diff --git a/.gitignore b/.gitignore index c905746..5c94db1 100644 --- a/.gitignore +++ b/.gitignore @@ -12,16 +12,30 @@ docs_public *.csv *.parquet *.ipc +*.mztab +*.fasta +*.mgf *.pkl *.json *.yaml +*.pdf +*.png + +*.ipynb examples/winnow-general-model examples/winnow-ms-datasets examples/output +# Sample data files +examples/example_data/*.ipc +examples/example_data/*.csv +examples/example_data/*.parquet + build/ +.cursorrules + # Coverage reports htmlcov/ .coverage diff --git a/Makefile b/Makefile index 31bc740..86a8f81 100644 --- a/Makefile +++ b/Makefile @@ -86,12 +86,16 @@ install-all: ## Development commands # ################################################################################# -.PHONY: tests test-docker bash set-gcp-credentials set-ceph-credentials +.PHONY: tests clean-coverage test-docker bash build-package clean-build clean-workspace test-build clean-all-build test-cli-isolated test-cli-config set-gcp-credentials set-ceph-credentials ## Run all tests tests: $(PYTEST) +## Clean coverage reports +clean-coverage: + rm -rf htmlcov/ .coverage coverage.xml pytest.xml + ## Run all tests in the Docker Image test-docker: docker run $(DOCKER_RUN_FLAGS) $(DOCKER_IMAGE) $(PYTEST) @@ -100,6 +104,17 @@ test-docker: bash: docker run -it $(DOCKER_RUN_FLAGS) $(DOCKER_IMAGE) /bin/bash +## Build the winnow-fdr package (creates wheel and sdist in dist/) +build-package: + uv build + +## Clean all build artifacts (dist/, build/, *.egg-info/) +clean-build: + rm -rf dist/ build/ *.egg-info/ winnow_fdr.egg-info/ + +## Build the package and then clean up (safe test build) +test-build: build-package clean-build + ## Set the GCP credentials set-gcp-credentials: uv run python scripts/set_gcp_credentials.py @@ -108,3 +123,28 @@ set-gcp-credentials: ## Set the Ceph credentials set-ceph-credentials: uv run python scripts/set_ceph_credentials.py + +################################################################################# +## Sample data and CLI commands # +################################################################################# + +.PHONY: sample-data train-sample predict-sample clean clean-all + +## Generate sample data files for testing +sample-data: + uv run python scripts/generate_sample_data.py + +## Run winnow train with sample data (uses defaults from config) +train-sample: + winnow train + +## Run winnow predict with sample data (uses locally trained model from models/new_model) +predict-sample: + winnow predict calibrator.pretrained_model_name_or_path=models/new_model + +## Clean output directories (does not delete sample data) +clean: + rm -rf models/ results/ + +## Clean outputs and regenerate sample data +clean-all: clean sample-data diff --git a/README.md b/README.md index 40629fe..a7be792 100644 --- a/README.md +++ b/README.md @@ -42,9 +42,9 @@ Explore the docs »

- Report Bug + Report bug · - Request Feature + Request feature

@@ -55,16 +55,13 @@ Table of Contents
  1. - About The Project + About the project
  2. Installation
    3. -
  3. Usage - +
  4. + Usage
  5. Contributing
@@ -77,7 +74,7 @@ -## About The Project +## About the project In bottom-up proteomics workflows, peptide sequencing—matching an MS2 spectrum to a peptide—is just the first step. The resulting peptide-spectrum matches (PSMs) often contain many incorrect identifications, which can negatively impact downstream tasks like protein assembly. @@ -87,7 +84,7 @@ To mitigate this, intermediate steps are introduced to: 1. Assign confidence scores to PSMs that better correlate with correctness. 2. Estimate and control the false discovery rate (FDR) by filtering identifications based on confidence scores. -For database search-based peptide sequencing, PSM rescoring and target-decoy competition (TDC) are standard approaches, supported by an extensive ecosystem of tools. However, *de novo* peptide sequencing lacks standardized methods for these tasks. +For database search-based peptide sequencing, PSM rescoring and target-decoy competition (TDC) are standard approaches, supported by an extensive ecosystem of tools. However, *de novo* peptide sequencing lacks standardised methods for these tasks. `winnow` aims to fill this gap by implementing the calibrate-estimate framework for FDR estimation. Unlike TDC, this approach is directly applicable to *de novo* sequencing models. Additionally, its calibration step naturally incorporates common confidence rescoring workflows as part of FDR estimation. @@ -109,7 +106,38 @@ uv pip install winnow-fdr ```

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+ +## Quick Start + +Get started with `winnow` in minutes using the included sample data: + +```bash +# Generate sample data (if not already present) +make sample-data + +# Train a calibrator on the sample data +make train-sample + +# Run prediction with the trained model +make predict-sample +``` + +The sample data is automatically configured in `configs/train.yaml` and `configs/predict.yaml`, so you can also use the commands directly: + +```bash +# Train with sample data +winnow train + +# Predict with pretrained HuggingFace model +winnow predict + +# Predict with your locally trained model +winnow predict calibrator.pretrained_model_name_or_path=models/new_model +``` + +**Note:** The sample data is minimal (20 spectra) and intended for testing only. For production use, replace with your own datasets. +

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## Usage @@ -126,13 +154,26 @@ Installing `winnow` provides the `winnow` command with two sub-commands: 1. `winnow train` – Performs confidence calibration on a dataset of annotated PSMs, outputting the fitted model checkpoint. 2. `winnow predict` – Performs confidence calibration using a fitted model checkpoint (defaults to a pretrained general model from HuggingFace), estimates and controls FDR using the calibrated confidence scores. -By default, `winnow predict` uses a pretrained general model (`InstaDeepAI/winnow-general-model`) hosted on HuggingFace Hub, allowing you to get started immediately without training. You can also specify custom HuggingFace models or use locally trained models. +By default, `winnow predict` uses a pretrained general model ([`InstaDeepAI/winnow-general-model`](https://huggingface.co/InstaDeepAI/winnow-general-model)) hosted on HuggingFace Hub, allowing you to get started immediately without training. You can also specify custom HuggingFace models or use locally trained models. + +Winnow uses [Hydra](https://hydra.cc/) for flexible, hierarchical configuration management. All parameters can be configured via YAML files or overridden on the command line: + +```bash +# Quick start with defaults +winnow predict -Refer to the documentation for details on command-line arguments and usage examples. +# Override specific parameters +winnow predict fdr_control.fdr_threshold=0.01 + +# Specify different data source and dataset paths +winnow predict data_loader=mztab dataset.spectrum_path_or_directory=data/spectra.parquet dataset.predictions_path=data/preds.mztab +``` + +Refer to the [CLI Guide](cli.md) and [Configuration Guide](configuration.md) for details on usage and configuration options. ### Package -The `winnow` package is organized into three sub-modules: +The `winnow` package is organised into three sub-modules: 1. `winnow.datasets` – Handles data loading and saving, including the `CalibrationDataset` class for mapping peptide sequencing output formats. 2. `winnow.calibration` – Implements confidence calibration. Key components include: @@ -152,10 +193,11 @@ For an example, check out the [example notebook](https://github.com/instadeepai/ Contributions are what make the open-source community such an amazing place to learn, inspire and create, and we welcome your support! Any contributions you make are **greatly appreciated**. If you have ideas for enhancements, you can: + - Fork the repository and submit a pull request. - Open an issue and tag it with "enhancement". -### Contribution Process +### Contribution process 1. Fork the repository. 2. Create a feature branch (`git checkout -b feature-amazing-feature`). @@ -166,7 +208,7 @@ Don't forget to give the project a star! Thanks again! :star:

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-### BibTeX entry and citation info +### BibTeX entry and citation information If you use `winnow` in your research, please cite the following preprint: @@ -175,7 +217,11 @@ If you use `winnow` in your research, please cite the following preprint: title={De novo peptide sequencing rescoring and FDR estimation with Winnow}, author={Amandla Mabona and Jemma Daniel and Henrik Servais Janssen Knudsen and Rachel Catzel and Kevin Michael Eloff and Erwin M. Schoof and Nicolas Lopez Carranza and Timothy P. Jenkins - and Jeroen Van Goey and Konstantinos Kalogeropoulos}, +@article{mabona2025novopeptidesequencingrescoring, + title={De novo peptide sequencing rescoring and FDR estimation with Winnow}, + author={Amandla Mabona and Jemma Daniel and Henrik Servais Janssen Knudsen + and Rachel Catzel and Kevin Michael Eloff and Erwin M. Schoof and Nicolas Lopez + Carranza and Timothy P. Jenkins and Jeroen Van Goey and Konstantinos Kalogeropoulos}, year={2025}, eprint={2509.24952}, archivePrefix={arXiv}, diff --git a/docs/api/calibration.md b/docs/api/calibration.md index e3170f7..69767c4 100644 --- a/docs/api/calibration.md +++ b/docs/api/calibration.md @@ -96,7 +96,7 @@ class CustomFeature(CalibrationFeatures): - **Column Specification**: Define output column names - **Dataset Integration**: Direct access to CalibrationDataset for computation -## Built-in Features +## Built-in features ### MassErrorFeature @@ -159,7 +159,7 @@ feature = RetentionTimeFeature(hidden_dim=10, train_fraction=0.1) **Purpose**: Incorporates chromatographic information for confidence calibration. -## Handling Missing Features +## Handling missing features Prosit-dependent features (PrositFeatures, ChimericFeatures, RetentionTimeFeature) may not be computable for all peptides due to limitations like: @@ -170,7 +170,7 @@ Prosit-dependent features (PrositFeatures, ChimericFeatures, RetentionTimeFeatur Winnow provides two strategies for handling such cases: -### Learn Strategy (Default, `learn_from_missing=True`) +### Learn strategy (Default, `learn_from_missing=True`) **Recommended for most use cases.** @@ -179,7 +179,7 @@ Winnow provides two strategies for handling such cases: - Uses all available data, maximising recall - More robust across diverse datasets -### Filter Strategy (`learn_from_missing=False`) +### Filter strategy (`learn_from_missing=False`) **Use when you want strict data quality requirements.** @@ -228,14 +228,14 @@ rt_feat = RetentionTimeFeature(hidden_dim=10, train_fraction=0.1, learn_from_mis ## Workflow -### Training Workflow +### Training workflow 1. **Create Calibrator**: Initialise `ProbabilityCalibrator` 2. **Add Features**: Use `add_feature()` to include desired calibration features 3. **Fit Model**: Call `fit()` with labelled `CalibrationDataset` 4. **Save Model**: Use `save()` to persist trained calibrator -### Prediction Workflow +### Prediction workflow 1. **Load Calibrator**: Use `load()` to restore trained model from a HuggingFace repository or a local directory ```python @@ -251,7 +251,7 @@ rt_feat = RetentionTimeFeature(hidden_dim=10, train_fraction=0.1, learn_from_mis 2. **Predict**: Call `predict()` with unlabelled `CalibrationDataset` 3. **Access Results**: Calibrated scores stored in dataset's "calibrated_confidence" column -## Feature Dependencies +## Feature dependencies The system automatically handles feature dependencies: diff --git a/docs/api/datasets.md b/docs/api/datasets.md index 638a456..7fe03a3 100644 --- a/docs/api/datasets.md +++ b/docs/api/datasets.md @@ -37,12 +37,12 @@ dataset.save(Path("output_directory")) **Key Features:** -- **Multiple Format Support**: Load data using specialized loaders for different file formats +- **Multiple Format Support**: Load data using specialised loaders for different file formats - **Data Integration**: Combines spectral data with prediction metadata - **Filtering**: Removes invalid tokens and unsupported modifications - **Evaluation**: Computes correctness labels when ground truth available -### Data Loaders +### Data loaders The datasets module provides several data loaders that implement the `DatasetLoader` protocol: diff --git a/docs/api/fdr.md b/docs/api/fdr.md index db73cec..6f3aa66 100644 --- a/docs/api/fdr.md +++ b/docs/api/fdr.md @@ -2,7 +2,7 @@ The `winnow.fdr` module implements false discovery rate (FDR) estimation and control methods for *de novo* peptide sequencing using both database-grounded and non-parametric approaches. -## Base Interface +## Base interface ### FDRControl @@ -108,9 +108,9 @@ dataset_with_q_values = fdr_control.add_psm_q_value(dataset, "confidence") - **Q-value**: `compute_q_value(score)` - Minimum FDR for significance - **No Ground Truth Required**: Works with confidence scores alone -## Additional Features +## Additional features -### PSM-Specific FDR +### PSM-specific FDR Both methods support PSM-specific FDR estimation: @@ -140,7 +140,7 @@ dataset_with_q_values = fdr_control.add_psm_q_value( psm_q_values = dataset_with_q_values["psm_q_value"] ``` -### Confidence Curves +### Confidence curves Generate FDR vs confidence curves for analysis: @@ -159,7 +159,7 @@ plt.xlabel("FDR Threshold") plt.ylabel("Confidence Cutoff") ``` -### Dataset Filtering +### Dataset filtering Filter PSM datasets at target FDR levels: @@ -175,7 +175,7 @@ filtered_psms = fdr_control.filter_entries( print(f"Retained {len(filtered_psms)} PSMs at 1% FDR") ``` -### FDR Estimation Method Selection +### FDR estimation method selection **Use DatabaseGroundedFDRControl when:** diff --git a/docs/cli.md b/docs/cli.md index 131c3cb..2720746 100644 --- a/docs/cli.md +++ b/docs/cli.md @@ -1,6 +1,8 @@ -# Command Line Interface +# Command line interface -The winnow CLI provides a simple interface for confidence calibration and FDR control workflows. It supports both training calibration models and applying them for prediction and FDR filtering. +This guide provides practical examples and workflows for using the `winnow` command-line interface. + +**Looking for configuration details?** See the **[Configuration guide](configuration.md)** for comprehensive documentation of the configuration system, YAML structure and advanced patterns. ## Installation @@ -12,148 +14,150 @@ pip install winnow-fdr uv pip install winnow-fdr ``` -## Commands +## Quick Start -### `winnow train` - -Train a confidence calibration model on labelled data. +Get started immediately with the included sample data: ```bash -winnow train \ - --data-source instanovo \ - --dataset-config-path train_config.yaml \ - --model-output-folder ./calibrator_model \ - --dataset-output-path ./training_results.csv +# Generate sample data (if not already present) +make sample-data + +# Train a calibrator on the sample data +make train-sample + +# Run prediction with the trained model +make predict-sample ``` -**Arguments:** +The sample data is pre-configured in the default config files (`configs/train.yaml` and `configs/predict.yaml`), pointing to `examples/example_data/`. You can also use the commands directly: -- `--data-source`: Type of dataset (`instanovo`, `mztab`, `winnow`) -- `--dataset-config-path`: Path to YAML configuration file -- `--model-output-folder`: Directory to save trained calibrator -- `--dataset-output-path`: Path to save training results CSV -- `--learn-prosit-missing` / `--no-learn-prosit-missing`: Whether to learn from missing Prosit features (default: True) -- `--learn-chimeric-missing` / `--no-learn-chimeric-missing`: Whether to learn from missing chimeric features (default: True) -- `--learn-retention-missing` / `--no-learn-retention-missing`: Whether to learn from missing retention time features (default: True) +```bash +# Train with sample data (uses defaults from configs/train.yaml) +winnow train -**Missingness Handling:** +# Predict with pretrained HuggingFace model (uses defaults from configs/predict.yaml) +winnow predict -By default, the calibrator learns from missing data by including missingness indicators as features. +# Predict with your locally trained model +winnow predict calibrator.pretrained_model_name_or_path=models/new_model +``` -If you wish to train only on complete data (training will fail if invalid spectra are found): +**Note:** The sample data is minimal (20 spectra) and intended for testing only. For production use, replace the dataset paths in the config files or override them on the command line. + +## Commands + +### `winnow config` + +Display the resolved configuration for any command without executing it. This is useful for inspecting how defaults and overrides are composed. ```bash -winnow train \ - --data-source instanovo \ - --dataset-config-path train_config.yaml \ - --model-output-folder ./calibrator_model \ - --dataset-output-path ./training_results.csv \ - --no-learn-prosit-missing \ - --no-learn-chimeric-missing \ - --no-learn-retention-missing +# Show training configuration +winnow config train + +# Show prediction configuration +winnow config predict + +# Check configuration with overrides +winnow config train data_loader=mztab model_output_dir=models/my_model +winnow config predict fdr_method=database_grounded fdr_control.fdr_threshold=0.01 ``` -See the [Handling Missing Features](api/calibration.md#handling-missing-features) section for more details. +This command prints the final YAML configuration with colour-coded hierarchical formatting, making it easy to read and verify your settings. Keys are coloured by nesting depth to help visualise the configuration structure. The output shows all defaults, composition and overrides after they have been applied. -### `winnow predict` +**Note:** Some keys appear with quotes (e.g. `'N'`, `'Y'`) because they are reserved words in YAML that would otherwise be interpreted as boolean values. The quotes ensure they are treated as strings. -Apply calibration and FDR control to new data using a trained model. By default, uses a pretrained general model from HuggingFace Hub. +### `winnow train` + +Train a confidence calibration model on labelled data. ```bash -# Use default pretrained model from HuggingFace (recommended for getting started) -winnow predict \ - --data-source instanovo \ - --dataset-config-path test_config.yaml \ - --method winnow \ - --fdr-threshold 0.01 \ - --confidence-column confidence \ - --output-folder ./predictions +# Use defaults (configured in configs/train.yaml) +winnow train -# Use a custom HuggingFace model -winnow predict \ - --data-source instanovo \ - --dataset-config-path test_config.yaml \ - --huggingface-model-name my-org/my-custom-model \ - --method winnow \ - --fdr-threshold 0.01 \ - --confidence-column confidence \ - --output-folder ./predictions +# Override specific parameters +winnow train data_loader=mztab model_output_dir=models/my_model -# Use a local model -winnow predict \ - --data-source instanovo \ - --dataset-config-path test_config.yaml \ - --local-model-folder ./calibrator_model \ - --method winnow \ - --fdr-threshold 0.01 \ - --confidence-column confidence \ - --output-folder ./predictions +# Specify dataset paths +winnow train dataset.spectrum_path_or_directory=data/spectra.parquet dataset.predictions_path=data/preds.csv ``` -**Arguments:** +**Common Parameters:** + +- `data_loader`: Type of dataset loader (`instanovo`, `mztab`, `pointnovo`, `winnow`) +- `dataset.spectrum_path_or_directory`: Path to spectrum/metadata file (or directory for winnow format) +- `dataset.predictions_path`: Path to predictions file (set to `null` for winnow format) +- `model_output_dir`: Directory to save trained calibrator +- `dataset_output_path`: Path to save training results CSV -- `--data-source`: Type of dataset (`instanovo`, `winnow`, `mztab`) -- `--dataset-config-path`: Path to YAML configuration file -- `--huggingface-model-name`: HuggingFace model identifier (defaults to `InstaDeepAI/winnow-general-model`). Use this to load models from HuggingFace Hub. -- `--local-model-folder`: Directory containing trained calibrator. Use this to load local models instead of HuggingFace models. -- `--method`: FDR estimation method (`winnow` or `database-ground`) -- `--fdr-threshold`: Target FDR threshold (e.g., 0.01 for 1%) -- `--confidence-column`: Name of confidence score column -- `--output-folder`: Folder path to write output files to (creates `metadata.csv` and `preds_and_fdr_metrics.csv`) +**Advanced calibrator configuration:** -**Note:** If neither `--local-model-folder` nor `--huggingface-model-name` are provided, the default pretrained general model from HuggingFace (`InstaDeepAI/winnow-general-model`) will be loaded automatically. +You can customise the calibrator architecture and features using nested parameters: -## Configuration Files +```bash +# Change MLP architecture +winnow train calibrator.hidden_layer_sizes=[100,50,25] -The CLI uses YAML configuration files to specify dataset locations and parameters. The format depends on the data source. +# Configure individual features +winnow train calibrator.features.prosit_features.mz_tolerance=0.01 +``` -### InstaNovo Configuration +For comprehensive calibrator configuration options, see: +- [Configuration guide](configuration.md) - Complete parameter reference +- [Calibration API](api/calibration.md#handling-missing-features) - Feature implementation details -For InstaNovo/CSV datasets: +### `winnow predict` -```yaml -# instanovo_config.yaml -beam_predictions_path: "/path/to/predictions.csv" -spectrum_path: "/path/to/spectra.csv" -``` +Apply calibration and FDR control to new data using a trained model. By default, uses a pretrained general model from HuggingFace Hub. -**Required files:** +```bash +# Use defaults (pretrained model from HuggingFace) +winnow predict -- **Spectrum file**: Parquet/IPC file containing spectral metadata and features -- **Predictions CSV**: Contains beam search results with columns like `preds`, `preds_beam_1`, confidence scores +# Override specific parameters +winnow predict data_loader=mztab fdr_control.fdr_threshold=0.01 fdr_method=database_grounded -### MZTab Configuration +# Specify dataset paths +winnow predict dataset.spectrum_path_or_directory=data/spectra.parquet dataset.predictions_path=data/preds.csv -For MZTab format datasets (traditional search engines and Casanovo outputs): +# Use a custom HuggingFace model +winnow predict calibrator.pretrained_model_name_or_path=my-org/my-custom-model -```yaml -# mztab_config.yaml -spectrum_path: "/path/to/spectra.parquet" -predictions_path: "/path/to/predictions.mztab" +# Use a local model +winnow predict calibrator.pretrained_model_name_or_path=models/my_model ``` -**Required files:** +**Common Parameters:** -- **Spectrum file**: Parquet/IPC file with spectrum metadata and row indices matching MZTab spectra_ref -- **MZTab file**: Standard MZTab format containing predictions +- `data_loader`: Type of dataset loader (`instanovo`, `mztab`, `pointnovo`, `winnow`) +- `dataset.spectrum_path_or_directory`: Path to spectrum/metadata file (or directory for winnow format) +- `dataset.predictions_path`: Path to predictions file +- `fdr_method`: FDR estimation method (`nonparametric` or `database_grounded`) +- `fdr_control.fdr_threshold`: Target FDR threshold (e.g. 0.01 for 1%) +- `output_folder`: Folder path to write output files -### Winnow Internal Configuration +By default, `winnow predict` uses the pretrained model `InstaDeepAI/winnow-general-model` from HuggingFace Hub. To use a different model, override the calibrator settings (see [Configuration guide](configuration.md#using-a-custom-model) for details). -For winnow's internal format: +## Configuration system -```yaml -# winnow_config.yaml -data_dir: "/path/to/winnow_dataset_directory" -``` +Winnow uses [Hydra](https://hydra.cc/) for