def build_data_set(self, root_dir: str):
entries = self.parse_folder(root_dir)
data_handler = DataHandler()
icd10_ontology = data_handler.read_icd10_ontology()
group_codes = set(data_handler.read_reduced_icd10_ontology().keys())
de_entries = dict()
en_entries = dict()
for entry in entries:
if "de_" + entry[0] in de_entries:
continue
icd10_codes = entry[3]
valid_group_codes = set()
main_chapter = ""
for code in icd10_codes:
if code in icd10_chapter_mappings:
code = icd10_chapter_mappings[code]
if not code in icd10_ontology and "." in code:
code = code[:code.index(".")]
if not code in icd10_ontology:
self.logger.error(
f"Can't find code {code} in ICD10 ontology")
continue
path_components = icd10_ontology[code].split("#")
if main_chapter == "":
main_chapter = path_components[0]
for path_comp in path_components:
if path_comp in group_codes:
valid_group_codes.add(path_comp)
valid_group_codes = "|".join(valid_group_codes)
de_entries["de_" + entry[0]] = {
"text": entry[1],
"language": "de",
"all_labels": valid_group_codes,
"main_chapter": main_chapter
}
en_entries["en_" + entry[0]] = {
"text": entry[2],
"language": "en",
"all_labels": valid_group_codes,
"main_chapter": main_chapter
}
de_df = DataFrame.from_dict(de_entries, orient="index")
en_df = DataFrame.from_dict(en_entries, orient="index")
de_train, de_dev = train_test_split(de_df,
train_size=0.8,
stratify=de_df["main_chapter"])
de_train["data_set"] = "train"
de_dev["data_set"] = "dev"
de_df = de_train.append(de_dev)
en_train, en_dev = train_test_split(en_df,
train_size=0.8,
stratify=en_df["main_chapter"])
en_train["data_set"] = "train"
en_dev["data_set"] = "dev"
en_df = en_train.append(en_dev)
full_df = de_df.append(en_df)
full_df = full_df.drop_duplicates()
full_df = full_df[full_df["text"].notna()]
#de_df.to_csv("drks_de.tsv", sep="\t", columns=["data_set", "main_chapter", "all_labels", "text"], index_label="id")
#en_df.to_csv("drks_en.tsv", sep="\t", columns=["data_set", "main_chapter", "all_labels", "text"], index_label="id")
output_dir = "data/drks/prepared"
os.makedirs(output_dir, exist_ok=True)
output_file = os.path.join(output_dir, "drks_full.tsv")
full_df.to_csv(output_file,
sep="\t",
columns=[
"language", "data_set", "main_chapter",
"all_labels", "text"
],
index_label="id")
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_set",
choices=DataHandler.ALL_DATA_SET_IDS,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help=
"Bert pre-trained model selected in the list: bert-base-multilingual-uncased, "
"bert-base-multilingual-cased")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
"--additional_data_set",
choices=DataHandler.ALL_DATA_SET_IDS,
required=False,
help="Additional data set to extend the basic training data set. "
"Only training data will be used! No additional evaluation data!")
## Other parameters
parser.add_argument(
"--cache_dir",
default="_cache",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=300,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_test",
action='store_true',
help="Whether to run eval on the test set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
args = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
data_handler = DataHandler()
train_data, dev_data = data_handler.get_data_set_by_id(args.data_set)
if args.additional_data_set is not None:
logger.info(
f"Extending training data with instances from {args.additional_data_set}"
)
add_train_data, add_dev_data = data_handler.get_data_set_by_id(
args.additional_data_set)
train_data = train_data.append(add_train_data)
train_data = train_data.append(add_dev_data)
logger.info(
f"Data set contains {len(train_data)} training and {len(dev_data)} development instances"
)
test_data = None
if args.do_test:
test_data = data_handler.get_test_data()
processor = DataProcessor(train_data, dev_data, test_data)
label_list = processor.get_labels()
logger.info(f"Labels: {str(label_list)}")
label_encoder = LabelEncoder()
label_encoder.fit(label_list)
num_labels = len(label_encoder.classes_)
logger.info(f"Num labels: {num_labels}")
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = processor.get_train_instances()
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(
args.local_rank))
model = BertForMultiLabelSequenceClassification.from_pretrained(
args.bert_model, cache_dir=cache_dir, num_labels=num_labels)
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError \
("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError \
("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
nb_tr_steps = 0
global_batch_no = 0
tr_loss = 0
if args.do_train:
train_features = convert_examples_to_features(train_examples,
label_encoder,
args.max_seq_length,
tokenizer)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_ids for f in train_features],
dtype=torch.float)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
max_f1 = 0.0
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
loss = model(input_ids, segment_ids, input_mask, label_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
