load_sider#

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

Load and return the SIDER (Side Effect Resource) dataset.

The task is to predict adverse drug reactions (ADRs) as drug side effects to 27 system organ classes in MedDRA classification [1] [2]. All tasks are binary.

Tasks

27

Task type

multitask classification

Total samples

1427

Recommended split

scaffold

Recommended metric

AUROC
Parameters:

  • 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 27 label columns, with names corresponding to MedDRA system organ classes (see [1] for details). Otherwise, returns SMILES as list of strings,and labels as a NumPy array (2D integer array).

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

Returns:

data – Depending on the as_frame argument, one of: - Pandas DataFrame with columns “SMILES” and 27 label columns - tuple of: list of strings (SMILES), NumPy array (labels)

Return type:

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

References