv('Walmart/stores.csv')
dfFeatures = pd.read_csv('Walmart/features.csv')
df = pd.merge(df, dfStores, on='Store', how='left')
df = pd.merge(df, dfFeatures, on=['Store', 'Date'], how='left')
df['IsHoliday'] = df['IsHoliday_x'] + df['IsHoliday_y']
df = df.drop(['IsHoliday_x', 'IsHoliday_y'], axis=1)
df['Date'] = pd.DatetimeIndex(df['Date'])
numerical_features = ['Size','Temperature','Fuel_Price','MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5','CPI','Unemployment']
df[numerical_features] /= df[numerical_features].max()
df = df.fillna(0)
df['Type'] = df['Type'].replace(['A','B','C'], [0,1,2])
df = df.set_index(['Store', 'Dept', 'Date'])
# Explicitly marking Holidays
df.loc[(slice(None), slice(None), ('2010-02-12', '2011-02-11', '2012-02-10', '2013-02-08')),'IsHoliday'] = True
df.loc[(slice(None), slice(None), ('2010-09-10', '2011-09-09', '2012-09-07', '2013-09-06')),'IsHoliday'] = True
df.loc[(slice(None), slice(None), ('2010-11-26', '2011-11-25', '2012-11-23', '2013-11-29')),'IsHoliday'] = True
df.loc[(slice(None), slice(None), ('2010-12-31', '2011-12-30', '2012-12-28', '2013-12-27')),'IsHoliday'] = True
df['IsHoliday'] *= 1
# print((df['Weekly_Sales'] < 0).sum())
df = df.loc[df['Weekly_Sales']>=0]
# Create individual timeseries from dataframe
XX = list()
lagXX = list()
YY = list()
for tsId, (store_dept, ts) in enumerate(df.groupby(level=['Store', 'Dept'])):
#print(store_dept)
ts = ts.reset_index(level=['Store', 'Dept'], drop=True) # Date is the new index now.
ts['Day'] = ts.index.day - 1
ts['Month'] = ts.index.month - 1
ts['Year'] = ts.index.year - 2010
ts['WeekOfYear'] = ts.index.weekofyear - 1
if params.isTsId:
ts['tsId'] = tsId
Y = ts[['Weekly_Sales']].values.tolist()
YY.append(Y)
lags = [{'weeks':1}]
if params.isLagFeat:
lags += [{'weeks':52}]
lagX = list()
for lag in lags:
lagX.append(ts['Weekly_Sales'].get(ts.index - pd.DateOffset(**lag)).fillna(0).reset_index(drop=True))
lagX = pd.concat(lagX, axis=1, ignore_index=True)
lagX = lagX.values.tolist()
lagXX.append(lagX)
ts = ts.drop(columns='Weekly_Sales')
if params.noFeat:
X = [[] for x in ts.values.tolist()]
else:
X = ts.values.tolist()
XX.append(X)
features = ts.columns.values
if params.noFeat:
feature_dict, emb_dict = {}, {}
else:
feature_dict = OrderedDict([(x,i) for i,x in enumerate(features)])
#for k,v in feature_dict.items():
# print('feature_dict', k,v)
emb_list = [('Type', (3, 1)), ('Day', (31, 10)), ('Month', (12, 6)), ('Year', (3, 1)), ('WeekOfYear', (52, 10))]
if params.isTsId:
emb_list += [('tsId', (len(XX), 50))]
emb_dict = OrderedDict(emb_list)
if writeToFile:
with open('datasets/walmart.pkl','wb') as f:
pickle.dump(XX, f)
pickle.dump(lagXX, f)
pickle.dump(YY, f)
pickle.dump(feature_dict, f)
pickle.dump(emb_dict, f)
return XX, lagXX, YY, feature_dict, emb_dict
def createPickleFromRawData_old():
df = pd.read_csv('Walmart/train.csv')
dfStores = pd.read_csv('Walmart/stores.csv')
dfFeatures = pd.read_csv('Walmart/features.csv')
df = pd.merge(df, dfStores, on='Store', how='left')
df = pd.merge(df, dfFeatures, on=['Store', 'Date'], how='left')
