Class: 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:
Constructors
new LassoLars()
new LassoLars(
opts?):LassoLars
Parameters
| Parameter | 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. |
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.max_iter? | number | Maximum number of iterations to perform. |
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. |
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 LassoLars
Defined in generated/linear_model/LassoLars.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/linear_model/LassoLars.ts:23 |
_isInitialized | boolean | false | generated/linear_model/LassoLars.ts:22 |
_py | PythonBridge | undefined | generated/linear_model/LassoLars.ts:21 |
id | string | undefined | generated/linear_model/LassoLars.ts:18 |
opts | any | undefined | generated/linear_model/LassoLars.ts:19 |
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/LassoLars.ts:417
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/LassoLars.ts:394
coef_
Get Signature
get coef_():
Promise<ArrayLike>
Parameter vector (w in the formulation formula).
Returns Promise<ArrayLike>
Defined in generated/linear_model/LassoLars.ts:463
coef_path_
Get Signature
get coef_path_():
Promise<ArrayLike[]>
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.
Returns Promise<ArrayLike[]>
Defined in generated/linear_model/LassoLars.ts:440
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/LassoLars.ts:557
intercept_
Get Signature
get intercept_():
Promise<number|ArrayLike>
Independent term in decision function.
Returns Promise<number | ArrayLike>
Defined in generated/linear_model/LassoLars.ts:486
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/LassoLars.ts:532
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/LassoLars.ts:509
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/LassoLars.ts:101
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/LassoLars.ts:152
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/LassoLars.ts:169
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/LassoLars.ts:213
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/LassoLars.ts:114
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/LassoLars.ts:247
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/LassoLars.ts:280
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/LassoLars.ts:326
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/LassoLars.ts:362