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
class Hybrid201AlphaRecommender(BaseRecommender):
"""Hybrid201AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid201AlphaRecommender"
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)
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 Hybrid202AlphaRecommender(BaseRecommender):
"""Hybrid202AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid202AlphaRecommender"
def __init__(self, data: DataObject):
super(Hybrid202AlphaRecommender, 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.random_seed = data.random_seed
self.slim = SLIMElasticNetRecommender(urm)
self.rp3 = RP3betaRecommender(urm)
self.itemcf = ItemKNNCFRecommender(self.URM_train)
self.alpha = 1
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'
)
try:
self.rp3.load_model(
'stored_recommenders/rp3_beta/',
f'with_icm_{self.random_seed}_topK=20_alpha=0.16_beta=0.24')
except:
self.rp3.fit(topK=20, alpha=0.16, beta=0.24)
self.rp3.save_model(
'stored_recommenders/rp3_beta/',
f'with_icm_{self.random_seed}_topK=20_alpha=0.16_beta=0.24')
try:
self.itemcf.load_model(
'stored_recommenders/item_cf/',
f'{self.random_seed}_topK=22_shrink=850_similarity=jaccard_feature_weighting=BM25'
)
except:
self.itemcf.fit(topK=22,
shrink=850,
similarity='jaccard',
feature_weighting='BM25')
self.itemcf.save_model(
'stored_recommenders/item_cf/',
f'{self.random_seed}_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 LinearHybridC001(BaseItemSimilarityMatrixRecommender):
RECOMMENDER_NAME = "LinearHybridC001"
"""
This hybrid works for users who have a profile length shorter or equal to 2 interactions
"""
# 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(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 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(
f'stored_recommenders/seed_{str(self.seed)}_{self.__rec1.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
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(
f'stored_recommenders/seed_{str(self.seed)}_{self.__rec1.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
try:
self.__rec2.load_model(
f'stored_recommenders/seed_{str(self.seed)}_{self.__rec2.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
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(
f'stored_recommenders/seed_{str(self.seed)}_{self.__rec2.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
try:
self.__rec3.load_model(
f'stored_recommenders/seed_{str(self.seed)}_{self.__rec3.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
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(
f'stored_recommenders/seed_{str(self.seed)}_{self.__rec3.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
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 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 LinearHybridW001(BaseItemSimilarityMatrixRecommender):
RECOMMENDER_NAME = "LinearHybridW001"
"""
This hybrid works for users who have a profile length greater than or equal to 3 interactions
"""
# 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(LinearHybridW001, self).__init__(URM_train, verbose=verbose)
self.URM_train = URM_train
self.ICM_train = ICM_train
# seed 1205: {'topK': 205, 'shrink': 1000, 'similarity': 'cosine',
# 'normalize': True, 'feature_weighting': 'BM25'}
self.__rec1 = ItemKNNCBFRecommender(URM_train,ICM_train, verbose=False)
self.__rec1_params = {'topK': 205, 'shrink': 1000, 'similarity': 'cosine', 'normalize': True,
'feature_weighting': 'BM25'}
# seed 1205: {'topK': 565, 'shrink': 554, 'similarity': 'tversky', 'normalize': True,
# 'tversky_alpha': 1.9109121434662428, 'tversky_beta': 1.7823834698905734}
self.__rec2 = ItemKNNCFRecommender(URM_train, verbose=False)
self.__rec2_params = {'topK': 565, 'shrink': 554, 'similarity': 'tversky', 'normalize': True,
'tversky_alpha': 1.9109121434662428, 'tversky_beta': 1.7823834698905734}
# seed 1205: {'topK': 753, 'alpha': 0.3873710051288722, 'beta': 0.0, 'normalize_similarity': False}
self.__rec3 = RP3betaRecommender(URM_train, verbose=False)
self.__rec3_params = {'topK': 753, 'alpha': 0.3873710051288722, 'beta': 0.0, 'normalize_similarity': False}
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(f'stored_recommenders/seed_{str(self.seed)}_{self.__rec1.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
