def make_classification(n_samples=100, n_features=20, n_informative=2,
n_redundant=2, n_repeated=0, n_classes=2,
n_clusters_per_class=2, weights=None, flip_y=0.01,
class_sep=1.0, hypercube=True, shift=0.0, scale=1.0,
shuffle=True, random_state=None, order='F',
dtype='float32', n_parts=None, client=None):
"""
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]``.
Examples
--------
.. code-block:: python
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
from cuml.dask.datasets.classification import make_classification
cluster = LocalCUDACluster()
client = Client(cluster)
X, y = make_classification(n_samples=10, n_features=4,
n_informative=2, n_classes=2)
print("X:")
print(X.compute())
print("y:")
print(y.compute())
Output:
.. code-block:: python
X:
[[-1.6990056 -0.8241044 -0.06997631 0.45107925]
[-1.8105277 1.7829906 0.492909 0.05390119]
[-0.18290454 -0.6155432 0.6667889 -1.0053712 ]
[-2.7530136 -0.888528 -0.5023055 1.3983376 ]
[-0.9788184 -0.89851004 0.10802134 -0.10021686]
[-0.76883423 -1.0689086 0.01249526 -0.1404741 ]
[-1.5676656 -0.83082974 -0.03072987 0.34499463]
[-0.9381793 -1.0971068 -0.07465998 0.02618019]
[-1.3021476 -0.87076336 0.02249984 0.15187258]
[ 1.1820307 1.7524253 1.5087451 -2.4626074 ]]
y:
[0 1 0 0 0 0 0 0 0 1]
Parameters
----------
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
hypercube.
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 : array-like of shape ``(n_classes,)`` or ``(n_classes - 1,)``, \
(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.
flip_y : float, optional (default=0.01)
The fraction of samples whose class is assigned randomly. Larger
values introduce noise in the labels and make the classification
task harder.
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.
See :term:`Glossary <random_state>`.
order: str, optional (default='F')
The order of the generated samples
dtype : str, optional (default='float32')
Dtype of the generated samples
n_parts : int (default = None)
number of partitions to generate (this can be greater
than the number of workers)
Returns
-------
X : dask.array backed by CuPy array of shape [n_samples, n_features]
The generated samples.
y : dask.array backed by CuPy array of shape [n_samples]
The integer labels for class membership of each sample.
Notes
-----
How we extended the dask MNMG version from the single GPU version:
1. We generate centroids of shape ``(n_centroids, n_informative)``
2. We generate an informative covariance of shape \
``(n_centroids, n_informative, n_informative)``
3. We generate a redundant covariance of shape \
``(n_informative, n_redundant)``
4. We generate the indices for the repeated features \
We pass along the references to the futures of the above arrays \
with each part to the single GPU \
`cuml.datasets.classification.make_classification` so that each \
part (and worker) has access to the correct values to generate \
data from the same covariances
"""
client = get_client(client=client)
rs = _create_rs_generator(random_state)
workers = list(client.scheduler_info()['workers'].keys())
n_parts = n_parts if n_parts is not None else len(workers)
parts_workers = (workers * n_parts)[:n_parts]
n_clusters = n_classes * n_clusters_per_class
# create centroids
centroids = cp.array(_generate_hypercube(n_clusters, n_informative,
rs)).astype(dtype, copy=False)
covariance_seeds = rs.randint(n_features, size=2)
informative_covariance = client.submit(_create_covariance,
(n_clusters, n_informative,
n_informative),
int(covariance_seeds[0]),
pure=False)
redundant_covariance = client.submit(_create_covariance,
(n_informative,
n_redundant),
int(covariance_seeds[1]),
pure=False)
# repeated indices
n = n_informative + n_redundant
repeated_indices = ((n - 1) * rs.rand(n_repeated, dtype=dtype)
+ 0.5).astype(np.intp)
# scale and shift
if shift is None:
shift = (2 * rs.rand(n_features, dtype=dtype) - 1) * class_sep
if scale is None:
scale = 1 + 100 * rs.rand(n_features, dtype=dtype)
# Create arrays on each worker (gpu)
rows_per_part = max(1, int(n_samples / n_parts))
worker_rows = [rows_per_part] * n_parts
worker_rows[-1] += (n_samples % n_parts)
worker_rows = tuple(worker_rows)
part_seeds = rs.permutation(n_parts)
parts = [client.submit(sg_make_classification, worker_rows[i], n_features,
n_informative, n_redundant, n_repeated, n_classes,
n_clusters_per_class, weights, flip_y, class_sep,
hypercube, shift, scale, shuffle,
int(part_seeds[i]), order, dtype, centroids,
informative_covariance, redundant_covariance,
repeated_indices, pure=False,
workers=[parts_workers[i]])
for i in range(len(parts_workers))]
X_parts = [client.submit(_get_X, f, pure=False)
for idx, f in enumerate(parts)]
y_parts = [client.submit(_get_labels, f, pure=False)
for idx, f in enumerate(parts)]
X_dela = _create_delayed(X_parts, dtype, worker_rows, n_features)
y_dela = _create_delayed(y_parts, dtype, worker_rows)
X = da.concatenate(X_dela)
y = da.concatenate(y_dela)
return X, y
def make_classification(n_samples=100,
n_features=20,
n_informative=2,
n_redundant=2,
n_repeated=0,
n_classes=2,
n_clusters_per_class=2,
weights=None,
flip_y=0.01,
class_sep=1.0,
hypercube=True,
shift=0.0,
scale=1.0,
shuffle=True,
random_state=None,
order='F',
dtype='float32',
n_parts=None,
client=None):
"""
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 :py:`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
:py:`X[:, :n_informative + n_redundant + n_repeated]`.
