def __init__(self, model, args, name='Alice'):
super(LstmAgent, self).__init__()
self.model = model
self.args = args
self.name = name
self.human = False
self.domain = domain.get_domain(args.domain)
def gather_information(name, url):
domain = get_domain(url)
ipaddress = get_address(domain)
nmap = get_nmap(domain, '-F')
robots = get_robots(url)
whois = get_whois(domain)
create_report(name, url, domain, ipaddress, nmap, robots, whois)
def __init__(self, agents, args):
# for now we only suppport dialog of 2 agents
assert len(agents) == 2
self.agents = agents
self.args = args
self.domain = domain.get_domain(args.domain)
self.metrics = MetricsContainer()
self._register_metrics()
def __init__(self, agents, args):
# for now we only suppport dialog of 2 agents
assert len(agents) == 2
self.agents = agents
self.args = args
self.domain = domain.get_domain(args.domain)
self.metrics = MetricsContainer()
self._register_metrics()
def main():
parser = argparse.ArgumentParser(description='Negotiator')
parser.add_argument('--dataset',
type=str,
default='./data/negotiate/val.txt',
help='location of the dataset')
parser.add_argument('--model_file', type=str, help='model file')
parser.add_argument('--smart_ai',
action='store_true',
default=False,
help='to use rollouts')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument('--temperature',
type=float,
default=1.0,
help='temperature')
parser.add_argument('--domain',
type=str,
default='object_division',
help='domain for the dialogue')
parser.add_argument('--log_file', type=str, default='', help='log file')
args = parser.parse_args()
utils.set_seed(args.seed)
model = utils.load_model(args.model_file)
ai = LstmAgent(model, args)
logger = DialogLogger(verbose=True, log_file=args.log_file)
domain = get_domain(args.domain)
score_func = rollout if args.smart_ai else likelihood
dataset, sents = read_dataset(args.dataset)
ranks, n, k = 0, 0, 0
for ctx, dialog in dataset:
start_time = time.time()
ai.feed_context(ctx)
for sent, you in dialog:
if you:
rank = compute_rank(sent, sents, ai, domain, args.temperature,
score_func)
# Compute lang_h for the groundtruth sentence
enc = ai._encode(sent, ai.model.word_dict)
_, ai.lang_h, lang_hs = ai.model.score_sent(
enc, ai.lang_h, ai.ctx_h, args.temperature)
ai.lang_hs.append(lang_hs)
ai.words.append(ai.model.word2var('YOU:'))
ai.words.append(Variable(enc))
ranks += rank
n += 1
else:
ai.read(sent)
k += 1
time_elapsed = time.time() - start_time
logger.dump('dialogue %d | avg rank %.3f | raw %d/%d | time %.3f' %
(k, 1. * ranks / n, ranks, n, time_elapsed))
logger.dump('final avg rank %.3f' % (1. * ranks / n))
def __init__(self, cfg):
Spider.config = cfg
Spider.project_name = Spider.config['project']
Spider.base_url = Spider.config['base_url']
Spider.domain_name = get_domain(Spider.base_url)
Spider.queue_file = Spider.config['queue']
Spider.crawled_file = Spider.config['crawled']
self.boot()
self.crawl_page('Spider one', Spider.base_url)
def __init__(self, sess, model, use_rollouts=False):
self.sess = sess
self.model = model
self.all_rewards = []
self.domain = domain.get_domain('object_division')
self.use_rollouts = use_rollouts # only used for generation
# params for rollouts
self.ncandidate = 10
self.nrollout = 5
self.rollout_len = 100
def __init__(self, word_dict, item_dict, context_dict, count_dict, args):
super(LatentClusteringPredictionModel, self).__init__()
self.lang_model = utils.load_model(args.lang_model_file)
self.lang_model.eval()
domain = get_domain(args.domain)
self.word_dict = word_dict
self.item_dict = item_dict
self.context_dict = context_dict
self.count_dict = count_dict
self.args = args
self.ctx_encoder = MlpContextEncoder(len(self.context_dict),
domain.input_length(),
args.nembed_ctx, args.nhid_ctx,
args.dropout, args.init_range,
False)
self.word_embed = nn.Embedding(len(self.word_dict), args.nembed_word)
self.encoder = nn.GRU(input_size=args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
self.embed2hid = nn.Sequential(
nn.Linear(args.nhid_lang + args.nhid_lang + args.nhid_ctx,
self.args.nhid_lang), nn.Tanh())
self.latent_bottleneck = ShardedLatentBottleneckModule(
num_shards=len(count_dict),
num_clusters=self.lang_model.cluster_model.args.num_clusters,
input_size=args.nhid_lang,
output_size=self.lang_model.cluster_model.args.nhid_cluster,
args=args)
# copy lat vars from the cluster model
self.latent_bottleneck.latent_vars.weight.data.copy_(
self.lang_model.cluster_model.latent_bottleneck.latent_vars.weight.
data)
self.memory = RecurrentUnit(
input_size=args.nhid_lang,
hidden_size=self.lang_model.cluster_model.args.nhid_cluster,
args=args)
self.dropout = nn.Dropout(args.dropout)
self.kldiv = nn.KLDivLoss(reduction='sum')
# init
self.word_embed.weight.data.uniform_(-args.init_range, args.init_range)
init_rnn(self.encoder, args.init_range)
init_cont(self.embed2hid, args.init_range)
def __init__(self, word_dict, item_dict, context_dict, count_dict, args):
super(LatentClusteringLanguageModel, self).__init__()
self.cluster_model = utils.load_model(args.cluster_model_file)
self.cluster_model.eval()
domain = get_domain(args.domain)
self.word_dict = word_dict
self.item_dict = item_dict
self.context_dict = context_dict
self.count_dict = count_dict
self.args = args
self.word_embed = nn.Embedding(len(self.word_dict), args.nembed_word)
self.encoder = nn.GRU(input_size=args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
self.hid2output = nn.Sequential(
nn.Linear(args.nhid_lang, args.nembed_word),
nn.Dropout(args.dropout))
self.cond2input = nn.Linear(
args.nhid_lang + self.cluster_model.args.nhid_cluster,
args.nembed_word)
self.decoder_reader = nn.GRU(input_size=args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
self.decoder_writer = nn.GRUCell(input_size=args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
# tie the weights between reader and writer
self.decoder_writer.weight_ih = self.decoder_reader.weight_ih_l0
self.decoder_writer.weight_hh = self.decoder_reader.weight_hh_l0
self.decoder_writer.bias_ih = self.decoder_reader.bias_ih_l0
self.decoder_writer.bias_hh = self.decoder_reader.bias_hh_l0
self.dropout = nn.Dropout(args.dropout)
self.special_token_mask = make_mask(len(word_dict), [
word_dict.get_idx(w) for w in ['<unk>', 'YOU:', 'THEM:', '<pad>']
])
# init
self.word_embed.weight.data.uniform_(-args.init_range, args.init_range)
init_rnn(self.decoder_reader, args.init_range)
init_linear(self.cond2input, args.init_range)
init_cont(self.hid2output, args.init_range)
init_rnn(self.encoder, args.init_range)
def main():
parser = argparse.ArgumentParser(description='Negotiator')
parser.add_argument('--dataset', type=str, default='./data/negotiate/val.txt',
help='location of the dataset')
parser.add_argument('--model_file', type=str,
help='model file')
parser.add_argument('--smart_ai', action='store_true', default=False,
help='to use rollouts')
parser.add_argument('--seed', type=int, default=1,
help='random seed')
parser.add_argument('--temperature', type=float, default=1.0,
help='temperature')
parser.add_argument('--domain', type=str, default='object_division',
help='domain for the dialogue')
parser.add_argument('--log_file', type=str, default='',
help='log file')
args = parser.parse_args()
utils.set_seed(args.seed)
model = utils.load_model(args.model_file)
ai = LstmAgent(model, args)
logger = DialogLogger(verbose=True, log_file=args.log_file)
domain = get_domain(args.domain)
score_func = rollout if args.smart_ai else likelihood
dataset, sents = read_dataset(args.dataset)
ranks, n, k = 0, 0, 0
for ctx, dialog in dataset:
start_time = time.time()
# start new conversation
ai.feed_context(ctx)
for sent, you in dialog:
if you:
# if it is your turn to say, take the target word and compute its rank
rank = compute_rank(sent, sents, ai, domain, args.temperature, score_func)
# compute lang_h for the groundtruth sentence
enc = ai._encode(sent, ai.model.word_dict)
_, ai.lang_h, lang_hs = ai.model.score_sent(enc, ai.lang_h, ai.ctx_h, args.temperature)
# save hidden states and the utterance
ai.lang_hs.append(lang_hs)
ai.words.append(ai.model.word2var('YOU:'))
ai.words.append(Variable(enc))
ranks += rank
n += 1
else:
ai.read(sent)
k += 1
time_elapsed = time.time() - start_time
logger.dump('dialogue %d | avg rank %.3f | raw %d/%d | time %.3f' % (k, 1. * ranks / n, ranks, n, time_elapsed))
logger.dump('final avg rank %.3f' % (1. * ranks / n))
def __init__(self, agents, args, markable_detector,
markable_detector_corpus):
# For now we only suppport dialog of 2 agents
assert len(agents) == 2
self.agents = agents
self.args = args
self.domain = domain.get_domain(args.domain)
self.metrics = MetricsContainer()
self._register_metrics()
self.markable_detector = markable_detector
self.markable_detector_corpus = markable_detector_corpus
self.selfplay_markables = {}