df['IsHoliday'] = df['IsHoliday_x'] + df['IsHoliday_y']
df = df.drop(['IsHoliday_x', 'IsHoliday_y'], axis=1)
# df = pd.merge(df, dfFeatures, on=['Store','Date'], how='left')
# print(df.loc[(df['Date'] == '2010-02-12')].shape)
#print(df['IsHoliday'].sum().sum())
df['Day'] = pd.DatetimeIndex(df['Date']).day
df['Month'] = pd.DatetimeIndex(df['Date']).month
df['Year'] = pd.DatetimeIndex(df['Date']).year
#df.columns = [u'Store', u'Dept', u'Date', u'Weekly_Sales', u'IsHoliday', u'Day',
# u'Month', u'Year', u'Type', u'Size', u'Store2', u'Date2',
# u'Temperature', u'Fuel_Price', u'MarkDown1', u'MarkDown2', u'MarkDown3',
# u'MarkDown4', u'MarkDown5', u'CPI', u'Unemployment', u'IsHoliday2']
numerical_features =['Size','Temperature','Fuel_Price','MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5','CPI','Unemployment']
# numerical_features =['Size']
# xF = ['Store','Dept','Day','Month','Year','Type','IsHoliday','isHA1','isHA2','isHA3','isHB1','isHB2','isHB3','Size','Temperature','Fuel_Price','MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5','CPI','Unemployment']
# xF = ['Store','Dept','Day','Month','Year','Type','IsHoliday','Size']
# xF = ['Store','Dept','Day','Month','Year','Type','IsHoliday','Size','Temperature','Fuel_Price','MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5','CPI','Unemployment']
xF = ['Day','Month','Year','Type','IsHoliday','Size','Temperature','Fuel_Price','MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5','CPI','Unemployment']
#print "@@@@@@@@@@@@@@@@@@@@@@@len = ",len(xF)
df = df.fillna(0)
df['IsHoliday']*=1
df['Type'] = df['Type'].replace(['A','B','C'], [0,1,2])
df['Year'] = df['Year'] - 2010
df['Month'] = df['Month'] - 1
df['Day'] = df['Day'] - 1
df['Store'] = df['Store'] - 1
df['Dept'] = df['Dept'] - 1
# for k in numerical_features:
# df[k] = df[k]/np.max(df[k].values)
ddf = df.loc[df['Weekly_Sales'] <= 0]
XX = list()
YY = list()
count = 0
yF = ['ScaledSales']
salesMax = max(df['Weekly_Sales'])
df['ScaledSales'] = df['Weekly_Sales'].values
pair2id = dict()
for item, shop in df[['Store','Dept']].values:
if pair2id.get((item, shop), -1) == -1:
pair2id[(item, shop)] = 1
else:
pair2id[(item, shop)] += 1
# for cnt, ((store, dept), sales) in enumerate(pair2id.items()):
for store in range(45):
for dept in range(100):
if len(ddf.loc[(ddf['Store'] == store) & (ddf['Dept'] == dept)]) == 0:
W = df.loc[(df['Store'] == store) & (df['Dept'] == dept)]
numWeeks = W.shape[0]
#print(W.isna().values.sum())
#print(store, dept, sales, sum([s<0 for s in W['ScaledSales']]))
#if sum([s<0 for s in W['ScaledSales']]) == 0: # No negative sales
print(store, dept)
# W['isHA1'] = W['IsHoliday'].shift(+1)
# W['isHA2'] = W['IsHoliday'].shift(+2)
# W['isHA3'] = W['IsHoliday'].shift(+3)
# W['isHB1'] = W['IsHoliday'].shift(-1)
# W['isHB2'] = W['IsHoliday'].shift(-2)
# W['isHB3'] = W['IsHoliday'].shift(-3)
W = W.fillna(0)
X = W[yF+xF]
X['ScaledSales'] = X['ScaledSales'].shift(1).fillna(0) # adding prev y features.
X.rename(columns={'ScaledSales':'PrevScaledSales'}, inplace=True)
X = X.values.tolist()
Y = W[yF].values.tolist()
XX.append(X)
YY.append(Y)
