deepsensor.model.model

class DeepSensorModel(data_processor=None, task_loader=None)

Bases: ProbabilisticModel

Implements DeepSensor prediction functionality of a ProbabilisticModel. Allows for outputting an xarray object containing on-grid predictions or a pandas object containing off-grid predictions.

Parameters:

• data_processor (DataProcessor) – DataProcessor object, used to unnormalise predictions.

• task_loader (TaskLoader) – TaskLoader object, used to determine target variables for unnormalising.

N_mixture_components = 1

predict(tasks, X_t, X_t_mask=None, X_t_is_normalised=False, aux_at_targets_override=None, aux_at_targets_override_is_normalised=False, resolution_factor=1, pred_params=('mean', 'std'), n_samples=0, ar_sample=False, ar_subsample_factor=1, unnormalise=True, seed=0, append_indexes=None, progress_bar=0, verbose=False)

Predict on a regular grid or at off-grid locations.

Parameters:

• tasks (List[Task] | Task) – List of tasks containing context data.

• X_t (xarray.Dataset | xarray.DataArray | pandas.DataFrame | pandas.Series | pandas.Index | numpy.ndarray) – Target locations to predict at. Can be an xarray object containingon-grid locations or a pandas object containing off-grid locations.

• X_t_mask – xarray.Dataset | xarray.DataArray, optional 2D mask to apply to gridded X_t (zero/False will be NaNs). Will be interpolated to the same grid as X_t. Default None (no mask).

• X_t_is_normalised (bool) – Whether the X_t coords are normalised. If False, will normalise the coords before passing to model. Default False.

• aux_at_targets_override (xarray.Dataset | xarray.DataArray) – Optional auxiliary xarray data to override from the task_loader.

• aux_at_targets_override_is_normalised (bool) – Whether the aux_at_targets_override coords are normalised. If False, the DataProcessor will normalise the coords before passing to model. Default False.

• pred_params (Tuple[str]) – Tuple of prediction parameters to return. The strings refer to methods of the model class which will be called and stored in the Prediction object. Default (“mean”, “std”).

• resolution_factor (float) – Optional factor to increase the resolution of the target grid by. E.g. 2 will double the target resolution, 0.5 will halve it.Applies to on-grid predictions only. Default 1.

• n_samples (int) – Number of joint samples to draw from the model. If 0, will not draw samples. Default 0.

• ar_sample (bool) – Whether to use autoregressive sampling. Default False.

• unnormalise (bool) – Whether to unnormalise the predictions. Only works if self hasa data_processor and task_loader attribute. Default True.

• seed (int) – Random seed for deterministic sampling. Default 0.

• append_indexes (dict) – Dictionary of index metadata to append to pandas indexes in the off-grid case. Default None.

• progress_bar (int) – Whether to display a progress bar over tasks. Default 0.

• verbose (bool) – Whether to print time taken for prediction. Default False.

Returns:

Prediction) – A dict-like object mapping from target variable IDs to xarray or pandas objects containing model predictions. - If X_t is a pandas object, returns pandas objects containing off-grid predictions. - If X_t is an xarray object, returns xarray object containing on-grid predictions. - If n_samples == 0, returns only mean and std predictions. - If n_samples > 0, returns mean, std and samples predictions.

Raises:

• ValueError – If X_t is not an xarray object and resolution_factor is not 1 or ar_subsample_factor is not 1.

• ValueError – If X_t is not a pandas object and append_indexes is not None.

• ValueError – If X_t is not an xarray, pandas or numpy object.

• ValueError – If append_indexes are not all the same length as X_t.

class ProbabilisticModel

Bases: object

Base class for probabilistic model used for DeepSensor. Ensures a set of methods required for DeepSensor are implemented by specific model classes that inherit from it.

covariance(task, *args, **kwargs)

Computes the model covariance matrix over target points based on given context data. Shape (N, N).

Parameters:

task (Task) – Task containing context data.

Returns:

numpy.ndarray – Covariance matrix over target points.

Raises:

NotImplementedError – If not implemented by child class.

joint_entropy(task, *args, **kwargs)

Computes the model joint entropy over target points based on given context data.

Parameters:

task (Task) – Task containing context data.

Returns:

float – Joint entropy over target points.

Raises:

NotImplementedError – If not implemented by child class.

logpdf(task, *args, **kwargs)

Computes the joint model logpdf over target points based on given context data.

Parameters:

task (Task) – Task containing context data.

Returns:

float – Joint logpdf over target points.

Raises:

NotImplementedError – If not implemented by child class.

loss(task, *args, **kwargs)

Computes the model loss over target points based on given context data.

Parameters:

task (Task) – Task containing context data.

Returns:

float – Loss over target points.

Raises:

NotImplementedError – If not implemented by child class.

mean(task, *args, **kwargs)

Computes the model mean prediction over target points based on given context data.

Parameters:

task (Task) – Task containing context data.

Returns:

numpy.ndarray – Mean prediction over target points.

Raises:

NotImplementedError – If not implemented by child class.

mean_marginal_entropy(task, *args, **kwargs)

Computes the mean marginal entropy over target points based on given context data.

Note

Note: Getting a vector of marginal entropies would be useful too.

Parameters:

task (Task) – Task containing context data.

Returns:

float – Mean marginal entropy over target points.

Raises:

NotImplementedError – If not implemented by child class.

sample(task, n_samples=1, *args, **kwargs)

Draws n_samples joint samples over target points based on given context data. Returned shape is (n_samples, n_target).

Parameters:

• task (Task) – Task containing context data.

• n_samples (int, optional) – Number of samples to draw. Defaults to 1.

Returns:

tuple[numpy.ndarray] – Joint samples over target points.

Raises:

NotImplementedError – If not implemented by child class.

std(task)

Model marginal standard deviation over target points given context points. Shape (N,).

Parameters:

task (Task) – Task containing context data.

Returns:

numpy.ndarray – Marginal standard deviation over target points.

stddev(*args, **kwargs)

variance(task, *args, **kwargs)

Model marginal variance over target points given context points. Shape (N,).

Parameters:

task (Task) – Task containing context data.

Returns:

numpy.ndarray – Marginal variance over target points.

Raises:

NotImplementedError – If not implemented by child class.

add_valid_time_coord_to_pred_and_move_time_dims(pred)

Add a valid time coordinate “time” to a Prediction object based on the initialisation times “init_time” and lead times “lead_time”, and reorder the time dims from (“lead_time”, “init_time”) to (“init_time”, “lead_time”).

Parameters:

pred (Prediction) – Prediction object to add valid time coordinate to.

Returns:

Prediction – Prediction object with valid time coordinate added.

main()