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trainer_vanilla.py
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trainer_vanilla.py
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from dataset import Dataset
from tester import Tester
from models import *
from utils import *
import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import random
from sklearn.utils import shuffle
torch.manual_seed(7)
random.seed(7)
class Trainer:
def __init__(self, dataset, model_name, args):
torch.manual_seed(7)
random.seed(7)
self.device = args.device
self.model_name = model_name
self.lmbda = args.reg
if self.model_name == 'TransE':
self.model_vanilla = TransE(
dataset.num_ent(),
dataset.num_rel(),
args.dim,
self.device)
if self.model_name == 'TransH':
self.model_vanilla = TransH(
dataset.num_ent(),
dataset.num_rel(),
args.dim,
self.device)
if self.model_name == 'TransD':
self.model_vanilla = TransD(
dataset.num_ent(),
dataset.num_rel(),
args.dim,
self.device)
if self.model_name == 'DistMult':
self.model_vanilla = DistMult(
dataset.num_ent(),
dataset.num_rel(),
args.dim,
self.device,
args)
if self.model_name == 'ComplEx':
self.model_vanilla = ComplEx(
dataset.num_ent(),
dataset.num_rel(),
args.dim,
self.device,
args)
if self.model_name == 'SimplE':
self.model_vanilla = SimplE(
dataset.num_ent(),
dataset.num_rel(),
args.dim,
self.device,
self.lmbda,
args)
if self.model_name == 'TuckER':
self.model_vanilla = TuckER(
dataset.num_ent(), dataset.num_rel(), args, self.device)
if self.model_name == 'ConvE':
self.model_vanilla = ConvE(
dataset.num_ent(), dataset.num_rel(), args, self.device)
self.dataset = dataset
self.args = args
self.directory = "models/" + self.dataset.name + "/" + self.model_name + "/"
self.lossfunc = args.lossfunc
self.labelsem = args.labelsem
def train(self):
self.model_vanilla.train()
optimizer_vanilla = torch.optim.Adam(
self.model_vanilla.parameters(),
lr=self.args.lr
)
if not self.args.resume_training:
start = 1
else:
start = self.args.resume_epoch + 1
for epoch in range(start, self.args.ne + 1):
last_batch = False
total_loss_vanilla = 0.0
while not last_batch:
batch_pos = self.dataset.next_pos_batch(self.args.batch_size)
if self.lossfunc != 'bce':
neg_batch_sem, neg_batch_nonsem = self.dataset.neg_picking(
batch_pos, neg_ratio=1)
last_batch = self.dataset.was_last_batch()
optimizer_vanilla.zero_grad()
if self.lossfunc != 'bce':
pos_scores_v, neg_scores_sem_v, neg_scores_nonsem_v = self.model_vanilla.forward(
batch_pos), self.model_vanilla.forward(neg_batch_sem), self.model_vanilla.forward(neg_batch_nonsem)
else:
loss_vanilla = self.model_vanilla._bce_vanilla(batch_pos)
if self.lossfunc == 'softplus':
loss_pos_v = self.model_vanilla._softplus(
pos_scores_v, self.args.neg_ratio, beta=1, label=1)
loss_nonsem_v = self.model_vanilla._softplus(
neg_scores_nonsem_v, self.args.neg_ratio, beta=1, label=-1)
loss_sem_v = self.model_vanilla._softplus(
neg_scores_sem_v, self.args.neg_ratio, beta=1, label=-1)
loss_vanilla = loss_pos_v + loss_sem_v + loss_nonsem_v
elif self.lossfunc == 'pairwise':
loss_sem_v = self.model_vanilla._loss(
pos_scores_v, neg_scores_sem_v, self.args.neg_ratio, gamma=self.args.gamma1)
loss_nonsem_v = self.model_vanilla._loss(
pos_scores_v, neg_scores_nonsem_v, self.args.neg_ratio, gamma=self.args.gamma1)
loss_vanilla = loss_sem_v + loss_nonsem_v
elif self.lossfunc == 'logistic_pairwise':
loss_sem_v = self.model_vanilla._logistic_pairwise(
pos_scores_v, neg_scores_sem_v, self.args.neg_ratio, gamma=1.0)
loss_nonsem_v = self.model_vanilla._logistic_pairwise(
pos_scores_v, neg_scores_nonsem_v, self.args.neg_ratio, gamma=1.0)
loss_vanilla = loss_sem_v + loss_nonsem_v
if self.args.reg != 0.0:
if self.lossfunc != 'bce':
batch = np.concatenate(
(batch_pos, neg_batch_sem, neg_batch_nonsem), axis=0)
batch = torch.tensor(batch)
loss_vanilla += self.args.reg * self.model_vanilla._regularization(batch[:, 0].to(
self.device), batch[:, 1].to(self.device), batch[:, 2].to(self.device))
