Source code for alibi_detect.od.sklearn.gmm

import numpy as np
from typing import Dict
from sklearn.mixture import GaussianMixture

from alibi_detect.od.sklearn.base import SklearnOutlierDetector

[docs]class GMMSklearn(SklearnOutlierDetector):
[docs] def __init__( self, n_components: int, ): """sklearn backend for the Gaussian Mixture Model (GMM) outlier detector. Parameters ---------- n_components Number of components in gaussian mixture model. Raises ------ ValueError If `n_components` is less than 1. """ super().__init__() if n_components < 1: raise ValueError('n_components must be at least 1') self.n_components = n_components
[docs] def fit( # type: ignore[override] self, x_ref: np.ndarray, tol: float = 1e-3, max_iter: int = 100, n_init: int = 1, init_params: str = 'kmeans', verbose: int = 0, ) -> Dict: """Fit the SKLearn GMM model`. Parameters ---------- x_ref Reference data. tol Convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold. max_iter Maximum number of EM iterations to perform. n_init Number of initializations to perform. init_params 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 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 ------- Dictionary with fit results. The dictionary contains the following keys: - converged: bool indicating whether EM algorithm converged. - n_iter: number of EM iterations performed. - lower_bound: log-likelihood lower bound. """ self.gmm = GaussianMixture( n_components=self.n_components, tol=tol, max_iter=max_iter, n_init=n_init, init_params=init_params, verbose=verbose, ) self.gmm = x_ref, ) self._set_fitted() return { 'converged': self.gmm.converged_, 'n_iter': self.gmm.n_iter_, 'lower_bound': self.gmm.lower_bound_ }
[docs] def format_fit_kwargs(self, fit_kwargs: Dict) -> Dict: """Format kwargs for `fit` method. Parameters ---------- kwargs dictionary of Kwargs to format. See `fit` method for details. Returns ------- Formatted kwargs. """ return dict( tol=fit_kwargs.get('tol', 1e-3), max_iter=(lambda v: 100 if v is None else v)(fit_kwargs.get('max_epochs', None)), n_init=fit_kwargs.get('n_init', 1), init_params=fit_kwargs.get('init_params', 'kmeans'), verbose=fit_kwargs.get('verbose', 0), )
[docs] def score(self, x: np.ndarray) -> np.ndarray: """Computes the score of `x` Parameters ---------- x `np.ndarray` with leading batch dimension. Returns ------- `np.ndarray` of scores with leading batch dimension. Raises ------ NotFittedError Raised if method called and detector has not been fit. """ self.check_fitted() return - self.gmm.score_samples(x)