"""K-means clustering.""" # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import warnings from abc import ABC, abstractmethod from numbers import Integral, Real import numpy as np import scipy.sparse as sp from ..base import ( BaseEstimator, ClassNamePrefixFeaturesOutMixin, ClusterMixin, TransformerMixin, _fit_context, ) from ..exceptions import ConvergenceWarning from ..metrics.pairwise import _euclidean_distances, euclidean_distances from ..utils import check_array, check_random_state from ..utils._openmp_helpers import _openmp_effective_n_threads from ..utils._param_validation import Interval, StrOptions, validate_params from ..utils.extmath import row_norms, stable_cumsum from ..utils.parallel import ( _get_threadpool_controller, _threadpool_controller_decorator, ) from ..utils.sparsefuncs import mean_variance_axis from ..utils.sparsefuncs_fast import assign_rows_csr from ..utils.validation import ( _check_sample_weight, _is_arraylike_not_scalar, check_is_fitted, validate_data, ) from ._k_means_common import ( CHUNK_SIZE, _inertia_dense, _inertia_sparse, _is_same_clustering, ) from ._k_means_elkan import ( elkan_iter_chunked_dense, elkan_iter_chunked_sparse, init_bounds_dense, init_bounds_sparse, ) from ._k_means_lloyd import lloyd_iter_chunked_dense, lloyd_iter_chunked_sparse from ._k_means_minibatch import _minibatch_update_dense, _minibatch_update_sparse ############################################################################### # Initialization heuristic @validate_params( { "X": ["array-like", "sparse matrix"], "n_clusters": [Interval(Integral, 1, None, closed="left")], "sample_weight": ["array-like", None], "x_squared_norms": ["array-like", None], "random_state": ["random_state"], "n_local_trials": [Interval(Integral, 1, None, closed="left"), None], }, prefer_skip_nested_validation=True, ) def kmeans_plusplus( X, n_clusters, *, sample_weight=None, x_squared_norms=None, random_state=None, n_local_trials=None, ): """Init n_clusters seeds according to k-means++. .. versionadded:: 0.24 Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data to pick seeds from. n_clusters : int The number of centroids to initialize. 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 ignored if `init` is a callable or a user provided array. .. versionadded:: 1.3 x_squared_norms : array-like of shape (n_samples,), default=None Squared Euclidean norm of each data point. random_state : int or RandomState instance, default=None Determines random number generation for centroid initialization. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. n_local_trials : int, default=None The number of seeding trials for each center (except the first), of which the one reducing inertia the most is greedily chosen. Set to None to make the number of trials depend logarithmically on the number of seeds (2+log(k)) which is the recommended setting. Setting to 1 disables the greedy cluster selection and recovers the vanilla k-means++ algorithm which was empirically shown to work less well than its greedy variant. Returns ------- centers : ndarray of shape (n_clusters, n_features) The initial centers for k-means. indices : ndarray of shape (n_clusters,) The index location of the chosen centers in the data array X. For a given index and center, X[index] = center. Notes ----- Selects initial cluster centers for k-mean clustering in a smart way to speed up convergence. see: Arthur, D. and Vassilvitskii, S. "k-means++: the advantages of careful seeding". ACM-SIAM symposium on Discrete algorithms. 2007 Examples -------- >>> from sklearn.cluster import kmeans_plusplus >>> import numpy as np >>> X = np.array([[1, 2], [1, 4], [1, 0], ... [10, 2], [10, 4], [10, 0]]) >>> centers, indices = kmeans_plusplus(X, n_clusters=2, random_state=0) >>> centers array([[10, 2], [ 1, 0]]) >>> indices array([3, 2]) """ # Check data check_array(X, accept_sparse="csr", dtype=[np.float64, np.float32]) sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype) if X.shape[0] < n_clusters: raise ValueError( f"n_samples={X.shape[0]} should be >= n_clusters={n_clusters}." ) # Check parameters if x_squared_norms is None: x_squared_norms = row_norms(X, squared=True) else: x_squared_norms = check_array(x_squared_norms, dtype=X.dtype, ensure_2d=False) if x_squared_norms.shape[0] != X.shape[0]: raise ValueError( f"The length of x_squared_norms {x_squared_norms.shape[0]} should " f"be equal to the length of n_samples {X.shape[0]}." ) random_state = check_random_state(random_state) # Call private k-means++ centers, indices = _kmeans_plusplus( X, n_clusters, x_squared_norms, sample_weight, random_state, n_local_trials ) return centers, indices def _kmeans_plusplus( X, n_clusters, x_squared_norms, sample_weight, random_state, n_local_trials=None ): """Computational component for initialization of n_clusters by k-means++. Prior validation of data is assumed. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) The data to pick seeds for. n_clusters : int The number of seeds to choose. sample_weight : ndarray of shape (n_samples,) The weights for each observation in `X`. x_squared_norms : ndarray of shape (n_samples,) Squared Euclidean norm of each data point. random_state : RandomState instance The generator used to initialize the centers. See :term:`Glossary `. n_local_trials : int, default=None The number of seeding trials for each center (except the first), of which the one reducing inertia the most is greedily chosen. Set to None to make the number of trials depend logarithmically on the number of seeds (2+log(k)); this is the default. Returns ------- centers : ndarray of shape (n_clusters, n_features) The initial centers for k-means. indices : ndarray of shape (n_clusters,) The index location of the chosen centers in the data array X. For a given index and center, X[index] = center. """ n_samples, n_features = X.shape centers = np.empty((n_clusters, n_features), dtype=X.dtype) # Set the number of local seeding trials if none is given if n_local_trials is None: # This is what Arthur/Vassilvitskii tried, but did not report # specific results for other than mentioning in the conclusion # that it helped. n_local_trials = 2 + int(np.log(n_clusters)) # Pick first center randomly and track index of point center_id = random_state.choice(n_samples, p=sample_weight / sample_weight.sum()) indices = np.full(n_clusters, -1, dtype=int) if sp.issparse(X): centers[0] = X[[center_id]].toarray() else: centers[0] = X[center_id] indices[0] = center_id # Initialize list of closest distances and calculate current potential closest_dist_sq = _euclidean_distances( centers[0, np.newaxis], X, Y_norm_squared=x_squared_norms, squared=True ) current_pot = closest_dist_sq @ sample_weight # Pick the remaining n_clusters-1 points for c in range(1, n_clusters): # Choose center candidates by sampling with probability proportional # to the squared distance to the closest existing center rand_vals = random_state.uniform(size=n_local_trials) * current_pot candidate_ids = np.searchsorted( stable_cumsum(sample_weight * closest_dist_sq), rand_vals ) # XXX: numerical imprecision can result in a candidate_id out of range np.clip(candidate_ids, None, closest_dist_sq.size - 1, out=candidate_ids) # Compute distances to center candidates distance_to_candidates = _euclidean_distances( X[candidate_ids], X, Y_norm_squared=x_squared_norms, squared=True ) # update closest distances squared and potential for each candidate np.minimum(closest_dist_sq, distance_to_candidates, out=distance_to_candidates) candidates_pot = distance_to_candidates @ sample_weight.reshape(-1, 1) # Decide which candidate is the best best_candidate = np.argmin(candidates_pot) current_pot = candidates_pot[best_candidate] closest_dist_sq = distance_to_candidates[best_candidate] best_candidate = candidate_ids[best_candidate] # Permanently add best center candidate found in local tries if sp.issparse(X): centers[c] = X[[best_candidate]].toarray() else: centers[c] = X[best_candidate] indices[c] = best_candidate return centers, indices ############################################################################### # K-means batch estimation by EM (expectation maximization) def _tolerance(X, tol): """Return a tolerance which is dependent on the dataset.""" if tol == 0: return 0 if sp.issparse(X): variances = mean_variance_axis(X, axis=0)[1] else: variances = np.var(X, axis=0) return np.mean(variances) * tol @validate_params( { "X": ["array-like", "sparse matrix"], "sample_weight": ["array-like", None], "return_n_iter": [bool], }, prefer_skip_nested_validation=False, ) def k_means( X, n_clusters, *, sample_weight=None, init="k-means++", n_init="auto", max_iter=300, verbose=False, tol=1e-4, random_state=None, copy_x=True, algorithm="lloyd", return_n_iter=False, ): """Perform K-means clustering algorithm. Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The observations 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. n_clusters : int The number of clusters to form as well as the number of centroids to generate. 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. 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 centers for k-mean clustering in a smart way to speed up convergence. See section Notes in k_init for more details. - `'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. n_init : 'auto' or int, default="auto" Number of time the k-means 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. When `n_init='auto'`, the number of runs depends on the value of init: 10 if using `init='random'` or `init` is a callable; 1 if using `init='k-means++'` or `init` is an array-like. .. versionadded:: 1.2 Added 'auto' option for `n_init`. .. versionchanged:: 1.4 Default value for `n_init` changed to `'auto'`. max_iter : int, default=300 Maximum number of iterations of the k-means algorithm to run. verbose : bool, default=False Verbosity mode. tol : float, default=1e-4 Relative tolerance with regards to Frobenius norm of the difference in the cluster centers of two consecutive iterations to declare convergence. random_state : int, RandomState instance or None, default=None Determines random number generation for centroid initialization. Use an int to make the randomness deterministic. See :term:`Glossary `. copy_x : bool, default=True When pre-computing distances it is more numerically accurate to center the data first. If `copy_x` is True (default), 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. Note that if the original data is not C-contiguous, a copy will be made even if `copy_x` is False. If the original data is sparse, but not in CSR format, a copy will be made even if `copy_x` is False. algorithm : {"lloyd", "elkan"}, default="lloyd" K-means algorithm to use. The classical EM-style algorithm is `"lloyd"`. The `"elkan"` variation can be more efficient on some datasets with well-defined clusters, by using the triangle inequality. However it's more memory intensive due to the allocation of an extra array of shape `(n_samples, n_clusters)`. .. versionchanged:: 0.18 Added Elkan algorithm .. versionchanged:: 1.1 Renamed "full" to "lloyd", and deprecated "auto" and "full". Changed "auto" to use "lloyd" instead of "elkan". return_n_iter : bool, default=False Whether or not to return the number of iterations. Returns ------- centroid : ndarray of shape (n_clusters, n_features) Centroids found at the last iteration of k-means. label : ndarray of shape (n_samples,) The `label[i]` is the code or index of the centroid the i'th observation is closest to. inertia : float The final value of the inertia criterion (sum of squared distances to the closest centroid for all observations in the training set). best_n_iter : int Number of iterations corresponding to the best results. Returned only if `return_n_iter` is set to True. Examples -------- >>> import numpy as np >>> from sklearn.cluster import k_means >>> X = np.array([[1, 2], [1, 4], [1, 0], ... [10, 2], [10, 4], [10, 0]]) >>> centroid, label, inertia = k_means( ... X, n_clusters=2, n_init="auto", random_state=0 ... ) >>> centroid array([[10., 2.], [ 1., 2.]]) >>> label array([1, 1, 1, 0, 0, 0], dtype=int32) >>> inertia 16.0 """ est = KMeans( n_clusters=n_clusters, init=init, n_init=n_init, max_iter=max_iter, verbose=verbose, tol=tol, random_state=random_state, copy_x=copy_x, algorithm=algorithm, ).fit(X, sample_weight=sample_weight) if return_n_iter: return est.cluster_centers_, est.labels_, est.inertia_, est.n_iter_ else: return est.cluster_centers_, est.labels_, est.inertia_ def _kmeans_single_elkan( X, sample_weight, centers_init, max_iter=300, verbose=False, tol=1e-4, n_threads=1, ): """A single run of k-means elkan, assumes preparation completed prior. