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.

Constructors

constructor()

Signature

new NeighborhoodComponentsAnalysis(opts?: object): NeighborhoodComponentsAnalysis;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.callback?`	`any`	If 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?`	`number`	Maximum number of iterations in the optimization. `Default Value` `50`
`opts.n_components?`	`number`	Preferred dimensionality of the projected space. If `undefined` it will be set to `n\_features`.
`opts.random_state?`	`number`	A 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?`	`number`	Convergence tolerance for the optimization. `Default Value` `0.00001`
`opts.verbose?`	`number`	If 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?`	`boolean`	If `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

Name	Type
`pythonBridge`	`PythonBridge`

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

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	The training samples.
`opts.y?`	`ArrayLike`	The 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

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Input samples.
`opts.fit_params?`	`any`	Additional fit parameters.
`opts.y?`	`ArrayLike`	Target 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

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/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

Name	Type
`py`	`PythonBridge`

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

Name	Type	Description
`opts`	`object`	-
`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

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Data samples.

Returns

Promise<any>

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

NearestNeighbors NMF