Outlier detection

class optbinning.binning.outlier.OutlierDetector

Bases: object

Base class for all outlier detectors.

fit(x: ArrayLike, y: ArrayLike | None = None) → Self

Fit outlier detector.

Parameters:

  • x (array-like, shape = (n_samples))

  • y (array-like, shape = (n_samples) or None (default=None))

Returns:

self

Return type:

OutlierDetector

get_support(indices: bool = False) → ndarray

Get a mask, or integer index, of the samples excluded, i.e, samples detected as outliers.

Parameters:

indices (boolean (default False)) – If True, the return value will be an array of integers, rather than a boolean mask.

Returns:

support – An index that selects the excluded samples from a vector. If indices is False, this is a boolean array, in which an element is True iff its corresponding sample is excluded. If indices is True, this is an integer array whose values are indices into the input vector.

Return type:

array, shape = (n_samples)

class optbinning.binning.outlier.RangeDetector(interval_length: float = 0.5, k: float = 1.5, method: str = 'ETI')

Bases: BaseEstimator, OutlierDetector

Interquartile range or interval based outlier detection method.

The default settings compute the usual interquartile range method.

Parameters:

  • interval_length (float (default=0.5)) – Compute interval_length% credible interval. This is a value in [0, 1].

  • k (float (default=1.5)) – Tukey’s factor.

  • method (str (default="ETI")) – Method to compute credible intervals. Supported methods are Highest Density interval (method="HDI") and Equal-tailed interval (method="ETI").

fit(x: ArrayLike, y: ArrayLike | None = None) → Self

Fit outlier detector.

Parameters:

  • x (array-like, shape = (n_samples))

  • y (array-like, shape = (n_samples) or None (default=None))

Returns:

self

Return type:

OutlierDetector

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

get_support(indices: bool = False) → ndarray

Get a mask, or integer index, of the samples excluded, i.e, samples detected as outliers.

Parameters:

indices (boolean (default False)) – If True, the return value will be an array of integers, rather than a boolean mask.

Returns:

support – An index that selects the excluded samples from a vector. If indices is False, this is a boolean array, in which an element is True iff its corresponding sample is excluded. If indices is True, this is an integer array whose values are indices into the input vector.

Return type:

array, shape = (n_samples)

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

class optbinning.binning.outlier.ModifiedZScoreDetector(threshold: float = 3.5)

Bases: BaseEstimator, OutlierDetector

Modified Z-score method.

Parameters:

threshold (float (default=3.5)) – Modified Z-scores with an absolute value of greater than the threshold are labeled as outliers.

References

[IH93]

B. Iglewicz and D. Hoaglin. “Volume 16: How to Detect and Handle Outliers”, The ASQC Basic References in Quality Control: Statistical Techniques, Edward F. Mykytka, Ph.D., Editor, 1993.

fit(x: ArrayLike, y: ArrayLike | None = None) → Self

Fit outlier detector.

Parameters:

  • x (array-like, shape = (n_samples))

  • y (array-like, shape = (n_samples) or None (default=None))

Returns:

self

Return type:

OutlierDetector

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

get_support(indices: bool = False) → ndarray

Get a mask, or integer index, of the samples excluded, i.e, samples detected as outliers.

Parameters:

indices (boolean (default False)) – If True, the return value will be an array of integers, rather than a boolean mask.

Returns:

support – An index that selects the excluded samples from a vector. If indices is False, this is a boolean array, in which an element is True iff its corresponding sample is excluded. If indices is True, this is an integer array whose values are indices into the input vector.

Return type:

array, shape = (n_samples)

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

class optbinning.binning.outlier.YQuantileDetector(outlier_detector: str | None = 'zscore', outlier_params: dict[str, Any] | None = None, n_bins: int = 5)

Bases: BaseEstimator, OutlierDetector

Outlier detector on the y-axis over quantiles.

Parameters:

  • outlier_detector (str or None, optional (default=None)) – The outlier detection method. Supported methods are “range” to use the interquartile range based method or “zcore” to use the modified Z-score method.

  • outlier_params (dict or None, optional (default=None)) – Dictionary of parameters to pass to the outlier detection method.

  • n_bins (int (default=5)) – The maximum number of bins to consider.

fit(x: ArrayLike, y: ArrayLike | None = None) → Self

Fit outlier detector.

Parameters:

  • x (array-like, shape = (n_samples))

  • y (array-like, shape = (n_samples) or None (default=None))

Returns:

self

Return type:

OutlierDetector

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

get_support(indices: bool = False) → ndarray

Get a mask, or integer index, of the samples excluded, i.e, samples detected as outliers.

Parameters:

indices (boolean (default False)) – If True, the return value will be an array of integers, rather than a boolean mask.

Returns:

support – An index that selects the excluded samples from a vector. If indices is False, this is a boolean array, in which an element is True iff its corresponding sample is excluded. If indices is True, this is an integer array whose values are indices into the input vector.

Return type:

array, shape = (n_samples)

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance