pydvl.valuation.utility.knn

KNNClassifierUtility

KNNClassifierUtility(
    model: KNeighborsClassifier,
    test_data: Dataset,
    *,
    catch_errors: bool = True,
    show_warnings: bool = False,
    cache_backend: CacheBackend | None = None,
    cached_func_options: CachedFuncConfig | None = None,
    clone_before_fit: bool = True
)

Bases: ModelUtility[Sample, KNeighborsClassifier]

Utility object for KNN Classifiers.

The utility function is the likelihood of the true class given the model's prediction.

This works both as a Utility object for general game theoretic valuation methods and for specialized valuation methods for KNN classifiers.

PARAMETER	DESCRIPTION
model	A KNN classifier model. TYPE: KNeighborsClassifier
test_data	The test data to evaluate the model on. TYPE: Dataset
catch_errors	set to True to catch the errors when fit() fails. This could happen in several steps of the pipeline, e.g. when too little training data is passed, which happens often during Shapley value calculations. When this happens, the scorer's default value is returned as a score and computation continues. TYPE: bool DEFAULT: True
show_warnings	Set to False to suppress warnings thrown by fit(). TYPE: bool DEFAULT: False
cache_backend	Optional instance of [CacheBackend][ pydvl.utils.caching.base.CacheBackend] used to wrap the _utility method of the Utility instance. By default, this is set to None and that means that the utility evaluations will not be cached. TYPE: CacheBackend | None DEFAULT: None
cached_func_options	Optional configuration object for cached utility evaluation. TYPE: CachedFuncConfig | None DEFAULT: None
clone_before_fit	If True, the model will be cloned before calling fit(). TYPE: bool DEFAULT: True

Source code in src/pydvl/valuation/utility/knn.py

def __init__(
    self,
    model: KNeighborsClassifier,
    test_data: Dataset,
    *,
    catch_errors: bool = True,
    show_warnings: bool = False,
    cache_backend: CacheBackend | None = None,
    cached_func_options: CachedFuncConfig | None = None,
    clone_before_fit: bool = True,
):
    scorer = KNNClassifierScorer(test_data)

    self.test_data = test_data

    super().__init__(
        model=model,
        scorer=scorer,
        catch_errors=catch_errors,
        show_warnings=show_warnings,
        cache_backend=cache_backend,
        cached_func_options=cached_func_options,
        clone_before_fit=clone_before_fit,
    )

cache_stats property

cache_stats: CacheStats | None

Cache statistics are gathered when cache is enabled. See CacheStats for all fields returned.

__call__

__call__(sample: SampleT | None) -> float

PARAMETER	DESCRIPTION
sample	contains a subset of valid indices for the x_train attribute of Dataset. TYPE: SampleT | None

Source code in src/pydvl/valuation/utility/modelutility.py

def __call__(self, sample: SampleT | None) -> float:
    """
    Args:
        sample: contains a subset of valid indices for the
            `x_train` attribute of [Dataset][pydvl.utils.dataset.Dataset].
    """
    if sample is None or len(sample.subset) == 0:
        return self.scorer.default

    return cast(float, self._utility_wrapper(sample))