Source code for Orange.classification.xgb

# 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
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


[docs]class XGBClassifier(XGBBase, Learner, _FeatureScorerMixin): __wraps__ = xgboost.XGBClassifier 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, use_label_encoder=False, preprocessors=preprocessors)
[docs]class XGBRFClassifier(XGBBase, Learner, _FeatureScorerMixin): __wraps__ = xgboost.XGBRFClassifier 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, use_label_encoder=False, preprocessors=preprocessors)