estimator¶

Module Contents¶

Classes Summary¶

Estimator

A component that fits and predicts given data.

Contents¶

class evalml.pipelines.components.estimators.estimator.Estimator(parameters=None, component_obj=None, random_seed=0, **kwargs)[source]¶

A component that fits and predicts given data.

To implement a new Estimator, define your own class which is a subclass of Estimator, 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 Estimator 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

model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
predict_uses_y	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
`feature_importance`	Returns importance associated with each feature.
`fit`	Fits component to data
`load`	Loads component at file path
`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
`predict`	Make predictions using selected features.
`predict_proba`	Make probability estimates for labels.
`save`	Saves component at file path
`supported_problem_types`	Problem types this estimator supports

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

property feature_importance(self)¶

Returns importance associated with each feature.

Returns: Importance associated with each feature
Return type: np.ndarray

fit(self, X, y=None)[source]¶

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 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

predict(self, X)[source]¶

Make predictions using selected features.

Parameters: X (pd.DataFrame, np.ndarray) – Data of shape [n_samples, n_features]
Returns: Predicted values
Return type: pd.Series

predict_proba(self, X)[source]¶

Make probability estimates for labels.

Parameters: X (pd.DataFrame, or np.ndarray) – Features
Returns: Probability estimates
Return type: pd.Series

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

property supported_problem_types(cls)¶: Problem types this estimator supports

xgboost_regressor transformers