Class: RadiusNeighborsRegressor

Regression based on neighbors within a fixed radius.

The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.

Constructors

new RadiusNeighborsRegressor()

new RadiusNeighborsRegressor(opts?): RadiusNeighborsRegressor

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.algorithm`?	`"auto"` \| `"ball_tree"` \| `"kd_tree"` \| `"brute"`	Algorithm used to compute the nearest neighbors:
`opts.leaf_size`?	`number`	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.
`opts.metric`?	`string`	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.
`opts.metric_params`?	`any`	Additional keyword arguments for the metric function.
`opts.n_jobs`?	`number`	The number of parallel jobs to run for neighbors search. `undefined` means 1 unless in a `joblib.parallel_backend` context. `\-1` means using all processors. See Glossary for more details.
`opts.p`?	`number`	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.
`opts.radius`?	`number`	Range of parameter space to use by default for `radius_neighbors` queries.
`opts.weights`?	`"uniform"` \| `"distance"`	Weight function used in prediction. Possible values:

Returns RadiusNeighborsRegressor

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/neighbors/RadiusNeighborsRegressor.ts:23
`_isInitialized`	`boolean`	`false`	generated/neighbors/RadiusNeighborsRegressor.ts:22
`_py`	`PythonBridge`	`undefined`	generated/neighbors/RadiusNeighborsRegressor.ts:21
`id`	`string`	`undefined`	generated/neighbors/RadiusNeighborsRegressor.ts:18
`opts`	`any`	`undefined`	generated/neighbors/RadiusNeighborsRegressor.ts:19

Accessors

effective_metric_

Get Signature

get effective_metric_(): Promise<string>

The distance metric to use. It will be same as the metric parameter or a synonym of it, e.g. ‘euclidean’ if the metric parameter set to ‘minkowski’ and p parameter set to 2.

Returns Promise<string>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:476

effective_metric_params_

Get Signature

get effective_metric_params_(): Promise<any>

Additional keyword arguments for the metric function. For most metrics will be same with metric_params parameter, but may also contain the p parameter value if the effective_metric_ attribute is set to ‘minkowski’.

Returns Promise<any>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:503

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

Names of features seen during fit. Defined only when X has feature names that are all strings.

Returns Promise<ArrayLike>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:557

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:530

n_samples_fit_

Get Signature

get n_samples_fit_(): Promise<number>

Number of samples in the fitted data.

Returns Promise<number>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:584

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:86

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Returns Promise<void>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:142

fit()

fit(opts): Promise<any>

Fit the radius neighbors regressor from the training dataset.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	Training data.
`opts.y`?	`any`	Target values.

Returns Promise<any>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:159

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.routing`?	`any`	A `MetadataRequest` encapsulating routing information.

Returns Promise<any>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:200

init()

init(py): Promise<void>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:99

predict()

predict(opts): Promise<ArrayLike>

Predict the target for the provided data.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`	Test samples.

Returns Promise<ArrayLike>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:236

radius_neighbors()

radius_neighbors(opts): Promise<any>

Find the neighbors within a given radius of a point or points.

Return the indices and distances of each point from the dataset lying in a ball with size radius around the points of the query array. Points lying on the boundary are included in the results.

The result points are not necessarily sorted by distance to their query point.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.radius`?	`number`	Limiting distance of neighbors to return. The default is the value passed to the constructor.
`opts.return_distance`?	`boolean`	Whether or not to return the distances.
`opts.sort_results`?	`boolean`	If `true`, the distances and indices will be sorted by increasing distances before being returned. If `false`, the results may not be sorted. If `return_distance=False`, setting `sort_results=True` will result in an error.
`opts.X`?	`any`	The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.

Returns Promise<any>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:276

radius_neighbors_graph()

radius_neighbors_graph(opts): Promise<any[]>

Compute the (weighted) graph of Neighbors for points in X.

Neighborhoods are restricted the points at a distance lower than radius.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.mode`?	`"connectivity"` \| `"distance"`	Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are distances between points, type of distance depends on the selected metric parameter in NearestNeighbors class.
`opts.radius`?	`number`	Radius of neighborhoods. The default is the value passed to the constructor.
`opts.sort_results`?	`boolean`	If `true`, in each row of the result, the non-zero entries will be sorted by increasing distances. If `false`, the non-zero entries may not be sorted. Only used with mode=’distance’.
`opts.X`?	`ArrayLike`	The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.

Returns Promise<any[]>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:333

score()

score(opts): Promise<number>

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true \- y_pred)\*\* 2).sum() and \(v\) is the total sum of squares ((y_true \- y_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`opts.X`?	`ArrayLike`[]	Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape `(n_samples, n_samples_fitted)`, where `n_samples_fitted` is the number of samples used in the fitting for the estimator.
`opts.y`?	`ArrayLike`	True values for `X`.

Returns Promise<number>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:390

set_score_request()

set_score_request(opts): Promise<any>

Request metadata passed to the score 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:

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`string` \| `boolean`	Metadata routing for `sample_weight` parameter in `score`.

Returns Promise<any>

Defined in generated/neighbors/RadiusNeighborsRegressor.ts:440

RadiusNeighborsClassifier RadiusNeighborsTransformer