DOBIN

class DOBIN(frac=0.95, k=None)[source]

Distance based Outlier BasIs using Neighbors (DOBIN).

DOBIN is a pre-processing algorithm that constructs a set of basis vectors tailored for outlier detection as described by _[1]. DOBIN has a simple mathematical foundation and can be used as a dimension reduction tool for outlier detection tasks.

Method assumes normalized data, the original R code implementation uses: from sklearn.preprocessing import MinMaxScaler, RobustScaler, StandardScaler This prevents variables with large variances having disproportional influence on Euclidean distances. The original implelemtation _[1] uses MinMaxScaler normalization, and removes NA values before normalization.

We emphasize that DOBIN is not an outlier detection method; rather it is a pre-processing step that can be used by any outlier detection method.

Parameters:

fracfloat (default=0.95): The cut-off quantile for Y space (parameter q in _[1]).
kint (default=None): Number of nearest neighbours considered (parameter k_2 on page 9 in _[1])

Attributes:

_basispd.DataFrame: The basis vectors suitable for outlier detection (denoted as Theta in _[1]).
_coordspd.DataFrame: The transformed coordinates of the data (denoted as tilde{X}, see equation 8 in _[1])

References

[1]

Kandanaarachchi, Sevvandi, and Rob J. Hyndman. “Dimension reduction

for outlier detection using DOBIN.” Journal of Computational and Graphical Statistics 30.1 (2021): 204-219.

Examples

>>> from sktime.transformations.dobin import DOBIN
>>> from sklearn.preprocessing import MinMaxScaler
>>> import numpy as np
>>> import pandas as pd
>>> from sktime.datasets import load_uschange
>>> _, X = load_uschange()
>>> scaler = MinMaxScaler()
>>> X = scaler.fit_transform(X)
>>> model = DOBIN()
>>> X_outlier = model.fit_transform(pd.DataFrame(X))
>>> X_outlier.head()
        DB0       DB1       DB2       DB3
0  1.151965  0.116488  0.286064  0.288140
1  1.191976  0.100772  0.050835  0.225985
2  1.221158  0.078031  0.034030  0.249676
3  1.042420  0.188494  0.218460  0.205251
4  1.224701  0.020028 -0.294705  0.199827

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X[, y])	Fit transformer to X, optionally to y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the skbase object.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.