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) The observations to cluster. If sparse matrix, must be in CSR format. sample_weight : array-like of shape (n_samples,) The weights for each observation in X. centers_init : ndarray of shape (n_clusters, n_features) The initial centers. max_iter : int, default=300 Maximum number of iterations of the k-means algorithm to run. verbose : bool, default=False Verbosity mode. tol : float, default=1e-4 Relative tolerance with regards to Frobenius norm of the difference in the cluster centers of two consecutive iterations to declare convergence. It's not advised to set `tol=0` since convergence might never be declared due to rounding errors. Use a very small number instead. n_threads : int, default=1 The number of OpenMP threads to use for the computation. Parallelism is sample-wise on the main cython loop which assigns each sample to its closest center. Returns ------- centroid : ndarray of shape (n_clusters, n_features) Centroids found at the last iteration of k-means. label : ndarray of shape (n_samples,) label[i] is the code or index of the centroid the i'th observation is closest to. inertia : float The final value of the inertia criterion (sum of squared distances to the closest centroid for all observations in the training set). n_iter : int Number of iterations run. """ n_samples = X.shape[0] n_clusters = centers_init.shape[0] # Buffers to avoid new allocations at each iteration. centers = centers_init centers_new = np.zeros_like(centers) weight_in_clusters = np.zeros(n_clusters, dtype=X.dtype) labels = np.full(n_samples, -1, dtype=np.int32) labels_old = labels.copy() center_half_distances = euclidean_distances(centers) / 2 distance_next_center = np.partition( np.asarray(center_half_distances), kth=1, axis=0 )[1] upper_bounds = np.zeros(n_samples, dtype=X.dtype) lower_bounds = np.zeros((n_samples, n_clusters), dtype=X.dtype) center_shift = np.zeros(n_clusters, dtype=X.dtype) if sp.issparse(X): init_bounds = init_bounds_sparse elkan_iter = elkan_iter_chunked_sparse _inertia = _inertia_sparse else: init_bounds = init_bounds_dense elkan_iter = elkan_iter_chunked_dense _inertia = _inertia_dense init_bounds( X, centers, center_half_distances, labels, upper_bounds, lower_bounds, n_threads=n_threads, ) strict_convergence = False for i in range(max_iter): elkan_iter( X, sample_weight, centers, centers_new, weight_in_clusters, center_half_distances, distance_next_center, upper_bounds, lower_bounds, labels, center_shift, n_threads, ) # compute new pairwise distances between centers and closest other # center of each center for next iterations center_half_distances = euclidean_distances(centers_new) / 2 distance_next_center = np.partition( np.asarray(center_half_distances), kth=1, axis=0 )[1] if verbose: inertia = _inertia(X, sample_weight, centers, labels, n_threads) print(f"Iteration {i}, inertia {inertia}") centers, centers_new = centers_new, centers if np.array_equal(labels, labels_old): # First check the labels for strict convergence. if verbose: print(f"Converged at iteration {i}: strict convergence.") strict_convergence = True break else: # No strict convergence, check for tol based convergence. center_shift_tot = (center_shift**2).sum() if center_shift_tot <= tol: if verbose: print( f"Converged at iteration {i}: center shift " f"{center_shift_tot} within tolerance {tol}." ) break labels_old[:] = labels if not strict_convergence: # rerun E-step so that predicted labels match cluster centers elkan_iter( X, sample_weight, centers, centers, weight_in_clusters, center_half_distances, distance_next_center, upper_bounds, lower_bounds, labels, center_shift, n_threads, update_centers=False, ) inertia = _inertia(X, sample_weight, centers, labels, n_threads) return labels, inertia, centers, i + 1 # Threadpoolctl context to limit the number of threads in second level of # nested parallelism (i.e. BLAS) to avoid oversubscription. @_threadpool_controller_decorator(limits=1, user_api="blas") def _kmeans_single_lloyd( X, sample_weight, centers_init, max_iter=300, verbose=False, tol=1e-4, n_threads=1, ): """A single run of k-means lloyd, assumes preparation completed prior. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) The observations to cluster. If sparse matrix, must be in CSR format. sample_weight : ndarray of shape (n_samples,) The weights for each observation in X. centers_init : ndarray of shape (n_clusters, n_features) The initial centers. max_iter : int, default=300 Maximum number of iterations of the k-means algorithm to run. verbose : bool, default=False Verbosity mode tol : float, default=1e-4 Relative tolerance with regards to Frobenius norm of the difference in the cluster centers of two consecutive iterations to declare convergence. It's not advised to set `tol=0` since convergence might never be declared due to rounding errors. Use a very small number instead. n_threads : int, default=1 The number of OpenMP threads to use for the computation. Parallelism is sample-wise on the main cython loop which assigns each sample to its closest center. Returns ------- centroid : ndarray of shape (n_clusters, n_features) Centroids found at the last iteration of k-means. label : ndarray of shape (n_samples,) label[i] is the code or index of the centroid the i'th observation is closest to. inertia : float The final value of the inertia criterion (sum of squared distances to the closest centroid for all observations in the training set). n_iter : int Number of iterations run. """ n_clusters = centers_init.shape[0] # Buffers to avoid new allocations at each iteration. centers = centers_init centers_new = np.zeros_like(centers) labels = np.full(X.shape[0], -1, dtype=np.int32) labels_old = labels.copy() weight_in_clusters = np.zeros(n_clusters, dtype=X.dtype) center_shift = np.zeros(n_clusters, dtype=X.dtype) if sp.issparse(X): lloyd_iter = lloyd_iter_chunked_sparse _inertia = _inertia_sparse else: lloyd_iter = lloyd_iter_chunked_dense _inertia = _inertia_dense strict_convergence = False for i in range(max_iter): lloyd_iter( X, sample_weight, centers, centers_new, weight_in_clusters, labels, center_shift, n_threads, ) if verbose: inertia = _inertia(X, sample_weight, centers, labels, n_threads) print(f"Iteration {i}, inertia {inertia}.") centers, centers_new = centers_new, centers if np.array_equal(labels, labels_old): # First check the labels for strict convergence. if verbose: print(f"Converged at iteration {i}: strict convergence.") strict_convergence = True break else: # No strict convergence, check for tol based convergence. center_shift_tot = (center_shift**2).sum() if center_shift_tot <= tol: if verbose: print( f"Converged at iteration {i}: center shift " f"{center_shift_tot} within tolerance {tol}." ) break labels_old[:] = labels if not strict_convergence: # rerun E-step so that predicted labels match cluster centers lloyd_iter( X, sample_weight, centers, centers, weight_in_clusters, labels, center_shift, n_threads, update_centers=False, ) inertia = _inertia(X, sample_weight, centers, labels, n_threads) return labels, inertia, centers, i + 1 def _labels_inertia(X, sample_weight, centers, n_threads=1, return_inertia=True): """E step of the K-means EM algorithm. Compute the labels and the inertia of the given samples and centers. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) The input samples to assign to the labels. If sparse matrix, must be in CSR format. sample_weight : ndarray of shape (n_samples,) The weights for each observation in X. x_squared_norms : ndarray of shape (n_samples,) Precomputed squared euclidean norm of each data point, to speed up computations. centers : ndarray of shape (n_clusters, n_features) The cluster centers. n_threads : int, default=1 The number of OpenMP threads to use for the computation. Parallelism is sample-wise on the main cython loop which assigns each sample to its closest center. return_inertia : bool, default=True Whether to compute and return the inertia. Returns ------- labels : ndarray of shape (n_samples,) The resulting assignment. inertia : float Sum of squared distances of samples to their closest cluster center. Inertia is only returned if return_inertia is True. """ n_samples = X.shape[0] n_clusters = centers.shape[0] labels = np.full(n_samples, -1, dtype=np.int32) center_shift = np.zeros(n_clusters, dtype=centers.dtype) if sp.issparse(X): _labels = lloyd_iter_chunked_sparse _inertia = _inertia_sparse else: _labels = lloyd_iter_chunked_dense _inertia = _inertia_dense _labels( X, sample_weight, centers, centers_new=None, weight_in_clusters=None, labels=labels, center_shift=center_shift, n_threads=n_threads, update_centers=False, ) if return_inertia: inertia = _inertia(X, sample_weight, centers, labels, n_threads) return labels, inertia return labels # Same as _labels_inertia but in a threadpool_limits context. _labels_inertia_threadpool_limit = _threadpool_controller_decorator( limits=1, user_api="blas" )(_labels_inertia) class _BaseKMeans( ClassNamePrefixFeaturesOutMixin, TransformerMixin, ClusterMixin, BaseEstimator, ABC ): """Base class for KMeans and MiniBatchKMeans""" _parameter_constraints: dict = { "n_clusters": [Interval(Integral, 1, None, closed="left")], "init": [StrOptions({"k-means++", "random"}), callable, "array-like"], "n_init": [ StrOptions({"auto"}), Interval(Integral, 1, None, closed="left"), ], "max_iter": [Interval(Integral, 1, None, closed="left")], "tol": [Interval(Real, 0, None, closed="left")], "verbose": ["verbose"], "random_state": ["random_state"], } def __init__( self, n_clusters, *, init, n_init, max_iter, tol, verbose, random_state, ): self.n_clusters = n_clusters self.init = init self.max_iter = max_iter self.tol = tol self.n_init = n_init self.verbose = verbose self.random_state = random_state def _check_params_vs_input(self, X, default_n_init=None): # n_clusters if X.shape[0] < self.n_clusters: raise ValueError( f"n_samples={X.shape[0]} should be >= n_clusters={self.n_clusters}." ) # tol self._tol = _tolerance(X, self.tol) # n-init if self.n_init == "auto": if isinstance(self.init, str) and self.init == "k-means++": self._n_init = 1 elif isinstance(self.init, str) and self.init == "random": self._n_init = default_n_init elif callable(self.init): self._n_init = default_n_init else: # array-like self._n_init = 1 else: self._n_init = self.n_init if _is_arraylike_not_scalar(self.init) and self._n_init != 1: warnings.warn( ( "Explicit initial center position passed: performing only" f" one init in {self.__class__.__name__} instead of " f"n_init={self._n_init}." ), RuntimeWarning, stacklevel=2, ) self._n_init = 1 @abstractmethod def _warn_mkl_vcomp(self, n_active_threads): """Issue an estimator specific warning when vcomp and mkl are both present This method is called by `_check_mkl_vcomp`. """ def _check_mkl_vcomp(self, X, n_samples): """Check when vcomp and mkl are both present""" # The BLAS call inside a prange in lloyd_iter_chunked_dense is known to # cause a small memory leak when there are less chunks than the number # of available threads. It only happens when the OpenMP library is # vcomp (microsoft OpenMP) and the BLAS library is MKL. see #18653 if sp.issparse(X): return n_active_threads = int(np.ceil(n_samples / CHUNK_SIZE)) if n_active_threads < self._n_threads: modules = _get_threadpool_controller().info() has_vcomp = "vcomp" in [module["prefix"] for module in modules] has_mkl = ("mkl", "intel") in [ (module["internal_api"], module.get("threading_layer", None)) for module in modules ] if has_vcomp and has_mkl: self._warn_mkl_vcomp(n_active_threads) def _validate_center_shape(self, X, centers): """Check if centers is compatible with X and n_clusters.""" if centers.shape[0] != self.n_clusters: raise ValueError( f"The shape of the initial centers {centers.shape} does not " f"match the number of clusters {self.n_clusters}." ) if centers.shape[1] != X.shape[1]: raise ValueError( f"The shape of the initial centers {centers.shape} does not " f"match the number of features of the data {X.shape[1]}." ) def _check_test_data(self, X): X = validate_data( self, X, accept_sparse="csr", reset=False, dtype=[np.float64, np.float32], order="C", accept_large_sparse=False, ) return X def _init_centroids( self, X, x_squared_norms, init, random_state, sample_weight, init_size=None, n_centroids=None, ): """Compute the initial centroids. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) The input samples. x_squared_norms : ndarray of shape (n_samples,) Squared euclidean norm of each data point. Pass it if you have it at hands already to avoid it being recomputed here. init : {'k-means++', 'random'}, callable or ndarray of shape \ (n_clusters, n_features) Method for initialization. random_state : RandomState instance Determines random number generation for centroid initialization. See :term:`Glossary `. sample_weight : ndarray of shape (n_samples,) The weights for each observation in X. `sample_weight` is not used during initialization if `init` is a callable or a user provided array. init_size : int, default=None Number of samples to randomly sample for speeding up the initialization (sometimes at the expense of accuracy). n_centroids : int, default=None Number of centroids to initialize. If left to 'None' the number of centroids will be equal to number of clusters to form (self.n_clusters). Returns ------- centers : ndarray of shape (n_clusters, n_features) Initial centroids of clusters. """ n_samples = X.shape[0] n_clusters = self.n_clusters if n_centroids is None else n_centroids if init_size is not None and init_size < n_samples: init_indices = random_state.randint(0, n_samples, init_size) X = X[init_indices] x_squared_norms = x_squared_norms[init_indices] n_samples = X.shape[0] sample_weight = sample_weight[init_indices] if isinstance(init, str) and init == "k-means++": centers, _ = _kmeans_plusplus( X, n_clusters, random_state=random_state, x_squared_norms=x_squared_norms, sample_weight=sample_weight, ) elif isinstance(init, str) and init == "random": seeds = random_state.choice( n_samples, size=n_clusters, replace=False, p=sample_weight / sample_weight.sum(), ) centers = X[seeds] elif _is_arraylike_not_scalar(self.init): centers = init elif callable(init): centers = init(X, n_clusters, random_state=random_state) centers = check_array(centers, dtype=X.dtype, copy=False, order="C") self._validate_center_shape(X, centers) if sp.issparse(centers): centers = centers.toarray() return centers def fit_predict(self, 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. """ return self.fit(X, sample_weight=sample_weight).labels_ def predict(self, X): """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. 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) # sample weights are not used by predict but cython helpers expect an array sample_weight = np.ones(X.shape[0], dtype=X.dtype) labels = _labels_inertia_threadpool_limit( X, sample_weight, self.cluster_centers_, n_threads=self._n_threads, return_inertia=False, ) return labels def fit_transform(self, 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. """ return self.fit(X, sample_weight=sample_weight)._transform(X) def transform(self, 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. """ check_is_fitted(self) X = self._check_test_data(X) return self._transform(X) def _transform(self, X): """Guts of transform method; no input validation.""" return euclidean_distances(X, self.cluster_centers_) def score(self, 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. """ check_is_fitted(self) X = self._check_test_data(X) sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype) _, scores = _labels_inertia_threadpool_limit( X, sample_weight, self.cluster_centers_, self._n_threads ) return -scores def __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.sparse = True return tags class KMeans(_BaseKMeans): """K-Means clustering. Read more in the :ref:`User Guide `. Parameters ---------- n_clusters : int, default=8 The number of clusters to form as well as the number of centroids to generate. For an example of how to choose an optimal value for `n_clusters` refer to :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py`. 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. For an example of how to use the different `init` strategies, see :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`. For an evaluation of the impact of initialization, see the example :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py`. n_init : 'auto' or int, default='auto' Number of times the k-means algorithm is run with different centroid seeds. The final results is the best output of `n_init` consecutive runs in terms of inertia. Several runs are recommended for sparse high-dimensional problems (see :ref:`kmeans_sparse_high_dim`). When `n_init='auto'`, the number of runs depends on the value of init: 10 if using `init='random'` or `init` is a callable; 1 if using `init='k-means++'` or `init` is an array-like. .. versionadded:: 1.2 Added 'auto' option for `n_init`. .. versionchanged:: 1.4 Default value for `n_init` changed to `'auto'`. max_iter : int, default=300 Maximum number of iterations of the k-means algorithm for a single run. tol : float, default=1e-4 Relative tolerance with regards to Frobenius norm of the difference in the cluster centers of two consecutive iterations to declare convergence. verbose : int, default=0 Verbosity mode. random_state : int, RandomState instance or None, default=None Determines random number generation for centroid initialization. Use an int to make the randomness deterministic. See :term:`Glossary `. copy_x : bool, default=True When pre-computing distances it is more numerically accurate to center the data first. If copy_x is True (default), 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. Note that if the original data is not C-contiguous, a copy will be made even if copy_x is False. If the original data is sparse, but not in CSR format, a copy will be made even if copy_x is False. algorithm : {"lloyd", "elkan"}, default="lloyd" K-means algorithm to use. The classical EM-style algorithm is `"lloyd"`. The `"elkan"` variation can be more efficient on some datasets with well-defined clusters, by using the triangle inequality. However it's more memory intensive due to the allocation of an extra array of shape `(n_samples, n_clusters)`. .. versionchanged:: 0.18 Added Elkan algorithm .. versionchanged:: 1.1 Renamed "full" to "lloyd", and deprecated "auto" and "full". Changed "auto" to use "lloyd" instead of "elkan". Attributes ---------- cluster_centers_ : ndarray of shape (n_clusters, n_features) Coordinates of cluster centers. If the algorithm stops before fully converging (see ``tol`` and ``max_iter``), these will not be consistent with ``labels_``. labels_ : ndarray of shape (n_samples,) Labels of each point inertia_ : float Sum of squared distances of samples to their closest cluster center, weighted by the sample weights if provided. n_iter_ : int Number of iterations run. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- MiniBatchKMeans : Alternative online implementation that does incremental updates of the centers positions using mini-batches. For large scale learning (say n_samples > 10k) MiniBatchKMeans is probably much faster than the default batch implementation. Notes ----- The k-means problem is solved using either Lloyd's or Elkan's algorithm. The average complexity is given by O(k n T), where 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. Refer to :doi:`"How slow is the k-means method?" D. Arthur and S. Vassilvitskii - SoCG2006.