def get_ICM_all(reader: RecSys2019Reader):
"""
It returns all the ICM_all after applying feature engineering
:param reader: data splitter
:return: return ICM_all
"""
URM_all = reader.get_URM_all()
UCM_all_dict = reader.get_loaded_UCM_dict()
ICM_all_dict = reader.get_loaded_ICM_dict()
ICM_all_dict.pop("ICM_all")
ICM_all_dict = apply_feature_engineering_ICM(
ICM_all_dict,
URM_all,
UCM_all_dict,
ICM_names_to_count=["ICM_sub_class"],
UCM_names_to_list=["UCM_age"])
ICM_all_dict = apply_filtering_ICM(
ICM_all_dict,
ICM_name_to_filter_mapper={
"ICM_asset":
lambda x: x < np.quantile(x, q=0.75) + 0.72 *
(np.quantile(x, q=0.75) - np.quantile(x, q=0.25)),
"ICM_price":
lambda x: x < np.quantile(x, q=0.75) + 4 *
(np.quantile(x, q=0.75) - np.quantile(x, q=0.25))
})
ICM_all_dict = apply_transformation_ICM(ICM_all_dict,
ICM_name_to_transform_mapper={
"ICM_asset":
lambda x: np.log1p(1 / x),
"ICM_price":
lambda x: np.log1p(1 / x),
"ICM_item_pop":
np.log1p,
"ICM_sub_class_count":
np.log1p,
"ICM_age":
lambda x: x**(1 / 2.5)
})
ICM_all_dict = apply_discretization_ICM(ICM_all_dict,
ICM_name_to_bins_mapper={
"ICM_asset": 200,
"ICM_price": 200,
"ICM_item_pop": 50,
"ICM_sub_class_count": 50
})
ICM_all = build_ICM_all_from_dict(ICM_all_dict)
return ICM_all
def get_ICM_all_new(reader: RecSys2019Reader):
"""
It returns all the ICM_all after applying feature engineering
:param reader: data splitter
:return: return ICM_all
"""
ICM_all_dict = reader.get_loaded_ICM_dict()
ICM_all_dict.pop("ICM_all")
ICM_all_dict = apply_filtering_ICM(
ICM_all_dict,
ICM_name_to_filter_mapper={
"ICM_asset":
lambda x: x < np.quantile(x, q=0.75) + 0.72 *
(np.quantile(x, q=0.75) - np.quantile(x, q=0.25)),
"ICM_price":
lambda x: x < np.quantile(x, q=0.75) + 4 *
(np.quantile(x, q=0.75) - np.quantile(x, q=0.25))
})
# Apply useful transformation
ICM_all_dict = apply_transformation_ICM(ICM_all_dict,
ICM_name_to_transform_mapper={
"ICM_asset":
lambda x: np.log1p(1 / x),
"ICM_price":
lambda x: np.log1p(1 / x),
"ICM_item_pop":
np.log1p
})
ICM_all_dict = apply_discretization_ICM(ICM_all_dict,
ICM_name_to_bins_mapper={
"ICM_asset": 200,
"ICM_price": 200,
"ICM_item_pop": 50
})
# Apply feature weighting
ICM_all_dict = apply_transformation_ICM(
ICM_all_dict,
ICM_name_to_transform_mapper={
"ICM_price": lambda x: x * 1.8474248499810804,
"ICM_asset": lambda x: x * 1.2232716972721878,
"ICM_sub_class": lambda x: x * 1.662671860026709,
"ICM_item_pop": lambda x: x * 0.886528360392298
})
ICM_all = build_ICM_all_from_dict(ICM_all_dict)
return ICM_all
def get_UCM_all(reader: RecSys2019Reader):
URM_all = reader.get_URM_all()
UCM_all_dict = reader.get_loaded_UCM_dict()
ICM_dict = reader.get_loaded_ICM_dict()
UCM_all_dict = apply_feature_engineering_UCM(
UCM_all_dict, URM_all, ICM_dict, ICM_names_to_UCM=["ICM_sub_class"])
# These are useful feature weighting for UserCBF_CF_Warm
UCM_all_dict = apply_transformation_UCM(UCM_all_dict,
UCM_name_to_transform_mapper={
"UCM_sub_class":
lambda x: x / 2,
"UCM_user_act": np.log1p
})
UCM_all_dict = apply_discretization_UCM(
UCM_all_dict, UCM_name_to_bins_mapper={"UCM_user_act": 50})
UCM_all = build_UCM_all_from_dict(UCM_all_dict)
return UCM_all
def get_UCM_all_new(reader: RecSys2019Reader):
URM_all = reader.get_URM_all()
UCM_all_dict = reader.get_loaded_UCM_dict()
ICM_dict = reader.get_loaded_ICM_dict()
# Preprocess ICM
ICM_dict.pop("ICM_all")
ICM_dict = apply_feature_engineering_ICM(
ICM_dict,
URM_all,
UCM_all_dict,
ICM_names_to_count=["ICM_sub_class"],
UCM_names_to_list=["UCM_age"])
ICM_dict = apply_filtering_ICM(
ICM_dict,
ICM_name_to_filter_mapper={
"ICM_asset":
lambda x: x < np.quantile(x, q=0.75) + 0.72 *
(np.quantile(x, q=0.75) - np.quantile(x, q=0.25)),
"ICM_price":
lambda x: x < np.quantile(x, q=0.75) + 4 *
(np.quantile(x, q=0.75) - np.quantile(x, q=0.25))
})
ICM_dict = apply_transformation_ICM(ICM_dict,
ICM_name_to_transform_mapper={
"ICM_asset":
lambda x: np.log1p(1 / x),
"ICM_price":
lambda x: np.log1p(1 / x),
"ICM_item_pop": np.log1p,
"ICM_sub_class_count": np.log1p,
"ICM_age": lambda x: x**(1 / 2.5)
})
ICM_dict = apply_discretization_ICM(ICM_dict,
ICM_name_to_bins_mapper={
"ICM_asset": 200,
"ICM_price": 200,
"ICM_item_pop": 50,
"ICM_sub_class_count": 50
})
# Preprocess UCM
UCM_all_dict = apply_feature_engineering_UCM(
UCM_all_dict,
URM_all,
ICM_dict,
ICM_names_to_UCM=["ICM_sub_class", "ICM_item_pop"])
UCM_all_dict = apply_feature_entropy_UCM(
UCM_all_dict, UCM_names_to_entropy=["UCM_sub_class"])
# Apply useful transformation
UCM_all_dict = apply_transformation_UCM(
UCM_all_dict, UCM_name_to_transform_mapper={"UCM_user_act": np.log1p})
UCM_all_dict = apply_discretization_UCM(UCM_all_dict,
UCM_name_to_bins_mapper={
"UCM_user_act": 50,
"UCM_sub_class_entropy": 20
})
UCM_all = build_UCM_all_from_dict(UCM_all_dict)
return UCM_all
