Upload preprocess.py

preprocess.py

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+from zipfile import ZipFile, ZIP_DEFLATED
+import json
+import os
+import copy
+import zipfile
+from tqdm import tqdm
+import re
+from collections import Counter
+from shutil import rmtree
+from convlab.util.file_util import read_zipped_json, write_zipped_json
+from pprint import pprint
+import random
+import glob
+
+
+descriptions = {
+    'movie': 'Book movie tickets for the user',
+    'name.movie': 'Name of the movie, e.g. Joker, Parasite, The Avengers',
+    'name.theater': 'Name of the theater, e.g. Century City, AMC Mercado 20',
+    'num.tickets': 'Number of tickets, e.g. two, me and my friend, John and I',
+    'time.preference': 'Preferred time or range, e.g. around 2pm, later in the evening, 4:30pm',
+    'time.showing': 'The showtimes published by the theater, e.g. 5:10pm, 8:30pm',
+    'date.showing': 'the date or day of the showing, e.g. today, tonight, tomrrow, April 12th.',
+    'location': 'The city, or city and state, zip code and sometimes more specific regions, e.g. downtown',
+    'type.screening': 'IMAX, Dolby, 3D, standard, or similar phrases for technology offerings',
+    'seating': 'Various phrases from specific "row 1" to "near the back", "on an aisle", etc.',
+    'date.release': 'Movie attribute published for the official movie release date.',
+    'price.ticket': 'Price per ticket',
+    'price.total': 'The total for the purchase of all tickets',
+    'name.genre': 'Includes a wide range from classic genres like action, drama, etc. to categories like "slasher" or series like Marvel or Harry Potter',
+    'description.plot': 'The movie synopsis or shorter description',
+    'description.other': 'Any other movie description that is not captured by genre, name, plot.',
+    'duration.movie': 'The movie runtime, e.g. 120 minutes',
+    'name.person': 'Names of actors, directors, producers but NOT movie characters',
+    'name.character': 'Character names like James Bond, Harry Potter, Wonder Woman',
+    'review.audience': 'The audience review',
+    'review.critic': 'Critic reviews like those from Rotten Tomatoes, IMDB, etc.',
+    'rating.movie': 'G, PG, PG-13, R, etc.',
+}
+
+anno2slot = {
+    "movie": {
+        "description.other": False,  # transform to binary dialog act
+        "description.plot": False,  # too long, 19 words in avg. transform to binary dialog act
+    }
+}
+
+
+def format_turns(ori_turns):
+    # delete invalid turns and merge continuous turns
+    new_turns = []
+    previous_speaker = None
+    utt_idx = 0
+    for i, turn in enumerate(ori_turns):
+        speaker = 'system' if turn['speaker'].upper() == 'ASSISTANT' else 'user'
+        turn['speaker'] = speaker
+        if turn['text'] == '(deleted)':
+            continue
+        if not previous_speaker:
+            # first turn
+            assert speaker != previous_speaker
+        if speaker != previous_speaker:
+            # switch speaker
+            previous_speaker = speaker
+            new_turns.append(copy.deepcopy(turn))
+            utt_idx += 1
+        else:
+            # continuous speaking of the same speaker
+            last_turn = new_turns[-1]
+            # skip repeated turn
+            if turn['text'] in ori_turns[i-1]['text']:
+                continue
+            # merge continuous turns
+            index_shift = len(last_turn['text']) + 1
+            last_turn['text'] += ' '+turn['text']
+            if 'segments' in turn:
+                last_turn.setdefault('segments', [])
+                for segment in turn['segments']:
+                    segment['start_index'] += index_shift
+                    segment['end_index'] += index_shift
+                last_turn['segments'] += turn['segments']
+    return new_turns
+
+
+def preprocess():
+    original_data_dir = 'Taskmaster-master'
+    new_data_dir = 'data'
+
+    if not os.path.exists(original_data_dir):
+        original_data_zip = 'master.zip'
+        if not os.path.exists(original_data_zip):
+            raise FileNotFoundError(f'cannot find original data {original_data_zip} in tm3/, should manually download master.zip from https://github.com/google-research-datasets/Taskmaster/archive/refs/heads/master.zip')
+        else:
+            archive = ZipFile(original_data_zip)
+            archive.extractall()
+
+    os.makedirs(new_data_dir, exist_ok=True)
+
+    ontology = {'domains': {},
+                'intents': {
+                    'inform': {'description': 'inform the value of a slot or general information.'}
+                },
+                'state': {},
+                'dialogue_acts': {
+                    "categorical": {},
+                    "non-categorical": {},
+                    "binary": {}
+                }}
+    global descriptions
+    global anno2slot
+    ori_ontology = json.load(open(os.path.join(original_data_dir, "TM-3-2020/ontology/entities.json")))
+    assert len(ori_ontology) == 1
+    domain = list(ori_ontology.keys())[0]
+    domain_ontology = ori_ontology[domain]
+    ontology['domains'][domain] = {'description': descriptions[domain], 'slots': {}}
+    ontology['state'][domain] = {}
+    for slot in domain_ontology['required']+domain_ontology['optional']:
+        ontology['domains'][domain]['slots'][slot] = {
+            'description': descriptions[slot],
+            'is_categorical': False,
+            'possible_values': [],
+        }
+        if slot not in anno2slot[domain]:
+            ontology['state'][domain][slot] = ''
+    
+    dataset = 'tm3'
+    splits = ['train', 'validation', 'test']
+    dialogues_by_split = {split:[] for split in splits}
+    data_files = sorted(glob.glob(os.path.join(original_data_dir, f"TM-3-2020/data/*.json")))
