msmbuilder.cluster.MiniBatchKMeans¶

class msmbuilder.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, optional 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, optional 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 k. 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=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 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, optional 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.

Notes

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

Attributes

cluster_centers_	(array, [n_clusters, n_features]) Coordinates of cluster centers
labels_	(list of arrays, each of shape [sequence_length, ]) The label of each point is an integer in [0, n_clusters).
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.

Methods

fit(sequences[, y])	Fit the clustering on the data
fit_predict(sequences[, y])	Performs clustering on X and returns cluster labels.
fit_transform(sequences[, y])	Alias for fit_predict
get_params([deep])	Get parameters for this estimator.
partial_fit(X[, y])	Update k means estimate on a single mini-batch X.
partial_predict(X[, y])	Predict the closest cluster each sample in X belongs to.
partial_transform(X)	Alias for partial_predict
predict(sequences[, y])	Predict the closest cluster each sample in each sequence in sequences belongs to.
score(X)	Opposite of the value of X on the K-means objective.
set_params(**params)	Set the parameters of this estimator.
summarize()	Return some diagnostic summary statistics about this Markov model
transform(sequences)	Alias for predict

__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)¶

Methods

__init__([n_clusters, init, max_iter, ...])
fit(sequences[, y])	Fit the clustering on the data
fit_predict(sequences[, y])	Performs clustering on X and returns cluster labels.
fit_transform(sequences[, y])	Alias for fit_predict
get_params([deep])	Get parameters for this estimator.
partial_fit(X[, y])	Update k means estimate on a single mini-batch X.
partial_predict(X[, y])	Predict the closest cluster each sample in X belongs to.
partial_transform(X)	Alias for partial_predict
predict(sequences[, y])	Predict the closest cluster each sample in each sequence in sequences belongs to.
score(X)	Opposite of the value of X on the K-means objective.
set_params(**params)	Set the parameters of this estimator.
summarize()	Return some diagnostic summary statistics about this Markov model
transform(sequences)	Alias for predict

fit(sequences, y=None)¶

Fit the clustering on the data

Parameters:	sequences : list of array-like, each of shape [sequence_length, n_features] A list of multivariate timeseries. Each sequence may have a different length, but they all must have the same number of features.
Returns:	self

fit_predict(sequences, y=None)¶

Performs clustering on X and returns cluster labels.

Parameters:	sequences : list of array-like, each of shape [sequence_length, n_features] A list of multivariate timeseries. Each sequence may have a different length, but they all must have the same number of features.
Returns:	Y : list of ndarray, each of shape [sequence_length, ] Cluster labels

fit_transform(sequences, y=None)¶

Alias for fit_predict

get_params(deep=True)¶

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)¶

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.

partial_predict(X, y=None)¶

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 shape=(n_samples, n_features) A single timeseries.
Returns:	Y : array, shape=(n_samples,) Index of the cluster that each sample belongs to

partial_transform(X)¶

Alias for partial_predict

predict(sequences, y=None)¶

Predict the closest cluster each sample in each sequence in sequences 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:	sequences : list of array-like, each of shape [sequence_length, n_features] A list of multivariate timeseries. Each sequence may have a different length, but they all must have the same number of features.
Returns:	Y : list of arrays, each of shape [sequence_length,] Index of the closest center each sample belongs to.

score(X)¶

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)¶

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

summarize()¶

Return some diagnostic summary statistics about this Markov model

transform(sequences)¶

Alias for predict