configuration management. All parameters can be configured via: -**Required structure:** +- **YAML config files** in the `configs/` directory (defines defaults) +- **Command-line overrides** using `key=value` syntax +- **Nested parameters** using dot notation (e.g., `calibrator.seed=42`) -- Directory containing `metadata.csv` and optionally `predictions.pkl` -- Created by `CalibrationDataset.save()` -## Data Requirements +For comprehensive configuration documentation, including: -### Training Data +- Full configuration file structure and composition +- Config interpolation and variable references +- Creating custom configurations +- Advanced patterns and debugging + +See the **[Configuration guide](configuration.md)**. + +## Data requirements + +### Training data For training (`winnow train`), you need: @@ -162,34 +166,43 @@ For training (`winnow train`), you need: - **Spectral data**: MS/MS spectra and metadata - **Unique identifiers**: Each PSM must have a unique `spectrum_id` in both input files -### Prediction Data +### Prediction data For prediction (`winnow predict`), you need: -- **Unlabelled dataset**: Predictions and spectra (no ground truth required) -- **Trained model**: Output from `winnow train` +- **Unlabelled dataset**: Predictions and spectra (no ground truth required for non-parametric FDR) +- **Trained model**: Pretrained model from HuggingFace or output from `winnow train` - **Confidence scores**: Raw confidence values to calibrate - **Unique identifiers**: Each PSM must have a unique `spectrum_id` in both input files -## FDR Methods +### Data formats + +Winnow supports multiple input formats: + +- **InstaNovo**: Parquet or IPC spectra + CSV predictions (beam search format) +- **MZTab**: Parquet or IPC spectra + MZTab predictions +- **PointNovo**: Similar to InstaNovo format +- **Winnow**: Internal format (directory with metadata.csv and predictions.pkl) -### Winnow Method (`--method winnow`) +Specify the format using `data_source=` parameter. + +**Note on MGF files**: While many users have their input data in `.mgf` format, Winnow currently requires spectrum data to be in `.parquet` or `.ipc` format. To convert `.mgf` files to `.parquet`, you can use InstaNovo's conversion utilities. See the [InstaNovo documentation](https://instadeepai.github.io/InstaNovo/) for instructions on using `instanovo convert` or the `SpectrumDataFrame` class to perform this conversion. + +## FDR methods + +### Non-parametric method (`fdr_method=nonparametric`) Uses non-parametric FDR estimation procedure: - **No ground truth required**: Works with confidence scores alone - **No correct/incorrect distribution modelling**: Process directly estimates FDR using calibrated confidence scores -- **Multiple metrics**: Provides FDR, PEP and q-values. +- **Multiple metrics**: Provides FDR, PEP and q-values ```bash -winnow predict \ - --method winnow \ - --fdr-threshold 0.01 \ - --confidence-column calibrated_confidence \ - # ... other args +winnow predict fdr_method=nonparametric fdr_control.fdr_threshold=0.01 ``` -### Database-Grounded Method (`--method database-ground`) +### Database-grounded method (`fdr_method=database_grounded`) Uses database search results for validation: @@ -198,28 +211,24 @@ Uses database search results for validation: - **Direct estimates**: FDR calculated from actual correct/incorrect labels ```bash -winnow predict \ - --method database-ground \ - --fdr-threshold 0.05 \ - --confidence-column confidence \ - # ... other args +winnow predict fdr_method=database_grounded fdr_control.fdr_threshold=0.05 ``` -## Output Files +## Output files -### Training Output +### Training output Training produces: -1. **Model checkpoints** (in `--model-output-folder`): +1. **Model checkpoints** (`model_output_dir`): - `calibrator.pkl`: Complete trained calibrator with all features and parameters -2. **Training results** (`--dataset-output-path`): +2. **Training results** (`dataset_output_path`): - CSV with calibrated scores and evaluation metrics -### Prediction Output +### Prediction output -Prediction produces two CSV files in the `--output-folder` directory: +Prediction produces two CSV files in the `output-folder` directory: 1. **`metadata.csv`**: Contains all metadata and feature columns from the input dataset - Original metadata columns (spectrum information, precursors, etc.) @@ -238,94 +247,147 @@ Prediction produces two CSV files in the `--output-folder` directory: This separation allows users to work with metadata and features separately from predictions and error metrics, making downstream analysis more convenient. -## Example Workflows +## Example workflows -### Complete Training and Prediction Pipeline +### Quick start with defaults + +```bash +# Predict using pretrained model, InstaNovo predictions and default settings +winnow predict \ + dataset.spectrum_path_or_directory=data/test_spectra.parquet \ + dataset.predictions_path=data/test_predictions.csv +``` + +### Complete training and prediction pipeline ```bash # Step 1: Train calibrator on labelled data winnow train \ - --data-source instanovo \ - --dataset-config-path configs/train_data.yaml \ - --model-output-folder models/my_calibrator \ - --dataset-output-folder results/training_output.csv + data_loader=instanovo \ + dataset.spectrum_path_or_directory=data/train_spectra.parquet \ + dataset.predictions_path=data/train_predictions.csv \ + model_output_dir=models/my_calibrator \ + dataset_output_path=results/training_output.csv -# Step 2: Apply to new data with FDR control (using default pretrained model) +# Step 2: Apply to new data with FDR control winnow predict \ - --data-source instanovo \ - --dataset-config-path configs/test_data.yaml \ - --method winnow \ - --fdr-threshold 0.01 \ - --confidence-column confidence \ - --output-folder results/predictions - -# Alternative: Use the locally trained model -winnow predict \ - --data-source instanovo \ - --dataset-config-path configs/test_data.yaml \ - --local-model-folder models/my_calibrator \ - --method winnow \ - --fdr-threshold 0.01 \ - --confidence-column confidence \ - --output-folder results/predictions + data_loader=instanovo \ + dataset.spectrum_path_or_directory=data/test_spectra.parquet \ + dataset.predictions_path=data/test_predictions.csv \ + calibrator.pretrained_model_name_or_path=models/my_calibrator \ + fdr_method=nonparametric \ + fdr_control.fdr_threshold=0.01 \ + output_folder=results/predictions ``` -### Configuration File Examples +### MZTab format -**Training configuration** (`configs/train_data.yaml`): -```yaml -beam_predictions_path: "data/train_predictions.csv" -spectrum_path: "data/train_spectra.csv" -``` +```bash +# Train with MZTab format +winnow train \ + data_loader=mztab \ + dataset.spectrum_path_or_directory=data/spectra.parquet \ + dataset.predictions_path=data/casanovo_results.mztab \ + model_output_dir=models/mztab_model -**Test configuration** (`configs/test_data.yaml`): -```yaml -beam_predictions_path: "data/test_predictions.csv" -spectrum_path: "data/test_spectra.csv" +# Predict with MZTab format +winnow predict \ + data_loader=mztab \ + dataset.spectrum_path_or_directory=data/test_spectra.parquet \ + dataset.predictions_path=data/test_results.mztab \ + calibrator.pretrained_model_name_or_path=models/mztab_model \ + fdr_control.fdr_threshold=0.05 ``` -## Built-in Features +### Advanced configuration -The CLI automatically includes these calibration features: +```bash +# Train with custom calibrator settings +winnow train \ + dataset.spectrum_path_or_directory=data/spectra.parquet \ + dataset.predictions_path=data/predictions.csv \ + calibrator.hidden_layer_sizes=[100,50,25] \ + calibrator.learning_rate_init=0.01 \ + calibrator.max_iter=500 \ + calibrator.features.prosit_features.mz_tolerance=0.01 + +# Predict with database-grounded FDR +winnow predict \ + dataset.spectrum_path_or_directory=data/test_spectra.parquet \ + dataset.predictions_path=data/test_predictions.csv \ + fdr_method=database_grounded \ + fdr_control.fdr_threshold=0.01 \ + calibrator.pretrained_model_name_or_path=models/custom_model +``` -- **Mass Error**: Difference between observed and theoretical mass -- **Prosit Features**: ML-based intensity predictions (`mz_tolerance=0.02`) -- **Retention Time**: iRT predictions (`hidden_dim=10`, `train_fraction=0.1`) -- **Chimeric Features**: Chimeric spectrum detection (`mz_tolerance=0.02`) -- **Beam Features**: Beam search diversity metrics +## Default configuration -## Default Parameters +Winnow comes with sensible default settings for all parameters: -The CLI uses these default parameters: +- **Calibrator**: 2-layer MLP with 50 hidden units per layer +- **Features**: Mass error, Prosit features, retention time, chimeric features, beam features +- **FDR**: Non-parametric method with 5% threshold +- **Model**: Pretrained general model from HuggingFace -- **Seed**: 42 (for reproducibility) -- **MZ Tolerance**: 0.02 Da -- **FDR Learning Rate**: 0.005 -- **FDR Training Steps**: 5000 -- **Retention Time Hidden Dim**: 10 -- **Retention Time Train Fraction**: 0.1 +All defaults are defined in YAML files under `configs/` and can be overridden via command line. For a complete reference of all default parameters and configuration options, see the **[Configuration guide](configuration.md)**. ## Troubleshooting -### Common Issues +### Common issues -**Missing columns**: Ensure your CSV files contain expected columns: +**Missing columns**: Ensure your data files contain expected columns: - `preds`: Main prediction - `confidence`: Confidence scores -- `sequence`: Ground truth (for training/database method) +- `sequence`: Ground truth (for training/database-grounded FDR) -**File paths**: Use absolute paths in configuration files to avoid path resolution issues. +**File paths**: Use absolute paths in dataset path overrides to avoid path resolution issues: +```bash +winnow predict dataset.spectrum_path_or_directory=/absolute/path/to/spectra.parquet +``` + +**Configuration errors**: If Hydra reports a missing config file: +```bash +winnow predict fdr_method=typo +# Error: Could not find 'fdr/typo' +# Available options in 'fdr': nonparametric, database_grounded +``` -**Memory issues**: Large datasets may require more memory. Consider filtering data or using smaller batch sizes. +**Memory issues**: Large datasets may require more memory. Consider: +- Filtering data before processing +- Using a machine with more RAM +- Processing in batches (requires custom Python script) -### Dataset Filtering +### Dataset filtering The CLI automatically filters out: - Empty predictions - Peptides longer than 30 amino acids (Prosit limitation) - Precursor charges above 6 (Prosit limitation) -- Invalid modifications and tokens +- Invalid modifications and tokens (defined in `configs/residues.yaml`) + +### Getting help + +View available options: + +```bash +winnow --help # List all commands +winnow train --help # Command-specific help +winnow predict --help +winnow config --help # Config command help + +winnow config train # View resolved training configuration +winnow config predict # View resolved prediction configuration +``` + +## Where to find information + +This CLI guide focuses on **practical command-line usage**. For other information, see: -For more advanced usage and customisation, refer to the [Python API documentation](api/calibration.md) and [examples notebook](https://github.com/instadeepai/winnow/blob/main/examples/getting_started_with_winnow.ipynb). +| Topic | Documentation | +|-------|---------------| +| Configuration system, YAML structure, advanced patterns | [Configuration guide](configuration.md) | +| Python API, feature implementation, programmatic usage | [API reference](api/calibration.md) | +| Interactive tutorials and examples | [Examples notebook](https://github.com/instadeepai/winnow/blob/main/examples/getting_started_with_winnow.ipynb) | +| Contributing, development setup | [Contributing guide](contributing.md) | diff --git a/docs/configuration.md b/docs/configuration.md new file mode 100644 index 0000000..84620e1 --- /dev/null +++ b/docs/configuration.md @@ -0,0 +1,656 @@ +# Configuration guide + +This guide provides comprehensive documentation of Winnow's configuration system, including YAML file structure, parameter reference, advanced patterns and customisation. + +**Looking for practical CLI usage?** See the **[CLI reference](cli.md)** for command examples and workflows. + +## Overview + +Winnow uses [Hydra](https://hydra.cc/) for flexible, hierarchical configuration management. This enables: + +- **Composable configs**: Build configurations from multiple YAML files +- **Flexibility**: Override any parameter via command line or config files +- **Reproducibility**: Full configuration is automatically logged + +## Quick start + +Winnow works out of the box with sensible defaults: + +```bash +# Train with default settings +winnow train + +# Predict with default settings +winnow predict +``` + +## Configuration files + +Winnow's configuration files are organised in the `configs/` directory: + +``` +configs/ +├── residues.yaml # Amino acid masses, modifications +├── data_loader/ # Dataset format loaders +│ ├── instanovo.yaml +│ ├── mztab.yaml +│ ├── pointnovo.yaml +│ └── winnow.yaml +├── fdr_method/ # FDR control methods +│ ├── nonparametric.yaml +│ └── database_grounded.yaml +├── train.yaml # Main training config +├── calibrator.yaml # Model architecture and features +└── predict.yaml # Main prediction config +``` + +## Overriding configuration + +All configuration parameters have default values defined in YAML files. You can override any parameter from the command line. + +### Command-line overrides + +Override any parameter from the command line: + +```bash +# Override dataset paths +winnow train dataset.spectrum_path_or_directory=data/my_spectra.parquet dataset.predictions_path=data/my_preds.csv + +# Change data loader +winnow train data_loader=mztab + +# Change output directory +winnow train model_output_dir=models/my_model + +# Change multiple parameters +winnow predict data_loader=mztab fdr_control.fdr_threshold=0.01 fdr_method=database_grounded +``` + +### Nested parameters + +Access nested configuration values using dot notation: + +```bash +# Change calibrator seed +winnow train calibrator.seed=123 + +# Change MLP hidden layer sizes +winnow train calibrator.hidden_layer_sizes=[100,50,25] + +# Change feature parameters +winnow train calibrator.features.prosit_features.mz_tolerance=0.01 +``` + +### Dataset configuration + +Specify dataset paths using nested notation: + +```bash +# For InstaNovo format +winnow train dataset.spectrum_path_or_directory=data/spectra.parquet dataset.predictions_path=data/preds.csv + +# For MZTab format +winnow train data_loader=mztab dataset.spectrum_path_or_directory=data/spectra.parquet dataset.predictions_path=data/results.mztab +``` + +## Training configuration + +### Main training config (`configs/train.yaml`) + +Controls dataset loading, output paths and composition: + +```yaml +defaults: + - _self_ + - residues + - calibrator + - data_loader: instanovo # Options: instanovo, mztab, pointnovo, winnow + +dataset: + # Path to the spectrum data file or to folder containing saved internal Winnow dataset + spectrum_path_or_directory: data/spectra.ipc + # Path to the beam predictions file + # Leave as null if data source is winnow, or loading will fail + predictions_path: data/predictions.csv + +# Output paths +model_output_dir: models/new_model +dataset_output_path: results/calibrated_dataset.csv +``` + +**Key parameters:** + +- `data_loader`: Format of input data loader to use (via defaults: `instanovo`, `mztab`, `pointnovo`, `winnow`) +- `dataset.spectrum_path_or_directory`: Path to spectrum/metadata file (or directory for winnow format) +- `dataset.predictions_path`: Path to predictions file (set to null for winnow format) +- `model_output_dir`: Where to save trained model +- `dataset_output_path`: Where to save calibrated training results + +### Calibrator config (`configs/calibrator.yaml`) + +Controls model architecture and calibration features: + +```yaml +calibrator: + _target_: winnow.calibration.calibrator.ProbabilityCalibrator + + seed: 42 + hidden_layer_sizes: [50, 50] # The number of neurons in each hidden layer of the MLP classifier + learning_rate_init: 0.001 # The initial learning rate for the MLP classifier + alpha: 0.0001 # L2 regularisation parameter for the MLP classifier + max_iter: 1000 # Maximum number of training iterations for the MLP classifier + early_stopping: true # Whether to use early stopping to terminate training + validation_fraction: 0.1 # Proportion of training data to use for early stopping validation + + features: + mass_error: + _target_: winnow.calibration.calibration_features.MassErrorFeature + residue_masses: ${residue_masses} + + prosit_features: + _target_: winnow.calibration.calibration_features.PrositFeatures + mz_tolerance: 0.02 + learn_from_missing: true + invalid_prosit_tokens: ${invalid_prosit_tokens} + prosit_intensity_model_name: Prosit_2020_intensity_HCD + + retention_time_feature: + _target_: winnow.calibration.calibration_features.RetentionTimeFeature + hidden_dim: 10 + train_fraction: 0.1 + learn_from_missing: true + seed: 42 + learning_rate_init: 0.001 + alpha: 0.0001 + max_iter: 200 + early_stopping: false + validation_fraction: 0.1 + invalid_prosit_tokens: ${invalid_prosit_tokens} + prosit_irt_model_name: Prosit_2019_irt + + chimeric_features: + _target_: winnow.calibration.calibration_features.ChimericFeatures + mz_tolerance: 0.02 + learn_from_missing: true + invalid_prosit_tokens: ${invalid_prosit_tokens} + prosit_intensity_model_name: Prosit_2020_intensity_HCD + + beam_features: + _target_: winnow.calibration.calibration_features.BeamFeatures +``` + +**Key parameters:** + +- `seed`: Random seed for reproducibility +- `hidden_layer_sizes`: Architecture of MLP classifier +- `learning_rate_init`: Initial learning rate +- `alpha`: L2 regularisation parameter +- `max_iter`: Maximum training iterations +- `early_stopping`: Whether to use early stopping +- `validation_fraction`: Proportion of data for validation +- `features.*`: Individual calibration feature configurations + +## Prediction configuration + +### Main prediction config (`configs/predict.yaml`) + +Controls dataset loading, FDR estimation and output: + +```yaml +defaults: + - _self_ + - residues + - data_loader: instanovo # Options: instanovo, mztab, pointnovo, winnow + - fdr_method: nonparametric # Options: nonparametric, database_grounded + +dataset: + # Path to the spectrum data file or to folder containing saved internal Winnow dataset + spectrum_path_or_directory: data/spectra.ipc + # Path to the beam predictions file + # Leave as null if data source is winnow, or loading will fail + predictions_path: data/predictions.csv + +calibrator: + # Path to the local calibrator directory or the HuggingFace model identifier + # If the path is a local directory path, it will be used directly + # If it is a HuggingFace repository identifier, it will be downloaded from HuggingFace + pretrained_model_name_or_path: InstaDeepAI/winnow-general-model + # Directory to cache the HuggingFace model + cache_dir: null # can be set to null if using local model or for the default cache directory + +fdr_control: + # Target FDR threshold (e.g. 0.01 for 1%, 0.05 for 5% etc.) + fdr_threshold: 0.05 + # Name of the column with confidence scores to use for FDR estimation + confidence_column: calibrated_confidence + +# Folder path to write the outputs to +output_folder: results/predictions +``` + +**Key parameters:** + +- `data_loader`: Format of input data loader to use (via defaults: `instanovo`, `mztab`, `pointnovo`, `winnow`) +- `dataset.spectrum_path_or_directory`: Path to spectrum/metadata file (or directory for winnow format) +- `dataset.predictions_path`: Path to predictions file +- `calibrator.pretrained_model_name_or_path`: HuggingFace model identifier or local model directory path +- `calibrator.cache_dir`: Directory to cache HuggingFace models (null for default) +- `fdr_method`: FDR estimation method (via defaults: `nonparametric` or `database_grounded`) +- `fdr_control.fdr_threshold`: Target FDR threshold (e.g. 0.01 for 1%, 0.05 for 5%) +- `fdr_control.confidence_column`: Column name with confidence scores +- `output_folder`: Where to save results + +### FDR method configs + +**Non-parametric FDR** (`configs/fdr_method/nonparametric.yaml`): + +```yaml +_target_: winnow.fdr.nonparametric.NonParametricFDRControl +``` + +No additional parameters required. + +**Database-grounded FDR** (`configs/fdr_method/database_grounded.yaml`): + +```yaml +_target_: winnow.fdr.database_grounded.DatabaseGroundedFDRControl +confidence_feature: ${fdr_control.confidence_column} +residue_masses: ${residue_masses} +isotope_error_range: [0, 1] +drop: 10 +``` + +Requires ground truth sequences in the dataset. + +**Key parameters:** + +- `confidence_feature`: Name of the column with confidence scores (interpolated from fdr_control) +- `residue_masses`: Amino acid and modification masses (interpolated from residues config) +- `isotope_error_range`: Range of isotope errors to consider when matching peptides +- `drop`: Number of top predictions to drop for stability + +## Shared configuration + +### Residues config (`configs/residues.yaml`) + +Defines amino acid masses, modifications and invalid tokens: + +```yaml +residue_masses: + "G": 57.021464 + "A": 71.037114 + "S": 87.032028 + # ... other amino acids + "M[UNIMOD:35]": 147.035400 # Oxidation + "C[UNIMOD:4]": 160.030649 # Carboxyamidomethylation + "N[UNIMOD:7]": 115.026943 # Deamidation + "Q[UNIMOD:7]": 129.042594 # Deamidation + # ... other modifications + "[UNIMOD:1]": 42.010565 # Acetylation (terminal) + "[UNIMOD:5]": 43.005814 # Carbamylation (terminal) + "[UNIMOD:385]": -17.026549 # NH3 loss (terminal) + +invalid_prosit_tokens: + # InstaNovo + - "[UNIMOD:7]" + - "[UNIMOD:21]" + - "[UNIMOD:1]" + - "[UNIMOD:5]" + - "[UNIMOD:385]" + # Casanovo + - "+0.984" + - "+42.011" + - "+43.006" + - "-17.027" + - "[Deamidated]" + # ... other unsupported modifications +``` + +This configuration is shared across all pipelines and referenced via `${residue_masses}` and `${invalid_prosit_tokens}` interpolation. + +### Data loader configs + +Each data format has a dedicated loader configuration in `configs/data_loader/`: + +**InstaNovo** (`configs/data_loader/instanovo.yaml`): +```yaml +_target_: winnow.datasets.data_loaders.InstaNovoDatasetLoader +residue_masses: ${residue_masses} +residue_remapping: + "M(ox)": "M[UNIMOD:35]" + "C(+57.02)": "C[UNIMOD:4]" + # ... maps legacy notations to UNIMOD tokens +``` + +**MZTab** (`configs/data_loader/mztab.yaml`): +```yaml +_target_: winnow.datasets.data_loaders.MZTabDatasetLoader +residue_masses: ${residue_masses} +residue_remapping: + "M+15.995": "M[UNIMOD:35]" + "C+57.021": "C[UNIMOD:4]" + "C[Carbamidomethyl]": "C[UNIMOD:4]" + # ... maps Casanovo notations to UNIMOD tokens +``` + +**PointNovo** (`configs/data_loader/pointnovo.yaml`): +```yaml +_target_: winnow.datasets.data_loaders.PointNovoDatasetLoader +residue_masses: ${residue_masses} +``` + +**Winnow** (`configs/data_loader/winnow.yaml`): +```yaml +_target_: winnow.datasets.data_loaders.WinnowDatasetLoader +residue_masses: ${residue_masses} +# Internal format uses UNIMOD tokens directly, no remapping needed +``` + +## Common configuration patterns + +### Using a custom model + +```bash +# Use a custom HuggingFace model +winnow predict calibrator.pretrained_model_name_or_path=my-org/my-model + +# Use a locally trained model +winnow predict calibrator.pretrained_model_name_or_path=models/my_model + +# Specify a custom HuggingFace cache directory +winnow predict calibrator.cache_dir=/path/to/cache +``` + +### Changing FDR method and threshold + +```bash +# Use database-grounded FDR at 1% +winnow predict fdr_method=database_grounded fdr_control.fdr_threshold=0.01 + +# Use non-parametric FDR at 5% +winnow predict fdr_method=nonparametric fdr_control.fdr_threshold=0.05 +``` + +### Training with different features + +```bash +# Change Prosit tolerance +winnow train calibrator.features.prosit_features.mz_tolerance=0.01 + +# Disable missing value handling for a feature +winnow train calibrator.features.prosit_features.learn_from_missing=false + +# Change retention time model architecture +winnow train calibrator.features.retention_time_feature.hidden_dim=20 +``` + +### Processing different data formats + +```bash +# MZTab format +winnow train data_loader=mztab dataset.spectrum_path_or_directory=data/spectra.parquet dataset.predictions_path=data/results.mztab + +# Previously saved Winnow dataset +winnow train data_loader=winnow dataset.spectrum_path_or_directory=data/winnow_dataset/ dataset.predictions_path=null +``` + +## Config interpolation + +Hydra supports variable interpolation using `${...