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LICENSE
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MIT License
Copyright (c) 2021 ZJUNLP
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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QA/__init__.py
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from transformers import BartForConditionalGeneration, T5ForConditionalGeneration, GPT2LMHeadModel
from .model import *

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QA/fbqa/relphormer-filtered/dev-stat.json
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{
"#examples": 3994,
"#kept_examples": 3994,
"#mappable_examples": 743,
"#multiple_answer_examples": 2
}

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{
"#examples": 3996,
"#kept_examples": 3996,
"#mappable_examples": 755,
"#multiple_answer_examples": 0
}

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{
"#examples": 20358,
"#kept_examples": 20358,
"#mappable_examples": 3713,
"#multiple_answer_examples": 4
}

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{
"#examples": 3994,
"#kept_examples": 3994,
"#mappable_examples": 743,
"#multiple_answer_examples": 2
}

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{
"#examples": 3996,
"#kept_examples": 3996,
"#mappable_examples": 755,
"#multiple_answer_examples": 0
}

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{
"#examples": 20358,
"#kept_examples": 20358,
"#mappable_examples": 3713,
"#multiple_answer_examples": 4
}

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import json
import math
import argparse
from pathlib import Path
from transformers import BertTokenizer, BertForMaskedLM, AdamW, get_linear_schedule_with_warmup, AutoConfig
import torch
from torch import device, nn
from torch.utils.data import DataLoader, Dataset
import pytorch_lightning as pl
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.utilities.seed import seed_everything
from transformers.tokenization_bert import BertTokenizerFast
from kge.model import KgeModel
from kge.util.io import load_checkpoint
from kge.util import sc
# from relphormer.lit_models import TransformerLitModel
from relphormer.models import BertKGC
# from relphormer.data import KGC
import os
os.environ['CUDA_VISIBLE_DEVICES']='4'
MODEL = 'bert-base-uncased'
tokenizer = BertTokenizer.from_pretrained(MODEL)
class FBQADataset(Dataset):
def __init__(self, file_dir):
self.examples = json.load(Path(file_dir).open("rb"))
def __len__(self):
return len(self.examples)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
return self.examples[idx]
def fbqa_collate(samples):
questions = []
answers = []
answer_ids = []
entities = []
entity_names = []
relations = []
for item in samples:
q = item["RawQuestion"] + "[MASK]" * len(item["AnswerEntity"]) + "."
questions.append(q)
answers.append(item["AnswerEntity"])
answer_ids.append(item["AnswerEntityID"])
entities.append(item["TopicEntityID"])
entity_names.append(item["TopicEntityName"])
relations.append(item["RelationID"])
questions = tokenizer(questions, return_tensors='pt', padding=True)
entity_names = tokenizer(entity_names, add_special_tokens=False)
answers, answers_lengths = sc.pad_seq_of_seq(answers)
answers = torch.LongTensor(answers)
answers_lengths = torch.LongTensor(answers_lengths)
answer_ids = torch.LongTensor(answer_ids)
input_ids = questions['input_ids']
masked_labels = torch.ones_like(input_ids) * -100
masked_labels[input_ids == tokenizer.mask_token_id] = answers[answers != 0]
entity_mask = torch.zeros_like(input_ids).bool()
entity_span_index = input_ids.new_zeros((len(input_ids), 2))
for i, e_tokens in enumerate(entity_names['input_ids']):
q_tokens = input_ids[i].tolist()
for s_index in range(len(q_tokens) - len(e_tokens)):
if all([e_token == q_tokens[s_index + j] for j, e_token in enumerate(e_tokens)]):
entity_mask[i][s_index:s_index + len(e_tokens)] = True
entity_span_index[i][0] = s_index
entity_span_index[i][1] = s_index + len(e_tokens) - 1
break
entities = torch.LongTensor(entities)
relations = torch.LongTensor(relations)
return questions.data, masked_labels, answers, answers_lengths, answer_ids, entities, relations, entity_mask, entity_span_index
class SelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class CrossAttention(nn.Module):
def __init__(self, config, ctx_hidden_size):
super().__init__()
self.self = CrossAttentionInternal(config, ctx_hidden_size)
self.output = SelfOutput(config)
self.config = config
self.apply(self._init_weights)
def _init_weights(self, module):
""" Initialize the weights """
if isinstance(module, (nn.Linear, nn.Embedding)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
output_attentions=False,
):
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class CrossAttentionInternal(nn.Module):
def __init__(self, config, ctx_hidden_size):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, config.num_attention_heads)
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(ctx_hidden_size, self.all_head_size)
self.value = nn.Linear(ctx_hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
output_attentions=False,
):
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
mixed_key_layer = self.key(encoder_hidden_states)
mixed_value_layer = self.value(encoder_hidden_states)
attention_mask = encoder_attention_mask
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, nn.Softmax(dim=-1)(attention_scores)) if output_attentions else (context_layer,)
return outputs
class CrossTrmFinetuner(pl.LightningModule):
def __init__(self, hparams, bertmodel):
super().__init__()
self._hparams = hparams
self.lr = hparams['lr']
self.weight_decay = hparams['weight_decay']
self.kg_dim = 320
# self.bert = BertForMaskedLM.from_pretrained(MODEL)
self.bert = bertmodel
if self._hparams['use_hitter']:
self.kg_layer_num = 10
self.cross_attentions = nn.ModuleList([CrossAttention(self.bert.config, self.kg_dim)
for _ in range(self.kg_layer_num)])
checkpoint = load_checkpoint('local/best/20200812-174221-trmeh-fb15k237-best/checkpoint_best.pt')
self.hitter = KgeModel.create_from(checkpoint)
def forward(self, batch):
sent_input, masked_labels, batch_labels, label_lens, answer_ids, s, p, entity_mask, entity_span_index = batch
if self._hparams['use_hitter']:
# kg_masks: [bs, 1, 1, length]
# kg_embeds: nlayer*[bs, length, dim]
kg_embeds, kg_masks = self.hitter('get_hitter_repr', s, p)
kg_attentions = [None] * 2 + [(self.cross_attentions[i], kg_embeds[(i + 2) // 2], kg_masks)
for i in range(self.kg_layer_num)]
else:
kg_attentions = []
out = self.bert(kg_attentions=kg_attentions,
output_attentions=True,
output_hidden_states=True,
return_dict=True,
labels=masked_labels,
**sent_input,
)
return out
def training_step(self, batch, batch_idx):
output = self(batch)
loss = output.loss
self.log('train_loss', loss, on_epoch=True, prog_bar=True)
return {'loss': loss}
def validation_step(self, batch, batch_idx):
batch_inputs, masked_labels, batch_labels, label_lens, answer_ids, s, p, entity_mask, _ = batch
output = self(batch)
input_tokens = batch_inputs["input_ids"].clone()
logits = output.logits[masked_labels != -100]
probs = logits.softmax(dim=-1)
values, predictions = probs.topk(1)
hits = []
now_pos = 0
for sample_i, label_length in enumerate(label_lens.tolist()):
failed = False
for i in range(label_length):
if (predictions[now_pos + i] == batch_labels[sample_i][i]).sum() != 1:
failed = True
break
hits += [1] if not failed else [0]
now_pos += label_length
hits = torch.tensor(hits)
input_tokens[input_tokens == tokenizer.mask_token_id] = predictions.flatten()
pred_strings = [str(hits[i].item()) + ' ' + tokenizer.decode(input_tokens[i], skip_special_tokens=True)
for i in range(input_tokens.size(0))]
return {'val_loss': output.loss,
'val_acc': hits.float(),
'pred_strings': pred_strings}
def validation_epoch_end(self, outputs):
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
avg_val_acc = torch.cat([x['val_acc'] for x in outputs]).mean().to(avg_loss.device)
if self.global_rank == 0:
tensorboard = self.logger.experiment
tensorboard.add_text('pred', '\n\n'.join(sum([x['pred_strings'] for x in outputs], [])), self.global_step)
self.log('avg_loss', avg_loss, on_epoch=True, prog_bar=True, sync_dist=True)
self.log('avg_val_acc', avg_val_acc, on_epoch=True, prog_bar=True, sync_dist=True)
return {'val_loss': avg_loss}
def train_dataloader(self):
return DataLoader(FBQADataset(self._hparams['train_dataset']),
self._hparams['batch_size'],
shuffle=True,
collate_fn=fbqa_collate,
num_workers=0)
def val_dataloader(self):
return DataLoader(FBQADataset(self._hparams['val_dataset']),
1,
shuffle=False,
collate_fn=fbqa_collate,
num_workers=0)
def test_dataloader(self):
return DataLoader(FBQADataset(self._hparams['test_dataset']),
1,
shuffle=False,
collate_fn=fbqa_collate,
num_workers=0)
def configure_optimizers(self):
no_decay = ['bias', 'LayerNorm.weight']
no_fine_tune = ['cross_attentions']
pgs = [{'params': [p for n, p in self.named_parameters() if not any(nd in n for nd in no_decay) and not any([i in n for i in no_fine_tune])],
'weight_decay': 0.01},
{'params': [p for n, p in self.named_parameters() if any(nd in n for nd in no_decay) and not any([i in n for i in no_fine_tune])],
'weight_decay': 0.0}]
if self._hparams['use_hitter']:
pgs.append({'params': self.cross_attentions.parameters(), 'lr': 5e-5, 'weight_decay': 0.01})
# bert_optimizer = AdamW(pgs, lr=3e-5, weight_decay=1e-2)
bert_optimizer = AdamW(pgs, lr=self.lr, weight_decay=self.weight_decay)
bert_scheduler = {
'scheduler': get_linear_schedule_with_warmup(bert_optimizer, self._hparams['max_steps'] // 10, self._hparams['max_steps']),
'interval': 'step',
'monitor': None
}
return [bert_optimizer], [bert_scheduler]
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--exp_name", default='default', nargs='?', help="Name of the experiment")
parser.add_argument('--dataset', choices=['fbqa', 'webqsp'], default='fbqa', help="fbqa or webqsp")
parser.add_argument('--filtered', default=False, action='store_true', help="Filtered or not")
parser.add_argument('--hitter', default=False, action='store_true', help="Use pretrained HittER or not")
parser.add_argument('--relphormer', default=False, action='store_true', help="Use pretrained relphormer or not")
parser.add_argument('--seed', default=333, type=int, help='Seed number')
parser.add_argument('--lr', default=3e-5, type=float, help='learning rate')
parser.add_argument('--weight_decay', default=1e-2, type=float, help='weight decay')
args = parser.parse_args()
seed_everything(args.seed)
QA_DATASET = args.dataset
if args.filtered and args.relphormer:
SUBSET = 'relphormer-filtered'
elif not args.filtered and args.relphormer:
SUBSET = 'relphormer'
elif args.filtered and not args.relphormer:
SUBSET = 'fb15k237-filtered'
else:
SUBSET = 'fb15k237'
hparams = {
'use_hitter': args.hitter,
'relphormer': args.relphormer,
'lr': args.lr,
'weight_decay': args.weight_decay,
'batch_size': 16,
'max_epochs': 20,
'train_dataset': f'data/{QA_DATASET}/{SUBSET}/train.json',
'val_dataset': f'data/{QA_DATASET}/{SUBSET}/test.json',
'test_dataset': f'data/{QA_DATASET}/{SUBSET}/test.json',
}
if hparams['relphormer']:
MODEL = "./local/relphormer/"
config = AutoConfig.from_pretrained(MODEL)
bertmodel = BertForMaskedLM.from_pretrained(MODEL, config=config)
model = CrossTrmFinetuner(hparams, bertmodel=bertmodel)
else:
bertmodel = BertForMaskedLM.from_pretrained(MODEL)
model = CrossTrmFinetuner(hparams, bertmodel=bertmodel)
model.hparams['max_steps'] = (len(model.train_dataloader().dataset) // hparams['batch_size'] + 1) * hparams['max_epochs']
base_path = '/tmp/hitbert-paper'
logger = TensorBoardLogger(base_path, args.exp_name)
checkpoint_callback = ModelCheckpoint(
monitor='avg_val_acc',
dirpath=base_path + '/' + args.exp_name,
filename='{epoch:02d}-{avg_val_acc:.3f}',
save_top_k=1,
mode='max')
trainer = pl.Trainer(gpus=1, accelerator="ddp",
max_epochs=hparams['max_epochs'], max_steps=model.hparams['max_steps'],
checkpoint_callback=True,
gradient_clip_val=1.0, logger=logger,
callbacks=[LearningRateMonitor(), checkpoint_callback])
trainer.fit(model)
print("QA Task End!")

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QA/model.py
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# from transformers.models.bert.modeling_bert import BertForMaskedLM
from models.huggingface_relformer import BertForMaskedLM
class BertKGC(BertForMaskedLM):
@staticmethod
def add_to_argparse(parser):
parser.add_argument("--pretrain", type=int, default=0, help="")
return parser

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QA/scripts/relphormer_fbqa.sh
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for SEED in 111 222 333 444 555 666 777 888 999
do
# echo ${LR} ${WD}
python hitter-bert.py --dataset fbqa \
--relphormer \
--seed ${SEED} \
--exp_name relphormer-fbqa \
--lr 3e-5 \
--weight_decay 1e-2
done

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QA/scripts/relphormer_fbqa_filtered.sh
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for SEED in 111 222 333 444 555 666 777 888 999
do
# echo ${LR} ${WD}
python hitter-bert.py --dataset fbqa \
--relphormer \
--filtered \
--seed ${SEED} \
--exp_name relphormer-filtered-fbqa \
--lr 3e-5 \
--weight_decay 1e-2
done

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QA/scripts/relphormer_webqsp.sh
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for SEED in 222 333 444 555 666 777 888 999
do
python hitter-bert.py --dataset webqsp \
--relphormer \
--seed ${SEED} \
--exp_name relphormer-webqsp \
--lr 3e-5 \
--weight_decay 1e-2
done

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QA/scripts/relphormer_webqsp_filtered.sh
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for SEED in 111 222 333 444 555 666 777 888 999
do
# echo ${LR} ${WD}
python hitter-bert.py --dataset webqsp \
--relphormer \
--filtered \
--seed ${SEED} \
--exp_name relphormer-filtered-webqsp \
--lr 3e-5 \
--weight_decay 1e-2
done

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{
"#examples": 1639,
"#kept_examples": 484,
"#mappable_examples": 484,
"#multiple_answer_examples": 800
}

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{
"#examples": 3098,
"#kept_examples": 850,
"#mappable_examples": 850,
"#multiple_answer_examples": 1437
}

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{
"#examples": 1639,
"#kept_examples": 1582,
"#mappable_examples": 484,
"#multiple_answer_examples": 800
}

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{
"#examples": 3098,
"#kept_examples": 2997,
"#mappable_examples": 850,
"#multiple_answer_examples": 1437
}

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README.md
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# Relphormer
Code for the paper: "Relphormer: Relational Graph Transformer for Knowledge Graph Representations".
> Transformers have achieved remarkable performance in widespread fields, including natural language processing, computer vision and graph mining. However, vanilla Transformer architectures have not yielded promising improvements in the Knowledge Graph (KG) representations, where the translational distance paradigm dominates this area. Note that vanilla Transformer architectures struggle to capture the intrinsically heterogeneous semantic and structural information of knowledge graphs. To this end, we propose a new variant of Transformer for knowledge graph representations dubbed Relphormer. Specifically, we introduce Triple2Seq which can dynamically sample contextualized sub-graph sequences as the input to alleviate the heterogeneity issue. We propose a novel structure-enhanced self-attention mechanism to encode the relational information and keep the globally semantic information among sub-graphs. Moreover, we propose masked knowledge modeling as a new paradigm for knowledge graph representation learning. We apply Relphormer to three tasks, namely, knowledge graph completion, KG-based question answering and KG-based recommendation for evaluation. Experimental results show that Relphormer can obtain better performance on benchmark datasets compared with baselines.
# Model Architecture
<div align=center>
<img src="./resource/model.png" width="85%" height="75%" />
</div>
The model architecture of Relphormer.
The contextualized sub-graph is sampled with Triple2Seq, and then it will be converted into sequences while maintaining its sub-graph structure.
Next, we conduct masked knowledge modeling, which randomly masks the nodes in the center triple in the contextualized sub-graph sequences.
For the transformer architecture, we design a novel structure-enhanced mechanism to preserve the structure feature.
Finally, we utilize our pre-trained KG transformer for KG-based downstream tasks.
# Environments
- python (3.8.13)
- cuda(11.2)
- Ubuntu-18.04.6 (4.15.0-156-generic)
# Requirements
To run the codes, you need to install the requirements:
```
pip install -r requirements.txt
```
The expected structure of files is:
```
── Relphormer
├── data
├── dataset
│   ├── FB15k-237
│   ├── WN18RR
│   ├── umls
│   ├── create_neighbor.py
├── lit_models
│   ├── _init_.py
│   ├── base.py
│   ├── transformer.py
│   └── utils.py
├── models
│   ├── _init_.py
│   ├── huggingface_relformer.py
│   ├── model.py
│   └── utils.py
├── resource
│   └── model.png
├── scripts
│ ├── fb15k-237
│ ├── wn18rr
│   └── umls
├── QA
├── logs
├── main.py
└── requirements.txt
```
# How to run
## KGC Task
### Generate Masked Neighbors
- Use the command below to generate the masked neighbors.
```shell
>> cd dataset
>> python create_neighbor.py --dataset xxx # like python create_neighbor.py --dataset umls
```
### Entity Embedding Initialization
- Then use the command below to add entities to BERT and initialize the entity embedding layer to be used in the later training. For other datasets `FB15k-237` and `WN18RR` , just replace the dataset name with `fb15k-237` and `wn18rr` will be fine.
```shell
>> cd pretrain
>> mkdir logs
>> bash scripts/pretrain_umls.sh
>> tail -f -n 2000 logs/pretrain_umls.log
```
The pretrained models are saved in the `Relphormer/pretrain/output` directory.
### Entity Prediction
- Next use the command below to train the model to predict the correct entity in the masked position. Same as above for other datasets.
```shell
>> cd Relphormer
>> mkdir logs
>> bash scripts/umls/umls.sh
>> tail -f -n 2000 logs/train_umls.log
```
The trained models are saved in the `Relphormer/output` directory.
## QA Task
The experimental settings in QA follow the [Hitter](https://arxiv.org/pdf/2008.12813.pdf) experimental settings, and the environment installation can be done by referring to [GitHub](https://github.com/microsoft/HittER). We only modified **hitter-best.py** to fit our model.
- The relphormer model used by QA can be downloaded [here](https://drive.google.com/file/d/1FK_A_kFq1ECoNm75RfkcvYv8rZiJL1Bw/view?usp=sharing).
```shell
>> cd QA
>> sh scripts/relphormer_fbqa.sh
>> sh scripts/relphormer_fbqa_filtered.sh
>> sh scripts/relphormer_webqsp.sh
>> sh scripts/relphormer_webqsp_filtered.sh
```

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@ -0,0 +1,24 @@
{
"version": 1,
"disable_existing_loggers": false,
"formatters": {
"simple": {
"format": "%(asctime)s - %(name)s - [%(levelname)s] - %(message)s"
}
},
"handlers": {
"file_handler": {
"class": "logging.FileHandler",
"level": "DEBUG",
"formatter": "simple",
"filename": "python_logging.log",
"encoding": "utf8"
}
},
"root": {
"level": "DEBUG",
"handlers": [
"file_handler"
]
}
}

0
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0
dataset/FB15k-237/train.tsv → data/FB15k-237/train.txt
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0
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0
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0
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0
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2
data/__init__.py
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from .data_module import KGC
from .processor import convert_examples_to_features, KGProcessor

63
data/algos.pyx
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@ -1,63 +0,0 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import cython
from cython.parallel cimport prange, parallel
cimport numpy
import numpy
def floyd_warshall(adjacency_matrix):
(nrows, ncols) = adjacency_matrix.shape
assert nrows == ncols
cdef unsigned int n = nrows
adj_mat_copy = adjacency_matrix.astype(long, order='C', casting='safe', copy=True)
assert adj_mat_copy.flags['C_CONTIGUOUS']
cdef numpy.ndarray[long, ndim=2, mode='c'] M = adj_mat_copy
cdef numpy.ndarray[long, ndim=2, mode='c'] path = numpy.zeros([n, n], dtype=numpy.int64)
cdef unsigned int i, j, k
cdef long M_ij, M_ik, cost_ikkj
cdef long* M_ptr = &M[0,0]
cdef long* M_i_ptr
cdef long* M_k_ptr
# set unreachable nodes distance to 510
for i in range(n):
for j in range(n):
if i == j:
M[i][j] = 0
elif M[i][j] == 0:
M[i][j] = 510
# floyed algo
for k in range(n):
M_k_ptr = M_ptr + n*k
for i in range(n):
M_i_ptr = M_ptr + n*i
M_ik = M_i_ptr[k]
for j in range(n):
cost_ikkj = M_ik + M_k_ptr[j]
M_ij = M_i_ptr[j]
if M_ij > cost_ikkj:
M_i_ptr[j] = cost_ikkj
path[i][j] = k
# set unreachable path to 510
for i in range(n):
for j in range(n):
if M[i][j] >= 510:
path[i][j] = 510
M[i][j] = 510
return M, path
def get_all_edges(path, i, j):
cdef unsigned int k = path[i][j]
if k == 0:
return []
else:
return get_all_edges(path, i, k) + [k] + get_all_edges(path, k, j)

71
data/base_data_module.py
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@ -1,71 +0,0 @@
"""Base DataModule class."""
from pathlib import Path
from typing import Dict
import argparse
import os
import pytorch_lightning as pl
from torch.utils.data import DataLoader
class Config(dict):
def __getattr__(self, name):
return self.get(name)
def __setattr__(self, name, val):
self[name] = val
BATCH_SIZE = 8
NUM_WORKERS = 8
class BaseDataModule(pl.LightningDataModule):
"""
Base DataModule.
Learn more at https://pytorch-lightning.readthedocs.io/en/stable/datamodules.html
"""
def __init__(self, args: argparse.Namespace = None) -> None:
super().__init__()
self.args = Config(vars(args)) if args is not None else {}
self.batch_size = self.args.get("batch_size", BATCH_SIZE)
self.num_workers = self.args.get("num_workers", NUM_WORKERS)
@staticmethod
def add_to_argparse(parser):
parser.add_argument(
"--batch_size", type=int, default=BATCH_SIZE, help="Number of examples to operate on per forward step."
)
parser.add_argument(
"--num_workers", type=int, default=0, help="Number of additional processes to load data."
)
parser.add_argument(
"--dataset", type=str, default="./dataset/NELL", help="Number of additional processes to load data."
)
return parser
def prepare_data(self):
"""
Use this method to do things that might write to disk or that need to be done only from a single GPU in distributed settings (so don't set state `self.x = y`).
"""
pass
def setup(self, stage=None):
"""
Split into train, val, test, and set dims.
Should assign `torch Dataset` objects to self.data_train, self.data_val, and optionally self.data_test.
"""
self.data_train = None
self.data_val = None
self.data_test = None
def train_dataloader(self):
return DataLoader(self.data_train, shuffle=True, batch_size=self.batch_size, num_workers=self.num_workers, pin_memory=True)
def val_dataloader(self):
return DataLoader(self.data_val, shuffle=False, batch_size=self.batch_size, num_workers=self.num_workers, pin_memory=True)
def test_dataloader(self):
return DataLoader(self.data_test, shuffle=False, batch_size=self.batch_size, num_workers=self.num_workers, pin_memory=True)

