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LassoLarsCV

LassoLarsCV

Cross-validated Lasso, using the LARS algorithm.

See glossary entry for cross-validation estimator.

The optimization objective for Lasso is:

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new LassoLarsCV(opts?: object): LassoLarsCV;

Parameters

NameTypeDescription
opts?object-
opts.copy_X?booleanIf true, X will be copied; else, it may be overwritten. Default Value true
opts.cv?numberDetermines the cross-validation splitting strategy. Possible inputs for cv are:
opts.eps?numberThe 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?booleanWhether 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). Default Value true
opts.max_iter?numberMaximum number of iterations to perform. Default Value 500
opts.max_n_alphas?numberThe maximum number of points on the path used to compute the residuals in the cross-validation. Default Value 1000
opts.n_jobs?numberNumber of CPUs to use during the cross validation. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details.
opts.normalize?booleanThis parameter is ignored when fit\_intercept is set to false. If true, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False. Default Value false
opts.positive?booleanRestrict coefficients to be >= 0. Be aware that you might want to remove fit_intercept which is set true by default. Under the positive restriction the model coefficients do not converge to the ordinary-least-squares solution for small values of alpha. Only coefficients up to the smallest alpha value (alphas\_\[alphas\_ > 0.\].min() when fit_path=true) reached by the stepwise Lars-Lasso algorithm are typically in congruence with the solution of the coordinate descent Lasso estimator. As a consequence using LassoLarsCV only makes sense for problems where a sparse solution is expected and/or reached. Default Value false
opts.precompute?boolean | "auto"Whether to use a precomputed Gram matrix to speed up calculations. If set to 'auto' let us decide. The Gram matrix cannot be passed as argument since we will use only subsets of X. Default Value 'auto'
opts.verbose?number | booleanSets the verbosity amount. Default Value false

Returns

LassoLarsCV

Defined in: generated/linear_model/LassoLarsCV.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/linear_model/LassoLarsCV.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/linear_model/LassoLarsCV.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/linear_model/LassoLarsCV.ts:21 (opens in a new tab)

id

string

Defined in: generated/linear_model/LassoLarsCV.ts:18 (opens in a new tab)

opts

any

Defined in: generated/linear_model/LassoLarsCV.ts:19 (opens in a new tab)

Accessors

active_

Indices of active variables at the end of the path.

Signature

active_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/linear_model/LassoLarsCV.ts:491 (opens in a new tab)

alpha_

the estimated regularization parameter alpha

Signature

alpha_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/LassoLarsCV.ts:374 (opens in a new tab)

alphas_

the different values of alpha along the path

Signature

alphas_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/LassoLarsCV.ts:397 (opens in a new tab)

coef_

parameter vector (w in the formulation formula)

Signature

coef_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/LassoLarsCV.ts:301 (opens in a new tab)

coef_path_

the varying values of the coefficients along the path

Signature

coef_path_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/linear_model/LassoLarsCV.ts:349 (opens in a new tab)

cv_alphas_

all the values of alpha along the path for the different folds

Signature

cv_alphas_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/LassoLarsCV.ts:420 (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/linear_model/LassoLarsCV.ts:539 (opens in a new tab)

intercept_

independent term in decision function.

Signature

intercept_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/LassoLarsCV.ts:324 (opens in a new tab)

mse_path_

the mean square error on left-out for each fold along the path (alpha values given by cv\_alphas)

Signature

mse_path_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/linear_model/LassoLarsCV.ts:445 (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/linear_model/LassoLarsCV.ts:514 (opens in a new tab)

n_iter_

the number of iterations run by Lars with the optimal alpha.

Signature

n_iter_(): Promise<number | ArrayLike>;

Returns

Promise<number | ArrayLike>

Defined in: generated/linear_model/LassoLarsCV.ts:468 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/linear_model/LassoLarsCV.ts:101 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/linear_model/LassoLarsCV.ts:105 (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/linear_model/LassoLarsCV.ts:164 (opens in a new tab)

fit()

Fit the model using X, y as training data.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Training data.
opts.y?ArrayLikeTarget values.

Returns

Promise<any>

Defined in: generated/linear_model/LassoLarsCV.ts:181 (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/linear_model/LassoLarsCV.ts:114 (opens in a new tab)

predict()

Predict using the linear model.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?anySamples.

Returns

Promise<any>

Defined in: generated/linear_model/LassoLarsCV.ts:221 (opens in a new tab)

score()

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.

Signature

score(opts: object): Promise<number>;

Parameters

NameTypeDescription
optsobject-
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.sample_weight?ArrayLikeSample weights.
opts.y?ArrayLikeTrue values for X.

Returns

Promise<number>

Defined in: generated/linear_model/LassoLarsCV.ts:254 (opens in a new tab)