TomekLinks

class hana_ml.algorithms.pal.preprocessing.TomekLinks(distance_level=None, minkowski_power=None, thread_ratio=None, search_method=None, sampling_strategy=None, category_weights=None)

This class is for performing under-sampling by removing Tomek's links.

Parameters:

distance_levelstr, optional

Specifies the distance method between train data and test data point.

• 'manhattan'

• 'euclidean'

• 'minkowski'

• 'chebyshev'

• 'cosine'

Defaults to 'euclidean'.

minkowski_powerfloat, optional

Specifies the value of power for Minkowski distance calculation.

Defaults to 3.

Valid only when distance_level is 'minkowski'.

thread_ratiofloat, optional

Adjusts the percentage of available threads to use, from 0 to 1. A value of 0 indicates the use of a single thread, while 1 implies the use of all possible current threads. Values outside the range will be ignored and this function heuristically determines the number of threads to use.

Default to 0.

search_methodstr, optional

Specifies the searching method when finding K nearest neighbour.

• 'brute-force'

• 'kd-tree'

Defaults to 'brute-force'.

sampling_strategystr, optional

Specifies the classes targeted by resampling:

• 'majority' : resamples only the majority class

• 'non-minority' : resamples all classes except the minority class

• 'non-majority' : resamples all classes except the majority class

• 'all' : resamples all classes

Defaults to 'majority'

category_weightsfloat, optional

Specifies the weight for categorical attributes.

Defaults to 0.707 if not provided.

Examples

>>> tomeklinks = TomekLinks(search_method='kd-tree',
                            sampling_strategy='majority')
>>> res = smotetomek.fit_transform(data=df, label='TYPE')

Attributes:

None

Methods

fit_transform(data[, key, label, ...])

Perform under-sampling on given datasets by removing Tomek's links.

fit_transform(data, key=None, label=None, categorical_variable=None, variable_weight=None)

Perform under-sampling on given datasets by removing Tomek's links.

Parameters:

dataDataFrame

Dataframe that contains the training data.

keystr, optional

Specifies the name of the ID column.

If key is not provided, then:

• if data is indexed by a single column, then key defaults to that index column;

• otherwise, it is assumed that data contains no ID column.

labelstr, optional

Specifies the dependent variable by name.

If not specified, defaults to the 1st non-key column in data.

categorical_variablestr or a list of str, optional

Specifies which INTEGER columns should be treated as categorical, with all other INTEGER columns treated as continuous.

No default value.

variable_weightdict, optional

Specifies the weights of variables participating in distance calculation in a dictionary:

• key : variable(column) name

• value : weight for distance calculation

No default value.

Returns:

DataFrame

• Undersampled result, the same structure as defined in the input data.

Inherited Methods from PALBase

Besides those methods mentioned above, the TomekLinks class also inherits methods from PALBase class, please refer to PAL Base for more details.