# for i in range(45):
# for j in range(100):
# if len(ddf.loc[(ddf['Store'] == i) & (ddf['Dept'] == j)]) == 0:
# XXF = df.loc[(df['Store'] == i) & (df['Dept'] == j)]
# if testFraction:
# test_length = int(testFraction*(len(XXF)-1))
# else:
# test_length = int(decoder_length)
# train_length = len(XXF) - 1 - test_length
# if train_length > sequence_length and test_length >= decoder_length:
# XXF = df.loc[(df['Store'] == i) & (df['Dept'] == j)]
# # XXF['isHA1'] = XXF['IsHoliday'].shift(+1)
# # XXF['isHA2'] = XXF['IsHoliday'].shift(+2)
# # XXF['isHA3'] = XXF['IsHoliday'].shift(+3)
# # XXF['isHB1'] = XXF['IsHoliday'].shift(-1)
# # XXF['isHB2'] = XXF['IsHoliday'].shift(-2)
# # XXF['isHB3'] = XXF['IsHoliday'].shift(-3)
# XXF = XXF.fillna(0)
# X = XXF[xF+yF]
# X['ScaledSales'] = X['ScaledSales'].shift(1).fillna(0) # adding prev y features.
# X.rename(columns={'ScaledSales':'PrevScaledSales'}, inplace=True)
# X = X.values.tolist()
# Y = XXF[yF].values.tolist()
# XX.append(X)
# YY.append(Y)
# count += 1
with open('datasets/walmart.pkl','wb') as f:
pickle.dump(XX, f)
pickle.dump(YY, f)
def getValuesWalmart(testFraction,sequence_length,decoder_length,modelToRun):
with open('datasets/walmart.pkl','r') as f:
pickle.load(XX, f)
pickle.load(YY, f)
XTrain, YTrain, XTest, YTest, maxYY = list(), list(), list(), list(), list()
for i in range(len(XTrain)): # Normalizing each feature by max value along the sequence
#XTrain[i] = np.exp(XTrain[i]).tolist()
maxX = np.array(XTrain[i]).max(axis=0)
XTrain[i] = (np.array(XTrain[i]) / maxX)
XTrain[i][np.isnan(XTrain[i])] = 0
XTrain[i][np.isinf(XTrain[i])] = 0
XTrain[i] = XTrain[i].tolist()
#XTest[i] = np.exp(XTest[i]).tolist()
XTest[i] = (np.array(XTest[i]) / maxX)
XTest[i][np.isnan(XTest[i])] = 0
XTest[i][np.isinf(XTest[i])] = 0
XTest[i] = XTest[i].tolist()
#YTrain[i] = np.exp(YTrain[i]).tolist()
maxy = np.array(YTrain[i]).max()
maxY.append(maxy)
YTrain[i] = (np.array(YTrain[i]) / maxy)
YTrain[i][np.isnan(YTrain[i])] = 0
YTrain[i][np.isinf(YTrain[i])] = 0
YTrain[i] = YTrain[i].tolist()
#YTest[i] = np.array(YTest[i]).tolist()
YTest[i] = (np.array(YTest[i]) / maxy)
YTest[i][np.isnan(YTest[i])] = 0
YTest[i][np.isinf(YTest[i])] = 0
YTest[i] = YTest[i].tolist()
assert len(XTrain) == len(YTrain)
# print('Number of timeseries:', len(XTrain))
# print('Len of each TS:', [len(i) for i in XTrain])
# print('XTrain[0].shape:',np.array(XTrain[0]).shape)
# print('YTrain[0].shape:',np.array(YTrain[0]).shape)
# print('XTest[0].shape',np.array(XTest[0]).shape)
# print('YTest[0].shape',np.array(YTest[0]).shape)
return XTrain, YTrain, XTest, YTest, count, maxY, len(XTrain[0][0])
def getWalmartEmbeddingTransform(input1,isEmbedding):
store_embed_size = 5 #45
dept_embed_size = 10 #99
day_embed_size = 10 #31
month_embed_size = 6 #12
year_embed_size = 1 #3
type_embed_size = 1 #3
hol_embed_size = 1 #2
structure = []
#prev Y
prevY = tf.expand_dims(tf.cast(input1[:,:,0],tf.float32), axis=2)
structure.append(prevY)
# #store
# v_store_index = tf.Variable(tf.random_uniform([45,store_embed_size], -1.0, 1.0))
# em_store_index = tf.nn.embedding_lookup(v_store_index, tf.cast(input1[:,:,1],tf.int32))
# if isEmbedding:
# structure.append(em_store_index)
#
# #dept
# v_dept_index = tf.Variable(tf.random_uniform([99,dept_embed_size], -1.0, 1.0))
# em_dept_index = tf.nn.embedding_lookup(v_dept_index, tf.cast(input1[:,:,2],tf.int32))
# if isEmbedding:
# structure.append(em_dept_index)
#day
v_day_index = tf.Variable(tf.random_uniform([31,day_embed_size], -1.0, 1.0, seed=12))
em_day_index = tf.nn.embedding_lookup(v_day_index, tf.cast(input1[:,:,1],tf.int32))
structure.append(em_day_index)
#month
