Class: Lars

Least Angle Regression model a.k.a. LAR.

Constructors

new Lars()

new Lars(opts?): Lars

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.copy_X`?	`boolean`	If `true`, X will be copied; else, it may be overwritten.
`opts.eps`?	`number`	The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the `tol` parameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization.
`opts.fit_intercept`?	`boolean`	Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).
`opts.fit_path`?	`boolean`	If `true` the full path is stored in the `coef_path_` attribute. If you compute the solution for a large problem or many targets, setting `fit_path` to `false` will lead to a speedup, especially with a small alpha.
`opts.jitter`?	`number`	Upper bound on a uniform noise parameter to be added to the `y` values, to satisfy the model’s assumption of one-at-a-time computations. Might help with stability.
`opts.n_nonzero_coefs`?	`number`	Target number of non-zero coefficients. Use `np.inf` for no limit.
`opts.precompute`?	`boolean` \| `ArrayLike` \| `"auto"`	Whether to use a precomputed Gram matrix to speed up calculations. If set to `'auto'` let us decide. The Gram matrix can also be passed as argument.
`opts.random_state`?	`number`	Determines random number generation for jittering. Pass an int for reproducible output across multiple function calls. See Glossary. Ignored if `jitter` is `undefined`.
`opts.verbose`?	`number` \| `boolean`	Sets the verbosity amount.

Returns Lars

Defined in generated/linear_model/Lars.ts:23

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/linear_model/Lars.ts:21
`_isInitialized`	`boolean`	`false`	generated/linear_model/Lars.ts:20
`_py`	`PythonBridge`	`undefined`	generated/linear_model/Lars.ts:19
`id`	`string`	`undefined`	generated/linear_model/Lars.ts:16
`opts`	`any`	`undefined`	generated/linear_model/Lars.ts:17

Accessors

active_

Get Signature

get active_(): Promise<any[]>

Indices of active variables at the end of the path. If this is a list of list, the length of the outer list is n_targets.

Returns Promise<any[]>

Defined in generated/linear_model/Lars.ts:397

alphas_

Get Signature

get alphas_(): Promise<ArrayLike>

Maximum of covariances (in absolute value) at each iteration. n_alphas is either max_iter, n_features or the number of nodes in the path with alpha >= alpha_min, whichever is smaller. If this is a list of array-like, the length of the outer list is n_targets.

Returns Promise<ArrayLike>

Defined in generated/linear_model/Lars.ts:375

coef_

Get Signature

get coef_(): Promise<ArrayLike>

Parameter vector (w in the formulation formula).

Returns Promise<ArrayLike>

Defined in generated/linear_model/Lars.ts:442

coef_path_

Get Signature

get coef_path_(): Promise<ArrayLike[]>

The varying values of the coefficients along the path. It is not present if the fit_path parameter is false. If this is a list of array-like, the length of the outer list is n_targets.

Returns Promise<ArrayLike[]>

Defined in generated/linear_model/Lars.ts:419

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/linear_model/Lars.ts:532

intercept_

Get Signature

get intercept_(): Promise<number | ArrayLike>

Independent term in decision function.

Returns Promise<number | ArrayLike>

Defined in generated/linear_model/Lars.ts:464

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/linear_model/Lars.ts:509

n_iter_

Get Signature

get n_iter_(): Promise<number | ArrayLike>

The number of iterations taken by lars_path to find the grid of alphas for each target.

Returns Promise<number | ArrayLike>

Defined in generated/linear_model/Lars.ts:487

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/linear_model/Lars.ts:85

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/linear_model/Lars.ts:136

fit()

fit(opts): Promise<any>

Fit the model using X, y as training data.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	Training data.
`opts.Xy`?	`ArrayLike`	Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed.
`opts.y`?	`ArrayLike`	Target values.

Returns Promise<any>

Defined in generated/linear_model/Lars.ts:153

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/linear_model/Lars.ts:196

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/linear_model/Lars.ts:98

predict()

predict(opts): Promise<any>

Predict using the linear model.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`	Samples.

Returns Promise<any>

Defined in generated/linear_model/Lars.ts:228

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/linear_model/Lars.ts:261

set_fit_request()

set_fit_request(opts): Promise<any>

Request metadata passed to the fit 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.Xy`?	`string` \| `boolean`	Metadata routing for `Xy` parameter in `fit`.

Returns Promise<any>

Defined in generated/linear_model/Lars.ts:307

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/linear_model/Lars.ts:343

LabelSpreading LarsCV