load_asap_dataset#

skfp.datasets.asap.load_asap_dataset(dataset_name: str, data_dir: str | PathLike | None = None, as_frame: bool = False, verbose: bool = False, force_update: bool = False) → DataFrame | tuple[list[str], ndarray]#

Load ASAP Discovery-OpenADMET challenge dataset by name.

Loads a given dataset from ASAP Discovery-OpenADMET challenge [1] by its name. This is a proxy for easier benchmarking that avoids looking for individual functions.

Dataset names here are the same as returned by load_asap_benchmark() function, and are case-sensitive.

Parameters:

  • dataset_name (str) – Name of the dataset to load.

  • data_dir ({None, str, path-like}, default=None) – Path to the root data directory. If None, currently set scikit-learn directory is used, by default $HOME/scikit_learn_data.

  • as_frame (bool, default=False) – If True, returns the raw DataFrame with columns “SMILES” and labels (dataset-dependent). Otherwise, returns SMILES as list of strings, and labels as a NumPy array (shape and type are dataset-dependent).

  • verbose (bool, default=False) – If True, progress bar will be shown for downloading or loading files.

  • force_update (bool, default=False) – If True, always re-download the dataset from HuggingFace Hub, even if it is already present locally. If False, the dataset is downloaded only if it is not yet available locally.

Returns:

data – Depending on the as_frame argument, one of: - Pandas DataFrame with columns depending on the dataset - tuple of: list of strings (SMILES), NumPy array (labels)

Return type:

pd.DataFrame or tuple(list[str], np.ndarray)

References

Examples

>> from skfp.datasets.asap import load_asap_dataset >> dataset = load_asap_dataset(“LogD”) >> dataset # doctest: +SKIP ([‘COC1=CC=CC(Cl)=C1NC(=O)N1CCC[C@H](C(N)=O)C1’, …, ‘])