package sklearn
Module Cluster.MiniBatchKMeansSource
Source
val create :
?n_clusters:int ->
?init:[ `K_means_ | `Random | `Arr of [> `ArrayLike ] Np.Obj.t ] ->
?max_iter:int ->
?batch_size:int ->
?verbose:int ->
?compute_labels:bool ->
?random_state:int ->
?tol:float ->
?max_no_improvement:int ->
?init_size:int ->
?n_init:int ->
?reassignment_ratio:float ->
unit ->
tMini-Batch K-Means clustering.
Read more in the :ref:`User Guide <mini_batch_kmeans>`.
Parameters ----------
n_clusters : int, default=8 The number of clusters to form as well as the number of centroids to generate.
init : 'k-means++', 'random' or ndarray of shape (n_clusters, n_features), default='k-means++' Method for initialization
'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, default=100 Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics.
batch_size : int, default=100 Size of the mini batches.
verbose : int, default=0 Verbosity mode.
compute_labels : bool, default=True Compute label assignment and inertia for the complete dataset once the minibatch optimization has converged in fit.
random_state : int, RandomState instance, default=None Determines random number generation for centroid initialization and random reassignment. Use an int to make the randomness deterministic. See :term:`Glossary <random_state>`.
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, default=None 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.
If `None`, `init_size= 3 * batch_size`.
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.
Attributes ----------
cluster_centers_ : ndarray of shape (n_clusters, n_features) Coordinates of cluster centers
labels_ : int 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.
See Also -------- 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.
Notes ----- See https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf
Examples -------- >>> from sklearn.cluster import MiniBatchKMeans >>> import numpy as np >>> X = np.array([1, 2], [1, 4], [1, 0], ... [4, 2], [4, 0], [4, 4], ... [4, 5], [0, 1], [2, 2], ... [3, 2], [5, 5], [1, -1]) >>> # manually fit on batches >>> kmeans = MiniBatchKMeans(n_clusters=2, ... random_state=0, ... batch_size=6) >>> kmeans = kmeans.partial_fit(X0:6,:) >>> kmeans = kmeans.partial_fit(X6:12,:) >>> kmeans.cluster_centers_ array([2. , 1. ], [3.5, 4.5]) >>> kmeans.predict([0, 0], [4, 4]) array(0, 1, dtype=int32) >>> # fit on the whole data >>> kmeans = MiniBatchKMeans(n_clusters=2, ... random_state=0, ... batch_size=6, ... max_iter=10).fit(X) >>> kmeans.cluster_centers_ array([3.95918367, 2.40816327], [1.12195122, 1.3902439 ]) >>> kmeans.predict([0, 0], [4, 4]) array(1, 0, dtype=int32)
Source
val fit :
?y:Py.Object.t ->
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tCompute the centroids on X by chunking it into mini-batches.
Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Training instances to cluster. It must be noted that the data will be converted to C ordering, which will cause a memory copy if the given data is not C-contiguous.
y : Ignored Not used, present here for API consistency by convention.
sample_weight : array-like, shape (n_samples,), optional The weights for each observation in X. If None, all observations are assigned equal weight (default: None).
.. versionadded:: 0.20
Returns ------- self
Source
val fit_predict :
?y:Py.Object.t ->
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tCompute cluster centers and predict cluster index for each sample.
Convenience method; equivalent to calling fit(X) followed by predict(X).
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) New data to transform.
y : Ignored Not used, present here for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None The weights for each observation in X. If None, all observations are assigned equal weight.
Returns ------- labels : ndarray of shape (n_samples,) Index of the cluster each sample belongs to.
Source
val fit_transform :
?y:Py.Object.t ->
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tCompute clustering and transform X to cluster-distance space.
Equivalent to fit(X).transform(X), but more efficiently implemented.
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) New data to transform.
y : Ignored Not used, present here for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None The weights for each observation in X. If None, all observations are assigned equal weight.
Returns ------- X_new : array of shape (n_samples, n_clusters) X transformed in the new space.
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True 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.
Source
val partial_fit :
?y:Py.Object.t ->
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tUpdate k means estimate on a single mini-batch X.
Parameters ---------- X : array-like of shape (n_samples, n_features) Coordinates of the data points to cluster. It must be noted that X will be copied if it is not C-contiguous.
y : Ignored Not used, present here for API consistency by convention.
sample_weight : array-like, shape (n_samples,), optional The weights for each observation in X. If None, all observations are assigned equal weight (default: None).
Returns ------- self
Source
val predict :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tPredict 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 of shape (n_samples, n_features) New data to predict.
sample_weight : array-like, shape (n_samples,), optional The weights for each observation in X. If None, all observations are assigned equal weight (default: None).
Returns ------- labels : array, shape n_samples, Index of the cluster each sample belongs to.
Source
val score :
?y:Py.Object.t ->
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatOpposite of the value of X on the K-means objective.
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) New data.
y : Ignored Not used, present here for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None The weights for each observation in X. If None, all observations are assigned equal weight.
Returns ------- score : float Opposite of the value of X on the K-means objective.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
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 of shape (n_samples, n_features) New data to transform.
Returns ------- X_new : ndarray of shape (n_samples, n_clusters) X transformed in the new space.
Attribute cluster_centers_: get value or raise Not_found if None.
Attribute cluster_centers_: get value as an option.
Pretty-print the object to a formatter.