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run_IJCAI_17_DELF.py
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run_IJCAI_17_DELF.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 06/07/19
@author: Simone Boglio
"""
import numpy as np
import os, traceback, argparse
import scipy.sparse as sps
from functools import partial
from Utils.plot_popularity import plot_popularity_bias, save_popularity_statistics
from Base.Evaluation.Evaluator import EvaluatorNegativeItemSample
from Recommender_import_list import *
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_disjoint_matrices, assert_implicit_data
from Conferences.IJCAI.DELF_our_interface.Movielens1MReader.Movielens1MReader import Movielens1MReader
from Conferences.IJCAI.DELF_our_interface.AmazonMusicReader.AmazonMusicReader import AmazonMusicReader
from Conferences.IJCAI.DELF_our_interface.DELFWrapper import DELF_MLP_RecommenderWrapper, DELF_EF_RecommenderWrapper
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 get_cold_items(URM):
cold_items_flag = np.ediff1d(sps.csc_matrix(URM).indptr) == 0
return np.arange(0, URM.shape[1])[cold_items_flag]
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 not os.path.exists(result_folder_path):
os.makedirs(result_folder_path)
# Ensure both experiments use the same data
dataset_folder_path = "result_experiments/{}/{}_{}/".format(CONFERENCE_NAME, ALGORITHM_NAME,
dataset_name.replace("_remove_cold_items", ""))
if not os.path.exists(dataset_folder_path):
os.makedirs(dataset_folder_path)
if 'amazon_music' in dataset_name:
dataset = AmazonMusicReader(dataset_folder_path)
elif 'movielens1m_ours' in dataset_name:
dataset = Movielens1MReader(dataset_folder_path, type ="ours")
elif 'movielens1m_original' in dataset_name:
dataset = Movielens1MReader(dataset_folder_path, type ="original")
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 IMPLICI data and DISJOINT matrices
assert_implicit_data([URM_train, URM_validation, URM_test, URM_test_negative])
assert_disjoint_matrices([URM_train, URM_validation, URM_test, URM_test_negative])
cold_items_statistics(URM_train, URM_validation, URM_test, URM_test_negative)
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]
if "_remove_cold_items" in dataset_name:
ignore_items_validation = get_cold_items(URM_train)
ignore_items_test = get_cold_items(URM_train + URM_validation)
else:
ignore_items_validation = None
ignore_items_test = None
evaluator_validation = EvaluatorNegativeItemSample(URM_validation, URM_test_negative, cutoff_list=cutoff_list_validation, ignore_items=ignore_items_validation)
evaluator_test = EvaluatorNegativeItemSample(URM_test, URM_test_negative, cutoff_list=cutoff_list_test, ignore_items=ignore_items_test)
# The Evaluator automatically skips users with no test interactions
# in this case we need the evaluation done with and without cold items to be comparable
# So we ensure the users that are included in the evaluation are the same in both cases.
evaluator_validation.users_to_evaluate = np.arange(URM_train.shape[0])
evaluator_test.users_to_evaluate = np.arange(URM_train.shape[0])
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': 5,
'evaluator_object': evaluator_validation,
'validation_metric': metric_to_optimize,
}
num_factors = 64
article_hyperparameters = {'epochs': 500,
'learning_rate': 0.001,
'batch_size': 256,
'num_negatives': 4,
'layers': (num_factors*4, num_factors*2, num_factors),
'regularization_layers': (0, 0, 0),
'learner': 'adam',
'verbose': False,
}
parameterSearch = SearchSingleCase(DELF_MLP_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 = DELF_MLP_RecommenderWrapper.RECOMMENDER_NAME)
parameterSearch = SearchSingleCase(DELF_EF_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=DELF_EF_RecommenderWrapper.RECOMMENDER_NAME)
################################################################################################
######
###### 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 = [DELF_MLP_RecommenderWrapper, DELF_EF_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_test,
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__':
CONFERENCE_NAME = 'IJCAI'
ALGORITHM_NAME = 'DELF'
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', 'movielens1m_ours', 'amazon_music_remove_cold_items', 'movielens1m_ours_remove_cold_items']
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=[DELF_MLP_RecommenderWrapper, DELF_EF_RecommenderWrapper],
KNN_similarity_to_report_list = KNN_similarity_to_report_list,
split_per_algorithm_type = True)