Examples
--------
.. code-block:: python
>>> from dask.distributed import Client
>>> from dask_cuda import LocalCUDACluster
>>> from cuml.dask.datasets.classification import make_classification
>>> cluster = LocalCUDACluster()
>>> client = Client(cluster)
>>> X, y = make_classification(n_samples=10, n_features=4,
... random_state=1, n_informative=2,
... n_classes=2)
>>> print(X.compute()) # doctest: +SKIP
[[-1.1273878 1.2844919 -0.32349187 0.1595734 ]
[ 0.80521786 -0.65946865 -0.40753683 0.15538901]
[ 1.0404129 -1.481386 1.4241115 1.2664981 ]
[-0.92821544 -0.6805706 -0.26001272 0.36004275]
[-1.0392245 -1.1977317 0.16345565 -0.21848428]
[ 1.2273135 -0.529214 2.4799604 0.44108105]
[-1.9163864 -0.39505136 -1.9588828 -1.8881643 ]
[-0.9788184 -0.89851004 -0.08339313 0.1130247 ]
[-1.0549078 -0.8993015 -0.11921967 0.04821599]
[-1.8388828 -1.4063598 -0.02838472 -1.0874642 ]]
>>> print(y.compute()) # doctest: +SKIP
[1 0 0 0 0 1 0 0 0 0]
>>> client.close()
>>> cluster.close()
Parameters
----------
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
:py:`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
hypercube.
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 : array-like of shape :py:`(n_classes,)` or :py:`(n_classes - 1,)`\
, (default=None)
The proportions of samples assigned to each class. If None, then
classes are balanced. Note that if :py:`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.
flip_y : float, optional (default=0.01)
The fraction of samples whose class is assigned randomly. Larger
values introduce noise in the labels and make the classification
task harder.
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.
See :term:`Glossary <random_state>`.
order: str, optional (default='F')
The order of the generated samples
dtype : str, optional (default='float32')
Dtype of the generated samples
n_parts : int (default = None)
number of partitions to generate (this can be greater
than the number of workers)
Returns
-------
X : dask.array backed by CuPy array of shape [n_samples, n_features]
The generated samples.
y : dask.array backed by CuPy array of shape [n_samples]
The integer labels for class membership of each sample.
Notes
-----
How we extended the dask MNMG version from the single GPU version:
1. We generate centroids of shape ``(n_centroids, n_informative)``
2. We generate an informative covariance of shape \
``(n_centroids, n_informative, n_informative)``
3. We generate a redundant covariance of shape \
``(n_informative, n_redundant)``
4. We generate the indices for the repeated features \
We pass along the references to the futures of the above arrays \
with each part to the single GPU \
`cuml.datasets.classification.make_classification` so that each \
part (and worker) has access to the correct values to generate \
data from the same covariances
"""
client = get_client(client=client)
rs = _create_rs_generator(random_state)
workers = list(client.scheduler_info()['workers'].keys())
n_parts = n_parts if n_parts is not None else len(workers)
parts_workers = (workers * n_parts)[:n_parts]
n_clusters = n_classes * n_clusters_per_class
# create centroids
centroids = cp.array(_generate_hypercube(n_clusters, n_informative,
rs)).astype(dtype, copy=False)
covariance_seeds = rs.randint(n_features, size=2)
informative_covariance = client.submit(
_create_covariance, (n_clusters, n_informative, n_informative),
int(covariance_seeds[0]),
pure=False)
redundant_covariance = client.submit(_create_covariance,
(n_informative, n_redundant),
int(covariance_seeds[1]),
pure=False)
# repeated indices
n = n_informative + n_redundant
repeated_indices = ((n - 1) * rs.rand(n_repeated, dtype=dtype) +
0.5).astype(np.intp)
# scale and shift
if shift is None:
shift = (2 * rs.rand(n_features, dtype=dtype) - 1) * class_sep
if scale is None:
scale = 1 + 100 * rs.rand(n_features, dtype=dtype)
# Create arrays on each worker (gpu)
rows_per_part = max(1, int(n_samples / n_parts))
worker_rows = [rows_per_part] * n_parts
worker_rows[-1] += (n_samples % n_parts)
worker_rows = tuple(worker_rows)
part_seeds = rs.permutation(n_parts)
parts = [
client.submit(sg_make_classification,
worker_rows[i],
n_features,
n_informative,
n_redundant,
n_repeated,
n_classes,
n_clusters_per_class,
weights,
flip_y,
class_sep,
hypercube,
shift,
scale,
shuffle,
int(part_seeds[i]),
order,
dtype,
centroids,
informative_covariance,
redundant_covariance,
repeated_indices,
pure=False,
workers=[parts_workers[i]])
for i in range(len(parts_workers))
]
X_parts = [
client.submit(_get_X, f, pure=False) for idx, f in enumerate(parts)
]
y_parts = [
client.submit(_get_labels, f, pure=False)
for idx, f in enumerate(parts)
]
X_dela = _create_delayed(X_parts, dtype, worker_rows, n_features)
y_dela = _create_delayed(y_parts, np.int64, worker_rows)
X = da.concatenate(X_dela)
y = da.concatenate(y_dela)
return X, y