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data/data_module.py
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from dataclasses import dataclass
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
from enum import Enum
import torch
from torch.utils.data import DataLoader
from transformers import AutoTokenizer, BertTokenizer
# from transformers.configuration_bert import BertTokenizer, BertTokenizerFast
from transformers.tokenization_utils_base import (BatchEncoding,
PreTrainedTokenizerBase)
from .base_data_module import BaseDataModule
from .processor import KGProcessor, get_dataset
import transformers
transformers.logging.set_verbosity_error()
class ExplicitEnum(Enum):
"""
Enum with more explicit error message for missing values.
"""
@classmethod
def _missing_(cls, value):
raise ValueError(
f"{value} is not a valid {cls.__name__}, please select one of {list(cls._value2member_map_.keys())}"
)
class PaddingStrategy(ExplicitEnum):
"""
Possible values for the ``padding`` argument in :meth:`PreTrainedTokenizerBase.__call__`. Useful for tab-completion
in an IDE.
"""
LONGEST = "longest"
MAX_LENGTH = "max_length"
DO_NOT_PAD = "do_not_pad"
import numpy as np
@dataclass
class DataCollatorForSeq2Seq:
"""
Data collator that will dynamically pad the inputs received, as well as the labels.
Args:
tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
The tokenizer used for encoding the data.
model (:class:`~transformers.PreTrainedModel`):
The model that is being trained. If set and has the `prepare_decoder_input_ids_from_labels`, use it to
prepare the `decoder_input_ids`
This is useful when using `label_smoothing` to avoid calculating loss twice.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
among:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence is provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
max_length (:obj:`int`, `optional`):
Maximum length of the returned list and optionally padding length (see above).
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.5 (Volta).
label_pad_token_id (:obj:`int`, `optional`, defaults to -100):
The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
"""
tokenizer: PreTrainedTokenizerBase
model: Optional[Any] = None
padding: Union[bool, str, PaddingStrategy] = True
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
label_pad_token_id: int = -100
return_tensors: str = "pt"
num_labels: int = 0
def __call__(self, features, return_tensors=None):
if return_tensors is None:
return_tensors = self.return_tensors
labels = [feature.pop("labels") for feature in features] if "labels" in features[0].keys() else None
label = [feature.pop("label") for feature in features]
features_keys = {}
name_keys = list(features[0].keys())
for k in name_keys:
# ignore the padding arguments
if k in ["input_ids", "attention_mask", "token_type_ids"]: continue
try:
features_keys[k] = [feature.pop(k) for feature in features]
except KeyError:
continue
# We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the
# same length to return tensors.
bsz = len(labels)
with torch.no_grad():
new_labels = torch.zeros(bsz, self.num_labels)
for i,l in enumerate(labels):
if isinstance(l, int):
new_labels[i][l] = 1
else:
for j in l:
new_labels[i][j] = 1
labels = new_labels
features = self.tokenizer.pad(
features,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors=return_tensors,
)
features['labels'] = labels
features['label'] = torch.tensor(label)
features.update(features_keys)
return features
class KGC(BaseDataModule):
def __init__(self, args, model) -> None:
super().__init__(args)
self.tokenizer = AutoTokenizer.from_pretrained(self.args.model_name_or_path, use_fast=False)
self.processor = KGProcessor(self.tokenizer, args)
self.label_list = self.processor.get_labels(args.data_dir)
entity_list = self.processor.get_entities(args.data_dir)
num_added_tokens = self.tokenizer.add_special_tokens({'additional_special_tokens': entity_list})
self.sampler = DataCollatorForSeq2Seq(self.tokenizer,
model=model,
label_pad_token_id=self.tokenizer.pad_token_id,
pad_to_multiple_of=8 if self.args.precision == 16 else None,
padding="longest",
max_length=self.args.max_seq_length,
num_labels = len(entity_list),
)
relations_tokens = self.processor.get_relations(args.data_dir)
self.num_relations = len(relations_tokens)
num_added_tokens = self.tokenizer.add_special_tokens({'additional_special_tokens': relations_tokens})
vocab = self.tokenizer.get_added_vocab()
self.relation_id_st = vocab[relations_tokens[0]]
self.relation_id_ed = vocab[relations_tokens[-1]] + 1
self.entity_id_st = vocab[entity_list[0]]
self.entity_id_ed = vocab[entity_list[-1]] + 1
def setup(self, stage=None):
self.data_train = get_dataset(self.args, self.processor, self.label_list, self.tokenizer, "train")
self.data_val = get_dataset(self.args, self.processor, self.label_list, self.tokenizer, "dev")
self.data_test = get_dataset(self.args, self.processor, self.label_list, self.tokenizer, "test")
def prepare_data(self):
pass
def get_config(self):
d = {}
for k, v in self.__dict__.items():
if "st" in k or "ed" in k:
d.update({k:v})
return d
@staticmethod
def add_to_argparse(parser):
BaseDataModule.add_to_argparse(parser)
parser.add_argument("--model_name_or_path", type=str, default="roberta-base", help="the name or the path to the pretrained model")
parser.add_argument("--data_dir", type=str, default="roberta-base", help="the name or the path to the pretrained model")
parser.add_argument("--max_seq_length", type=int, default=256, help="Number of examples to operate on per forward step.")
parser.add_argument("--warm_up_radio", type=float, default=0.1, help="Number of examples to operate on per forward step.")
parser.add_argument("--eval_batch_size", type=int, default=8)
parser.add_argument("--overwrite_cache", action="store_true", default=False)
return parser
def get_tokenizer(self):
return self.tokenizer
def train_dataloader(self):
return DataLoader(self.data_train, num_workers=self.num_workers, pin_memory=True, collate_fn=self.sampler, batch_size=self.args.batch_size, shuffle=not self.args.faiss_init)
def val_dataloader(self):
return DataLoader(self.data_val, num_workers=self.num_workers, pin_memory=True, collate_fn=self.sampler, batch_size=self.args.eval_batch_size)
def test_dataloader(self):
return DataLoader(self.data_test, num_workers=self.num_workers, pin_memory=True, collate_fn=self.sampler, batch_size=self.args.eval_batch_size)

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from hashlib import new
from re import DEBUG
import contextlib
import sys
from collections import Counter
from multiprocessing import Pool
from torch._C import HOIST_CONV_PACKED_PARAMS
from torch.utils.data import Dataset, Sampler, IterableDataset
from collections import defaultdict
from functools import partial
from multiprocessing import Pool
import os
import random
import json
import torch
import copy
import numpy as np
import pickle
from tqdm import tqdm
from dataclasses import dataclass, asdict, replace
import inspect
from transformers.models.auto.tokenization_auto import AutoTokenizer
from models.utils import get_entity_spans_pre_processing
import pyximport
pyximport.install(setup_args={'include_dirs': np.get_include()})
import data.algos as algos
def lmap(a, b):
return list(map(a,b)) # a是个函数，b是个值列表，返回函数值列表
def cache_results(_cache_fp, _refresh=False, _verbose=1):
r"""
cache_results是fastNLP中用于cache数据的装饰器。通过下面的例子看一下如何使用::
import time
import numpy as np
from fastNLP import cache_results
@cache_results('cache.pkl')
def process_data():
# 一些比较耗时的工作，比如读取数据，预处理数据等，这里用time.sleep()代替耗时
time.sleep(1)
return np.random.randint(10, size=(5,))
start_time = time.time()
print("res =",process_data())
print(time.time() - start_time)
start_time = time.time()
print("res =",process_data())
print(time.time() - start_time)
# 输出内容如下，可以看到两次结果相同，且第二次几乎没有花费时间
# Save cache to cache.pkl.
# res = [5 4 9 1 8]
# 1.0042750835418701
# Read cache from cache.pkl.
# res = [5 4 9 1 8]
# 0.0040721893310546875
可以看到第二次运行的时候，只用了0.0001s左右，是由于第二次运行将直接从cache.pkl这个文件读取数据，而不会经过再次预处理::
# 还是以上面的例子为例，如果需要重新生成另一个cache，比如另一个数据集的内容，通过如下的方式调用即可
process_data(_cache_fp='cache2.pkl') # 完全不影响之前的‘cache.pkl'
上面的_cache_fp是cache_results会识别的参数，它将从'cache2.pkl'这里缓存/读取数据，即这里的'cache2.pkl'覆盖默认的
'cache.pkl'。如果在你的函数前面加上了@cache_results()则你的函数会增加三个参数[_cache_fp, _refresh, _verbose]。
上面的例子即为使用_cache_fp的情况，这三个参数不会传入到你的函数中，当然你写的函数参数名也不可能包含这三个名称::
process_data(_cache_fp='cache2.pkl', _refresh=True) # 这里强制重新生成一份对预处理的cache。
# _verbose是用于控制输出信息的，如果为0,则不输出任何内容;如果为1,则会提醒当前步骤是读取的cache还是生成了新的cache
:param str _cache_fp: 将返回结果缓存到什么位置;或从什么位置读取缓存。如果为None，cache_results没有任何效用，除非在
函数调用的时候传入_cache_fp这个参数。
:param bool _refresh: 是否重新生成cache。
:param int _verbose: 是否打印cache的信息。
:return:
"""
def wrapper_(func):
signature = inspect.signature(func)
for key, _ in signature.parameters.items():
if key in ('_cache_fp', '_refresh', '_verbose'):
raise RuntimeError("The function decorated by cache_results cannot have keyword `{}`.".format(key))
def wrapper(*args, **kwargs):
my_args = args[0]
mode = args[-1]
if '_cache_fp' in kwargs:
cache_filepath = kwargs.pop('_cache_fp')
assert isinstance(cache_filepath, str), "_cache_fp can only be str."
else:
cache_filepath = _cache_fp
if '_refresh' in kwargs:
refresh = kwargs.pop('_refresh')
assert isinstance(refresh, bool), "_refresh can only be bool."
else:
refresh = _refresh
if '_verbose' in kwargs:
verbose = kwargs.pop('_verbose')
assert isinstance(verbose, int), "_verbose can only be integer."
else:
verbose = _verbose
refresh_flag = True
model_name = my_args.model_name_or_path.split("/")[-1]
is_pretrain = my_args.pretrain
cache_filepath = os.path.join(my_args.data_dir, f"cached_{mode}_features{model_name}_pretrain{is_pretrain}_faiss{my_args.faiss_init}_seqlength{my_args.max_seq_length}_{my_args.litmodel_class}.pkl")
refresh = my_args.overwrite_cache
if cache_filepath is not None and refresh is False:
# load data
if os.path.exists(cache_filepath):
with open(cache_filepath, 'rb') as f:
results = pickle.load(f)
if verbose == 1:
logger.info("Read cache from {}.".format(cache_filepath))
refresh_flag = False
if refresh_flag:
results = func(*args, **kwargs)
if cache_filepath is not None:
if results is None:
raise RuntimeError("The return value is None. Delete the decorator.")
with open(cache_filepath, 'wb') as f:
pickle.dump(results, f)
logger.info("Save cache to {}.".format(cache_filepath))
return results
return wrapper
return wrapper_
import argparse
import csv
import logging
import os
import random
import sys
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
# from torch.nn import CrossEntropyLoss, MSELoss
# from scipy.stats import pearsonr, spearmanr
# from sklearn.metrics import matthews_corrcoef, f1_scoreclass
logger = logging.getLogger(__name__)
class InputExample(object):
"""A single training/test example for simple sequence classification."""
def __init__(self, guid, text_a, text_b=None, text_c=None, text_d=None, label=None, real_label=None, en=None, en_id=None, rel=None, text_d_id=None, graph_inf=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
text_c: (Optional) string. The untokenized text of the third sequence.
Only must be specified for sequence triple tasks.
label: (Optional) string. list of entities
"""
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.text_c = text_c
self.text_d = text_d
self.label = label
self.real_label = real_label
self.en = en
self.rel = rel # rel id
self.text_d_id = text_d_id
self.graph_inf = graph_inf
self.en_id = en_id
@dataclass
class InputFeatures:
"""A single set of features of data."""
input_ids: torch.Tensor
attention_mask: torch.Tensor
labels: torch.Tensor = None
label: torch.Tensor = None
en: torch.Tensor = 0
rel: torch.Tensor = 0
pos: torch.Tensor = 0
graph: torch.Tensor = 0
distance_attention: torch.Tensor = 0
# attention_bias: torch.Tensor = 0
class DataProcessor(object):
"""Base class for data converters for sequence classification data sets."""
def get_train_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the train set."""
raise NotImplementedError()
def get_dev_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
def get_labels(self, data_dir):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r", encoding="utf-8") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
if sys.version_info[0] == 2:
line = list(unicode(cell, 'utf-8') for cell in line)
lines.append(line)
return lines
import copy
def solve_get_knowledge_store(line, set_type="train", pretrain=1):
"""
use the LM to get the entity embedding.
Transductive: triples + text description
Inductive: text description
"""
examples = []
head_ent_text = ent2text[line[0]]
tail_ent_text = ent2text[line[2]]
relation_text = rel2text[line[1]]
i=0
a = tail_filter_entities["\t".join([line[0],line[1]])]
b = head_filter_entities["\t".join([line[2],line[1]])]
guid = "%s-%s" % (set_type, i)
text_a = head_ent_text
text_b = relation_text
text_c = tail_ent_text
# use the description of c to predict A
examples.append(
InputExample(guid=guid, text_a="[PAD]", text_b=text_b + "[PAD]", text_c = "[PAD]" + " " + text_c, label=lmap(lambda x: ent2id[x], b), real_label=ent2id[line[0]], en=[ent2id[line[0]], rel2id[line[1]], ent2id[line[2]]], rel=0)
)
examples.append(
InputExample(guid=guid, text_a="[PAD]", text_b=text_b + "[PAD]", text_c = "[PAD]" + " " + text_a, label=lmap(lambda x: ent2id[x], b), real_label=ent2id[line[2]], en=[ent2id[line[0]], rel2id[line[1]], ent2id[line[2]]], rel=0)
)
return examples
def solve(line, set_type="train", pretrain=1, max_triplet=32):
examples = []
head_ent_text = ent2text[line[0]]
tail_ent_text = ent2text[line[2]]
relation_text = rel2text[line[1]]
i=0
a = tail_filter_entities["\t".join([line[0],line[1]])]
b = head_filter_entities["\t".join([line[2],line[1]])]
guid = "%s-%s" % (set_type, i)
text_a = head_ent_text
text_b = relation_text
text_c = tail_ent_text
if pretrain:
text_a_tokens = text_a.split()
for i in range(10):
st = random.randint(0, len(text_a_tokens))
examples.append(
InputExample(guid=guid, text_a="[MASK]", text_b=" ".join(text_a_tokens[st:min(st+64, len(text_a_tokens))]), text_c = "", label=ent2id[line[0]], real_label=ent2id[line[0]], en=0, rel=0)
)
examples.append(
InputExample(guid=guid, text_a="[MASK]", text_b=text_a, text_c = "", label=ent2id[line[0]], real_label=ent2id[line[0]], en=0, rel=0)
)
# examples.append(
# InputExample(guid=guid, text_a="[MASK]", text_b=text_c, text_c = "", label=ent2id[line[2]], real_label=ent2id[line[2]], en=0, rel=0)
# )
else:
# 主要是对text_c进行包装，不再是原来的文本，而是对应子图的graph（变量graph_seq）。如果mask的是尾实体，那么就让text_c在后面加入graph_seq
# masked_head_seq = []
# masked_tail_seq = []
# masked_tail_graph_list = masked_tail_neighbor["\t".join([line[0],line[1]])]
# masked_head_graph_list = masked_head_neighbor["\t".join([line[2],line[1]])]
# for item in masked_head_graph_list:
# masked_head_seq.append(ent2id[item[0]])
# masked_head_seq.append(rel2id[item[1]])
# masked_head_seq.append(ent2id[item[2]])
# for item in masked_tail_graph_list:
# masked_tail_seq.append(ent2id[item[0]])
# masked_tail_seq.append(rel2id[item[1]])
# masked_tail_seq.append(ent2id[item[2]])
masked_head_seq = set()
masked_head_seq_id = set()
masked_tail_seq = set()
masked_tail_seq_id = set()
masked_tail_graph_list = masked_tail_neighbor["\t".join([line[0],line[1]])] if len(masked_tail_neighbor["\t".join([line[0],line[1]])]) < max_triplet else \
random.sample(masked_tail_neighbor["\t".join([line[0],line[1]])], max_triplet)
masked_head_graph_list = masked_head_neighbor["\t".join([line[2],line[1]])] if len(masked_head_neighbor["\t".join([line[2],line[1]])]) < max_triplet else \
random.sample(masked_head_neighbor["\t".join([line[2],line[1]])], max_triplet)
# masked_tail_graph_list = masked_tail_neighbor["\t".join([line[0],line[1]])][:16]
# masked_head_graph_list = masked_head_neighbor["\t".join([line[2],line[1]])][:16]
for item in masked_head_graph_list:
masked_head_seq.add(item[0])
masked_head_seq.add(item[1])
masked_head_seq.add(item[2])
masked_head_seq_id.add(ent2id[item[0]])
masked_head_seq_id.add(rel2id[item[1]])
masked_head_seq_id.add(ent2id[item[2]])
for item in masked_tail_graph_list:
masked_tail_seq.add(item[0])
masked_tail_seq.add(item[1])
masked_tail_seq.add(item[2])
masked_tail_seq_id.add(ent2id[item[0]])
masked_tail_seq_id.add(rel2id[item[1]])
masked_tail_seq_id.add(ent2id[item[2]])
# print(masked_tail_seq)
masked_head_seq = masked_head_seq.difference({line[0]})
masked_head_seq = masked_head_seq.difference({line[2]})
masked_head_seq = masked_head_seq.difference({line[1]})
masked_head_seq_id = masked_head_seq_id.difference({ent2id[line[0]]})
masked_head_seq_id = masked_head_seq_id.difference({rel2id[line[1]]})
masked_head_seq_id = masked_head_seq_id.difference({ent2id[line[2]]})
masked_tail_seq = masked_tail_seq.difference({line[0]})
masked_tail_seq = masked_tail_seq.difference({line[2]})
masked_tail_seq = masked_tail_seq.difference({line[1]})
masked_tail_seq_id = masked_tail_seq_id.difference({ent2id[line[0]]})
masked_tail_seq_id = masked_tail_seq_id.difference({rel2id[line[1]]})
masked_tail_seq_id = masked_tail_seq_id.difference({ent2id[line[2]]})
# examples.append(
# InputExample(guid=guid, text_a="[MASK]", text_b=' '.join(text_b.split(' ')[:16]) + " [PAD]", text_c = "[PAD]" + " " + ' '.join(text_c.split(' ')[:16]), text_d = masked_head_seq, label=lmap(lambda x: ent2id[x], b), real_label=ent2id[line[0]], en=[rel2id[line[1]], ent2id[line[2]]], rel=rel2id[line[1]]))
# examples.append(
# InputExample(guid=guid, text_a="[PAD] ", text_b=' '.join(text_b.split(' ')[:16]) + " [PAD]", text_c = "[MASK]" +" " + ' '.join(text_a.split(' ')[:16]), text_d = masked_tail_seq, label=lmap(lambda x: ent2id[x], a), real_label=ent2id[line[2]], en=[ent2id[line[0]], rel2id[line[1]]], rel=rel2id[line[1]]))
examples.append(
InputExample(guid=guid, text_a="[MASK]", text_b="[PAD]", text_c = "[PAD]", text_d = list(masked_head_seq), label=lmap(lambda x: ent2id[x], b), real_label=ent2id[line[0]], en=[line[1], line[2]], en_id = [rel2id[line[1]], ent2id[line[2]]], rel=rel2id[line[1]], text_d_id = list(masked_head_seq_id), graph_inf = masked_head_graph_list))
examples.append(
InputExample(guid=guid, text_a="[PAD]", text_b="[PAD]", text_c = "[MASK]", text_d = list(masked_tail_seq), label=lmap(lambda x: ent2id[x], a), real_label=ent2id[line[2]], en=[line[0], line[1]], en_id = [ent2id[line[0]], rel2id[line[1]]], rel=rel2id[line[1]], text_d_id = list(masked_tail_seq_id), graph_inf = masked_tail_graph_list))
return examples
def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_, masked_head_neighbor_, masked_tail_neighbor_, rel2token_):
global head_filter_entities
global tail_filter_entities
global ent2text
global rel2text
global ent2id
global ent2token
global rel2id
global masked_head_neighbor
global masked_tail_neighbor
global rel2token
head_filter_entities = head
tail_filter_entities = tail
ent2text =t1
rel2text =t2
ent2id = ent2id_
ent2token = ent2token_
rel2id = rel2id_
masked_head_neighbor = masked_head_neighbor_
masked_tail_neighbor = masked_tail_neighbor_
rel2token = rel2token_
def delete_init(ent2text_):
global ent2text
ent2text = ent2text_
class KGProcessor(DataProcessor):
"""Processor for knowledge graph data set."""
def __init__(self, tokenizer, args):
self.labels = set()
self.tokenizer = tokenizer
self.args = args
self.entity_path = os.path.join(args.data_dir, "entity2textlong.txt") if os.path.exists(os.path.join(args.data_dir, 'entity2textlong.txt')) \
else os.path.join(args.data_dir, "entity2text.txt")
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train", data_dir, self.args)
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev", data_dir, self.args)
def get_test_examples(self, data_dir, chunk=""):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, f"test{chunk}.tsv")), "test", data_dir, self.args)
def get_relations(self, data_dir):
"""Gets all labels (relations) in the knowledge graph."""
# return list(self.labels)
with open(os.path.join(data_dir, "relations.txt"), 'r') as f:
lines = f.readlines()
relations = []
for line in lines:
relations.append(line.strip().split('\t')[0])
rel2token = {ent : f"[RELATION_{i}]" for i, ent in enumerate(relations)}
return list(rel2token.values())
def get_labels(self, data_dir):
"""Gets all labels (0, 1) for triples in the knowledge graph."""
relation = []
with open(os.path.join(data_dir, "relation2text.txt"), 'r') as f:
lines = f.readlines()
entities = []
for line in lines:
relation.append(line.strip().split("\t")[-1])
return relation
def get_entities(self, data_dir):
"""Gets all entities in the knowledge graph."""
with open(self.entity_path, 'r') as f:
lines = f.readlines()
entities = []
for line in lines:
entities.append(line.strip().split("\t")[0])
ent2token = {ent : f"[ENTITY_{i}]" for i, ent in enumerate(entities)}
return list(ent2token.values())
def get_train_triples(self, data_dir):
"""Gets training triples."""
return self._read_tsv(os.path.join(data_dir, "train.tsv"))
def get_dev_triples(self, data_dir):
"""Gets validation triples."""
return self._read_tsv(os.path.join(data_dir, "dev.tsv"))
def get_test_triples(self, data_dir, chunk=""):
"""Gets test triples."""
return self._read_tsv(os.path.join(data_dir, f"test{chunk}.tsv"))
def _create_examples(self, lines, set_type, data_dir, args):
"""Creates examples for the training and dev sets."""
# entity to text
ent2text = {}
ent2text_with_type = {}
with open(self.entity_path, 'r') as f:
ent_lines = f.readlines()
for line in ent_lines:
temp = line.strip().split('\t')
try:
end = temp[1]#.find(',')
if "wiki" in data_dir:
assert "Q" in temp[0]
ent2text[temp[0]] = temp[1].replace("\\n", " ").replace("\\", "") #[:end]
except IndexError:
# continue
end = " "#.find(',')
if "wiki" in data_dir:
assert "Q" in temp[0]
ent2text[temp[0]] = end #[:end]
entities = list(ent2text.keys())
ent2token = {ent : f"[ENTITY_{i}]" for i, ent in enumerate(entities)}
ent2id = {ent : i for i, ent in enumerate(entities)}
rel2text = {}
with open(os.path.join(data_dir, "relation2text.txt"), 'r') as f:
rel_lines = f.readlines()
for line in rel_lines:
temp = line.strip().split('\t')
rel2text[temp[0]] = temp[1]
relation_names = {}
with open(os.path.join(data_dir, "relations.txt"), "r") as file:
for line in file.readlines():
t = line.strip()
relation_names[t] = rel2text[t]
tmp_lines = []
not_in_text = 0
for line in tqdm(lines, desc="delete entities without text name."):
if (line[0] not in ent2text) or (line[2] not in ent2text) or (line[1] not in rel2text):
not_in_text += 1
continue
tmp_lines.append(line)
lines = tmp_lines
print(f"total entity not in text : {not_in_text} ")
relations = list(rel2text.keys())
rel2token = {rel : f"[RELATION_{i}]" for i, rel in enumerate(relations)}
# rel id -> relation token id
num_entities = len(self.get_entities(args.data_dir))
rel2id = {w:i+num_entities for i,w in enumerate(relation_names.keys())}
with open(os.path.join(data_dir, "masked_head_neighbor.txt"), 'r') as file:
masked_head_neighbor = json.load(file)
with open(os.path.join(data_dir, "masked_tail_neighbor.txt"), 'r') as file:
masked_tail_neighbor = json.load(file)
examples = []
# head filter head entity
head_filter_entities = defaultdict(list)
tail_filter_entities = defaultdict(list)
dataset_list = ["train.tsv", "dev.tsv", "test.tsv"]
# in training, only use the train triples
if set_type == "train" and not args.pretrain: dataset_list = dataset_list[0:1]
for m in dataset_list:
with open(os.path.join(data_dir, m), 'r') as file:
train_lines = file.readlines()
for idx in range(len(train_lines)):
train_lines[idx] = train_lines[idx].strip().split("\t")
for line in train_lines:
tail_filter_entities["\t".join([line[0], line[1]])].append(line[2])
head_filter_entities["\t".join([line[2], line[1]])].append(line[0])
max_head_entities = max(len(_) for _ in head_filter_entities.values())
max_tail_entities = max(len(_) for _ in tail_filter_entities.values())
# use bce loss, ignore the mlm
if set_type == "train" and args.bce:
lines = []
for k, v in tail_filter_entities.items():
h, r = k.split('\t')
t = v[0]
lines.append([h, r, t])
for k, v in head_filter_entities.items():
t, r = k.split('\t')
h = v[0]
lines.append([h, r, t])
# for training , select each entity as for get mask embedding.
if args.pretrain:
rel = list(rel2text.keys())[0]
lines = []
for k in ent2text.keys():
lines.append([k, rel, k])
print(f"max number of filter entities : {max_head_entities} {max_tail_entities}")
# 把子图信息加入到filter_init中（初始化为文件夹，及固定子图），设置为全局变量，solve中调用
from os import cpu_count
threads = min(1, cpu_count())
filter_init(head_filter_entities, tail_filter_entities,ent2text, rel2text, ent2id, ent2token, rel2id, masked_head_neighbor, masked_tail_neighbor, rel2token
)
if hasattr(args, "faiss_init") and args.faiss_init:
annotate_ = partial(
solve_get_knowledge_store,
pretrain=self.args.pretrain
)
else:
annotate_ = partial(
solve,
pretrain=self.args.pretrain,
max_triplet=self.args.max_triplet
)
examples = list(
tqdm(
map(annotate_, lines),
total=len(lines),
desc="convert text to examples"
)
)
tmp_examples = []
for e in examples:
for ee in e:
tmp_examples.append(ee)
examples = tmp_examples
# delete vars
del head_filter_entities, tail_filter_entities, ent2text, rel2text, ent2id, ent2token, rel2id
return examples
class Verbalizer(object):
def __init__(self, args):
if "WN18RR" in args.data_dir:
self.mode = "WN18RR"
elif "FB15k" in args.data_dir:
self.mode = "FB15k"
elif "umls" in args.data_dir:
self.mode = "umls"
elif "codexs" in args.data_dir:
self.mode = "codexs"
elif "FB13" in args.data_dir:
self.mode = "FB13"
elif "WN11" in args.data_dir:
self.mode = "WN11"
def _convert(self, head, relation, tail):
if self.mode == "umls":
return f"The {relation} {head} is "
return f"{head} {relation}"
class KGCDataset(Dataset):
def __init__(self, features):
self.features = features
def __getitem__(self, index):
return self.features[index]
def __len__(self):
return len(self.features)
def convert_examples_to_features_init(tokenizer_for_convert):
global tokenizer
tokenizer = tokenizer_for_convert
def convert_examples_to_features(example, max_seq_length, mode, pretrain=1):
"""Loads a data file into a list of `InputBatch`s."""
text_a = " ".join(example.text_a.split()[:128])
text_b = " ".join(example.text_b.split()[:128])
text_c = " ".join(example.text_c.split()[:128])
if pretrain:
input_text_a = text_a
input_text_b = text_b
else:
input_text_a = " ".join([text_a, text_b])
input_text_b = text_c
inputs = tokenizer(
input_text_a,
input_text_b,
truncation="longest_first",
max_length=max_seq_length,
padding="longest",
add_special_tokens=True,
)
# assert tokenizer.mask_token_id in inputs.input_ids, "mask token must in input"
features = asdict(InputFeatures(input_ids=inputs["input_ids"],
attention_mask=inputs['attention_mask'],
labels=torch.tensor(example.label),
label=torch.tensor(example.real_label)
)
)
return features
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def _truncate_seq_triple(tokens_a, tokens_b, tokens_c, max_length):
"""Truncates a sequence triple in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b) + len(tokens_c)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b) and len(tokens_a) > len(tokens_c):
tokens_a.pop()
elif len(tokens_b) > len(tokens_a) and len(tokens_b) > len(tokens_c):
tokens_b.pop()
elif len(tokens_c) > len(tokens_a) and len(tokens_c) > len(tokens_b):
tokens_c.pop()
else:
tokens_c.pop()
@cache_results(_cache_fp="./dataset")
def get_dataset(args, processor, label_list, tokenizer, mode):
assert mode in ["train", "dev", "test"], "mode must be in train dev test!"
# use training data to construct the entity embedding
combine_train_and_test = False
if args.faiss_init and mode == "test" and not args.pretrain:
mode = "train"
if "ind" in args.data_dir: combine_train_and_test = True
else:
pass
if mode == "train":
train_examples = processor.get_train_examples(args.data_dir)
elif mode == "dev":
train_examples = processor.get_dev_examples(args.data_dir)
else:
train_examples = processor.get_test_examples(args.data_dir)
if combine_train_and_test:
logger.info("use all the dataset for getting the entity mask embedding in pretraining pretraining")
logger.info("use all the dataset for getting the entity mask embedding in pretraining pretraining")
train_examples = processor.get_test_examples(args.data_dir) + processor.get_train_examples(args.data_dir) + processor.get_dev_examples(args.data_dir)
from os import cpu_count
with open(os.path.join(args.data_dir, f"examples_{mode}.txt"), 'w') as file:
for line in train_examples:
d = {}
d.update(line.__dict__)
file.write(json.dumps(d) + '\n')
# 这里应该不需要重新from_pretrain，必须沿用加入token的
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=False)
features = []
file_inputs = [os.path.join(args.data_dir, f"examples_{mode}.txt")]
file_outputs = [os.path.join(args.data_dir, f"features_{mode}.txt")]
with contextlib.ExitStack() as stack:
inputs = [
stack.enter_context(open(input, "r", encoding="utf-8"))
if input != "-" else sys.stdin
for input in file_inputs
]
outputs = [
stack.enter_context(open(output, "w", encoding="utf-8"))
if output != "-" else sys.stdout
for output in file_outputs
]
encoder = MultiprocessingEncoder(tokenizer, args)
pool = Pool(16, initializer=encoder.initializer)
encoder.initializer()
encoded_lines = pool.imap(encoder.encode_lines, zip(*inputs), 1000)
# encoded_lines = map(encoder.encode_lines, zip(*inputs))
stats = Counter()
for i, (filt, enc_lines) in tqdm(enumerate(encoded_lines, start=1), total=len(train_examples)):
if filt == "PASS":
for enc_line, output_h in zip(enc_lines, outputs):
features.append(eval(enc_line))
# features.append(enc_line)
# print(enc_line, file=output_h)
else:
stats["num_filtered_" + filt] += 1
for k, v in stats.most_common():
print("[{}] filtered {} lines".format(k, v), file=sys.stderr)
for f_id, f in enumerate(features):
en = features[f_id].pop("en")
rel = features[f_id].pop("rel")
graph = features[f_id].pop("graph")
real_label = f['label']
features[f_id]['distance_attention'] = torch.Tensor(features[f_id]['distance_attention'])
cnt = 0
cnt_2 = 0
if not isinstance(en, list): break
pos = 0
for i,t in enumerate(f['input_ids']):
if t == tokenizer.pad_token_id:
features[f_id]['input_ids'][i] = en[cnt] + len(tokenizer)
cnt += 1
if t == tokenizer.unk_token_id:
features[f_id]['input_ids'][i] = graph[cnt_2] + len(tokenizer)
cnt_2 += 1
if features[f_id]['input_ids'][i] == real_label + len(tokenizer):
pos = i
if cnt_2 == len(graph) and cnt == len(en): break
# 如果等于UNK， pop出图节点list，然后替换
assert not (args.faiss_init and pos == 0)
features[f_id]['pos'] = pos
# for i,t in enumerate(f['input_ids']):
# if t == tokenizer.pad_token_id:
# features[f_id]['input_ids'][i] = rel + len(tokenizer) + num_entities
# break
features = KGCDataset(features)
return features
class MultiprocessingEncoder(object):
def __init__(self, tokenizer, args):
self.tokenizer = tokenizer
self.pretrain = args.pretrain
self.max_seq_length = args.max_seq_length
def initializer(self):
global bpe
bpe = self.tokenizer
def encode(self, line):
global bpe
ids = bpe.encode(line)
return list(map(str, ids))
def decode(self, tokens):
global bpe
return bpe.decode(tokens)
def encode_lines(self, lines):
"""
Encode a set of lines. All lines will be encoded together.
"""
enc_lines = []
for line in lines:
line = line.strip()
if len(line) == 0:
return ["EMPTY", None]
# enc_lines.append(" ".join(tokens))
enc_lines.append(json.dumps(self.convert_examples_to_features(example=eval(line))))
# enc_lines.append(" ")
# enc_lines.append("123")
return ["PASS", enc_lines]
def decode_lines(self, lines):
dec_lines = []
for line in lines:
tokens = map(int, line.strip().split())
dec_lines.append(self.decode(tokens))
return ["PASS", dec_lines]
def convert_examples_to_features(self, example):
pretrain = self.pretrain
max_seq_length = self.max_seq_length
global bpe
"""Loads a data file into a list of `InputBatch`s."""
# tokens_a = tokenizer.tokenize(example.text_a)
# tokens_b = tokenizer.tokenize(example.text_b)
# tokens_c = tokenizer.tokenize(example.text_c)
# _truncate_seq_triple(tokens_a, tokens_b, tokens_c, max_length= max_seq_length)
# text_a = " ".join(example['text_a'].split()[:128])
# text_b = " ".join(example['text_b'].split()[:128])
# text_c = " ".join(example['text_c'].split()[:128])
text_a = example['text_a']
text_b = example['text_b']
text_c = example['text_c']
text_d = example['text_d']
graph_list = example['graph_inf']
if pretrain:
# the des of xxx is [MASK] .
input_text = f"The description of {text_a} is that {text_b} ."
inputs = bpe(
input_text,
truncation="longest_first",
max_length=max_seq_length,
padding="longest",
add_special_tokens=True,
)
else:
if text_a == "[MASK]":
input_text_a = " ".join([text_a, text_b])
input_text_b = text_c
origin_triplet = ["MASK"] + example['en']
graph_seq = ["MASK"] + example['en'] + text_d
else:
input_text_a = text_a
input_text_b = " ".join([text_b, text_c])
origin_triplet = example['en'] + ["MASK"]
graph_seq = example['en'] + ["MASK"] + text_d
# 加入graph信息, 拼接等量[UNK]
input_text_b = " ".join(["[CLS]", input_text_a, input_text_b, bpe.unk_token * len(text_d)])
inputs = bpe(
input_text_b,
truncation="longest_first",
max_length=max_seq_length,
padding="longest",
add_special_tokens=False,
)
# assert bpe.mask_token_id in inputs.input_ids, "mask token must in input"
# graph_seq = input_text_b[] 把图结构信息读取出来
# [CLS] [ENTITY_13258] [RELATION_68] [MASK] [ENTITY_4] [RELATION_127] [ENTITY_8] [RELATION_9] [ENTITY_9011] [ENTITY_12477] [PAD] [PAD]
# 获取图结构信息
# 首先在solve中加入一个存储所有子图三元组的临时存储变量
# 在这里graph_information = example['graph']
new_rel = set()
new_rel.add(tuple((origin_triplet[0], origin_triplet[1])))
new_rel.add(tuple((origin_triplet[1], origin_triplet[0])))
new_rel.add(tuple((origin_triplet[1], origin_triplet[2])))
new_rel.add(tuple((origin_triplet[2], origin_triplet[1])))
for triplet in graph_list:
rel1, rel2, rel3, rel4 = tuple((triplet[0], triplet[1])), tuple((triplet[1], triplet[2])), tuple((triplet[1], triplet[0])), tuple((triplet[2], triplet[1]))
new_rel.add(rel1)
new_rel.add(rel2)
new_rel.add(rel3)
new_rel.add(rel4)
# 这里的三元组转换为new_rel
KGid2Graphid_map = defaultdict(int)
for i in range(len(graph_seq)):
KGid2Graphid_map[graph_seq[i]] = i
N = len(graph_seq)
adj = torch.zeros([N, N], dtype=torch.bool)
for item in list(new_rel):
adj[KGid2Graphid_map[item[0]], KGid2Graphid_map[item[1]]] = True
shortest_path_result, _ = algos.floyd_warshall(adj.numpy())
max_dist = np.amax(shortest_path_result)
# [PAD]部分， [CLS]部分补全， [SEP]额外引入也当作[PAD]处理
# 加上一个attention_bias, PAD部分设置为-inf，在送入model前，对其进行处理, 将其相加（让模型无法关注PAD）
# 加入attention到huggingface的BertForMaskedLM（这个可能需要再去查查）
# attention_bias = torch.zero(N, N, dtype=torch.float)
# attention_bias[torch.tensor(shortest_path_result == )]
features = asdict(InputFeatures(input_ids=inputs["input_ids"],
attention_mask=inputs['attention_mask'],
labels=example['label'],
label=example['real_label'],
en=example['en_id'],
rel=example['rel'],
graph=example['text_d_id'],
distance_attention = shortest_path_result.tolist(),
)
)
return features

