KNeighborsTransformer
Transform X into a (weighted) graph of k nearest neighbors.
The transformed data is a sparse graph as returned by kneighbors_graph.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new KNeighborsTransformer(opts?: object): KNeighborsTransformer;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.algorithm? | "auto" | "ball_tree" | "kd_tree" | "brute" | Algorithm used to compute the nearest neighbors: Default Value 'auto' |
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. Default Value 30 |
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 (opens in a new tab) 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. Default Value 'minkowski' |
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. Default Value 'distance' |
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. Default Value 5 |
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. Default Value 2 |
Returns
Defined in: generated/neighbors/KNeighborsTransformer.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/neighbors/KNeighborsTransformer.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/neighbors/KNeighborsTransformer.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/neighbors/KNeighborsTransformer.ts:21 (opens in a new tab)
id
string
Defined in: generated/neighbors/KNeighborsTransformer.ts:18 (opens in a new tab)
opts
any
Defined in: generated/neighbors/KNeighborsTransformer.ts:19 (opens in a new tab)
Accessors
effective_metric_
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.
Signature
effective_metric_(): Promise<string>;Returns
Promise<string>
Defined in: generated/neighbors/KNeighborsTransformer.ts:472 (opens in a new tab)
effective_metric_params_
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’.
Signature
effective_metric_params_(): Promise<any>;Returns
Promise<any>
Defined in: generated/neighbors/KNeighborsTransformer.ts:499 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/neighbors/KNeighborsTransformer.ts:553 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/neighbors/KNeighborsTransformer.ts:526 (opens in a new tab)
n_samples_fit_
Number of samples in the fitted data.
Signature
n_samples_fit_(): Promise<number>;Returns
Promise<number>
Defined in: generated/neighbors/KNeighborsTransformer.ts:580 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/neighbors/KNeighborsTransformer.ts:86 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/neighbors/KNeighborsTransformer.ts:90 (opens in a new tab)
Methods
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/neighbors/KNeighborsTransformer.ts:149 (opens in a new tab)
fit()
Fit the k-nearest neighbors transformer from the training dataset.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | 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:166 (opens in a new tab)
fit_transform()
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Signature
fit_transform(opts: object): Promise<any[]>;Parameters
| Name | 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:208 (opens in a new tab)
get_feature_names_out()
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: \["class\_name0", "class\_name1", "class\_name2"\].
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | 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:253 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/neighbors/KNeighborsTransformer.ts:99 (opens in a new tab)
kneighbors()
Find the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.
Signature
kneighbors(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
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. |
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. Default Value true |
Returns
Promise<ArrayLike[]>
Defined in: generated/neighbors/KNeighborsTransformer.ts:293 (opens in a new tab)
kneighbors_graph()
Compute the (weighted) graph of k-Neighbors for points in X.
Signature
kneighbors_graph(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
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). |
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. Default Value 'connectivity' |
opts.n_neighbors? | number | Number of neighbors for each sample. The default is the value passed to the constructor. |
Returns
Promise<any[]>
Defined in: generated/neighbors/KNeighborsTransformer.ts:344 (opens in a new tab)
set_output()
Set output container.
See Introducing the set_output API for an example on how to use the API.
Signature
set_output(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | Configure output of transform and fit\_transform. |
Returns
Promise<any>
Defined in: generated/neighbors/KNeighborsTransformer.ts:398 (opens in a new tab)
transform()
Compute the (weighted) graph of Neighbors for points in X.
Signature
transform(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Sample data. |
Returns
Promise<any[]>
Defined in: generated/neighbors/KNeighborsTransformer.ts:435 (opens in a new tab)