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run_IJCAI_18_CoupledCF.py
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run_IJCAI_18_CoupledCF.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 13/03/19
@author: Simone Boglio
"""
from ParameterTuning.run_parameter_search import runParameterSearch_Collaborative, runParameterSearch_Content, runParameterSearch_Hybrid
# from ParameterTuning.parameter_search_ensemble import runParameterSearch_Ensemble
from ParameterTuning.SearchSingleCase import SearchSingleCase
from ParameterTuning.SearchAbstractClass import SearchInputRecommenderArgs
from Recommender_import_list import *
from Utils.ResultFolderLoader import ResultFolderLoader, generate_latex_hyperparameters
from Utils.print_negative_items_stats import print_negative_items_stats
from functools import partial
import numpy as np
import os, traceback, argparse
from Conferences.IJCAI.CoupledCF_our_interface.Movielens1MReader.Movielens1MReader import Movielens1MReader
from Conferences.IJCAI.CoupledCF_our_interface.TafengReader.TafengReader import TafengReader
from Conferences.IJCAI.CoupledCF_our_interface.CoupledCFWrapper import CoupledCF_RecommenderWrapper
from Conferences.IJCAI.CoupledCF_our_interface.DeepCFWrapper import DeepCF_RecommenderWrapper
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/IJCAI/CoupledCF_{}/".format(dataset_name)
#Logger(path=result_folder_path, name_file='CoupledCF_' + dataset_name)
if dataset_name.startswith("movielens1m"):
if dataset_name.endswith("_original"):
dataset = Movielens1MReader(result_folder_path, type ='original')
elif dataset_name.endswith("_ours"):
dataset = Movielens1MReader(result_folder_path, type ='ours')
else:
print("Dataset name not supported, current is {}".format(dataset_name))
return
UCM_to_report = ["UCM_all"]
ICM_to_report = ["ICM_all"]
UCM_CoupledCF = dataset.ICM_DICT["UCM_all"]
ICM_CoupledCF = dataset.ICM_DICT["ICM_all"]
elif dataset_name.startswith("tafeng"):
if dataset_name.endswith("_original"):
dataset = TafengReader(result_folder_path, type ='original')
elif dataset_name.endswith("_ours"):
dataset = TafengReader(result_folder_path, type ='ours')
else:
print("Dataset name not supported, current is {}".format(dataset_name))
return
UCM_to_report = ["UCM_all"]
ICM_to_report = ["ICM_original"]
UCM_CoupledCF = dataset.ICM_DICT["UCM_all"]
ICM_CoupledCF = dataset.ICM_DICT["ICM_original"]
else:
print("Dataset name not supported, current is {}".format(dataset_name))
return
print ('Current dataset is: {}'.format(dataset_name))
UCM_dict = {UCM_name:UCM_object for (UCM_name,UCM_object) in dataset.ICM_DICT.items() if "UCM" in UCM_name}
ICM_dict = {UCM_name:UCM_object for (UCM_name,UCM_object) in dataset.ICM_DICT.items() if "ICM" in UCM_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()
# Matrices are 1-indexed, so remove first row
print_negative_items_stats(URM_train[1:], URM_validation[1:], URM_test[1:], URM_test_negative[1:])
# 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, URM_test_negative])
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 EvaluatorNegativeItemSample
cutoff_list_validation = [5]
cutoff_list_test = [1,2,3,4,5,6,7,8,9,10]
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()
###############################################################################################
##### Item Content Baselines
for ICM_name, ICM_object in ICM_dict.items():
try:
runParameterSearch_Content(ItemKNNCBFRecommender,
URM_train = URM_train,
URM_train_last_test = URM_train + URM_validation,
metric_to_optimize = metric_to_optimize,
evaluator_validation = evaluator_validation,
evaluator_test = evaluator_test,
output_folder_path = result_folder_path,
parallelizeKNN = False,
allow_weighting = True,
resume_from_saved = True,
ICM_name = ICM_name,
ICM_object = ICM_object.copy(),
n_cases = n_cases,
n_random_starts = n_random_starts)
runParameterSearch_Hybrid(ItemKNN_CFCBF_Hybrid_Recommender,
URM_train = URM_train,
URM_train_last_test = URM_train + URM_validation,
metric_to_optimize = metric_to_optimize,
evaluator_validation = evaluator_validation,
evaluator_test = evaluator_test,
output_folder_path = result_folder_path,
parallelizeKNN = False,
allow_weighting = True,
resume_from_saved = True,
ICM_name = ICM_name,
ICM_object = ICM_object.copy(),
n_cases = n_cases,
n_random_starts = n_random_starts)
