opendataval.dataval.AME

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

Implementation of Average Marginal Effect Data Valuation.

References

[1]

J. Lin, A. Zhang, M. Lecuyer, J. Li, A. Panda, and S. Sen, Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments, arXiv.org, 2022. Available: https://arxiv.org/abs/2206.10013.

Parameters

num_modelsint, optional

Number of models to bag/aggregate, by default 1000

random_stateRandomState, optional

Random initial state, by default None

__init__(num_models: int = 1000, random_state: RandomState | None = None)

Methods

__init__([num_models, random_state])
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 DataEvaluator.
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)	Trains model to predict data values.

Attributes

Evaluators
data_values	Cached data values.