def fit(self):
self.cold_recommender.fit()
rec_augmenter_cold_item = ItemKNNCBFOnlyColdRecommender(self.data)
# r2 = ItemKNNCBFRecommender(data.urm_train, data.icm_all_augmented)
rec_augmenter_cold_item.fit(topK=10,
shrink=1,
feature_weighting="BM25")
self.warm_recommender.augment(rec_augmenter_cold_item,
self.data.ids_warm_train_users,
self.warm_recommender.augmented_matrix,
cutoff=3,
amount=1)
rec_warm = RP3betaRecommender(self.warm_recommender.augmented_matrix)
rec_warm.fit(topK=20, alpha=0.16, beta=0.24)
self.warm_recommender.fit(rec_warm)
self.warm_1_recommender.fit()
self.warm_7_recommender.augment(
rec_augmenter_cold_item,
self.data.ids_warm_train_users,
self.warm_7_recommender.augmented_matrix,
cutoff=3,
amount=1)
rec_warm_7 = ItemKNNCFRecommender(
self.warm_7_recommender.augmented_matrix)
rec_warm_7.fit(topK=22,
shrink=850,
feature_weighting="BM25",
similarity="jaccard")
self.warm_7_recommender.fit(rec_warm_7)
def __init__(self, data: DataObject, random_seed: int, alpha=1):
super(Hybrid3ScoreRecommender, self).__init__(data.urm_train)
self.random_seed = random_seed
self.slim = SLIMElasticNetRecommender(self.URM_train)
self.rp3 = RP3betaRecommender(self.URM_train)
self.itemcf = ItemKNNCFRecommender(self.URM_train)
self.alpha = alpha
class Hybrid105AlphaRecommender(BaseRecommender):
"""Hybrid105AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid105AlphaRecommender"
def __init__(self, data: DataObject):
super(Hybrid105AlphaRecommender, self).__init__(data.urm_train)
self.data = data
# self.rec1 = SLIM_BPR_Cython(data.urm_train)
self.rec2 = ItemKNNCFRecommender(data.urm_train)
self.rec3 = RP3betaRecommender(data.urm_train)
# self.rec1.fit(sgd_mode="adagrad", topK=50, epochs=150, learning_rate=1e-05, lambda_i=1, lambda_j=0.001)
self.rec2.fit(topK=10, shrink=30, similarity="tanimoto")
self.rec3.fit(topK=30, alpha=0.24, beta=0.24, implicit=True, normalize_similarity=True)
self.hybrid_rec = Hybrid1XXAlphaRecommender(data, recommenders=[self.rec2, self.rec3], max_cutoff=20)
def fit(self):
weights = np.array([[91.45, 46.9, 36.6, 26.75, 19.85, 18.45, 14.95, 11.9, 14.25, 9.9, 10.65, 10.65,
9.85, 9.2, 7.6, 7.8, 6.6, 7.05, 5.8, 6.25, 4.7, 5., 5.45, 4.45,
3.8, 4.05, 4.05, 4.05, 3.75, 3.95],
[94.7, 51.2, 34.25, 26.45, 22.25, 17.55, 15.7, 13.7, 10.75, 9.9, 9.15, 8.2,
6.6, 6.35, 6.65, 5.45, 5.25, 4.4, 5.6, 3.95, 3.2, 3.85, 2.6, 2.85,
2.75, 2.3, 2.55, 2.4, 2.9, 1.6]])
weights[0] = weights[0] / 3 * 2
self.hybrid_rec.fit(weights=weights)
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):
return self.hybrid_rec.recommend(user_id_array=user_id_array, cutoff=cutoff)
def __init__(self, urm_train):
super(HybridNorm3Recommender, self).__init__(urm_train)
urm_train = check_matrix(urm_train.copy(), 'csr')
self.num_users = urm_train.shape[0]
# recommender_1 = HybridGenRecommender(urm_train)
# recommender_1.fit()
recommender_1 = RP3betaRecommender(urm_train)
recommender_1.fit(topK=16,
alpha=0.03374950051351756,
beta=0.24087176329409027,
normalize_similarity=True)
recommender_3 = UserKNNCFRecommender(urm_train)
recommender_3.fit(shrink=2, topK=600, normalize=True)
recommender_2 = ItemKNNCFRecommender(urm_train)
recommender_2.fit(topK=5,
shrink=500,
feature_weighting='BM25',
similarity='tversky',
normalize=False,
tversky_alpha=0.0,
tversky_beta=1.0)
self.recommender_1 = recommender_1
self.recommender_2 = recommender_2
self.recommender_3 = recommender_3
class Hybrid001AlphaRecommender(BaseRecommender):
"""Hybrid001AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid001AlphaRecommender"
def __init__(self, URM_train, UCM, cold_users, warm_users):
