mojolearn.MinCovDet

class mojolearn.MinCovDet(*, store_precision=True, assume_centered=False, support_fraction=None, random_state=None, numeric_mode=None)

Bases: _Base

Minimum covariance determinant: a robust covariance estimator.

Follows scikit-learn’s MinCovDet (FastMCD), including the consistency correction and the reweighting step. Random subsets come from mojolearn’s own generator and ties are broken by row index, so results can differ from scikit-learn’s for the same seed.

Parameters:

• store_precision (bool, default True) – Store precision_; when False it is None.

• assume_centered (bool, default False) – Treat the data as centered (location fixed at zero).

• support_fraction (float or None, default None) – Fraction of points in the raw support. None uses ceil((n_samples + n_features + 1) / 2) points.

• random_state (int or None, default None) – Seed for the random subsets; None means 0.

• numeric_mode ({“identical”, “fast”} or None, default None) – Numeric mode for this estimator. None uses the package default ("identical" unless changed). "identical" gives the same bits on NVIDIA and AMD GPUs; "fast" applies on Apple GPUs.

Variables:

• location_ (ndarray of shape (n_features,)) – Robust location (reweighted).

• covariance_ (ndarray of shape (n_features, n_features)) – Robust covariance (reweighted).

• precision_ (ndarray of shape (n_features, n_features) or None)

• support_ (ndarray of shape (n_samples,), uint8) – 1 for points used in the reweighted estimate.

• raw_location_ (ndarray of shape (n_features,))

• raw_covariance_ (ndarray of shape (n_features, n_features))

• raw_support_ (ndarray of shape (n_samples,), uint8)

• dist_ (ndarray of shape (n_samples,)) – Squared Mahalanobis distances of the training points.

• n_features_in_ (int)

Examples

>>> import numpy as np, mojolearn
>>> X = np.random.default_rng(0).normal(size=(500, 3)).astype(np.float32)
>>> mcd = mojolearn.MinCovDet(random_state=0).fit(X)
>>> d = mcd.mahalanobis(X)

__init__(*, store_precision=True, assume_centered=False, support_fraction=None, random_state=None, numeric_mode=None)

Methods

fit(X[, y])	Fit the robust location and covariance.
fit_transform(X[, y])	Fit to X, then return transform(X).
get_params([deep])	Return this estimator's constructor parameters.
get_precision()	Return the precision matrix (pseudo-inverse of covariance_).
mahalanobis(X)	Return squared Mahalanobis distances under the fitted model.
score(X[, y])	Return the Gaussian log-likelihood of X under the fitted model.
set_params(**params)	Set constructor parameters by keyword.

fit(X, y=None)

Fit the robust location and covariance.

Parameters:

• X (array-like of shape (n_samples, n_features))

• y (ignored)

Returns:

self

mahalanobis(X)

Return squared Mahalanobis distances under the fitted model.

Parameters:

X (array-like of shape (n_samples, n_features))

Returns:

ndarray of shape (n_samples,), float32

Raises:

RuntimeError – If the model is not fitted.

get_precision()

Return the precision matrix (pseudo-inverse of covariance_).

Available even when store_precision=False.

Returns:

ndarray of shape (n_features, n_features), float32

score(X, y=None)

Return the Gaussian log-likelihood of X under the fitted model.

As scikit-learn’s EmpiricalCovariance.score: uses X’s empirical covariance around location_ and the fitted precision.

Parameters:

• X (array-like of shape (n_samples, n_features))

• y (ignored)

Returns:

float