OutlierSampleDetection#
- class OutlierSampleDetection[source]#
Detects outliers in a dataset using the LoOP algorithm.
The LoOP algorithm is a robust method for detecting outliers in a dataset across multiple variables by comparing the density in the area of a sample with the densities in the areas of its nearest neighbors. The output of the algorithm is highly dependent on the number of nearest neighbors, it is recommended to select a value k that represent the maximum cluster size that will still be considered as “outliers”. See https://www.dbs.ifi.lmu.de/Publikationen/Papers/LoOP1649.pdf for more details. LoOP relies on a distance matrix, in our implementation we use the Gower distance that measure the distance between two samples based on its numeric and categorical features. See https://statisticaloddsandends.wordpress.com/2021/02/23/what-is-gowers-distance/ for further details.
- Parameters
- columnsUnion[Hashable, List[Hashable]] , default: None
Columns to check, if none are given checks all columns except ignored ones.
- ignore_columnsUnion[Hashable, List[Hashable]] , default: None
Columns to ignore, if none given checks based on columns variable
- nearest_neighbors_percentfloat, default: 0.01
Percent of the dataset to use as K, nearest neighbors for the LoOP outlier detection. It is recommended to select a percentage that represent the maximum cluster size that will still be considered as “outliers”.
- extent_parameter: int, default: 3
Extend parameter for LoOP algorithm.
- n_samplesint , default: 5_000
number of samples to use for this check.
- n_to_showint , default: 5
number of data elements with the highest outlier score to show (out of sample).
- random_stateint, default: 42
random seed for all check internals.
- timeoutint, default: 10
Check will be interrupted if it takes more than this number of seconds. If 0, check will not be interrupted.
- __init__(columns: Optional[Union[Hashable, List[Hashable]]] = None, ignore_columns: Optional[Union[Hashable, List[Hashable]]] = None, nearest_neighbors_percent: float = 0.01, extent_parameter: int = 3, n_samples: int = 5000, n_to_show: int = 5, random_state: int = 42, timeout: int = 10, **kwargs)[source]#
- __new__(*args, **kwargs)#
Methods
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Add new condition function to the check. |
Add condition - no elements over outlier threshold are allowed. |
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Add condition - ratio of samples over outlier score is less or equal to the threshold. |
Remove all conditions from this check instance. |
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Run conditions on given result. |
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Return check configuration (conditions' configuration not yet supported). |
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Return check object from a CheckConfig object. |
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Return check metadata. |
Name of class in split camel case. |
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Return parameters to show when printing the check. |
Remove given condition by index. |
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Run check. |
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Run check. |