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import torch
import numpy as np
from torch.utils.data import Dataset
class TrainDataset(Dataset):
"""
Training Dataset class.
Parameters
----------
triples: The triples used for training the model
params: Parameters for the experiments
Returns
-------
A training Dataset class instance used by DataLoader
"""
def __init__(self, triples, params):
self.triples = triples
self.p = params
self.strategy = self.p.train_strategy
self.entities = np.arange(self.p.num_ent, dtype=np.int32)
def __len__(self):
return len(self.triples)
def __getitem__(self, idx):
ele = self.triples[idx]
triple, label, sub_samp = torch.LongTensor(ele['triple']), np.int32(
ele['label']), np.float32(ele['sub_samp'])
trp_label = self.get_label(label)
if self.p.lbl_smooth != 0.0:
trp_label = (1.0 - self.p.lbl_smooth) * \
trp_label + (1.0/self.p.num_ent)
if self.strategy == 'one_to_n':
return triple, trp_label, None, None
elif self.strategy == 'one_to_x':
sub_samp = torch.FloatTensor([sub_samp])
neg_ent = torch.LongTensor(self.get_neg_ent(triple, label))
return triple, trp_label, neg_ent, sub_samp
else:
raise NotImplementedError
@staticmethod
def collate_fn(data):
triple = torch.stack([_[0] for _ in data], dim=0)
trp_label = torch.stack([_[1] for _ in data], dim=0)
if not data[0][2] is None: # one_to_x
neg_ent = torch.stack([_[2] for _ in data], dim=0)
sub_samp = torch.cat([_[3] for _ in data], dim=0)
return triple, trp_label, neg_ent, sub_samp
else:
return triple, trp_label
def get_neg_ent(self, triple, label):
def get(triple, label):
if self.strategy == 'one_to_x':
pos_obj = triple[2]
mask = np.ones([self.p.num_ent], dtype=np.bool)
mask[label] = 0
neg_ent = np.int32(np.random.choice(
self.entities[mask], self.p.neg_num, replace=False)).reshape([-1])
neg_ent = np.concatenate((pos_obj.reshape([-1]), neg_ent))
else:
pos_obj = label
mask = np.ones([self.p.num_ent], dtype=np.bool)
mask[label] = 0
neg_ent = np.int32(np.random.choice(
self.entities[mask], self.p.neg_num - len(label), replace=False)).reshape([-1])
neg_ent = np.concatenate((pos_obj.reshape([-1]), neg_ent))
if len(neg_ent) > self.p.neg_num:
import pdb
pdb.set_trace()
return neg_ent
neg_ent = get(triple, label)
return neg_ent
def get_label(self, label):
if self.strategy == 'one_to_n':
y = np.zeros([self.p.num_ent], dtype=np.float32)
for e2 in label:
y[e2] = 1.0
elif self.strategy == 'one_to_x':
y = [1] + [0] * self.p.neg_num
else:
raise NotImplementedError
return torch.FloatTensor(y)
class TestDataset(Dataset):
"""
Evaluation Dataset class.
Parameters
----------
triples: The triples used for evaluating the model
params: Parameters for the experiments
Returns
-------
An evaluation Dataset class instance used by DataLoader for model evaluation
"""
def __init__(self, triples, params):
self.triples = triples
self.p = params
def __len__(self):
return len(self.triples)
def __getitem__(self, idx):
ele = self.triples[idx]
triple, label = torch.LongTensor(ele['triple']), np.int32(ele['label'])
label = self.get_label(label)
return triple, label
@staticmethod
def collate_fn(data):
triple = torch.stack([_[0] for _ in data], dim=0)
label = torch.stack([_[1] for _ in data], dim=0)
return triple, label
def get_label(self, label):
y = np.zeros([self.p.num_ent], dtype=np.float32)
for e2 in label:
y[e2] = 1.0
return torch.FloatTensor(y)

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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"path1 = './entities.txt'\n",
"path2 = './relations.txt'\n",
"path3 = './train.tsv'\n",
"path4 = './dev.tsv'\n",
"path5 = './test.tsv'\n",
"path6 = './get_neighbor/entity2id.txt'\n",
"path7 = './get_neighbor/relation2id.txt'\n",
"path8 = './get_neighbor/train2id.txt'\n",
"path9 = './get_neighbor/valid2id.txt'\n",
"path10 = './get_neighbor/test2id.txt'"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"with open(path1, 'r') as f:\n",
" a = f.readlines()\n",
"cnt = 0\n",
"with open(path6, 'w') as f:\n",
" for line in a:\n",
" en = line.strip()\n",
" f.write(en + '\\t' + str(cnt) + '\\n')\n",
" cnt += 1\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"with open(path2, 'r') as f:\n",
" a = f.readlines()\n",
"cnt = 0\n",
"with open(path7, 'w') as f:\n",
" for line in a:\n",
" re = line.strip()\n",
" f.write(re + '\\t' + str(cnt) + '\\n')\n",
" cnt += 1\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"with open(path6, 'r') as f:\n",
" a = f.readlines()\n",
"en2id = {}\n",
"for line in a:\n",
" b = line.strip().split('\\t')\n",
" en, num = b[0], b[1]\n",
" en2id[en] = num"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"with open(path7, 'r') as f:\n",
" a = f.readlines()\n",
"re2id = {}\n",
"for line in a:\n",
" b = line.strip().split('\\t')\n",
" re, num = b[0], b[1]\n",
" re2id[re] = num"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"with open(path3, 'r') as f:\n",
" a = f.readlines()\n",
"with open(path8, 'w') as f:\n",
" for line in a:\n",
" b = line.strip().split('\\t')\n",
" h, r, t = b[0], b[1], b[2]\n",
" f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"with open(path4, 'r') as f:\n",
" a = f.readlines()\n",
"with open(path9, 'w') as f:\n",
" for line in a:\n",
" b = line.strip().split('\\t')\n",
" h, r, t = b[0], b[1], b[2]\n",
" f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open(path5, 'r') as f:\n",
" a = f.readlines()\n",
"with open(path10, 'w') as f:\n",
" for line in a:\n",
" b = line.strip().split('\\t')\n",
" h, r, t = b[0], b[1], b[2]\n",
" f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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dataset/FB15k-237/get_neighbor/relation2id.txt
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/soccer/football_team/current_roster./soccer/football_roster_position/position 0
/music/artist/origin 1
/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position 2
/food/food/nutrients./food/nutrition_fact/nutrient 3
/film/actor/film./film/performance/film 4
/award/award_nominee/award_nominations./award/award_nomination/nominated_for 5
/government/political_party/politicians_in_this_party./government/political_party_tenure/politician 6
/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency 7
/people/deceased_person/place_of_death 8
/people/person/profession 9
/location/administrative_division/first_level_division_of 10
/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team 11
/education/university/international_tuition./measurement_unit/dated_money_value/currency 12
/location/us_county/county_seat 13
/location/location/partially_contains 14
/tv/tv_program/program_creator 15
/film/film/music 16
/tv/tv_program/languages 17
/common/topic/webpage./common/webpage/category 18
/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy 19
/education/field_of_study/students_majoring./education/education/major_field_of_study 20
/business/business_operation/assets./measurement_unit/dated_money_value/currency 21
/film/film_set_designer/film_sets_designed 22
/dataworld/gardening_hint/split_to 23
/people/person/languages 24
/business/job_title/people_with_this_title./business/employment_tenure/company 25
/location/country/form_of_government 26
/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language 27
/people/person/place_of_birth 28
/sports/sports_team/colors 29
/education/educational_institution/school_type 30
/award/award_category/winners./award/award_honor/award_winner 31
/organization/organization/headquarters./location/mailing_address/citytown 32
/education/educational_degree/people_with_this_degree./education/education/student 33
/government/legislative_session/members./government/government_position_held/legislative_sessions 34
/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium 35
/education/educational_degree/people_with_this_degree./education/education/major_field_of_study 36
/location/hud_county_place/county 37
/location/administrative_division/country 38
/film/film/film_production_design_by 39
/award/award_winning_work/awards_won./award/award_honor/award 40
/organization/organization/headquarters./location/mailing_address/state_province_region 41
/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category 42
/tv/tv_program/country_of_origin 43
/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal 44
/location/country/second_level_divisions 45
/award/award_ceremony/awards_presented./award/award_honor/honored_for 46
/organization/organization_member/member_of./organization/organization_membership/organization 47
/education/educational_institution/campuses 48
/music/artist/contribution./music/recording_contribution/performance_role 49
/award/ranked_item/appears_in_ranked_lists./award/ranking/list 50
/people/person/religion 51
/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month 52
/film/special_film_performance_type/film_performance_type./film/performance/film 53
/award/award_nominee/award_nominations./award/award_nomination/award 54
/location/statistical_region/religions./location/religion_percentage/religion 55
/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school 56
/film/film/distributors./film/film_film_distributor_relationship/region 57
/government/politician/government_positions_held./government/government_position_held/legislative_sessions 58
/organization/role/leaders./organization/leadership/organization 59
/tv/tv_network/programs./tv/tv_network_duration/program 60
/soccer/football_team/current_roster./sports/sports_team_roster/position 61
/music/instrument/instrumentalists 62
/business/business_operation/operating_income./measurement_unit/dated_money_value/currency 63
/people/cause_of_death/people 64
/film/film/film_art_direction_by 65
/people/person/sibling_s./people/sibling_relationship/sibling 66
/film/film/cinematography 67
/film/actor/dubbing_performances./film/dubbing_performance/language 68
/base/biblioness/bibs_location/state 69
/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed 70
/people/person/gender 71
/education/field_of_study/students_majoring./education/education/student 72
/base/popstra/celebrity/dated./base/popstra/dated/participant 73
/sports/sports_team/roster./american_football/football_roster_position/position 74
/award/award_winner/awards_won./award/award_honor/award_winner 75
/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics 76
/film/director/film 77
/tv/tv_producer/programs_produced./tv/tv_producer_term/program 78
/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film 79
/olympics/olympic_games/sports 80
/music/record_label/artist 81
/education/university/local_tuition./measurement_unit/dated_money_value/currency 82
/film/film/story_by 83
/people/person/spouse_s./people/marriage/spouse 84
/sports/sports_league/teams./sports/sports_league_participation/team 85
/people/profession/specialization_of 86
/base/americancomedy/celebrity_impressionist/celebrities_impersonated 87
/tv/tv_program/genre 88
/award/award_category/nominees./award/award_nomination/nominated_for 89
/language/human_language/countries_spoken_in 90
/organization/organization/headquarters./location/mailing_address/country 91
/location/statistical_region/gdp_real./measurement_unit/adjusted_money_value/adjustment_currency 92
/education/university/fraternities_and_sororities 93
/award/award_nominee/award_nominations./award/award_nomination/award_nominee 94
/military/military_combatant/military_conflicts./military/military_combatant_group/combatants 95
/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for 96
/location/location/time_zones 97
/film/film/dubbing_performances./film/dubbing_performance/actor 98
/film/film_subject/films 99
/education/educational_degree/people_with_this_degree./education/education/institution 100
/education/educational_institution/colors 101
/award/award_category/category_of 102
/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program 103
/film/film/language 104
/music/group_member/membership./music/group_membership/group 105
/business/business_operation/revenue./measurement_unit/dated_money_value/currency 106
/film/film/film_festivals 107
/film/actor/film./film/performance/special_performance_type 108
/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency 109
/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office 110
/base/aareas/schema/administrative_area/administrative_parent 111
/award/award_winning_work/awards_won./award/award_honor/award_winner 112
/organization/organization/place_founded 113
/soccer/football_player/current_team./sports/sports_team_roster/team 114
/government/politician/government_positions_held./government/government_position_held/basic_title 115
/music/artist/track_contributions./music/track_contribution/role 116
/base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months 117
/celebrities/celebrity/celebrity_friends./celebrities/friendship/friend 118
/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school 119
/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee 120
/influence/influence_node/peers./influence/peer_relationship/peers 121
/medicine/disease/risk_factors 122
/broadcast/content/artist 123
/film/film/estimated_budget./measurement_unit/dated_money_value/currency 124
/military/military_conflict/combatants./military/military_combatant_group/combatants 125
/location/capital_of_administrative_division/capital_of./location/administrative_division_capital_relationship/administrative_division 126
/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor 127
/people/deceased_person/place_of_burial 128
/location/location/adjoin_s./location/adjoining_relationship/adjoins 129
/music/group_member/membership./music/group_membership/role 130
/award/award_ceremony/awards_presented./award/award_honor/award_winner 131
/film/film/prequel 132
/film/film/produced_by 133
/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type 134
/sports/sports_position/players./sports/sports_team_roster/team 135
/olympics/olympic_games/participating_countries 136
/music/genre/parent_genre 137
/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program 138
/music/genre/artists 139
/film/film/genre 140
/people/person/employment_history./business/employment_tenure/company 141
/education/university/domestic_tuition./measurement_unit/dated_money_value/currency 142
/people/person/nationality 143
/location/country/capital 144
/location/statistical_region/gni_per_capita_in_ppp_dollars./measurement_unit/dated_money_value/currency 145
/base/aareas/schema/administrative_area/capital 146
/business/business_operation/industry 147
/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source 148
/film/film/other_crew./film/film_crew_gig/crewmember 149
/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer 150
/film/film/film_format 151
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/music/performance_role/track_performances./music/track_contribution/role 234
/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal 235
/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members 236

