1051 lines
39 KiB
Python
1051 lines
39 KiB
Python
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"""Ordering Points To Identify the Clustering Structure (OPTICS)
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These routines execute the OPTICS algorithm, and implement various
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cluster extraction methods of the ordered list.
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Authors: Shane Grigsby <refuge@rocktalus.com>
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Adrin Jalali <adrinjalali@gmail.com>
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Erich Schubert <erich@debian.org>
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Hanmin Qin <qinhanmin2005@sina.com>
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License: BSD 3 clause
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"""
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from numbers import Integral, Real
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import warnings
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import numpy as np
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from ..exceptions import DataConversionWarning
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from ..metrics.pairwise import PAIRWISE_BOOLEAN_FUNCTIONS
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from ..metrics.pairwise import _VALID_METRICS
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from ..utils import gen_batches, get_chunk_n_rows
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from ..utils._param_validation import Interval, HasMethods, StrOptions
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from ..utils.validation import check_memory
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from ..neighbors import NearestNeighbors
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from ..base import BaseEstimator, ClusterMixin
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from ..metrics import pairwise_distances
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from scipy.sparse import issparse, SparseEfficiencyWarning
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class OPTICS(ClusterMixin, BaseEstimator):
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"""Estimate clustering structure from vector array.
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OPTICS (Ordering Points To Identify the Clustering Structure), closely
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related to DBSCAN, finds core sample of high density and expands clusters
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from them [1]_. Unlike DBSCAN, keeps cluster hierarchy for a variable
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neighborhood radius. Better suited for usage on large datasets than the
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current sklearn implementation of DBSCAN.
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Clusters are then extracted using a DBSCAN-like method
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(cluster_method = 'dbscan') or an automatic
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technique proposed in [1]_ (cluster_method = 'xi').
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This implementation deviates from the original OPTICS by first performing
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k-nearest-neighborhood searches on all points to identify core sizes, then
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computing only the distances to unprocessed points when constructing the
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cluster order. Note that we do not employ a heap to manage the expansion
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candidates, so the time complexity will be O(n^2).
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Read more in the :ref:`User Guide <optics>`.
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Parameters
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----------
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min_samples : int > 1 or float between 0 and 1, default=5
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The number of samples in a neighborhood for a point to be considered as
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a core point. Also, up and down steep regions can't have more than
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``min_samples`` consecutive non-steep points. Expressed as an absolute
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number or a fraction of the number of samples (rounded to be at least
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2).
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max_eps : float, default=np.inf
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The maximum distance between two samples for one to be considered as
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in the neighborhood of the other. Default value of ``np.inf`` will
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identify clusters across all scales; reducing ``max_eps`` will result
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in shorter run times.
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metric : str or callable, default='minkowski'
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Metric to use for distance computation. Any metric from scikit-learn
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or scipy.spatial.distance can be used.
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If metric is a callable function, it is called on each
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pair of instances (rows) and the resulting value recorded. The callable
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should take two arrays as input and return one value indicating the
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distance between them. This works for Scipy's metrics, but is less
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efficient than passing the metric name as a string. If metric is
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"precomputed", `X` is assumed to be a distance matrix and must be
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square.
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Valid values for metric are:
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- from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
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'manhattan']
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- from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
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'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
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'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao',
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'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
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'yule']
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Sparse matrices are only supported by scikit-learn metrics.
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See the documentation for scipy.spatial.distance for details on these
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metrics.
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p : float, default=2
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Parameter for the Minkowski metric from
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:class:`~sklearn.metrics.pairwise_distances`. When p = 1, this is
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equivalent to using manhattan_distance (l1), and euclidean_distance
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(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
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metric_params : dict, default=None
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Additional keyword arguments for the metric function.
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cluster_method : str, default='xi'
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The extraction method used to extract clusters using the calculated
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reachability and ordering. Possible values are "xi" and "dbscan".
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eps : float, default=None
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The maximum distance between two samples for one to be considered as
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in the neighborhood of the other. By default it assumes the same value
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as ``max_eps``.
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Used only when ``cluster_method='dbscan'``.
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xi : float between 0 and 1, default=0.05
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Determines the minimum steepness on the reachability plot that
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constitutes a cluster boundary. For example, an upwards point in the
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reachability plot is defined by the ratio from one point to its
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successor being at most 1-xi.
