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main_vanilla.py
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main_vanilla.py
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from trainer_vanilla import Trainer
from tester import Tester
from dataset import Dataset
import numpy as np
import pandas as pd
import argparse
import time
import os
import torch
import json
from datetime import datetime
date_today = datetime.today().strftime('%d-%m-%Y')
def get_parameter():
parser = argparse.ArgumentParser()
parser.add_argument('-ne', default=400, type=int, help="number of epochs")
parser.add_argument('-lr', default=0.001, type=float, help="learning rate")
parser.add_argument('-reg', default=0.0, type=float,
help="l2 regularization parameter")
parser.add_argument('-margin', default=2.0, type=float, help="margin")
parser.add_argument(
'-dataset',
default="Yago14k",
type=str,
help="dataset")
parser.add_argument(
'-model',
default="TransE",
type=str,
help="knowledge graph embedding model")
parser.add_argument(
'-dim',
default=100,
type=int,
help="embedding dimension")
parser.add_argument(
'-neg_ratio',
default=1,
type=int,
help="number of negative examples per positive example")
parser.add_argument(
'-neg_sampler',
default="rns",
type=str,
help="negative sampling strategy")
parser.add_argument(
'-batch_size',
default=1024,
type=int,
help="batch size")
parser.add_argument(
'-save_each',
default=10,
type=int,
help="validate every k epochs")
parser.add_argument(
'-criterion_validation',
default="mrr",
type=str,
help="criterion for keeping best epoch")
parser.add_argument(
'-metrics',
default="all",
type=str,
help="metrics to compute on test set (sem|ranks|all)")
parser.add_argument(
'-pipeline',
default="both",
type=str,
help="(train|test|both)")
parser.add_argument(
'-device',
default="cuda:0",
type=str,
help="(cpu|cuda:0)")
parser.add_argument(
'-setting',
default="CWA",
type=str,
help="CWA|OWA|both")
parser.add_argument(
'-sem',
default="both",
type=str,
help="schema|extensional|both")
parser.add_argument('-hierarchy', default=False, type=bool)
parser.add_argument('-resume_training', default=False, type=bool)
parser.add_argument(
'-resume_epoch',
default=0,
type=int,
help='epoch at which resuming training (0 means: last epoch the model was saved)')
parser.add_argument('-test_one_epoch', default=False, type=bool)
parser.add_argument(
'-loss_strategy',
default='vanilla',
type=str,
help='vanilla|sem')
parser.add_argument('-lossfunc', default='pairwise', type=str)
parser.add_argument('-monitor_metrics', default=0, type=int)
parser.add_argument('-gamma1', default=2.0, type=float)
parser.add_argument('-gamma2', default=0.5, type=float)
parser.add_argument('-fast_testing', default=0, type=int)
parser.add_argument('-labelsem', default=0.0001, type=float)
parser.add_argument('-alpha', default=0.15, type=float)
# ConvE
parser.add_argument('-input_drop', default=0.2, type=float)
parser.add_argument('-hidden_drop', default=0.3, type=float)
parser.add_argument('-feat_drop', default=0.3, type=float)
parser.add_argument('-hidden_size', default=9728, type=int)
parser.add_argument('-embedding_shape1', default=20, type=int)
