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# `sklearn.cluster`.MiniBatchKMeans

class sklearn.cluster.MiniBatchKMeans(n_clusters=8, *, init='k-means++', max_iter=100, batch_size=1024, verbose=0, compute_labels=True, random_state=None, tol=0.0, max_no_improvement=10, init_size=None, n_init='warn', reassignment_ratio=0.01)

  Mini-Batch K-Means clustering.

  Read more in the User Guide.

  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’}, callable or array-like of shape (n_clusters, n_features), default=’k-means++’

      Method for initialization:

      ‘k-means++’ : selects initial cluster centroids using sampling based on an empirical probability distribution of the points’ contribution to the overall inertia. This technique speeds up convergence. The algorithm implemented is “greedy k-means++”. It differs from the vanilla k-means++ by making several trials at each sampling step and choosing the best centroid among them.

      ‘random’: choose `n_clusters` observations (rows) at random from data for the initial centroids.

      If an array is passed, it should be of shape (n_clusters, n_features) and gives the initial centers.

      If a callable is passed, it should take arguments X, n_clusters and a random state and return an initialization.

    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=1024

      Size of the mini batches. For faster computations, you can set the `batch_size` greater than 256 * number of cores to enable parallelism on all cores.

      Changed in version 1.0: `batch_size` default changed from 100 to 1024.

    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 or None, default=None

      Determines random number generation for centroid initialization and random reassignment. Use an int to make the randomness deterministic. See Glossary.

    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`, the heuristic is `init_size = 3 * batch_size` if `3 * batch_size < n_clusters`, else `init_size = 3 * n_clusters`.

    n_init‘auto’ or 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. Several runs are recommended for sparse high-dimensional problems (see Clustering sparse data with k-means).

      When `n_init='auto'`, the number of runs depends on the value of init: 3 if using `init='random'` or `init` is a callable; 1 if using `init='k-means++'` or `init` is an array-like.

      New in version 1.2: Added ‘auto’ option for `n_init`.

      Changed in version 1.4: Default value for `n_init` will change from 3 to `'auto'` in version 1.4.

    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. However, too high a value may cause convergence issues, especially with a small batch size.

  Attributes:
    cluster_centers_ndarray of shape (n_clusters, n_features)

      Coordinates of cluster centers.

    labels_ndarray of shape (n_samples,)

      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. If compute_labels is set to False, it’s an approximation of the inertia based on an exponentially weighted average of the batch inertiae. The inertia is defined as the sum of square distances of samples to their cluster center, weighted by the sample weights if provided.

    n_iter_int

      Number of iterations over the full dataset.

    n_steps_int

      Number of minibatches processed.

      New in version 1.0.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

  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

  When there are too few points in the dataset, some centers may be duplicated, which means that a proper clustering in terms of the number of requesting clusters and the number of returned clusters will not always match. One solution is to set `reassignment_ratio=0`, which prevents reassignments of clusters that are too small.

  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,
      ...                          n_init="auto")
      >>> kmeans = kmeans.partial_fit(X[0:6,:])
      >>> kmeans = kmeans.partial_fit(X[6:12,:])
      >>> kmeans.cluster_centers_
      array([[3.375, 3.  ],
             [0.75 , 0.5 ]])
      >>> kmeans.predict([[0, 0], [4, 4]])
      array([1, 0], dtype=int32)
      >>> # fit on the whole data
      >>> kmeans = MiniBatchKMeans(n_clusters=2,
      ...                          random_state=0,
      ...                          batch_size=6,
      ...                          max_iter=10,
      ...                          n_init="auto").fit(X)
      >>> kmeans.cluster_centers_
      array([[3.55102041, 2.48979592],
             [1.06896552, 1.        ]])
      >>> kmeans.predict([[0, 0], [4, 4]])
      array([1, 0], dtype=int32)

