dianna.utils.maskers

Functions

generate_tabular_masks(input_data_shape, number_of_masks)	Generator function to create masks for tabular data.
generate_time_series_masks(input_data_shape, ...[, ...])	Generate masks for time series data given a probability of keeping any time step or channel unmasked.
generate_channel_masks(input_data_shape, ...)	Generate masks that mask one or multiple channels independently at a time.
mask_data_tabular(→ numpy.array)	Mask tabular data given using a set of masks.
mask_data(data, masks, mask_type)	Mask data given using a set of masks.
_get_mask_value(→ int)	Calculates a masking value of the given type for the data.
_determine_number_masked(→ int)	Determine the number of time steps that need to be masked.
generate_time_step_masks(input_data_shape, ...)	Generate masks that mask all channels simultaneously for clusters of time steps.
_mask_bottom_ratio(→ numpy.ndarray)	Return a bool mask given a mask of floats and a ratio.
generate_interpolated_float_masks_for_image(...)	Generates a set of random masks of float values to mask image data.
generate_interpolated_float_masks_for_timeseries(...)	Generates a set of random masks to mask time-series data.
_project_grids_to_masks(→ numpy.ndarray)	Projects a set of (low resolution) grids onto a target resolution masks.
_upscale(grid_i, up_size)	Up samples and crops the grid to result in an array with size up_size.

Module Contents

dianna.utils.maskers.generate_tabular_masks(input_data_shape: tuple[int], number_of_masks: int, p_keep: float = 0.5)

Generator function to create masks for tabular data.

Parameters:

• input_data_shape – Shape of the tabular data to be masked.

• number_of_masks – Number of masks to generate.

• p_keep – probability that any value should remain unmasked.

Returns: Single array containing all masks where the first dimension represents the batch.

dianna.utils.maskers.generate_time_series_masks(input_data_shape: tuple[int], number_of_masks: int, feature_res: int = 8, p_keep: float = 0.5)

Generate masks for time series data given a probability of keeping any time step or channel unmasked.

Note that, for multivariate data, the resulting masks will be an evenly distributed sample of the following 3 kinds of masks: - Channel masks. These are masks that mask a whole channel at the time, masking all its time steps simultaneously. - Time step masks. These mask all channels simultaneously for selected time steps. Masked time steps are selected randomly, while grouping adjacent time steps. See for a complete description of how we generate time step masks in our blog post: https://medium.com/escience-center/masking-time-series-for-explainable-ai-90247ac252b4 - Combination masks: These masks are a combination of the above 2 types.

For univariate data, only time step masks are returned.

Parameters:

• input_data_shape – Shape of the time series data to be masked.

• number_of_masks – Number of masks to generate.

• p_keep – the probability that any value remains unmasked.

• feature_res – Resolution of features in masks.

Returns: Single array containing all masks where the first dimension represents the batch.

dianna.utils.maskers.generate_channel_masks(input_data_shape: tuple[int], number_of_masks: int, p_keep: float)

Generate masks that mask one or multiple channels independently at a time.

dianna.utils.maskers.mask_data_tabular(data: numpy.array, masks: numpy.array, training_data: numpy.array, mask_type: object | str) → numpy.array

Mask tabular data given using a set of masks.

Parameters:

• data – Input data.

• masks – an array with shape [number_of_masks] + data.shape

• mask_type – Masking strategy. Can be ‘most_frequent’, ‘mean’ or a function f(data, masks, training_data).

• training_data – Data used to sample from for imputation of masked values.

Returns:

Single array containing all masked input where the first dimension represents the batch.

dianna.utils.maskers.mask_data(data: numpy.array, masks: numpy.array, mask_type: object | str)

Mask data given using a set of masks.

Parameters:

• data – Input data.

• masks – an array with shape [number_of_masks] + data.shape

• mask_type – Masking strategy.

Returns: Single array containing all masked input where the first dimension represents the batch.

dianna.utils.maskers._get_mask_value(data: numpy.array, mask_type: object) → int

Calculates a masking value of the given type for the data.

dianna.utils.maskers._determine_number_masked(p_keep: float, series_length: int, element_name='feature') → int

Determine the number of time steps that need to be masked.

dianna.utils.maskers.generate_time_step_masks(input_data_shape: tuple[int], number_of_masks: int, p_keep: float, number_of_features: int)

Generate masks that mask all channels simultaneously for clusters of time steps.

For a conceptual description see: https://medium.com/escience-center/masking-time-series-for-explainable-ai-90247ac252b4.

dianna.utils.maskers._mask_bottom_ratio(float_mask: numpy.ndarray, p_keep: float) → numpy.ndarray

Return a bool mask given a mask of floats and a ratio.

Return a mask containing bool values where the top p_keep values of the float mask remain unmasked and the rest is masked.

Parameters:

• float_mask – a mask containing float values

• p_keep – the ratio of keeping cells unmasked

Returns:

a mask containing bool

dianna.utils.maskers.generate_interpolated_float_masks_for_image(image_shape: Iterable[int], p_keep: float, number_of_masks: int, number_of_features: int)

Generates a set of random masks of float values to mask image data.

Parameters:

• image_shape (int) – Size of a single sample of input data, for images without the channel axis.

• p_keep –

  ?

• number_of_masks – Number of masks

• number_of_features – Number of features (or blobs) in both dimensions

Returns:

The generated masks (np.ndarray)

dianna.utils.maskers.generate_interpolated_float_masks_for_timeseries(time_series_shape: Iterable[int], number_of_masks: int, number_of_features: int) → numpy.ndarray

Generates a set of random masks to mask time-series data.

Parameters:

• time_series_shape (int) – Size of a single sample of input time series.

• number_of_masks – Number of masks

• number_of_features – Number of features in the time dimension

Returns:

The generated masks (np.ndarray)

dianna.utils.maskers._project_grids_to_masks(grids: numpy.ndarray, masks_shape: tuple) → numpy.ndarray

Projects a set of (low resolution) grids onto a target resolution masks.

Parameters:

• grids – Set of grids with a pattern for each resulting mask

• masks_shape – Resolution of the resulting masks

Returns:

Set of masks with specified shape based on the grids

dianna.utils.maskers._upscale(grid_i, up_size)

Up samples and crops the grid to result in an array with size up_size.