LassoLarsIC
Lasso model fit with Lars using BIC or AIC for model selection.
The optimization objective for Lasso is:
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new LassoLarsIC(opts?: object): LassoLarsIC;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.copy_X? | boolean | If true, X will be copied; else, it may be overwritten. Default Value true |
opts.criterion? | "aic" | "bic" | The type of criterion to use. Default Value 'aic' |
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.max_iter? | number | Maximum number of iterations to perform. Can be used for early stopping. Default Value 500 |
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.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 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. 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.verbose? | number | boolean | Sets the verbosity amount. Default Value false |
Returns
Defined in: generated/linear_model/LassoLarsIC.ts:23 (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/LassoLarsIC.ts:157 (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.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.y? | ArrayLike | Target values. Will be cast to X’s dtype if necessary. |
Returns
Promise<any>
Defined in: generated/linear_model/LassoLarsIC.ts:174 (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/LassoLarsIC.ts:223 (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/LassoLarsIC.ts:107 (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/LassoLarsIC.ts:258 (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/LassoLarsIC.ts:291 (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.copy_X? | string | boolean | Metadata routing for copy\_X parameter in fit. |
Returns
Promise<any>
Defined in: generated/linear_model/LassoLarsIC.ts:342 (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/LassoLarsIC.ts:379 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/linear_model/LassoLarsIC.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/linear_model/LassoLarsIC.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/linear_model/LassoLarsIC.ts:19 (opens in a new tab)
id
string
Defined in: generated/linear_model/LassoLarsIC.ts:16 (opens in a new tab)
opts
any
Defined in: generated/linear_model/LassoLarsIC.ts:17 (opens in a new tab)
Accessors
alpha_
the alpha parameter chosen by the information criterion
Signature
alpha_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/LassoLarsIC.ts:460 (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 a list, it will be of length n\_targets.
Signature
alphas_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/linear_model/LassoLarsIC.ts:483 (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/LassoLarsIC.ts:412 (opens in a new tab)
criterion_
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].
Signature
criterion_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/linear_model/LassoLarsIC.ts:529 (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/LassoLarsIC.ts:604 (opens in a new tab)
intercept_
independent term in decision function.
Signature
intercept_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/LassoLarsIC.ts:435 (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/LassoLarsIC.ts:579 (opens in a new tab)
n_iter_
number of iterations run by lars_path to find the grid of alphas.
Signature
n_iter_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/LassoLarsIC.ts:506 (opens in a new tab)
noise_variance_
The estimated noise variance from the data used to compute the criterion.
Signature
noise_variance_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/LassoLarsIC.ts:554 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/linear_model/LassoLarsIC.ts:94 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/linear_model/LassoLarsIC.ts:98 (opens in a new tab)