def run(self): opt = self.opt world = self._setup_world() logger = TodWorldLogger(opt) # set up logging log_every_n_secs = opt.get("log_every_n_secs", -1) if log_every_n_secs <= 0: log_every_n_secs = float("inf") log_time = TimeLogger() # episode counter max_episodes = opt.get("num_episodes", -1) if max_episodes < 0: max_episodes = float("inf") world_num_episodes = world.num_episodes() if world_num_episodes > 0: max_episodes = min(max_episodes, world_num_episodes) ep_count = 0 episode_metrics = [] while not world.epoch_done() and ep_count < max_episodes: episode_metrics.extend(self._run_episode(opt, world, logger)) ep_count += opt.get("batchsize", 1) if log_time.time() > log_every_n_secs: report = world.report() text, report = log_time.log(ep_count, max_episodes, report) logging.info(text) return self._save_outputs(opt, world, logger, episode_metrics)
def _eval_single_world(opt, agent, task): print('[ Evaluating task {} using datatype {}. ] '.format( task, opt.get('datatype', 'N/A'))) task_opt = opt.copy() # copy opt since we're editing the task task_opt['task'] = task world = create_task(task_opt, agent) # create worlds for tasks # set up logging log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() # max number of examples to evaluate max_cnt = opt['num_examples'] if opt['num_examples'] > 0 else float('inf') cnt = 0 while not world.epoch_done() and cnt < max_cnt: cnt += opt.get('batchsize', 1) world.parley() if opt['display_examples']: # display examples print(world.display() + '\n~~') if log_time.time() > log_every_n_secs: report = world.report() text, report = log_time.log(report['exs'], world.num_examples(), report) print(text) report = world.report() world.reset() return report
def self_chat(opt, print_parser=None): if print_parser is not None: if print_parser is True and isinstance(opt, ParlaiParser): print_parser = opt elif print_parser is False: print_parser = None if isinstance(opt, ParlaiParser): print('[ Deprecated Warning: self_chat should be passed opt not Parser ]') opt = opt.parse_args() random.seed(opt['seed']) # Create models agent1 = create_agent(opt, requireModelExists=True) agent2 = agent1.clone() if hasattr(agent2, 'id'): agent2.id = agent2.id + "2" # Check for `selfchat` in the task name if 'selfchat' not in opt['task']: warn_once( 'You are using self chat with task {}. '.format(opt['task']) + 'If your task has an existing self chat world, then run with ' '-t {}:selfchat'.format(opt['task']) ) world = create_task(opt, [agent1, agent2]) if print_parser: # Show arguments after loading model print_parser.opt = agent1.opt print_parser.print_args() # set up logging log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() logger = WorldLogger(opt) # Run some self chats. max_cnt = opt['num_examples'] cnt = 0 while cnt < max_cnt: cnt += opt.get('batchsize', 1) world.parley() logger.log(world) if opt.get('display_examples'): print(world.display()) if log_time.time() > log_every_n_secs: text = log_time.log(cnt, max_cnt) print(text) if opt.get('display_examples'): print('-- end of episode --') logger.reset_world() # flush last episode logger.write(opt['outfile'], opt['format']) return logger.get_logs()
def detect(opt, printargs=None, print_parser=None): """ Checks a task for offensive language. """ if print_parser is not None: if print_parser is True and isinstance(opt, ParlaiParser): print_parser = opt elif print_parser is False: print_parser = None random.seed(42) # Create model and assign it to the specified task agent = create_agent(opt, requireModelExists=True) world = create_task(opt, agent) bad = OffensiveStringMatcher() if print_parser: # Show arguments after loading model print_parser.opt = agent.opt print_parser.print_args() log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() # Show some example dialogs: cnt = 0 while not world.epoch_done(): world.parley() words = [] for a in world.acts: offensive = bad.contains_offensive_language(a.get('text', '')) if offensive: words.append(offensive) labels = a.get('labels', a.get('eval_labels', '')) for l in labels: offensive = bad.contains_offensive_language(l) if offensive: words.append(offensive) if len(words) > 0 and opt['display_examples']: print(world.display()) print("[Offensive words detected:]", ', '.join(words)) print("\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n") cnt += len(words) if log_time.time() > log_every_n_secs: report = world.report() log = {'offenses': cnt} text, log = log_time.log(report['exs'], world.num_examples(), log) print(text) if world.epoch_done(): print("EPOCH DONE") print( str(cnt) + " offensive messages found out of " + str(world.num_examples()) + " messages.") return world.report()
def get_word_counts(opt, count_inputs): """ Goes through the dataset specified in opt, returns word counts and all utterances. Inputs: count_inputs: If True, include both input and reply when counting words and utterances. Otherwise, only include reply text. Returns: word_counter: a Counter mapping each word to the total number of times it appears total_count: int. total word count, i.e. the sum of the counts for each word all_utts: list of strings. all the utterances that were used for counting words """ # Create repeat label agent and assign it to the specified task agent = RepeatLabelAgent(opt) world = create_task(opt, agent) # Count word frequency for all words in dataset word_counter = Counter() total_count = 0 all_utts = [] log_timer = TimeLogger() while True: world.parley() # Count words in reply reply = world.acts[0].get('labels', world.acts[0].get('eval_labels'))[0] words = reply.split() word_counter.update(words) total_count += len(words) all_utts.append(reply) # Optionally count words in input text if count_inputs: input = world.acts[0]['text'] input = input.split('\n')[ -1] # e.g. in ConvAI2, this removes persona words = input.split() word_counter.update(words) total_count += len(words) all_utts.append(input) if log_timer.time() > opt['log_every_n_secs']: text, _log = log_timer.log(world.total_parleys, world.num_examples()) print(text) if world.epoch_done(): print('EPOCH DONE') break assert total_count == sum(word_counter.values()) return word_counter, total_count, all_utts
