def generate_images():
data = dataset()
shops = get_shops(data)
items = get_items(data)
applied_image_item = map(lambda x: partial(image_item, x), shops)
mapnp(lambda f: pmap(f, items), applied_image_item)
def image_item(shop, item):
item_sales = partition(calendar_map(item, shop_sales(shop)))
train, test = mapnp(annotate_year, item_sales)
applied_img = partial(img, shop, item)
mapnp(lambda t: applied_img("train", t[1], t[0]), train)
mapnp(lambda t: applied_img("test", t[1], t[0]), test)
def partition(sales_list):
"""
Splits the item sales into annual sales + November sales
"""
sales_nparr = mapnp(np.array, sales_list).astype(np.uint8)
train = mapnp(lambda i: sales_nparr[12 * i: 12 * i + 11], range(3))
test = mapnp(lambda i: [sales_nparr[12 * i + 11],], range(3))
return [train, test]
def calendar_map(item, shop_data):
item_results = []
for year in (2013, 2014, 2015):
for month in (12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11):
monthly_sales = mapnp(lambda d: date_loopup(item, shop_data, d, month, year), range(1, 32))
item_results = enqueue(item_results, monthly_sales)
return item_results
def annotate_year(col):
return mapnp(arrayl, zip(col, (2013, 2014, 2015)))
def split_date(date):
return mapnp(int, date.split("."))