<10.1145/1137856.1137880>` for more details. In practice, the k-means 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. If the algorithm stops before fully converging (because of ``tol`` or ``max_iter``), ``labels_`` and ``cluster_centers_`` will not be consistent, i.e. the ``cluster_centers_`` will not be the means of the points in each cluster. Also, the estimator will reassign ``labels_`` after the last iteration to make ``labels_`` consistent with ``predict`` on the training set. Examples -------- >>> from sklearn.cluster import KMeans >>> import numpy as np >>> X = np.array([[1, 2], [1, 4], [1, 0], ... [10, 2], [10, 4], [10, 0]]) >>> kmeans = KMeans(n_clusters=2, random_state=0, n_init="auto").fit(X) >>> kmeans.labels_ array([1, 1, 1, 0, 0, 0], dtype=int32) >>> kmeans.predict([[0, 0], [12, 3]]) array([1, 0], dtype=int32) >>> kmeans.cluster_centers_ array([[10., 2.], [ 1., 2.]]) For examples of common problems with K-Means and how to address them see :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_assumptions.py`. For a demonstration of how K-Means can be used to cluster text documents see :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`. For a comparison between K-Means and MiniBatchKMeans refer to example :ref:`sphx_glr_auto_examples_cluster_plot_mini_batch_kmeans.py`. For a comparison between K-Means and BisectingKMeans refer to example :ref:`sphx_glr_auto_examples_cluster_plot_bisect_kmeans.py`. """ _parameter_constraints: dict = { **_BaseKMeans._parameter_constraints, "copy_x": ["boolean"], "algorithm": [StrOptions({"lloyd", "elkan"})], } def __init__( self, n_clusters=8, *, init="k-means++", n_init="auto", max_iter=300, tol=1e-4, verbose=0, random_state=None, copy_x=True, algorithm="lloyd", ): super().__init__( n_clusters=n_clusters, init=init, n_init=n_init, max_iter=max_iter, tol=tol, verbose=verbose, random_state=random_state, ) self.copy_x = copy_x self.algorithm = algorithm def _check_params_vs_input(self, X): super()._check_params_vs_input(X, default_n_init=10) self._algorithm = self.algorithm if self._algorithm == "elkan" and self.n_clusters == 1: warnings.warn( ( "algorithm='elkan' doesn't make sense for a single " "cluster. Using 'lloyd' instead." ), RuntimeWarning, ) self._algorithm = "lloyd" def _warn_mkl_vcomp(self, n_active_threads): """Warn when vcomp and mkl are both present""" warnings.warn( "KMeans is known to have a memory leak on Windows " "with MKL, when there are less chunks than available " "threads. You can avoid it by setting the environment" f" variable OMP_NUM_THREADS={n_active_threads}." ) @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None, sample_weight=None): """Compute k-means clustering. 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. .. versionadded:: 0.20 Returns ------- self : object Fitted estimator. """ X = validate_data( self, 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) 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() # Validate init array init = self.init init_is_array_like = _is_arraylike_not_scalar(init) if init_is_array_like: init = check_array(init, dtype=X.dtype, copy=True, order="C") self._validate_center_shape(X, init) # subtract of mean of x for more accurate distance computations if not sp.issparse(X): X_mean = X.mean(axis=0) # The copy was already done above X -= X_mean if init_is_array_like: init -= X_mean # precompute squared norms of data points x_squared_norms = row_norms(X, squared=True) if self._algorithm == "elkan": kmeans_single = _kmeans_single_elkan else: kmeans_single = _kmeans_single_lloyd self._check_mkl_vcomp(X, X.shape[0]) best_inertia, best_labels = None, None for i in range(self._n_init): # Initialize centers centers_init = self._init_centroids( X, x_squared_norms=x_squared_norms, init=init, random_state=random_state, sample_weight=sample_weight, ) if self.verbose: print("Initialization complete") # run a k-means once labels, inertia, centers, n_iter_ = kmeans_single( X, sample_weight, centers_init, max_iter=self.max_iter, verbose=self.verbose, tol=self._tol, n_threads=self._n_threads, ) # determine if these results are the best so far # we chose a new run if it has a better inertia and the clustering is # different from the best so far (it's possible that the inertia is # slightly better even if the clustering is the same with potentially # permuted labels, due to rounding errors) if best_inertia is None or ( inertia < best_inertia and not _is_same_clustering(labels, best_labels, self.n_clusters) ): best_labels = labels best_centers = centers best_inertia = inertia best_n_iter = n_iter_ if not sp.issparse(X): if not self.copy_x: X += X_mean best_centers += X_mean distinct_clusters = len(set(best_labels)) if distinct_clusters < self.n_clusters: warnings.warn( "Number of distinct clusters ({}) found smaller than " "n_clusters ({}). Possibly due to duplicate points " "in X.".format(distinct_clusters, self.n_clusters), ConvergenceWarning, stacklevel=2, ) self.cluster_centers_ = best_centers self._n_features_out = self.cluster_centers_.shape[0] self.labels_ = best_labels self.inertia_ = best_inertia self.n_iter_ = best_n_iter return self def _mini_batch_step( X, sample_weight, centers, centers_new, weight_sums, random_state, random_reassign=False, reassignment_ratio=0.01, verbose=False, n_threads=1, ): """Incremental update of the centers for the Minibatch K-Means algorithm. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) The original data array. If sparse, must be in CSR format. x_squared_norms : ndarray of shape (n_samples,) Squared euclidean norm of each data point. sample_weight : ndarray of shape (n_samples,) The weights for each observation in `X`. centers : ndarray of shape (n_clusters, n_features) The cluster centers before the current iteration centers_new : ndarray of shape (n_clusters, n_features) The cluster centers after the current iteration. Modified in-place. weight_sums : ndarray of shape (n_clusters,) The vector in which we keep track of the numbers of points in a cluster. This array is modified in place. random_state : RandomState instance Determines random number generation for low count centers reassignment. See :term:`Glossary `. random_reassign : boolean, default=False If True, centers with very low counts are randomly reassigned to observations. 