def _eval_single_world(opt, agent, task): print('[ Evaluating task {} using datatype {}. ] '.format( task, opt.get('datatype', 'N/A'))) # set up world logger world_logger = WorldLogger(opt) if opt['save_world_logs'] else None task_opt = opt.copy() # copy opt since we're editing the task # task_opt['task'] = task world = create_task(task_opt, agent) # create worlds for tasks # set up logging log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() # max number of examples to evaluate max_cnt = opt['num_examples'] if opt['num_examples'] > 0 else float('inf') cnt = 0 while not world.epoch_done() and cnt < max_cnt: cnt += opt.get('batchsize', 1) world.parley() if world_logger is not None: world_logger.log(world) if opt['display_examples']: # display examples print(world.display() + '\n~~') # for a in world.acts: # print (a) # print (world.get_acts()) # print (world.acts) if log_time.time() > log_every_n_secs: report = world.report() text, report = log_time.log(report.get('exs', 0), world.num_examples(), report) print(text) report = world.report() print("Printing Report") print(report) world.reset() if world_logger is not None: # dump world acts to file world_logger.reset() # add final acts to logs base_outfile = opt['report_filename'].split('.')[0] print("filename: ", base_outfile) outfile = base_outfile + f'_{task}_replies.jsonl' # world_logger.write_jsonl_format(outfile) world_logger.write_parlai_format(outfile) return report
def get_word_counts(opt, count_inputs): """ Goes through the dataset specified in opt and gets word counts. Inputs: count_inputs: If True, include both input and reply when counting words and utterances. Otherwise, only include reply text. Returns: word_counter_per_sent: a Counter mapping each word to the number of utterances in which it appears. num_sents: int. number of utterances counted """ # Create repeat label agent and assign it to the specified task agent = RepeatLabelAgent(opt) world = create_task(opt, agent) # Count word frequency for all words in dataset word_counter_per_sent = Counter() num_sents = 0 count = 0 log_timer = TimeLogger() while True: count += 1 world.parley() reply = world.acts[0].get('labels', world.acts[0].get('eval_labels'))[0] words = reply.split() words_no_dups = list(set(words)) # remove duplicates word_counter_per_sent.update(words_no_dups) num_sents += 1 # Optionally count words in input text if count_inputs: input = world.acts[0]['text'] input = input.split('\n')[ -1] # e.g. in ConvAI2, this removes persona words = input.split() words_no_dups = list(set(words)) # remove duplicates word_counter_per_sent.update(words_no_dups) num_sents += 1 if log_timer.time() > opt['log_every_n_secs']: text, _log = log_timer.log(world.total_parleys, world.num_examples()) print(text) if world.epoch_done(): print('EPOCH DONE') break return word_counter_per_sent, num_sents
def _eval_single_world(opt, agent, task): logging.info( f'Evaluating task {task} using datatype {opt.get("datatype")}.') # set up world logger world_logger = WorldLogger(opt) if opt['world_logs'] else None task_opt = opt.copy() # copy opt since we're editing the task task_opt['task'] = task world = create_task(task_opt, agent) # create worlds for tasks # set up logging log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() # max number of examples to evaluate max_cnt = opt['num_examples'] if opt['num_examples'] > 0 else float('inf') cnt = 0 total_cnt = world.num_examples() if is_distributed(): logging.warning('Progress bar is approximate in distributed mode.') while not world.epoch_done() and cnt < max_cnt: cnt += opt.get('batchsize', 1) world.parley() if world_logger is not None: world_logger.log(world) if opt['display_examples']: # display examples print(world.display() + '\n~~') if log_time.time() > log_every_n_secs: report = world.report() text, report = log_time.log(report.get('exs', 0), min(max_cnt, total_cnt), report) logging.info(text) if world_logger is not None: # dump world acts to file world_logger.reset() # add final acts to logs if is_distributed(): rank = get_rank() base_outfile, extension = os.path.splitext(opt['world_logs']) outfile = base_outfile + f'_{rank}' + extension else: outfile = opt['world_logs'] world_logger.write(outfile, world, file_format=opt['save_format']) report = aggregate_unnamed_reports(all_gather_list(world.report())) world.reset() return report
def build_cands(opt): # create repeat label agent and assign it to the specified task if opt['numthreads'] > 1: # Broken in hogwild mode. Just fall back to single processing mode opt['numthreads'] = 1 agent = RepeatLabelAgent(opt) world = create_task(opt, agent) if opt['outfile'] is None: outfile = tempfile.mkstemp(prefix='{}_{}_'.format( opt['task'], opt['datatype']), suffix='.txt')[1] else: outfile = opt['outfile'] if opt.get('num_examples', -1) == -1: num_examples = world.num_examples() else: num_examples = opt['num_examples'] log_timer = TimeLogger() print('[ starting to build candidates from task.. (ex:' + str(num_examples) + ')]') print('[ saving output to {} ]'.format(outfile)) cands = set() for _ in range(num_examples): world.parley() # We get the acts of the first agent, which is the teacher. # this part is modified to get all utterances for acts in world.get_acts(): acts = world.get_acts()[0] if isinstance(acts, dict): # We turn into a batch of 1 example, in case batching is being used. acts = [acts] for a in acts: candidate = a.get('labels', a.get('eval_labels', None)) if candidate is not None: candidate = candidate[0] cands.add(candidate) if log_timer.time() > opt['log_every_n_secs']: text, _log = log_timer.log(world.total_parleys, world.num_examples()) print(text) if world.epoch_done(): print('EPOCH DONE') break fw = open(outfile, 'w') fw.write('\n'.join(cands)) fw.close()
def self_chat(opt, print_parser=None): if print_parser is not None: if print_parser is True and isinstance(opt, ParlaiParser): print_parser = opt elif print_parser is False: print_parser = None if isinstance(opt, ParlaiParser): print( '[ Deprecated Warning: self_chat should be passed opt not Parser ]' ) opt = opt.parse_args() random.seed(opt['seed']) # Create models agent1 = create_agent(opt, requireModelExists=True) agent2 = agent1.clone() if hasattr(agent2, 'id'): agent2.id = agent2.id + "2" world = create_task(opt, [agent1, agent2]) if print_parser: # Show arguments after loading model print_parser.opt = agent1.opt print_parser.print_args() # set up logging log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() logger = WorldLogger(opt) # Run some self chats. max_cnt = opt['num_examples'] cnt = 0 while cnt < max_cnt: cnt += opt.get('batchsize', 1) world.parley() logger.log(world) if opt.get('display_examples'): print("---") print(world.display()) if log_time.time() > log_every_n_secs: text = log_time.log(cnt, max_cnt) print(text) logger.write(opt['outfile'], opt['format'])
