boosters.sklearn.GBDTRegressor

class boosters.sklearn.GBDTRegressor

Bases: _GBDTEstimatorBase, RegressorMixin

Gradient Boosted Decision Tree Regressor.

A sklearn-compatible wrapper around GBDTModel for regression.

Parameters:

• n_estimators (int, default=100) – Number of boosting rounds.

• learning_rate (float, default=0.1) – Learning rate shrinks the contribution of each tree.

• max_depth (int, default=6) – Maximum depth of each tree.

• min_child_weight (float, default=1.0) – Minimum sum of instance weight (hessian) in a child node.

• max_leaves (int, default=31) – Maximum number of leaves per tree.

• grow_strategy (GrowthStrategy, default=GrowthStrategy.Depthwise) – Tree growing strategy.

• colsample_bytree (float, default=1.0) – Subsample ratio of columns for each tree.

• subsample (float, default=1.0) – Subsample ratio of training instances.

• gamma (float, default=0.0) – Minimum loss reduction required for split.

• reg_alpha (float, default=0.0) – L1 regularization on weights.

• reg_lambda (float, default=1.0) – L2 regularization on weights.

• early_stopping_rounds (int or None, default=None) – Stop if no improvement for this many rounds.

• seed (int, default=42) – Random seed.

• n_threads (int, default=0) – Number of threads (0 = auto).

• objective (Objective or None, default=None) – Loss function. Must be a regression objective (e.g., Objective.squared()). If None, uses Objective.squared().

• metric (Metric or None, default=None) – Evaluation metric. If None, uses Metric.rmse().

• Attributes

• ----------

• model (GBDTModel) – The fitted core model.

• n_features_in (int) – Number of features seen during fit.

• feature_importances (ndarray of shape (n_features,)) – Feature importance scores (gain-based).

__init__(n_estimators=100, learning_rate=0.1, max_depth=6, min_child_weight=1.0, max_leaves=31, grow_strategy=GrowthStrategy.Depthwise, colsample_bytree=1.0, subsample=1.0, gamma=0.0, reg_alpha=0.0, reg_lambda=1.0, early_stopping_rounds=None, seed=42, n_threads=0, verbose=1, objective=None, metric=None)

Parameters:

• n_estimators (int)

• learning_rate (float)

• max_depth (int)

• min_child_weight (float)

• max_leaves (int)

• grow_strategy (GrowthStrategy)

• colsample_bytree (float)

• subsample (float)

• gamma (float)

• reg_alpha (float)

• reg_lambda (float)

• early_stopping_rounds (int | None)

• seed (int)

• n_threads (int)

• verbose (int)

• objective (Objective | None)

• metric (Metric | None)

Return type:

None

property feature_importances_: ndarray[tuple[Any, ...], dtype[float32]]

Return feature importances (gain-based).

fit(X, y, eval_set=None, sample_weight=None)

Fit the estimator.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Training input samples.

• y (array-like of shape (n_samples,)) – Target values.

• eval_set (tuple of (X, y), optional) – Validation set as (X_val, y_val) tuple.

• sample_weight (array-like of shape (n_samples,), optional) – Sample weights.

• Returns

• -------

• self – Fitted estimator.

Return type:

Self

get_feature_importance(importance_type=ImportanceType.Gain)

Get feature importance scores.

Parameters:

• importance_type (ImportanceType, default=ImportanceType.Gain) – Type of feature importance to compute.

• Returns

• -------

• importance (ndarray of shape (n_features,)) – Feature importance scores.

Return type:

ndarray[tuple[Any, ...], dtype[float32]]

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

predict(X)

Predict using the fitted model.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Input samples.

• Returns

• -------

• y_pred (ndarray of shape (n_samples,)) – Predicted values.

Return type:

ndarray[tuple[Any, ...], dtype[float32]]

score(X, y, sample_weight=None)

Return coefficient of determination on test data.

The coefficient of determination, \(R^2\), is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

• sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – \(R^2\) of self.predict(X) w.r.t. y.

Return type:

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_fit_request(*, eval_set='$UNCHANGED$', sample_weight='$UNCHANGED$')

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

• eval_set (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for eval_set parameter in fit.

• sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

• self (GBDTRegressor)

Returns:

self – The updated object.

Return type:

object

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

set_score_request(*, sample_weight='$UNCHANGED$')

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

• sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

• self (GBDTRegressor)

Returns:

self – The updated object.

Return type:

object

model_: GBDTModel

n_features_in_: int