transformer

A component that may or may not need fitting that transforms data. These components are used before an estimator.

Module Contents

Classes Summary

Transformer

A component that may or may not need fitting that transforms data. These components are used before an estimator.

Contents

class evalml.pipelines.components.transformers.transformer.Transformer(parameters=None, component_obj=None, random_seed=0, **kwargs)

A component that may or may not need fitting that transforms data. These components are used before an estimator.

To implement a new Transformer, define your own class which is a subclass of Transformer, including a name and a list of acceptable ranges for any parameters to be tuned during the automl search (hyperparameters). Define an __init__ method which sets up any necessary state and objects. Make sure your __init__ only uses standard keyword arguments and calls super().__init__() with a parameters dict. You may also override the fit, transform, fit_transform and other methods in this class if appropriate.

To see some examples, check out the definitions of any Transformer component.

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	False
training_only	False

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 component to data.
fit_transform	Fits on X and transforms X.
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 data X.

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=None)

Fits component to data.

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

self

Raises

MethodPropertyNotFoundError – If component does not have a fit method or a component_obj that implements fit.

fit_transform(self, X, y=None)

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

Raises

MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.

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.

abstract transform(self, X, y=None)

Transforms data X.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Target data.

Returns

Transformed X

Return type

pd.DataFrame

Raises

MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.