530 lines
19 KiB
Python
530 lines
19 KiB
Python
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"""Bisecting K-means clustering."""
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# Author: Michal Krawczyk <mkrwczyk.1@gmail.com>
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import warnings
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import numpy as np
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import scipy.sparse as sp
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from ..base import _fit_context
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from ..utils._openmp_helpers import _openmp_effective_n_threads
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from ..utils._param_validation import Integral, Interval, StrOptions
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from ..utils.extmath import row_norms
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from ..utils.validation import _check_sample_weight, check_is_fitted, check_random_state
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from ._k_means_common import _inertia_dense, _inertia_sparse
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from ._kmeans import (
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_BaseKMeans,
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_kmeans_single_elkan,
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_kmeans_single_lloyd,
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_labels_inertia_threadpool_limit,
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)
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class _BisectingTree:
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"""Tree structure representing the hierarchical clusters of BisectingKMeans."""
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def __init__(self, center, indices, score):
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"""Create a new cluster node in the tree.
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The node holds the center of this cluster and the indices of the data points
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that belong to it.
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"""
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self.center = center
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self.indices = indices
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self.score = score
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self.left = None
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self.right = None
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def split(self, labels, centers, scores):
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"""Split the cluster node into two subclusters."""
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self.left = _BisectingTree(
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indices=self.indices[labels == 0], center=centers[0], score=scores[0]
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)
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self.right = _BisectingTree(
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indices=self.indices[labels == 1], center=centers[1], score=scores[1]
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)
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# reset the indices attribute to save memory
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self.indices = None
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def get_cluster_to_bisect(self):
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"""Return the cluster node to bisect next.
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It's based on the score of the cluster, which can be either the number of
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data points assigned to that cluster or the inertia of that cluster
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(see `bisecting_strategy` for details).
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"""
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max_score = None
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for cluster_leaf in self.iter_leaves():
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if max_score is None or cluster_leaf.score > max_score:
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max_score = cluster_leaf.score
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best_cluster_leaf = cluster_leaf
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return best_cluster_leaf
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def iter_leaves(self):
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"""Iterate over all the cluster leaves in the tree."""
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if self.left is None:
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yield self
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else:
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yield from self.left.iter_leaves()
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yield from self.right.iter_leaves()
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class BisectingKMeans(_BaseKMeans):
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"""Bisecting K-Means clustering.
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Read more in the :ref:`User Guide <bisect_k_means>`.
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.. versionadded:: 1.1
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Parameters
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----------
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n_clusters : int, default=8
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The number of clusters to form as well as the number of
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centroids to generate.
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init : {'k-means++', 'random'} or callable, default='random'
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Method for initialization:
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'k-means++' : selects initial cluster centers for k-mean
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clustering in a smart way to speed up convergence. See section
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Notes in k_init for more details.
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'random': choose `n_clusters` observations (rows) at random from data
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for the initial centroids.
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If a callable is passed, it should take arguments X, n_clusters and a
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random state and return an initialization.
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n_init : int, default=1
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Number of time the inner k-means algorithm will be run with different
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centroid seeds in each bisection.
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That will result producing for each bisection best output of n_init
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consecutive runs in terms of inertia.
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random_state : int, RandomState instance or None, default=None
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Determines random number generation for centroid initialization
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in inner K-Means. Use an int to make the randomness deterministic.
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See :term:`Glossary <random_state>`.
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max_iter : int, default=300
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Maximum number of iterations of the inner k-means algorithm at each
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bisection.
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verbose : int, default=0
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Verbosity mode.
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tol : float, default=1e-4
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Relative tolerance with regards to Frobenius norm of the difference
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in the cluster centers of two consecutive iterations to declare
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convergence. Used in inner k-means algorithm at each bisection to pick
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best possible clusters.
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copy_x : bool, default=True
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When pre-computing distances it is more numerically accurate to center
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the data first. If copy_x is True (default), then the original data is
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not modified. If False, the original data is modified, and put back
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before the function returns, but small numerical differences may be
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introduced by subtracting and then adding the data mean. Note that if
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the original data is not C-contiguous, a copy will be made even if
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copy_x is False. If the original data is sparse, but not in CSR format,
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a copy will be made even if copy_x is False.
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algorithm : {"lloyd", "elkan"}, default="lloyd"
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Inner K-means algorithm used in bisection.
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The classical EM-style algorithm is `"lloyd"`.
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The `"elkan"` variation can be more efficient on some datasets with
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well-defined clusters, by using the triangle inequality. However it's
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more memory intensive due to the allocation of an extra array of shape
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`(n_samples, n_clusters)`.