def run(self): self.opt['no_cuda'] = True if 'ordered' not in self.opt['datatype'] and 'train' in self.opt[ 'datatype']: self.opt['datatype'] = self.opt['datatype'] + ':ordered' agent = create_agent(self.opt) agent.opt.log() num_examples = self.opt['num_examples'] field = self.opt['field'] + '_vec' if num_examples < 0: num_examples = float('inf') assert self.opt['batchsize'] == 1 assert isinstance(agent, TorchAgent) world = create_task(self.opt, agent) teacher = world.get_task_agent() # set up logging log_every_n_secs = self.opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() lengths = [] cnt = 0 total = min(teacher.num_examples(), num_examples) while not teacher.epoch_done() and cnt < num_examples: act = teacher.act() processed = agent.observe(act) try: text_vec = processed[field] except KeyError: raise KeyError(f"Pick one of {list(processed.keys())}") if text_vec is not None and (not self.opt['final_only'] or act.get('episode_done')): cnt += 1 lengths.append(float(len(text_vec))) agent.self_observe({}) if log_time.time() > log_every_n_secs: report = self._compute_stats(lengths) text, report = log_time.log(report['exs'], total, report) logging.info(text) report = self._compute_stats(lengths) print(nice_report(report)) return report
def run_generation(self): """ Actually run the evaluations. """ # set up logging log_every_n_secs = self.opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() # max number of examples to evaluate max_cnt = ( self.opt['num_examples'] if self.opt['num_examples'] > 0 else float('inf') ) self.cnt = 0 self.n_valid = 0 self.log_count = 0 total_cnt = self.world.num_examples() while not self.world.epoch_done() and self.cnt < max_cnt: self.cnt += self.opt.get('batchsize', 1) self.world.parley() acts = self.world.get_acts() if acts[-1]['text'] != INVALID: try: self.world.acts[0]['text'] += f"\n{acts[-1]['knowledge']}" except RuntimeError: self.world.acts[0].force_set( 'text', f"{self.world.acts[0]['text']}\n{acts[-1]['knowledge']}" ) self.world.acts[0]['f1_overlap'] = acts[-1]['f1_overlap'] self.world_logger.log(self.world) self.n_valid += 1 if ( self.n_valid > 0 and self.n_valid % self.opt['write_every_n_valid_exs'] == 0 ): self.log() if log_time.time() > log_every_n_secs: report = self.world.report() report['n_valid'] = self.n_valid text, report = log_time.log( report.get('exs', 0), min(max_cnt, total_cnt), report ) logging.info(text)
def build_cands(opt): opt.log() # create repeat label agent and assign it to the specified task agent = RepeatLabelAgent(opt) world = create_task(opt, agent) if opt['outfile'] is None: outfile = tempfile.mkstemp(prefix='{}_{}_'.format( opt['task'], opt['datatype']), suffix='.txt')[1] else: outfile = opt['outfile'] if opt.get('num_examples', -1) == -1: num_examples = world.num_examples() else: num_examples = opt['num_examples'] log_timer = TimeLogger() logging.info( f'Starting to build candidates from task.. (ex: {num_examples})') logging.info(f'Saving output to {outfile}') cands = set() for _ in range(num_examples): world.parley() # We get the acts of the first agent, which is the teacher. acts = world.get_acts()[0] if isinstance(acts, dict): # We turn into a batch of 1 example, in case batching is being used. acts = [acts] for a in acts: candidate = a.get('labels', a.get('eval_labels', None)) if candidate is not None: candidate = candidate[0] cands.add(candidate) if log_timer.time() > opt['log_every_n_secs']: text, _log = log_timer.log(world.total_parleys, world.num_examples()) logging.info(text) if world.epoch_done(): logging.info('epoch done') break fw = open(outfile, 'w') fw.write('\n'.join(cands)) fw.close()
def dump_data(opt): # create repeat label agent and assign it to the specified task agent = RepeatLabelAgent(opt) world = create_task(opt, agent) opt.log() if opt['outfile'] is None: outfile = tempfile.mkstemp(prefix='{}_{}_'.format( opt['task'], opt['datatype']), suffix='.txt')[1] else: outfile = opt['outfile'] if opt['num_examples'] == -1: num_examples = world.num_examples() else: num_examples = opt['num_examples'] log_timer = TimeLogger() logging.debug('starting to convert...') logging.info(f'saving output to {outfile}') fw = open(outfile, 'w') text = '' for _ in range(num_examples): world.parley() world.acts[0]['labels'] = world.acts[0].get( 'labels', world.acts[0].pop('eval_labels', None)) samp = world.acts[0] text += samp["text"].replace("\n", " ") + " " fw.write("__label__%s %s\n" % (samp["labels"][0].replace(' ', '_'), text)) if world.acts[0].get('episode_done', False): text = '' if log_timer.time() > opt['log_every_n_secs']: text, _log = log_timer.log(world.total_parleys, world.num_examples()) logging.info(text) if world.epoch_done(): logging.info('epoch done') break fw.close()
def dump_data(opt): # create repeat label agent and assign it to the specified task agent = RepeatLabelAgent(opt) world = create_task(opt, agent) opt.log() ignorefields = opt.get('ignore_fields', '') if opt['outfile'] is None: outfile = tempfile.mkstemp(prefix='{}_{}_'.format( opt['task'], opt['datatype']), suffix='.txt')[1] else: outfile = opt['outfile'] if opt['num_examples'] == -1: num_examples = world.num_examples() else: num_examples = opt['num_examples'] log_timer = TimeLogger() logging.debug('starting to convert...') logging.info(f'saving output to {outfile}') fw = open(outfile, 'w') for _ in range(num_examples): world.parley() acts = world.get_acts() value = acts[0].get('labels', acts[0].pop('eval_labels', None)) acts[0].force_set('labels', value) txt = msg_to_str(acts[0], ignore_fields=ignorefields) fw.write(txt + '\n') if acts[0].get('episode_done', False): fw.write('\n') if log_timer.time() > opt['log_every_n_secs']: text, _log = log_timer.log(world.total_parleys, world.num_examples()) logging.info(text) if world.epoch_done(): logging.info('epoch done') break fw.close()
