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run_RecSys_18_SpectralCF.py
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run_RecSys_18_SpectralCF.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 22/11/17
@author: Maurizio Ferrari Dacrema
@author: Simone Boglio
"""
from Recommender_import_list import *
from Conferences.RecSys.SpectralCF_our_interface.SpectralCF_RecommenderWrapper import SpectralCF_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 functools import partial
import numpy as np
import os, traceback, argparse
from Conferences.RecSys.SpectralCF_our_interface.Movielens1M.Movielens1MReader import Movielens1MReader
from Conferences.RecSys.SpectralCF_our_interface.MovielensHetrec2011.MovielensHetrec2011Reader import MovielensHetrec2011Reader
from Conferences.RecSys.SpectralCF_our_interface.AmazonInstantVideo.AmazonInstantVideoReader import AmazonInstantVideoReader
######################################################################
from skopt.space import Real, Integer, Categorical
from ParameterTuning.SearchBayesianSkopt import SearchBayesianSkopt
from ParameterTuning.SearchSingleCase import SearchSingleCase
from ParameterTuning.SearchAbstractClass import SearchInputRecommenderArgs
def runParameterSearch_SpectralCF(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)
##########################################################################################################
if recommender_class is SpectralCF_RecommenderWrapper:
hyperparameters_range_dictionary = {}
hyperparameters_range_dictionary["batch_size"] = Categorical([128, 256, 512, 1024, 2048])
hyperparameters_range_dictionary["embedding_size"] = Categorical([4, 8, 16, 32])
hyperparameters_range_dictionary["decay"] = Real(low = 1e-5, high = 1e-1, prior = 'log-uniform')
hyperparameters_range_dictionary["learning_rate"] = Real(low = 1e-5, high = 1e-2, prior = 'log-uniform')
hyperparameters_range_dictionary["k"] = Integer(low = 1, high = 6)
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [URM_train],
CONSTRUCTOR_KEYWORD_ARGS = {},
FIT_POSITIONAL_ARGS = [],
FIT_KEYWORD_ARGS = earlystopping_hyperparameters
)
#########################################################################################################
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,
resume_from_saved = True,
output_folder_path = output_folder_path,
output_file_name_root = output_file_name_root,
metric_to_optimize = metric_to_optimize,
recommender_input_args_last_test = recommender_input_args_last_test)
def read_data_split_and_search(dataset_name, cold_start = False, cold_items=None,
flag_baselines_tune = False,
flag_DL_article_default = False, flag_DL_tune = False,
flag_print_results = False):
if not cold_start:
result_folder_path = "result_experiments/{}/{}_{}/".format(CONFERENCE_NAME, ALGORITHM_NAME, dataset_name)
else:
result_folder_path = "result_experiments/{}/{}_cold_{}_{}/".format(CONFERENCE_NAME, ALGORITHM_NAME, cold_items, dataset_name)
if dataset_name == "movielens1m_original":
assert(cold_start is not True)
dataset = Movielens1MReader(result_folder_path, type ="original")
elif dataset_name == "movielens1m_ours":
dataset = Movielens1MReader(result_folder_path, type ="ours", cold_start=cold_start, cold_items=cold_items)
elif dataset_name == "hetrec":
assert (cold_start is not True)
dataset = MovielensHetrec2011Reader(result_folder_path)
elif dataset_name == "amazon_instant_video":
assert (cold_start is not True)
dataset = AmazonInstantVideoReader(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()
# Ensure IMPLICIT data and DISJOINT sets
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)
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")
metric_to_optimize = "RECALL"
n_cases = 50
n_random_starts = 15
from Base.Evaluation.Evaluator import EvaluatorHoldout
if not cold_start:
cutoff_list_validation = [50]
cutoff_list_test = [20, 30, 40, 50, 60, 70, 80, 90, 100]
else:
cutoff_list_validation = [20]
cutoff_list_test = [20]
evaluator_validation = EvaluatorHoldout(URM_validation, cutoff_list=cutoff_list_validation)
evaluator_test = EvaluatorHoldout(URM_test, cutoff_list=cutoff_list_test)
################################################################################################
###### KNN CF
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,
]
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:
spectralCF_article_hyperparameters = {
"epochs": 1000,
"batch_size": 1024,
"embedding_size": 16,
"decay": 0.001,
"k": 3,
"learning_rate": 1e-3,
}
spectralCF_earlystopping_hyperparameters = {
"validation_every_n": 5,
"stop_on_validation": True,
"lower_validations_allowed": 5,
