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LassoLars

LassoLars

Lasso model fit with Least Angle Regression a.k.a. Lars.

It is a Linear Model trained with an L1 prior as regularizer.

The optimization objective for Lasso is:

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new LassoLars(opts?: object): LassoLars;

Parameters

NameTypeDescription
opts?object-
opts.alpha?numberConstant that multiplies the penalty term. Defaults to 1.0. alpha \= 0 is equivalent to an ordinary least square, solved by LinearRegression. For numerical reasons, using alpha \= 0 with the LassoLars object is not advised and you should prefer the LinearRegression object. Default Value 1
opts.copy_X?booleanIf true, X will be copied; else, it may be overwritten. Default Value true
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.fit_path?booleanIf 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. Default Value true
opts.jitter?numberUpper 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.max_iter?numberMaximum number of iterations to perform. Default Value 500
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 will 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. Default Value false
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. Default Value 'auto'
opts.random_state?numberDetermines 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 | booleanSets the verbosity amount. Default Value false

Returns

LassoLars

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

Properties

_isDisposed

boolean = false

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

_isInitialized

boolean = false

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

_py

PythonBridge

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

id

string

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

opts

any

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

Accessors

active_

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.

Signature

active_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/linear_model/LassoLars.ts:339 (opens in a new tab)

alphas_

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.

Signature

alphas_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/LassoLars.ts:316 (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/LassoLars.ts:385 (opens in a new tab)

coef_path_

If a list is passed it’s expected to be one of n_targets such arrays. 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.

Signature

coef_path_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/linear_model/LassoLars.ts:362 (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/LassoLars.ts:479 (opens in a new tab)

intercept_

Independent term in decision function.

Signature

intercept_(): Promise<number | ArrayLike>;

Returns

Promise<number | ArrayLike>

Defined in: generated/linear_model/LassoLars.ts:408 (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/LassoLars.ts:454 (opens in a new tab)

n_iter_

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

Signature

n_iter_(): Promise<number | ArrayLike>;

Returns

Promise<number | ArrayLike>

Defined in: generated/linear_model/LassoLars.ts:431 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/linear_model/LassoLars.ts:108 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/linear_model/LassoLars.ts:112 (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/LassoLars.ts:172 (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.Xy?ArrayLikeXy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed.
opts.y?ArrayLikeTarget values.

Returns

Promise<any>

Defined in: generated/linear_model/LassoLars.ts:189 (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/LassoLars.ts:121 (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/LassoLars.ts:236 (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/LassoLars.ts:269 (opens in a new tab)