self.selfplay_referents = {}
def main():
parser = argparse.ArgumentParser(description='chat utility')
parser.add_argument('--model_file', type=str,
help='model file')
parser.add_argument('--domain', type=str, default='object_division',
help='domain for the dialogue')
parser.add_argument('--context_file', type=str, default='',
help='context file')
parser.add_argument('--temperature', type=float, default=1.0,
help='temperature')
parser.add_argument('--num_types', type=int, default=3,
help='number of object types')
parser.add_argument('--num_objects', type=int, default=6,
help='total number of objects')
parser.add_argument('--max_score', type=int, default=10,
help='max score per object')
parser.add_argument('--score_threshold', type=int, default=6,
help='successful dialog should have more than score_threshold in score')
parser.add_argument('--seed', type=int, default=1,
help='random seed')
parser.add_argument('--smart_ai', action='store_true', default=False,
help='make AI smart again')
parser.add_argument('--ai_starts', action='store_true', default=False,
help='allow AI to start the dialog')
parser.add_argument('--ref_text', type=str,
help='file with the reference text')
args = parser.parse_args()
utils.set_seed(args.seed)
human = HumanAgent(domain.get_domain(args.domain))
alice_ty = LstmRolloutAgent if args.smart_ai else LstmAgent
ai = alice_ty(utils.load_model(args.model_file), args)
agents = [ai, human] if args.ai_starts else [human, ai]
dialog = Dialog(agents, args)
logger = DialogLogger(verbose=True)
# either take manually produced contextes, or relay on the ones from the dataset
if args.context_file == '':
ctx_gen = ManualContextGenerator(args.num_types, args.num_objects, args.max_score)
else:
ctx_gen = ContextGenerator(args.context_file)
chat = Chat(dialog, ctx_gen, logger)
chat.run()
def main():
parser = argparse.ArgumentParser(description='chat utility')
parser.add_argument('--model_file', type=str,
help='model file')
parser.add_argument('--domain', type=str, default='object_division',
help='domain for the dialogue')
parser.add_argument('--context_file', type=str, default='',
help='context file')
parser.add_argument('--temperature', type=float, default=1.0,
help='temperature')
parser.add_argument('--num_types', type=int, default=3,
help='number of object types')
parser.add_argument('--num_objects', type=int, default=6,
help='total number of objects')
parser.add_argument('--max_score', type=int, default=10,
help='max score per object')
parser.add_argument('--score_threshold', type=int, default=6,
help='successful dialog should have more than score_threshold in score')
parser.add_argument('--seed', type=int, default=1,
help='random seed')
parser.add_argument('--smart_ai', action='store_true', default=False,
help='make AI smart again')
parser.add_argument('--ai_starts', action='store_true', default=False,
help='allow AI to start the dialog')
parser.add_argument('--ref_text', type=str,
help='file with the reference text')
args = parser.parse_args()
utils.set_seed(args.seed)
human = HumanAgent(domain.get_domain(args.domain))
alice_ty = LstmRolloutAgent if args.smart_ai else LstmAgent
ai = alice_ty(utils.load_model(args.model_file), args)
agents = [ai, human] if args.ai_starts else [human, ai]
dialog = Dialog(agents, args)
logger = DialogLogger(verbose=True)
if args.context_file == '':
ctx_gen = ManualContextGenerator(args.num_types, args.num_objects, args.max_score)
else:
ctx_gen = ContextGenerator(args.context_file)
chat = Chat(dialog, ctx_gen, logger)
chat.run()
def main():
parser = argparse.ArgumentParser(
description='A script to compute Pareto efficiency')
parser.add_argument('--log_file', type=str, default='',
help='location of the log file')
parser.add_argument('--domain', type=str, default='object_division',
help='domain for the dialogue')
args = parser.parse_args()
domain = get_domain(args.domain)
dataset = parse_log(args.log_file, domain)
avg_agree, avg_can_improve = 0, 0
avg_score1, avg_score2 = 0, 0
avg_max_score1, avg_max_score2 = 0, 0
for cnts, vals1, picks1, vals2, picks2 in dataset:
if np.min(picks1) == -1 or np.min(picks2) == -1:
continue
agree = True
for p1, p2, n in zip(picks1, picks2, cnts):
agree = agree and (p1 + p2 == n)
if not agree:
continue
avg_agree += 1
score1 = compute_score(vals1, picks1)
score2 = compute_score(vals2, picks2)
choices = gen_choices(cnts)
can_improve = False
for cand1, cand2 in choices:
cand_score1 = compute_score(vals1, cand1)
cand_score2 = compute_score(vals2, cand2)
if (cand_score1 > score1 and cand_score2 >= score2) or (cand_score1 >= score1 and cand_score2 > score2):
can_improve = True
avg_score1 += score1
avg_score2 += score2
avg_can_improve += int(can_improve)
print('pareto opt (%%)\t:\t%.2f' % (100. * (1 - avg_can_improve / avg_agree)))
print('agree (%%)\t:\t%.2f' % (100. * avg_agree / len(dataset)))
print('score (all)\t:\t%.2f vs. %.2f' % (
1. * avg_score1 / len(dataset), 1. * avg_score2 / len(dataset)))
print('score (agreed)\t:\t%.2f vs. %.2f' % (
1. * avg_score1 / avg_agree, 1. * avg_score2 / avg_agree))
def __init__(self, word_dict, output_length, args, device):
super(SelectModel, self).__init__()
domain = get_domain(args.domain)
self.word_dict = word_dict
self.args = args
self.device = device
self.num_ent = domain.num_ent()
# embedding for words
self.word_encoder = nn.Embedding(len(self.word_dict), args.nembed_word)
# context encoder
if args.rel_ctx_encoder:
self.ctx_encoder = modules.RelationalContextEncoder(
domain.num_ent(), domain.dim_ent(), args.rel_hidden,
args.nembed_ctx, args.dropout, args.init_range, device)
else:
self.ctx_encoder = modules.MlpContextEncoder(
domain.input_length(), args.nembed_ctx, args.dropout,
args.init_range, device)
self.dropout = nn.Dropout(args.dropout)
# a bidirectional selection RNN
# it will go through input words and generate by the reader hidden states
# to produce a hidden representation
self.sel_rnn = nn.GRU(input_size=args.nembed_word,
hidden_size=args.nhid_lang,
bias=True,
bidirectional=True)
self.sel_encoder = nn.Sequential(
torch.nn.Linear(2 * args.nhid_lang + args.nembed_ctx,
args.nhid_sel), nn.Tanh())
# selection decoder
self.sel_decoder = nn.Linear(args.nhid_sel, self.num_ent)
if self.args.context_only:
self.sel_encoder = nn.Sequential(
torch.nn.Linear(args.nembed_ctx, args.nhid_sel), nn.Tanh())
self.sel_decoder = nn.Linear(args.nhid_sel, self.num_ent)
self.init_weights()
def __init__(self, model, args, name='Alice', train=False):
super(RnnAgent, self).__init__()
self.model = model
self.args = args
self.name = name
self.human = False
self.domain = domain.get_domain(args.domain)
self.train = train
if train:
self.model.train()
self.opt = optim.RMSprop(
self.model.parameters(),
lr=args.rl_lr,
momentum=self.args.momentum)
self.all_rewards = []
self.t = 0
else:
self.model.eval()
def __init__(self, word_dict, item_dict, context_dict, count_dict, args):
super(SelectionModel, self).__init__()
self.nhid_pos = 32
self.nhid_speaker = 32
self.len_cutoff = 10
domain = get_domain(args.domain)
self.word_dict = word_dict
self.item_dict = item_dict
self.context_dict = context_dict
self.count_dict = count_dict
self.args = args
self.word_encoder = nn.Embedding(len(self.word_dict), args.nembed_word)
self.pos_encoder = nn.Embedding(self.len_cutoff, self.nhid_pos)
self.speaker_encoder = nn.Embedding(len(self.word_dict),
self.nhid_speaker)
self.ctx_encoder = MlpContextEncoder(len(self.context_dict),
domain.input_length(),
args.nembed_ctx, args.nhid_ctx,
args.dropout, args.init_range,
args.skip_values)
self.sel_head = SelectionModule(query_size=args.nhid_ctx,
value_size=args.nembed_word +
self.nhid_pos + self.nhid_speaker,
hidden_size=args.nhid_attn,
selection_size=args.nhid_sel,
num_heads=6,
output_size=len(item_dict),
args=args)
self.dropout = nn.Dropout(args.dropout)
# init embeddings
self.word_encoder.weight.data.uniform_(-self.args.init_range,
self.args.init_range)
self.pos_encoder.weight.data.uniform_(-self.args.init_range,
self.args.init_range)
self.speaker_encoder.weight.data.uniform_(-self.args.init_range,
self.args.init_range)
def print_items(sess, model, count, val, words):
# to print output choices, we need to find max probable that is in valid set
my_domain = domain.get_domain('object_division')
count_w, val_w = model.corpus.context_dict.i2w(
count[0]), model.corpus.context_dict.i2w(val[0])
ctx = [
str(count_w[0]),
str(val_w[0]),
str(count_w[1]),
str(val_w[1]),
str(count_w[2]),
str(val_w[2])
]
choices = my_domain.generate_choices(ctx)
idxs = [model.corpus.item_dict.w2i(c) for c in choices]
probs = sess.run(
model.item_softmax, {
model.ctx_count: count,
model.ctx_val: val,
model.inpt: words,
model.init_lang_state: model.lang_state_one,
})
probs = np.array(probs)[:, 0, :]
probs_arr = []
for i in range(len(idxs)):
choice = idxs[i]
prob_of_choice = 1.