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Used only when ``cluster_method='xi'``.
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predecessor_correction : bool, default=True
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Correct clusters according to the predecessors calculated by OPTICS
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[2]_. This parameter has minimal effect on most datasets.
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Used only when ``cluster_method='xi'``.
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min_cluster_size : int > 1 or float between 0 and 1, default=None
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Minimum number of samples in an OPTICS cluster, expressed as an
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absolute number or a fraction of the number of samples (rounded to be
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at least 2). If ``None``, the value of ``min_samples`` is used instead.
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Used only when ``cluster_method='xi'``.
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algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
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Algorithm used to compute the nearest neighbors:
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- 'ball_tree' will use :class:`BallTree`.
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- 'kd_tree' will use :class:`KDTree`.
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- 'brute' will use a brute-force search.
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- 'auto' (default) will attempt to decide the most appropriate
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algorithm based on the values passed to :meth:`fit` method.
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Note: fitting on sparse input will override the setting of
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this parameter, using brute force.
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leaf_size : int, default=30
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Leaf size passed to :class:`BallTree` or :class:`KDTree`. This can
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affect the speed of the construction and query, as well as the memory
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required to store the tree. The optimal value depends on the
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nature of the problem.
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memory : str or object with the joblib.Memory interface, default=None
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Used to cache the output of the computation of the tree.
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By default, no caching is done. If a string is given, it is the
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path to the caching directory.
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n_jobs : int, default=None
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The number of parallel jobs to run for neighbors search.
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``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
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``-1`` means using all processors. See :term:`Glossary <n_jobs>`
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for more details.
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Attributes
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----------
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labels_ : ndarray of shape (n_samples,)
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Cluster labels for each point in the dataset given to fit().
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Noisy samples and points which are not included in a leaf cluster
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of ``cluster_hierarchy_`` are labeled as -1.
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reachability_ : ndarray of shape (n_samples,)
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Reachability distances per sample, indexed by object order. Use
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``clust.reachability_[clust.ordering_]`` to access in cluster order.
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ordering_ : ndarray of shape (n_samples,)
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The cluster ordered list of sample indices.
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core_distances_ : ndarray of shape (n_samples,)
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Distance at which each sample becomes a core point, indexed by object
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order. Points which will never be core have a distance of inf. Use
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``clust.core_distances_[clust.ordering_]`` to access in cluster order.
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predecessor_ : ndarray of shape (n_samples,)
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Point that a sample was reached from, indexed by object order.
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Seed points have a predecessor of -1.
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cluster_hierarchy_ : ndarray of shape (n_clusters, 2)
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The list of clusters in the form of ``[start, end]`` in each row, with
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all indices inclusive. The clusters are ordered according to
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``(end, -start)`` (ascending) so that larger clusters encompassing
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smaller clusters come after those smaller ones. Since ``labels_`` does
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not reflect the hierarchy, usually
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``len(cluster_hierarchy_) > np.unique(optics.labels_)``. Please also
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note that these indices are of the ``ordering_``, i.e.
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``X[ordering_][start:end + 1]`` form a cluster.
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Only available when ``cluster_method='xi'``.
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X`
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has feature names that are all strings.
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.. versionadded:: 1.0
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See Also
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--------
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DBSCAN : A similar clustering for a specified neighborhood radius (eps).
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Our implementation is optimized for runtime.
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References
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----------
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.. [1] Ankerst, Mihael, Markus M. Breunig, Hans-Peter Kriegel,
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and Jörg Sander. "OPTICS: ordering points to identify the clustering
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structure." ACM SIGMOD Record 28, no. 2 (1999): 49-60.
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.. [2] Schubert, Erich, Michael Gertz.
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"Improving the Cluster Structure Extracted from OPTICS Plots." Proc. of
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the Conference "Lernen, Wissen, Daten, Analysen" (LWDA) (2018): 318-329.
