alibi_detect.od.mahalanobis

Constants

logger

logger: logging.Logger = <Logger alibi_detect.od.mahalanobis (WARNING)>

Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting.

EPSILON

EPSILON: float = 1e-08

Convert a string or number to a floating point number, if possible.

Mahalanobis

Inherits from: BaseDetector, FitMixin, ThresholdMixin, ABC

Constructor

Mahalanobis(self, threshold: float = None, n_components: int = 3, std_clip: int = 3, start_clip: int = 100, max_n: int = None, cat_vars: dict = None, ohe: bool = False, data_type: str = 'tabular') -> None

Name	Type	Default	Description
threshold	Optional[float]	None	Mahalanobis distance threshold used to classify outliers.
n_components	int	3	Number of principal components used.
std_clip	int	3	Feature-wise stdev used to clip the observations before updating the mean and cov.
start_clip	int	100	Number of observations before clipping is applied.
max_n	Optional[int]	None	Algorithm behaves as if it has seen at most max_n points.
cat_vars	Optional[dict]	None	Dict with as keys the categorical columns and as values the number of categories per categorical variable.
ohe	bool	False	Whether the categorical variables are one-hot encoded (OHE) or not. If not OHE, they are assumed to have ordinal encodings.
data_type	str	'tabular'	Optionally specifiy the data type (tabular, image or time-series). Added to metadata.

Methods

cat2num

cat2num(X: numpy.ndarray) -> numpy.ndarray

Convert categorical variables to numerical values.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances to analyze.

Returns

• Type: numpy.ndarray

fit

fit(X: numpy.ndarray, y: Optional[numpy.ndarray] = None, d_type: str = 'abdm', w: Optional[float] = None, disc_perc: list = [25, 50, 75], standardize_cat_vars: bool = True, feature_range: tuple = (-10000000000.0, 10000000000.0), smooth: float = 1.0, center: bool = True) -> None

If categorical variables are present, then transform those to numerical values.

This step is not necessary in the absence of categorical variables.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances used to infer distances between categories from.
y	Optional[numpy.ndarray]	None	Model class predictions or ground truth labels for X. Used for 'mvdm' and 'abdm-mvdm' pairwise distance metrics. Note that this is only compatible with classification problems. For regression problems, use the 'abdm' distance metric.
d_type	str	'abdm'	Pairwise distance metric used for categorical variables. Currently, 'abdm', 'mvdm' and 'abdm-mvdm' are supported. 'abdm' infers context from the other variables while 'mvdm' uses the model predictions. 'abdm-mvdm' is a weighted combination of the two metrics.
w	Optional[float]	None	Weight on 'abdm' (between 0. and 1.) distance if d_type equals 'abdm-mvdm'.
disc_perc	list	[25, 50, 75]	List with percentiles used in binning of numerical features used for the 'abdm' and 'abdm-mvdm' pairwise distance measures.
standardize_cat_vars	bool	True	Standardize numerical values of categorical variables if True.
feature_range	tuple	(-10000000000.0, 10000000000.0)	Tuple with min and max ranges to allow for perturbed instances. Min and max ranges can be floats or numpy arrays with dimension (1x nb of features) for feature-wise ranges.
smooth	float	1.0	Smoothing exponent between 0 and 1 for the distances. Lower values of l will smooth the difference in distance metric between different features.
center	bool	True	Whether to center the scaled distance measures. If False, the min distance for each feature except for the feature with the highest raw max distance will be the lower bound of the feature range, but the upper bound will be below the max feature range.

Returns

• Type: None

infer_threshold

infer_threshold(X: numpy.ndarray, threshold_perc: float = 95.0) -> None

Update threshold by a value inferred from the percentage of instances considered to be

outliers in a sample of the dataset.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances.
threshold_perc	float	95.0	Percentage of X considered to be normal based on the outlier score.

Returns

• Type: None

predict

predict(X: numpy.ndarray, return_instance_score: bool = True) -> Dict[Dict[str, str], Dict[numpy.ndarray, numpy.ndarray]]

Compute outlier scores and transform into outlier predictions.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances.
return_instance_score	bool	True	Whether to return instance level outlier scores.

Returns

• Type: Dict[Dict[str, str], Dict[numpy.ndarray, numpy.ndarray]]

score

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

Compute outlier scores.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances to analyze.

Returns

• Type: numpy.ndarray