BaseModel#

class pytorch_forecasting.models.base_model.BaseModel(dataset_parameters: Dict[str, Any] | None = None, log_interval: int | float = -1, log_val_interval: float | int | None = None, learning_rate: float | List[float] = 0.001, log_gradient_flow: bool = False, loss: Metric = SMAPE(), logging_metrics: ModuleList = ModuleList(), reduce_on_plateau_patience: int = 1000, reduce_on_plateau_reduction: float = 2.0, reduce_on_plateau_min_lr: float = 1e-05, weight_decay: float = 0.0, optimizer_params: Dict[str, Any] | None = None, monotone_constaints: Dict[str, int] = {}, output_transformer: Callable | None = None, optimizer='Ranger')[source]#

Bases: InitialParameterRepresenterMixIn, LightningModule, TupleOutputMixIn

BaseModel from which new timeseries models should inherit from. The hparams of the created object will default to the parameters indicated in __init__().

The forward() method should return a named tuple with at least the entry prediction that contains the network’s output. See the function’s documentation for more details.

The idea of the base model is that common methods do not have to be re-implemented for every new architecture. The class is a [LightningModule](https://pytorch-lightning.readthedocs.io/en/latest/lightning_module.html) and follows its conventions. However, there are important additions:

You need to specify a loss attribute that stores the function to calculate the MultiHorizonLoss for backpropagation.

The from_dataset() method can be used to initialize a network using the specifications of a dataset. Often, parameters such as the number of features can be easily deduced from the dataset. Further, the method will also store how to rescale normalized predictions into the unnormalized prediction space. Override it to pass additional arguments to the __init__ method of your network that depend on your dataset.

The transform_output() method rescales the network output using the target normalizer from thedataset.

The step() method takes care of calculating the loss, logging additional metrics defined in the logging_metrics attribute and plots of sample predictions. You can override this method to add custom interpretations or pass extra arguments to the networks forward method.

The on_epoch_end() method can be used to calculate summaries of each epoch such as statistics on the encoder length, etc and needs to return the outputs.

The predict() method makes predictions using a dataloader or dataset. Override it if you need to pass additional arguments to forward by default.

To implement your own architecture, it is best to go throught the Using custom data and implementing custom models and to look at existing ones to understand what might be a good approach.

Example

class Network(BaseModel):

    def __init__(self, my_first_parameter: int=2, loss=SMAPE()):
        self.save_hyperparameters()
        super().__init__(loss=loss)

    def forward(self, x):
        normalized_prediction = self.module(x)
        prediction = self.transform_output(prediction=normalized_prediction, target_scale=x["target_scale"])
        return self.to_network_output(prediction=prediction)

BaseModel for timeseries forecasting from which to inherit from

Parameters:

log_interval (Union[int, float], optional) – Batches after which predictions are logged. If < 1.0, will log multiple entries per batch. Defaults to -1.
log_val_interval (Union[int, float], optional) – batches after which predictions for validation are logged. Defaults to None/log_interval.
learning_rate (float, optional) – Learning rate. Defaults to 1e-3.
log_gradient_flow (bool) – If to log gradient flow, this takes time and should be only done to diagnose training failures. Defaults to False.
loss (Metric, optional) – metric to optimize, can also be list of metrics. Defaults to SMAPE().
logging_metrics (nn.ModuleList[MultiHorizonMetric]) – list of metrics that are logged during training. Defaults to [].
reduce_on_plateau_patience (int) – patience after which learning rate is reduced by a factor of 10. Defaults to 1000
reduce_on_plateau_reduction (float) – reduction in learning rate when encountering plateau. Defaults to 2.0.
reduce_on_plateau_min_lr (float) – minimum learning rate for reduce on plateua learning rate scheduler. Defaults to 1e-5
weight_decay (float) – weight decay. Defaults to 0.0.
optimizer_params (Dict[str, Any]) – additional parameters for the optimizer. Defaults to {}.
monotone_constaints (Dict[str, int]) – dictionary of monotonicity constraints for continuous decoder variables mapping position (e.g. "0" for first position) to constraint (-1 for negative and +1 for positive, larger numbers add more weight to the constraint vs. the loss but are usually not necessary). This constraint significantly slows down training. Defaults to {}.
output_transformer (Callable) – transformer that takes network output and transforms it to prediction space. Defaults to None which is equivalent to lambda out: out["prediction"].
optimizer (str) – Optimizer, “ranger”, “sgd”, “adam”, “adamw” or class name of optimizer in torch.optim or pytorch_optimizer. Alternatively, a class or function can be passed which takes parameters as first argument and a lr argument (optionally also weight_decay). Defaults to “ranger”.

