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
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
class HybridScoreRecommender(BaseRecommender):
"""HybridScoreRecommender recommender"""
RECOMMENDER_NAME = "HybridScoreRecommender"
def __init__(self, data: DataObject, random_seed: int):
super(HybridScoreRecommender, self).__init__(data.urm_train)
self.random_seed = random_seed
self.slim = SLIMElasticNetRecommender(self.URM_train)
self.rp3 = RP3betaRecommender(self.URM_train)
def fit(self, alpha=0.5):
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.alpha = alpha
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)
# normalization
slim_max = scores_slim.max()
rp3_max = scores_rp3.max()
if not slim_max == 0:
scores_slim /= slim_max
if not rp3_max == 0:
scores_rp3 /= rp3_max
scores_total = self.alpha * scores_slim + (1 - self.alpha) * scores_rp3
return scores_total
class HybridNico(BaseRecommender):
"""Hybrid100AlphaRecommender recommender"""
RECOMMENDER_NAME = "HybridNico"
def __init__(self, data: DataObject, random_seed: int):
super(HybridNico, self).__init__(data.urm_train)
self.random_seed = random_seed
self.slim = SLIMElasticNetRecommender(self.URM_train)
self.rp3 = RP3betaRecommender(self.URM_train)
self.number_of_users = data.number_of_users
def fit(self, alpha=0.5):
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)
path_slim = 'slim_item_scores_random_seed=' + str(self.random_seed)
path_rp3 = 'slim_item_scores_random_seed=' + str(self.random_seed)
# cache = (os.path.exists(path_slim + '.npy') and os.path.exists(path_rp3 + '.npy'))
cache = False
print("cache is hardcoded to:")
print(cache)
if cache:
self.fit_cached(path_slim=path_slim,
path_rp3=path_rp3,
alpha=alpha)
else:
self.fit_no_cached(path_slim=path_slim,
path_rp3=path_rp3,
alpha=alpha)
def fit_cached(self, path_slim, path_rp3, alpha=0.5):
mat_scores_slim = np.load(path_slim + '.npy')
mat_scores_rp3 = np.load(path_rp3 + '.npy')
self.score_matrix = alpha * mat_scores_slim + (1 -
alpha) * mat_scores_rp3
def fit_no_cached(self, path_slim, path_rp3, alpha=0.5):
user_id_array = np.array(range(self.number_of_users))
self.mat_scores_slim = self.slim._compute_item_score(
user_id_array=user_id_array)
self.mat_scores_rp3 = self.rp3._compute_item_score(
user_id_array=user_id_array)
# normalization
self.mat_scores_slim /= self.mat_scores_slim.max()
self.mat_scores_rp3 /= self.mat_scores_rp3.max()
# np.save(path_slim, arr=mat_scores_slim)
# np.save(path_rp3, arr=mat_scores_rp3)
self.score_matrix = alpha * self.mat_scores_slim + (
1 - alpha) * self.mat_scores_rp3
def _compute_item_score(self, user_id_array, items_to_compute=None):
return self.score_matrix[user_id_array]
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
urm = sps.vstack([data.urm_train, data.icm_all_augmented.T])
urm = urm.tocsr()
self.slim = SLIMElasticNetRecommender(urm)
self.rp3 = RP3betaRecommender(urm)
self.alpha = alpha
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=191_l1_ratio=0.0458089_alpha=0.000707_positive_only=True_max_iter=100'
)
except:
self.slim.fit(topK=191,
l1_ratio=0.0458089,
alpha=0.000707,
positive_only=True,
max_iter=100)
self.slim.save_model(
'stored_recommenders/slim_elastic_net/',
f'with_icm_{self.random_seed}_topK=191_l1_ratio=0.0458089_alpha=0.000707_positive_only=True_max_iter=100'
)
try:
self.rp3.load_model(
'stored_recommenders/rp3_beta/',
f'with_icm_{self.random_seed}_topK=40_alpha=0.4_beta=0.2')
except:
self.rp3.fit(topK=40, alpha=0.4, beta=0.2)
self.rp3.save_model(
'stored_recommenders/rp3_beta/',
f'with_icm_{self.random_seed}_topK=40_alpha=0.4_beta=0.2')
# 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)
# normalization
slim_max = scores_slim.max()
rp3_max = scores_rp3.max()
if not slim_max == 0:
scores_slim /= slim_max
if not rp3_max == 0:
scores_rp3 /= rp3_max
scores_total = self.alpha * scores_slim + self.beta * scores_rp3
return scores_total
class LinearHybrid001(BaseItemSimilarityMatrixRecommender):
RECOMMENDER_NAME = "LinearHybrid001"
# set the seed equal to the one of the parameter search!!!!
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 fit(self, alpha=0.5, l1_ratio=0.5):
self.__a = alpha * l1_ratio
self.__b = alpha - self.__a
self.__c = 1 - self.__a - self.__b
if not self.__submission:
try:
self.__rec1.load_model('stored_recommenders/'+self.__rec1.RECOMMENDER_NAME+'/', f'seed_{str(self.seed)}_best_for_LinearHybrid001')
print(f"{self.__rec1.RECOMMENDER_NAME} loaded.")