for j in range(6):
prob_of_choice *= probs[j][choice[j]]
probs_arr.append(prob_of_choice)
best_choce = idxs[np.argmax(probs_arr)]
items = model.corpus.item_dict.i2w(best_choce)
for item in items:
print(item, end=" ")
print("\n")
def __init__(self, word_dict, item_dict, context_dict, count_dict, args):
super(BaselineClusteringModel, self).__init__()
domain = get_domain(args.domain)
self.word_dict = word_dict
self.item_dict = item_dict
self.context_dict = context_dict
self.count_dict = count_dict
self.args = args
self.ctx_encoder = MlpContextEncoder(len(self.context_dict),
domain.input_length(),
args.nembed_ctx, args.nhid_lang,
args.dropout, args.init_range,
False)
self.word_embed = nn.Embedding(len(self.word_dict), args.nembed_word)
self.encoder = nn.GRU(input_size=args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
self.latent_bottleneck = ShardedLatentBottleneckModule(
num_shards=len(count_dict),
num_clusters=self.args.num_clusters,
input_size=args.nhid_lang,
output_size=self.args.nhid_cluster,
args=args)
self.dropout = nn.Dropout(args.dropout)
self.decoder_reader = nn.GRU(input_size=args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
self.decoder_writer = nn.GRUCell(input_size=args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
self.cond2input = nn.Linear(args.nhid_cluster, args.nembed_word)
self.hid2output = nn.Sequential(
nn.Linear(args.nhid_lang, args.nembed_word),
nn.Dropout(args.dropout))
self.memory = RecurrentUnit(input_size=args.nhid_lang,
hidden_size=args.nhid_lang,
args=args)
# tie the weights between reader and writer
self.decoder_writer.weight_ih = self.decoder_reader.weight_ih_l0
self.decoder_writer.weight_hh = self.decoder_reader.weight_hh_l0
self.decoder_writer.bias_ih = self.decoder_reader.bias_ih_l0
self.decoder_writer.bias_hh = self.decoder_reader.bias_hh_l0
self.special_token_mask = make_mask(len(word_dict), [
word_dict.get_idx(w) for w in ['<unk>', 'YOU:', 'THEM:', '<pad>']
])
# init
self.word_embed.weight.data.uniform_(-args.init_range, args.init_range)
init_rnn(self.encoder, args.init_range)
init_rnn(self.decoder_reader, args.init_range)
init_linear(self.cond2input, args.init_range)
init_cont(self.hid2output, args.init_range)
def __init__(self, word_dict, item_dict, context_dict, output_length, args,
device_id):
super(DialogModel, self).__init__(device_id)
domain = get_domain(args.domain)
self.word_dict = word_dict
self.item_dict = item_dict
self.context_dict = context_dict
self.args = args
# embedding for words
self.word_encoder = nn.Embedding(len(self.word_dict), args.nembed_word)
# context encoder
ctx_encoder_ty = modules.RnnContextEncoder if args.rnn_ctx_encoder \
else modules.MlpContextEncoder
self.ctx_encoder = ctx_encoder_ty(len(self.context_dict),
domain.input_length(),
args.nembed_ctx, args.nhid_ctx,
args.init_range, device_id)
# a reader RNN, to encode words
self.reader = nn.GRU(input_size=args.nhid_ctx + args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
self.decoder = nn.Linear(args.nhid_lang, args.nembed_word)
# a writer, a RNNCell that will be used to generate utterances
self.writer = nn.GRUCell(input_size=args.nhid_ctx + args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
# tie the weights of reader and writer
self.writer.weight_ih = self.reader.weight_ih_l0
self.writer.weight_hh = self.reader.weight_hh_l0
self.writer.bias_ih = self.reader.bias_ih_l0
self.writer.bias_hh = self.reader.bias_hh_l0
self.dropout = nn.Dropout(args.dropout)
# a bidirectional selection RNN
# it will go through input words and generate by the reader hidden states
# to produce a hidden representation
self.sel_rnn = nn.GRU(input_size=args.nhid_lang + args.nembed_word,
hidden_size=args.nhid_attn,
bias=True,
bidirectional=True)
# mask for disabling special tokens when generating sentences
self.special_token_mask = torch.FloatTensor(len(self.word_dict))
# attention to combine selection hidden states
self.attn = nn.Sequential(
torch.nn.Linear(2 * args.nhid_attn, args.nhid_attn), nn.Tanh(),
torch.nn.Linear(args.nhid_attn, 1))
# selection encoder, takes attention output and context hidden and combines them
self.sel_encoder = nn.Sequential(
torch.nn.Linear(2 * args.nhid_attn + args.nhid_ctx, args.nhid_sel),
nn.Tanh())
# selection decoders, one per each item
self.sel_decoders = nn.ModuleList()
for i in range(output_length):
self.sel_decoders.append(
nn.Linear(args.nhid_sel, len(self.item_dict)))
self.init_weights()
# fill in the mask
for i in range(len(self.word_dict)):
w = self.word_dict.get_word(i)
special = domain.item_pattern.match(w) or w in ('<unk>', 'YOU:',
'THEM:', '<pad>')
self.special_token_mask[i] = -999 if special else 0.0
self.special_token_mask = self.to_device(self.special_token_mask)
def __init__(self, word_dict, item_dict, context_dict, count_dict, args):
super(LatentClusteringModel, self).__init__()
domain = get_domain(args.domain)
self.word_dict = word_dict
self.item_dict = item_dict
self.context_dict = context_dict
self.count_dict = count_dict
self.args = args
self.ctx_encoder = MlpContextEncoder(len(self.context_dict),
domain.input_length(),
args.nembed_ctx, args.nhid_ctx,
args.dropout, args.init_range,
args.skip_values)
self.word_embed = nn.Embedding(len(self.word_dict), args.nembed_word)
self.hid2output = nn.Sequential(
nn.Linear(args.nhid_lang, args.nembed_word),
nn.Dropout(args.dropout))
self.mem2input = nn.Linear(args.nhid_lang, args.nembed_word)
self.encoder = nn.GRU(input_size=args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
self.embed2hid = nn.Sequential(
nn.Linear(args.nhid_lang + args.nhid_lang + args.nhid_ctx,
args.nhid_cluster), nn.Tanh())
self.decoder_reader = nn.GRU(input_size=args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
self.decoder_writer = nn.GRUCell(input_size=args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
# tie the weights between reader and writer
self.decoder_writer.weight_ih = self.decoder_reader.weight_ih_l0
self.decoder_writer.weight_hh = self.decoder_reader.weight_hh_l0
self.decoder_writer.bias_ih = self.decoder_reader.bias_ih_l0
self.decoder_writer.bias_hh = self.decoder_reader.bias_hh_l0
self.latent_bottleneck = ShardedLatentBottleneckModule(
num_shards=len(count_dict),
num_clusters=args.num_clusters,
input_size=args.nhid_lang,
output_size=args.nhid_cluster,
args=args)
self.memory = nn.GRUCell(input_size=args.nhid_cluster,
hidden_size=args.nhid_lang,
bias=True)
self.dropout = nn.Dropout(args.dropout)
self.selection = SimpleSeparateSelectionModule(
input_size=args.nhid_cluster,
hidden_size=args.nhid_sel,
output_size=len(item_dict),
args=args)
# init
self.word_embed.weight.data.uniform_(-args.init_range, args.init_range)
init_rnn(self.encoder, args.init_range)
init_rnn(self.decoder_reader, args.init_range)
init_rnn_cell(self.memory, args.init_range)
init_linear(self.mem2input, args.init_range)
init_cont(self.hid2output, args.init_range)
init_cont(self.embed2hid, args.init_range)
def __init__(self, word_dict, args):
super(RnnReferenceModel, self).__init__()
domain = get_domain(args.domain)
self.word_dict = word_dict
self.args = args
self.num_ent = domain.num_ent()
# define modules:
self.word_embed = nn.Embedding(len(self.word_dict), args.nembed_word)
ctx_encoder_ty = models.get_ctx_encoder_type(args.ctx_encoder_type)
self.ctx_encoder = ctx_encoder_ty(domain, args)
self.reader = nn.GRU(input_size=args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
self.writer = nn.GRUCell(input_size=args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
self.hid2output = nn.Sequential(
nn.Linear(args.nhid_lang + args.nembed_ctx, args.nembed_word),
nn.Tanh(),
nn.Dropout(args.dropout),
)
if args.share_attn:
self.attn = nn.Sequential(
nn.Linear(args.nhid_lang + args.nembed_ctx, args.nhid_attn),
nn.Tanh(), nn.Dropout(args.dropout),
torch.nn.Linear(args.nhid_attn, args.nhid_attn), nn.Tanh(),
nn.Dropout(args.dropout), torch.nn.Linear(args.nhid_attn, 1))
else:
self.attn = nn.Sequential(
nn.Linear(args.nhid_lang + args.nembed_ctx, args.nhid_sel),
nn.Tanh(), nn.Dropout(args.dropout))
self.lang_attn = nn.Sequential(
torch.nn.Linear(args.nhid_sel, args.nhid_attn), nn.Tanh(),
nn.Dropout(args.dropout), torch.nn.Linear(args.nhid_attn, 1))
self.sel_attn = nn.Sequential(
torch.nn.Linear(args.nhid_sel, args.nhid_sel), nn.Tanh(),
nn.Dropout(args.dropout), torch.nn.Linear(args.nhid_sel, 1))
self.ref_attn = nn.Sequential(
torch.nn.Linear(args.nhid_sel, args.nhid_sel), nn.Tanh(),
nn.Dropout(args.dropout), torch.nn.Linear(args.nhid_sel, 1))
# tie the weights between reader and writer
self.writer.weight_ih = self.reader.weight_ih_l0
self.writer.weight_hh = self.reader.weight_hh_l0
self.writer.bias_ih = self.reader.bias_ih_l0
self.writer.bias_hh = self.reader.bias_hh_l0
self.dropout = nn.Dropout(args.dropout)
# mask for disabling special tokens when generating sentences
self.special_token_mask = make_mask(len(word_dict), [
word_dict.get_idx(w) for w in ['<unk>', 'YOU:', 'THEM:', '<pad>']
])
# init
self.word_embed.weight.data.uniform_(-args.init_range, args.init_range)
init_rnn(self.reader, args.init_range)
init_cont(self.hid2output, args.init_range)
if args.share_attn:
init_cont(self.attn, args.init_range)
else:
init_cont(self.attn, args.init_range)
init_cont(self.lang_attn, args.init_range)
init_cont(self.sel_attn, args.init_range)
init_cont(self.ref_attn, args.init_range)
def main():
parser = argparse.ArgumentParser(description='training script')
parser.add_argument('--data',
type=str,
default='data/negotiate',
help='location of the data corpus')
parser.add_argument('--nembed_word',
type=int,
default=256,
help='size of word embeddings')
parser.add_argument('--nembed_ctx',
type=int,
default=64,
help='size of context embeddings')
parser.add_argument(
