# alibi_detect.od.mahalanobis module¶

class alibi_detect.od.mahalanobis.Mahalanobis(threshold=None, n_components=3, std_clip=3, start_clip=100, max_n=None, cat_vars=None, ohe=False, data_type='tabular')[source]
__init__(threshold=None, n_components=3, std_clip=3, start_clip=100, max_n=None, cat_vars=None, ohe=False, data_type='tabular')[source]

Outlier detector for tabular data using the Mahalanobis distance.

Parameters
Return type

None

cat2num(X)[source]

Convert categorical variables to numerical values.

Parameters

X (numpy.ndarray) – Batch of instances to analyze.

Return type

numpy.ndarray

Returns

Batch of instances where categorical variables are converted to numerical values.

fit(X, y=None, d_type='abdm', w=None, disc_perc=[25, 50, 75], standardize_cat_vars=True, feature_range=(-10000000000.0, 10000000000.0), smooth=1.0, center=True)[source]

If categorical variables are present, then transform those to numerical values. This step is not necessary in the absence of categorical variables.

Parameters
• X (numpy.ndarray) – Batch of instances used to infer distances between categories from.

• y (Optional[numpy.ndarray]) – 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) – 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]) – Weight on ‘abdm’ (between 0. and 1.) distance if d_type equals ‘abdm-mvdm’.

• disc_perc (list) – List with percentiles used in binning of numerical features used for the ‘abdm’ and ‘abdm-mvdm’ pairwise distance measures.

• standardize_cat_vars (bool) – Standardize numerical values of categorical variables if True.

• feature_range (tuple) – 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) – 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) – 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.

Return type

None

infer_threshold(X, threshold_perc=95.0)[source]

Update threshold by a value inferred from the percentage of instances considered to be outliers in a sample of the dataset.

Parameters
• X (numpy.ndarray) – Batch of instances.

• threshold_perc (float) – Percentage of X considered to be normal based on the outlier score.

Return type

None

predict(X, return_instance_score=True)[source]

Compute outlier scores and transform into outlier predictions.

Parameters
• X (numpy.ndarray) – Batch of instances.

• return_instance_score (bool) – Whether to return instance level outlier scores.

Return type

Dict[Dict[str, str], Dict[numpy.ndarray, numpy.ndarray]]

Returns

• Dictionary containing ‘meta’ and ‘data’ dictionaries.

• ’meta’ has the model’s metadata.

• ’data’ contains the outlier predictions and instance level outlier scores.

score(X)[source]

Compute outlier scores.

Parameters

X (numpy.ndarray) – Batch of instances to analyze.

Return type

numpy.ndarray

Returns

Array with outlier scores for each instance in the batch.