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[WIP] Remove parent spec and see what happens #478

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40f2b9e
remove parent spec and see what happens
hmacdope Feb 16, 2026
252b79a
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Feb 16, 2026
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hmacdope Feb 16, 2026
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hmacdope Feb 16, 2026
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80 changes: 39 additions & 41 deletions openadmet/models/anvil/workflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@

from openadmet.models.drivers import DriverType
from openadmet.models.anvil.workflow_base import AnvilWorkflowBase
from openadmet.models.features.pairwise_featurizer import PairwiseFeaturizer


def _safe_to_numpy(X):
Expand Down Expand Up @@ -72,8 +73,8 @@ def check_no_finetuning(self):
# Ensemble specified
if self.ensemble:
# Fine-tuning paths specified
if (self.parent_spec.procedure.ensemble.param_paths is not None) or (
self.parent_spec.procedure.ensemble.serial_paths is not None
if (self.procedure.ensemble.param_paths is not None) or (
self.procedure.ensemble.serial_paths is not None
):
raise ValueError(
"Finetuning from serialized ensemble models is not supported in this workflow."
Expand All @@ -82,8 +83,8 @@ def check_no_finetuning(self):
# No ensemble
else:
# Fine-tuning paths supplied
if (self.parent_spec.procedure.model.param_path is not None) or (
self.parent_spec.procedure.model.serial_path is not None
if (self.procedure.model.param_path is not None) or (
self.procedure.model.serial_path is not None
):
raise ValueError(
"Finetuning from serialized model is not supported in this workflow."
Expand Down Expand Up @@ -117,7 +118,7 @@ def _train_ensemble(self, X_train_feat, y_train, output_dir, **kwargs):

# Bootstrap iterations
models = []
for i in range(self.parent_spec.procedure.ensemble.n_models):
for i in range(self.procedure.ensemble.n_models):
# Manage bootstrap directory
bootstrap_dir = output_dir / f"bootstrap_{i}"
bootstrap_dir.mkdir(parents=True, exist_ok=True)
Expand Down Expand Up @@ -216,17 +217,19 @@ def run(
data_dir.mkdir(parents=True, exist_ok=True)

# Write recipe to output directory
self.parent_spec.to_recipe(output_dir / "anvil_recipe.yaml")
if self.parent_spec is not None:
self.parent_spec.to_recipe(output_dir / "anvil_recipe.yaml")

# Split recipe into components and save
recipe_components = Path(output_dir / "recipe_components")
recipe_components.mkdir(parents=True, exist_ok=True)
self.parent_spec.to_multi_yaml(
metadata_yaml=recipe_components / "metadata.yaml",
procedure_yaml=recipe_components / "procedure.yaml",
data_yaml=recipe_components / "data.yaml",
report_yaml=recipe_components / "eval.yaml",
)
if self.parent_spec is not None:
self.parent_spec.to_multi_yaml(
metadata_yaml=recipe_components / "metadata.yaml",
procedure_yaml=recipe_components / "procedure.yaml",
data_yaml=recipe_components / "data.yaml",
report_yaml=recipe_components / "eval.yaml",
)

# Log output directory information
logger.info(f"Running workflow from directory {output_dir}")
Expand Down Expand Up @@ -322,7 +325,7 @@ def run(
self.model.calibrate_uncertainty(
X_val_feat,
y_val,
method=self.parent_spec.procedure.ensemble.calibration_method,
method=self.ensemble.calibration_method,
)

# Save
Expand Down Expand Up @@ -449,26 +452,21 @@ def _train(
self, train_dataloader, val_dataloader, train_scaler, output_dir, **kwargs
):
# Load model from disk
if (
self.parent_spec.procedure.model.param_path is not None
and self.parent_spec.procedure.model.serial_path is not None
):
if self.model.param_path is not None and self.model.serial_path is not None:
logger.info("Loading model from disk, overrides any specified parameters.")
self.model = self.model.deserialize(
self.parent_spec.procedure.model.param_path,
self.parent_spec.procedure.model.serial_path,
self.model.param_path,
self.model.serial_path,
scaler=train_scaler,
**kwargs,
)

logger.info("Model loaded")