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Examples
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--------
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>>> from sklearn.cluster import OPTICS
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>>> import numpy as np
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>>> X = np.array([[1, 2], [2, 5], [3, 6],
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... [8, 7], [8, 8], [7, 3]])
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>>> clustering = OPTICS(min_samples=2).fit(X)
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>>> clustering.labels_
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array([0, 0, 0, 1, 1, 1])
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"""
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_parameter_constraints: dict = {
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"min_samples": [
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Interval(Integral, 2, None, closed="left"),
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Interval(Real, 0, 1, closed="both"),
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],
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"max_eps": [Interval(Real, 0, None, closed="both")],
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"metric": [StrOptions(set(_VALID_METRICS) | {"precomputed"}), callable],
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"p": [Interval(Real, 1, None, closed="left")],
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"metric_params": [dict, None],
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"cluster_method": [StrOptions({"dbscan", "xi"})],
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"eps": [Interval(Real, 0, None, closed="both"), None],
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"xi": [Interval(Real, 0, 1, closed="both")],
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"predecessor_correction": ["boolean"],
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"min_cluster_size": [
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Interval(Integral, 2, None, closed="left"),
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Interval(Real, 0, 1, closed="right"),
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None,
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],
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"algorithm": [StrOptions({"auto", "brute", "ball_tree", "kd_tree"})],
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"leaf_size": [Interval(Integral, 1, None, closed="left")],
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"memory": [str, HasMethods("cache"), None],
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"n_jobs": [Integral, None],
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}
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def __init__(
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self,
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*,
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min_samples=5,
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max_eps=np.inf,
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metric="minkowski",
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p=2,
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metric_params=None,
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cluster_method="xi",
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eps=None,
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xi=0.05,
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predecessor_correction=True,
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min_cluster_size=None,
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algorithm="auto",
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leaf_size=30,
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memory=None,
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n_jobs=None,
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):
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self.max_eps = max_eps
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self.min_samples = min_samples
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self.min_cluster_size = min_cluster_size
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self.algorithm = algorithm
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self.metric = metric
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self.metric_params = metric_params
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self.p = p
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self.leaf_size = leaf_size
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self.cluster_method = cluster_method
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self.eps = eps
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self.xi = xi
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self.predecessor_correction = predecessor_correction
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self.memory = memory
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self.n_jobs = n_jobs
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def fit(self, X, y=None):
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"""Perform OPTICS clustering.
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Extracts an ordered list of points and reachability distances, and
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performs initial clustering using ``max_eps`` distance specified at
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OPTICS object instantiation.
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Parameters
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----------
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X : {ndarray, sparse matrix} of shape (n_samples, n_features), or \
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(n_samples, n_samples) if metric='precomputed'
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A feature array, or array of distances between samples if
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metric='precomputed'. If a sparse matrix is provided, it will be
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converted into CSR format.
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y : Ignored
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Not used, present for API consistency by convention.
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Returns
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-------
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self : object
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Returns a fitted instance of self.
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"""
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self._validate_params()
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dtype = bool if self.metric in PAIRWISE_BOOLEAN_FUNCTIONS else float