+    for data_file in tqdm(data_files, desc='processing taskmaster-{}'.format(domain)):
+        data = json.load(open(data_file))
+        # random split, train:validation:test = 8:1:1
+        random.seed(42)
+        dial_ids = list(range(len(data)))
+        random.shuffle(dial_ids)
+        dial_id2split = {}
+        for dial_id in dial_ids[:int(0.8*len(dial_ids))]:
+            dial_id2split[dial_id] = 'train'
+        for dial_id in dial_ids[int(0.8*len(dial_ids)):int(0.9*len(dial_ids))]:
+            dial_id2split[dial_id] = 'validation'
+        for dial_id in dial_ids[int(0.9*len(dial_ids)):]:
+            dial_id2split[dial_id] = 'test'
+
+        for dial_id, d in enumerate(data):
+            # delete empty dialogs and invalid dialogs
+            if len(d['utterances']) == 0:
+                continue
+            if len(set([t['speaker'] for t in d['utterances']])) == 1:
+                continue
+            data_split = dial_id2split[dial_id]
+            dialogue_id = f'{dataset}-{data_split}-{len(dialogues_by_split[data_split])}'
+            cur_domains = [domain]
+            goal = {
+                'description': d['instructions'],
+                'inform': {},
+                'request': {}
+            }
+            dialogue = {
+                'dataset': dataset,
+                'data_split': data_split,
+                'dialogue_id': dialogue_id,
+                'original_id': d["conversation_id"],
+                'domains': cur_domains,
+                'goal': goal,
+                'turns': []
+            }
+            turns = format_turns(d['utterances'])
+            prev_state = {}
+            prev_state.setdefault(domain, copy.deepcopy(ontology['state'][domain]))
+            
+            for utt_idx, uttr in enumerate(turns):
+                speaker = uttr['speaker']
+                turn = {
+                    'speaker': speaker,
+                    'utterance': uttr['text'],
+                    'utt_idx': utt_idx,
+                    'dialogue_acts': {
+                        'binary': [],
+                        'categorical': [],
+                        'non-categorical': [],
+                    },
+                }
+                in_span = [0] * len(turn['utterance'])
+
+                if 'segments' in uttr:
+                    # sort the span according to the length
+                    segments = sorted(uttr['segments'], key=lambda x: len(x['text']))
+                    for segment in segments:
+                        assert len(['annotations']) == 1
+                        item = segment['annotations'][0]
+                        intent = 'inform'  # default intent
+                        slot = item['name'].strip()
+                        assert slot in ontology['domains'][domain]['slots']
+                        if slot in anno2slot[domain]:
+                            # binary dialog act
+                            turn['dialogue_acts']['binary'].append({
+                                'intent': intent,
+                                'domain': domain,
+                                'slot': slot,
+                            })
+                            continue
+                        assert turn['utterance'][segment['start_index']:segment['end_index']] == segment['text']
+                        # skip overlapped spans, keep the shortest one
+                        if sum(in_span[segment['start_index']: segment['end_index']]) > 0:
+                            continue
+                        else:
+                            in_span[segment['start_index']: segment['end_index']] = [1]*(segment['end_index']-segment['start_index'])
+                        turn['dialogue_acts']['non-categorical'].append({
+                            'intent': intent,
+                            'domain': domain,
+                            'slot': slot,
+                            'value': segment['text'],
+                            'start': segment['start_index'],
+                            'end': segment['end_index']
+                        })
+
+                turn['dialogue_acts']['non-categorical'] = sorted(turn['dialogue_acts']['non-categorical'], key=lambda x: x['start'])
+                
+                bdas = set()
+                for da in turn['dialogue_acts']['binary']:
+                    da_tuple = (da['intent'], da['domain'], da['slot'],)
+                    bdas.add(da_tuple)
+                turn['dialogue_acts']['binary'] = [{'intent':bda[0],'domain':bda[1],'slot':bda[2]} for bda in sorted(bdas)]
+                # add to dialogue_acts dictionary in the ontology
+                for da_type in turn['dialogue_acts']:
+                    das = turn['dialogue_acts'][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'])][speaker] = True
+
+                for da in turn['dialogue_acts']['non-categorical']:
+                    slot, value = da['slot'], da['value']
+                    assert slot in prev_state[domain], print(da)
+                    prev_state[domain][slot] = value
+                
+                if speaker == 'user':
+                    turn['state'] = copy.deepcopy(prev_state)
+                else:
+                    turn['db_results'] = {}
+                    if 'apis' in turns[utt_idx-1]:
+                        turn['db_results'].setdefault(domain, [])
+                        apis = turns[utt_idx-1]['apis']
+                        turn['db_results'][domain] += apis
+
+                dialogue['turns'].append(turn)
+            dialogues_by_split[data_split].append(dialogue)
+
+    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()])
+    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()