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Fix intercept handling of xgboost starting with version 3.1.0 #733

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Fix intercept handling of xgboost starting with version 3.1.0 #733

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18 changes: 15 additions & 3 deletions onnxmltools/convert/xgboost/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,9 +29,16 @@ def get_xgb_params(xgb_node):
if xgb_node.n_estimators is not None:
params["n_estimators"] = xgb_node.n_estimators
if "base_score" in config["learner"]["learner_model_param"]:
bs = float(config["learner"]["learner_model_param"]["base_score"])
# xgboost >= 2.0
params["base_score"] = bs
base_score = config["learner"]["learner_model_param"]["base_score"]
if(base_score.startswith('[') and base_score.endswith(']')):
# xgboost >= 3.1, see
base_score = [float(score) for score in base_score.strip('[]').split(',')]
if len(base_score) == 1:
base_score = base_score[0]
else:
#xgboost >= 2.0 and < 3.1
base_score = float(base_score)
params["base_score"] = base_score
if "num_target" in config["learner"]["learner_model_param"]:
params["n_targets"] = int(
config["learner"]["learner_model_param"]["num_target"]
Expand All @@ -48,6 +55,11 @@ def get_xgb_params(xgb_node):
params["best_ntree_limit"] = int(gbp["num_trees"])
return params

def base_score_as_list(base_score):
if isinstance(base_score, list):
return base_score
return [base_score]


def get_n_estimators_classifier(xgb_node, params, js_trees):
if "n_estimators" not in params:
Expand Down
17 changes: 10 additions & 7 deletions onnxmltools/convert/xgboost/operator_converters/XGBoost.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
except ImportError:
XGBRFClassifier = None
from ...common._registration import register_converter
from ..common import get_xgb_params, get_n_estimators_classifier
from ..common import get_xgb_params, get_n_estimators_classifier, base_score_as_list


class XGBConverter:
Expand Down Expand Up @@ -259,7 +259,7 @@ def convert(scope, operator, container):
raise RuntimeError("Objective '{}' not supported.".format(objective))

attr_pairs = XGBRegressorConverter._get_default_tree_attribute_pairs()
attr_pairs["base_values"] = [base_score]
attr_pairs["base_values"] = base_score_as_list(base_score)

if best_ntree_limit and best_ntree_limit < len(js_trees):
js_trees = js_trees[:best_ntree_limit]
Expand Down Expand Up @@ -350,17 +350,20 @@ def convert(scope, operator, container):
attr_pairs["post_transform"] = "LOGISTIC"
attr_pairs["class_ids"] = [0 for v in attr_pairs["class_treeids"]]
if js_trees[0].get("leaf", None) == 0:
attr_pairs["base_values"] = [base_score]
attr_pairs["base_values"] = base_score_as_list(base_score)
elif base_score != 0.5:
# 0.5 -> cst = 0
cst = -np.log(1 / np.float32(base_score) - 1.0)
attr_pairs["base_values"] = [cst]
cst = -np.log(1 / np.array(base_score_as_list(base_score), dtype=np.float32) - 1.0)
attr_pairs["base_values"] = cst.tolist()
else:
attr_pairs["base_values"] = [base_score]
attr_pairs["base_values"] = base_score_as_list(base_score)
else:
# See https://github.com/dmlc/xgboost/blob/main/src/common/math.h#L35.
attr_pairs["post_transform"] = "SOFTMAX"
attr_pairs["base_values"] = [base_score for n in range(ncl)]
if isinstance(base_score, list):
attr_pairs["base_values"] = base_score
else:
attr_pairs["base_values"] = [base_score for n in range(ncl)]
attr_pairs["class_ids"] = [v % ncl for v in attr_pairs["class_treeids"]]

classes = xgb_node.classes_
Expand Down