r"""Performs a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss.

Convert output to quantiles using the loss metric. Args: out (Dict[str, Any]): output of network where "prediction" has

Extract prediction from network output and rescale it to real space / de-normalize it. Args: prediction (Union[torch.Ten

Assemble the given data into a dictionary for training input. Parameters ---------- data : Input data from datalo

Assemble the given data into a dictionary for validating input. Parameters ---------- data : Data output from dat

Check value type and shape of input X and y Parameters ---------- X : Time-series data that must have a shape lik

Create index of samples. Args: data (pd.DataFrame): preprocessed data predict_mode (bool): if to create one

Convert data to tensors for faster access with :py:meth:`~__getitem__`. Args: data (pd.DataFrame): preprocessed data

Identify end indices in series even if some values are missing. Args: diffs (np.ndarray): array of differences to next time step. na

Calculate the consistency loss between the imputation from two RITS models. Parameters ---------- pred_f : The im

Detect the data sample sizes in the dataset and return the numbers. Returns ------- n_samples : The number of the

Generate the time-gap matrix (i.e. the delta metrix) from the missing mask. Please refer to :cite:`che2018GRUD` for its math definition. Para

Scale continuous variables, encode categories and set aside target and weight. Args: data (pd.DataFrame): original data

Reverse the array values on the time dimension in the given dictionary.

Determine target normalizer. Args: data (pd.DataFrame): input data

Validate that data will not cause hick-ups later on.