from typing import Sequence, Dict, Optional, Union
from ..base import CheLoDataset
from ..registry import register_dataset
from ..utils.downloader import DatasetDownloader
import pandas as pd
[docs]
@register_dataset
class BCFactorDataset(CheLoDataset):
_URL: str = "https://raw.githubusercontent.com/edgarsmdn/MLCE_book/main/references/BCF_training.csv"
_FILE_NAME: str = "BCF_training.csv"
_CHECKSUM: str = "bcf4ea5fa670952cbead1dd9b3091028"
[docs]
def __init__(
self,
selected_features: Optional[Sequence[str]] = None,
selected_targets: Optional[Sequence[str]] = None,
) -> None:
"""
Initialize the Bioconcentration Factor (BCF) dataset.
:param selected_features: Features to select (default: all features).
:param selected_targets: Targets to select (default: all targets).
"""
super().__init__(selected_features, selected_targets)
self.dataset_name: str = "Bioconcentration Factor (BCF) Dataset"
self.dataset_url: str = "https://edgarsmdn.github.io/MLCE_book/02_kNN_QSPR.html"
[docs]
def load_data(self) -> None:
"""
Load the dataset into memory.
"""
downloader: DatasetDownloader = DatasetDownloader()
file_path: str = downloader.download(
self._URL,
dataset_name="bcf",
filename=self._FILE_NAME,
checksum=self._CHECKSUM,
)
data: pd.DataFrame = pd.read_csv(file_path, sep=",")
columns_to_drop = ["CAS", "SMILES", "Experimental value [log(L/kg)]"]
self.raw_features: Dict[str, Sequence[Union[int, float]]] = data.drop(
columns=columns_to_drop,
).to_dict(orient="list")
self.raw_targets: Dict[str, Sequence[float]] = {
"bcf": data["Experimental value [log(L/kg)]"].tolist()
}
self._apply_initial_selections()
[docs]
def get_dataset_info(self) -> Dict[str, Union[str, Sequence[str]]]:
"""
Retrieve metadata about the dataset.
:return: A dictionary containing dataset metadata.
"""
return {
"name": self.dataset_name,
"description": (
"Dataset containing chemical properties and experimental "
"bioconcentration factor (BCF)."
),
"features": self.list_features(),
"targets": self.list_targets(),
"url": self.dataset_url,
}