This module is a decoder with Gaussian output distribution. Parameters ---------- output_size : int, the feature

Length of dataset. Returns: int: length

Assemble the given data into a dictionary for testing input. Notes ----- The processing functions of train/val/test stages ar

Collate function to combine items into mini-batch for dataloader. Args: batches (List[Tuple[Dict[str, torch.Tensor], tor

Fetch data from self.X if it is given. Parameters ---------- idx : The index of the sample to be return.

Fetch data according to index. Parameters ---------- idx : The index to fetch the specified sample. Retu

Fetch data according to index. Parameters ---------- idx : The index to fetch the specified sample. Retu

Fetch data from self.X if it is given. Parameters ---------- idx : The index of the sample to be return.

Fetch data according to index. Parameters ---------- idx : The index to fetch the specified sample. Retu

Fetch data from self.X if it is given. Parameters ---------- idx : The index of the sample to be return.

Fetch data from self.X if it is given. Parameters ---------- idx : The index of the sample to be return.

Fetch data with the lazy-loading strategy, i.e. only loading data from the file while requesting for samples. Here the opened file handle does

Fetch data with the lazy-loading strategy, i.e. only loading data from the file while requesting for samples. Here the opened file handle does

Fetch data with the lazy-loading strategy, i.e. only loading data from the file while requesting for samples. Here the opened file handle does

Fetch data with the lazy-loading strategy, i.e. only loading data from the file while requesting for samples. Here the opened file handle does

Fetch data with the lazy-loading strategy, i.e. only loading data from the file while requesting for samples. Here the opened file handle does

Fetch data with the lazy-loading strategy, i.e. only loading data from the file while requesting for samples. Here the opened file handle does

Fetch data with the lazy-loading strategy, i.e. only loading data from the file while requesting for samples. Here the opened file handle does

Group ids used to identify series in dataset. See :py:meth:`~TimeSeriesDataSet._group_ids_mapping` for details.

Mapping of group id names to group ids used to identify series in dataset - group ids can also be used for target normalizer.

Add a param group to the optimizer param_groups. Parameters ---------- param_group : Specifies the parameters to

r"""Aggregates messages from neighbors as :math:`\square_{j \in \mathcal{N}(i)}`. Takes in the output of message computation as first

Calculate the evaluation metrics for the binary classification task, including accuracy, precision, recall, f1 score, area under ROC curve, and ar

Computer all external cluster validation metrics available in PyPOTS and return as a dictionary. Parameters ---------- class_predictions

Computer all internal cluster validation metrics available in PyPOTS and return as a dictionary. Parameters ---------- X : array-like of

Calculate the Mean Relative Error between ``predictions`` and ``targets``. ``masks`` can be used for filtering. For values==0 in ``masks``, va

Continuous rank probability score for distributional predictions. Parameters ---------- predictions : The prediction data to be e

Sum continuous rank probability score for distributional predictions. Parameters ---------- predictions : The prediction data to

Calculate predictions and actuals by variable averaged by ``bins`` bins spanning from ``-std`` to ``+std`` Args: x: inpu

Mapping of categorical variables to categorical groups

Categorical variables as used for modelling. Returns: List[str]: list of variables

List of all categorical variables in model

Organize clustered arrays into format ready for plotting. Parameters __________ cluster_members : Output from get_cluster_me

Configure optimizers. Uses single Ranger optimizer. Depending if learning rate is a list or a single float, implement dynamic

Available inside lightning loops. :return: current trainer stage. One of ["train", "val", "test", "predict", "sanity_check"]

Make predictions in auto-regressive manner. Supports only continuous targets. Args: decode_one (Callable): func

Get interpretable version of index. DataFrame contains - group_id columns in original encoding - time_idx_first colu

List of all decoder variables in model (excluding static variables)

list of categorical variables that are unknown when making a forecast without observed history

List of all encoder variables in model (excluding static variables)

run at epoch end for training or validation

Extract features Args: x (Dict[str, torch.Tensor]): input from the dataloader embeddings (MultiEmbedding): e

Filter subsequences in dataset. Uses interpretable version of index :py:meth:`~decoded_index` to filter subsequences in data

Train the imputer on the given data. Parameters ---------- train_set : The dataset for model training, should be

Train the imputer on the given data. Parameters ---------- train_set : The dataset for model training, should be

Train the imputer on the given data. Warnings -------- Median imputation class does not need to run fit(). Please run

Train the imputer on the given data. Warnings -------- Mean imputation class does not need to run fit(). Please run f