def test(model, epoch, batch_size, data_path, fold, gpu, dicts, model_dir, testing, thresh = None, obs_limit = None):
"""
Testing loop. Returns metrics on desired fold (validation or test).
"""
filename = data_path.replace('train', fold)
print('\nfile for evaluation: %s' % filename)
y, yhat, yhat_raw, hids, losses = [], [], [], [], []
model.eval()
gen = datasets.data_generator(filename, dicts, batch_size)
for batch_idx, tup in enumerate(gen):
if obs_limit: # Early stopping for debugging
obs_seen = batch_idx * batch_size
if obs_seen > obs_limit:
print("Reached {} of test/val set".format(obs_limit))
break
data, target, hadm_ids = tup
data, target = Variable(torch.LongTensor(data), volatile=True), Variable(torch.FloatTensor(target))
if gpu:
data = data.cuda()
target = target.cuda()
model.zero_grad()
output, loss = model(data, target) # Forward pass
output = output.data.cpu().numpy()
losses.append(loss.data[0])
target_data = target.data.cpu().numpy()
#save predictions, target, hadm ids
yhat_raw.append(output)
output = np.round(output) # Rounds to 0 for <= 0.5, up to one for > 0.5
yhat.append(output)
y.append(target_data)
hids.extend(hadm_ids)
y = np.concatenate(y, axis=0)
yhat = np.concatenate(yhat, axis=0)
yhat_raw = np.concatenate(yhat_raw, axis=0)
print("\nMax Prediction This Epoch:")
print(max(yhat_raw))
#write the predictions
persistence.write_preds(yhat, model_dir, hids, fold, yhat_raw)
metrics = evaluation.all_metrics(yhat_raw, y, thresh)
evaluation.print_metrics(metrics)
metrics['loss_%s' % fold] = np.float64(np.mean(losses)) #float64 for json serialization
return metrics
def test(model,
epoch,
batch_size,
data_path,
struc_feats,
fold,
gpu,
dicts,
model_dir,
testing,
test_frac=1,
thresh=None):
"""
Testing loop.
Returns metrics
"""
filename = data_path.replace('train', fold)
print('\nfile for evaluation: %s' % filename)
y, yhat, yhat_raw, hids, losses = [], [], [], [], []
print(struc_feats.shape[0])
model.eval()
gen = datasets.data_generator(filename, dicts, batch_size)
for batch_idx, tup in enumerate(gen):
data, target, hadm_ids = tup
batch_size_safe = min(
batch_size, struc_feats.shape[0] -
batch_idx * batch_size) # Avoiding going out of range
if batch_idx * batch_size > test_frac * struc_feats.shape[0]:
print("Reached {} of test/val set".format(test_frac))
break
struc_data = struc_feats[
batch_idx * batch_size:batch_idx * batch_size +
batch_size_safe].todense(
) # Only need in second index b/c batch_size_safe should be < batch_size only once
data, struc_data, target = Variable(torch.LongTensor(data)), Variable(
torch.FloatTensor(struc_data)), Variable(torch.FloatTensor(target))
if gpu:
data = data.cuda()
struc_data = struc_data.cuda()
target = target.cuda()
model.zero_grad()
output, loss = model(data, struc_data, target) # Forward pass
output = output.data.cpu().numpy()
losses.append(loss.data[0])
target_data = target.data.cpu().numpy()
#save predictions, target, hadm ids
yhat_raw.append(output)
output = np.round(
output) # Rounds to 0 for <= 0.5, up to one for > 0.5
yhat.append(output)
y.append(target_data)
hids.extend(hadm_ids)
y = np.concatenate(y, axis=0)
yhat = np.concatenate(yhat, axis=0)
yhat_raw = np.concatenate(yhat_raw, axis=0)
print("\nMax Prediction:")
print(max(yhat_raw))
#write the predictions
persistence.write_preds(yhat, model_dir, hids, fold, yhat_raw)
metrics = evaluation.all_metrics(yhat_raw, y, thresh)
evaluation.print_metrics(metrics)
metrics['loss_%s' % fold] = np.float64(
np.mean(losses)) #float64 for json serialization
return metrics
def test(model, epoch, batch_size, data_path, fold, gpu, dicts, model_dir,
testing):
"""
Testing loop.
Returns metrics
"""
filename = data_path.replace('train', fold)
print('\nfile for evaluation: %s' % filename)
y, yhat, yhat_raw, hids, losses = [], [], [], [], []
model.eval()
gen = datasets.data_generator(filename, dicts, batch_size)
for batch_idx, tup in enumerate(gen):
data, target, hadm_ids = tup
data, target = Variable(torch.LongTensor(data),
volatile=True), Variable(
torch.FloatTensor(target))
if gpu:
data = data.cuda()
target = target.cuda()
model.zero_grad()
output, loss = model(data, target) # Forward pass
output = output.data.cpu().numpy()
losses.append(loss.data[0])
target_data = target.data.cpu().numpy()
#save predictions, target, hadm ids
yhat_raw.append(output)
output = np.round(
output) # Rounds to 0 for <= 0.5, up to one for > 0.5
yhat.append(output)
y.append(target_data)
hids.extend(hadm_ids)
y = np.concatenate(y, axis=0)
yhat = np.concatenate(yhat, axis=0)
yhat_raw = np.concatenate(yhat_raw, axis=0)
print("\nMax Prediction:")
print(max(yhat_raw))
# print("y shape: " + str(y.shape))
# print("yhat shape: " + str(yhat.shape))
#write the predictions
persistence.write_preds(yhat, model_dir, hids, fold, yhat_raw)
metrics = evaluation.all_metrics(yhat_raw, y)
evaluation.print_metrics(metrics)
metrics['loss_%s' % fold] = np.float64(
np.mean(losses)) #float64 for json serialization
return metrics
