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run_IJCAI_18_NeuRec.py
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run_IJCAI_18_NeuRec.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 20/02/19
@author: Simone Boglio
"""
from Recommender_import_list import *
from ParameterTuning.run_parameter_search import runParameterSearch_Collaborative
from ParameterTuning.SearchSingleCase import SearchSingleCase
from ParameterTuning.SearchAbstractClass import SearchInputRecommenderArgs
from Conferences.IJCAI.NeuRec_our_interface.UNeuRecWrapper import UNeuRec_RecommenderWrapper
from Conferences.IJCAI.NeuRec_our_interface.INeuRecWrapper import INeuRec_RecommenderWrapper
from Utils.ResultFolderLoader import ResultFolderLoader, generate_latex_hyperparameters
from functools import partial
import numpy as np
import os, traceback, argparse
from Conferences.IJCAI.NeuRec_our_interface.Movielens1M.Movielens1MReader import Movielens1MReader
from Conferences.IJCAI.NeuRec_our_interface.FilmTrust.FilmTrustReader import FilmTrustReader
from Conferences.IJCAI.NeuRec_our_interface.MovielensHetrec2011.MovielensHetrec2011Reader import MovielensHetrec2011Reader
from Conferences.IJCAI.NeuRec_our_interface.Frappe.FrappeReader import FrappeReader
######################################################################
from skopt.space import Real, Integer, Categorical
import traceback
from ParameterTuning.SearchBayesianSkopt import SearchBayesianSkopt
from ParameterTuning.SearchSingleCase import SearchSingleCase
from ParameterTuning.SearchAbstractClass import SearchInputRecommenderArgs
def runParameterSearch_NeuRec(recommender_class, URM_train, earlystopping_hyperparameters, output_file_name_root, URM_train_last_test = None,
n_cases = 35, n_random_starts = 5,
evaluator_validation= None, evaluator_test=None, metric_to_optimize = "RECALL",
output_folder_path ="result_experiments/"):
# If directory does not exist, create
if not os.path.exists(output_folder_path):
os.makedirs(output_folder_path)
parameterSearch = SearchBayesianSkopt(recommender_class, evaluator_validation=evaluator_validation, evaluator_test=evaluator_test)
##########################################################################################################
hyperparameters_range_dictionary = {}
hyperparameters_range_dictionary["epochs"] = Categorical([1500])
hyperparameters_range_dictionary["num_neurons"] = Integer(100, 400)
hyperparameters_range_dictionary["num_factors"] = Integer(20, 70)
hyperparameters_range_dictionary["dropout_percentage"] = Real(low = 0.0, high = 0.1, prior = 'uniform')
hyperparameters_range_dictionary["learning_rate"] = Real(low = 1e-5, high = 1e-3, prior = 'log-uniform')
hyperparameters_range_dictionary["regularization_rate"] = Real(low = 0.0, high = 0.2, prior = 'uniform')
hyperparameters_range_dictionary["batch_size"] = Categorical([128, 256, 512, 1024, 2048])
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [URM_train],
CONSTRUCTOR_KEYWORD_ARGS = {},
FIT_POSITIONAL_ARGS = [],
FIT_KEYWORD_ARGS = {**earlystopping_hyperparameters,
"use_gpu": False,
"epochs_min": 200,
"display_epoch": None,
"display_step": None,
"verbose": False}
)
#########################################################################################################
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[0] = URM_train_last_test
else:
recommender_input_args_last_test = None
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 = True,
recommender_input_args_last_test = recommender_input_args_last_test)
def read_data_split_and_search(dataset_name,
