Class: LassoCV
Lasso linear model with iterative fitting along a regularization path.
See glossary entry for cross-validation estimator.
The best model is selected by cross-validation.
The optimization objective for Lasso is:
Constructors
new LassoCV()
new LassoCV(
opts?):LassoCV
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.alphas? | ArrayLike | List of alphas where to compute the models. If undefined alphas are set automatically. |
opts.copy_X? | boolean | If true, X will be copied; else, it may be overwritten. |
opts.cv? | number | Determines the cross-validation splitting strategy. Possible inputs for cv are: |
opts.eps? | number | Length of the path. eps=1e-3 means that alpha_min / alpha_max \= 1e-3. |
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 | The maximum number of iterations. |
opts.n_alphas? | number | Number of alphas along the regularization path. |
opts.n_jobs? | number | Number of CPUs to use during the cross validation. undefined means 1 unless in a joblib.parallel_backend context. \-1 means using all processors. See Glossary for more details. |
opts.positive? | boolean | If positive, restrict regression coefficients to be positive. |
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 | The seed of the pseudo random number generator that selects a random feature to update. Used when selection == ‘random’. Pass an int for reproducible output across multiple function calls. See Glossary. |
opts.selection? | "random" | "cyclic" | If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e-4. |
opts.tol? | number | The tolerance for the optimization: if the updates are smaller than tol, the optimization code checks the dual gap for optimality and continues until it is smaller than tol. |
opts.verbose? | number | boolean | Amount of verbosity. |
Returns LassoCV
Defined in generated/linear_model/LassoCV.ts:27
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/linear_model/LassoCV.ts:25 |
_isInitialized | boolean | false | generated/linear_model/LassoCV.ts:24 |
_py | PythonBridge | undefined | generated/linear_model/LassoCV.ts:23 |
id | string | undefined | generated/linear_model/LassoCV.ts:20 |
opts | any | undefined | generated/linear_model/LassoCV.ts:21 |
Accessors
alpha_
Get Signature
get alpha_():
Promise<number>
The amount of penalization chosen by cross validation.
Returns Promise<number>
Defined in generated/linear_model/LassoCV.ts:530
alphas_
Get Signature
get alphas_():
Promise<ArrayLike>
The grid of alphas used for fitting.
Returns Promise<ArrayLike>
Defined in generated/linear_model/LassoCV.ts:620
coef_
Get Signature
get coef_():
Promise<ArrayLike>
Parameter vector (w in the cost function formula).
Returns Promise<ArrayLike>
Defined in generated/linear_model/LassoCV.ts:552
dual_gap_
Get Signature
get dual_gap_():
Promise<number|ArrayLike>
The dual gap at the end of the optimization for the optimal alpha (alpha_).
Returns Promise<number | ArrayLike>
Defined in generated/linear_model/LassoCV.ts:643
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/LassoCV.ts:714
intercept_
Get Signature
get intercept_():
Promise<number|ArrayLike>
Independent term in decision function.
Returns Promise<number | ArrayLike>
Defined in generated/linear_model/LassoCV.ts:574
mse_path_
Get Signature
get mse_path_():
Promise<ArrayLike[]>
Mean square error for the test set on each fold, varying alpha.
Returns Promise<ArrayLike[]>
Defined in generated/linear_model/LassoCV.ts:597
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/LassoCV.ts:689
n_iter_
Get Signature
get n_iter_():
Promise<number>
Number of iterations run by the coordinate descent solver to reach the specified tolerance for the optimal alpha.
Returns Promise<number>
Defined in generated/linear_model/LassoCV.ts:666
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/LassoCV.ts:122
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/LassoCV.ts:173
fit()
fit(
opts):Promise<any>
Fit Lasso model with coordinate descent.
Fit is on grid of alphas and best alpha estimated by cross-validation.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.params? | any | Parameters to be passed to the CV splitter. |
opts.sample_weight? | number | ArrayLike | Sample weights used for fitting and evaluation of the weighted mean squared error of each cv-fold. Note that the cross validated MSE that is finally used to find the best model is the unweighted mean over the (weighted) MSEs of each test fold. |
opts.X? | ArrayLike | Training data. Pass directly as Fortran-contiguous data to avoid unnecessary memory duplication. If y is mono-output, X can be sparse. Note that large sparse matrices and arrays requiring int64 indices are not accepted. |
opts.y? | ArrayLike | Target values. |
Returns Promise<any>
Defined in generated/linear_model/LassoCV.ts:192
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 MetadataRouter encapsulating routing information. |
Returns Promise<any>
Defined in generated/linear_model/LassoCV.ts:241
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/LassoCV.ts:135
path()
path(
opts):Promise<ArrayLike>
Compute Lasso path with coordinate descent.
The Lasso optimization function varies for mono and multi-outputs.
For mono-output tasks it is:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.alphas? | ArrayLike | List of alphas where to compute the models. If undefined alphas are set automatically. |
opts.coef_init? | ArrayLike | The initial values of the coefficients. |
opts.copy_X? | boolean | If true, X will be copied; else, it may be overwritten. |
opts.eps? | number | Length of the path. eps=1e-3 means that alpha_min / alpha_max \= 1e-3. |
opts.n_alphas? | number | Number of alphas along the regularization path. |
opts.params? | any | Keyword arguments passed to the coordinate descent solver. |
opts.positive? | boolean | If set to true, forces coefficients to be positive. (Only allowed when y.ndim \== 1). |
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.return_n_iter? | boolean | Whether to return the number of iterations or not. |
opts.verbose? | number | boolean | Amount of verbosity. |
opts.X? | ArrayLike | Training data. Pass directly as Fortran-contiguous data to avoid unnecessary memory duplication. If y is mono-output then X can be sparse. |
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? | any | Target values. |
Returns Promise<ArrayLike>
Defined in generated/linear_model/LassoCV.ts:277
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/LassoCV.ts:383
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/LassoCV.ts:416
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.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in fit. |
Returns Promise<any>
Defined in generated/linear_model/LassoCV.ts:462
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/LassoCV.ts:498