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NeighborhoodComponentsAnalysis

NeighborhoodComponentsAnalysis

Neighborhood Components Analysis.

Neighborhood Component Analysis (NCA) is a machine learning algorithm for metric learning. It learns a linear transformation in a supervised fashion to improve the classification accuracy of a stochastic nearest neighbors rule in the transformed space.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new NeighborhoodComponentsAnalysis(opts?: object): NeighborhoodComponentsAnalysis;

Parameters

NameTypeDescription
opts?object-
opts.callback?anyIf not undefined, this function is called after every iteration of the optimizer, taking as arguments the current solution (flattened transformation matrix) and the number of iterations. This might be useful in case one wants to examine or store the transformation found after each iteration.
opts.init?ArrayLike[] | "auto" | "random" | "identity" | "pca" | "lda"Initialization of the linear transformation. Possible options are 'auto', 'pca', 'lda', 'identity', 'random', and a numpy array of shape (n\_features\_a, n\_features\_b). Default Value 'auto'
opts.max_iter?numberMaximum number of iterations in the optimization. Default Value 50
opts.n_components?numberPreferred dimensionality of the projected space. If undefined it will be set to n\_features.
opts.random_state?numberA pseudo random number generator object or a seed for it if int. If init='random', random\_state is used to initialize the random transformation. If init='pca', random\_state is passed as an argument to PCA when initializing the transformation. Pass an int for reproducible results across multiple function calls. See Glossary.
opts.tol?numberConvergence tolerance for the optimization. Default Value 0.00001
opts.verbose?numberIf 0, no progress messages will be printed. If 1, progress messages will be printed to stdout. If > 1, progress messages will be printed and the disp parameter of scipy.optimize.minimize (opens in a new tab) will be set to verbose \- 2. Default Value 0
opts.warm_start?booleanIf true and fit has been called before, the solution of the previous call to fit is used as the initial linear transformation (n\_components and init will be ignored). Default Value false

Returns

NeighborhoodComponentsAnalysis

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:21 (opens in a new tab)

id

string

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:18 (opens in a new tab)

opts

any

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:19 (opens in a new tab)

Accessors

components_

The linear transformation learned during fitting.

Signature

components_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:380 (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/NeighborhoodComponentsAnalysis.ts:488 (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/NeighborhoodComponentsAnalysis.ts:407 (opens in a new tab)

n_iter_

Counts the number of iterations performed by the optimizer.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:434 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:82 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:86 (opens in a new tab)

random_state_

Pseudo random number generator object used during initialization.

Signature

random_state_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:461 (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/NeighborhoodComponentsAnalysis.ts:147 (opens in a new tab)

fit()

Fit the model according to the given training data.

Signature

fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The training samples.
opts.y?ArrayLikeThe corresponding training labels.

Returns

Promise<any>

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:164 (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

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Input samples.
opts.fit_params?anyAdditional fit parameters.
opts.y?ArrayLikeTarget values (undefined for unsupervised transformations).

Returns

Promise<any[]>

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:210 (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

NameTypeDescription
optsobject-
opts.input_features?anyOnly used to validate feature names with the names seen in fit.

Returns

Promise<any>

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:264 (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

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:95 (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

NameTypeDescription
optsobject-
opts.transform?"default" | "pandas"Configure output of transform and fit\_transform.

Returns

Promise<any>

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:304 (opens in a new tab)

transform()

Apply the learned transformation to the given data.

Signature

transform(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Data samples.

Returns

Promise<any>

Defined in: generated/neighbors/NeighborhoodComponentsAnalysis.ts:342 (opens in a new tab)