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"""Crystals crf training utilities."""
import logging
from dataclasses import dataclass, field
from typing import Any, Dict, Optional
from ...frameworks.crystals_rfc.rf_classifier import RFC
from ..core import TrainingPipeline, TrainingPipelineArguments
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
[docs]class CrystalsRFCTrainingPipeline(TrainingPipeline):
"""Crystals RFC training pipelines for crystals."""
[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 Crystals RFC models.
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.
"""
rfc = RFC(crystal_sys=model_args["sym"])
df = rfc.load_data(file_name=dataset_args["datapath"])
train_x, test_x, train_y, test_y = rfc.split_data(
df, test_size=dataset_args["test_size"]
)
train_x, test_x, train_y, test_y = rfc.normalize_data(
train_x, test_x, train_y, test_y
)
rfc.train(train_x, train_y)
rfc.save(training_args["output_path"])
[docs]@dataclass
class CrystalsRFCDataArguments(TrainingPipelineArguments):
"""Data arguments related to crystals RFC trainer."""
__name__ = "dataset_args"
datapath: str = field(
metadata={
"help": "Path to the dataset."
"The dataset should follow the directory structure as described in https://github.com/dilangaem/SemiconAI."
},
)
test_size: Optional[int] = field(
default=None, metadata={"help": "Testing set percentage."}
)
[docs]@dataclass
class CrystalsRFCModelArguments(TrainingPipelineArguments):
"""Model arguments related to crystals RFC trainer."""
__name__ = "model_args"
sym: str = field(
default="all",
metadata={
"help": "Crystal systems to be used. 'all' for all the crystal systems. Other seven options are: 'monoclinic', 'triclinic', 'orthorhombic', 'trigonal', 'hexagonal', 'cubic', 'tetragonal'"
},
)
[docs]@dataclass
class CrystalsRFCTrainingArguments(TrainingPipelineArguments):
"""Training arguments related to crystals RFC trainer."""
__name__ = "training_args"
output_path: str = field(
default=".",
metadata={"help": "Path to the store the checkpoints."},
)
[docs]@dataclass
class CrystalsRFCSavingArguments(TrainingPipelineArguments):
"""Saving arguments related to crystals RFC trainer."""
__name__ = "saving_args"