# TuckER
parser.add_argument('-dim_e', default=100, type=int)
parser.add_argument('-dim_r', default=100, type=int)
parser.add_argument('-input_dropout', default=0.3, type=float)
parser.add_argument('-hidden_dropout1', default=0.4, type=float)
parser.add_argument('-hidden_dropout2', default=0.5, type=float)
parser.add_argument('-label_smoothing', default=0.0, type=float)
args = parser.parse_args()
np.random.seed(7)
torch.manual_seed(7)
return args
if __name__ == '__main__':
args = get_parameter()
dataset = Dataset(args.dataset, args)
model = args.model
if args.pipeline == 'both' or args.pipeline == 'train':
if not args.resume_training:
print("------- Training -------")
start = time.time()
trainer = Trainer(dataset, model, args)
trainer.train()
else:
print("------- Training -------")
start = time.time()
trainer = Trainer(dataset, model, args)
trainer.resume_training()
print("Training time: ", time.time() - start)
if args.pipeline == 'both' or args.pipeline == 'test':
print("------- Select best epoch on validation set -------")
if not args.resume_training:
epochs2test = [str(int(args.save_each * (i + 1)))
for i in range(args.ne // args.save_each)]
else:
if args.resume_epoch == 0:
# get last pt file
resume_epoch = max([int(f[-11:].split('=')[-1].split('.')[0]) for f in os.listdir(
"models/" + str(dataset.name) + "/" + str(model) + "/")]) - args.ne
else:
resume_epoch = args.resume_epoch
print('Resuming at epoch ' + str(resume_epoch))
epochs2test = [str(int(resume_epoch + args.save_each * (i + 1)))
for i in range(args.ne // args.save_each)]
if args.test_one_epoch:
resume_epoch = args.resume_epoch
print(resume_epoch)
epochs2test = [str(resume_epoch)]
dataset = Dataset(args.dataset, args)
best_mrr = -1.0
best_loss = + np.inf
results = {}
best_epoch = "0"
directory = "models/" + dataset.name + "/" + model + "/"
if not os.path.exists(
'results/' +
dataset.name +
"/" +
args.model +
'/' +
args.sem +
'-' +
args.setting +
'/'):
os.makedirs(
'results/' +
dataset.name +
"/" +
args.model +
'/' +
args.sem +
'-' +
args.setting +
'/')
for epoch in epochs2test:
print("Epoch n°", epoch, " | Loss strategy: ", args.loss_strategy)
if str(model) == 'ConvE':
model_path = directory + args.loss_strategy + "__dim=" + str(args.dim) + "_loss=" + str(args.lossfunc) +\
"_lr=" + str(args.lr) + "_input_drop=" + str(args.input_drop) + "_hid_dropout=" + str(args.hidden_drop) + \
"_feat_drop=" + str(args.feat_drop) + "_neg=" + str(args.neg_ratio) + "_bs=" + str(args.batch_size) + "_gamma1=" + str(args.gamma1) + "_gamma2=" + str(args.gamma2) + \
"_labelsem=" + str(args.labelsem) + "_alpha=" + str(args.alpha) + "__epoch=" + str(epoch) + ".pt"
elif str(model) == 'TuckER':
model_path = directory + args.loss_strategy + "__dim_e=" + str(args.dim_e) + "_dim_r=" + str(args.dim_r) + \
"_loss=" + str(args.lossfunc) + "_lr=" + str(args.lr) + "_input_drop=" + str(args.input_dropout) + "_hid_dropout1=" + str(args.hidden_dropout1) + \
"_hid_dropout2=" + str(args.hidden_dropout2) + "_neg=" + str(args.neg_ratio) + "_bs=" + str(args.batch_size) + \
"_gamma1=" + str(args.gamma1) + "_gamma2=" + str(args.gamma2) + "_labelsem=" + str(args.labelsem) + "_alpha=" + str(args.alpha) + "__epoch=" + str(epoch) + ".pt"