"evaluator_object": evaluator_validation,
"validation_metric": metric_to_optimize,
"epochs_min": 400,
}
parameterSearch = SearchSingleCase(SpectralCF_RecommenderWrapper,
evaluator_validation=evaluator_validation,
evaluator_test=evaluator_test)
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [URM_train],
FIT_KEYWORD_ARGS = spectralCF_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= spectralCF_article_hyperparameters,
output_folder_path = result_folder_path,
resume_from_saved = True,
output_file_name_root = SpectralCF_RecommenderWrapper.RECOMMENDER_NAME + "_article_default")
except Exception as e:
print("On recommender {} Exception {}".format(SpectralCF_RecommenderWrapper, str(e)))
traceback.print_exc()
if flag_DL_tune:
try:
spectralCF_earlystopping_hyperparameters = {
"validation_every_n": 5,
"stop_on_validation": True,
"lower_validations_allowed": 5,
"evaluator_object": evaluator_validation,
"validation_metric": metric_to_optimize,
"epochs_min": 400,
"epochs": 2000
}
runParameterSearch_SpectralCF(SpectralCF_RecommenderWrapper,
URM_train = URM_train,
URM_train_last_test = URM_train + URM_validation,
earlystopping_hyperparameters = spectralCF_earlystopping_hyperparameters,
metric_to_optimize = metric_to_optimize,
evaluator_validation = evaluator_validation,
evaluator_test = evaluator_test,
output_folder_path = result_folder_path,
n_cases = n_cases,
n_random_starts = n_random_starts,
output_file_name_root = SpectralCF_RecommenderWrapper.RECOMMENDER_NAME)
except Exception as e:
print("On recommender {} Exception {}".format(SpectralCF_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 if not cold_start else "{}_cold_{}".format(ALGORITHM_NAME, cold_items),
dataset_name)
if cold_start:
cutoffs_to_report_list = [20]
else:
cutoffs_to_report_list = [20, 40, 60, 80, 100]
result_loader = ResultFolderLoader(result_folder_path,
base_algorithm_list = None,
other_algorithm_list = other_algorithm_list,
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", "MAP_MIN_DEN"],
cutoffs_list = cutoffs_to_report_list,
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 = [50],
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 = "SpectralCF"
CONFERENCE_NAME = "RecSys"
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('-t', '--DL_tune', help="DL model hyperparameter search", type = bool, default = False)
parser.add_argument('-c', '--cold_start', help="DL model cold start experiment", type = bool, default = False)
input_flags = parser.parse_args()
print(input_flags)
dataset_list = ["movielens1m_ours", "movielens1m_original", "hetrec", "amazon_instant_video"]
dataset_cold_start_list = ["movielens1m_ours"]
cold_start_items_list = [1, 2, 3, 4, 5]
KNN_similarity_to_report_list = ["cosine", "dice", "jaccard", "asymmetric", "tversky"]
from collections import namedtuple
CustomRecommenderName = namedtuple('CustomRecommenderName', ['RECOMMENDER_NAME'])
other_algorithm_list_names = [SpectralCF_RecommenderWrapper.RECOMMENDER_NAME + hyperparameter_set for hyperparameter_set in ["", "_article_default"]]
other_algorithm_list = [CustomRecommenderName(RECOMMENDER_NAME = recommender_name) for recommender_name in other_algorithm_list_names]
if input_flags.cold_start:
for dataset_name in dataset_cold_start_list:
for cold_start_items in cold_start_items_list:
read_data_split_and_search(dataset_name,
cold_start = input_flags.cold_start,
cold_items=cold_start_items,
flag_baselines_tune=input_flags.baseline_tune,
flag_DL_article_default = input_flags.DL_article_default,
flag_DL_tune = input_flags.DL_tune,
flag_print_results= input_flags.print_results
)
else:
for dataset_name in dataset_list:
read_data_split_and_search(dataset_name,
cold_start = input_flags.cold_start,
flag_baselines_tune=input_flags.baseline_tune,
flag_DL_article_default = input_flags.DL_article_default,
flag_DL_tune = input_flags.DL_tune,
flag_print_results= input_flags.print_results
)
# mantain compatibility with latex parameteres function
if input_flags.cold_start and input_flags.print_results:
for n_cold_item in cold_start_items_list:
generate_latex_hyperparameters(result_folder_path ="result_experiments/{}/".format(CONFERENCE_NAME),
algorithm_name="{}_cold_{}".format(ALGORITHM_NAME, n_cold_item),
experiment_subfolder_list = dataset_cold_start_list,
other_algorithm_list = other_algorithm_list,
KNN_similarity_to_report_list = KNN_similarity_to_report_list,
split_per_algorithm_type = True)
elif not input_flags.cold_start and 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 = other_algorithm_list,
KNN_similarity_to_report_list = KNN_similarity_to_report_list,
split_per_algorithm_type = True)