}` syntax: + +```yaml +# Reference from residues config (loaded via defaults) +features: + mass_error: + residue_masses: ${residue_masses} # References residue_masses from residues.yaml + +# Reference nested values +fdr_control: + confidence_column: calibrated_confidence + +database_grounded: + confidence_feature: ${fdr_control.confidence_column} # References nested value + +# Use in defaults for dynamic composition +defaults: + - fdr_method: nonparametric # Loads fdr_method/nonparametric.yaml +``` + +Common interpolation patterns in Winnow configs: +- `${residue_masses}` - References amino acid masses from residues.yaml +- `${invalid_prosit_tokens}` - References invalid tokens from residues.yaml +- `${fdr_control.confidence_column}` - References FDR confidence column setting + +## Creating custom configurations + +### Add a custom data loader + +1. Create loader class implementing `DatasetLoader` protocol +2. Add configuration file: `configs/data_loader/custom.yaml` +3. Use with: `winnow train data_loader=custom` + +Example `configs/data_loader/custom.yaml`: +```yaml +_target_: my_module.CustomDatasetLoader +residue_masses: ${residue_masses} +custom_param: value +``` + +### Add custom calibration features + +1. Create feature class inheriting from `CalibrationFeatures` +2. Add to `configs/calibrator.yaml`: + ```yaml + features: + custom_feature: + _target_: my_module.CustomFeature + param1: value1 + param2: value2 + ``` + +### Add custom FDR method + +1. Create FDR class implementing the FDR interface +2. Add configuration file: `configs/fdr_method/custom_method.yaml` +3. Use with: `winnow predict fdr_method=custom_method` + +Example `configs/fdr_method/custom_method.yaml`: +```yaml +_target_: my_module.CustomFDRControl +confidence_feature: ${fdr_control.confidence_column} +custom_param: value +``` + +## Debugging configuration + +### View resolved configuration + +To see the final composed configuration without running the pipeline, use the `winnow config` command: + +```bash +# View training configuration +winnow config train + +# View prediction configuration +winnow config predict + +# View configuration with overrides +winnow config train data_loader=mztab model_output_dir=custom/path +winnow config predict fdr_method=database_grounded fdr_control.fdr_threshold=0.01 +``` + +This prints the complete resolved YAML configuration with colour-coded hierarchical formatting for easy readability. Keys are coloured by nesting depth to help visualise the configuration structure. The output shows all defaults, composition and command-line overrides after they have been applied. + +**Note:** Some keys appear with quotes (e.g. `'N'`, `'Y'`) because they are reserved words in YAML that would otherwise be interpreted as boolean values. The quotes ensure they are treated as strings. + +### Configuration validation + +Hydra will validate that configuration files exist and can be composed. Invalid configurations will fail early with clear error messages: + +```bash +winnow predict fdr_method=typo +# Error: Could not find 'fdr_method/typo' +# Available options in 'fdr_method': nonparametric, database_grounded +``` + +## Advanced: custom config directories + +For advanced users who have installed winnow as a package and need to customise multiple configuration files, you can use the `--config-dir` flag to specify a custom configuration directory. This is particularly useful when you have complex customisations that would be verbose to specify via command-line overrides. + +**When to use custom config directories:** +- **CLI overrides**: For simple parameter changes (1-3 values) - use command-line overrides like `winnow train calibrator.seed=42` +- **Custom config dirs**: For complex configurations with many custom settings (advanced users) - use `--config-dir` +- **Cloning repo**: For extending Winnow (developers) - clone the repository, make changes, and modify configs directly + +### Config file structure and maming + +Your custom config directory should mirror the structure of the package configs: + +``` +my_configs/ +├── residues.yaml # Override residue masses/modifications +├── calibrator.yaml # Override calibrator features +├── train.yaml # Override training config (if needed) +├── predict.yaml # Override prediction config (if needed) +├── data_loader/ # Override data loaders (if needed) +│ └── instanovo.yaml +│ └── mztab.yaml +│ └── winnow.yaml +└── fdr_method/ # Override FDR methods (if needed) +│ └── database_grounded.yaml +│ └── nonparametric.yaml +``` + +**Important requirements:** +- File names must match package config names **exactly** (case-sensitive) +- Directory structure should mirror package structure (e.g., `data_loader/`, `fdr_method/`) +- Only include files you want to override - you don't need to include everything +- YAML files must be valid and follow the same structure as package configs + +### Partial configs + +You can use **partial configs at the file level** - only include the files you want to override. For example, if you only want to customise residue masses and calibrator settings: + +```bash +my_configs/ +├── residues.yaml # Your custom residues +└── calibrator.yaml # Your custom calibrator config +``` + +When you use `--config-dir`, winnow will: +1. Use your custom files for files present in your directory (these completely replace package versions) +2. Use package defaults for files not in your directory + +**Important limitation**: Partial configs work at the **file level**, not the **key level** within a file. If you provide a custom `calibrator.yaml`, it must contain the complete structure - you can't just override `seed` and expect other settings to come from package defaults. See "Behaviour with variables" below for details. + +### Behaviour with variables + +**How config files work**: When you provide a custom config file (e.g., `calibrator.yaml`), it **completely replaces** the package version of that file. Hydra does not merge keys within the same file - it uses your file exactly as written. + +**What this means:** +- ✅ **Partial configs at file level**: You only need to include the files you want to override (e.g., just `residues.yaml` and `calibrator.yaml`). Files not in your custom directory use package defaults. +- ❌ **Partial configs at key level don't work**: If you provide `calibrator.yaml` with only `seed: 999`, the other settings (`hidden_layer_sizes`, `features`, etc.) will be **missing**, not using package defaults. This will cause errors. + +**Example - What happens with minimal config:** +```yaml +# custom/calibrator.yaml - TOO MINIMAL +calibrator: + _target_: winnow.calibration.calibrator.ProbabilityCalibrator + seed: 99999 +``` + +**Result**: Only `_target_` and `seed` are present. All other keys (`hidden_layer_sizes`, `learning_rate_init`, `features`, etc.) are **missing** from the final config. This will cause errors when running the pipeline in most cases. + +**Example - What you need (complete structure):** +```yaml +# custom/calibrator.yaml - COMPLETE STRUCTURE REQUIRED +calibrator: + _target_: winnow.calibration.calibrator.ProbabilityCalibrator + seed: 99999 # Your custom value + hidden_layer_sizes: [50, 50] # Must include all settings + learning_rate_init: 0.001 + alpha: 0.0001 + max_iter: 1000 + early_stopping: true + validation_fraction: 0.1 + features: + mass_error: + _target_: winnow.calibration.calibration_features.MassErrorFeature + residue_masses: ${residue_masses} + prosit_features: + # ... include all features you want to keep + # Features you don't include will be missing (not using defaults) +``` + +**Removing features**: To remove features, simply **don't include them** in your custom `calibrator.yaml`. Since your file completely replaces the package version, any features you omit will be absent from the final config. It is also possible to specify this using a tilde with CLI overrides (e.g., `~calibrator.prosit_features`). + +**New variables**: Adding new keys that don't exist in package configs will cause them to be ignored (Hydra is not strict by default). They won't cause errors, but they also won't be used unless your code explicitly accesses them. **Stick to overriding existing keys** from package configs. + +**Recommendation**: Always start by copying the complete package config file, then modify only the values you need. You can get the package config structure by running `winnow config train` and copying the relevant section, or by copying from `winnow/configs/` in the [repository](https://github.com/instadeepai/winnow). + +### Getting package config structure + +Before creating custom configs, you need to know the structure of package configs. Here are ways to get them: + +1. **View resolved config**: Run `winnow config train` or `winnow config predict` to see the complete resolved configuration +2. **Clone the repository**: Visit the winnow [repository](https://github.com/instadeepai/winnow) and check `winnow/configs/` directory +3. **Inspect installed package**: Find the package location (e.g., `python -c "import winnow; print(winnow.__file__)"`) and navigate to `configs/` + +**Recommended workflow**: Start by creating a new config directory, copying in the package config file you want to customise, and then modify only the values you need. + +### Troubleshooting + +**Common mistakes:** + +1. **Wrong file names**: File names must match exactly (case-sensitive) + - ❌ `Residues.yaml` (wrong case) + - ✅ `residues.yaml` (correct) + +2. **Incorrect structure**: Directory structure must match package structure + - ❌ `my_configs/data_loaders/` (wrong directory name) + - ✅ `my_configs/data_loader/` (correct) + +3. **Typos in keys**: YAML keys must match package config keys exactly + - Check package configs for correct key names + +4. **Invalid YAML**: Ensure your YAML files are valid + - Use a YAML validator if unsure + +**How to check if custom config is being used:** + +1. Use `winnow config train --config-dir my_configs` and search for your custom values +2. Compare output with `winnow config train` (without custom dir) to see differences +3. Check logs - winnow logs which config directory is being used + +## Additional resources + +- [Hydra documentation](https://hydra.cc/docs/intro/) +- [OmegaConf documentation](https://omegaconf.readthedocs.io/) +- [Winnow API documentation](api/calibration.md) +- [Example notebook](https://github.com/instadeepai/winnow/blob/main/examples/getting_started_with_winnow.ipynb) + +## Migration from old CLI + +If you're migrating from the old argument-based CLI: + +| Old CLI Argument | New Hydra Config Parameter | +|------------------|---------------------------| +| `--data-source instanovo` | `data_loader=instanovo` | +| `--model-output-folder models/X` | `model_output_dir=models/X` | +| `--dataset-output-path results/X.csv` | `dataset_output_path=results/X.csv` | +| `--fdr-threshold 0.01` | `fdr_control.fdr_threshold=0.01` | +| `--method winnow` | `fdr_method=nonparametric` | +| `--method database-ground` | `fdr_method=database_grounded` | +| `--local-model-folder models/X` | `calibrator.pretrained_model_name_or_path=models/X` | +| `--huggingface-model-name X` | `calibrator.pretrained_model_name_or_path=X` | +| `--confidence-column X` | `fdr_control.confidence_column=X` | +| `--output-folder results/` | `output_folder=results/` | + +**Key changes:** + +- `data_source` renamed to `data_loader` (references configs/data_loader/*.yaml) +- `fdr_threshold` and `confidence_column` now nested under `fdr_control` +- `local_model_folder` and `huggingface_model_name` merged into `pretrained_model_name_or_path` +- Dataset paths are now specified directly as Hydra parameters instead of via separate YAML files: + - **Old**: Create `config.yaml` with `spectrum_path` and `predictions_path`, then use `--dataset-config-path config.yaml` + - **New**: Use `dataset.spectrum_path_or_directory=... dataset.predictions_path=...` directly on command line diff --git a/docs/contributing.md b/docs/contributing.md index afe987d..638abb0 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -2,7 +2,7 @@ Contributions are what make the open-source community such an amazing place to learn, inspire and create, and we welcome your support! Any contributions you make are **greatly appreciated**. -## Ways to Contribute +## Ways to contribute If you have ideas for enhancements, you can: @@ -12,7 +12,7 @@ If you have ideas for enhancements, you can: - Improve documentation - Add examples or tutorials -## Contribution Process +## Contribution process 1. **Fork the repository** ```bash @@ -63,9 +63,9 @@ If you have ideas for enhancements, you can: - Reference any related issues - Include examples if applicable -## Development Guidelines +## Development guidelines -### Code Style +### Code style This project uses: @@ -85,17 +85,17 @@ This project uses: - Add examples to demonstrate usage - Update the API documentation pages if you add new modules -### Issues and Bug Reports +### Issues and bug reports When reporting bugs, please include: - A clear and descriptive title - Steps to reproduce the issue -- Expected vs. actual behavior +- Expected vs. actual behaviour - Your environment details (Python version, OS, etc.) - Any relevant error messages or logs -### Feature Requests +### Feature requests For feature requests, please: @@ -104,7 +104,7 @@ For feature requests, please: - Describe the proposed solution - Consider alternatives you've thought about -## Getting Help +## Getting help If you need help or have questions: diff --git a/docs/examples.md b/docs/examples.md index 1b390f2..ecfc413 100644 --- a/docs/examples.md +++ b/docs/examples.md @@ -1,8 +1,27 @@ # Examples +## Quick Start with Sample Data + +Get started with `winnow` in minutes using the included sample data: + +```bash +# Generate sample data +make sample-data + +# Train a calibrator +make train-sample + +# Run prediction +make predict-sample +``` + +The sample data is automatically configured in the default config files. See the [CLI guide](cli.md#quick-start) for more details. + +## Comprehensive Example Notebook + For a comprehensive example demonstrating the full winnow workflow, see our example notebook: -📓 **[FDR Plots Example Notebook](https://github.com/instadeepai/winnow/blob/main/examples/getting_started_with_winnow.ipynb)** +📓 **[FDR plots example notebook](https://github.com/instadeepai/winnow/blob/main/examples/getting_started_with_winnow.ipynb)** This notebook shows you how to: diff --git a/examples/example_data/predictions.csv b/examples/example_data/predictions.csv new file mode 100644 index 0000000..5055776 --- /dev/null +++ b/examples/example_data/predictions.csv @@ -0,0 +1,21 @@ 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K",-0.1396097391739677,PEPTIDEK,PEPTIDEK,-0.8580417332931659,"[-0.36418526090876374, -0.3750170473528839, -0.5575436066762, -0.6385560199240516, -0.351885897096517, -0.4576945870020608, -0.5347783329153784, -0.620151848601525]",KEVPYKAD,-0.8307558545201461,"[-0.16665506888765855, -0.4580902089789952, -0.36259850517015, -0.5314076669237787, -0.4903893178624768, -0.4222928673951614, -0.5519651708245514, -0.3675151987697704]",AVRCIYPR,-1.2215795426005904,"[-0.44094024791973685, -0.687912908195114, -0.3110916469162062, -0.4517853577435027, -0.5866364468446136, -0.21182272556682824, -0.24681439659654547, -0.13596327596018673]" +spectrum_17,MASSIVE,"M, A, S, S, I, V, E",-1.673576044172536,MASSIVE,MASSIVE,-1.0416352236295878,"[-0.18011733411718406, -0.44309690273126257, -0.18546967224676997, -0.6725901169561864, -0.6913571219485906, -0.13952980371681353, -0.26191907305593165]",AVCNQEV,-2.1064853589028734,"[-0.49941457645173465, -0.5613620275510856, -0.12683698539601138, -0.576995184417916, -0.2158555530642328, -0.18840672391507085, -0.17063769574233772]",FIDVAFY,-0.720662642157043,"[-0.158819522076645, -0.12507331977330707, -0.3182599244225264, -0.4554459665475208, -0.11464413536235571, -0.55061860938854, -0.6838185169116369]" +spectrum_18,PEPTIDES,"P, E, P, T, I, D, E, S",-0.20385092564668866,PEPTIDES,PEPTIDES,-1.8243023305661654,"[-0.10766780647118387, -0.3684554790772888, -0.5733129192232955, -0.12679615833858135, -0.5684051802426422, -0.4343058484652547, -0.13487630398608988, -0.6298786133036921]",YEEWHLMF,-0.21998080903850434,"[-0.34970116792464323, -0.12510327399597215, -0.19074060012399183, -0.3713073916915177, -0.11676484767902127, -0.1917769333555045, -0.3137903773577807, -0.15334435903655044]",VARIMPMC,-2.155753908791302,"[-0.28882205067883104, -0.39508986900075516, -0.42653791045742573, -0.3632701174324455, -0.2525320992759492, -0.522647013018777, -0.1830132796459985, -0.31472506833943226]" +spectrum_19,SEQQENCR,"S, E, Q, Q, E, N, C, R",-0.11296685048715463,SEQQENCR,SEQQENCR,-0.2554346478178205,"[-0.3833345369220081, -0.15675178365843412, -0.4655941268240131, -0.12209625224365629, -0.10652893116790317, -0.3646981732451785, -0.46439278292724706, -0.11723563636988465]",KLQESVHN,-0.5398811568573243,"[-0.37747579886843446, -0.2831553354880077, -0.40827869647373755, -0.41762472834219044, -0.18005039232318404, -0.5154473495901091, -0.22414917531599954, -0.24503592841782743]",HDETWCYM,-1.2772633021909128,"[-0.2731991203786418, -0.1850045539068488, -0.45263593167185406, -0.49574184487856604, -0.2299389411312791, -0.2216644179226556, -0.6078837089643009, -0.3635693939132283]" diff --git a/examples/example_data/spectra.ipc b/examples/example_data/spectra.ipc new file mode 100644 index 0000000..1360c33 Binary files /dev/null and b/examples/example_data/spectra.ipc differ diff --git a/examples/getting_started_with_winnow.ipynb b/examples/getting_started_with_winnow.ipynb index c9fccab..c8c9623 100644 --- a/examples/getting_started_with_winnow.ipynb +++ b/examples/getting_started_with_winnow.ipynb @@ -70,23 +70,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
[11/06/25 18:46:36] INFO     Enabling RDKit 2024.09.6 jupyter extensions                                                                                         __init__.py:22\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[2;36m[11/06/25 18:46:36]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Enabling RDKit \u001b[1;36m2024.09\u001b[0m.\u001b[1;36m6\u001b[0m jupyter extensions \u001b]8;id=885939;file:///home/j-daniel/repos/winnow/.venv/lib/python3.12/site-packages/rdkit/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=21754;file:///home/j-daniel/repos/winnow/.venv/lib/python3.12/site-packages/rdkit/__init__.py#22\u001b\\\u001b[2m22\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import logging\n", "import warnings\n", @@ -96,6 +82,8 @@ "import pandas as pd\n", "import seaborn as sns\n", "from huggingface_hub import list_repo_files, snapshot_download\n", + "from hydra import initialize, compose\n", + "from hydra.utils import instantiate\n", "\n", "from winnow.calibration.calibration_features import (\n", " BeamFeatures,\n", @@ -106,9 +94,7 @@ " RetentionTimeFeature,\n", ")\n", "from winnow.calibration.calibrator import ProbabilityCalibrator\n", - "from winnow.constants import RESIDUE_MASSES\n", "from winnow.datasets.calibration_dataset import CalibrationDataset\n", - "from winnow.datasets.data_loaders import InstaNovoDatasetLoader\n", "from winnow.fdr.database_grounded import DatabaseGroundedFDRControl\n", "from winnow.fdr.nonparametric import NonParametricFDRControl\n", "\n", @@ -141,17 +127,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['.gitattributes', 'README.md', 'celegans_labelled.parquet', 'celegans_labelled_beams.csv', 'celegans_raw.parquet', 'celegans_raw_beams.csv', 'general_test.parquet', 'general_test_beams.csv', 'general_train.parquet', 'general_train_beams.csv', 'general_val.parquet', 'general_val_beams.csv', 'helaqc_labelled.parquet', 'helaqc_labelled_beams.csv', 'helaqc_raw_less_train.parquet', 'helaqc_raw_less_train_beams.csv', 'immuno2_labelled.parquet', 'immuno2_labelled_beams.csv', 'immuno2_raw.parquet', 'immuno2_raw_beams.csv']\n" - ] - } - ], + "outputs": [], "source": [ "repo_id = \"InstaDeepAI/winnow-ms-datasets\"\n", "save_dir = \"winnow-ms-datasets\"\n", @@ -162,34 +140,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5a543758fdac4806b5ff0a3ee5c9cdf7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Fetching 4 files: 0%| | 0/4 [00:00=0.15.2", "instanovo>=1.1.4", "huggingface-hub>=0.35.3", + "hydra-core>=1.3.2", ] [project.urls] @@ -32,6 +33,9 @@ build-backend = "setuptools.build_meta" [tool.setuptools] packages = ["winnow", "winnow.scripts", "winnow.fdr", "winnow.datasets", "winnow.calibration"] +[tool.setuptools.package-data] +winnow = ["configs/**/*.yaml", "configs/**/*.yml"] + [dependency-groups] dev = [ "pre-commit>=4.1.0", diff --git a/requirements.txt b/requirements.txt index 3e08759..cc118ec 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,37 +1,38 @@ # This file was autogenerated by uv via the following command: # uv pip compile pyproject.toml -o requirements.txt -absl-py==2.1.0 +absl-py==2.3.1 # via tensorboard -aiobotocore==2.21.1 +accelerate==1.12.0 + # via instanovo +aiobotocore==2.26.0 # via s3fs -aiohappyeyeballs==2.5.0 +aiohappyeyeballs==2.6.1 # via aiohttp -aiohttp==3.11.13 +aiohttp==3.13.3 # via # aiobotocore - # datasets # fsspec # s3fs -aioitertools==0.12.0 +aioitertools==0.13.0 # via aiobotocore -aiosignal==1.3.2 +aiosignal==1.4.0 # via aiohttp antlr4-python3-runtime==4.9.3 # via # hydra-core # omegaconf -appdirs==1.4.4 - # via spectrum-utils -arrow==1.3.0 +anyio==4.12.1 + # via httpx +arrow==1.4.0 # via isoduration -attrs==25.1.0 +attrs==25.4.0 # via # aiohttp # jsonschema # referencing -boto3==1.37.1 +boto3==1.41.5 # via neptune -botocore==1.37.1 +botocore==1.41.5 # via # aiobotocore # boto3 @@ -40,93 +41,108 @@ bravado==11.1.0 # via neptune bravado-core==6.1.1 # via bravado -certifi==2025.1.31 - # via requests -charset-normalizer==3.4.1 +certifi==2026.1.4 + # via + # httpcore + # httpx + # requests +charset-normalizer==3.4.4 # via requests -click==8.1.8 +click==8.3.1 # via # jiwer # neptune # typer -contourpy==1.3.1 + # typer-slim +contourpy==1.3.3 # via matplotlib cycler==0.12.1 # via matplotlib -datasets==3.3.2 +datasets==4.5.0 # via instanovo -deprecated==1.2.18 +deprecated==1.3.1 # via matchms -dill==0.3.8 +dill==0.4.0 # via # datasets # multiprocess -fastobo==0.13.0 +fastobo==0.14.1 # via spectrum-utils -filelock==3.17.0 +filelock==3.20.3 # via # datasets # huggingface-hub # torch -fonttools==4.56.0 +fonttools==4.61.1 # via matplotlib fqdn==1.5.1 # via jsonschema -frozenlist==1.5.0 +frozenlist==1.8.0 # via # aiohttp # aiosignal -fsspec==2024.12.0 +fsspec==2025.10.0 # via # datasets # huggingface-hub - # lightning - # pytorch-lightning # s3fs # torch future==1.0.0 # via neptune gitdb==4.0.12 # via gitpython -gitpython==3.1.44 +gitpython==3.1.46 # via # instanovo # neptune -grpcio==1.70.0 +grpcio==1.67.1 # via # tensorboard # tritonclient -huggingface-hub==0.29.2 - # via datasets +h11==0.16.0 + # via