else:
model_path = directory + args.loss_strategy + "__dim=" + str(args.dim) + "_loss=" + str(args.lossfunc) +\
"_lr=" + str(args.lr) + "_reg=" + str(args.reg) + "_neg=" + str(args.neg_ratio) + "_bs=" + str(args.batch_size) + "_gamma1=" + str(args.gamma1) + "_gamma2=" + str(args.gamma2) + \
"_labelsem=" + str(args.labelsem) + "_alpha=" + str(args.alpha) + "__epoch=" + str(epoch) + ".pt"
tester = Tester(dataset, args, model_path, "valid")
start = time.time()
if args.criterion_validation == 'mrr':
if args.metrics == 'sem' or args.metrics == 'all':
filt_mrr, filtered_hits_at_1, filtered_hits_at_3, filtered_hits_at_5, filtered_hits_at_10, filt_mrr_h, filt_mrr_t, \
filt_h1_h, filt_h1_t, filt_h3_h, filt_h3_t, filt_h5_h, filt_h5_t, filt_h10_h, filt_h10_t, schema_CWA, schema_OWA = tester.calc_valid_mrr()
if args.sem == 'both':
if args.setting == 'both':
results[int(epoch)] = {"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t,
"CWA_Sem@1": schema_CWA['sem1'],
"CWA_Sem@1_h": schema_CWA['sem1_h'],
"CWA_Sem@1_t": schema_CWA['sem1_t'],
"CWA_Sem@3": schema_CWA['sem3'],
"CWA_Sem@3_h": schema_CWA['sem3_h'],
"CWA_Sem@3_t": schema_CWA['sem3_t'],
"CWA_Sem@5": schema_CWA['sem5'],
"CWA_Sem@5_h": schema_CWA['sem5_h'],
"CWA_Sem@5_t": schema_CWA['sem5_t'],
"CWA_Sem@10": schema_CWA['sem10'],
"CWA_Sem@10_h": schema_CWA['sem10_h'],
"CWA_Sem@10_t": schema_CWA['sem10_t'],
"OWA_Sem@1": schema_OWA['sem1'],
"OWA_Sem@1_h": schema_OWA['sem1_h'],
"OWA_Sem@1_t": schema_OWA['sem1_t'],
"OWA_Sem@3": schema_OWA['sem3'],
"OWA_Sem@3_h": schema_OWA['sem3_h'],
"OWA_Sem@3_t": schema_OWA['sem3_t'],
"OWA_Sem@5": schema_OWA['sem5'],
"OWA_Sem@5_h": schema_OWA['sem5_h'],
"OWA_Sem@5_t": schema_OWA['sem5_t'],
"OWA_Sem@10": schema_OWA['sem10'],
"OWA_Sem@10_h": schema_OWA['sem10_h'],
"OWA_Sem@10_t": schema_OWA['sem10_t']}
elif args.setting == 'CWA':
results[int(epoch)] = {"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t,
"CWA_Sem@1": schema_CWA['sem1'],
"CWA_Sem@1_h": schema_CWA['sem1_h'],
"CWA_Sem@1_t": schema_CWA['sem1_t'],
"CWA_Sem@3": schema_CWA['sem3'],
"CWA_Sem@3_h": schema_CWA['sem3_h'],
"CWA_Sem@3_t": schema_CWA['sem3_t'],
"CWA_Sem@5": schema_CWA['sem5'],
"CWA_Sem@5_h": schema_CWA['sem5_h'],
"CWA_Sem@5_t": schema_CWA['sem5_t'],
"CWA_Sem@10": schema_CWA['sem10'],
"CWA_Sem@10_h": schema_CWA['sem10_h'],
"CWA_Sem@10_t": schema_CWA['sem10_t']}
else:
results[int(epoch)] = {"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t,
"OWA_Sem@1": schema_OWA['sem1'],
"OWA_Sem@1_h": schema_OWA['sem1_h'],
"OWA_Sem@1_t": schema_OWA['sem1_t'],
"OWA_Sem@3": schema_OWA['sem3'],
"OWA_Sem@3_h": schema_OWA['sem3_h'],
"OWA_Sem@3_t": schema_OWA['sem3_t'],
"OWA_Sem@5": schema_OWA['sem5'],
"OWA_Sem@5_h": schema_OWA['sem5_h'],
"OWA_Sem@5_t": schema_OWA['sem5_t'],
"OWA_Sem@10": schema_OWA['sem10'],
"OWA_Sem@10_h": schema_OWA['sem10_h'],
"OWA_Sem@10_t": schema_OWA['sem10_t']}
elif args.sem == 'schema':
if args.setting == 'both':
results[int(epoch)] = {"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t,
"CWA_Sem@1": schema_CWA['sem1'],
"CWA_Sem@1_h": schema_CWA['sem1_h'],
"CWA_Sem@1_t": schema_CWA['sem1_t'],
"CWA_Sem@3": schema_CWA['sem3'],
"CWA_Sem@3_h": schema_CWA['sem3_h'],