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if dtype == bool and X.dtype != bool:
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msg = (
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"Data will be converted to boolean for"
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f" metric {self.metric}, to avoid this warning,"
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" you may convert the data prior to calling fit."
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)
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warnings.warn(msg, DataConversionWarning)
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X = self._validate_data(X, dtype=dtype, accept_sparse="csr")
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if self.metric == "precomputed" and issparse(X):
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", SparseEfficiencyWarning)
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# Set each diagonal to an explicit value so each point is its
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# own neighbor
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X.setdiag(X.diagonal())
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memory = check_memory(self.memory)
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(
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self.ordering_,
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self.core_distances_,
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self.reachability_,
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self.predecessor_,
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) = memory.cache(compute_optics_graph)(
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X=X,
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min_samples=self.min_samples,
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algorithm=self.algorithm,
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leaf_size=self.leaf_size,
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metric=self.metric,
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metric_params=self.metric_params,
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p=self.p,
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n_jobs=self.n_jobs,
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max_eps=self.max_eps,
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)
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# Extract clusters from the calculated orders and reachability
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if self.cluster_method == "xi":
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labels_, clusters_ = cluster_optics_xi(
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reachability=self.reachability_,
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predecessor=self.predecessor_,
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ordering=self.ordering_,
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min_samples=self.min_samples,
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min_cluster_size=self.min_cluster_size,
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xi=self.xi,
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|
|
predecessor_correction=self.predecessor_correction,
|
||
|
|
)
|
||
|
|
self.cluster_hierarchy_ = clusters_
|
||
|
|
elif self.cluster_method == "dbscan":
|
||
|
|
if self.eps is None:
|
||
|
|
eps = self.max_eps
|
||
|
|
else:
|
||
|
|
eps = self.eps
|
||
|
|
|
||
|
|
if eps > self.max_eps:
|
||
|
|
raise ValueError(
|
||
|
|
"Specify an epsilon smaller than %s. Got %s." % (self.max_eps, eps)
|
||
|
|
)
|
||
|
|
|
||
|
|
labels_ = cluster_optics_dbscan(
|
||
|
|
reachability=self.reachability_,
|
||
|
|
core_distances=self.core_distances_,
|
||
|
|
ordering=self.ordering_,
|
||
|
|
eps=eps,
|
||
|
|
)
|
||
|
|
|
||
|
|
self.labels_ = labels_
|
||
|
|
return self
|
||
|
|
|
||
|
|
|
||
|
|
def _validate_size(size, n_samples, param_name):
|
||
|
|
if size > n_samples:
|
||
|
|
raise ValueError(
|
||
|
|
"%s must be no greater than the number of samples (%d). Got %d"
|
||
|
|
% (param_name, n_samples, size)
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
# OPTICS helper functions
|
||
|
|
def _compute_core_distances_(X, neighbors, min_samples, working_memory):
|
||
|
|
"""Compute the k-th nearest neighbor of each sample.
|
||
|
|
|
||
|
|
Equivalent to neighbors.kneighbors(X, self.min_samples)[0][:, -1]
|
||
|
|
but with more memory efficiency.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like of shape (n_samples, n_features)
|
||
|
|
The data.
|
||
|
|
neighbors : NearestNeighbors instance
|
||
|
|
The fitted nearest neighbors estimator.
|
||
|
|
working_memory : int, default=None
|
||
|
|
The sought maximum memory for temporary distance matrix chunks.
|
||
|
|
When None (default), the value of
|
||
|
|
``sklearn.get_config()['working_memory']`` is used.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
core_distances : ndarray of shape (n_samples,)
|
||
|
|
Distance at which each sample becomes a core point.
|
||
|
|
Points which will never be core have a distance of inf.
|
||
|
|
"""
|
||
|
|
n_samples = X.shape[0]
|
||
|
|
core_distances = np.empty(n_samples)
|
||
|
|
core_distances.fill(np.nan)
|
||
|
|
|
||
|
|
chunk_n_rows = get_chunk_n_rows(
|
||
|
|
row_bytes=16 * min_samples, max_n_rows=n_samples, working_memory=working_memory
|
||
|
|
)
|
||
|
|
slices = gen_batches(n_samples, chunk_n_rows)
|
||
|
|
for sl in slices:
|
||
|
|
core_distances[sl] = neighbors.kneighbors(X[sl], min_samples)[0][:, -1]
|
||
|
|
return core_distances
|
||
|
|
|
||
|
|
|
||
|
|
def compute_optics_graph(
|
||
|
|
X, *, min_samples, max_eps, metric, p, metric_params, algorithm, leaf_size, n_jobs
|
||
|
|
):
|
||
|
|
"""Compute the OPTICS reachability graph.
|
||
|
|
|
||
|
|
Read more in the :ref:`User Guide <optics>`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : ndarray of shape (n_samples, n_features), or \
|
||
|
|
(n_samples, n_samples) if metric='precomputed'
|
||
|
|
A feature array, or array of distances between samples if
|
||
|
|
metric='precomputed'.
|
||
|
|
|
||
|
|
min_samples : int > 1 or float between 0 and 1
|
||
|
|
The number of samples in a neighborhood for a point to be considered
|
||
|
|
as a core point. Expressed as an absolute number or a fraction of the
|
||
|
|
number of samples (rounded to be at least 2).
|
||
|
|
|
||
|
|
max_eps : float, default=np.inf
|
||
|
|
The maximum distance between two samples for one to be considered as
|
||
|
|
in the neighborhood of the other. Default value of ``np.inf`` will
|
||
|
|
identify clusters across all scales; reducing ``max_eps`` will result
|
||
|
|
in shorter run times.
|
||
|
|
|
||
|
|
metric : str or callable, default='minkowski'
|
||
|
|
Metric to use for distance computation. Any metric from scikit-learn
|
||
|
|
or scipy.spatial.distance can be used.