20466
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@ -1,237 +0,0 @@
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/tv/tv_program/country_of_origin tv tv program country of origin
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/film/film/dubbing_performances./film/dubbing_performance/actor film film dubbing performances. film dubbing performance actor
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/sports/sports_position/players./sports/sports_team_roster/position sports sports position players. sports sports team roster position
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/film/film/personal_appearances./film/personal_film_appearance/person film film personal appearances. film personal film appearance person
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/base/popstra/celebrity/friendship./base/popstra/friendship/participant base popstra celebrity friendship. base popstra friendship participant
/music/performance_role/regular_performances./music/group_membership/group music performance role regular performances. music group membership group
/film/film/edited_by film film edited by
/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club base x2010fifaworldcupsouthafrica world cup squad current world cup squad. base x2010fifaworldcupsouthafrica current world cup squad current club
/base/popstra/celebrity/canoodled./base/popstra/canoodled/participant base popstra celebrity canoodled. base popstra canoodled participant
/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium film film release date s. film film regional release date film release distribution medium
/film/film/other_crew./film/film_crew_gig/film_crew_role film film other crew. film film crew gig film crew role
/base/popstra/celebrity/breakup./base/popstra/breakup/participant base popstra celebrity breakup. base popstra breakup participant
/film/film/country film film country
/music/performance_role/regular_performances./music/group_membership/role music performance role regular performances. music group membership role
/sports/sports_team/roster./american_football/football_historical_roster_position/position_s sports sports team roster. american football football historical roster position position s
/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue film film release date s. film film regional release date film regional debut venue
/time/event/instance_of_recurring_event time event instance of recurring event
/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics olympics olympic participating country athletes. olympics olympic athlete affiliation olympics
/organization/endowed_organization/endowment./measurement_unit/dated_money_value/currency organization endowed organization endowment. measurement unit dated money value currency
/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation travel travel destination how to get here. travel transportation mode of transportation
/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season baseball baseball team team stats. baseball baseball team stats season
/award/award_category/winners./award/award_honor/ceremony award award category winners. award award honor ceremony
/government/legislative_session/members./government/government_position_held/district_represented government legislative session members. government government position held district represented
/influence/influence_node/influenced_by influence influence node influenced by
/base/culturalevent/event/entity_involved base culturalevent event entity involved
/people/ethnicity/people people ethnicity people
/sports/sport/pro_athletes./sports/pro_sports_played/athlete sports sport pro athletes. sports pro sports played athlete
/location/statistical_region/gdp_nominal_per_capita./measurement_unit/dated_money_value/currency location statistical region gdp nominal per capita. measurement unit dated money value currency
/location/hud_county_place/place location hud county place place
/base/aareas/schema/administrative_area/administrative_area_type base aareas schema administrative area administrative area type
/base/locations/continents/countries_within base locations continents countries within
/sports/sports_position/players./american_football/football_historical_roster_position/position_s sports sports position players. american football football historical roster position position s
/people/person/spouse_s./people/marriage/location_of_ceremony people person spouse s. people marriage location of ceremony
/education/educational_institution/students_graduates./education/education/major_field_of_study education educational institution students graduates. education education major field of study
/film/film/written_by film film written by
/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country olympics olympic sport athletes. olympics olympic athlete affiliation country
/music/performance_role/guest_performances./music/recording_contribution/performance_role music performance role guest performances. music recording contribution performance role
/film/film/featured_film_locations film film featured film locations
/education/educational_institution_campus/educational_institution education educational institution campus educational institution
/sports/pro_athlete/teams./sports/sports_team_roster/team sports pro athlete teams. sports sports team roster team
/people/ethnicity/languages_spoken people ethnicity languages spoken
/film/film/executive_produced_by film film executive produced by
/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type tv tv producer programs produced. tv tv producer term producer type
/location/location/contains location location contains
/base/biblioness/bibs_location/country base biblioness bibs location country
/user/ktrueman/default_domain/international_organization/member_states user ktrueman default domain international organization member states
/music/performance_role/track_performances./music/track_contribution/role music performance role track performances. music track contribution role
/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal olympics olympic games medals awarded. olympics olympic medal honor medal
/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members base saturdaynightlive snl cast member seasons. base saturdaynightlive snl season tenure cast members

237
dataset/FB15k-237/relations.txt
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@ -1,237 +0,0 @@
/soccer/football_team/current_roster./soccer/football_roster_position/position
/music/artist/origin
/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position
/food/food/nutrients./food/nutrition_fact/nutrient
/film/actor/film./film/performance/film
/award/award_nominee/award_nominations./award/award_nomination/nominated_for
/government/political_party/politicians_in_this_party./government/political_party_tenure/politician
/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency
/people/deceased_person/place_of_death
/people/person/profession
/location/administrative_division/first_level_division_of
/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team
/education/university/international_tuition./measurement_unit/dated_money_value/currency
/location/us_county/county_seat
/location/location/partially_contains
/tv/tv_program/program_creator
/film/film/music
/tv/tv_program/languages
/common/topic/webpage./common/webpage/category
/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy
/education/field_of_study/students_majoring./education/education/major_field_of_study
/business/business_operation/assets./measurement_unit/dated_money_value/currency
/film/film_set_designer/film_sets_designed
/dataworld/gardening_hint/split_to
/people/person/languages
/business/job_title/people_with_this_title./business/employment_tenure/company
/location/country/form_of_government
/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language
/people/person/place_of_birth
/sports/sports_team/colors
/education/educational_institution/school_type
/award/award_category/winners./award/award_honor/award_winner
/organization/organization/headquarters./location/mailing_address/citytown
/education/educational_degree/people_with_this_degree./education/education/student
/government/legislative_session/members./government/government_position_held/legislative_sessions
/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium
/education/educational_degree/people_with_this_degree./education/education/major_field_of_study
/location/hud_county_place/county
/location/administrative_division/country
/film/film/film_production_design_by
/award/award_winning_work/awards_won./award/award_honor/award
/organization/organization/headquarters./location/mailing_address/state_province_region
/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category
/tv/tv_program/country_of_origin
/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal
/location/country/second_level_divisions
/award/award_ceremony/awards_presented./award/award_honor/honored_for
/organization/organization_member/member_of./organization/organization_membership/organization
/education/educational_institution/campuses
/music/artist/contribution./music/recording_contribution/performance_role
/award/ranked_item/appears_in_ranked_lists./award/ranking/list
/people/person/religion
/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month
/film/special_film_performance_type/film_performance_type./film/performance/film
/award/award_nominee/award_nominations./award/award_nomination/award
/location/statistical_region/religions./location/religion_percentage/religion
/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school
/film/film/distributors./film/film_film_distributor_relationship/region
/government/politician/government_positions_held./government/government_position_held/legislative_sessions
/organization/role/leaders./organization/leadership/organization
/tv/tv_network/programs./tv/tv_network_duration/program
/soccer/football_team/current_roster./sports/sports_team_roster/position
/music/instrument/instrumentalists
/business/business_operation/operating_income./measurement_unit/dated_money_value/currency
/people/cause_of_death/people
/film/film/film_art_direction_by
/people/person/sibling_s./people/sibling_relationship/sibling
/film/film/cinematography
/film/actor/dubbing_performances./film/dubbing_performance/language
/base/biblioness/bibs_location/state
/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed
/people/person/gender
/education/field_of_study/students_majoring./education/education/student
/base/popstra/celebrity/dated./base/popstra/dated/participant
/sports/sports_team/roster./american_football/football_roster_position/position
/award/award_winner/awards_won./award/award_honor/award_winner
/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics
/film/director/film
/tv/tv_producer/programs_produced./tv/tv_producer_term/program
/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film
/olympics/olympic_games/sports
/music/record_label/artist
/education/university/local_tuition./measurement_unit/dated_money_value/currency
/film/film/story_by
/people/person/spouse_s./people/marriage/spouse
/sports/sports_league/teams./sports/sports_league_participation/team
/people/profession/specialization_of
/base/americancomedy/celebrity_impressionist/celebrities_impersonated
/tv/tv_program/genre
/award/award_category/nominees./award/award_nomination/nominated_for
/language/human_language/countries_spoken_in
/organization/organization/headquarters./location/mailing_address/country
/location/statistical_region/gdp_real./measurement_unit/adjusted_money_value/adjustment_currency
/education/university/fraternities_and_sororities
/award/award_nominee/award_nominations./award/award_nomination/award_nominee
/military/military_combatant/military_conflicts./military/military_combatant_group/combatants
/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for
/location/location/time_zones
/film/film/dubbing_performances./film/dubbing_performance/actor
/film/film_subject/films
/education/educational_degree/people_with_this_degree./education/education/institution
/education/educational_institution/colors
/award/award_category/category_of
/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program
/film/film/language
/music/group_member/membership./music/group_membership/group
/business/business_operation/revenue./measurement_unit/dated_money_value/currency
/film/film/film_festivals
/film/actor/film./film/performance/special_performance_type
/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency
/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office
/base/aareas/schema/administrative_area/administrative_parent
/award/award_winning_work/awards_won./award/award_honor/award_winner
/organization/organization/place_founded
/soccer/football_player/current_team./sports/sports_team_roster/team
/government/politician/government_positions_held./government/government_position_held/basic_title
/music/artist/track_contributions./music/track_contribution/role
/base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months
/celebrities/celebrity/celebrity_friends./celebrities/friendship/friend
/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school
/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee
/influence/influence_node/peers./influence/peer_relationship/peers
/medicine/disease/risk_factors
/broadcast/content/artist
/film/film/estimated_budget./measurement_unit/dated_money_value/currency
/military/military_conflict/combatants./military/military_combatant_group/combatants
/location/capital_of_administrative_division/capital_of./location/administrative_division_capital_relationship/administrative_division
/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor
/people/deceased_person/place_of_burial
/location/location/adjoin_s./location/adjoining_relationship/adjoins
/music/group_member/membership./music/group_membership/role
/award/award_ceremony/awards_presented./award/award_honor/award_winner
/film/film/prequel
/film/film/produced_by
/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type
/sports/sports_position/players./sports/sports_team_roster/team
/olympics/olympic_games/participating_countries
/music/genre/parent_genre
/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program
/music/genre/artists
/film/film/genre
/people/person/employment_history./business/employment_tenure/company
/education/university/domestic_tuition./measurement_unit/dated_money_value/currency
/people/person/nationality
/location/country/capital
/location/statistical_region/gni_per_capita_in_ppp_dollars./measurement_unit/dated_money_value/currency
/base/aareas/schema/administrative_area/capital
/business/business_operation/industry
/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source
/film/film/other_crew./film/film_crew_gig/crewmember
/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer
/film/film/film_format
/medicine/disease/notable_people_with_this_condition
/film/film/costume_design_by
/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office
/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency
/sports/sports_team/roster./baseball/baseball_roster_position/position
/award/award_winning_work/awards_won./award/award_honor/honored_for
/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics
/celebrities/celebrity/sexual_relationships./celebrities/romantic_relationship/celebrity
/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony
/organization/organization/child./organization/organization_relationship/child
/organization/organization_founder/organizations_founded
/sports/sports_team/sport
/people/ethnicity/geographic_distribution
/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to
/location/country/official_language
/film/film/production_companies
/user/jg/default_domain/olympic_games/sports
/time/event/locations
/people/person/spouse_s./people/marriage/type_of_union
/government/governmental_body/members./government/government_position_held/legislative_sessions
/media_common/netflix_genre/titles
/user/alexander/philosophy/philosopher/interests
/film/film/runtime./film/film_cut/film_release_region
/education/educational_institution/students_graduates./education/education/student
/base/eating/practicer_of_diet/diet
/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person
/sports/sports_position/players./sports/sports_team_roster/position
/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft
/medicine/symptom/symptom_of
/film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/type_of_appearance
/sports/sports_team_location/teams
/american_football/football_team/current_roster./sports/sports_team_roster/position
/people/person/places_lived./people/place_lived/location
/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency
/film/film/personal_appearances./film/personal_film_appearance/person
/music/instrument/family
/sports/sports_team/roster./basketball/basketball_roster_position/position
/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location
/film/film/release_date_s./film/film_regional_release_date/film_release_region
/award/award_category/disciplines_or_subjects
/base/popstra/celebrity/friendship./base/popstra/friendship/participant
/music/performance_role/regular_performances./music/group_membership/group
/film/film/edited_by
/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club
/base/popstra/celebrity/canoodled./base/popstra/canoodled/participant
/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium
/film/film/other_crew./film/film_crew_gig/film_crew_role
/base/popstra/celebrity/breakup./base/popstra/breakup/participant
/film/film/country
/music/performance_role/regular_performances./music/group_membership/role
/sports/sports_team/roster./american_football/football_historical_roster_position/position_s
/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue
/time/event/instance_of_recurring_event
/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics
/organization/endowed_organization/endowment./measurement_unit/dated_money_value/currency
/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation
/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season
/award/award_category/winners./award/award_honor/ceremony
/government/legislative_session/members./government/government_position_held/district_represented
/influence/influence_node/influenced_by
/base/culturalevent/event/entity_involved
/people/ethnicity/people
/sports/sport/pro_athletes./sports/pro_sports_played/athlete
/location/statistical_region/gdp_nominal_per_capita./measurement_unit/dated_money_value/currency
/location/hud_county_place/place
/base/aareas/schema/administrative_area/administrative_area_type
/base/locations/continents/countries_within
/sports/sports_position/players./american_football/football_historical_roster_position/position_s
/people/person/spouse_s./people/marriage/location_of_ceremony
/education/educational_institution/students_graduates./education/education/major_field_of_study
/film/film/written_by
/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country
/music/performance_role/guest_performances./music/recording_contribution/performance_role
/film/film/featured_film_locations
/education/educational_institution_campus/educational_institution
/sports/pro_athlete/teams./sports/sports_team_roster/team
/people/ethnicity/languages_spoken
/film/film/executive_produced_by
/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type
/location/location/contains
/base/biblioness/bibs_location/country
/user/ktrueman/default_domain/international_organization/member_states
/music/performance_role/track_performances./music/track_contribution/role
/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal
/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members

40943
dataset/WN18RR/entities.txt
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dataset/WN18RR/entity2text.txt
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dataset/WN18RR/entity2text_clean.txt
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dataset/WN18RR/get_neighbor.ipynb
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@ -1,155 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"path1 = './entities.txt'\n",
"path2 = './relations.txt'\n",
"path3 = './train.tsv'\n",
"path4 = './dev.tsv'\n",
"path5 = './test.tsv'\n",
"path6 = './get_neighbor/entity2id.txt'\n",
"path7 = './get_neighbor/relation2id.txt'\n",
"path8 = './get_neighbor/train2id.txt'\n",
"path9 = './get_neighbor/valid2id.txt'\n",
"path10 = './get_neighbor/test2id.txt'"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"with open(path1, 'r') as f:\n",
" a = f.readlines()\n",
"cnt = 0\n",
"with open(path6, 'w') as f:\n",
" for line in a:\n",
" en = line.strip()\n",
" f.write(en + '\\t' + str(cnt) + '\\n')\n",
" cnt += 1\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"with open(path2, 'r') as f:\n",
" a = f.readlines()\n",
"cnt = 0\n",
"with open(path7, 'w') as f:\n",
" for line in a:\n",
" re = line.strip()\n",
" f.write(re + '\\t' + str(cnt) + '\\n')\n",
" cnt += 1\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"with open(path6, 'r') as f:\n",
" a = f.readlines()\n",
"en2id = {}\n",
"for line in a:\n",
" b = line.strip().split('\\t')\n",
" en, num = b[0], b[1]\n",
" en2id[en] = num"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"with open(path7, 'r') as f:\n",
" a = f.readlines()\n",
"re2id = {}\n",
"for line in a:\n",
" b = line.strip().split('\\t')\n",
" re, num = b[0], b[1]\n",
" re2id[re] = num"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"with open(path3, 'r') as f:\n",
" a = f.readlines()\n",
"with open(path8, 'w') as f:\n",
" for line in a:\n",
" b = line.strip().split('\\t')\n",
" h, r, t = b[0], b[1], b[2]\n",
" f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"with open(path4, 'r') as f:\n",
" a = f.readlines()\n",
"with open(path9, 'w') as f:\n",
" for line in a:\n",
" b = line.strip().split('\\t')\n",
" h, r, t = b[0], b[1], b[2]\n",
" f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open(path5, 'r') as f:\n",
" a = f.readlines()\n",
"with open(path10, 'w') as f:\n",
" for line in a:\n",
" b = line.strip().split('\\t')\n",
" h, r, t = b[0], b[1], b[2]\n",
" f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

40943
dataset/WN18RR/get_neighbor/entity2id.txt
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11
dataset/WN18RR/get_neighbor/relation2id.txt
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@ -1,11 +0,0 @@
_member_of_domain_usage 0
_has_part 1
_also_see 2
_hypernym 3
_synset_domain_topic_of 4
_derivationally_related_form 5
_similar_to 6
_instance_hypernym 7
_verb_group 8
_member_meronym 9
_member_of_domain_region 10

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dataset/WN18RR/get_neighbor/train2id.txt
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dataset/WN18RR/get_neighbor/valid2id.txt
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dataset/WN18RR/relation2text.txt
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_member_of_domain_usage member of domain usage
_has_part has part
_also_see also see
_hypernym hypernym
_synset_domain_topic_of synset domain topic of
_derivationally_related_form derivationally related form
_similar_to similar to
_instance_hypernym instance hypernym
_verb_group verb group
_member_meronym member meronym
_member_of_domain_region member of domain region

11
dataset/WN18RR/relations.txt
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_member_of_domain_usage
_has_part
_also_see
_hypernym
_synset_domain_topic_of
_derivationally_related_form
_similar_to
_instance_hypernym
_verb_group
_member_meronym
_member_of_domain_region

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dataset/create_neighbor.py
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from collections import defaultdict
import time
import argparse
id2entity_name = defaultdict(str)
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", type=str, default=None)
args = parser.parse_args()
# dataset_name = 'FB15k-237'
with open('./' + args.dataset + '/get_neighbor/entity2id.txt', 'r') as file:
entity_lines = file.readlines()
for line in entity_lines:
_name, _id = line.strip().split("\t")
id2entity_name[int(_id)] = _name
id2relation_name = defaultdict(str)
with open('./' + args.dataset + '/get_neighbor/relation2id.txt', 'r') as file:
relation_lines = file.readlines()
for line in relation_lines:
_name, _id = line.strip().split("\t")
id2relation_name[int(_id)] = _name
train_triplet = []
for line in open('./' + args.dataset + '/get_neighbor/train2id.txt', 'r'):
head, relation, tail = line.strip('\n').split()
train_triplet.append(list((int(head), int(relation), int(tail))))
for line in open('./' + args.dataset + '/get_neighbor/test2id.txt', 'r'):
head, relation, tail = line.strip('\n').split()
train_triplet.append(list((int(head), int(relation), int(tail))))
for line in open('./'+args.dataset+'/get_neighbor/valid2id.txt', 'r'):
head, relation, tail = line.strip('\n').split()
train_triplet.append(list((int(head), int(relation), int(tail))))
graph = {}
reverse_graph = {}
def init_graph(graph_triplet):
for triple in graph_triplet:
head = triple[0]
rela = triple[1]
tail = triple[2]
if(head not in graph.keys()):
graph[head] = {}
graph[head][tail] = rela
else:
graph[head][tail] = rela
if(tail not in reverse_graph.keys()):
reverse_graph[tail] = {}
reverse_graph[tail][head] = rela
else:
reverse_graph[tail][head] = rela
# return graph, reverse_graph, node_indegree, node_outdegree
init_graph(train_triplet)
import random
def random_delete(triplet, reserved_num):
reserved = random.sample(triplet, reserved_num)
return reserved
def get_onestep_neighbors(graph, source, sample_num):
triplet = []
try:
nei = list(graph[source].keys())
# nei = random.sample(graph[source].keys(), sample_num)
triplet = [tuple((source, graph[source][nei[i]], nei[i])) for i in range(len(nei))]
except KeyError:
pass
except ValueError:
nei = list(graph[source].keys())
triplet = [tuple((source, graph[source][nei[i]], nei[i])) for i in range(len(nei))]
return triplet
def get_entity_neighbors(traget_entity, max_triplet):
as_head_neighbors = get_onestep_neighbors(graph, traget_entity, max_triplet // 2)
as_tail_neighbors = get_onestep_neighbors(reverse_graph, traget_entity, max_triplet // 2)
all_triplet = as_head_neighbors + as_tail_neighbors
return all_triplet
def get_triplet(triplet):
head_entity = triplet[0]
tail_entity = triplet[2]
triplet = tuple((triplet[0], triplet[1], triplet[2]))
head_triplet = get_entity_neighbors(head_entity, 4)
tail_triplet = get_entity_neighbors(tail_entity, 4)
temp_triplet = list(set(head_triplet + tail_triplet))
temp_triplet = list(set(temp_triplet) - set([triplet]))
# if len(temp_triplet) > 8:
# del_triplet = list(set(temp_triplet) - set([triplet]))
# temp_triplet = random_delete(del_triplet, 7)
return temp_triplet
import copy
def change_(triplet_list):
tri_text = []
for item in triplet_list:
# text = id2entity_name[item[0]] + '\t' + id2relation_name[item[1]] + '\t' + id2entity_name[item[2]]
h = id2entity_name[item[0]]
r = id2relation_name[item[1]]
t = id2entity_name[item[2]]
tri_text.append([h, r, t])
return tri_text
mask_idx = 99999999
masked_tail_neighbor = defaultdict(list)
masked_head_neighbor = defaultdict(list)
for triplet in train_triplet:
tail_masked = copy.deepcopy(triplet)
head_masked = copy.deepcopy(triplet)
tail_masked[2] = mask_idx
head_masked[0] = mask_idx
masked_tail_neighbor['\t'.join([id2entity_name[triplet[0]], id2relation_name[triplet[1]]])] = change_(get_triplet(tail_masked))
masked_head_neighbor['\t'.join([id2entity_name[triplet[2]], id2relation_name[triplet[1]]])] = change_(get_triplet(head_masked))
import json
with open("./" + args.dataset + "/masked_tail_neighbor.txt", "w") as file:
file.write(json.dumps(masked_tail_neighbor, indent=1))
with open("./" + args.dataset + "/masked_head_neighbor.txt", "w") as file:
file.write(json.dumps(masked_head_neighbor, indent=1))

135
dataset/umls/entities.txt
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@ -1,135 +0,0 @@
idea_or_concept
virus
spatial_concept
human_caused_phenomenon_or_process
human
organ_or_tissue_function
daily_or_recreational_activity
steroid
biomedical_or_dental_material
vertebrate
immunologic_factor
inorganic_chemical
invertebrate
embryonic_structure
functional_concept
amino_acid_peptide_or_protein
fish
reptile
physical_object
disease_or_syndrome
biologically_active_substance
physiologic_function
population_group
group
body_space_or_junction
bird
qualitative_concept
bacterium
cell_function
enzyme
organophosphorus_compound
nucleic_acid_nucleoside_or_nucleotide
cell
language
antibiotic
indicator_reagent_or_diagnostic_aid
fungus
chemical_viewed_functionally
rickettsia_or_chlamydia
patient_or_disabled_group
professional_society
health_care_related_organization
clinical_attribute
biomedical_occupation_or_discipline
temporal_concept
phenomenon_or_process
family_group
chemical_viewed_structurally
regulation_or_law
acquired_abnormality
experimental_model_of_disease
professional_or_occupational_group
injury_or_poisoning
receptor
drug_delivery_device
hazardous_or_poisonous_substance
organism
neoplastic_process
mammal
molecular_function
lipid
group_attribute
nucleotide_sequence
biologic_function
chemical
cell_component
intellectual_product
manufactured_object
classification
geographic_area
vitamin
gene_or_genome
self_help_or_relief_organization
pathologic_function
amphibian
laboratory_or_test_result
organism_attribute
cell_or_molecular_dysfunction
therapeutic_or_preventive_procedure
sign_or_symptom
occupational_activity
anatomical_abnormality
hormone
fully_formed_anatomical_structure
educational_activity
quantitative_concept
tissue
organism_function
social_behavior
mental_or_behavioral_dysfunction
governmental_or_regulatory_activity
molecular_biology_research_technique
occupation_or_discipline
conceptual_entity
body_location_or_region
pharmacologic_substance
clinical_drug
food
substance
genetic_function
congenital_abnormality
medical_device
carbohydrate
health_care_activity
eicosanoid
element_ion_or_isotope
diagnostic_procedure
entity
event
laboratory_procedure
environmental_effect_of_humans
body_part_organ_or_organ_component
molecular_sequence
mental_process
research_device
alga
natural_phenomenon_or_process
anatomical_structure
animal
body_system
behavior
carbohydrate_sequence
archaeon
research_activity
organization
individual_behavior
organic_chemical
finding
age_group
activity
machine_activity
plant
body_substance
amino_acid_sequence
neuroreactive_substance_or_biogenic_amine

