lale.lib.autogen.radius_neighbors_regressor module

class lale.lib.autogen.radius_neighbors_regressor.RadiusNeighborsRegressor(*, radius=1.0, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • radius (float, not for optimizer, default 1.0) – Range of parameter space to use by default for radius_neighbors() queries.

  • weights (union type, default 'uniform') –

    weight function used in prediction

    • callable, not for optimizer

    • or ‘distance’ or ‘uniform’

  • algorithm (‘auto’, ‘ball_tree’, ‘kd_tree’, or ‘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

  • leaf_size (integer, >=30 for optimizer, <=31 for optimizer, uniform distribution, default 30) – Leaf size passed to BallTree or KDTree

  • p (integer, >=1 for optimizer, <=3 for optimizer, uniform distribution, default 2) – Power parameter for the Minkowski metric

  • metric (union type, default 'minkowski') –

    the distance metric to use for the tree

    • callable, not for optimizer

    • or ‘euclidean’, ‘manhattan’, ‘minkowski’, or ‘precomputed’

  • metric_params (union type, not for optimizer, default None) –

    Additional keyword arguments for the metric function.

    • dict

    • or None

  • n_jobs (union type, not for optimizer, default None) –

    The number of parallel jobs to run for neighbors search

    • integer

    • or None

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

  • X (array of items : Any) – Training data

  • y (array of items : Any) – Target values, array of float values, shape = [n_samples] or [n_samples, n_outputs]

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (Any) – Test samples.

Returns

result – Target values

Return type

Any