'--nhid_lang',
type=int,
default=256,
help='size of the hidden state for the language module')
parser.add_argument(
'--nhid_cluster',
type=int,
default=256,
help='size of the hidden state for the language module')
parser.add_argument('--nhid_ctx',
type=int,
default=64,
help='size of the hidden state for the context module')
parser.add_argument(
'--nhid_strat',
type=int,
default=64,
help='size of the hidden state for the strategy module')
parser.add_argument(
'--nhid_attn',
type=int,
default=64,
help='size of the hidden state for the attention module')
parser.add_argument(
'--nhid_sel',
type=int,
default=64,
help='size of the hidden state for the selection module')
parser.add_argument('--lr',
type=float,
default=20.0,
help='initial learning rate')
parser.add_argument('--min_lr',
type=float,
default=1e-5,
help='min threshold for learning rate annealing')
parser.add_argument('--decay_rate',
type=float,
default=9.0,
help='decrease learning rate by this factor')
parser.add_argument('--decay_every',
type=int,
default=1,
help='decrease learning rate after decay_every epochs')
parser.add_argument('--momentum',
type=float,
default=0.0,
help='momentum for sgd')
parser.add_argument('--clip',
type=float,
default=0.2,
help='gradient clipping')
parser.add_argument('--dropout',
type=float,
default=0.5,
help='dropout rate in embedding layer')
parser.add_argument('--init_range',
type=float,
default=0.1,
help='initialization range')
parser.add_argument('--max_epoch',
type=int,
default=30,
help='max number of epochs')
parser.add_argument('--num_clusters',
type=int,
default=50,
help='number of clusters')
parser.add_argument('--bsz', type=int, default=25, help='batch size')
parser.add_argument('--unk_threshold',
type=int,
default=20,
help='minimum word frequency to be in dictionary')
parser.add_argument('--temperature',
type=float,
default=0.1,
help='temperature')
parser.add_argument('--partner_ctx_weight',
type=float,
default=0.0,
help='selection weight')
parser.add_argument('--sel_weight',
type=float,
default=0.6,
help='selection weight')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument('--cuda',
action='store_true',
default=False,
help='use CUDA')
parser.add_argument('--model_file',
type=str,
default='',
help='path to save the final model')
parser.add_argument('--prediction_model_file',
type=str,
default='',
help='path to save the prediction model')
parser.add_argument('--selection_model_file',
type=str,
default='',
help='path to save the selection model')
parser.add_argument('--cluster_model_file',
type=str,
default='',
help='path to save the cluster model')
parser.add_argument('--lang_model_file',
type=str,
default='',
help='path to save the language model')
parser.add_argument('--visual',
action='store_true',
default=False,
help='plot graphs')
parser.add_argument('--skip_values',
action='store_true',
default=False,
help='skip values in ctx encoder')
parser.add_argument('--model_type',
type=str,
default='rnn_model',
help='model type',
choices=models.get_model_names())
parser.add_argument('--domain',
type=str,
default='object_division',
help='domain for the dialogue')
parser.add_argument('--clustering',
action='store_true',
default=False,
help='use clustering')
parser.add_argument('--sep_sel',
action='store_true',
default=False,
help='use separate classifiers for selection')
args = parser.parse_args()
utils.use_cuda(args.cuda)
utils.set_seed(args.seed)
domain = get_domain(args.domain)
model_ty = models.get_model_type(args.model_type)
corpus = model_ty.corpus_ty(domain,
args.data,
freq_cutoff=args.unk_threshold,
verbose=True,
sep_sel=args.sep_sel)
model = model_ty(corpus.word_dict, corpus.item_dict_old,
corpus.context_dict, corpus.count_dict, args)
if args.cuda:
model.cuda()
engine = model_ty.engine_ty(model, args, verbose=True)
train_loss, valid_loss, select_loss, extra = engine.train(corpus)
utils.save_model(engine.get_model(), args.model_file)
def main():
parser = argparse.ArgumentParser(
description='training script for reference resolution')
parser.add_argument('--data',
type=str,
default='data/onecommon',
help='location of the data corpus')
parser.add_argument('--model_type',
type=str,
default='rnn_reference_model',
help='type of model to use',
choices=models.get_model_names())
parser.add_argument('--ctx_encoder_type',
type=str,
default='mlp_encoder',
help='type of context encoder to use',
choices=models.get_ctx_encoder_names())
parser.add_argument('--attention',
action='store_true',
default=False,
help='use attention')
parser.add_argument('--nembed_word',
type=int,
default=128,
help='size of word embeddings')
parser.add_argument(
'--nhid_rel',
type=int,
default=64,
help='size of the hidden state for the language module')
parser.add_argument('--nembed_ctx',
type=int,
default=128,
help='size of context embeddings')
parser.add_argument('--nembed_cond',
type=int,
default=128,
help='size of condition embeddings')
parser.add_argument(
'--nhid_lang',
type=int,
default=128,
help='size of the hidden state for the language module')
parser.add_argument(
'--nhid_strat',
type=int,
default=128,
help='size of the hidden state for the strategy module')
parser.add_argument(
'--nhid_attn',
type=int,
default=64,
help='size of the hidden state for the attention module')
parser.add_argument(
'--nhid_sel',
type=int,
default=64,
help='size of the hidden state for the selection module')
parser.add_argument(
'--share_attn',
action='store_true',
default=False,
help='share attention modules for selection and language output')
parser.add_argument('--optimizer',
choices=['adam', 'rmsprop'],
default='adam',
help='optimizer to use')
parser.add_argument('--lr',
type=float,
default=0.001,
help='initial learning rate')
parser.add_argument('--min_lr',
type=float,
default=1e-5,
help='min threshold for learning rate annealing')
parser.add_argument('--decay_rate',
type=float,
default=9.0,
help='decrease learning rate by this factor')
parser.add_argument('--decay_every',
type=int,
default=1,
help='decrease learning rate after decay_every epochs')
parser.add_argument('--momentum',
type=float,
default=0.0,
help='momentum for sgd')
parser.add_argument('--clip',
type=float,
default=0.5,
help='gradient clipping')
parser.add_argument('--dropout',
type=float,
default=0.5,
help='dropout rate in embedding layer')
parser.add_argument('--init_range',
type=float,
default=0.01,
help='initialization range')
parser.add_argument('--max_epoch',
type=int,
default=20,
help='max number of epochs')
parser.add_argument('--bsz', type=int, default=16, help='batch size')
parser.add_argument('--unk_threshold',
type=int,
default=20,
help='minimum word frequency to be in dictionary')
parser.add_argument('--temperature',
type=float,
default=0.1,
help='temperature')
parser.add_argument('--lang_weight',
type=float,
default=1.0,
help='language loss weight')
parser.add_argument('--ref_weight',
type=float,
default=1.0,
help='reference loss weight')
parser.add_argument('--num_ref_weight',
type=float,
default=1.0,
help='reference loss weight')
parser.add_argument('--sel_weight',
type=float,
default=1.0,
help='selection loss weight')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument('--cuda',
action='store_true',
default=False,
help='use CUDA')
parser.add_argument('--model_file',
type=str,
default='tmp.th',
help='path to save the final model')
parser.add_argument('--domain',
type=str,
default='one_common',
help='domain for the dialogue')
parser.add_argument('--tensorboard_log',
action='store_true',
default=False,
help='log training with tensorboard')
parser.add_argument('--repeat_train',
action='store_true',
default=False,
help='repeat training n times')
parser.add_argument('--corpus_type',
choices=['full', 'uncorrelated', 'success_only'],
default='full',
help='type of training corpus to use')
parser.add_argument('--remove_location',
action='store_true',
default=False,
help='remove locative information from input')
parser.add_argument('--remove_size',
action='store_true',
default=False,
help='remove size information from input')
parser.add_argument('--remove_color',
action='store_true',
default=False,
help='remove color information from input')
parser.add_argument('--remove_size_color',
action='store_true',
default=False,
help='remove size and color information from input')
args = parser.parse_args()
if args.repeat_train:
seeds = list(range(10))
else:
seeds = [1]
for seed in seeds:
utils.use_cuda(args.cuda)
utils.set_seed(args.seed)
domain = get_domain(args.domain)
model_ty = models.get_model_type(args.model_type)
corpus = model_ty.corpus_ty(
domain,
args.data,
train='train_reference_shift_{}.txt'.format(seed),
valid='valid_reference_shift_{}.txt'.format(seed),
test='test_reference_shift_{}.txt'.format(seed),
freq_cutoff=args.unk_threshold,
verbose=True)
model = model_ty(corpus.word_dict, args)
if args.cuda:
model.cuda()
engine = model_ty.engine_ty(model, args, verbose=True)
if args.optimizer == 'adam':
best_valid_loss, best_model = engine.train(corpus)
elif args.optimizer == 'rmsprop':
best_valid_loss, best_model = engine.train_scheduled(corpus)
utils.save_model(best_model, args.model_file + '_' + str(seed) + '.th')
utils.save_model(best_model.state_dict(), 'stdict_' + args.model_file)
def main():
parser = argparse.ArgumentParser(description='Reinforce')
parser.add_argument('--alice_model_file',
type=str,
help='Alice model file')
parser.add_argument('--bob_model_file', type=str, help='Bob model file')
parser.add_argument('--output_model_file',
type=str,
help='output model file')
parser.add_argument('--context_file', type=str, help='context file')
parser.add_argument('--temperature',
type=float,
default=1.0,
help='temperature')
parser.add_argument('--pred_temperature',
type=float,