def make_dataset(opt): # Initialize control information so we can compute sentence attributes. # Here we set build_task=False so we don't download data/controllable_dialogue # (because we're trying to create it instead). initialize_control_information(opt, build_task=False) # Create repeat label agent and assign it to the specified task agent = RepeatLabelAgent(opt) world = create_task(opt, agent) ignorefields = opt.get('ignore_fields', '') outfile = opt['outfile'] # Number of examples to process if opt['num_examples'] == -1: num_examples = world.num_examples() else: num_examples = opt['num_examples'] # List of controls to include: controls = opt['controls'].split(',') if opt['controls'] != '' else [] print('[ starting to convert.. ]') print('[ saving output to {} ]'.format(outfile)) fw = open(outfile, 'w') log_timer = TimeLogger() for _ in range(num_examples): world.parley() world.acts[0]['labels'] = world.acts[0].get( 'labels', world.acts[0].pop('eval_labels', None)) # Need to get history in order to compute control values hist = ConvAI2History(world.acts[0]['text'], assume_persontokens=False) response = world.acts[0]['labels'][0] # Compute control values for ctrl in controls: ctrl_val = eval_attr(response, hist, ctrl) if ctrl == 'avg_nidf': assert ctrl_val >= 0 assert ctrl_val <= 1 elif ctrl == 'question': assert ctrl_val in [0, 1] elif ctrl == 'lastuttsim': if ctrl_val is not None: assert ctrl_val >= -1 assert ctrl_val <= 1 else: raise Exception('unexpected ctrl name: %s' % ctrl) world.acts[0][ctrl] = ctrl_val # add control value to act # Write to file txt = msg_to_str(world.acts[0], ignore_fields=ignorefields) fw.write(txt + '\n') if world.acts[0].get('episode_done', False): fw.write('\n') if log_timer.time() > opt['log_every_n_secs']: text, _log = log_timer.log(world.total_parleys, world.num_examples()) print(text) if world.epoch_done(): print('EPOCH DONE') break fw.close()
def verify(opt, printargs=None, print_parser=None): if opt['datatype'] == 'train': logging.warn("changing datatype from train to train:ordered") opt['datatype'] = 'train:ordered' # create repeat label agent and assign it to the specified task agent = RepeatLabelAgent(opt) world = create_task(opt, agent) log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() counts = {} counts['missing_text'] = 0 counts['missing_labels'] = 0 counts['missing_label_candidates'] = 0 counts['empty_string_label_candidates'] = 0 counts['label_candidates_with_missing_label'] = 0 counts['did_not_return_message'] = 0 # Show some example dialogs. while not world.epoch_done(): world.parley() act = world.acts[0] if not isinstance(act, Message): counts['did_not_return_message'] += 1 if 'text' not in act and 'image' not in act: warn("warning: missing text field:\n", act, opt) counts['missing_text'] += 1 if 'labels' not in act and 'eval_labels' not in act: warn("warning: missing labels/eval_labels field:\n", act, opt) counts['missing_labels'] += 1 else: if 'label_candidates' not in act: counts['missing_label_candidates'] += 1 else: labels = act.get('labels', act.get('eval_labels')) is_label_cand = {} for l in labels: is_label_cand[l] = False for c in act['label_candidates']: if c == '': warn("warning: empty string label_candidate:\n", act, opt) counts['empty_string_label_candidates'] += 1 if c in is_label_cand: if is_label_cand[c] is True: warn( "warning: label mentioned twice in candidate_labels:\n", act, opt, ) is_label_cand[c] = True for _, has in is_label_cand.items(): if has is False: warn("warning: label missing in candidate_labels:\n", act, opt) counts['label_candidates_with_missing_label'] += 1 if log_time.time() > log_every_n_secs: text, log = report(world, counts, log_time) if print_parser: print(text) try: # print dataset size if available logging.info(f'Loaded {world.num_episodes()} episodes with a ' f'total of {world.num_examples()} examples') except Exception: pass return report(world, counts, log_time)
def eval_wordstat(opt): """ Evaluates a model. :param opt: tells the evaluation function how to run """ random.seed(42) # Create model and assign it to the specified task agent = create_agent(opt, requireModelExists=True) world = create_task(opt, agent) agent.opt.log() if opt.get('external_dict'): print('[ Using external dictionary from: {} ]'.format( opt['external_dict'])) dict_opt = copy.deepcopy(opt) dict_opt['dict_file'] = opt['external_dict'] dictionary = DictionaryAgent(dict_opt) else: print('[ Using model bundled dictionary ]') dictionary = agent.dict batch_size = opt['batchsize'] log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() cnt = 0 max_cnt = opt['num_examples'] if opt['num_examples'] > 0 else float('inf') word_statistics = { 'mean_wlength': [], 'mean_clength': [], 'freqs_cnt': Counter(), 'word_cnt': 0, 'pred_list': [], 'pure_pred_list': [], 'context_list': [], 'unique_words': set(), } bins = [int(i) for i in opt['freq_bins'].split(',')] def process_prediction(prediction, word_statistics): normalized = normalize_answer(prediction) word_statistics['pred_list'].append(normalized) freqs, _cnt, wlength, clength = get_word_stats(prediction, dictionary, bins=bins) word_statistics['word_cnt'] += _cnt word_statistics['mean_wlength'].append(wlength) word_statistics['mean_clength'].append(clength) word_statistics['freqs_cnt'] += Counter(freqs) word_statistics['unique_words'] |= set(normalized.split(" ")) return word_statistics while not world.epoch_done(): world.parley() if batch_size == 1: cnt += 1 prediction = world.acts[-1]['text'] word_statistics['context_list'].append(world.acts[0]['text']) word_statistics['pure_pred_list'].append(prediction) word_statistics = process_prediction(prediction, word_statistics) else: for w in world.worlds: try: if 'text' not in w.acts[-1]: continue prediction = w.acts[-1]['text'] word_statistics['context_list'].append(w.acts[0]['text']) word_statistics['pure_pred_list'].append(prediction) except IndexError: continue cnt += 1 word_statistics = process_prediction(prediction, word_statistics) if log_time.time() > log_every_n_secs: report = world.report() text, report = log_time.log(report['exs'], min(max_cnt, world.num_examples()), report) print(text) stat_str = 'total_words: {}, '.format(word_statistics['word_cnt']) stat_str += ', '.join([ '<{}:{} ({:.{prec}f}%)'.format( b, word_statistics['freqs_cnt'].get(b, 0), (word_statistics['freqs_cnt'].get(b, 0) / word_statistics['word_cnt']) * 100, prec=2, ) for b in bins ]) print("Word statistics: {}, avg_word_length: {:.{prec}f}, " "avg_char_length: {:.{prec}f}".format( stat_str, numpy.array(word_statistics['mean_wlength']).mean(), numpy.array(word_statistics['mean_clength']).mean(), prec=2, )) if cnt >= max_cnt: break if world.epoch_done(): print("EPOCH DONE") if opt['compute_unique'] is True: unique_list = [] cntr = Counter(word_statistics['pred_list']) for k, v in cntr.items(): if v == 1: unique_list.append(k) print("Unique responses: {:.{prec}f}%".format( len(unique_list) / len(word_statistics['pred_list']) * 100, prec=2)) print("Total unique tokens:", len(word_statistics['unique_words'])) if opt['dump_predictions_path'] is not None: with PathManager.open(opt['dump_predictions_path'], 'w') as f: f.writelines([ 'CONTEXT: {}\nPREDICTION:{}\n\n'.format(c, p) for c, p in zip( word_statistics['context_list'], word_statistics['pure_pred_list'], ) ]) if opt['compute_unique'] is True: with PathManager.open(opt['dump_predictions_path'] + '_unique', 'w') as f: f.writelines(['{}\n'.format(i) for i in unique_list]) stat_str = 'total_words: {}, '.format(word_statistics['word_cnt']) stat_str += ', '.join([ '<{}:{} ({:.{prec}f}%)'.format( b, word_statistics['freqs_cnt'].get(b, 0), (word_statistics['freqs_cnt'].get(b, 0) / word_statistics['word_cnt']) * 100, prec=2, ) for b in bins ]) print("Word statistics: {}, avg_word_length: {:.{prec}f}, " "avg_char_length: {:.{prec}f}".format( stat_str, numpy.array(word_statistics['mean_wlength']).mean(), numpy.array(word_statistics['mean_clength']).mean(), prec=2, )) report = world.report() print(report) return report
