ovwt

Trains one XGBoost binary classifier per variant vs. wild-type using CellProfiler morphological features. For each variant in the dataset, the tool subsets to that variant and the wild-type, trains a model on the training split, selects the best round using a validation split, and reports AUROC and accuracy on all three splits.

How it works

1. Read a feature file (Parquet or CSV) containing CellProfiler features and a label column.
2. Perform a single stratified 80/10/10 train/val/test split across all cells.
3. For each non-wild-type label found in the dataset:
   • Filter each split to rows belonging to that variant or the wild-type.
   • Optionally compute balanced sample weights to correct for class imbalance.
   • Train an XGBoost classifier with early stopping on the validation set.
   • Evaluate AUROC and accuracy on all three splits.
4. Write results.csv and models.pkl to the output directory.

Feature columns are identified automatically as any column whose name starts with an uppercase letter and contains an underscore — the standard CellProfiler naming convention (e.g. Intensity_MeanIntensity_DAPI, Texture_Variance_CY5_3_00).

Outputs

All outputs are written to out_dir.

File	Description
results.csv	One row per variant with columns variant, train_auroc, train_accuracy, val_auroc, val_accuracy, test_auroc, test_accuracy.
models.pkl	Python pickle file containing a dict mapping each variant label to its trained xgboost.Booster.
ovwt.log	Full log of the run, mirrored from stdout.

Installation

Requires Python 3.13+. Install with uv:

uv sync