135
dataset/umls/entity2text.txt
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@ -1,135 +0,0 @@
idea_or_concept idea or concept
virus virus
spatial_concept spatial concept
human_caused_phenomenon_or_process human caused phenomenon or process
human human
organ_or_tissue_function organ or tissue function
daily_or_recreational_activity daily or recreational activity
steroid steroid
biomedical_or_dental_material biomedical or dental material
vertebrate vertebrate
immunologic_factor immunologic factor
inorganic_chemical inorganic chemical
invertebrate invertebrate
embryonic_structure embryonic structure
functional_concept functional concept
amino_acid_peptide_or_protein amino acid peptide or protein
fish fish
reptile reptile
physical_object physical object
disease_or_syndrome disease or syndrome
biologically_active_substance biologically active substance
physiologic_function physiologic function
population_group population group
group group
body_space_or_junction body space or junction
bird bird
qualitative_concept qualitative concept
bacterium bacterium
cell_function cell function
enzyme enzyme
organophosphorus_compound organophosphorus compound
nucleic_acid_nucleoside_or_nucleotide nucleic acid nucleoside or nucleotide
cell cell
language language
antibiotic antibiotic
indicator_reagent_or_diagnostic_aid indicator reagent or diagnostic aid
fungus fungus
chemical_viewed_functionally chemical viewed functionally
rickettsia_or_chlamydia rickettsia or chlamydia
patient_or_disabled_group patient or disabled group
professional_society professional society
health_care_related_organization health care related organization
clinical_attribute clinical attribute
biomedical_occupation_or_discipline biomedical occupation or discipline
temporal_concept temporal concept
phenomenon_or_process phenomenon or process
family_group family group
chemical_viewed_structurally chemical viewed structurally
regulation_or_law regulation or law
acquired_abnormality acquired abnormality
experimental_model_of_disease experimental model of disease
professional_or_occupational_group professional or occupational group
injury_or_poisoning injury or poisoning
receptor receptor
drug_delivery_device drug delivery device
hazardous_or_poisonous_substance hazardous or poisonous substance
organism organism
neoplastic_process neoplastic process
mammal mammal
molecular_function molecular function
lipid lipid
group_attribute group attribute
nucleotide_sequence nucleotide sequence
biologic_function biologic function
chemical chemical
cell_component cell component
intellectual_product intellectual product
manufactured_object manufactured object
classification classification
geographic_area geographic area
vitamin vitamin
gene_or_genome gene or genome
self_help_or_relief_organization self help or relief organization
pathologic_function pathologic function
amphibian amphibian
laboratory_or_test_result laboratory or test result
organism_attribute organism attribute
cell_or_molecular_dysfunction cell or molecular dysfunction
therapeutic_or_preventive_procedure therapeutic or preventive procedure
sign_or_symptom sign or symptom
occupational_activity occupational activity
anatomical_abnormality anatomical abnormality
hormone hormone
fully_formed_anatomical_structure fully formed anatomical structure
educational_activity educational activity
quantitative_concept quantitative concept
tissue tissue
organism_function organism function
social_behavior social behavior
mental_or_behavioral_dysfunction mental or behavioral dysfunction
governmental_or_regulatory_activity governmental or regulatory activity
molecular_biology_research_technique molecular biology research technique
occupation_or_discipline occupation or discipline
conceptual_entity conceptual entity
body_location_or_region body location or region
pharmacologic_substance pharmacologic substance
clinical_drug clinical drug
food food
substance substance
genetic_function genetic function
congenital_abnormality congenital abnormality
medical_device medical device
carbohydrate carbohydrate
health_care_activity health care activity
eicosanoid eicosanoid
element_ion_or_isotope element ion or isotope
diagnostic_procedure diagnostic procedure
entity entity
event event
laboratory_procedure laboratory procedure
environmental_effect_of_humans environmental effect of humans
body_part_organ_or_organ_component body part organ or organ component
molecular_sequence molecular sequence
mental_process mental process
research_device research device
alga alga
natural_phenomenon_or_process natural phenomenon or process
anatomical_structure anatomical structure
animal animal
body_system body system
behavior behavior
carbohydrate_sequence carbohydrate sequence
archaeon archaeon
research_activity research activity
organization organization
individual_behavior individual behavior
organic_chemical organic chemical
finding finding
age_group age group
activity activity
machine_activity machine activity
plant plant
body_substance body substance
amino_acid_sequence amino acid sequence
neuroreactive_substance_or_biogenic_amine neuroreactive substance or biogenic amine

135
dataset/umls/entity2textlong.txt
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155
dataset/umls/get_neighbor.ipynb
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@ -1,155 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"path1 = './entities.txt'\n",
"path2 = './relations.txt'\n",
"path3 = './train.tsv'\n",
"path4 = './dev.tsv'\n",
"path5 = './test.tsv'\n",
"path6 = './get_neighbor/entity2id.txt'\n",
"path7 = './get_neighbor/relation2id.txt'\n",
"path8 = './get_neighbor/train2id.txt'\n",
"path9 = './get_neighbor/valid2id.txt'\n",
"path10 = './get_neighbor/test2id.txt'"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"with open(path1, 'r') as f:\n",
" a = f.readlines()\n",
"cnt = 0\n",
"with open(path6, 'w') as f:\n",
" for line in a:\n",
" en = line.strip()\n",
" f.write(en + '\\t' + str(cnt) + '\\n')\n",
" cnt += 1\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"with open(path2, 'r') as f:\n",
" a = f.readlines()\n",
"cnt = 0\n",
"with open(path7, 'w') as f:\n",
" for line in a:\n",
" re = line.strip()\n",
" f.write(re + '\\t' + str(cnt) + '\\n')\n",
" cnt += 1\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"with open(path6, 'r') as f:\n",
" a = f.readlines()\n",
"en2id = {}\n",
"for line in a:\n",
" b = line.strip().split('\\t')\n",
" en, num = b[0], b[1]\n",
" en2id[en] = num"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"with open(path7, 'r') as f:\n",
" a = f.readlines()\n",
"re2id = {}\n",
"for line in a:\n",
" b = line.strip().split('\\t')\n",
" re, num = b[0], b[1]\n",
" re2id[re] = num"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"with open(path3, 'r') as f:\n",
" a = f.readlines()\n",
"with open(path8, 'w') as f:\n",
" for line in a:\n",
" b = line.strip().split('\\t')\n",
" h, r, t = b[0], b[1], b[2]\n",
" f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"with open(path4, 'r') as f:\n",
" a = f.readlines()\n",
"with open(path9, 'w') as f:\n",
" for line in a:\n",
" b = line.strip().split('\\t')\n",
" h, r, t = b[0], b[1], b[2]\n",
" f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open(path5, 'r') as f:\n",
" a = f.readlines()\n",
"with open(path10, 'w') as f:\n",
" for line in a:\n",
" b = line.strip().split('\\t')\n",
" h, r, t = b[0], b[1], b[2]\n",
" f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

135
dataset/umls/get_neighbor/entity2id.txt
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@ -1,135 +0,0 @@
idea_or_concept 0
virus 1
spatial_concept 2
human_caused_phenomenon_or_process 3
human 4
organ_or_tissue_function 5
daily_or_recreational_activity 6
steroid 7
biomedical_or_dental_material 8
vertebrate 9
immunologic_factor 10
inorganic_chemical 11
invertebrate 12
embryonic_structure 13
functional_concept 14
amino_acid_peptide_or_protein 15
fish 16
reptile 17
physical_object 18
disease_or_syndrome 19
biologically_active_substance 20
physiologic_function 21
population_group 22
group 23
body_space_or_junction 24
bird 25
qualitative_concept 26
bacterium 27
cell_function 28
enzyme 29
organophosphorus_compound 30
nucleic_acid_nucleoside_or_nucleotide 31
cell 32
language 33
antibiotic 34
indicator_reagent_or_diagnostic_aid 35
fungus 36
chemical_viewed_functionally 37
rickettsia_or_chlamydia 38
patient_or_disabled_group 39
professional_society 40
health_care_related_organization 41
clinical_attribute 42
biomedical_occupation_or_discipline 43
temporal_concept 44
phenomenon_or_process 45
family_group 46
chemical_viewed_structurally 47
regulation_or_law 48
acquired_abnormality 49
experimental_model_of_disease 50
professional_or_occupational_group 51
injury_or_poisoning 52
receptor 53
drug_delivery_device 54
hazardous_or_poisonous_substance 55
organism 56
neoplastic_process 57
mammal 58
molecular_function 59
lipid 60
group_attribute 61
nucleotide_sequence 62
biologic_function 63
chemical 64
cell_component 65
intellectual_product 66
manufactured_object 67
classification 68
geographic_area 69
vitamin 70
gene_or_genome 71
self_help_or_relief_organization 72
pathologic_function 73
amphibian 74
laboratory_or_test_result 75
organism_attribute 76
cell_or_molecular_dysfunction 77
therapeutic_or_preventive_procedure 78
sign_or_symptom 79
occupational_activity 80
anatomical_abnormality 81
hormone 82
fully_formed_anatomical_structure 83
educational_activity 84
quantitative_concept 85
tissue 86
organism_function 87
social_behavior 88
mental_or_behavioral_dysfunction 89
governmental_or_regulatory_activity 90
molecular_biology_research_technique 91
occupation_or_discipline 92
conceptual_entity 93
body_location_or_region 94
pharmacologic_substance 95
clinical_drug 96
food 97
substance 98
genetic_function 99
congenital_abnormality 100
medical_device 101
carbohydrate 102
health_care_activity 103
eicosanoid 104
element_ion_or_isotope 105
diagnostic_procedure 106
entity 107
event 108
laboratory_procedure 109
environmental_effect_of_humans 110
body_part_organ_or_organ_component 111
molecular_sequence 112
mental_process 113
research_device 114
alga 115
natural_phenomenon_or_process 116
anatomical_structure 117
animal 118
body_system 119
behavior 120
carbohydrate_sequence 121
archaeon 122
research_activity 123
organization 124
individual_behavior 125
organic_chemical 126
finding 127
age_group 128
activity 129
machine_activity 130
plant 131
body_substance 132
amino_acid_sequence 133
neuroreactive_substance_or_biogenic_amine 134

46
dataset/umls/get_neighbor/relation2id.txt
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@ -1,46 +0,0 @@
measures 0
derivative_of 1
disrupts 2
prevents 3
conceptually_related_to 4
manifestation_of 5
diagnoses 6
evaluation_of 7
contains 8
co-occurs_with 9
conceptual_part_of 10
performs 11
degree_of 12
interacts_with 13
uses 14
issue_in 15
assesses_effect_of 16
property_of 17
precedes 18
result_of 19
causes 20
practices 21
ingredient_of 22
analyzes 23
surrounds 24
indicates 25
associated_with 26
affects 27
location_of 28
produces 29
process_of 30
measurement_of 31
connected_to 32
carries_out 33
method_of 34
adjacent_to 35
occurs_in 36
consists_of 37
interconnects 38
manages 39
complicates 40
part_of 41
treats 42
isa 43
developmental_form_of 44
exhibits 45

661
dataset/umls/get_neighbor/test2id.txt
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@ -1,661 +0,0 @@
7 13 104
42 43 93
94 28 21
57 43 19
102 27 59
19 27 5
98 15 92
75 7 99
37 43 107
30 22 96
89 27 88
3 19 73
15 13 34
82 27 50
34 27 28
13 41 25
86 29 126
99 30 5
100 41 58
11 20 77
53 2 32
40 28 109
87 9 28
10 20 77
70 27 57
34 40 81
85 31 21
73 27 63
100 15 92
86 35 24
70 2 5
53 43 98
113 43 87
9 45 125
94 28 78
18 15 43
11 27 28
57 27 9
28 27 58
22 14 101
3 19 59
95 42 57
13 28 77
50 19 109
29 2 28
73 40 52
89 12 19
131 13 118
19 30 73
73 19 106
81 5 63
75 5 73
16 15 43
102 27 77
63 27 1
113 18 28
50 36 89
100 41 27
3 19 21
32 28 78
50 30 116
57 19 45
134 27 87
89 30 122
100 41 1
73 5 99
20 20 89
95 42 19
24 28 28
99 27 1
49 19 106
21 27 122
65 28 73
91 0 37
59 19 3
51 14 67
53 2 59
5 30 63
91 0 105
21 19 50
21 29 20
83 41 74
29 40 87
86 28 1
12 43 118
113 9 99
81 28 27
83 28 77
5 9 99
21 19 3
59 19 77
106 26 73
21 19 57
60 13 95
65 29 53
21 19 100
128 11 130
100 26 76
109 23 15
70 27 113
65 41 56
68 43 93
87 19 73
87 29 70
109 6 89
102 27 73
65 43 107
73 27 56
113 27 27
75 25 5
86 29 132
65 41 111
59 27 89
102 13 10
6 26 49
59 19 19
57 36 52
83 41 122
102 27 113
105 13 55
52 2 59
51 13 22
89 27 122
126 27 57
98 20 19
20 20 19
52 2 86
73 15 92
89 30 17
49 5 73
105 15 43
120 26 128
19 40 77
83 29 53
64 20 52
89 27 17
63 27 87
34 13 134
20 27 89
109 16 99
123 0 15
19 27 77
73 19 19
19 36 89
88 26 39
34 6 89
73 19 87
131 13 17
89 27 74
79 6 89
63 19 89
63 27 77
81 41 9
46 13 23
50 30 16
89 27 116
87 27 115
65 28 24
111 28 99
64 27 99
37 27 21
33 15 43
111 28 5
123 43 80
106 23 55
50 36 23
31 43 107
106 26 100
80 26 77
5 19 49
59 27 28
47 22 96
89 19 120
47 13 102
28 27 57
73 36 89
46 11 78
46 29 114
15 27 113
82 40 63
81 19 103
87 30 63
10 40 19
62 17 31
124 28 80
42 19 99
70 20 81
89 27 5
57 19 89
111 29 126
1 43 56
5 30 21
125 26 92
55 27 19
77 27 21
82 2 86
59 27 118
21 27 59
63 27 21
75 25 113
47 27 5
28 19 19
15 27 63
50 27 63
8 27 63
91 0 15
50 27 27
34 27 5
4 43 58
22 11 6
65 10 119
32 41 17
5 27 4
35 13 64
77 19 87
91 0 104
91 0 116
76 19 19
95 42 52
99 27 63
23 45 125
3 19 45
34 27 99
82 13 29
73 30 122
25 13 58
134 2 5
102 20 77
28 27 19
32 41 36
87 30 4
53 40 89
99 43 59
89 12 77
61 17 46
95 6 50
73 27 115
86 28 63
87 9 113
80 26 57
35 27 99
102 13 8
87 36 44
11 20 81
77 27 87
74 45 88
117 41 115
60 43 107
77 19 19
88 26 51
32 29 82
12 43 107
126 20 77
49 19 3
73 5 19
37 15 92
50 9 81
109 16 105
106 0 28
47 15 92
99 27 19
75 9 79
15 13 37
32 41 27
28 27 42
83 41 131
47 13 60
91 0 59
36 13 56
29 13 70
100 5 89
78 40 73
64 27 5
1 28 82
5 29 82
115 28 134
109 27 5
73 30 12
67 20 77
57 27 38
77 19 49
99 27 131
115 43 18
46 11 109
19 12 77
17 45 88
78 27 39
26 7 125
22 14 48
34 20 77
77 36 89
49 5 99
27 43 107
50 36 128
10 20 100
109 0 95
19 27 16
63 19 57
78 26 49
32 29 70
113 30 9
113 19 57
106 6 77
38 28 70
57 5 73
19 18 57
21 19 116
75 31 97
106 16 105
70 20 77
121 43 0
3 19 52
105 20 77
126 20 100
3 19 50
50 40 89
5 43 116
62 43 112
21 27 36
50 43 45
77 5 52
96 20 49
65 28 99
80 26 19
75 26 81
128 11 88
83 28 21
82 13 70
59 18 87
3 43 108
51 11 106
19 9 52
113 29 20
59 29 82
57 40 100
57 19 113
104 15 43
41 43 107
28 43 63
106 23 95
10 40 99
21 18 99
10 40 21
89 30 58
10 25 57
57 30 99
63 27 16
5 27 25
89 19 110
55 40 57
77 19 88
50 19 3
105 13 70
54 20 52
83 28 36
83 28 27
116 19 19
29 40 50
125 5 89
69 43 0
86 43 83
79 6 50
84 26 73
53 27 63
5 9 21
89 29 70
50 19 21
82 40 21
72 33 84
110 43 108
64 20 19
106 6 100
65 41 4
50 19 103
75 5 50
76 31 113
28 27 99
117 41 131
116 19 73
100 19 50
87 29 53
97 20 57
82 27 99
106 15 43
5 30 89
25 13 122
109 23 30
118 13 56
109 16 19
131 13 115
78 3 57
100 40 81
34 2 87
128 11 6
71 41 131
15 13 134
95 20 73
60 15 92
114 20 81
19 30 115
81 19 28
34 42 50
34 40 113
52 19 28
21 18 5
99 19 3
85 31 113
36 20 73
38 28 10
104 13 105
11 20 57
117 15 43
10 40 52
54 42 52
114 43 107
20 13 134
30 13 20
59 27 74
89 9 52
57 5 50
27 28 20
126 13 8
21 27 116
109 43 103
57 40 81
81 27 9
42 5 5
13 41 36
11 13 29
89 9 50
29 40 57
79 5 5
5 9 59
128 43 23
7 27 57
128 45 120
19 5 21
106 43 108
20 2 71
81 5 113
28 19 21
113 30 4
64 15 43
115 13 4
70 27 63
83 29 102
110 19 49
19 19 3
126 13 7
77 30 116
81 41 118
106 14 54
91 34 106
20 20 52
81 27 131
59 30 12
106 0 95
105 27 59
89 19 57
130 43 129
31 13 82
109 27 57
8 43 64
73 27 118
53 20 73
35 20 81
134 27 77
28 27 38
13 28 1
78 27 28
4 13 56
36 20 19
32 29 53
22 29 48
46 11 123
70 20 52
112 15 43
7 15 43
27 13 16
28 19 113
76 17 58
81 5 19
77 19 110
21 27 58
83 41 25
126 13 82
0 15 92
39 14 114
53 20 49
63 19 19
20 13 29
21 43 63
34 40 28
55 2 113
73 18 77
76 17 56
30 13 102
27 28 53
5 19 50
83 28 87
127 43 93
100 43 107
86 15 43
116 19 57
76 5 5
78 40 28
22 29 101
34 13 20
34 20 49
28 29 70
57 27 21
110 19 89
5 27 87
60 27 73
109 27 113
20 2 65
127 5 87
87 27 25
99 27 21
28 19 99
34 27 21
30 20 73
116 27 99
57 29 53
109 0 8
76 31 59
21 27 63
50 19 57
115 13 1
100 28 36
34 6 77
106 0 44
89 19 63
95 40 63
95 2 87
81 19 52
83 28 59
31 13 34
134 43 20
50 30 27
134 13 64
77 27 25
75 43 93
73 26 76
49 9 52
51 14 54
51 6 50
77 12 57
57 15 43
19 19 113
57 30 25
73 19 81
100 5 19
76 19 57
52 15 92
53 20 89
42 17 27
31 27 50
60 20 100
47 13 37
34 3 73
104 43 126
20 2 5
5 27 99
34 27 57
83 28 1
26 7 129
13 41 32
29 2 86
90 26 19
71 28 113
57 30 87
95 15 43
53 40 19
19 30 99
81 28 1
13 41 9
87 27 50
67 20 89
32 41 111
59 19 50
101 42 49
19 27 4
111 28 59
19 36 57
70 43 37
65 15 92
65 29 31
27 43 56
77 36 52
55 15 92
124 28 84
86 29 20
36 43 18
87 19 45
87 43 63
126 13 31
126 27 116
106 26 57
59 29 134
89 30 25
47 27 63
50 29 20
89 26 76
109 23 134
126 43 98
89 27 131
6 26 50
113 18 5
64 27 57
82 22 96
82 43 20
91 0 53
50 18 57
95 13 134
1 13 16
49 27 1
73 19 59
13 28 36
95 27 77
63 19 49
57 9 81
57 19 49
111 29 132
77 30 89
84 15 92
73 5 57
1 20 73
52 40 50
104 13 95
59 19 116
57 29 86
106 16 70
81 5 5
55 22 96
30 27 77
109 0 76
37 13 10
106 6 19
52 40 19
59 19 100
63 27 27
87 29 82
125 26 57
116 27 28
6 26 73
83 28 38
5 27 73
57 27 74
49 36 128
113 27 76
91 0 57
19 36 39
89 27 58
110 43 45
77 18 50
75 43 107
1 13 122
35 20 89
117 41 36
77 30 25

