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name: model-ops-interpretability description: "Guides StellarGraph saved-model loading, custom Keras layers, calibration, ensembles, saliency and integrated gradients, randomness utilities, plotting, and optional Neo4j connector workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Model Operations and Interpretability

Use this sub-skill for operational tasks around StellarGraph models after or around training: saved-model loading, probability calibration, ensembles, interpretability, plotting/history, random seeds, and optional Neo4j connector workflows.

Read first

• references/operations.md for custom_keras_layers, calibration, ensembles, history plotting, and seed utilities.
• references/interpretability.md for GCN/GAT saliency and integrated-gradient workflows.
• references/neo4j.md for optional Neo4j connector classes, service requirements, ID/features properties, and warnings.
• references/troubleshooting.md for saved model, calibration/ensemble, saliency, TensorFlow, and Neo4j failures.
• scripts/calibration_smoke.py for a safe tiny calibration smoke.

Route here when the user asks to

• load a Keras model containing StellarGraph custom layers;
• calibrate model outputs with TemperatureCalibration, IsotonicCalibration, expected calibration error, or reliability diagrams;
• build Ensemble or BaggingEnsemble wrappers;
• explain node/edge importance with GCN/GAT saliency maps or integrated gradients;
• check reproducibility utilities such as random seeds;
• use Neo4jStellarGraph, Neo4jStellarDiGraph, or Neo4j GraphSAGE generators.

Route elsewhere

Core model training still belongs to the owning workflow route:

• node models: ../node-classification-gnns/SKILL.md;
• link prediction/KG: ../link-prediction-kg/SKILL.md;
• embeddings: ../embedding-workflows/SKILL.md;
• graph/time-series: ../graph-time-series-workflows/SKILL.md.

Operating workflow

1. Identify the operational surface: saved model, probability outputs, uncertainty/ensemble, saliency, or Neo4j-backed graph access.
2. Verify package/TensorFlow import with the root diagnostic if the environment is suspect.
3. For saved models, pass stellargraph.custom_keras_layers as Keras custom_objects.
4. For calibration, use logits or probabilities with the shapes required by the calibration class; validate with a tiny smoke before applying to model outputs.
5. For saliency, keep generator sparse/dense settings consistent with the model and use node indices/classes that exist.
6. For Neo4j, confirm py2neo, service reachability, ID property, feature property, and uniqueness constraints before expecting model generators to work.

Safe check

python sub-skills/model-ops-interpretability/scripts/calibration_smoke.py --help
python sub-skills/model-ops-interpretability/scripts/calibration_smoke.py