component_base#

Base class for all components.

Module Contents#

Classes Summary#

ComponentBase

Base class for all components.

Contents#

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

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.
`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 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.
`training_only`	Returns whether or not this component should be evaluated during training-time only, or during both training and prediction time.

clone(self)[source]#

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)[source]#

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)[source]#

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.

static load(file_path)[source]#

Loads component at file path.

Parameters: file_path (str) – Location to load file.
Returns: ComponentBase object

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.

Returns: True.

property parameters(self)#: Returns the parameters which were used to initialize the component.

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)[source]#

Saves component at file path.

Parameters

file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.

property training_only(cls)#: Returns whether or not this component should be evaluated during training-time only, or during both training and prediction time.