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(f'stored_recommenders/seed_{str(self.seed)}_{self.__rec1.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
try:
self.__rec2.load_model(f'stored_recommenders/seed_{str(self.seed)}_{self.__rec2.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
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(f'stored_recommenders/seed_{str(self.seed)}_{self.__rec2.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
try:
self.__rec3.load_model(f'stored_recommenders/seed_{str(self.seed)}_{self.__rec3.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
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(f'stored_recommenders/seed_{str(self.seed)}_{self.__rec3.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
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")
def read_data_split_and_search():
"""
This function provides a simple example on how to tune parameters of a given algorithm
The BayesianSearch object will save:
- A .txt file with all the cases explored and the recommendation quality
- A _best_model file which contains the trained model and can be loaded with recommender.load_model()
- A _best_parameter file which contains a dictionary with all the fit parameters, it can be passed to recommender.fit(**_best_parameter)
- A _best_result_validation file which contains a dictionary with the results of the best solution on the validation
- A _best_result_test file which contains a dictionary with the results, on the test set, of the best solution chosen using the validation set
"""
seed = 1205
parser = DataParser()
URM_all = parser.get_URM_all()
ICM_obj = parser.get_ICM_all()
# SPLIT TO GET TEST PARTITION
URM_train, URM_test = split_train_in_two_percentage_global_sample(URM_all, train_percentage=0.90, seed=seed)
# SPLIT TO GET THE HYBRID VALID PARTITION
URM_train, URM_valid_hybrid = split_train_in_two_percentage_global_sample(URM_train, train_percentage=0.85,
seed=seed)
collaborative_algorithm_list = [
# EASE_R_Recommender
# PipeHybrid001,
# Random,
# TopPop,
# P3alphaRecommender,
# RP3betaRecommender,
# ItemKNNCFRecommender,
# UserKNNCFRecommender,
# MatrixFactorization_BPR_Cython,
# MatrixFactorization_FunkSVD_Cython,
# PureSVDRecommender,
# NMFRecommender,
# PureSVDItemRecommender
# SLIM_BPR_Cython,
# SLIMElasticNetRecommender
# IALSRecommender
# MF_MSE_PyTorch
# MergedHybrid000
# LinearHybrid002ggg
HybridCombinationSearch
]
content_algorithm_list = [
# ItemKNNCBFRecommender
]
from Base.Evaluation.Evaluator import EvaluatorHoldout
evaluator_valid_hybrid = EvaluatorHoldout(URM_valid_hybrid, cutoff_list=[10])
evaluator_test = EvaluatorHoldout(URM_test, cutoff_list=[10])
"""
earlystopping_keywargs = {"validation_every_n": 5,
"stop_on_validation": True,
"evaluator_object": evaluator_valid_hybrid,
"lower_validations_allowed": 5,
"validation_metric": 'MAP',
}
print('IALS training...')
ials = IALSRecommender(URM_train, verbose=False)
ials_params = {'num_factors': 83, 'confidence_scaling': 'linear', 'alpha': 28.4278070726612,
'epsilon': 1.0234211788885077, 'reg': 0.0027328110246575004, 'epochs': 20}
ials.fit(**ials_params, **earlystopping_keywargs)
print("Done")
print("PureSVD training...")
psvd = PureSVDRecommender(URM_train, verbose=False)
psvd_params = {'num_factors': 711}
psvd.fit(**psvd_params)
print("Done")
"""
rp3b = RP3betaRecommender(URM_train, verbose=False)
rp3b_params = {'topK': 1000, 'alpha': 0.38192761611274967, 'beta': 0.0, 'normalize_similarity': False}
try:
rp3b.load_model(f'stored_recommenders/seed_{str(seed)}_hybrid_search/',
f'{rp3b.RECOMMENDER_NAME}_for_second_search')
print(f"{rp3b.RECOMMENDER_NAME} loaded.")
except:
print(f"Fitting {rp3b.RECOMMENDER_NAME} ...")
rp3b.fit(**rp3b_params)
print(f"done.")
rp3b.save_model(f'stored_recommenders/seed_{str(seed)}_hybrid_search/',
f'{rp3b.RECOMMENDER_NAME}_for_second_search')
p3a = P3alphaRecommender(URM_train, verbose=False)
p3a_params = {'topK': 131, 'alpha': 0.33660811631883863, 'normalize_similarity': False}
try:
p3a.load_model(f'stored_recommenders/seed_{str(seed)}_hybrid_search/',
f'{p3a.RECOMMENDER_NAME}_for_second_search')
print(f"{p3a.RECOMMENDER_NAME} loaded.")
except:
print(f"Fitting {p3a.RECOMMENDER_NAME} ...")
p3a.fit(**p3a_params)
print(f"done.")