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 likely to be reassigned, which means that the model will take longer to converge, but should converge in a better clustering. verbose : bool, default=False Controls the verbosity. n_threads : int, default=1 The number of OpenMP threads to use for the computation. Returns ------- inertia : float Sum of squared distances of samples to their closest cluster center. The inertia is computed after finding the labels and before updating the centers. """ # Perform label assignment to nearest centers # For better efficiency, it's better to run _mini_batch_step in a # threadpool_limit context than using _labels_inertia_threadpool_limit here labels, inertia = _labels_inertia(X, sample_weight, centers, n_threads=n_threads) # Update centers according to the labels if sp.issparse(X): _minibatch_update_sparse( X, sample_weight, centers, centers_new, weight_sums, labels, n_threads ) else: _minibatch_update_dense( X, sample_weight, centers, centers_new, weight_sums, labels, n_threads, ) # Reassign clusters that have very low weight if random_reassign and reassignment_ratio > 0: to_reassign = weight_sums < reassignment_ratio * weight_sums.max() # pick at most .5 * batch_size samples as new centers if to_reassign.sum() > 0.5 * X.shape[0]: indices_dont_reassign = np.argsort(weight_sums)[int(0.5 * X.shape[0]) :] to_reassign[indices_dont_reassign] = False n_reassigns = to_reassign.sum() if n_reassigns: # Pick new clusters amongst observations with uniform probability new_centers = random_state.choice( X.shape[0], replace=False, size=n_reassigns ) if verbose: print(f"[MiniBatchKMeans] Reassigning {n_reassigns} cluster centers.") if sp.issparse(X): assign_rows_csr( X, new_centers.astype(np.intp, copy=False), np.where(to_reassign)[0].astype(np.intp, copy=False), centers_new, ) else: centers_new[to_reassign] = X[new_centers] # reset counts of reassigned centers, but don't reset them too small # to avoid instant reassignment. This is a pretty dirty hack as it # also modifies the learning rates. weight_sums[to_reassign] = np.min(weight_sums[~to_reassign]) return inertia class MiniBatchKMeans(_BaseKMeans): """ Mini-Batch K-Means clustering. Read more in the :ref:`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. For an evaluation of the impact of initialization, see the example :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py`. 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. .. versionchanged:: 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 :term:`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="auto" 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 :ref:`kmeans_sparse_high_dim`). 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. .. versionadded:: 1.2 Added 'auto' option for `n_init`. .. versionchanged:: 1.4 Default value for `n_init` changed to `'auto'` in version. 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. .. versionadded:: 1.0 n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 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. See :ref:`sphx_glr_auto_examples_cluster_plot_birch_vs_minibatchkmeans.py` for a comparison with :class:`~sklearn.cluster.BIRCH`. 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) """ _parameter_constraints: dict = { **_BaseKMeans._parameter_constraints, "batch_size": [Interval(Integral, 1, None, closed="left")], "compute_labels": ["boolean"], "max_no_improvement": [Interval(Integral, 0, None, closed="left"), None], "init_size": [Interval(Integral, 1, None, closed="left"), None], "reassignment_ratio": [Interval(Real, 0, None, closed="left")], } def __init__( self, 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="auto", reassignment_ratio=0.01, ): super().__init__( n_clusters=n_clusters, init=init, max_iter=max_iter, verbose=verbose, random_state=random_state, tol=tol, n_init=n_init, ) self.max_no_improvement = max_no_improvement self.batch_size = batch_size self.compute_labels = compute_labels self.init_size = init_size self.reassignment_ratio = reassignment_ratio def _check_params_vs_input(self, X): super()._check_params_vs_input(X, default_n_init=3) self._batch_size = min(self.batch_size, X.shape[0]) # init_size self._init_size = self.init_size if self._init_size is None: self._init_size = 3 * self._batch_size if self._init_size < self.n_clusters: self._init_size = 3 * self.n_clusters elif self._init_size < self.n_clusters: warnings.warn( ( f"init_size={self._init_size} should be larger than " f"n_clusters={self.n_clusters}. Setting it to " "min(3*n_clusters, n_samples)" ), RuntimeWarning, stacklevel=2, ) self._init_size = 3 * self.n_clusters self._init_size = min(self._init_size, X.shape[0]) # reassignment_ratio if self.reassignment_ratio < 0: raise ValueError( "reassignment_ratio should be >= 0, got " f"{self.reassignment_ratio} instead." ) def _warn_mkl_vcomp(self, n_active_threads): """Warn when vcomp and mkl are both present""" warnings.warn( "MiniBatchKMeans is known to have a memory leak on " "Windows with MKL, when there are less chunks than " "available threads. You can prevent it by setting " f"batch_size >= {self._n_threads * CHUNK_SIZE} or by " "setting the environment variable " f"OMP_NUM_THREADS={n_active_threads}" ) def _mini_batch_convergence( self, step, n_steps, n_samples, centers_squared_diff, batch_inertia ): """Helper function to encapsulate the early stopping logic""" # Normalize inertia to be able to compare values when # batch_size changes batch_inertia /= self._batch_size # count steps starting from 1 for user friendly verbose mode. step = step + 1 # Ignore first iteration because it's inertia from initialization. if step == 1: if self.verbose: print( f"Minibatch step {step}/{n_steps}: mean batch " f"inertia: {batch_inertia}" ) return False # Compute an Exponentially Weighted Average of the inertia to # monitor the convergence while discarding minibatch-local stochastic # variability: https://en.wikipedia.org/wiki/Moving_average if self._ewa_inertia is None: self._ewa_inertia = batch_inertia else: alpha = self._batch_size * 2.0 / (n_samples + 1) alpha = min(alpha, 1) self._ewa_inertia = self._ewa_inertia * (1 - alpha) + batch_inertia * alpha # Log progress to be able to monitor convergence if self.verbose: print( f"Minibatch step {step}/{n_steps}: mean batch inertia: " f"{batch_inertia}, ewa inertia: {self._ewa_inertia}" ) # Early stopping based on absolute tolerance on squared change of # centers position if self._tol > 0.0 and centers_squared_diff <= self._tol: if self.verbose: print(f"Converged (small