"CWA_Sem@3_t": schema_CWA['sem3_t'],
"CWA_Sem@5": schema_CWA['sem5'],
"CWA_Sem@5_h": schema_CWA['sem5_h'],
"CWA_Sem@5_t": schema_CWA['sem5_t'],
"CWA_Sem@10": schema_CWA['sem10'],
"CWA_Sem@10_h": schema_CWA['sem10_h'],
"CWA_Sem@10_t": schema_CWA['sem10_t'],
"OWA_Sem@1": schema_OWA['sem1'],
"OWA_Sem@1_h": schema_OWA['sem1_h'],
"OWA_Sem@1_t": schema_OWA['sem1_t'],
"OWA_Sem@3": schema_OWA['sem3'],
"OWA_Sem@3_h": schema_OWA['sem3_h'],
"OWA_Sem@3_t": schema_OWA['sem3_t'],
"OWA_Sem@5": schema_OWA['sem5'],
"OWA_Sem@5_h": schema_OWA['sem5_h'],
"OWA_Sem@5_t": schema_OWA['sem5_t'],
"OWA_Sem@10": schema_OWA['sem10'],
"OWA_Sem@10_h": schema_OWA['sem10_h'],
"OWA_Sem@10_t": schema_OWA['sem10_t']}
elif args.setting == 'CWA':
results[int(epoch)] = {"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t}
else:
results[int(epoch)] = {"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t,
"OWA_Sem@1": schema_OWA['sem1'],
"OWA_Sem@1_h": schema_OWA['sem1_h'],
"OWA_Sem@1_t": schema_OWA['sem1_t'],
"OWA_Sem@3": schema_OWA['sem3'],
"OWA_Sem@3_h": schema_OWA['sem3_h'],
"OWA_Sem@3_t": schema_OWA['sem3_t'],
"OWA_Sem@5": schema_OWA['sem5'],
"OWA_Sem@5_h": schema_OWA['sem5_h'],
"OWA_Sem@5_t": schema_OWA['sem5_t'],
"OWA_Sem@10": schema_OWA['sem10'],
"OWA_Sem@10_h": schema_OWA['sem10_h'],
"OWA_Sem@10_t": schema_OWA['sem10_t']}
elif args.sem == 'extensional':
results[int(epoch)] = {"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t}
df_valid = pd.DataFrame.from_dict(results)
if model == 'ConvE':
df_valid.to_csv('results/' +
dataset.name +
"/" +
args.model +
'/' +
args.sem +
'-' +
args.setting +
'/' +
'[' +
date_today +
']' +
args.model +
'-valid_results-mrr-sem-' +
args.dataset +
'-' +
model +
'-loss_str=' +
args.loss_strategy +
'-dim=' +
str(args.dim) +
"-loss=" +
str(args.lossfunc) +
'-lr=' +
str(args.lr) +
"-input_drop=" +
str(args.input_drop) +
"-hid_dropout=" +
str(args.hidden_drop) +
"-feat_drop=" +
str(args.feat_drop) +
'-neg=' +
str(args.neg_ratio) +
'-bs=' +
str(args.batch_size) +
"-gamma1=" +
str(args.gamma1) +
"-gamma2=" +
str(args.gamma2) +
"-labelsem=" +
str(args.labelsem) +
"-alpha=" +
str(args.alpha) +
'.csv')
with open('results/' + dataset.name + "/" + args.model + '/' + args.sem + '-' + args.setting + '/' + '[' + date_today + ']' + args.model + '-valid_results-mrr-sem-' +
args.dataset + '-' + model + '-loss_str=' + args.loss_strategy + '-dim=' +
str(args.dim) + "-loss=" + str(args.lossfunc) + '-lr=' + str(args.lr) + "-input_drop=" + str(args.input_drop) + "-hid_dropout=" + str(args.hidden_drop) +
"-feat_drop=" + str(args.feat_drop) + '-neg=' + str(args.neg_ratio) + '-bs=' + str(args.batch_size) +
"-gamma1=" + str(args.gamma1) + "-gamma2=" + str(args.gamma2) + "-labelsem=" + str(args.labelsem) + "-alpha=" + str(args.alpha) + '.json', 'w') as fp:
json.dump(results, fp)
elif model == 'TuckER':
df_valid.to_csv('results/' +
dataset.name +
"/" +
args.model +
'/' +
args.sem +
'-' +
args.setting +
'/' +
'[' +
date_today +
']' +
args.model +
'-valid_results-mrr-sem-' +
args.dataset +
'-' +
model +
'-loss_str=' +
args.loss_strategy +
"-dim_e=" +
str(args.dim_e) +
"-dim_r=" +
str(args.dim_r) +
"-loss=" +
str(args.lossfunc) +
'-lr=' +
str(args.lr) +
