ArbitraryOutlierCapper

class feature_engine.outliers.ArbitraryOutlierCapper(max_capping_dict=None, min_capping_dict=None, missing_values='raise')

The ArbitraryOutlierCapper() caps the maximum or minimum values of a variable at an arbitrary value indicated by the user.

You must provide the maximum or minimum values that will be used to cap each variable in a dictionary containing the features as keys and the capping values as values.

More details in the User Guide.

Parameters

max_capping_dict: dictionary, default=None

Dictionary containing the user specified capping values for the right tail of the distribution of each variable to cap (maximum values).

min_capping_dict: dictionary, default=None

Dictionary containing user specified capping values for the eft tail of the distribution of each variable to cap (minimum values).

missing_values: string, default=’raise’

Indicates if missing values should be ignored or raised. If 'raise' the transformer will return an error if the the datasets to fit or transform contain missing values. If 'ignore', missing data will be ignored when learning parameters or performing the transformation.

Attributes

right_tail_caps_:

Dictionary with the maximum values beyond which a value will be considered an outlier.

left_tail_caps_:

Dictionary with the minimum values beyond which a value will be considered an outlier.

variables_:

The group of variables that will be transformed.

feature_names_in_:

List with the names of features seen during fit.

n_features_in_:

The number of features in the train set used in fit.

Examples

>>> import pandas as pd
>>> from feature_engine.outliers import ArbitraryOutlierCapper
>>> X = pd.DataFrame(dict(x1 = [1,2,3,4,5,6,7,8,9,10]))
>>> aoc = ArbitraryOutlierCapper(max_capping_dict=dict(x1 =  8),
>>>                              min_capping_dict=dict(x1 = 2))
>>> aoc.fit(X)
>>> aoc.transform(X)
   x1
0   2
1   2
2   3
3   4
4   5
5   6
6   7
7   8
8   8
9   8

Methods

fit:	This transformer does not learn parameters.
fit_transform:	Fit to data, then transform it.
get_feature_names_out:	Get output feature names for transformation.
get_params:	Get parameters for this estimator.
set_params:	Set the parameters of this estimator.
transform:	Cap the variables.

fit(X, y=None)

This transformer does not learn any parameter.

Parameters

X: pandas dataframe of shape = [n_samples, n_features]

The training input samples.

y: pandas Series, default=None

y is not needed in this transformer. You can pass y or None.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)

Get output feature names for transformation. In other words, returns the variable names of transformed dataframe.

Parameters

input_featuresarray or list, default=None

This parameter exits only for compatibility with the Scikit-learn pipeline.

• If None, then feature_names_in_ is used as feature names in.

• If an array or list, then input_features must match feature_names_in_.

Returns

feature_names_out: list

Transformed feature names.

rtype

List[Union[str, int]] ..

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict

Parameter names mapped to their values.

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

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

transform(X)

Cap the variable values.

Parameters

X: pandas dataframe of shape = [n_samples, n_features]

The data to be transformed.

Returns

X_new: pandas dataframe of shape = [n_samples, n_features]

The dataframe with the capped variables.

rtype

DataFrame ..