Class: LedoitWolf
LedoitWolf Estimator.
Ledoit-Wolf is a particular form of shrinkage, where the shrinkage coefficient is computed using O. Ledoit and M. Wolf’s formula as described in “A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices”, Ledoit and Wolf, Journal of Multivariate Analysis, Volume 88, Issue 2, February 2004, pages 365-411.
Read more in the User Guide.
Constructors
new LedoitWolf()
new LedoitWolf(
opts?):LedoitWolf
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.assume_centered? | boolean | If true, data will not be centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If false (default), data will be centered before computation. |
opts.block_size? | number | Size of blocks into which the covariance matrix will be split during its Ledoit-Wolf estimation. This is purely a memory optimization and does not affect results. |
opts.store_precision? | boolean | Specify if the estimated precision is stored. |
Returns LedoitWolf
Defined in generated/covariance/LedoitWolf.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/covariance/LedoitWolf.ts:23 |
_isInitialized | boolean | false | generated/covariance/LedoitWolf.ts:22 |
_py | PythonBridge | undefined | generated/covariance/LedoitWolf.ts:21 |
id | string | undefined | generated/covariance/LedoitWolf.ts:18 |
opts | any | undefined | generated/covariance/LedoitWolf.ts:19 |
Accessors
covariance_
Get Signature
get covariance_():
Promise<ArrayLike[]>
Estimated covariance matrix.
Returns Promise<ArrayLike[]>
Defined in generated/covariance/LedoitWolf.ts:385
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/LedoitWolf.ts:504
location_
Get Signature
get location_():
Promise<ArrayLike>
Estimated location, i.e. the estimated mean.
Returns Promise<ArrayLike>
Defined in generated/covariance/LedoitWolf.ts:410
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/covariance/LedoitWolf.ts:479
precision_
Get Signature
get precision_():
Promise<ArrayLike[]>
Estimated pseudo inverse matrix. (stored only if store_precision is true)
Returns Promise<ArrayLike[]>
Defined in generated/covariance/LedoitWolf.ts:433
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/LedoitWolf.ts:51
shrinkage_
Get Signature
get shrinkage_():
Promise<number>
Coefficient in the convex combination used for the computation of the shrunk estimate. Range is [0, 1].
Returns Promise<number>
Defined in generated/covariance/LedoitWolf.ts:456
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/LedoitWolf.ts:103
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/LedoitWolf.ts:120
fit()
fit(
opts):Promise<any>
Fit the Ledoit-Wolf shrunk covariance model to X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training data, where n_samples is the number of samples and n_features is the number of features. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns Promise<any>
Defined in generated/covariance/LedoitWolf.ts:173
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 MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/covariance/LedoitWolf.ts:212
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/LedoitWolf.ts:246
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/LedoitWolf.ts:64
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/LedoitWolf.ts:278
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/LedoitWolf.ts:312
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/LedoitWolf.ts:353