GraphicalLassoCV
Sparse inverse covariance w/ cross-validated choice of the l1 penalty.
See glossary entry for cross-validation estimator.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new GraphicalLassoCV(opts?: object): GraphicalLassoCV;Parameters
| Name | 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. Default Value 4 |
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. Default Value false |
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]. Default Value 0.0001 |
opts.max_iter? | number | Maximum number of iterations. Default Value 100 |
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. Default Value 'cd' |
opts.n_jobs? | number | Number of jobs to run in parallel. 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.n_refinements? | number | The number of times the grid is refined. Not used if explicit values of alphas are passed. Range is [1, inf). Default Value 4 |
opts.tol? | number | The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped. Range is (0, inf]. Default Value 0.0001 |
opts.verbose? | boolean | If verbose is true, the objective function and duality gap are printed at each iteration. Default Value false |
Returns
Defined in: generated/covariance/GraphicalLassoCV.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/covariance/GraphicalLassoCV.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/covariance/GraphicalLassoCV.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/covariance/GraphicalLassoCV.ts:21 (opens in a new tab)
id
string
Defined in: generated/covariance/GraphicalLassoCV.ts:18 (opens in a new tab)
opts
any
Defined in: generated/covariance/GraphicalLassoCV.ts:19 (opens in a new tab)
Accessors
alpha_
Penalization parameter selected.
Signature
alpha_(): Promise<number>;Returns
Promise<number>
Defined in: generated/covariance/GraphicalLassoCV.ts:476 (opens in a new tab)
covariance_
Estimated covariance matrix.
Signature
covariance_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/covariance/GraphicalLassoCV.ts:422 (opens in a new tab)
cv_results_
A dict with keys:
Signature
cv_results_(): Promise<any>;Returns
Promise<any>
Defined in: generated/covariance/GraphicalLassoCV.ts:503 (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/covariance/GraphicalLassoCV.ts:584 (opens in a new tab)
location_
Estimated location, i.e. the estimated mean.
Signature
location_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/covariance/GraphicalLassoCV.ts:395 (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/covariance/GraphicalLassoCV.ts:557 (opens in a new tab)
n_iter_
Number of iterations run for the optimal alpha.
Signature
n_iter_(): Promise<number>;Returns
Promise<number>
Defined in: generated/covariance/GraphicalLassoCV.ts:530 (opens in a new tab)
precision_
Estimated precision matrix (inverse covariance).
Signature
precision_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/covariance/GraphicalLassoCV.ts:449 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/covariance/GraphicalLassoCV.ts:96 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/covariance/GraphicalLassoCV.ts:100 (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/covariance/GraphicalLassoCV.ts:161 (opens in a new tab)
error_norm()
Compute the Mean Squared Error between two covariance estimators.
Signature
error_norm(opts: object): Promise<number>;Parameters
| Name | 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\_). Default Value 'frobenius' |
opts.scaling? | boolean | If true (default), the squared error norm is divided by n_features. If false, the squared error norm is not rescaled. Default Value true |
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. Default Value true |
Returns
Promise<number>
Defined in: generated/covariance/GraphicalLassoCV.ts:178 (opens in a new tab)
fit()
Fit the GraphicalLasso covariance model to X.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
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:238 (opens in a new tab)
get_precision()
Getter for the precision matrix.
Signature
get_precision(opts: object): Promise<any>;Parameters
| Name | 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:278 (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/covariance/GraphicalLassoCV.ts:109 (opens in a new tab)
mahalanobis()
Compute the squared Mahalanobis distances of given observations.
Signature
mahalanobis(opts: object): Promise<ArrayLike>;Parameters
| Name | 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:316 (opens in a new tab)
score()
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\_.
Signature
score(opts: object): Promise<number>;Parameters
| Name | 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:353 (opens in a new tab)