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"""TorchDrug training utilities."""
import os
from dataclasses import dataclass, field
from typing import Any, Dict, Optional
from ...configuration import gt4sd_configuration_instance
from ..core import TrainingPipeline, TrainingPipelineArguments
from . import DATASET_FACTORY
DATA_ROOT = os.path.join(
gt4sd_configuration_instance.gt4sd_local_cache_path, "data", "torchdrug"
)
os.makedirs(DATA_ROOT, exist_ok=True)
[docs]class TorchDrugTrainingPipeline(TrainingPipeline):
"""TorchDrug training pipelines."""
[docs] def train( # type: ignore
self,
training_args: Dict[str, Any],
model_args: Dict[str, Any],
dataset_args: Dict[str, Any],
) -> None:
"""Generic training function for launching a TorchDrug training.
Args:
training_args: training arguments passed to the configuration.
model_args: model arguments passed to the configuration.
dataset_args: dataset arguments passed to the configuration.
Raises:
NotImplementedError: the generic trainer does not implement the pipeline.
"""
raise NotImplementedError
[docs]@dataclass
class TorchDrugTrainingArguments(TrainingPipelineArguments):
"""Arguments related to torchDrug trainer."""
__name__ = "training_args"
model_path: str = field(
metadata={"help": "Path where the model artifacts are stored."}
)
training_name: str = field(metadata={"help": "Name used to identify the training."})
epochs: int = field(default=10, metadata={"help": "Number of epochs."})
batch_size: int = field(default=16, metadata={"help": "Size of the batch."})
learning_rate: float = field(
default=1e-5, metadata={"help": "Learning rate used in training."}
)
log_interval: int = field(
default=100, metadata={"help": "Number of steps between log intervals."}
)
gradient_interval: int = field(
default=1, metadata={"help": "Gradient accumulation steps"}
)
num_worker: int = field(
default=0, metadata={"help": "Number of CPU workers per GPU."}
)
task: Optional[str] = field(
default=None,
metadata={
"help": "Optimization task for goal-driven generation."
"Currently, TorchDrug only supports `plogp` and `qed`."
},
)
[docs]@dataclass
class TorchDrugDataArguments(TrainingPipelineArguments):
"""Arguments related to TorchDrug data loading."""
__name__ = "dataset_args"
dataset_name: str = field(
metadata={
"help": f"Identifier for the dataset. Has to be in {DATASET_FACTORY.keys()}"
". Can either point to one of the predefined TorchDrug datasets or it can "
"be `custom` if the user brings their own dataset. If `custom`, then the "
"arguments `file_path`, `target_field` and `smiles_field` below have to be"
" specified."
}
)
file_path: str = field(
default="",
metadata={
"help": "Ignored unless `datase_name` is `custom`. In that case it's "
"a path to a .csv file containing the training data."
},
)
dataset_path: str = field(
default=DATA_ROOT,
metadata={
"help": "Path where the TorchDrug dataset will be stored. This is ignored "
"if `datase_name` is `custom`."
},
)
target_field: str = field(
default="",
metadata={
"help": "Ignored unless `datase_name` is `custom`. In that case it's a str "
"with name of the column containing the property that can be optimized."
"Currently TorchDrug only supports `plogp` and `qed`."
},
)
smiles_field: str = field(
default="smiles",
metadata={
"help": "Ignored unless `datase_name` is `custom`. In that case it's the "
"name of the column containing the SMILES strings."
},
)
transform: str = field(
default="lambda x: x",
metadata={
"help": "Optional data transformation function. Has to be a lambda function"
" (written as a string) that operates on the batch dictionary."
"See torchdrug docs for details."
},
)
verbose: int = field(
default=1, metadata={"help": "Output verbosity level for dataset."}
)
lazy: bool = field(
default=False,
metadata={
"help": "If yes, molecules are processed in the dataloader. This is faster "
"for setup but slower at training time."
},
)
node_feature: str = field(
default="default",
metadata={"help": "Node features (or node feature list) to extract."},
)
edge_feature: str = field(
default="default",
metadata={"help": "Edge features (or edge feature list) to extract."},
)
graph_feature: Optional[str] = field(
default=None,
metadata={"help": "Graph features (or graph feature list) to extract."},
)
with_hydrogen: bool = field(
default=False,
metadata={"help": "Whether hydrogens are stored in molecular graph."},
)
no_kekulization: bool = field(
default=False,
metadata={
"help": "Whether SMILES kekulization is used. Per default, it is used."
},
)
[docs]@dataclass
class TorchDrugSavingArguments(TrainingPipelineArguments):
"""Saving arguments related to TorchDrug trainer."""
__name__ = "saving_args"
model_path: str = field(
metadata={"help": "Path where the model artifacts are stored."}
)
training_name: str = field(metadata={"help": "Name used to identify the training."})
dataset_name: str = field(
metadata={
"help": f"Identifier for the dataset. Has to be in {DATASET_FACTORY.keys()}"
". Can either point to one of the predefined TorchDrug datasets or it can "
"be `custom` if the user brings their own dataset. If `custom`, then the "
"arguments `file_path`, `target_field` and `smiles_field` below have to be"
" specified."
}
)
task: Optional[str] = field(
default=None,
metadata={
"help": "Optimization task for goal-driven generation."
"Currently, TorchDrug only supports `plogp` and `qed`."
},
)
file_path: str = field(
default="",
metadata={
"help": "Ignored unless `datase_name` is `custom`. In that case it's "
"a path to a .csv file containing the training data."
},
)
epochs: int = field(default=10, metadata={"help": "Number of epochs."})