def dsinfo(request):
from gluonts import time_feature
from gluonts.dataset.artificial import constant_dataset, default_synthetic
if request.param == "constant":
ds_info, train_ds, test_ds = constant_dataset()
return AttrDict(
name="constant",
cardinality=int(ds_info.metadata.feat_static_cat[0].cardinality),
freq=ds_info.metadata.freq,
num_parallel_samples=2,
prediction_length=ds_info.prediction_length,
# FIXME: Should time features should not be needed for GP
time_features=[time_feature.DayOfWeek(),
time_feature.HourOfDay()],
train_ds=train_ds,
test_ds=test_ds,
)
elif request.param == "synthetic":
ds_info, train_ds, test_ds = default_synthetic()
return AttrDict(
name="synthetic",
batch_size=32,
cardinality=int(ds_info.metadata.feat_static_cat[0].cardinality),
context_length=2,
freq=ds_info.metadata.freq,
prediction_length=ds_info.prediction_length,
num_parallel_samples=2,
train_ds=train_ds,
test_ds=test_ds,
time_features=None,
)
def test_simple_model():
dsinfo, training_data, test_data = default_synthetic()
freq = dsinfo.metadata.freq
prediction_length = dsinfo.prediction_length
context_length = 2 * prediction_length
hidden_dimensions = [10, 10]
net = LightningFeedForwardNetwork(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
hidden_dimensions=hidden_dimensions,
distr_output=NormalOutput(),
batch_norm=True,
scaling=mean_abs_scaling,
)
transformation = Chain([
AddObservedValuesIndicator(
target_field=FieldName.TARGET,
output_field=FieldName.OBSERVED_VALUES,
),
InstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
train_sampler=ExpectedNumInstanceSampler(num_instances=1),
past_length=context_length,
future_length=prediction_length,
time_series_fields=[FieldName.OBSERVED_VALUES],
),
])
data_loader = TrainDataLoader(
training_data,
batch_size=8,
stack_fn=batchify,
transform=transformation,
num_batches_per_epoch=5,
)
trainer = pl.Trainer(max_epochs=3, callbacks=[], weights_summary=None)
trainer.fit(net, train_dataloader=data_loader)
predictor = net.get_predictor(transformation)
forecast_it, ts_it = make_evaluation_predictions(
dataset=test_data,
predictor=predictor,
num_samples=100,
)
evaluator = Evaluator(quantiles=[0.5, 0.9], num_workers=None)
agg_metrics, _ = evaluator(ts_it, forecast_it)
def create_multivariate_datasets(data_dir: str) -> None:
info, train_ds, test_ds = default_synthetic()
save_datasets(TrainDatasets(metadata=info.metadata, train=train_ds, test=test_ds), data_dir)
return
# permissions and limitations under the License.
# Third-party imports
import pytest
# First-party imports
from gluonts.dataset.artificial import default_synthetic
from gluonts.evaluation.backtest import backtest_metrics
from gluonts.model.deepar import DeepAREstimator
from gluonts.model.deep_factor import DeepFactorEstimator
from gluonts.model.simple_feedforward import SimpleFeedForwardEstimator
from gluonts.model.gp_forecaster import GaussianProcessEstimator
from gluonts.model.wavenet import WaveNetEstimator
from gluonts.model.transformer import TransformerEstimator
dataset_info, train_ds, test_ds = default_synthetic()
freq = dataset_info.metadata.freq
prediction_length = dataset_info.prediction_length
cardinality = int(dataset_info.metadata.feat_static_cat[0].cardinality)
batch_size = 32
context_length = 2
epochs = 1
def simple_feedforward_estimator(hybridize: bool = True, batches_per_epoch=1):
return (
SimpleFeedForwardEstimator,
dict(
ctx="cpu",
epochs=epochs,
learning_rate=1e-2,