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"""Feature pipeline construction utilities."""
from __future__ import annotations
from dataclasses import asdict, dataclass, field
from typing import Iterable, Sequence
import numpy as np
from sklearn.preprocessing import StandardScaler
from ..config import Config, DescriptorSettings, FeatureBackendSettings
from .descriptors import DescriptorConfig, DescriptorFeaturizer
from .plm import PLMEmbedder
@dataclass(slots=True)
class FeaturePipelineState:
backend_type: str
descriptor_featurizer: DescriptorFeaturizer | None
plm_scaler: StandardScaler | None
descriptor_config: DescriptorConfig | None
plm_model_name: str | None
plm_layer_pool: str | None
cache_dir: str | None
device: str
feature_names: list[str] = field(default_factory=list)
class FeaturePipeline:
"""Fit/transform feature matrices according to configuration."""
def __init__(
self,
*,
backend: FeatureBackendSettings,
descriptors: DescriptorSettings,
device: str,
cache_dir_override: str | None = None,
plm_model_override: str | None = None,
layer_pool_override: str | None = None,
) -> None:
self.backend = backend
self.descriptor_settings = descriptors
self.device = device
self.cache_dir_override = cache_dir_override
self.plm_model_override = plm_model_override
self.layer_pool_override = layer_pool_override
self._descriptor: DescriptorFeaturizer | None = None
self._plm: PLMEmbedder | None = None
self._plm_scaler: StandardScaler | None = None
self._feature_names: list[str] = []
def fit_transform(self, df, *, heavy_only: bool, batch_size: int = 8) -> np.ndarray: # noqa: ANN001
backend_type = self.backend.type if self.backend.type else "descriptors"
self._validate_heavy_support(backend_type, heavy_only)
sequences = _extract_sequences(df, heavy_only=heavy_only)
if backend_type == "descriptors":
self._descriptor = _build_descriptor_featurizer(self.descriptor_settings)
features = self._descriptor.fit_transform(sequences)
self._feature_names = list(self._descriptor.feature_names_ or [])
self._plm = None
self._plm_scaler = None
return features.astype(np.float32)
if backend_type == "plm":
self._descriptor = None
self._plm = _build_plm_embedder(
self.backend,
device=self.device,
cache_dir_override=self.cache_dir_override,
plm_model_override=self.plm_model_override,
layer_pool_override=self.layer_pool_override,
)
embeddings = self._plm.embed(sequences, batch_size=batch_size)
if self.backend.standardize:
self._plm_scaler = StandardScaler()
embeddings = self._plm_scaler.fit_transform(embeddings)
else:
self._plm_scaler = None
self._feature_names = [f"plm_{i}" for i in range(embeddings.shape[1])]
return embeddings.astype(np.float32)
if backend_type == "concat":
descriptor = _build_descriptor_featurizer(self.descriptor_settings)
desc_features = descriptor.fit_transform(sequences)
plm = _build_plm_embedder(
self.backend,
device=self.device,
cache_dir_override=self.cache_dir_override,
plm_model_override=self.plm_model_override,
layer_pool_override=self.layer_pool_override,
)
embeddings = plm.embed(sequences, batch_size=batch_size)
if self.backend.standardize:
plm_scaler = StandardScaler()
embeddings = plm_scaler.fit_transform(embeddings)
else:
plm_scaler = None
self._descriptor = descriptor
self._plm = plm
self._plm_scaler = plm_scaler
self._feature_names = list(descriptor.feature_names_ or []) + [
f"plm_{i}" for i in range(embeddings.shape[1])
]
return np.concatenate([desc_features, embeddings], axis=1).astype(np.float32)
msg = f"Unsupported feature backend: {backend_type}"
raise ValueError(msg)
def fit(self, df, *, heavy_only: bool, batch_size: int = 8) -> "FeaturePipeline": # noqa: ANN001
backend_type = self.backend.type if self.backend.type else "descriptors"
self._validate_heavy_support(backend_type, heavy_only)
sequences = _extract_sequences(df, heavy_only=heavy_only)
