def write(filename, filename2, rows):
options_file = 'biomed_elmo_options.json'
weight_file = 'biomed_elmo_weights.hdf5'
elmo = Elmo(options_file, weight_file, 1, dropout=0)
elmo = elmo.to("cuda")
df = pd.read_csv(filename, nrows=rows)
x = df["TEXT"].values
y = df[[
'No Finding', 'Enlarged Cardiomediastinum', 'Cardiomegaly',
'Airspace Opacity', 'Lung Lesion', 'Edema', 'Consolidation',
'Pneumonia', 'Atelectasis', 'Pneumothorax', 'Pleural Effusion',
'Pleural Other', 'Fracture', 'Support Devices'
]].values
for i in range(rows):
print(str(i) + " of " + str(rows))
text = x[i]
token_list = []
for word in tokenizer(text):
token_list.append(word)
for n in range(MAXLEN - len(token_list)):
token_list.append('PAD')
token_list = np.array([token_list])
character_ids = batch_to_ids(token_list)
character_ids = character_ids.to("cuda")
word_emb = elmo(character_ids)['elmo_representations'][0]
character_ids.to("cpu")
word_emb = word_emb.data.cpu().numpy()
label = y[i]
batch = np.array([word_emb, label])
final_filename = "./Data/" + filename2 + "_instance_" + str(i)
np.save(final_filename, batch)
class SeqPrototypicalNetwork(nn.Module):
def __init__(self, config):
super(SeqPrototypicalNetwork, self).__init__()
self.base_path = config['base_path']
self.early_stopping = config['early_stopping']
self.lr = config.get('meta_lr', 1e-3)
self.weight_decay = config.get('meta_weight_decay', 0.0)
if 'seq' in config['learner_model']:
self.learner = RNNSequenceModel(config['learner_params'])
elif 'mlp' in config['learner_model']:
self.learner = MLPModel(config['learner_params'])
elif 'bert' in config['learner_model']:
self.learner = BERTSequenceModel(config['learner_params'])
self.num_outputs = config['learner_params']['num_outputs']
self.vectors = config.get('vectors', 'glove')
if self.vectors == 'elmo':
self.elmo = Elmo(
options_file=
"https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway_5.5B/elmo_2x4096_512_2048cnn_2xhighway_5.5B_options.json",
weight_file=
"https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway_5.5B/elmo_2x4096_512_2048cnn_2xhighway_5.5B_weights.hdf5",
num_output_representations=1,
dropout=0,
requires_grad=False)
elif self.vectors == 'glove':
self.glove = torchtext.vocab.GloVe(name='840B', dim=300)
elif self.vectors == 'bert':
self.bert_tokenizer = BertTokenizer.from_pretrained(
'bert-base-cased')
self.loss_fn = {}
for task in config['learner_params']['num_outputs']:
self.loss_fn[task] = nn.CrossEntropyLoss(ignore_index=-1)
if config.get('trained_learner', False):
self.learner.load_state_dict(
torch.load(
os.path.join(self.base_path, 'saved_models',
config['trained_learner'])))
logger.info('Loaded trained learner model {}'.format(
config['trained_learner']))
self.device = torch.device(config.get('device', 'cpu'))
self.to(self.device)
if self.vectors == 'elmo':
self.elmo.to(self.device)
self.initialize_optimizer_scheduler()
def initialize_optimizer_scheduler(self):
learner_params = [
p for p in self.learner.parameters() if p.requires_grad
]
if isinstance(self.learner, BERTSequenceModel):
self.optimizer = AdamW(learner_params,
lr=self.lr,
weight_decay=self.weight_decay)
self.lr_scheduler = get_constant_schedule_with_warmup(
self.optimizer, num_warmup_steps=100)
else:
self.optimizer = optim.Adam(learner_params,
lr=self.lr,
weight_decay=self.weight_decay)
self.lr_scheduler = optim.lr_scheduler.StepLR(self.optimizer,
step_size=500,
gamma=0.5)
def vectorize(self, batch_x, batch_len, batch_y):
with torch.no_grad():
if self.vectors == 'elmo':
char_ids = batch_to_ids(batch_x)
char_ids = char_ids.to(self.device)
batch_x = self.elmo(char_ids)['elmo_representations'][0]
elif self.vectors == 'glove':
max_batch_len = max(batch_len)
vec_batch_x = torch.ones((len(batch_x), max_batch_len, 300))
