oversamplers¶

Module Contents¶

Classes Summary¶

`SMOTENCOversampler`	SMOTENC Oversampler component. Uses SMOTENC to generate synthetic samples. Works on a mix of numerical and categorical columns.
`SMOTENOversampler`	SMOTEN Oversampler component. Uses SMOTEN to generate synthetic samples. Works for purely categorical datasets.
`SMOTEOversampler`	SMOTE Oversampler component. Works on numerical datasets only. This component is only run during training and not during predict.

Contents¶

class evalml.pipelines.components.transformers.samplers.oversamplers.SMOTENCOversampler(sampling_ratio=0.25, k_neighbors_default=5, n_jobs=- 1, random_seed=0, **kwargs)[source]¶

SMOTENC Oversampler component. Uses SMOTENC to generate synthetic samples. Works on a mix of numerical and categorical columns. Input data must be Woodwork type, and this component is only run during training and not during predict.

Parameters

sampling_ratio (float) – This is the goal ratio of the minority to majority class, with range (0, 1]. A value of 0.25 means we want a 1:4 ratio of the minority to majority class after oversampling. We will create the a sampling dictionary using this ratio, with the keys corresponding to the class and the values responding to the number of samples. Defaults to 0.25.
k_neighbors_default (int) – The number of nearest neighbors used to construct synthetic samples. This is the default value used, but the actual k_neighbors value might be smaller if there are less samples. Defaults to 5.
n_jobs (int) – The number of CPU cores to use. Defaults to -1.
random_seed (int) – The seed to use for random sampling. Defaults to 0.

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	True
name	SMOTENC Oversampler

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_transform`	Fits on X and transforms X
`load`	Loads component at file path
`needs_fitting`	Returns boolean determining if component needs fitting before
`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

prints and returns dictionary

Return type

None or dict

fit(self, X, y)[source]¶

Fits the sampler to the data.

Parameters

X (pd.DataFrame) – Input features.
y (pd.Series) – Target.

Returns

self

fit_transform(self, X, y)¶

Fits on X and transforms X

Parameters

X (pd.DataFrame) – Data to fit and transform
y (pd.Series) – Target data

Returns

Transformed X

Return type

pd.DataFrame

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.

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.

Returns

None

transform(self, X, y=None)¶

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

class evalml.pipelines.components.transformers.samplers.oversamplers.SMOTENOversampler(sampling_ratio=0.25, k_neighbors_default=5, n_jobs=- 1, random_seed=0, **kwargs)[source]¶

SMOTEN Oversampler component. Uses SMOTEN to generate synthetic samples. Works for purely categorical datasets. This component is only run during training and not during predict.

Parameters

sampling_ratio (float) – This is the goal ratio of the minority to majority class, with range (0, 1]. A value of 0.25 means we want a 1:4 ratio of the minority to majority class after oversampling. We will create the a sampling dictionary using this ratio, with the keys corresponding to the class and the values responding to the number of samples. Defaults to 0.25.
k_neighbors_default (int) – The number of nearest neighbors used to construct synthetic samples. This is the default value used, but the actual k_neighbors value might be smaller if there are less samples. Defaults to 5.
n_jobs (int) – The number of CPU cores to use. Defaults to -1.
random_seed (int) – The seed to use for random sampling. Defaults to 0.

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	True
name	SMOTEN Oversampler

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_transform`	Fits on X and transforms X
`load`	Loads component at file path
`needs_fitting`	Returns boolean determining if component needs fitting before
`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

prints and returns dictionary

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

fit_transform(self, X, y)¶

Fits on X and transforms X

Parameters

X (pd.DataFrame) – Data to fit and transform
y (pd.Series) – Target data

Returns

Transformed X

Return type

pd.DataFrame

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.

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.

Returns

None

transform(self, X, y=None)¶

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

class evalml.pipelines.components.transformers.samplers.oversamplers.SMOTEOversampler(sampling_ratio=0.25, k_neighbors_default=5, n_jobs=- 1, random_seed=0, **kwargs)[source]¶

SMOTE Oversampler component. Works on numerical datasets only. This component is only run during training and not during predict.

Parameters

sampling_ratio (float) – This is the goal ratio of the minority to majority class, with range (0, 1]. A value of 0.25 means we want a 1:4 ratio of the minority to majority class after oversampling. We will create the a sampling dictionary using this ratio, with the keys corresponding to the class and the values responding to the number of samples. Defaults to 0.25.
k_neighbors_default (int) – The number of nearest neighbors used to construct synthetic samples. This is the default value used, but the actual k_neighbors value might be smaller if there are less samples. Defaults to 5.
n_jobs (int) – The number of CPU cores to use. Defaults to -1.
random_seed (int) – The seed to use for random sampling. Defaults to 0.

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	True
name	SMOTE Oversampler

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_transform`	Fits on X and transforms X
`load`	Loads component at file path
`needs_fitting`	Returns boolean determining if component needs fitting before
`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

prints and returns dictionary

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

fit_transform(self, X, y)¶

Fits on X and transforms X

Parameters

X (pd.DataFrame) – Data to fit and transform
y (pd.Series) – Target data

Returns

Transformed X

Return type

pd.DataFrame

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.

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.

Returns

None

transform(self, X, y=None)¶

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

base_sampler undersampler