httpcore +hf-xet==1.2.0 + # via huggingface-hub +httpcore==1.0.9 + # via httpx +httpx==0.28.1 + # via + # datasets + # huggingface-hub +huggingface-hub==1.3.2 + # via + # winnow-fdr (pyproject.toml) + # accelerate + # datasets hydra-core==1.3.2 - # via instanovo -idna==3.10 # via + # winnow-fdr (pyproject.toml) + # instanovo +idna==3.11 + # via + # anyio + # httpx # jsonschema # requests # yarl importlib-resources==6.5.2 # via swagger-spec-validator -instanovo==1.1.4 +instanovo==1.2.2 # via winnow-fdr (pyproject.toml) isoduration==20.11.0 # via jsonschema -jax==0.7.2 - # via winnow-fdr (pyproject.toml) -jaxlib==0.7.2 - # via jax -jaxtyping==0.2.38 +jaxtyping==0.3.5 # via instanovo jinja2==3.1.6 # via torch -jiwer==3.1.0 +jiwer==4.0.0 # via instanovo jmespath==1.0.1 # via # aiobotocore # boto3 # botocore -joblib==1.4.2 +joblib==1.5.3 # via # pynndescent # scikit-learn @@ -134,69 +150,61 @@ jsonpointer==3.0.0 # via jsonschema jsonref==1.1.0 # via bravado-core -jsonschema==4.23.0 +jsonschema==4.26.0 # via # bravado-core # swagger-spec-validator -jsonschema-specifications==2024.10.1 +jsonschema-specifications==2025.9.1 # via jsonschema -kiwisolver==1.4.8 +kiwisolver==1.4.9 # via matplotlib koinapy==0.0.10 # via winnow-fdr (pyproject.toml) -lark==1.2.2 - # via spectrum-utils -lightning==2.5.2 - # via instanovo -lightning-utilities==0.14.0 +lark==1.3.1 # via - # lightning - # pytorch-lightning - # torchmetrics + # rfc3987-syntax + # spectrum-utils llvmlite==0.44.0 # via # numba # pynndescent lxml==5.4.0 # via matchms -markdown==3.7 +markdown==3.10 # via tensorboard -markdown-it-py==3.0.0 +markdown-it-py==4.0.0 # via rich -markupsafe==3.0.2 +markupsafe==3.0.3 # via # jinja2 # werkzeug -matchms==0.30.2 +matchms==0.31.0 # via instanovo -matplotlib==3.10.1 +matplotlib==3.10.8 # via # matchms # spectrum-utils mdurl==0.1.2 # via markdown-it-py -ml-dtypes==0.5.3 - # via - # jax - # jaxlib monotonic==1.6 # via bravado mpmath==1.3.0 # via sympy -msgpack==1.1.0 +msgpack==1.1.2 # via # bravado # bravado-core -multidict==6.1.0 + # signalrcore +multidict==6.7.0 # via # aiobotocore # aiohttp # yarl -multiprocess==0.70.16 +multiprocess==0.70.18 # via datasets -neptune==1.13.0 +neptune==1.14.0 # via instanovo -networkx==3.4.2 +networkx==3.6.1 # via # matchms # torch @@ -208,14 +216,12 @@ numba==0.61.2 # spectrum-utils numpy==2.2.6 # via + # accelerate # contourpy # datasets # instanovo - # jax - # jaxlib # matchms # matplotlib - # ml-dtypes # numba # pandas # rdkit @@ -224,7 +230,6 @@ numpy==2.2.6 # sparsestack # spectrum-utils # tensorboard - # torchmetrics # tritonclient nvidia-cublas-cu12==12.8.4.1 # via @@ -253,7 +258,7 @@ nvidia-cusparse-cu12==12.5.8.93 # torch nvidia-cusparselt-cu12==0.7.1 # via torch -nvidia-nccl-cu12==2.27.3 +nvidia-nccl-cu12==2.27.5 # via torch nvidia-nvjitlink-cu12==12.8.93 # via @@ -261,9 +266,11 @@ nvidia-nvjitlink-cu12==12.8.93 # nvidia-cusolver-cu12 # nvidia-cusparse-cu12 # torch +nvidia-nvshmem-cu12==3.3.20 + # via torch nvidia-nvtx-cu12==12.8.90 # via torch -oauthlib==3.2.2 +oauthlib==3.3.1 # via # neptune # requests-oauthlib @@ -271,22 +278,17 @@ omegaconf==2.3.0 # via # hydra-core # instanovo -opt-einsum==3.4.0 - # via jax -packaging==24.2 +packaging==25.0 # via + # accelerate # datasets # huggingface-hub # hydra-core - # lightning - # lightning-utilities # matplotlib # neptune - # pytorch-lightning # tensorboard - # torchmetrics # tritonclient -pandas==2.2.3 +pandas==2.3.3 # via # datasets # instanovo @@ -295,35 +297,43 @@ pandas==2.2.3 # neptune pickydict==0.5.0 # via matchms -pillow==11.1.0 +pillow==12.1.0 # via - # matchms # matplotlib # neptune # rdkit -polars==1.24.0 + # tensorboard +platformdirs==4.5.1 + # via spectrum-utils +polars==1.37.1 # via instanovo -propcache==0.3.0 +polars-runtime-32==1.37.1 + # via polars +propcache==0.4.1 # via # aiohttp # yarl -protobuf==4.25.6 +protobuf==5.29.5 # via # tensorboard # tritonclient -psutil==7.0.0 - # via neptune -pubchempy==1.0.4 - # via matchms -pyarrow==19.0.1 +psutil==7.2.1 + # via + # accelerate + # neptune +pubchempy==1.0.5 + # via + # instanovo + # matchms +pyarrow==22.0.0 # via datasets -pygments==2.19.1 +pygments==2.19.2 # via rich pyjwt==2.10.1 # via neptune -pynndescent==0.5.13 +pynndescent==0.6.0 # via matchms -pyparsing==3.2.1 +pyparsing==3.3.1 # via matplotlib pyteomics==4.7.5 # via @@ -339,84 +349,85 @@ python-dateutil==2.9.0.post0 # bravado-core # matplotlib # pandas -python-dotenv==1.0.1 +python-dotenv==1.2.1 # via instanovo -python-rapidjson==1.20 +python-rapidjson==1.23 # via tritonclient -pytorch-lightning==2.5.0.post0 - # via lightning -pytz==2025.1 +pytz==2025.2 # via # bravado-core # pandas -pyyaml==6.0.2 +pyyaml==6.0.3 # via + # accelerate # bravado # bravado-core # datasets # huggingface-hub # instanovo - # lightning # matchms # omegaconf - # pytorch-lightning # swagger-spec-validator -rapidfuzz==3.12.2 +rapidfuzz==3.14.3 # via jiwer -rdkit==2024.9.5 +rdkit==2024.9.6 # via matchms -referencing==0.36.2 +referencing==0.37.0 # via # jsonschema # jsonschema-specifications -requests==2.32.3 +requests==2.32.5 # via # bravado # bravado-core # datasets - # huggingface-hub # instanovo # matchms # neptune # requests-oauthlib + # signalrcore requests-oauthlib==2.0.0 # via neptune rfc3339-validator==0.1.4 # via jsonschema rfc3986-validator==0.1.1 # via jsonschema -rich==13.9.4 +rfc3987-syntax==1.1.0 + # via jsonschema +rich==14.2.0 # via typer -rpds-py==0.23.1 +rpds-py==0.30.0 # via # jsonschema # referencing -s3fs==2024.12.0 +s3fs==2025.10.0 # via instanovo -s3transfer==0.11.3 +s3transfer==0.15.0 # via boto3 -scikit-learn==1.6.1 +safetensors==0.7.0 + # via accelerate +scikit-learn==1.8.0 # via # instanovo # pynndescent -scipy==1.16.2 +scipy==1.17.0 # via - # jax - # jaxlib # matchms # pynndescent # scikit-learn # sparsestack -setuptools==75.8.2 +setuptools==80.9.0 # via - # lightning-utilities # neptune # tensorboard # torch - # triton shellingham==1.5.4 - # via typer -simplejson==3.20.1 + # via + # huggingface-hub + # typer +signalrcore==0.8.8 + # via instanovo +simplejson==3.20.2 # via # bravado # bravado-core @@ -427,91 +438,83 @@ six==1.17.0 # neptune # python-dateutil # rfc3339-validator - # tensorboard smmap==5.0.2 # via gitdb sparsestack==0.7.0 # via matchms -spectrum-utils==0.4.2 +spectrum-utils==0.5.0 # via instanovo swagger-spec-validator==3.0.4 # via # bravado-core # neptune -sympy==1.13.3 +sympy==1.14.0 # via torch -tensorboard==2.19.0 +tensorboard==2.20.0 # via instanovo tensorboard-data-server==0.7.2 # via tensorboard -threadpoolctl==3.5.0 +threadpoolctl==3.6.0 # via scikit-learn -tomli==2.2.1 +tomli==2.4.0 # via winnow-fdr (pyproject.toml) -torch==2.8.0 - # via - # lightning - # pytorch-lightning - # torchmetrics -torchmetrics==1.6.2 - # via - # lightning - # pytorch-lightning +torch==2.9.1 + # via accelerate tqdm==4.67.1 # via # datasets # huggingface-hub # instanovo # koinapy - # lightning # matchms - # pytorch-lightning transfusion-asr==0.1.0 # via instanovo -triton==3.4.0 +triton==3.5.1 # via torch -tritonclient==2.46.0 +tritonclient==2.64.0 # via koinapy -typer==0.15.2 +typer==0.21.1 # via # winnow-fdr (pyproject.toml) # instanovo -types-python-dateutil==2.9.0.20241206 - # via arrow -typing-extensions==4.12.2 +typer-slim==0.21.1 + # via huggingface-hub +typing-extensions==4.15.0 # via # bravado # huggingface-hub - # lightning - # lightning-utilities # neptune - # pytorch-lightning # swagger-spec-validator # torch # typer -tzdata==2025.1 - # via pandas + # typer-slim +tzdata==2025.3 + # via + # arrow + # pandas uri-template==1.3.0 # via jsonschema -urllib3==2.3.0 +urllib3==2.6.3 # via # botocore # neptune # requests # tritonclient -wadler-lindig==0.1.3 +wadler-lindig==0.1.7 # via jaxtyping -webcolors==24.11.1 +webcolors==25.10.0 # via jsonschema -websocket-client==1.8.0 - # via neptune -werkzeug==3.1.3 +websocket-client==1.9.0 + # via + # neptune + # signalrcore +werkzeug==3.1.5 # via tensorboard -wrapt==1.17.2 +wrapt==1.17.3 # via # aiobotocore # deprecated -xxhash==3.5.0 +xxhash==3.6.0 # via datasets -yarl==1.18.3 +yarl==1.22.0 # via aiohttp diff --git a/scripts/generate_sample_data.py b/scripts/generate_sample_data.py new file mode 100755 index 0000000..97d1897 --- /dev/null +++ b/scripts/generate_sample_data.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 +"""Generate minimal sample data for winnow train and predict commands.""" + +import numpy as np +import pandas as pd +import polars as pl +from pathlib import Path + + +def generate_sample_data(): + """Generate minimal sample IPC and CSV files for InstaNovo format.""" + output_dir = Path("examples/example_data") + output_dir.mkdir(parents=True, exist_ok=True) + + n_samples = 20 + spectrum_ids = [f"spectrum_{i}" for i in range(n_samples)] + + # Generate peptides using only valid amino acids (A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y) + # Note: Must avoid O, U, X, Z, B, J which are not standard amino acids + peptides = [ + "PEPTIDEK", + "MASSIVE", + "PEPTIDES", + "SEQQENCR", + "PEPTIDE", + "MASSIVE", + "PEPTIDES", + "SEQQENCR", + "PEPTIDEK", + "MASSIVE", + "PEPTIDES", + "SEQQENCR", + "PEPTIDE", + "MASSIVE", + "PEPTIDES", + "SEQQENCR", + "PEPTIDEK", + "MASSIVE", + "PEPTIDES", + "SEQQENCR", + ] + + # Generate spectrum data (IPC format) + # Calculate precursor_mass from mz and charge + np.random.seed(42) # For reproducibility + precursor_mz = np.random.uniform(400, 1200, n_samples) + precursor_charge = np.random.choice([2, 3, 4], n_samples) + proton_mass = 1.007276 + precursor_mass = precursor_mz * precursor_charge - proton_mass * precursor_charge + + # Generate spectrum arrays (mz_array and intensity_array) + mz_arrays = [] + intensity_arrays = [] + for _ in range(n_samples): + n_peaks = np.random.randint(10, 50) + mz_array = np.random.uniform(100, 1000, n_peaks).tolist() + intensity_array = np.random.uniform(0.1, 1.0, n_peaks).tolist() + mz_arrays.append(mz_array) + intensity_arrays.append(intensity_array) + + # Create spectrum data DataFrame using polars + spectrum_data = pl.DataFrame( + { + "spectrum_id": spectrum_ids, + "precursor_mz": precursor_mz, + "precursor_charge": precursor_charge.astype(int), + "precursor_mass": precursor_mass, + "retention_time": np.random.uniform(10, 60, n_samples), + "sequence": peptides, # Ground truth for training + "mz_array": mz_arrays, + "intensity_array": intensity_arrays, + } + ) + + # Generate predictions (CSV format) + predictions_data = { + "spectrum_id": spectrum_ids, + "predictions": peptides, + "predictions_tokenised": [ + ", ".join(list(p)) + for p in peptides # "P, E, P, T, I, D, E, K" + ], + "log_probs": np.log(np.random.uniform(0.1, 0.9, n_samples)), + "sequence": peptides, # Ground truth + } + + # Add beam predictions (top 3 beams) + # Generate valid alternative peptides for runner-up beams + valid_aa = list("ACDEFGHIKLMNPQRSTVWY") + np.random.seed(43) # Different seed for beam alternatives + for beam_idx in range(3): + if beam_idx == 0: + # Top beam uses the main prediction + beam_predictions = peptides + else: + # Generate valid alternative peptides for runner-up beams + beam_predictions = [ + "".join(np.random.choice(valid_aa, size=len(peptides[i]))) + for i in range(n_samples) + ] + predictions_data[f"instanovo_predictions_beam_{beam_idx}"] = beam_predictions + predictions_data[f"instanovo_log_probabilities_beam_{beam_idx}"] = [ + np.log(np.random.uniform(0.1, 0.9)) for _ in range(n_samples) + ] + # Token log probabilities as string representation of list + predictions_data[f"token_log_probabilities_beam_{beam_idx}"] = [ + str([float(np.log(np.random.uniform(0.5, 0.9))) for _ in range(len(p))]) + for p in peptides + ] + + predictions_df = pd.DataFrame(predictions_data) + + # Save files + spectrum_path = output_dir / "spectra.ipc" + predictions_path = output_dir / "predictions.csv" + + spectrum_data.write_ipc(str(spectrum_path)) + predictions_df.to_csv(predictions_path, index=False) + + print("✓ Generated sample data:") + print(f" - {spectrum_path}") + print(f" - {predictions_path}") + print("\n✓ You can now run:") + print(" winnow train # Uses sample data from config defaults") + + +if __name__ == "__main__": + generate_sample_data() diff --git a/tests/calibration/test_calibration_features.py b/tests/calibration/test_calibration_features.py index 90aba49..d785d26 100644 --- a/tests/calibration/test_calibration_features.py +++ b/tests/calibration/test_calibration_features.py @@ -16,7 +16,6 @@ _raise_value_error, ) from winnow.datasets.calibration_dataset import CalibrationDataset -from winnow.constants import RESIDUE_MASSES class TestUtilityFunctions: @@ -105,7 +104,23 @@ class TestMassErrorFeature: @pytest.fixture() def mass_error_feature(self): """Create a MassErrorFeature instance for testing.""" - return MassErrorFeature(residue_masses=RESIDUE_MASSES) + residue_masses = { + "G": 57.021464, + "A": 71.037114, + "P": 97.052764, + "E": 129.042593, + "T": 101.047670, + "I": 113.084064, + "D": 115.026943, + "R": 156.101111, + "O": 237.147727, + "N": 114.042927, + "S": 87.032028, + "M": 131.040485, + "L": 113.084064, + "V": 99.068414, + } + return MassErrorFeature(residue_masses=residue_masses) @pytest.fixture() def sample_dataset(self): @@ -469,7 +484,9 @@ class TestRetentionTimeFeature: @pytest.fixture() def retention_time_feature(self): """Create a RetentionTimeFeature instance for testing.""" - return RetentionTimeFeature(hidden_dim=10, train_fraction=0.8) + return RetentionTimeFeature( + hidden_dim=10, train_fraction=0.8, invalid_prosit_tokens=["U", "O", "X"] + ) @pytest.fixture() def sample_dataset_with_rt(self): @@ -494,7 +511,9 @@ def test_properties(self, retention_time_feature): def test_initialization_parameters(self): """Test initialization with custom parameters.""" - feature = RetentionTimeFeature(hidden_dim=10, train_fraction=0.8) + feature = RetentionTimeFeature( + hidden_dim=10, train_fraction=0.8, invalid_prosit_tokens=["U", "O", "X"] + ) assert feature.hidden_dim == 10 assert feature.train_fraction == 0.8 assert feature.prosit_irt_model_name == "Prosit_2019_irt" @@ -779,7 +798,7 @@ class TestPrositFeatures: @pytest.fixture() def prosit_features(self): """Create a PrositFeatures instance for testing.""" - return PrositFeatures(mz_tolerance=0.02) + return PrositFeatures(mz_tolerance=0.02, invalid_prosit_tokens=["U", "O", "X"]) @pytest.fixture() def sample_dataset_with_spectra(self): @@ -818,7 +837,9 @@ def test_properties(self, prosit_features): def test_initialization_with_tolerance(self): """Test initialization with custom tolerance.""" - feature = PrositFeatures(mz_tolerance=0.01) + feature = PrositFeatures( + mz_tolerance=0.01, invalid_prosit_tokens=["U", "O", "X"] + ) assert feature.mz_tolerance == 0.01 assert feature.prosit_intensity_model_name == "Prosit_2020_intensity_HCD" @@ -1152,7 +1173,9 @@ class TestChimericFeatures: @pytest.fixture() def chimeric_features(self): """Create a ChimericFeatures instance for testing.""" - return ChimericFeatures(mz_tolerance=0.02) + return ChimericFeatures( + mz_tolerance=0.02, invalid_prosit_tokens=["U", "O", "X"] + ) @pytest.fixture() def sample_dataset_with_beam_predictions(self): @@ -1205,7 +1228,9 @@ def test_properties(self, chimeric_features): def test_initialization_with_tolerance(self): """Test initialization with custom tolerance.""" - feature = ChimericFeatures(mz_tolerance=0.01) + feature = ChimericFeatures( + mz_tolerance=0.01, invalid_prosit_tokens=["U", "O", "X"] + ) assert feature.mz_tolerance == 0.01 def test_prepare_does_nothing( diff --git a/tests/fdr/test_database_grounded.py b/tests/fdr/test_database_grounded.py index b5d9a2b..8e5f051 100644 --- a/tests/fdr/test_database_grounded.py +++ b/tests/fdr/test_database_grounded.py @@ -1,7 +1,6 @@ """Unit tests for winnow DatabaseGroundedFDRControl.""" import pytest -from unittest.mock import patch, Mock import pandas as pd from winnow.fdr.database_grounded import DatabaseGroundedFDRControl @@ -12,7 +11,24 @@ class TestDatabaseGroundedFDRControl: @pytest.fixture() def db_fdr_control(self): """Create a DatabaseGroundedFDRControl instance for testing.""" - return DatabaseGroundedFDRControl(confidence_feature="confidence") + residue_masses = { + "G": 57.021464, + "A": 71.037114, + "P": 97.052764, + "E": 129.042593, + "T": 101.047670, + "I": 113.084064, + "D": 115.026943, + "R": 156.101111, + "O": 237.147727, + "N": 114.042927, + "S": 87.032028, + "M": 131.040485, + "L": 113.084064, + } + return DatabaseGroundedFDRControl( + confidence_feature="confidence", residue_masses=residue_masses + ) @pytest.fixture() def sample_dataset_df(self): @@ -32,59 +48,40 @@ def test_initialization(self, db_fdr_control): assert db_fdr_control._fdr_values is None assert db_fdr_control._confidence_scores is None - @patch("winnow.fdr.database_grounded.Metrics") - def test_fit_basic(self, mock_metrics, db_fdr_control, sample_dataset_df): + def test_fit_basic(self, db_fdr_control, sample_dataset_df): """Test basic fitting functionality.""" - # Mock the Metrics class and its methods - mock_metrics_instance = Mock() - mock_metrics.return_value = mock_metrics_instance - mock_metrics_instance._split_peptide = lambda x: list(x) - - residue_masses = { - "P": 100.0, - "E": 110.0, - "T": 120.0, - "I": 130.0, - "D": 140.0, - "R": 150.0, - "O": 160.0, - "N": 170.0, - "S": 180.0, - "A": 190.0, - "M": 200.0, - "L": 210.0, - } + # Convert sequences to list format as expected by the implementation + sample_dataset_df = sample_dataset_df.copy() + sample_dataset_df["prediction"] = sample_dataset_df["prediction"].apply(list) # Should not raise an exception - db_fdr_control.fit(sample_dataset_df, residue_masses) + db_fdr_control.fit(sample_dataset_df) - # Check that metrics was called - mock_metrics.assert_called_once() + # Check that fit created the required attributes + assert hasattr(db_fdr_control, "preds") + assert hasattr(db_fdr_control, "_fdr_values") + assert hasattr(db_fdr_control, "_confidence_scores") + assert db_fdr_control._fdr_values is not None + assert db_fdr_control._confidence_scores is not None def test_fit_with_parameters(self, db_fdr_control): """Test fit with custom parameters.""" sample_df = pd.DataFrame( - {"sequence": ["TEST"], "prediction": ["TEST"], "confidence": [0.9]} + {"sequence": ["TEST"], "prediction": [list("TEST")], "confidence": [0.9]} ) - residue_masses = {"T": 100.0, "E": 110.0, "S": 120.0} - - with patch("winnow.fdr.database_grounded.Metrics") as mock_metrics: - mock_metrics_instance = Mock() - mock_metrics.return_value = mock_metrics_instance - mock_metrics_instance._split_peptide = lambda x: list(x) - db_fdr_control.fit( - sample_df, residue_masses, isotope_error_range=(0, 2), drop=5 - ) + db_fdr_control.fit(sample_df) - # Check that Metrics was initialized with correct parameters - mock_metrics.assert_called_once() + # Check that fit created the required attributes + assert hasattr(db_fdr_control, "preds") + assert len(db_fdr_control.preds) == 1 + assert db_fdr_control.preds.iloc[0]["confidence"] == 0.9 def test_fit_with_empty_data(self, db_fdr_control): """Test that fit method handles empty data.""" empty_data = pd.DataFrame() with pytest.raises(AssertionError, match="Fit method requires non-empty data"): - db_fdr_control.fit(empty_data, residue_masses={"A": 71.03}) + db_fdr_control.fit(empty_data) def test_get_confidence_cutoff_requires_fitting(self, db_fdr_control): """Test that get_confidence_cutoff requires fitting first.""" diff --git a/tests/scripts/test_config_paths.py b/tests/scripts/test_config_paths.py new file mode 100644 index 0000000..ac3ccf1 --- /dev/null +++ b/tests/scripts/test_config_paths.py @@ -0,0 +1,199 @@ +"""Tests for config path resolution utilities.""" + +from __future__ import annotations + +import pytest +from pathlib import Path +from unittest.mock import patch, MagicMock + +from winnow.scripts.config_path_utils import ( + get_config_dir, + get_config_search_path, + get_primary_config_dir, +) + + +class TestGetConfigDir: + """Tests for get_config_dir() function.""" + + def test_package_mode(self, tmp_path): + """Test config dir resolution in package mode.""" + # Mock importlib.resources to simulate installed package + package_configs = tmp_path / "package_configs" + package_configs.mkdir() + + # Create a mock that simulates files("winnow").joinpath("configs") + mock_winnow_files = MagicMock() + mock_configs = MagicMock() + mock_configs.is_dir.return_value = True + # Make str() return the path + type(mock_configs).__str__ = lambda self: str(package_configs) + mock_winnow_files.joinpath.return_value = mock_configs + + # Mock files("winnow") to return mock_winnow_files + mock_files = MagicMock(return_value=mock_winnow_files) + + # Patch importlib.resources.files (where it's imported from) + with patch("importlib.resources.files", mock_files): + config_dir = get_config_dir() + assert config_dir == package_configs + + def test_dev_mode(self, tmp_path): + """Test config dir resolution in dev mode.""" + # Mock importlib.resources to fail (simulating dev mode) + with patch("importlib.resources.files", side_effect=ModuleNotFoundError()): + # Create a mock repo structure + repo_root = tmp_path / "repo" + winnow_dir = repo_root / "winnow" + configs_dir = winnow_dir / "configs" + configs_dir.mkdir(parents=True) + + # Mock __file__ to point to winnow/scripts/config_paths.py + with patch( + "winnow.scripts.config_path_utils.__file__", + str(winnow_dir / "scripts" / "config_paths.py"), + ): + config_dir = get_config_dir() + assert config_dir == configs_dir + + def test_dev_mode_alt_location(self, tmp_path): + """Test config dir resolution in dev mode with configs at repo root.""" + # Mock importlib.resources to fail + with patch("importlib.resources.files", side_effect=ModuleNotFoundError()): + # Create a mock repo structure with configs at root + repo_root = tmp_path / "repo" + configs_dir = repo_root / "configs" + configs_dir.mkdir(parents=True) + + # Mock __file__ to point to winnow/scripts/config_paths.py + with patch( + "winnow.scripts.config_path_utils.__file__", + str(repo_root / "winnow" / "scripts" / "config_paths.py"), + ): + config_dir = get_config_dir() + assert config_dir == configs_dir + + def test_not_found(self): + """Test error when config dir cannot be found.""" + with patch("importlib.resources.files", side_effect=ModuleNotFoundError()): + with patch( + "winnow.scripts.config_path_utils.