opendataval.dataval.GrTMCSampler

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

TMC Sampler with terminator for semivalue-based methods of computing data values.

Evaluators that share marginal contributions should share a sampler.

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.

[2]

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

gr_thresholdfloat, optional

Convergence threshold for the Gelman-Rubin statistic. Shapley values are NP-hard so we resort to MCMC sampling, by default 1.05

max_mc_epochsint, optional

Max number of outer epochs of MCMC sampling, by default 100

models_per_epochint, optional

Number of model fittings to take per epoch prior to checking GR convergence, by default 100

min_modelsint, optional

Minimum samples before checking MCMC convergence, by default 1000

min_cardinalityint, optional

Minimum cardinality of a training set, must be passed as kwarg, by default 5

cache_namestr, optional

Unique cache_name of the model to cache marginal contributions, set to None to disable caching, by default “” which is set to a unique value for a object

random_stateRandomState, optional

Random initial state, by default None

__init__(gr_threshold: float = 1.05, max_mc_epochs: int = 100, models_per_epoch: int = 100, min_models: int = 1000, min_cardinality: int = 5, cache_name: str | None = '', random_state: RandomState | None = None)

Methods

__init__([gr_threshold, max_mc_epochs, ...])
compute_marginal_contribution(*args, **kwargs)	Compute the marginal contributions for semivalue based data evaluators.
set_coalition(coalition)	Initializes storage to find marginal contribution of each data point
set_evaluator(value_func)	Sets the evaluator function to evaluate the utility of a coalition

Attributes

CACHE	Cached marginal contributions.
GR_MAX	Default maximum Gelman-Rubin statistic.