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 = Builder()
URM_train = dataReader.get_URM_train()
URM_validation = dataReader.get_URM_validation()
URM_test = dataReader.get_URM_test()
output_root_path = "result_experiments/"
# If directory does not exist, create
if not os.path.exists(output_root_path):
os.makedirs(output_root_path)
collaborative_algorithm_list = [HybridRec]
from ParameterTuning.AbstractClassSearch import EvaluatorWrapper
from Base.Evaluation.Evaluator import SequentialEvaluator
evaluator_validation_earlystopping = SequentialEvaluator(URM_validation,
cutoff_list=[5])
evaluator_test = SequentialEvaluator(URM_test, cutoff_list=[5, 10])
evaluator_validation = EvaluatorWrapper(evaluator_validation_earlystopping)
evaluator_test = EvaluatorWrapper(evaluator_test)
runParameterSearch_Collaborative_partial = partial(
runParameterSearch_Collaborative,
URM_train=URM_train,
metric_to_optimize="MAP",
evaluator_validation_earlystopping=evaluator_validation_earlystopping,
evaluator_validation=evaluator_validation,
evaluator_test=evaluator_test,
output_root_path=output_root_path)
# pool = multiprocessing.Pool(processes=int(multiprocessing.cpu_count()), maxtasksperchild=1)
# resultList = pool.map(runParameterSearch_Collaborative_partial, collaborative_algorithm_list)
for recommender_class in collaborative_algorithm_list:
try:
runParameterSearch_Collaborative_partial(recommender_class)
except Exception as e:
print("On recommender {} Exception {}".format(
recommender_class, str(e)))
traceback.print_exc()
