Class: LassoLarsIC
Lasso model fit with Lars using BIC or AIC for model selection.
The optimization objective for Lasso is:
Constructors
new LassoLarsIC()
new LassoLarsIC(
opts?):LassoLarsIC
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.copy_X? | boolean | If true, X will be copied; else, it may be overwritten. |
opts.criterion? | "aic" | "bic" | The type of criterion to use. |
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.max_iter? | number | Maximum number of iterations to perform. Can be used for early stopping. |
opts.noise_variance? | number | The estimated noise variance of the data. If undefined, an unbiased estimate is computed by an OLS model. However, it is only possible in the case where n_samples > n_features + fit_intercept. |
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 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 LassoLarsIC only makes sense for problems where a sparse solution is expected and/or reached. |
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.verbose? | number | boolean | Sets the verbosity amount. |
Returns LassoLarsIC
Defined in generated/linear_model/LassoLarsIC.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/linear_model/LassoLarsIC.ts:21 |
_isInitialized | boolean | false | generated/linear_model/LassoLarsIC.ts:20 |
_py | PythonBridge | undefined | generated/linear_model/LassoLarsIC.ts:19 |
id | string | undefined | generated/linear_model/LassoLarsIC.ts:16 |
opts | any | undefined | generated/linear_model/LassoLarsIC.ts:17 |
Accessors
alpha_
Get Signature
get alpha_():
Promise<number>
the alpha parameter chosen by the information criterion
Returns Promise<number>
Defined in generated/linear_model/LassoLarsIC.ts:429
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 a list, it will be of length n_targets.
Returns Promise<ArrayLike>
Defined in generated/linear_model/LassoLarsIC.ts:452
coef_
Get Signature
get coef_():
Promise<ArrayLike>
parameter vector (w in the formulation formula)
Returns Promise<ArrayLike>
Defined in generated/linear_model/LassoLarsIC.ts:381
criterion_
Get Signature
get criterion_():
Promise<ArrayLike>
The value of the information criteria (‘aic’, ‘bic’) across all alphas. The alpha which has the smallest information criterion is chosen, as specified in [1].
Returns Promise<ArrayLike>
Defined in generated/linear_model/LassoLarsIC.ts:498
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/LassoLarsIC.ts:573
intercept_
Get Signature
get intercept_():
Promise<number>
independent term in decision function.
Returns Promise<number>
Defined in generated/linear_model/LassoLarsIC.ts:404
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/LassoLarsIC.ts:548
n_iter_
Get Signature
get n_iter_():
Promise<number>
number of iterations run by lars_path to find the grid of alphas.
Returns Promise<number>
Defined in generated/linear_model/LassoLarsIC.ts:475
noise_variance_
Get Signature
get noise_variance_():
Promise<number>
The estimated noise variance from the data used to compute the criterion.
Returns Promise<number>
Defined in generated/linear_model/LassoLarsIC.ts:523
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/LassoLarsIC.ts:87
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/LassoLarsIC.ts:139
fit()
fit(
opts):Promise<any>
Fit the model using X, y as training data.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.copy_X? | boolean | If provided, this parameter will override the choice of copy_X made at instance creation. If true, X will be copied; else, it may be overwritten. |
opts.X? | ArrayLike[] | Training data. |
opts.y? | ArrayLike | Target values. Will be cast to X’s dtype if necessary. |
Returns Promise<any>
Defined in generated/linear_model/LassoLarsIC.ts:156
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/LassoLarsIC.ts:200
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/LassoLarsIC.ts:100
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/LassoLarsIC.ts:234
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/LassoLarsIC.ts:267
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.copy_X? | string | boolean | Metadata routing for copy_X parameter in fit. |
Returns Promise<any>
Defined in generated/linear_model/LassoLarsIC.ts:313
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/LassoLarsIC.ts:349