flag_baselines_tune = False,
flag_DL_article_default = False, flag_DL_tune = False,
flag_print_results = False):
result_folder_path = "result_experiments/{}/{}_{}/".format(CONFERENCE_NAME, ALGORITHM_NAME, dataset_name)
if dataset_name == "movielens1m":
dataset = Movielens1MReader(result_folder_path)
article_hyperparameters = {'num_neurons': 300,
'num_factors': 50,
'dropout_percentage': 0.03,
'learning_rate': 1e-4,
'regularization_rate': 0.1,
'epochs': 1500,
'batch_size': 1024,
'display_epoch': None,
'display_step': None,
'verbose': True
}
early_stopping_epochs_min = 800
elif dataset_name == "hetrec":
dataset = MovielensHetrec2011Reader(result_folder_path)
article_hyperparameters = {'num_neurons': 300,
'num_factors': 50,
'dropout_percentage': 0.03,
'learning_rate': 1e-4,
'regularization_rate': 0.1,
'epochs': 1500,
'batch_size': 1024,
'display_epoch': None,
'display_step': None,
'verbose': True
}
early_stopping_epochs_min = 800
elif dataset_name == "filmtrust":
dataset = FilmTrustReader(result_folder_path)
article_hyperparameters = {'num_neurons': 150,
'num_factors': 40,
'dropout_percentage': 0.00,
'learning_rate': 5e-5,
'regularization_rate': 0.1,
'epochs': 100,
'batch_size': 1024,
'display_epoch': None,
'display_step': None,
'verbose': True
}
early_stopping_epochs_min = 0
elif dataset_name == "frappe":
dataset = FrappeReader(result_folder_path)
article_hyperparameters = {'num_neurons': 300,
'num_factors': 50,
'dropout_percentage': 0.03,
'learning_rate': 1e-4,
'regularization_rate': 0.01,
'epochs': 100,
'batch_size': 1024,
'display_epoch': None,
'display_step': None,
'verbose': True
}
early_stopping_epochs_min = 0
print ('Current dataset is: {}'.format(dataset_name))
URM_train = dataset.URM_DICT["URM_train"].copy()
URM_validation = dataset.URM_DICT["URM_validation"].copy()
URM_test = dataset.URM_DICT["URM_test"].copy()
# Ensure IMPLICIT data
from Utils.assertions_on_data_for_experiments import assert_implicit_data, assert_disjoint_matrices
assert_implicit_data([URM_train, URM_validation, URM_test])
assert_disjoint_matrices([URM_train, URM_validation, URM_test])
# If directory does not exist, create
if not os.path.exists(result_folder_path):
os.makedirs(result_folder_path)
collaborative_algorithm_list = [
Random,
TopPop,
UserKNNCFRecommender,
ItemKNNCFRecommender,
P3alphaRecommender,
RP3betaRecommender,
PureSVDRecommender,
NMFRecommender,
IALSRecommender,
MatrixFactorization_BPR_Cython,
MatrixFactorization_FunkSVD_Cython,
EASE_R_Recommender,
SLIM_BPR_Cython,
SLIMElasticNetRecommender,
]
metric_to_optimize = "NDCG"
n_cases = 50
n_random_starts = 15
from Base.Evaluation.Evaluator import EvaluatorHoldout
# use max cutoff to compute full MAP and NDCG
max_cutoff = URM_train.shape[1]-1
cutoff_list_validation = [10]
cutoff_list_test=[5, 10, 50, max_cutoff]
evaluator_validation = EvaluatorHoldout(URM_validation, cutoff_list=cutoff_list_validation)
evaluator_test = EvaluatorHoldout(URM_test, cutoff_list=cutoff_list_test)
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()
################################################################################################
######
###### DL ALGORITHM
######
if flag_DL_article_default:
earlystopping_hyperparameters = {'validation_every_n': 5,
'stop_on_validation': True,
'lower_validations_allowed': 20,
'evaluator_object': evaluator_validation,
'validation_metric': metric_to_optimize,
'epochs_min': early_stopping_epochs_min
}
try:
parameterSearch = SearchSingleCase(UNeuRec_RecommenderWrapper,
evaluator_validation=evaluator_validation,
evaluator_test=evaluator_test)
