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"""PaccMannVAE Algorithm.
PaccMannVAE is an unconditional molecular generative model.
"""
import logging
from dataclasses import field
from typing import Any, ClassVar, Dict, Optional, Set, TypeVar
from ....domains.materials import SMILES
from ...conditional_generation.paccmann_rl.core import PaccMannRLProteinBasedGenerator
from ...core import AlgorithmConfiguration, GeneratorAlgorithm, Untargeted
from ...registry import ApplicationsRegistry
from .implementation import PaccMannVaeDefaultGenerator
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
T = type(None)
S = TypeVar("S", bound=SMILES)
[docs]class PaccMannVAE(GeneratorAlgorithm[S, T]):
"""Molecular VAE as in the PaccMann\\ :superscript:`RL` paper."""
[docs] def __init__(
self, configuration: AlgorithmConfiguration[S, T], target: Optional[T] = None
):
"""Instantiate PaccMannVAE ready to generate molecules.
Args:
configuration: domain and application specification defining parameters,
types and validations.
target: unused since it is not a conditional generator.
Example:
An example for unconditional generation of small molecules::
config = PaccMannVAEGenerator()
algorithm = PaccMannVAE(configuration=config)
items = list(algorithm.sample(10))
print(items)
"""
configuration = self.validate_configuration(configuration)
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 PaccMannVAE.
Args:
configuration: helps to set up specific application of PaccMannVAE.
Returns:
callable with target generating a batch of items.
"""
implementation: PaccMannVaeDefaultGenerator = (
configuration.get_conditional_generator() # type: ignore
)
return implementation.generate
[docs]@ApplicationsRegistry.register_algorithm_application(PaccMannVAE)
class PaccMannVAEGenerator(AlgorithmConfiguration[SMILES, Any]):
"""
Configuration to generate molecules with PaccMannVAE.
Implementation from the paper: https://doi.org/10.1016/j.isci.2021.102269
"""
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."),
)
temperature: float = field(
default=1.4,
metadata=dict(
description="Temperature parameter for the softmax sampling in decoding."
),
)
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.
"""
return None
[docs] def get_conditional_generator(self) -> PaccMannVaeDefaultGenerator:
"""Instantiate the actual generator implementation.
Args:
resources_path: local path to model files.
Returns:
instance with :meth:`generate_batch<gt4sd.algorithms.conditional_generation.paccmann_rl.implementation.ConditionalGenerator.generate_batch>` method for targeted generation.
"""
return PaccMannVaeDefaultGenerator(
temperature=self.temperature,
generated_length=self.generated_length,
algorithm_version=self.algorithm_version,
batch_size=self.batch_size,
)
[docs] @classmethod
def list_versions(cls) -> Set[str]:
"""Get possible algorithm versions.
S3 is searched as well as the local cache is searched for matching versions.
Returns:
viable values as :attr:`algorithm_version` for the environment.
"""
return PaccMannRLProteinBasedGenerator().list_versions()