def test_distribution():
"""
Makes sure additional tensors can be accessed and have expected shapes
"""
prediction_length = ds_info.prediction_length
estimator = DeepAREstimator(
freq=freq,
prediction_length=prediction_length,
trainer=Trainer(epochs=2, num_batches_per_epoch=1),
distr_output=StudentTOutput(),
)
train_output = estimator.train_model(train_ds, test_ds)
# todo adapt loader to anomaly detection use-case
batch_size = 2
num_samples = 3
training_data_loader = TrainDataLoader(
dataset=train_ds,
transform=train_output.transformation,
batch_size=batch_size,
num_batches_per_epoch=estimator.trainer.num_batches_per_epoch,
ctx=mx.cpu(),
)
seq_len = 2 * ds_info.prediction_length
for data_entry in islice(training_data_loader, 1):
input_names = get_hybrid_forward_input_names(train_output.trained_net)
distr = train_output.trained_net.distribution(
*[data_entry[k] for k in input_names]
)
assert distr.sample(num_samples).shape == (
num_samples,
batch_size,
seq_len,
)
def test_shape():
"""
Makes sure additional tensors can be accessed and have expected shapes
"""
prediction_length = ds_info.prediction_length
estimator = DeepAREstimator(
freq=freq,
prediction_length=prediction_length,
trainer=Trainer(epochs=1, num_batches_per_epoch=1),
distr_output=StudentTOutput(),
)
training_transformation, trained_net = estimator.train_model(train_ds)
# todo adapt loader to anomaly detection use-case
batch_size = 2
training_data_loader = TrainDataLoader(
dataset=train_ds,
transform=training_transformation,
batch_size=batch_size,
num_batches_per_epoch=estimator.trainer.num_batches_per_epoch,
ctx=mx.cpu(),
)
seq_len = 2 * ds_info.prediction_length
for data_entry in islice(training_data_loader, 1):
input_names = get_hybrid_forward_input_names(trained_net)
loss, likelihoods, *distr_args = trained_net(
*[data_entry[k] for k in input_names])
distr = StudentT(*distr_args)
assert likelihoods.shape == (batch_size, seq_len)
assert distr.mu.shape == (batch_size, seq_len)
assert distr.sigma.shape == (batch_size, seq_len)
assert distr.nu.shape == (batch_size, seq_len)
from gluonts.dataset.repository.datasets import get_dataset
if __name__ == '__main__':
dataset = get_dataset(dataset_name="electricity")
estimator = DeepAREstimator(
prediction_length=dataset.metadata.prediction_length,
freq=dataset.metadata.time_granularity,
trainer=Trainer(learning_rate=1e-3,
epochs=50,
num_batches_per_epoch=100),
)
# instead of calling `train` method, we call `train_model` that returns more things including the training model
train_output = estimator.train_model(dataset.train)
# we construct a data_entry that contains 500 random windows
batch_size = 500
num_samples = 100
training_data_loader = TrainDataLoader(
dataset=dataset.train,
transform=train_output.transformation,
batch_size=batch_size,
num_batches_per_epoch=estimator.trainer.num_batches_per_epoch,
ctx=mx.cpu(),
)
for data_entry in islice(training_data_loader, 1):
pass
