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
+
+
+descriptions = {
+    "uber_lyft": {
+        "uber_lyft": "order a car for a ride inside a city",
+        "location.from": "pickup location",
+        "location.to": "destination of the ride",
+        "type.ride": "type of ride",
+        "num.people": "number of people",
+        "price.estimate": "estimated cost of the ride",
+        "duration.estimate": "estimated duration of the ride",
+        "time.pickup": "time of pickup",
+        "time.dropoff": "time of dropoff",
+    },
+    "movie_ticket": {
+        "movie_ticket": "book movie tickets for a film",
+        "name.movie": "name of the movie",
+        "name.theater": "name of the theater",
+        "num.tickets": "number of tickets",
+        "time.start": "start time of the movie",
+        "location.theater": "location of the theater",
+        "price.ticket": "price of the ticket",
+        "type.screening": "type of the screening",
+        "time.end": "end time of the movie",
+        "time.duration": "duration of the movie",
+    },
+    "restaurant_reservation": {
+        "restaurant_reservation": "searching for a restaurant and make reservation",
+        "name.restaurant": "name of the restaurant",
+        "name.reservation": "name of the person who make the reservation",
+        "num.guests": "number of guests",
+        "time.reservation": "time of the reservation",
+        "type.seating": "type of the seating",
+        "location.restaurant": "location of the restaurant",
+    },
+    "coffee_ordering": {
+        "coffee_ordering": "order a coffee drink from either Starbucks or Peets for pick up",
+        "location.store": "location of the coffee store",
+        "name.drink": "name of the drink",
+        "size.drink": "size of the drink",
+        "num.drink": "number of drinks",
+        "type.milk": "type of the milk",
+        "preference": "user preference of the drink",
+    },
+    "pizza_ordering": {
+        "pizza_ordering": "order a pizza",
+        "name.store": "name of the pizza store",
+        "name.pizza": "name of the pizza",
+        "size.pizza": "size of the pizza",
+        "type.topping": "type of the topping",
+        "type.crust": "type of the crust",
+        "preference": "user preference of the pizza",
+        "location.store": "location of the pizza store",
+    },
+    "auto_repair": {
+        "auto_repair": "set up an auto repair appointment with a repair shop",
+        "name.store": "name of the repair store",
+        "name.customer": "name of the customer",
+        "date.appt": "date of the appointment",
+        "time.appt": "time of the appointment",
+        "reason.appt": "reason of the appointment",
+        "name.vehicle": "name of the vehicle",
+        "year.vehicle": "year of the vehicle",
+        "location.store": "location of the repair store",
+    }
+}
+
+def normalize_domain_name(domain):
+    if domain == 'auto':
+        return 'auto_repair'
+    elif domain == 'pizza':
+        return 'pizza_ordering'
+    elif domain == 'coffee':
+        return 'coffee_ordering'
+    elif domain == 'uber':
+        return 'uber_lyft'
+    elif domain == 'restaurant':
+        return 'restaurant_reservation'
+    elif domain == 'movie':
+        return 'movie_ticket'
+    assert 0
+
+
+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'] == '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 tm1/, 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.'},
+                    'accept': {'description': 'accept the value of a slot or a transaction'},
+                    'reject': {'description': 'reject the value of a slot or a transaction'}
+                },
+                'state': {},
+                'dialogue_acts': {
+                    "categorical": {},
+                    "non-categorical": {},
+                    "binary": {}
+                }}
+    global descriptions
+    ori_ontology = {}
+    for _, item in json.load(open(os.path.join(original_data_dir, "TM-1-2019/ontology.json"))).items():
+        ori_ontology[item["id"]] = item
+    
+    for domain, item in ori_ontology.items():
+        ontology['domains'][domain] = {'description': descriptions[domain][domain], 'slots': {}}
+        ontology['state'][domain] = {}
+        for slot in item['required']+item['optional']:
+            ontology['domains'][domain]['slots'][slot] = {
+                'description': descriptions[domain][slot],
+                'is_categorical': False,
+                'possible_values': [],
+            }
+            ontology['state'][domain][slot] = ''
+
+    dataset = 'tm1'
+    splits = ['train', 'validation', 'test']
+    dialogues_by_split = {split:[] for split in splits}
+    dialog_files = ["TM-1-2019/self-dialogs.json", "TM-1-2019/woz-dialogs.json"]
+    for file_idx, filename in enumerate(dialog_files):
+        data = json.load(open(os.path.join(original_data_dir, filename)))
+        if file_idx == 0:
+            # original split for self dialogs
+            dial_id2split = {}
+            for data_split in ['train', 'dev', 'test']:
+                with open(os.path.join(original_data_dir, f"TM-1-2019/train-dev-test/{data_split}.csv")) as f:
+                    for line in f:
+                        dial_id = line.split(',')[0]
+                        dial_id2split[dial_id] = data_split if data_split != 'dev' else 'validation'
+        else:
+            # random split for woz dialogs 8:1:1
+            random.seed(42)
+            dial_ids = [d['conversation_id'] for d in 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 d in tqdm(data, desc='processing taskmaster-{}'.format(filename)):
+            # 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[d["conversation_id"]]
+            dialogue_id = f'{dataset}-{data_split}-{len(dialogues_by_split[data_split])}'
+            cur_domains = [normalize_domain_name(d["instruction_id"].split('-', 1)[0])]
+            assert len(cur_domains) == 1 and cur_domains[0] in ontology['domains']
+            domain = cur_domains[0]
+            dialogue = {
+                'dataset': dataset,
+                'data_split': data_split,
+                'dialogue_id': dialogue_id,
+                'original_id': d["conversation_id"],
+                'domains': cur_domains,
+                '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:
+                        # Each conversation was annotated by two workers.
+                        # only keep the first annotation for the span
+                        item = segment['annotations'][0]
+                        intent = 'inform'  # default intent
+                        slot = item['name'].split('.', 1)[-1]
+                        if slot.endswith('.accept') or slot.endswith('.reject'):
+                            # intent=accept/reject
+                            intent = slot[-6:]
+                            slot = slot[:-7]
+                        if slot not in ontology['domains'][domain]['slots']:
+                            # no slot, only general reference to a transaction, binary dialog act
+                            turn['dialogue_acts']['binary'].append({
+                                'intent': intent,
+                                'domain': domain,
+                                'slot': '',
+                            })
+                        else:
+                            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]
+                    # not add reject slot-value into state
+                    if da['intent'] != 'reject':
+                        prev_state[domain][slot] = value
+                
+                if speaker == 'user':
+                    turn['state'] = copy.deepcopy(prev_state)
+
+                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()