__file__", "/nonexistent/path" + ): + with pytest.raises(FileNotFoundError): + get_config_dir() + + +class TestGetConfigSearchPath: + """Tests for get_config_search_path() function.""" + + def test_custom_dir_only(self, tmp_path): + """Test search path with custom directory.""" + custom_dir = tmp_path / "custom_configs" + custom_dir.mkdir() + + with patch( + "winnow.scripts.config_path_utils.get_config_dir", + return_value=Path("/package/configs"), + ): + search_path = get_config_search_path(str(custom_dir)) + assert len(search_path) == 2 + assert search_path[0] == custom_dir.resolve() + assert search_path[1] == Path("/package/configs").resolve() + + def test_no_custom_dir(self, tmp_path): + """Test search path without custom directory.""" + package_dir = tmp_path / "package_configs" + package_dir.mkdir() + + with patch( + "winnow.scripts.config_path_utils.get_config_dir", return_value=package_dir + ): + search_path = get_config_search_path() + assert len(search_path) == 1 + assert search_path[0] == package_dir.resolve() + + def test_custom_dir_not_exists(self): + """Test error when custom directory doesn't exist.""" + with pytest.raises(FileNotFoundError, match="does not exist"): + get_config_search_path("/nonexistent/path") + + def test_custom_dir_not_directory(self, tmp_path): + """Test error when custom path is not a directory.""" + file_path = tmp_path / "not_a_dir" + file_path.touch() + + with pytest.raises(ValueError, match="not a directory"): + get_config_search_path(str(file_path)) + + +class TestGetPrimaryConfigDir: + """Tests for get_primary_config_dir() function.""" + + def test_no_custom_dir(self, tmp_path): + """Test primary config dir without custom directory.""" + package_dir = tmp_path / "package_configs" + package_dir.mkdir() + + with patch( + "winnow.scripts.config_path_utils.get_config_dir", return_value=package_dir + ): + primary_dir = get_primary_config_dir() + assert primary_dir == package_dir.resolve() + + def test_with_custom_dir(self, tmp_path): + """Test primary config dir with custom directory (merged).""" + custom_dir = tmp_path / "custom_configs" + custom_dir.mkdir() + (custom_dir / "residues.yaml").write_text("custom: true") + + package_dir = tmp_path / "package_configs" + package_dir.mkdir() + (package_dir / "train.yaml").write_text("package: true") + (package_dir / "residues.yaml").write_text("package: true") + + with patch( + "winnow.scripts.config_path_utils.get_config_dir", return_value=package_dir + ): + primary_dir = get_primary_config_dir(str(custom_dir)) + + # Should be a temporary merged directory + assert primary_dir.exists() + assert primary_dir.is_dir() + + # Custom config should override package config + residues_content = (primary_dir / "residues.yaml").read_text() + assert "custom: true" in residues_content + + # Package config should be available for files not in custom dir + assert (primary_dir / "train.yaml").exists() + train_content = (primary_dir / "train.yaml").read_text() + assert "package: true" in train_content + + def test_partial_configs(self, tmp_path): + """Test that partial configs work (only custom residues.yaml).""" + custom_dir = tmp_path / "custom_configs" + custom_dir.mkdir() + (custom_dir / "residues.yaml").write_text("custom_residues: true") + + package_dir = tmp_path / "package_configs" + package_dir.mkdir() + (package_dir / "train.yaml").write_text("train_config: true") + (package_dir / "residues.yaml").write_text("package_residues: true") + (package_dir / "calibrator.yaml").write_text("calibrator_config: true") + + with patch( + "winnow.scripts.config_path_utils.get_config_dir", return_value=package_dir + ): + primary_dir = get_primary_config_dir(str(custom_dir)) + + # Custom residues should override + residues_content = (primary_dir / "residues.yaml").read_text() + assert "custom_residues" in residues_content + assert "package_residues" not in residues_content + + # Package files not in custom dir should be available + assert (primary_dir / "train.yaml").exists() + assert (primary_dir / "calibrator.yaml").exists() + + train_content = (primary_dir / "train.yaml").read_text() + assert "train_config" in train_content diff --git 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b5a820e..3fd17df 100644 --- a/winnow/calibration/calibration_features.py +++ b/winnow/calibration/calibration_features.py @@ -13,7 +13,6 @@ import koinapy from winnow.datasets.calibration_dataset import CalibrationDataset -from winnow.constants import INVALID_PROSIT_TOKENS def map_modification(peptide: List[str]) -> List[str]: @@ -197,18 +196,28 @@ def compute_ion_identifications( class PrositFeatures(CalibrationFeatures): """A class for extracting features related to Prosit: a machine learning-based intensity prediction tool for peptide fragmentation.""" - def __init__(self, mz_tolerance: float, learn_from_missing: bool = True) -> None: + def __init__( + self, + mz_tolerance: float, + invalid_prosit_tokens: List[str], + learn_from_missing: bool = True, + prosit_intensity_model_name: str = "Prosit_2020_intensity_HCD", + ) -> None: """Initialize PrositFeatures. Args: mz_tolerance (float): The mass-to-charge tolerance for ion matching. + invalid_prosit_tokens (List[str]): The tokens to consider as invalid for Prosit intensity prediction. learn_from_missing (bool): Whether to learn from missing data by including a missingness indicator column. If False, an error will be raised when invalid spectra are encountered. Defaults to True. + prosit_intensity_model_name (str): The name of the Prosit intensity model to use. + Defaults to "Prosit_2020_intensity_HCD". """ self.mz_tolerance = mz_tolerance + self.invalid_prosit_tokens = invalid_prosit_tokens self.learn_from_missing = learn_from_missing - self.prosit_intensity_model_name = "Prosit_2020_intensity_HCD" + self.prosit_intensity_model_name = prosit_intensity_model_name @property def dependencies(self) -> List[FeatureDependency]: @@ -266,7 +275,7 @@ def check_valid_prosit_prediction(self, dataset: CalibrationDataset) -> pd.Serie metadata_predicate=lambda row: ( any( token in row["prediction_untokenised"] - for token in INVALID_PROSIT_TOKENS + for token in self.invalid_prosit_tokens ) ) ) @@ -322,7 +331,7 @@ def compute(self, dataset: CalibrationDataset) -> None: f"Please filter your dataset to remove:\n" f" - Peptides longer than 30 amino acids\n" f" - Precursor charges greater than 6\n" - f" - Peptides with unsupported modifications (e.g., {', '.join(INVALID_PROSIT_TOKENS[:3])}...)\n" + f" - Peptides with unsupported modifications (e.g., {', '.join(self.invalid_prosit_tokens[:3])}...)\n" f"Or set learn_from_missing=True to handle missing data automatically." ) @@ -413,18 +422,28 @@ class ChimericFeatures(CalibrationFeatures): are stored in the dataset metadata. """ - def __init__(self, mz_tolerance: float, learn_from_missing: bool = True) -> None: + def __init__( + self, + mz_tolerance: float, + invalid_prosit_tokens: List[str], + learn_from_missing: bool = True, + prosit_intensity_model_name: str = "Prosit_2020_intensity_HCD", + ) -> None: """Initialize ChimericFeatures. Args: mz_tolerance (float): The mass-to-charge tolerance for ion matching. + invalid_prosit_tokens (List[str]): The tokens to consider as invalid for Prosit intensity prediction. learn_from_missing (bool): Whether to learn from missing data by including a missingness indicator column. If False, an error will be raised when invalid spectra are encountered. Defaults to True. + prosit_intensity_model_name (str): The name of the Prosit intensity model to use. + Defaults to "Prosit_2020_intensity_HCD". """ self.mz_tolerance = mz_tolerance self.learn_from_missing = learn_from_missing - self.prosit_intensity_model_name = "Prosit_2020_intensity_HCD" + self.invalid_prosit_tokens = invalid_prosit_tokens + self.prosit_intensity_model_name = prosit_intensity_model_name @property def dependencies(self) -> List[FeatureDependency]: @@ -488,7 +507,7 @@ def check_valid_chimeric_prosit_prediction( len(beam) > 1 and any( token in "".join(beam[1].sequence) - for token in INVALID_PROSIT_TOKENS + for token in self.invalid_prosit_tokens ) ) ) @@ -552,7 +571,7 @@ def compute(self, dataset: CalibrationDataset) -> None: f" - Spectra without runner-up sequences (beam search required)\n" f" - Runner-up peptides longer than 30 amino acids\n" f" - Runner-up peptides with precursor charges greater than 6\n" - f" - Runner-up peptides with unsupported modifications (e.g., {', '.join(INVALID_PROSIT_TOKENS[:3])}...)\n" + f" - Runner-up peptides with unsupported modifications (e.g., {', '.join(self.invalid_prosit_tokens[:3])}...)\n" f"Or set learn_from_missing=True to handle missing data automatically." ) @@ -834,23 +853,50 @@ class RetentionTimeFeature(CalibrationFeatures): irt_predictor: MLPRegressor def __init__( - self, hidden_dim: int, train_fraction: float, learn_from_missing: bool = True + self, + hidden_dim: int, + train_fraction: float, + invalid_prosit_tokens: List[str], + learn_from_missing: bool = True, + seed: int = 42, + learning_rate_init: float = 0.001, + alpha: float = 0.0001, + max_iter: int = 200, + early_stopping: bool = False, + validation_fraction: float = 0.1, + prosit_irt_model_name: str = "Prosit_2019_irt", ) -> None: """Initialize RetentionTimeFeature. Args: hidden_dim (int): Hidden dimension size for the MLP regressor. train_fraction (float): Fraction of data to use for training the iRT calibrator. + invalid_prosit_tokens (List[str]): The tokens to consider as invalid for Prosit iRT prediction. learn_from_missing (bool): Whether to learn from missing data by including a missingness indicator column. If False, an error will be raised when invalid spectra are encountered. Defaults to True. + seed (int): Random seed for the regressor. Defaults to 42. + learning_rate_init (float): The initial learning rate. Defaults to 0.001. + alpha (float): L2 regularisation parameter. Defaults to 0.0001. + max_iter (int): Maximum number of training iterations. Defaults to 200. + early_stopping (bool): Whether to use early stopping to terminate training. Defaults to False. + validation_fraction (float): Proportion of training data to use for early stopping validation. Defaults to 0.1. + prosit_irt_model_name (str): The name of the Prosit iRT model to use. + Defaults to "Prosit_2019_irt". """ self.train_fraction = train_fraction self.hidden_dim = hidden_dim self.learn_from_missing = learn_from_missing - self.prosit_irt_model_name = "Prosit_2019_irt" + self.invalid_prosit_tokens = invalid_prosit_tokens + self.prosit_irt_model_name = prosit_irt_model_name self.irt_predictor = MLPRegressor( - hidden_layer_sizes=[hidden_dim], random_state=42 + hidden_layer_sizes=[hidden_dim], + random_state=seed, + learning_rate_init=learning_rate_init, + alpha=alpha, + max_iter=max_iter, + early_stopping=early_stopping, + validation_fraction=validation_fraction, ) @property @@ -906,7 +952,7 @@ def check_valid_irt_prediction(self, dataset: CalibrationDataset) -> pd.Series: metadata_predicate=lambda row: ( any( token in row["prediction_untokenised"] - for token in INVALID_PROSIT_TOKENS + for token in self.invalid_prosit_tokens ) ) ) @@ -1003,7 +1049,7 @@ def compute(self, dataset: CalibrationDataset) -> None: f" - Spectra without retention time data\n" f" - Peptides longer than 30 amino acids\n" f" - Precursor charges greater than 6\n" - f" - Peptides with unsupported modifications (e.g., {', '.join(INVALID_PROSIT_TOKENS[:3])}...)\n" + f" - Peptides with unsupported modifications (e.g., {', '.join(self.invalid_prosit_tokens[:3])}...)\n" f"Or set learn_from_missing=True to handle missing data automatically." ) diff --git a/winnow/calibration/calibrator.py b/winnow/calibration/calibrator.py index 1ea2d6a..b3b927d 100644 --- a/winnow/calibration/calibrator.py +++ b/winnow/calibration/calibrator.py @@ -8,6 +8,8 @@ from sklearn.preprocessing import StandardScaler from numpy.typing import NDArray from huggingface_hub import snapshot_download +from omegaconf import DictConfig + from winnow.calibration.calibration_features import ( CalibrationFeatures, FeatureDependency, @@ -21,21 +23,54 @@ class ProbabilityCalibrator: This class provides functionality to recalibrate predicted probabilities by fitting an MLP classifier using various features computed from a calibration dataset. """ - def __init__(self, seed: int = 42) -> None: + def __init__( + self, + seed: int = 42, + features: Optional[ + Union[List[CalibrationFeatures], Dict[str, CalibrationFeatures], DictConfig] + ] = None, + hidden_layer_sizes: Tuple[int, ...] = (50, 50), + learning_rate_init: float = 0.001, + alpha: float = 0.0001, + max_iter: int = 1000, + early_stopping: bool = True, + validation_fraction: float = 0.1, + ) -> None: + """Initialise the probability calibrator. + + Args: + seed (int): Random seed for the classifier. Defaults to 42. + features (Optional[Union[List[CalibrationFeatures], Dict[str, CalibrationFeatures], DictConfig]]): + Features to add to the calibrator. Can be a list or dict of CalibrationFeatures objects. + If None, no features are added. Defaults to None. + hidden_layer_sizes (Tuple[int, ...]): The number of neurons in each hidden layer. Defaults to (50, 50). + learning_rate_init (float): The initial learning rate. Defaults to 0.001. + alpha (float): L2 regularisation parameter. Defaults to 0.0001. + max_iter (int): Maximum number of training iterations. Defaults to 1000. + early_stopping (bool): Whether to use early stopping to terminate training. Defaults to True. + validation_fraction (float): Proportion of training data to use for early stopping validation. Defaults to 0.1. + """ self.feature_dict: Dict[str, CalibrationFeatures] = {} self.dependencies: Dict[str, FeatureDependency] = {} self.dependency_reference_counter: Dict[str, int] = {} self.classifier = MLPClassifier( random_state=seed, - hidden_layer_sizes=(50, 50), - learning_rate_init=0.001, - alpha=0.0001, - max_iter=1000, - early_stopping=True, - validation_fraction=0.1, + hidden_layer_sizes=hidden_layer_sizes, + learning_rate_init=learning_rate_init, + alpha=alpha, + max_iter=max_iter, + early_stopping=early_stopping, + validation_fraction=validation_fraction, ) self.scaler = StandardScaler() + # Add features if provided + if features is not None: + if isinstance(features, (dict, DictConfig)): + self.add_features(list(features.values())) + else: + self.add_features(list(features)) + @property def columns(self) -> List[str]: """Returns the list of column names corresponding to the features added to the calibrator. @@ -59,14 +94,18 @@ def features(self) -> List[str]: return list(self.feature_dict.keys()) @classmethod - def save(cls, calibrator: "ProbabilityCalibrator", dir_path: Path) -> None: + def save( + cls, calibrator: "ProbabilityCalibrator", dir_path: Union[Path, str] + ) -> None: """Save the calibrator to a file. Args: calibrator (ProbabilityCalibrator): The calibrator to save. dir_path (Path): The path to the directory where the calibrator checkpoint will be saved. """ - dir_path.mkdir(parents=True) + if isinstance(dir_path, str): + dir_path = Path(dir_path) + dir_path.mkdir(parents=True, exist_ok=True) pickle.dump(calibrator, open(dir_path / "calibrator.pkl", "wb")) @classmethod diff --git a/winnow/compat/__init__.py b/winnow/compat/__init__.py new file mode 100644 index 0000000..10ffa9e --- /dev/null +++ b/winnow/compat/__init__.py @@ -0,0 +1 @@ +"""Compatibility shims for external dependencies.""" diff --git a/winnow/compat/instanovo.py b/winnow/compat/instanovo.py new file mode 100644 index 0000000..16d7c21 --- /dev/null +++ b/winnow/compat/instanovo.py @@ -0,0 +1,33 @@ +"""Compatibility layer for InstaNovo imports across versions. + +This module provides a stable import interface for InstaNovo dependencies, +handling API changes between different InstaNovo versions. +""" + +import warnings + +# Try to import from new InstaNovo location first +try: + from instanovo.constants import ScoredSequence + + _INSTANOVO_NEW_API = True +except ImportError: + # Fall back to old InstaNovo location + try: + from instanovo.inference.interfaces import ScoredSequence + + _INSTANOVO_NEW_API = False + warnings.warn( + "You are using an older version of InstaNovo with deprecated import paths. " + "Please upgrade to InstaNovo >= 2.0.0 for the latest features and API. " + "Support for older InstaNovo versions will be removed in a future release.", + DeprecationWarning, + stacklevel=2, + ) + except ImportError as e: + raise ImportError( + "Failed to import ScoredSequence from InstaNovo. " + "Please ensure InstaNovo is installed: pip install instanovo>=1.1.4" + ) from e + +__all__ = ["ScoredSequence"] diff --git a/winnow/configs/calibrator.yaml b/winnow/configs/calibrator.yaml new file mode 100644 index 0000000..0816814 --- /dev/null +++ b/winnow/configs/calibrator.yaml @@ -0,0 +1,48 @@ +# --- Calibrator configuration --- + +calibrator: + _target_: winnow.calibration.calibrator.ProbabilityCalibrator + + seed: 42 + hidden_layer_sizes: [50, 50] # The number of neurons in each hidden layer of the MLP classifier. + learning_rate_init: 0.001 # The initial learning rate for the MLP classifier. + alpha: 0.0001 # L2 regularisation parameter for the MLP classifier. + max_iter: 1000 # Maximum number of training iterations for the MLP classifier. + early_stopping: true # Whether to use early stopping to terminate training. + validation_fraction: 0.1 # Proportion of training data to use for early stopping validation. + + features: + mass_error: + _target_: winnow.calibration.calibration_features.MassErrorFeature + residue_masses: ${residue_masses} # The residue masses to use for the mass error feature. + + prosit_features: + _target_: winnow.calibration.calibration_features.PrositFeatures + mz_tolerance: 0.02 + learn_from_missing: true # Whether to learn from missing Prosit features. If False, errors will be raised when invalid spectra are encountered. + invalid_prosit_tokens: ${invalid_prosit_tokens} # The tokens to consider as invalid for Prosit features. + prosit_intensity_model_name: Prosit_2020_intensity_HCD # The name of the Prosit intensity model to use. + + retention_time_feature: + _target_: winnow.calibration.calibration_features.RetentionTimeFeature + hidden_dim: 10 # The hidden dimension size for the MLP regressor used to predict iRT from observed retention times. + train_fraction: 0.1 # The fraction of the data to use for training the iRT predictor. + learn_from_missing: true # Whether to learn from missing retention time features. If False, errors will be raised when invalid spectra are encountered. + seed: 42 # Random seed for the MLP regressor. + learning_rate_init: 0.001 # The initial learning rate for the MLP regressor. + alpha: 0.0001 # L2 regularisation parameter for the MLP regressor. + max_iter: 200 # Maximum number of training iterations for the MLP regressor. + early_stopping: false # Whether to use early stopping for the MLP regressor. + validation_fraction: 0.1 # Proportion of training data to use for early stopping validation. + invalid_prosit_tokens: ${invalid_prosit_tokens} # The tokens to consider as invalid for Prosit iRT features. + prosit_irt_model_name: Prosit_2019_irt # The name of the Prosit iRT model to use. + + chimeric_features: + _target_: winnow.calibration.calibration_features.ChimericFeatures + mz_tolerance: 0.02 + learn_from_missing: true # Whether to learn from missing chimeric features. If False, errors will be raised when invalid spectra are encountered. + invalid_prosit_tokens: ${invalid_prosit_tokens} # The tokens to consider as invalid for Prosit chimeric intensity features. + prosit_intensity_model_name: Prosit_2020_intensity_HCD # The name of the Prosit intensity model to use. + + beam_features: + _target_: winnow.calibration.calibration_features.BeamFeatures diff --git a/winnow/configs/data_loader/instanovo.yaml b/winnow/configs/data_loader/instanovo.yaml new file mode 100644 index 0000000..caf142e --- /dev/null +++ b/winnow/configs/data_loader/instanovo.yaml @@ -0,0 +1,23 @@ +# --- InstaNovo data loading configuration --- + +_target_: winnow.datasets.data_loaders.InstaNovoDatasetLoader + +residue_masses: ${residue_masses} +residue_remapping: # Used to map InstaNovo legacy notations to UNIMOD tokens. + "M(ox)": "M[UNIMOD:35]" # Oxidation + "M(+15.99)": "M[UNIMOD:35]" # Oxidation + "S(p)": "S[UNIMOD:21]" # Phosphorylation + "T(p)": "T[UNIMOD:21]" # Phosphorylation + "Y(p)": "Y[UNIMOD:21]" # Phosphorylation + "S(+79.97)": "S[UNIMOD:21]" # Phosphorylation + "T(+79.97)": "T[UNIMOD:21]" # Phosphorylation + "Y(+79.97)": "Y[UNIMOD:21]" # Phosphorylation + "Q(+0.98)": "Q[UNIMOD:7]" # Deamidation + "N(+0.98)": "N[UNIMOD:7]" # Deamidation + "Q(+.98)": "Q[UNIMOD:7]" # Deamidation + "N(+.98)": "N[UNIMOD:7]" # Deamidation + "C(+57.02)": "C[UNIMOD:4]" # Carbamidomethylation + # N-terminal modifications. + "(+42.01)": "[UNIMOD:1]" # Acetylation + "(+43.01)": "[UNIMOD:5]" # Carbamylation + "(-17.03)": "[UNIMOD:385]" # Ammonia loss diff --git a/winnow/configs/data_loader/mztab.yaml b/winnow/configs/data_loader/mztab.yaml new file mode 100644 index 0000000..dc184ea --- /dev/null +++ b/winnow/configs/data_loader/mztab.yaml @@ -0,0 +1,20 @@ +# --- MZTab data loading configuration --- +_target_: winnow.datasets.data_loaders.MZTabDatasetLoader + +residue_masses: ${residue_masses} +residue_remapping: # Used to map Casanovo-specific notations to UNIMOD tokens. + "M+15.995": "M[UNIMOD:35]" # Oxidation + "Q+0.984": "Q[UNIMOD:7]" # Deamidation + "N+0.984": "N[UNIMOD:7]" # Deamidation + "+42.011": "[UNIMOD:1]" # Acetylation + "+43.006": "[UNIMOD:5]" # Carbamylation + "-17.027": "[UNIMOD:385]" # Ammonia loss + "C+57.021": "C[UNIMOD:4]" # Carbamidomethylation + "C[Carbamidomethyl]": "C[UNIMOD:4]" # Carbamidomethylation + "M[Oxidation]": "M[UNIMOD:35]" # Oxidation + "N[Deamidated]": "N[UNIMOD:7]" # Deamidation + "Q[Deamidated]": "Q[UNIMOD:7]" # Deamidation + # N-terminal modifications. + "[Acetyl]-": "[UNIMOD:1]" # Acetylation + "[Carbamyl]-": "[UNIMOD:5]" # Carbamylation + "[Ammonia-loss]-": "[UNIMOD:385]" # Ammonia loss diff --git a/winnow/configs/data_loader/pointnovo.yaml b/winnow/configs/data_loader/pointnovo.yaml new file mode 100644 index 0000000..022691a --- /dev/null +++ b/winnow/configs/data_loader/pointnovo.yaml @@ -0,0 +1,5 @@ +# --- PointNovo data loading configuration --- + +_target_: winnow.datasets.data_loaders.PointNovoDatasetLoader + +residue_masses: ${residue_masses} diff --git a/winnow/configs/data_loader/winnow.yaml b/winnow/configs/data_loader/winnow.yaml new file mode 100644 index 0000000..dbfb632 --- /dev/null +++ b/winnow/configs/data_loader/winnow.yaml @@ -0,0 +1,7 @@ +# --- Winnow data loading configuration --- + +_target_: winnow.datasets.data_loaders.WinnowDatasetLoader + +residue_masses: ${residue_masses} +# The internal Winnow dataset loader does not need a residue remapping +# since it uses the UNIMOD tokens directly. diff --git a/winnow/configs/fdr_method/database_grounded.yaml b/winnow/configs/fdr_method/database_grounded.yaml new file mode 100644 index 0000000..41a6cc3 --- /dev/null +++ b/winnow/configs/fdr_method/database_grounded.yaml @@ -0,0 +1,8 @@ +# --- Database-grounded FDR control configuration --- + +_target_: winnow.fdr.database_grounded.DatabaseGroundedFDRControl + +confidence_feature: ${fdr_control.confidence_column} # Name of the column with confidence scores to use for FDR estimation. +residue_masses: ${residue_masses} # The residue masses from global `residues` config +isotope_error_range: [0, 1] # The isotope error range for matching peptides +drop: 10 # The number of top predictions to drop for stability diff --git a/winnow/configs/fdr_method/nonparametric.yaml b/winnow/configs/fdr_method/nonparametric.yaml new file mode 100644 index 0000000..2d8c5a3 --- /dev/null +++ b/winnow/configs/fdr_method/nonparametric.yaml @@ -0,0 +1,3 @@ +# --- Non-parametric FDR control configuration --- + +_target_: winnow.fdr.nonparametric.NonParametricFDRControl diff --git a/winnow/configs/predict.yaml b/winnow/configs/predict.yaml new file mode 100644 index 0000000..fa53a6a --- /dev/null +++ b/winnow/configs/predict.yaml @@ -0,0 +1,38 @@ +# --- Predicting scores and applying FDR control --- +defaults: + - _self_ + - residues + - data_loader: instanovo # Options: instanovo, mztab, pointnovo, winnow + - fdr_method: nonparametric # Options: nonparametric, database_grounded + +# --- Pipeline Execution Configuration --- + +dataset: + # Dataset paths: + # Path to the spectrum data file or to folder containing saved internal Winnow dataset. + spectrum_path_or_directory: examples/example_data/spectra.ipc + # Path to the beam predictions file. + # Leave as `null` if data source is `winnow`, or loading will fail. + predictions_path: examples/example_data/predictions.csv + # NOTE: Make sure that the data loader type matches the data source type in this dataset section. + +calibrator: + # Model loading: + # Path to the local calibrator directory or the HuggingFace model identifier. + # If the path is a local directory path, it will be used directly. If it is a HuggingFace repository identifier, it will be downloaded from HuggingFace. + pretrained_model_name_or_path: InstaDeepAI/winnow-general-model + # Directory to cache the HuggingFace model. + cache_dir: null # can be set to `null` if using local model or for the default cache directory from HuggingFace. + +fdr_control: + # FDR settings: + # Target FDR threshold (e.g. 0.01 for 1%, 0.05 for 5% etc.). + fdr_threshold: 0.05 + # Name of the