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run_WWW_18_Mult_VAE.py
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run_WWW_18_Mult_VAE.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 22/11/17
@author: Maurizio Ferrari Dacrema
"""
from Recommender_import_list import *
from Conferences.WWW.MultiVAE_our_interface.MultiVAE_RecommenderWrapper import Mult_VAE_RecommenderWrapper
from ParameterTuning.SearchSingleCase import SearchSingleCase
from ParameterTuning.SearchAbstractClass import SearchInputRecommenderArgs
from ParameterTuning.run_parameter_search import runParameterSearch_Collaborative
from Utils.ResultFolderLoader import ResultFolderLoader, generate_latex_hyperparameters
from functools import partial
import os, traceback, argparse
import numpy as np
from Conferences.WWW.MultiVAE_our_interface.EvaluatorUserSubsetWrapper import EvaluatorUserSubsetWrapper, MF_cold_user_wrapper
from Utils.assertions_on_data_for_experiments import assert_implicit_data, assert_disjoint_matrices
######################################################################
from skopt.space import Real, Integer, Categorical
from ParameterTuning.SearchBayesianSkopt import SearchBayesianSkopt
from ParameterTuning.SearchSingleCase import SearchSingleCase
from ParameterTuning.SearchAbstractClass import SearchInputRecommenderArgs
def runParameterSearch_cold_user_MF(recommender_class, URM_train, URM_train_last_test = None, metric_to_optimize = "PRECISION",
evaluator_validation = None, evaluator_test = None, evaluator_validation_earlystopping = None,
output_folder_path ="result_experiments/",
n_cases = 35, n_random_starts = 5, resume_from_saved = True):
# If directory does not exist, create
if not os.path.exists(output_folder_path):
os.makedirs(output_folder_path)
earlystopping_keywargs = {"validation_every_n": 5,
"stop_on_validation": True,
"evaluator_object": evaluator_validation_earlystopping,
"lower_validations_allowed": 5,
"validation_metric": metric_to_optimize,
}
URM_train = URM_train.copy()
if URM_train_last_test is not None:
URM_train_last_test = URM_train_last_test.copy()
try:
output_file_name_root = recommender_class.RECOMMENDER_NAME
##########################################################################################################
if recommender_class is MatrixFactorization_FunkSVD_Cython:
hyperparameters_range_dictionary = {}
hyperparameters_range_dictionary["sgd_mode"] = Categorical(["sgd", "adagrad", "adam"])
hyperparameters_range_dictionary["epochs"] = Categorical([500])
hyperparameters_range_dictionary["use_bias"] = Categorical([True, False])
hyperparameters_range_dictionary["batch_size"] = Categorical([1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024])
hyperparameters_range_dictionary["num_factors"] = Integer(1, 200)
hyperparameters_range_dictionary["item_reg"] = Real(low = 1e-5, high = 1e-2, prior = 'log-uniform')
hyperparameters_range_dictionary["user_reg"] = Real(low = 1e-5, high = 1e-2, prior = 'log-uniform')
hyperparameters_range_dictionary["learning_rate"] = Real(low = 1e-4, high = 1e-1, prior = 'log-uniform')
hyperparameters_range_dictionary["negative_interactions_quota"] = Real(low = 0.0, high = 0.5, prior = 'uniform')
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [recommender_class, URM_train],
CONSTRUCTOR_KEYWORD_ARGS = {},
FIT_POSITIONAL_ARGS = [],
FIT_KEYWORD_ARGS = earlystopping_keywargs
)
##########################################################################################################
if recommender_class is MatrixFactorization_AsySVD_Cython:
hyperparameters_range_dictionary = {}
hyperparameters_range_dictionary["sgd_mode"] = Categorical(["sgd", "adagrad", "adam"])
hyperparameters_range_dictionary["epochs"] = Categorical([500])
hyperparameters_range_dictionary["use_bias"] = Categorical([True, False])
hyperparameters_range_dictionary["batch_size"] = Categorical([1])
hyperparameters_range_dictionary["num_factors"] = Integer(1, 200)
hyperparameters_range_dictionary["item_reg"] = Real(low = 1e-5, high = 1e-2, prior = 'log-uniform')
hyperparameters_range_dictionary["user_reg"] = Real(low = 1e-5, high = 1e-2, prior = 'log-uniform')
hyperparameters_range_dictionary["learning_rate"] = Real(low = 1e-4, high = 1e-1, prior = 'log-uniform')