  Methods

  `fit`(X[, y, sample_weight]) | Compute the centroids on X by chunking it into mini-batches.
  `fit_predict`(X[, y, sample_weight]) | Compute cluster centers and predict cluster index for each sample.
  `fit_transform`(X[, y, sample_weight]) | Compute clustering and transform X to cluster-distance space.
  `get_feature_names_out`([input_features]) | Get output feature names for transformation.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `partial_fit`(X[, y, sample_weight]) | Update k means estimate on a single mini-batch X.
  `predict`(X[, sample_weight]) | Predict the closest cluster each sample in X belongs to.
  `score`(X[, y, sample_weight]) | Opposite of the value of X on the K-means objective.
  `set_fit_request`(*[, sample_weight]) | Request metadata passed to the `fit` method.
  `set_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_partial_fit_request`(*[, sample_weight]) | Request metadata passed to the `partial_fit` method.
  `set_predict_request`(*[, sample_weight]) | Request metadata passed to the `predict` method.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.
  `transform`(X) | Transform X to a cluster-distance space.

  fit(X, y=None, sample_weight=None)

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

    Parameters:
      X{array-like, sparse matrix} of 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. If a sparse matrix is passed, a copy will be made if it’s not in CSR format.

      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. `sample_weight` is not used during initialization if `init` is a callable or a user provided array.

        New in version 0.20.

    Returns:
      self object

        Fitted estimator.

  fit_predict(X, y=None, sample_weight=None)

    Compute 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.

  fit_transform(X, y=None, sample_weight=None)

    Compute 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 ndarray of shape (n_samples, n_clusters)

        X transformed in the new space.

  get_feature_names_out(input_features=None)

    Get output feature names for transformation.

    The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: `["class_name0", "class_name1", "class_name2"]`.

    Parameters:
      input_features array-like of str or None, default=None

        Only used to validate feature names with the names seen in `fit`.

    Returns:
      feature_names_out ndarray of str objects

        Transformed feature names.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    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 dict

        Parameter names mapped to their values.

  partial_fit(X, y=None, sample_weight=None)

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

    Parameters:
      X{array-like, sparse matrix} of 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. If a sparse matrix is passed, a copy will be made if it’s not in CSR format.

      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. `sample_weight` is not used during initialization if `init` is a callable or a user provided array.

    Returns:
      self object

        Return updated estimator.

  predict(X, sample_weight='deprecated')

    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} of shape (n_samples, n_features)

        New data to predict.

      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.

        Deprecated since version 1.3: The parameter `sample_weight` is deprecated in version 1.3 and will be removed in 1.5.

    Returns:
      labels ndarray of shape (n_samples,)

        Index of the cluster each sample belongs to.

  score(X, y=None, sample_weight=None)

    Opposite 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_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → MiniBatchKMeans

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_output(*, transform=None)

    Set output container.

    See Introducing the set_output API for an example on how to use the API.

    Parameters:
      transform{“default”, “pandas”}, default=None

        Configure output of `transform` and `fit_transform`.

        - `"default"`: Default output format of a transformer

        - `"pandas"`: DataFrame output

        - `None`: Transform configuration is unchanged

    Returns:
      self estimator instance

        Estimator instance.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). 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 estimator instance

        Estimator instance.

  set_partial_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → MiniBatchKMeans

    Request metadata passed to the `partial_fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `partial_fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `partial_fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `partial_fit`.

    Returns:
      self object

        The updated object.

  set_predict_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → MiniBatchKMeans

    Request metadata passed to the `predict` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `predict` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `predict`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `predict`.

    Returns:
      self object

        The updated object.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → MiniBatchKMeans

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

  transform(X)

    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.

## Examples using `sklearn.cluster.MiniBatchKMeans`

Biclustering documents with the Spectral Co-clustering algorithm

Biclustering documents with the Spectral Co-clustering algorithm

Compare BIRCH and MiniBatchKMeans

Compare BIRCH and MiniBatchKMeans

Comparing different clustering algorithms on toy datasets

Comparing different clustering algorithms on toy datasets

Comparison of the K-Means and MiniBatchKMeans clustering algorithms

Comparison of the K-Means and MiniBatchKMeans clustering algorithms

Empirical evaluation of the impact of k-means initialization

Empirical evaluation of the impact of k-means initialization

Online learning of a dictionary of parts of faces

Online learning of a dictionary of parts of faces

Faces dataset decompositions

Faces dataset decompositions

Clustering text documents using k-means

Clustering text documents using k-means