default=1.0,
help='temperature')
parser.add_argument('--cuda',
action='store_true',
default=False,
help='use CUDA')
parser.add_argument('--verbose',
action='store_true',
default=False,
help='print out converations')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument(
'--score_threshold',
type=int,
default=6,
help='successful dialog should have more than score_threshold in score'
)
parser.add_argument('--log_file',
type=str,
default='',
help='log successful dialogs to file for training')
parser.add_argument('--smart_bob',
action='store_true',
default=False,
help='make Bob smart again')
parser.add_argument('--gamma',
type=float,
default=0.99,
help='discount factor')
parser.add_argument('--eps', type=float, default=0.5, help='eps greedy')
parser.add_argument('--momentum',
type=float,
default=0.1,
help='momentum for sgd')
parser.add_argument('--lr', type=float, default=0.1, help='learning rate')
parser.add_argument('--clip',
type=float,
default=0.1,
help='gradient clip')
parser.add_argument('--rl_lr',
type=float,
default=0.002,
help='RL learning rate')
parser.add_argument('--rl_clip',
type=float,
default=2.0,
help='RL gradient clip')
parser.add_argument('--ref_text',
type=str,
help='file with the reference text')
parser.add_argument('--sv_train_freq',
type=int,
default=-1,
help='supervision train frequency')
parser.add_argument('--nepoch',
type=int,
default=1,
help='number of epochs')
parser.add_argument('--hierarchical',
action='store_true',
default=False,
help='use hierarchical training')
parser.add_argument('--visual',
action='store_true',
default=False,
help='plot graphs')
parser.add_argument('--domain',
type=str,
default='object_division',
help='domain for the dialogue')
parser.add_argument('--selection_model_file',
type=str,
default='',
help='path to save the final model')
parser.add_argument('--data',
type=str,
default='data/negotiate',
help='location of the data corpus')
parser.add_argument('--unk_threshold',
type=int,
default=20,
help='minimum word frequency to be in dictionary')
parser.add_argument('--bsz', type=int, default=16, help='batch size')
parser.add_argument('--validate',
action='store_true',
default=False,
help='plot graphs')
parser.add_argument('--scratch',
action='store_true',
default=False,
help='erase prediciton weights')
parser.add_argument('--sep_sel',
action='store_true',
default=False,
help='use separate classifiers for selection')
args = parser.parse_args()
utils.use_cuda(args.cuda)
utils.set_seed(args.seed)
alice_model = utils.load_model(args.alice_model_file) # RnnModel
alice_ty = get_agent_type(alice_model) # RnnRolloutAgent
alice = alice_ty(alice_model, args, name='Alice', train=True)
alice.vis = args.visual
bob_model = utils.load_model(args.bob_model_file) # RnnModel
bob_ty = get_agent_type(bob_model) # RnnAgent
bob = bob_ty(bob_model, args, name='Bob', train=False)
dialog = Dialog([alice, bob], args)
logger = DialogLogger(verbose=args.verbose, log_file=args.log_file)
ctx_gen = ContextGenerator(args.context_file)
domain = get_domain(args.domain)
corpus = alice_model.corpus_ty(domain,
args.data,
freq_cutoff=args.unk_threshold,
verbose=True,
sep_sel=args.sep_sel)
engine = alice_model.engine_ty(alice_model, args)
reinforce = Reinforce(dialog, ctx_gen, args, engine, corpus, logger)
reinforce.run()
utils.save_model(alice.model, args.output_model_file)
def __init__(self, word_dict, item_dict, context_dict, output_length, args, device_id):
super(DialogModel, self).__init__(device_id)
domain = get_domain(args.domain)
self.word_dict = word_dict
self.item_dict = item_dict
self.context_dict = context_dict
self.args = args
self.word_encoder = nn.Embedding(len(self.word_dict), args.nembed_word)
ctx_encoder_ty = modules.RnnContextEncoder if args.rnn_ctx_encoder \
else modules.MlpContextEncoder
self.ctx_encoder = ctx_encoder_ty(len(self.context_dict), domain.input_length(),
args.nembed_ctx, args.nhid_ctx, args.init_range, device_id)
self.reader = nn.GRU(
input_size=args.nhid_ctx + args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
self.decoder = nn.Linear(args.nhid_lang, args.nembed_word)
self.writer = nn.GRUCell(
input_size=args.nhid_ctx + args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
# Tie the weights of reader and writer
self.writer.weight_ih = self.reader.weight_ih_l0
self.writer.weight_hh = self.reader.weight_hh_l0
self.writer.bias_ih = self.reader.bias_ih_l0
self.writer.bias_hh = self.reader.bias_hh_l0
self.dropout = nn.Dropout(args.dropout)
self.sel_rnn = nn.GRU(
input_size=args.nhid_lang + args.nembed_word,
hidden_size=args.nhid_attn,
bias=True,
bidirectional=True)
# Mask for disabling special tokens when generating sentences
self.special_token_mask = torch.FloatTensor(len(self.word_dict))
self.sel_encoder = nn.Sequential(
torch.nn.Linear(2 * args.nhid_attn + args.nhid_ctx, args.nhid_sel),
nn.Tanh()
)
self.attn = nn.Sequential(
torch.nn.Linear(2 * args.nhid_attn, args.nhid_attn),
nn.Tanh(),
torch.nn.Linear(args.nhid_attn, 1)
)
self.sel_decoders = nn.ModuleList()
for i in range(output_length):
self.sel_decoders.append(nn.Linear(args.nhid_sel, len(self.item_dict)))
self.init_weights()
for i in range(len(self.word_dict)):
w = self.word_dict.get_word(i)
special = domain.item_pattern.match(w) or w in ('<unk>', 'YOU:', 'THEM:', '<pad>')
self.special_token_mask[i] = -999 if special else 0.0
self.special_token_mask = self.to_device(self.special_token_mask)
def main():
parser = argparse.ArgumentParser(
description='training script for markable detection')
parser.add_argument('--data',
type=str,
default='data/onecommon',
help='location of the data corpus')
parser.add_argument('--nembed_word',
type=int,
default=128,
help='size of word embeddings')
parser.add_argument('--nembed_ctx',
type=int,
default=128,
help='size of context embeddings')
parser.add_argument(
'--nhid_lang',
type=int,
default=128,
help='size of the hidden state for the language module')
parser.add_argument('--optimizer',
choices=['adam', 'rmsprop'],
default='adam',
help='optimizer to use')
parser.add_argument('--lr',
type=float,
default=0.001,
help='initial learning rate')
parser.add_argument('--min_lr',
type=float,
default=1e-5,
help='min threshold for learning rate annealing')
parser.add_argument('--decay_rate',
type=float,
default=9.0,
help='decrease learning rate by this factor')
parser.add_argument('--decay_every',
type=int,
default=1,
help='decrease learning rate after decay_every epochs')
parser.add_argument('--momentum',
type=float,
default=0.0,
help='momentum for sgd')
parser.add_argument('--clip',
type=float,
default=0.5,
help='gradient clipping')
parser.add_argument('--dropout',
type=float,
default=0.5,
help='dropout rate in embedding layer')
parser.add_argument('--init_range',
type=float,
default=0.01,
help='initialization range')
parser.add_argument('--max_epoch',
type=int,
default=10,
help='max number of epochs')
parser.add_argument('--bsz', type=int, default=1, help='batch size')
parser.add_argument('--unk_threshold',
type=int,
default=20,
help='minimum word frequency to be in dictionary')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument('--cuda',
action='store_true',
default=False,
help='use CUDA')
parser.add_argument('--model_file',
type=str,
default='markable_detector',
help='path to save the final model')
parser.add_argument('--domain',
type=str,
default='one_common',
help='domain for the dialogue')
parser.add_argument('--tensorboard_log',
action='store_true',
default=False,
help='log training with tensorboard')
parser.add_argument('--repeat_train',
action='store_true',
default=False,
help='repeat training n times')
parser.add_argument('--test_only',
action='store_true',
default=False,
help='test only')
parser.add_argument('--corpus_type',
choices=['full', 'uncorrelated', 'success_only'],
default='full',
help='type of training corpus to use')
args = parser.parse_args()
if args.repeat_train:
seeds = list(range(10))
else:
seeds = [1]
for seed in seeds:
utils.use_cuda(args.cuda)
utils.set_seed(args.seed)
domain = get_domain(args.domain)
corpus = BiLSTM_CRF.corpus_ty(
domain,
args.data,
train='train_markable_{}.txt'.format(seed),
valid='valid_markable_{}.txt'.format(seed),
test='test_markable_{}.txt'.format(seed),
verbose=True)
if args.test_only:
best_model = utils.load_model(args.model_file + '_' + str(seed) +
'.th')
if args.cuda:
best_model.cuda()
else:
device = torch.device("cpu")
best_model.to(device)
best_model.eval()
else:
model = BiLSTM_CRF(len(corpus.word_dict), corpus.bio_dict,
args.nembed_word, args.nhid_lang)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
if args.cuda:
model.cuda()
best_model, best_valid_loss = copy.deepcopy(model), 1e100
validdata = corpus.valid_dataset(args.bsz)
for epoch in range(1, args.max_epoch + 1):
traindata = corpus.train_dataset(args.bsz)
trainset, trainset_stats = traindata
validset, validset_stats = validdata
# train pass
model.train()
total_lang_loss, total_select_loss, total_num_correct, total_num_select = 0, 0, 0, 0
start_time = time.time()
for batch in tqdm(trainset):
model.zero_grad()
ctx, words, markables, scenario_ids, agents, chat_ids = batch
ctx = Variable(ctx)
words = Variable(words)
markables = Variable(markables)
loss = model.neg_log_likelihood(words, markables)
loss.sum().backward()
optimizer.step()
# valid pass
model.eval()
with torch.no_grad():
correct = 0
total = 0
valid_loss = 0
for batch in tqdm(validset):
ctx, words, markables, scenario_ids, agents, chat_ids = batch
valid_loss += model.neg_log_likelihood(
words, markables).sum().item()
score, tag_seq = model(words)
correct += (torch.Tensor(tag_seq).long() == markables
).sum().item()
total += len(tag_seq)
print("epoch {}".format(epoch))
print("valid loss: {:.5f}".format(valid_loss))