for i, sent in enumerate(batch_x):
sent_emb = self.glove.get_vecs_by_tokens(
sent, lower_case_backup=True)
vec_batch_x[i, :len(sent_emb)] = sent_emb
batch_x = vec_batch_x.to(self.device)
elif self.vectors == 'bert':
max_batch_len = max(batch_len) + 2
input_ids = torch.zeros((len(batch_x), max_batch_len)).long()
for i, sent in enumerate(batch_x):
sent_token_ids = self.bert_tokenizer.encode(
sent, add_special_tokens=True)
input_ids[i, :len(sent_token_ids)] = torch.tensor(
sent_token_ids)
batch_x = input_ids.to(self.device)
batch_len = torch.tensor(batch_len).to(self.device)
batch_y = torch.tensor(batch_y).to(self.device)
return batch_x, batch_len, batch_y
def forward(self, episodes, updates=1, testing=False):
query_losses, query_accuracies, query_precisions, query_recalls, query_f1s = [], [], [], [], []
n_episodes = len(episodes)
for episode_id, episode in enumerate(episodes):
batch_x, batch_len, batch_y = next(iter(episode.support_loader))
batch_x, batch_len, batch_y = self.vectorize(
batch_x, batch_len, batch_y)
self.train()
support_repr, support_label = [], []
batch_x_repr = self.learner(batch_x, batch_len)
support_repr.append(batch_x_repr)
support_label.append(batch_y)
prototypes = self._build_prototypes(support_repr, support_label,
episode.n_classes)
# Run on query
query_loss = 0.0
all_predictions, all_labels = [], []
for module in self.learner.modules():
if isinstance(module, nn.Dropout):
module.eval()
for n_batch, (batch_x, batch_len,
batch_y) in enumerate(episode.query_loader):
batch_x, batch_len, batch_y = self.vectorize(
batch_x, batch_len, batch_y)
batch_x_repr = self.learner(batch_x, batch_len)
output = self._normalized_distances(prototypes, batch_x_repr)
output = output.view(output.size()[0] * output.size()[1], -1)
batch_y = batch_y.view(-1)
loss = self.loss_fn[episode.base_task](output, batch_y)
query_loss += loss.item()
if not testing:
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.lr_scheduler.step()
relevant_indices = torch.nonzero(
batch_y != -1).view(-1).detach()
all_predictions.extend(
make_prediction(output[relevant_indices]).cpu())
all_labels.extend(batch_y[relevant_indices].cpu())
query_loss /= n_batch + 1
# Calculate metrics
accuracy, precision, recall, f1_score = utils.calculate_metrics(
all_predictions, all_labels, binary=False)
logger.info(
'Episode {}/{}, task {} [query set]: Loss = {:.5f}, accuracy = {:.5f}, precision = {:.5f}, '
'recall = {:.5f}, F1 score = {:.5f}'.format(
episode_id + 1, n_episodes, episode.task_id, query_loss,
accuracy, precision, recall, f1_score))
query_losses.append(query_loss)
query_accuracies.append(accuracy)
query_precisions.append(precision)
query_recalls.append(recall)
query_f1s.append(f1_score)
return query_losses, query_accuracies, query_precisions, query_recalls, query_f1s
def _build_prototypes(self, data_repr, data_label, num_outputs):
n_dim = data_repr[0].shape[2]
data_repr = torch.cat(tuple([x.view(-1, n_dim) for x in data_repr]),
dim=0)
data_label = torch.cat(tuple([y.view(-1) for y in data_label]), dim=0)
prototypes = torch.zeros((num_outputs, n_dim), device=self.device)
for c in range(num_outputs):
idx = torch.nonzero(data_label == c).view(-1)
if idx.nelement() != 0:
prototypes[c] = torch.mean(data_repr[idx], dim=0)
return prototypes
def _normalized_distances(self, prototypes, q):
d = torch.stack(tuple(
[q.sub(p).pow(2).sum(dim=-1) for p in prototypes]),
dim=-1)
return d.neg()
class NearestNeighborClassifier():
def __init__(self, config):
self.vectors = config.get('vectors', 'elmo')
self.device = torch.device(config.get('device', 'cpu'))
if self.vectors == 'elmo':
self.elmo = Elmo(
options_file=
"https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway_5.5B/elmo_2x4096_512_2048cnn_2xhighway_5.5B_options.json",
