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main.py
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main.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 9 19:57:37 2020
"""
from __future__ import unicode_literals, print_function, division
import os
import os.path as path
import sys
import argparse
import torch
from gensim.models.keyedvectors import KeyedVectors
import numpy as np
import time
import math
from data_loader import split_data, load_emb
from train import train_iter
from generate_summary import generate_summary, ensembled_generating_summary
IN_DBPEDIA_DIR = os.path.join(path.dirname(os.getcwd()), 'GATES/data/ESBM_benchmark_v1.2', 'dbpedia_data')
IN_LMDB_DIR = os.path.join(path.dirname(os.getcwd()), 'GATES/data/ESBM_benchmark_v1.2', 'lmdb_data')
OUT_DIR = os.path.join(path.dirname(os.getcwd()), 'GATES')
IN_FACES_DIR = os.path.join(path.dirname(os.getcwd()), 'GATES/data/FACES', 'faces_data')
FILE_N = 6
TOP_K = [5, 10]
DS_NAME = ['dbpedia', 'lmdb', 'faces']
DEVICE = torch.device("cpu")
def asHours(s):
m = math.floor(s / 60)
h = math.floor(m / 60)
s -= m * 60
m -= h * 60
return '%dh %dm %ds' % (h, m, s)
def _read_epochs_from_log(ds_name, topk):
log_file_path = os.path.join(OUT_DIR, 'GATES_log.txt')
key = '{}-top{}'.format(ds_name, topk)
epoch_list = None
with open(log_file_path, 'r', encoding='utf-8') as f:
for line in f:
if line.startswith(key):
epoch_list = list(eval(line.split('\t')[1]))
return epoch_list
def get_embeddings(word_emb_model):
if word_emb_model == "fasttext":
word_emb = KeyedVectors.load_word2vec_format("data/wiki-news-300d-1M.vec")
elif word_emb_model=="Glove":
word_emb = {}
with open("data/glove.6B/glove.6B.300d.txt", 'r') as f:
for line in f:
values = line.split()
word = values[0]
vector = np.asarray(values[1:], "float32")
word_emb[word] = vector
else:
print("please choose the correct word embedding model")
sys.exit()
return word_emb
def main(mode, emb_model, loss_type, ent_emb_dim, pred_emb_dim, hidden_layers, nheads, lr, dropout, reg, weight_decay, n_epoch, save_every, word_emb_model, word_emb_calc, use_epoch, concat_model, weighted_edges_method):
word_emb = get_embeddings(word_emb_model)
if loss_type == "BCE":
loss_function = torch.nn.BCELoss()
elif loss_type == "MSE":
loss_function = torch.nn.MSELoss()
elif loss_type == "NLLL":
loss_function = torch.nn.NLLLoss()
elif loss_type=="CE":
loss_function=torch.nn.CrossEntropyLoss()
else:
print("please choose the correct loss fucntion")
sys.exit()
if reg==True:
weight_decay==weight_decay
else:
weight_decay==0
print('Hyper paramters:')
print("Loss function: {}".format(loss_function))
print("Learning rate: {}".format(lr))
print("Dropout: {}".format(dropout))
if reg==True:
print("Weight Decay: {}".format(weight_decay))
print("n Epochs: {}".format(n_epoch))
print("Regularization: {}".format(reg))
if mode == "train" or mode =="test":
log_file_path = os.path.join(OUT_DIR, 'GATES_log.txt')
if mode=="train":
with open(log_file_path,'w') as log_file:pass
for ds_name in DS_NAME:
if ds_name == "dbpedia":
db_dir = IN_DBPEDIA_DIR
elif ds_name == "lmdb":
db_dir = IN_LMDB_DIR
elif ds_name == "faces":
db_dir = IN_FACES_DIR
else:
raise ValueError("The database's name must be dbpedia or lmdb")
sys.exit()
print('loading embeddings and dictionaries for {} ...'.format(ds_name))
entity2vec, pred2vec, entity2ix, pred2ix = load_emb(ds_name, emb_model)
entity_dict = entity2vec
pred_dict = pred2vec
pred2ix_size = len(pred2ix)
entity2ix_size = len(entity2ix)
hidden_size = ent_emb_dim + pred_emb_dim
start = time.time()
if mode=="train":
print_to = 'model-training-{}.txt'.format(ds_name)
with open(print_to, 'w+') as f:
f.write("Starting Training \n")
f.write("Training model is processed to {}\n".format(ds_name))
f.write("hyperparameters:\n")
f.write("Loss function: {}\n".format(loss_function))
f.write("Learning rate: {}\n".format(lr))
f.write("Dropout: {}\n".format(dropout))
if reg==True:
f.write("Weight Decay: {}\n".format(weight_decay))
f.write("n Epochs: {}\n".format(n_epoch))
f.write("Regularization: {}\n".format(reg))
f.write("nhead: {}\n".format(nheads))
f.write("Hidden layers: {}\n".format(hidden_layers))
f.write("Text embedding: {}\n".format(word_emb_model))
f.write("KGE model: {}\n".format(emb_model))
if mode=="test":
print_to = 'model-testing-dbpedia-lmdb.txt'
if ds_name=='faces':
print_to = 'model-testing-{}.txt'.format(ds_name)
with open(print_to, 'w+') as f:
f.write("dsFile:true {}\n".format(IN_FACES_DIR))
