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name: training-evaluation description: "Prepare CLAM dataset CSVs and splits, train CLAM or MIL models, evaluate checkpoints, and diagnose task/model/data mismatches." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

CLAM Training and Evaluation

Use this sub-skill when a user needs to turn extracted CLAM feature bags into train/validation/test splits, train CLAM_SB, CLAM_MB, or MIL baselines, evaluate checkpoints, or debug task/model/data mismatches.

Route First

• For raw whole-slide segmentation, patch coordinates, presets, or stitched QC images, use ../wsi-preprocessing/SKILL.md.
• For ResNet50, UNI, or CONCH feature extraction and pt_files/ creation, use ../feature-extraction/SKILL.md.
• For attention heatmaps and interpretation from trained checkpoints, use ../heatmap-visualization/SKILL.md.
• Stay here for dataset CSV schemas, split generation, main.py training, eval.py checkpoint evaluation, model constructor choices, and metrics outputs.

Operating Checklist

1. Confirm the dataset CSV has case_id, slide_id, and the selected label column; slide_id must match feature file basenames.
2. Confirm features are arranged as DATA_ROOT_DIR/<dataset_feature_dir>/pt_files/<slide_id>.pt and that --embed_dim matches the encoder used upstream.
3. Generate or select splits_<fold>.csv files with train, val, and test columns before training.
4. For custom tasks, update the CLAM task choices and dataset constructor branches consistently in split, training, and evaluation entrypoints.
5. Train with main.py, matching --task, --model_type, --subtyping, loss flags, fold range, and split directory.
6. Evaluate with eval.py, matching the checkpoint directory, model flags, task branch, fold range, split files, and feature dimension.

Bundled References

• Read references/data-formats.md when preparing dataset CSVs, feature folder layouts, label dictionaries, or split files.
• Read references/training-reference.md when assembling a main.py command or explaining training outputs.
• Read references/evaluation-reference.md when evaluating checkpoints or independent cohorts with eval.py.
• Read references/model-api.md when selecting CLAM_SB, CLAM_MB, MIL_fc, MIL_fc_mc, dataset classes, losses, or embed_dim values.
• Read references/troubleshooting.md when diagnosing missing columns, label mismatches, split paths, --subtyping assertions, SVM loss imports, or checkpoint shape errors.

Bundled Scripts

• Run scripts/clam_split_recipe.py to validate a CLAM dataset CSV and render a safe create_splits_seq.py command without creating split files by default.
• Run scripts/clam_train_eval_command_builder.py to render safe main.py or eval.py commands, check task/model/embed-dimension consistency, and preview output paths without training or loading checkpoints.

Safety Notes

• CLAM training, evaluation, and feature loading can require GPU memory, large .pt bags, valid checkpoints, and optional packages such as smooth-topk; bundled helpers only validate inputs and build commands.
• The CLAM source entrypoints are task-branch based. For new tasks, future agents should edit their own working CLAM copy rather than relying on hard-coded dummy tasks.