opendataval.dataval.KNNShapley

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

Data valuation using KNNShapley implementation.

KNN Shapley is a model-less mixin. This means we cannot specify an underlying prediction model for the DataEvaluator. However, we can specify a pretrained embedding model.

References

[1]

R. Jia et al., Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms, arXiv.org, 2019. Available: https://arxiv.org/abs/1908.08619.

Parameters

k_neighborsint, optional

Number of neighbors to group the data points, by default 10

batch_sizeint, optional

Batch size of tensors to load at a time during training, by default 32

embedding_modelModel, optional

Pre-trained embedding model used by DataEvaluator, by default None

random_stateRandomState, optional

Random initial state, by default None

__init__(k_neighbors: int = 10, batch_size: int = 32, embedding_model: Model | None = None, random_state: RandomState | None = None)

Methods

__init__([k_neighbors, batch_size, ...])
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.
match(y)	\(1.\) for all matching rows and \(0.\) otherwise.
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.