def _eval_single_world(opt, agent, task): logging.info( f'Evaluating task {task} using datatype {opt.get("datatype")}.') # set up world logger task_opt = opt.copy() # copy opt since we're editing the task task_opt['task'] = task # add task suffix in case of multi-tasking if opt['world_logs']: task_opt['world_logs'] = get_task_world_logs( task, task_opt['world_logs'], is_multitask=len(opt['task'].split(',')) > 1) world_logger = WorldLogger(task_opt) if task_opt['world_logs'] else None world = create_task(task_opt, agent) # create worlds for tasks # set up logging log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() # max number of examples to evaluate max_cnt = opt['num_examples'] if opt['num_examples'] > 0 else float('inf') cnt = 0 total_cnt = world.num_examples() if is_distributed(): logging.warning('Progress bar is approximate in distributed mode.') while not world.epoch_done() and cnt < max_cnt: cnt += opt.get('batchsize', 1) world.parley() if world_logger is not None: world_logger.log(world) if opt['display_examples']: # display examples print(world.display() + '\n~~') if log_time.time() > log_every_n_secs: report = world.report() text, report = log_time.log(report.get('exs', 0), min(max_cnt, total_cnt), report) logging.info(text) if world_logger is not None: # dump world acts to file world_logger.reset() # add final acts to logs if is_distributed(): rank = get_rank() base_outfile, extension = os.path.splitext(task_opt['world_logs']) outfile = base_outfile + f'_{rank}' + extension else: outfile = task_opt['world_logs'] world_logger.write(outfile, world, file_format=opt['save_format']) report = aggregate_unnamed_reports(all_gather_list(world.report())) if isinstance(world.agents, list) and len(world.agents) > 1: classifier_agent = world.agents[CLASSIFIER_AGENT] if hasattr(classifier_agent, 'calc_auc') and classifier_agent.calc_auc: for class_indices, curr_auc in zip( classifier_agent.auc_class_indices, classifier_agent.aucs): report[ f'AUC_{classifier_agent.class_list[class_indices]}'] = curr_auc classifier_agent.reset_auc() # for safety measures agent.reset_auc() world.reset() return report
def verify(opt): if opt['datatype'] == 'train': logging.warn('changing datatype from train to train:ordered') opt['datatype'] = 'train:ordered' # create repeat label agent and assign it to the specified task opt['fixed_response'] = None agent = FixedResponseAgent(opt) world = create_task(opt, agent) opt.log() log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() dictionary = DictionaryAgent(opt) ignore_tokens = opt.get('ignore_tokens').split(',') counts = {} for t in {'input', 'labels', 'both'}: counts[f'{t}/tokens'] = 0 counts[f'{t}/utterances'] = 0 counts[f'{t}/avg_utterance_length'] = None counts[f'{t}/unique_tokens'] = 0 counts[f'{t}/unique_utterances'] = 0 # for counting the stats.. counts[f'{t}/token_dict'] = {} counts[f'{t}/utterance_dict'] = {} def tokenize(txt): return dictionary.tokenize(txt) def keep_token(t): for s in ignore_tokens: if s != '' and s in t: return False return True # max number of examples to evaluate max_cnt = opt['num_examples'] if opt['num_examples'] > 0 else float('inf') cnt = 0 # Show some example dialogs. while not world.epoch_done() and world.total_exs < max_cnt: world.parley() act = world.get_acts()[opt.get('agent')] for itype in {'input', 'labels'}: if itype == 'input': if opt.get('new_line_new_utt'): txts = act.get('text').split('\n') else: txts = [act.get('text')] else: txts = act.get('labels', act.get('eval_labels', [''])) for txt in txts: tokens = tokenize(txt) retxt = [t for t in tokens if keep_token(t)] counts[f'{itype}/tokens'] += len(retxt) counts['both/tokens'] += len(retxt) counts[f'{itype}/utterances'] += 1 counts['both/utterances'] += 1 counts[f'{itype}/avg_utterance_length'] += AverageMetric( len(retxt), 1) counts[f'both/avg_utterance_length'] += AverageMetric( len(retxt), 1) for t in retxt: if t not in counts[f'{itype}/token_dict']: counts[f'{itype}/unique_tokens'] += 1 counts[f'{itype}/token_dict'][t] = True if t not in counts['both/token_dict']: counts['both/unique_tokens'] += 1 counts['both/token_dict'][t] = True retxt = ' '.join(retxt) if retxt not in counts[f'{itype}/utterance_dict']: counts[f'{itype}/unique_utterances'] += 1 counts[f'{itype}/utterance_dict'][retxt] = True if retxt not in counts['both/utterance_dict']: counts['both/unique_utterances'] += 1 counts['both/utterance_dict'][retxt] = True if log_time.time() > log_every_n_secs: report = _report(world, counts) cnt = report.pop('exs') text, log = log_time.log(cnt, world.num_examples(), report) logging.info(text) try: # print dataset size if available logging.info(f'loaded {world.num_episodes()} episodes with a total ' f'of {world.num_examples()} examples') except AttributeError: pass retval = _report(world, counts) retval.pop('exs') return retval
