GraphicalLasso
Sparse inverse covariance estimation with an l1-penalized estimator.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new GraphicalLasso(opts?: object): GraphicalLasso;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | The regularization parameter: the higher alpha, the more regularization, the sparser the inverse covariance. Range is (0, inf]. Default Value 0.01 |
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.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 | The 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 p > n. Elsewhere prefer cd which is more numerically stable. Default Value 'cd' |
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 dual gap are plotted at each iteration. Default Value false |
Returns
Defined in: generated/covariance/GraphicalLasso.ts:23 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/covariance/GraphicalLasso.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/covariance/GraphicalLasso.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/covariance/GraphicalLasso.ts:19 (opens in a new tab)
id
string
Defined in: generated/covariance/GraphicalLasso.ts:16 (opens in a new tab)
opts
any
Defined in: generated/covariance/GraphicalLasso.ts:17 (opens in a new tab)
Accessors
covariance_
Estimated covariance matrix
Signature
covariance_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/covariance/GraphicalLasso.ts:383 (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/GraphicalLasso.ts:483 (opens in a new tab)
location_
Estimated location, i.e. the estimated mean.
Signature
location_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/covariance/GraphicalLasso.ts:358 (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/GraphicalLasso.ts:458 (opens in a new tab)
n_iter_
Number of iterations run.
Signature
n_iter_(): Promise<number>;Returns
Promise<number>
Defined in: generated/covariance/GraphicalLasso.ts:433 (opens in a new tab)
precision_
Estimated pseudo inverse matrix.
Signature
precision_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/covariance/GraphicalLasso.ts:408 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/covariance/GraphicalLasso.ts:77 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/covariance/GraphicalLasso.ts:81 (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/GraphicalLasso.ts:136 (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/GraphicalLasso.ts:153 (opens in a new tab)
fit()
Fit the GraphicalLasso 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/GraphicalLasso.ts:211 (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/GraphicalLasso.ts:249 (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/GraphicalLasso.ts:90 (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/GraphicalLasso.ts:283 (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/GraphicalLasso.ts:318 (opens in a new tab)