msmbuilder.cluster.KMeans¶

class
msmbuilder.cluster.
KMeans
(n_clusters=8, init='kmeans++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=1)¶ KMeans clustering
Read more in the User Guide.
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, default: 300
Maximum number of iterations of the kmeans algorithm for a single run.
n_init : int, default: 10
Number of time the kmeans 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.
init : {‘kmeans++’, ‘random’ or an ndarray}
Method for initialization, defaults to ‘kmeans++’:
‘kmeans++’ : selects initial cluster centers for kmean 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.
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
tol : float, default: 1e4
Relative tolerance with regards to inertia to declare convergence
n_jobs : int
The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel.
If 1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below 1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = 2, all CPUs but one are used.
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.
verbose : int, default 0
Verbosity mode.
copy_x : boolean, default True
When precomputing distances it is more numerically accurate to center the data first. If copy_x is True, then the original data is not modified. 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.
See also
MiniBatchKMeans
 Alternative online implementation that does incremental updates of the centers positions using minibatches. For large scale learning (say n_samples > 10k) MiniBatchKMeans is probably much faster to than the default batch implementation.
Notes
The kmeans problem is solved using Lloyd’s algorithm.
The average complexity is given by O(k n T), were n is the number of samples and T is the number of iteration.
The worst case complexity is given by O(n^(k+2/p)) with n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii, ‘How slow is the kmeans method?’ SoCG2006)
In practice, the kmeans algorithm is very fast (one of the fastest clustering algorithms available), but it falls in local minima. That’s why it can be useful to restart it several times.
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) Sum of distances of samples to their closest cluster center. 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_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[, y])Opposite of the value of X on the Kmeans 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='kmeans++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=1)¶
Methods
__init__
([n_clusters, init, n_init, ...])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_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[, y])Opposite of the value of X on the Kmeans 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 arraylike, 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 arraylike, 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_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 : arraylike 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 arraylike, 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, y=None)¶ Opposite of the value of X on the Kmeans objective.
Parameters: X : {arraylike, sparse matrix}, shape = [n_samples, n_features]
New data.
Returns: score : float
Opposite of the value of X on the Kmeans 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