from Forecast.Grouping import *
import numpy as np
from os import path, makedirs
dept_id = 'FOODS_1'
store_id = 'CA_1'
training_years = range(2012, 2015)
test_years = [2015]
group_path = '../data/M5/' + dept_id + '_' + store_id + '/groups/vol_4.txt'
base_path = '../data/M5/' + dept_id + '_' + store_id + '/RNN/vol_4'
perform_path = path.join(base_path, 'performance')
model_path = path.join(base_path, 'models')
print('Loading Data ...')
sales, prices = M5.import_weekly_sales_and_prices('../data/m5-forecasting-accuracy')
dates = M5.import_weekly_dates('../data/m5-forecasting-accuracy')
name = 'M5_' + dept_id + '_' + store_id
category = {'dept_id': dept_id, 'store_id':store_id}
train_dates = M5.select_dates(dates, training_years, range(1, 13))
train_sales = M5.select_data(sales, category, train_dates)
train_prices = M5.select_data(prices, category, train_dates)
test_dates = M5.select_dates(dates, test_years, range(1, 13))
test_sales = M5.select_data(sales, category, test_dates)
test_prices = M5.select_data(prices, category, test_dates)
print('Setting Parameters ...')
groups = import_groups(group_path)
init_model_params = {
'--dataset_path',
type = str,
default = '../data/m5-forecasting-accuracy',
help = 'path of dataset'
)
parser.add_argument(
'--base_path',
type = str,
default = '../data/M5',
help = 'base path'
)
args = parser.parse_args()
print('Loading Data ...')
sales, prices = M5.import_weekly_sales_and_prices(args.dataset_path)
dates = M5.import_weekly_dates(args.dataset_path)
dept_id = args.dept_id
store_id = args.store_id
training_years = args.training_years
test_years = [args.test_year]
name = 'M5_' +\
dept_id + \
'_' + store_id + \
'_' + str(args.test_year) + \
'_' + args.model + \
'_' + args.group_type
base_path = path.join(