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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
"""
import multiprocessing
import os
from functools import partial
from multiprocessing.pool import ThreadPool as Pool1
import scipy.sparse as sps
from Utils.split_train_validation_random_holdout import \
split_train_in_two_percentage_global_sample
from Evaluation.Evaluator import EvaluatorHoldout
from HyperparameterTuning.run_hyperparameter_search import runHyperparameterSearch_Collaborative, \
runHyperparameterSearch_Content, runHyperparameterSearch_Hybrid
from Recommenders.Hybrids.HybridRatings_IALS_hybrid_EASE_R_hybrid_SLIM_Rp3 import \
HybridRatings_IALS_hybrid_EASE_R_hybrid_SLIM_Rp3
from Recommenders.KNN.ItemKNNCBFWeightedSimilarityRecommender import ItemKNNCBFWeightedSimilarityRecommender
from Recommenders.Recommender_import_list import *
from reader import load_urm, load_icm, get_k_folds_URM
output_folder_path = "result_experiments/"
# If directory does not exist, create
if not os.path.exists(output_folder_path):
os.makedirs(output_folder_path)
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
"""
collaborative_algorithm_list = [
# P3alphaRecommender,
# RP3betaRecommender,
# ItemKNNCFRecommender,
# UserKNNCFRecommender,
# MatrixFactorization_BPR_Cython, # bad
# MatrixFactorization_FunkSVD_Cython,
# PureSVDRecommender,
# SLIM_BPR_Cython,
# SLIM_BPR_Cython
# SLIMElasticNetRecommender,
# IALSRecommender
# MultVAERecommender
# IALSRecommender_implicit
# EASE_R_Recommender
]
content_algorithm_list = [
ItemKNNCBFRecommender,
# ItemKNNCBFWeightedSimilarityRecommender,
]
hybrid_algorithm_list = [
# ScoresHybridRecommender,
# HybridWsparseSLIMRp3,
# Hybrid_SlimElastic_Rp3_IALS,
# ItemKNNScoresHybridRecommender
# RankingHybrid
# Hybrid_SlimElastic_Rp3_PureSVD
# HybridSimilarity_withGroupedusers
# Hybrid_SLIM_EASE_R_IALS
# HybridRatings_EASE_R_hybrid_SLIM_Rp3
HybridRatings_IALS_hybrid_EASE_R_hybrid_SLIM_Rp3
]
URM_all = load_urm()
URM_train, URM_validation = split_train_in_two_percentage_global_sample(URM_all, train_percentage=0.80)
cutoff_list = [10]
metric_to_optimize = "MAP"
cutoff_to_optimize = 10
n_cases = 100
n_random_starts = int(n_cases / 3)
# new function to evaluate 1 group of users (for now split at 50%)
# evaluator_validation = group_users_in_urm(URM_train, URM_validation, 1)
evaluator_validation = EvaluatorHoldout(URM_validation, cutoff_list=cutoff_list)
# COLLABORATIVE
# 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=None,
# output_folder_path=output_folder_path,
# resume_from_saved=True,
# save_model="no",
# similarity_type_list=None, # ["cosine"],
# allow_weighting=True,
# parallelizeKNN=False)
#
# pool_collab = multiprocessing.Pool(processes=int(multiprocessing.cpu_count()), maxtasksperchild=1)
# pool_collab.map(runParameterSearch_Collaborative_partial, collaborative_algorithm_list)
# CONTENT RECS
ICM_channel = load_icm("data_ICM_channel.csv")
# ICM_event = load_icm("data_ICM_event.csv")
# ICM_genre = load_icm("data_ICM_genre.csv")
# ICM_subgenre = load_icm("data_ICM_subgenre.csv")
# ICM_all = sps.hstack([ICM_channel, ICM_event, ICM_genre, ICM_subgenre]).tocsr()
#
# runParameterSearch_Content_partial = partial(runHyperparameterSearch_Content,
# URM_train=URM_train,
# ICM_object=ICM_all,
# ICM_name="ICM_all",
# metric_to_optimize=metric_to_optimize,
# cutoff_to_optimize=cutoff_to_optimize,
# n_cases=n_cases,
# n_random_starts=n_random_starts,
# evaluator_validation=evaluator_validation,
# evaluator_test=None,
# output_folder_path=output_folder_path,
# resume_from_saved=False,
# save_model="no",
# similarity_type_list=None,
# parallelizeKNN=False)
#
# pool_collab = Pool1(processes=int(multiprocessing.cpu_count()))
# pool_collab.map(runParameterSearch_Content_partial, content_algorithm_list)
# HYBRID
runParameterSearch_Hybrid_partial = partial(runHyperparameterSearch_Hybrid,
URM_train=URM_train,
W_train=None,
ICM_object=ICM_channel,
ICM_name="ICM_all",
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=None,
output_folder_path=output_folder_path)
pool_collab = Pool1(processes=int(multiprocessing.cpu_count()))
pool_collab.map(runParameterSearch_Hybrid_partial, hybrid_algorithm_list)
if __name__ == '__main__':
read_data_split_and_search()