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package sklearn

sklearn
- Sklearn

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type tag = [

| `MinCovDet

]

type t = [ `BaseEstimator | `MinCovDet | `Object ] Obj.t

val of_pyobject : Py.Object.t -> t

val to_pyobject : [> tag ] Obj.t -> Py.Object.t

val as_estimator : t -> [ `BaseEstimator ] Obj.t

val create : 
  ?store_precision:bool ->
  ?assume_centered:bool ->
  ?support_fraction:float ->
  ?random_state:int ->
  unit ->
  t

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 :ref:`User Guide <robust_covariance>`.

Parameters ---------- store_precision : bool, default=True Specify if the estimated precision is stored.

assume_centered : bool, default=False 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.

support_fraction : float, default=None The proportion of points to be included in the support of the raw MCD estimate. Default is None, 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).

random_state : int or RandomState instance, default=None Determines the pseudo random number generator for shuffling the data. Pass an int for reproducible results across multiple function calls. See :term: `Glossary <random_state>`.

Attributes ---------- raw_location_ : ndarray of shape (n_features,) The raw robust estimated location before correction and re-weighting.

raw_covariance_ : ndarray of shape (n_features, n_features) The raw robust estimated covariance before correction and re-weighting.

raw_support_ : ndarray of shape (n_samples,) A mask of the observations that have been used to compute the raw robust estimates of location and shape, before correction and re-weighting.

location_ : ndarray of shape (n_features,) Estimated robust location.

covariance_ : ndarray of shape (n_features, n_features) Estimated robust covariance matrix.

precision_ : ndarray of shape (n_features, n_features) Estimated pseudo inverse matrix. (stored only if store_precision is True)

support_ : ndarray of shape (n_samples,) A mask of the observations that have been used to compute the robust estimates of location and shape.

dist_ : ndarray of shape (n_samples,) Mahalanobis distances of the training set (on which :meth:`fit` is called) observations.

Examples -------- >>> import numpy as np >>> from sklearn.covariance import MinCovDet >>> from sklearn.datasets import make_gaussian_quantiles >>> real_cov = np.array([.8, .3], ... [.3, .4]) >>> rng = np.random.RandomState(0) >>> X = rng.multivariate_normal(mean=0, 0, ... cov=real_cov, ... size=500) >>> cov = MinCovDet(random_state=0).fit(X) >>> cov.covariance_ array([0.7411..., 0.2535...], [0.2535..., 0.3053...]) >>> cov.location_ array(0.0813... , 0.0427...)

References ----------

.. Rouseeuw1984 P. J. Rousseeuw. Least median of squares regression. J. Am Stat Ass, 79:871, 1984. .. Rousseeuw A Fast Algorithm for the Minimum Covariance Determinant Estimator, 1999, American Statistical Association and the American Society for Quality, TECHNOMETRICS .. ButlerDavies R. W. Butler, P. L. Davies and M. Jhun, Asymptotics For The Minimum Covariance Determinant Estimator, The Annals of Statistics, 1993, Vol. 21, No. 3, 1385-1400

val correct_covariance : 
  data:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Apply a correction to raw Minimum Covariance Determinant estimates.

Correction using the empirical correction factor suggested by Rousseeuw and Van Driessen in RVD_.

Parameters ---------- data : array-like of shape (n_samples, n_features) 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 ------- covariance_corrected : ndarray of shape (n_features, n_features) Corrected robust covariance estimate.

References ----------

.. RVD A Fast Algorithm for the Minimum Covariance Determinant Estimator, 1999, American Statistical Association and the American Society for Quality, TECHNOMETRICS

val error_norm : 
  ?norm:[ `Frobenius | `Spectral ] ->
  ?scaling:bool ->
  ?squared:bool ->
  comp_cov:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  float

Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm).

Parameters ---------- comp_cov : array-like of shape (n_features, n_features) The covariance to compare with.

norm : 'frobenius', 'spectral', default='frobenius' 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_)``.

scaling : bool, default=True If True (default), the squared error norm is divided by n_features. If False, the squared error norm is not rescaled.

squared : bool, default=True 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 ------- result : float The Mean Squared Error (in the sense of the Frobenius norm) between `self` and `comp_cov` covariance estimators.

val fit : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> t

Fits a Minimum Covariance Determinant with the FastMCD algorithm.

Parameters ---------- X : array-like of shape (n_samples, n_features) Training data, where `n_samples` is the number of samples and `n_features` is the number of features.

y: Ignored Not used, present for API consistence purpose.

Returns ------- self : object

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

val get_precision : [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Getter for the precision matrix.

Returns ------- precision_ : array-like of shape (n_features, n_features) The precision matrix associated to the current covariance object.

val mahalanobis : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Computes the squared Mahalanobis distances of given observations.

Parameters ---------- X : array-like of shape (n_samples, n_features) 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 ------- dist : ndarray of shape (n_samples,) Squared Mahalanobis distances of the observations.

val reweight_covariance : 
  data:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t * Py.Object.t

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 ---------- data : array-like of shape (n_samples, n_features) 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 ------- location_reweighted : ndarray of shape (n_features,) Re-weighted robust location estimate.

covariance_reweighted : ndarray of shape (n_features, n_features) Re-weighted robust covariance estimate.

support_reweighted : ndarray of shape (n_samples,), dtype=bool A mask of the observations that have been used to compute the re-weighted robust location and covariance estimates.

References ----------

.. RVDriessen A Fast Algorithm for the Minimum Covariance Determinant Estimator, 1999, American Statistical Association and the American Society for Quality, TECHNOMETRICS

val score : 
  ?y:Py.Object.t ->
  x_test:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  float

Computes the log-likelihood of a Gaussian data set with `self.covariance_` as an estimator of its covariance matrix.

Parameters ---------- X_test : array-like of shape (n_samples, n_features) 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).

y : Ignored Not used, present for API consistence purpose.

Returns ------- res : float The likelihood of the data set with `self.covariance_` as an estimator of its covariance matrix.

val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

val raw_location_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute raw_location_: get value or raise Not_found if None.

val raw_location_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute raw_location_: get value as an option.

val raw_covariance_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute raw_covariance_: get value or raise Not_found if None.

val raw_covariance_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute raw_covariance_: get value as an option.

val raw_support_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute raw_support_: get value or raise Not_found if None.

val raw_support_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute raw_support_: get value as an option.

val location_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute location_: get value or raise Not_found if None.

val location_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute location_: get value as an option.

val covariance_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute covariance_: get value or raise Not_found if None.

val covariance_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute covariance_: get value as an option.

val precision_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute precision_: get value or raise Not_found if None.

val precision_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute precision_: get value as an option.

val support_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute support_: get value or raise Not_found if None.

val support_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute support_: get value as an option.

val dist_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute dist_: get value or raise Not_found if None.

val dist_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute dist_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.