def __init__(self):
self.bow_enc = BoW_encoder()
self.emb = UtteranceEmbed()
self.at = ActionTracker(interact=True)
print(self.at.dialog_acts)
_, _, self.other_da, self.other_da_id = self.at.get_data()
self.action_templates = self.at.action_templates
self.action_size = self.at.action_size
#self.responses = [r[0] for r in self.at.responses]
#print(self.emb.dim , self.bow_enc.vocab_size , self.action_size)
self.obs_size = self.emb.dim + self.bow_enc.vocab_size + self.action_size
nb_hidden = 128
self.exercises = list(self.at.action_templates.keys())
self.net = LSTM_net(obs_size=self.obs_size,
action_size=self.action_size,
nb_hidden=nb_hidden)
# restore checkpoint
self.net.restore()
# setup remote interaction via zeromq
context = zmq.Context.instance()
# PUB socket for sending system-output
self.pubSocket: zmq.Socket = context.socket(zmq.PUB)
self.pubSocket.bind(Config.SYSTEM_OUTPUT_ADDR)
# SUB socket for receiving user-input
self.subSocket: zmq.Socket = context.socket(zmq.SUB)
self.subSocket.connect(Config.USER_INPUT_ADDR)
self.subSocket.setsockopt_string(zmq.SUBSCRIBE,
Config.USER_INPUT_SUBJECT)
self.lastOutputMessage: str = None
self.requestHandler: threading.Thread = None
self.requestInterrupted: threading.Lock = None
def __init__(self):
import os
#实体追踪
et = EntityTracker()
#词袋 word2vec
self.bow_enc = BoW_encoder()
#加载word2vec embedding
self.emb = UtteranceEmbed()
#将实体追踪器添加到动作追踪器中
at = ActionTracker(et)
#得到数据集和对话开始 结束行数
self.dataset, dialog_indices = Data(et, at).trainset
#划分数据集：200对做训练 50对做测试
self.dialog_indices_tr = dialog_indices
self.dialog_indices_dev = dialog_indices
#obs_size 300维的词向量 + 85个袋中的词 + 4个槽位
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features
#话术模板
self.action_templates = at.get_action_templates()
#动作个数
action_size = at.action_size
#隐藏层神经元个数
nb_hidden = 128
self.net = LSTM_net(obs_size=obs_size,
action_size=action_size,
nb_hidden=nb_hidden)
def __init__(self):
et = EntityTracker()
self.bow_enc = BoW_encoder()
self.emb = UtteranceEmbed()
at = ActionTracker(et)
self.train_dataset, train_dialog_indices = Data(et, at).train_set
self.test_dataset, test_dialog_indices = Data(et, at).test_set
print('=========================\n')
print('length of Train dialog indices : ', len(train_dialog_indices))
print('=========================\n')
print('=========================\n')
print('length of Test dialog indices : ', len(test_dialog_indices))
print('=========================\n')
# Shuffle Training Dataset
random.shuffle(train_dialog_indices)
self.dialog_indices_tr = train_dialog_indices
self.dialog_indices_dev = test_dialog_indices
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features
self.action_templates = at.get_action_templates()
action_size = at.action_size
# nb_hidden = 128
nb_hidden = 150
print('=========================\n')
print('Action_templates: ', action_size)
print('=========================\n')
self.net = LSTM_net(obs_size=obs_size,
action_size=action_size,
nb_hidden=nb_hidden)
self.et = et
self.at = at
action_projection = []
for action in self.action_templates:
action_projection.append(self.emb.encode(action))
self.action_projection = np.transpose(action_projection)
self.action_size = action_size
def main(in_dataset_folder, in_model_folder, in_no_ood_evaluation):
rev_vocab, kb, action_templates, config = load_model(in_model_folder)
test_dialogs, test_indices = read_dialogs(os.path.join(
in_dataset_folder, 'dialog-babi-task6-dstc2-tst.txt'),
with_indices=True)
et = EntityTracker(kb)
at = ActionTracker(None, et)
at.set_action_templates(action_templates)
vocab = {word: idx for idx, word in enumerate(rev_vocab)}
X, action_masks, sequence_masks, y = make_dataset(
test_dialogs, test_indices, vocab, et, at, config['max_input_length'])
net = LSTM_net(config, X.shape[-1], action_masks.shape[-1])
net.restore(in_model_folder)
eval_stats_full_dataset = evaluate_advanced(
net, (X, action_masks, sequence_masks, y), test_indices,
at.action_templates)
print(
'Full dataset: {} turns overall, {} turns after the first OOD'.format(
eval_stats_full_dataset['total_turns'],
eval_stats_full_dataset['total_turns_after_ood']))
print('Accuracy:')
accuracy = eval_stats_full_dataset[
'correct_turns'] / eval_stats_full_dataset['total_turns']
accuracy_after_ood = eval_stats_full_dataset['correct_turns_after_ood'] / eval_stats_full_dataset['total_turns_after_ood'] \
if eval_stats_full_dataset['total_turns_after_ood'] != 0 \
else 0
print('overall: {:.3f}; after first OOD: {:.3f}'.format(
