load_peptides_struct#

skfp.datasets.lrgb.load_peptides_struct(data_dir: str | PathLike | None = None, mol_type: str = 'SMILES', standardize_labels: bool = True, as_frame: bool = False, verbose: bool = False) → DataFrame | tuple[list[str]] | ndarray#

Load and return the Peptides-struct dataset.

The task is to predict structural properties (real values) for a set of peptides (small proteins) [1].

Tasks

11

Task type

classification

Total samples

15535

Recommended split

stratified random

Recommended metric

MAE
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.

  • mol_type ({"SMILES", "aminoseq"}, default="SMILES") – Which molecule representation to return, either SMILES strings or aminoacid sequences.

  • standardize_labels (bool, default=True) – Whether to standardize labels to mean 0 and standard deviation 1, following the recommendation from the original paper [1]. Otherwise, the raw property values are returned.

  • as_frame (bool, default=False) – If True, returns the raw DataFrame with columns: “SMILES”/”aminoseq” and “label”. This depends on the mol_type parameter. Otherwise, returns molecules as a list of strings (either SMILES or aminoacid sequences), 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 and mol_type parameters, one of: - Pandas DataFrame with columns: “SMILES”/”aminoseq”, “label” - tuple of: list of strings (SMILES / aminoacid sequences), NumPy array (labels)

Return type:

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

References