5216
dataset/umls/get_neighbor/train2id.txt
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652
dataset/umls/get_neighbor/valid2id.txt
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31 27 89
39 11 125
57 30 59
60 27 63
57 27 115
34 27 77
104 27 89
83 28 52
130 34 109
77 43 73
91 0 87
87 27 118
39 11 90
109 0 113
86 24 24
81 27 87
131 28 20
73 12 89
38 28 134
7 20 81
30 43 126
15 13 104
128 29 114
49 19 99
126 13 64
12 13 16
71 29 31
29 43 20
32 28 24
42 12 76
70 43 20
118 13 58
52 19 50
87 9 21
15 13 20
95 2 28
113 30 25
49 19 88
123 0 44
7 43 98
59 30 28
51 11 103
19 19 21
94 28 52
34 40 49
30 13 15
134 40 52
71 29 132
52 26 42
28 27 4
127 26 52
109 0 126
19 19 89
21 18 113
24 15 92
89 30 118
49 19 21
49 19 52
0 43 93
59 30 122
81 19 87
41 28 91
104 20 57
73 18 57
110 19 52
105 20 57
59 27 9
134 20 49
7 20 100
11 13 64
86 29 31
111 28 28
76 17 118
104 13 35
19 27 113
77 30 19
73 19 63
127 5 89
100 28 27
8 20 57
37 13 8
50 30 19
73 27 50
53 40 73
47 27 50
16 45 125
10 43 107
106 0 59
102 43 107
73 30 131
133 17 71
77 27 63
97 43 107
57 30 4
55 40 81
94 28 19
28 30 118
116 27 5
134 43 37
30 13 105
99 19 19
57 30 12
109 16 50
115 43 56
42 31 5
4 43 107
112 43 0
55 27 59
15 43 47
128 15 43
75 31 105
124 28 109
7 43 126
78 27 19
116 19 5
37 20 81
69 26 52
121 19 113
99 19 110
8 27 77
37 27 73
59 27 122
57 5 5
86 29 134
35 20 77
75 7 113
8 20 77
57 19 5
99 29 134
89 19 5
113 27 12
35 27 116
89 26 73
113 27 57
28 27 63
50 5 99
11 20 100
75 31 126
18 43 107
94 28 73
134 40 28
123 27 113
109 0 73
15 20 77
49 27 4
106 27 73
10 40 77
27 15 43
53 40 99
57 40 50
5 27 28
78 43 103
50 19 88
78 34 43
104 27 113
54 20 100
87 27 38
89 29 29
67 20 57
47 13 10
28 30 36
21 30 12
116 19 100
70 40 100
71 41 111
19 19 45
19 27 118
39 11 80
76 19 87
20 27 73
13 28 50
70 27 21
101 20 77
123 0 34
54 42 49
56 43 18
59 30 36
21 19 5
34 3 19
101 20 89
31 27 113
77 30 21
64 27 89
31 13 134
131 13 27
126 13 37
50 26 42
100 41 56
71 28 50
111 28 36
15 27 73
99 29 82
109 26 81
34 20 73
49 27 21
51 43 23
79 26 49
29 20 100
99 30 28
70 40 21
42 31 59
13 28 89
52 19 45
47 27 116
28 27 89
113 27 88
63 30 1
106 23 35
50 27 21
1 28 53
26 7 103
28 27 115
89 30 63
113 30 5
5 19 52
57 18 77
19 12 89
39 29 101
34 13 64
19 5 57
28 30 87
76 5 28
115 15 43
40 15 43
45 19 87
64 27 87
75 5 87
100 27 87
6 26 52
75 31 29
100 41 25
57 5 113
109 23 47
19 19 63
82 2 32
77 5 59
128 29 48
113 27 25
101 42 89
45 19 113
13 41 1
59 27 17
78 3 50
60 43 98
109 16 21
16 13 56
131 43 18
71 43 107
42 17 12
106 23 105
34 27 116
71 29 70
57 27 116
57 19 103
106 0 53
27 13 122
21 27 76
82 13 53
40 33 109
32 28 5
15 22 96
3 19 116
123 15 92
37 20 49
17 43 9
63 27 12
57 27 56
70 27 116
34 6 19
49 5 21
95 43 64
128 45 88
87 30 118
51 11 130
50 43 108
57 30 19
49 28 19
108 15 43
89 36 51
35 27 50
89 43 63
103 34 92
105 27 50
131 13 36
39 15 92
72 33 80
123 0 59
49 41 74
53 27 113
31 20 52
77 27 5
76 19 50
73 27 27
40 28 103
55 2 13
118 45 88
63 19 100
73 27 113
106 0 15
59 9 21
46 14 101
23 11 130
109 26 73
57 9 100
109 0 35
81 19 73
94 28 28
123 0 7
12 20 57
109 23 82
19 27 63
73 27 99
86 15 92
63 27 131
81 27 17
94 28 89
101 42 73
76 19 28
71 28 1
71 41 86
86 29 82
75 25 57
89 40 52
20 20 50
78 15 43
85 31 94
51 43 107
71 27 5
104 27 19
10 40 87
71 41 17
75 5 59
89 36 46
78 42 89
22 43 23
94 28 86
85 31 112
109 43 129
106 16 30
71 15 92
5 30 17
69 43 93
134 27 89
20 43 64
29 2 13
1 28 70
51 14 48
50 19 78
35 20 57
79 7 63
21 30 74
68 15 43
87 29 20
75 31 64
10 2 111
103 15 43
102 13 34
57 19 106
89 19 87
65 28 5
30 15 92
65 28 50
60 20 49
50 19 113
81 19 77
28 43 21
49 5 28
75 26 19
113 29 82
58 45 120
6 26 57
96 20 52
123 26 73
77 30 4
111 41 12
54 42 79
134 27 19
9 43 18
50 19 106
54 43 107
78 14 96
29 27 77
106 23 134
74 45 125
89 30 21
109 6 77
78 40 113
7 13 11
21 27 131
43 43 93
109 23 102
104 13 53
128 11 91
105 13 29
55 2 65
100 19 21
30 13 134
81 41 27
96 20 81
24 15 43
78 27 113
103 26 52
59 18 5
41 33 123
28 30 59
57 27 50
106 27 77
106 15 92
90 34 43
75 5 28
51 29 48
75 31 95
95 27 50
53 27 28
134 20 81
111 29 70
82 27 63
83 28 19
53 27 21
123 0 76
127 5 5
89 5 21
103 27 89
34 13 10
19 29 132
106 0 8
64 27 116
123 0 8
111 10 119
19 27 27
64 20 81
87 19 113
77 36 128
73 27 74
59 43 45
109 23 70
90 26 73
113 19 49
86 29 30
71 41 65
89 27 118
10 20 49
34 42 49
104 43 60
134 20 73
34 42 100
49 41 131
89 30 113
51 45 125
65 28 63
55 43 37
28 19 59
105 22 96
49 27 74
23 14 68
126 13 104
53 43 20
20 27 59
73 18 89
109 16 20
28 29 82
20 2 13
63 29 53
115 28 82
50 29 53
5 36 113
31 27 59
49 41 38
101 42 50
57 30 50
69 26 77
30 13 7
28 43 116
19 19 88
89 36 39
52 36 51
55 40 100
12 20 73
49 36 51
60 27 89
42 26 76
60 27 113
12 13 17
71 41 9
5 30 58
132 10 119
111 29 134
102 13 11
81 41 58
116 27 59
98 20 77
13 24 32
52 43 45
106 6 81
24 28 52
28 19 50
134 40 99
50 19 110
103 27 77
40 33 106
103 27 113
23 29 114
65 28 100
9 43 118
91 0 8
40 29 68
133 43 0
99 9 21
89 5 99
63 30 58
125 27 88
73 9 52
12 20 50
16 13 122
114 20 19
85 15 43
40 28 78
54 3 19
83 41 12
58 43 107
111 29 53
59 27 58
109 23 8
3 43 45
50 30 9
40 33 123
50 18 77
50 27 74
109 16 55
81 15 43
82 27 113
109 23 95
94 28 99
19 19 52
109 16 57
100 27 118
8 13 10
87 43 116
68 43 66
116 19 81
37 27 57
133 19 113
42 17 17
58 45 125
116 27 19
5 30 57
20 40 113
109 16 8
8 13 64
57 26 77
26 43 0
79 7 50
134 13 53
32 28 73
106 16 29
49 41 115
30 13 55
106 16 60
36 13 12
75 31 21
49 27 113
19 27 17
133 43 107
113 19 63
126 27 63
7 13 82
73 19 49
123 0 47
78 26 89
21 19 113
42 19 3
109 0 34
32 41 12
70 40 77
42 5 59
87 19 49
51 13 128
116 27 57
124 33 123
13 41 27
83 29 29
126 13 35
116 19 3
57 27 73
83 15 43
110 19 50
50 5 21
111 28 113
53 20 52
79 6 19
34 2 113
113 18 87
47 27 77
70 2 59
95 20 52
51 11 90
84 43 129
100 28 19
57 9 73
37 20 89
63 30 4
82 40 89
13 28 38
100 19 89
5 29 29
91 0 50
19 30 87
127 5 19
73 30 58
5 30 4
35 27 21
41 33 91
55 2 5
113 30 12
86 28 50
34 43 95
78 3 89
7 27 19
95 3 19
120 19 113
88 26 69
86 41 111
59 27 38
22 11 90
20 2 87
49 43 81
59 27 115
100 19 3
100 19 110
57 30 89
14 43 107
2 43 93
89 30 77
8 20 81
55 20 100
34 2 32
19 27 115
127 5 50
105 27 116
74 13 122
24 28 113
98 20 57
63 27 99
35 20 52
123 0 95
52 19 110
124 15 92
5 30 113
123 26 89
4 15 43
59 27 19
104 27 73

46
dataset/umls/relation2text.txt
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@ -1,46 +0,0 @@
measures measures
derivative_of derivative of
disrupts disrupts
prevents prevents
conceptually_related_to conceptually related to
manifestation_of manifestation of
diagnoses diagnoses
evaluation_of evaluation of
contains contains
co-occurs_with co-occurs with
conceptual_part_of conceptual part of
performs performs
degree_of degree of
interacts_with interacts with
uses uses
issue_in issue in
assesses_effect_of assesses effect of
property_of property of
precedes precedes
result_of result of
causes causes
practices practices
ingredient_of ingredient of
analyzes analyzes
surrounds surrounds
indicates indicates
associated_with associated with
affects affects
location_of location of
produces produces
process_of process of
measurement_of measurement of
connected_to connected to
carries_out carries out
method_of method of
adjacent_to adjacent to
occurs_in occurs in
consists_of consists of
interconnects interconnects
manages manages
complicates complicates
part_of part of
treats treats
isa is a
developmental_form_of developmental form of
exhibits exhibits

46
dataset/umls/relations.txt
View File

@ -1,46 +0,0 @@
measures
derivative_of
disrupts
prevents
conceptually_related_to
manifestation_of
diagnoses
evaluation_of
contains
co-occurs_with
conceptual_part_of
performs
degree_of
interacts_with
uses
issue_in
assesses_effect_of
property_of
precedes
result_of
causes
practices
ingredient_of
analyzes
surrounds
indicates
associated_with
affects
location_of
produces
process_of
measurement_of
connected_to
carries_out
method_of
adjacent_to
occurs_in
consists_of
interconnects
manages
complicates
part_of
treats
isa
developmental_form_of
exhibits

22
layers.py Normal file
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@ -0,0 +1,22 @@
import torch
from torch import nn
from torch.nn import functional as F
class PreNormResidual(torch.nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.fn = fn
self.norm = torch.nn.LayerNorm(dim)
def forward(self, x):
return self.fn(self.norm(x)) + x
def FeedForward(dim, expansion_factor=4, dropout=0., dense=torch.nn.Linear):
inner_dim = int(dim * expansion_factor)
return torch.nn.Sequential(
dense(dim, inner_dim),
torch.nn.GELU(),
torch.nn.Dropout(dropout*2),
dense(inner_dim, dim),
torch.nn.Dropout(dropout)
)

2
lit_models/__init__.py
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@ -1,2 +0,0 @@
from .transformer import *
from .base import *

97
lit_models/base.py
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@ -1,97 +0,0 @@
import argparse
import pytorch_lightning as pl
import torch
from typing import Dict, Any
OPTIMIZER = "AdamW"
LR = 5e-5
LOSS = "cross_entropy"
ONE_CYCLE_TOTAL_STEPS = 100
class Config(dict):
def __getattr__(self, name):
return self.get(name)
def __setattr__(self, name, val):
self[name] = val
class BaseLitModel(pl.LightningModule):
"""
Generic PyTorch-Lightning class that must be initialized with a PyTorch module.
"""
def __init__(self, model, args: argparse.Namespace = None):
super().__init__()
self.model = model
self.args = Config(vars(args)) if args is not None else {}
optimizer = self.args.get("optimizer", OPTIMIZER)
self.optimizer_class = getattr(torch.optim, optimizer)
self.lr = self.args.get("lr", LR)
@staticmethod
def add_to_argparse(parser):
parser.add_argument("--optimizer", type=str, default=OPTIMIZER, help="optimizer class from torch.optim")
parser.add_argument("--lr", type=float, default=LR)
parser.add_argument("--weight_decay", type=float, default=0.01)
return parser
def configure_optimizers(self):
optimizer = self.optimizer_class(self.parameters(), lr=self.lr)
if self.one_cycle_max_lr is None:
return optimizer
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer=optimizer, max_lr=self.one_cycle_max_lr, total_steps=self.one_cycle_total_steps)
return {"optimizer": optimizer, "lr_scheduler": scheduler, "monitor": "val_loss"}
def forward(self, x):
return self.model(x)
def training_step(self, batch, batch_idx): # pylint: disable=unused-argument
x, y = batch
logits = self(x)
loss = self.loss_fn(logits, y)
self.log("train_loss", loss)
self.train_acc(logits, y)
self.log("train_acc", self.train_acc, on_step=False, on_epoch=True)
return loss
def validation_step(self, batch, batch_idx): # pylint: disable=unused-argument
x, y = batch
logits = self(x)
loss = self.loss_fn(logits, y)
self.log("val_loss", loss, prog_bar=True)
self.val_acc(logits, y)
self.log("val_acc", self.val_acc, on_step=False, on_epoch=True, prog_bar=True)
def test_step(self, batch, batch_idx): # pylint: disable=unused-argument
x, y = batch
logits = self(x)
self.test_acc(logits, y)
self.log("test_acc", self.test_acc, on_step=False, on_epoch=True)
@property
def num_training_steps(self) -> int:
"""Total training steps inferred from datamodule and devices."""
if isinstance(self.trainer.limit_train_batches, int) and self.trainer.limit_train_batches != 0:
dataset_size = self.trainer.limit_train_batches
elif isinstance(self.trainer.limit_train_batches, float):
# limit_train_batches is a percentage of batches
dataset_size = len(self.trainer.datamodule.train_dataloader())
dataset_size = int(dataset_size * self.trainer.limit_train_batches)
else:
dataset_size = len(self.trainer.datamodule.train_dataloader())
num_devices = max(1, self.trainer.num_gpus, self.trainer.num_processes)
if self.trainer.tpu_cores:
num_devices = max(num_devices, self.trainer.tpu_cores)
effective_batch_size = self.trainer.accumulate_grad_batches * num_devices
max_estimated_steps = (dataset_size // effective_batch_size) * self.trainer.max_epochs
if self.trainer.max_steps and self.trainer.max_steps < max_estimated_steps:
return self.trainer.max_steps
return max_estimated_steps

521
lit_models/transformer.py
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@ -1,521 +0,0 @@
from logging import debug
import random
from turtle import distance
import pytorch_lightning as pl
import torch
import pickle
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import json
# from transformers.utils.dummy_pt_objects import PrefixConstrainedLogitsProcessor
from .base import BaseLitModel
from transformers.optimization import get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup
from functools import partial
from .utils import rank_score, acc, LabelSmoothSoftmaxCEV1
from typing import Callable, Iterable, List
def pad_distance(pad_length, distance):
pad = nn.ConstantPad2d(padding=(0, pad_length, 0, pad_length), value=float('-inf'))
distance = pad(distance)
return distance
def lmap(f: Callable, x: Iterable) -> List:
"""list(map(f, x))"""
return list(map(f, x))
def multilabel_categorical_crossentropy(y_pred, y_true):
y_pred = (1 - 2 * y_true) * y_pred
y_pred_neg = y_pred - y_true * 1e12
y_pred_pos = y_pred - (1 - y_true) * 1e12
zeros = torch.zeros_like(y_pred[..., :1])
y_pred_neg = torch.cat([y_pred_neg, zeros], dim=-1)
y_pred_pos = torch.cat([y_pred_pos, zeros], dim=-1)
neg_loss = torch.logsumexp(y_pred_neg, dim=-1)
pos_loss = torch.logsumexp(y_pred_pos, dim=-1)
return (neg_loss + pos_loss).mean()
def decode(output_ids, tokenizer):
return lmap(str.strip, tokenizer.batch_decode(output_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True))
class TransformerLitModel(BaseLitModel):
def __init__(self, model, args, tokenizer=None, data_config={}):
super().__init__(model, args)
self.save_hyperparameters(args)
if args.bce:
self.loss_fn = torch.nn.BCEWithLogitsLoss()
elif args.label_smoothing != 0.0:
self.loss_fn = LabelSmoothSoftmaxCEV1(lb_smooth=args.label_smoothing)
else:
self.loss_fn = nn.CrossEntropyLoss()
self.best_acc = 0
self.first = True
self.tokenizer = tokenizer
self.num_heads = 12
self.__dict__.update(data_config)
# resize the word embedding layer
self.model.resize_token_embeddings(len(self.tokenizer))
self.decode = partial(decode, tokenizer=self.tokenizer)
if args.pretrain:
self._freaze_attention()
elif "ind" in args.data_dir:
# for inductive setting, use feeaze the word embedding
self._freaze_word_embedding()
self.spatial_pos_encoder = nn.Embedding(5, self.num_heads, padding_idx=0)
self.graph_token_virtual_distance = nn.Embedding(1, self.num_heads)
def forward(self, x):
return self.model(x)
def training_step(self, batch, batch_idx): # pylint: disable=unused-argument
# embed();exit()
# print(self.optimizers().param_groups[1]['lr'])
labels = batch.pop("labels")
label = batch.pop("label")
pos = batch.pop("pos")
try:
en = batch.pop("en")
rel = batch.pop("rel")
except KeyError:
pass
input_ids = batch['input_ids']
distance_attention = torch.stack([pad_distance(len(input_ids[i]) - len(distance) - 1, distance) for i, distance in enumerate(batch['distance_attention'])])
distance = batch.pop("distance_attention")
graph_attn_bias = torch.zeros(input_ids.size(0), input_ids.size(1), input_ids.size(1), device='cuda')
graph_attn_bias[:, 1:, 1:][distance_attention == float('-inf')] = float('-inf')
graph_attn_bias = graph_attn_bias.unsqueeze(1).repeat(1, self.num_heads, 1, 1)
distance_attention = self.spatial_pos_encoder(distance_attention.long()).permute(0, 3, 1, 2)
graph_attn_bias[:, :, 1:, 1:] = graph_attn_bias[:, :, 1:, 1:] + distance_attention
if self.args.use_global_node:
t = self.graph_token_virtual_distance.weight.view(1, self.num_heads, 1)
graph_attn_bias[:, :, 1:, 0] = graph_attn_bias[:, :, 1:, 0] + t
graph_attn_bias[:, :, 0, :] = graph_attn_bias[:, :, 0, :] + t
if self.args.add_attn_bias:
logits = self.model(**batch, return_dict=True, distance_attention=graph_attn_bias).logits
else:
logits = self.model(**batch, return_dict=True, distance_attention=None).logits
_, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True)
bs = input_ids.shape[0]
mask_logits = logits[torch.arange(bs), mask_idx][:, self.entity_id_st:self.entity_id_ed]
assert mask_idx.shape[0] == bs, "only one mask in sequence!"
if self.args.bce:
loss = self.loss_fn(mask_logits, labels)
else:
loss = self.loss_fn(mask_logits, label)
if batch_idx == 0:
print('\n'.join(self.decode(batch['input_ids'][:4])))
return loss
def _eval(self, batch, batch_idx, ):
labels = batch.pop("labels")
input_ids = batch['input_ids']
# single label
label = batch.pop('label')
pos = batch.pop('pos')
distance_attention = torch.stack([pad_distance(len(input_ids[i]) - len(distance) - 1, distance) for i, distance in enumerate(batch['distance_attention'])])
distance = batch.pop("distance_attention")
graph_attn_bias = torch.zeros(input_ids.size(0), input_ids.size(1), input_ids.size(1), device='cuda')
graph_attn_bias[:, 1:, 1:][distance_attention == float('-inf')] = float('-inf')
graph_attn_bias = graph_attn_bias.unsqueeze(1).repeat(1, self.num_heads, 1, 1)
distance_attention = self.spatial_pos_encoder(distance_attention.long()).permute(0, 3, 1, 2)
graph_attn_bias[:, :, 1:, 1:] = graph_attn_bias[:, :, 1:, 1:] + distance_attention
# distance_attention = torch.stack([pad_distance(len(input_ids[i]) - len(distance), distance) for i, distance in enumerate(batch['distance_attention'])])
# distance = batch.pop("distance_attention")
# distance_attention = self.spatial_pos_encoder(distance_attention.long()).permute(0, 3, 1, 2)
my_keys = list(batch.keys())
for k in my_keys:
if k not in ["input_ids", "attention_mask", "token_type_ids"]:
batch.pop(k)
if self.args.add_attn_bias:
logits = self.model(**batch, return_dict=True, distance_attention=graph_attn_bias).logits[:, :, self.entity_id_st:self.entity_id_ed]
else:
logits = self.model(**batch, return_dict=True, distance_attention=None).logits[:, :, self.entity_id_st:self.entity_id_ed]
_, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True)
bsz = input_ids.shape[0]
logits = logits[torch.arange(bsz), mask_idx]
# get the entity ranks
# filter the entity
assert labels[0][label[0]], "correct ids must in filiter!"
labels[torch.arange(bsz), label] = 0
assert logits.shape == labels.shape
logits += labels * -100 # mask entityj
# for i in range(bsz):
# logits[i][labels]
_, outputs = torch.sort(logits, dim=1, descending=True)
_, outputs = torch.sort(outputs, dim=1)
ranks = outputs[torch.arange(bsz), label].detach().cpu() + 1
return dict(ranks = np.array(ranks))
def validation_step(self, batch, batch_idx):
result = self._eval(batch, batch_idx)
return result
def validation_epoch_end(self, outputs) -> None:
ranks = np.concatenate([_['ranks'] for _ in outputs])
total_ranks = ranks.shape[0]
if not self.args.pretrain:
l_ranks = ranks[np.array(list(np.arange(0, total_ranks, 2)))]
r_ranks = ranks[np.array(list(np.arange(0, total_ranks, 2))) + 1]
self.log("Eval/lhits10", (l_ranks<=10).mean())
self.log("Eval/rhits10", (r_ranks<=10).mean())
hits20 = (ranks<=20).mean()
hits10 = (ranks<=10).mean()
hits3 = (ranks<=3).mean()
hits1 = (ranks<=1).mean()
self.log("Eval/hits10", hits10)
self.log("Eval/hits20", hits20)
self.log("Eval/hits3", hits3)
self.log("Eval/hits1", hits1)
self.log("Eval/mean_rank", ranks.mean())
self.log("Eval/mrr", (1. / ranks).mean())
self.log("hits10", hits10, prog_bar=True)
self.log("hits1", hits1, prog_bar=True)
def test_step(self, batch, batch_idx): # pylint: disable=unused-argument
# ranks = self._eval(batch, batch_idx)
result = self._eval(batch, batch_idx)
# self.log("Test/ranks", np.mean(ranks))
return result
def test_epoch_end(self, outputs) -> None:
ranks = np.concatenate([_['ranks'] for _ in outputs])
hits20 = (ranks<=20).mean()
hits10 = (ranks<=10).mean()
hits3 = (ranks<=3).mean()
hits1 = (ranks<=1).mean()
self.log("Test/hits10", hits10)
self.log("Test/hits20", hits20)
self.log("Test/hits3", hits3)
self.log("Test/hits1", hits1)
self.log("Test/mean_rank", ranks.mean())
self.log("Test/mrr", (1. / ranks).mean())
def configure_optimizers(self):
no_decay_param = ["bias", "LayerNorm.weight"]
optimizer_group_parameters = [
{"params": [p for n, p in self.model.named_parameters() if p.requires_grad and not any(nd in n for nd in no_decay_param)], "weight_decay": self.args.weight_decay},
{"params": [p for n, p in self.model.named_parameters() if p.requires_grad and any(nd in n for nd in no_decay_param)], "weight_decay": 0}
]
optimizer = self.optimizer_class(optimizer_group_parameters, lr=self.lr, eps=1e-8)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=self.num_training_steps * self.args.warm_up_radio, num_training_steps=self.num_training_steps)
return {
"optimizer": optimizer,
"lr_scheduler":{
'scheduler': scheduler,
'interval': 'step', # or 'epoch'
'frequency': 1,
}
}
def _freaze_attention(self):
for k, v in self.model.named_parameters():
if "word" not in k:
v.requires_grad = False
else:
print(k)
def _freaze_word_embedding(self):
for k, v in self.model.named_parameters():
if "word" in k:
print(k)
v.requires_grad = False
@staticmethod
def add_to_argparse(parser):
parser = BaseLitModel.add_to_argparse(parser)
parser.add_argument("--label_smoothing", type=float, default=0.1, help="")
parser.add_argument("--bce", type=int, default=0, help="")
return parser
import faiss
import os
class GetEntityEmbeddingLitModel(TransformerLitModel):
def __init__(self, model, args, tokenizer, data_config={}):
super().__init__(model, args, tokenizer, data_config)
self.faissid2entityid = {}
# self.index = faiss.IndexFlatL2(d) # build the index
d, measure = self.model.config.hidden_size, faiss.METRIC_L2
# param = 'HNSW64'
# self.index = faiss.index_factory(d, param, measure)
self.index = faiss.IndexFlatL2(d) # build the index
# print(self.index.is_trained) # 此时输出为True
# index.add(xb)
self.cnt_batch = 0
self.total_embedding = []
def test_step(self, batch, batch_idx):
labels = batch.pop("labels")
mask_idx = batch.pop("pos")
input_ids = batch['input_ids']
# single label
label = batch.pop('label')
# last layer
hidden_states = self.model(**batch, return_dict=True, output_hidden_states=True).hidden_states[-1]
# _, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True)
bsz = input_ids.shape[0]
entity_embedding = hidden_states[torch.arange(bsz), mask_idx].cpu()
# use normalize or not ?
# entity_embedding = F.normalize(entity_embedding, dim=-1, p = 2)
self.total_embedding.append(entity_embedding)
# self.index.add(np.array(entity_embedding, dtype=np.float32))
for i, l in zip(range(bsz), label):
self.faissid2entityid[i+self.cnt_batch] = l.cpu()
self.cnt_batch += bsz
def test_epoch_end(self, outputs) -> None:
self.total_embedding = np.concatenate(self.total_embedding, axis=0)
# self.index.train(self.total_embedding)
print(faiss.MatrixStats(self.total_embedding).comments)
self.index.add(self.total_embedding)
faiss.write_index(self.index, os.path.join(self.args.data_dir, "faiss_dump.index"))
with open(os.path.join(self.args.data_dir, "faissid2entityid.pkl") ,'wb') as file:
pickle.dump(self.faissid2entityid, file)
with open(os.path.join(self.args.data_dir, "total_embedding.pkl") ,'wb') as file:
pickle.dump(self.total_embedding, file)
# print(f"number of entity embedding : {len(self.faissid2entityid)}")
@staticmethod
def add_to_argparse(parser):
parser = TransformerLitModel.add_to_argparse(parser)
parser.add_argument("--faiss_init", type=int, default=1, help="get the embedding and save it the file.")
return parser
class UseEntityEmbeddingLitModel(TransformerLitModel):
def __init__(self, model, args, tokenizer, data_config={}):
super().__init__(model, args, tokenizer, data_config)
self.faissid2entityid = pickle.load(open(os.path.join(self.args.data_dir, "faissid2entityid.pkl") ,'rb'))
self.index = faiss.read_index(os.path.join(self.args.data_dir, "faiss_dump.index"))
self.dis2logits = distance2logits_2
def _eval(self, batch, batch_idx, ):
labels = batch.pop("labels")
pos = batch.pop("pos")
input_ids = batch['input_ids']
# single label
label = batch.pop('label')
hidden_states = self.model(**batch, return_dict=True, output_hidden_states=True).hidden_states[-1]
_, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True)
bsz = input_ids.shape[0]
mask_embedding = np.array(hidden_states[torch.arange(bsz), mask_idx].cpu(), dtype=np.float32)
topk = 200
D, I = self.index.search(mask_embedding, topk)
labels[torch.arange(bsz), label] = 0
entity_logits = torch.full(labels.shape, -100.).to(self.device)
D = self.dis2logits(D)
for i in range(bsz):
for j in range(topk):
# filter entity in labels
if I[i][j] not in self.faissid2entityid:
print(I[i][j])
break
# assert I[i][j] in self.faissid2entityid, print(I[i][j])
if labels[i][self.faissid2entityid[I[i][j]]]: continue
if entity_logits[i][self.faissid2entityid[I[i][j]]] == -100.:
entity_logits[i][self.faissid2entityid[I[i][j]]] = D[i][j]
# no added together
# else:
# entity_logits[i][self.faissid2entityid[I[i][j]]] += D[i][j]
# get the entity ranks
# filter the entity
assert entity_logits.shape == labels.shape
_, outputs = torch.sort(entity_logits, dim=1, descending=True)
_, outputs = torch.sort(outputs, dim=1)
ranks = outputs[torch.arange(bsz), label].detach().cpu() + 1
return dict(ranks = np.array(ranks))
@staticmethod
def add_to_argparse(parser):
parser = TransformerLitModel.add_to_argparse(parser)
parser.add_argument("--faiss_init", type=int, default=0, help="get the embedding and save it the file.")
parser.add_argument("--faiss_use", type=int, default=1, help="get the embedding and save it the file.")
return parser
class CombineEntityEmbeddingLitModel(UseEntityEmbeddingLitModel):
def __init__(self, model, args, tokenizer, data_config={}):
super().__init__(model, args, tokenizer, data_config=data_config)
self.dis2logits = distance2logits_2
self.id2entity = {}
with open("./dataset/FB15k-237/entity2textlong.txt", 'r') as file:
cnt = 0
for line in file.readlines():
e, d = line.strip().split("\t")
self.id2entity[cnt] = e
cnt += 1
self.id2entity_t = {}
with open("./dataset/FB15k-237/entity2text.txt", 'r') as file:
for line in file.readlines():
e, d = line.strip().split("\t")
self.id2entity_t[e] = d
for k, v in self.id2entity.items():
self.id2entity[k] = self.id2entity_t[v]
def _eval(self, batch, batch_idx, ):
labels = batch.pop("labels")
input_ids = batch['input_ids']
# single label
label = batch.pop('label')
pos = batch.pop("pos")
result = self.model(**batch, return_dict=True, output_hidden_states=True)
hidden_states = result.hidden_states[-1]
_, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True)
bsz = input_ids.shape[0]
mask_embedding = np.array(hidden_states[torch.arange(bsz), mask_idx].cpu(), dtype=np.float32)
# mask_embedding = np.array(hidden_states[torch.arange(bsz), mask_idx].cpu(), dtype=np.float32)
topk = self.args.knn_topk
D, I = self.index.search(mask_embedding, topk)
D = torch.from_numpy(D).to(self.device)
assert labels[0][label[0]], "correct ids must in filiter!"
labels[torch.arange(bsz), label] = 0
mask_logits = result.logits[:, :, self.entity_id_st:self.entity_id_ed]
mask_logits = mask_logits[torch.arange(bsz), mask_idx]
entity_logits = torch.full(labels.shape, 1000.).to(self.device)
# D = self.dis2logits(D)
for i in range(bsz):
for j in range(topk):
# filter entity in labels
if labels[i][self.faissid2entityid[I[i][j]]]: continue
if entity_logits[i][self.faissid2entityid[I[i][j]]] == 1000.:
entity_logits[i][self.faissid2entityid[I[i][j]]] = D[i][j]
# else:
# entity_logits[i][self.faissid2entityid[I[i][j]]] += D[i][j]
entity_logits = self.dis2logits(entity_logits)
# get the entity ranks
# filter the entity
assert entity_logits.shape == labels.shape
assert mask_logits.shape == labels.shape
# entity_logits = torch.softmax(entity_logits + labels * -100, dim=-1) # mask entityj
entity_logits = entity_logits + labels* -100.
mask_logits = torch.softmax(mask_logits + labels* -100, dim=-1)
# logits = mask_logits
logits = combine_knn_and_vocab_probs(entity_logits, mask_logits, self.args.knn_lambda)
# logits = entity_logits + mask_logits
knn_topk_logits, knn_topk_id = entity_logits.topk(20)
mask_topk_logits, mask_topk_id = mask_logits.topk(20)
union_topk = []
for i in range(bsz):
num_same = len(list(set(knn_topk_id[i].cpu().tolist()) & set(mask_topk_id[i].cpu().tolist())))
union_topk.append(num_same/ 20.)
knn_topk_id = knn_topk_id.to("cpu")
mask_topk_id = mask_topk_id.to("cpu")
mask_topk_logits = mask_topk_logits.to("cpu")
knn_topk_logits = knn_topk_logits.to("cpu")
label = label.to("cpu")
for t in range(bsz):
if knn_topk_id[t][0] == label[t] and knn_topk_logits[t][0] > mask_topk_logits[t][0] and mask_topk_logits[t][0] <= 0.4:
print(knn_topk_logits[t], knn_topk_id[t])
print(lmap(lambda x: self.id2entity[x.item()], knn_topk_id[t]))
print(mask_topk_logits[t], mask_topk_id[t])
print(lmap(lambda x: self.id2entity[x.item()], mask_topk_id[t]))
print(label[t])
print()
_, outputs = torch.sort(logits, dim=1, descending=True)
_, outputs = torch.sort(outputs, dim=1)
ranks = outputs[torch.arange(bsz), label].detach().cpu() + 1
return dict(ranks = np.array(ranks), knn_topk_id=knn_topk_id, knn_topk_logits=knn_topk_logits,
mask_topk_id=mask_topk_id, mask_topk_logits=mask_topk_logits, num_same = np.array(union_topk))
def test_epoch_end(self, outputs) -> None:
ranks = np.concatenate([_['ranks'] for _ in outputs])
num_same = np.concatenate([_['num_same'] for _ in outputs])
results_keys = list(outputs[0].keys())
results = {}
# for k in results_keys:
# results.
self.log("Test/num_same", num_same.mean())
hits20 = (ranks<=20).mean()
hits10 = (ranks<=10).mean()
hits3 = (ranks<=3).mean()
hits1 = (ranks<=1).mean()
self.log("Test/hits10", hits10)
self.log("Test/hits20", hits20)
self.log("Test/hits3", hits3)
self.log("Test/hits1", hits1)
self.log("Test/mean_rank", ranks.mean())
self.log("Test/mrr", (1. / ranks).mean())
def add_to_argparse(parser):
parser = TransformerLitModel.add_to_argparse(parser)
parser.add_argument("--knn_lambda", type=float, default=0.5, help="lambda * knn + (1-lambda) * mask logits , lambda of knn logits and mask logits.")
parser.add_argument("--knn_topk", type=int, default=100, help="")
return parser
def combine_knn_and_vocab_probs(knn_p, vocab_p, coeff=0.5):
combine_probs = torch.stack([vocab_p, knn_p], dim=0)
coeffs = torch.ones_like(combine_probs)
coeffs[0] = np.log(1 - coeff)
coeffs[1] = np.log(coeff)
curr_prob = torch.logsumexp(combine_probs + coeffs, dim=0)
return curr_prob
def distance2logits(D):
return torch.softmax( -1. * torch.tensor(D) / 30., dim=-1)
def distance2logits_2(D, n=10):
if not isinstance(D, torch.Tensor):
D = torch.tensor(D)
if torch.sum(D) != 0.0:
distances = torch.exp(-D/n) / torch.sum(torch.exp(-D/n), dim=-1, keepdim=True)
return distances