hyperparameters_range_dictionary["negative_interactions_quota"] = Real(low = 0.0, high = 0.5, prior = 'uniform')
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [recommender_class, URM_train],
CONSTRUCTOR_KEYWORD_ARGS = {},
FIT_POSITIONAL_ARGS = [],
FIT_KEYWORD_ARGS = earlystopping_keywargs
)
##########################################################################################################
if recommender_class is MatrixFactorization_BPR_Cython:
hyperparameters_range_dictionary = {}
hyperparameters_range_dictionary["sgd_mode"] = Categorical(["sgd", "adagrad", "adam"])
hyperparameters_range_dictionary["epochs"] = Categorical([1500])
hyperparameters_range_dictionary["num_factors"] = Integer(1, 200)
hyperparameters_range_dictionary["batch_size"] = Categorical([1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024])
hyperparameters_range_dictionary["positive_reg"] = Real(low = 1e-5, high = 1e-2, prior = 'log-uniform')
hyperparameters_range_dictionary["negative_reg"] = Real(low = 1e-5, high = 1e-2, prior = 'log-uniform')
hyperparameters_range_dictionary["learning_rate"] = Real(low = 1e-4, high = 1e-1, prior = 'log-uniform')
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [recommender_class, URM_train],
CONSTRUCTOR_KEYWORD_ARGS = {},
FIT_POSITIONAL_ARGS = [],
FIT_KEYWORD_ARGS = {**earlystopping_keywargs,
"positive_threshold_BPR": None}
)
##########################################################################################################
if recommender_class is IALSRecommender:
hyperparameters_range_dictionary = {}
hyperparameters_range_dictionary["num_factors"] = Integer(1, 200)
hyperparameters_range_dictionary["confidence_scaling"] = Categorical(["linear", "log"])
hyperparameters_range_dictionary["alpha"] = Real(low = 1e-3, high = 50.0, prior = 'log-uniform')
hyperparameters_range_dictionary["epsilon"] = Real(low = 1e-3, high = 10.0, prior = 'log-uniform')
hyperparameters_range_dictionary["reg"] = Real(low = 1e-5, high = 1e-2, prior = 'log-uniform')
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [recommender_class, URM_train],
CONSTRUCTOR_KEYWORD_ARGS = {},
FIT_POSITIONAL_ARGS = [],
FIT_KEYWORD_ARGS = earlystopping_keywargs
)
##########################################################################################################
if recommender_class is PureSVDRecommender:
hyperparameters_range_dictionary = {}
hyperparameters_range_dictionary["num_factors"] = Integer(1, 350)
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [recommender_class, URM_train],
CONSTRUCTOR_KEYWORD_ARGS = {},
FIT_POSITIONAL_ARGS = [],
FIT_KEYWORD_ARGS = {}
)
##########################################################################################################
if recommender_class is NMFRecommender:
hyperparameters_range_dictionary = {}
hyperparameters_range_dictionary["num_factors"] = Integer(1, 350)
hyperparameters_range_dictionary["solver"] = Categorical(["coordinate_descent", "multiplicative_update"])
hyperparameters_range_dictionary["init_type"] = Categorical(["random", "nndsvda"])
hyperparameters_range_dictionary["beta_loss"] = Categorical(["frobenius", "kullback-leibler"])
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [recommender_class, URM_train],
CONSTRUCTOR_KEYWORD_ARGS = {},
FIT_POSITIONAL_ARGS = [],
FIT_KEYWORD_ARGS = {}
)
#########################################################################################################
if URM_train_last_test is not None:
recommender_input_args_last_test = recommender_input_args.copy()
recommender_input_args_last_test.CONSTRUCTOR_POSITIONAL_ARGS[1] = URM_train_last_test
else:
recommender_input_args_last_test = None
parameterSearch = SearchBayesianSkopt(MF_cold_user_wrapper, evaluator_validation=evaluator_validation, evaluator_test=evaluator_test)
hyperparameters_range_dictionary["estimate_model_for_cold_users"] = Categorical(["itemKNN", "mean_item_factors"])
hyperparameters_range_dictionary["estimate_model_for_cold_users_topK"] = Integer(5, 1000)
## Final step, after the hyperparameter range has been defined for each type of algorithm
parameterSearch.search(recommender_input_args,
parameter_search_space = hyperparameters_range_dictionary,
n_cases = n_cases,
