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run_hyperparameter_search.py
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run_hyperparameter_search.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 22/11/17
@author: Maurizio Ferrari Dacrema
"""
from Recommenders.Recommender_import_list import *
import traceback
import os, multiprocessing
from functools import partial
from Data_manager.Movielens.Movielens1MReader import Movielens1MReader
from Data_manager.split_functions.split_train_validation_random_holdout import split_train_in_two_percentage_global_sample
from HyperparameterTuning.run_hyperparameter_search import runHyperparameterSearch_Collaborative, runHyperparameterSearch_Content, runHyperparameterSearch_Hybrid
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 = Movielens1MReader()
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 Evaluation.Evaluator import EvaluatorHoldout
cutoff_list = [5, 10, 20]
metric_to_optimize = "MAP"
cutoff_to_optimize = 10
n_cases = 10
n_random_starts = int(n_cases/3)
evaluator_validation = EvaluatorHoldout(URM_validation, cutoff_list = cutoff_list)
evaluator_test = EvaluatorHoldout(URM_test, cutoff_list = cutoff_list)
runParameterSearch_Collaborative_partial = partial(runHyperparameterSearch_Collaborative,
URM_train = URM_train,
metric_to_optimize = metric_to_optimize,
cutoff_to_optimize = cutoff_to_optimize,
n_cases = n_cases,
n_random_starts = n_random_starts,
evaluator_validation_earlystopping = evaluator_validation,
evaluator_validation = evaluator_validation,
evaluator_test = evaluator_test,
output_folder_path = output_folder_path,
resume_from_saved = True,
similarity_type_list = ["cosine"],
parallelizeKNN = False)
pool = multiprocessing.Pool(processes=int(multiprocessing.cpu_count()), maxtasksperchild=1)
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()
#
################################################################################################
###### Content Baselines
for ICM_name, ICM_object in dataset.get_loaded_ICM_dict().items():
try:
runHyperparameterSearch_Content(ItemKNNCBFRecommender,
URM_train = URM_train,
URM_train_last_test = URM_train + URM_validation,
metric_to_optimize = metric_to_optimize,
cutoff_to_optimize = cutoff_to_optimize,
evaluator_validation = evaluator_validation,
evaluator_test = evaluator_test,
output_folder_path = output_folder_path,
parallelizeKNN = True,
allow_weighting = True,
resume_from_saved = True,
similarity_type_list = ["cosine"],
ICM_name = ICM_name,
ICM_object = ICM_object.copy(),
n_cases = n_cases,
n_random_starts = n_random_starts)
except Exception as e:
print("On CBF recommender for ICM {} Exception {}".format(ICM_name, str(e)))
traceback.print_exc()
try:
runHyperparameterSearch_Hybrid(ItemKNN_CFCBF_Hybrid_Recommender,
URM_train = URM_train,
URM_train_last_test = URM_train + URM_validation,
metric_to_optimize = metric_to_optimize,
cutoff_to_optimize = cutoff_to_optimize,
evaluator_validation = evaluator_validation,
evaluator_test = evaluator_test,
output_folder_path = output_folder_path,
parallelizeKNN = True,
allow_weighting = True,
resume_from_saved = True,
similarity_type_list = ["cosine"],
ICM_name = ICM_name,
ICM_object = ICM_object.copy(),
n_cases = n_cases,
n_random_starts = n_random_starts)
except Exception as e:
print("On recommender {} Exception {}".format(ItemKNN_CFCBF_Hybrid_Recommender, str(e)))
traceback.print_exc()
if __name__ == '__main__':
read_data_split_and_search()