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bisecting_strategy : {"biggest_inertia", "largest_cluster"},\
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default="biggest_inertia"
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Defines how bisection should be performed:
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- "biggest_inertia" means that BisectingKMeans will always check
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all calculated cluster for cluster with biggest SSE
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(Sum of squared errors) and bisect it. This approach concentrates on
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precision, but may be costly in terms of execution time (especially for
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larger amount of data points).
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- "largest_cluster" - BisectingKMeans will always split cluster with
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largest amount of points assigned to it from all clusters
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previously calculated. That should work faster than picking by SSE
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('biggest_inertia') and may produce similar results in most cases.
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Attributes
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----------
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cluster_centers_ : ndarray of shape (n_clusters, n_features)
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Coordinates of cluster centers. If the algorithm stops before fully
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converging (see ``tol`` and ``max_iter``), these will not be
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consistent with ``labels_``.
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labels_ : ndarray of shape (n_samples,)
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Labels of each point.
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inertia_ : float
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Sum of squared distances of samples to their closest cluster center,
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weighted by the sample weights if provided.
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X`
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has feature names that are all strings.
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See Also
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--------
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KMeans : Original implementation of K-Means algorithm.
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Notes
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-----
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It might be inefficient when n_cluster is less than 3, due to unnecessary
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calculations for that case.
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Examples
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--------
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>>> from sklearn.cluster import BisectingKMeans
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>>> import numpy as np
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>>> X = np.array([[1, 1], [10, 1], [3, 1],
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... [10, 0], [2, 1], [10, 2],
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... [10, 8], [10, 9], [10, 10]])
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>>> bisect_means = BisectingKMeans(n_clusters=3, random_state=0).fit(X)
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>>> bisect_means.labels_
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array([0, 2, 0, 2, 0, 2, 1, 1, 1], dtype=int32)
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>>> bisect_means.predict([[0, 0], [12, 3]])
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array([0, 2], dtype=int32)
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>>> bisect_means.cluster_centers_
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array([[ 2., 1.],
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[10., 9.],
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[10., 1.]])
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"""
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_parameter_constraints: dict = {
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**_BaseKMeans._parameter_constraints,
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"init": [StrOptions({"k-means++", "random"}), callable],
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"n_init": [Interval(Integral, 1, None, closed="left")],
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"copy_x": ["boolean"],
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"algorithm": [StrOptions({"lloyd", "elkan"})],
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"bisecting_strategy": [StrOptions({"biggest_inertia", "largest_cluster"})],
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}
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def __init__(
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self,
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n_clusters=8,
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*,
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init="random",
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n_init=1,
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random_state=None,
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max_iter=300,
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verbose=0,
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tol=1e-4,
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copy_x=True,
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algorithm="lloyd",
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bisecting_strategy="biggest_inertia",
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):
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super().__init__(
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n_clusters=n_clusters,
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init=init,
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max_iter=max_iter,
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verbose=verbose,
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random_state=random_state,
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tol=tol,
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n_init=n_init,
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)
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self.copy_x = copy_x
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self.algorithm = algorithm
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self.bisecting_strategy = bisecting_strategy
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def _warn_mkl_vcomp(self, n_active_threads):
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"""Warn when vcomp and mkl are both present"""
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warnings.warn(
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"BisectingKMeans is known to have a memory leak on Windows "
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"with MKL, when there are less chunks than available "
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"threads. You can avoid it by setting the environment"
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f" variable OMP_NUM_THREADS={n_active_threads}."
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)
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def _inertia_per_cluster(self, X, centers, labels, sample_weight):
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"""Calculate the sum of squared errors (inertia) per cluster.
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Parameters
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----------
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X : {ndarray, csr_matrix} of shape (n_samples, n_features)
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The input samples.
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centers : ndarray of shape (n_clusters=2, n_features)
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The cluster centers.
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labels : ndarray of shape (n_samples,)
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Index of the cluster each sample belongs to.
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sample_weight : ndarray of shape (n_samples,)
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The weights for each observation in X.
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Returns
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-------
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inertia_per_cluster : ndarray of shape (n_clusters=2,)
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Sum of squared errors (inertia) for each cluster.
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"""
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n_clusters = centers.shape[0] # = 2 since centers comes from a bisection
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_inertia = _inertia_sparse if sp.issparse(X) else _inertia_dense
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inertia_per_cluster = np.empty(n_clusters)
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for label in range(n_clusters):
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inertia_per_cluster[label] = _inertia(
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X, sample_weight, centers, labels, self._n_threads, single_label=label
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)
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return inertia_per_cluster
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def _bisect(self, X, x_squared_norms, sample_weight, cluster_to_bisect):
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"""Split a cluster into 2 subsclusters.
