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
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | Constant 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? | boolean | If true, X will be copied; else, it may be overwritten. Default Value true |
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). Default Value true |
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. Default Value true |
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.max_iter? | number | Maximum number of iterations to perform. Default Value 500 |
opts.normalize? | boolean | This 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? | boolean | Restrict 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? | 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. Default Value false |
Returns
Defined in: generated/linear_model/LassoLars.ts:25 (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
| Name | 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/LassoLars.ts:189 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/linear_model/LassoLars.ts:238 (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/linear_model/LassoLars.ts:121 (opens in a new tab)
predict()
Predict using the linear model.
Signature
predict(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Samples. |
Returns
Promise<any>
Defined in: generated/linear_model/LassoLars.ts:273 (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
| Name | Type | Description |
|---|---|---|
opts | object | - |
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? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True values for X. |
Returns
Promise<number>
Defined in: generated/linear_model/LassoLars.ts:306 (opens in a new tab)
set_fit_request()
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:
Signature
set_fit_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.Xy? | string | boolean | Metadata routing for Xy parameter in fit. |
Returns
Promise<any>
Defined in: generated/linear_model/LassoLars.ts:357 (opens in a new tab)
set_score_request()
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:
Signature
set_score_request(opts: object): Promise<any>;Parameters
| Name | 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/LassoLars.ts:394 (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:450 (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:427 (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:496 (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:473 (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:590 (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:519 (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:565 (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:542 (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
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/linear_model/LassoLars.ts:112 (opens in a new tab)