base_sampler¶

Base Sampler component. Used as the base class of all sampler components.

Module Contents¶

Classes Summary¶

BaseSampler

Base Sampler component. Used as the base class of all sampler components.

Contents¶

class evalml.pipelines.components.transformers.samplers.base_sampler.BaseSampler(parameters=None, component_obj=None, random_seed=0, **kwargs)[source]¶

Base Sampler component. Used as the base class of all sampler components.

Parameters

parameters (dict) – Dictionary of parameters for the component. Defaults to None.
component_obj (obj) – Third-party objects useful in component implementation. Defaults to None.
random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

modifies_features	True
modifies_target	True
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_transform`	Fit and transform data using the sampler component.
`load`	Loads component at file path.
`name`	Returns string name of this component.
`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)[source]¶

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_transform(self, X, y)[source]¶

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

property name(cls)¶: Returns string name of this component.

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

samplers

oversampler