def learn_arora(opt): """ Go through ConvAI2 data and collect word counts, thus compute the unigram probability distribution. Use those probs to compute weighted sentence embeddings for all utterances, thus compute first principal component. Save all info to arora.pkl file. """ arora_file = os.path.join(opt['datapath'], 'controllable_dialogue', 'arora.pkl') opt['task'] = 'fromfile:parlaiformat' opt['log_every_n_secs'] = 2 print('Getting word counts from ConvAI2 train set...') opt['datatype'] = 'train:ordered' opt['fromfile_datapath'] = os.path.join(opt['datapath'], 'controllable_dialogue', 'ConvAI2_parlaiformat', 'train.txt') # Do include inputs because ConvAI2 train set reverses every convo: word_counter_train, total_count_train, all_utts_train = get_word_counts( opt, count_inputs=False) print('Getting word counts from ConvAI2 val set...') opt['datatype'] = 'valid' opt['fromfile_datapath'] = os.path.join(opt['datapath'], 'controllable_dialogue', 'ConvAI2_parlaiformat', 'valid.txt') # Don't include inputs because ConvAI2 val set doesn't reverses convos: word_counter_valid, total_count_valid, all_utts_valid = get_word_counts( opt, count_inputs=True) # Merge word counts word_counter = word_counter_train for word, count in word_counter_valid.items(): word_counter[word] += count total_count = total_count_train + total_count_valid # Merge all_utts all_utts = all_utts_train + all_utts_valid # Compute unigram prob for every word print("Computing unigram probs for all words...") word2prob = {w: c / total_count for w, c in word_counter.items()} # Settings for sentence embedder arora_a = 0.0001 glove_name = '840B' glove_dim = 300 glove_cache = modelzoo_path(opt['datapath'], 'models:glove_vectors') # Embed every sentence, without removing first singular value print('Embedding all sentences...') sent_embedder = SentenceEmbedder( word2prob, arora_a, glove_name, glove_dim, first_sv=None, glove_cache=glove_cache, ) utt_embs = [] log_timer = TimeLogger() for n, utt in enumerate(all_utts): utt_emb = sent_embedder.embed_sent(utt.split(), rem_first_sv=False) utt_embs.append(utt_emb) if log_timer.time() > opt['log_every_n_secs']: text, _log = log_timer.log(n, len(all_utts)) print(text) # Use SVD to calculate singular vector # https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.linalg.svd.html print('Calculating SVD...') utt_embs = np.stack(utt_embs, axis=0) # shape (num_embs, glove_dim) U, s, V = np.linalg.svd(utt_embs, full_matrices=False) first_sv = V[0, :] # first row of V. shape (glove_dim) # Remove singular vector from all embs to get complete Arora-style sent embs print('Removing singular vec from all sentence embeddings...') utt_embs_adj = [ remove_first_sv(torch.Tensor(emb), torch.Tensor(first_sv)).numpy() for emb in utt_embs ] # list of np arrays shape (glove_dim) # Make dict mapping ConvAI2 dataset utterances to Arora sent emb # We save this to file for convenience (e.g. if you want to inspect) utt2emb = {utt: emb for (utt, emb) in zip(all_utts, utt_embs_adj)} # Save unigram distribution, first singular value, hyperparameter value for a, # info about GloVe vectors used, and full dict of utt->emb to file print("Saving Arora embedding info to %s..." % arora_file) with open(arora_file, "wb") as f: pickle.dump( { 'word2prob': word2prob, # dict: string to float between 0 and 1 'first_sv': first_sv, # np array shape (glove_dim) 'arora_a': arora_a, # float, 0.0001 'glove_name': glove_name, # string, '840B' 'glove_dim': glove_dim, # int, 300 'utt2emb': utt2emb, # dict: string to np array shape (glove_dim) }, f, )
def eval_wordstat(opt): """ Evaluates a model. :param opt: tells the evaluation function how to run """ random.seed(42) # Setup control information initialize_control_information(opt) # Create model and assign it to the specified task agent = create_agent(opt, requireModelExists=True) world = create_task(opt, agent) if opt.get('external_dict'): print('[ Using external dictionary from: {} ]'.format(opt['external_dict'])) dict_opt = copy.deepcopy(opt) dict_opt['dict_file'] = opt['external_dict'] dictionary = DictionaryAgent(dict_opt) else: print('[ Using model bundled dictionary ]') dictionary = agent.dict batch_size = opt['batchsize'] log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() data = {} # This will be written to the output json file data['opt'] = agent.opt # Save the opt to json # Determine the output filename if opt['gold_response']: # Special output file for gold response model_dir, _ = os.path.split(opt.get('model_file')) outfile = os.path.join(model_dir, 'goldresponse') if opt['use_reply'] != 'label': raise ValueError( 'You should set --use-reply label (not --use-reply model) ' 'when measuring goldresponse stats' ) else: outfile = "%s.%s.%s.%s" % ( opt.get('model_file'), opt.get('datatype'), "use%sreply" % agent.opt['use_reply'], "beam%i" % agent.opt['beam_size'], ) if agent.opt['beam_size'] > 1: outfile += ".beamminnbest%i" % agent.opt['beam_min_n_best'] if len(agent.control_settings) > 0: outfile += ".setcontrols:" + "_".join( [ "%s%s" % (c, str(agent.control_settings[c]['set_value'])) for c in sorted(agent.control_settings.keys()) ] ) if agent.opt['beam_reorder'] not in ['none', False]: outfile += ".beamreorder_%s" % agent.opt['beam_reorder'] if len(agent.wd_features) > 0: sorted_bfw = sorted( list(zip(agent.wd_features, agent.wd_wts)), key=lambda x: x[0] ) outfile += ".WDfeatures:" + "_".join( ["%s%s" % (f, str(w)) for f, w in sorted_bfw] ) if opt['num_examples'] != -1: outfile += ".numex%i" % opt['num_examples'] outfile += ".wordstats.json" print("\nOutfile: %s\n" % outfile) cnt = 0 word_statistics = { 'mean_wlength': [], # list of length (in words) of utterances 'mean_clength': [], # list of length (in chars) of utterances 'freqs_cnt': Counter(), # Counter for word frequencies, bucketed 'word_cnt': 0, # total number of words in all utterances 'pred_list': [], # list of generated utterances after applying normalize_answer 'pure_pred_list': [], # list of generated utterances 'context_list': [], # list of text inputs (persona and conversation history) } bins = [int(i) for i in opt['freq_bins'].split(',')] # This dictionary records all the sentence-level controllable attributes # For each attribute, we have a list of all the values sent_attrs = {attr: [] for attr in ATTR2SENTSCOREFN.keys()} # str to list of floats # histories will be a list of ConvAI2History objects histories = [] def process_prediction(prediction, word_statistics): word_statistics['pred_list'].append(normalize_answer(prediction)) freqs, _cnt, wlength, clength = get_word_stats( prediction, dictionary, bins=bins ) word_statistics['word_cnt'] += _cnt word_statistics['mean_wlength'].append(wlength) word_statistics['mean_clength'].append(clength) word_statistics['freqs_cnt'] += Counter(freqs) return word_statistics t0 = time.time() while not world.epoch_done(): world.parley() # orig eval_wordstat.py handles bsz=1 but for simplicity we assume bsz>1 assert batch_size != 1 for w in world.worlds: try: try: response_act = w.acts[-1] prediction = response_act['text'] except KeyError: continue if opt['gold_response']: # If we're measuring gold response, use eval_label as prediction prediction = w.acts[0]['eval_labels'][0] response_act = {'text': prediction} word_statistics['context_list'].append(w.acts[0]['text']) word_statistics['pure_pred_list'].append(prediction) except IndexError: continue cnt += 1 word_statistics = process_prediction(prediction, word_statistics) # Compute and record sentence-level attributes history = ConvAI2History(w.acts[0]['text']) histories.append(history) sent_attrs = update_sent_attr_stats(sent_attrs, history, prediction) # Periodically log some info if log_time.time() > log_every_n_secs: report = world.report() text, report = log_time.log(report['exs'], world.num_examples(), report) print(text) if opt['num_examples'] > 0 and cnt >= opt['num_examples']: break if world.epoch_done(): print("EPOCH DONE") print("Time to process %i examples: %f seconds" % (cnt, time.time() - t0)) # Compute percent unique # Note this is w.r.t. normalized pred_list not original pure_pred_list unique_list = [] cntr = Counter(word_statistics['pred_list']) for k, v in cntr.items(): if v == 1: unique_list.append(k) unique_percent = len(unique_list) / len(word_statistics['pred_list']) * 100 # Print a final report report = world.report() if opt['gold_response']: report['ppl'] = 0.0 # For gold responses, overwrite the perplexity print(report) # Put all information in data dict data['unique_percent'] = unique_percent # percent of all responses that are unique data['word_statistics'] = word_statistics # word stats, as in orig eval_wordstat data['report'] = report # the final report data['histories'] = [ (hist.persona_lines, hist.partner_utts, hist.own_utts) for hist in histories ] # history for each example data['sent_attrs'] = sent_attrs # all sentence attribute values for responses # Write data to outfile print("Writing to %s..." % outfile) with open(outfile, 'w') as f: json.dump(data, f)
def dump_data(opt): """ Dump task data to ACUTE-Eval. """ # create repeat label agent and assign it to the specified task agent = RepeatLabelAgent(opt) world = create_task(opt, agent) task = opt.get('task') speaker_0_id = opt.get('speaker_0_id') or f'{task}_as_human' speaker_1_id = opt.get('speaker_1_id') or f'{task}_as_model' if opt['outfile'] is None: outfile = tempfile.mkstemp(prefix='{}_{}_'.format( opt['task'], opt['datatype']), suffix='.txt')[1] else: outfile = opt['outfile'] num_episodes = (world.num_episodes() if opt['num_episodes'] == -1 else min( opt['num_episodes'], world.num_episodes())) log_timer = TimeLogger() print(f'[ starting to convert, saving output to {outfile} ]') dialogues = [] for _ in range(num_episodes): episode = [] episode_done = False while not episode_done: world.parley() acts = world.get_acts() text = acts[0].get('text') split_text = text.split('\n') label = random.choice(acts[0].get('labels', acts[0].pop('eval_labels', None))) if not episode and opt.get('prepended_context'): # first turn context = split_text[:-1] text = split_text[-1] context_turn = [{ 'text': context, 'episode_done': False, 'id': 'context' } for _ in range(2)] episode.append(context_turn) turn = [ { 'text': text, 'episode_done': False, 'id': speaker_0_id }, { 'text': label, 'episode_done': False, 'id': speaker_1_id }, ] episode.append(turn) if acts[0].get('episode_done', False): episode[-1][-1]['episode_done'] = True episode_done = True dialogues.append(episode) if log_timer.time() > opt['log_every_n_secs']: text, _log = log_timer.log(world.total_parleys, world.num_examples()) print(text) if world.epoch_done(): break Conversations.save_conversations(dialogues, outfile, opt)
def detect(opt, printargs=None, print_parser=None): """ Checks a task for offensive language. """ if print_parser is not None: if print_parser is True and isinstance(opt, ParlaiParser): print_parser = opt elif print_parser is False: print_parser = None random.seed(42) # Create model and assign it to the specified task agent = create_agent(opt, requireModelExists=True) world = create_task(opt, agent) if opt['safety'] == 'string_matcher' or opt['safety'] == 'all': offensive_string_matcher = OffensiveStringMatcher() if opt['safety'] == 'classifier' or opt['safety'] == 'all': offensive_classifier = OffensiveLanguageClassifier() if print_parser: # Show arguments after loading model print_parser.opt = agent.opt print_parser.print_args() log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() stats = { 'bad_words': [], 'bad_words_cnt': 0, 'string_offensive': 0, 'classifier_offensive': 0, 'total_offensive': 0, 'total': 0, } def report(world, stats): report = world.report() log = { 'word_offenses': stats['bad_words_cnt'], 'classifier_offenses%': 100 * (stats['classifier_offensive'] / stats['total']), 'string_offenses%': 100 * (stats['string_offensive'] / stats['total']), 'total_offenses%': 100 * (stats['total_offensive'] / stats['total']), } text, log = log_time.log(report['exs'], world.num_examples(), log) print(text) def classify(text, stats): offensive = False stats['total'] += 1 if opt['safety'] == 'string_matcher' or opt['safety'] == 'all': bad_words = offensive_string_matcher.contains_offensive_language( text) if bad_words: stats['string_offensive'] += 1 offensive = True stats['bad_words'].append(bad_words) if opt['safety'] == 'classifier' or opt['safety'] == 'all': if text in offensive_classifier: stats['classifier_offensive'] += 1 offensive = True if offensive: stats['total_offensive'] += 1 while not world.epoch_done(): world.parley() stats['bad_words'] = [] for a in world.acts: text = a.get('text', '') classify(text, stats) labels = a.get('labels', a.get('eval_labels', '')) for l in labels: classify(l, stats) if len(stats['bad_words']) > 0 and opt['display_examples']: print(world.display()) print("[Offensive words detected:]", ', '.join(stats['bad_words'])) print("\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n") stats['bad_words_cnt'] += len(stats['bad_words']) if log_time.time() > log_every_n_secs: report(world, stats) if world.epoch_done(): print("EPOCH DONE") report(world, stats) return world.report()