weight_file=
"https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway_5.5B/elmo_2x4096_512_2048cnn_2xhighway_5.5B_weights.hdf5",
num_output_representations=1,
dropout=0,
requires_grad=False)
self.elmo.to(self.device)
elif self.vectors == 'glove':
self.glove = torchtext.vocab.GloVe(name='840B', dim=300)
elif self.vectors == 'bert':
self.bert_tokenizer = BertTokenizer.from_pretrained(
'bert-base-cased')
self.bert = BertModel.from_pretrained('bert-base-cased')
self.bert.to(self.device)
logger.info('Nearest neighbor classifier instantiated')
def vectorize(self, batch_x, batch_len, batch_y):
with torch.no_grad():
if self.vectors == 'elmo':
char_ids = batch_to_ids(batch_x)
char_ids = char_ids.to(self.device)
batch_x = self.elmo(char_ids)['elmo_representations'][0]
elif self.vectors == 'glove':
max_batch_len = max(batch_len)
vec_batch_x = torch.ones((len(batch_x), max_batch_len, 300))
for i, sent in enumerate(batch_x):
sent_emb = self.glove.get_vecs_by_tokens(
sent, lower_case_backup=True)
vec_batch_x[i, :len(sent_emb)] = sent_emb
batch_x = vec_batch_x.to(self.device)
elif self.vectors == 'bert':
max_batch_len = max(batch_len) + 2
input_ids = torch.zeros((len(batch_x), max_batch_len)).long()
for i, sent in enumerate(batch_x):
sent_token_ids = self.bert_tokenizer.encode(
sent, add_special_tokens=True)
input_ids[i, :len(sent_token_ids)] = torch.tensor(
sent_token_ids)
batch_x = input_ids.to(self.device)
attention_mask = (batch_x.detach() != 0).float()
batch_x, _ = self.bert(batch_x, attention_mask=attention_mask)
batch_x = batch_x[:, 1:-1, :]
batch_len = torch.tensor(batch_len).to(self.device)
batch_y = torch.tensor(batch_y).to(self.device)
return batch_x, batch_len, batch_y
def training(self, train_episodes, val_episodes):
return 0
def testing(self, test_episodes):
episode_accuracies, episode_precisions, episode_recalls, episode_f1s = [], [], [], []
for episode_id, episode in enumerate(test_episodes):
batch_x, batch_len, batch_y = next(iter(episode.support_loader))
support_repr, _, support_labels = self.vectorize(
batch_x, batch_len, batch_y)
support_repr = support_repr.reshape(
support_repr.shape[0] * support_repr.shape[1], -1)
support_labels = support_labels.view(-1)
support_repr = support_repr[support_labels != -1].cpu().numpy()
support_labels = support_labels[support_labels != -1].cpu().numpy()
batch_x, batch_len, batch_y = next(iter(episode.query_loader))
query_repr, _, true_labels = self.vectorize(
batch_x, batch_len, batch_y)
query_repr = query_repr.reshape(
query_repr.shape[0] * query_repr.shape[1], -1)
true_labels = true_labels.view(-1)
query_repr = query_repr[true_labels != -1].cpu().numpy()
true_labels = true_labels[true_labels != -1].cpu().numpy()
dist = cdist(query_repr, support_repr, metric='cosine')
nearest_neighbor = np.argmin(dist, axis=1)
predictions = support_labels[nearest_neighbor]
accuracy = metrics.accuracy_score(true_labels, predictions)
precision = metrics.precision_score(true_labels,
predictions,
average='macro')
recall = metrics.recall_score(true_labels,
predictions,
average='macro')
f1_score = metrics.f1_score(true_labels,
predictions,
average='macro')
logger.info(
'Episode {}/{}, task {} [query set]: Accuracy = {:.5f}, precision = {:.5f}, '
'recall = {:.5f}, F1 score = {:.5f}'.format(
episode_id + 1, len(test_episodes), episode.task_id,
accuracy, precision, recall, f1_score))
episode_accuracies.append(accuracy)
episode_precisions.append(precision)
episode_recalls.append(recall)
episode_f1s.append(f1_score)
logger.info(
'Avg meta-testing metrics: Accuracy = {:.5f}, precision = {:.5f}, recall = {:.5f}, '
'F1 score = {:.5f}'.format(np.mean(episode_accuracies),
np.mean(episode_precisions),
np.mean(episode_recalls),
np.mean(episode_f1s)))
return np.mean(episode_f1s)
