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# pylint: disable=too-many-arguments
from typing import Tuple
import numpy as np
import xgboost
from Orange.base import XGBBase
from Orange.classification import Learner, SklModel
from Orange.data import Variable, DiscreteVariable, Table
from Orange.preprocess.score import LearnerScorer
__all__ = ["XGBClassifier", "XGBRFClassifier"]
class _FeatureScorerMixin(LearnerScorer):
feature_type = Variable
class_type = DiscreteVariable
def score(self, data: Table) -> Tuple[np.ndarray, Tuple[Variable]]:
model: XGBBase = self(data)
return model.skl_model.feature_importances_, model.domain.attributes
class XGBClassifier(XGBBase, Learner, _FeatureScorerMixin):
__wraps__ = xgboost.XGBClassifier
__returns__ = SklModel
supports_weights = True
def __init__(self,
max_depth=None,
learning_rate=None,
n_estimators=100,
verbosity=None,
objective="binary:logistic",
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)
class XGBRFClassifier(XGBBase, Learner, _FeatureScorerMixin):
__wraps__ = xgboost.XGBRFClassifier
__returns__ = SklModel
supports_weights = True
def __init__(self,
max_depth=None,
learning_rate=None,
n_estimators=100,
verbosity=None,
objective="binary:logistic",
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)
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