print("valid accuracy: {:.5f}".format(correct / total))
if valid_loss < best_valid_loss:
print("update best model")
best_model = copy.deepcopy(model)
best_valid_loss = valid_loss
# test pass
testdata = corpus.test_dataset(args.bsz)
testset, testset_stats = testdata
best_model.eval()
with torch.no_grad():
correct = 0
total = 0
test_loss = 0
for batch in tqdm(testset):
ctx, words, markables, scenario_ids, agents, chat_ids = batch
test_loss += best_model.neg_log_likelihood(
words, markables).sum().item()
score, tag_seq = best_model(words)
correct += (
torch.Tensor(tag_seq).long() == markables).sum().item()
total += len(tag_seq)
print("final test {}".format(epoch))
print("test loss: {:.5f}".format(test_loss))
print("test accuracy: {:.5f}".format(correct / total))
if not args.test_only:
utils.save_model(best_model,
args.model_file + '_' + str(seed) + '.th')
utils.save_model(best_model.state_dict(),
'stdict_' + args.model_file)
def main():
parser = argparse.ArgumentParser(
description='testing script for reference resolution')
parser.add_argument('--data',
type=str,
default='data/onecommon',
help='location of the data corpus')
parser.add_argument('--unk_threshold',
type=int,
default=10,
help='minimum word frequency to be in dictionary')
parser.add_argument('--model_file',
type=str,
required=True,
help='pretrained model file')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument('--hierarchical',
action='store_true',
default=False,
help='use hierarchical model')
parser.add_argument('--bsz', type=int, default=16, help='batch size')
parser.add_argument('--cuda',
action='store_true',
default=False,
help='use CUDA')
parser.add_argument('--domain',
type=str,
default='one_common',
help='domain for the dialogue')
parser.add_argument('--vocab_corpus',
choices=['full', 'uncorrelated', 'success_only'],
default='full',
help='vocabulary of the corpus to use')
parser.add_argument('--corpus_type',
choices=['full', 'uncorrelated', 'success_only'],
default='full',
help='type of test corpus to use')
parser.add_argument('--bleu_n',
type=int,
default=0,
help='test ngram bleu')
parser.add_argument('--temperature',
type=float,
default=0.1,
help='temperature')
# for error analysis
parser.add_argument('--transcript_file',
type=str,
default='final_transcripts.json',
help='scenario file')
parser.add_argument('--markable_file',
type=str,
default='markable_annotation.json',
help='scenario file')
parser.add_argument('--show_errors',
action='store_true',
default=False,
help='show errors')
# analysis parameters
parser.add_argument('--fix_misspellings',
action='store_true',
default=False,
help='fix misspellings')
parser.add_argument('--shuffle_utterance',
action='store_true',
default=False,
help='shuffle order of words in the utterance')
parser.add_argument('--shuffle_word_types',
type=str,
nargs='*',
default=[],
help='shuffle specified class of words in the output')
parser.add_argument('--drop_word_types',
type=str,
nargs='*',
default=[],
help='drop specified class of words in the output')
parser.add_argument('--replace_word_types',
type=str,
nargs='*',
default=[],
help='replace specified class of words in the output')
parser.add_argument('--repeat_test',
action='store_true',
default=False,
help='repeat training n times')
parser.add_argument('--test_ref_forward',
action='store_true',
default=False,
help='test forward reference instead')
args = parser.parse_args()
if args.bleu_n > 0:
# current support
args.bsz = 1
if args.repeat_test:
seeds = list(range(10))
else:
seeds = [args.seed]
repeat_results = defaultdict(list)
model_referent_annotation = {}
init2num_referents = defaultdict(Counter)
for seed in seeds:
device_id = utils.use_cuda(args.cuda)
utils.set_seed(args.seed)
domain = get_domain(args.domain)
model = utils.load_model(args.model_file + '_' + str(seed) + '.th')
if args.cuda:
model.cuda()
else:
device = torch.device("cpu")
model.to(device)
model.eval()
corpus = model.corpus_ty(
domain,
args.data,
train='train_reference_shift_{}.txt'.format(seed),
valid='valid_reference_shift_{}.txt'.format(seed),
test='test_reference_shift_{}.txt'.format(seed),
freq_cutoff=args.unk_threshold,
verbose=True)
with open(os.path.join(args.data, args.transcript_file), "r") as f:
dialog_corpus = json.load(f)
with open(os.path.join(args.data, args.markable_file), "r") as f:
markable_annotation = json.load(f)
with open(
os.path.join(args.data, "aggregated_referent_annotation.json"),
"r") as f:
aggregated_referent_annotation = json.load(f)
scenarios = {
scenario['scenario_uuid']: scenario
for scenario in dialog_corpus
}
crit = Criterion(model.word_dict, device_id=device_id)
sel_crit = nn.CrossEntropyLoss()
ref_crit = nn.BCEWithLogitsLoss()
testset, testset_stats = corpus.test_dataset(args.bsz)
test_lang_loss, test_select_loss, test_reference_loss, test_select_correct, test_select_total, test_reference_correct, test_reference_total, test_num_ref_correct, test_num_ref_total = 0, 0, 0, 0, 0, 0, 0, 0, 0
"""
Variables to keep track of the results for analysis
"""
# num_referents --> count, count correct
num_markables = 0
num_markables_counter = Counter()
num_markables_correct = Counter()
exact_match = 0
exact_match_counter = Counter()
# location of markable --> count, count correct, count exact match
location_counter = Counter()
location_correct = Counter()
location_exact_match = Counter()
# information to compute correlation between selection and reference score
select_correct = {}
reference_correct = {}
reference_total = {}
# markable text --> count, count correct, count exact match
text_counter = Counter()
text_correct = Counter()
text_exact_match = Counter()
# init token --> count, count correct
init_counter = Counter()
init_correct = Counter()
init_exact_match = Counter()
# num ref confusion
num_ref_confusion = np.zeros([8, 8], dtype=int)
anaphora_list = [
"it", "that", "thats", "this", "its", "they", "their", "itself",
"them", "those", "it's"
]
total_anaphora = 0
correct_anaphora = 0
bleu_scores = []
for batch in testset:
ctx, inpt, tgt, ref_inpt, ref_tgt, num_ref_tgt, sel_tgt, scenario_ids, real_ids, agents, chat_ids, sel_idx = batch
ctx = Variable(ctx)
inpt = Variable(inpt)
if ref_inpt is not None:
ref_inpt = Variable(ref_inpt)
out, ref_out, num_ref_out, sel_out = model.forward(
ctx, inpt, ref_inpt, sel_idx)
tgt = Variable(tgt)
sel_tgt = Variable(sel_tgt)
lang_loss = crit(out, tgt)
if ref_inpt is not None:
ref_tgt = Variable(ref_tgt)
ref_tgt = torch.transpose(ref_tgt, 0, 1).contiguous().float()
ref_loss = ref_crit(ref_out, ref_tgt)
t = Variable(torch.FloatTensor([0])) # threshold
if model.args.num_ref_weight > 0:
num_ref_pred = num_ref_out.max(dim=2)[1]
ref_results = torch.zeros_like(ref_tgt)
ref_correct = 0
for i in range(ref_out.size(0)):
for j in range(ref_out.size(1)):
ref_pred = torch.zeros_like(ref_tgt[i][j])
for ref_idx in range(ref_pred.size(0)):
if ref_idx in ref_out[i][j].topk(
num_ref_pred[i][j])[1]:
ref_pred[ref_idx] = 1.0
ref_results[i][j] = (
ref_pred.long() == ref_tgt[i][j].long())
ref_correct += (ref_pred.long() == ref_tgt[i]
[j].long()).sum().item()
ref_total = ref_tgt.size(0) * ref_tgt.size(
1) * ref_tgt.size(2)
else:
ref_results = ((ref_out > 0).long() == ref_tgt.long())
ref_correct = ((ref_out >
0).long() == ref_tgt.long()).sum().item()
ref_total = ref_tgt.size(0) * ref_tgt.size(
1) * ref_tgt.size(2)
# compute more details of reference resolution
for i in range(ref_tgt.size(0)): # markable idx
for j in range(ref_tgt.size(1)): # batch idx
chat_id = chat_ids[j]
# add chat level details if not exists
if chat_id not in reference_correct:
reference_correct[chat_id] = ref_results[:,
j, :].sum(
).item()
if chat_id not in reference_total:
reference_total[
chat_id] = ref_results[:, j, :].size(
0) * ref_results[:, j, :].size(1)
if chat_id not in model_referent_annotation:
model_referent_annotation[chat_id] = {}
markables = []
# markables information from aggregated_referent_annotation
for markable in markable_annotation[chat_id][
"markables"]:
markable_id = markable["markable_id"]
if markable_id in aggregated_referent_annotation[
chat_id] and markable["speaker"] == agents[
j]:
if "unidentifiable" in aggregated_referent_annotation[
chat_id][
markable_id] and aggregated_referent_annotation[
chat_id][markable_id][
"unidentifiable"]:
if markable_id not in model_referent_annotation[
chat_id] and markable[
"speaker"] == agents[j]:
model_referent_annotation[chat_id][
markable_id] = {
'ambiguous': False,
'referents': [],
'unidentifiable': True
}
continue
markables.append(markable)
assert len(markables) == ref_tgt.size(0)
if model.args.num_ref_weight > 0:
ref_pred = torch.zeros_like(ref_tgt[i][j])
for ref_idx in range(ref_pred.size(0)):
#if ref_idx in ref_out[i][j].topk(num_ref_tgt[i][j])[1]:
if ref_idx in ref_out[i][j].topk(
num_ref_pred[i][j])[1]:
ref_pred[ref_idx] = 1.0
correct_result = (ref_pred.long() == ref_tgt[i]
[j].long()).sum().item()
exact_match_result = torch.equal(
ref_pred.long(), ref_tgt[i][j].long())
num_referents = ref_tgt[i][j].long().sum().item()
else:
correct_result = ((ref_out > 0).long(
)[i][j] == ref_tgt.long())[i][j].sum().item()
exact_match_result = torch.equal(
(ref_out > 0).long()[i][j],
ref_tgt.long()[i][j])
num_referents = ref_tgt.long()[i][j].sum().item()
ref_pred = (ref_out > 0).long()[i][j]
"""
Add information to variables
"""
num_markables += 1
num_markables_counter[num_referents] += 1
num_markables_correct[num_referents] += correct_result
# compute exact match
if exact_match_result:
exact_match += 1
exact_match_counter[ref_tgt.long()[i]
[j].sum().item()] += 1
location_exact_match[i] += 1
if num_referents == 1: # temporal condition
text_exact_match[markables[i]
["text"].lower()] += 1
init_exact_match[markables[i]["text"].lower().