v_month_index = tf.Variable(tf.random_uniform([12,month_embed_size], -1.0, 1.0, seed=12))
em_month_index = tf.nn.embedding_lookup(v_month_index, tf.cast(input1[:,:,2],tf.int32))
structure.append(em_month_index)
#year
v_year_index = tf.Variable(tf.random_uniform([3,year_embed_size], -1.0, 1.0, seed=12))
#v_year_index = tf.Print(v_year_index, [ v_year_index, input1[:,:,3]], message="HERE", summarize=30000)
em_year_index = tf.nn.embedding_lookup(v_year_index, tf.cast(input1[:,:,3],tf.int32))
structure.append(em_year_index)
#type
v_type_index = tf.Variable(tf.random_uniform([3,type_embed_size], -1.0, 1.0, seed=12))
em_type_index = tf.nn.embedding_lookup(v_type_index, tf.cast(input1[:,:,4],tf.int32))
if isEmbedding:
structure.append(em_type_index)
#hol
v_hol_index = tf.Variable(tf.random_uniform([2,hol_embed_size], -1.0, 1.0, seed=12))
em_hol_index = tf.nn.embedding_lookup(v_hol_index, tf.cast(input1[:,:,5],tf.int32))
structure.append(em_hol_index)
#hol1
# v_hol_index = tf.Variable(tf.random_uniform([2,hol_embed_size], -1.0, 1.0))
# em_hol_index = tf.nn.embedding_lookup(v_hol_index, tf.cast(input1[:,:,7],tf.int32))
# structure.append(em_hol_index)
# #hol2
# v_hol_index = tf.Variable(tf.random_uniform([2,hol_embed_size], -1.0, 1.0))
# em_hol_index = tf.nn.embedding_lookup(v_hol_index, tf.cast(input1[:,:,8],tf.int32))
# structure.append(em_hol_index)
# #hol3
# v_hol_index = tf.Variable(tf.random_uniform([2,hol_embed_size], -1.0, 1.0))
# em_hol_index = tf.nn.embedding_lookup(v_hol_index, tf.cast(input1[:,:,9],tf.int32))
# structure.append(em_hol_index)
# #hol4
# v_hol_index = tf.Variable(tf.random_uniform([2,hol_embed_size], -1.0, 1.0))
# em_hol_index = tf.nn.embedding_lookup(v_hol_index, tf.cast(input1[:,:,10],tf.int32))
# structure.append(em_hol_index)
# #hol5
# v_hol_index = tf.Variable(tf.random_uniform([2,hol_embed_size], -1.0, 1.0))
# em_hol_index = tf.nn.embedding_lookup(v_hol_index, tf.cast(input1[:,:,11],tf.int32))
# structure.append(em_hol_index)
# #hol6
# v_hol_index = tf.Variable(tf.random_uniform([2,hol_embed_size], -1.0, 1.0))
# em_hol_index = tf.nn.embedding_lookup(v_hol_index, tf.cast(input1[:,:,12],tf.int32))
# structure.append(em_hol_index)
#num_f_index
num_f_index = tf.cast(input1[:,:,6:],tf.float32)
structure.append(num_f_index)
input11 = tf.concat(structure, axis=2)
#print "@@@@@@@@@@@@@@@@@@@@@@@@transform",input11
return input11
if __name__ == '__main__':
testFraction = 0
sequence_length = 24
decoder_length = 16
modelToRun = 'baseline'
createPickleFromRawData(params, writeToFile=True)
#XTrain, YTrain, XTest, YTest, count, maxY, numFW = getValuesWalmart(testFraction,sequence_length,decoder_length,modelToRun)
tsId = np.random.randint(len(XTrain))
X_tr = np.array(XTrain[tsId])
y_tr = np.array(YTrain[tsId])[:,0].tolist()
X_test = np.array(XTest[tsId])
y_test = np.array(YTest[tsId])[:,0].tolist()
#---- Plotter Block Start -----#
plt.plot([len(y_tr)-1, len(y_tr)],[y_tr[-1], y_test[0]], 'r') # Connecting conditioning and prediction range
#plt.plot((len(y_tr)-1+len(y_tr))*1.0/2, 'r') # TODO:Separator line between conditioning and prediction range
plt.plot(range(len(y_tr)), y_tr, 'b') # Plotting conditioning range
#plt.plot(range(len(y_tr)), y_tr, 'bo') # plotting conditioning range (with marker)
plt.plot(range(len(y_tr), len(y_tr)+len(y_test)), y_test, 'r') # Plotting prediction range
#plt.plot(range(len(y_tr), len(y_tr)+len(y_test)), y_test, 'ro') # Plotting prediction range (with marker)
plt.show()
#---- Plotter Block End -----#
# for i in range(X.shape[1]):
# #pltX = X[:,i].tolist()
# #print(pltX)
# #print(y)
# plt.plot(y, 'b')
# plt.plot(y, 'k*')
# plt.show()