component_base

Module Contents

Classes Summary

ComponentBase

Base class for all components.

Attributes Summary

logger

Contents

class evalml.pipelines.components.component_base.ComponentBase(parameters=None, component_obj=None, random_seed=0, **kwargs)

Base class for all 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.

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
load	Loads component at file path
model_family	Returns ModelFamily of this component
modifies_features	Returns whether this component modifies (subsets or transforms) the features variable during transform.
modifies_target	Returns whether this component modifies (subsets or transforms) the target variable during transform.
name	Returns string name of this component
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

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=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

static load(file_path)

Loads component at file path

Parameters

file_path (str) – Location to load file

Returns

ComponentBase object

property model_family(cls)

Returns ModelFamily of this component

property modifies_features(cls)

Returns whether this component modifies (subsets or transforms) the features variable during transform. For Estimator objects, this attribute determines if the return value from predict or predict_proba should be used as features or targets.

property modifies_target(cls)

Returns whether this component modifies (subsets or transforms) the target variable during transform. For Estimator objects, this attribute determines if the return value from predict or predict_proba should be used as features or targets.

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.

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

evalml.pipelines.components.component_base.logger

transformer component_base_meta