base_sampler#
Base Sampler component. Used as the base class of all sampler components.
Module Contents#
Classes Summary#
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
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 the sampler to the data.
Fit and transform data using the sampler component.
Loads component at file path.
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.
Transforms the input data by sampling the data.
Updates the parameter dictionary of the component.
- 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)[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
- update_parameters(self, update_dict, reset_fit=True)#
Updates the parameter dictionary of the component.
- Parameters
update_dict (dict) – A dict of parameters to update.
reset_fit (bool, optional) – If True, will set _is_fitted to False.