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

Project links

Key dates

PyPI data
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  • Released:
Newer release available (1.0.6)

1 maintainer

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Credits

Author: Simon Pelletier

License

MIT License (MIT)

Requires

Python >=3.10

Provides Extra

minimal core-extended deep-learning experiment-tracking notebooks tools python311-plus tools-with-ax web web-dev external-tools typing r-integration dev-tools special ml-full analysis analysis-with-ax development modern-web ide-tools python313-ml-minimal python313-ml-stable python313-minimal-safe python313-safe full-no-ax full full-safe py311-plus py312-plus py313-plus

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