cli-anything-unimol-tools

# Uni-Mol Tools - Molecular Property Prediction CLI **Package**: `cli-anything-unimol-tools` **Command**: `python3 -m cli_anything.unimol_tools` ## Description Interactive CLI for training and inference of molecular property prediction models using Uni-Mol Tools. Supports 5 task types: binary classification, regression, multiclass, multilabel classification, and multilabel regression. ## Key Features - **Project Management**: Organize experiments with named projects - **5 Task Types**: Classification, regression, multiclass, multilabel variants - **Model Tracking**: Automatic performance history and rankings - **Smart Storage**: Analyze usage and clean up underperformers - **JSON API**: Full automation support with `--json` flag ## Common Commands ### Project Management ```bash # Create a new project project create --name drug_discovery # List all projects project list # Switch to a project project switch --name drug_discovery ``` ### Training ```bash # Train a classification model train --data-path train.csv --target-col active --task-type classification --epochs 10 # Train a regression model train --data-path train.csv --target-col affinity --task-type regression --epochs 10 ``` ### Model Management ```bash # List all trained models models list # Show model details and performance models show --model-id <id> # Rank models by performance models rank ``` ### Storage & Cleanup ```bash # Analyze storage usage storage analyze # Automatic cleanup of poor performers cleanup auto # Manual cleanup with criteria cleanup manual --max-models 10 --min-score 0.7 ``` ### Prediction ```bash # Make predictions with a trained model predict --model-id <id> --data-path test.csv ``` ## Data Format CSV files must contain: - `SMILES` column: Molecular structures in SMILES format - Target column(s): Values to predict (name specified via `--target-col`) Example: ```csv SMILES,target CCO,1 CCCO,0 CC(C)O,1 ``` ## Task Types 1. **classification**: Binary classification (0/1) 2. **regression**: Continuous value prediction 3. **multiclass**: Multiple class classification 4. **multilabel_classification**: Multiple binary labels 5. **multilabel_regression**: Multiple continuous values ## JSON Mode Add `--json` flag to any command for machine-readable output: ```bash python3 -m cli_anything.unimol_tools --json models list ``` Output format: ```json { "status": "success", "data": [...], "message": "..." } ``` ## Interactive Mode Launch without commands for interactive REPL: ```bash python3 -m cli_anything.unimol_tools ``` Features: - Tab completion - Command history - Contextual help - Project state persistence ## Test Data Example datasets available at: https://github.com/545487677/CLI-Anything-unimol-tools/tree/main/unimol_tools/examples Includes data for all 5 task types. ## Requirements - Python 3.8+ - PyTorch 1.12+ - Uni-Mol Tools backend - 4GB+ RAM (8GB+ recommended for training) ## Installation ```bash cd unimol_tools/agent-harness pip install -e . ``` ## Documentation - **SOP**: [UNIMOL_TOOLS.md](../UNIMOL_TOOLS.md) - **Quick Start**: [docs/guides/02-QUICK-START.md](../docs/guides/02-QUICK-START.md) - **Full Documentation**: [docs/README.md](../docs/README.md) ## Testing ```bash cd docs/test bash run_tests.sh --unit -v # Unit tests (67 tests) bash run_tests.sh --full -v # Full test suite ``` ## Performance Tips - Start with 10 epochs for initial experiments - Use smaller batch sizes if memory is limited - Monitor storage with `storage analyze` - Use `models rank` to identify best performers - Clean up regularly with `cleanup auto` ## Troubleshooting - **CUDA errors**: Reduce batch size or use CPU mode - **CSV not recognized**: Verify SMILES column exists - **Low accuracy**: Try more epochs or adjust learning rate - **Storage full**: Run `cleanup auto` to free space ## Related - **Uni-Mol Tools**: https://github.com/dptech-corp/Uni-Mol/tree/main/unimol_tools - **Uni-Mol Paper**: https://arxiv.org/abs/2209.11126 - **CLI-Anything**: https://github.com/HKUDS/CLI-Anything