66
lit_models/utils.py
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@ -1,66 +0,0 @@
import json
import numpy as np
def rank_score(ranks):
# prepare the dataset
len_samples = len(ranks)
hits10 = [0] * len_samples
hits5 = [0] * len_samples
hits1 = [0] * len_samples
mrr = []
for idx, rank in enumerate(ranks):
if rank <= 10:
hits10[idx] = 1.
if rank <= 5:
hits5[idx] = 1.
if rank <= 1:
hits1[idx] = 1.
mrr.append(1./rank)
return np.mean(hits10), np.mean(hits5), np.mean(hits1), np.mean(mrr)
def acc(logits, labels):
preds = np.argmax(logits, axis=-1)
return (preds == labels).mean()
import torch.nn as nn
import torch
class LabelSmoothSoftmaxCEV1(nn.Module):
'''
This is the autograd version, you can also try the LabelSmoothSoftmaxCEV2 that uses derived gradients
'''
def __init__(self, lb_smooth=0.1, reduction='mean', ignore_index=-100):
super(LabelSmoothSoftmaxCEV1, self).__init__()
self.lb_smooth = lb_smooth
self.reduction = reduction
self.lb_ignore = ignore_index
self.log_softmax = nn.LogSoftmax(dim=1)
def forward(self, logits, label):
'''
args: logits: tensor of shape (N, C, H, W)
args: label: tensor of shape(N, H, W)
'''
# overcome ignored label
with torch.no_grad():
num_classes = logits.size(1)
label = label.clone().detach()
ignore = label == self.lb_ignore
n_valid = (ignore == 0).sum()
label[ignore] = 0
lb_pos, lb_neg = 1. - self.lb_smooth, self.lb_smooth / num_classes
label = torch.empty_like(logits).fill_(
lb_neg).scatter_(1, label.unsqueeze(1), lb_pos).detach()
logs = self.log_softmax(logits)
loss = -torch.sum(logs * label, dim=1)
loss[ignore] = 0
if self.reduction == 'mean':
loss = loss.sum() / n_valid
if self.reduction == 'sum':
loss = loss.sum()
return loss

