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"""PaccMann vanilla generator trained on polymer building blocks (catalysts/monomers)."""
import logging
import os
from dataclasses import field
from typing import ClassVar, Dict, Optional, TypeVar
from ....domains.materials import SMILES, MoleculeFormat, validate_molecules
from ....exceptions import InvalidItem
from ....training_pipelines.core import TrainingPipelineArguments
from ....training_pipelines.paccmann.core import PaccMannSavingArguments
from ...core import AlgorithmConfiguration, GeneratorAlgorithm, Untargeted
from ...registry import ApplicationsRegistry
from .implementation import Generator
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
T = type(None)
S = TypeVar("S", bound=SMILES)
[docs]class PolymerBlocks(GeneratorAlgorithm[S, T]):
[docs] def __init__(
self, configuration: AlgorithmConfiguration, target: Optional[T] = None
):
"""Polymer blocks generation.
Args:
configuration: domain and application
specification, defining types and validations.
target: unused since it is not a conditional generator.
Example:
An example for generating small molecules (SMILES) that resembles
monomers/catalysts for polymer synthesis::
configuration = PolymerBlocksGenerator()
polymer_blocks = PolymerBlocks(configuration=configuration)
items = list(polymer_blocks.sample(10))
print(items)
"""
configuration = self.validate_configuration(configuration)
# TODO there might also be a validation/check on the target input
super().__init__(
configuration=configuration,
target=None, # type:ignore
)
[docs] def get_generator(
self,
configuration: AlgorithmConfiguration[S, T],
target: Optional[T],
) -> Untargeted:
"""Get the function to sample batches via the Generator.
Args:
configuration: helps to set up the application.
target: context or condition for the generation. Unused in the algorithm.
Returns:
callable generating a batch of items.
"""
logger.info("ensure artifacts for the application are present.")
self.local_artifacts = configuration.ensure_artifacts()
implementation: Generator = configuration.get_conditional_generator( # type: ignore
self.local_artifacts
)
return implementation.sample
[docs] def validate_configuration(
self, configuration: AlgorithmConfiguration
) -> AlgorithmConfiguration:
# TODO raise InvalidAlgorithmConfiguration
assert isinstance(configuration, AlgorithmConfiguration)
return configuration
[docs]@ApplicationsRegistry.register_algorithm_application(PolymerBlocks)
class PolymerBlocksGenerator(AlgorithmConfiguration[SMILES, None]):
"""Configuration to generate subunits of polymers."""
algorithm_type: ClassVar[str] = "generation"
domain: ClassVar[str] = "materials"
algorithm_version: str = "v0"
batch_size: int = field(
default=32,
metadata=dict(description="Batch size used for the generative model sampling."),
)
generated_length: int = field(
default=100,
metadata=dict(
description="Maximum length in tokens of the generated molcules (relates to the SMILES length)."
),
)
[docs] def get_target_description(self) -> Optional[Dict[str, str]]:
"""Get description of the target for generation.
Returns:
target description, returns None in case no target is used.
"""
return None
[docs] def get_conditional_generator(self, resources_path: str) -> Generator:
return Generator(
resources_path=resources_path,
generated_length=self.generated_length,
batch_size=self.batch_size,
)
[docs] def validate_item(self, item: str) -> SMILES:
(
molecules,
_,
) = validate_molecules([item], MoleculeFormat.smiles)
if molecules[0] is None:
raise InvalidItem(
title="InvalidSMILES",
detail=f'rdkit.Chem.MolFromSmiles returned None for "{item}"',
)
return SMILES(item)
[docs] @classmethod
def get_filepath_mappings_for_training_pipeline_arguments(
cls, training_pipeline_arguments: TrainingPipelineArguments
) -> Dict[str, str]:
"""Ger filepath mappings for the given training pipeline arguments.
Args:
training_pipeline_arguments: training pipeline arguments.
Returns:
a mapping between artifacts' files and training pipeline's output files.
"""
if isinstance(training_pipeline_arguments, PaccMannSavingArguments):
return {
"smiles_language.pkl": os.path.join(
training_pipeline_arguments.model_path,
f"{training_pipeline_arguments.training_name}.lang",
),
"params.json": os.path.join(
training_pipeline_arguments.model_path,
training_pipeline_arguments.training_name,
"model_params.json",
),
"weights.pt": os.path.join(
training_pipeline_arguments.model_path,
training_pipeline_arguments.training_name,
"weights",
"best_rec.pt",
),
}
else:
return super().get_filepath_mappings_for_training_pipeline_arguments(
training_pipeline_arguments
)