def compute(self, verbose=True, metrics=None, verbose_evaluation=True, as_table=False, table_sep='\t'):
if verbose:
super(MatrixFactorization, self).compute(verbose=verbose)
self.init_model()
print("training_time:: %4f sec" % timed(self.fit))
if self.extra_info_header is not None:
print(self.extra_info_header)
search_time = timed(self.search_new_items)
replace_time = timed(self.replace_vector_new_item)
prediction_time = timed(self.predict)
print("search_new_items_time:: %4f sec" % search_time)
print("vectors_replacement_time:: %4f sec" % replace_time)
print("prediction_time:: %4f sec" % prediction_time)
print("total_prediction_time:: %4f sec" % (search_time + replace_time + prediction_time))
print("\n")
else:
# Execute all in silence without prints
super(MatrixFactorization, self).compute(verbose=verbose)
self.init_model()
self.fit()
self.search_new_items()
self.replace_vector_new_item()
self.predict()
self.write_predictions()
if self.test_file is not None:
return self.evaluate(metrics, verbose_evaluation, as_table=as_table, table_sep=table_sep)
def execute(self,
measures=('Prec@5', 'Prec@10', 'NDCG@5', 'NDCG@10', 'MAP@5',
'MAP@10')):
print(
"[Case Recommender: Item Recommendation > Item Attribute KNN Algorithm]\n"
)
print("training data:: ", len(self.train_set['users']), " users and ",
len(self.train_set['items']), " items and ",
self.train_set['ni'], " interactions | sparsity ",
self.train_set['sparsity'])
if self.test_file is not None:
test_set = ReadFile(
self.test_file,
space_type=self.space_type).return_information()
print("test data:: ", len(self.test_set['users']), " users and ",
len(self.test_set['items']), " items and ",
(self.test_set['ni']), " interactions | sparsity ",
self.test_set['sparsity'])
del test_set
if self.similarity_matrix_file is not None:
print("training time:: ", timed(self.read_matrix), " sec")
else:
print("training time:: ", timed(self.compute_similarity), " sec")
print("prediction_time:: ", timed(self.predict), " sec\n")
if self.test_file is not None:
self.evaluate(measures)
def compute(self,
verbose=True,
metrics=None,
verbose_evaluation=True,
as_table=False,
table_sep='\t',
n_ranks=None):
"""
Extends compute method from BaseItemRecommendation. Method to run recommender algorithm
:param verbose: Print recommender and database information
:type verbose: bool, default True
:param metrics: List of evaluation metrics
:type metrics: list, default None
:param verbose_evaluation: Print the evaluation results
:type verbose_evaluation: bool, default True
:param as_table: Print the evaluation results as table
:type as_table: bool, default False
:param table_sep: Delimiter for print results (only work with verbose=True and as_table=True)
:type table_sep: str, default '\t'
:param n_ranks: List of positions to evaluate the ranking
:type n_ranks: list, None
"""
super(ContentBased, self).compute(verbose=verbose)
if verbose:
print("training_time:: %4f sec" % timed(self.init_model))
if self.extra_info_header is not None:
print(self.extra_info_header)
self.create_user_profile()
print("prediction_time:: %4f sec" % timed(self.predict))
print('\n')
else:
self.init_model()
self.create_user_profile()
self.predict()
self.write_ranking()
if self.test_file is not None:
self.evaluate(metrics,
verbose_evaluation,
as_table=as_table,
table_sep=table_sep,
n_ranks=n_ranks)
def compute(self,
verbose=True,
metrics=None,
verbose_evaluation=True,
as_table=False,
table_sep='\t'):
"""
Extends compute method from BaseRatingPrediction. Method to run recommender algorithm
:param verbose: Print recommender and database information
:type verbose: bool, default True
:param metrics: List of evaluation measures
:type metrics: list, default None
:param verbose_evaluation: Print the evaluation results
:type verbose_evaluation: bool, default True
:param as_table: Print the evaluation results as table
:type as_table: bool, default False
:param table_sep: Delimiter for print results (only work with verbose=True and as_table=True)
:type table_sep: str, default '\t'
"""
super(MatrixFactorization, self).compute(verbose=verbose)
if verbose:
self.init_model()
print("training_time:: %4f sec" % timed(self.fit))
if self.extra_info_header is not None:
print(self.extra_info_header)
print("prediction_time:: %4f sec" % timed(self.predict))
print('\n')
else:
# Execute all in silence without prints
self.init_model()
self.fit()
self.predict()
self.write_predictions()
if self.test_file is not None:
self.evaluate(metrics,
verbose_evaluation,
as_table=as_table,
table_sep=table_sep)
def execute(self):
# methods
print("[Case Recommender: Rating Prediction > SVD++]\n")
print("training data:: ", len(self.train_set['users']), " users and ",
len(self.train_set['items']), " items and ",
self.train_set['ni'], " interactions | sparsity ",
self.train_set['sparsity'])
print("test data:: ", len(self.test_set['users']), " users and ",
len(self.test_set['items']), " items and ",
(self.test_set['ni']), " interactions | sparsity ",
self.test_set['sparsity'])
print("training time:: ", timed(self.train_model), " sec")
print("\nprediction_time:: ", timed(self.predict), " sec\n")