centers change) at step {step}/{n_steps}") return True # Early stopping heuristic due to lack of improvement on smoothed # inertia if self._ewa_inertia_min is None or self._ewa_inertia < self._ewa_inertia_min: self._no_improvement = 0 self._ewa_inertia_min = self._ewa_inertia else: self._no_improvement += 1 if ( self.max_no_improvement is not None and self._no_improvement >= self.max_no_improvement ): if self.verbose: print( "Converged (lack of improvement in inertia) at step " f"{step}/{n_steps}" ) return True return False def _random_reassign(self): """Check if a random reassignment needs to be done. Do random reassignments each time 10 * n_clusters samples have been processed. If there are empty clusters we always want to reassign. """ self._n_since_last_reassign += self._batch_size if (self._counts == 0).any() or self._n_since_last_reassign >= ( 10 * self.n_clusters ): self._n_since_last_reassign = 0 return True return False @_fit_context(prefer_skip_nested_validation=True) def fit(self, 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. .. versionadded:: 0.20 Returns ------- self : object Fitted estimator. """ X = validate_data( self, X, accept_sparse="csr", dtype=[np.float64, np.float32], order="C", accept_large_sparse=False, ) self._check_params_vs_input(X) 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() n_samples, n_features = X.shape # Validate init array init = self.init if _is_arraylike_not_scalar(init): init = check_array(init, dtype=X.dtype, copy=True, order="C") self._validate_center_shape(X, init) self._check_mkl_vcomp(X, self._batch_size) # precompute squared norms of data points x_squared_norms = row_norms(X, squared=True) # Validation set for the init validation_indices = random_state.randint(0, n_samples, self._init_size) X_valid = X[validation_indices] sample_weight_valid = sample_weight[validation_indices] # perform several inits with random subsets best_inertia = None for init_idx in range(self._n_init): if self.verbose: print(f"Init {init_idx + 1}/{self._n_init} with method {init}") # Initialize the centers using only a fraction of the data as we # expect n_samples to be very large when using MiniBatchKMeans. cluster_centers = self._init_centroids( X, x_squared_norms=x_squared_norms, init=init, random_state=random_state, init_size=self._init_size, sample_weight=sample_weight, ) # Compute inertia on a validation set. _, inertia = _labels_inertia_threadpool_limit( X_valid, sample_weight_valid, cluster_centers, n_threads=self._n_threads, ) if self.verbose: print(f"Inertia for init {init_idx + 1}/{self._n_init}: {inertia}") if best_inertia is None or inertia < best_inertia: init_centers = cluster_centers best_inertia = inertia centers = init_centers centers_new = np.empty_like(centers) # Initialize counts self._counts = np.zeros(self.n_clusters, dtype=X.dtype) # Attributes to monitor the convergence self._ewa_inertia = None self._ewa_inertia_min = None self._no_improvement = 0 # Initialize number of samples seen since last reassignment self._n_since_last_reassign = 0 n_steps = (self.max_iter * n_samples) // self._batch_size with _get_threadpool_controller().limit(limits=1, user_api="blas"): # Perform the iterative optimization until convergence for i in range(n_steps): # Sample a minibatch from the full dataset minibatch_indices = random_state.randint(0, n_samples, self._batch_size) # Perform the actual update step on the minibatch data batch_inertia = _mini_batch_step( X=X[minibatch_indices], sample_weight=sample_weight[minibatch_indices], centers=centers, centers_new=centers_new, weight_sums=self._counts, random_state=random_state, random_reassign=self._random_reassign(), reassignment_ratio=self.reassignment_ratio, verbose=self.verbose, n_threads=self._n_threads, ) if self._tol > 0.0: centers_squared_diff = np.sum((centers_new - centers) ** 2) else: centers_squared_diff = 0 centers, centers_new = centers_new, centers # Monitor convergence and do early stopping if necessary if self._mini_batch_convergence( i, n_steps, n_samples, centers_squared_diff, batch_inertia ): break self.cluster_centers_ = centers self._n_features_out = self.cluster_centers_.shape[0] self.n_steps_ = i + 1 self.n_iter_ = int(np.ceil(((i + 1) * self._batch_size) / n_samples)) if self.compute_labels: self.labels_, self.inertia_ = _labels_inertia_threadpool_limit( X, sample_weight, self.cluster_centers_, n_threads=self._n_threads, ) else: self.inertia_ = self._ewa_inertia * n_samples return self @_fit_context(prefer_skip_nested_validation=True) def partial_fit(self, 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. """ has_centers = hasattr(self, "cluster_centers_") X = validate_data( self, X, accept_sparse="csr", dtype=[np.float64, np.float32], order="C", accept_large_sparse=False, reset=not has_centers, ) self._random_state = getattr( self, "_random_state", check_random_state(self.random_state) ) sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype) self.n_steps_ = getattr(self, "n_steps_", 0) # precompute squared norms of data points x_squared_norms = row_norms(X, squared=True) if not has_centers: # this instance has not been fitted yet (fit or partial_fit) self._check_params_vs_input(X) self._n_threads = _openmp_effective_n_threads() # Validate init array init = self.init if _is_arraylike_not_scalar(init): init = check_array(init, dtype=X.dtype, copy=True, order="C") self._validate_center_shape(X, init) self._check_mkl_vcomp(X, X.shape[0]) # initialize the cluster centers self.cluster_centers_ = self._init_centroids( X, x_squared_norms=x_squared_norms, init=init, random_state=self._random_state, init_size=self._init_size, sample_weight=sample_weight, ) # Initialize counts self._counts = np.zeros(self.n_clusters, dtype=X.dtype) # Initialize number of samples seen since last reassignment self._n_since_last_reassign = 0 with _get_threadpool_controller().limit(limits=1, user_api="blas"): _mini_batch_step( X, sample_weight=sample_weight, centers=self.cluster_centers_, centers_new=self.cluster_centers_, weight_sums=self._counts, random_state=self._random_state, random_reassign=self._random_reassign(), reassignment_ratio=self.reassignment_ratio, verbose=self.verbose, n_threads=self._n_threads, ) if self.compute_labels: self.labels_, self.inertia_ = _labels_inertia_threadpool_limit( X, sample_weight, self.cluster_centers_, n_threads=self._n_threads, ) self.n_steps_ += 1 self._n_features_out = self.cluster_centers_.shape[0] return self