Model Hyperparameter Transfer And Tuning

---
name: model-hyperparameter-transfer-and-tuning
description: Use when you have a trained baseline GNN model with established hyperparameters
  (dropout rate, learning rate, epochs, optimizer settings) and want to evaluate whether
  alternative message-passing GNN architectures (Graph Attention Networks, Message-Passing
  Neural Networks) achieve comparable or.
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3445
  edam_topics:
  - http://edamontology.org/topic_0091
  - http://edamontology.org/topic_3407
  tools:
  - PyTorch Geometric (PyG)
  - PyTorch
  - enveda/ccs-prediction
  techniques:
  - ion-mobility-MS
  license_tier: open
  provenance_tier: literature
derived_from:
- doi: 10.1186/s13321-024-00899-w
  title: mol2ccs
evidence_spans:
- enveda/ccs-prediction
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_mol2ccs
    doi: 10.1186/s13321-024-00899-w
    title: mol2ccs
  dedup_kept_from: coll_mol2ccs
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.1186/s13321-024-00899-w
  all_source_dois:
  - 10.1186/s13321-024-00899-w
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# model-hyperparameter-transfer-and-tuning

## Summary

Transfer hyperparameters from a baseline GNN model to alternative architectures (e.g., GAT, MPNN) for collision cross section prediction, then evaluate held-out performance to compare generalization. This skill enables rapid architecture exploration without exhaustive hyperparameter search for each variant.

## When to use

You have a trained baseline GNN model with established hyperparameters (dropout rate, learning rate, epochs, optimizer settings) and want to evaluate whether alternative message-passing GNN architectures (Graph Attention Networks, Message-Passing Neural Networks) achieve comparable or better CCS prediction performance on the same train/validation/test split without retuning each hyperparameter independently.

## When NOT to use

- The baseline model has not yet been trained or validated; hyperparameter transfer requires a stable baseline.
- Input/output dimensions or the molecular graph encoding differ between the baseline and alternative architecture; transferred hyperparameters may not be applicable.
- The alternative architecture requires fundamentally different optimization strategies (e.g., recurrent vs. feedforward); direct hyperparameter transfer may lead to underfitting or training instability.

## Inputs

- Baseline GNN model checkpoint (.h5 or equivalent)
- Baseline model hyperparameter configuration (JSON or YAML)
- Preprocessed CCS dataset split (training set, validation set, test set as Parquet or similar)
- Molecular structure input (SMILES strings or 3D coordinates)
- Adduct ion types and ground-truth CCS values

## Outputs

- Trained alternative GNN model checkpoint
- Prediction output file for test set (prefix-based naming, e.g., 'train-metlin-test-ccsbase.out')
- Comparative performance table (RMSE, MAE, accuracy, training time, inference speed for both architectures)
- Evaluation metrics (test-set regression metrics for held-out performance comparison)

## How to apply

Load the baseline model's hyperparameters (dropout rate, epochs, loss function, optimization settings) from the original training configuration. Implement an alternative GNN architecture with equivalent input/output dimensions to the baseline, ensuring the graph representation (molecular structure encoded as nodes/edges) remains unchanged. Train the alternative architecture on the same training set using the transferred hyperparameters and monitor validation performance against the validation set. Evaluate both architectures on the held-out test set, computing regression metrics (RMSE, MAE, prediction accuracy) side-by-side. Document training time and inference speed for each architecture to assess computational trade-offs. Use this comparative table to determine whether the alternative architecture generalizes as well as the baseline despite architectural differences.

## Related tools

- **PyTorch Geometric (PyG)** (Provides graph neural network architectures (GAT, MPNN, GCN) and graph convolution operations required to implement alternative GNN architectures with equivalent dimensions to the baseline.)
- **PyTorch** (Deep learning framework for training, monitoring validation performance, and computing loss functions and optimization settings transferred from the baseline model.)
- **enveda/ccs-prediction** (Source repository containing baseline model architecture, preprocessed CCS dataset splits (METLIN, CCSBase), training scripts (train-test.py), and Makefile commands for model training reproducibility.) — https://github.com/enveda/ccs-prediction

## Examples

```
poetry run python scripts/train-test.py --prefix "train-gat-test-ccsbase" --train-input-file "ccs-prediction/metlin_train_3d.parquet" --test-input-file "ccs-prediction/ccsbase_3d.parquet" --parameter-path "parameter/parameter-train-metlin-test-metlin.json" --model-output-file "model/train-gat-test-ccsbase.h5" --dropout-rate 0.1 --epochs 400
```

## Evaluation signals

- Comparative test-set RMSE, MAE, and prediction accuracy metrics should be within a reasonable margin (e.g., ±5%) of the baseline, indicating the alternative architecture generalizes similarly.
- Training curves (validation loss) should show convergence behavior consistent with the baseline, confirming that transferred hyperparameters are appropriate for the alternative architecture.
- Inference speed and training time comparisons should be numerically reported for both architectures; faster inference with comparable accuracy indicates successful transfer.
- The test-set predictions should show no systematic bias or outliers compared to the baseline (e.g., checked via residual plots or distribution analysis).
- Hyperparameter configuration file and training logs should be reproducible via the documented Makefile commands and parameter files (e.g., 'parameter-train-metlin-test-metlin.json').

## Limitations

- Transferred hyperparameters are optimized for the baseline architecture and may be suboptimal for the alternative; this skill assumes architectural differences are modest (e.g., attention vs. convolution, not fundamental changes in graph encoding).
- No adaptive tuning is performed; if the alternative architecture significantly underperforms, manual hyperparameter adjustment may be required, undermining the efficiency of transfer.
- Comparison is limited to a single data split; generalization across multiple train/test dataset pairs (e.g., METLIN training on CCSBase test, vice versa) is not addressed in this workflow.
- The skill does not account for differences in computational requirements (memory, GPU utilization) between architectures; an architecture may require lower batch sizes or dropout rates to train without out-of-memory errors despite identical nominal hyperparameters.

## Evidence

- [other] Implement an alternative message-passing GNN architecture (Graph Attention Network or Message-Passing Neural Network) with equivalent input/output dimensions to the original model.: "Implement an alternative message-passing GNN architecture (Graph Attention Network or Message-Passing Neural Network) with equivalent input/output dimensions to the original model."
- [other] Train the alternative GNN on the training set using the same hyperparameters, loss function, and optimization settings as the original baseline, monitoring validation performance.: "Train the alternative GNN on the training set using the same hyperparameters, loss function, and optimization settings as the original baseline, monitoring validation performance."
- [other] Evaluate both the original and alternative architectures on the held-out test set, computing prediction accuracy, RMSE, MAE, and other relevant regression metrics for CCS prediction.: "Evaluate both the original and alternative architectures on the held-out test set, computing prediction accuracy, RMSE, MAE, and other relevant regression metrics for CCS prediction."
- [readme] poetry run python scripts/train-test.py \
	--prefix "train-metlin-test-ccsbase" \
	--parameter-path "parameter/parameter-train-metlin-test-metlin.json" \
	--model-output-file "model/train-metlin-test-metlin.h5" \
	--dropout-rate 0.1 \
	--epochs 400: "poetry run python scripts/train-test.py \
	--prefix "train-metlin-test-ccsbase" \
	--parameter-path "parameter/parameter-train-metlin-test-metlin.json""
- [other] Generate a comparative performance table documenting metric values, training time, and inference speed for both architectures side-by-side.: "Generate a comparative performance table documenting metric values, training time, and inference speed for both architectures side-by-side."