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [URM_train],
FIT_KEYWORD_ARGS = 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=article_hyperparameters,
output_folder_path = result_folder_path,
resume_from_saved = True,
output_file_name_root = UNeuRec_RecommenderWrapper.RECOMMENDER_NAME)
except Exception as e:
print("On recommender {} Exception {}".format(UNeuRec_RecommenderWrapper, str(e)))
traceback.print_exc()
try:
parameterSearch = SearchSingleCase(INeuRec_RecommenderWrapper,
evaluator_validation=evaluator_validation,
evaluator_test=evaluator_test)
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [URM_train],
FIT_KEYWORD_ARGS = 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=article_hyperparameters,
output_folder_path = result_folder_path,
resume_from_saved = True,
output_file_name_root = INeuRec_RecommenderWrapper.RECOMMENDER_NAME)
except Exception as e:
print("On recommender {} Exception {}".format(INeuRec_RecommenderWrapper, str(e)))
traceback.print_exc()
# if isUNeuRec_tune:
#
# try:
#
# runParameterSearch_NeuRec(UNeuRec_RecommenderWrapper,
# URM_train = URM_train,
# URM_train_last_test = URM_train + URM_validation,
# earlystopping_hyperparameters = earlystopping_hyperparameters,
# metric_to_optimize = metric_to_optimize,
# evaluator_validation = evaluator_validation,
# evaluator_test = evaluator_test,
# result_folder_path = result_folder_path,
# n_cases = n_cases,
# n_random_starts = n_random_starts,
# output_file_name_root = UNeuRec_RecommenderWrapper.RECOMMENDER_NAME)
#
#
# except Exception as e:
#
# print("On recommender {} Exception {}".format(UNeuRec_RecommenderWrapper, str(e)))
# traceback.print_exc()
#
#
#
#
#
# if isINeuRec_tune:
#
# try:
#
# runParameterSearch_NeuRec(INeuRec_RecommenderWrapper,
# URM_train = URM_train,
# URM_train_last_test = URM_train + URM_validation,
# earlystopping_hyperparameters = earlystopping_hyperparameters,
# metric_to_optimize = metric_to_optimize,
# evaluator_validation = evaluator_validation,
# evaluator_test = evaluator_test,
# result_folder_path = result_folder_path,
# n_cases = n_cases,
# n_random_starts = n_random_starts,
# output_file_name_root = INeuRec_RecommenderWrapper.RECOMMENDER_NAME)
#
#
# except Exception as e:
#
# print("On recommender {} Exception {}".format(INeuRec_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 = [INeuRec_RecommenderWrapper, UNeuRec_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 = ["PRECISION", "RECALL", "MAP_MIN_DEN", "NDCG", "MRR"],
cutoffs_list = [5, 10, 50],
table_title = None,
highlight_best = True)
result_loader.generate_latex_results(file_name + "{}_latex_results.txt".format("beyond_accuracy_metrics"),
metrics_list = ["DIVERSITY_MEAN_INTER_LIST", "DIVERSITY_HERFINDAHL", "COVERAGE_ITEM", "DIVERSITY_GINI", "SHANNON_ENTROPY"],
cutoffs_list = [10],
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)
if __name__ == '__main__':
ALGORITHM_NAME = "NeuREC"
CONFERENCE_NAME = "IJCAI"
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)
input_flags = parser.parse_args()
print(input_flags)
KNN_similarity_to_report_list = ["cosine", "dice", "jaccard", "asymmetric", "tversky"]
dataset_list = ['frappe', 'filmtrust', 'movielens1m', 'hetrec']
for dataset_name in dataset_list:
read_data_split_and_search(dataset_name,
flag_baselines_tune=input_flags.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=dataset_list,
other_algorithm_list=[INeuRec_RecommenderWrapper, UNeuRec_RecommenderWrapper],
KNN_similarity_to_report_list = KNN_similarity_to_report_list,
split_per_algorithm_type = True)