class Hybrid000AlphaRecommender(BaseRecommender):
"""Hybrid000AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid000AlphaRecommender"
def __init__(self, URM_train, UCM, cold_users, warm_users):
super(Hybrid000AlphaRecommender, self).__init__(URM_train)
self.warm_recommender = SLIM_BPR_Cython(URM_train)
self.cold_recommender = UserKNNCBFRecommender(UCM, URM_train)
self.cold_users = cold_users
self.warm_users = warm_users
def fit(self,
random_seed=42,
epochs=500,
topK=14000,
shrink=2,
lambda_i=0.0,
lambda_j=0.0):
self.warm_recommender.fit(epochs=epochs,
lambda_i=lambda_i,
lambda_j=lambda_j,
random_seed=random_seed)
self.cold_recommender.fit(topK=topK,
shrink=shrink,
feature_weighting="BM25")
def recommend(self,
user_id_array,
cutoff=None,
remove_seen_flag=True,
items_to_compute=None,
remove_top_pop_flag=False,
remove_CustomItems_flag=False,
return_scores=False):
if user_id_array in self.warm_users:
return self.warm_recommender.recommend(user_id_array,
cutoff=cutoff)
if user_id_array in self.cold_users:
return self.cold_recommender.recommend(user_id_array,
cutoff=cutoff)
class Hybrid110Recommender(BaseItemSimilarityMatrixRecommender):
RECOMMENDER_NAME = "Hybrid110Recommender"
def __init__(self, URM_train, seed: int):
super(Hybrid110Recommender, self).__init__(URM_train)
self.number_of_interactions_per_user = (self.URM_train > 0).sum(axis=1)
self.highRange = SLIM_BPR_Cython(URM_train)
self.lowRange = P3alphaRecommender(URM_train)
self.midRange = RP3betaRecommender(URM_train)
#self.itemcf = ItemKNNCFRecommender(urm)
def fit(self):
try:
self.highRange.load_model("result_experiments/range_200--1/","SLIM_BPR_Recommender_best_model")
except:
self.highRange.fit(topK= 100, epochs= 70, symmetric= False, sgd_mode= 'adam', lambda_i= 0.01, lambda_j= 1e-05, learning_rate= 0.0001)
self.lowRange.fit(topK= 685, alpha= 0.41303525095465676, normalize_similarity= False)
self.midRange.fit(topK= 979, alpha= 0.42056182126095865, beta= 0.03446674275249296, normalize_similarity= False)
#self.gamma = alpha_gamma_ratio
def recommend(self, user_id_array, cutoff=None, remove_seen_flag=True, items_to_compute=None,
remove_top_pop_flag=False, remove_custom_items_flag = False, return_scores=False):
if user_id_array in self._user_with_interactions_within(0, 50):
return self.lowRange.recommend(user_id_array, cutoff=cutoff)
elif user_id_array in self._user_with_interactions_within(51, 200):
return self.midRange.recommend(user_id_array, cutoff=cutoff)
elif user_id_array in self._user_with_interactions_over(200):
return self.highRange.recommend(user_id_array, cutoff=cutoff)
"""recs = []
for user_id in user_id_array:
if user_id in self._user_with_interactions_within(0, 200):
rec = self.lowRange.recommend(user_id_array, cutoff=cutoff)
elif user_id in self._user_with_interactions_over(200):
rec = self.highRange.recommend(user_id_array, cutoff=cutoff)
recs.append(rec)
scores_batch = self._compute_item_score(user_id_array, items_to_compute=items_to_compute)
return np.array(recs), scores_batch"""
def _user_with_interactions_within(self, x=0, y=200):
a = self.number_of_interactions_per_user
x1 = np.where(a >= x)
x2 = np.where(a <= y)
return np.array([n for n in x1[0] if n in x2[0]])
def _user_with_interactions_over(self, x=0):
a = self.number_of_interactions_per_user
x1 = np.where(a > x)
return np.array([n for n in x1[0]])
def save_model(self, folder_path, file_name = None):
if file_name is None:
file_name = self.RECOMMENDER_NAME
self._print("Saving model in file '{}'".format(folder_path + file_name))
dataIO = DataIO(folder_path=folder_path)
dataIO.save_data(file_name=file_name, data_dict_to_save = {})
self._print("Saving complete")