column with confidence scores to use for FDR estimation. + confidence_column: calibrated_confidence + +# Folder path to write the outputs to. +# This will create two CSV files in the output folder: +# - metadata.csv: Contains all metadata and feature columns from the input dataset. +# - preds_and_fdr_metrics.csv: Contains predictions and FDR metrics. +output_folder: results/predictions diff --git a/winnow/configs/residues.yaml b/winnow/configs/residues.yaml new file mode 100644 index 0000000..d76d4a3 --- /dev/null +++ b/winnow/configs/residues.yaml @@ -0,0 +1,64 @@ +# --- Residues configuration --- + +# This is Winnow's internal residue representation. +# We use this to calculate the mass error feature and during database-grounded FDR control. +# We also use this to initialise the residue set for the Metrics class. +residue_masses: + "G": 57.021464 + "A": 71.037114 + "S": 87.032028 + "P": 97.052764 + "V": 99.068414 + "T": 101.047670 + "C": 103.009185 + "L": 113.084064 + "I": 113.084064 + "N": 114.042927 + "D": 115.026943 + "Q": 128.058578 + "K": 128.094963 + "E": 129.042593 + "M": 131.040485 + "H": 137.058912 + "F": 147.068414 + "R": 156.101111 + "Y": 163.063329 + "W": 186.079313 + # Modifications + "M[UNIMOD:35]": 147.035400 # Oxidation + "C[UNIMOD:4]": 160.030649 # Carboxyamidomethylation + "N[UNIMOD:7]": 115.026943 # Deamidation + "Q[UNIMOD:7]": 129.042594 # Deamidation + "R[UNIMOD:7]": 157.085127 # Arginine citrullination + "P[UNIMOD:35]": 113.047679 # Proline hydroxylation + "S[UNIMOD:21]": 166.998028 # Phosphorylation + 79.966 + "T[UNIMOD:21]": 181.01367 # Phosphorylation + 79.966 + "Y[UNIMOD:21]": 243.029329 # Phosphorylation + 79.966 + "C[UNIMOD:312]": 222.013284 # Cysteinylation + "E[UNIMOD:27]": 111.032028 # Glu -> pyro-Glu + "Q[UNIMOD:28]": 111.032029 # Gln -> pyro-Gln + # Terminal modifications + "[UNIMOD:1]": 42.010565 # Acetylation + "[UNIMOD:5]": 43.005814 # Carbamylation + "[UNIMOD:385]": -17.026549 # NH3 loss + "(+25.98)": 25.980265 # Carbamylation & NH3 loss (legacy notation) + +# The tokens to consider as invalid for Prosit features. +# We also filter out non-carboxyamidomethylated Cysteine in a separate step. +invalid_prosit_tokens: + # InstaNovo + - "[UNIMOD:7]" + - "[UNIMOD:21]" + - "[UNIMOD:1]" + - "[UNIMOD:5]" + - "[UNIMOD:385]" + - "(+25.98)" # (legacy notation) + # Casanovo + - "+0.984" + - "+42.011" + - "+43.006" + - "-17.027" + - "[Ammonia-loss]-" + - "[Carbamyl]-" + - "[Acetyl]-" + - "[Deamidated]" diff --git a/winnow/configs/train.yaml b/winnow/configs/train.yaml new file mode 100644 index 0000000..839d3f8 --- /dev/null +++ b/winnow/configs/train.yaml @@ -0,0 +1,21 @@ +# --- Training a calibrator --- +defaults: + - _self_ + - residues + - calibrator + - data_loader: instanovo # Options: instanovo, mztab, pointnovo, winnow + +# --- Pipeline Execution Configuration --- + +dataset: + # Dataset paths: + # Path to the spectrum data file or to folder containing saved internal Winnow dataset. + spectrum_path_or_directory: examples/example_data/spectra.ipc + # Path to the beam predictions file. + # Leave as `null` if data source is `winnow`, or loading will fail. + predictions_path: examples/example_data/predictions.csv + # NOTE: Make sure that the data loader type matches the data source type in this dataset section. + +# Output paths: +model_output_dir: models/new_model +dataset_output_path: results/calibrated_dataset.csv diff --git a/winnow/constants.py b/winnow/constants.py deleted file mode 100644 index a4eeb31..0000000 --- a/winnow/constants.py +++ /dev/null @@ -1,103 +0,0 @@ -from instanovo.utils.residues import ResidueSet -from instanovo.utils.metrics import Metrics - -RESIDUE_MASSES: dict[str, float] = { - "G": 57.021464, - "A": 71.037114, - "S": 87.032028, - "P": 97.052764, - "V": 99.068414, - "T": 101.047670, - "C": 103.009185, - "L": 113.084064, - "I": 113.084064, - "N": 114.042927, - "D": 115.026943, - "Q": 128.058578, - "K": 128.094963, - "E": 129.042593, - "M": 131.040485, - "H": 137.058912, - "F": 147.068414, - "R": 156.101111, - "Y": 163.063329, - "W": 186.079313, - # Modifications - "M[UNIMOD:35]": 147.035400, # Oxidation - "N[UNIMOD:7]": 115.026943, # Deamidation - "Q[UNIMOD:7]": 129.042594, # Deamidation - "C[UNIMOD:4]": 160.030649, # Carboxyamidomethylation - "S[UNIMOD:21]": 166.998028, # Phosphorylation - "T[UNIMOD:21]": 181.01367, # Phosphorylation - "Y[UNIMOD:21]": 243.029329, # Phosphorylation - "[UNIMOD:385]": -17.026549, # Ammonia Loss - "[UNIMOD:5]": 43.005814, # Carbamylation - "[UNIMOD:1]": 42.010565, # Acetylation - "C[UNIMOD:312]": 222.013284, # Cysteinylation - "E[UNIMOD:27]": 111.032028, # Glu -> pyro-Glu - "Q[UNIMOD:28]": 111.032029, # Gln -> pyro-Gln - "(+25.98)": 25.980265, # Carbamylation & NH3 loss -} - -RESIDUE_REMAPPING: dict[str, str] = { - "M(ox)": "M[UNIMOD:35]", # Oxidation - "M(+15.99)": "M[UNIMOD:35]", - "S(p)": "S[UNIMOD:21]", # Phosphorylation - "T(p)": "T[UNIMOD:21]", - "Y(p)": "Y[UNIMOD:21]", - "S(+79.97)": "S[UNIMOD:21]", - "T(+79.97)": "T[UNIMOD:21]", - "Y(+79.97)": "Y[UNIMOD:21]", - "Q(+0.98)": "Q[UNIMOD:7]", # Deamidation - "N(+0.98)": "N[UNIMOD:7]", - "Q(+.98)": "Q[UNIMOD:7]", - "N(+.98)": "N[UNIMOD:7]", - "C(+57.02)": "C[UNIMOD:4]", # Carbamidomethylation - "(+42.01)": "[UNIMOD:1]", # Acetylation - "(+43.01)": "[UNIMOD:5]", # Carbamylation - "(-17.03)": "[UNIMOD:385]", # Loss of ammonia -} - -CASANOVO_RESIDUE_REMAPPING: dict[str, str] = { - "M+15.995": "M[UNIMOD:35]", # Oxidation - "Q+0.984": "Q[UNIMOD:7]", # Deamidation - "N+0.984": "N[UNIMOD:7]", # Deamidation - "+42.011": "[UNIMOD:1]", # Acetylation - "+43.006": "[UNIMOD:5]", # Carbamylation - "-17.027": "[UNIMOD:385]", # Loss of ammonia - "C+57.021": "C[UNIMOD:4]", # Carbamidomethylation - # "+43.006-17.027": "[UNIMOD:5][UNIMOD:385]", # Carbamylation and Loss of ammonia - "C[Carbamidomethyl]": "C[UNIMOD:4]", # Carbamidomethylation - "M[Oxidation]": "M[UNIMOD:35]", # Met oxidation: 131.040485 + 15.994915 - "N[Deamidated]": "N[UNIMOD:7]", # Asn deamidation: 114.042927 + 0.984016 - "Q[Deamidated]": "Q[UNIMOD:7]", # Gln deamidation: 128.058578 + 0.984016 - # N-terminal modifications. - "[Acetyl]-": "[UNIMOD:1]", # Acetylation - "[Carbamyl]-": "[UNIMOD:5]", # Carbamylation - "[Ammonia-loss]-": "[UNIMOD:385]", # Ammonia loss - # "[+25.980265]-": 25.980265 # Carbamylation and ammonia loss -} - -# Each C is also treated as Cysteine with carbamidomethylation in Prosit. -INVALID_PROSIT_TOKENS: list = [ - "(+25.98)", - "[UNIMOD:7]", - "[UNIMOD:21]", - "[UNIMOD:1]", - "[UNIMOD:5]", - "[UNIMOD:385]", - "+0.984", - "+42.011", - "+43.006", - "-17.027", - "[Ammonia-loss]-", - "[Carbamyl]-", - "[Acetyl]-", - "[Deamidated]", -] - - -residue_set = ResidueSet( - residue_masses=RESIDUE_MASSES, residue_remapping=RESIDUE_REMAPPING -) -metrics = Metrics(residue_set=residue_set, isotope_error_range=[0, 1]) diff --git a/winnow/datasets/calibration_dataset.py b/winnow/datasets/calibration_dataset.py index 118387a..a5a579c 100644 --- a/winnow/datasets/calibration_dataset.py +++ b/winnow/datasets/calibration_dataset.py @@ -9,13 +9,13 @@ from dataclasses import dataclass from pathlib import Path -from typing import Any, Callable, List, Optional, Tuple +from typing import Any, Callable, List, Optional, Tuple, Union import pickle import numpy as np import pandas as pd -from instanovo.inference.beam_search import ScoredSequence +from winnow.compat.instanovo import ScoredSequence @dataclass @@ -48,7 +48,7 @@ def save(self, data_dir: Path) -> None: data_dir (Path): Directory to save the dataset. This will contain `metadata.csv` and optionally, `predictions.pkl` for serialized beam search results. """ - data_dir.mkdir(parents=True) + data_dir.mkdir(parents=True, exist_ok=True) with (data_dir / "metadata.csv").open(mode="w") as metadata_file: output_metadata = self.metadata.copy(deep=True) if "sequence" in output_metadata.columns: @@ -127,20 +127,26 @@ def filter_entries( return CalibrationDataset(predictions=predictions, metadata=metadata) - def to_csv(self, path: Path) -> None: + def to_csv(self, path: Union[Path, str]) -> None: """Saves the dataset metadata to a CSV file. Args: path (str): Path to the output CSV file. """ + if isinstance(path, str): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) self.metadata.to_csv(path) - def to_parquet(self, path: str) -> None: + def to_parquet(self, path: Union[Path, str]) -> None: """Saves the dataset metadata to a parquet file. Args: path (str): Path to the output parquet file. """ + if isinstance(path, str): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) self.metadata.to_parquet(path) def _create_predicate_error_message( diff --git a/winnow/datasets/data_loaders.py b/winnow/datasets/data_loaders.py index ae2acca..37d3436 100644 --- a/winnow/datasets/data_loaders.py +++ b/winnow/datasets/data_loaders.py @@ -9,36 +9,61 @@ import re from pathlib import Path from typing import Any, List, Optional, Tuple - import numpy as np import pandas as pd import polars as pl import polars.selectors as cs from pyteomics import mztab +from instanovo.utils.residues import ResidueSet +from instanovo.utils.metrics import Metrics from winnow.datasets.interfaces import DatasetLoader from winnow.datasets.calibration_dataset import ( CalibrationDataset, ScoredSequence, ) -from winnow.constants import metrics, CASANOVO_RESIDUE_REMAPPING class InstaNovoDatasetLoader(DatasetLoader): """Loader for InstaNovo predictions in CSV format.""" + def __init__( + self, + residue_masses: dict[str, float], + residue_remapping: dict[str, str], + isotope_error_range: Tuple[int, int] = (0, 1), + ) -> None: + """Initialise the InstaNovoDatasetLoader. + + Args: + residue_masses: The mapping of residue masses to UNIMOD tokens. + residue_remapping: The mapping of residue notations to UNIMOD tokens. + isotope_error_range: The range of isotope errors to consider when matching peptides. + """ + self.metrics = Metrics( + residue_set=ResidueSet( + residue_masses=residue_masses, residue_remapping=residue_remapping + ), + isotope_error_range=isotope_error_range, + ) + @staticmethod def _load_beam_preds( - predictions_path: Path, + predictions_path: Path | str, ) -> Tuple[pl.DataFrame, pl.DataFrame]: """Loads a dataset from a CSV file and optionally filters it. Args: - predictions_path (Path): The path to the CSV file containing the predictions. + predictions_path (Path | str): The path to the CSV file containing the predictions. Returns: Tuple[pl.DataFrame, pl.DataFrame]: A tuple containing the predictions and beams dataframes. """ + predictions_path = Path(predictions_path) + if predictions_path.suffix != ".csv": + raise ValueError( + f"Unsupported file format for InstaNovo beam predictions: {predictions_path.suffix}. Supported format is .csv." + ) df = pl.read_csv(predictions_path) # Use polars column selectors to split dataframe beam_df = df.select( @@ -54,7 +79,103 @@ def _load_beam_preds( return preds_df, beam_df @staticmethod - def _process_beams(beam_df: pl.DataFrame) -> List[Optional[List[ScoredSequence]]]: + def _load_spectrum_data(spectrum_path: Path | str) -> Tuple[pl.DataFrame, bool]: + """Loads spectrum data from either a Parquet or IPC file. + + Args: + spectrum_path (Path | str): The path to the spectrum data file. + + Returns: + Tuple[pl.DataFrame, bool]: A tuple containing the spectrum data and a boolean indicating whether the dataset has ground truth labels. + """ + spectrum_path = Path(spectrum_path) + + if spectrum_path.suffix == ".parquet": + df = pl.read_parquet(spectrum_path) + elif spectrum_path.suffix == ".ipc": + df = pl.read_ipc(spectrum_path) + else: + raise ValueError( + f"Unsupported file format for spectrum data: {spectrum_path.suffix}. Supported formats are .parquet and .ipc." + ) + + if "sequence" in df.columns: + has_labels = True + else: + has_labels = False + + return df, has_labels + + @staticmethod + def _merge_spectrum_data( + beam_dataset: pd.DataFrame, spectrum_dataset: pd.DataFrame + ) -> pd.DataFrame: + """Merge the input and output data from the de novo sequencing model. + + Args: + beam_dataset (pd.DataFrame): The dataframe containing the beam predictions. + spectrum_dataset (pd.DataFrame): The dataframe containing the spectrum data. + + Returns: + pd.DataFrame: The merged dataframe. + """ + merged_df = pd.merge( + beam_dataset, + spectrum_dataset, + on=["spectrum_id"], + suffixes=("_from_beams", ""), + ) + merged_df = merged_df.drop( + columns=[ + col + "_from_beams" + for col in beam_dataset.columns + if col in spectrum_dataset.columns and col != "spectrum_id" + ], + axis=1, + ) + + if len(merged_df) != len(beam_dataset): + raise ValueError( + f"Merge conflict: Expected {len(beam_dataset)} rows, but got {len(merged_df)}." + ) + + return merged_df + + def load( + self, *, data_path: Path, predictions_path: Optional[Path] = None, **kwargs: Any + ) -> CalibrationDataset: + """Load a CalibrationDataset from InstaNovo CSV predictions. + + Args: + data_path: Path to the spectrum data file + predictions_path: Path to the IPC or parquet beam predictions file + **kwargs: Not used + + Returns: + CalibrationDataset: An instance of the CalibrationDataset class containing metadata and predictions. + + Raises: + ValueError: If predictions_path is None + """ + if predictions_path is None: + raise ValueError("predictions_path is required for InstaNovoDatasetLoader") + + beam_predictions_path = predictions_path + inputs, has_labels = self._load_spectrum_data(data_path) + inputs = self._process_spectrum_data(inputs, has_labels) + + predictions, beams = self._load_beam_preds(beam_predictions_path) + beams = self._process_beams(beams) + predictions = self._process_predictions(predictions.to_pandas(), has_labels) + + predictions = self._merge_spectrum_data(predictions, inputs) + predictions = self._evaluate_predictions(predictions, has_labels) + + return CalibrationDataset(metadata=predictions, predictions=beams) + + def _process_beams( + self, beam_df: pl.DataFrame + ) -> List[Optional[List[ScoredSequence]]]: """Processes beam predictions into scored sequences. Args: @@ -85,7 +206,7 @@ def convert_row_to_scored_sequences( if sequence and log_prob > float("-inf"): scored_sequences.append( ScoredSequence( - sequence=metrics._split_peptide(sequence), + sequence=self.metrics._split_peptide(sequence), mass_error=None, sequence_log_probability=log_prob, token_log_probabilities=token_log_prob, @@ -110,8 +231,35 @@ def convert_row_to_scored_sequences( for row in beam_df.iter_rows(named=True) ] - @staticmethod - def _process_predictions(dataset: pd.DataFrame, has_labels: bool) -> pd.DataFrame: + def _process_spectrum_data( + self, df: pl.DataFrame, has_labels: bool + ) -> pd.DataFrame: + """Processes the input data from the de novo sequencing model. + + Args: + df (pl.DataFrame): The dataframe containing the spectrum data. + has_labels (bool): Whether the dataset has ground truth labels. + + Returns: + pd.DataFrame: The processed dataframe. + """ + # Convert to pandas for downstream compatibility + df = df.to_pandas() + if has_labels: + df["sequence"] = ( + df["sequence"] + .apply( + lambda peptide: peptide.replace("L", "I") + if isinstance(peptide, str) + else peptide + ) + .apply(self.metrics._split_peptide) + ) + return df + + def _process_predictions( + self, dataset: pd.DataFrame, has_labels: bool + ) -> pd.DataFrame: """Processes the predictions obtained from saved beams. Args: @@ -144,7 +292,7 @@ def _process_predictions(dataset: pd.DataFrame, has_labels: bool) -> pd.DataFram else peptide ) dataset["sequence"] = dataset["sequence_untokenised"].apply( - metrics._split_peptide + self.metrics._split_peptide ) dataset["prediction"] = dataset["prediction"].apply( lambda peptide: [ @@ -161,96 +309,9 @@ def _process_predictions(dataset: pd.DataFrame, has_labels: bool) -> pd.DataFram return dataset - @staticmethod - def _load_spectrum_data(spectrum_path: Path | str) -> Tuple[pl.DataFrame, bool]: - """Loads spectrum data from either a Parquet or IPC file. - - Args: - spectrum_path (Path | str): The path to the spectrum data file. - - Returns: - Tuple[pl.DataFrame, bool]: A tuple containing the spectrum data and a boolean indicating whether the dataset has ground truth labels. - """ - spectrum_path = Path(spectrum_path) - - if spectrum_path.suffix == ".parquet": - df = pl.read_parquet(spectrum_path) - elif spectrum_path.suffix == ".ipc": - df = pl.read_ipc(spectrum_path) - else: - raise ValueError( - f"Unsupported file format: {spectrum_path.suffix}. Supported formats are .parquet and .ipc." - ) - - if "sequence" in df.columns: - has_labels = True - else: - has_labels = False - - return df, has_labels - - @staticmethod - def _process_spectrum_data(df: pl.DataFrame, has_labels: bool) -> pd.DataFrame: - """Processes the input data from the de novo sequencing model. - - Args: - df (pl.DataFrame): The dataframe containing the spectrum data. - has_labels (bool): Whether the dataset has ground truth labels. - - Returns: - pd.DataFrame: The processed dataframe. - """ - # Convert to pandas for downstream compatibility - df = df.to_pandas() - if has_labels: - df["sequence"] = ( - df["sequence"] - .apply( - lambda peptide: peptide.replace("L", "I") - if isinstance(peptide, str) - else peptide - ) - .apply(metrics._split_peptide) - ) - return df - - @staticmethod - def _merge_spectrum_data( - beam_dataset: pd.DataFrame, spectrum_dataset: pd.DataFrame + def _evaluate_predictions( + self, dataset: pd.DataFrame, has_labels: bool ) -> pd.DataFrame: - """Merge the input and output data from the de novo sequencing model. - - Args: - beam_dataset (pd.DataFrame): The dataframe containing the beam predictions. - spectrum_dataset (pd.DataFrame): The dataframe containing the spectrum data. - - Returns: - pd.DataFrame: The merged dataframe. - """ - merged_df = pd.merge( - beam_dataset, - spectrum_dataset, - on=["spectrum_id"], - suffixes=("_from_beams", ""), - ) - merged_df = merged_df.drop( - columns=[ - col + "_from_beams" - for col in beam_dataset.columns - if col in spectrum_dataset.columns and col != "spectrum_id" - ], - axis=1, - ) - - if len(merged_df) != len(beam_dataset): - raise ValueError( - f"Merge conflict: Expected {len(beam_dataset)} rows, but got {len(merged_df)}." - ) - - return merged_df - - @staticmethod - def _evaluate_predictions(dataset: pd.DataFrame, has_labels: bool) -> pd.DataFrame: """Evaluates predictions in a dataset by checking validity and accuracy. Args: @@ -269,7 +330,9 @@ def _evaluate_predictions(dataset: pd.DataFrame, has_labels: bool) -> pd.DataFra ) if has_labels: dataset["num_matches"] = dataset.apply( - lambda row: metrics._novor_match(row["sequence"], row["prediction"]) + lambda row: self.metrics._novor_match( + row["sequence"], row["prediction"] + ) if isinstance(row["sequence"], list) and isinstance(row["prediction"], list) else 0, @@ -286,38 +349,6 @@ def _evaluate_predictions(dataset: pd.DataFrame, has_labels: bool) -> pd.DataFra ) return dataset - def load( - self, *, data_path: Path, predictions_path: Optional[Path] = None, **kwargs: Any - ) -> CalibrationDataset: - """Load a CalibrationDataset from InstaNovo CSV predictions. - - Args: - data_path: Path to the spectrum data file - predictions_path: Path to the IPC or parquet beam predictions file - **kwargs: Not used - - Returns: - CalibrationDataset: An instance of the CalibrationDataset class containing metadata and predictions. - - Raises: - ValueError: If predictions_path is None - """ - if predictions_path is None: - raise ValueError("predictions_path is required for InstaNovoDatasetLoader") - - beam_predictions_path = predictions_path - inputs, has_labels = self._load_spectrum_data(data_path) - inputs = self._process_spectrum_data(inputs, has_labels) - - predictions, beams = self._load_beam_preds(beam_predictions_path) - beams = self._process_beams(beams) - predictions = self._process_predictions(predictions.to_pandas(), has_labels) - - predictions = self._merge_spectrum_data(predictions, inputs) - predictions = self._evaluate_predictions(predictions, has_labels) - - return CalibrationDataset(metadata=predictions, predictions=beams) - class MZTabDatasetLoader(DatasetLoader): """Loader for MZTab predictions from both traditional search engines and Casanovo outputs. @@ -344,36 +375,25 @@ class MZTabDatasetLoader(DatasetLoader): """ def __init__( - self, residue_remapping: dict[str, str] | None = None, *args: Any, **kwargs: Any + self, + residue_masses: dict[str, float], + residue_remapping: dict[str, str], + isotope_error_range: Tuple[int, int] = (0, 1), ) -> None: """Initialise the MZTabDatasetLoader. Args: - residue_remapping: Optional dictionary mapping modification strings to UNIMOD format. - If None, uses the default CASANOVO_RESIDUE_REMAPPING. - *args: Additional positional arguments for parent class - **kwargs: Additional keyword arguments for parent class + residue_masses: The mapping of residue masses to UNIMOD tokens. + residue_remapping: The mapping of residue notations to UNIMOD tokens. + isotope_error_range: The range of isotope errors to consider when matching peptides. """ - super().__init__(*args, **kwargs) - self.residue_remapping = ( - residue_remapping - if residue_remapping is not None - else CASANOVO_RESIDUE_REMAPPING + self.metrics = Metrics( + residue_set=ResidueSet( + residue_masses=residue_masses, residue_remapping=residue_remapping + ), + isotope_error_range=isotope_error_range, ) - @staticmethod - def _load_dataset(predictions_path: Path) -> pl.DataFrame: - """Load predictions from mzTab file. - - Args: - predictions_path: Path to mzTab file containing predictions - - Returns: - DataFrame containing predictions - """ - predictions = mztab.MzTab(str(predictions_path)).spectrum_match_table - return pl.DataFrame(predictions) - @staticmethod def _load_spectrum_data(spectrum_path: Path | str) -> Tuple[pl.DataFrame, bool]: """Load spectrum data from either a Parquet or IPC file. @@ -393,7 +413,7 @@ def _load_spectrum_data(spectrum_path: Path | str) -> Tuple[pl.DataFrame, bool]: df = pl.read_ipc(spectrum_path) else: raise ValueError( - f"Unsupported file format: {spectrum_path.suffix}. Supported formats are .parquet and .ipc." + f"Unsupported file format for spectrum data: {spectrum_path.suffix}. Supported formats are .parquet and .ipc." ) if "sequence" in df.columns: @@ -401,6 +421,24 @@ def _load_spectrum_data(spectrum_path: Path | str) -> Tuple[pl.DataFrame, bool]: return df, has_labels + @staticmethod + def _load_dataset(predictions_path: Path | str) -> pl.DataFrame: + """Load predictions from mzTab file. + + Args: + predictions_path: Path to mzTab file containing predictions + + Returns: + DataFrame containing predictions + """ + predictions_path = Path(predictions_path) + if predictions_path.suffix != ".mztab": + raise ValueError( + f"Unsupported file format for MZTab predictions: {predictions_path.suffix}. Supported format is .mztab." + ) + predictions = mztab.MzTab(str(predictions_path)).spectrum_match_table + return pl.DataFrame(predictions) + def load( self, *, data_path: Path, predictions_path: Optional[Path] = None, **kwargs: Any ) -> CalibrationDataset: @@ -528,7 +566,7 @@ def _tokenize( ).with_columns( # Split sequence string into list of amino acid tokens pl.col(tokenised_column) - .map_elements(metrics._split_peptide, return_dtype=pl.List(pl.Utf8)) + .map_elements(self.metrics._split_peptide, return_dtype=pl.List(pl.Utf8)) .alias(tokenised_column) ) @@ -569,7 +607,7 @@ def _create_beam_predictions( def _map_modifications(self, sequence: str) -> str: """Map modifications to UNIMOD.""" - for mod, unimod in self.residue_remapping.items(): + for mod, unimod in self.metrics.residue_remapping.items(): sequence = sequence.replace(mod, unimod) return sequence @@ -667,7 +705,9 @@ def _evaluate_predictions( # Count matching amino acids between prediction and ground truth pl.struct(["sequence", "prediction"]) .map_elements( - lambda row: metrics._novor_match(row["sequence"], row["prediction"]) + lambda row: self.metrics._novor_match( + row["sequence"], row["prediction"] + ) if isinstance(row["sequence"], list) and isinstance(row["prediction"], list) else 0, @@ -724,6 +764,26 @@ def load( class WinnowDatasetLoader(DatasetLoader): """Loader for previously saved CalibrationDataset instances.""" + def __init__( + self, + residue_masses: dict[str, float], + residue_remapping: dict[str, str], + isotope_error_range: Tuple[int, int] = (0, 1), + ) -> None: + """Initialise the WinnowDatasetLoader. + + Args: + residue_masses: The mapping of residue masses to UNIMOD tokens. + residue_remapping: The mapping of residue notations to UNIMOD tokens. + isotope_error_range: The range of isotope errors to consider when matching peptides. + """ + self.metrics = Metrics( + residue_set=ResidueSet( + residue_masses=residue_masses, residue_remapping=residue_remapping + ), + isotope_error_range=isotope_error_range, + ) + def load( self, *, data_path: Path, predictions_path: Optional[Path] = None, **kwargs: Any ) -> CalibrationDataset: @@ -740,34 +800,52 @@ def load( if predictions_path is not None: raise ValueError("predictions_path is not used for WinnowDatasetLoader") - with (data_path / "metadata.csv").open(mode="r") as metadata_file: - metadata = pd.read_csv(metadata_file) - if "sequence" in metadata.columns: - metadata["sequence"] = metadata["sequence"].apply( - metrics._split_peptide - ) - metadata["prediction"] = metadata["prediction"].apply( - metrics._split_peptide + metadata_csv_path = data_path / "metadata.csv" + if not metadata_csv_path.exists(): + raise FileNotFoundError( + f"Winnow dataset loader expects a CSV file containing metadata at {metadata_csv_path}. " + f"The specified directory {data_path} should contain a 'metadata.csv' file " + f"with PSM metadata from a previously saved Winnow dataset." ) - metadata["mz_array"] = metadata["mz_array"].apply( - lambda s: ast.literal_eval(s) - if "," in s - else ast.literal_eval( - re.sub(r"(\n?)