component_base¶
Base class for all components.
Module Contents¶
Classes Summary¶
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
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits component to data.
Loads component at file path.
Returns whether this component modifies (subsets or transforms) the features variable during transform.
Returns whether this component modifies (subsets or transforms) the target variable during transform.
Returns string name of this component.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Returns whether or not this component should be evaluated during training-time only, or during both training and prediction time.
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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.
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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
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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
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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.
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static
load(file_path)[source]¶ Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
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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.
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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.
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property
name(cls)¶ Returns string name of this component.
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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.
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property
parameters(self)¶ Returns the parameters which were used to initialize the component.
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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.
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property
training_only(cls)¶ Returns whether or not this component should be evaluated during training-time only, or during both training and prediction time.