def verify(opt, printargs=None, print_parser=None): if opt['datatype'] == 'train': logging.warn('changing datatype from train to train:ordered') opt['datatype'] = 'train:ordered' # create repeat label agent and assign it to the specified task agent = RepeatLabelAgent(opt) world = create_task(opt, agent) log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() dictionary = DictionaryAgent(opt) ignore_tokens = opt.get('ignore_tokens').split(',') counts = {} for t in {'input', 'labels', 'both'}: counts['tokens_in_' + t] = 0 counts['utterances_in_' + t] = 0 counts['avg_utterance_length_in_' + t] = 0 counts['unique_tokens_in_' + t] = 0 counts['unique_utterances_in_' + t] = 0 # for counting the stats.. counts['token_dict_' + t] = {} counts['utterance_dict_' + t] = {} def tokenize(txt): return dictionary.tokenize(txt) def keep_token(t): for s in ignore_tokens: if s != '' and s in t: return False return True # max number of examples to evaluate max_cnt = opt['num_examples'] if opt['num_examples'] > 0 else float('inf') cnt = 0 # Show some example dialogs. while not world.epoch_done() and cnt < max_cnt: cnt += opt.get('batchsize', 1) world.parley() act = world.get_acts()[opt.get('agent')] for itype in {'input', 'labels'}: if itype == 'input': if opt.get('new_line_new_utt'): txts = act.get('text').split('\n') else: txts = [act.get('text')] else: txts = act.get('labels', act.get('eval_labels', [''])) for txt in txts: tokens = tokenize(txt) retxt = [] for t in tokens: if keep_token(t): retxt.append(t) counts['tokens_in_' + itype] += len(retxt) counts['tokens_in_' + 'both'] += len(retxt) counts['utterances_in_' + itype] += 1 counts['utterances_in_' + 'both'] += 1 counts['avg_utterance_length_in_' + itype] = ( counts['tokens_in_' + itype] / counts['utterances_in_' + itype] ) counts['avg_utterance_length_in_' + 'both'] = ( counts['tokens_in_' + 'both'] / counts['utterances_in_' + 'both'] ) for t in retxt: if t not in counts['token_dict_' + itype]: counts['unique_tokens_in_' + itype] += 1 counts['token_dict_' + itype][t] = True if t not in counts['token_dict_' + 'both']: counts['unique_tokens_in_' + 'both'] += 1 counts['token_dict_' + 'both'][t] = True retxt = ' '.join(retxt) if retxt not in counts['utterance_dict_' + itype]: counts['unique_utterances_in_' + itype] += 1 counts['utterance_dict_' + itype][retxt] = True if retxt not in counts['utterance_dict_' + 'both']: counts['unique_utterances_in_' + 'both'] += 1 counts['utterance_dict_' + 'both'][retxt] = True if log_time.time() > log_every_n_secs: text, log = report(world, counts, log_time) if print_parser: logging.info(text) try: # print dataset size if available logging.info( f'loaded {world.num_episodes()} episodes with a total ' f'of {world.num_examples()} examples' ) except Exception: pass return report(world, counts, log_time)
def detect(opt): """ Checks a task for offensive language. """ # Create model and assign it to the specified task agent = create_agent(opt, requireModelExists=True) world = create_task(opt, agent) agent.opt.log() if opt['safety'] == 'string_matcher' or opt['safety'] == 'all': offensive_string_matcher = OffensiveStringMatcher() if opt['safety'] == 'classifier' or opt['safety'] == 'all': offensive_classifier = OffensiveLanguageClassifier() log_every_n_secs = opt.get('log_every_n_secs', -1) if log_every_n_secs <= 0: log_every_n_secs = float('inf') log_time = TimeLogger() stats = { 'bad_words': [], 'bad_words_cnt': 0, 'string_offensive': 0, 'classifier_offensive': 0, 'total_offensive': 0, 'total': 0, } def report(world, stats): report = world.report() log = { 'word_offenses': stats['bad_words_cnt'], 'classifier_offenses%': 100 * (stats['classifier_offensive'] / stats['total']), 'string_offenses%': 100 * (stats['string_offensive'] / stats['total']), 'total_offenses%': 100 * (stats['total_offensive'] / stats['total']), } text, log = log_time.log(report['exs'], world.num_examples(), log) logging.info(text) return log def classify(text, stats): offensive = False stats['total'] += 1 if opt['safety'] == 'string_matcher' or opt['safety'] == 'all': bad_words = offensive_string_matcher.contains_offensive_language( text) if bad_words: stats['string_offensive'] += 1 offensive = True stats['bad_words'].append(bad_words) if opt['safety'] == 'classifier' or opt['safety'] == 'all': if text in offensive_classifier: stats['classifier_offensive'] += 1 offensive = True if offensive: stats['total_offensive'] += 1 while not world.epoch_done(): world.parley() stats['bad_words'] = [] for a in world.acts: text = a.get('text', '') classify(text, stats) labels = a.get('labels', a.get('eval_labels', '')) for l in labels: classify(l, stats) if len(stats['bad_words']) > 0 and opt['display_examples']: logging.info(world.display()) logging.info("Offensive words detected: {}".format(', '.join( stats['bad_words']))) stats['bad_words_cnt'] += len(stats['bad_words']) if log_time.time() > log_every_n_secs: report(world, stats) if world.epoch_done(): logging.info("epoch done") return report(world, stats)
def bucket_data(opt): # create repeat label agent and assign it to the specified task agent = RepeatLabelAgent(opt) world = create_task(opt, agent) if opt['num_examples'] == -1: num_examples = world.num_examples() else: num_examples = opt['num_examples'] log_timer = TimeLogger() assert opt['control'] != '' ctrl = opt['control'] num_buckets = opt['num_buckets'] ctrl_vals = [] # list of floats for _ in range(num_examples): world.parley() world.acts[0]['labels'] = world.acts[0].get( 'labels', world.acts[0].pop('eval_labels', None)) if ctrl not in world.acts[0].keys(): raise Exception( 'Error: control %s isn\'t in the data. available keys: %s' % (ctrl, ', '.join(world.acts[0].keys()))) ctrl_val = world.acts[0][ctrl] if ctrl_val == "None": assert ctrl == 'lastuttsim' ctrl_val = None else: ctrl_val = float(ctrl_val) if ctrl == 'avg_nidf': assert ctrl_val >= 0 assert ctrl_val <= 1 elif ctrl == 'question': assert ctrl_val in [0, 1] elif ctrl == 'lastuttsim': if ctrl_val is not None: assert ctrl_val >= -1 assert ctrl_val <= 1 else: raise Exception('Unexpected ctrl name: %s' % ctrl) ctrl_vals.append(ctrl_val) if log_timer.time() > opt['log_every_n_secs']: text, _log = log_timer.log(world.total_parleys, world.num_examples()) print(text) if world.epoch_done(): print('EPOCH DONE') break if ctrl == 'lastuttsim': num_nones = len([v for v in ctrl_vals if v is None]) ctrl_vals = [v for v in ctrl_vals if v is not None] print("Have %i Nones for lastuttsim; these have been removed " "for bucket calculation" % num_nones) print('Collected %i control vals between %.6f and %.6f' % (len(ctrl_vals), min(ctrl_vals), max(ctrl_vals))) # Calculate bucket lower bounds print('Calculating lowerbounds for %i buckets...' % num_buckets) ctrl_vals = sorted(ctrl_vals) lb_indices = [ int(len(ctrl_vals) * i / num_buckets) for i in range(num_buckets) ] lbs = [ctrl_vals[idx] for idx in lb_indices] print('\nBucket lowerbounds for control %s: ' % ctrl) print(lbs) # Calculate the actual bucket sizes bucket_sizes = Counter() bucket_ids = [sort_into_bucket(ctrl_val, lbs) for ctrl_val in ctrl_vals] bucket_sizes.update(bucket_ids) print('\nBucket sizes: ') for bucket_id in sorted(bucket_sizes.keys()): print("%i: %i" % (bucket_id, bucket_sizes[bucket_id]))