self.evaluate(self.predictions)
def init_model(self):
super(ItemMSMF, self).init_model()
if self.verbose:
print("\nread_similarity_matrix_time:: %4f sec" %
timed(self.fill_similarity_matrix))
else:
self.fill_similarity_matrix()
def execute(self):
print("[Case Recommender: Cross-Fold-Validation Process]\n")
print("number of folds:: ", self.n_folds, " write folds in:: ", self.dir_folds)
print("total process time:: ", timed(self.divide_dataset), "sec")
print("dataset info:: #users: ", len(self.tp['users']), " #items: ", len(self.tp['items']),
" #number of interactions: ", self.tp['ni'], " #range of feedback: [",
self.tp['min'], "-", self.tp['max'], "]\n")
def compute(self, verbose=True, metrics=None, verbose_evaluation=True, as_table=False, table_sep='\t'):
"""
Extends compute method from BaseItemRecommendation. Method to run recommender algorithm
:param verbose: Print recommender and database information
:type verbose: bool, default True
:param metrics: List of evaluation measures
:type metrics: list, default None
:param verbose_evaluation: Print the evaluation results
:type verbose_evaluation: bool, default True
:param as_table: Print the evaluation results as table
:type as_table: bool, default False
:param table_sep: Delimiter for print results (only work with verbose=True and as_table=True)
:type table_sep: str, default '\t'
"""
super(MostPopular, self).compute(verbose=verbose)
if verbose:
print("prediction_time:: %4f sec" % timed(self.predict))
print('\n')
else:
self.predict()
self.write_predictions()
if self.test_file is not None:
self.evaluate(metrics, verbose_evaluation, as_table=as_table, table_sep=table_sep)
def execute(self):
# methods
print("[Case Recommender: Rating Prediction > User Algorithm]\n")
print("training data:: ", len(self.train_set['users']), " users and ",
len(self.train_set['items']), " items and ",
self.train_set['ni'], " interactions | sparsity ",
self.train_set['sparsity'])
print("test data:: ", len(self.test_set['users']), " users and ",
len(self.test_set['items']), " items and ",
(self.test_set['ni']), " interactions | sparsity ",
self.test_set['sparsity'])
# training baselines bui
self.fill_matrix()
print("training time:: ", timed(self.train_baselines), " sec")
self.compute_similarity()
print("prediction_time:: ", timed(self.predict), " sec\n")
self.evaluate(self.predictions)
def execute(self):
# methods
print("[Case Recommender: Rating Prediction > User NSVD1]\n")
print("training data:: ", len(self.train_set['users']), " users and ", len(self.train_set['items']),
" items and ", self.train_set['ni'], " interactions | sparsity ", self.train_set['sparsity'])
print("test data:: ", len(self.test_set['users']), " users and ", len(self.test_set['items']),
" items and ", (self.test_set['ni']), " interactions | sparsity ", self.test_set['sparsity'])
print("metadata:: ", len(self.metadata['items']), " users and ", len(self.metadata['metadata']),
" metadata and ", self.metadata['ni'], " interactions\n")
self._create_factors()
if self.batch:
print("training time:: ", timed(self.train_batch_model), " sec")
else:
print("training time:: ", timed(self.train_model), " sec")
print("\nprediction_time:: ", timed(self.predict), " sec\n")
self.evaluate(self.predictions)
def execute(self):
# methods
print(
"[Case Recommender: Rating Prediction > User Attribute KNN Algorithm]\n"
)
print("training data:: ", len(self.train_set['users']), " users and ",
len(self.train_set['items']), " items and ",
self.train_set['ni'], " interactions")
print("test data:: ", len(self.test_set['users']), " users and ",
len(self.test_set['items']), " items and ", self.test_set['ni'],
" interactions")
# training baselines bui
print("training time:: ", timed(self.train_baselines), " sec")
if self.similarity_matrix_file is not None:
print("compute similarity:: ", timed(self.read_matrix), " sec")
else:
print("compute similarity time:: ", timed(self.compute_similarity),
" sec")
print("prediction_time:: ", timed(self.predict), " sec\n")
self.evaluate(self.predictions)
def execute(self,
measures=('Prec@5', 'Prec@10', 'NDCG@5', 'NDCG@10', 'MAP@5',
'MAP@10')):
# methods
print("[Case Recommender: Item Recommendation > BPR MF Algorithm]\n")
print("training data:: ", len(self.train_set['users']), " users and ",
len(self.train_set['items']), " items and ",
self.train_set['ni'], " interactions | sparsity ",
self.train_set['sparsity'])
if self.test_file is not None:
test_set = ReadFile(self.test_file).return_information()
print("test data:: ", len(self.test_set['users']), " users and ",
len(self.test_set['items']), " items and ",
(self.test_set['ni']), " interactions | sparsity ",
self.test_set['sparsity'])
del test_set
self._create_factors()
print("training time:: ", timed(self.train_model), " sec")
print("prediction_time:: ", timed(self.predict), " sec\n")
if self.test_file is not None:
self.evaluate(measures)
def execute(self):
print("[Case Recommender: Split Dataset Process]\n")
print("number of folds:: ", self.n_folds, " write folds in:: ",
self.dir_folds)
print("total process time:: ", timed(self.divide_dataset), "sec")