except:
print(f"Fitting {self.__rec1.RECOMMENDER_NAME} ...")
self.__rec1.fit(**self.__rec1_params)
print(f"done.")
self.__rec1.save_model('stored_recommenders/'+self.__rec1.RECOMMENDER_NAME+'/', f'seed_{str(self.seed)}_best_for_LinearHybrid001')
try:
self.__rec2.load_model('stored_recommenders/'+self.__rec2.RECOMMENDER_NAME+'/', f'seed_{str(self.seed)}_best_for_LinearHybrid001')
print(f"{self.__rec2.RECOMMENDER_NAME} loaded.")
except:
print(f"Fitting {self.__rec2.RECOMMENDER_NAME} ...")
self.__rec2.fit(**self.__rec2_params)
print(f"done.")
self.__rec2.save_model('stored_recommenders/'+self.__rec2.RECOMMENDER_NAME+'/', f'seed_{str(self.seed)}_best_for_LinearHybrid001')
try:
self.__rec3.load_model('stored_recommenders/'+self.__rec3.RECOMMENDER_NAME+'/', f'seed_{str(self.seed)}_best_for_LinearHybrid001')
print(f"{self.__rec3.RECOMMENDER_NAME} loaded.")
except:
print(f"Fitting {self.__rec3.RECOMMENDER_NAME} ...")
self.__rec3.fit(**self.__rec3_params)
print(f"done.")
self.__rec3.save_model('stored_recommenders/'+self.__rec3.RECOMMENDER_NAME+'/', f'seed_{str(self.seed)}_best_for_LinearHybrid001')
else:
self.__rec1.fit(**self.__rec1_params)
self.__rec2.fit(**self.__rec2_params)
self.__rec3.fit(**self.__rec3_params)
def _compute_item_score(self, user_id_array, items_to_compute=None):
item_weights_1 = self.__rec1._compute_item_score(user_id_array)
item_weights_2 = self.__rec2._compute_item_score(user_id_array)
item_weights_3 = self.__rec3._compute_item_score(user_id_array)
item_weights = item_weights_1 * self.__a + item_weights_2 * self.__b + item_weights_3 * self.__c
return item_weights
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")
class HybridNicoSubmission(BaseRecommender):
"""HybridNicoSubmission recommender"""
RECOMMENDER_NAME = "HybridNicoSubmission"
def __init__(self, data: DataObject):
super(HybridNicoSubmission, self).__init__(data.urm_train)
self.data = data
self.slim = SLIMElasticNetRecommender(data.urm)
self.rp3 = RP3betaRecommender(data.urm)
def fit(self, alpha=0.5):
self.slim.load_model(
'',
'SLIM_ElasticNetFULL_URM_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.fit_no_cached(path_slim=None, path_rp3=None, alpha=alpha)
def fit_cached(self, path_slim, path_rp3, alpha=0.5):
mat_scores_slim = np.load(path_slim + '.npy')
mat_scores_rp3 = np.load(path_rp3 + '.npy')
self.score_matrix = alpha * mat_scores_slim + (1 -
alpha) * mat_scores_rp3
def fit_no_cached(self, path_slim, path_rp3, alpha=0.5):
n_users = self.data.number_of_users
n_item = self.data.number_of_items
list_slim = []
list_rp3 = []
for u_id in range(n_users):
# if u_id % 1000 == 0:
# print('user: {} / {}'.format(u_id, n_users - 1))
list_slim.append(
np.squeeze(self.slim._compute_item_score(user_id_array=u_id)))
list_rp3.append(
np.squeeze(self.rp3._compute_item_score(user_id_array=u_id)))
mat_scores_slim = np.stack(list_slim, axis=0)
mat_scores_rp3 = np.stack(list_rp3, axis=0)
'''print("slim scores stats:")
print("min = {}".format(mat_scores_slim.min()))
print("max = {}".format(mat_scores_slim.max()))
print("average = {}".format(mat_scores_slim.mean()))
print("rp3 scores stats:")
print("min = {}".format(mat_scores_rp3.min()))
print("max = {}".format(mat_scores_rp3.max()))
print("average = {}".format(mat_scores_rp3.mean()))'''
# normalization
mat_scores_slim /= mat_scores_slim.max()
mat_scores_rp3 /= mat_scores_rp3.max()
self.mat_scores_slim = mat_scores_slim
self.mat_scores_rp3 = mat_scores_rp3
'''print("slim scores stats:")
print("min = {}".format(mat_scores_slim.min()))
print("max = {}".format(mat_scores_slim.max()))
print("average = {}".format(mat_scores_slim.mean()))
print("rp3 scores stats:")
print("min = {}".format(mat_scores_rp3.min()))
print("max = {}".format(mat_scores_rp3.max()))
print("average = {}".format(mat_scores_rp3.mean()))'''
# np.save(path_slim, arr=mat_scores_slim)
# np.save(path_rp3, arr=mat_scores_rp3)
self.score_matrix = alpha * mat_scores_slim + (1 -
alpha) * mat_scores_rp3
def _compute_item_score(self, user_id_array, items_to_compute=None):
return self.score_matrix[user_id_array]