|
||
|
|
|
||
|
|
If metric is a callable function, it is called on each
|
||
|
|
pair of instances (rows) and the resulting value recorded. The callable
|
||
|
|
should take two arrays as input and return one value indicating the
|
||
|
|
distance between them. This works for Scipy's metrics, but is less
|
||
|
|
efficient than passing the metric name as a string. If metric is
|
||
|
|
"precomputed", X is assumed to be a distance matrix and must be square.
|
||
|
|
|
||
|
|
Valid values for metric are:
|
||
|
|
|
||
|
|
- from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
|
||
|
|
'manhattan']
|
||
|
|
|
||
|
|
- from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
|
||
|
|
'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
|
||
|
|
'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao',
|
||
|
|
'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
|
||
|
|
'yule']
|
||
|
|
|
||
|
|
See the documentation for scipy.spatial.distance for details on these
|
||
|
|
metrics.
|
||
|
|
|
||
|
|
p : int, default=2
|
||
|
|
Parameter for the Minkowski metric from
|
||
|
|
:class:`~sklearn.metrics.pairwise_distances`. 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.
|
||
|
|
|
||
|
|
metric_params : dict, default=None
|
||
|
|
Additional keyword arguments for the metric function.
|
||
|
|
|
||
|
|
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
|
||
|
|
Algorithm used to compute the nearest neighbors:
|
||
|
|
|
||
|
|
- 'ball_tree' will use :class:`BallTree`.
|
||
|
|
- 'kd_tree' will use :class:`KDTree`.
|
||
|
|
- 'brute' will use a brute-force search.
|
||
|
|
- 'auto' will attempt to decide the most appropriate algorithm
|
||
|
|
based on the values passed to :meth:`fit` method. (default)
|
||
|
|
|
||
|
|
Note: fitting on sparse input will override the setting of
|
||
|
|
this parameter, using brute force.
|
||
|
|
|
||
|
|
leaf_size : int, default=30
|
||
|
|
Leaf size passed to :class:`BallTree` or :class:`KDTree`. 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.
|
||
|
|
|
||
|
|
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
|
||
|
|
-------
|
||
|
|
ordering_ : array of shape (n_samples,)
|
||
|
|
The cluster ordered list of sample indices.
|
||
|
|
|
||
|
|
core_distances_ : array of shape (n_samples,)
|
||
|
|
Distance at which each sample becomes a core point, indexed by object
|
||
|
|
order. Points which will never be core have a distance of inf. Use
|
||
|
|
``clust.core_distances_[clust.ordering_]`` to access in cluster order.
|
||
|
|
|
||
|
|
reachability_ : array of shape (n_samples,)
|
||
|
|
Reachability distances per sample, indexed by object order. Use
|
||
|
|
``clust.reachability_[clust.ordering_]`` to access in cluster order.
|
||
|
|
|
||
|
|
predecessor_ : array of shape (n_samples,)
|
||
|
|
Point that a sample was reached from, indexed by object order.
|
||
|
|
Seed points have a predecessor of -1.
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] Ankerst, Mihael, Markus M. Breunig, Hans-Peter Kriegel,
|
||
|
|
and Jörg Sander. "OPTICS: ordering points to identify the clustering
|
||
|
|
structure." ACM SIGMOD Record 28, no. 2 (1999): 49-60.
|
||
|
|
"""
|
||
|
|
n_samples = X.shape[0]
|
||
|
|
_validate_size(min_samples, n_samples, "min_samples")
|
||
|
|
if min_samples <= 1:
|
||
|
|
min_samples = max(2, int(min_samples * n_samples))
|
||
|
|
|
||
|
|
# Start all points as 'unprocessed' ##
|
||
|
|
reachability_ = np.empty(n_samples)
|
||
|
|
reachability_.fill(np.inf)
|
||
|
|
predecessor_ = np.empty(n_samples, dtype=int)
|
||
|
|
predecessor_.fill(-1)
|
||
|
|
|
||
|
|
nbrs = NearestNeighbors(
|
||
|
|
n_neighbors=min_samples,
|
||
|
|
algorithm=algorithm,
|
||
|
|
leaf_size=leaf_size,
|
||
|
|
metric=metric,
|
||
|
|
metric_params=metric_params,
|
||
|
|
p=p,
|
||
|
|
n_jobs=n_jobs,
|
||
|
|
)
|
||
|
|
|
||
|
|
nbrs.fit(X)