loss_value = loss.item()
tr_loss += loss_value
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
global_batch_no += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used that handles this automatically
lr_this_step = args.learning_rate * warmup_linear(
global_step / num_train_optimization_steps,
args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
if (epoch + 1) % 2 == 0:
eval_examples = processor.get_dev_instances()
eval_features = convert_examples_to_features(
eval_examples, label_encoder, args.max_seq_length,
tokenizer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor(
[f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor(
[f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor(
[f.segment_ids for f in eval_features], dtype=torch.long)
all_label_ids = torch.tensor(
[f.label_ids for f in eval_features], dtype=torch.float)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
y_dev = None
y_dev_pred = None
y_dev_sigmoid = None
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids,
input_mask, label_ids)
logits = model(input_ids, segment_ids, input_mask)
# logits = logits.detach().cpu().numpy()
# prediction = np.argmax(logits, axis=1)
logits_cpu = logits.detach().cpu()
logits_sigmoid = logits_cpu.sigmoid()
if y_dev_pred is None:
y_dev_sigmoid = logits_sigmoid
else:
y_dev_sigmoid = np.concatenate(
(y_dev_sigmoid, logits_sigmoid), axis=0)
pred_logits = logits.detach().cpu().numpy()
if y_dev_pred is None:
y_dev_pred = pred_logits
else:
y_dev_pred = np.concatenate((y_dev_pred, pred_logits),
axis=0)
if y_dev is None:
y_dev = label_ids.detach().cpu().numpy()
else:
y_dev = np.concatenate(
(y_dev, label_ids.detach().cpu().numpy()), axis=0)
tmp_eval_accuracy = accuracy_thresh(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
loss = tr_loss / nb_tr_steps if args.do_train else None
i = 0
gold_labels = dict()
pred_labels = dict()
prediction_output = dict()
for example, true_logits, pred_logits in zip(
eval_examples, y_dev, y_dev_sigmoid):
true_indexes = np.argwhere(true_logits > 0.0)
labels = [
label_encoder.inverse_transform(y)[0]
for y in true_indexes
]
pred_indexes = np.argwhere(pred_logits > 0.5)
pred = [
label_encoder.inverse_transform(y)[0]
for y in pred_indexes
]
gold_labels[str(example.guid)] = labels
pred_labels[str(example.guid)] = pred
class_logits = {
label_encoder.inverse_transform([j])[0]:
float(pred_logits[j])
for j in range(len(label_encoder.classes_))
}
prediction_output[example.guid] = class_logits
i += 1
pred_output_file = os.path.join(
args.output_dir, f"prediction_output_{epoch+1}.json")
json.dump(prediction_output,
open(pred_output_file, 'w'),
sort_keys=True,
indent=2)
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': loss,
}
eval_util = EvaluationUtil()
pred_file = os.path.join(args.output_dir,
f"dev_pred_{epoch+1}.txt")
eval_util.save_predictions(pred_labels, pred_file)
clef19_result = eval_util.evaluate(pred_labels, gold_labels)
f1_score = clef19_result["eval_fscore"]
if f1_score > max_f1:
# Save a trained model and the associated configuration
model_to_save = model.module if hasattr(
model,
'module') else model # Only save the model it-self
output_model_file = os.path.join(args.output_dir,
WEIGHTS_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
output_config_file = os.path.join(args.output_dir,
CONFIG_NAME)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
max_f1 = f1_score
icd10_ontology = data_handler.read_icd10_ontology()
pred_labels_extended = eval_util.extend_paths(
pred_labels, icd10_ontology)
pred_extended_file = os.path.join(
args.output_dir, f"dev_pred_extended_{epoch+1}.txt")
eval_util.save_predictions(pred_labels_extended,
pred_extended_file)
extended_clef19_result = eval_util.evaluate(
pred_labels_extended, gold_labels)
output_eval_file = os.path.join(args.output_dir,
f"eval_results_{epoch+1}.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
clef10_result_str = eval_util.format_result(clef19_result)
logger.info(f"CLEF19 evaluation: {clef10_result_str}")
writer.write("\nResults prediction:\n")
writer.write(clef10_result_str)
extended_clef10_result_str = eval_util.format_result(
extended_clef19_result)
logger.info(
f"CLEF19 evaluation (extended): {extended_clef10_result_str}"
)
writer.write("\n\nResults extended prediction:\n")
writer.write(extended_clef10_result_str)
#if not args.do_train:
# (Re-) Load best model
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
config = BertConfig(output_config_file)
model = BertForMultiLabelSequenceClassification(config,
num_labels=num_labels)
model.load_state_dict(torch.load(output_model_file))
model.to(device)
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_instances()
eval_features = convert_examples_to_features(eval_examples,
label_encoder,
args.max_seq_length,
tokenizer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_ids for f in eval_features],
dtype=torch.float)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
y_dev = None
y_dev_pred = None
y_dev_sigmoid = None