super(Hybrid001AlphaRecommender, self).__init__(URM_train)
self.warm_recommender = ItemKNNCFRecommender(URM_train)
self.cold_recommender = UserKNNCBFRecommender(UCM, URM_train)
self.cold_users = cold_users
self.warm_users = warm_users
def fit(self):
self.warm_recommender.fit(topK=12, shrink=16)
self.cold_recommender.fit(topK=11000, shrink=2)
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)
def __init__(self, data: DataObject):
super(Hybrid3ScoreSubRecommender, self).__init__(data.urm_train)
urm = sps.vstack([data.urm_train, data.icm_all_augmented.T])
urm = urm.tocsr()
self.slim = SLIMElasticNetRecommender(urm)
self.rp3 = RP3betaRecommender(self.URM_train)
self.itemcf = ItemKNNCFRecommender(self.URM_train)
self.random_seed = data.random_seed
def objective(**params):
recommender = ItemKNNCFRecommender(URM_train)
recommender.fit(**params)
print(f"RUN#{run}:\n> space: [ topK={recommender.topK}, shrink={recommender.shrink} ]")
result_dict, _ = evaluator_validation.evaluateRecommender(recommender)
MAP = result_dict[10]['MAP']
print(f"> result: [ MAP={MAP} ]")
return -MAP
class Hybrid3ScoreSubRecommender(BaseRecommender):
"""Hybrid3ScoreSubRecommender recommender"""
RECOMMENDER_NAME = "Hybrid3ScoreRecommender"
def __init__(self, data: DataObject):
super(Hybrid3ScoreSubRecommender, self).__init__(data.urm_train)
urm = sps.vstack([data.urm_train, data.icm_all_augmented.T])
urm = urm.tocsr()
self.slim = SLIMElasticNetRecommender(urm)
self.rp3 = RP3betaRecommender(self.URM_train)
self.itemcf = ItemKNNCFRecommender(self.URM_train)
self.random_seed = data.random_seed
def fit(self, alpha_beta_ratio=1, alpha_gamma_ratio=1):
try:
self.slim.load_model('stored_recommenders/slim_elastic_net/',
f'with_icm_{self.random_seed}_topK=100_l1_ratio=0.04705_alpha=0.00115_positive_only=True_max_iter=35')
except:
self.slim.fit(topK=100, l1_ratio=0.04705, alpha=0.00115, positive_only=True, max_iter=35)
self.slim.save_model('stored_recommenders/slim_elastic_net/',
f'with_icm_{self.random_seed}_topK=100_l1_ratio=0.04705_alpha=0.00115_positive_only=True_max_iter=35')
self.rp3.fit(topK=20, alpha=0.16, beta=0.24)
self.itemcf.fit(topK=22, shrink=850, similarity='jaccard', feature_weighting='BM25')
self.alpha = 1
self.beta = alpha_beta_ratio
self.gamma = alpha_gamma_ratio
def _compute_item_score(self, user_id_array, items_to_compute = None):
# ATTENTION!
# THIS METHOD WORKS ONLY IF user_id_array IS A SCALAR AND NOT AN ARRAY
# TODO
scores_slim = self.slim._compute_item_score(user_id_array=user_id_array)
scores_rp3 = self.rp3._compute_item_score(user_id_array=user_id_array)
scores_itemcf = self.itemcf._compute_item_score(user_id_array=user_id_array)
# normalization
slim_max = scores_slim.max()
rp3_max = scores_rp3.max()
itemcf_max = scores_itemcf.max()
if not slim_max == 0:
scores_slim /= slim_max
if not rp3_max == 0:
scores_rp3 /= rp3_max
if not itemcf_max == 0:
scores_itemcf /= itemcf_max
scores_total = self.alpha * scores_slim + self.beta * scores_rp3 + self.gamma * scores_itemcf
return scores_total
def __init__(self, data: DataObject):
super(Hybrid201AlphaRecommender, self).__init__(data.urm_train)
self.data = data
urm = data.urm_train
urm = sps.vstack([data.urm_train, data.icm_all_augmented.T])
urm = urm.tocsr()
self.rec1 = SLIM_BPR_Cython(urm)
self.rec2 = ItemKNNCFRecommender(urm)
self.rec3 = RP3betaRecommender(urm)
self.random_seed = data.random_seed
try:
self.rec1.load_model(
"stored_recommenders/slim_bpr/",
f'with_icm_{self.random_seed}_topK=15000_epochs=250_learning_rate=1e-05_lambda_i=0.01_lambda_j=0.01'
)
except:
self.rec1.fit(sgd_mode="adagrad",
topK=15000,
epochs=250,
learning_rate=1e-05,
lambda_i=0.01,
lambda_j=0.01)
self.rec1.save_model(
"stored_recommenders/slim_bpr/",
f'with_icm_{self.random_seed}_topK=15000_epochs=250_learning_rate=1e-05_lambda_i=0.01_lambda_j=0.01'
)
try:
self.rec2.load_model(
"stored_recommenders/item_cf/",
f'with_icm_{self.random_seed}_topK=20000_shrink=20000_feature_weighting=TF-IDF'
)
except:
self.rec2.fit(topK=20000, shrink=20000, feature_weighting="TF-IDF")
self.rec2.save_model(
"stored_recommenders/item_cf/",
f'with_icm_{self.random_seed}_topK=20000_shrink=20000_feature_weighting=TF-IDF'
)
try:
self.rec3.load_model(
"stored_recommenders/rp3_beta/",
f'with_icm_{self.random_seed}_topK=10000_alpha=0.55_beta=0.01_implicit=True_normalize_similarity=True'
)
except:
self.rec3.fit(topK=10000,
alpha=0.55,
beta=0.01,
implicit=True,
normalize_similarity=True)
self.rec3.save_model(
"stored_recommenders/rp3_beta/",
f'with_icm_{self.random_seed}_topK=10000_alpha=0.55_beta=0.01_implicit=True_normalize_similarity=True'
)
self.hybrid_rec = Hybrid1XXAlphaRecommender(
data,
recommenders=[self.rec1, self.rec2, self.rec3],
max_cutoff=20)
class Hybrid101AlphaRecommender(BaseRecommender):
"""Hybrid101AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid101AlphaRecommender"
def __init__(self, data: DataObject):
super(Hybrid101AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = SLIM_BPR_Cython(data.urm_train)
self.rec2 = ItemKNNCFRecommender(data.urm_train)
self.rec3 = RP3betaRecommender(data.urm_train)
self.rec1.fit(sgd_mode="adagrad",
topK=15000,
epochs=250,
learning_rate=1e-05,
lambda_i=0.01,
lambda_j=0.01)
# self.rec1.fit(topK=435, epochs=115, symmetric=True, sgd_mode='adagrad', lambda_i=0.0010067865845523253,
# lambda_j=3.764224446055186e-05, learning_rate=0.00024125117955549121)
self.rec2.fit(topK=20000, shrink=20000, feature_weighting="TF-IDF")
self.rec3.fit(topK=10000,
alpha=0.55,
beta=0.01,
implicit=True,
normalize_similarity=True)
self.hybrid_rec = Hybrid1XXAlphaRecommender(
data,
recommenders=[self.rec1, self.rec2, self.rec3],
max_cutoff=20)
def fit(self):
weights = [[
69.4, 25.7, 11.7, 9.4, 8.4, 5.4, 6.6, 6., 5.5, 5.6, 5., 4.4, 3.3,
5.7, 4.2, 3.7, 4.5, 2.8, 3.8, 3.4
],
[
77.8, 29.3, 17.4, 9., 8.5, 8.9, 5.9, 5.9, 5.4, 5.1, 6.,
6.3, 4.4, 4.6, 5.2, 4.9, 3.5, 3.3, 3.5, 4.3
],
[
78.5, 29.2, 15.6, 10.9, 9.4, 6.5, 8.3, 5.7, 6.3, 6.6,
4.3, 4.2, 4.3, 4.6, 6.1, 4.7, 5.1, 4.7, 4.9, 5.1
]]
self.hybrid_rec.fit(weights=weights)
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):
return self.hybrid_rec.recommend(user_id_array=user_id_array,
cutoff=cutoff)
class Hybrid3ScoreRecommender(BaseRecommender):
"""Hybrid3ScoreRecommender recommender"""
RECOMMENDER_NAME = "Hybrid3ScoreRecommender"
def __init__(self, data: DataObject, random_seed: int, alpha=1):
super(Hybrid3ScoreRecommender, self).__init__(data.urm_train)
self.random_seed = random_seed
self.slim = SLIMElasticNetRecommender(self.URM_train)
self.rp3 = RP3betaRecommender(self.URM_train)
self.itemcf = ItemKNNCFRecommender(self.URM_train)
self.alpha = alpha
def fit(self, alpha_beta_ratio=1, alpha_gamma_ratio=1):
self.slim.load_model(
'', 'SLIM_ElasticNetURM_seed=' + str(self.random_seed) +
'_topK=100_l1_ratio=0.04705_alpha=0.00115_positive_only=True_max_iter=35'
)
self.rp3.fit(topK=20, alpha=0.16, beta=0.24)
self.itemcf.fit(topK=22,
shrink=850,
similarity='jaccard',
feature_weighting='BM25')
# self.alpha = 1
self.beta = self.alpha * alpha_beta_ratio
self.gamma = self.alpha * alpha_gamma_ratio
def _compute_item_score(self, user_id_array, items_to_compute=None):