p3a.save_model(f'stored_recommenders/seed_{str(seed)}_hybrid_search/',
f'{p3a.RECOMMENDER_NAME}_for_second_search')
icf = ItemKNNCFRecommender(URM_train, verbose=False)
icf_params = {'topK': 55, 'shrink': 1000, 'similarity': 'asymmetric', 'normalize': True, 'asymmetric_alpha': 0.0}
try:
icf.load_model(f'stored_recommenders/seed_{str(seed)}_hybrid_search/',
f'{icf.RECOMMENDER_NAME}_for_second_search')
print(f"{icf.RECOMMENDER_NAME} loaded.")
except:
print(f"Fitting {icf.RECOMMENDER_NAME} ...")
icf.fit(**icf_params)
print(f"done.")
icf.save_model(f'stored_recommenders/seed_{str(seed)}_hybrid_search/',
f'{icf.RECOMMENDER_NAME}_for_second_search')
ucf = UserKNNCFRecommender(URM_train, verbose=False)
ucf_params = {'topK': 190, 'shrink': 0, 'similarity': 'cosine', 'normalize': True}
try:
ucf.load_model(f'stored_recommenders/seed_{str(seed)}_hybrid_search/',
f'{ucf.RECOMMENDER_NAME}_for_second_search')
print(f"{ucf.RECOMMENDER_NAME} loaded.")
except:
print(f"Fitting {ucf.RECOMMENDER_NAME} ...")
ucf.fit(**ucf_params)
print(f"done.")
ucf.save_model(f'stored_recommenders/seed_{str(seed)}_hybrid_search/',
f'{ucf.RECOMMENDER_NAME}_for_second_search')
icb = ItemKNNCBFRecommender(URM_train, ICM_obj, verbose=False)
icb_params = {'topK': 65, 'shrink': 0, 'similarity': 'dice', 'normalize': True}
try:
icb.load_model(f'stored_recommenders/seed_{str(seed)}_hybrid_search/',
f'{icb.RECOMMENDER_NAME}_for_second_search')
print(f"{icb.RECOMMENDER_NAME} loaded.")
except:
print(f"Fitting {icf.RECOMMENDER_NAME} ...")
icb.fit(**icb_params)
print(f"done.")
icb.save_model(f'stored_recommenders/seed_{str(seed)}_hybrid_search/',
f'{icb.RECOMMENDER_NAME}_for_second_search')
sen = SLIMElasticNetRecommender(URM_train, verbose=False)
sen_params = {'topK': 992, 'l1_ratio': 0.004065081925341167, 'alpha': 0.003725005053334143}
try:
sen.load_model(f'stored_recommenders/seed_{str(seed)}_hybrid_search/',
f'{sen.RECOMMENDER_NAME}_for_second_search')
print(f"{sen.RECOMMENDER_NAME} loaded.")
except:
print(f"Fitting {sen.RECOMMENDER_NAME} ...")
sen.fit(**sen_params)
print(f"done.")
sen.save_model(f'stored_recommenders/seed_{str(seed)}_hybrid_search/',
f'{sen.RECOMMENDER_NAME}_for_second_search')
print("\nStart.")
list_recommender = [icb, icf, ucf, p3a, rp3b, sen]
list_already_seen = []
combinations_already_seen = []
"""
(icb, icf, p3a), (icb, icf, rp3b), (icb, icf, sen), (icb, p3a, rp3b), (icb, p3a, sen),
(icb, rp3b, sen), (icf, p3a, rp3b), (icf, p3a, sen)
"""
for rec_perm in combinations(list_recommender, 3):
if rec_perm not in combinations_already_seen:
recommender_names = '_'.join([r.RECOMMENDER_NAME for r in rec_perm])
output_folder_path = "result_experiments_v3/seed_" + str(
seed) + '/linear_combination/' + recommender_names + '/'
print(F"\nTESTING THE COMBO {recommender_names}")
# If directory does not exist, create
if not os.path.exists(output_folder_path):
os.makedirs(output_folder_path)
# TODO: setta I GIUSTI EVALUATOR QUI!!!!
runParameterSearch_Collaborative_partial = partial(runParameterSearch_Collaborative,
URM_train=URM_train,
ICM_train=ICM_obj,
metric_to_optimize="MAP",
n_cases=50,
n_random_starts=20,
evaluator_validation_earlystopping=evaluator_valid_hybrid,
evaluator_validation=evaluator_valid_hybrid,
#evaluator_test=evaluator_test,
output_folder_path=output_folder_path,
allow_weighting=False,
# similarity_type_list = ["cosine", 'jaccard'],
parallelizeKNN=False,
list_rec=rec_perm)
pool = multiprocessing.Pool(processes=int(multiprocessing.cpu_count()), maxtasksperchild=1)
pool.map(runParameterSearch_Collaborative_partial, collaborative_algorithm_list)