compatibility with xgboost 2.0.3
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 66
- Forks
- 10
- PR merge metrics
- No merged PRs in 30d
Description
Using xgboost 2.0.3, I found the following error:
(with categorical support)
model_explainer = ModelExplainer(
File "/mllab/miniconda3/envs/llm-3.9/lib/python3.9/site-packages/te2rules/explainer.py", line 110, in __init__
self.random_forest = XgboostXGBClassifierAdapter(
File "/mllab/miniconda3/envs/llm-3.9/lib/python3.9/site-packages/te2rules/adapter.py", line 254, in __init__
self.random_forest = self._convert()
File "/mllab/miniconda3/envs/llm-3.9/lib/python3.9/site-packages/te2rules/adapter.py", line 290, in _convert
node = self._build_tree(tree_dict)
File "/mllab/miniconda3/envs/llm-3.9/lib/python3.9/site-packages/te2rules/adapter.py", line 266, in _build_tree
i = int(tree_dict["split"][1:])
ValueError: invalid literal for int() with base 10
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Reproduce the categorical-support failure with xgboost 2.0.3, starting from te2rules/explainer.py line 110 and following the adapter construction into te2rules/adapter.py lines 254-290. Inspect _build_tree at line 266 and _convert at line 290; done means the model conversion completes without the reported ValueError.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100