# Optionally freeze weights
if self.parent_spec.procedure.model.freeze_weights is not None:
if self.model.freeze_weights is not None:
logger.info(f"Freezing model weights")
self.model.freeze_weights(
**self.parent_spec.procedure.model.freeze_weights
)
self.model.freeze_weights(**self.model.freeze_weights)
logger.info(f"Model weights frozen")

# Build model from scratch
Expand Down Expand Up @@ -507,7 +505,7 @@ def _train_ensemble(self, X_train, y_train, val_dataloader, output_dir, **kwargs

# Bootstrap iterations
models = []
for i in range(self.parent_spec.procedure.ensemble.n_models):
for i in range(self.ensemble.n_models):
# Manage bootstrap directory
bootstrap_dir = output_dir / f"bootstrap_{i}"
bootstrap_dir.mkdir(parents=True, exist_ok=True)
Expand Down Expand Up @@ -540,26 +538,24 @@ def _train_ensemble(self, X_train, y_train, val_dataloader, output_dir, **kwargs
logger.info("Data featurized")

# Load model from disk
if (self.parent_spec.procedure.ensemble.param_paths is not None) and (
self.parent_spec.procedure.ensemble.serial_paths is not None
if (self.param_paths is not None) and (
self.ensemble.serial_paths is not None
):
logger.info(
f"Loading model {i} from disk, overrides any specified parameters."
)
self.model = self.model.deserialize(
self.parent_spec.procedure.ensemble.param_paths[i],
self.parent_spec.procedure.ensemble.serial_paths[i],
self.ensemble.param_paths[i],
self.ensemble.serial_paths[i],
scaler=bootstrap_scaler,
**kwargs,
)
logger.info(f"Model {i} loaded")

# Optionally freeze weights
if self.parent_spec.procedure.model.freeze_weights is not None:
if self.model.freeze_weights is not None:
logger.info(f"Freezing weights for model {i}")
self.model.freeze_weights(
**self.parent_spec.procedure.model.freeze_weights
)
self.model.freeze_weights(**self.model.freeze_weights)
logger.info(f"Model {i} frozen")

# Build model from scratch
Expand Down Expand Up @@ -655,17 +651,19 @@ def run(
data_dir.mkdir(parents=True, exist_ok=True)

# Write recipe to output directory
self.parent_spec.to_recipe(output_dir / "anvil_recipe.yaml")
if self.parent_spec is not None:
self.parent_spec.to_recipe(output_dir / "anvil_recipe.yaml")

# Split recipe into components and save
recipe_components = Path(output_dir / "recipe_components")
recipe_components.mkdir(parents=True, exist_ok=True)
self.parent_spec.to_multi_yaml(
metadata_yaml=recipe_components / "metadata.yaml",
procedure_yaml=recipe_components / "procedure.yaml",
data_yaml=recipe_components / "data.yaml",
report_yaml=recipe_components / "eval.yaml",
)
if self.parent_spec is not None:
self.parent_spec.to_multi_yaml(
metadata_yaml=recipe_components / "metadata.yaml",
procedure_yaml=recipe_components / "procedure.yaml",
data_yaml=recipe_components / "data.yaml",
report_yaml=recipe_components / "eval.yaml",
)

# Log output directory information
logger.info(f"Running workflow from directory {output_dir}")
Expand Down Expand Up @@ -733,7 +731,7 @@ def run(
logger.info("Data featurized")

kwargs = {}
if self.parent_spec.procedure.feat.type == "PairwiseFeaturizer":
if isinstance(self.feat, PairwiseFeaturizer):
kwargs["input_dim"] = train_dataset[0][0].shape[
-1
] # this is the dimension of # of features, e.g. 1024 for ECFP4, variable for descriptors
Expand All @@ -756,7 +754,7 @@ def run(
self.model.calibrate_uncertainty(
val_dataloader,
y_val,
method=self.parent_spec.procedure.ensemble.calibration_method,
method=self.ensemble.calibration_method,
accelerator=self.trainer.accelerator,
devices=self.trainer.devices,
)
Expand Down
4 changes: 3 additions & 1 deletion openadmet/models/anvil/workflow_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,9 @@ class AnvilWorkflowBase(BaseModel):
ensemble: EnsembleBase | None = None
trainer: TrainerBase
evals: list[EvalBase]
parent_spec: AnvilSpecification
parent_spec: Optional[AnvilSpecification] = (
None # Optional reference to parent specification
)
debug: bool = False

@abstractmethod
Expand Down
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