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Parameters
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----------
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X : {ndarray, csr_matrix} of shape (n_samples, n_features)
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Training instances to cluster.
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x_squared_norms : ndarray of shape (n_samples,)
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Squared euclidean norm of each data point.
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sample_weight : ndarray of shape (n_samples,)
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The weights for each observation in X.
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cluster_to_bisect : _BisectingTree node object
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The cluster node to split.
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"""
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X = X[cluster_to_bisect.indices]
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x_squared_norms = x_squared_norms[cluster_to_bisect.indices]
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sample_weight = sample_weight[cluster_to_bisect.indices]
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best_inertia = None
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# Split samples in X into 2 clusters.
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# Repeating `n_init` times to obtain best clusters
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for _ in range(self.n_init):
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centers_init = self._init_centroids(
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X,
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x_squared_norms=x_squared_norms,
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init=self.init,
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random_state=self._random_state,
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n_centroids=2,
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sample_weight=sample_weight,
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)
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labels, inertia, centers, _ = self._kmeans_single(
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X,
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sample_weight,
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centers_init,
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max_iter=self.max_iter,
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verbose=self.verbose,
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tol=self.tol,
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n_threads=self._n_threads,
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)
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# allow small tolerance on the inertia to accommodate for
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# non-deterministic rounding errors due to parallel computation
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if best_inertia is None or inertia < best_inertia * (1 - 1e-6):
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best_labels = labels
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best_centers = centers
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best_inertia = inertia
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if self.verbose:
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print(f"New centroids from bisection: {best_centers}")
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if self.bisecting_strategy == "biggest_inertia":
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scores = self._inertia_per_cluster(
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X, best_centers, best_labels, sample_weight
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)
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else: # bisecting_strategy == "largest_cluster"
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# Using minlength to make sure that we have the counts for both labels even
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# if all samples are labelled 0.
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scores = np.bincount(best_labels, minlength=2)
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cluster_to_bisect.split(best_labels, best_centers, scores)
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@_fit_context(prefer_skip_nested_validation=True)
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def fit(self, X, y=None, sample_weight=None):
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"""Compute bisecting k-means clustering.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Training instances to cluster.
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.. note:: The data will be converted to C ordering,
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which will cause a memory copy
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if the given data is not C-contiguous.
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y : Ignored
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Not used, present here for API consistency by convention.
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sample_weight : array-like of shape (n_samples,), default=None
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The weights for each observation in X. If None, all observations
