dataeval.data.Metadata¶

class dataeval.data.Metadata(dataset, *, continuous_factor_bins=None, auto_bin_method='uniform_width', exclude=None, include=None)¶

Class containing binned metadata.

discrete_factor_names¶

List containing factor names for the original data that was discrete and the binned continuous data

discrete_data¶

Array containing values for the original data that was discrete and the binned continuous data

continuous_factor_names¶

List containing factor names for the original continuous data

continuous_data¶

Array containing values for the original continuous data or None if there was no continuous data

class_labels¶

Numerical class labels for the images/objects

class_names¶

List of unique class names

total_num_factors¶

Sum of discrete_factor_names and continuous_factor_names plus 1 for class

image_indices¶

Array of the image index that is mapped by the index of the factor

Parameters:¶

dataset : ImageClassificationDataset or ObjectDetectionDataset¶: Dataset to access original targets and metadata from.
continuous_factor_bins : Mapping[str, int | Sequence[float]] | None, default None¶: Mapping from continuous factor name to the number of bins or bin edges
auto_bin_method : Literal["uniform_width", "uniform_count", "clusters"], default "uniform_width"¶: Method for automatically determining the number of bins for continuous factors
exclude : Sequence[str] | None, default None¶: Filter metadata factors to exclude the specified factors, cannot be set with include
include : Sequence[str] | None, default None¶: Filter metadata factors to include the specified factors, cannot be set with exclude

add_factors(factors)¶

Add additional factors to the metadata.

The number of measures per factor must match the number of images in the dataset or the number of detections in the dataset.

Parameters:¶

factors : Mapping[str, ArrayLike]¶: Dictionary of factors to add to the metadata.

Return type:¶

None