else:
batch = torch.tensor(batch_pos)
loss_vanilla += self.args.reg * self.model_vanilla._regularization(batch[:, 0].to(
self.device), batch[:, 1].to(self.device), batch[:, 2].to(self.device))
loss_vanilla.backward()
optimizer_vanilla.step()
total_loss_vanilla += loss_vanilla.cpu().item()
if epoch % self.args.save_each == 0:
print(
"Vanilla Loss in iteration " +
str(epoch) +
": " +
str(total_loss_vanilla))
if epoch % self.args.save_each == 0:
self.save_model(self.model_name, epoch)
if self.args.monitor_metrics == 1:
if self.model_name == 'ConvE':
model_path_vanilla = self.directory + "vanilla__dim=" + str(self.args.dim) + \
"_loss=" + str(self.args.lossfunc) + "_lr=" + str(self.args.lr) + "_input_drop=" + str(self.args.input_drop) + "_hid_dropout=" + str(self.args.hidden_drop) + \
"_feat_drop=" + str(self.args.feat_drop) + "_neg=" + str(self.args.neg_ratio) + "_bs=" + str(self.args.batch_size) + \
"_gamma1=" + str(self.args.gamma1) + "_gamma2=" + str(self.args.gamma2) + "_labelsem=" + str(self.args.labelsem) + "_alpha=" + str(self.args.alpha) + "__epoch=" + str(epoch) + ".pt"
elif self.model_name == 'TuckER':
model_path_vanilla = self.directory + "vanilla__dim_e=" + str(self.args.dim_e) + "_dim_r=" + str(self.args.dim_r) + \
"_loss=" + str(self.args.lossfunc) + "_lr=" + str(self.args.lr) + "_input_drop=" + str(self.args.input_dropout) + "_hid_dropout1=" + str(self.args.hidden_dropout1) + \
"_hid_dropout2=" + str(self.args.hidden_dropout2) + "_neg=" + str(self.args.neg_ratio) + "_bs=" + str(self.args.batch_size) + \
"_gamma1=" + str(self.args.gamma1) + "_gamma2=" + str(self.args.gamma2) + "_labelsem=" + str(self.args.labelsem) + "_alpha=" + str(self.args.alpha) + "__epoch=" + str(epoch) + ".pt"
else:
model_path_vanilla = self.directory + "vanilla__dim=" + str(self.args.dim) + \
"_loss=" + str(self.args.lossfunc) + "_lr=" + str(self.args.lr) + "_reg=" + str(self.args.reg) + "_neg=" + str(self.args.neg_ratio) + "_bs=" + str(self.args.batch_size) + \
"_gamma1=" + str(self.args.gamma1) + "_gamma2=" + str(self.args.gamma2) + "_labelsem=" + str(self.args.labelsem) + "_alpha=" + str(self.args.alpha) + "__epoch=" + str(epoch) + ".pt"
tester = Tester(
self.dataset,
self.args,
model_path_vanilla,
"valid")
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, ext_CWA, schema_WUP = tester.calc_valid_mrr()
def save_model(self, model, chkpnt):
print("Saving the model")
directory = "models/" + self.dataset.name + "/" + model + "/"
if not os.path.exists(directory):
os.makedirs(directory)
if self.model_name == 'ConvE':
torch.save(self.model_vanilla.state_dict(), directory +
"vanilla__dim=" +
str(self.args.dim) +
"_loss=" +
str(self.args.lossfunc) +
"_lr=" +
str(self.args.lr) +
"_input_drop=" +
str(self.args.input_drop) +
"_hid_dropout=" +
str(self.args.hidden_drop) +
"_feat_drop=" +
str(self.args.feat_drop) +
"_neg=" +
str(self.args.neg_ratio) +
"_bs=" +
str(self.args.batch_size) +
"_gamma1=" +
str(self.args.gamma1) +
"_gamma2=" +
str(self.args.gamma2) +
"_labelsem=" +
str(self.args.labelsem) +
"_alpha=" +
str(self.args.alpha) +
"__epoch=" +
str(chkpnt) +
".pt")
elif self.model_name == 'TuckER':
torch.save(self.model_vanilla.state_dict(), directory +
"vanilla__dim_e=" +
str(self.args.dim_e) +
"_dim_r=" +
str(self.args.dim_r) +
"_loss=" +
str(self.args.lossfunc) +
"_lr=" +
str(self.args.lr) +
"_input_drop=" +
str(self.args.input_dropout) +
"_hid_dropout1=" +
str(self.args.hidden_dropout1) +
"_hid_dropout2=" +
str(self.args.hidden_dropout2) +
"_neg=" +
str(self.args.neg_ratio) +
"_bs=" +
str(self.args.batch_size) +
"_gamma1=" +
str(self.args.gamma1) +
"_gamma2=" +
str(self.args.gamma2) +
"_labelsem=" +
str(self.args.labelsem) +
"_alpha=" +
str(self.args.alpha) +
"__epoch=" +
str(chkpnt) +
".pt")
else:
torch.save(self.model_vanilla.state_dict(), directory +
"vanilla__dim=" +
str(self.args.dim) +
"_loss=" +
str(self.args.lossfunc) +
"_lr=" +
str(self.args.lr) +
"_reg=" +