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are assigned equal weight. `sample_weight` is not used during
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||
|
|
initialization if `init` is a callable.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
self
|
||
|
|
Fitted estimator.
|
||
|
|
"""
|
||
|
|
X = self._validate_data(
|
||
|
|
X,
|
||
|
|
accept_sparse="csr",
|
||
|
|
dtype=[np.float64, np.float32],
|
||
|
|
order="C",
|
||
|
|
copy=self.copy_x,
|
||
|
|
accept_large_sparse=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
self._check_params_vs_input(X)
|
||
|
|
|
||
|
|
self._random_state = check_random_state(self.random_state)
|
||
|
|
sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
|
||
|
|
self._n_threads = _openmp_effective_n_threads()
|
||
|
|
|
||
|
|
if self.algorithm == "lloyd" or self.n_clusters == 1:
|
||
|
|
self._kmeans_single = _kmeans_single_lloyd
|
||
|
|
self._check_mkl_vcomp(X, X.shape[0])
|
||
|
|
else:
|
||
|
|
self._kmeans_single = _kmeans_single_elkan
|
||
|
|
|
||
|
|
# Subtract of mean of X for more accurate distance computations
|
||
|
|
if not sp.issparse(X):
|
||
|
|
self._X_mean = X.mean(axis=0)
|
||
|
|
X -= self._X_mean
|
||
|
|
|
||
|
|
# Initialize the hierarchical clusters tree
|
||
|
|
self._bisecting_tree = _BisectingTree(
|
||
|
|
indices=np.arange(X.shape[0]),
|
||
|
|
center=X.mean(axis=0),
|
||
|
|
score=0,
|
||
|
|
)
|
||
|
|
|
||
|
|
x_squared_norms = row_norms(X, squared=True)
|
||
|
|
|
||
|
|
for _ in range(self.n_clusters - 1):
|
||
|
|
# Chose cluster to bisect
|
||
|
|
cluster_to_bisect = self._bisecting_tree.get_cluster_to_bisect()
|
||
|
|
|
||
|
|
# Split this cluster into 2 subclusters
|
||
|
|
self._bisect(X, x_squared_norms, sample_weight, cluster_to_bisect)
|
||
|
|
|
||
|
|
# Aggregate final labels and centers from the bisecting tree
|
||
|
|
self.labels_ = np.full(X.shape[0], -1, dtype=np.int32)
|
||
|
|
self.cluster_centers_ = np.empty((self.n_clusters, X.shape[1]), dtype=X.dtype)
|
||
|
|
|
||
|
|
for i, cluster_node in enumerate(self._bisecting_tree.iter_leaves()):
|
||
|
|
self.labels_[cluster_node.indices] = i
|
||
|
|
self.cluster_centers_[i] = cluster_node.center
|
||
|
|
cluster_node.label = i # label final clusters for future prediction
|
||
|
|
cluster_node.indices = None # release memory
|
||
|
|
|
||
|
|
# Restore original data
|
||
|
|
if not sp.issparse(X):
|
||
|
|
X += self._X_mean
|
||
|
|
self.cluster_centers_ += self._X_mean
|
||
|
|
|
||
|
|
_inertia = _inertia_sparse if sp.issparse(X) else _inertia_dense
|
||
|
|
self.inertia_ = _inertia(
|
||
|
|
X, sample_weight, self.cluster_centers_, self.labels_, self._n_threads
|
||
|
|
)
|
||
|
|
|
||
|
|
self._n_features_out = self.cluster_centers_.shape[0]
|
||
|
|
|
||
|
|
return self
|
||
|
|
|
||
|
|
def predict(self, X):
|
||
|
|
"""Predict which cluster each sample in X belongs to.
|
||
|
|
|
||
|
|
Prediction is made by going down the hierarchical tree
|
||
|
|
in searching of closest leaf cluster.
|
||
|
|
|
||
|
|
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.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
labels : ndarray of shape (n_samples,)
|
||
|
|
Index of the cluster each sample belongs to.
|
||
|
|
"""
|
||
|
|
check_is_fitted(self)
|
||
|
|
|
||
|
|
X = self._check_test_data(X)
|
||
|
|
x_squared_norms = row_norms(X, squared=True)
|
||
|
|
|
||
|
|
# sample weights are unused but necessary in cython helpers
|
||
|
|
sample_weight = np.ones_like(x_squared_norms)
|
||
|
|
|
||
|
|
labels = self._predict_recursive(X, sample_weight, self._bisecting_tree)
|
||
|
|
|
||
|
|
return labels
|
||
|
|
|
||
|
|
def _predict_recursive(self, X, sample_weight, cluster_node):
|
||
|
|
"""Predict recursively by going down the hierarchical tree.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : {ndarray, csr_matrix} of shape (n_samples, n_features)
|
||
|
|
The data points, currently assigned to `cluster_node`, to predict between
|
||
|
|
the subclusters of this node.
|
||
|
|
|
||
|
|
sample_weight : ndarray of shape (n_samples,)
|
||
|
|
The weights for each observation in X.
|
||
|
|
|
||
|
|
cluster_node : _BisectingTree node object
|
||
|
|
The cluster node of the hierarchical tree.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
labels : ndarray of shape (n_samples,)
|
||
|
|
Index of the cluster each sample belongs to.
|
||
|
|
"""
|
||
|
|
if cluster_node.left is None:
|
||
|
|
# This cluster has no subcluster. Labels are just the label of the cluster.
|
||
|
|
return np.full(X.shape[0], cluster_node.label, dtype=np.int32)
|
||
|
|
|
||
|
|
# Determine if data points belong to the left or right subcluster
|
||
|
|
centers = np.vstack((cluster_node.left.center, cluster_node.right.center))
|
||
|
|
if hasattr(self, "_X_mean"):
|
||
|
|
centers += self._X_mean
|
||
|
|
|
||
|
|
cluster_labels = _labels_inertia_threadpool_limit(
|
||
|
|
X,
|
||
|
|
sample_weight,
|
||
|
|
centers,
|
||
|
|
self._n_threads,
|
||
|
|
return_inertia=False,
|
||
|
|
)
|
||
|
|
mask = cluster_labels == 0
|
||
|
|
|
||
|
|
# Compute the labels for each subset of the data points.
|
||
|
|
labels = np.full(X.shape[0], -1, dtype=np.int32)
|
||
|
|
|
||
|
|
labels[mask] = self._predict_recursive(
|
||
|
|
X[mask], sample_weight[mask], cluster_node.left
|
||
|
|
)
|
||
|
|
|
||
|
|
labels[~mask] = self._predict_recursive(
|
||
|
|
X[~mask], sample_weight[~mask], cluster_node.right
|
||
|
|
)
|
||
|
|
|
||
|
|
return labels
|
||
|
|
|
||
|
|
def _more_tags(self):
|
||
|
|
return {"preserves_dtype": [np.float64, np.float32]}
|