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:
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new LassoCV(opts?: object): LassoCV;Parameters
| Name | 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. Default Value true |
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. Default Value 0.001 |
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 | The maximum number of iterations. Default Value 1000 |
opts.n_alphas? | number | Number of alphas along the regularization path. Default Value 100 |
opts.n_jobs? | number | Number of CPUs to use during the cross validation. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details. |
opts.positive? | boolean | If positive, restrict regression coefficients to be positive. 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 | 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. Default Value 'cyclic' |
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. Default Value 0.0001 |
opts.verbose? | number | boolean | Amount of verbosity. Default Value false |
Returns
Defined in: generated/linear_model/LassoCV.ts:27 (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/LassoCV.ts:188 (opens in a new tab)
fit()
Fit linear model with coordinate descent.
Fit is on grid of alphas and best alpha estimated by cross-validation.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
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. |
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.y? | ArrayLike | Target values. |
Returns
Promise<any>
Defined in: generated/linear_model/LassoCV.ts:207 (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/LassoCV.ts:256 (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/LassoCV.ts:135 (opens in a new tab)
path()
Compute Lasso path with coordinate descent.
The Lasso optimization function varies for mono and multi-outputs.
For mono-output tasks it is:
Signature
path(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
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.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. Default Value true |
opts.eps? | number | Length of the path. eps=1e-3 means that alpha\_min / alpha\_max \= 1e-3. Default Value 0.001 |
opts.n_alphas? | number | Number of alphas along the regularization path. Default Value 100 |
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). 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.return_n_iter? | boolean | Whether to return the number of iterations or not. Default Value false |
opts.verbose? | number | boolean | Amount of verbosity. Default Value false |
opts.y? | any | Target values. |
Returns
Promise<ArrayLike>
Defined in: generated/linear_model/LassoCV.ts:293 (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/LassoCV.ts:418 (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/LassoCV.ts:451 (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.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in fit. |
Returns
Promise<any>
Defined in: generated/linear_model/LassoCV.ts:502 (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/LassoCV.ts:539 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/linear_model/LassoCV.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/linear_model/LassoCV.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/linear_model/LassoCV.ts:23 (opens in a new tab)
id
string
Defined in: generated/linear_model/LassoCV.ts:20 (opens in a new tab)
opts
any
Defined in: generated/linear_model/LassoCV.ts:21 (opens in a new tab)
Accessors
alpha_
The amount of penalization chosen by cross validation.
Signature
alpha_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/LassoCV.ts:572 (opens in a new tab)
alphas_
The grid of alphas used for fitting.
Signature
alphas_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/linear_model/LassoCV.ts:662 (opens in a new tab)
coef_
Parameter vector (w in the cost function formula).
Signature
coef_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/linear_model/LassoCV.ts:594 (opens in a new tab)
dual_gap_
The dual gap at the end of the optimization for the optimal alpha (alpha\_).
Signature
dual_gap_(): Promise<number | ArrayLike>;Returns
Promise<number | ArrayLike>
Defined in: generated/linear_model/LassoCV.ts:685 (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/LassoCV.ts:756 (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/LassoCV.ts:616 (opens in a new tab)
mse_path_
Mean square error for the test set on each fold, varying alpha.
Signature
mse_path_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/linear_model/LassoCV.ts:639 (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/LassoCV.ts:731 (opens in a new tab)
n_iter_
Number of iterations run by the coordinate descent solver to reach the specified tolerance for the optimal alpha.
Signature
n_iter_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/LassoCV.ts:708 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/linear_model/LassoCV.ts:122 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/linear_model/LassoCV.ts:126 (opens in a new tab)