/
githubmirror
/
scikit-learn
Обзор
Документация
Войти
/
githubmirror
/
scikit-learn
Код
Запросы
0
Пакеты
0
Релизы
0
Аналитика
Безопасность
main
sklearn/cluster/_mean_shift.py
579 строк
20 KB
Kropiunig
DOC fix incorrect default for random_state in estimate_bandwidth docstring (#34291)
27 июн 2026, 20:16
Не верифицирован
27 июн 2026, 20:16
e504dc6
Код
Авторство
О чём код?
"""Mean shift clustering algorithm. Mean shift clustering aims to discover *blobs* in a smooth density of samples. It is a centroid based algorithm, which works by updating candidates for centroids to be the mean of the points within a given region. These candidates are then filtered in a post-processing stage to eliminate near-duplicates to form the final set of centroids. Seeding is performed using a binning technique for scalability. """ # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import warnings from collections import defaultdict from numbers import Integral, Real import numpy as np from sklearn._config import config_context from sklearn.base import BaseEstimator, ClusterMixin, _fit_context from sklearn.metrics.pairwise import pairwise_distances_argmin from sklearn.neighbors import NearestNeighbors from sklearn.utils import check_array, check_random_state, gen_batches from sklearn.utils._param_validation import Interval, validate_params from sklearn.utils.parallel import Parallel, delayed from sklearn.utils.validation import check_is_fitted, validate_data @validate_params( { "X": ["array-like"], "quantile": [Interval(Real, 0, 1, closed="both")], "n_samples": [Interval(Integral, 1, None, closed="left"), None], "random_state": ["random_state"], "n_jobs": [Integral, None], }, prefer_skip_nested_validation=True, ) def estimate_bandwidth(X, *, quantile=0.3, n_samples=None, random_state=0, n_jobs=None): """Estimate the bandwidth to use with the mean-shift algorithm. This function takes time at least quadratic in `n_samples`. For large datasets, it is wise to subsample by setting `n_samples`. Alternatively, the parameter `bandwidth` can be set to a small value without estimating it. Parameters ---------- X : array-like of shape (n_samples, n_features) Input points. quantile : float, default=0.3 Should be between [0, 1] 0.5 means that the median of all pairwise distances is used. n_samples : int, default=None The number of samples to use. If not given, all samples are used. random_state : int, RandomState instance, default=0 The generator used to randomly select the samples from input points for bandwidth estimation. Use an int to make the randomness deterministic. See :term:`Glossary <random_state>`. n_jobs : int, default=None The number of parallel jobs to run for neighbors search. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. Returns ------- bandwidth : float The bandwidth parameter. Examples -------- >>> import numpy as np >>> from sklearn.cluster import estimate_bandwidth >>> X = np.array([[1, 1], [2, 1], [1, 0], ... [4, 7], [3, 5], [3, 6]]) >>> estimate_bandwidth(X, quantile=0.5) np.float64(1.61) """ X = check_array(X) random_state = check_random_state(random_state) if n_samples is not None: idx = random_state.permutation(X.shape[0])[:n_samples] X = X[idx] n_neighbors = int(X.shape[0] * quantile) if n_neighbors < 1: # cannot fit NearestNeighbors with n_neighbors = 0 n_neighbors = 1 nbrs = NearestNeighbors(n_neighbors=n_neighbors, n_jobs=n_jobs) nbrs.fit(X) bandwidth = 0.0 for batch in gen_batches(len(X), 500): d, _ = nbrs.kneighbors(X[batch, :], return_distance=True) bandwidth += np.max(d, axis=1).sum() return bandwidth / X.shape[0] # separate function for each seed's iterative loop def _mean_shift_single_seed(my_mean, X, nbrs, max_iter): # For each seed, climb gradient until convergence or max_iter bandwidth = nbrs.get_params()["radius"] stop_thresh = 1e-3 * bandwidth # when mean has converged completed_iterations = 0 while True: # Find mean of points within bandwidth i_nbrs = nbrs.radius_neighbors([my_mean], bandwidth, return_distance=False)[0] points_within = X[i_nbrs] if len(points_within) == 0: break # Depending on seeding strategy this condition may occur my_old_mean = my_mean # save the old mean