str(self.args.reg) +
"_neg=" +
str(self.args.neg_ratio) +
"_bs=" +
str(self.args.batch_size) +
"_gamma1=" +
str(self.args.gamma1) +
"_gamma2=" +
str(self.args.gamma2) +
"_labelsem=" +
str(self.args.labelsem) +
"_alpha=" +
str(self.args.alpha) +
"__epoch=" +
str(chkpnt) +
".pt")
def resume_training(self):
directory = "models/" + self.dataset.name + "/" + self.model_name + "/"
resume_epoch = self.args.resume_epoch
if resume_epoch == 0:
resume_epoch = max([int(f[-11:].split('=')[-1].split('.')[0]) for f in os.listdir(
"models/" + self.dataset.name + "/" + self.model_name + "/")])
if self.model_name == 'ConvE':
model_path = directory + "vanilla__dim=" + str(self.args.dim) + \
"_loss=" + str(self.args.lossfunc) + "_lr=" + str(self.args.lr) + "_input_drop=" + str(self.args.input_drop) + "_hid_dropout=" + str(self.args.hidden_drop) + \
"_feat_drop=" + str(self.args.feat_drop) + "_neg=" + str(self.args.neg_ratio) + "_bs=" + str(self.args.batch_size) + \
"_gamma1=" + str(self.args.gamma1) + "_gamma2=" + str(self.args.gamma2) + "_labelsem=" + str(self.args.labelsem) + "_alpha=" + str(self.args.alpha) + "__epoch=" + str(resume_epoch) + ".pt"
elif self.model_name == 'TuckER':
model_path = directory + "vanilla__dim_e=" + str(self.args.dim_e) + "_dim_r=" + str(self.args.dim_r) + \
"_loss=" + str(self.args.lossfunc) + "_lr=" + str(self.args.lr) + "_input_drop=" + str(self.args.input_dropout) + "_hid_dropout1=" + str(self.args.hidden_dropout1) + \
"_hid_dropout2=" + str(self.args.hidden_dropout2) + "_neg=" + str(self.args.neg_ratio) + "_bs=" + str(self.args.batch_size) + \
"_gamma1=" + str(self.args.gamma1) + "_gamma2=" + str(self.args.gamma2) + "_labelsem=" + str(self.args.labelsem) + "_alpha=" + str(self.args.alpha) + "__epoch=" + str(resume_epoch) + ".pt"
else:
model_path = directory + "vanilla__dim=" + str(self.args.dim) + \
"_loss=" + str(self.args.lossfunc) + "_lr=" + str(self.args.lr) + "_reg=" + str(self.args.reg) + "_neg=" + str(self.args.neg_ratio) + "_bs=" + str(self.args.batch_size) + \
"_gamma1=" + str(self.args.gamma1) + "_gamma2=" + str(self.args.gamma2) + "_labelsem=" + str(self.args.labelsem) + "_alpha=" + str(self.args.alpha) + "__epoch=" + str(resume_epoch) + ".pt"
else:
if self.model_name == 'ConvE':
model_path = directory + "vanilla__dim=" + str(self.args.dim) + \
"_loss=" + str(self.args.lossfunc) + "_lr=" + str(self.args.lr) + "_input_drop=" + str(self.args.input_drop) + "_hid_dropout=" + str(self.args.hidden_drop) + \
"_feat_drop=" + str(self.args.feat_drop) + "_neg=" + str(self.args.neg_ratio) + "_bs=" + str(self.args.batch_size) + \
"_gamma1=" + str(self.args.gamma1) + "_gamma2=" + str(self.args.gamma2) + "_labelsem=" + str(self.args.labelsem) + "_alpha=" + str(self.args.alpha) + "__epoch=" + str(resume_epoch) + ".pt"
elif self.model_name == 'TuckER':
model_path = directory + "vanilla__dim_e=" + str(self.args.dim_e) + "_dim_r=" + str(self.args.dim_r) + \
"_loss=" + str(self.args.lossfunc) + "_lr=" + str(self.args.lr) + "_input_drop=" + str(self.args.input_dropout) + "_hid_dropout1=" + str(self.args.hidden_dropout1) + \
"_hid_dropout2=" + str(self.args.hidden_dropout2) + "_neg=" + str(self.args.neg_ratio) + "_bs=" + str(self.args.batch_size) + \
"_gamma1=" + str(self.args.gamma1) + "_gamma2=" + str(self.args.gamma2) + "_labelsem=" + str(self.args.labelsem) + "_alpha=" + str(self.args.alpha) + "__epoch=" + str(resume_epoch) + ".pt"
else:
model_path = directory + "vanilla__dim=" + str(self.args.dim) + \
"_loss=" + str(self.args.lossfunc) + "_lr=" + str(self.args.lr) + "_reg=" + str(self.args.reg) + "_neg=" + str(self.args.neg_ratio) + "_bs=" + str(self.args.batch_size) + \
"_gamma1=" + str(self.args.gamma1) + "_gamma2=" + str(self.args.gamma2) + "_labelsem=" + str(self.args.labelsem) + "_alpha=" + str(self.args.alpha) + "__epoch=" + str(resume_epoch) + ".pt"
print('Resuming from ' + str(model_path))
self.model_vanilla.load_state_dict(torch.load(model_path))
self.train()