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run_IJCAI_17_DMF.py
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run_IJCAI_17_DMF.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 28/04/19
@author: Simone Boglio
"""
import numpy as np
import os, traceback, argparse
from functools import partial
from Base.Evaluation.Evaluator import EvaluatorNegativeItemSample
from Recommender_import_list import *
# from Conferences.IJCAI.DMF_our_interface.DMFWrapper import DMF_BCE_RecommenderWrapper, DMF_NCE_RecommenderWrapper
from ParameterTuning.run_parameter_search import runParameterSearch_Collaborative
from ParameterTuning.SearchSingleCase import SearchSingleCase
from ParameterTuning.SearchAbstractClass import SearchInputRecommenderArgs
from Utils.ResultFolderLoader import ResultFolderLoader, generate_latex_hyperparameters
from Utils.assertions_on_data_for_experiments import assert_implicit_data, assert_disjoint_matrices
from Utils.plot_popularity import plot_popularity_bias, save_popularity_statistics
from Conferences.IJCAI.DMF_our_interface.Movielens1MReader.Movielens1MReader import Movielens1MReader
from Conferences.IJCAI.DMF_our_interface.Movielens100KReader.Movielens100KReader import Movielens100KReader
from Conferences.IJCAI.DMF_our_interface.AmazonMusicReader.AmazonMusicReader import AmazonMusicReader
from Conferences.IJCAI.DMF_our_interface.AmazonMovieReader.AmazonMovieReader import AmazonMovieReader
def cold_items_statistics(URM_train, URM_validation, URM_test, URM_test_negative):
# Cold items experiment
import scipy.sparse as sps
URM_train_validation = URM_train + URM_validation
n_users, n_items = URM_train_validation.shape
item_in_train_flag = np.ediff1d(sps.csc_matrix(URM_train_validation).indptr) > 0
item_in_test_flag = np.ediff1d(sps.csc_matrix(URM_test).indptr) > 0
test_item_not_in_train_flag = np.logical_and(item_in_test_flag, np.logical_not(item_in_train_flag))
test_item_in_train_flag = np.logical_and(item_in_test_flag, item_in_train_flag)
print("The test data contains {} unique items, {} ({:.2f} %) of them never appear in train data".format(
item_in_test_flag.sum(),
test_item_not_in_train_flag.sum(),
test_item_not_in_train_flag.sum()/item_in_test_flag.sum()*100,
))
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 == 'amazon_music_original':
dataset = AmazonMusicReader(result_folder_path, original = True)
elif dataset_name == 'amazon_music_ours':
dataset = AmazonMusicReader(result_folder_path, original = False)
elif dataset_name == 'amazon_movie':
dataset = AmazonMovieReader(result_folder_path)
elif dataset_name == 'movielens100k':
dataset = Movielens100KReader(result_folder_path)
elif dataset_name == 'movielens1m':
dataset = Movielens1MReader(result_folder_path)
else:
print("Dataset name not supported, current is {}".format(dataset_name))
return
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()
URM_test_negative = dataset.URM_DICT["URM_test_negative"].copy()
# Ensure DISJOINT sets. Do not ensure IMPLICIT data because the algorithm needs explicit data
assert_disjoint_matrices([URM_train, URM_validation, URM_test, URM_test_negative])
cold_items_statistics(URM_train, URM_validation, URM_test, URM_test_negative)
# If directory does not exist, create
if not os.path.exists(result_folder_path):
os.makedirs(result_folder_path)
algorithm_dataset_string = "{}_{}_".format(ALGORITHM_NAME, dataset_name)
plot_popularity_bias([URM_train + URM_validation, URM_test],
["Training data", "Test data"],
result_folder_path + algorithm_dataset_string + "popularity_plot")
save_popularity_statistics([URM_train + URM_validation + URM_test, URM_train + URM_validation, URM_test],
["Full data", "Training data", "Test data"],
result_folder_path + algorithm_dataset_string + "popularity_statistics")
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
cutoff_list_validation = [10]
cutoff_list_test = [5, 10, 20]
evaluator_validation = EvaluatorNegativeItemSample(URM_validation, URM_test_negative, cutoff_list=cutoff_list_validation)
evaluator_test = EvaluatorNegativeItemSample(URM_test, URM_test_negative, 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
######
"""
NOTICE: We did not upload the source code of DMF as it was not publicly available and the original
authors did not respond to our request to add it to this repository
"""
if flag_DL_article_default:
if dataset_name in ['amazon_music_original', 'amazon_music_ours']:
last_layer_size = 128
else:
last_layer_size = 64
article_hyperparameters = {'epochs': 300,
'learning_rate': 0.0001,
'batch_size': 256,
'num_negatives': 7, # As reported in the "Detailed implementation" section of the original paper
'last_layer_size': last_layer_size,
}
earlystopping_hyperparameters = {'validation_every_n': 5,
'stop_on_validation': True,
'lower_validations_allowed': 5,
'evaluator_object': evaluator_validation,
'validation_metric': metric_to_optimize,
}
#
# try:
#
#
# parameterSearch = SearchSingleCase(DMF_NCE_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 = DMF_NCE_RecommenderWrapper.RECOMMENDER_NAME)
#
#
#
# except Exception as e:
#
# print("On recommender {} Exception {}".format(DMF_NCE_RecommenderWrapper, str(e)))
# traceback.print_exc()
#
#
#
# try:
#
#
# parameterSearch = SearchSingleCase(DMF_BCE_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 = DMF_BCE_RecommenderWrapper.RECOMMENDER_NAME)
#
#
# except Exception as e:
#
# print("On recommender {} Exception {}".format(DMF_BCE_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 = [DMF_NCE_RecommenderWrapper, DMF_BCE_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 = ["HIT_RATE", "NDCG"],
cutoffs_list = cutoff_list_validation,
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 = [10],
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 = "DMF"
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 = ['amazon_music_original', 'amazon_music_ours', 'movielens100k', 'amazon_movie', 'movielens1m']
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= [DMF_NCE_RecommenderWrapper, DMF_BCE_RecommenderWrapper],
KNN_similarity_to_report_list = KNN_similarity_to_report_list,
split_per_algorithm_type = True)