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