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Classes
ElasticNet

ElasticNet

Linear regression with combined L1 and L2 priors as regularizer.

Minimizes the objective function:

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new ElasticNet(opts?: object): ElasticNet;

Parameters

NameTypeDescription
opts?object-
opts.alpha?numberConstant that multiplies the penalty terms. Defaults to 1.0. See the notes for the exact mathematical meaning of this parameter. alpha \= 0 is equivalent to an ordinary least square, solved by the LinearRegression object. For numerical reasons, using alpha \= 0 with the Lasso object is not advised. Given this, you should use the LinearRegression object. Default Value 1
opts.copy_X?booleanIf true, X will be copied; else, it may be overwritten. Default Value true
opts.fit_intercept?booleanWhether the intercept should be estimated or not. If false, the data is assumed to be already centered. Default Value true
opts.l1_ratio?numberThe ElasticNet mixing parameter, with 0 <= l1\_ratio <= 1. For l1\_ratio \= 0 the penalty is an L2 penalty. For l1\_ratio \= 1 it is an L1 penalty. For 0 < l1\_ratio < 1, the penalty is a combination of L1 and L2. Default Value 0.5
opts.max_iter?numberThe maximum number of iterations. Default Value 1000
opts.positive?booleanWhen 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?numberThe 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?numberThe 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?booleanWhen 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

ElasticNet

Defined in: generated/linear_model/ElasticNet.ts:23 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/linear_model/ElasticNet.ts:21 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/linear_model/ElasticNet.ts:20 (opens in a new tab)

_py

PythonBridge

Defined in: generated/linear_model/ElasticNet.ts:19 (opens in a new tab)

id

string

Defined in: generated/linear_model/ElasticNet.ts:16 (opens in a new tab)

opts

any

Defined in: generated/linear_model/ElasticNet.ts:17 (opens in a new tab)

Accessors

coef_

Parameter vector (w in the cost function formula).

Signature

coef_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/ElasticNet.ts:464 (opens in a new tab)

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/ElasticNet.ts:533 (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/ElasticNet.ts:581 (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/ElasticNet.ts:487 (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/ElasticNet.ts:556 (opens in a new tab)

n_iter_

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

Signature

n_iter_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/linear_model/ElasticNet.ts:510 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/linear_model/ElasticNet.ts:103 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/linear_model/ElasticNet.ts:107 (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/ElasticNet.ts:166 (opens in a new tab)

fit()

Fit model with coordinate descent.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?anyData.
opts.check_input?booleanAllow to bypass several input checking. Don’t use this parameter unless you know what you do. Default Value true
opts.sample_weight?number | ArrayLikeSample weights. Internally, the sample\_weight vector will be rescaled to sum to n\_samples.
opts.y?anyTarget. Will be cast to X’s dtype if necessary.

Returns

Promise<any>

Defined in: generated/linear_model/ElasticNet.ts:183 (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

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/linear_model/ElasticNet.ts:116 (opens in a new tab)

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

NameTypeDescription
optsobject-
opts.X?ArrayLikeTraining data. Pass directly as Fortran-contiguous data to avoid unnecessary memory duplication. If y is mono-output then X can be sparse.
opts.Xy?ArrayLikeXy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed.
opts.alphas?ArrayLikeList of alphas where to compute the models. If undefined alphas are set automatically.
opts.check_input?booleanIf 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?ArrayLikeThe initial values of the coefficients.
opts.copy_X?booleanIf true, X will be copied; else, it may be overwritten. Default Value true
opts.eps?numberLength of the path. eps=1e-3 means that alpha\_min / alpha\_max \= 1e-3. Default Value 0.001
opts.l1_ratio?numberNumber 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?numberNumber of alphas along the regularization path. Default Value 100
opts.params?anyKeyword arguments passed to the coordinate descent solver.
opts.positive?booleanIf 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?booleanWhether to return the number of iterations or not. Default Value false
opts.verbose?number | booleanAmount of verbosity. Default Value false
opts.y?anyTarget values.

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/ElasticNet.ts:243 (opens in a new tab)

predict()

Predict using the linear model.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?anySamples.

Returns

Promise<any>

Defined in: generated/linear_model/ElasticNet.ts:384 (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

NameTypeDescription
optsobject-
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?ArrayLikeSample weights.
opts.y?ArrayLikeTrue values for X.

Returns

Promise<number>

Defined in: generated/linear_model/ElasticNet.ts:417 (opens in a new tab)