Lasso

Linear Model trained with L1 prior as regularizer (aka the Lasso).

The optimization objective for Lasso is:

Constructors

constructor()

Signature

new Lasso(opts?: object): Lasso;

Parameters

Name	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. `Default Value` `1`
`opts.copy_X?`	`boolean`	If `true`, X will be copied; else, it may be overwritten. `Default Value` `true`
`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.positive?`	`boolean`	When set to `true`, forces the coefficients to be positive. `Default Value` `false`
`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. `Default Value` `false`
`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`, see Notes below. `Default Value` `0.0001`
`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. `Default Value` `false`

Returns

Lasso

Defined in: generated/linear_model/Lasso.ts:23

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/Lasso.ts:160

fit()

Fit model with coordinate descent.

Signature

fit(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`	Data.
`opts.check_input?`	`boolean`	Allow to bypass several input checking. Don’t use this parameter unless you know what you do. `Default Value` `true`
`opts.sample_weight?`	`number` \| `ArrayLike`	Sample weights. Internally, the `sample\_weight` vector will be rescaled to sum to `n\_samples`.
`opts.y?`	`ArrayLike`	Target. Will be cast to X’s dtype if necessary.

Returns

Promise<any>

Defined in: generated/linear_model/Lasso.ts:177

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/Lasso.ts:235

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/Lasso.ts:111

path()

Compute elastic net path with coordinate descent.

The elastic net 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.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. `Default Value` `true`
`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.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. `Default Value` `0.5`
`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/Lasso.ts:272

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/Lasso.ts:413

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/Lasso.ts:446

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.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:497

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/Lasso.ts:539

Properties

_isDisposed

boolean = false

Defined in: generated/linear_model/Lasso.ts:21

_isInitialized

boolean = false

Defined in: generated/linear_model/Lasso.ts:20

_py

PythonBridge

Defined in: generated/linear_model/Lasso.ts:19

id

string

Defined in: generated/linear_model/Lasso.ts:16

opts

any

Defined in: generated/linear_model/Lasso.ts:17

Accessors

coef_

Parameter vector (w in the cost function formula).

Signature

coef_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/Lasso.ts:572

dual_gap_

Given param alpha, the dual gaps at the end of the optimization, same shape as each observation of y.

Signature

dual_gap_(): Promise<number | ArrayLike>;

Returns

Promise<number | ArrayLike>

Defined in: generated/linear_model/Lasso.ts:594

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/Lasso.ts:685

intercept_

Independent term in decision function.

Signature

intercept_(): Promise<number | ArrayLike>;

Returns

Promise<number | ArrayLike>

Defined in: generated/linear_model/Lasso.ts:617

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/Lasso.ts:662

n_iter_

Number of iterations run by the coordinate descent solver to reach the specified tolerance.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/Lasso.ts:640

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/linear_model/Lasso.ts:98

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/linear_model/Lasso.ts:102

Files

Lasso.md

Latest commit

History

Lasso.md

File metadata and controls

Lasso

Constructors

constructor()

Signature

Parameters

Returns

Methods

dispose()

Signature

Returns

fit()

Signature

Parameters

Returns

get_metadata_routing()

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Parameters

Returns

init()

Signature

Parameters

Returns

path()

Signature

Parameters

Returns

predict()

Signature

Parameters

Returns

score()

Signature

Parameters

Returns

set_fit_request()

Signature

Parameters

Returns

set_score_request()

Signature

Parameters

Returns

Properties

_isDisposed

_isInitialized

_py

id

opts

Accessors

coef_

Signature

Returns

dual_gap_

Signature

Returns

feature_names_in_

Signature

Returns

intercept_

Signature

Returns

n_features_in_

Signature

Returns

n_iter_

Signature

Returns

py

Signature

Returns

Signature

Parameters

Returns