"-input_drop=" +
str(args.input_dropout) +
"-hid_dropout1=" +
str(args.hidden_dropout1) +
"-hid_dropout2=" +
str(args.hidden_dropout2) +
'-neg=' +
str(args.neg_ratio) +
'-bs=' +
str(args.batch_size) +
"-gamma1=" +
str(args.gamma1) +
"-gamma2=" +
str(args.gamma2) +
"-labelsem=" +
str(args.labelsem) +
"-alpha=" +
str(args.alpha) +
'.csv')
with open('results/' + dataset.name + "/" + args.model + '/' + args.sem + '-' + args.setting + '/' + '[' + date_today + ']' + args.model + '-valid_results-mrr-sem-' +
args.dataset + '-' + model + '-loss_str=' + args.loss_strategy + "-dim_e=" + str(args.dim_e) + "-dim_r=" + str(args.dim_r) +
"-loss=" + str(args.lossfunc) + '-lr=' + str(args.lr) + "-input_drop=" + str(args.input_dropout) + "-hid_dropout1=" + str(args.hidden_dropout1) +
"-hid_dropout2=" + str(args.hidden_dropout2) + '-neg=' + str(args.neg_ratio) + '-bs=' + str(args.batch_size) +
"-gamma1=" + str(args.gamma1) + "-gamma2=" + str(args.gamma2) + "-labelsem=" + str(args.labelsem) + "-alpha=" + str(args.alpha) + '.json', 'w') as fp:
json.dump(results, fp)
else:
df_valid.to_csv('results/' +
dataset.name +
"/" +
args.model +
'/' +
args.sem +
'-' +
args.setting +
'/' +
'[' +
date_today +
']' +
args.model +
'-valid_results-mrr-sem-' +
args.dataset +
'-' +
model +
'-loss_str=' +
args.loss_strategy +
'-dim=' +
str(args.dim) +
"-loss=" +
str(args.lossfunc) +
'-lr=' +
str(args.lr) +
"-reg=" +
str(args.reg) +
'-neg=' +
str(args.neg_ratio) +
'-bs=' +
str(args.batch_size) +
"-gamma1=" +
str(args.gamma1) +
"-gamma2=" +
str(args.gamma2) +
"-labelsem=" +
str(args.labelsem) +
"-alpha=" +
str(args.alpha) +
'.csv')
with open('results/' + dataset.name + "/" + args.model + '/' + args.sem + '-' + args.setting + '/' '[' + date_today + ']' + args.model + '-valid_results-mrr-sem-' + args.dataset + '-' + model + '-loss_str=' + args.loss_strategy + '-dim=' +
str(args.dim) + "-loss=" + str(args.lossfunc) + '-lr=' + str(args.lr) + "-reg=" + str(args.reg) + '-neg=' + str(args.neg_ratio) + '-bs=' + str(args.batch_size) +
"-gamma1=" + str(args.gamma1) + "-gamma2=" + str(args.gamma2) + "-labelsem=" + str(args.labelsem) + "-alpha=" + str(args.alpha) + '.json', 'w') as fp:
json.dump(results, fp)
if filt_mrr > best_mrr:
best_mrr = filt_mrr
best_epoch = epoch
print(time.time() - start)
print("Best epoch: " + best_epoch)
print("------- Testing on the best epoch -------")
if model == 'ConvE':
best_model_path = directory + args.loss_strategy + "__dim=" + str(args.dim) + "_loss=" + str(args.lossfunc) + \
"_lr=" + str(args.lr) + "_input_drop=" + str(args.input_drop) + "_hid_dropout=" + str(args.hidden_drop) + \
"_feat_drop=" + str(args.feat_drop) + "_neg=" + str(args.neg_ratio) + "_bs=" + str(args.batch_size) + "_gamma1=" + str(args.gamma1) + "_gamma2=" + str(args.gamma2) + \
"_labelsem=" + str(args.labelsem) + "_alpha=" + str(args.alpha) + "__epoch=" + best_epoch + ".pt"
elif model == 'TuckER':
best_model_path = directory + args.loss_strategy + "__dim_e=" + str(args.dim_e) + "_dim_r=" + str(args.dim_r) + \
"_loss=" + str(args.lossfunc) + "_lr=" + str(args.lr) + "_input_drop=" + str(args.input_dropout) + "_hid_dropout1=" + str(args.hidden_dropout1) + \
