Class: GraphicalLassoCV

Sparse inverse covariance w/ cross-validated choice of the l1 penalty.

See glossary entry for cross-validation estimator.

Constructors

new GraphicalLassoCV()

new GraphicalLassoCV(opts?): GraphicalLassoCV

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.alphas`?	`number` \| `ArrayLike`	If an integer is given, it fixes the number of points on the grids of alpha to be used. If a list is given, it gives the grid to be used. See the notes in the class docstring for more details. Range is [1, inf) for an integer. Range is (0, inf] for an array-like of floats.
`opts.assume_centered`?	`boolean`	If `true`, data are not centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If `false`, data are centered before computation.
`opts.cv`?	`number`	Determines the cross-validation splitting strategy. Possible inputs for cv are:
`opts.enet_tol`?	`number`	The tolerance for the elastic net solver used to calculate the descent direction. This parameter controls the accuracy of the search direction for a given column update, not of the overall parameter estimate. Only used for mode=’cd’. Range is (0, inf].
`opts.eps`?	`number`	The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Default is `np.finfo(np.float64).eps`.
`opts.max_iter`?	`number`	Maximum number of iterations.
`opts.mode`?	`"cd"` \| `"lars"`	The Lasso solver to use: coordinate descent or LARS. Use LARS for very sparse underlying graphs, where number of features is greater than number of samples. Elsewhere prefer cd which is more numerically stable.
`opts.n_jobs`?	`number`	Number of jobs to run in parallel. `undefined` means 1 unless in a `joblib.parallel_backend` context. `\-1` means using all processors. See Glossary for more details.
`opts.n_refinements`?	`number`	The number of times the grid is refined. Not used if explicit values of alphas are passed. Range is [1, inf).
`opts.tol`?	`number`	The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped. Range is (0, inf].
`opts.verbose`?	`boolean`	If verbose is `true`, the objective function and duality gap are printed at each iteration.

Returns GraphicalLassoCV

Defined in generated/covariance/GraphicalLassoCV.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/covariance/GraphicalLassoCV.ts:23
`_isInitialized`	`boolean`	`false`	generated/covariance/GraphicalLassoCV.ts:22
`_py`	`PythonBridge`	`undefined`	generated/covariance/GraphicalLassoCV.ts:21
`id`	`string`	`undefined`	generated/covariance/GraphicalLassoCV.ts:18
`opts`	`any`	`undefined`	generated/covariance/GraphicalLassoCV.ts:19

Accessors

alpha_

Get Signature

get alpha_(): Promise<number>

Penalization parameter selected.

Returns Promise<number>

Defined in generated/covariance/GraphicalLassoCV.ts:568

costs_

Get Signature

get costs_(): Promise<any>

The list of values of the objective function and the dual gap at each iteration. Returned only if return_costs is true.

Returns Promise<any>

Defined in generated/covariance/GraphicalLassoCV.ts:541

covariance_

Get Signature

get covariance_(): Promise<ArrayLike[]>

Estimated covariance matrix.

Returns Promise<ArrayLike[]>

Defined in generated/covariance/GraphicalLassoCV.ts:487

cv_results_

Get Signature

get cv_results_(): Promise<any>

A dict with keys:

Returns Promise<any>

Defined in generated/covariance/GraphicalLassoCV.ts:595

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/covariance/GraphicalLassoCV.ts:676

location_

Get Signature

get location_(): Promise<ArrayLike>

Estimated location, i.e. the estimated mean.

Returns Promise<ArrayLike>

Defined in generated/covariance/GraphicalLassoCV.ts:460

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/covariance/GraphicalLassoCV.ts:649

n_iter_

Get Signature

get n_iter_(): Promise<number>

Number of iterations run for the optimal alpha.

Returns Promise<number>

Defined in generated/covariance/GraphicalLassoCV.ts:622

precision_

Get Signature

get precision_(): Promise<ArrayLike[]>

Estimated precision matrix (inverse covariance).

Returns Promise<ArrayLike[]>

Defined in generated/covariance/GraphicalLassoCV.ts:514

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/covariance/GraphicalLassoCV.ts:101

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/covariance/GraphicalLassoCV.ts:155

error_norm()

error_norm(opts): Promise<number>

Compute the Mean Squared Error between two covariance estimators.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.comp_cov`?	`ArrayLike`[]	The covariance to compare with.
`opts.norm`?	`"frobenius"` \| `"spectral"`	The type of norm used to compute the error. Available error types: - ‘frobenius’ (default): sqrt(tr(A^t.A)) - ‘spectral’: sqrt(max(eigenvalues(A^t.A)) where A is the error `(comp_cov \- self.covariance_)`.
`opts.scaling`?	`boolean`	If `true` (default), the squared error norm is divided by n_features. If `false`, the squared error norm is not rescaled.
`opts.squared`?	`boolean`	Whether to compute the squared error norm or the error norm. If `true` (default), the squared error norm is returned. If `false`, the error norm is returned.

Returns Promise<number>

Defined in generated/covariance/GraphicalLassoCV.ts:172

fit()

fit(opts): Promise<any>

Fit the GraphicalLasso covariance model to X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.params`?	`any`	Parameters to be passed to the CV splitter and the cross_val_score function.
`opts.X`?	`ArrayLike`[]	Data from which to compute the covariance estimate.
`opts.y`?	`any`	Not used, present for API consistency by convention.

Returns Promise<any>

Defined in generated/covariance/GraphicalLassoCV.ts:227

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/covariance/GraphicalLassoCV.ts:273

get_precision()

get_precision(opts): Promise<any>

Getter for the precision matrix.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.precision_`?	`ArrayLike`[]	The precision matrix associated to the current covariance object.

Returns Promise<any>

Defined in generated/covariance/GraphicalLassoCV.ts:309

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/covariance/GraphicalLassoCV.ts:114

mahalanobis()

mahalanobis(opts): Promise<ArrayLike>

Compute the squared Mahalanobis distances of given observations.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit.

Returns Promise<ArrayLike>

Defined in generated/covariance/GraphicalLassoCV.ts:345

score()

score(opts): Promise<number>

Compute the log-likelihood of X_test under the estimated Gaussian model.

The Gaussian model is defined by its mean and covariance matrix which are represented respectively by self.location_ and self.covariance_.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X_test`?	`ArrayLike`[]	Test data of which we compute the likelihood, where `n_samples` is the number of samples and `n_features` is the number of features. `X_test` is assumed to be drawn from the same distribution than the data used in fit (including centering).
`opts.y`?	`any`	Not used, present for API consistency by convention.

Returns Promise<number>

Defined in generated/covariance/GraphicalLassoCV.ts:381

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.X_test`?	`string` \| `boolean`	Metadata routing for `X_test` parameter in `score`.

Returns Promise<any>

Defined in generated/covariance/GraphicalLassoCV.ts:424

GraphicalLasso GridSearchCV