# ATTENTION!
# THIS METHOD WORKS ONLY IF user_id_array IS A SCALAR AND NOT AN ARRAY
# TODO
scores_slim = self.slim._compute_item_score(
user_id_array=user_id_array)
scores_rp3 = self.rp3._compute_item_score(user_id_array=user_id_array)
scores_itemcf = self.itemcf._compute_item_score(
user_id_array=user_id_array)
# normalization
slim_max = scores_slim.max()
rp3_max = scores_rp3.max()
itemcf_max = scores_itemcf.max()
if not slim_max == 0:
scores_slim /= slim_max
if not rp3_max == 0:
scores_rp3 /= rp3_max
if not itemcf_max == 0:
scores_itemcf /= itemcf_max
scores_total = self.alpha * scores_slim + self.beta * scores_rp3 + self.gamma * scores_itemcf
return scores_total
def __init__(self, data: DataObject):
super(Hybrid105AlphaRecommender, self).__init__(data.urm_train)
self.data = data
# self.rec1 = SLIM_BPR_Cython(data.urm_train)
self.rec2 = ItemKNNCFRecommender(data.urm_train)
self.rec3 = RP3betaRecommender(data.urm_train)
# self.rec1.fit(sgd_mode="adagrad", topK=50, epochs=150, learning_rate=1e-05, lambda_i=1, lambda_j=0.001)
self.rec2.fit(topK=10, shrink=30, similarity="tanimoto")
self.rec3.fit(topK=30, alpha=0.24, beta=0.24, implicit=True, normalize_similarity=True)
self.hybrid_rec = Hybrid1XXAlphaRecommender(data, recommenders=[self.rec2, self.rec3], max_cutoff=20)
def __init__(self, data: DataObject):
super(Hybrid104AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = RP3betaRecommender(data.urm_train)
self.rec1.fit(topK=26, alpha=0.25, beta=0.21)
self.rec2 = ItemKNNCFRecommender(data.urm_train)
self.rec2.fit(topK=10, shrink=1000, similarity="tanimoto", feature_weighting="BM25")
target_users = data.urm_train_users_by_type[4][1]
self.target_users = target_users
self.hybrid_rec = Hybrid1CXAlphaRecommender(data, recommenders=[self.rec1, self.rec2],
recommended_users=target_users, max_cutoff=30)
def __init__(self, data: DataObject):
super(Hybrid109AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = RP3betaRecommender(data.urm_train)
self.rec2 = ItemKNNCFRecommender(data.urm_train)
self.rec1.fit(topK=20, alpha=0.11, beta=0.18)
self.rec2.fit(topK=18, shrink=850, similarity='jaccard', feature_weighting='BM25')
cold = data.ids_cold_user
self.target_users = data.urm_train_users_by_type[10][1]
self.hybrid_rec = Hybrid1CXAlphaRecommender(data, recommenders=[self.rec1, self.rec2],
recommended_users=self.target_users, max_cutoff=30)
class Hybrid102AlphaRecommender(BaseRecommender):
"""Hybrid102AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid102AlphaRecommender"
def __init__(self, data: DataObject):
super(Hybrid102AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = UserKNNCFRecommender(data.urm_train)
self.rec1.fit(topK=1000, shrink=4500, similarity="cosine", feature_weighting="TF-IDF")
self.rec2 = ItemKNNCFRecommender(data.urm_train)
self.rec2.fit(topK=2000, shrink=800, similarity="cosine", feature_weighting="TF-IDF")
self.rec3 = SLIM_BPR_Cython(data.urm_train)
self.rec3.fit(epochs=120, topK=800, lambda_i=0.1, lambda_j=0.1, learning_rate=0.0001)
self.rec4 = RP3betaRecommender(data.urm_train)
self.rec4.fit(topK=30, alpha=0.21, beta=0.25)
target_users = data.urm_train_users_by_type[2][1]
self.target_users = target_users
self.hybrid_rec = Hybrid1CXAlphaRecommender(data, recommenders=[self.rec1, self.rec2, self.rec3],
recommended_users=target_users, max_cutoff=30)
def fit(self):
weights1 = np.array([[0.37625119, 0.43193487, 0.17444842, 0.16197883, 0.18204363,
0.17016599, 0.14983434, 0.11938279, 0.09980418, 0.1147748,
0.12762677, 0.08689066, 0.09533745, 0.10492991, 0.097475,
0.05278562, 0.05244627, 0.0602501, 0.06743845, 0.06145589,
0.07008017, 0.07410305, 0.07170746, 0.04231058, 0.04493697,
0.02516579, 0.0176046, 0.01360429, 0., 0.],
[0.55298149, 0.27456885, 0.2278579, 0.25095311, 0.24721051,
0.09937549, 0.09209609, 0.07158969, 0.07174988, 0.08251237,
0.09157335, 0.10530935, 0.1106961, 0.12150468, 0.12001527,
0.10052318, 0.09536568, 0.10770821, 0.08553278, 0.06198749,
0.05708056, 0.05176975, 0.05953521, 0.05567152, 0.06083775,
0.02776653, 0.02663699, 0.01181728, 0.01168978, 0.],