except Exception as e:
print("On CBF recommender for ICM {} Exception {}".format(ICM_name, str(e)))
traceback.print_exc()
################################################################################################
###### User Content Baselines
for UCM_name, UCM_object in UCM_dict.items():
try:
runParameterSearch_Content(UserKNNCBFRecommender,
URM_train = URM_train,
URM_train_last_test = URM_train + URM_validation,
metric_to_optimize = metric_to_optimize,
evaluator_validation = evaluator_validation,
evaluator_test = evaluator_test,
output_folder_path = result_folder_path,
parallelizeKNN = False,
allow_weighting = True,
resume_from_saved = True,
ICM_name = UCM_name,
ICM_object = UCM_object.copy(),
n_cases = n_cases,
n_random_starts = n_random_starts)
runParameterSearch_Hybrid(UserKNN_CFCBF_Hybrid_Recommender,
URM_train = URM_train,
URM_train_last_test = URM_train + URM_validation,
metric_to_optimize = metric_to_optimize,
evaluator_validation = evaluator_validation,
evaluator_test = evaluator_test,
output_folder_path = result_folder_path,
parallelizeKNN = False,
allow_weighting = True,
resume_from_saved = True,
ICM_name = UCM_name,
ICM_object = UCM_object.copy(),
n_cases = n_cases,
n_random_starts = n_random_starts)
except Exception as e:
print("On CBF recommender for UCM {} Exception {}".format(UCM_name, str(e)))
traceback.print_exc()
################################################################################################
######
###### DL ALGORITHM
######
if flag_DL_article_default:
model_name = dataset.DATASET_NAME
earlystopping_hyperparameters = {
'validation_every_n': 5,
'stop_on_validation': True,
'lower_validations_allowed': 5,
'evaluator_object': evaluator_validation,
'validation_metric': metric_to_optimize
}
if 'tafeng' in dataset_name:
model_number = 3
article_hyperparameters = {'learning_rate': 0.005,
'epochs': 100,
'n_negative_sample': 4,
'temp_file_folder': None,
'dataset_name': model_name,
'number_model': model_number,
'verbose': 0,
'plot_model': False,
}
else:
# movielens1m and other dataset
model_number = 3
article_hyperparameters = {'learning_rate': 0.001,
'epochs': 100,
'n_negative_sample': 4,
'temp_file_folder': None,
'dataset_name': model_name,
'number_model': model_number,
'verbose': 0,
'plot_model': False,
}
parameterSearch = SearchSingleCase(DeepCF_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=DeepCF_RecommenderWrapper.RECOMMENDER_NAME)
if 'tafeng' in dataset_name:
# tafeng model has a different structure
model_number = 2
article_hyperparameters = {'learning_rate': 0.005,
'epochs': 100,
'n_negative_sample': 4,
'temp_file_folder': None,
'dataset_name': "Tafeng",
'number_model': model_number,
'verbose': 0,
'plot_model': False,
}
else:
# movielens1m use this tructure with model 2
model_number = 2
article_hyperparameters = {'learning_rate': 0.001,
'epochs': 100,
'n_negative_sample': 4,
'temp_file_folder': None,
'dataset_name': "Movielens1M",
'number_model': model_number,
'verbose': 0,
'plot_model': False,
}
parameterSearch = SearchSingleCase(CoupledCF_RecommenderWrapper,
evaluator_validation=evaluator_validation,
evaluator_test=evaluator_test)
recommender_input_args = SearchInputRecommenderArgs(CONSTRUCTOR_POSITIONAL_ARGS=[URM_train, UCM_CoupledCF, ICM_CoupledCF],
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=CoupledCF_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 = [DeepCF_RecommenderWrapper, CoupledCF_RecommenderWrapper],
KNN_similarity_list = KNN_similarity_to_report_list,
ICM_names_list = ICM_to_report,
UCM_names_list = UCM_to_report)
result_loader.generate_latex_results(file_name + "{}_latex_results.txt".format("article_metrics"),
metrics_list = ["HIT_RATE", "NDCG"],
cutoffs_list = [1, 5, 10],
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 = [5],
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 = [5],
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 = "CoupledCF"
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 = ['movielens1m_original', 'movielens1m_ours', 'tafeng_original', 'tafeng_ours']
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,
ICM_names_to_report_list=["ICM_all", "ICM_original"],
UCM_names_to_report_list=["UCM_all"],
other_algorithm_list=[DeepCF_RecommenderWrapper, CoupledCF_RecommenderWrapper],
KNN_similarity_to_report_list = KNN_similarity_to_report_list,
split_per_algorithm_type = True)