my_mean = np.mean(points_within, axis=0) # If converged or at max_iter, adds the cluster if ( np.linalg.norm(my_mean - my_old_mean) <= stop_thresh or completed_iterations == max_iter ): break completed_iterations += 1 return tuple(my_mean), len(points_within), completed_iterations @validate_params( {"X": ["array-like"]}, prefer_skip_nested_validation=False, ) def mean_shift( X, *, bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, max_iter=300, n_jobs=None, ): """Perform mean shift clustering of data using a flat kernel. Read more in the :ref:`User Guide <mean_shift>`. Parameters ---------- X : array-like of shape (n_samples, n_features) Input data. bandwidth : float, default=None Kernel bandwidth. If not None, must be in the range [0, +inf). If None, the bandwidth is determined using a heuristic based on the median of all pairwise distances. This will take quadratic time in the number of samples. The sklearn.cluster.estimate_bandwidth function can be used to do this more efficiently. seeds : array-like of shape (n_seeds, n_features) or None Point used as initial kernel locations. If None and bin_seeding=False, each data point is used as a seed. If None and bin_seeding=True, see bin_seeding. bin_seeding : bool, default=False If true, initial kernel locations are not locations of all points, but rather the location of the discretized version of points, where points are binned onto a grid whose coarseness corresponds to the bandwidth. Setting this option to True will speed up the algorithm because fewer seeds will be initialized. Ignored if seeds argument is not None. min_bin_freq : int, default=1 To speed up the algorithm, accept only those bins with at least min_bin_freq points as seeds. cluster_all : bool, default=True If true, then all points are clustered, even those orphans that are not within any kernel. Orphans are assigned to the nearest kernel. If false, then orphans are given cluster label -1. max_iter : int, default=300 Maximum number of iterations, per seed point before the clustering operation terminates (for that seed point), if has not converged yet. n_jobs : int, default=None The number of jobs to use for the computation. The following tasks benefit from the parallelization: - The search of nearest neighbors for bandwidth estimation and label assignments. See the details in the docstring of the ``NearestNeighbors`` class. - Hill-climbing optimization for all seeds. See :term:`Glossary <n_jobs>` for more details. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. .. versionadded:: 0.17 Parallel Execution using *n_jobs*. Returns ------- cluster_centers : ndarray of shape (n_clusters, n_features) Coordinates of cluster centers. labels : ndarray of shape (n_samples,) Cluster labels for each point. Notes ----- For a usage example, see :ref:`sphx_glr_auto_examples_cluster_plot_mean_shift.py`. Examples -------- >>> import numpy as np >>> from sklearn.cluster import mean_shift >>> X = np.array([[1, 1], [2, 1], [1, 0], ... [4, 7], [3, 5], [3, 6]]) >>> cluster_centers, labels = mean_shift(X, bandwidth=2) >>> cluster_centers array([[3.33, 6. ], [1.33, 0.66]]) >>> labels array([1, 1, 1, 0, 0, 0]) """ model = MeanShift( bandwidth=bandwidth, seeds=seeds, min_bin_freq=min_bin_freq, bin_seeding=bin_seeding, cluster_all=cluster_all, n_jobs=n_jobs, max_iter=max_iter, ).fit(X) return model.cluster_centers_, model.labels_ def get_bin_seeds(X, bin_size, min_bin_freq=1): """Find seeds for mean_shift. Finds seeds by first binning data onto a grid whose lines are spaced bin_size apart, and then choosing those bins with at least min_bin_freq points. Parameters ---------- X : array-like of shape (n_samples, n_features) Input points, the same points that will be used in mean_shift. bin_size : float Controls the coarseness of the binning. Smaller values lead to more seeding (which is computationally more expensive). If you're not sure how to set this, set it to the value of the bandwidth used in clustering.mean_shift. min_bin_freq : int, default=1 Only bins with at least min_bin_freq will be selected as seeds. Raising this value decreases the number of seeds found, which makes mean_shift computationally