class LinearHybrid003(BaseItemSimilarityMatrixRecommender):
RECOMMENDER_NAME = "LinearHybrid003"
# 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(LinearHybrid003, 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
}
self.__rec2 = P3alphaRecommender(URM_train, verbose=False)
self.__rec2_params = {
'topK': 131,
'alpha': 0.33660811631883863,
'normalize_similarity': False
}
# 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 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")
print(f"Fitting {ucf.RECOMMENDER_NAME} ...")
ucf.fit(**ucf_params)
print(f"done.")
ucf.save_model(f'stored_recommenders/seed_{str(seed)}_{ucf.RECOMMENDER_NAME}/', 'for_notebook_analysis')
icb = ItemKNNCBFRecommender(URM_train, ICM_all, verbose=False)
icb_params = {'topK': 65, 'shrink': 0, 'similarity': 'dice', 'normalize': True}
try:
icb.load_model(f'stored_recommenders/seed_{str(seed)}_{icb.RECOMMENDER_NAME}/', 'for_notebook_analysis')
print(f"{icb.RECOMMENDER_NAME} loaded.")
except:
print(f"Fitting {icb.RECOMMENDER_NAME} ...")
icb.fit(**icb_params)
print(f"done.")
icb.save_model(f'stored_recommenders/seed_{str(seed)}_{icb.RECOMMENDER_NAME}/', 'for_notebook_analysis')
list_recommender = [sslim, icb, ucf]
best_recommender = HybridCombinationSearch(URM_train, ICM_all, list_recommender)
params = {'alpha': 0.6461624491197696, 'l1_ratio': 0.7617220099582368}
best_recommender.fit(**params)
user_ids = parser.get_ratings().user_id.unique()
cutoff = 20
user_recommendations_items = []
user_recommendations_user_id = []
target = []
for n_user in user_ids:
recommendations = best_recommender.recommend(n_user, cutoff=20)
user_recommendations_items.extend(recommendations)
class PipeHybrid001(RP3betaRecommender):
RECOMMENDER_NAME = "PipeHybrid001"
def __init__(self, URM_train, ICM_train,verbose=True):
super(PipeHybrid001, self).__init__(URM_train, verbose = verbose)
self.URM_train_recommendation = URM_train
self.ICM_train = ICM_train
self.__content_recommender = ItemKNNCBFRecommender(URM_train, ICM_train)
#print("fitting ItemKNNCBF...")
try:
self.__content_recommender.load_model('stored_recommenders/ItemKNNCBFRecommender/best_at_26_10_20')
except:
self.__content_recommender.fit(topK=140, shrink=1000, similarity='cosine', normalize=True,
feature_weighting='BM25') # best parameter up to now
self.__content_recommender.save_model('stored_recommenders/ItemKNNCBFRecommender/best_at_26_10_20')
#print("... done")
#print(f"URM_train shape: {URM_train.shape}")
#print(f"W_sparse knn shape: {self.__content_recommender.W_sparse.shape}")
self.URM_train = URM_train.dot(self.__content_recommender.W_sparse)
self._URM_train_format_checked = False
self._W_sparse_format_checked = False
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):
"""
redefinition using self.URM_train_recommendation, not the new URM train of the RP3Beta algorithm
"""
# If is a scalar transform it in a 1-cell array
if np.isscalar(user_id_array):
user_id_array = np.atleast_1d(user_id_array)
single_user = True
else:
single_user = False
if cutoff is None:
cutoff = self.URM_train_recommendation.shape[1] - 1
# Compute the scores using the model-specific function
# Vectorize over all users in user_id_array
scores_batch = self._compute_item_score(user_id_array, items_to_compute=items_to_compute)
for user_index in range(len(user_id_array)):
user_id = user_id_array[user_index]
if remove_seen_flag:
scores_batch[user_index,:] = self._remove_seen_on_scores(user_id, scores_batch[user_index, :])
# Sorting is done in three steps. Faster then plain np.argsort for higher number of items
# - Partition the data to extract the set of relevant items
# - Sort only the relevant items
# - Get the original item index
# relevant_items_partition = (-scores_user).argpartition(cutoff)[0:cutoff]
# relevant_items_partition_sorting = np.argsort(-scores_user[relevant_items_partition])
# ranking = relevant_items_partition[relevant_items_partition_sorting]
#
# ranking_list.append(ranking)
if remove_top_pop_flag:
scores_batch = self._remove_TopPop_on_scores(scores_batch)
if remove_custom_items_flag:
scores_batch = self._remove_custom_items_on_scores(scores_batch)
# relevant_items_partition is block_size x cutoff
relevant_items_partition = (-scores_batch).argpartition(cutoff, axis=1)[:,0:cutoff]
# Get original value and sort it
# [:, None] adds 1 dimension to the array, from (block_size,) to (block_size,1)
# This is done to correctly get scores_batch value as [row, relevant_items_partition[row,:]]
relevant_items_partition_original_value = scores_batch[np.arange(scores_batch.shape[0])[:, None], relevant_items_partition]
relevant_items_partition_sorting = np.argsort(-relevant_items_partition_original_value, axis=1)
ranking = relevant_items_partition[np.arange(relevant_items_partition.shape[0])[:, None], relevant_items_partition_sorting]
ranking_list = [None] * ranking.shape[0]
# Remove from the recommendation list any item that has a -inf score
# Since -inf is a flag to indicate an item to remove
for user_index in range(len(user_id_array)):
user_recommendation_list = ranking[user_index]
user_item_scores = scores_batch[user_index, user_recommendation_list]
not_inf_scores_mask = np.logical_not(np.isinf(user_item_scores))
user_recommendation_list = user_recommendation_list[not_inf_scores_mask]
ranking_list[user_index] = user_recommendation_list.tolist()
#TEST
"""
user_profile_array = self.URM_train[user_id_array[user_index]]
if np.empty(user_profile_array):
print(f"WARNING! {user_index} is a cold user!")
