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[DOC] Add RecurrentNetwork usage example #2397

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[DOC] Add RecurrentNetwork usage example #2397

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[DOC]: Add RecurrentNetwork usage example
obaidsafi51 Aug 26, 2026
26279f8
Code Quality check fixed
obaidsafi51 Aug 27, 2026
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74 changes: 57 additions & 17 deletions pytorch_forecasting/models/rnn/_rnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -98,6 +98,49 @@ def __init__(
loss (MultiHorizonMetric, optional): loss: loss function taking prediction and targets.
logging_metrics (nn.ModuleList, optional): Metrics to log during training.
Defaults to nn.ModuleList([SMAPE(), MAE(), RMSE(), MAPE(), MASE()]).
Example:

@phoeenniixx phoeenniixx Aug 31, 2026

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THis should be like this:

Example
-------------

>>> import lightning.pytorch as pl
>>> from pytorch_forcasting import RecurrentNetwork,TimeSeriesDataSet
>>> from pytorch_forcasting.data.examples import generate_ar_data
>>> data = generate_ar_data(n_series=10, timesteps = 100, seed = 42)
>>> data["time_idx"] = data["time_idx"].astype(int)
>>> max_encoder_length = 24
>>> max_prediction_length = 6
training = TimeSeriesDataSet(
... data,
... time_idx = "time_idx",
... target = "value",
... group_ids = ["series"],
... max_encoder_length = max_encoder_length,
... max_prediction_length = max_prediction_length,
... time_vary_unknown_reals= ["value"],
... target_lags = {"value": [1, 2, 3, 6, 12, 24]},
... add_relative_time_idx = True,
... add_target_scales = True,
... add_encoder_length = True,
... )
>>> validation = TimeSeriesDataSet.from_dataset(
... training, data, predict=True, stop_randomization=True
... )
>>> train_dataloader = training.to_dataloader(train=True, batch_size=32, num_workers=0)
>>> val_dataloader = validation.to_dataloader(train=False, batch_size=32, num_workers=0)
>>> rnn = RecurrentNetwork.from_dataset(
... training,
... cell_type = "LSTM",
... hidden_size = 32,
... rnn_layers = 2,
... dropout = 0.1,
... learning_rate = 1e-3,
... log_interval = 10,
...)
>>> trainer = pl.Trainer(
... max_epochs = 1,
... accelerator = "cpu",
... enable_checkpointing = False,
... logger = False
... )
>>> trainer.fit(rnn, train_dataloaders = train_dataloader, val_dataloaders = val_dataloader)
>>> predictions = rnn.predict(val_dataloader, trainer = trainer)
""" # noqa : E501
if static_categoricals is None:
static_categoricals = []
Expand Down Expand Up @@ -148,9 +191,9 @@ def __init__(
" be the same apart from target variable"
)
for targeti in to_list(target):
assert (
targeti in time_varying_reals_encoder
), f"target {targeti} has to be real" # todo: remove this restriction
assert targeti in time_varying_reals_encoder, (
f"target {targeti} has to be real"
) # todo: remove this restriction
assert (isinstance(target, str) and isinstance(loss, MultiHorizonMetric)) or (
isinstance(target, tuple | list)
and isinstance(loss, MultiLoss)
Expand All @@ -174,9 +217,9 @@ def __init__(
self.output_projector = nn.Linear(
self.hparams.hidden_size, self.hparams.output_size
)
assert not isinstance(
self.loss, QuantileLoss
), "QuantileLoss does not work with recurrent network"
assert not isinstance(self.loss, QuantileLoss), (
"QuantileLoss does not work with recurrent network"
)
else: # multi target
self.output_projector = nn.ModuleList(
[
Expand All @@ -185,9 +228,9 @@ def __init__(
]
)
for l in self.loss:
assert not isinstance(
l, QuantileLoss
), "QuantileLoss does not work with recurrent network"
assert not isinstance(l, QuantileLoss), (
"QuantileLoss does not work with recurrent network"
)

@classmethod
def from_dataset(
Expand All @@ -213,14 +256,11 @@ def from_dataset(
dataset=dataset, kwargs=kwargs, default_loss=MAE()
)
)
assert (
not isinstance(dataset.target_normalizer, NaNLabelEncoder)
and (
not isinstance(dataset.target_normalizer, MultiNormalizer)
or all(
not isinstance(normalizer, NaNLabelEncoder)
for normalizer in dataset.target_normalizer
)
assert not isinstance(dataset.target_normalizer, NaNLabelEncoder) and (
not isinstance(dataset.target_normalizer, MultiNormalizer)
or all(
not isinstance(normalizer, NaNLabelEncoder)
for normalizer in dataset.target_normalizer
)
), (
"target(s) should be continuous - categorical targets are not supported"
Expand Down
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