opendataval.dataval.BetaShapley

class opendataval.dataval.BetaShapley(*args, **kwargs)

Beta Shapley implementation. Must specify alpha/beta values for beta function.

References

[1]

Y. Kwon and J. Zou, Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning, arXiv.org, 2021. Available: https://arxiv.org/abs/2110.14049.

Parameters

samplerSampler, optional

Sampler used to compute the marginal contributions. Can be found in sampler, by default uses *args, **kwargs for GrTMCSampler.

alphaint, optional

Alpha parameter for beta distribution used in the weight function, by default 4

betaint, optional

Beta parameter for beta distribution used in the weight function, by default 1

__init__(sampler: Sampler | None = None, alpha: int = 4, beta: int = 1, *args, **kwargs)

Methods

__init__([sampler, alpha, beta])
compute_weight()	Compute weights for each cardinality of training set.
evaluate(y, y_hat)	Evaluate performance of the specified metric between label and predictions.
evaluate_data_values()	Return data values for each training data point.
input_data(x_train, y_train, x_valid, y_valid)	Store and transform input data for semi-value samplers.
input_fetcher(fetcher)	Input data from a DataFetcher object.
input_metric(metric)	Input the evaluation metric.
input_model(pred_model)	Input the prediction model.
input_model_metric(pred_model, metric)	Input the prediction model and the evaluation metric.
setup(fetcher[, pred_model, metric])	Inputs model, metric and data into Data Evaluator.
train(fetcher[, pred_model, metric])	Store and transform data, then train model to predict data values.
train_data_values(*args, **kwargs)	Uses sampler to trains model to find marginal contribs and data values.

Attributes

Evaluators
data_values	Cached data values.