lightgbm_regressor¶

LightGBM Regressor.

Module Contents¶

Classes Summary¶

LightGBMRegressor

LightGBM Regressor.

Contents¶

class evalml.pipelines.components.estimators.regressors.lightgbm_regressor.LightGBMRegressor(boosting_type='gbdt', learning_rate=0.1, n_estimators=20, max_depth=0, num_leaves=31, min_child_samples=20, bagging_fraction=0.9, bagging_freq=0, n_jobs=- 1, random_seed=0, **kwargs)[source]¶

LightGBM Regressor.

Parameters

boosting_type (string) – Type of boosting to use. Defaults to “gbdt”. - ‘gbdt’ uses traditional Gradient Boosting Decision Tree - “dart”, uses Dropouts meet Multiple Additive Regression Trees - “goss”, uses Gradient-based One-Side Sampling - “rf”, uses Random Forest
learning_rate (float) – Boosting learning rate. Defaults to 0.1.
n_estimators (int) – Number of boosted trees to fit. Defaults to 100.
max_depth (int) – Maximum tree depth for base learners, <=0 means no limit. Defaults to 0.
num_leaves (int) – Maximum tree leaves for base learners. Defaults to 31.
min_child_samples (int) – Minimum number of data needed in a child (leaf). Defaults to 20.
bagging_fraction (float) – LightGBM will randomly select a subset of features on each iteration (tree) without resampling if this is smaller than 1.0. For example, if set to 0.8, LightGBM will select 80% of features before training each tree. This can be used to speed up training and deal with overfitting. Defaults to 0.9.
bagging_freq (int) – Frequency for bagging. 0 means bagging is disabled. k means perform bagging at every k iteration. Every k-th iteration, LightGBM will randomly select bagging_fraction * 100 % of the data to use for the next k iterations. Defaults to 0.
n_jobs (int or None) – Number of threads to run in parallel. -1 uses all threads. Defaults to -1.
random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

hyperparameter_ranges	{ “learning_rate”: Real(0.000001, 1), “boosting_type”: [“gbdt”, “dart”, “goss”, “rf”], “n_estimators”: Integer(10, 100), “max_depth”: Integer(0, 10), “num_leaves”: Integer(2, 100), “min_child_samples”: Integer(1, 100), “bagging_fraction”: Real(0.000001, 1), “bagging_freq”: Integer(0, 1),}
model_family	ModelFamily.LIGHTGBM
modifies_features	True
modifies_target	False
name	LightGBM Regressor
SEED_MAX	SEED_BOUNDS.max_bound
SEED_MIN	0
supported_problem_types	[ProblemTypes.REGRESSION]
training_only	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 LightGBM regressor to data.
`load`	Loads component at file path.
`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.
`predict`	Make predictions using fitted LightGBM regressor.
`predict_proba`	Make probability estimates for labels.
`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

Returns dictionary if return_dict is True, else None.

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
Raises: MethodPropertyNotFoundError – If estimator does not have a feature_importance method or a component_obj that implements feature_importance.

fit(self, X, y=None)[source]¶

Fits LightGBM regressor to data.

Parameters

X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series) – 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

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.

predict(self, X)[source]¶

Make predictions using fitted LightGBM regressor.

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

predict_proba(self, X)¶

Make probability estimates for labels.

Parameters: X (pd.DataFrame) – Features.
Returns: Probability estimates.
Return type: pd.Series
Raises: MethodPropertyNotFoundError – If estimator does not have a predict_proba method or a component_obj that implements predict_proba.

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.

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