Merge branch 'unified_dataset' into pre-training

convlab2/util/unified_datasets_util.py

@@ -19,11 +19,12 @@ class BaseDatabase(ABC):
         """return a list of topk entities (dict containing slot-value pairs) for a given domain based on the dialogue state."""
 
 
-def load_dataset(dataset_name:str) -> Dict:
+def load_dataset(dataset_name:str, dial_ids_order=None) -> Dict:
     """load unified dataset from `data/unified_datasets/$dataset_name`
 
     Args:
         dataset_name (str): unique dataset name in `data/unified_datasets`
+        dial_ids_order (int): idx of shuffled dial order in `data/unified_datasets/$dataset_name/shuffled_dial_ids.json`
 
     Returns:
         dataset (dict): keys are data splits and the values are lists of dialogues
@@ -33,6 +34,11 @@ def load_dataset(dataset_name:str) -> Dict:
     with archive.open('data/dialogues.json') as f:
         dialogues = json.loads(f.read())
     dataset = {}
+    if dial_ids_order is not None:
+        dial_ids = json.load(open(os.path.join(data_dir, 'shuffled_dial_ids.json')))[dial_ids_order]
+        for data_split in dial_ids:
+            dataset[data_split] = [dialogues[i] for i in dial_ids[data_split]]
+    else:
         for dialogue in dialogues:
             if dialogue['data_split'] not in dataset:
                 dataset[dialogue['data_split']] = [dialogue]
@@ -187,8 +193,12 @@ def load_rg_data(dataset, data_split='all', speaker='system', context_window_siz
     return load_unified_data(dataset, **kwargs)
 
 
-def create_delex_data(dataset, delex_format='[({domain})-({slot})]', ignore_values=['yes', 'no']):
-    # add delex_utterance to the dataset according to dialogue acts and belief_state
+def create_delex_data(dataset, delex_func=lambda d,s,v: f'[({d})-({s})]', ignore_values=['yes', 'no']):
+    """add delex_utterance to the dataset according to dialogue acts and belief_state
+    delex_func: function that return the placeholder (e.g. "[(domain_name)-(slot_name)]") given (domain, slot, value)
+    ignore_values: ignored values when delexicalizing using the categorical acts and states
+    """
+    # 
 
     def delex_inplace(texts_placeholders, value_pattern):
         res = []
@@ -226,7 +236,7 @@ def create_delex_data(dataset, delex_format='[({domain})-({slot})]', ignore_valu
                     assert utt[start:end] == value
                     # make sure there are no words/number prepend & append and no overlap with other spans
                     if start >= last_end and (start == 0 or re.match('\W', utt[start-1])) and (end == len(utt) or re.match('\W', utt[end])):
-                        placeholder = delex_format.format(domain=domain, slot=slot, value=value)
+                        placeholder = delex_func(domain, slot, value)
                         delex_vocab.add(placeholder)
                         delex_utt.append((utt[last_end:start], False))
                         delex_utt.append((placeholder, True))
@@ -237,7 +247,7 @@ def create_delex_data(dataset, delex_format='[({domain})-({slot})]', ignore_valu
                 for da in sorted(turn['dialogue_acts']['categorical'], key=lambda x: len(x['value'])):
                     domain, slot, value = da['domain'], da['slot'], da['value']
                     if value.lower() not in ignore_values:
-                        placeholder = delex_format.format(domain=domain, slot=slot, value=value)
+                        placeholder = delex_func(domain, slot, value)
                         pattern = re.compile(r'\b({})\b'.format(value), flags=re.I)
                         if delex_inplace(delex_utt, pattern):
                             delex_vocab.add(placeholder)
@@ -251,7 +261,7 @@ def create_delex_data(dataset, delex_format='[({domain})-({slot})]', ignore_valu
                             # has value
                             for value in values.split('|'):
                                 if value.lower() not in ignore_values:
-                                    placeholder = delex_format.format(domain=domain, slot=slot, value=value)
+                                    placeholder = delex_func(domain, slot, value)
                                     pattern = re.compile(r'\b({})\b'.format(value), flags=re.I)
                                     if delex_inplace(delex_utt, pattern):
                                         delex_vocab.add(placeholder)
@@ -262,7 +272,10 @@ def create_delex_data(dataset, delex_format='[({domain})-({slot})]', ignore_valu
 
 
 if __name__ == "__main__":
-    dataset = load_dataset('multiwoz21')
+    dataset = load_dataset('multiwoz21', dial_ids_order=0)
+    train_ratio = 0.1
+    dataset['train'] = dataset['train'][:round(len(dataset['train'])*train_ratio)]
+    print(len(dataset['train']))
     print(dataset.keys())
     print(len(dataset['test']))
 