Methods

`configure_optimizers`()	Configure optimizers.
`create_log`(x, y, out, batch_idx[, ...])	Create the log used in the training and validation step.
`deduce_default_output_parameters`(dataset, kwargs)	Deduce default parameters for output for from_dataset() method.
`forward`(x)	Network forward pass.
`from_dataset`(dataset, **kwargs)	Create model from dataset, i.e. save dataset parameters in model.
`log`(args, *kwargs)	See `lightning.pytorch.core.lightning.LightningModule.log()`.
`log_gradient_flow`(named_parameters)	log distribution of gradients to identify exploding / vanishing gradients
`log_metrics`(x, y, out[, prediction_kwargs])	Log metrics every training/validation step.
`log_prediction`(x, out, batch_idx, **kwargs)	Log metrics every training/validation step.
`on_after_backward`()	Log gradient flow for debugging.
`on_epoch_end`(outputs)	Run at epoch end for training or validation.
`on_load_checkpoint`(checkpoint)	Called by Lightning to restore your model.
`on_save_checkpoint`(checkpoint)	Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save.
`on_test_epoch_end`()	Called in the test loop at the very end of the epoch.
`on_train_epoch_end`()	Called in the training loop at the very end of the epoch.
`on_validation_epoch_end`()	Called in the validation loop at the very end of the epoch.
`plot_prediction`(x, out[, idx, ...])	Plot prediction of prediction vs actuals
`predict`(data[, mode, return_index, ...])	Run inference / prediction.
`predict_dependency`(data, variable, values[, ...])	Predict partial dependency.
`predict_step`(batch, batch_idx)	Step function called during `predict()`.
`size`()	get number of parameters in model
`step`(x, y, batch_idx, **kwargs)	Run for each train/val step.
`test_step`(batch, batch_idx)	Operates on a single batch of data from the test set.
`to_prediction`(out[, use_metric])	Convert output to prediction using the loss metric.
`to_quantiles`(out[, use_metric])	Convert output to quantiles using the loss metric.
`training_step`(batch, batch_idx)	Train on batch.
`transform_output`(prediction, target_scale[, ...])	Extract prediction from network output and rescale it to real space / de-normalize it.
`validation_step`(batch, batch_idx)	Operates on a single batch of data from the validation set.

configure_optimizers()[source]#

Configure optimizers.

Uses single Ranger optimizer. Depending if learning rate is a list or a single float, implement dynamic learning rate scheduler or deterministic version

Returns:: first entry is list of optimizers and second is list of schedulers
Return type:: Tuple[List]

create_log(x: Dict[str, Tensor], y: Tuple[Tensor, Tensor], out: Dict[str, Tensor], batch_idx: int, prediction_kwargs: Dict[str, Any] = {}, quantiles_kwargs: Dict[str, Any] = {}) → Dict[str, Any][source]#

Create the log used in the training and validation step.

Parameters:

x (Dict[str, torch.Tensor]) – x as passed to the network by the dataloader
y (Tuple[torch.Tensor, torch.Tensor]) – y as passed to the loss function by the dataloader
out (Dict[str, torch.Tensor]) – output of the network
batch_idx (int) – batch number
prediction_kwargs (Dict[str, Any], optional) – arguments to pass to to_prediction(). Defaults to {}.
quantiles_kwargs (Dict[str, Any], optional) – to_quantiles(). Defaults to {}.

