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RepEngineBase — Abstract Representation Engine

Module: autopeptideml.reps.engine

Overview

RepEngineBase is the abstract base class for all molecular representation engines in AutoPeptideML. It defines the standard interface for batch-level representation computation, serialisation, and property management. All concrete engines — fingerprints, language models, one-hot encoding — inherit from this class.

Subclasses must implement:

Attributes

Attribute Type Description
engine str Class-level identifier for the engine type (e.g. 'fp', 'lm', 'one-hot').
rep str Instance-level representation name passed at construction.
properties dict Deep copy of all instance attributes captured at __init__ time. Used for serialisation.

Constructor

RepEngineBase(rep: str, **args)
Parameter Type Description
rep str Representation identifier (e.g. 'ecfp', 'esm2-8m').
**args Any Additional keyword arguments added as instance attributes and captured in self.properties.

Public Methods

compute_reps

compute_reps(
    mols: List[str],
    verbose: Optional[bool] = False,
    batch_size: Optional[int] = 12
) -> Union[np.ndarray, List[np.ndarray]]

Computes representations for a list of molecules by calling _preprocess_batch then _rep_batch on successive batches.

Parameter Type Default Description
mols List[str] — Input molecules as SMILES strings or amino-acid sequences.
verbose bool False Show a tqdm progress bar over batches.
batch_size int 12 Number of molecules per batch.

Returns:

  • np.ndarray — stacked array of shape (n_mols, dim) when average_pooling is True or not set.
  • List[np.ndarray] — list of per-molecule arrays (variable length) when average_pooling=False.

dim

dim() -> int

Returns the dimensionality of the computed representation vector.

Raises: NotImplementedError — must be implemented by subclasses.

get_num_params

get_num_params() -> int

Returns the total number of learnable parameters in the engine. The base implementation returns 0; language model engines override this to return the actual parameter count.

save

save(filename: str)

Serialises self.properties to a YAML file at filename. This enables reloading the engine configuration later.

Parameter Type Description
filename str Destination path for the YAML file.

__str__

__str__() -> str

Returns a JSON string representation of self.properties.

Abstract Methods (must be implemented by subclasses)

_rep_batch

_rep_batch(batch: List[str]) -> np.ndarray

Compute and return representations for a single batch.

Raises: NotImplementedError

_preprocess_batch

_preprocess_batch(batch: List[str]) -> List[str]

Apply any necessary preprocessing to a batch before representation computation (e.g. tokenisation, canonical conversion).

Raises: NotImplementedError

Subclasses

Class Module Description
RepEngineFP autopeptideml.reps.fps Molecular fingerprints via RDKit (ECFP, FCFP, PepFuNN).
RepEngineSkfp autopeptideml.reps.fps 28 fingerprint families via scikit-fingerprints (MACCS, AtomPair, Avalon, PubChem, Mordred, …).
RepEngineLM autopeptideml.reps.lms Pre-trained language model embeddings (ESM2, ProtT5, MoLFormer, …).
RepEngineOnehot autopeptideml.reps.seq_based Fixed-length one-hot encoding for canonical amino acid sequences.

Design Notes

  • Batch processing is handled centrally by compute_reps; subclasses only need to implement single-batch logic in _rep_batch.
  • Pooling control: setting self.average_pooling = False before calling compute_reps causes it to return raw per-residue tensors rather than pooled vectors. This is useful for sequence-level models.
  • Python compatibility: batched from itertools is used (Python ≥ 3.12) with a fallback islice-based implementation for earlier versions.