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):
random.seed(opt['seed'])
partner = opt['partner_model_file']
partner_opt_file = opt.get('partner_opt_file')
# Create agents
agent1 = create_agent(opt, requireModelExists=True)
if partner is None:
# Self chat with same model
agent2 = agent1.clone()
else:
# Self chat with different models
if partner_opt_file:
print(f"WARNING: Loading override opts from: {partner_opt_file}")
with open(partner_opt_file) as f:
partner_opt = json.load(f)
else:
partner_opt = {}
partner_opt['interactive_mode'] = opt.get('interactive_mode', True)
print(
f"WARNING: Setting partner interactive mode to: {partner_opt['interactive_mode']}"
)
agent2 = create_agent_from_model_file(partner, partner_opt)
# Set IDs
agent1.id = agent1.id + "_1"
agent2.id = agent2.id + "_2"
model_id = agent1.id + "_" + agent2.id
world = create_task(opt, user_agents=[agent1, agent2])
# Set up world logging
logger = WorldLogger(opt)
log_time = TimeLogger()
# Run some self chats.
for i in range(opt['num_self_chats']):
_run_self_chat_episode(opt, world, logger)
report = world.report()
text, report = log_time.log(i + 1, opt['num_self_chats'], report)
logging.info(text)
# Save chats
if opt['outfile'] is None:
outfile = '/tmp/{}_selfchat'.format(model_id)
else:
outfile = opt['outfile']
if opt['save_format'] == 'conversations' and hasattr(world, 'write'):
# use self chat specific world to write conversation
# this might be useful for logging extra contextual
# information (like personas)
world.write(logger, outfile)
else:
# use default logger write function
logger.write(outfile, world, opt['save_format'])
return logger.get_logs()
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 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):
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 _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 self_chat(opt):
random.seed(opt['seed'])
# Create agents
agent1 = create_agent(opt, requireModelExists=True)
agent2 = agent1.clone()
# Set IDs
model_id = agent1.id
agent1.id = model_id + "_1"
agent2.id = model_id + "_2"
world = create_task(opt, user_agents=[agent1, agent2])
# Set up world logging
logger = WorldLogger(opt)
log_time = TimeLogger()
# Run some self chats.
for i in range(opt['num_self_chats']):
_run_self_chat_episode(opt, world, logger)
report = world.report()
text, report = log_time.log(i + 1, opt['num_self_chats'], report)
logging.info(text)
# Save chats
if opt['outfile'] is None:
outfile = '/tmp/{}_selfchat'.format(model_id)
else:
outfile = opt['outfile']
if opt['save_format'] == 'conversations' and hasattr(world, 'write'):
# use self chat specific world to write conversation
# this might be useful for logging extra contextual
# information (like personas)
world.write(logger, outfile)
else:
# use default logger write function
logger.write(outfile, world, opt['save_format'])
return logger.get_logs()
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 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]))
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 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 self_chat(opt):
random.seed(opt['seed'])
partner = opt['partner_model_file']
assert partner is not None
partner_opt_file = opt.get('partner_opt_file')
if partner_opt_file:
assert partner_opt_file == partner + '.opt', (
'Unless you think it is save,'
' you can remove assert')
else:
partner_opt_file = partner + '.opt'
# Create agents
if opt['model_file'].split(':')[0] == 'human':
agent1 = MyLocalHumanAgent(opt)
assert partner is not None
else:
agent1 = create_agent(opt, requireModelExists=True)
if partner is None:
# Self chat with same model
agent2 = agent1.clone()
else:
# Self chat with different models
if partner_opt_file:
print(f"WARNING: Loading override opts from: {partner_opt_file}")
with open(partner_opt_file) as f:
partner_opt = json.load(f)
else:
partner_opt = {}
partner_opt['interactive_mode'] = opt.get('interactive_mode', True)
print(
f"WARNING: Setting partner interactive mode to: {partner_opt['interactive_mode']}"
)
agent2 = create_agent_from_model_file(partner, partner_opt)
# Set IDs
agent1.id = agent1.id + '_1'
agent2.id = agent2.id + '_2'
model_id = agent1.id + '_' + agent2.id
world = create_task(opt, user_agents=[agent1, agent2])
# Set up world logging
logger = WorldLogger(opt)
log_time = TimeLogger()
# Run some self chats.
all_report = []
if opt['num_self_chats'] < 0:
opt['num_self_chats'] = len(world.messages)
for i in range(opt['num_self_chats']):
_run_self_chat_episode(opt, world, logger)
report = world.report()
text, report = log_time.log(i + 1, opt['num_self_chats'], report)
logging.info(text)
all_report.append(report)
world.write(logger, all_report, opt['outfile'])
# Save chats
if opt['outfile'] is None:
outfile = '/tmp/{}_selfchat'.format(model_id)
else:
outfile = opt['outfile']
if opt['save_format'] == 'conversations' and hasattr(world, 'write'):
# use self chat specific world to write conversation
# this might be useful for logging extra contextual
# information (like personas)
world.write(logger, all_report, outfile)
else:
# use default logger write function
logger.write(outfile, world, opt['save_format'])
return logger.get_logs()
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)