Returns:

log dictionary to be returned by training and validation steps

Return type:

Dict[str, Any]

static deduce_default_output_parameters(dataset: TimeSeriesDataSet, kwargs: Dict[str, Any], default_loss: MultiHorizonMetric | None = None) → Dict[str, Any][source]#

Deduce default parameters for output for from_dataset() method.

Determines output_size and loss parameters.

Parameters:

dataset (TimeSeriesDataSet) – timeseries dataset
kwargs (Dict[str, Any]) – current hyperparameters
default_loss (MultiHorizonMetric, optional) – default loss function. Defaults to MAE.

Returns:

dictionary with output_size and loss.

Return type:

Dict[str, Any]

forward(x: Dict[str, List[Tensor] | Tensor]) → Dict[str, List[Tensor] | Tensor][source]#

Network forward pass.

Parameters:

x (Dict[str, Union[torch.Tensor, List[torch.Tensor]]]) – network input (x as returned by the dataloader). See to_dataloader() method that returns a tuple of x and y. This function expects x.

Returns:

network outputs / dictionary of tensors or list

of tensors. Create it using the to_network_output() method. The minimal required entries in the dictionary are (and shapes in brackets):

prediction (batch_size x n_decoder_time_steps x n_outputs or list thereof with each entry for a different target): re-scaled predictions that can be fed to metric. List of tensors if multiple targets are predicted at the same time.

Before passing outputting the predictions, you want to rescale them into real space. By default, you can use the transform_output() method to achieve this.

Return type:

NamedTuple[Union[torch.Tensor, List[torch.Tensor]]]

Example

def forward(self, x:
    # x is a batch generated based on the TimeSeriesDataset, here we just use the
    # continuous variables for the encoder
    network_input = x["encoder_cont"].squeeze(-1)
    prediction = self.linear(network_input)  #

    # rescale predictions into target space
    prediction = self.transform_output(prediction, target_scale=x["target_scale"])

    # We need to return a dictionary that at least contains the prediction
    # The parameter can be directly forwarded from the input.
    # The conversion to a named tuple can be directly achieved with the `to_network_output` function.
    return self.to_network_output(prediction=prediction)

classmethod from_dataset(dataset: TimeSeriesDataSet, **kwargs) → LightningModule[source]#

Create model from dataset, i.e. save dataset parameters in model

This function should be called as super().from_dataset() in a derived models that implement it

Parameters:: dataset (TimeSeriesDataSet) – timeseries dataset
Returns:: Model that can be trained
Return type:: BaseModel

log(*args, **kwargs)[source]#: See lightning.pytorch.core.lightning.LightningModule.log().

log_gradient_flow(named_parameters: Dict[str, Tensor]) → None[source]#: log distribution of gradients to identify exploding / vanishing gradients

log_metrics(x: Dict[str, Tensor], y: Tensor, out: Dict[str, Tensor], prediction_kwargs: Dict[str, Any] | None = None) → None[source]#

Log metrics every training/validation step.

Parameters:

x (Dict[str, torch.Tensor]) – x as passed to the network by the dataloader
y (torch.Tensor) – y as passed to the loss function by the dataloader
out (Dict[str, torch.Tensor]) – output of the network
prediction_kwargs (Dict[str, Any]) – parameters for to_prediction() of the loss metric.

log_prediction(x: Dict[str, Tensor], out: Dict[str, Tensor], batch_idx: int, **kwargs) → None[source]#

Log metrics every training/validation step.

Parameters:

x (Dict[str, torch.Tensor]) – x as passed to the network by the dataloader
out (Dict[str, torch.Tensor]) – output of the network
batch_idx (int) – current batch index
**kwargs – paramters to pass to plot_prediction

on_after_backward()[source]#: Log gradient flow for debugging.

on_epoch_end(outputs)[source]#: Run at epoch end for training or validation. Can be overriden in models.

on_load_checkpoint(checkpoint: Dict[str, Any]) → None[source]#

Called by Lightning to restore your model. If you saved something with on_save_checkpoint() this is your chance to restore this.