n_random_starts = n_random_starts,
output_folder_path = output_folder_path,
output_file_name_root = output_file_name_root,
metric_to_optimize = metric_to_optimize,
resume_from_saved = resume_from_saved,
recommender_input_args_last_test = recommender_input_args_last_test)
except Exception as e:
print("On recommender {} Exception {}".format(recommender_class, str(e)))
traceback.print_exc()
error_file = open(output_folder_path + "ErrorLog.txt", "a")
error_file.write("On recommender {} Exception {}\n".format(recommender_class, str(e)))
error_file.close()
def read_data_split_and_search(dataset_name,
flag_baselines_tune = False,
flag_DL_article_default = False, flag_MF_baselines_tune = False, flag_DL_tune = False,
flag_print_results = False):
from Conferences.WWW.MultiVAE_our_interface.Movielens20M.Movielens20MReader import Movielens20MReader
from Conferences.WWW.MultiVAE_our_interface.NetflixPrize.NetflixPrizeReader import NetflixPrizeReader
split_type = "cold_user"
result_folder_path = "result_experiments/{}/{}_{}_{}/".format(CONFERENCE_NAME, ALGORITHM_NAME, dataset_name, split_type)
if dataset_name == "movielens20m":
dataset = Movielens20MReader(result_folder_path, split_type = split_type)
elif dataset_name == "netflixPrize":
dataset = NetflixPrizeReader(result_folder_path)
# If directory does not exist, create
if not os.path.exists(result_folder_path):
os.makedirs(result_folder_path)
metric_to_optimize = "NDCG"
n_cases = 50
n_random_starts = 15
if split_type == "cold_user":
collaborative_algorithm_list = [
Random,
TopPop,
# UserKNNCFRecommender,
ItemKNNCFRecommender,
P3alphaRecommender,
RP3betaRecommender,
# PureSVDRecommender,
# IALSRecommender,
# NMFRecommender,
# MatrixFactorization_BPR_Cython,
# MatrixFactorization_FunkSVD_Cython,
EASE_R_Recommender,
SLIM_BPR_Cython,
SLIMElasticNetRecommender,
]
URM_train = dataset.URM_DICT["URM_train"].copy()
URM_train_all = dataset.URM_DICT["URM_train_all"].copy()
URM_validation = dataset.URM_DICT["URM_validation"].copy()
URM_test = dataset.URM_DICT["URM_test"].copy()
# Ensure IMPLICIT data and DISJOINT sets
assert_implicit_data([URM_train, URM_train_all, URM_validation, URM_test])
assert_disjoint_matrices([URM_train, URM_validation, URM_test])
assert_disjoint_matrices([URM_train_all, URM_validation, URM_test])
from Base.Evaluation.Evaluator import EvaluatorHoldout
evaluator_validation = EvaluatorHoldout(URM_validation, cutoff_list=[100])
evaluator_test = EvaluatorHoldout(URM_test, cutoff_list=[20, 50, 100])
evaluator_validation = EvaluatorUserSubsetWrapper(evaluator_validation, URM_train_all)
evaluator_test = EvaluatorUserSubsetWrapper(evaluator_test, URM_train_all)
runParameterSearch_Collaborative_partial = partial(runParameterSearch_Collaborative,
URM_train = URM_train,
URM_train_last_test = URM_train + URM_validation,
metric_to_optimize = metric_to_optimize,
evaluator_validation_earlystopping = evaluator_validation,
evaluator_validation = evaluator_validation,
evaluator_test = evaluator_test,
output_folder_path = result_folder_path,
parallelizeKNN = False,
allow_weighting = True,
resume_from_saved = True,
n_cases = n_cases,
n_random_starts = n_random_starts)
if flag_baselines_tune:
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()
################################################################################################
###### Matrix Factorization Cold users
collaborative_MF_algorithm_list = [
PureSVDRecommender,
IALSRecommender,
NMFRecommender,
MatrixFactorization_BPR_Cython,
MatrixFactorization_FunkSVD_Cython,
]
runParameterSearch_cold_user_MF_partial = partial(runParameterSearch_cold_user_MF,
URM_train = URM_train,
URM_train_last_test = URM_train + URM_validation,
metric_to_optimize = metric_to_optimize,
evaluator_validation_earlystopping = evaluator_validation,
evaluator_validation = evaluator_validation,
evaluator_test = evaluator_test,
output_folder_path = result_folder_path,
resume_from_saved = True,
n_cases = n_cases,
n_random_starts = n_random_starts)
if flag_MF_baselines_tune:
for recommender_class in collaborative_MF_algorithm_list:
try:
runParameterSearch_cold_user_MF_partial(recommender_class)
except Exception as e:
print("On recommender {} Exception {}".format(recommender_class, str(e)))