if backend_type == "descriptors":
self._descriptor = _build_descriptor_featurizer(self.descriptor_settings)
self._descriptor.fit(sequences)
self._feature_names = list(self._descriptor.feature_names_ or [])
self._plm = None
self._plm_scaler = None
elif backend_type == "plm":
self._descriptor = None
self._plm = _build_plm_embedder(
self.backend,
device=self.device,
cache_dir_override=self.cache_dir_override,
plm_model_override=self.plm_model_override,
layer_pool_override=self.layer_pool_override,
)
embeddings = self._plm.embed(sequences, batch_size=batch_size)
if self.backend.standardize:
self._plm_scaler = StandardScaler()
embeddings = self._plm_scaler.fit_transform(embeddings)
else:
self._plm_scaler = None
self._feature_names = [f"plm_{i}" for i in range(embeddings.shape[1])]
elif backend_type == "concat":
descriptor = _build_descriptor_featurizer(self.descriptor_settings)
desc_features = descriptor.fit_transform(sequences)
plm = _build_plm_embedder(
self.backend,
device=self.device,
cache_dir_override=self.cache_dir_override,
plm_model_override=self.plm_model_override,
layer_pool_override=self.layer_pool_override,
)
embeddings = plm.embed(sequences, batch_size=batch_size)
if self.backend.standardize:
plm_scaler = StandardScaler()
embeddings = plm_scaler.fit_transform(embeddings)
else:
plm_scaler = None
self._descriptor = descriptor
self._plm = plm
self._plm_scaler = plm_scaler
self._feature_names = list(descriptor.feature_names_ or []) + [
f"plm_{i}" for i in range(embeddings.shape[1])
]
else: # pragma: no cover - defensive branch
msg = f"Unsupported feature backend: {backend_type}"
raise ValueError(msg)
return self
def transform(self, df, *, heavy_only: bool, batch_size: int = 8) -> np.ndarray: # noqa: ANN001
backend_type = self.backend.type if self.backend.type else "descriptors"
self._validate_heavy_support(backend_type, heavy_only)
sequences = _extract_sequences(df, heavy_only=heavy_only)
if backend_type == "descriptors":
if self._descriptor is None:
msg = "Descriptor featurizer is not fitted"
raise RuntimeError(msg)
features = self._descriptor.transform(sequences)
elif backend_type == "plm":
if self._plm is None:
msg = "PLM embedder is not initialised"
raise RuntimeError(msg)
embeddings = self._plm.embed(sequences, batch_size=batch_size)
if self.backend.standardize and self._plm_scaler is not None:
embeddings = self._plm_scaler.transform(embeddings)
features = embeddings
elif backend_type == "concat":
if self._descriptor is None or self._plm is None:
msg = "Feature pipeline not fitted"
raise RuntimeError(msg)
desc_features = self._descriptor.transform(sequences)
embeddings = self._plm.embed(sequences, batch_size=batch_size)
if self.backend.standardize and self._plm_scaler is not None:
embeddings = self._plm_scaler.transform(embeddings)
features = np.concatenate([desc_features, embeddings], axis=1)
else: # pragma: no cover - defensive branch
msg = f"Unsupported feature backend: {backend_type}"
raise ValueError(msg)
return features.astype(np.float32)
@property
def feature_names(self) -> list[str]:
return self._feature_names
def get_state(self) -> FeaturePipelineState:
descriptor = self._descriptor
if descriptor is not None and descriptor.numberer is not None:
if hasattr(descriptor.numberer, "_runner"):
descriptor.numberer._runner = None # type: ignore[attr-defined]
return FeaturePipelineState(
backend_type=self.backend.type,
descriptor_featurizer=descriptor,
plm_scaler=self._plm_scaler,
descriptor_config=_build_descriptor_config(self.descriptor_settings),
plm_model_name=self._effective_plm_model_name,
plm_layer_pool=self._effective_layer_pool,
cache_dir=self._effective_cache_dir,
device=self.device,
feature_names=self._feature_names,
)
def load_state(self, state: FeaturePipelineState) -> None:
self.backend.type = state.backend_type
if state.plm_model_name:
