Discontinued Support for old Json model

[LukasFehring]

I installed the libary using pypi (pip install pip install mf-prior-bench) and I am working with the PD1 model lm1b_transformer_2048.
When executing the follwoing basic example

import mfpbench

benchmark = mfpbench.get("lm1b_transformer_2048") # example pd1 benchmark
# This example is based on https://github.com/automl/mf-prior-bench/blob/main/docs/quickstart.md


print(benchmark.name)# There is a list of attributes accessible from the benchmark object 
config = benchmark.sample(n = 1, seed=0)[0]
print(config)
result = benchmark.query(config)
print(result)

the following warnings are raised

ARNING: ../src/learner.cc:888: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.
[10:38:01] WARNING: ../src/learner.cc:888: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.
/home/lukas/anaconda3/envs/automatic_stopping/lib/python3.9/site-packages/xgboost/data.py:312: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.
  if is_sparse(dtype):
/home/lukas/anaconda3/envs/automatic_stopping/lib/python3.9/site-packages/xgboost/data.py:314: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  elif is_categorical_dtype(dtype) and enable_categorical:
/home/lukas/anaconda3/envs/automatic_stopping/lib/python3.9/site-packages/xgboost/data.py:345: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if is_categorical_dtype(dtype)
/home/lukas/anaconda3/envs/automatic_stopping/lib/python3.9/site-packages/xgboost/data.py:336: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  return is_int or is_bool or is_float or is_categorical_dtype(dtype)
/home/lukas/anaconda3/envs/automatic_stopping/lib/python3.9/site-packages/xgboost/data.py:312: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.
  if is_sparse(dtype):
/home/lukas/anaconda3/envs/automatic_stopping/lib/python3.9/site-packages/xgboost/data.py:314: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  elif is_categorical_dtype(dtype) and enable_categorical:
/home/lukas/anaconda3/envs/automatic_stopping/lib/python3.9/site-packages/xgboost/data.py:345: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if is_categorical_dtype(dtype)
/home/lukas/anaconda3/envs/automatic_stopping/lib/python3.9/site-packages/xgboost/data.py:336: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  return is_int or is_bool or is_float or is_categorical_dtype(dtype)

[eddiebergman]

Hi @LukasFehring,

Sorry, I somehow completely missed this notification. Thanks for the heads up.
This should be doable by loading and saving the current surrogate models so I can try doing that soon.

Best,
Eddie