#
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
import logging
from dataclasses import field
from typing import Any, Callable, ClassVar, Dict, Iterable, Optional, TypeVar
from ....domains.materials import SMILES, MoleculeFormat, validate_molecules
from ....exceptions import InvalidItem
from ...core import AlgorithmConfiguration, GeneratorAlgorithm
from ...registry import ApplicationsRegistry
from .implementation import ReinventConditionalGenerator
T = TypeVar("T", bound=Any)
S = TypeVar("S", bound=Any)
Targeted = Callable[[T], Iterable[Any]]
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
[docs]class Reinvent(GeneratorAlgorithm[S, T]):
"""Reinvent sample generation algorithm."""
[docs] def __init__(
self,
configuration: AlgorithmConfiguration[S, T],
target: Optional[T],
):
"""Instantiate Reinvent ready to generate samples.
Args:
configuration: domain and application
specification defining parameters, types and validations.
target: a target for which to generate items.
Example:
An example for predicting topics for a given text::
config = ReinventGenerator()
algorithm = Reinvent(configuration=config, target="CCO")
items = list(algorithm.sample(1))
print(items)
"""
configuration = self.validate_configuration(configuration)
# TODO there might also be a validation/check on the target input
super().__init__(
configuration=configuration, # type:ignore
target=target, # type:ignore
)
[docs] def get_generator(
self,
configuration: AlgorithmConfiguration[S, T],
target: Optional[T],
) -> Targeted[T]:
"""Get the function to perform the prediction via Reinvent's generator.
Args:
configuration: helps to set up specific application of Reinvent.
target: context or condition for the generation.
Returns:
callable with target generating samples.
"""
logger.info("ensure artifacts for the application are present.")
self.local_artifacts = configuration.ensure_artifacts()
implementation: ReinventConditionalGenerator = configuration.get_conditional_generator( # type: ignore
self.local_artifacts
)
return implementation.generate_samples
[docs]@ApplicationsRegistry.register_algorithm_application(Reinvent)
class ReinventGenerator(AlgorithmConfiguration[str, str]):
"""Configuration to generate molecules using the REINVENT algorithm. It generates the molecules minimizing the distances between the scaffolds."""
algorithm_name: ClassVar[str] = Reinvent.__name__
algorithm_type: ClassVar[str] = "conditional_generation"
domain: ClassVar[str] = "materials"
algorithm_version: str = "v0"
batch_size: int = field(
default=20,
metadata=dict(description=("Number of samples to generate per scaffold")),
)
randomize: bool = field(
default=True,
metadata=dict(description=("Randomize the scaffolds if set to true")),
)
sample_uniquely: bool = field(
default=True,
metadata=dict(description=("Generate unique sample sequences if set to true")),
)
max_sequence_length: int = field(
default=256,
metadata=dict(description=("Maximal length of SMILES sequences")),
)
[docs] def get_target_description(self) -> Dict[str, str]:
"""Get description of the target for generation.
Returns:
target description.
"""
return {
"title": "SMILES for sample generation",
"description": "SMILES considered for the samples generation.",
"type": "string",
}
[docs] def get_conditional_generator(
self, resources_path: str
) -> ReinventConditionalGenerator:
"""Instantiate the actual generator implementation.
Args:
resources_path: local path to model files.
Returns:
instance with :meth:`generate_samples<gt4sd.algorithms.conditional_generation.reinvent.implementation.ReinventConditionalGenerator.generate_samples>` method for targeted generation.
"""
return ReinventConditionalGenerator(
resources_path=resources_path,
batch_size=self.batch_size,
randomize=self.randomize,
sample_uniquely=self.sample_uniquely,
max_sequence_length=self.max_sequence_length,
)
[docs] def validate_item(self, item: str) -> SMILES:
"""Check that item is a valid SMILES.
Args:
item: a generated item that is possibly not valid.
Raises:
InvalidItem: in case the item can not be validated.
Returns:
the validated SMILES.
"""
molecules, _ = validate_molecules(
pattern_list=[item], input_type=MoleculeFormat.smiles
)
if molecules[0] is None:
raise InvalidItem(
title="InvalidSMILES",
detail=f'rdkit.Chem.MolFromSmiles returned None for "{item}"',
)
return SMILES(item)