Parameters:: checkpoint – Loaded checkpoint

Example:

def on_load_checkpoint(self, checkpoint):
    # 99% of the time you don't need to implement this method
    self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']

Note

Lightning auto-restores global step, epoch, and train state including amp scaling. There is no need for you to restore anything regarding training.

on_save_checkpoint(checkpoint: Dict[str, Any]) → None[source]#

Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save.

Parameters:: checkpoint – The full checkpoint dictionary before it gets dumped to a file. Implementations of this hook can insert additional data into this dictionary.

Example:

def on_save_checkpoint(self, checkpoint):
    # 99% of use cases you don't need to implement this method
    checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object

Note

Lightning saves all aspects of training (epoch, global step, etc…) including amp scaling. There is no need for you to store anything about training.

on_test_epoch_end()[source]#: Called in the test loop at the very end of the epoch.

on_train_epoch_end()[source]#

Called in the training loop at the very end of the epoch.

To access all batch outputs at the end of the epoch, you can cache step outputs as an attribute of the LightningModule and access them in this hook:

class MyLightningModule(L.LightningModule):
    def __init__(self):
        super().__init__()
        self.training_step_outputs = []

    def training_step(self):
        loss = ...
        self.training_step_outputs.append(loss)
        return loss

    def on_train_epoch_end(self):
        # do something with all training_step outputs, for example:
        epoch_mean = torch.stack(self.training_step_outputs).mean()
        self.log("training_epoch_mean", epoch_mean)
        # free up the memory
        self.training_step_outputs.clear()

on_validation_epoch_end()[source]#: Called in the validation loop at the very end of the epoch.

plot_prediction(x: Dict[str, Tensor], out: Dict[str, Tensor], idx: int = 0, add_loss_to_title: Metric | Tensor | bool = False, show_future_observed: bool = True, ax=None, quantiles_kwargs: Dict[str, Any] = {}, prediction_kwargs: Dict[str, Any] = {}) → Figure[source]#

Plot prediction of prediction vs actuals

Parameters:

x – network input
out – network output
idx – index of prediction to plot
add_loss_to_title – if to add loss to title or loss function to calculate. Can be either metrics, bool indicating if to use loss metric or tensor which contains losses for all samples. Calcualted losses are determined without weights. Default to False.
show_future_observed – if to show actuals for future. Defaults to True.
ax – matplotlib axes to plot on
quantiles_kwargs (Dict[str, Any]) – parameters for to_quantiles() of the loss metric.
prediction_kwargs (Dict[str, Any]) – parameters for to_prediction() of the loss metric.

Returns:

matplotlib figure

predict(data: DataLoader | DataFrame | TimeSeriesDataSet, mode: str | Tuple[str, str] = 'prediction', return_index: bool = False, return_decoder_lengths: bool = False, batch_size: int = 64, num_workers: int = 0, fast_dev_run: bool = False, return_x: bool = False, return_y: bool = False, mode_kwargs: Dict[str, Any] | None = None, trainer_kwargs: Dict[str, Any] | None = None, write_interval: Literal['batch', 'epoch', 'batch_and_epoch'] = 'batch', output_dir: str | None = None, **kwargs) → Prediction[source]#

Run inference / prediction.

Parameters:

dataloader – dataloader, dataframe or dataset
mode – one of “prediction”, “quantiles”, or “raw”, or tuple ("raw", output_name) where output_name is a name in the dictionary returned by forward()
return_index – if to return the prediction index (in the same order as the output, i.e. the row of the dataframe corresponds to the first dimension of the output and the given time index is the time index of the first prediction)
return_decoder_lengths – if to return decoder_lengths (in the same order as the output
batch_size – batch size for dataloader - only used if data is not a dataloader is passed
num_workers – number of workers for dataloader - only used if data is not a dataloader is passed
fast_dev_run – if to only return results of first batch
return_x – if to return network inputs (in the same order as prediction output)
return_y – if to return network targets (in the same order as prediction output)
mode_kwargs (Dict[str, Any]) – keyword arguments for to_prediction() or to_quantiles() for modes “prediction” and “quantiles”
trainer_kwargs (Dict[str, Any], optional) – keyword arguments for the trainer
write_interval – interval to write predictions to disk
output_dir – directory to write predictions to. Defaults to None. If set function will return empty list
**kwargs – additional arguments to network’s forward method

Returns:

if one of the `return arguments is present,: prediction tuple with fields prediction, x, y, index and decoder_lengths

Return type:

Prediction

Predict partial dependency.

