API Reference#

DataEval’s API is split into several submodules which support specific goals and are detailed below. The base module is empty except for __version__ by design.

Submodules#

`dataeval.detectors`	Detectors can determine if a dataset or individual images in a dataset are indicative of a specific issue.
`dataeval.detectors.drift`	Drift detectors identify if the statistical properties of the data has changed.
`dataeval.detectors.linters`	Linters help identify potential issues in training and test data and are an important aspect of data cleaning.
`dataeval.detectors.ood`	Out-of-distribution (OOD)` detectors identify data that is different from the data used to train a particular model.
`dataeval.metrics`	Metrics are a way to measure the performance of your models or datasets that can then be analyzed in the context of a given problem.
`dataeval.metrics.bias`	Bias metrics check for skewed or imbalanced datasets and incomplete feature representation which may impact model performance.
`dataeval.metrics.estimators`	Estimators calculate performance bounds and the statistical distance between datasets.
`dataeval.metrics.stats`	Statistics metrics calculate a variety of image properties and pixel statistics and label statistics against the images and labels of a dataset.
`dataeval.utils`	The utility classes and functions are provided by DataEval to assist users in setting up architectures that are guaranteed to work with applicable DataEval metrics.
`dataeval.utils.tensorflow`	TensorFlow models are used in out of distribution detectors in the `dataeval.detectors.ood` module.
`dataeval.utils.torch`	PyTorch is the primary backend for metrics that require neural networks.
`dataeval.workflows`	Workflows perform a sequence of actions to analyze the dataset and make predictions.