[0.25041731, 0.15536414, 0.16953122, 0.17164006, 0.11443169,
0.11873982, 0.07100542, 0.06452205, 0.06123626, 0.06430055,
0.06311274, 0.05618836, 0.05331187, 0.04611177, 0.04239514,
0.03824963, 0.04398116, 0.04738213, 0.04862799, 0.03962175,
0.04556502, 0.04738956, 0.054498, 0.0626727, 0.04973429,
0.03219802, 0.03227312, 0.0307041, 0.03396853, 0.],
[1., 0.5538608, 0.44692181, 0.20321725, 0.22012478,
0.1873366, 0.14329206, 0.09783222, 0.10765581, 0.10658318,
0.12257066, 0.13699397, 0.15743225, 0.12181424, 0.13897041,
0.08672218, 0.09188654, 0.05170634, 0.04459521, 0.04785834,
0.05248675, 0.06035977, 0.06733202, 0.06760871, 0.07775002,
0.0720465, 0.05977294, 0.04260028, 0.00546561, 0.0055422]])
self.hybrid_rec.weights = weights1
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.target_users:
return self.hybrid_rec.recommend(user_id_array=user_id_array, cutoff=cutoff)
else:
return []
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 eval_one_data(random_seed):
data_reader = DataReader()
data = DataObject(data_reader, 1, random_seed=random_seed)
result = []
recommender = ItemKNNCFRecommender(data.urm_train)
recommender.fit(topK = args["topK"], shrink=args["shrink"], similarity=args["similarity"], feature_weighting=args["feature_weighting"])
for n, users, description in data.urm_train_users_by_type:
eval, map = MyEvaluator.evaluate_algorithm(data.urm_test, users, recommender, at=10, remove_top=0)
result.append(map)
users = data.ids_target_users
eval, map = MyEvaluator.evaluate_algorithm(data.urm_test, users, recommender, at=10, remove_top=0)
result.append(map)
return result
def __init__(self, data: DataObject):
super(Hybrid108AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = ItemKNNCFRecommender(data.urm_train)
self.rec2 = P3alphaRecommender(data.urm_train)
self.rec3 = RP3betaRecommender(data.urm_train)
self.rec1.fit(shrink=15, topK=12, feature_weighting="none")
self.rec2.fit(topK=170, implicit=True, alpha=0.5)
self.rec3.fit(topK=60, alpha=0.5, beta=0.1, implicit=True)
self.hybrid_rec = Hybrid1XXAlphaRecommender(
data,
recommenders=[self.rec1, self.rec2, self.rec3],
max_cutoff=12)
def __init__(self, urm_train, eurm=False):
super(HybridNorm2Recommender, self).__init__(urm_train)
self.data_folder = Path(__file__).parent.parent.absolute()
self.eurm = eurm
self.num_users = urm_train.shape[0]
urm_train = check_matrix(urm_train.copy(), 'csr')
recommender_2 = ItemKNNCFRecommender(urm_train)
recommender_2.fit(topK=5, shrink=500, feature_weighting='BM25', similarity='tversky', normalize=False, tversky_alpha=0.0, tversky_beta=1.0)
recommender_3 = UserKNNCFRecommender(urm_train)
recommender_3.fit(shrink=2, topK=600, normalize=True)
# recommender_3 = UserKNNCFRecommender(urm_train)
# recommender_3.fit(topK=697, shrink=1000, feature_weighting='TF-IDF', similarity='tversky', normalize=False,
# tversky_alpha=1.0, tversky_beta=1.0)
recommender_4 = RP3betaRecommender(urm_train)
recommender_4.fit(topK=16, alpha=0.03374950051351756, beta=0.24087176329409027, normalize_similarity=True)
recommender_5 = SLIM_BPR_Cython(urm_train)
recommender_5.fit(lambda_i=0.0926694015, lambda_j=0.001697250, learning_rate=0.002391, epochs=65, topK=200)
recommender_6 = ALSRecommender(urm_train)
recommender_6.fit(alpha=5, iterations=40, reg=0.3)
self.recommender_2 = recommender_2
self.recommender_3 = recommender_3
self.recommender_4 = recommender_4
self.recommender_5 = recommender_5
self.recommender_6 = recommender_6
if self.eurm:
self.score_matrix_1 = sps.load_npz(self.data_folder / 'Data/icm_sparse.npz')
self.score_matrix_2 = self.recommender_2._compute_item_matrix_score(np.arange(self.num_users))
self.score_matrix_3 = self.recommender_3._compute_item_matrix_score(np.arange(self.num_users))
self.score_matrix_4 = self.recommender_4._compute_item_matrix_score(np.arange(self.num_users))
self.score_matrix_5 = self.recommender_5._compute_item_matrix_score(np.arange(self.num_users))
self.score_matrix_6 = self.recommender_6._compute_item_score(np.arange(self.num_users))
self.score_matrix_7 = sps.load_npz(self.data_folder / 'Data/ucm_sparse.npz')
