skada.NearestNeighborReweightAdapter

class skada.NearestNeighborReweightAdapter(n_neighbors=1, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None, laplace_smoothing=False)[source]

Adapter based on re-weighting samples using a KNN,

See [24] for details.

Parameters:
n_neighborsint, default=1

Number of neighbors to use for the KNN

laplace_smoothingbool, default=False, optional

True if we want to use laplace smoothing, and thus adding 1 to all our weights (to prevent some of them to be 0)

weights{'uniform', 'distance'}, callable or None, default='uniform'

Weight function used in prediction. Possible values:

  • 'uniform' : uniform weights. All points in each neighborhood are weighted equally.

  • 'distance' : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.

  • [callable] : a user-defined function which accepts an array of distances, and returns an array of the same shape containing the weights.

Refer to the example entitled Nearest Neighbors Classification showing the impact of the weights parameter on the decision boundary.

algorithm{'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'

Algorithm used to compute the nearest neighbors:

  • 'ball_tree' will use BallTree

  • 'kd_tree' will use KDTree

  • 'brute' will use a brute-force search.

  • 'auto' will attempt to decide the most appropriate algorithm based on the values passed to fit() method.

Note: fitting on sparse input will override the setting of this parameter, using brute force.

leaf_sizeint, default=30

Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.

pfloat, default=2

Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected to be positive.

metricstr or callable, default='minkowski'

Metric to use for distance computation. Default is "minkowski", which results in the standard Euclidean distance when p = 2. See the documentation of scipy.spatial.distance and the metrics listed in distance_metrics for valid metric values.

If metric is "precomputed", X is assumed to be a distance matrix and must be square during fit. X may be a sparse graph, in which case only "nonzero" elements may be considered neighbors.

If metric is a callable function, it takes two arrays representing 1D vectors as inputs and must return one value indicating the distance between those vectors. This works for Scipy's metrics, but is less efficient than passing the metric name as a string.

metric_paramsdict, default=None

Additional keyword arguments for the metric function.

n_jobsint, default=None

The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details. Doesn't affect fit() method.

References

[24]

Nearest neighbor-based importance weighting. In 2012 IEEE International Workshop on Machine Learning for Signal Processing, pages 1–6. IEEE.

fit(X, y=None, *, sample_domain=None)[source]

Fit adaptation parameters.

Parameters:
Xarray-like, shape (n_samples, n_features)

The source data.

yarray-like, shape (n_samples,)

The source labels.

sample_domainarray-like, shape (n_samples,)

The domain labels (same as sample_domain).

Returns:
selfobject

Returns self.

set_fit_request(*, sample_domain: bool | None | str = '$UNCHANGED$') → NearestNeighborReweightAdapter

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sample_domainstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_domain parameter in fit.

Returns:
selfobject

The updated object.

set_transform_request(*, allow_source: bool | None | str = '$UNCHANGED$', sample_domain: bool | None | str = '$UNCHANGED$') → NearestNeighborReweightAdapter

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
allow_sourcestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for allow_source parameter in transform.

sample_domainstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_domain parameter in transform.

Returns:
selfobject

The updated object.