tapas.attacks.set_classifiers.HistSetFeature
- class tapas.attacks.set_classifiers.HistSetFeature(num_bins: int = 10, bounds: tuple[float, float] = (0, 1))
Bases:
tapas.attacks.set_classifiers.SetFeatureF_Hist set feature from Stadler et al. Compute a histogram of each column, with binning for continuous variables.
- __init__(num_bins: int = 10, bounds: tuple[float, float] = (0, 1))
- Parameters
num_bins (int (default, 10)) – Number of bins to use when categorising continuous columns, for the computation of histograms.
bounds ((float, float) (default, (0,1))) – Bounds on continuous attributes, within which the histograms are computed.
Methods
__init__([num_bins, bounds])- param num_bins
Number of bins to use when categorising continuous columns, for
extract(datasets)Extract features from each dataset in a list.
Attributes
label- extract(datasets: list[TabularDataset]) np.array
Extract features from each dataset in a list.
- Parameters
datasets (list of Datasets.) – Datasets to extract features from.
- Returns
features – Array of size len(datasets) x k, where the number of features k can be estimated by self.size(dataset.description). Each row is a dataset, and each column a different feature.
- Return type
np.array