Finalized model code

README.md

@@ -68,8 +68,8 @@ Once the predictative files are generated, we will depend on the [AirDialogue to
 We are currently working on the scoring script.
 ```
 airdialogue score --pred_data ./data/out_dir/dev_inference_out.txt \
-                  --true_data ./data/airdialogue/json/dev_data.json \
-                  --true_kb ./data/airdialogue/json/dev_kb.json \
+                  --true_data ./data/airdialogue/tokenized/dev.infer.tar.data \
+                  --true_kb ./data/airdialogue/tokenied/dev.infer.kb \
                   --task infer \
                   --output ./data/out_dir/dev_bleu.json
 ```
@@ -155,8 +155,8 @@ bash ./scripts/evaluate.sh -p dev -a ood1 -m ./data/synthesized_out_dir -o ./dat
 ###### Scoring
 ```
 airdialogue score --pred_data ./data/synthesized_out_dir/dev_inference_out.txt \
-                  --true_data ./data/synthesized/json/dev_data.json \
-                  --true_kb ./data/airdialogue/json/dev_kb.json \
+                  --true_data ./data/synthesized/tokenized/dev.infer.tar.data \
+                  --true_kb ./data/airdialogue/tokenized/dev.infer.kb \
                   --task infer \
                   --output ./data/synthesized_out_dir/dev_bleu.json
 ```

airdialogue_model_tf.py

@@ -358,6 +358,11 @@ def add_arguments(parser):
       type=int,
       default=50,
       help="maximum sentence length for dialogue inference")
+  parser.add_argument(
+      "--self_play_start_turn",
+      type=str,
+      default=None,
+      help="Force self-play to run for an agent/customer start. [agent | customer]")
   parser.add_argument(
       "--num_kb_fields_per_entry",
       type=int,
@@ -592,6 +597,7 @@ def create_hparams(flags):
       vocab_file=flags.vocab_file,
       max_dialogue_len=flags.max_dialogue_len,
       max_inference_len=flags.max_inference_len,
+      self_play_start_turn=flags.self_play_start_turn,
       num_kb_fields_per_entry=flags.num_kb_fields_per_entry,
       len_action=flags.len_action,
       # selfplay

scripts/evaluate.sh

@@ -63,9 +63,9 @@ echo "out_dir", ${out_dir}
 echo "num_gpus", ${num_gpus}
 
 # run in foreground once and display the results
-# python airdialogue_model_tf.py --task_type INFER --eval_prefix $partition --num_gpus $num_gpus \
-#                       --input_dir ${input_dir} --out_dir ${out_dir} \
-#                       --inference_output_file ${out_dir}/dev_inference_out.txt
+python airdialogue_model_tf.py --task_type INFER --eval_prefix $partition --num_gpus $num_gpus \
+                      --input_dir ${input_dir} --out_dir ${out_dir} \
+                      --inference_output_file ${out_dir}/dev_inference_out.txt
 
 # run in foreground once and display the results
 python airdialogue_model_tf.py --task_type SP_EVAL --eval_prefix $partition --num_gpus $num_gpus \

utils/dialogue_utils.py

@@ -16,6 +16,7 @@
 
 import codecs
 import random
+import re
 import numpy as np
 import tensorflow.compat.v1 as tf
 from airdialogue.evaluator.metrics import f1
@@ -217,7 +218,7 @@ def decode_and_evaluate(name,
       trans_f.write('')  # Write empty string to ensure file is created.
       while True:
         try:
-          ut1, ut2, _ = model.generate_infer_utterance(sess,
+          ut1, ut2, action = model.generate_infer_utterance(sess,
                                                        data_iterator_handle)
           batch_size = ut1.shape[0]
           for sent_id in range(batch_size):
@@ -226,7 +227,19 @@ def decode_and_evaluate(name,
             nmt_outputs = [ut1, ut2][speaker]
             translation = get_translation_cut_both(nmt_outputs, sent_id,
                                                    hparams.t1.encode(), hparams.t2.encode())
-            trans_f.write((translation + b'\n').decode('utf-8'))
+            translation = translation.decode('utf-8')
+            if hparams.self_play_start_turn == 'agent':
+              if '<eod>' in translation:
+                ac_arr = [w.decode('utf-8') for w in action[sent_id]]
+                name = ac_arr[0] + ' ' + ac_arr[1]
+                flight = re.match(r'<fl_(\d+)>', ac_arr[2])
+                flight = flight.group(1) if flight else ''
+                status = re.match(r'<st_(\w+)>', ac_arr[3])
+                status = status.group(1) if status else ''
+                translation += '|' + '|'.join([name, flight, status])
+              else:
+                translation += '|||'
+            trans_f.write(translation + '\n')
             cnt += 1
           if last_cnt - cnt >= 10000:  # 400k in total
             utils.print_out('cnt= ' + str(cnt))

utils/iterator_utils.py

@@ -150,7 +150,7 @@ def get_sub_items_supervised(data, kb):
 
 def get_sub_items_infer(data, kb):
   """process procedure for inference."""
-  all_data = tf.string_split([data], sep="|").values
+  all_data = tf.string_split([data], sep="|", skip_empty=False).values
   intent, dialogue_context = all_data[0], all_data[1]
   return intent, dialogue_context, kb