"_hid_dropout2=" + str(args.hidden_dropout2) + "_neg=" + str(args.neg_ratio) + "_bs=" + str(args.batch_size) + \
"_gamma1=" + str(args.gamma1) + "_gamma2=" + str(args.gamma2) + "_labelsem=" + str(args.labelsem) + "_alpha=" + str(args.alpha) + "__epoch=" + best_epoch + ".pt"
else:
best_model_path = directory + args.loss_strategy + "__dim=" + str(args.dim) + "_loss=" + str(args.lossfunc) + "_lr=" + str(args.lr) + "_reg=" + str(args.reg) + "_neg=" + str(args.neg_ratio) + "_bs=" + str(
args.batch_size) + "_gamma1=" + str(args.gamma1) + "_gamma2=" + str(args.gamma2) + "_labelsem=" + str(args.labelsem) + "_alpha=" + str(args.alpha) + "__epoch=" + best_epoch + ".pt"
tester = Tester(dataset, args, best_model_path, "test")
if args.metrics == 'sem' or args.metrics == 'all':
filt_mrr, filtered_hits_at_1, filtered_hits_at_3, filtered_hits_at_5, filtered_hits_at_10,\
filt_mrr_h, filt_mrr_t, filt_h1_h, filt_h1_t, filt_h3_h, filt_h3_t, filt_h5_h, filt_h5_t, filt_h10_h, filt_h10_t, schema_CWA, schema_OWA = tester.test()
if args.sem == 'both':
if args.setting == 'both':
best_ep_results = {
"Epoch": int(best_epoch),
"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t,
"CWA_Sem@1": schema_CWA['sem1'],
"CWA_Sem@1_h": schema_CWA['sem1_h'],
"CWA_Sem@1_t": schema_CWA['sem1_t'],
"CWA_Sem@3": schema_CWA['sem3'],
"CWA_Sem@3_h": schema_CWA['sem3_h'],
"CWA_Sem@3_t": schema_CWA['sem3_t'],
"CWA_Sem@5": schema_CWA['sem5'],
"CWA_Sem@5_h": schema_CWA['sem5_h'],
"CWA_Sem@5_t": schema_CWA['sem5_t'],
"CWA_Sem@10": schema_CWA['sem10'],
"CWA_Sem@10_h": schema_CWA['sem10_h'],
"CWA_Sem@10_t": schema_CWA['sem10_t'],
"OWA_Sem@1": schema_OWA['sem1'],
"OWA_Sem@1_h": schema_OWA['sem1_h'],
"OWA_Sem@1_t": schema_OWA['sem1_t'],
"OWA_Sem@3": schema_OWA['sem3'],
"OWA_Sem@3_h": schema_OWA['sem3_h'],
"OWA_Sem@3_t": schema_OWA['sem3_t'],
"OWA_Sem@5": schema_OWA['sem5'],
"OWA_Sem@5_h": schema_OWA['sem5_h'],
"OWA_Sem@5_t": schema_OWA['sem5_t'],
"OWA_Sem@10": schema_OWA['sem10'],
"OWA_Sem@10_h": schema_OWA['sem10_h'],
"OWA_Sem@10_t": schema_OWA['sem10_t']}
elif args.setting == 'CWA':
best_ep_results = {
"Epoch": int(best_epoch),
"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t,
"CWA_Sem@1": schema_CWA['sem1'],
"CWA_Sem@1_h": schema_CWA['sem1_h'],
"CWA_Sem@1_t": schema_CWA['sem1_t'],
"CWA_Sem@3": schema_CWA['sem3'],
"CWA_Sem@3_h": schema_CWA['sem3_h'],
"CWA_Sem@3_t": schema_CWA['sem3_t'],
"CWA_Sem@5": schema_CWA['sem5'],
"CWA_Sem@5_h": schema_CWA['sem5_h'],
"CWA_Sem@5_t": schema_CWA['sem5_t'],
"CWA_Sem@10": schema_CWA['sem10'],
"CWA_Sem@10_h": schema_CWA['sem10_h'],
"CWA_Sem@10_t": schema_CWA['sem10_t']}
else:
best_ep_results = {
"Epoch": int(best_epoch),
"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t,
"OWA_Sem@1": schema_OWA['sem1'],
"OWA_Sem@1_h": schema_OWA['sem1_h'],
"OWA_Sem@1_t": schema_OWA['sem1_t'],
"OWA_Sem@3": schema_OWA['sem3'],
"OWA_Sem@3_h": schema_OWA['sem3_h'],
"OWA_Sem@3_t": schema_OWA['sem3_t'],
"OWA_Sem@5": schema_OWA['sem5'],
"OWA_Sem@5_h": schema_OWA['sem5_h'],
"OWA_Sem@5_t": schema_OWA['sem5_t'],
"OWA_Sem@10": schema_OWA['sem10'],
"OWA_Sem@10_h": schema_OWA['sem10_h'],
"OWA_Sem@10_t": schema_OWA['sem10_t']}
elif args.sem == 'schema':
if args.setting == 'both':
best_ep_results = {
