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from _library.data_utils.data_loader import load_indacoOrders, load_collectionTypes
from _library import toolkit
from _library.data_utils.io_toolkit import read_settings
from components.mine_assRules import MineAssociationRules

from numpy import nan
import numpy as np
from os import path

from pandas import DataFrame
from sqlalchemy import create_engine,text
import pandas as pd

if __name__ == '__main__':
    
    appSettings = read_settings()
    
    # Load the orders 
    print("\n" + 120 * "-")
    print("-" * 50, f"(0) Loading orders", "-" * 50)
    print(120 * "-", "\n")

    # Get the products, orders, sku mapping
    indaco_products, indaco_orders, indaco_categories, sku_mapping = load_indacoOrders(
        "etl",
        consider_delatedProducts = True, 
        consider_unavailableProducts = True)
    
    # Visualize orders
    toolkit.visualize_orders(indaco_orders, last_k_orders = -1)
    
    # Visualize products
    toolkit.visualize_products(indaco_products, num_products = 20)
    
    # Read the collections
    collectionTypes, rawCollectionTypes = load_collectionTypes(filepath = path.join('_library', 'INDACO_collectionCodes.json'), 
                                                               verbose = True)
    collections = list(collectionTypes.keys())
    
    # Attach the collection to the orders (and visualize collections)
    for collection_type in collections:
        
        products_view = indaco_products[['SKU', collection_type]]
        indaco_orders = indaco_orders.merge(products_view, how = 'inner', on = 'SKU')
        indaco_orders[collection_type] = indaco_orders[collection_type].replace('', nan)
        
        toolkit.visualize_collection(indaco_products, collection_type)
    
    # ------------------------------------------------------------------------------------------
    # ---------------------------- (A) Mine the association rules ------------------------------
    # ------------------------------------------------------------------------------------------
    print("\n" + 120 * "-")
    print("-" * 44, f"(A) Mine the association rules", "-" * 44)
    print(120 * "-", "\n")
    
    assRules_identifiers = ['SKU', 'Product Type']
    for assRules_identifier in assRules_identifiers:
        
        print("\n" + 120 * "-")
        print("-" * 43, f"(A0) Ass. rules based on {assRules_identifier.upper()}", "-" * 43)
        print(120 * "-", "\n")
    
        # Initialize the miner
        associationRulesMiner = MineAssociationRules(indaco_orders, product_identifier = assRules_identifier)
        
        # Find frequent patterns
        print(120 * "-")
        print("-" * 46, f"(B1) Find frequent patterns", "-" * 45)
        print(120 * "-", "\n")
        frequentPatterns = associationRulesMiner.find_frequentPatterns(min_support = 0.01, limit_dim_pattern = 6)
        
        # Define the trehsold for the minimum confidence 
        min_confidence = 0.7
        if assRules_identifier == 'Product Type':
            min_confidence -= 0.3
        
        # Generate simple association rules
        print("\n" + 120 * "-")
        print("-" * 44, f"(B2) Generate association rules", "-" * 43)
        print(120 * "-", "\n")
        associationRules = associationRulesMiner.mine_associationRules(frequentPatterns, min_confidence, min_lift = 2)
        
        # Generate the enhanced association rules (i.e., with linked collections)
        print("\n" + 120 * "-")
        print("-" * 39, f"(B3) Generate enhanced association rules", "-" * 39)
        print(120 * "-", "\n")
                  

        enhanced_associationRules = associationRulesMiner.mine_enhancedAssociationRules(
            associationRules = associationRules, 
            grouped_collections = collectionTypes)
        
        # Save the outcomes
        print("\n" + 120 * "-")
        print("-" * 43, f"(C) Saving the findings locally", "-" * 44)
        print(120 * "-", "\n")
        associationRulesMiner.save_outcomes_asExcel(frequentPatterns, associationRules, enhanced_associationRules,
                                                    timestamp_col = 'timestamp', folder_path = "_tmp", 
                                                    version_name = appSettings['versionName'])
        
        # Save remotely 
        if appSettings['saveRemotely']:
            print("\n" + 120 * "-")
            print("-" * 43, f"(D) Saving the findings remotely", "-" * 43)
            print(120 * "-", "\n")
            associationRulesMiner.save_excel_remotely()