split(" ")[0]] += 1
location_correct[i] += correct_result
location_counter[i] += 1
if num_referents == 1: # temporal condition
text_counter[markables[i]["text"].lower()] += 1
text_correct[markables[i]
["text"].lower()] += correct_result
init_counter[markables[i]["text"].lower().split(
" ")[0]] += 1
init_correct[markables[i]["text"].lower().split(
" ")[0]] += correct_result
init2num_referents[markables[i]["text"].lower().split(
" ")[0]][num_referents] += 1
# test anaphora
if markables[i]["text"].lower() in anaphora_list:
total_anaphora += 1
if exact_match_result:
correct_anaphora += 1
# keep track of model predictions for later visualization
chat = [
chat for chat in dialog_corpus
if chat['uuid'] == chat_id
]
chat = chat[0]
if markables[i][
'markable_id'] not in model_referent_annotation[
chat_id]:
model_referent_annotation[chat_id][
markables[i]['markable_id']] = {}
model_referent_annotation[chat_id][
markables[i]['markable_id']]['referents'] = []
model_referent_annotation[chat_id][markables[i][
'markable_id']]['ambiguous'] = False
model_referent_annotation[chat_id][markables[i][
'markable_id']]['unidentifiable'] = False
for ent, is_referent in zip(
chat['scenario']['kbs'][agents[j]],
ref_pred.long().tolist()):
#for ent, is_referent in zip(chat['scenario']['kbs'][agents[j]], (ref_out > 0).long()[i][j].tolist()):
if is_referent:
model_referent_annotation[chat_id][
markables[i]
['markable_id']]['referents'].append(
"agent_{}_{}".format(
agents[j], ent['id']))
else:
ref_loss = None
ref_correct = 0
ref_total = 0
sel_loss = sel_crit(sel_out, sel_tgt)
sel_correct = (sel_out.max(dim=1)[1] == sel_tgt).sum().item()
sel_total = sel_out.size(0)
for i in range(sel_tgt.size(0)): # batch idx
chat_id = chat_ids[i]
sel_resuts = (sel_out.max(dim=1)[1] == sel_tgt)
if sel_resuts[i]:
select_correct[chat_id] = 1
else:
select_correct[chat_id] = 0
if model.args.num_ref_weight > 0 and num_ref_out is not None:
num_ref_out = num_ref_out.view(-1, num_ref_out.size(2))
num_ref_tgt = torch.transpose(num_ref_tgt, 0, 1).contiguous()
num_ref_tgt = num_ref_tgt.view(-1)
num_ref_loss = sel_crit(num_ref_out, num_ref_tgt)
num_ref_correct = (num_ref_out.max(
dim=1)[1] == num_ref_tgt).sum().item()
num_ref_total = num_ref_tgt.size(0)
for mi in range(num_ref_out.size(0)):
model_pred = num_ref_out[mi].max(dim=0)[1].item()
ground_truth = num_ref_tgt[mi].item()
num_ref_confusion[ground_truth][model_pred] += 1
else:
num_ref_correct = 0
num_ref_total = 0
test_lang_loss += lang_loss.item()
test_select_loss += sel_loss.item()
if ref_loss:
test_reference_loss += ref_loss.item()
test_select_correct += sel_correct
test_select_total += sel_total
test_reference_correct += ref_correct
test_reference_total += ref_total
test_num_ref_correct += num_ref_correct
test_num_ref_total += num_ref_total
if args.bleu_n > 0:
ctx_h = model.ctx_encoder(ctx.transpose(0, 1))
my_utterance = None
idx = 0
while True:
if inpt[idx] == model.word_dict.word2idx['YOU:']:
start = idx
my_utterance = model.read_and_write(
inpt[:idx],
ctx_h,
30,
temperature=args.temperature)
my_utterance = model.word_dict.i2w(my_utterance)
#print(my_utterance)
while not inpt[idx] in [
model.word_dict.word2idx[stop_token]
for stop_token in data.STOP_TOKENS
]:
idx += 1
end = idx
golden_utterance = inpt[start:end]
golden_utterance = model.word_dict.i2w(
golden_utterance)
bleu_scores.append(100 * sentence_bleu(
[golden_utterance],
my_utterance,
weights=[
1 for i in range(4) if args.bleu_n == i
], #weights=[1 / args.bleu_n] * args.bleu_n,
smoothing_function=SmoothingFunction().method7))
if inpt[idx] == model.word_dict.word2idx['<selection>']:
break
idx += 1
# Main results:
# Dividing by the number of words in the input, not the tokens modeled,
# because the latter includes padding
test_lang_loss /= testset_stats['nonpadn']
test_select_loss /= len(testset)
test_select_accuracy = test_select_correct / test_select_total
test_reference_accuracy = test_reference_correct / test_reference_total
if test_num_ref_total > 0:
test_num_ref_accuracy = test_num_ref_correct / test_num_ref_total
else:
test_num_ref_accuracy = 0
print('testlangloss %.8f | testlangppl %.8f' %
(test_lang_loss, np.exp(test_lang_loss)))
print('testselectloss %.8f | testselectaccuracy %.6f' %
(test_select_loss, test_select_accuracy))
print('testreferenceloss %.8f | testreferenceaccuracy %.6f' %
(test_reference_loss, test_reference_accuracy))
print('reference_exact_match %.6f' % (exact_match / num_markables))
for k in num_markables_counter.keys():
print('{}: {:.4f} {:.4f} (out of {})'.format(
k, num_markables_correct[k] / (num_markables_counter[k] * 7),
exact_match_counter[k] / num_markables_counter[k],
num_markables_counter[k]))
print('test anaphora: {} (out of {})'.format(
correct_anaphora / total_anaphora, total_anaphora))
if args.bleu_n > 0:
print('average bleu score {}'.format(np.mean(bleu_scores)))
# reference/selection correlation
reference_score = []
selection_score = []
for chat_id in reference_correct.keys():
reference_score.append(reference_correct[chat_id] /
reference_total[chat_id])
selection_score.append(select_correct[chat_id])
plt.xlabel('reference score', fontsize=14)
plt.ylabel('selection score', fontsize=14)
sns.regplot(x=reference_score, y=selection_score)
plt.savefig('reference_selection_{}.png'.format(seed), dpi=300)
plt.clf()
reference_score = np.array(reference_score)
selection_score = np.array(selection_score)
print("reference selection correlation: {}".format(
np.corrcoef(reference_score, selection_score)))
# keep track of results for this run
repeat_results["test_lang_loss"].append(test_lang_loss)
repeat_results["test_select_loss"].append(test_select_loss)
repeat_results["test_select_accuracy"].append(test_select_accuracy)
repeat_results["test_reference_loss"].append(test_reference_loss)
repeat_results["test_reference_accuracy"].append(
test_reference_accuracy)
repeat_results["test_num_ref_accuracy"].append(test_num_ref_accuracy)
repeat_results["correlation_score"].append(
np.corrcoef(reference_score, selection_score)[0][1])
repeat_results["num_markables_counter"].append(
copy.copy(num_markables_counter))
repeat_results["exact_match_counter"].append(
copy.copy(exact_match_counter))
repeat_results["num_markables_correct"].append(
copy.copy(num_markables_correct))
repeat_results["reference_exact_match"].append(exact_match /
num_markables)
repeat_results["test_perplexity"].append(np.exp(test_lang_loss))
repeat_results["location_counter"].append(copy.copy(location_counter))
repeat_results["location_correct"].append(copy.copy(location_correct))
repeat_results["location_exact_match"].append(
copy.copy(location_exact_match))
repeat_results["init_counter"].append(copy.copy(init_counter))
repeat_results["init_correct"].append(copy.copy(init_correct))
repeat_results["init_exact_match"].append(copy.copy(init_exact_match))
print("=================================\n\n")
print("repeat test lang loss %.8f" %
np.mean(repeat_results["test_lang_loss"]))
print("repeat test select loss %.8f" %
np.mean(repeat_results["test_select_loss"]))
print("repeat test select accuracy %.8f ( %.8f )" %
(np.mean(repeat_results["test_select_accuracy"]),
np.std(repeat_results["test_select_accuracy"])))
print("repeat test reference loss %.8f" %
np.mean(repeat_results["test_reference_loss"]))
print("repeat test reference accuracy %.8f ( %.8f )" %
(np.mean(repeat_results["test_reference_accuracy"]),
np.std(repeat_results["test_reference_accuracy"])))
print("repeat test num ref accuracy %.8f ( %.8f )" %
(np.mean(repeat_results["test_num_ref_accuracy"]),
np.std(repeat_results["test_reference_accuracy"])))
print("repeat correlation score %.8f ( %.8f )" %
(np.mean(repeat_results["correlation_score"]),
np.std(repeat_results["correlation_score"])))
print("repeat correlation score %.8f ( %.8f )" %
(np.mean(repeat_results["correlation_score"]),
np.std(repeat_results["correlation_score"])))
print("repeat reference exact match %.8f ( %.8f )" %
(np.mean(repeat_results["reference_exact_match"]),
np.std(repeat_results["reference_exact_match"])))
print("repeat test perplexity %.8f ( %.8f )" %
(np.mean(repeat_results["test_perplexity"]),
np.std(repeat_results["test_perplexity"])))
for k in num_markables_counter.keys():
print("repeat accuracy and exact match:")
num_markables = []
exact_match = []
exact_match_rate = []
num_markables_correct = []
for seed in range(len(seeds)):
num_markables.append(
repeat_results["num_markables_counter"][seed][k])
exact_match.append(repeat_results["exact_match_counter"][seed][k])
exact_match_rate.append(
repeat_results["exact_match_counter"][seed][k] /
repeat_results["num_markables_counter"][seed][k])
num_markables_correct.append(
repeat_results["num_markables_correct"][seed][k] /
(repeat_results["num_markables_counter"][seed][k] * 7))
print('{}: {:.5f} (std {}) {:.5f} (std {}) (count {})'.format(
k, np.mean(num_markables_correct), np.std(num_markables_correct),
np.mean(exact_match_rate), np.std(exact_match_rate),
np.mean(num_markables)))
dump_json(model_referent_annotation,
"{}_referent_annotation.json".format(args.model_file))
print("exact match at each location:")
markable_location_plot = []
exact_match_rate_plot = []
accuracy_plot = []
for loc in range(12):
accuracy = []
exact_match_rate = []
total_count = 0
for seed in range(len(seeds)):
if repeat_results["location_counter"][seed][loc] > 0:
exact_match_rate.append(
repeat_results["location_exact_match"][seed][loc] /
repeat_results["location_counter"][seed][loc])
total_count += repeat_results["location_counter"][seed][loc]
markable_location_plot.append(loc + 1)
exact_match_rate_plot.append(
repeat_results["location_exact_match"][seed][loc] /
repeat_results["location_counter"][seed][loc])
accuracy_plot.append(
repeat_results["location_correct"][seed][loc] /
(7 * repeat_results["location_counter"][seed][loc]))
if len(exact_match_rate) > 0:
print('Loc @ {}: {:.5f} (std {:.5f}) (valid runs: {}, total: {})'.