"Epoch": int(best_epoch),
"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t,
"CWA_Sem@1": schema_CWA['sem1'],
"CWA_Sem@1_h": schema_CWA['sem1_h'],
"CWA_Sem@1_t": schema_CWA['sem1_t'],
"CWA_Sem@3": schema_CWA['sem3'],
"CWA_Sem@3_h": schema_CWA['sem3_h'],
"CWA_Sem@3_t": schema_CWA['sem3_t'],
"CWA_Sem@5": schema_CWA['sem5'],
"CWA_Sem@5_h": schema_CWA['sem5_h'],
"CWA_Sem@5_t": schema_CWA['sem5_t'],
"CWA_Sem@10": schema_CWA['sem10'],
"CWA_Sem@10_h": schema_CWA['sem10_h'],
"CWA_Sem@10_t": schema_CWA['sem10_t'],
"OWA_Sem@1": schema_OWA['sem1'],
"OWA_Sem@1_h": schema_OWA['sem1_h'],
"OWA_Sem@1_t": schema_OWA['sem1_t'],
"OWA_Sem@3": schema_OWA['sem3'],
"OWA_Sem@3_h": schema_OWA['sem3_h'],
"OWA_Sem@3_t": schema_OWA['sem3_t'],
"OWA_Sem@5": schema_OWA['sem5'],
"OWA_Sem@5_h": schema_OWA['sem5_h'],
"OWA_Sem@5_t": schema_OWA['sem5_t'],
"OWA_Sem@10": schema_OWA['sem10'],
"OWA_Sem@10_h": schema_OWA['sem10_h'],
"OWA_Sem@10_t": schema_OWA['sem10_t']}
elif args.setting == 'CWA':
best_ep_results = {
"Epoch": int(best_epoch),
"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t,
"CWA_Sem@1": schema_CWA['sem1'],
"CWA_Sem@1_h": schema_CWA['sem1_h'],
"CWA_Sem@1_t": schema_CWA['sem1_t'],
"CWA_Sem@3": schema_CWA['sem3'],
"CWA_Sem@3_h": schema_CWA['sem3_h'],
"CWA_Sem@3_t": schema_CWA['sem3_t'],
"CWA_Sem@5": schema_CWA['sem5'],
"CWA_Sem@5_h": schema_CWA['sem5_h'],
"CWA_Sem@5_t": schema_CWA['sem5_t'],
"CWA_Sem@10": schema_CWA['sem10'],
"CWA_Sem@10_h": schema_CWA['sem10_h'],
"CWA_Sem@10_t": schema_CWA['sem10_t']}
else:
best_ep_results = {
"Epoch": int(best_epoch),
"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t,
"OWA_Sem@1": schema_OWA['sem1'],
"OWA_Sem@1_h": schema_OWA['sem1_h'],
"OWA_Sem@1_t": schema_OWA['sem1_t'],
"OWA_Sem@3": schema_OWA['sem3'],
"OWA_Sem@3_h": schema_OWA['sem3_h'],
"OWA_Sem@3_t": schema_OWA['sem3_t'],
"OWA_Sem@5": schema_OWA['sem5'],
"OWA_Sem@5_h": schema_OWA['sem5_h'],
"OWA_Sem@5_t": schema_OWA['sem5_t'],
"OWA_Sem@10": schema_OWA['sem10'],
"OWA_Sem@10_h": schema_OWA['sem10_h'],
"OWA_Sem@10_t": schema_OWA['sem10_t']}
elif args.sem == 'extensional':
best_ep_results = {
"Epoch": int(best_epoch),
"MRR": filt_mrr,
"MRR_h": filt_mrr_h,
"MRR_t": filt_mrr_t,
"H@1": filtered_hits_at_1,
"H@1_h": filt_h1_h,
"H@1_t": filt_h1_t,
"H@3": filtered_hits_at_3,
"H@3_h": filt_h3_h,
"H@3_t": filt_h3_t,
"H@5": filtered_hits_at_5,
"H@5_h": filt_h5_h,
"H@5_t": filt_h5_t,
"H@10": filtered_hits_at_10,
"H@10_h": filt_h10_h,
"H@10_t": filt_h10_t}
df_test = pd.DataFrame.from_dict([best_ep_results])
if args.model == 'ConvE':
df_test.to_csv('results/' +
dataset.name +
"/" +
args.model +
'/' +
args.sem +
'-' +
args.setting +
'/' +
'[' +
date_today +
']best-epoch_results-ALL-METRICS-' +
args.dataset +
'-' +
model +
'-loss_str=' +
args.loss_strategy +
'-dim=' +
str(args.dim) +
"-loss=" +
str(args.lossfunc) +
'-lr=' +
str(args.lr) +
"-input_drop=" +
str(args.input_drop) +
"-hid_dropout=" +
str(args.hidden_drop) +
"-feat_drop=" +
str(args.feat_drop) +
'-neg=' +
str(args.neg_ratio) +
'-bs=' +
str(args.batch_size) +
"-gamma1=" +
str(args.gamma1) +
"-gamma2=" +
str(args.gamma2) +
"-labelsem=" +
str(args.labelsem) +
"-alpha=" +
str(args.alpha) +
'.csv')
