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from components.ProfilingUsers import ProfilingUsers
from components.ProfileBased_RS import ProfileBased_RS
from _library.visual_utils import visualize_user_profile
from _library.data_loader import load_indacoProducts
from _library.mongodb_utils import simplified_SKUs
from _library.io_toolkit import saveComputationalTimes, read_settings
from _library.profiling_utils import write_profiles
if __name__ == '__main__':
appSettings = read_settings()
# Read data
db_service,indacoProducts_df, indacoOrders_df, indacoCategories = load_indacoProducts("etl",visualize_aggregated_territories = True)
import pdb;
# pdb.set_trace()
# ----------------------------------- PRE-PROCESSING ----------------------------------------------
# [PRE-PROCESSING A] Simplify SKUs
indacoProducts_df, product_names, sku_mapping = simplified_SKUs(indacoProducts_df, product_identifier = 'SKU')
# [PRE-PROCESSING B] Add the simplified SKUs to the orders
reversed_skuMapping = {indaco_sku: simplified_sku for simplified_sku, indaco_sku in sku_mapping.items()}
indacoOrders_df.insert(loc = 4, column = 'SKU', value = indacoOrders_df['sku'].apply(
lambda indaco_sku: reversed_skuMapping[indaco_sku]))
indacoOrders_df.rename(columns = {'sku': 'indaco_sku'}, inplace = True)
# Enhanced the order with information of the products
duplicate_columns = ['Product Type', 'indaco_sku', 'Title']
enhancedOrders = indacoOrders_df.merge(indacoProducts_df.drop(columns = duplicate_columns), how = 'left', on ='SKU')
enhancedOrders = enhancedOrders.dropna(subset = ['SKU'])
print(enhancedOrders)
# Generate profile for the users users
profiler = ProfilingUsers(orders = enhancedOrders)
userProfiles = profiler.mine_orders()
# --------------------------- RECOMMENDER SYSTEM ----------------------------
if appSettings['generate_recommendations']:
verbose = False
# Initialize
userBased_recomSys = ProfileBased_RS(
platformProducts = indacoProducts_df,
platfromOrders = enhancedOrders)
# Generate recommendations
userRecommendations_byUsers, computationalTime_byUser = dict(), dict()
channels = indacoProducts_df['channel'].unique()
collectionBased_bundleDim = 2
for channel in channels:
channel_products = indacoProducts_df[indacoProducts_df['channel'] == channel]
# if(len(channel_products) < collectionBased_bundleDim):
# continue
userBased_recomSys.platformProducts = channel_products
userRecommendations_byUsers[channel] = {}
for user_id, user_profile in userProfiles.items():
# Personalized recommendations
user_recommendations, user_computationalTime = userBased_recomSys.userWise_recommendations(
user_id, user_profile, collectionBased_bundleDim, verbose)
userRecommendations_byUsers[channel][user_id] = user_recommendations
if(appSettings['generate_recommendations']):
write_profiles(db_service,userRecommendations_byUsers[channel], sku_mapping=sku_mapping, collectionName = 'userbasedrecommendations', overwriteCollection = True,channel=channel)
#computationalTime[channel][user_id] = user_computationalTime
#saveComputationalTimes(computationalTime_byUser)
# ----------------------------------------------------------------------------
# Visualize the user profiles
print("\nUSER PROFILES:")
for user_id, user_profile in userProfiles.items():
# Visualize profile
visualize_user_profile(user_id, user_profile)
# Merge the user profile with the recommendended products
# if appSettings["generate_recommendations"]:
# visualize_user_profile(user_id, userRecommendations_byUsers[user_id])
# Write profiles
write_profiles(db_service,userProfiles, sku_mapping,collectionName = 'userprofiles', overwriteCollection = True)
if appSettings['generate_recommendations']:
print("\nGENERATING RECOMMENDATIONS")
#write_recommendations(db_service,userRecommendations_byUsers, sku_mapping, overwriteCollection = True)