f.write("===============================================================================\n")
f.write("dataset:{}\n".format(ds_name))
for topk in TOP_K:
train_adjs, train_facts, train_labels, val_adjs, val_facts, val_labels, test_adjs, test_facts, test_labels = split_data(ds_name, db_dir, topk, FILE_N, weighted_edges_method)
if mode == "train" or mode=="all":
use_epoch = train_iter(ds_name, train_adjs, train_facts, train_labels, val_adjs, val_facts, val_labels, reg, n_epoch, save_every, DEVICE, entity_dict, \
pred_dict, loss_function, pred2ix_size, hidden_size, pred_emb_dim, ent_emb_dim, lr, dropout, entity2ix_size, hidden_layers, nheads, \
word_emb, db_dir, weight_decay, word_emb_calc, topk, FILE_N, concat_model, print_to, weighted_edges_method)
with open(log_file_path,'a') as log_file:
line = '{}-top{} epoch:\t{}\n'.format(ds_name,topk, str(use_epoch))
log_file.write(line)
if mode == "test" or mode=="all":
print("Testing processes for {}@top{}".format(ds_name, topk))
use_epoch = _read_epochs_from_log(ds_name, topk) if mode=='test' or mode=='all' else []
generate_summary(ds_name, test_adjs, test_facts, test_labels, pred_dict, entity_dict, pred2ix_size, pred_emb_dim, ent_emb_dim, \
DEVICE, use_epoch, db_dir, dropout, entity2ix_size, hidden_layers, nheads, word_emb, word_emb_calc, topk, FILE_N, concat_model, print_to, weighted_edges_method)
ensembled_generating_summary(ds_name, test_adjs, test_facts, test_labels, pred_dict, entity_dict, pred2ix_size, pred_emb_dim, ent_emb_dim, \
DEVICE, use_epoch, db_dir, dropout, entity2ix_size, hidden_layers, nheads, word_emb, word_emb_calc, topk, FILE_N, concat_model, print_to, weighted_edges_method)
total_time = time.time()-start
if mode=="train":
print("Training processes time", asHours(total_time))
elif mode=="test":
print("Testing processes time", asHours(total_time))
else:
print("All processing time", asHours(total_time))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='GATES: Graph Attention Network for Entity Summarization')
parser.add_argument("--mode", type=str, default="test", help="use which mode type: train/test/all")
parser.add_argument("--kge_model", type=str, default="ComplEx", help="use ComplEx/DistMult/ConEx")
parser.add_argument("--loss_function", type=str, default="BCE", help="use which loss type: BCE/MSE")
parser.add_argument("--ent_emb_dim", type=int, default=300, help="the embeddiing dimension of entity")
parser.add_argument("--pred_emb_dim", type=int, default=300, help="the embeddiing dimension of predicate")
parser.add_argument("--hidden_layers", type=int, default=2, help="the number of hidden layers")
parser.add_argument("--nheads", type=int, default=1, help="the number of heads attention")
parser.add_argument("--lr", type=float, default=0.05, help="use to define learning rate hyperparameter")
parser.add_argument("--dropout", type=float, default='0.0', help="use to define dropout hyperparameter")
parser.add_argument("--weight_decay", type=float, default='1e-5', help="use to define weight decay hyperparameter if the regularization set to True")
parser.add_argument("--regularization", type=bool, default=False, help="use to define regularization: True/False")
parser.add_argument("--save_every", type=int, default=1, help="save model in every n epochs")
parser.add_argument("--n_epoch", type=int, default=50, help="train model in total n epochs")
parser.add_argument("--word_emb_model", type=str, default="Glove", help="use which word embedding model: fasttext/Glove")
parser.add_argument("--word_emb_calc", type=str, default="AVG", help="use which method to compute textual form: SUM/AVG")
parser.add_argument("--use_epoch", type=int, nargs='+', help="how many epochs to train the model")
parser.add_argument("--concat_model", type=int, default=1, help="use which concatenation model (1 or 2). In which, 1 refers to KGE + Word embedding, and 2 refers to KGE + (KGE/Word embeddings) depends on the object value")
parser.add_argument("--weighted_edges_method", type=str, default="", help="use which apply the initialize weighted edges method: tf-idf")
args = parser.parse_args()
main(args.mode, args.kge_model, args.loss_function, args.ent_emb_dim, args.pred_emb_dim, args.hidden_layers, args.nheads, args.lr, args.dropout, args.regularization, args.weight_decay, \
args.n_epoch, args.save_every, args.word_emb_model, args.word_emb_calc, args.use_epoch, args.concat_model, args.weighted_edges_method)