Batch Prediction (batch_predict)

sparrow.predictors.batch_predict.batch_predict() runs a single ALBATROSS network over a whole set of sequences in batched forward passes. For large collections this is substantially faster than constructing one Protein per sequence and calling its predictor, and it returns identical values.

Note

Batch prediction covers the polymer-dimension networks only: rg, re, asphericity, scaling_exponent, prefactor, scaled_rg, scaled_re. For other predictors (disorder, DSSP, phosphorylation, …) use the per-protein Protein.predictor API (The Protein Object).

Quick start

from sparrow.predictors.batch_predict import batch_predict

sequences = {
    "p1": "MEEEKKKKSSSTTTDDD",
    "p2": "GRGRGGYGGRGGYGGSRGGYGG",
    "p3": "QQQQQQAASSSSTTTTQQQQQ",
}

results = batch_predict(sequences, network="rg", batch_size=64)
#   {"p1": ["MEEE...", 14.7], "p2": [...], "p3": [...]}

Inputs and outputs

  • Input – a dict (keys preserved in the output) or a list (mapped to integer keys 0..n). Values may be sequence strings or sparrow.protein.Protein objects.

  • Output (default) – a dict mapping each input key to [sequence, prediction].

  • Output (``return_seq2prediction=True``) – a dict mapping each unique sequence directly to its prediction.

  • Order – the returned dictionary preserves input order.

# sequence -> prediction
seq2rg = batch_predict(sequences, network="rg", return_seq2prediction=True)

# list input -> integer-keyed output
from_list = batch_predict(["MEEE...", "QQQQ..."], network="asphericity")

Key options

  • network – which network to run (see the list above).

  • batch_size – forward-pass batch size (default 32). Larger values are faster on GPU; results are independent of batch size.

  • version – network version (default 2).

  • gpuid – GPU index to use when CUDA is available (CPU otherwise).

  • batch_algorithm – "default" (recommended) selects "pad-n-pack" on modern PyTorch, which batches mixed-length sequences together via pack_padded_sequence; "size-collect" groups equal-length sequences instead. Both give identical values.

  • safe – for rg/re, automatically routes sequences shorter than the minimum reliable length to the corresponding scaled network. Leave this True (the default) unless you have a specific reason not to.

Performance notes

  • pad-n-pack (the default) is typically several times faster than size-collect on sequence sets with varied lengths, and equal when all lengths match.

  • Inference runs under torch.no_grad() in evaluation mode.

API Reference