"""Module for wine dataset."""
from __future__ import annotations
import numpy as np
from numpy.typing import NDArray
from sklearn import datasets
from tno.quantum.utils.validation import check_int
from tno.quantum.ml.datasets._utils import _pre_process_data, _safe_train_test_split
[docs]
def get_wine_dataset(
n_features: int = 13,
n_classes: int = 3,
random_seed: int = 0,
test_size: float | int | None = None,
) -> tuple[
NDArray[np.float64], NDArray[np.int_], NDArray[np.float64], NDArray[np.int_]
]:
r"""Load wine dataset.
This function wraps :func:`~sklearn.datasets.load_wine` of :mod:`sklearn.datasets`.
The dataset is loaded and split into training and validation data.
Example usage::
>>> from tno.quantum.ml.datasets import get_wine_dataset
>>> X_train, y_train, X_val, y_val = get_wine_dataset()
>>> print(f"{X_train.shape=}\n{y_train.shape=}\n{X_val.shape=}\n{y_val.shape=}")
X_train.shape=(133, 13)
y_train.shape=(133,)
X_val.shape=(45, 13)
y_val.shape=(45,)
Args:
n_features: Number of features. Defaults to 13.
n_classes: Number of classes, must be 1, 2 or 3. Defaults to 3.
random_seed: Seed to give to the random number generator. Defaults to 0.
test_size: The proportion of the dataset that is included in the test-split.
Either represented by a percentage in the range [0.0, 1.0) or as absolute
number of test samples in the range [1, inf). Defaults to 0.25.
Returns:
A tuple containing ``X_training``, ``y_training``, ``X_validation`` and
``y_validation`` of the wine dataset.
"""
# Validate input
n_features = check_int(n_features, name="n_features", l_bound=1)
n_classes = check_int(n_classes, name="n_classes", l_bound=1)
random_seed = check_int(random_seed, name="random_seed", l_bound=0)
# Load data and take subset
X, y = datasets.load_wine(return_X_y=True)
X, y = _pre_process_data(X, y, n_features, n_classes)
# Split into training and validation data sets
return _safe_train_test_split(X, y, test_size=test_size, random_state=random_seed)