# pylint: disable=too-many-arguments
from typing import Tuple
import numpy as np
import xgboost
from Orange.base import XGBBase
from Orange.data import Variable, ContinuousVariable, Table
from Orange.preprocess.score import LearnerScorer
from Orange.regression import Learner
__all__ = ["XGBRegressor", "XGBRFRegressor"]
class _FeatureScorerMixin(LearnerScorer):
feature_type = Variable
class_type = ContinuousVariable
def score(self, data: Table) -> Tuple[np.ndarray, Tuple[Variable]]:
model: XGBBase = self(data)
return model.skl_model.feature_importances_, model.domain.attributes
[docs]
class XGBRegressor(XGBBase, Learner, _FeatureScorerMixin):
__wraps__ = xgboost.XGBRegressor
supports_weights = True
def __init__(self,
max_depth=None,
learning_rate=None,
n_estimators=100,
verbosity=None,
objective="reg:squarederror",
booster=None,
tree_method=None,
n_jobs=None,
gamma=None,
min_child_weight=None,
max_delta_step=None,
subsample=None,
colsample_bytree=None,
colsample_bylevel=None,
colsample_bynode=None,
reg_alpha=None,
reg_lambda=None,
scale_pos_weight=None,
base_score=None,
random_state=None,
missing=np.nan,
num_parallel_tree=None,
monotone_constraints=None,
interaction_constraints=None,
importance_type="gain",
gpu_id=None,
validate_parameters=None,
preprocessors=None):
super().__init__(max_depth=max_depth, learning_rate=learning_rate,
n_estimators=n_estimators, verbosity=verbosity,
objective=objective, booster=booster,
tree_method=tree_method, n_jobs=n_jobs, gamma=gamma,
min_child_weight=min_child_weight,
max_delta_step=max_delta_step, subsample=subsample,
colsample_bytree=colsample_bytree,
colsample_bylevel=colsample_bylevel,
colsample_bynode=colsample_bynode,
reg_alpha=reg_alpha, reg_lambda=reg_lambda,
scale_pos_weight=scale_pos_weight,
base_score=base_score, random_state=random_state,
missing=missing, num_parallel_tree=num_parallel_tree,
monotone_constraints=monotone_constraints,
interaction_constraints=interaction_constraints,
importance_type=importance_type, gpu_id=gpu_id,
validate_parameters=validate_parameters,
preprocessors=preprocessors)
[docs]
class XGBRFRegressor(XGBBase, Learner, _FeatureScorerMixin):
__wraps__ = xgboost.XGBRFRegressor
supports_weights = True
def __init__(self,
max_depth=None,
learning_rate=None,
n_estimators=100,
verbosity=None,
objective="reg:squarederror",
booster=None,
tree_method=None,
n_jobs=None,
gamma=None,
min_child_weight=None,
max_delta_step=None,
subsample=None,
colsample_bytree=None,
colsample_bylevel=None,
colsample_bynode=None,
reg_alpha=None,
reg_lambda=None,
scale_pos_weight=None,
base_score=None,
random_state=None,
missing=np.nan,
num_parallel_tree=None,
monotone_constraints=None,
interaction_constraints=None,
importance_type="gain",
gpu_id=None,
validate_parameters=None,
preprocessors=None):
super().__init__(max_depth=max_depth, learning_rate=learning_rate,
n_estimators=n_estimators, verbosity=verbosity,
objective=objective, booster=booster,
tree_method=tree_method, n_jobs=n_jobs, gamma=gamma,
min_child_weight=min_child_weight,
max_delta_step=max_delta_step, subsample=subsample,
colsample_bytree=colsample_bytree,
colsample_bylevel=colsample_bylevel,
colsample_bynode=colsample_bynode,
reg_alpha=reg_alpha, reg_lambda=reg_lambda,
scale_pos_weight=scale_pos_weight,
base_score=base_score, random_state=random_state,
missing=missing, num_parallel_tree=num_parallel_tree,
monotone_constraints=monotone_constraints,
interaction_constraints=interaction_constraints,
importance_type=importance_type, gpu_id=gpu_id,
validate_parameters=validate_parameters,
preprocessors=preprocessors)