updates

.gitignore

@@ -0,0 +1,6 @@
+*.json
+datasets/webqsp/
+*.tar.gz
+*.pt
+__pycache__/
+tf_logs/*

README.md

@@ -15,30 +15,21 @@ Model Overview:
 
 ### Prepare data
 ```
-mkdir datasets && cd datasets && wget http://nlp.cs.ucsb.edu/data/webqsp.tar.gz && tar -xzvf webqsp.tar.gz
+mkdir datasets && cd datasets && wget http://nlp.cs.ucsb.edu/data/webqsp.tar.gz && tar -xzvf webqsp.tar.gz && cd ..
 ```
 
 ### Full KB setting
-**Training**
 ```
-CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_full_kb --num_layer 1 --max_num_neighbors 50 --label_smooth 0.1 --data_folder datasets/webqsp/full/ 
-```
-**Testing**
-```
-CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_full_kb --num_layer 1 --max_num_neighbors 50 --label_smooth 0.1 --data_folder datasets/webqsp/full/ --mode test
+CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_full_kb --max_num_neighbors 50 --label_smooth 0.1 --data_folder datasets/webqsp/full/ 
 ```
 
-### Incomplete KB setting (50%)
-**Training**
-```
-CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_kb_05 --num_layer 1 --max_num_neighbors 100 --use_doc --label_smooth 0.1 --data_folder datasets/webqsp/kb_05/
-```
-**Testing**
+### Incomplete KB setting
+#### 50% KB
 ```
-CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_kb_05 --num_layer 1 --max_num_neighbors 100 --use_doc --label_smooth 0.1 --data_folder datasets/webqsp/kb_05/ --mode test --eps 0.12
+CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_kb_05 --max_num_neighbors 50 --use_doc --data_folder datasets/webqsp/kb_05/ --eps 0.12
 ```
 
-### Bibtex
+### Citation
 ```
 @article{xiong2019improving,
   title={Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader},

model.py

@@ -132,8 +132,8 @@ def forward(self, feed):
 
         # prepare pagerank scores
         ent_seed_info = feed['query_entities'].float() # seed entity will have 1.0 score
-        ent_pagerank = torch.cat([torch.zeros(1).to(torch.device('cuda')), ent_seed_info.view(-1)], dim=0)
-        pagerank = torch.index_select(ent_pagerank, dim=0, index=neighbor_ent_local_index).view(B*max_num_candidates, max_num_neighbors)
+        ent_is_seed = torch.cat([torch.zeros(1).to(torch.device('cuda')), ent_seed_info.view(-1)], dim=0)
+        ent_seed_indicator = torch.index_select(ent_is_seed, dim=0, index=neighbor_ent_local_index).view(B*max_num_candidates, max_num_neighbors)
 
         # v0.0 more find-grained attention
         q_emb_expand = q_emb.unsqueeze(1).expand(B, max_num_candidates, max_q_len, -1).contiguous()
@@ -163,7 +163,7 @@ def forward(self, feed):
         neighbor_ent_emb = torch.index_select(ent_emb_for_lookup, dim=0, index=neighbor_ent_local_index)
         neighbor_ent_emb = neighbor_ent_emb.view(B*max_num_candidates, max_num_neighbors, -1)
         neighbor_vec = torch.cat([neighbor_rel_emb, neighbor_ent_emb], dim =-1).view(B*max_num_candidates, max_num_neighbors, -1) # for propagation
-        neighbor_scores = q_rel_simi * pagerank
+        neighbor_scores = q_rel_simi * ent_seed_indicator
         neighbor_scores = neighbor_scores - (1 - neighbor_mask.view(B*max_num_candidates, max_num_neighbors)) * 1e8
         attn_score = F.softmax(neighbor_scores, dim=1)
         aggregate = self.kg_prop(neighbor_vec) * attn_score.unsqueeze(2)

train.py

@@ -1,7 +1,6 @@
 import torch
 import numpy as np
 import random
-import json
 
 from data_generator import DataLoader
 from model import KAReader
@@ -44,17 +43,14 @@ def get_best_ans(candidate2prob):
     return best_ans
 
 def train(cfg):
-    tf_logger = SummaryWriter('my_logs/' + cfg['model_id'])
+    tf_logger = SummaryWriter('tf_logs/' + cfg['model_id'])
 
     # train and test share the same set of documents
     documents = load_documents(cfg['data_folder'] + cfg['{}_documents'.format(cfg['mode'])])
 
-
-
     # train data
     train_data = DataLoader(cfg, documents)
     valid_data = DataLoader(cfg, documents, mode='dev')
-    test_data = DataLoader(cfg, documents, mode='test')
 
     model = KAReader(cfg)
     model = model.to(torch.device('cuda'))
@@ -97,12 +93,6 @@ def train(cfg):
         print('evaluation best f1:{} current:{}'.format(best_val_f1, val_f1))
         print('evaluation best hits:{} current:{}'.format(best_val_hits, val_hits))
 
-        if epoch % 5 == 0:
-            test_f1, test_hits = test(model, test_data, cfg['eps'])
-            tf_logger.add_scalar('test_hits', test_hits, epoch)
-            tf_logger.add_scalar('test_f1', test_f1, epoch)
-            torch.save(model.state_dict(), 'model/{}/{}_{}.pt'.format(cfg['name'], cfg['model_id'], test_hits))
-
     print('save final model')
     torch.save(model.state_dict(), 'model/{}/{}_final.pt'.format(cfg['name'], cfg['model_id']))
 
@@ -111,6 +101,8 @@ def train(cfg):
     model.load_state_dict(torch.load(model_save_path))
     model.eval()
 
+    print('Testing....')
+    test_data = DataLoader(cfg, documents, mode='test')
     test(model, test_data, cfg['eps'])
 
 def test(model, test_data, eps):
@@ -148,18 +140,11 @@ def test(model, test_data, eps):
             f1s.append(f1)
             hits.append(hit)
     print('evaluation.......')
-    print('how many samples......', len(f1s))
+    print('how many eval samples......', len(f1s))
     print('avg_f1', np.mean(f1s))
     print('avg_hits', np.mean(hits))
 
-    for q, f1, hit in zip(questions, f1s, hits):
-        q_to_metrics[q] = (f1, hit)
-    json.dump(q_to_metrics, open('q_to_metrics_kb_only.json', 'w'))
 
-    q_to_ans = {}
-    for q, best_ans in zip(questions, pred_answers):
-        q_to_ans[q] = best_ans
-    json.dump(q_to_ans, open('q_to_ans_kb_only.json', 'w'))
 
     model.train()
     return np.mean(f1s), np.mean(hits)

util.py

@@ -8,8 +8,6 @@
 from collections import Counter
 
 from torch.autograd import Variable
-from torch.nn.utils.rnn import pack_padded_sequence
-from torch.nn.utils.rnn import pad_packed_sequence
 from tqdm import tqdm
 
 import argparse
@@ -61,7 +59,6 @@ def get_config(config_path=None):
 
         # model options
         parser.add_argument('--use_doc', action='store_true')
-        parser.add_argument('--use_kb', action='store_true')
         parser.add_argument('--use_inverse_relation', action='store_true')
         parser.add_argument('--model_id', default='debug', type=str)
         parser.add_argument('--load_model_file', default=None, type=str)