self.score_matrix_1 = normalize(self.score_matrix_1, norm='l2', axis=1)
self.score_matrix_2 = normalize(self.score_matrix_2, norm='l2', axis=1)
self.score_matrix_3 = normalize(self.score_matrix_3, norm='l2', axis=1)
self.score_matrix_4 = normalize(self.score_matrix_4, norm='l2', axis=1)
self.score_matrix_5 = normalize(self.score_matrix_5, norm='l2', axis=1)
self.score_matrix_6 = normalize(self.score_matrix_6, norm='l2', axis=1)
self.score_matrix_7 = normalize(self.score_matrix_7, norm='l2', axis=1)
def __init__(self, URM_train, verbose=True):
super(MergedHybrid000, self).__init__(URM_train, verbose=verbose)
rec1 = RP3betaRecommender(URM_train, verbose=False)
try:
rec1.load_model(
'stored_recommenders/RP3betaRecommender/best_at_26_10_20')
except:
rec1.fit(alpha=0.4530815441932864,
beta=0.008742088319964482,
topK=104,
normalize_similarity=False)
rec1.save_model(
'stored_recommenders/RP3betaRecommender/best_at_26_10_20')
rec2 = ItemKNNCFRecommender(URM_train, verbose=False)
try:
rec2.load_model(
'stored_recommenders/ItemKNNCFRecommender/best_at_26_10_20')
except:
rec2.fit(topK=967, shrink=356, similarity='cosine', normalize=True)
rec2.save_model(
'stored_recommenders/ItemKNNCFRecommender/best_at_26_10_20')
self.rec1 = rec1
self.rec2 = rec2
self.rec1_W_sparse = rec1.W_sparse.copy()
self.rec2_W_sparse = rec2.W_sparse.copy()
self.URM_train = URM_train
def __init__(self, data: DataObject):
super(Hybrid102AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = UserKNNCFRecommender(data.urm_train)
self.rec1.fit(topK=1000, shrink=4500, similarity="cosine", feature_weighting="TF-IDF")
self.rec2 = ItemKNNCFRecommender(data.urm_train)
self.rec2.fit(topK=2000, shrink=800, similarity="cosine", feature_weighting="TF-IDF")
self.rec3 = SLIM_BPR_Cython(data.urm_train)
self.rec3.fit(epochs=120, topK=800, lambda_i=0.1, lambda_j=0.1, learning_rate=0.0001)
self.rec4 = RP3betaRecommender(data.urm_train)
self.rec4.fit(topK=30, alpha=0.21, beta=0.25)
target_users = data.urm_train_users_by_type[2][1]
self.target_users = target_users
self.hybrid_rec = Hybrid1CXAlphaRecommender(data, recommenders=[self.rec1, self.rec2, self.rec3],
recommended_users=target_users, max_cutoff=30)
def __init__(self, urm_train):
super(HybridNorm1Recommender, self).__init__(urm_train)
self.num_users = urm_train.shape[0]
urm_train = check_matrix(urm_train.copy(), 'csr')
recommender_1 = HybridGen2Recommender(urm_train)
recommender_1.fit()
recommender_2 = ItemKNNCFRecommender(urm_train)
recommender_2.fit(topK=5,
shrink=500,
feature_weighting='BM25',
similarity='tversky',
normalize=False,
tversky_alpha=0.0,
tversky_beta=1.0)
recommender_3 = UserKNNCFRecommender(urm_train)
recommender_3.fit(topK=697,
shrink=1000,
feature_weighting='TF-IDF',
similarity='tversky',
normalize=False,
tversky_alpha=1.0,
tversky_beta=1.0)
recommender_4 = RP3betaRecommender(urm_train)
recommender_4.fit(topK=16,
alpha=0.03374950051351756,
beta=0.24087176329409027,
normalize_similarity=True)
recommmender_5 = SLIM_BPR_Cython(urm_train)
recommmender_5.fit(lambda_i=0.0926694015,
lambda_j=0.001697250,
learning_rate=0.002391,
epochs=65,
topK=200)
recommender_6 = ALSRecommender(urm_train)
recommender_6.fit(alpha=5, iterations=40, reg=0.3)
self.recommender_1 = recommender_1
self.recommender_2 = recommender_2
self.recommender_3 = recommender_3
self.recommender_4 = recommender_4
self.recommender_5 = recommmender_5
self.recommender_6 = recommender_6
def __init__(self,
URM_train,
ICM_train,
submission=False,
verbose=True,
seed=1205):
super(LinearHybridC001, self).__init__(URM_train, verbose=verbose)
self.URM_train = URM_train
self.ICM_train = ICM_train
# seed 1205: {'num_factors': 83, 'confidence_scaling': 'linear', 'alpha': 28.4278070726612, 'epsilon':
# 1.0234211788885077, 'reg': 0.0027328110246575004, 'epochs': 20}
self.__rec1 = IALSRecommender(URM_train, verbose=False)
self.__rec1_params = {
'num_factors': 83,
'confidence_scaling': 'linear',
'alpha': 28.4278070726612,
'epsilon': 1.0234211788885077,
'reg': 0.0027328110246575004,
'epochs': 15
} #### -5!!