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
#logits = logits.detach().cpu().numpy()
#prediction = np.argmax(logits, axis=1)
logits_cpu = logits.detach().cpu()
logits_sigmoid = logits_cpu.sigmoid()
if y_dev_sigmoid is None:
y_dev_sigmoid = logits_sigmoid
else:
y_dev_sigmoid = np.concatenate((y_dev_sigmoid, logits_sigmoid),
axis=0)
pred_logits = logits.detach().cpu().numpy()
if y_dev_pred is None:
y_dev_pred = pred_logits
else:
y_dev_pred = np.concatenate((y_dev_pred, pred_logits), axis=0)
if y_dev is None:
y_dev = label_ids.detach().cpu().numpy()
else:
y_dev = np.concatenate(
(y_dev, label_ids.detach().cpu().numpy()), axis=0)
tmp_eval_accuracy = accuracy_thresh(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
loss = tr_loss / nb_tr_steps if args.do_train else None
i = 0
gold_labels = dict()
pred_labels = dict()
prediction_output = dict()
for example, true_logits, pred_logits in zip(eval_examples, y_dev,
y_dev_sigmoid):
true_indexes = np.argwhere(true_logits > 0.0)
labels = [
label_encoder.inverse_transform(y)[0] for y in true_indexes
]
pred_indexes = np.argwhere(pred_logits > 0.5)
pred = [
label_encoder.inverse_transform(y)[0] for y in pred_indexes
]
if i < 2:
logger.info(f"Example: {example.guid}")
logger.info(f"Example labels: {example.labels}")
logger.info(f"True labels: {labels}")
logger.info(f"Pred labels: {pred}")
gold_labels[str(example.guid)] = labels
pred_labels[str(example.guid)] = pred
class_logits = {
label_encoder.inverse_transform([j])[0]: float(pred_logits[j])
for j in range(len(label_encoder.classes_))
}
prediction_output[example.guid] = class_logits
i += 1
pred_output_file = os.path.join(args.output_dir,
"prediction_output.json")
json.dump(prediction_output,
open(pred_output_file, 'w'),
sort_keys=True,
indent=2)
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': loss,
}
eval_util = EvaluationUtil()
pred_file = os.path.join(args.output_dir, "dev_pred.txt")
eval_util.save_predictions(pred_labels, pred_file)
clef19_result = eval_util.evaluate(pred_labels, gold_labels)
icd10_ontology = data_handler.read_icd10_ontology()
pred_labels_extended = eval_util.extend_paths(pred_labels,
icd10_ontology)
pred_extended_file = os.path.join(args.output_dir,
"dev_pred_extended.txt")
eval_util.save_predictions(pred_labels_extended, pred_extended_file)
extended_clef19_result = eval_util.evaluate(pred_labels_extended,
gold_labels)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
clef10_result_str = eval_util.format_result(clef19_result)
logger.info(f"CLEF19 evaluation: {clef10_result_str}")
writer.write("\nResults prediction:\n")
writer.write(clef10_result_str)
extended_clef10_result_str = eval_util.format_result(
extended_clef19_result)
logger.info(
f"CLEF19 evaluation (extended): {extended_clef10_result_str}")
writer.write("\n\nResults extended prediction:\n")
writer.write(extended_clef10_result_str)
if args.do_test:
test_examples = processor.get_test_instances()
test_features = convert_examples_to_features(test_examples,
label_encoder,
args.max_seq_length,
tokenizer)
logger.info("***** Running test *****")
logger.info(" Num examples = %d", len(test_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in test_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in test_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in test_features],
dtype=torch.long)
test_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids)
# Run prediction for full data
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data,
sampler=test_sampler,
batch_size=args.eval_batch_size)
model.eval()
y_dev_sigmoid = None
for input_ids, input_mask, segment_ids in tqdm(test_dataloader,
desc="Testing"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
logits = model(input_ids, segment_ids, input_mask)
logits_sigmoid = logits.detach().cpu().sigmoid()
if y_dev_sigmoid is None:
y_dev_sigmoid = logits_sigmoid
else:
y_dev_sigmoid = np.concatenate((y_dev_sigmoid, logits_sigmoid),
axis=0)
i = 0
test_labels = dict()
test_output = dict()
for example, pred_logits in zip(test_examples, y_dev_sigmoid):
pred_indexes = np.argwhere(pred_logits > 0.5)
pred = [
label_encoder.inverse_transform(y)[0] for y in pred_indexes
]
if i < 2:
logger.info(f"Example: {example.guid}")
logger.info(f"Pred labels: {pred}")
test_labels[str(example.guid)] = pred
class_logits = {
label_encoder.inverse_transform([j])[0]: float(pred_logits[j])
for j in range(len(label_encoder.classes_))
}
test_output[example.guid] = class_logits
i += 1
pred_output_file = os.path.join(args.output_dir, "test_output.json")
json.dump(test_output,
open(pred_output_file, 'w'),
sort_keys=True,
indent=2)
eval_util = EvaluationUtil()
test_file = os.path.join(args.output_dir, "test_pred.txt")
eval_util.save_predictions(test_labels, test_file)
icd10_ontology = data_handler.read_icd10_ontology()
test_labels_extended = eval_util.extend_paths(test_labels,
icd10_ontology)
test_extended_file = os.path.join(args.output_dir,
"test_pred_extended.txt")
eval_util.save_predictions(test_labels_extended, test_extended_file)
