Upload preprocess.py

preprocess.py

@@ -0,0 +1,232 @@
+import copy
+import json
+import os
+from zipfile import ZipFile, ZIP_DEFLATED
+from shutil import rmtree
+
+ontology = {
+    'domains': {
+        'restaurant': {
+            'description': 'search for a restaurant to dine',
+            'slots': {
+                'food': {
+                    'description': 'food type of the restaurant',
+                    'is_categorical': False,
+                    'possible_values': []
+                },
+                'area': {
+                    'description': 'area of the restaurant',
+                    'is_categorical': True,
+                    'possible_values': ["east", "west", "centre", "north", "south"]
+                },
+                'postcode': {
+                    'description': 'postal code of the restaurant',
+                    'is_categorical': False,
+                    'possible_values': []
+                },
+                'phone': {
+                    'description': 'phone number of the restaurant',
+                    'is_categorical': False,
+                    'possible_values': []
+                },
+                'address': {
+                    'description': 'address of the restaurant',
+                    'is_categorical': False,
+                    'possible_values': []
+                },
+                'price range': {
+                    'description': 'price range of the restaurant',
+                    'is_categorical': True,
+                    'possible_values': ["expensive", "moderate", "cheap"]
+                },
+                'name': {
+                    'description': 'name of the restaurant',
+                    'is_categorical': False,
+                    'possible_values': []
+                }
+            }
+        }
+    },
+    'intents': {
+        'inform': {
+            'description': 'system informs user the value of a slot'
+        },
+        'request': {
+            'description': 'system asks the user to provide value of a slot'
+        }
+    },
+    'state': {
+        'restaurant': {
+            'food': '',
+            'area': '',
+            'postcode': '',
+            'phone': '',
+            'address': '',
+            'price range': '',
+            'name': ''
+        }
+    },
+    "dialogue_acts": {
+        "categorical": {},
+        "non-categorical": {},
+        "binary": {}
+    }
+}
+
+
+def convert_da(da, utt):
+    global ontology
+
+    converted = {
+        'binary': [],
+        'categorical': [],
+        'non-categorical': []
+    }
+
+    for s, v in da:
+        if s == 'request':
+            converted['binary'].append({
+                'intent': 'request',
+                'domain': 'restaurant',
+                'slot': v,
+            })
+
+        else:
+            slot_type = 'categorical' if ontology['domains']['restaurant']['slots'][s]['is_categorical'] else 'non-categorical'
+
+            v = v.strip()
+            if v != 'dontcare' and ontology['domains']['restaurant']['slots'][s]['is_categorical']:
+                if v == 'center':
+                    v = 'centre'
+                elif v == 'east side':
+                    v = 'east'
+                assert v in ontology['domains']['restaurant']['slots'][s]['possible_values'], print([s,v, utt])
+
+            converted[slot_type].append({
+                'intent': 'inform',
+                'domain': 'restaurant',
+                'slot': s,
+                'value': v
+            })
+
+            if slot_type == 'non-categorical' and v != 'dontcare':
+
+                start = utt.lower().find(v)
+
+                if start != -1:
+                    end = start + len(v)
+                    converted[slot_type][-1]['start'] = start
+                    converted[slot_type][-1]['end'] = end
+                    converted[slot_type][-1]['value'] = utt[start:end]
+
+    return converted
+
+
+def preprocess():
+    original_data_dir = 'woz'
+    new_data_dir = 'data'
+    os.makedirs(new_data_dir, exist_ok=True)
+
+    dataset = 'woz'
+    splits = ['train', 'validation', 'test']
+    domain = 'restaurant'
+    dialogues_by_split = {split: [] for split in splits}
+    global ontology
+    
+    for split in splits:
+        if split != 'validation':
+            filename = os.path.join(original_data_dir, f'woz_{split}_en.json')
+        else:
+            filename = os.path.join(original_data_dir, 'woz_validate_en.json')
+        if not os.path.exists(filename):
+            raise FileNotFoundError(
+                f'cannot find {filename}, should manually download from https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz')
+
+        data = json.load(open(filename))
+
+        for item in data:
+            dialogue = {
+                'dataset': dataset,
+                'data_split': split,
+                'dialogue_id': f'{dataset}-{split}-{len(dialogues_by_split[split])}',
+                'original_id': item['dialogue_idx'],
+                'domains': [domain],
+                'turns': []
+            }
+
+            turns = item['dialogue']
+            n_turn = len(turns)
+
+            for i in range(n_turn):
+                sys_utt = turns[i]['system_transcript'].strip()
+                usr_utt = turns[i]['transcript'].strip()
+                usr_da = turns[i]['turn_label']
+
+                for s, v in usr_da:
+                    if s == 'request':
+                        assert v in ontology['domains']['restaurant']['slots']
+                    else:
+                        assert s in ontology['domains']['restaurant']['slots']
+
+                if i != 0:
+                    dialogue['turns'].append({
+                        'utt_idx': len(dialogue['turns']),
+                        'speaker': 'system',
+                        'utterance': sys_utt,
+                    })
+
+                cur_state = copy.deepcopy(ontology['state'])
+                for act_slots in turns[i]['belief_state']:
+                    act, slots = act_slots['act'], act_slots['slots']
+                    if act == 'inform':
+                        for s, v in slots:
+                            v = v.strip()
+                            if v != 'dontcare' and ontology['domains']['restaurant']['slots'][s]['is_categorical']:
+                                if v not in ontology['domains']['restaurant']['slots'][s]['possible_values']:
+                                    if v == 'center':
+                                        v = 'centre'
+                                    elif v == 'east side':
+                                        v = 'east'
+                                    assert v in ontology['domains']['restaurant']['slots'][s]['possible_values']
+                                
+                            cur_state[domain][s] = v
+
+                cur_usr_da = convert_da(usr_da, usr_utt)
+
+                # add to dialogue_acts dictionary in the ontology
+                for da_type in cur_usr_da:
+                    das = cur_usr_da[da_type]
+                    for da in das:
+                        ontology["dialogue_acts"][da_type].setdefault((da['intent'], da['domain'], da['slot']), {})
+                        ontology["dialogue_acts"][da_type][(da['intent'], da['domain'], da['slot'])]['user'] = True
+
+                dialogue['turns'].append({
+                    'utt_idx': len(dialogue['turns']),
+                    'speaker': 'user',
+                    'utterance': usr_utt,
+                    'state': cur_state,
+                    'dialogue_acts': cur_usr_da,
+                })
+
+            dialogues_by_split[split].append(dialogue)
+
+    dialogues = []
+    for split in splits:
+        dialogues += dialogues_by_split[split]
+    for da_type in ontology['dialogue_acts']:
+        ontology["dialogue_acts"][da_type] = sorted([str(
+            {'user': speakers.get('user', False), 'system': speakers.get('system', False), 'intent': da[0],
+             'domain': da[1], 'slot': da[2]}) for da, speakers in ontology["dialogue_acts"][da_type].items()])
+    json.dump(dialogues[:10], open(f'dummy_data.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
+    json.dump(ontology, open(f'{new_data_dir}/ontology.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
+    json.dump(dialogues, open(f'{new_data_dir}/dialogues.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
+    with ZipFile('data.zip', 'w', ZIP_DEFLATED) as zf:
+        for filename in os.listdir(new_data_dir):
+            zf.write(f'{new_data_dir}/{filename}')
+    rmtree(original_data_dir)
+    rmtree(new_data_dir)
+    return dialogues, ontology
+
+
+if __name__ == '__main__':
+    preprocess()