@@ -274,7 +287,11 @@ if __name__ == "__main__":
     data_by_split = load_nlu_data(dataset, data_split='test', speaker='user')
     pprint(data_by_split['test'][0])
 
-    dataset, delex_vocab = create_delex_data(dataset)
+    def delex_slot(domain, slot, value):
+        # only use slot name for delexicalization
+        return f'[{slot}]'
+
+    dataset, delex_vocab = create_delex_data(dataset, delex_slot)
     json.dump(dataset['test'], open('new_delex_multiwoz21_test.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
     json.dump(delex_vocab, open('new_delex_vocab.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
     with open('new_delex_cmp.txt', 'w') as f:

data/unified_datasets/README.md

@@ -42,6 +42,7 @@ if __name__ == '__main__':
   - `dialogues.json`: a list of all dialogues in the dataset.
   - other necessary files such as databases.
 - `dummy_data.json`: a list of 10 dialogues from `dialogues.json` for illustration.
+- `shuffled_dial_ids.json`: 10 random shuffled data orders created by `check.py` for experiment reproducibility, can be used in `load_dataset` function by passing the `dial_ids_order` in [0, 9]
 
 Datasets that require database interaction should also include the following file:
 - `database.py`: load the database and define the query function:
@@ -60,7 +61,7 @@ class Database(BaseDatabase):
 We first introduce the unified format of `ontology` and `dialogues`. To transform a new dataset into the unified format:
 1. Create `data/unified_datasets/$dataset` folder, where `$dataset` is the name of the dataset.
 2. Write `preprocess.py` to transform the original dataset into the unified format, producing `data.zip` and `dummy_data.json`.
-3. Run `python check.py $dataset` in the `data/unified_datasets` directory to check the validation of processed dataset and get data statistics.
+3. Run `python check.py $dataset` in the `data/unified_datasets` directory to check the validation of processed dataset and get data statistics and shuffled dialog ids.
 4. Write `README.md` to describe the data following [How to create dataset README](#how-to-create-dataset-readme).
 
 ### Ontology
@@ -120,7 +121,7 @@ Note that multiple descriptions/values are separated by `"|"`.
 
 Other attributes are optional.
 
-> **Necessary**: Run `python check.py $dataset` in the `data/unified_datasets` directory to check the validation of processed dataset and get data statistics in `data/unified_datasets/$dataset/stat.txt`.
+> **Necessary**: Run `python check.py $dataset` in the `data/unified_datasets` directory to check the validation of processed dataset and get data statistics in `data/unified_datasets/$dataset/stat.txt` as well as shuffled dialog ids in `data/unified_datasets/$dataset/shuffled_dial_ids.json`.
 
 ### How to create dataset README
 Each dataset has a README.md to describe the original and transformed data. Please follow the `README_TEMPLATE.md` and make sure that you:

data/unified_datasets/check.py

@@ -4,6 +4,7 @@ from copy import deepcopy
 from zipfile import ZipFile
 import importlib
 from tabulate import tabulate
+import random
 
 special_values = ['', 'dontcare', None, '?']
 
@@ -279,6 +280,20 @@ def check_dialogues(name, dialogues, ontology):
     return tabulate(table, headers='keys', tablefmt='github')
 
 
+def create_shuffled_dial_ids(dialogues, rng=random.Random(42), num_orders=10):
+    dial_ids = {}
+    for i, dialogue in enumerate(dialogues):
+        dial_ids.setdefault(dialogue['data_split'], [])
+        dial_ids[dialogue['data_split']].append(i)
+    
+    id_orders = []
+    for _ in range(num_orders):
+        for data_split in dial_ids:
+            rng.shuffle(dial_ids[data_split])
+        id_orders.append(deepcopy(dial_ids))
+    return id_orders
+
+
 if __name__ == '__main__':
     from argparse import ArgumentParser
 
@@ -339,6 +354,10 @@ if __name__ == '__main__':
                     dialogues = json.load(f)
                     stat = check_dialogues(name, dialogues, ontology)
                     print('pass')
+                    print('creating shuffled_dial_ids')
+                    id_orders = create_shuffled_dial_ids(dialogues)
+                    with open(os.path.join(name, 'shuffled_dial_ids.json'), 'w', encoding='utf-8') as f:
+                        json.dump(id_orders, f, ensure_ascii=False)
                 
                 print(f'Please copy and paste the statistics in {name}/stat.txt to dataset README.md->Data Splits section\n')
                 with open(f'{name}/stat.txt', 'w') as f: