Open ekamioka opened 1 year ago
executed code:
>>> axgb = AutoXGB( ... train_filename="X_train.csv", ... output="output", ... test_filename="X_valid.csv", ... task="classification", ... idx=None, ... targets=["label"], ... features=['feat1', 'feat2', 'feat3', 'feat4', 'feat5'], ... categorical_features=None, ... use_gpu=False, ... num_folds=5, ... seed=42, ... num_trials=100, ... time_limit=360, ... fast=False, ... )
error log:
2023-04-21 04:37:30.385 | INFO | autoxgb.autoxgb:_process_data:149 - Reading training data 2023-04-21 04:37:30.727 | INFO | autoxgb.utils:reduce_memory_usage:48 - Mem. usage decreased to 5.07 Mb (80.9% reduction) 2023-04-21 04:37:30.732 | INFO | autoxgb.autoxgb:_determine_problem_type:140 - Problem type: multi_class_classification 2023-04-21 04:37:30.851 | INFO | autoxgb.utils:reduce_memory_usage:48 - Mem. usage decreased to 1.87 Mb (82.4% reduction) 2023-04-21 04:37:30.851 | INFO | autoxgb.autoxgb:_create_folds:58 - Creating folds 2023-04-21 04:37:30.868 | INFO | autoxgb.autoxgb:_process_data:170 - Encoding target(s) 2023-04-21 04:37:30.875 | INFO | autoxgb.autoxgb:_process_data:195 - Found 0 categorical features. 2023-04-21 04:37:31.084 | INFO | autoxgb.autoxgb:_process_data:236 - Model config: train_filename='X_train.csv' test_filename='X_valid.csv' idx='id' targets=['label'] problem_type=<ProblemType.multi_class_classification: 2> output='output' features=['feat1', 'feat2', 'feat3', 'feat4', 'feat5'] num_folds=5 use_gpu=False seed=42 categorical_features=[] num_trials=100 time_limit=360 fast=False 2023-04-21 04:37:31.084 | INFO | autoxgb.autoxgb:_process_data:237 - Saving model config 2023-04-21 04:37:31.085 | INFO | autoxgb.autoxgb:_process_data:241 - Saving encoders Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/workspace/models/venv-autoxgb/lib/python3.8/site-packages/autoxgb/autoxgb.py", line 247, in train best_params = train_model(self.model_config) File "/workspace/models/venv-autoxgb/lib/python3.8/site-packages/autoxgb/utils.py", line 207, in train_model study = optuna.create_study( File "/workspace/models/venv-autoxgb/lib/python3.8/site-packages/optuna/study/study.py", line 1136, in create_study storage = storages.get_storage(storage) File "/workspace/models/venv-autoxgb/lib/python3.8/site-packages/optuna/storages/__init__.py", line 31, in get_storage return _CachedStorage(RDBStorage(storage)) File "/workspace/models/venv-autoxgb/lib/python3.8/site-packages/optuna/storages/_rdb/storage.py", line 187, in __init__ self._version_manager.check_table_schema_compatibility() File "/workspace/models/venv-autoxgb/lib/python3.8/site-packages/optuna/storages/_rdb/storage.py", line 1310, in check_table_schema_compatibility current_version = self.get_current_version() File "/workspace/models/venv-autoxgb/lib/python3.8/site-packages/optuna/storages/_rdb/storage.py", line 1337, in get_current_version assert version is not None AssertionError
pip freeze:
alembic==1.10.3 anyio==3.6.2 asgiref==3.6.0 attrs==23.1.0 autopage==0.5.1 autoxgb==0.2.2 click==8.1.3 cliff==4.2.0 cmaes==0.9.1 cmd2==2.4.3 colorlog==6.7.0 fastapi==0.70.0 greenlet==2.0.2 h11==0.14.0 idna==3.4 importlib-metadata==6.5.0 importlib-resources==5.12.0 joblib==1.1.0 loguru==0.5.3 Mako==1.2.4 MarkupSafe==2.1.2 numpy==1.21.3 optuna==2.10.0 packaging==23.1 pandas==1.3.4 pbr==5.11.1 prettytable==3.7.0 pyarrow==6.0.0 pydantic==1.8.2 pyperclip==1.8.2 python-dateutil==2.8.2 pytz==2023.3 PyYAML==6.0 scikit-learn==1.0.1 scipy==1.10.1 six==1.16.0 sniffio==1.3.0 SQLAlchemy==2.0.9 starlette==0.16.0 stevedore==5.0.0 threadpoolctl==3.1.0 tqdm==4.65.0 typing_extensions==4.5.0 uvicorn==0.15.0 wcwidth==0.2.6 xgboost==1.5.0 zipp==3.15.0
This is because the version of your sqlalchemy library is too high and needs to be downgraded to sqlalchemy=1.4.45 to solve the problem
executed code:
error log:
pip freeze: