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.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new MinCovDet(opts?: object): MinCovDet;Parameters
| Name | 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. Default Value false |
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. Default Value true |
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\_sample + n\_features + 1) / 2. The parameter must be in the range (0, 1]. |
Returns
Defined in: generated/covariance/MinCovDet.ts:25 (opens in a new tab)
Methods
correct_covariance()
Apply a correction to raw Minimum Covariance Determinant estimates.
Correction using the empirical correction factor suggested by Rousseeuw and Van Driessen in [RVD].
Signature
correct_covariance(opts: object): Promise<ArrayLike[]>;Parameters
| Name | 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:129 (opens in a new tab)
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/MinCovDet.ts:110 (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/MinCovDet.ts:162 (opens in a new tab)
fit()
Fit a Minimum Covariance Determinant with the FastMCD algorithm.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | 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:220 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/covariance/MinCovDet.ts:260 (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/MinCovDet.ts:295 (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/MinCovDet.ts:67 (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/MinCovDet.ts:328 (opens in a new tab)
reweight_covariance()
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].
Signature
reweight_covariance(opts: object): Promise<ArrayLike>;Parameters
| Name | 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:363 (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/MinCovDet.ts:398 (opens in a new tab)
set_score_request()
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:
Signature
set_score_request(opts: object): Promise<any>;Parameters
| Name | 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:442 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/covariance/MinCovDet.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/covariance/MinCovDet.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/covariance/MinCovDet.ts:21 (opens in a new tab)
id
string
Defined in: generated/covariance/MinCovDet.ts:18 (opens in a new tab)
opts
any
Defined in: generated/covariance/MinCovDet.ts:19 (opens in a new tab)
Accessors
covariance_
Estimated robust covariance matrix.
Signature
covariance_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/covariance/MinCovDet.ts:573 (opens in a new tab)
dist_
Mahalanobis distances of the training set (on which fit is called) observations.
Signature
dist_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/covariance/MinCovDet.ts:642 (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/MinCovDet.ts:690 (opens in a new tab)
location_
Estimated robust location.
Signature
location_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/covariance/MinCovDet.ts:550 (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/MinCovDet.ts:665 (opens in a new tab)
precision_
Estimated pseudo inverse matrix. (stored only if store_precision is true)
Signature
precision_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/covariance/MinCovDet.ts:596 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/covariance/MinCovDet.ts:54 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/covariance/MinCovDet.ts:58 (opens in a new tab)
raw_covariance_
The raw robust estimated covariance before correction and re-weighting.
Signature
raw_covariance_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/covariance/MinCovDet.ts:500 (opens in a new tab)
raw_location_
The raw robust estimated location before correction and re-weighting.
Signature
raw_location_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/covariance/MinCovDet.ts:475 (opens in a new tab)
raw_support_
A mask of the observations that have been used to compute the raw robust estimates of location and shape, before correction and re-weighting.
Signature
raw_support_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/covariance/MinCovDet.ts:525 (opens in a new tab)
support_
A mask of the observations that have been used to compute the robust estimates of location and shape.
Signature
support_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/covariance/MinCovDet.ts:619 (opens in a new tab)