BERNN-MSMS

Batch-effect-aware representation learning and classification with an estimator-style fit(...); predict(...) interface.

Install

pip install bernn

Basic usage

from bernn import TrainAEClassifierHoldout
from bernn.config.training_config import TrainingConfig

config = TrainingConfig(
    n_epochs=100,
    optimize_hyperparams=False,
    device="cpu",
)
model = TrainAEClassifierHoldout(config=config, log_metrics=False)

# Inductive fit: training data only.
model.fit(X_train, y_train, groups_train=batch_train)
y_pred = model.predict(X_new, groups_test=batch_new)

Read the complete BERNN usage guide for input shapes, inductive and fully transductive fitting, prediction, and hyperparameter guidance.

Important runtime contract:

• groups_train is mandatory.
• If X_valid or X_test is supplied, its matching batch vector is mandatory.
• BERNN hyperparameters are dataset-dependent. The examples are interface demonstrations, not universal performance-optimal defaults.

Important parameters

Focus on these first:

• optimize_hyperparams: enable/disable Ax optimization.
• n_trials: number of optimization trials.
• fixed_hyperparams: force values and remove them from search.
• n_repeats: number of holdout repeats.
• n_layers, layer1: classifier depth and width seed.
• dloss: domain loss mode.
• warmup, n_epochs: core training schedule.
• device: cpu/cuda target.
• scaler, bs: preprocessing and batch size.
• num_workers: PyTorch DataLoader subprocesses; defaults to 0.

Documentation

• Usage tutorial