dataeval.workflows.Sufficiency#

class dataeval.workflows.Sufficiency(model, train_ds, test_ds, train_fn, eval_fn, runs=1, substeps=5, train_kwargs=None, eval_kwargs=None)#

Project dataset sufficiency using given a model and evaluation criteria

Parameters:

  • model (nn.Module) – Model that will be trained for each subset of data

  • train_ds (torch.Dataset) – Full training data that will be split for each run

  • test_ds (torch.Dataset) – Data that will be used for every run’s evaluation

  • train_fn (Callable[[nn.Module, Dataset, Sequence[int]], None]) – Function which takes a model (torch.nn.Module), a dataset (torch.utils.data.Dataset), indices to train on and executes model training against the data.

  • eval_fn (Callable[[nn.Module, Dataset], Mapping[str, float | ArrayLike]]) – Function which takes a model (torch.nn.Module), a dataset (torch.utils.data.Dataset) and returns a dictionary of metric values (Mapping[str, float]) which is used to assess model performance given the model and data.

  • runs (int, default 1) – Number of models to run over all subsets

  • substeps (int, default 5) – Total number of dataset partitions that each model will train on

  • train_kwargs (Mapping | None, default None) – Additional arguments required for custom training function

  • eval_kwargs (Mapping | None, default None) – Additional arguments required for custom evaluation function

evaluate(eval_at=None, niter=1000)#

Creates data indices, trains models, and returns plotting data

Parameters:

  • eval_at (int | Iterable[int] | None, default None) – Specify this to collect accuracies over a specific set of dataset lengths, rather than letting sufficiency internally create the lengths to evaluate at.

  • niter (int, default 1000) – Iterations to perform when using the basin-hopping method to curve-fit measure(s).

Returns:

Dataclass containing the average of each measure per substep

Return type:

SufficiencyOutput

Raises:

ValueError – If eval_at is not numerical

Examples

>>> suff = Sufficiency(
...     model=model,
...     train_ds=train_ds,
...     test_ds=test_ds,
...     train_fn=train_fn,
...     eval_fn=eval_fn,
...     runs=3,
...     substeps=5,
... )
>>> suff.evaluate()
SufficiencyOutput(steps=array([  1,   3,  10,  31, 100], dtype=uint32), params={'test': array([ 0., 42.,  0.])}, measures={'test': array([1., 1., 1., 1., 1.])})