|
||
|
|
# Here we first do a kNN query for each point, this differs from
|
||
|
|
# the original OPTICS that only used epsilon range queries.
|
||
|
|
# TODO: handle working_memory somehow?
|
||
|
|
core_distances_ = _compute_core_distances_(
|
||
|
|
X=X, neighbors=nbrs, min_samples=min_samples, working_memory=None
|
||
|
|
)
|
||
|
|
# OPTICS puts an upper limit on these, use inf for undefined.
|
||
|
|
core_distances_[core_distances_ > max_eps] = np.inf
|
||
|
|
np.around(
|
||
|
|
core_distances_,
|
||
|
|
decimals=np.finfo(core_distances_.dtype).precision,
|
||
|
|
out=core_distances_,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Main OPTICS loop. Not parallelizable. The order that entries are
|
||
|
|
# written to the 'ordering_' list is important!
|
||
|
|
# Note that this implementation is O(n^2) theoretically, but
|
||
|
|
# supposedly with very low constant factors.
|
||
|
|
processed = np.zeros(X.shape[0], dtype=bool)
|
||
|
|
ordering = np.zeros(X.shape[0], dtype=int)
|
||
|
|
for ordering_idx in range(X.shape[0]):
|
||
|
|
# Choose next based on smallest reachability distance
|
||
|
|
# (And prefer smaller ids on ties, possibly np.inf!)
|
||
|
|
index = np.where(processed == 0)[0]
|
||
|
|
point = index[np.argmin(reachability_[index])]
|
||
|
|
|
||
|
|
processed[point] = True
|
||
|
|
ordering[ordering_idx] = point
|
||
|
|
if core_distances_[point] != np.inf:
|
||
|
|
_set_reach_dist(
|
||
|
|
core_distances_=core_distances_,
|
||
|
|
reachability_=reachability_,
|
||
|
|
predecessor_=predecessor_,
|
||
|
|
point_index=point,
|
||
|
|
processed=processed,
|
||
|
|
X=X,
|
||
|
|
nbrs=nbrs,
|
||
|
|
metric=metric,
|
||
|
|
metric_params=metric_params,
|
||
|
|
p=p,
|
||
|
|
max_eps=max_eps,
|
||
|
|
)
|
||
|
|
if np.all(np.isinf(reachability_)):
|
||
|
|
warnings.warn(
|
||
|
|
"All reachability values are inf. Set a larger"
|
||
|
|
" max_eps or all data will be considered outliers.",
|
||
|
|
UserWarning,
|
||
|
|
)
|
||
|
|
return ordering, core_distances_, reachability_, predecessor_
|
||
|
|
|
||
|
|
|
||
|
|
def _set_reach_dist(
|
||
|
|
core_distances_,
|
||
|
|
reachability_,
|
||
|
|
predecessor_,
|
||
|
|
point_index,
|
||
|
|
processed,
|
||
|
|
X,
|
||
|
|
nbrs,
|
||
|
|
metric,
|
||
|
|
metric_params,
|
||
|
|
p,
|
||
|
|
max_eps,
|
||
|
|
):
|
||
|
|
P = X[point_index : point_index + 1]
|
||
|
|
# Assume that radius_neighbors is faster without distances
|
||
|
|
# and we don't need all distances, nevertheless, this means
|
||
|
|
# we may be doing some work twice.
|
||
|
|
indices = nbrs.radius_neighbors(P, radius=max_eps, return_distance=False)[0]
|
||
|
|
|
||
|
|
# Getting indices of neighbors that have not been processed
|
||
|
|
unproc = np.compress(~np.take(processed, indices), indices)