Parameters:

data (Union[DataLoader, pd.DataFrame, TimeSeriesDataSet]) – data
variable (str) – variable which to modify
values (Iterable) – array of values to probe
mode (str, optional) –
Output mode. Defaults to “dataframe”. Either
- ”series”: values are average prediction and index are probed values
- ”dataframe”: columns are as obtained by the dataset.x_to_index() method,
  prediction (which is the mean prediction over the time horizon), normalized_prediction (which are predictions devided by the prediction for the first probed value) the variable name for the probed values
- ”raw”: outputs a tensor of shape len(values) x prediction_shape
target – Defines which values are overwritten for making a prediction. Same as in set_overwrite_values(). Defaults to “decoder”.
show_progress_bar – if to show progress bar. Defaults to False.
**kwargs – additional kwargs to predict() method

Returns:

output

Return type:

Union[np.ndarray, torch.Tensor, pd.Series, pd.DataFrame]

predict_step(batch, batch_idx)[source]#

Step function called during predict(). By default, it calls forward(). Override to add any processing logic.

The predict_step() is used to scale inference on multi-devices.

To prevent an OOM error, it is possible to use BasePredictionWriter callback to write the predictions to disk or database after each batch or on epoch end.

The BasePredictionWriter should be used while using a spawn based accelerator. This happens for Trainer(strategy="ddp_spawn") or training on 8 TPU cores with Trainer(accelerator="tpu", devices=8) as predictions won’t be returned.

Parameters:

batch – The output of your data iterable, normally a DataLoader.
batch_idx – The index of this batch.
dataloader_idx – The index of the dataloader that produced this batch. (only if multiple dataloaders used)

Returns:

Predicted output (optional).

Example

class MyModel(LightningModule):

    def predict_step(self, batch, batch_idx, dataloader_idx=0):
        return self(batch)

dm = ...
model = MyModel()
trainer = Trainer(accelerator="gpu", devices=2)
predictions = trainer.predict(model, dm)

size() → int[source]#: get number of parameters in model

step(x: Dict[str, Tensor], y: Tuple[Tensor, Tensor], batch_idx: int, **kwargs) → Tuple[Dict[str, Tensor], Dict[str, Tensor]][source]#

Run for each train/val step.

Parameters:

x (Dict[str, torch.Tensor]) – x as passed to the network by the dataloader
y (Tuple[torch.Tensor, torch.Tensor]) – y as passed to the loss function by the dataloader
batch_idx (int) – batch number
**kwargs – additional arguments to pass to the network apart from x

Returns:

tuple where the first: entry is a dictionary to which additional logging results can be added for consumption in the on_epoch_end hook and the second entry is the model’s output.

Return type:

Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]

test_step(batch, batch_idx)[source]#

Operates on a single batch of data from the test set. In this step you’d normally generate examples or calculate anything of interest such as accuracy.

Parameters:

batch – The output of your data iterable, normally a DataLoader.
batch_idx – The index of this batch.
dataloader_idx – The index of the dataloader that produced this batch. (only if multiple dataloaders used)

Returns:

Tensor - The loss tensor
dict - A dictionary. Can include any keys, but must include the key 'loss'.
None - Skip to the next batch.