(\s+)", ", ", re.sub(r"\[\s+", "[", s)) - ) - ) - metadata["intensity_array"] = metadata["intensity_array"].apply( - lambda s: ast.literal_eval(s) - if "," in s - else ast.literal_eval( - re.sub(r"(\n?)(\s+)", ", ", re.sub(r"\[\s+", "[", s)) - ) + + try: + with metadata_csv_path.open(mode="r") as metadata_file: + metadata = pd.read_csv(metadata_file) + except Exception as e: + raise ValueError( + f"Failed to read metadata.csv from Winnow dataset directory {data_path}. " + f"The file should be a valid CSV containing PSM metadata. Error: {e}" + ) from e + + if "sequence" in metadata.columns: + metadata["sequence"] = metadata["sequence"].apply( + self.metrics._split_peptide ) + metadata["prediction"] = metadata["prediction"].apply( + self.metrics._split_peptide + ) + metadata["mz_array"] = metadata["mz_array"].apply( + lambda s: ast.literal_eval(s) + if "," in s + else ast.literal_eval(re.sub(r"(\n?)(\s+)", ", ", re.sub(r"\[\s+", "[", s))) + ) + metadata["intensity_array"] = metadata["intensity_array"].apply( + lambda s: ast.literal_eval(s) + if "," in s + else ast.literal_eval(re.sub(r"(\n?)(\s+)", ", ", re.sub(r"\[\s+", "[", s))) + ) - predictions_path = data_path / "predictions.pkl" - if predictions_path.exists(): - with predictions_path.open(mode="rb") as predictions_file: - predictions = pickle.load(predictions_file) + predictions_pkl_path = data_path / "predictions.pkl" + if predictions_pkl_path.exists(): + try: + with predictions_pkl_path.open(mode="rb") as predictions_file: + predictions = pickle.load(predictions_file) + except Exception as e: + raise ValueError( + f"Failed to load predictions.pkl from Winnow dataset directory {data_path}. " + f"The file should be a pickled beam predictions object from a previously saved Winnow dataset. " + f"Error: {e}" + ) from e else: predictions = None return CalibrationDataset(metadata=metadata, predictions=predictions) diff --git a/winnow/datasets/interfaces.py b/winnow/datasets/interfaces.py index 91260be..7abfd7a 100644 --- a/winnow/datasets/interfaces.py +++ b/winnow/datasets/interfaces.py @@ -3,7 +3,7 @@ This module provides abstract interfaces that define the contract for dataset loaders. """ -from typing import Protocol, Optional +from typing import Protocol, Optional, Tuple from pathlib import Path from winnow.datasets.calibration_dataset import CalibrationDataset @@ -14,6 +14,21 @@ class DatasetLoader(Protocol): Any class implementing this protocol must provide a load method that returns a CalibrationDataset. """ + def __init__( + self, + residue_masses: dict[str, float], + residue_remapping: dict[str, str] | None = None, + isotope_error_range: Tuple[int, int] = (0, 1), + ) -> None: + """Initialise the DatasetLoader. + + Args: + residue_masses: The mapping of residue masses to UNIMOD tokens. + residue_remapping: Optional mapping of residue notations to UNIMOD tokens. Defaults to None. + isotope_error_range: The range of isotope errors to consider when matching peptides. Defaults to (0, 1). + """ + ... + def load( self, *, data_path: Path, predictions_path: Optional[Path] = None, **kwargs ) -> CalibrationDataset: diff --git a/winnow/fdr/database_grounded.py b/winnow/fdr/database_grounded.py index 141405c..90ba323 100644 --- a/winnow/fdr/database_grounded.py +++ b/winnow/fdr/database_grounded.py @@ -2,9 +2,9 @@ import pandas as pd import numpy as np from instanovo.utils.metrics import Metrics +from instanovo.utils.residues import ResidueSet from winnow.fdr.base import FDRControl -from winnow.constants import residue_set class DatabaseGroundedFDRControl(FDRControl): @@ -13,16 +13,27 @@ class DatabaseGroundedFDRControl(FDRControl): This method estimates FDR thresholds by comparing model-predicted peptides to ground-truth peptides from a database. """ - def __init__(self, confidence_feature: str) -> None: + def __init__( + self, + confidence_feature: str, + residue_masses: dict[str, float], + isotope_error_range: Tuple[int, int] = (0, 1), + drop: int = 10, + ) -> None: super().__init__() self.confidence_feature = confidence_feature + self.residue_masses = residue_masses + self.isotope_error_range = isotope_error_range + self.drop = drop + + self.metrics = Metrics( + residue_set=ResidueSet(residue_masses=residue_masses), + isotope_error_range=isotope_error_range, + ) def fit( # type: ignore self, dataset: pd.DataFrame, - residue_masses: dict[str, float], - isotope_error_range: Tuple[int, int] = (0, 1), - drop: int = 10, ) -> None: """Computes the precision-recall curve by comparing model predictions to database-grounded peptide sequences. @@ -32,25 +43,14 @@ def fit( # type: ignore - 'peptide': Ground-truth peptide sequences. - 'prediction': Model-predicted peptide sequences. - 'confidence': Confidence scores associated with predictions. - - residue_masses (dict[str, float]): A dictionary mapping amino acid residues to their respective masses. - - isotope_error_range (Tuple[int, int], optional): Range of isotope errors to consider when matching peptides. Defaults to (0, 1). - - drop (int): Number of top-scoring predictions to exclude when computing FDR thresholds. Defaults to 10. """ assert len(dataset) > 0, "Fit method requires non-empty data" - metrics = Metrics( - residue_set=residue_set, isotope_error_range=isotope_error_range - ) - - dataset["sequence"] = dataset["sequence"].apply(metrics._split_peptide) - # dataset["prediction"] = dataset["prediction"].apply(metrics._split_peptide) + dataset["sequence"] = dataset["sequence"].apply(self.metrics._split_peptide) dataset["num_matches"] = dataset.apply( lambda row: ( - metrics._novor_match(row["sequence"], row["prediction"]) + self.metrics._novor_match(row["sequence"], row["prediction"]) if isinstance(row["prediction"], list) else 0 ), @@ -70,5 +70,5 @@ def fit( # type: ignore precision = np.cumsum(dataset["correct"]) / np.arange(1, len(dataset) + 1) confidence = np.array(dataset[self.confidence_feature]) - self._fdr_values = np.array(1 - precision[drop:]) - self._confidence_scores = confidence[drop:] + self._fdr_values = np.array(1 - precision[self.drop :]) + self._confidence_scores = confidence[self.drop :] diff --git a/winnow/scripts/config_formatter.py b/winnow/scripts/config_formatter.py new file mode 100644 index 0000000..1bb88e4 --- /dev/null +++ b/winnow/scripts/config_formatter.py @@ -0,0 +1,160 @@ +"""Configuration output formatter with hierarchical colour-coding.""" + +from rich.console import Console +from rich.text import Text +from omegaconf import DictConfig, OmegaConf + + +class ConfigFormatter: + """Format Hydra configuration with hierarchical colour-coding based on nesting depth. + + Keys are coloured according to their indentation level to help visualise + the configuration structure. + """ + + # Colour palette for different indentation levels (similar to Typer's style) + INDENT_COLOURS = [ + "bright_cyan", # Level 0 (root keys) + "bright_green", # Level 1 + "bright_yellow", # Level 2 + "bright_magenta", # Level 3 + "bright_blue", # Level 4 + "cyan", # Level 5 + "green", # Level 6 + "yellow", # Level 7+ + ] + + def __init__(self): + """Initialise the formatter.""" + self.console = Console() + + def print_config(self, cfg: DictConfig) -> None: + """Print configuration with hierarchical colour-coding. + + Args: + cfg: OmegaConf configuration object to format and print + """ + yaml_str = OmegaConf.to_yaml(cfg) + output = Text() + + for line in yaml_str.split("\n"): + formatted_line = self._format_line(line) + output.append(formatted_line) + + self.console.print(output, end="") + + def _format_line(self, line: str) -> Text: + """Format a single line of YAML with appropriate colouring. + + Args: + line: A single line from the YAML output + + Returns: + Rich Text object with formatted content + """ + output = Text() + + # Handle empty lines + if not line.strip(): + output.append("\n") + return output + + indent_level = self._get_indent_level(line) + colour = self._get_colour_for_level(indent_level) + + # Handle list items specially (they contain '- ' prefix) + if self._is_list_item(line): + output.append(line) + output.append("\n") + return output + + # Handle key-value pairs + separator_idx = self._find_key_value_separator(line) + if separator_idx != -1: + self._append_key_value_pair(output, line, separator_idx, colour) + else: + # Lines without key-value separator + output.append(line) + output.append("\n") + + return output + + def _get_indent_level(self, line: str) -> int: + """Calculate the indentation level of a line. + + Args: + line: Line to analyse + + Returns: + Indentation level (0 for root, 1 for first nested level, etc.) + """ + return (len(line) - len(line.lstrip())) // 2 + + def _get_colour_for_level(self, indent_level: int) -> str: + """Get the colour for a given indentation level. + + Args: + indent_level: The indentation level + + Returns: + Colour name for Rich + """ + return self.INDENT_COLOURS[min(indent_level, len(self.INDENT_COLOURS) - 1)] + + def _is_list_item(self, line: str) -> bool: + """Check if a line is a YAML list item. + + Args: + line: Line to check + + Returns: + True if line is a list item (starts with '- ') + """ + return line.lstrip().startswith("- ") + + def _find_key_value_separator(self, line: str) -> int: + """Find the position of the YAML key-value separator. + + This finds colons that are followed by a space or end of line, + avoiding colons inside keys like M[UNIMOD:35]. + + Args: + line: Line to search + + Returns: + Index of the separator colon, or -1 if not found + """ + for i, char in enumerate(line): + if char == ":": + # Check if this is followed by space, end of line, or is the last char + if i + 1 >= len(line) or line[i + 1] == " ": + return i + return -1 + + def _append_key_value_pair( + self, output: Text, line: str, separator_idx: int, colour: str + ) -> None: + """Append a formatted key-value pair to the output. + + Args: + output: Text object to append to + line: Original line + separator_idx: Index of the separator colon + colour: Colour to use for the key + """ + key_part = line[:separator_idx] + value_part = line[separator_idx + 1 :] + indent = " " * (len(line) - len(line.lstrip())) + + # Add indentation + output.append(indent) + + # Add coloured key + output.append(key_part.lstrip(), style=f"bold {colour}") + output.append(":") + + # Add value without formatting (plain text) + if value_part: + output.append(value_part) + + output.append("\n") diff --git a/winnow/scripts/config_path_utils.py b/winnow/scripts/config_path_utils.py new file mode 100644 index 0000000..18b50b9 --- /dev/null +++ b/winnow/scripts/config_path_utils.py @@ -0,0 +1,190 @@ +"""Configuration path resolution utilities. + +This module provides robust path resolution for config directories that works +in both development (cloned repo) and package (installed) modes. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Optional, List +import logging +import shutil +import tempfile +import atexit + +logger = logging.getLogger(__name__) + +# Track temporary directories for cleanup +_temp_dirs: List[Path] = [] + + +def _cleanup_temp_dirs() -> None: + """Clean up temporary directories on exit.""" + for temp_dir in _temp_dirs: + if temp_dir.exists(): + shutil.rmtree(temp_dir, ignore_errors=True) + + +atexit.register(_cleanup_temp_dirs) + + +def get_config_dir() -> Path: + """Get the primary config directory (package or dev mode). + + Returns: + Path to the config directory. In package mode, returns the package + config directory. In dev mode, returns the repo root config directory. + + Raises: + FileNotFoundError: If config directory cannot be found in either mode. + """ + # Try package mode first (when installed) + try: + from importlib.resources import files + + config_path = files("winnow").joinpath("configs") + if config_path.is_dir(): + return Path(str(config_path)) + except (ModuleNotFoundError, TypeError, AttributeError): + pass + + # Fallback to dev mode (cloned repo) + # This file is in winnow/scripts/, so go up to repo root + script_dir = Path(__file__).parent + repo_root = script_dir.parent.parent + dev_configs = repo_root / "winnow" / "configs" + + if dev_configs.exists() and dev_configs.is_dir(): + return dev_configs + + # If neither works, try alternative dev location (configs at repo root) + alt_dev_configs = repo_root / "configs" + if alt_dev_configs.exists() and alt_dev_configs.is_dir(): + return alt_dev_configs + + raise FileNotFoundError( + f"Could not locate configs directory. Tried:\n" + f" - Package configs: winnow.configs\n" + f" - Dev configs: {dev_configs}\n" + f" - Alt dev configs: {alt_dev_configs}" + ) + + +def get_config_search_path(custom_config_dir: Optional[str] = None) -> List[Path]: + """Get ordered list of config directories for Hydra search path. + + The search path is ordered by priority (first directory has highest priority): + 1. Custom config directory (if provided) + 2. Package configs (when installed) + 3. Development configs (when running from cloned repo) + + Args: + custom_config_dir: Optional path to custom config directory. + If provided, this takes highest priority. + + Returns: + List of config directory paths in priority order (highest first). + All paths are absolute. + + Raises: + FileNotFoundError: If custom_config_dir is provided but doesn't exist. + ValueError: If custom_config_dir is provided but is not a directory. + """ + search_path: List[Path] = [] + + # 1. Custom config directory (highest priority) + if custom_config_dir: + custom_path = Path(custom_config_dir).resolve() + if not custom_path.exists(): + raise FileNotFoundError( + f"Custom config directory does not exist: {custom_config_dir}" + ) + if not custom_path.is_dir(): + raise ValueError( + f"Custom config path is not a directory: {custom_config_dir}" + ) + search_path.append(custom_path) + logger.info(f"Using custom config directory: {custom_path}") + + # 2. Package configs (fallback for files not in custom dir) + try: + package_config_dir = get_config_dir() + # Only add if it's different from custom dir (avoid duplicates) + if not search_path or package_config_dir.resolve() != search_path[0].resolve(): + search_path.append(package_config_dir.resolve()) + logger.debug(f"Added package config directory: {package_config_dir}") + except FileNotFoundError: + logger.warning("Package config directory not found, skipping") + + return search_path + + +def _merge_config_dirs(custom_dir: Path, package_dir: Path) -> Path: + """Create a merged config directory with custom configs overriding package configs. + + Creates a temporary directory containing: + - All files from custom_dir (highest priority) + - Files from package_dir that don't exist in custom_dir (fallback) + + This allows partial configs to work with Hydra's single-directory search. + + Args: + custom_dir: Custom config directory (highest priority). + package_dir: Package config directory (fallback). + + Returns: + Path to temporary merged config directory. + """ + temp_dir = Path(tempfile.mkdtemp(prefix="winnow_configs_")) + _temp_dirs.append(temp_dir) + + # First, copy all package configs (this provides fallback for missing files) + if package_dir.exists(): + for item in package_dir.rglob("*"): + if item.is_file(): + rel_path = item.relative_to(package_dir) + dest_path = temp_dir / rel_path + dest_path.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(item, dest_path) + + # Then, copy/override with custom configs (this takes precedence) + if custom_dir.exists(): + for item in custom_dir.rglob("*"): + if item.is_file(): + rel_path = item.relative_to(custom_dir) + dest_path = temp_dir / rel_path + dest_path.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(item, dest_path) + logger.debug(f"Merged custom config: {rel_path}") + + return temp_dir + + +def get_primary_config_dir(custom_config_dir: Optional[str] = None) -> Path: + """Get the primary config directory to use with Hydra. + + If custom_config_dir is provided, creates a merged directory containing + both custom and package configs (custom takes precedence). This allows + partial configs to work - users only need to include files they want to override. + + Otherwise returns package/dev config directory. + + Args: + custom_config_dir: Optional path to custom config directory. + + Returns: + Path to primary config directory (absolute). May be a temporary directory + if custom_config_dir is provided. + """ + if custom_config_dir: + custom_path = Path(custom_config_dir).resolve() + package_path = get_config_dir().resolve() + # Merge custom and package configs so partial configs work + merged_dir = _merge_config_dirs(custom_path, package_path) + logger.info( + f"Using merged config directory (custom: {custom_path}, " + f"package: {package_path}) -> {merged_dir}" + ) + return merged_dir + return get_config_dir().resolve() diff --git a/winnow/scripts/main.py b/winnow/scripts/main.py index 5ba18ed..ec57e01 100644 --- a/winnow/scripts/main.py +++ b/winnow/scripts/main.py @@ -1,90 +1,26 @@ -# -- Import -from winnow.calibration.calibration_features import ( - PrositFeatures, - MassErrorFeature, - RetentionTimeFeature, - ChimericFeatures, - BeamFeatures, -) -from winnow.calibration.calibrator import ProbabilityCalibrator -from winnow.datasets.calibration_dataset import CalibrationDataset -from winnow.datasets.data_loaders import ( - InstaNovoDatasetLoader, - MZTabDatasetLoader, - PointNovoDatasetLoader, - WinnowDatasetLoader, -) -from winnow.fdr.nonparametric import NonParametricFDRControl -from winnow.fdr.database_grounded import DatabaseGroundedFDRControl -from winnow.constants import RESIDUE_MASSES +"""CLI entry point for winnow. + +Note: This module uses lazy imports to minimise CLI startup time. +Heavy dependencies (PyTorch, InstaNovo, etc.) are imported only when +needed, significantly reducing --help and config command times. +""" -from dataclasses import dataclass -from enum import Enum +from __future__ import annotations + +from typing import Union, Tuple, Optional, List, TYPE_CHECKING, Annotated import typer -from typing_extensions import Annotated -from typing import Union, Optional import logging from rich.logging import RichHandler from pathlib import Path -import yaml -import pandas as pd - - -# --- Configuration --- -SEED = 42 -MZ_TOLERANCE = 0.02 -HIDDEN_DIM = 10 -TRAIN_FRACTION = 0.1 - - -class DataSource(Enum): - """Source of a dataset to be used for calibration.""" - - winnow = "winnow" - instanovo = "instanovo" - pointnovo = "pointnovo" - mztab = "mztab" - - -@dataclass -class WinnowDatasetConfig: - """Config for calibration datasets saved through `winnow`.""" - - data_dir: Path - - -@dataclass -class InstaNovoDatasetConfig: - """Config for calibration datasets generated by InstaNovo.""" - - beam_predictions_path: Path - spectrum_path: Path - - -@dataclass -class MZTabDatasetConfig: - """Config for calibration datasets saved in MZTab format.""" - - spectrum_path: Path - predictions_path: Path - - -@dataclass -class PointNovoDatasetConfig: - """Config for calibration datasets generated by PointNovo.""" - - mgf_path: Path - predictions_path: Path - -class FDRMethod(Enum): - """FDR estimation method.""" +# Lazy imports for heavy dependencies - only imported when actually needed +if TYPE_CHECKING: + import pandas as pd + from winnow.datasets.calibration_dataset import CalibrationDataset + from winnow.fdr.nonparametric import NonParametricFDRControl + from winnow.fdr.database_grounded import DatabaseGroundedFDRControl - database = "database-ground" - winnow = "winnow" - - -# --- Logging Setup --- +# Logging setup logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) # Prevent duplicate messages by disabling propagation and using only RichHandler @@ -92,68 +28,33 @@ class FDRMethod(Enum): if not logger.handlers: logger.addHandler(RichHandler()) + +# Typer CLI setup app = typer.Typer( name="winnow", - help=""" - Confidence calibration and FDR estimation for de novo peptide sequencing. - """, + help="""Confidence calibration and FDR estimation for de novo peptide sequencing.""", + rich_markup_mode="rich", ) +# Config command group +config_app = typer.Typer( + name="config", + help="Configuration utilities for inspecting resolved settings.", + rich_markup_mode="rich", +) +app.add_typer(config_app) -def load_dataset( - data_source: DataSource, dataset_config_path: Path -) -> CalibrationDataset: - """Load PSM dataset into a `CalibrationDataset` object. - - Args: - data_source (DataSource): The source of the dataset - dataset_config_path (Path): Path to a `.yaml` file containing arguments - for the load method for the data source. - Raises: - TypeError: If `data_source` is not one of the supported data sources +def print_config(cfg) -> None: + """Print configuration with hierarchical colour-coding based on nesting depth. - Returns: - CalibrationDataset: A calibration dataset + Args: + cfg: OmegaConf configuration object to print """ - logger.info(f"Loading dataset from {data_source}.") - with open(dataset_config_path) as dataset_config_file: - if data_source is DataSource.winnow: - winnow_dataset_config = WinnowDatasetConfig( - **yaml.safe_load(dataset_config_file) - ) - dataset = WinnowDatasetLoader().load( - data_path=Path(winnow_dataset_config.data_dir) - ) - elif data_source is DataSource.instanovo: - instanovo_dataset_config = InstaNovoDatasetConfig( - **yaml.safe_load(dataset_config_file) - ) - dataset = InstaNovoDatasetLoader().load( - data_path=Path(instanovo_dataset_config.spectrum_path), - predictions_path=Path(instanovo_dataset_config.beam_predictions_path), - ) - elif data_source is DataSource.mztab: - mztab_dataset_config = MZTabDatasetConfig( - **yaml.safe_load(dataset_config_file) - ) - dataset = MZTabDatasetLoader().load( - data_path=Path(mztab_dataset_config.spectrum_path), - predictions_path=Path(mztab_dataset_config.predictions_path), - ) - elif data_source is DataSource.pointnovo: - pointnovo_dataset_config = PointNovoDatasetConfig( - **yaml.safe_load(dataset_config_file) - ) - dataset = PointNovoDatasetLoader().load( - data_path=Path(pointnovo_dataset_config.mgf_path), - predictions_path=Path(pointnovo_dataset_config.predictions_path), - ) - else: - raise TypeError( - f"Data source was {data_source}. Only 'instanovo', 'mztab' and 'pointnovo' are supported." - ) - return dataset + from winnow.scripts.config_formatter import ConfigFormatter + + formatter = ConfigFormatter() + formatter.print_config(cfg) def filter_dataset(dataset: CalibrationDataset) -> CalibrationDataset: @@ -165,7 +66,6 @@ def filter_dataset(dataset: CalibrationDataset) -> CalibrationDataset: Returns: CalibrationDataset: The filtered dataset """ - logger.info("Filtering dataset.") filtered_dataset = ( dataset.filter_entries( # Filter out non-list predictions @@ -177,47 +77,6 @@ def filter_dataset(dataset: CalibrationDataset) -> CalibrationDataset: return filtered_dataset -def initialise_calibrator( - learn_prosit_missing: bool = True, - learn_chimeric_missing: bool = True, - learn_retention_missing: bool = True, -) -> ProbabilityCalibrator: - """Set up the probability calibrator with features. - - Args: - learn_prosit_missing: Whether to learn from missing Prosit features. If False, - errors will be raised when invalid spectra are encountered. - learn_chimeric_missing: Whether to learn from missing chimeric features. If False, - errors will be raised when invalid spectra are encountered. - learn_retention_missing: Whether to learn from missing retention time features. If False, - errors will be raised when invalid spectra are encountered. - - Returns: - ProbabilityCalibrator: Configured calibrator with specified features. - """ - calibrator = ProbabilityCalibrator(SEED) - calibrator.add_feature(MassErrorFeature(residue_masses=RESIDUE_MASSES)) - calibrator.add_feature( - PrositFeatures( - mz_tolerance=MZ_TOLERANCE, learn_from_missing=learn_prosit_missing - ) - ) - calibrator.add_feature( - RetentionTimeFeature( - hidden_dim=HIDDEN_DIM, - train_fraction=TRAIN_FRACTION, - learn_from_missing=learn_retention_missing, - ) - ) - calibrator.add_feature( - ChimericFeatures( - mz_tolerance=MZ_TOLERANCE, learn_from_missing=learn_chimeric_missing - ) - ) - calibrator.add_feature(BeamFeatures()) - return calibrator - - def apply_fdr_control( fdr_control: Union[NonParametricFDRControl, DatabaseGroundedFDRControl], dataset: CalibrationDataset, @@ -225,14 +84,14 @@ def apply_fdr_control( confidence_column: str, ) -> pd.DataFrame: """Apply FDR control to a dataset.""" + from winnow.fdr.nonparametric import NonParametricFDRControl + if isinstance(fdr_control, NonParametricFDRControl): fdr_control.fit(dataset=dataset.metadata[confidence_column]) dataset.metadata = fdr_control.add_psm_pep(dataset.metadata, confidence_column) else: - fdr_control.fit( - dataset=dataset.metadata[confidence_column], - residue_masses=RESIDUE_MASSES, - ) + fdr_control.fit(dataset=dataset.metadata[confidence_column]) + dataset.metadata = fdr_control.add_psm_fdr(dataset.metadata, confidence_column) dataset.metadata = fdr_control.add_psm_q_value(dataset.metadata, confidence_column) confidence_cutoff = fdr_control.get_confidence_cutoff(threshold=fdr_threshold) @@ -249,197 +108,352 @@ def check_if_labelled(dataset: CalibrationDataset) -> None: ) -@app.command(name="train", help="Fit a calibration model.") -def train( - data_source: Annotated[ - DataSource, typer.Option(help="The type of PSM dataset to be calibrated.") - ], - dataset_config_path: Annotated[ - Path, - typer.Option( - help="The path to the config with the specification of the calibration dataset." - ), - ], - model_output_dir: Annotated[ - Path, - typer.Option( - help="The path to the directory where the fitted model checkpoint will be saved." - ), - ], - dataset_output_path: Annotated[ - Path, typer.Option(help="The path to write the output to.") - ], - learn_prosit_missing: Annotated[ - bool, - typer.Option( - help="Whether to learn from missing Prosit features. If False, training will fail if any spectra have invalid Prosit predictions." - ), - ] = True, - learn_chimeric_missing: Annotated[ - bool, - typer.Option( - help="Whether to learn from missing chimeric features. If False, training will fail if any spectra have invalid predictions for chimeric feature computation." - ), - ] = True, - learn_retention_missing: Annotated[ - bool, - typer.Option( - help="Whether to learn from missing retention time features. If False, training will fail if any spectra have invalid retention time predictions." - ), - ] = True, -): - """Fit the calibration model. +def separate_metadata_and_predictions( + dataset_metadata: pd.DataFrame, + fdr_control: Union[NonParametricFDRControl, DatabaseGroundedFDRControl], + confidence_column: str, +) -> Tuple[pd.DataFrame, pd.DataFrame]: + """Separate out metadata from prediction and FDR metrics. Args: - data_source (Annotated[ DataSource, typer.Option, optional): The type of PSM dataset to be calibrated. - dataset_config_path (Annotated[ Path, typer.Option, optional): The path to the config with the specification of the calibration dataset. - model_output_dir (Annotated[Path, typer.Option, optional]): The path to the directory where the fitted model checkpoint will be saved. - dataset_output_path (Annotated[Path, typer.Option, optional): The path to write the output to. + dataset_metadata: The metadata dataframe to separate out prediction and FDR metrics from metadata and computed features. + fdr_control: The FDR control object used (to determine which columns were added). + confidence_column: The name of the confidence column. + + Returns: + Tuple[pd.DataFrame, pd.DataFrame]: A tuple containing the metadata dataframe and the prediction and FDR metrics dataframe. """ - # -- Load dataset - logger.info("Loading datasets.") - annotated_dataset = load_dataset( - data_source=data_source, - dataset_config_path=dataset_config_path, - ) + from winnow.fdr.nonparametric import NonParametricFDRControl + + # Separate out metadata from prediction and FDR metrics + preds_and_fdr_metrics_cols = [ + confidence_column, + "prediction", + "psm_fdr", + "psm_q_value", + ] + if "sequence" in dataset_metadata.columns: + preds_and_fdr_metrics_cols.append("sequence") + # NonParametricFDRControl adds psm_pep column + if isinstance(fdr_control, NonParametricFDRControl): + preds_and_fdr_metrics_cols.append("psm_pep") + dataset_preds_and_fdr_metrics = dataset_metadata[ + preds_and_fdr_metrics_cols + ["spectrum_id"] + ] + dataset_metadata = dataset_metadata.drop(columns=preds_and_fdr_metrics_cols) + return dataset_metadata, dataset_preds_and_fdr_metrics + + +def train_entry_point( + overrides: Optional[List[str]] = None, + execute: bool = True, + config_dir: Optional[str] = None, +) -> None: + """The main training pipeline entry point. + + Args: + overrides: Optional list of config overrides. + execute: If False, only print the configuration and return without executing the pipeline. + config_dir: Optional path to custom config directory. If provided, configs in this + directory take precedence over package configs. Files not in custom dir will use package defaults (file-by-file resolution). + """ + from hydra import initialize_config_dir, compose + from hydra.utils import instantiate + from winnow.scripts.config_path_utils import get_primary_config_dir + + # Get primary config directory (custom if provided, otherwise package/dev) + primary_config_dir = get_primary_config_dir(config_dir) + # Initialise Hydra with primary config directory + with initialize_config_dir( + config_dir=str(primary_config_dir), + version_base="1.3", + job_name="winnow_train", + ): + cfg = compose(config_name="train", overrides=overrides) + + if not execute: + print_config(cfg) + return + + from winnow.calibration.calibrator import ProbabilityCalibrator + + logger.info("Starting training pipeline.") + logger.info(f"Training configuration: {cfg}") + + # Load dataset - Hydra creates the DatasetLoader object + logger.info("Loading dataset.") + data_loader = instantiate(cfg.data_loader) + + # Extract dataset loading parameters and convert to dict for flexible kwargs + dataset_params = dict(cfg.dataset) + # Rename config keys to match the Protocol interface + dataset_params["data_path"] = dataset_params.pop("spectrum_path_or_directory") + dataset_params["predictions_path"] = dataset_params.pop("predictions_path", None) + + annotated_dataset = data_loader.load(**dataset_params) + + logger.info("Filtering dataset.") annotated_dataset = filter_dataset(annotated_dataset) - # Train - logger.info("Training calibrator.") - calibrator = initialise_calibrator( - learn_prosit_missing=learn_prosit_missing, - learn_chimeric_missing=learn_chimeric_missing, - learn_retention_missing=learn_retention_missing, - ) + # Instantiate the calibrator from the config + logger.info("Instantiating calibrator from config.") + calibrator = instantiate(cfg.calibrator) + + # Fit the calibrator to the dataset + logger.info("Fitting calibrator to dataset.") calibrator.fit(annotated_dataset) - # -- Write model checkpoints - logger.info(f"Saving model to {model_output_dir}") - ProbabilityCalibrator.save(calibrator, model_output_dir) + # Save the model + logger.info(f"Saving model to {cfg.model_output_dir}") + ProbabilityCalibrator.save(calibrator, cfg.model_output_dir) - # -- Write output - logger.info("Writing output.") - annotated_dataset.to_csv(dataset_output_path) - logger.info(f"Training dataset results saved: {dataset_output_path}") + # Save the training dataset results + logger.info(f"Saving training dataset results to {cfg.dataset_output_path}") + annotated_dataset.to_csv(cfg.dataset_output_path) + logger.info("Training pipeline completed successfully.") -@app.command( - name="predict", - help="Calibrate scores and optionally filter results to a target FDR.", -) -def predict( - data_source: Annotated[ - DataSource, typer.Option(help="The type of PSM dataset to be calibrated.") - ], - dataset_config_path: Annotated[ - Path, - typer.Option( - help="The path to the config with the specification of the calibration dataset." - ), - ], - method: Annotated[ - FDRMethod, typer.Option(help="Method to use for FDR estimation.") - ], - fdr_threshold: Annotated[ - float, - typer.Option( - help="The target FDR threshold (e.g. 0.01 for 1%, 0.05 for 5% etc.)" - ), - ], - confidence_column: Annotated[ - str, typer.Option(help="Name of the column with confidence scores.") - ], - output_folder: Annotated[ - Path, typer.Option(help="The folder path to write the outputs to.") - ], - huggingface_model_name: Annotated[ - str, - typer.Option( - help="HuggingFace model identifier. If neither this nor `--local-model-folder` are provided, loads default model from HuggingFace.", - ), - ] = "InstaDeepAI/winnow-general-model", - local_model_folder: Annotated[ - Optional[Path], - typer.Option( - help="Path to local calibrator directory. If neither this nor `--huggingface-model-name` are provided, loads default pretrained model from HuggingFace.", - ), - ] = None, -): - """Calibrate model scores, estimate FDR and filter for a threshold. + +def predict_entry_point( + overrides: Optional[List[str]] = None, + execute: bool = True, + config_dir: Optional[str] = None, +) -> None: + """The main prediction pipeline entry point. Args: - data_source (Annotated[ DataSource, typer.Option, optional): The type of PSM dataset to be calibrated. - dataset_config_path (Annotated[ Path, typer.Option, optional): The path to the config with the specification of the dataset. - method (Annotated[ FDRMethod, typer.Option, optional): Method to use for FDR estimation. - fdr_threshold (Annotated[ float, typer.Option, optional): The target FDR threshold (e.g. 0.01 for 1%, 0.05 for 5% etc.). - confidence_column (Annotated[ str, typer.Option, optional): Name of the column with confidence scores. - output_folder (Annotated[ Path, typer.Option, optional): The folder path to write the outputs to: `metadata.csv` and `preds_and_fdr_metrics.csv`. - huggingface_model_name (Annotated[str, typer.Option, optional): HuggingFace model identifier. - local_model_folder (Annotated[Path, typer.Option, optional): Path to local calibrator directory (e.g., Path("./my-model-directory")). - - Note that either `local_model_folder` or `huggingface-model-name` may be overwritten, but not both. - If neither `local_model_folder` nor `huggingface-model-name` are provided, the general model from HuggingFace will be loaded by default (i.e., `InstaDeepAI/winnow-general-model`). + overrides: Optional list of config overrides. + execute: If False, only print the configuration and return without executing the pipeline. + config_dir: Optional path to custom config directory. If provided, configs in this + directory take precedence over package configs. Files not in custom dir will use + package defaults (file-by-file resolution). """ - # -- Load dataset - logger.info("Loading datasets.") - dataset = load_dataset( - data_source=data_source, - dataset_config_path=dataset_config_path, - ) + from hydra import initialize_config_dir, compose + from hydra.utils import instantiate + from winnow.scripts.config_path_utils import get_primary_config_dir + + # Get primary config directory (custom if provided, otherwise package/dev) + primary_config_dir = get_primary_config_dir(config_dir) + + # Initialize Hydra with primary config directory + with initialize_config_dir( + config_dir=str(primary_config_dir), + version_base="1.3", + job_name="winnow_predict", + ): + cfg = compose(config_name="predict", overrides=overrides) + + if not execute: + print_config(cfg) + return + + from winnow.calibration.calibrator import ProbabilityCalibrator + from winnow.fdr.database_grounded import DatabaseGroundedFDRControl + + logger.info("Starting prediction pipeline.") + logger.info(f"Prediction configuration: {cfg}") + + # Load dataset - Hydra creates the DatasetLoader object + logger.info("Loading dataset.") + data_loader = instantiate(cfg.data_loader) + + # Extract dataset loading parameters and convert to dict for flexible kwargs + dataset_params = dict(cfg.dataset) + # Rename config keys to match the Protocol interface + dataset_params["data_path"] = dataset_params.pop("spectrum_path_or_directory") + dataset_params["predictions_path"] = dataset_params.pop("predictions_path", None) + + dataset = data_loader.load(**dataset_params) + logger.info("Filtering dataset.") dataset = filter_dataset(dataset) - # Predict - # If local_model_folder is an empty string, load the HuggingFace model - if local_model_folder is None: - logger.info(f"Loading HuggingFace model: {huggingface_model_name}") - calibrator = ProbabilityCalibrator.load(huggingface_model_name) - # Otherwise, load the model from the local folder path - else: - logger.info(f"Loading local model from: {local_model_folder}") - calibrator = ProbabilityCalibrator.load(local_model_folder) + # Load trained calibrator + logger.info("Loading trained calibrator.") + calibrator = ProbabilityCalibrator.load( + pretrained_model_name_or_path=cfg.calibrator.pretrained_model_name_or_path, + cache_dir=cfg.calibrator.cache_dir, + ) + # Calibrate scores logger.info("Calibrating scores.") calibrator.predict(dataset) - if method is FDRMethod.winnow: - logger.info("Applying FDR control.") - dataset_metadata = apply_fdr_control( - NonParametricFDRControl(), dataset, fdr_threshold, confidence_column - ) - elif method is FDRMethod.database: - logger.info("Applying FDR control.") + # Instantiate FDR control from config - Hydra handles which FDR method to use + logger.info("Instantiating FDR control from config.") + fdr_control = instantiate(cfg.fdr_method) + + # Check if dataset is labelled for database-grounded FDR + if isinstance(fdr_control, DatabaseGroundedFDRControl): check_if_labelled(dataset) - dataset_metadata = apply_fdr_control( - DatabaseGroundedFDRControl(confidence_feature=confidence_column), - dataset, - fdr_threshold, - confidence_column, - ) - # -- Write output - logger.info("Writing output.") - # Separate out metadata from prediction and FDR metrics - preds_and_fdr_metrics_cols = [ - confidence_column, - "prediction", - "psm_fdr", - "psm_q_value", - ] - if "sequence" in dataset_metadata.columns: - preds_and_fdr_metrics_cols.append("sequence") - if method is FDRMethod.winnow: - preds_and_fdr_metrics_cols.append("psm_pep") - dataset_preds_and_fdr_metrics = dataset_metadata[ - preds_and_fdr_metrics_cols + ["spectrum_id"] - ] - dataset_metadata = dataset_metadata.drop(columns=preds_and_fdr_metrics_cols) - # Write outputs - output_folder.mkdir(parents=True) - dataset_metadata.to_csv(output_folder / "metadata.csv") - dataset_preds_and_fdr_metrics.to_csv(output_folder / "preds_and_fdr_metrics.csv") - logger.info(f"Outputs saved: {output_folder}") + # Apply FDR control + logger.info(f"Applying {fdr_control.__class__.__name__} FDR control.") + dataset_metadata = apply_fdr_control( + fdr_control, + dataset, + cfg.fdr_control.fdr_threshold, + cfg.fdr_control.confidence_column, + ) + + # Write output + logger.info(f"Writing output to {cfg.output_folder}") + dataset_metadata, dataset_preds_and_fdr_metrics = separate_metadata_and_predictions( + dataset_metadata, fdr_control, cfg.fdr_control.confidence_column + ) + output_folder = Path(cfg.output_folder) + output_folder.mkdir(parents=True, exist_ok=True) + dataset_metadata.to_csv(output_folder.joinpath("metadata.csv")) + dataset_preds_and_fdr_metrics.to_csv( + output_folder.joinpath("preds_and_fdr_metrics.csv") + ) + + logger.info("Prediction pipeline completed successfully.") + + +@app.command( + name="train", + help=( + "Train a probability calibration model on annotated peptide sequencing data.\n\n" + "This command loads your dataset, trains calibration features, and saves the trained model.\n\n" + "[bold cyan]Quick start:[/bold cyan]\n" + " [dim]winnow train[/dim] # Uses default config from config/train.yaml\n\n" + "[bold cyan]Override parameters:[/bold cyan]\n" + " [dim]winnow train data_loader=mztab[/dim] # Use MZTab format instead of InstaNovo\n" + " [dim]winnow train model_output_dir=models/my_model[/dim] # Custom output location\n" + " [dim]winnow train calibrator.seed=42[/dim] # Set random seed\n\n" + "[bold cyan]Custom config directory:[/bold cyan]\n" + " [dim]winnow train --config-dir /path/to/configs[/dim] # Use custom config directory\n" + " [dim]winnow train -cp ./my_configs[/dim] # Short form (relative or absolute path)\n" + " See docs for advanced usage.\n\n" + "[bold cyan]Configuration files to customise:[/bold cyan]\n" + " • config/train.yaml - Main config (data paths, output locations)\n" + " • config/calibrator.yaml - Model architecture and features\n" + " • config/data_loader/ - Dataset format loaders\n" + " • config/residues.yaml - Amino acid masses and modifications" + ), + context_settings={"allow_extra_args": True, "ignore_unknown_options": True}, +) +def train( + ctx: typer.Context, + config_dir: Annotated[ + Optional[str], + typer.Option( + "--config-dir", + "-cp", + help="Path to custom config directory (relative or absolute). See documentation for advanced usage.", + ), + ] = None, +) -> None: + """Passes control directly to the Hydra training pipeline.""" + # Capture extra arguments as Hydra overrides (--config-dir already parsed out by Typer) + overrides = ctx.args if ctx.args else None + train_entry_point(overrides, config_dir=config_dir) + + +@app.command( + name="predict", + help=( + "Calibrate confidence scores and filter peptide predictions by false discovery rate (FDR).\n\n" + "This command loads your dataset, applies a trained calibrator to improve confidence scores, " + "estimates FDR using your chosen method, and outputs filtered predictions at your target FDR threshold.\n\n" + "[bold cyan]Quick start:[/bold cyan]\n" + " [dim]winnow predict[/dim] # Uses default config from config/predict.yaml\n\n" + "[bold cyan]Override parameters:[/bold cyan]\n" + " [dim]winnow predict data_loader=mztab[/dim] # Use MZTab format instead of InstaNovo\n" + " [dim]winnow predict fdr_method=database_grounded[/dim] # Use database-grounded FDR\n" + " [dim]winnow predict fdr_threshold=0.01[/dim] # Target 1% FDR instead of 5%\n" + " [dim]winnow predict output_folder=results/my_run[/dim] # Custom output location\n\n" + "[bold cyan]Custom config directory:[/bold cyan]\n" + " [dim]winnow predict --config-dir /path/to/configs[/dim] # Use custom config directory\n" + " [dim]winnow predict -cp ./my_configs[/dim] # Short form (relative or absolute path)\n" + " See docs for advanced usage.\n\n" + "[bold cyan]Configuration files to customise:[/bold cyan]\n" + " • config/predict.yaml - Main config (data paths, FDR settings, output)\n" + " • config/fdr_method/ - FDR methods (nonparametric, database_grounded)\n" + " • config/data_loader/ - Dataset format loaders\n" + " • config/residues.yaml - Amino acid masses and modifications" + ), + context_settings={"allow_extra_args": True, "ignore_unknown_options": True}, +) +def predict( + ctx: typer.Context, + config_dir: Annotated[ + Optional[str], + typer.Option( + "--config-dir", + "-cp", + help="Path to custom config directory (relative or absolute). See documentation for advanced usage.", + ), + ] = None, +) -> None: + """Passes control directly to the Hydra predict pipeline.""" + # Capture extra arguments as Hydra overrides (--config-dir already parsed out by Typer) + overrides = ctx.args if ctx.args else None + predict_entry_point(overrides, config_dir=config_dir) + + +@config_app.command( + name="train", + help=( + "Display the resolved training configuration without running the pipeline.\n\n" + "This is useful for inspecting the final configuration after all defaults " + "and overrides have been applied.\n\n" + "[bold cyan]Usage:[/bold cyan]\n" + " [dim]winnow config train[/dim] # Show default config\n" + " [dim]winnow config train data_loader=mztab[/dim] # Show config with overrides\n" + " [dim]winnow config train calibrator.seed=42[/dim] # Check override application\n" + " [dim]winnow config train --config-dir /path/to/configs[/dim] # Show config with custom directory\n" + " [dim]winnow config train -cp ./my_configs[/dim] # Short form (relative or absolute path)" + ), + context_settings={"allow_extra_args": True, "ignore_unknown_options": True}, +) +def config_train( + ctx: typer.Context, + config_dir: Annotated[ + Optional[str], + typer.Option( + "--config-dir", + "-cp", + help="Path to custom config directory (relative or absolute). See documentation for advanced usage.", + ), + ] = None, +) -> None: + """Display the resolved training configuration.""" + overrides = ctx.args if ctx.args else None + train_entry_point(overrides, execute=False, config_dir=config_dir) + + +@config_app.command( + name="predict", + help=( + "Display the resolved prediction configuration without running the pipeline.\n\n" + "This is useful for inspecting the final configuration after all defaults " + "and overrides have been applied.\n\n" + "[bold cyan]Usage:[/bold cyan]\n" + " [dim]winnow config predict[/dim] # Show default config\n" + " [dim]winnow config predict fdr_method=database_grounded[/dim] # Show config with overrides\n" + " [dim]winnow config predict fdr_control.fdr_threshold=0.01[/dim] # Check override application\n" + " [dim]winnow config predict --config-dir /path/to/configs[/dim] # Show config with custom directory\n" + " [dim]winnow config predict -cp ./my_configs[/dim] # Short form (relative or absolute path)" + ), + context_settings={"allow_extra_args": True, "ignore_unknown_options": True}, +) +def config_predict( + ctx: typer.Context, + config_dir: Annotated[ + Optional[str], + typer.Option( + "--config-dir", + "-cp", + help="Path to custom config directory (relative or absolute). See documentation for advanced usage.", + ), + ] = None, +) -> None: + """Display the resolved prediction configuration.""" + overrides = ctx.args if ctx.args else None + predict_entry_point(overrides, execute=False, config_dir=config_dir) if __name__ == "__main__":