cheaper. Returns ------- bin_seeds : array-like of shape (n_samples, n_features) Points used as initial kernel positions in clustering.mean_shift. """ if bin_size == 0: return X # Bin points bin_sizes = defaultdict(int) for point in X: binned_point = np.round(point / bin_size) bin_sizes[tuple(binned_point)] += 1 # Select only those bins as seeds which have enough members bin_seeds = np.array( [point for point, freq in bin_sizes.items() if freq >= min_bin_freq], dtype=np.float32, ) if len(bin_seeds) == len(X): warnings.warn( "Binning data failed with provided bin_size=%f, using data points as seeds." % bin_size ) return X bin_seeds = bin_seeds * bin_size return bin_seeds class MeanShift(ClusterMixin, BaseEstimator): """Mean shift clustering using a flat kernel. Mean shift clustering aims to discover "blobs" in a smooth density of samples. It is a centroid-based algorithm, which works by updating candidates for centroids to be the mean of the points within a given region. These candidates are then filtered in a post-processing stage to eliminate near-duplicates to form the final set of centroids. Seeding is performed using a binning technique for scalability. For an example of how to use MeanShift clustering, refer to: :ref:`sphx_glr_auto_examples_cluster_plot_mean_shift.py`. Read more in the :ref:`User Guide <mean_shift>`. Parameters ---------- bandwidth : float, default=None Bandwidth used in the flat kernel. If not given, the bandwidth is estimated using sklearn.cluster.estimate_bandwidth; see the documentation for that function for hints on scalability (see also the Notes, below). seeds : array-like of shape (n_samples, n_features), default=None Seeds used to initialize kernels. If not set, the seeds are calculated by clustering.get_bin_seeds with bandwidth as the grid size and default values for other parameters. bin_seeding : bool, default=False If true, initial kernel locations are not locations of all points, but rather the location of the discretized version of points, where points are binned onto a grid whose coarseness corresponds to the bandwidth. Setting this option to True will speed up the algorithm because fewer seeds will be initialized. The default value is False. Ignored if seeds argument is not None. min_bin_freq : int, default=1 To speed up the algorithm, accept only those bins with at least min_bin_freq points as seeds. cluster_all : bool, default=True If true, then all points are clustered, even those orphans that are not within any kernel. Orphans are assigned to the nearest kernel. If false, then orphans are given cluster label -1. n_jobs : int, default=None The number of jobs to use for the computation. The following tasks benefit from the parallelization: - The search of nearest neighbors for bandwidth estimation and label assignments. See the details in the docstring of the ``NearestNeighbors`` class. - Hill-climbing optimization for all seeds. See :term:`Glossary <n_jobs>` for more details. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. max_iter : int, default=300 Maximum number of iterations, per seed point before the clustering operation terminates (for that seed point), if has not converged yet. .. versionadded:: 0.22 Attributes ---------- cluster_centers_ : ndarray of shape (n_clusters, n_features) Coordinates of cluster centers. labels_ : ndarray of shape (n_samples,) Labels of each point. n_iter_ : int Maximum number of iterations performed on each seed. .. versionadded:: 0.22 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 : K-Means clustering. Notes ----- Scalability: Because this implementation uses a flat kernel and a Ball Tree to look up members of each kernel, the complexity will tend towards O(T*n*log(n)) in lower dimensions, with n the number of samples and T the number of points. In higher dimensions the complexity will tend towards O(T*n^2). Scalability can be boosted by using fewer seeds, for example by using a higher value of min_bin_freq in the get_bin_seeds function. Note that the estimate_bandwidth function is much less scalable than the mean shift algorithm and will be the bottleneck if it is used. References ---------- Dorin