Troubleshooting

Common issues and solutions when using Skyulf.

"Unknown transformer type"

Your step's transformer string does not match any registered node in the NodeRegistry.

Fix: Check spelling and casing. Use the exact key from the Configuration page (e.g., OneHotEncoder, not one_hot_encoder).

"Unknown model type: ..."

The type value in your modeling config is not registered.

Fix: Ensure you are using one of the 20 supported model keys listed in Configuration - Modeling config. If using XGBoost, install the extra: pip install skyulf-core[modeling-xgboost].

Resampling errors about non-numeric columns

Oversampling / undersampling (SMOTE, RandomOverSampler, etc.) require all features to be numeric.

Fix: Apply an encoder (e.g., OneHotEncoder or OrdinalEncoder) before any resampling step in your preprocessing config.

SMOTE not found / ImportError

The imbalanced-learn package is an optional dependency.

Fix:

pip install skyulf-core[preprocessing-imbalanced]

XGBoost ImportError

XGBoost estimators are optional and not installed by default.

Fix:

pip install skyulf-core[modeling-xgboost]

Optuna / TuningCalculator ImportError

Hyperparameter tuning with Optuna requires the tuning extra.

Fix:

pip install skyulf-core[tuning]

Target column not found after encoding

If your target column is categorical and you apply OneHotEncoder with drop_original=True, the target column may be dropped or expanded.

Fix: Always run TrainTestSplitter (which separates X and y) before encoding. This ensures the target is safely stored in y and never touched by the encoder.

Pickle loading errors

If SkyulfPipeline.load() fails, the saved model was pickled with a different library version.

Fix: Ensure compatible versions of:

Python (same minor version, e.g., 3.10.x)
scikit-learn (same minor version)
pandas (same major version)

Pin versions in requirements.txt for reproducibility across environments.

Polars version mismatch

Skyulf requires polars >= 1.36. Older versions may cause schema or type errors during data ingestion.

Fix:

pip install --upgrade polars

Celery / Redis connection refused (full platform)

The backend's async task queue requires a running Redis instance.

Fix:

Ensure Redis is running: redis-cli ping should return PONG.
Check CELERY_BROKER_URL in your .env file (default: redis://localhost:6379/0).
If using Docker: docker-compose up redis.

Feature scaling produces NaN

Standard scaling can produce NaN or Inf if a column has zero variance (all identical values).

Fix: Remove constant columns before scaling, or use auto_detect: True in the StandardScaler params - it automatically skips non-numeric and constant columns.