undersampler#

An undersampling transformer to downsample the majority classes in the dataset.

Module Contents#

Classes Summary#

Undersampler

Initializes an undersampling transformer to downsample the majority classes in the dataset.

Contents#

class evalml.pipelines.components.transformers.samplers.undersampler.Undersampler(sampling_ratio=0.25, sampling_ratio_dict=None, min_samples=100, min_percentage=0.1, random_seed=0, **kwargs)[source]#

Initializes an undersampling transformer to downsample the majority classes in the dataset.

This component is only run during training and not during predict.

Parameters
  • sampling_ratio (float) – The smallest minority:majority ratio that is accepted as ‘balanced’. For instance, a 1:4 ratio would be represented as 0.25, while a 1:1 ratio is 1.0. Must be between 0 and 1, inclusive. Defaults to 0.25.

  • sampling_ratio_dict (dict) – A dictionary specifying the desired balanced ratio for each target value. For instance, in a binary case where class 1 is the minority, we could specify: sampling_ratio_dict={0: 0.5, 1: 1}, which means we would undersample class 0 to have twice the number of samples as class 1 (minority:majority ratio = 0.5), and don’t sample class 1. Overrides sampling_ratio if provided. Defaults to None.

  • min_samples (int) – The minimum number of samples that we must have for any class, pre or post sampling. If a class must be downsampled, it will not be downsampled past this value. To determine severe imbalance, the minority class must occur less often than this and must have a class ratio below min_percentage. Must be greater than 0. Defaults to 100.

  • min_percentage (float) – The minimum percentage of the minimum class to total dataset that we tolerate, as long as it is above min_samples. If min_percentage and min_samples are not met, treat this as severely imbalanced, and we will not resample the data. Must be between 0 and 0.5, inclusive. Defaults to 0.1.

  • random_seed (int) – The seed to use for random sampling. Defaults to 0.

Raises
  • ValueError – If sampling_ratio is not in the range (0, 1].

  • ValueError – If min_sample is not greater than 0.

  • ValueError – If min_percentage is not between 0 and 0.5, inclusive.

Attributes

hyperparameter_ranges

{}

modifies_features

True

modifies_target

True

name

Undersampler

training_only

True

Methods

clone

Constructs a new component with the same parameters and random state.

default_parameters

Returns the default parameters for this component.

describe

Describe a component and its parameters.

fit

Fits the sampler to the data.

fit_resample

Resampling technique for this sampler.

fit_transform

Fit and transform data using the sampler component.

load

Loads component at file path.

needs_fitting

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

parameters

Returns the parameters which were used to initialize the component.

save

Saves component at file path.

transform

Transforms the input data by sampling the data.

clone(self)#

Constructs a new component with the same parameters and random state.

Returns

A new instance of this component with identical parameters and random state.

default_parameters(cls)#

Returns the default parameters for this component.

Our convention is that Component.default_parameters == Component().parameters.

Returns

Default parameters for this component.

Return type

dict

describe(self, print_name=False, return_dict=False)#

Describe a component and its parameters.

Parameters
  • print_name (bool, optional) – whether to print name of component

  • return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

fit(self, X, y)#

Fits the sampler to the data.

Parameters
  • X (pd.DataFrame) – Input features.

  • y (pd.Series) – Target.

Returns

self

Raises

ValueError – If y is None.

fit_resample(self, X, y)[source]#

Resampling technique for this sampler.

Parameters
  • X (pd.DataFrame) – Training data to fit and resample.

  • y (pd.Series) – Training data targets to fit and resample.

Returns

Indices to keep for training data.

Return type

list

fit_transform(self, X, y)#

Fit and transform data using the sampler component.

Parameters
  • X (pd.DataFrame) – The input training data of shape [n_samples, n_features].

  • y (pd.Series, optional) – The target training data of length [n_samples].

Returns

Transformed data.

Return type

(pd.DataFrame, pd.Series)

static load(file_path)#

Loads component at file path.

Parameters

file_path (str) – Location to load file.

Returns

ComponentBase object

needs_fitting(self)#

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.

Returns

True.

property parameters(self)#

Returns the parameters which were used to initialize the component.

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#

Saves component at file path.

Parameters
  • file_path (str) – Location to save file.

  • pickle_protocol (int) – The pickle data stream format.

transform(self, X, y=None)[source]#

Transforms the input data by sampling the data.

Parameters
  • X (pd.DataFrame) – Training features.

  • y (pd.Series) – Target.

Returns

Transformed features and target.

Return type

pd.DataFrame, pd.Series