lightgbm_regressor¶
LightGBM Regressor.
Module Contents¶
Classes Summary¶
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
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Returns importance associated with each feature.
Fits LightGBM regressor to data.
Loads component at file path.
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.
Make predictions using fitted LightGBM regressor.
Make probability estimates for labels.
Saves component at file path.
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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.
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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)¶ 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.
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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
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static
load
(file_path)¶ Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
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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.
-
property
parameters
(self)¶ Returns the parameters which were used to initialize the component.
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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
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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.
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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.