BoxplotOutlierDetector#

class skfda.exploratory.outliers.BoxplotOutlierDetector(*, depth_method=None, factor=1.5)[source]#

Outlier detector using the interquartile range.

Detects as outliers functions that have one or more points outside factor times the interquartile range plus or minus the central envelope, given a functional depth measure. This corresponds to the points selected as outliers by the functional boxplot.

Parameters:

depth_method (Callable) – The functional depth measure used.
factor (float) – The number of times the IQR is multiplied.

Example

Function \(f : \mathbb{R}\longmapsto\mathbb{R}\).

>>> import skfda
>>> data_matrix = [[1, 1, 2, 3, 2.5, 2],
...                [0.5, 0.5, 1, 2, 1.5, 1],
...                [-1, -1, -0.5, 1, 1, 0.5],
...                [-0.5, -0.5, -0.5, -1, -1, -1]]
>>> grid_points = [0, 2, 4, 6, 8, 10]
>>> fd = skfda.FDataGrid(data_matrix, grid_points)
>>> out_detector = BoxplotOutlierDetector()
>>> out_detector.fit_predict(fd)
array([-1, 1, 1, -1])

Methods

`fit`(X[, y])
`fit_predict`(X[, y])	Perform fit on X and returns labels for X.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)
`set_params`(**params)	Set the parameters of this estimator.

fit(X, y=None)[source]#

Parameters:

X (FDataGrid) –
y (None) –

Return type:

BoxplotOutlierDetector

fit_predict(X, y=None)[source]#

Perform fit on X and returns labels for X.

Returns -1 for outliers and 1 for inliers.

Parameters:

X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input samples.
y (Ignored) – Not used, present for API consistency by convention.
**kwargs (dict) –
Arguments to be passed to fit.

New in version 1.4.

Returns:

y – 1 for inliers, -1 for outliers.

Return type:

ndarray of shape (n_samples,)

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

predict(X)[source]#

Parameters:: X (FDataGrid) –
Return type:: ndarray[Any, dtype[int64]]

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