alibi_detect.od.sklearn.gmm

GMMSklearn

Inherits from: SklearnOutlierDetector, FitMixinSklearn, ABC

Constructor

GMMSklearn(self, n_components: int)

Name	Type	Default	Description
n_components	int		Number of components in gaussian mixture model.

Methods

fit

fit(x_ref: numpy.ndarray, tol: float = 0.001, max_iter: int = 100, n_init: int = 1, init_params: str = 'kmeans', verbose: int = 0) -> Dict

Fit the SKLearn GMM model`.

Name	Type	Default	Description
x_ref	numpy.ndarray		Reference data.
tol	float	0.001	Convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold.
max_iter	int	100	Maximum number of EM iterations to perform.
n_init	int	1	Number of initializations to perform.
init_params	str	'kmeans'	Method used to initialize the weights, the means and the precisions. Must be one of: 'kmeans' : responsibilities are initialized using kmeans. 'kmeans++' : responsibilities are initialized using kmeans++. 'random' : responsibilities are initialized randomly. 'random_from_data' : responsibilities are initialized randomly from the data.
verbose	int	0	Enable verbose output. If 1 then it prints the current initialization and each iteration step. If greater than 1 then it prints also the log probability and the time needed for each step.

Returns

• Type: Dict

format_fit_kwargs

format_fit_kwargs(fit_kwargs: Dict) -> Dict

Format kwargs for fit method.

Name	Type	Default	Description
fit_kwargs	Dict
kwargs			dictionary of Kwargs to format. See fit method for details.

Returns

• Type: Dict

score

score(x: numpy.ndarray) -> numpy.ndarray

Computes the score of x

Name	Type	Default	Description
x	numpy.ndarray		np.ndarray with leading batch dimension.

Returns

• Type: numpy.ndarray