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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]

"X merges N parallel branches…" (merge advisory banner)

You see an amber banner in the Results panel like:

Drop Missing Columns merges 2 parallel branches: TransformationNode + MissingIndicator. 6 overlapping columns take values from MissingIndicator; unique columns from the others are kept as-is. For sequential application, chain them instead.

What it means

A node has two or more incoming edges that trace back to a common ancestor (sibling fan-in). The engine merges them with two rules:

  1. Union of columns — any column unique to one branch is kept as-is.
  2. Last-wins on overlap — for columns present in two or more branches, the last input in the node's input list overwrites the earlier ones. Earlier branches' modifications to those columns are silently dropped.

Why it's usually a bug

If both branches modify the same columns (e.g. TransformationNode rescales SepalLengthCm and MissingIndicator outputs the original SepalLengthCm plus *_missing flags), only the last branch's values survive. You lose the work the other branch did.

How to entirely fix it

Chain the nodes instead of fanning them out. Instead of:

Dataset ──> TransformationNode ──┐
        ──> MissingIndicator ────┴──> DropMissingColumns

wire:

Dataset ──> TransformationNode ──> MissingIndicator ──> DropMissingColumns

Now MissingIndicator reads TransformationNode's output, adds *_missing flags on top, and passes the combined frame downstream. No fan-in, no warning, all changes preserved.

In the canvas:

  1. Delete the Dataset → MissingIndicator edge.
  2. Drag a new edge from TransformationNode → MissingIndicator.
  3. Re-run preview — the banner will disappear.

When fan-in is fine

  • Disjoint columns — branches output non-overlapping columns. The banner says "No column overlap — all columns from all branches are kept." You can ignore it.
  • Redundant edge (e.g. Splitter → Scaler plus a direct Splitter → Encoder while Scaler → Encoder also exists) — the engine detects this and suppresses the warning entirely because the descendant supersedes the ancestor under last-wins.

What the engine guarantees

Pattern Behavior
Linear chain A → B → C No warning, full data flow
Redundant ancestor edge Suppressed silently
Disjoint-column fan-in Advisory only, all columns kept
Overlapping-column fan-in Last input wins — chain instead
Cycles Engine rejects

Rule of thumb: if two branches touch the same columns, chain them; if they touch different columns, fanning in is fine.

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:

  1. Ensure Redis is running: redis-cli ping should return PONG.
  2. Check CELERY_BROKER_URL in your .env file (default: redis://localhost:6379/0).
  3. 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.