""" DBSCAN: Density-Based Spatial Clustering of Applications with Noise """ # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import warnings from numbers import Integral, Real import numpy as np from scipy import sparse from sklearn.base import BaseEstimator, ClusterMixin, _fit_context from sklearn.cluster._dbscan_inner import dbscan_inner from sklearn.metrics.pairwise import _VALID_METRICS from sklearn.neighbors import NearestNeighbors from sklearn.utils._param_validation import Interval, StrOptions, validate_params from sklearn.utils.validation import _check_sample_weight, validate_data @validate_params( { "X": ["array-like", "sparse matrix"], "sample_weight": ["array-like", None], }, prefer_skip_nested_validation=False, ) def dbscan( X, eps=0.5, *, min_samples=5, metric="minkowski", metric_params=None, algorithm="auto", leaf_size=30, p=2, sample_weight=None, n_jobs=None, ): """Perform DBSCAN clustering from vector array or distance matrix. This function is a wrapper around :class:`~cluster.DBSCAN`, suitable for quick, standalone clustering tasks. For estimator-based workflows, where estimator attributes or pipeline integration is required, prefer :class:`~cluster.DBSCAN`. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that groups together points that are closely packed while marking points in low-density regions as outliers. Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, scipy sparse matrix} of shape (n_samples, n_features) or \ (n_samples, n_samples) A feature array, or array of distances between samples if ``metric='precomputed'``. When using precomputed distances, X must be a square symmetric matrix. eps : float, default=0.5 The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function. Smaller values result in more clusters, while larger values result in fewer, larger clusters. min_samples : int, default=5 The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself. Higher values yield fewer, denser clusters, while lower values yield more, sparser clusters. metric : str or callable, default='minkowski' The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by :func:`sklearn.metrics.pairwise_distances` for its metric parameter. If metric is "precomputed", X is assumed to be a distance matrix and must be square during fit. X may be a :term:`sparse graph `, in which case only "nonzero" elements may be considered neighbors. metric_params : dict, default=None Additional keyword arguments for the metric function. .. versionadded:: 0.19 algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' The algorithm to be used by the NearestNeighbors module to compute pointwise distances and find nearest neighbors. 'auto' will attempt to decide the most appropriate algorithm based on the values passed to :meth:`fit` method. See :class:`~sklearn.neighbors.NearestNeighbors` documentation for details. leaf_size : int, default=30 Leaf size passed to BallTree or cKDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. Generally, smaller leaf sizes lead to faster queries but slower construction. p : float, default=2 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected to be positive. sample_weight : array-like of shape (n_samples,), default=None Weight of each sample, such that a sample with a weight of at least ``min_samples`` is by itself a core sample; a sample with negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1. 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 ` for more details. If precomputed distances are used, parallel execution is not available and thus n_jobs will have no effect. Returns ------- core_samples : ndarray of shape (n_core_samples,) Indices of core samples. labels : ndarray of shape (n_samples,) Cluster labels for each point. Noisy samples are given the label -1. Non-negative integers indicate cluster membership. See Also -------- DBSCAN : An estimator interface for this clustering algorithm. OPTICS : A similar estimator interface clustering at multiple values of eps. Our implementation is optimized for memory usage. Notes ----- For an example, see :ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py`. This implementation bulk-computes all neighborhood queries, which increases the memory complexity to O(n.d) where d is the average number of neighbors, while original DBSCAN had memory complexity O(n). It may attract a higher memory complexity when querying these nearest neighborhoods, depending on the ``algorithm``. One way to avoid the query complexity is to pre-compute sparse neighborhoods in chunks using :func:`NearestNeighbors.radius_neighbors_graph ` with ``mode='distance'``, then using ``metric='precomputed'`` here. Another way to reduce memory and computation time is to remove (near-)duplicate points and use ``sample_weight`` instead. :class:`~sklearn.cluster.OPTICS` provides a similar clustering with lower memory usage. References ---------- Ester, M., H. P. Kriegel, J. Sander, and X. Xu, `"A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise" `_. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996 Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). :doi:`"DBSCAN revisited, revisited: why and how you should (still) use DBSCAN." <10.1145/3068335>` ACM Transactions on Database Systems (TODS), 42(3), 19. Examples -------- >>> from sklearn.cluster import dbscan >>> X = [[1, 2], [2, 2], [2, 3], [8, 7], [8, 8], [25, 80]] >>> core_samples, labels = dbscan(X, eps=3, min_samples=2) >>> core_samples array([0, 1, 2, 3, 4]) >>> labels array([ 0, 0, 0, 1, 1, -1]) """ est = DBSCAN( eps=eps, min_samples=min_samples, metric=metric, metric_params=metric_params, algorithm=algorithm, leaf_size=leaf_size, p=p, n_jobs=n_jobs, ) est.fit(X, sample_weight=sample_weight) return est.core_sample_indices_, est.labels_ class DBSCAN(ClusterMixin, BaseEstimator): """Perform DBSCAN clustering from vector array or distance matrix. DBSCAN - Density-Based Spatial Clustering of Applications with Noise. Finds core samples of high density and expands clusters from them. This algorithm is particularly good for data which contains clusters of similar density and can find clusters of arbitrary shape. Unlike K-means, DBSCAN does not require specifying the number of clusters in advance and can identify outliers as noise points. This implementation has a worst case memory complexity of :math:`O({n}^2)`, which can occur when the `eps` param is large and `min_samples` is low, while the original DBSCAN only uses linear memory. For further details, see the Notes below. Read more in the :ref:`User Guide `. Parameters ---------- eps : float, default=0.5 The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function. Smaller values generally lead to more clusters. min_samples : int, default=5 The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself. If `min_samples` is set to a higher value, DBSCAN will find denser clusters, whereas if it is set to a lower value, the found clusters will be more sparse. metric : str, or callable, default='euclidean' The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by :func:`sklearn.metrics.pairwise_distances` for its metric parameter. If metric is "precomputed", X is assumed to be a distance matrix and must be square. X may be a :term:`sparse graph`, in which case only "nonzero" elements may be considered neighbors for DBSCAN. .. versionadded:: 0.17 metric *precomputed* to accept precomputed sparse matrix. metric_params : dict, default=None Additional keyword arguments for the metric function. .. versionadded:: 0.19 algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' The algorithm to be used by the NearestNeighbors module to compute pointwise distances and find nearest neighbors. 'auto' will attempt to decide the most appropriate algorithm based on the values passed to :meth:`fit` method. See :class:`~sklearn.neighbors.NearestNeighbors` documentation for details. leaf_size : int, default=30 Leaf size passed to BallTree or cKDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. p : float, default=None The power of the Minkowski metric to be used to calculate distance between points. If None, then ``p=2`` (equivalent to the Euclidean distance). When p=1, this is equivalent to Manhattan distance. n_jobs : int, default=None The number of parallel jobs to run. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. Attributes ---------- core_sample_indices_ : ndarray of shape (n_core_samples,) Indices of core samples. components_ : ndarray of shape (n_core_samples, n_features) Copy of each core sample found by training. labels_ : ndarray of shape (n_samples,) Cluster labels for each point in the dataset given to fit(). Noisy samples are given the label -1. Non-negative integers indicate cluster membership. 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 -------- OPTICS : A similar clustering at multiple values of eps. Our implementation is optimized for memory usage. Notes ----- This implementation bulk-computes all neighborhood queries, which increases the memory complexity to O(n.d) where d is the average number of neighbors, while original DBSCAN had memory complexity O(n). It may attract a higher memory complexity when querying these nearest neighborhoods, depending on the ``algorithm``. One way to avoid the query complexity is to pre-compute sparse neighborhoods in chunks using :func:`NearestNeighbors.radius_neighbors_graph ` with ``mode='distance'``, then using ``metric='precomputed'`` here. Another way to reduce memory and computation time is to remove (near-)duplicate points and use ``sample_weight`` instead. :class:`~sklearn.cluster.OPTICS` provides a similar clustering with lower memory usage. References ---------- Ester, M., H. P. Kriegel, J. Sander, and X. Xu, `"A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise" `_. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996 Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). :doi:`"DBSCAN revisited, revisited: why and how you should (still) use DBSCAN." <10.1145/3068335>` ACM Transactions on Database Systems (TODS), 42(3), 19. Examples -------- >>> from sklearn.cluster import DBSCAN >>> import numpy as np >>> X = np.array([[1, 2], [2, 2], [2, 3], ... [8, 7], [8, 8], [25, 80]]) >>> clustering = DBSCAN(eps=3, min_samples=2).fit(X) >>> clustering.labels_ array([ 0, 0, 0, 1, 1, -1]) >>> clustering DBSCAN(eps=3, min_samples=2) For an example, see :ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py`. For a comparison of DBSCAN with other clustering algorithms, see :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py` """ _parameter_constraints: dict = { "eps": [Interval(Real, 0.0, None, closed="neither")], "min_samples": [Interval(Integral, 1, None, closed="left")], "metric": [ StrOptions(set(_VALID_METRICS) | {"precomputed"}), callable, ], "metric_params": [dict, None], "algorithm": [StrOptions({"auto", "ball_tree", "kd_tree", "brute"})], "leaf_size": [Interval(Integral, 1, None, closed="left")], "p": [Interval(Real, 0.0, None, closed="left"), None], "n_jobs": [Integral, None], } def __init__( self, eps=0.5, *, min_samples=5, metric="euclidean", metric_params=None, algorithm="auto", leaf_size=30, p=None, n_jobs=None, ): self.eps = eps self.min_samples = min_samples self.metric = metric self.metric_params = metric_params self.algorithm = algorithm self.leaf_size = leaf_size self.p = p self.n_jobs = n_jobs @_fit_context( # DBSCAN.metric is not validated yet prefer_skip_nested_validation=False ) def fit(self, X, y=None, sample_weight=None): """Perform DBSCAN clustering from features, or distance matrix. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features), or \ (n_samples, n_samples) Training instances to cluster, or distances between instances if ``metric='precomputed'``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``. y : Ignored Not used, present here for API consistency by convention. sample_weight : array-like of shape (n_samples,), default=None Weight of each sample, such that a sample with a weight of at least ``min_samples`` is by itself a core sample; a sample with a negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1. Returns ------- self : object Returns a fitted instance of self. """ X = validate_data(self, X, accept_sparse="csr") if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X) # Calculate neighborhood for all samples. This leaves the original # point in, which needs to be considered later (i.e. point i is in the # neighborhood of point i. While True, its useless information) if self.metric == "precomputed" and sparse.issparse(X): # set the diagonal to explicit values, as a point is its own # neighbor X = X.copy() # copy to avoid in-place modification with warnings.catch_warnings(): warnings.simplefilter("ignore", sparse.SparseEfficiencyWarning) X.setdiag(X.diagonal()) neighbors_model = NearestNeighbors( radius=self.eps, algorithm=self.algorithm, leaf_size=self.leaf_size, metric=self.metric, metric_params=self.metric_params, p=self.p, n_jobs=self.n_jobs, ) neighbors_model.fit(X) # This has worst case O(n^2) memory complexity neighborhoods = neighbors_model.radius_neighbors(X, return_distance=False) if sample_weight is None: n_neighbors = np.array([len(neighbors) for neighbors in neighborhoods]) else: n_neighbors = np.array( [np.sum(sample_weight[neighbors]) for neighbors in neighborhoods] ) # Initially, all samples are noise. labels = np.full(X.shape[0], -1, dtype=np.intp) # A list of all core samples found. core_samples = np.asarray(n_neighbors >= self.min_samples, dtype=np.uint8) dbscan_inner(core_samples, neighborhoods, labels) self.core_sample_indices_ = np.where(core_samples)[0] self.labels_ = labels if len(self.core_sample_indices_): # fix for scipy sparse indexing issue self.components_ = X[self.core_sample_indices_].copy() else: # no core samples self.components_ = np.empty((0, X.shape[1])) return self def fit_predict(self, X, y=None, sample_weight=None): """Compute clusters from a data or distance matrix and predict labels. This method fits the model and returns the cluster labels in a single step. It is equivalent to calling fit(X).labels_. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features), or \ (n_samples, n_samples) Training instances to cluster, or distances between instances if ``metric='precomputed'``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``. y : Ignored Not used, present here for API consistency by convention. sample_weight : array-like of shape (n_samples,), default=None Weight of each sample, such that a sample with a weight of at least ``min_samples`` is by itself a core sample; a sample with a negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1. Returns ------- labels : ndarray of shape (n_samples,) Cluster labels. Noisy samples are given the label -1. Non-negative integers indicate cluster membership. """ self.fit(X, sample_weight=sample_weight) return self.labels_ def __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.pairwise = self.metric == "precomputed" tags.input_tags.sparse = True return tags