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import xgboost as xgb
import numpy as np
import m2cgen as m2c
from sklearn.datasets import load_boston
# train a model and save it as c code with m2cgen
X, y = load_boston(return_X_y=True)
y = np.random.choice([0, 1], size=len(y))
model = xgb.XGBClassifier(n_estimators=10, max_depth=3)
model.fit(X, y)
code = m2c.export_to_c(model)
Works fine with XGBoostRegressor
Full trace:
Traceback (most recent call last):
File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/pydevd.py", line 1500, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "/Users/judewells/Documents/dataScienceProgramming/tethir/ir3flamedetect2/experiment_scripts/temp_file_m2c.py", line 13, in <module>
code = m2c.export_to_c(model)
^^^^^^^^^^^^^^^^^^^^^^
File "/Users/judewells/miniforge3/envs/bayes_opt/lib/python3.11/site-packages/m2cgen/exporters.py", line 81, in export_to_c
return _export(model, interpreter)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/judewells/miniforge3/envs/bayes_opt/lib/python3.11/site-packages/m2cgen/exporters.py", line 459, in _export
model_ast = assembler_cls(model).assemble()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/judewells/miniforge3/envs/bayes_opt/lib/python3.11/site-packages/m2cgen/assemblers/boosting.py", line 214, in assemble
return self.assembler.assemble()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/judewells/miniforge3/envs/bayes_opt/lib/python3.11/site-packages/m2cgen/assemblers/boosting.py", line 34, in assemble
return self._assemble_bin_class_output(self._all_estimator_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/judewells/miniforge3/envs/bayes_opt/lib/python3.11/site-packages/m2cgen/assemblers/boosting.py", line 80, in _assemble_bin_class_output
base_score = -math.log(1.0 / self._base_score - 1.0)
~~~~^~~~~~~~~~~~~~~~~~
TypeError: unsupported operand type(s) for /: 'float' and 'NoneType'
Using m1 MacBook pro.
xgboost version '2.0.3' (also got the same error using older version)
The text was updated successfully, but these errors were encountered:
You can fix this by setting the base_score parameter in your xgboost model before passing to m2cgen:
model = xgb.XGBClassifier(n_estimators=10, max_depth=3, base_score=0.5)
You can fix this by setting the base_score parameter in your xgboost model before passing to m2cgen: model = xgb.XGBClassifier(n_estimators=10, max_depth=3, base_score=0.5)
Works fine with XGBoostRegressor
Full trace:
Using m1 MacBook pro.
xgboost version '2.0.3' (also got the same error using older version)
The text was updated successfully, but these errors were encountered: