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run_KDD_17_CollaborativeVAE.py
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run_KDD_17_CollaborativeVAE.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.KDD.CollaborativeVAE_our_interface.CollaborativeVAE_RecommenderWrapper import CollaborativeVAE_RecommenderWrapper
from ParameterTuning.run_parameter_search import runParameterSearch_Collaborative, runParameterSearch_Content, runParameterSearch_Hybrid
from ParameterTuning.SearchSingleCase import SearchSingleCase
from ParameterTuning.SearchAbstractClass import SearchInputRecommenderArgs
from functools import partial
import os, traceback, argparse
import numpy as np
from Utils.ResultFolderLoader import ResultFolderLoader, generate_latex_hyperparameters
from Utils.assertions_on_data_for_experiments import assert_implicit_data, assert_disjoint_matrices
def read_data_split_and_search(dataset_variant, train_interactions,
flag_baselines_tune = False,
flag_DL_article_default = False, flag_DL_tune = False,
flag_print_results = False):
from Conferences.KDD.CollaborativeVAE_our_interface.Citeulike.CiteulikeReader import CiteulikeReader
result_folder_path = "result_experiments/{}/{}_citeulike_{}_{}/".format(CONFERENCE_NAME, ALGORITHM_NAME, dataset_variant, train_interactions)
dataset = CiteulikeReader(result_folder_path, dataset_variant = dataset_variant, train_interactions = train_interactions)
URM_train = dataset.URM_DICT["URM_train"].copy()
URM_validation = dataset.URM_DICT["URM_validation"].copy()
URM_test = dataset.URM_DICT["URM_test"].copy()
del dataset.ICM_DICT["ICM_tokens_bool"]
# Ensure IMPLICIT data
assert_implicit_data([URM_train, URM_validation, URM_test])
# Due to the sparsity of the dataset, choosing an evaluation as subset of the train
# While keeping validation interaction in the train set
if train_interactions == 1:
# In this case the train data will contain validation data to avoid cold users
assert_disjoint_matrices([URM_train, URM_test])
assert_disjoint_matrices([URM_validation, URM_test])
exclude_seen_validation = False
URM_train_last_test = URM_train
else:
assert_disjoint_matrices([URM_train, URM_validation, URM_test])
exclude_seen_validation = True
URM_train_last_test = URM_train + URM_validation
assert_implicit_data([URM_train_last_test])
# If directory does not exist, create
if not os.path.exists(result_folder_path):
os.makedirs(result_folder_path)
from Base.Evaluation.Evaluator import EvaluatorHoldout
evaluator_validation = EvaluatorHoldout(URM_validation, cutoff_list=[150], exclude_seen = exclude_seen_validation)
evaluator_test = EvaluatorHoldout(URM_test, cutoff_list=[50, 100, 150, 200, 250, 300])
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 = "RECALL"
n_cases = 50
n_random_starts = 15
runParameterSearch_Collaborative_partial = partial(runParameterSearch_Collaborative,
URM_train = URM_train,
URM_train_last_test = URM_train_last_test,
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()
################################################################################################
###### Content Baselines
for ICM_name, ICM_object in dataset.ICM_DICT.items():
try:
runParameterSearch_Content(ItemKNNCBFRecommender,
URM_train = URM_train,
URM_train_last_test = URM_train_last_test,
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()
################################################################################################
###### Hybrid
for ICM_name, ICM_object in dataset.ICM_DICT.items():
try:
runParameterSearch_Hybrid(ItemKNN_CFCBF_Hybrid_Recommender,
URM_train = URM_train,
URM_train_last_test = URM_train_last_test,
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 recommender {} Exception {}".format(ItemKNN_CFCBF_Hybrid_Recommender, str(e)))
traceback.print_exc()
################################################################################################
######
###### DL ALGORITHM
######
if flag_DL_article_default:
try:
cvae_recommender_article_hyperparameters = {
"epochs": 200,
"learning_rate_vae": 1e-2,
"learning_rate_cvae": 1e-3,
"num_factors": 50,
"dimensions_vae": [200, 100],
"epochs_vae": [50, 50],
"batch_size": 128,
"lambda_u": 0.1,
"lambda_v": 10,
"lambda_r": 1,
"a": 1,
"b": 0.01,
"M": 300,
}
cvae_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(CollaborativeVAE_RecommenderWrapper,
evaluator_validation=evaluator_validation,
evaluator_test=evaluator_test)
recommender_input_args = SearchInputRecommenderArgs(
CONSTRUCTOR_POSITIONAL_ARGS = [URM_train, dataset.ICM_DICT["ICM_tokens_TFIDF"]],
FIT_KEYWORD_ARGS = cvae_earlystopping_hyperparameters)
recommender_input_args_last_test = recommender_input_args.copy()
recommender_input_args_last_test.CONSTRUCTOR_POSITIONAL_ARGS[0] = URM_train_last_test
parameterSearch.search(recommender_input_args,
recommender_input_args_last_test = recommender_input_args_last_test,
fit_hyperparameters_values=cvae_recommender_article_hyperparameters,
output_folder_path = result_folder_path,
resume_from_saved = True,
output_file_name_root = CollaborativeVAE_RecommenderWrapper.RECOMMENDER_NAME)
except Exception as e:
print("On recommender {} Exception {}".format(CollaborativeVAE_RecommenderWrapper, str(e)))
traceback.print_exc()
################################################################################################
######
###### PRINT RESULTS
######
if flag_print_results:
n_test_users = np.sum(np.ediff1d(URM_test.indptr)>=1)
ICM_names_to_report_list = list(dataset.ICM_DICT.keys())
dataset_name = "{}_{}".format(dataset_variant, train_interactions)
file_name = "{}..//{}_{}_".format(result_folder_path, ALGORITHM_NAME, dataset_name)
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 = ICM_names_to_report_list,
UCM_names_list = None)
result_loader.generate_latex_results(file_name + "{}_latex_results.txt".format("article_metrics"),
metrics_list = ["RECALL"],
cutoffs_list = [50, 100, 150, 200, 250, 300],
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 = [150],
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)
__name__ = "__main__"
if __name__ == '__main__':
ALGORITHM_NAME = "CollaborativeVAE"
CONFERENCE_NAME = "KDD"
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_variant_list = ["a", "t"]
train_interactions_list = [1, 10]
from collections import namedtuple
CustomRecommenderName = namedtuple('CustomRecommenderName', ['RECOMMENDER_NAME'])
other_algorithm_list_names = [CollaborativeVAE_RecommenderWrapper.RECOMMENDER_NAME, "CollaborativeDL_Matlab_RecommenderWrapper"]
other_algorithm_list = [CustomRecommenderName(RECOMMENDER_NAME = recommender_name) for recommender_name in other_algorithm_list_names]
for dataset_variant in dataset_variant_list:
for train_interactions in train_interactions_list:
read_data_split_and_search(dataset_variant, train_interactions,
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 = [
"citeulike_{}_{}".format(dataset_variant, train_interactions) for dataset_variant in dataset_variant_list for train_interactions in train_interactions_list
],
ICM_names_to_report_list = ["ICM_tokens_TFIDF"],
KNN_similarity_to_report_list = KNN_similarity_to_report_list,
other_algorithm_list = other_algorithm_list,
split_per_algorithm_type = True)