opendataval.dataval.LavaEvaluator

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

Data valuation using LAVA implementation.

References

[1]

H. A. Just, F. Kang, T. Wang, Y. Zeng, M. Ko, M. Jin, and R. Jia, LAVA: Data Valuation without Pre-Specified Learning Algorithms, 2023. Available: https://openreview.net/forum?id=JJuP86nBl4q

Parameters

devicetorch.device, optional

Tensor device for acceleration, by default torch.device(“cpu”)

random_state: RandomState, optional

Random initial state, by default None

Mixins

ModelLessMixin

Mixin for a data evaluator that doesn’t require a model or evaluation metric.

__init__(device: device = device(type='cpu'), embedding_model: Model | None = None, random_state: RandomState | None = None)

Methods

__init__([device, embedding_model, random_state])
embeddings(*tensors)	Returns Embeddings for the input tensors
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 DataEvaluator.
input_fetcher(fetcher)	Input data from a DataFetcher object.
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)	Trains model to predict data values.

Attributes

Evaluators
data_values	Cached data values.