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MinCovDet

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

NameTypeDescription
opts?object-
opts.assume_centered?booleanIf 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?numberDetermines the pseudo random number generator for shuffling the data. Pass an int for reproducible results across multiple function calls. See Glossary.
opts.store_precision?booleanSpecify if the estimated precision is stored. Default Value true
opts.support_fraction?numberThe 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

MinCovDet

Defined in: generated/covariance/MinCovDet.ts:25 (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:499 (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:568 (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:616 (opens in a new tab)

location_

Estimated robust location.

Signature

location_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/covariance/MinCovDet.ts:476 (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:591 (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:522 (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

NameType
pythonBridgePythonBridge

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:426 (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:401 (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:451 (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:545 (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

NameTypeDescription
optsobject-
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

NameTypeDescription
optsobject-
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?booleanIf 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?booleanWhether 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

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Training data, where n\_samples is the number of samples and n\_features is the number of features.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/covariance/MinCovDet.ts:220 (opens in a new tab)

get_precision()

Getter for the precision matrix.

Signature

get_precision(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.precision_?ArrayLike[]The precision matrix associated to the current covariance object.

Returns

Promise<any>

Defined in: generated/covariance/MinCovDet.ts:258 (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

NameType
pyPythonBridge

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

NameTypeDescription
optsobject-
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:291 (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

NameTypeDescription
optsobject-
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:326 (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

NameTypeDescription
optsobject-
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?anyNot used, present for API consistency by convention.

Returns

Promise<number>

Defined in: generated/covariance/MinCovDet.ts:361 (opens in a new tab)