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from _library.recom_utils import assRules_utils
from _library import toolkit
from _library.data_utils import data_loader
class associationRules_RS:
def __init__(self, platform_products, coPurchase_versionName, product_identifier, load_remotely):
# Load association rules
association_rules_df, enhanced_association_rules_df = data_loader.load_coPurchases(coPurchase_versionName, product_identifier,load_remotely=load_remotely)
# Pre-process the loaded data
if coPurchase_versionName == 'OLDinTrentino':
name_mapping = {'Linked regions': 'production_areas'}
else:
name_mapping = None
association_rules_df = assRules_utils.preProcessing_coPurchases(association_rules_df, name_mapping)
enhanced_association_rules_df = assRules_utils.preProcessing_coPurchases(enhanced_association_rules_df, name_mapping)
# Save the association rules
self.association_rules_df = association_rules_df
self.enhanced_association_rules_df = enhanced_association_rules_df
# Save the products of inTrentino
self.platform_products = platform_products
# Save the product identifier
self.product_identifier = product_identifier
self.category_based = True if self.product_identifier == 'Product Type' else False
# Flag to check whether the parameters have been set
self.flag_params = False
def set_params(self, unique_product_identifier, excluded_products = [], excluded_link_types = [], force_perfect_match = False,
hide_raw_scores = True, filter_source_platform = True, verbose = False):
# Set the parameters
# A list of unique product identifiers.
self.unique_product_identifier = unique_product_identifier
self.excluded_products = excluded_products
self.force_perfect_match = True if self.category_based else force_perfect_match
self.excluded_link_types = excluded_link_types
self.hide_raw_scores = hide_raw_scores
self.filter_source_platform = filter_source_platform
self.verbose = verbose
self.flag_params = True
def generate_associationRules(self, reference_items, flag_enhanced_assRules = False):
if flag_enhanced_assRules:
assRules_df = self.enhanced_association_rules_df
else:
assRules_df = self.association_rules_df
# Discover the recommendations
recommended_items = assRules_utils.find_recommendations(assRules_df,
self.platform_products,
self.product_identifier,
reference_items,
self.excluded_products,
self.excluded_link_types,
self.force_perfect_match,
self.hide_raw_scores,
self.filter_source_platform,
self.verbose)
# Add the name of this method to the recommendations
recommended_items = toolkit.add_recommendationSource(recommended_items, self.rs_name)
# [IF CATEGORY BASED] If the recommendations are based on categories
if self.category_based and len(recommended_items) > 0:
# Fill recommended categories with products
recommended_items = assRules_utils.addItems_byCategory(
recommendations = recommended_items,
reference_items = reference_items,
all_products = self.platform_products,
unique_product_identifier = self.unique_product_identifier,
filter_source_platform = self.filter_source_platform,
single_item = self.filter_source_platform)
return recommended_items
def itemWise_assRulesBased_recommendations(self, reference_items, flag_enhanced_assRules):
# Slight artefact
if (not isinstance(reference_items, list)) or (not isinstance(reference_items, set)):
reference_items = [reference_items]
# Generate the recommendations
recommended_items = self.generate_associationRules(reference_items, flag_enhanced_assRules)
return recommended_items
def generate_assRulesBased_recommendations(self, flag_enhanced_assRules):
if not self.flag_params:
self.set_params()
self.rs_name = 'enhanced' if flag_enhanced_assRules else 'simple'
self.rs_name += '_assRules'
if self.category_based:
self.rs_name += '_cat'
# Pre-processing the products
reference_products = self.platform_products.apply(
func = lambda df_row: toolkit.extract_referenceProduct(df_row, self.product_identifier),
axis = 1)
# Compute the recommendation for each products
recommendations = reference_products.apply(
func = lambda product: self.itemWise_assRulesBased_recommendations(product, flag_enhanced_assRules))
# Improve the data representation
identifier = 'item_name'
if self.category_based:
if self.unique_product_identifier == 'Title':
identifier = 'product_name'
elif self.unique_product_identifier == 'SKU':
identifier = 'sku'
recommendations.index = reference_products.apply(lambda product: product[identifier])
recommendations = recommendations.to_dict()
return recommendations