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run_SIGIR_18_CMN.py
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run_SIGIR_18_CMN.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.SIGIR.CMN_our_interface.CMN_RecommenderWrapper import CMN_RecommenderWrapper
from ParameterTuning.run_parameter_search import runParameterSearch_Collaborative
import os, traceback, argparse
from functools import partial
import numpy as np
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
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):
from Conferences.SIGIR.CMN_our_interface.CiteULike.CiteULikeReader import CiteULikeReader
from Conferences.SIGIR.CMN_our_interface.Pinterest.PinterestICCVReader import PinterestICCVReader
from Conferences.SIGIR.CMN_our_interface.Epinions.EpinionsReader import EpinionsReader
result_folder_path = "result_experiments/{}/{}_{}/".format(CONFERENCE_NAME, ALGORITHM_NAME, dataset_name)
if dataset_name == "citeulike":
dataset = CiteULikeReader(result_folder_path)
elif dataset_name == "epinions":
dataset = EpinionsReader(result_folder_path)
elif dataset_name == "pinterest":
dataset = PinterestICCVReader(result_folder_path)
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 IMPLICIT data and DISJOINT sets
assert_implicit_data([URM_train, URM_validation, URM_test, URM_test_negative])
if dataset_name == "citeulike":
assert_disjoint_matrices([URM_train, URM_validation, URM_test])
assert_disjoint_matrices([URM_test, URM_test_negative])
elif dataset_name == "pinterest":
assert_disjoint_matrices([URM_train, URM_validation, URM_test])
assert_disjoint_matrices([URM_train, URM_validation, URM_test_negative])
else:
assert_disjoint_matrices([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)
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 = "HIT_RATE"
n_cases = 50
n_random_starts = 15
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")
from Base.Evaluation.Evaluator import EvaluatorNegativeItemSample
evaluator_validation = EvaluatorNegativeItemSample(URM_validation, URM_test_negative, cutoff_list=[5])
evaluator_test = EvaluatorNegativeItemSample(URM_test, URM_test_negative, cutoff_list=[5, 10])
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:
try:
CMN_article_hyperparameters = {
"epochs": 100,
"epochs_gmf": 100,
"hops": 3,
"neg_samples": 4,
"reg_l2_cmn": 1e-1,
"reg_l2_gmf": 1e-4,
"pretrain": True,
"learning_rate": 1e-3,
"verbose": False,
}
if dataset_name == "citeulike":
CMN_article_hyperparameters["batch_size"] = 128
CMN_article_hyperparameters["embed_size"] = 50
elif dataset_name == "epinions":
CMN_article_hyperparameters["batch_size"] = 128
CMN_article_hyperparameters["embed_size"] = 40
elif dataset_name == "pinterest":
CMN_article_hyperparameters["batch_size"] = 256
CMN_article_hyperparameters["embed_size"] = 50
CMN_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(CMN_RecommenderWrapper,
evaluator_validation=evaluator_validation,
evaluator_test=evaluator_test)
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [URM_train],
FIT_KEYWORD_ARGS = CMN_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=CMN_article_hyperparameters,
output_folder_path = result_folder_path,
resume_from_saved = True,
output_file_name_root = CMN_RecommenderWrapper.RECOMMENDER_NAME)
except Exception as e:
print("On recommender {} Exception {}".format(CMN_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 = [CMN_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 = [5, 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 = [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 = "CMN"
CONFERENCE_NAME = "SIGIR"
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 = ["citeulike", "pinterest", "epinions"]
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 = [CMN_RecommenderWrapper],
KNN_similarity_to_report_list = KNN_similarity_to_report_list,
split_per_algorithm_type = True)