rec = TopPop(URM_train)
rec.fit()
ranking_list[user_index]=rec.recommend([user_id_array[user_index]], cutoff=cutoff)
"""
# Return single list for one user, instead of list of lists
if single_user:
ranking_list = ranking_list[0]
if return_scores:
return ranking_list, scores_batch
else:
return ranking_list
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 LinearHybrid008(BaseItemSimilarityMatrixRecommender):
RECOMMENDER_NAME = "LinearHybrid008"
# 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(LinearHybrid008, self).__init__(URM_train, verbose=verbose)
self.URM_train = URM_train
self.ICM_train = ICM_train
self.__rec1 = SSLIMElasticNet(URM_train, ICM_train, verbose=False)
self.__rec1_params = {
'beta': 0.4849594591575789,
'topK': 1000,
'l1_ratio': 1e-05,
'alpha': 0.001
}
self.__rec2 = ItemKNNCBFRecommender(URM_train,
ICM_train,
verbose=False)
self.__rec2_params = {
'topK': 65,
'shrink': 0,
'similarity': 'dice',
'normalize': True
}
self.__rec3 = UserKNNCFRecommender(URM_train, verbose=False)
self.__rec3_params = {
'topK': 190,
'shrink': 0,
'similarity': 'cosine',
'normalize': True
}
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)
# normalization
item_weights_1_max = item_weights_1.max()
item_weights_2_max = item_weights_2.max()
item_weights_3_max = item_weights_3.max()
if not item_weights_1_max == 0:
item_weights_1 /= item_weights_1_max
if not item_weights_2_max == 0:
item_weights_2 /= item_weights_2_max
if not item_weights_3_max == 0:
item_weights_3 /= item_weights_3_max
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 __init__(self,
URM_train,
ICM_train,
submission=False,
verbose=True,
seed=1205):
super(LinearOverMerged001, self).__init__(URM_train, verbose=verbose)
self.URM_train = URM_train
self.ICM_train = ICM_train
self.__submission = submission
self.__rec1 = UserKNNCFRecommender(URM_train, verbose=False)
self.__rec1_params = {
'topK': 190,
'shrink': 0,
'similarity': 'cosine',
'normalize': True
}
self.seed = seed
icb = ItemKNNCBFRecommender(URM_train, ICM_train, verbose=False)
icb_params = {
'topK': 65,
'shrink': 0,
'similarity': 'dice',
'normalize': True
}
rp3b = RP3betaRecommender(URM_train, verbose=False)
rp3b_params = {
'topK': 1000,
'alpha': 0.38192761611274967,
'beta': 0.0,
'normalize_similarity': False
}
sen = SLIMElasticNetRecommender(URM_train, verbose=False)
sen_params = {
'topK': 992,
'l1_ratio': 0.004065081925341167,
'alpha': 0.003725005053334143
}
if not self.__submission:
try:
icb.load_model(
f'stored_recommenders/seed_{str(self.seed)}_{icb.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
print(f"{icb.RECOMMENDER_NAME} loaded.")
except:
print(f"Fitting {icb.RECOMMENDER_NAME} ...")
icb.fit(**icb_params)
print(f"done.")
icb.save_model(
f'stored_recommenders/seed_{str(self.seed)}_{icb.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
try:
rp3b.load_model(
f'stored_recommenders/seed_{str(self.seed)}_{rp3b.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
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(self.seed)}_{rp3b.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
try:
sen.load_model(
f'stored_recommenders/seed_{str(self.seed)}_{sen.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
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(self.seed)}_{sen.RECOMMENDER_NAME}/',
f'best_for_{self.RECOMMENDER_NAME}')
else:
icb.fit(**icb_params)
rp3b.fit(**rp3b_params)
sen.fit(**sen_params)
self.__rec2 = HiddenMergedRecommender(URM_train,
ICM_train, [icb, rp3b, sen],
verbose=False)
self.__rec2_params = {
'alpha': 0.6355738550417837,
'l1_ratio': 0.6617849709204384,
'topK': 538
}
self.__a = self.__b = None
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)
