Generate a random n-class classification problem.
This initially creates clusters of points normally distributed (std=1)
about vertices of an n_informative-dimensional hypercube with sides of
length 2*class_sep and assigns an equal number of clusters to each
class. It introduces interdependence between these features and adds
various types of further noise to the data.
Without shuffling, X horizontally stacks features in the following
order: the primary n_informative features, followed by n_redundant
linear combinations of the informative features, followed by n_repeated
duplicates, drawn randomly with replacement from the informative and
redundant features. The remaining features are filled with random noise.
Thus, without shuffling, all useful features are contained in the columns
X[:, :n_informative + n_redundant + n_repeated].
X[:, :n_informative + n_redundant + n_repeated]
Read more in the User Guide.
n_samples (int, optional (default=100)) – The number of samples.
n_features (int, optional (default=20)) – The total number of features. These comprise n_informative
informative features, n_redundant redundant features,
n_repeated duplicated features and
n_features-n_informative-n_redundant-n_repeated useless features
drawn at random.
n_informative (int, optional (default=2)) – The number of informative features. Each class is composed of a number
of gaussian clusters each located around the vertices of a hypercube
in a subspace of dimension n_informative. For each cluster,
informative features are drawn independently from N(0, 1) and then
randomly linearly combined within each cluster in order to add
covariance. The clusters are then placed on the vertices of the
n_redundant (int, optional (default=2)) – The number of redundant features. These features are generated as
random linear combinations of the informative features.
n_repeated (int, optional (default=0)) – The number of duplicated features, drawn randomly from the informative
and the redundant features.
n_classes (int, optional (default=2)) – The number of classes (or labels) of the classification problem.
n_clusters_per_class (int, optional (default=2)) – The number of clusters per class.
weights (list of floats or None (default=None)) – The proportions of samples assigned to each class. If None, then
classes are balanced. Note that if len(weights) == n_classes - 1,
then the last class weight is automatically inferred.
More than n_samples samples may be returned if the sum of
weights exceeds 1.
len(weights) == n_classes - 1
flip_y (float, optional (default=0.01)) – The fraction of samples whose class are randomly exchanged. Larger
values introduce noise in the labels and make the classification
class_sep (float, optional (default=1.0)) – The factor multiplying the hypercube size. Larger values spread
out the clusters/classes and make the classification task easier.
hypercube (boolean, optional (default=True)) – If True, the clusters are put on the vertices of a hypercube. If
False, the clusters are put on the vertices of a random polytope.
shift (float, array of shape [n_features] or None, optional (default=0.0)) – Shift features by the specified value. If None, then features
are shifted by a random value drawn in [-class_sep, class_sep].
scale (float, array of shape [n_features] or None, optional (default=1.0)) – Multiply features by the specified value. If None, then features
are scaled by a random value drawn in [1, 100]. Note that scaling
happens after shifting.
shuffle (boolean, optional (default=True)) – Shuffle the samples and the features.
random_state (int, RandomState instance or None (default)) – Determines random number generation for dataset creation. Pass an int
for reproducible output across multiple function calls.
X (tensor of shape [n_samples, n_features]) – The generated samples.
y (tensor of shape [n_samples]) – The integer labels for class membership of each sample.
The algorithm is adapted from Guyon  and was designed to generate
the “Madelon” dataset.
I. Guyon, “Design of experiments for the NIPS 2003 variable
selection benchmark”, 2003.
unrelated generator for multilabel tasks