Model hyperparameter transfer and tuning

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."