`sklearn.cluster`.MiniBatchKMeans¶

class sklearn.cluster.MiniBatchKMeans(n_clusters=8, init='k-means++', max_iter=100, batch_size=100, verbose=0, compute_labels=True, random_state=None, tol=0.0, max_no_improvement=10, init_size=None, n_init=3, reassignment_ratio=0.01)[source]¶

Mini-Batch K-Means clustering

Parameters:

n_clusters : int, optional, default: 8

The number of clusters to form as well as the number of centroids to generate.

max_iter : int, optional

Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics.

max_no_improvement : int, default: 10

Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed inertia.

To disable convergence detection based on inertia, set max_no_improvement to None.

tol : float, default: 0.0

Control early stopping based on the relative center changes as measured by a smoothed, variance-normalized of the mean center squared position changes. This early stopping heuristics is closer to the one used for the batch variant of the algorithms but induces a slight computational and memory overhead over the inertia heuristic.

To disable convergence detection based on normalized center change, set tol to 0.0 (default).

batch_size : int, optional, default: 100

Size of the mini batches.

init_size : int, optional, default: 3 * batch_size

Number of samples to randomly sample for speeding up the initialization (sometimes at the expense of accuracy): the only algorithm is initialized by running a batch KMeans on a random subset of the data. This needs to be larger than n_clusters.

init : {‘k-means++’, ‘random’ or an ndarray}, default: ‘k-means++’

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=3

Number of random initializations that are tried. In contrast to KMeans, the algorithm is only run once, using the best of the n_init initializations as measured by inertia.

compute_labels : boolean, default=True

Compute label assignment and inertia for the complete dataset once the minibatch optimization has converged in fit.

random_state : integer or numpy.RandomState, optional

The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.

reassignment_ratio : float, default: 0.01

Control the fraction of the maximum number of counts for a center to be reassigned. A higher value means that low count centers are more easily reassigned, which means that the model will take longer to converge, but should converge in a better clustering.

verbose : boolean, optional

Verbosity mode.

Attributes:

cluster_centers_ : array, [n_clusters, n_features]

Coordinates of cluster centers

labels_ : :

Labels of each point (if compute_labels is set to True).

inertia_ : float

The value of the inertia criterion associated with the chosen partition (if compute_labels is set to True). The inertia is defined as the sum of square distances of samples to their nearest neighbor.

Notes

See http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf

Methods

`fit`(X[, y])	Compute the centroids on X by chunking it into mini-batches.
`fit_predict`(X[, y])	Compute cluster centers and predict cluster index for each sample.
`fit_transform`(X[, y])	Compute clustering and transform X to cluster-distance space.
`get_params`([deep])	Get parameters for this estimator.
`partial_fit`(X[, y])	Update k means estimate on a single mini-batch X.
`predict`(X)	Predict the closest cluster each sample in X belongs to.
`score`(X[, y])	Opposite of the value of X on the K-means objective.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X[, y])	Transform X to a cluster-distance space.

__init__(n_clusters=8, init='k-means++', max_iter=100, batch_size=100, verbose=0, compute_labels=True, random_state=None, tol=0.0, max_no_improvement=10, init_size=None, n_init=3, reassignment_ratio=0.01)[source]¶

fit(X, y=None)[source]¶

Compute the centroids on X by chunking it into mini-batches.

Parameters:

X : array-like, shape = [n_samples, n_features]

Coordinates of the data points to cluster

fit_predict(X, y=None)[source]¶

Compute cluster centers and predict cluster index for each sample.

Convenience method; equivalent to calling fit(X) followed by predict(X).

fit_transform(X, y=None)[source]¶

Compute clustering and transform X to cluster-distance space.

Equivalent to fit(X).transform(X), but more efficiently implemented.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

partial_fit(X, y=None)[source]¶

Update k means estimate on a single mini-batch X.

Parameters:

X : array-like, shape = [n_samples, n_features]

Coordinates of the data points to cluster.

predict(X)[source]¶

Predict the closest cluster each sample in X belongs to.

In the vector quantization literature, cluster_centers_ is called the code book and each value returned by predict is the index of the closest code in the code book.

Parameters:

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

New data to predict.

Returns:

labels : array, shape [n_samples,]

Index of the cluster each sample belongs to.

score(X, y=None)[source]¶

Opposite of the value of X on the K-means objective.

Parameters:

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

New data.

Returns:

score : float

Opposite of the value of X on the K-means objective.

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:	self :

transform(X, y=None)[source]¶

Transform X to a cluster-distance space.

In the new space, each dimension is the distance to the cluster centers. Note that even if X is sparse, the array returned by transform will typically be dense.

Parameters:

X : {array-like, sparse matrix}, shape = [n_samples, n_features]