Source code for qlearnkit.datasets.iris
import sklearn.datasets as skdatasets
from .dataset_helper import features_labels_from_data
from typing import Optional, Union
[docs]def load_iris(train_size: Optional[Union[float, int]] = None,
test_size: Optional[Union[float, int]] = None,
n_features: Optional[int] = None,
*,
use_pca: Optional[bool] = False,
return_bunch: Optional[bool] = False):
"""
This script loads iris dataset from sklearn and splits it according to
the required train size, test size and number of features
Args:
test_size :
float or int, default=None
If float, should be between 0.0 and 1.0 and represent the proportion
of the dataset to include in the test split. If int, represents the
absolute number of test samples. If None, the value is set to the
complement of the train size. If ``train_size`` is also None, it will
be set to 0.25.
train_size : float or int, default=None
If float, should be between 0.0 and 1.0 and represent the
proportion of the dataset to include in the train split. If
int, represents the absolute number of train samples. If None,
the value is automatically set to the complement of the test size.
n_features:
number of desired features
use_pca:
whether to use PCA for dimensionality reduction or not
default False
return_bunch:
whether to return a :class:`~sklearn.utils.Bunch`
(similar to a dictionary) or not
Returns:
Iris dataset as available in sklearn
"""
# X: data
# y: labels
X, y = skdatasets.load_iris(return_X_y=True)
return features_labels_from_data(
X, y, train_size, test_size, n_features,
use_pca=use_pca,
return_bunch=return_bunch
)