Calculate accuracy score of model predictions. Parameters ---------- class_predictions : Estimated classification predictions ret

Calculate adjusted Rand Index. Parameters ---------- class_predictions : Clustering results returned by a clusterer. targets

Compute the Calinski and Harabasz score (also known as the Variance Ratio Criterion). X : array-like of shape (n_samples_a, n_features) A

Calculate cluster purity. Parameters ---------- class_predictions : Clustering results returned by a clusterer. targets :

Compute the Davies-Bouldin score. Parameters ---------- X : array-like of shape (n_samples_a, n_features) A feature array, or lea

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

Calculate Normalized Mutual Information between two clusterings. Parameters ---------- class_predictions : Clustering results ret

Calculate precisions, recalls, and area under PR curve of model predictions. Parameters ---------- prob_predictions : Estimated p

Calculate precision, recall, and F1-score of model predictions. Parameters ---------- prob_predictions : Estimated probability pr

Calculate Rand Index, a measure of the similarity between two data clusterings. Refer to :cite:`rand1971RandIndex`. Parameters ----------

Calculate false positive rates, true positive rates, and area under AUC curve of model predictions. Parameters ---------- prob_prediction

Compute the mean Silhouette Coefficient of all samples. Parameters ---------- X : array-like of shape (n_samples_a, n_features) A

Deduce default parameters for output for `from_dataset()` method. Determines ``output_size`` and ``loss`` parameters. Args:

Generate dataset with different underlying data but same variable encoders and scalers, etc. Calls :py:meth:`~from_parameters` under

Generate complete random walk time-series data, i.e. having no missing values. Parameters ---------- n_samples : int, default=1000

Generate complete random walk time-series data for the classification task. Parameters ---------- n_classes : int, must >=1, default=2

Returns causal mask to apply for self-attention layer.

Compute learning rate.

Impute missing values in the given data with the trained model. Warnings -------- The method impute is deprecated. Please use

Load specific datasets supported by PyPOTS. Different from tsdb.load_dataset(), which only produces merely raw data, load_specific_dataset her

Numpy implementation of LOCF. Parameters ---------- X : np.ndarray, Time series containing missing values (NaN) to be imputed.

Torch implementation of LOCF. Parameters ---------- X : tensor, Time series containing missing values (NaN) to be imputed. f

Log embeddings to tensorboard

log distribution of gradients to identify exploding / vanishing gradients

Log interpretation metrics to tensorboard.

Log metrics every training/validation step. Args: x (Dict[str, torch.Tensor]): x as passed to the network by the dataloa

Log metrics every training/validation step. Args: x (Dict[str, torch.Tensor]): x as passed to the network by the dataloa

This function used to construct neural network consisting of one 1d-convolutional layer that utilizes temporal dependencies, fully conne

This function used to creates fully connected neural network. Parameters ---------- input_size : int, the dimension of input embe

De-Normalization from Non-stationary Transformer. Please refer to :cite:`liu2022nonstationary` for more details. Parameters ---------- X

Normalization from Non-stationary Transformer. Please refer to :cite:`liu2022nonstationary` for more details. Parameters ---------- X : t

Convert network output to rescaled and normalized prediction. Function is typically not called directly but via :py:meth:`~decode_au

Make figures that interpret model. * Attention * Variable selection weights / importances Args: interpr

Make predictions for the input data with the trained model. Parameters ---------- test_set : The dataset for mode

Make predictions for the input data with the trained model. Parameters ---------- test_set : dict or str The data

Make predictions for the input data with the trained model. Parameters ---------- test_set : dict or str The data

Make predictions for the input data with the trained model. Parameters ---------- test_set : dict or str The data

Make predictions for the input data with the trained model. Parameters ---------- test_set : dict or str The data

Make predictions for the input data with the trained model. Parameters ---------- test_set : dict or str The data

Parameters ---------- test_set : dict or str The dataset for model validating, should be a dictionary including