Upload preprocess.py

preprocess.py

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+from zipfile import ZipFile, ZIP_DEFLATED
+from shutil import rmtree
+import json
+import os
+from tqdm import tqdm
+from collections import Counter
+from pprint import pprint
+from nltk.tokenize import sent_tokenize, word_tokenize
+from nltk.tokenize.treebank import TreebankWordDetokenizer
+import re
+
+topic_map = {
+    1: "Ordinary Life", 
+    2: "School Life", 
+    3: "Culture & Education",
+    4: "Attitude & Emotion", 
+    5: "Relationship", 
+    6: "Tourism", 
+    7: "Health", 
+    8: "Work", 
+    9: "Politics", 
+    10: "Finance"
+}
+
+act_map = {
+    1: "inform", 
+    2: "question", 
+    3: "directive", 
+    4: "commissive"
+}
+
+emotion_map = {
+    0: "no emotion", 
+    1: "anger", 
+    2: "disgust", 
+    3: "fear", 
+    4: "happiness", 
+    5: "sadness", 
+    6: "surprise"
+}
+
+def preprocess():
+    original_data_dir = 'ijcnlp_dailydialog'
+    new_data_dir = 'data'
+
+    if not os.path.exists(original_data_dir):
+        original_data_zip = 'ijcnlp_dailydialog.zip'
+        if not os.path.exists(original_data_zip):
+            raise FileNotFoundError(f'cannot find original data {original_data_zip} in dailydialog/, should manually download ijcnlp_dailydialog.zip from http://yanran.li/files/ijcnlp_dailydialog.zip')
+        else:
+            archive = ZipFile(original_data_zip)
+            archive.extractall()
+
+    os.makedirs(new_data_dir, exist_ok=True)
+
+    dataset = 'dailydialog'
+    splits = ['train', 'validation', 'test']
+    dialogues_by_split = {split:[] for split in splits}
+    dial2topics = {}
+    with open(os.path.join(original_data_dir, 'dialogues_text.txt')) as dialog_file, \
+        open(os.path.join(original_data_dir, 'dialogues_topic.txt')) as topic_file:
+        for dialog, topic in zip(dialog_file, topic_file):
+            topic = int(topic.strip())
+            dialog = dialog.replace(' __eou__ ', ' ')
+            if dialog in dial2topics:
+                dial2topics[dialog].append(topic)
+            else:
+                dial2topics[dialog] = [topic]
+
+    global topic_map, act_map, emotion_map
+
+    ontology = {'domains': {x:{'description': '', 'slots': {}} for x in topic_map.values()},
+                'intents': {x:{'description': ''} for x in act_map.values()},
+                'state': {},
+                'dialogue_acts': {
+                    "categorical": [],
+                    "non-categorical": [],
+                    "binary": {}
+                }}
+
+    detokenizer = TreebankWordDetokenizer()
+
+    for data_split in splits:
+        archive = ZipFile(os.path.join(original_data_dir, f'{data_split}.zip'))
+        with archive.open(f'{data_split}/dialogues_{data_split}.txt') as dialog_file, \
+            archive.open(f'{data_split}/dialogues_act_{data_split}.txt') as act_file, \
+            archive.open(f'{data_split}/dialogues_emotion_{data_split}.txt') as emotion_file:
+            for dialog_line, act_line, emotion_line in tqdm(zip(dialog_file, act_file, emotion_file)):
+                if not dialog_line.strip():
+                    break
+                utts = dialog_line.decode().split("__eou__")[:-1]
+                acts = act_line.decode().split(" ")[:-1]
+                emotions = emotion_line.decode().split(" ")[:-1]
+                assert (len(utts) == len(acts) == len(emotions)), "Different turns btw dialogue & emotion & action"
+
+                topics = dial2topics[dialog_line.decode().replace(' __eou__ ', ' ')]
+                topic = Counter(topics).most_common(1)[0][0]
+                domain = topic_map[topic]
+                
+                dialogue_id = f'{dataset}-{data_split}-{len(dialogues_by_split[data_split])}'
+                dialogue = {
+                    'dataset': dataset,
+                    'data_split': data_split,
+                    'dialogue_id': dialogue_id,
+                    'original_id': f'{data_split}-{len(dialogues_by_split[data_split])}',
+                    'domains': [domain],
+                    'turns': []
+                }
+
+                for utt, act, emotion in zip(utts, acts, emotions):
+                    speaker = 'user' if len(dialogue['turns']) % 2 == 0 else 'system'
+                    intent = act_map[int(act)]
+                    emotion = emotion_map[int(emotion)]
+                    # re-tokenize
+                    utt = ' '.join([detokenizer.detokenize(word_tokenize(s)) for s in sent_tokenize(utt)])
+                    # replace with common apostrophe
+                    utt = utt.replace(' ’ ', "'")
+                    # add space after full-stop
+                    utt = re.sub('\.(?!com)(\w)', lambda x: '. '+x.group(1), utt)
+
+                    dialogue['turns'].append({
+                        'speaker': speaker,
+                        'utterance': utt.strip(),
+                        'utt_idx': len(dialogue['turns']),
+                        'dialogue_acts': {
+                            'binary': [{
+                                'intent': intent, 
+                                'domain': '', 
+                                'slot': ''
+                            }],
+                            'categorical': [],
+                            'non-categorical': [],
+                        },
+                        'emotion': emotion,
+                    })
+
+                    ontology["dialogue_acts"]['binary'].setdefault((intent, '', ''), {})
+                    ontology["dialogue_acts"]['binary'][(intent, '', '')][speaker] = True
+
+                dialogues_by_split[data_split].append(dialogue)
+
+    ontology["dialogue_acts"]['binary'] = 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"]['binary'].items()])
+    dialogues = dialogues_by_split['train']+dialogues_by_split['validation']+dialogues_by_split['test']
+    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()