# seed 1205: {'topK': 225, 'shrink': 1000, 'similarity': 'cosine', 'normalize': True, 'feature_weighting':
# 'BM25'}
self.__rec2 = ItemKNNCBFRecommender(URM_train,
ICM_train,
verbose=False)
self.__rec2_params = {
'topK': 225,
'shrink': 1000,
'similarity': 'cosine',
'normalize': True,
'feature_weighting': 'BM25'
}
# seed 1205: {'topK': 220, 'shrink': 175, 'similarity': 'cosine', 'normalize': False}
self.__rec3 = ItemKNNCFRecommender(URM_train, verbose=False)
self.__rec3_params = {
'topK': 220,
'shrink': 175,
'similarity': 'cosine',
'normalize': False
}
self.__a = self.__b = self.__c = None
self.seed = seed
self.__submission = submission
def __init__(self,
URM_train,
ICM_train,
submission=False,
verbose=True,
seed=1205):
super(LinearHybrid002, self).__init__(URM_train, verbose=verbose)
self.URM_train = URM_train
self.ICM_train = ICM_train
# seed 1205: 'topK': 190, 'shrink': 0, 'similarity': 'cosine', 'normalize': True
self.__rec1 = UserKNNCFRecommender(URM_train, verbose=False)
self.__rec1_params = {
'topK': 190,
'shrink': 0,
'similarity': 'cosine',
'normalize': True
}
# seed 1205: 'topK': 100, 'shrink': 1000, 'similarity': 'asymmetric', 'normalize': True, 'asymmetric_alpha': 0.0
self.__rec2 = ItemKNNCFRecommender(URM_train, verbose=False)
self.__rec2_params = {
'topK': 100,
'shrink': 1000,
'similarity': 'asymmetric',
'normalize': True,
'asymmetric_alpha': 0.0
}
# seed 1205: 'topK': 205, 'shrink': 1000, 'similarity': 'cosine', 'normalize': True, 'feature_weighting': 'BM25'
self.__rec3 = ItemKNNCBFRecommender(URM_train,
ICM_train,
verbose=False)
self.__rec3_params = {
'topK': 205,
'shrink': 1000,
'similarity': 'cosine',
'normalize': True,
'feature_weighting': 'BM25'
}
self.__a = self.__b = self.__c = None
self.seed = seed
self.__submission = submission
def __init__(self, URM_train):
super(HybridWarmRecommender, self).__init__(URM_train)
recommender_1 = ItemKNNCFRecommender(URM_train)
recommender_1.fit(topK=20, shrink=30)
recommender_2 = SLIM_BPR_Cython(URM_train)
recommender_2.fit(epochs=40,
lambda_i=0.01,
lambda_j=0.001,
learning_rate=1e-4,
topK=200)
# recommender_3 = TopPop(URM_train)
# recommender_3.fit()
self.URM_train = check_matrix(URM_train.copy(), 'csr')
self.recommender_1 = recommender_1
self.recommender_2 = recommender_2
def __init__(self, URM_train, ICM_train, submission=False, verbose=True, seed=1205):
super(LinearHybrid001, self).__init__(URM_train, verbose = verbose)
self.URM_train = URM_train
self.ICM_train = ICM_train
self.__rec1 = SLIMElasticNetRecommender(URM_train, verbose=False)
self.__rec1_params = {'topK': 120, 'l1_ratio': 1e-5, 'alpha': 0.066}
# seed 1205: 'topK': 620, 'shrink': 121, 'similarity': 'asymmetric', 'normalize': True, 'asymmetric_alpha': 0.5526988987666924
self.__rec2 = ItemKNNCFRecommender(URM_train, verbose=False)
self.__rec2_params = {'topK': 620, 'shrink': 121, 'similarity': 'asymmetric', 'normalize': True, 'asymmetric_alpha': 0.5526988987666924}
# seed 1205: 'topK': 115, 'shrink': 1000, 'similarity': 'cosine', 'normalize': True, 'feature_weighting': 'BM25'
self.__rec3 = ItemKNNCBFRecommender(URM_train, ICM_train, verbose=False)
self.__rec3_params = {'topK': 115, 'shrink': 1000, 'similarity': 'cosine', 'normalize': True, 'feature_weighting': 'BM25'}
self.__a = self.__b = self.__c = None
self.seed=seed
self.__submission=submission
def split_and_fit(self, random_seed):
print(random_seed)
data = self.data
# Split policy
new_urm_train = split_with_triple(data.augmented_urm, self.split_policy)[0]
rec = ItemKNNCFRecommender(new_urm_train)
rec.fit(
topK=self.topK,
shrink=self.shrink,
similarity=self.similarity,
feature_weighting=self.feature_weighting
)
rec.URM_train = data.urm_train
return rec
class Hybrid111AlphaRecommender(BaseRecommender):
"""Hybrid111AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid111AlphaRecommender"
def __init__(self, data: DataObject):
super(Hybrid111AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = RP3betaRecommender(data.urm_train)
self.rec2 = ItemKNNCFRecommender(data.urm_train)
self.rec1.fit(topK=20, alpha=0.11, beta=0.18)
self.rec2.fit(topK=18, shrink=850, similarity='jaccard', feature_weighting='BM25')
cold = data.ids_cold_user
self.target_users = data.urm_train_users_by_type[11][1]
self.hybrid_rec = Hybrid1CXAlphaRecommender(data, recommenders=[self.rec1, self.rec2],
recommended_users=self.target_users, max_cutoff=30)
def fit(self):
weights = [
[0.36263544103305234, 0.2821869186577171, 0.2958040731688242, 0.29923612866193294, 0.25220249762628333,
0.2761756648779422, 0.18915192083976606, 0.182117148228788, 0.1842783740517236, 0.1486885469397763,
0.15841860217081513, 0.1313525617771801, 0.1495204046677669, 0.1442400061423464, 0.14584743502512043,
0.12150982415065534, 0.12535649682927805, 0.08906867097805428, 0.1012469101805646, 0.08566556941254282,
0.08865930444526858, 0.0910510345404526, 0.06557183202757007, 0.05584782961979419, 0.05557650728442622,
0.030967327271124927, 0.029196638757279396, 0.025490236322759728, 0.01569027879218803,
0.015055828167915425],
[1.0, 0.5214104256707622, 0.20746796675006374, 0.23560220633621676, 0.2575770382835032, 0.2512775337476986,
0.28896916380985344, 0.3287565885723311, 0.19352951261468151, 0.22255893950688374, 0.12074099390243273,
0.13073528889178715, 0.13251088387516363, 0.13834537392481033, 0.10694898922318279, 0.1211777917594245,
0.1276279143114306, 0.14661389007809034, 0.14142761528430872, 0.11969443714497587, 0.13398226206599023,
0.14707641785555184, 0.11689418050769908, 0.0471984606450728, 0.049494500313232624, 0.04680805679550986,
0.042358887859941775, 0.04342842109287542, 0.0, 0.0]]
self.hybrid_rec.weights = weights
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.target_users:
return self.hybrid_rec.recommend(user_id_array=user_id_array, cutoff=cutoff)
else:
return []
def __init__(self,
URM_train,
ICM_train,
submission=False,
verbose=True,
seed=1205):
super(LinearHybridC002, self).__init__(URM_train, verbose=verbose)
self.URM_train = URM_train
self.ICM_train = ICM_train
self.__rec1 = ItemKNNCBFRecommender(URM_train,
ICM_train,
verbose=False)
self.__rec1_params = {
'topK': 225,
'shrink': 1000,
'similarity': 'cosine',
'normalize': True,
'feature_weighting': 'BM25'
}
self.__rec2 = ItemKNNCFRecommender(URM_train, verbose=False)
self.__rec2_params = {
'topK': 220,
'shrink': 175,
'similarity': 'cosine',
'normalize': False
}
self.__rec3 = RP3betaRecommender(URM_train, verbose=False)
self.__rec3_params = {
'topK': 974,
'alpha': 0.190821920493987,
'beta': 0.001834105482327875,
'normalize_similarity': False
}
self.__a = self.__b = self.__c = None
self.seed = seed
self.__submission = submission
class Hybrid104AlphaRecommender(BaseRecommender):
"""Hybrid104AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid104AlphaRecommender"
def __init__(self, data: DataObject):
super(Hybrid104AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = RP3betaRecommender(data.urm_train)
self.rec1.fit(topK=26, alpha=0.25, beta=0.21)
self.rec2 = ItemKNNCFRecommender(data.urm_train)
self.rec2.fit(topK=10, shrink=1000, similarity="tanimoto", feature_weighting="BM25")
target_users = data.urm_train_users_by_type[4][1]
self.target_users = target_users
self.hybrid_rec = Hybrid1CXAlphaRecommender(data, recommenders=[self.rec1, self.rec2],
recommended_users=target_users, max_cutoff=30)
def fit(self):
weights1 = [
[1.0, 0.4517179068981683, 0.3979690096061162, 0.4068926692801725, 0.3404408707736778, 0.35438712937353284,
0.3107497990263178, 0.277027821536975, 0.22460270448263392, 0.258293110155029, 0.22565957216308793,
0.1427039025093434, 0.14850612508172897, 0.1427281482083753, 0.1513486708877009, 0.14931902151854687,
0.1717168747463289, 0.0783259533439393, 0.08066358870496233, 0.0799579856421651, 0.06310732697276022,
0.0679217517962877, 0.07689888001915678, 0.06585136121968413, 0.05989375609327328, 0.05668195370281998,
0.06377071839974079, 0.017091307253589913, 0.017276230828121276, 0.012865982715760244],
[0.38688356604557783, 0.3653660032353448, 0.30862315365153425, 0.21699342252527684, 0.24954243590406833,
0.204684429683601, 0.11665613023016438, 0.12839454425138505, 0.14122535566771405, 0.1504381157229052,
0.17300383308134096, 0.1309799007170742, 0.1354656505605249, 0.08481274450716023, 0.09302787057145857,
0.09643918642755862, 0.10731710201865315, 0.07003981095966517, 0.06217937411084906, 0.0457095602617101,
0.04370382323931054, 0.050259396725207114, 0.05401448061464968, 0.03742468372661131, 0.038730399329483335,
0.04401751666811602, 0.01793705784582081, 0.020478791947760068, 0.010738842299817633, 0.0]
]
self.hybrid_rec.weights = weights1
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.target_users:
return self.hybrid_rec.recommend(user_id_array=user_id_array, cutoff=cutoff)
else:
return []
