Class: MinCovDet
Minimum Covariance Determinant (MCD): robust estimator of covariance.
The Minimum Covariance Determinant covariance estimator is to be applied on Gaussian-distributed data, but could still be relevant on data drawn from a unimodal, symmetric distribution. It is not meant to be used with multi-modal data (the algorithm used to fit a MinCovDet object is likely to fail in such a case). One should consider projection pursuit methods to deal with multi-modal datasets.
Read more in the User Guide.
Constructors
new MinCovDet()
new MinCovDet(
opts?):MinCovDet
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.assume_centered? | boolean | If true, the support of the robust location and the covariance estimates is computed, and a covariance estimate is recomputed from it, without centering the data. Useful to work with data whose mean is significantly equal to zero but is not exactly zero. If false, the robust location and covariance are directly computed with the FastMCD algorithm without additional treatment. |
opts.random_state? | number | Determines the pseudo random number generator for shuffling the data. Pass an int for reproducible results across multiple function calls. See Glossary. |
opts.store_precision? | boolean | Specify if the estimated precision is stored. |
opts.support_fraction? | number | The proportion of points to be included in the support of the raw MCD estimate. Default is undefined, which implies that the minimum value of support_fraction will be used within the algorithm: (n_samples + n_features + 1) / 2 \* n_samples. The parameter must be in the range (0, 1]. |
Returns MinCovDet
Defined in generated/covariance/MinCovDet.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/covariance/MinCovDet.ts:23 |
_isInitialized | boolean | false | generated/covariance/MinCovDet.ts:22 |
_py | PythonBridge | undefined | generated/covariance/MinCovDet.ts:21 |
id | string | undefined | generated/covariance/MinCovDet.ts:18 |
opts | any | undefined | generated/covariance/MinCovDet.ts:19 |
Accessors
covariance_
Get Signature
get covariance_():
Promise<ArrayLike[]>
Estimated robust covariance matrix.
Returns Promise<ArrayLike[]>
Defined in generated/covariance/MinCovDet.ts:553
dist_
Get Signature
get dist_():
Promise<ArrayLike>
Mahalanobis distances of the training set (on which fit is called) observations.
Returns Promise<ArrayLike>
Defined in generated/covariance/MinCovDet.ts:622
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/MinCovDet.ts:670
location_
Get Signature
get location_():
Promise<ArrayLike>
Estimated robust location.
Returns Promise<ArrayLike>
Defined in generated/covariance/MinCovDet.ts:530
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/covariance/MinCovDet.ts:645
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/MinCovDet.ts:576
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/MinCovDet.ts:54
raw_covariance_
Get Signature
get raw_covariance_():
Promise<ArrayLike[]>
The raw robust estimated covariance before correction and re-weighting.
Returns Promise<ArrayLike[]>
Defined in generated/covariance/MinCovDet.ts:480
raw_location_
Get Signature
get raw_location_():
Promise<ArrayLike>
The raw robust estimated location before correction and re-weighting.
Returns Promise<ArrayLike>
Defined in generated/covariance/MinCovDet.ts:455
raw_support_
Get Signature
get raw_support_():
Promise<ArrayLike>
A mask of the observations that have been used to compute the raw robust estimates of location and shape, before correction and re-weighting.
Returns Promise<ArrayLike>
Defined in generated/covariance/MinCovDet.ts:505
support_
Get Signature
get support_():
Promise<ArrayLike>
A mask of the observations that have been used to compute the robust estimates of location and shape.
Returns Promise<ArrayLike>
Defined in generated/covariance/MinCovDet.ts:599
Methods
correct_covariance()
correct_covariance(
opts):Promise<ArrayLike[]>
Apply a correction to raw Minimum Covariance Determinant estimates.
Correction using the empirical correction factor suggested by Rousseeuw and Van Driessen in [RVD].
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.data? | ArrayLike[] | The data matrix, with p features and n samples. The data set must be the one which was used to compute the raw estimates. |
Returns Promise<ArrayLike[]>
Defined in generated/covariance/MinCovDet.ts:124
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/MinCovDet.ts:105
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/MinCovDet.ts:156
fit()
fit(
opts):Promise<any>
Fit a Minimum Covariance Determinant with the FastMCD algorithm.
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/MinCovDet.ts:209
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/MinCovDet.ts:248
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/MinCovDet.ts:282
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/MinCovDet.ts:67
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/MinCovDet.ts:314
reweight_covariance()
reweight_covariance(
opts):Promise<ArrayLike>
Re-weight raw Minimum Covariance Determinant estimates.
Re-weight observations using Rousseeuw’s method (equivalent to deleting outlying observations from the data set before computing location and covariance estimates) described in [RVDriessen].
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.data? | ArrayLike[] | The data matrix, with p features and n samples. The data set must be the one which was used to compute the raw estimates. |
Returns Promise<ArrayLike>
Defined in generated/covariance/MinCovDet.ts:348
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/MinCovDet.ts:382
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/MinCovDet.ts:423