accuracy, accuracy_after_ood))
print('Loss : {:.3f}'.format(eval_stats_full_dataset['avg_loss']))
if in_no_ood_evaluation:
eval_stats_no_ood = evaluate_advanced(
net, (X, action_masks, sequence_masks, y),
test_indices,
at.action_templates,
ignore_ood_accuracy=True)
print('Accuracy (OOD turns ignored):')
accuracy = eval_stats_no_ood['correct_turns'] / eval_stats_no_ood[
'total_turns']
accuracy_after_ood = eval_stats_no_ood['correct_turns_after_ood'] / eval_stats_no_ood['total_turns_after_ood'] \
if eval_stats_no_ood['total_turns_after_ood'] != 0 \
else 0
print('overall: {:.3f}; after first OOD: {:.3f}'.format(
accuracy, accuracy_after_ood))
print('Loss : {:.3f}'.format(eval_stats_no_ood['avg_loss']))
def __init__(self):
et = EntityTracker()
self.bow_enc = BoW_encoder()
self.emb = UtteranceEmbed()
at = ActionTracker(et)
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features
self.action_templates = at.get_action_templates()
action_size = at.action_size
nb_hidden = 128
self.net = LSTM_net(obs_size=obs_size, action_size=action_size, nb_hidden=nb_hidden)
# restore checkpoint
self.net.restore()
def __init__(self):
et = EntityTracker()
self.bow_enc = BoW_encoder()
self.emb = UtteranceEmbed()
at = ActionTracker(et)
self.dataset, dialog_indices = Data(et, at).trainset
self.dialog_indices_tr = dialog_indices[:200]
self.dialog_indices_dev = dialog_indices[200:250]
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features
self.action_templates = at.get_action_templates()
action_size = at.action_size
nb_hidden = 128
self.net = LSTM_net(obs_size=obs_size,
action_size=action_size,
nb_hidden=nb_hidden)
def __init__(self, train_ratio=15/20, \
epochs=100, \
train_whole=True, \
):
self.train_ratio = train_ratio
self.epochs = epochs
#flag für fehleranalyse
self.startCountingErrors = False
self.count_all_one_eval = 0
self.count_all_one_train = 0
self.count_all_train = 0
self.count_all_eval = 0
#data.trainset = [(u,da_indx), ...]
#HIER acttemps und size
self.at = ActionTracker()
self.action_templates = self.at.action_templates
self.at.dialog_acts = self.at.dialog_acts
self.at.dialog_act_ids = self.at.dialog_act_ids
self.dataset, self.dialog_indices, self.other_da, self.other_da_id = self.at.get_data()
self.action_size = self.at.action_size
self.accs = []
self.loss = []
self.dev_accs =[]
self.dev_loss=[]
self.countErrors = Counter()
self.countPredicitions = 0
self.errorLog = {}
self.highest_accuracy = 0
#pickle.load(open("data/errors/errorLog.p", "rb"))
if train_whole:
self.bow_enc = BoW_encoder()
self.emb = UtteranceEmbed()
#HIER numfeats
obs_size = self.emb.dim + self.bow_enc.vocab_size + self.action_size# + self.et.size_context_features
nb_hidden = 128
self.net = LSTM_net(obs_size,
self.action_size,
nb_hidden=nb_hidden)
def __init__(self):
et = EntityTracker()
self.bow_enc = BoW_encoder()
self.emb = UtteranceEmbed()
at = ActionTracker(et)
'''
['any preference on a type of cuisine', 'api_call <cuisine> <location> <party_size> <rest_type>', 'great let me do the reservation', 'hello what can i help you with today', 'here it is <info_address>', 'here it is <info_phone>', 'how many people would be in your party', "i'm on it", 'is there anything i can help you with', 'ok let me look into some options for you', 'sure is there anything else to update', 'sure let me find an other option for you', 'what do you think of this option: <restaurant>', 'where should it be', 'which price range are looking for', "you're welcome"]
'''
self.dataset, dialog_indices = Data(et, at).trainset
self.dialog_indices_tr = dialog_indices[:200]
self.dialog_indices_dev = dialog_indices[200:250]
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features
self.action_templates = at.get_action_templates()
action_size = at.action_size
nb_hidden = 128
self.net = LSTM_net(obs_size=obs_size,
action_size=action_size,
nb_hidden=nb_hidden)
def __init__(self):
et = EntityTracker()
self.bow_enc = BoW_encoder()
self.emb = UtteranceEmbed()
at = ActionTracker(et)
self.dataset, dialog_indices = Data(et, at).trainset
train_indices = joblib.load('data/train_test_list/train_indices_759')
test_indices = joblib.load('data/train_test_list/test_indices_759_949')
self.dialog_indices_tr = train_indices
self.dialog_indices_dev = test_indices
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features
self.action_templates = at.get_action_templates()
action_size = at.action_size
nb_hidden = 128
self.net = LSTM_net(obs_size=obs_size,
action_size=action_size,
nb_hidden=nb_hidden)