Summary

This PR implements the Typer + Hydra hybrid architecture proposed in #146, refactoring Winnow's configuration management from flat CLI signatures to a flexible, hierarchical system that enables scalable configuration of complex nested components and automatic object instantiation.

Implementation Details

1. Typer + Hydra Hybrid Architecture

Typer now acts as a thin command dispatcher, passing all configuration to Hydra:

def train(ctx: typer.Context) -> None:
    """Passes control directly to the Hydra training pipeline."""
    overrides = ctx.args if ctx.args else None
    train_entry_point(overrides)Pipeline logic moved to `train_entry_point()` and `predict_entry_point()` functions that handle Hydra initialization, configuration composition and pipeline execution.

2. Structured Configuration with Composition

Created modular configuration structure in config/:

• train.yaml / predict.yaml - Main pipeline configurations
• calibrator.yaml - Model architecture and features
• residues.yaml - Amino acid masses and modifications (shared via composition)
• data_loader/ - Pluggable dataset format loaders (InstaNovo, MZTab, PointNovo, Winnow)
• fdr_method/ - Pluggable FDR methods (nonparametric, database-grounded)

Configuration files use Hydra's defaults mechanism to compose shared components.

3. Hydra-Based Object Instantiation

Used Hydra's _target_ field for automatic instantiation:

• Data loaders instantiated from configuration without manual if/elif logic
• FDR methods selected and configured via YAML
• Users can inject custom implementations by creating YAML configs with _target_ pointing to their classes

4. Configuration Inspection Commands

Added winnow config command group:

• winnow config train - Display resolved training configuration
• winnow config predict - Display resolved prediction configuration

Implemented custom ConfigFormatter class with hierarchical colour-coding based on YAML nesting depth for improved terminal readability.

5. Lazy Imports for CLI Performance

Implemented lazy import pattern using TYPE_CHECKING to defer heavy dependencies (PyTorch, InstaNovo, etc.) until command execution. This makes --help and config commands respond instantly whilst pipeline commands still have access to all required dependencies.

Added module-level docstring in main.py explaining the rationale.

6. Documentation Updates

Minor improvements to CLI help text and documentation to reflect the new Hydra-based configuration system with examples of dot-notation overrides.

Migration Notes

Existing users will need to:

• Use configuration files in config/ instead of passing all parameters via CLI flags
• Override parameters using dot notation: winnow train calibrator.seed=42
• Consult winnow config <pipeline> to inspect resolved configurations