OpenADMET Models documentation

openadmet-models

scikit-learn Splitting Strategies

Sklearn-based data splitting implementations.

class openadmet.models.split.sklearn.ShuffleSplitter(*, train_size: float = 0.8, val_size: float = 0.0, test_size: float = 0.2, random_state: int = 42)

Bases: SplitterBase

Vanilla splitter, uses sklearn’s train_test_split which wraps ShuffleSplit.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

split(X, y)

Split the data.

Parameters:

• X (array-like) – Feature data.

• y (array-like) – Target data.

Returns:

Tuple containing: - X_train: Training set features. - X_val: Validation set features (or None if val_size=0). - X_test: Test set features (or None if test_size=0). - y_train: Training set target values. - y_val: Validation set target values (or None if val_size=0). - y_test: Test set target values (or None if test_size=0).

Return type:

tuple