opendataval.dataval.DataShapley

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

Data Shapley implementation.

References

[1]

A. Ghorbani and J. Zou, Data Shapley: Equitable Valuation of Data for Machine Learning, arXiv.org, 2019. Available: https://arxiv.org/abs/1904.02868.

Parameters

samplerSampler, optional

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

__init__(sampler: Sampler | None = None, *args, **kwargs)

Methods

__init__([sampler])
compute_weight()	Compute weights (uniform) 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.