# if you have one test dataloader:
def test_step(self, batch, batch_idx):
    ...


# if you have multiple test dataloaders:
def test_step(self, batch, batch_idx, dataloader_idx=0):
    ...

Examples:

# CASE 1: A single test dataset
def test_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever
    sample_imgs = x[:6]
    grid = torchvision.utils.make_grid(sample_imgs)
    self.logger.experiment.add_image('example_images', grid, 0)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs!
    self.log_dict({'test_loss': loss, 'test_acc': test_acc})

If you pass in multiple test dataloaders, test_step() will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

# CASE 2: multiple test dataloaders
def test_step(self, batch, batch_idx, dataloader_idx=0):
    # dataloader_idx tells you which dataset this is.
    ...

Note

If you don’t need to test you don’t need to implement this method.

Note

When the test_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of the test epoch, the model goes back to training mode and gradients are enabled.

to_prediction(out: Dict[str, Any], use_metric: bool = True, **kwargs)[source]#

Convert output to prediction using the loss metric.

Parameters:

out (Dict[str, Any]) – output of network where “prediction” has been transformed with transform_output()
use_metric (bool) – if to use metric to convert for conversion, if False, simply take the average over out["prediction"]
**kwargs – arguments to metric to_quantiles method

Returns:

predictions of shape batch_size x timesteps

Return type:

torch.Tensor

to_quantiles(out: Dict[str, Any], use_metric: bool = True, **kwargs)[source]#

Convert output to quantiles using the loss metric.

Parameters:

out (Dict[str, Any]) – output of network where “prediction” has been transformed with transform_output()
use_metric (bool) – if to use metric to convert for conversion, if False, simply take the quantiles over out["prediction"]
**kwargs – arguments to metric to_quantiles method

Returns:

quantiles of shape batch_size x timesteps x n_quantiles

Return type:

torch.Tensor

training_step(batch, batch_idx)[source]#: Train on batch.

transform_output(prediction: Tensor | List[Tensor], target_scale: Tensor | List[Tensor], loss: Metric | None = None) → Tensor[source]#

Extract prediction from network output and rescale it to real space / de-normalize it.

Parameters:

prediction (Union[torch.Tensor, List[torch.Tensor]]) – normalized prediction
target_scale (Union[torch.Tensor, List[torch.Tensor]]) – scale to rescale prediction
loss (Optional[Metric]) – metric to use for transform

Returns:

rescaled prediction

Return type:

torch.Tensor

validation_step(batch, batch_idx)[source]#

Operates on a single batch of data from the validation set. In this step you’d might generate examples or calculate anything of interest like accuracy.

Parameters:

batch – The output of your data iterable, normally a DataLoader.
batch_idx – The index of this batch.
dataloader_idx – The index of the dataloader that produced this batch. (only if multiple dataloaders used)

Returns:

Tensor - The loss tensor
dict - A dictionary. Can include any keys, but must include the key 'loss'.
None - Skip to the next batch.

# if you have one val dataloader:
def validation_step(self, batch, batch_idx):
    ...


# if you have multiple val dataloaders:
def validation_step(self, batch, batch_idx, dataloader_idx=0):
    ...

Examples:

# CASE 1: A single validation dataset
def validation_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever
    sample_imgs = x[:6]
    grid = torchvision.utils.make_grid(sample_imgs)
    self.logger.experiment.add_image('example_images', grid, 0)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs!
    self.log_dict({'val_loss': loss, 'val_acc': val_acc})

If you pass in multiple val dataloaders, validation_step() will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

# CASE 2: multiple validation dataloaders
def validation_step(self, batch, batch_idx, dataloader_idx=0):
    # dataloader_idx tells you which dataset this is.
    ...

Note

If you don’t need to validate you don’t need to implement this method.

Note

When the validation_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of validation, the model goes back to training mode and gradients are enabled.

property current_stage: str#: Available inside lightning loops. :return: current trainer stage. One of [“train”, “val”, “test”, “predict”, “sanity_check”]

property log_interval: float#: Log interval depending if training or validating

property n_targets: int#

Number of targets to forecast.

Based on loss function.

Returns:: number of targets
Return type:: int

property target_names: List[str]#

List of targets that are predicted.

Returns:: list of target names
Return type:: List[str]