format(loc + 1, np.mean(exact_match_rate),
np.std(exact_match_rate), len(exact_match_rate),
total_count))
plt.xlabel('markable location', fontsize=14)
plt.ylabel('exact match rate', fontsize=14)
sns.lineplot(x=markable_location_plot, y=exact_match_rate_plot)
plt.savefig('location_exact_match_rate.png', dpi=300)
plt.clf()
plt.xlabel('markable location', fontsize=14)
plt.ylabel('accuracy', fontsize=14)
sns.lineplot(x=markable_location_plot, y=accuracy_plot)
plt.savefig('location_accuracy.png', dpi=300)
plt.clf()
plt.xlabel('markable position', fontsize=14)
plt.ylabel('percentage', fontsize=14)
sns.lineplot(x=markable_location_plot,
y=accuracy_plot,
legend="brief",
label="accuracy")
sns.lineplot(x=markable_location_plot,
y=exact_match_rate_plot,
legend="brief",
label="exact match")
plt.savefig('location_results.png', dpi=300)
plt.clf()
print("compute results based on initial token:")
#for tok in model.word_dict.w2i.keys():
definite_toks = ["the"]
indefinite_toks = ["a", "an"]
definite_accuracies = []
indefinite_accuracies = []
other_accuracies = []
definite_exact_matches = []
indefinite_exact_matches = []
other_exact_matches = []
definite_counts = []
indefinite_counts = []
other_counts = []
for seed in range(len(seeds)):
num_correct = 0
num_exact_match = 0
num_total = 0
for tok in definite_toks:
num_total += repeat_results["init_counter"][seed][tok]
num_correct += repeat_results["init_correct"][seed][tok]
num_exact_match += repeat_results["init_exact_match"][seed][tok]
definite_accuracies.append(num_correct / (7 * num_total))
definite_exact_matches.append(num_exact_match / num_total)
definite_counts.append(num_total)
num_correct = 0
num_exact_match = 0
num_total = 0
for tok in indefinite_toks:
num_total += repeat_results["init_counter"][seed][tok]
num_correct += repeat_results["init_correct"][seed][tok]
num_exact_match += repeat_results["init_exact_match"][seed][tok]
indefinite_accuracies.append(num_correct / (7 * num_total))
indefinite_exact_matches.append(num_exact_match / num_total)
indefinite_counts.append(num_total)
num_correct = 0
num_exact_match = 0
num_total = 0
for tok in repeat_results["init_counter"][seed].keys():
if tok not in definite_toks + indefinite_toks:
num_total += repeat_results["init_counter"][seed][tok]
num_correct += repeat_results["init_correct"][seed][tok]
num_exact_match += repeat_results["init_exact_match"][seed][
tok]
other_accuracies.append(num_correct / (7 * num_total))
other_exact_matches.append(num_exact_match / num_total)
other_counts.append(num_total)
print(
"definite: accuracies {} (std {}), exact match rate {} (std {}), total count {} (std {})"
.format(np.mean(definite_accuracies), np.std(definite_accuracies),
np.mean(definite_exact_matches),
np.std(definite_exact_matches), np.mean(definite_counts),
np.std(definite_counts)))
print(
"indefinite: accuracies {} (std {}), exact match rate {} (std {}), total count {} (std {})"
.format(np.mean(indefinite_accuracies), np.std(indefinite_accuracies),
np.mean(indefinite_exact_matches),
np.std(indefinite_exact_matches), np.mean(indefinite_counts),
np.std(indefinite_counts)))
print(
"other: accuracies {} (std {}), exact match rate {} (std {}), total count {} (std {})"
.format(np.mean(other_accuracies), np.std(other_accuracies),
np.mean(other_exact_matches), np.std(other_exact_matches),
np.mean(other_counts), np.std(other_counts)))
valid_markables = 0
for chat_id in model_referent_annotation.keys():
for markable_id in model_referent_annotation[chat_id].keys():
if 'unidentifiable' in aggregated_referent_annotation[chat_id][
markable_id] and aggregated_referent_annotation[chat_id][
markable_id]['unidentifiable']:
continue
valid_markables += 1
print("model valid markables: {}".format(valid_markables))
valid_markables = 0
for chat_id in aggregated_referent_annotation.keys():
for markable_id in aggregated_referent_annotation[chat_id].keys():
if 'unidentifiable' in aggregated_referent_annotation[chat_id][
markable_id] and aggregated_referent_annotation[chat_id][
markable_id]['unidentifiable']:
continue
valid_markables += 1
print("aggregated valid markables: {}".format(valid_markables))
def __init__(self, word_dict, item_dict, context_dict, count_dict, args):
super(RnnVariationalModel, self).__init__()
domain = get_domain(args.domain)
self.word_dict = word_dict
self.item_dict = item_dict
self.context_dict = context_dict
self.count_dict = count_dict
self.args = args
self.word_encoder = nn.Embedding(len(self.word_dict), args.nembed_word)
self.word_encoder_dropout = nn.Dropout(args.dropout)
ctx_encoder_ty = MlpContextEncoder
self.ctx_encoder = nn.Sequential(
ctx_encoder_ty(len(self.context_dict), domain.input_length(),
args.nembed_ctx, args.nhid_ctx, args.dropout,
args.init_range), nn.Dropout(args.dropout))
# Encoder GRU outputs
self.reader = nn.GRU(args.nhid_ctx + args.nembed_word,
args.nhid_lang * 2,
bias=True)
self.reader_dropout = nn.Dropout(args.dropout)
self.decoder = nn.Sequential(
nn.Linear(args.nhid_lang, args.nembed_word),
nn.Dropout(args.dropout))
self.writer = nn.GRUCell(input_size=args.nhid_ctx + args.nembed_word,
hidden_size=args.nhid_lang,
bias=True)
# Tie the weights of reader and writer
self.writer.weight_ih = self.reader.weight_ih_l0
self.writer.weight_hh = self.reader.weight_hh_l0
self.writer.bias_ih = self.reader.bias_ih_l0
self.writer.bias_hh = self.reader.bias_hh_l0
self.sel_rnn = nn.GRU(input_size=args.nhid_lang + args.nembed_word,
hidden_size=args.nhid_attn,
bias=True,
bidirectional=True)
self.sel_dropout = nn.Dropout(args.dropout)
# Mask for disabling special tokens when generating sentences
self.special_token_mask = torch.FloatTensor(len(self.word_dict))
self.sel_encoder = nn.Sequential(
torch.nn.Linear(2 * args.nhid_attn + args.nhid_ctx, args.nhid_sel),
nn.Tanh(), nn.Dropout(args.dropout))
self.attn = nn.Sequential(
torch.nn.Linear(2 * args.nhid_attn, args.nhid_attn), nn.Tanh(),
torch.nn.Linear(args.nhid_attn, 1))
self.sel_decoders = nn.ModuleList()
for i in range(domain.selection_length()):
self.sel_decoders.append(
nn.Linear(args.nhid_sel, len(self.item_dict)))
self.init_weights()
self.special_token_mask = make_mask(len(word_dict), [
word_dict.get_idx(w) for w in ['<unk>', 'YOU:', 'THEM:', '<pad>']
])
