# MiniBatchKMeans¶

class ibex.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)

Bases: sklearn.cluster.k_means_.MiniBatchKMeans, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Mini-Batch K-Means clustering

Read more in the User Guide.

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}, 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.

max_iter : int, optional
Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics.
batch_size : int, optional, default: 100
Size of the mini batches.
verbose : boolean, optional
Verbosity mode.
compute_labels : boolean, default=True
Compute label assignment and inertia for the complete dataset once the minibatch optimization has converged in fit.
random_state : int, RandomState instance or None, optional, default: None
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
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).

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.

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.
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.
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.
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.
KMeans
The classic implementation of the clustering method based on the Lloyd’s algorithm. It consumes the whole set of input data at each iteration.
fit(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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

X : array-like or sparse matrix, shape=(n_samples, n_features)
Training instances to cluster.

y : Ignored

fit_predict(X, y=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Compute cluster centers and predict cluster index for each sample.

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

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
New data to transform.

u : Ignored

labels : array, shape [n_samples,]
Index of the cluster each sample belongs to.
fit_transform(X, y=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Compute clustering and transform X to cluster-distance space.

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

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
New data to transform.

y : Ignored

X_new : array, shape [n_samples, k]
X transformed in the new space.
partial_fit(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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

X : array-like, shape = [n_samples, n_features]
Coordinates of the data points to cluster.

y : Ignored

predict(X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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.

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
New data to predict.
labels : array, shape [n_samples,]
Index of the cluster each sample belongs to.
score(X, y=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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

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

y : Ignored

score : float
Opposite of the value of X on the K-means objective.
transform(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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.

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
New data to transform.
X_new : array, shape [n_samples, k]
X transformed in the new space.