with open('results/' + dataset.name + "/" + args.model + '/' + args.sem + '-' + args.setting + '/' + '[' + date_today + ']best-epoch_results-ALL-METRICS-' + args.dataset + '-' + model + '-loss_str=' + args.loss_strategy + '-dim=' +
str(args.dim) + "-loss=" + str(args.lossfunc) + '-lr=' + str(args.lr) + "-input_drop=" + str(args.input_drop) + "-hid_dropout=" + str(args.hidden_drop) +
"-feat_drop=" + str(args.feat_drop) + '-neg=' + str(args.neg_ratio) + '-bs=' + str(args.batch_size) +
"-gamma1=" + str(args.gamma1) + "-gamma2=" + str(args.gamma2) + "-labelsem=" + str(args.labelsem) + "-alpha=" + str(args.alpha) + '.json', 'w') as fp:
json.dump(best_ep_results, fp)
elif args.model == 'TuckER':
df_test.to_csv('results/' +
dataset.name +
"/" +
args.model +
'/' +
args.sem +
'-' +
args.setting +
'/' +
'[' +
date_today +
']best-epoch_results-ALL-METRICS-' +
args.dataset +
'-' +
model +
'-loss_str=' +
args.loss_strategy +
"-dim_e=" +
str(args.dim_e) +
"-dim_r=" +
str(args.dim_r) +
"-loss=" +
str(args.lossfunc) +
'-lr=' +
str(args.lr) +
"-input_drop=" +
str(args.input_dropout) +
"-hid_dropout1=" +
str(args.hidden_dropout1) +
"-hid_dropout2=" +
str(args.hidden_dropout2) +
'-neg=' +
str(args.neg_ratio) +
'-bs=' +
str(args.batch_size) +
"-gamma1=" +
str(args.gamma1) +
"-gamma2=" +
str(args.gamma2) +
"-labelsem=" +
str(args.labelsem) +
"-alpha=" +
str(args.alpha) +
'.csv')
with open('results/' + dataset.name + "/" + args.model + '/' + args.sem + '-' + args.setting + '/' + '[' + date_today + ']best-epoch_results-ALL-METRICS-' + args.dataset + '-' + model + '-loss_str=' + args.loss_strategy + '-dim=' +
str(args.dim) + "-loss=" + str(args.lossfunc) + '-lr=' + str(args.lr) + "-input_drop=" + str(args.input_dropout) + "-hid_dropout1=" + str(args.hidden_dropout1) +
"-hid_dropout2=" + str(args.hidden_dropout2) + '-neg=' + str(args.neg_ratio) + '-bs=' + str(args.batch_size) +
"-gamma1=" + str(args.gamma1) + "-gamma2=" + str(args.gamma2) + "-labelsem=" + str(args.labelsem) + "-alpha=" + str(args.alpha) + '.json', 'w') as fp:
json.dump(best_ep_results, fp)
else:
df_test.to_csv('results/' +
dataset.name +
"/" +
args.model +
'/' +
args.sem +
'-' +
args.setting +
'/' +
'[' +
date_today +
']best-epoch_results-ALL-METRICS-' +
args.dataset +
'-' +
model +
'-loss_str=' +
args.loss_strategy +
'-dim=' +
str(args.dim) +
"-loss=" +
str(args.lossfunc) +
'-lr=' +
str(args.lr) +
"-reg=" +
str(args.reg) +
'-neg=' +
str(args.neg_ratio) +
'-bs=' +
str(args.batch_size) +
"-gamma1=" +
str(args.gamma1) +
"-gamma2=" +
str(args.gamma2) +
"-labelsem=" +
str(args.labelsem) +
"-alpha=" +
str(args.alpha) +
'.csv')
with open('results/' + dataset.name + "/" + args.model + '/' + args.sem + '-' + args.setting + '/' + '[' + date_today + ']best-epoch_results-ALL-METRICS-' + args.dataset + '-' + model + '-loss_str=' + args.loss_strategy + '-dim=' +
str(args.dim) + "-loss=" + str(args.lossfunc) + '-lr=' + str(args.lr) + "-reg=" + str(args.reg) + '-neg=' + str(args.neg_ratio) + '-bs=' + str(args.batch_size) +
"-gamma1=" + str(args.gamma1) + "-gamma2=" + str(args.gamma2) + "-labelsem=" + str(args.labelsem) + "-alpha=" + str(args.alpha) + '.json', 'w') as fp:
json.dump(best_ep_results, fp)