Comaniciu and Peter Meer, "Mean Shift: A robust approach toward feature space analysis". IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. pp. 603-619. Examples -------- >>> from sklearn.cluster import MeanShift >>> import numpy as np >>> X = np.array([[1, 1], [2, 1], [1, 0], ... [4, 7], [3, 5], [3, 6]]) >>> clustering = MeanShift(bandwidth=2).fit(X) >>> clustering.labels_ array([1, 1, 1, 0, 0, 0]) >>> clustering.predict([[0, 0], [5, 5]]) array([1, 0]) >>> clustering MeanShift(bandwidth=2) For a comparison of Mean Shift clustering with other clustering algorithms, see :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py` """ _parameter_constraints: dict = { "bandwidth": [Interval(Real, 0, None, closed="neither"), None], "seeds": ["array-like", None], "bin_seeding": ["boolean"], "min_bin_freq": [Interval(Integral, 1, None, closed="left")], "cluster_all": ["boolean"], "n_jobs": [Integral, None], "max_iter": [Interval(Integral, 0, None, closed="left")], } def __init__( self, *, bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, n_jobs=None, max_iter=300, ): self.bandwidth = bandwidth self.seeds = seeds self.bin_seeding = bin_seeding self.cluster_all = cluster_all self.min_bin_freq = min_bin_freq self.n_jobs = n_jobs self.max_iter = max_iter @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Perform clustering. Parameters ---------- X : array-like of shape (n_samples, n_features) Samples to cluster. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object Fitted instance. """ X = validate_data(self, X) bandwidth = self.bandwidth if bandwidth is None: bandwidth = estimate_bandwidth(X, n_jobs=self.n_jobs) seeds = self.seeds if seeds is None: if self.bin_seeding: seeds = get_bin_seeds(X, bandwidth, self.min_bin_freq) else: seeds = X n_samples, n_features = X.shape center_intensity_dict = {} # We use n_jobs=1 because this will be used in nested calls under # parallel calls to _mean_shift_single_seed so there is no need for # for further parallelism. nbrs = NearestNeighbors(radius=bandwidth, n_jobs=1).fit(X) # execute iterations on all seeds in parallel all_res = Parallel(n_jobs=self.n_jobs)( delayed(_mean_shift_single_seed)(seed, X, nbrs, self.max_iter) for seed in seeds ) # copy results in a dictionary for i in range(len(seeds)): if all_res[i][1]: # i.e. len(points_within) > 0 center_intensity_dict[all_res[i][0]] = all_res[i][1] self.n_iter_ = max([x[2] for x in all_res]) if not center_intensity_dict: # nothing near seeds raise ValueError( "No point was within bandwidth=%f of any seed. Try a different seeding" " strategy or increase the bandwidth." % bandwidth ) # POST PROCESSING: remove near duplicate points # If the distance between two kernels is less than the bandwidth, # then we have to remove one because it is a duplicate. Remove the # one with fewer points. sorted_by_intensity = sorted( center_intensity_dict.items(), key=lambda tup: (tup[1], tup[0]), reverse=True, ) sorted_centers = np.array([tup[0] for tup in sorted_by_intensity]) unique = np.ones(len(sorted_centers), dtype=bool) nbrs = NearestNeighbors(radius=bandwidth, n_jobs=self.n_jobs).fit( sorted_centers ) for i, center in enumerate(sorted_centers): if unique[i]: neighbor_idxs = nbrs.radius_neighbors([center], return_distance=False)[ 0 ] unique[neighbor_idxs] = 0 unique[i] = 1 # leave the current point as unique cluster_centers = sorted_centers[unique] # ASSIGN LABELS: a point belongs to the cluster that it is closest to nbrs = NearestNeighbors(n_neighbors=1, n_jobs=self.n_jobs).fit(cluster_centers) labels = np.zeros(n_samples, dtype=int) distances, idxs = nbrs.kneighbors(X) if self.cluster_all: labels = idxs.flatten() else: labels.fill(-1) bool_selector = distances.flatten() <= bandwidth labels[bool_selector] = idxs.flatten()[bool_selector] self.cluster_centers_, self.labels_ = cluster_centers, labels return self def predict(self, X): """Predict the closest cluster each sample in X belongs to. Parameters ---------- X : array-like 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 = validate_data(self, X, reset=False) with config_context(assume_finite=True): return pairwise_distances_argmin(X, self.cluster_centers_)