self.backend.plm_model_name = state.plm_model_name
self.plm_model_override = state.plm_model_name
if state.plm_layer_pool:
self.backend.layer_pool = state.plm_layer_pool
self.layer_pool_override = state.plm_layer_pool
if state.cache_dir:
self.backend.cache_dir = state.cache_dir
self.cache_dir_override = state.cache_dir
if state.descriptor_config:
self.descriptor_settings = DescriptorSettings(
use_anarci=state.descriptor_config.use_anarci,
regions=tuple(state.descriptor_config.regions),
features=tuple(state.descriptor_config.features),
ph=state.descriptor_config.ph,
)
self._descriptor = state.descriptor_featurizer
self._plm_scaler = state.plm_scaler
self._feature_names = state.feature_names
if self.backend.type in {"plm", "concat"}:
self._plm = _build_plm_embedder(
self.backend,
device=self.device,
cache_dir_override=self.backend.cache_dir,
plm_model_override=self.backend.plm_model_name,
layer_pool_override=self.backend.layer_pool,
)
else:
self._plm = None
@property
def _effective_plm_model_name(self) -> str | None:
if self.backend.type not in {"plm", "concat"}:
return None
return self.plm_model_override or self.backend.plm_model_name
@property
def _effective_layer_pool(self) -> str | None:
if self.backend.type not in {"plm", "concat"}:
return None
return self.layer_pool_override or self.backend.layer_pool
@property
def _effective_cache_dir(self) -> str | None:
if self.backend.type not in {"plm", "concat"}:
return None
if self.cache_dir_override is not None:
return self.cache_dir_override
return self.backend.cache_dir
def _validate_heavy_support(self, backend_type: str, heavy_only: bool) -> None:
if heavy_only:
return
if backend_type == "descriptors" and self.descriptor_settings.use_anarci:
msg = "Descriptor backend with ANARCI currently supports heavy-chain only inference."
raise ValueError(msg)
if backend_type == "concat" and self.descriptor_settings.use_anarci:
msg = "Concat backend with descriptors requires heavy-chain only data."
raise ValueError(msg)
def build_feature_pipeline(
config: Config,
*,
backend_override: str | None = None,
plm_model_override: str | None = None,
cache_dir_override: str | None = None,
layer_pool_override: str | None = None,
) -> FeaturePipeline:
backend = FeatureBackendSettings(**asdict(config.feature_backend))
if backend_override:
backend.type = backend_override
pipeline = FeaturePipeline(
backend=backend,
descriptors=config.descriptors,
device=config.device,
cache_dir_override=cache_dir_override,
plm_model_override=plm_model_override,
layer_pool_override=layer_pool_override,
)
return pipeline
def _build_descriptor_featurizer(settings: DescriptorSettings) -> DescriptorFeaturizer:
descriptor_config = _build_descriptor_config(settings)
return DescriptorFeaturizer(config=descriptor_config, standardize=True)
def _build_descriptor_config(settings: DescriptorSettings) -> DescriptorConfig:
return DescriptorConfig(
use_anarci=settings.use_anarci,
regions=tuple(settings.regions),
features=tuple(settings.features),
ph=settings.ph,
)
def _build_plm_embedder(
backend: FeatureBackendSettings,
*,
device: str,
cache_dir_override: str | None,
plm_model_override: str | None,
layer_pool_override: str | None,
) -> PLMEmbedder:
model_name = plm_model_override or backend.plm_model_name
cache_dir = cache_dir_override or backend.cache_dir
layer_pool = layer_pool_override or backend.layer_pool
return PLMEmbedder(
model_name=model_name,
layer_pool=layer_pool,
device=device,
cache_dir=cache_dir,
)
def _extract_sequences(df, heavy_only: bool) -> Sequence[str]: # noqa: ANN001
if heavy_only or "light_seq" not in df.columns:
return df["heavy_seq"].fillna("").astype(str).tolist()
heavy = df["heavy_seq"].fillna("").astype(str)
light = df["light_seq"].fillna("").astype(str)
return (heavy + "|" + light).tolist()
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