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2023-05-02 04:18:49,398 - fb2 - [INFO] - {'dataset': 'FB15k-237', 'name': 'fb2', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 1e-05, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.1, 'drop': 0.2, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}
2023-05-02 04:19:01,259 - fb2 - [INFO] - [E:0| 0]: Train Loss:0.69689, Val MRR:0.0, fb2
2023-05-02 04:20:32,640 - fb2 - [INFO] - [E:0| 100]: Train Loss:0.36744, Val MRR:0.0, fb2
2023-05-02 04:22:04,584 - fb2 - [INFO] - [E:0| 200]: Train Loss:0.24098, Val MRR:0.0, fb2
2023-05-02 04:23:36,730 - fb2 - [INFO] - [E:0| 300]: Train Loss:0.17938, Val MRR:0.0, fb2
2023-05-02 04:25:05,852 - fb2 - [INFO] - [E:0| 400]: Train Loss:0.14323, Val MRR:0.0, fb2
2023-05-02 04:26:37,938 - fb2 - [INFO] - [E:0| 500]: Train Loss:0.11952, Val MRR:0.0, fb2
2023-05-02 04:28:09,889 - fb2 - [INFO] - [E:0| 600]: Train Loss:0.10275, Val MRR:0.0, fb2
2023-05-02 04:29:41,867 - fb2 - [INFO] - [E:0| 700]: Train Loss:0.090272, Val MRR:0.0, fb2
2023-05-02 04:31:07,818 - fb2 - [INFO] - [E:0| 800]: Train Loss:0.080642, Val MRR:0.0, fb2
2023-05-02 04:32:39,703 - fb2 - [INFO] - [E:0| 900]: Train Loss:0.07296, Val MRR:0.0, fb2
2023-05-02 04:34:11,338 - fb2 - [INFO] - [E:0| 1000]: Train Loss:0.066702, Val MRR:0.0, fb2
2023-05-02 04:35:42,626 - fb2 - [INFO] - [E:0| 1100]: Train Loss:0.061509, Val MRR:0.0, fb2
2023-05-02 04:36:45,446 - fb2 - [INFO] - [Epoch:0]: Training Loss:0.05842
2023-05-02 04:36:45,724 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 04:37:14,248 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 04:37:24,208 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 04:37:57,309 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 04:38:09,080 - fb2 - [INFO] - [Evaluating Epoch 0 valid]:
MRR: Tail : 0.05276, Head : 0.00751, Avg : 0.03014
2023-05-02 04:38:10,219 - fb2 - [INFO] - [Epoch 0]: Training Loss: 0.058417, Valid MRR: 0.03014,
2023-05-02 04:38:11,161 - fb2 - [INFO] - [E:1| 0]: Train Loss:0.0083711, Val MRR:0.03014, fb2
2023-05-02 04:39:42,529 - fb2 - [INFO] - [E:1| 100]: Train Loss:0.0085887, Val MRR:0.03014, fb2
2023-05-02 04:41:14,306 - fb2 - [INFO] - [E:1| 200]: Train Loss:0.0084227, Val MRR:0.03014, fb2
2023-05-02 04:42:45,633 - fb2 - [INFO] - [E:1| 300]: Train Loss:0.0082433, Val MRR:0.03014, fb2
2023-05-02 04:44:11,829 - fb2 - [INFO] - [E:1| 400]: Train Loss:0.0080888, Val MRR:0.03014, fb2
2023-05-02 04:45:43,750 - fb2 - [INFO] - [E:1| 500]: Train Loss:0.007955, Val MRR:0.03014, fb2
2023-05-02 04:47:15,788 - fb2 - [INFO] - [E:1| 600]: Train Loss:0.0078681, Val MRR:0.03014, fb2
2023-05-02 04:48:48,084 - fb2 - [INFO] - [E:1| 700]: Train Loss:0.0077871, Val MRR:0.03014, fb2
2023-05-02 04:50:18,308 - fb2 - [INFO] - [E:1| 800]: Train Loss:0.0077091, Val MRR:0.03014, fb2
2023-05-02 04:51:49,440 - fb2 - [INFO] - [E:1| 900]: Train Loss:0.00764, Val MRR:0.03014, fb2
2023-05-02 04:53:21,113 - fb2 - [INFO] - [E:1| 1000]: Train Loss:0.007576, Val MRR:0.03014, fb2
2023-05-02 04:54:52,877 - fb2 - [INFO] - [E:1| 1100]: Train Loss:0.0075201, Val MRR:0.03014, fb2
2023-05-02 04:55:55,526 - fb2 - [INFO] - [Epoch:1]: Training Loss:0.007478
2023-05-02 04:55:55,884 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 04:56:25,912 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 04:56:36,106 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 04:57:04,951 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 04:57:16,876 - fb2 - [INFO] - [Evaluating Epoch 1 valid]:
MRR: Tail : 0.05304, Head : 0.00764, Avg : 0.03034
2023-05-02 04:57:19,016 - fb2 - [INFO] - [Epoch 1]: Training Loss: 0.007478, Valid MRR: 0.03034,
2023-05-02 04:57:19,964 - fb2 - [INFO] - [E:2| 0]: Train Loss:0.0077412, Val MRR:0.03034, fb2
2023-05-02 04:58:51,692 - fb2 - [INFO] - [E:2| 100]: Train Loss:0.006784, Val MRR:0.03034, fb2
2023-05-02 05:00:23,348 - fb2 - [INFO] - [E:2| 200]: Train Loss:0.0067657, Val MRR:0.03034, fb2
2023-05-02 05:01:55,157 - fb2 - [INFO] - [E:2| 300]: Train Loss:0.0067572, Val MRR:0.03034, fb2
2023-05-02 05:03:25,371 - fb2 - [INFO] - [E:2| 400]: Train Loss:0.0067142, Val MRR:0.03034, fb2
2023-05-02 05:04:55,830 - fb2 - [INFO] - [E:2| 500]: Train Loss:0.0067221, Val MRR:0.03034, fb2
2023-05-02 05:06:27,700 - fb2 - [INFO] - [E:2| 600]: Train Loss:0.0067123, Val MRR:0.03034, fb2
2023-05-02 05:07:59,354 - fb2 - [INFO] - [E:2| 700]: Train Loss:0.0066851, Val MRR:0.03034, fb2
2023-05-02 05:09:31,441 - fb2 - [INFO] - [E:2| 800]: Train Loss:0.0066677, Val MRR:0.03034, fb2
2023-05-02 05:10:58,593 - fb2 - [INFO] - [E:2| 900]: Train Loss:0.0066402, Val MRR:0.03034, fb2
2023-05-02 05:12:30,077 - fb2 - [INFO] - [E:2| 1000]: Train Loss:0.0066205, Val MRR:0.03034, fb2
2023-05-02 05:14:01,720 - fb2 - [INFO] - [E:2| 1100]: Train Loss:0.0065907, Val MRR:0.03034, fb2
2023-05-02 05:15:04,976 - fb2 - [INFO] - [Epoch:2]: Training Loss:0.006572
2023-05-02 05:15:05,303 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 05:15:35,236 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 05:15:46,422 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 05:16:18,357 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 05:16:29,632 - fb2 - [INFO] - [Evaluating Epoch 2 valid]:
MRR: Tail : 0.05261, Head : 0.00737, Avg : 0.02999
2023-05-02 05:16:29,632 - fb2 - [INFO] - [Epoch 2]: Training Loss: 0.0065725, Valid MRR: 0.03034,
2023-05-02 05:16:30,314 - fb2 - [INFO] - [E:3| 0]: Train Loss:0.0058567, Val MRR:0.03034, fb2
2023-05-02 05:18:01,365 - fb2 - [INFO] - [E:3| 100]: Train Loss:0.0062464, Val MRR:0.03034, fb2
2023-05-02 05:19:33,019 - fb2 - [INFO] - [E:3| 200]: Train Loss:0.0061913, Val MRR:0.03034, fb2
2023-05-02 05:21:04,158 - fb2 - [INFO] - [E:3| 300]: Train Loss:0.006163, Val MRR:0.03034, fb2
2023-05-02 05:22:35,273 - fb2 - [INFO] - [E:3| 400]: Train Loss:0.0061644, Val MRR:0.03034, fb2
2023-05-02 05:24:00,356 - fb2 - [INFO] - [E:3| 500]: Train Loss:0.0061382, Val MRR:0.03034, fb2
2023-05-02 05:25:31,092 - fb2 - [INFO] - [E:3| 600]: Train Loss:0.0061044, Val MRR:0.03034, fb2
2023-05-02 05:27:01,421 - fb2 - [INFO] - [E:3| 700]: Train Loss:0.0060781, Val MRR:0.03034, fb2
2023-05-02 05:28:31,802 - fb2 - [INFO] - [E:3| 800]: Train Loss:0.0060643, Val MRR:0.03034, fb2
2023-05-02 05:29:59,573 - fb2 - [INFO] - [E:3| 900]: Train Loss:0.0060521, Val MRR:0.03034, fb2
2023-05-02 05:31:29,674 - fb2 - [INFO] - [E:3| 1000]: Train Loss:0.0060237, Val MRR:0.03034, fb2
2023-05-02 05:32:59,535 - fb2 - [INFO] - [E:3| 1100]: Train Loss:0.0059936, Val MRR:0.03034, fb2
2023-05-02 05:34:00,924 - fb2 - [INFO] - [Epoch:3]: Training Loss:0.005975
2023-05-02 05:34:01,312 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 05:34:31,261 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 05:34:42,271 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 05:35:15,204 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 05:35:26,943 - fb2 - [INFO] - [Evaluating Epoch 3 valid]:
MRR: Tail : 0.05358, Head : 0.00717, Avg : 0.03037
2023-05-02 05:35:28,082 - fb2 - [INFO] - [Epoch 3]: Training Loss: 0.0059753, Valid MRR: 0.03037,
2023-05-02 05:35:28,897 - fb2 - [INFO] - [E:4| 0]: Train Loss:0.0053117, Val MRR:0.03037, fb2
2023-05-02 05:36:53,156 - fb2 - [INFO] - [E:4| 100]: Train Loss:0.0056516, Val MRR:0.03037, fb2
2023-05-02 05:38:22,908 - fb2 - [INFO] - [E:4| 200]: Train Loss:0.0056337, Val MRR:0.03037, fb2
2023-05-02 05:39:52,263 - fb2 - [INFO] - [E:4| 300]: Train Loss:0.0055847, Val MRR:0.03037, fb2
2023-05-02 05:41:21,010 - fb2 - [INFO] - [E:4| 400]: Train Loss:0.0055579, Val MRR:0.03037, fb2
2023-05-02 05:42:47,682 - fb2 - [INFO] - [E:4| 500]: Train Loss:0.0055457, Val MRR:0.03037, fb2
2023-05-02 05:44:16,669 - fb2 - [INFO] - [E:4| 600]: Train Loss:0.0055229, Val MRR:0.03037, fb2
2023-05-02 05:45:46,114 - fb2 - [INFO] - [E:4| 700]: Train Loss:0.0055053, Val MRR:0.03037, fb2
2023-05-02 05:47:14,834 - fb2 - [INFO] - [E:4| 800]: Train Loss:0.0054886, Val MRR:0.03037, fb2
2023-05-02 05:48:43,883 - fb2 - [INFO] - [E:4| 900]: Train Loss:0.0054641, Val MRR:0.03037, fb2
2023-05-02 05:50:07,436 - fb2 - [INFO] - [E:4| 1000]: Train Loss:0.0054569, Val MRR:0.03037, fb2
2023-05-02 05:51:36,436 - fb2 - [INFO] - [E:4| 1100]: Train Loss:0.0054476, Val MRR:0.03037, fb2
2023-05-02 05:52:37,532 - fb2 - [INFO] - [Epoch:4]: Training Loss:0.005427
2023-05-02 05:52:37,851 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 05:53:07,350 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 05:53:18,338 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 05:53:50,675 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 05:54:02,474 - fb2 - [INFO] - [Evaluating Epoch 4 valid]:
MRR: Tail : 0.05373, Head : 0.00834, Avg : 0.03104
2023-05-02 05:54:03,608 - fb2 - [INFO] - [Epoch 4]: Training Loss: 0.0054269, Valid MRR: 0.03104,
2023-05-02 05:54:04,547 - fb2 - [INFO] - [E:5| 0]: Train Loss:0.011799, Val MRR:0.03104, fb2
2023-05-02 05:55:32,136 - fb2 - [INFO] - [E:5| 100]: Train Loss:0.0052331, Val MRR:0.03104, fb2
2023-05-02 05:56:59,892 - fb2 - [INFO] - [E:5| 200]: Train Loss:0.0051725, Val MRR:0.03104, fb2
2023-05-02 05:58:28,729 - fb2 - [INFO] - [E:5| 300]: Train Loss:0.005151, Val MRR:0.03104, fb2
2023-05-02 05:59:57,684 - fb2 - [INFO] - [E:5| 400]: Train Loss:0.0051306, Val MRR:0.03104, fb2
2023-05-02 06:01:26,978 - fb2 - [INFO] - [E:5| 500]: Train Loss:0.0050962, Val MRR:0.03104, fb2
2023-05-02 06:02:50,567 - fb2 - [INFO] - [E:5| 600]: Train Loss:0.0050567, Val MRR:0.03104, fb2
2023-05-02 06:04:19,772 - fb2 - [INFO] - [E:5| 700]: Train Loss:0.005027, Val MRR:0.03104, fb2
2023-05-02 06:05:48,574 - fb2 - [INFO] - [E:5| 800]: Train Loss:0.0050262, Val MRR:0.03104, fb2
2023-05-02 06:07:17,606 - fb2 - [INFO] - [E:5| 900]: Train Loss:0.0050039, Val MRR:0.03104, fb2
2023-05-02 06:08:44,259 - fb2 - [INFO] - [E:5| 1000]: Train Loss:0.0049983, Val MRR:0.03104, fb2
2023-05-02 06:10:12,405 - fb2 - [INFO] - [E:5| 1100]: Train Loss:0.0049798, Val MRR:0.03104, fb2
2023-05-02 06:11:13,490 - fb2 - [INFO] - [Epoch:5]: Training Loss:0.004961
2023-05-02 06:11:13,729 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 06:11:43,033 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 06:11:53,943 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 06:12:26,527 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 06:12:38,133 - fb2 - [INFO] - [Evaluating Epoch 5 valid]:
MRR: Tail : 0.04854, Head : 0.00726, Avg : 0.0279
2023-05-02 06:12:38,133 - fb2 - [INFO] - [Epoch 5]: Training Loss: 0.0049607, Valid MRR: 0.03104,
2023-05-02 06:12:39,021 - fb2 - [INFO] - [E:6| 0]: Train Loss:0.0061745, Val MRR:0.03104, fb2
2023-05-02 06:14:08,189 - fb2 - [INFO] - [E:6| 100]: Train Loss:0.0046955, Val MRR:0.03104, fb2
2023-05-02 06:15:31,880 - fb2 - [INFO] - [E:6| 200]: Train Loss:0.0046845, Val MRR:0.03104, fb2
2023-05-02 06:17:00,642 - fb2 - [INFO] - [E:6| 300]: Train Loss:0.0047069, Val MRR:0.03104, fb2
2023-05-02 06:18:29,603 - fb2 - [INFO] - [E:6| 400]: Train Loss:0.0046892, Val MRR:0.03104, fb2
2023-05-02 06:19:58,015 - fb2 - [INFO] - [E:6| 500]: Train Loss:0.0046968, Val MRR:0.03104, fb2
2023-05-02 06:21:26,175 - fb2 - [INFO] - [E:6| 600]: Train Loss:0.0046998, Val MRR:0.03104, fb2
2023-05-02 06:22:53,128 - fb2 - [INFO] - [E:6| 700]: Train Loss:0.0046795, Val MRR:0.03104, fb2
2023-05-02 06:24:22,260 - fb2 - [INFO] - [E:6| 800]: Train Loss:0.0046533, Val MRR:0.03104, fb2
2023-05-02 06:25:51,084 - fb2 - [INFO] - [E:6| 900]: Train Loss:0.0046311, Val MRR:0.03104, fb2
2023-05-02 06:27:19,752 - fb2 - [INFO] - [E:6| 1000]: Train Loss:0.0046129, Val MRR:0.03104, fb2
2023-05-02 06:28:43,764 - fb2 - [INFO] - [E:6| 1100]: Train Loss:0.004602, Val MRR:0.03104, fb2
2023-05-02 06:29:44,669 - fb2 - [INFO] - [Epoch:6]: Training Loss:0.004588
2023-05-02 06:29:45,063 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 06:30:14,453 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 06:30:25,422 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 06:30:57,700 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 06:31:09,385 - fb2 - [INFO] - [Evaluating Epoch 6 valid]:
MRR: Tail : 0.05203, Head : 0.00718, Avg : 0.0296
2023-05-02 06:31:09,385 - fb2 - [INFO] - [Epoch 6]: Training Loss: 0.0045885, Valid MRR: 0.03104,
2023-05-02 06:31:10,424 - fb2 - [INFO] - [E:7| 0]: Train Loss:0.0040115, Val MRR:0.03104, fb2
2023-05-02 06:32:38,839 - fb2 - [INFO] - [E:7| 100]: Train Loss:0.0043937, Val MRR:0.03104, fb2
2023-05-02 06:34:07,833 - fb2 - [INFO] - [E:7| 200]: Train Loss:0.0043624, Val MRR:0.03104, fb2
2023-05-02 06:35:34,371 - fb2 - [INFO] - [E:7| 300]: Train Loss:0.0043875, Val MRR:0.03104, fb2
2023-05-02 06:37:03,680 - fb2 - [INFO] - [E:7| 400]: Train Loss:0.0043683, Val MRR:0.03104, fb2
2023-05-02 06:38:32,695 - fb2 - [INFO] - [E:7| 500]: Train Loss:0.0043403, Val MRR:0.03104, fb2
2023-05-02 06:40:01,879 - fb2 - [INFO] - [E:7| 600]: Train Loss:0.0043325, Val MRR:0.03104, fb2
2023-05-02 06:41:25,014 - fb2 - [INFO] - [E:7| 700]: Train Loss:0.0043239, Val MRR:0.03104, fb2
2023-05-02 06:42:53,634 - fb2 - [INFO] - [E:7| 800]: Train Loss:0.0043244, Val MRR:0.03104, fb2
2023-05-02 06:44:23,433 - fb2 - [INFO] - [E:7| 900]: Train Loss:0.0043176, Val MRR:0.03104, fb2
2023-05-02 06:46:45,886 - fb2 - [INFO] - [E:7| 1000]: Train Loss:0.0043261, Val MRR:0.03104, fb2
2023-05-02 06:48:56,506 - fb2 - [INFO] - [E:7| 1100]: Train Loss:0.0043094, Val MRR:0.03104, fb2
2023-05-02 06:49:57,310 - fb2 - [INFO] - [Epoch:7]: Training Loss:0.004304
2023-05-02 06:49:57,584 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 06:50:27,212 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 06:50:38,099 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 06:51:10,813 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 06:51:22,553 - fb2 - [INFO] - [Evaluating Epoch 7 valid]:
MRR: Tail : 0.05444, Head : 0.00794, Avg : 0.03119
2023-05-02 06:51:23,715 - fb2 - [INFO] - [Epoch 7]: Training Loss: 0.0043042, Valid MRR: 0.03119,
2023-05-02 06:51:24,590 - fb2 - [INFO] - [E:8| 0]: Train Loss:0.0037442, Val MRR:0.03119, fb2
2023-05-02 06:52:53,796 - fb2 - [INFO] - [E:8| 100]: Train Loss:0.0043246, Val MRR:0.03119, fb2
2023-05-02 06:54:21,907 - fb2 - [INFO] - [E:8| 200]: Train Loss:0.0042743, Val MRR:0.03119, fb2
2023-05-02 06:55:46,156 - fb2 - [INFO] - [E:8| 300]: Train Loss:0.004254, Val MRR:0.03119, fb2
2023-05-02 06:57:15,114 - fb2 - [INFO] - [E:8| 400]: Train Loss:0.0042282, Val MRR:0.03119, fb2
2023-05-02 06:58:43,879 - fb2 - [INFO] - [E:8| 500]: Train Loss:0.0042196, Val MRR:0.03119, fb2
2023-05-02 07:00:12,524 - fb2 - [INFO] - [E:8| 600]: Train Loss:0.0042126, Val MRR:0.03119, fb2
2023-05-02 07:01:33,515 - fb2 - [INFO] - [E:8| 700]: Train Loss:0.004186, Val MRR:0.03119, fb2
2023-05-02 07:02:38,712 - fb2 - [INFO] - [E:8| 800]: Train Loss:0.0041633, Val MRR:0.03119, fb2
2023-05-02 07:03:43,849 - fb2 - [INFO] - [E:8| 900]: Train Loss:0.0041458, Val MRR:0.03119, fb2
2023-05-02 07:04:49,035 - fb2 - [INFO] - [E:8| 1000]: Train Loss:0.0041378, Val MRR:0.03119, fb2
2023-05-02 07:05:54,251 - fb2 - [INFO] - [E:8| 1100]: Train Loss:0.0041268, Val MRR:0.03119, fb2
2023-05-02 07:06:38,939 - fb2 - [INFO] - [Epoch:8]: Training Loss:0.004119
2023-05-02 07:06:39,157 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 07:07:00,572 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 07:07:08,535 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 07:07:32,722 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 07:07:41,389 - fb2 - [INFO] - [Evaluating Epoch 8 valid]:
MRR: Tail : 0.04819, Head : 0.00725, Avg : 0.02772
2023-05-02 07:07:41,390 - fb2 - [INFO] - [Epoch 8]: Training Loss: 0.0041194, Valid MRR: 0.03119,
2023-05-02 07:07:42,060 - fb2 - [INFO] - [E:9| 0]: Train Loss:0.003654, Val MRR:0.03119, fb2
2023-05-02 07:08:47,270 - fb2 - [INFO] - [E:9| 100]: Train Loss:0.0040588, Val MRR:0.03119, fb2
2023-05-02 07:09:52,478 - fb2 - [INFO] - [E:9| 200]: Train Loss:0.0040426, Val MRR:0.03119, fb2
2023-05-02 07:10:57,634 - fb2 - [INFO] - [E:9| 300]: Train Loss:0.0040221, Val MRR:0.03119, fb2
2023-05-02 07:12:02,837 - fb2 - [INFO] - [E:9| 400]: Train Loss:0.0039971, Val MRR:0.03119, fb2
2023-05-02 07:13:08,035 - fb2 - [INFO] - [E:9| 500]: Train Loss:0.0039898, Val MRR:0.03119, fb2
2023-05-02 07:14:13,321 - fb2 - [INFO] - [E:9| 600]: Train Loss:0.0039806, Val MRR:0.03119, fb2
2023-05-02 07:15:18,533 - fb2 - [INFO] - [E:9| 700]: Train Loss:0.0039814, Val MRR:0.03119, fb2
2023-05-02 07:16:23,815 - fb2 - [INFO] - [E:9| 800]: Train Loss:0.003962, Val MRR:0.03119, fb2
2023-05-02 07:17:29,003 - fb2 - [INFO] - [E:9| 900]: Train Loss:0.0039572, Val MRR:0.03119, fb2
2023-05-02 07:18:34,329 - fb2 - [INFO] - [E:9| 1000]: Train Loss:0.0039594, Val MRR:0.03119, fb2
2023-05-02 07:19:39,427 - fb2 - [INFO] - [E:9| 1100]: Train Loss:0.0039596, Val MRR:0.03119, fb2
2023-05-02 07:20:24,100 - fb2 - [INFO] - [Epoch:9]: Training Loss:0.003968
2023-05-02 07:20:24,318 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 07:20:45,699 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 07:20:53,656 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 07:21:17,766 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 07:21:26,504 - fb2 - [INFO] - [Evaluating Epoch 9 valid]:
MRR: Tail : 0.05162, Head : 0.0051, Avg : 0.02836
MR: Tail : 3521.8, Head : 4816.5, Avg : 4169.1
Hit-1: Tail : 0.01745, Head : 0.0, Avg : 0.00873
Hit-3: Tail : 0.05252, Head : 0.00473, Avg : 0.02863
Hit-10: Tail : 0.13162, Head : 0.00941, Avg : 0.07052
2023-05-02 07:21:26,504 - fb2 - [INFO] - [Epoch 9]: Training Loss: 0.0039679, Valid MRR: 0.03119,
2023-05-02 07:21:27,168 - fb2 - [INFO] - [E:10| 0]: Train Loss:0.0034523, Val MRR:0.03119, fb2
2023-05-02 07:22:32,379 - fb2 - [INFO] - [E:10| 100]: Train Loss:0.0039456, Val MRR:0.03119, fb2
2023-05-02 07:23:37,573 - fb2 - [INFO] - [E:10| 200]: Train Loss:0.0039008, Val MRR:0.03119, fb2
2023-05-02 07:24:42,704 - fb2 - [INFO] - [E:10| 300]: Train Loss:0.0039139, Val MRR:0.03119, fb2
2023-05-02 07:25:47,783 - fb2 - [INFO] - [E:10| 400]: Train Loss:0.0038921, Val MRR:0.03119, fb2
2023-05-02 07:26:52,916 - fb2 - [INFO] - [E:10| 500]: Train Loss:0.0038931, Val MRR:0.03119, fb2
2023-05-02 07:27:58,012 - fb2 - [INFO] - [E:10| 600]: Train Loss:0.0038964, Val MRR:0.03119, fb2
2023-05-02 07:29:03,119 - fb2 - [INFO] - [E:10| 700]: Train Loss:0.0039048, Val MRR:0.03119, fb2
2023-05-02 07:30:08,304 - fb2 - [INFO] - [E:10| 800]: Train Loss:0.0038894, Val MRR:0.03119, fb2
2023-05-02 07:31:13,434 - fb2 - [INFO] - [E:10| 900]: Train Loss:0.0038943, Val MRR:0.03119, fb2
2023-05-02 07:32:18,598 - fb2 - [INFO] - [E:10| 1000]: Train Loss:0.0038952, Val MRR:0.03119, fb2
2023-05-02 07:33:23,724 - fb2 - [INFO] - [E:10| 1100]: Train Loss:0.0039006, Val MRR:0.03119, fb2
2023-05-02 07:34:08,316 - fb2 - [INFO] - [Epoch:10]: Training Loss:0.003898
2023-05-02 07:34:08,533 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 07:34:29,851 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 07:34:37,787 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 07:35:01,819 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 07:35:10,489 - fb2 - [INFO] - [Evaluating Epoch 10 valid]:
MRR: Tail : 0.05283, Head : 0.00678, Avg : 0.0298
2023-05-02 07:35:10,490 - fb2 - [INFO] - [Epoch 10]: Training Loss: 0.0038978, Valid MRR: 0.03119,
2023-05-02 07:35:11,153 - fb2 - [INFO] - [E:11| 0]: Train Loss:0.0037874, Val MRR:0.03119, fb2
2023-05-02 07:36:16,325 - fb2 - [INFO] - [E:11| 100]: Train Loss:0.0039696, Val MRR:0.03119, fb2
2023-05-02 07:37:21,496 - fb2 - [INFO] - [E:11| 200]: Train Loss:0.0038908, Val MRR:0.03119, fb2
2023-05-02 07:38:26,681 - fb2 - [INFO] - [E:11| 300]: Train Loss:0.0038632, Val MRR:0.03119, fb2
2023-05-02 07:39:31,748 - fb2 - [INFO] - [E:11| 400]: Train Loss:0.0038869, Val MRR:0.03119, fb2
2023-05-02 07:40:36,924 - fb2 - [INFO] - [E:11| 500]: Train Loss:0.0038894, Val MRR:0.03119, fb2
2023-05-02 07:41:42,072 - fb2 - [INFO] - [E:11| 600]: Train Loss:0.0038788, Val MRR:0.03119, fb2
2023-05-02 07:42:47,120 - fb2 - [INFO] - [E:11| 700]: Train Loss:0.0038572, Val MRR:0.03119, fb2
2023-05-02 07:43:52,259 - fb2 - [INFO] - [E:11| 800]: Train Loss:0.0038689, Val MRR:0.03119, fb2
2023-05-02 07:44:57,400 - fb2 - [INFO] - [E:11| 900]: Train Loss:0.0038614, Val MRR:0.03119, fb2
2023-05-02 07:46:02,534 - fb2 - [INFO] - [E:11| 1000]: Train Loss:0.003855, Val MRR:0.03119, fb2
2023-05-02 07:47:07,616 - fb2 - [INFO] - [E:11| 1100]: Train Loss:0.0038534, Val MRR:0.03119, fb2
2023-05-02 07:47:52,272 - fb2 - [INFO] - [Epoch:11]: Training Loss:0.003852
2023-05-02 07:47:52,489 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 07:48:13,780 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 07:48:21,688 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 07:48:45,765 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 07:48:54,380 - fb2 - [INFO] - [Evaluating Epoch 11 valid]:
MRR: Tail : 0.04712, Head : 0.00767, Avg : 0.0274
2023-05-02 07:48:54,380 - fb2 - [INFO] - [Epoch 11]: Training Loss: 0.0038525, Valid MRR: 0.03119,
2023-05-02 07:48:55,042 - fb2 - [INFO] - [E:12| 0]: Train Loss:0.0037471, Val MRR:0.03119, fb2
2023-05-02 07:50:00,130 - fb2 - [INFO] - [E:12| 100]: Train Loss:0.0037951, Val MRR:0.03119, fb2
2023-05-02 07:51:05,193 - fb2 - [INFO] - [E:12| 200]: Train Loss:0.0038364, Val MRR:0.03119, fb2
2023-05-02 07:52:10,256 - fb2 - [INFO] - [E:12| 300]: Train Loss:0.003826, Val MRR:0.03119, fb2
2023-05-02 07:53:15,321 - fb2 - [INFO] - [E:12| 400]: Train Loss:0.0038137, Val MRR:0.03119, fb2
2023-05-02 07:54:20,366 - fb2 - [INFO] - [E:12| 500]: Train Loss:0.0038102, Val MRR:0.03119, fb2
2023-05-02 07:55:25,458 - fb2 - [INFO] - [E:12| 600]: Train Loss:0.0038274, Val MRR:0.03119, fb2
2023-05-02 07:56:30,547 - fb2 - [INFO] - [E:12| 700]: Train Loss:0.003833, Val MRR:0.03119, fb2
2023-05-02 07:57:35,610 - fb2 - [INFO] - [E:12| 800]: Train Loss:0.0038263, Val MRR:0.03119, fb2
2023-05-02 07:58:40,626 - fb2 - [INFO] - [E:12| 900]: Train Loss:0.00383, Val MRR:0.03119, fb2
2023-05-02 07:59:45,777 - fb2 - [INFO] - [E:12| 1000]: Train Loss:0.0038273, Val MRR:0.03119, fb2
2023-05-02 08:00:50,702 - fb2 - [INFO] - [E:12| 1100]: Train Loss:0.0038211, Val MRR:0.03119, fb2
2023-05-02 08:01:35,279 - fb2 - [INFO] - [Epoch:12]: Training Loss:0.003815
2023-05-02 08:01:35,494 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 08:01:56,640 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 08:02:04,508 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 08:02:28,485 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 08:02:37,052 - fb2 - [INFO] - [Evaluating Epoch 12 valid]:
MRR: Tail : 0.05335, Head : 0.0075, Avg : 0.03043
2023-05-02 08:02:37,052 - fb2 - [INFO] - [Epoch 12]: Training Loss: 0.0038146, Valid MRR: 0.03119,
2023-05-02 08:02:37,718 - fb2 - [INFO] - [E:13| 0]: Train Loss:0.0033164, Val MRR:0.03119, fb2
2023-05-02 08:03:42,647 - fb2 - [INFO] - [E:13| 100]: Train Loss:0.0037194, Val MRR:0.03119, fb2
2023-05-02 08:04:47,608 - fb2 - [INFO] - [E:13| 200]: Train Loss:0.003769, Val MRR:0.03119, fb2
2023-05-02 08:05:52,600 - fb2 - [INFO] - [E:13| 300]: Train Loss:0.0037668, Val MRR:0.03119, fb2
2023-05-02 08:06:57,520 - fb2 - [INFO] - [E:13| 400]: Train Loss:0.0037588, Val MRR:0.03119, fb2
2023-05-02 08:08:02,439 - fb2 - [INFO] - [E:13| 500]: Train Loss:0.0037793, Val MRR:0.03119, fb2
2023-05-02 08:09:07,395 - fb2 - [INFO] - [E:13| 600]: Train Loss:0.0037882, Val MRR:0.03119, fb2
2023-05-02 08:10:12,351 - fb2 - [INFO] - [E:13| 700]: Train Loss:0.0037882, Val MRR:0.03119, fb2
2023-05-02 08:11:18,698 - fb2 - [INFO] - [E:13| 800]: Train Loss:0.0037905, Val MRR:0.03119, fb2
2023-05-02 08:12:28,320 - fb2 - [INFO] - [E:13| 900]: Train Loss:0.0037888, Val MRR:0.03119, fb2
2023-05-02 08:13:38,365 - fb2 - [INFO] - [E:13| 1000]: Train Loss:0.0037947, Val MRR:0.03119, fb2
2023-05-02 08:14:47,505 - fb2 - [INFO] - [E:13| 1100]: Train Loss:0.0037969, Val MRR:0.03119, fb2
2023-05-02 08:15:34,543 - fb2 - [INFO] - [Epoch:13]: Training Loss:0.003798
2023-05-02 08:15:34,776 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 08:15:57,326 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 08:16:05,504 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 08:16:33,537 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 08:16:42,828 - fb2 - [INFO] - [Evaluating Epoch 13 valid]:
MRR: Tail : 0.0532, Head : 0.00766, Avg : 0.03043
2023-05-02 08:16:42,829 - fb2 - [INFO] - [Epoch 13]: Training Loss: 0.0037978, Valid MRR: 0.03119,
2023-05-02 08:16:43,534 - fb2 - [INFO] - [E:14| 0]: Train Loss:0.0041683, Val MRR:0.03119, fb2
2023-05-02 08:17:53,001 - fb2 - [INFO] - [E:14| 100]: Train Loss:0.0040882, Val MRR:0.03119, fb2
2023-05-02 08:19:02,369 - fb2 - [INFO] - [E:14| 200]: Train Loss:0.0039416, Val MRR:0.03119, fb2
2023-05-02 08:20:13,079 - fb2 - [INFO] - [E:14| 300]: Train Loss:0.0038661, Val MRR:0.03119, fb2
2023-05-02 08:21:23,507 - fb2 - [INFO] - [E:14| 400]: Train Loss:0.0038449, Val MRR:0.03119, fb2
2023-05-02 08:22:33,698 - fb2 - [INFO] - [E:14| 500]: Train Loss:0.0038566, Val MRR:0.03119, fb2
2023-05-02 08:23:39,541 - fb2 - [INFO] - [E:14| 600]: Train Loss:0.0038399, Val MRR:0.03119, fb2
2023-05-02 08:24:44,366 - fb2 - [INFO] - [E:14| 700]: Train Loss:0.0038305, Val MRR:0.03119, fb2
2023-05-02 08:25:49,869 - fb2 - [INFO] - [E:14| 800]: Train Loss:0.0038338, Val MRR:0.03119, fb2
2023-05-02 08:26:57,839 - fb2 - [INFO] - [E:14| 900]: Train Loss:0.0037821, Val MRR:0.03119, fb2
2023-05-02 08:28:03,354 - fb2 - [INFO] - [E:14| 1000]: Train Loss:0.0037456, Val MRR:0.03119, fb2
2023-05-02 08:29:10,644 - fb2 - [INFO] - [E:14| 1100]: Train Loss:0.0037076, Val MRR:0.03119, fb2
2023-05-02 08:29:57,907 - fb2 - [INFO] - [Epoch:14]: Training Loss:0.003683
2023-05-02 08:29:58,121 - fb2 - [INFO] - [Valid, Tail_Batch Step 0] fb2
2023-05-02 08:30:19,870 - fb2 - [INFO] - [Valid, Tail_Batch Step 100] fb2
2023-05-02 08:30:28,247 - fb2 - [INFO] - [Valid, Head_Batch Step 0] fb2
2023-05-02 08:30:53,528 - fb2 - [INFO] - [Valid, Head_Batch Step 100] fb2
2023-05-02 08:31:03,475 - fb2 - [INFO] - [Evaluating Epoch 14 valid]:
MRR: Tail : 0.05567, Head : 0.00908, Avg : 0.03237
2023-05-02 08:31:05,395 - fb2 - [INFO] - [Epoch 14]: Training Loss: 0.0036829, Valid MRR: 0.03237,
2023-05-02 08:31:06,199 - fb2 - [INFO] - [E:15| 0]: Train Loss:0.0039411, Val MRR:0.03237, fb2
2023-05-02 08:32:16,283 - fb2 - [INFO] - [E:15| 100]: Train Loss:0.0033314, Val MRR:0.03237, fb2
2023-05-02 08:33:25,619 - fb2 - [INFO] - [E:15| 200]: Train Loss:0.0034024, Val MRR:0.03237, fb2
2023-05-02 08:34:34,742 - fb2 - [INFO] - [E:15| 300]: Train Loss:0.0034511, Val MRR:0.03237, fb2
2023-05-02 08:35:44,359 - fb2 - [INFO] - [E:15| 400]: Train Loss:0.0034416, Val MRR:0.03237, fb2
2023-05-02 08:36:53,600 - fb2 - [INFO] - [E:15| 500]: Train Loss:0.0034373, Val MRR:0.03237, fb2
2023-05-02 08:38:02,489 - fb2 - [INFO] - [E:15| 600]: Train Loss:0.003452, Val MRR:0.03237, fb2
2023-05-02 08:39:11,979 - fb2 - [INFO] - [E:15| 700]: Train Loss:0.0034426, Val MRR:0.03237, fb2
2023-05-02 08:40:21,547 - fb2 - [INFO] - [E:15| 800]: Train Loss:0.0034303, Val MRR:0.03237, fb2
2023-05-02 08:41:30,473 - fb2 - [INFO] - [E:15| 900]: Train Loss:0.0034168, Val MRR:0.03237, fb2

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2023-05-02 03:31:33,715 - fourier_wn_400_400 - [INFO] - {'dataset': 'WN18RR', 'name': 'fourier_wn_400_400', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 600, 'ent_vec_dim': 600, 'rel_vec_dim': 300, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.1, 'drop': 0.2, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': True}
2023-05-02 03:32:22,939 - fourier_wn_400_400 - [INFO] - {'dataset': 'WN18RR', 'name': 'fourier_wn_400_400', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.1, 'drop': 0.2, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': True}
2023-05-02 03:32:36,858 - fourier_wn_400_400 - [INFO] - [Test, Tail_Batch Step 0] fourier_wn_400_400
2023-05-02 03:32:55,123 - fourier_wn_400_400 - [INFO] - [Test, Head_Batch Step 0] fourier_wn_400_400
2023-05-02 03:33:15,785 - fourier_wn_400_400 - [INFO] - [Evaluating Epoch 0 test]:
MRR: Tail : 0.4518, Head : 0.42163, Avg : 0.43672
MR: Tail : 5866.5, Head : 5955.9, Avg : 5911.2
Hit-1: Tail : 0.42884, Head : 0.39789, Avg : 0.41337
Hit-3: Tail : 0.46171, Head : 0.43235, Avg : 0.44703
Hit-10: Tail : 0.49394, Head : 0.46362, Avg : 0.47878

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2023-04-28 18:22:54,643 - testrun_02f34acb - [INFO] - {'dataset': 'FB15k-237', 'name': 'testrun_02f34acb', 'gpu': '-1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 600, 'rel_vec_dim': 300, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.1, 'drop': 0.2, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/'}

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