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
"""
dataReader = Movielens10MReader()
dataset = dataReader.load_data()
URM_train, URM_test = split_train_in_two_percentage_global_sample(
dataset.get_URM_all(), train_percentage=0.80)
URM_train, URM_validation = split_train_in_two_percentage_global_sample(
URM_train, train_percentage=0.80)
output_folder_path = "result_experiments/"
# If directory does not exist, create
if not os.path.exists(output_folder_path):
os.makedirs(output_folder_path)
collaborative_algorithm_list = [
Random, TopPop, P3alphaRecommender, RP3betaRecommender,
ItemKNNCFRecommender, UserKNNCFRecommender,
MatrixFactorization_BPR_Cython, MatrixFactorization_FunkSVD_Cython,
PureSVDRecommender, SLIM_BPR_Cython, SLIMElasticNetRecommender
]
from Base.Evaluation.Evaluator import EvaluatorHoldout
evaluator_validation = EvaluatorHoldout(URM_validation, cutoff_list=[5])
evaluator_test = EvaluatorHoldout(URM_test, cutoff_list=[5, 10])
runParameterSearch_Collaborative_partial = partial(
runParameterSearch_Collaborative,
URM_train=URM_train,
metric_to_optimize="MAP",
n_cases=10,
evaluator_validation_earlystopping=evaluator_validation,
evaluator_validation=evaluator_validation,
evaluator_test=evaluator_test,
output_folder_path=output_folder_path,
similarity_type_list=["cosine"],
parallelizeKNN=False)
pool = multiprocessing.Pool(processes=int(multiprocessing.cpu_count()),
maxtasksperchild=1)
pool.map(runParameterSearch_Collaborative_partial,
collaborative_algorithm_list)
"""
Created on 09/11/2020
@author: Maurizio Ferrari Dacrema
"""
from Data_manager.split_functions.split_train_validation_random_holdout import split_train_in_two_percentage_global_sample
from Data_manager.Movielens.Movielens10MReader import Movielens10MReader
data_reader = Movielens10MReader()
data_loaded = data_reader.load_data()
URM_all = data_loaded.get_URM_all()
URM_train, URM_test = split_train_in_two_percentage_global_sample(
URM_all, train_percentage=0.8)
from Cython_examples.FunkSVD_fastest import train_multiple_epochs
train_multiple_epochs(URM_train, 1e-3, 10)