def __init__(self, data: DataObject):
super(CarmenTran_Recommender, self).__init__(data.urm_train)
self.data = data
self.cold_recommender = Hybrid100AlphaRecommender(data)
self.warm_1_recommender = Hybrid201AlphaRecommender(data)
self.warm_recommender = Hybrid3ScoreRecommender(
data=data, random_seed=data.random_seed)
def __init__(self, data: DataObject):
super(Hybrid600AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.cold_recommender = Hybrid100AlphaRecommender(data)
self.warm_1_recommender = Hybrid101AlphaRecommender(data)
self.warm_recommender = Hybrid3ScoreSubRecommender(data=data)
def __init__(self, data: DataObject):
super(Hybrid004AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.warm_recommender = Hybrid300AlphaRecommender(data)
self.warm_1_recommender = Hybrid101AlphaRecommender(data)
# self.warm_2_recommender = Hybrid102AlphaRecommender(data)
self.warm_7_recommender = Hybrid300AlphaRecommender(data)
self.cold_recommender = Hybrid100AlphaRecommender(data)
def __init__(self,
data: DataObject,
k: int,
leave_k_out=0,
threshold=0,
probability=0.2):
super(Hybrid400AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.max_cutoff = 30
rec = ItemKNNCBFRecommender(data.urm_train, data.icm_all_augmented)
rec.fit(topK=10)
print(f"Not augmented {data.augmented_urm.nnz}")
data.augmented_urm = augment_with_item_similarity_best_scores(
data.augmented_urm,
rec.W_sparse,
500,
value=0.3,
remove_seen=False)
# print(f"After User CBF {data.augmented_urm.nnz}")
# data.augmented_urm = augment_with_best_recommended_items(data.augmented_urm, rec,
# data.urm_train_users_by_type[1][1], 1, value=0.2)
#
print(f"After Item CBF {data.augmented_urm.nnz}")
rec = Hybrid100AlphaRecommender(data)
rec.fit()
data.augmented_urm = augment_with_best_recommended_items(
data.augmented_urm,
rec,
data.urm_train_users_by_type[0][1],
1,
value=1)
# data.augmented_urm = augment_with_best_recommended_items(data.augmented_urm, rec,
# data.ids_warm_train_users, 2, value=0.1)
print(f"After User CBF {data.augmented_urm.nnz}")
# print(f"After User CBF {data.augmented_urm.nnz}")
rec = None
recs = Parallel(n_jobs=6)(delayed(
par(data,
leave_k_out=leave_k_out,
threshold=threshold,
probability=probability).split_and_fit)(i) for i in range(k))
self.hybrid_rec = Hybrid1CXAlphaRecommender(
data,
recommenders=recs,
recommended_users=data.ids_user,
max_cutoff=self.max_cutoff)
self.hybrid_rec.weights = np.array([
np.sqrt(np.array(fib(30)[::-1])).astype(np.int).tolist()
for _ in range(k)
])
def __init__(self, data: DataObject):
super(Hybrid003AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.poco_warm_recommender = Hybrid101AlphaRecommender(data)
self.quasi_warm_recommender = Hybrid102AlphaRecommender(data)
# self.warm_recommender = ItemKNNCFRecommender(data.urm_train)
# self.warm_recommender = Hybrid105AlphaRecommender(data)
self.warm_recommender = RP3betaRecommender(data.urm_train)
self.warm_7_recommender = ItemKNNCFRecommender(data.urm_train)
self.warm_8_recommender = Hybrid108AlphaRecommender(data)
self.warm_9_recommender = Hybrid109AlphaRecommender(data)
self.cold_recommender = Hybrid100AlphaRecommender(data)
def __init__(self, data: DataObject):
super(Hybrid005AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.cold_recommender = Hybrid100AlphaRecommender(data)
self.cold_recommender.fit()
# rec1 = RP3betaRecommender(data.urm_train)
# rec1.fit(topK=20, alpha=0.12, beta=0.24)
# rec2 = ItemKNNCFRecommender(data.urm_train)
# rec2.fit(topK=22, shrink=850, similarity='jaccard', feature_weighting='BM25')
# self.warm_2_recommender = ItemKNNSimilarityHybridRecommender(data.urm_train, rec1.W_sparse, rec2.W_sparse)
urm = data.urm_train
urm = sps.vstack([data.urm_train, data.icm_all_augmented.T])
urm = urm.tocsr()
self.warm_recommender = MultiThreadSLIM_ElasticNet(data.urm_train)
self.warm_2_3_recommender = MultiThreadSLIM_ElasticNet(data.urm_train)
self.warm_1_recommender = Hybrid101AlphaRecommender(data)