Class: Lasso
Linear Model trained with L1 prior as regularizer (aka the Lasso).
The optimization objective for Lasso is:
Constructors
new Lasso()
new Lasso(
opts?):Lasso
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | Constant that multiplies the L1 term, controlling regularization strength. alpha must be a non-negative float i.e. in \[0, inf). When alpha \= 0, the objective is equivalent to ordinary least squares, solved by the LinearRegression object. For numerical reasons, using alpha \= 0 with the Lasso object is not advised. Instead, you should use the LinearRegression object. |
opts.copy_X? | boolean | If true, X will be copied; else, it may be overwritten. |
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.positive? | boolean | When set to true, forces the coefficients to be positive. |
opts.precompute? | boolean | ArrayLike[] | Whether to use a precomputed Gram matrix to speed up calculations. The Gram matrix can also be passed as argument. For sparse input this option is always false to preserve sparsity. |
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, see Notes below. |
opts.warm_start? | boolean | When set to true, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary. |
Returns Lasso
Defined in generated/linear_model/Lasso.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/linear_model/Lasso.ts:21 |
_isInitialized | boolean | false | generated/linear_model/Lasso.ts:20 |
_py | PythonBridge | undefined | generated/linear_model/Lasso.ts:19 |
id | string | undefined | generated/linear_model/Lasso.ts:16 |
opts | any | undefined | generated/linear_model/Lasso.ts:17 |
Accessors
coef_
Get Signature
get coef_():
Promise<ArrayLike>
Parameter vector (w in the cost function formula).
Returns Promise<ArrayLike>
Defined in generated/linear_model/Lasso.ts:527
dual_gap_
Get Signature
get dual_gap_():
Promise<number|ArrayLike>
Given param alpha, the dual gaps at the end of the optimization, same shape as each observation of y.
Returns Promise<number | ArrayLike>
Defined in generated/linear_model/Lasso.ts:549
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/Lasso.ts:640
intercept_
Get Signature
get intercept_():
Promise<number|ArrayLike>
Independent term in decision function.
Returns Promise<number | ArrayLike>
Defined in generated/linear_model/Lasso.ts:572
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/Lasso.ts:617
n_iter_
Get Signature
get n_iter_():
Promise<number>
Number of iterations run by the coordinate descent solver to reach the specified tolerance.
Returns Promise<number>
Defined in generated/linear_model/Lasso.ts:595
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/Lasso.ts:98
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/Lasso.ts:149
fit()
fit(
opts):Promise<any>
Fit model with coordinate descent.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.check_input? | boolean | Allow to bypass several input checking. Don’t use this parameter unless you know what you do. |
opts.sample_weight? | number | ArrayLike | Sample weights. Internally, the sample_weight vector will be rescaled to sum to n_samples. |
opts.X? | any | Data. Note that large sparse matrices and arrays requiring int64 indices are not accepted. |
opts.y? | ArrayLike | Target. Will be cast to X’s dtype if necessary. |
Returns Promise<any>
Defined in generated/linear_model/Lasso.ts:166
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/Lasso.ts:219
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/Lasso.ts:111
path()
path(
opts):Promise<ArrayLike>
Compute elastic net path with coordinate descent.
The elastic net 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.check_input? | boolean | If set to false, the input validation checks are skipped (including the Gram matrix when provided). It is assumed that they are handled by the caller. |
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.l1_ratio? | number | Number between 0 and 1 passed to elastic net (scaling between l1 and l2 penalties). l1_ratio=1 corresponds to the Lasso. |
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/Lasso.ts:255
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/Lasso.ts:375
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/Lasso.ts:408
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.check_input? | string | boolean | Metadata routing for check_input parameter in fit. |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in fit. |
Returns Promise<any>
Defined in generated/linear_model/Lasso.ts:454
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/Lasso.ts:495