Class: KNeighborsTransformer

Transform X into a (weighted) graph of k nearest neighbors.

The transformed data is a sparse graph as returned by kneighbors_graph.

Constructors

new KNeighborsTransformer()

new KNeighborsTransformer(opts?): KNeighborsTransformer

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 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. Distance matrices are not supported.
`opts.metric_params`?	`any`	Additional keyword arguments for the metric function.
`opts.mode`?	`"connectivity"` \| `"distance"`	Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, and ‘distance’ will return the distances between neighbors according to the given metric.
`opts.n_jobs`?	`number`	The number of parallel jobs to run for neighbors search. If `\-1`, then the number of jobs is set to the number of CPU cores.
`opts.n_neighbors`?	`number`	Number of neighbors for each sample in the transformed sparse graph. For compatibility reasons, as each sample is considered as its own neighbor, one extra neighbor will be computed when mode == ‘distance’. In this case, the sparse graph contains (n_neighbors + 1) neighbors.
`opts.p`?	`number`	Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. 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.

Returns KNeighborsTransformer

Defined in generated/neighbors/KNeighborsTransformer.ts:25

Properties

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

Accessors

effective_metric_

Get Signature

get effective_metric_(): Promise<string>

The distance metric used. 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/KNeighborsTransformer.ts:489

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/KNeighborsTransformer.ts:516

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/KNeighborsTransformer.ts:570

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/neighbors/KNeighborsTransformer.ts:543

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/KNeighborsTransformer.ts:597

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/KNeighborsTransformer.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/KNeighborsTransformer.ts:142

fit()

fit(opts): Promise<any>

Fit the k-nearest neighbors transformer from the training dataset.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	Training data.
`opts.y`?	`any`	Not used, present for API consistency by convention.

Returns Promise<any>

Defined in generated/neighbors/KNeighborsTransformer.ts:159

fit_transform()

fit_transform(opts): Promise<any[]>

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	Training set.
`opts.y`?	`any`	Not used, present for API consistency by convention.

Returns Promise<any[]>

Defined in generated/neighbors/KNeighborsTransformer.ts:200

get_feature_names_out()

get_feature_names_out(opts): Promise<any>

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: \["class_name0", "class_name1", "class_name2"\].

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.input_features`?	`any`	Only used to validate feature names with the names seen in `fit`.

Returns Promise<any>

Defined in generated/neighbors/KNeighborsTransformer.ts:243

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/KNeighborsTransformer.ts:281

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/KNeighborsTransformer.ts:99

kneighbors()

kneighbors(opts): Promise<ArrayLike[]>

Find the K-neighbors of a point.

Returns indices of and distances to the neighbors of each point.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.n_neighbors`?	`number`	Number of neighbors required for each sample. The default is the value passed to the constructor.
`opts.return_distance`?	`boolean`	Whether or not to return the distances.
`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<ArrayLike[]>

Defined in generated/neighbors/KNeighborsTransformer.ts:319

kneighbors_graph()

kneighbors_graph(opts): Promise<any[]>

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

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.n_neighbors`?	`number`	Number of neighbors for each sample. The default is the value passed to the constructor.
`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. For `metric='precomputed'` the shape should be (n_queries, n_indexed). Otherwise the shape should be (n_queries, n_features).