cluster.KMeans (uses cuML)¶

class pai4sk.cluster.KMeans(n_clusters=8, max_iter=300, tol=0.0001, verbose=0, random_state=1, precompute_distances='auto', init='k-means++', n_init=1, algorithm='auto', copy_x=True, n_jobs=None, use_gpu=True, oversampling_factor=2.0, max_samples_per_batch=32768, handle=None)¶

K-Means clustering.

If cudf dataframe is passed as input, then pai4sk will try to use the accelerated KMeans algorithm from cuML. Otherwise, scikit-learn’s KMeans algorithm will be used.

cuML in pai4sk is currently supported only without MPI. | If KMeans from cuML is run, then the return values from the APIs will be cudf dataframe and cudf Series objects instead of the return types of scikit-learn API.

Parameters

n_clusters (int, optional, default: 8) – The number of clusters to form as well as the number of centroids to generate.
init ({'k-means++', 'random' or an ndarray}) –
Method for initialization, defaults to ‘k-means++’:

’k-means++’ : selects initial cluster centers for k-mean clustering in a smart way to speed up convergence. See section Notes in k_init for more details.

’random’: choose k observations (rows) at random from data for the initial centroids.

If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers.
n_init (int, default: 10) – Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.
max_iter (int, default: 300) – Maximum number of iterations of the k-means algorithm for a single run.
tol (float, default: 1e-4) – Relative tolerance with regards to inertia to declare convergence
precompute_distances ({'auto', True, False}) –
Precompute distances (faster but takes more memory).

’auto’ : do not precompute distances if n_samples * n_clusters > 12 million. This corresponds to about 100MB overhead per job using double precision.

True : always precompute distances

False : never precompute distances
verbose (int, default 0) – Verbosity mode to print diagnostic information.
random_state (int, RandomState instance or None (default)) – Determines random number generation for centroid initialization. Use an int to make the randomness deterministic. See Glossary.
copy_x (boolean, optional) – When pre-computing distances it is more numerically accurate to center the data first. If copy_x is True (default), then the original data is not modified, ensuring X is C-contiguous. If False, the original data is modified, and put back before the function returns, but small numerical differences may be introduced by subtracting and then adding the data mean, in this case it will also not ensure that data is C-contiguous which may cause a significant slowdown.
n_jobs (int or None, optional (default=None)) –
The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel.

None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
algorithm ("auto", "full" or "elkan", "cuml", default="auto") –
K-means algorithm to use. The classical EM-style algorithm is “full”. The “elkan” variation is more efficient by using the triangle inequality, but currently doesn’t support sparse data. “auto” chooses “elkan” for dense data and “full” for sparse data.

If cudf dataframe is passed as input, then if either

(1) algorithm is set to “cuml” or

(2) algorithm is “auto”,

then pai4sk will try to use kmeans algorithm from RAPIDS cuML.

cuML in pai4sk is currently supported only without MPI.

If KMeans from cuML is run, then the return values of the APIs will be cudf dataframe and cudf Series objects instead of the return types of scikit-learn API.
use_gpu (boolean, Default is True) – If True, cuML will use GPU(s). Applicable only for cuML.
handle (cuml.Handle, Default is None) – If it is None, a new one is created just for this class. Applicable only for cuML.
oversampling_factor ((default = 2.0) The amount of points to sample) –
oversampling_factor – in scalable k-means++ initialization for potential centroids. Increasing this value can lead to better initial centroids at the cost of memory. The total number of centroids sampled in scalable k-means++ is oversampling_factor * n_clusters * 8. Applicable only for cuML.
max_samples_per_batch (int (default = 32768) The number of data) – of the pairwise distance computation.
max_samples_per_batch – samples to use for batches of the pairwise distance computation. This computation is done throughout both fit predict. The default should suit most cases. The total number of elements in the batched pairwise distance computation is max_samples_per_batch * n_clusters. It might become necessary to lower this number when n_clusters becomes prohibitively large. Applicable only for cuML.

Variables

cluster_centers_ (array, [n_clusters, n_features] or cudf dataframe) – Coordinates of cluster centers. If the algorithm stops before fully converging (see tol and max_iter), these will not be consistent with labels_. If KMeans from cuML is run, then the return values of some of the APIs will be cudf dataframe and cudf Series objects instead of the return types of scikit-learn API.
labels_ (array or cudf Series) – Labels of each point
inertia_ (float) – Sum of squared distances of samples to their closest cluster center.
n_iter_ (int) – Number of iterations run.

Examples

>>> from pai4sk.cluster import KMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
...               [4, 2], [4, 4], [4, 0]])
>>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
>>> kmeans.labels_
array([0, 0, 0, 1, 1, 1], dtype=int32)
>>> kmeans.predict([[0, 0], [4, 4]])
array([0, 1], dtype=int32)
>>> kmeans.cluster_centers_
array([[1., 2.],
       [4., 2.]])