opendataval.dataval.TMCSampler

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

TMCShapley sampler 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.

Parameters

mc_epochsint, optional

Number of outer epochs of MCMC sampling, 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__(mc_epochs: int = 1000, min_cardinality: int = 5, cache_name: str | None = '', random_state: RandomState | None = None)

Methods

__init__([mc_epochs, min_cardinality, ...])
compute_marginal_contribution(*args, **kwargs)	Computes marginal contribution through TMC Shapley.
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.