traceback.print_exc()
################################################################################################
######
###### DL ALGORITHM
######
if flag_DL_article_default:
try:
if dataset_name == "movielens20m":
epochs = 100
elif dataset_name == "netflixPrize":
epochs = 200
multiVAE_article_hyperparameters = {
"epochs": epochs,
"batch_size": 500,
"total_anneal_steps": 200000,
"p_dims": None,
}
multiVAE_earlystopping_hyperparameters = {
"validation_every_n": 5,
"stop_on_validation": True,
"evaluator_object": evaluator_validation,
"lower_validations_allowed": 5,
"validation_metric": metric_to_optimize,
}
parameterSearch = SearchSingleCase(Mult_VAE_RecommenderWrapper,
evaluator_validation=evaluator_validation,
evaluator_test=evaluator_test)
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [URM_train],
FIT_KEYWORD_ARGS = multiVAE_earlystopping_hyperparameters)
recommender_input_args_last_test = recommender_input_args.copy()
recommender_input_args_last_test.CONSTRUCTOR_POSITIONAL_ARGS[0] = URM_train + URM_validation
parameterSearch.search(recommender_input_args,
recommender_input_args_last_test = recommender_input_args_last_test,
fit_hyperparameters_values=multiVAE_article_hyperparameters,
output_folder_path = result_folder_path,
resume_from_saved = True,
output_file_name_root = Mult_VAE_RecommenderWrapper.RECOMMENDER_NAME)
except Exception as e:
print("On recommender {} Exception {}".format(Mult_VAE_RecommenderWrapper, str(e)))
traceback.print_exc()
################################################################################################
######
###### PRINT RESULTS
######
if flag_print_results:
n_test_users = np.sum(np.ediff1d(URM_test.indptr)>=1)
file_name = "{}..//{}_{}_".format(result_folder_path, ALGORITHM_NAME, dataset_name)
result_loader = ResultFolderLoader(result_folder_path,
base_algorithm_list = None,
other_algorithm_list = [Mult_VAE_RecommenderWrapper],
KNN_similarity_list = KNN_similarity_to_report_list,
ICM_names_list = None,
UCM_names_list = None)
result_loader.generate_latex_results(file_name + "{}_latex_results.txt".format("article_metrics"),
metrics_list = ["RECALL", "NDCG"],
cutoffs_list = [20, 50, 100],
table_title = None,
highlight_best = True)
result_loader.generate_latex_results(file_name + "{}_latex_results.txt".format("all_metrics"),
metrics_list = ["PRECISION", "RECALL", "MAP_MIN_DEN", "MRR", "NDCG", "F1", "HIT_RATE", "ARHR_ALL_HITS",
"NOVELTY", "DIVERSITY_MEAN_INTER_LIST", "DIVERSITY_HERFINDAHL", "COVERAGE_ITEM", "DIVERSITY_GINI", "SHANNON_ENTROPY"],
cutoffs_list = [50],
table_title = None,
highlight_best = True)
result_loader.generate_latex_time_statistics(file_name + "{}_latex_results.txt".format("time"),
n_evaluation_users=n_test_users,
table_title = None)
from functools import partial
if __name__ == '__main__':
ALGORITHM_NAME = "Mult_VAE"
CONFERENCE_NAME = "WWW"
parser = argparse.ArgumentParser()
parser.add_argument('-b', '--baseline_tune', help="Baseline hyperparameter search", type = bool, default = False)
parser.add_argument('-a', '--DL_article_default', help="Train the DL model with article hyperparameters", type = bool, default = False)
parser.add_argument('-p', '--print_results', help="Print results", type = bool, default = True)
parser.add_argument('-m', '--MF_baseline_tune', help="Matrix Factorization hyperparameter search", type = bool, default = False)
input_flags = parser.parse_args()
print(input_flags)
KNN_similarity_to_report_list = ["cosine", "dice", "jaccard", "asymmetric", "tversky"]
dataset_list = ["movielens20m", "netflixPrize"]
for dataset_name in dataset_list:
read_data_split_and_search(dataset_name,
flag_baselines_tune=input_flags.baseline_tune,
flag_MF_baselines_tune = input_flags.MF_baseline_tune,
flag_DL_article_default= input_flags.DL_article_default,
flag_print_results = input_flags.print_results,
)
if input_flags.print_results:
generate_latex_hyperparameters(result_folder_path ="result_experiments/{}/".format(CONFERENCE_NAME),
algorithm_name= ALGORITHM_NAME,
experiment_subfolder_list = ["{}_cold_user".format(dataset) for dataset in dataset_list],
other_algorithm_list = [Mult_VAE_RecommenderWrapper],
KNN_similarity_to_report_list = KNN_similarity_to_report_list,
split_per_algorithm_type = True)