|
||
|
|
# Neighbors of current point are already processed.
|
||
|
|
if not unproc.size:
|
||
|
|
return
|
||
|
|
|
||
|
|
# Only compute distances to unprocessed neighbors:
|
||
|
|
if metric == "precomputed":
|
||
|
|
dists = X[point_index, unproc]
|
||
|
|
if issparse(dists):
|
||
|
|
dists.sort_indices()
|
||
|
|
dists = dists.data
|
||
|
|
else:
|
||
|
|
_params = dict() if metric_params is None else metric_params.copy()
|
||
|
|
if metric == "minkowski" and "p" not in _params:
|
||
|
|
# the same logic as neighbors, p is ignored if explicitly set
|
||
|
|
# in the dict params
|
||
|
|
_params["p"] = p
|
||
|
|
dists = pairwise_distances(P, X[unproc], metric, n_jobs=None, **_params).ravel()
|
||
|
|
|
||
|
|
rdists = np.maximum(dists, core_distances_[point_index])
|
||
|
|
np.around(rdists, decimals=np.finfo(rdists.dtype).precision, out=rdists)
|
||
|
|
improved = np.where(rdists < np.take(reachability_, unproc))
|
||
|
|
reachability_[unproc[improved]] = rdists[improved]
|
||
|
|
predecessor_[unproc[improved]] = point_index
|
||
|
|
|
||
|
|
|
||
|
|
def cluster_optics_dbscan(*, reachability, core_distances, ordering, eps):
|
||
|
|
"""Perform DBSCAN extraction for an arbitrary epsilon.
|
||
|
|
|
||
|
|
Extracting the clusters runs in linear time. Note that this results in
|
||
|
|
``labels_`` which are close to a :class:`~sklearn.cluster.DBSCAN` with
|
||
|
|
similar settings and ``eps``, only if ``eps`` is close to ``max_eps``.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
reachability : array of shape (n_samples,)
|
||
|
|
Reachability distances calculated by OPTICS (``reachability_``).
|
||
|
|
|
||
|
|
core_distances : array of shape (n_samples,)
|
||
|
|
Distances at which points become core (``core_distances_``).
|
||
|
|
|
||
|
|
ordering : array of shape (n_samples,)
|
||
|
|
OPTICS ordered point indices (``ordering_``).
|
||
|
|
|
||
|
|
eps : float
|
||
|
|
DBSCAN ``eps`` parameter. Must be set to < ``max_eps``. Results
|
||
|
|
will be close to DBSCAN algorithm if ``eps`` and ``max_eps`` are close
|
||
|
|
to one another.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
labels_ : array of shape (n_samples,)
|
||
|
|
The estimated labels.
|
||
|
|
"""
|
||
|
|
n_samples = len(core_distances)
|
||
|
|
labels = np.zeros(n_samples, dtype=int)
|
||
|
|
|
||
|
|
far_reach = reachability > eps
|
||
|
|
near_core = core_distances <= eps
|
||
|
|
labels[ordering] = np.cumsum(far_reach[ordering] & near_core[ordering]) - 1
|
||
|
|
labels[far_reach & ~near_core] = -1
|
||
|
|
return labels
|
||
|
|
|
||
|
|
|
||
|
|
def cluster_optics_xi(
|
||
|
|
*,
|
||
|
|
reachability,
|
||
|
|
predecessor,
|
||
|
|
ordering,
|
||
|
|
min_samples,
|
||
|
|
min_cluster_size=None,
|
||
|
|
xi=0.05,
|
||
|
|
predecessor_correction=True,
|
||
|
|
):
|
||
|
|
"""Automatically extract clusters according to the Xi-steep method.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
reachability : ndarray of shape (n_samples,)
|
||
|
|
Reachability distances calculated by OPTICS (`reachability_`).
|
||
|
|
|
||
|
|
predecessor : ndarray of shape (n_samples,)
|
||
|
|
Predecessors calculated by OPTICS.
|
||
|
|
|
||
|
|
ordering : ndarray of shape (n_samples,)
|
||
|
|
OPTICS ordered point indices (`ordering_`).
|
||
|
|
|
||
|
|
min_samples : int > 1 or float between 0 and 1
|
||
|
|
The same as the min_samples given to OPTICS. Up and down steep regions
|
||
|
|
can't have more then ``min_samples`` consecutive non-steep points.
|
||
|
|
Expressed as an absolute number or a fraction of the number of samples
|
||
|
|
(rounded to be at least 2).
|
||
|
|
|
||
|
|
min_cluster_size : int > 1 or float between 0 and 1, default=None
|
||
|
|
Minimum number of samples in an OPTICS cluster, expressed as an
|
||
|
|
absolute number or a fraction of the number of samples (rounded to be
|
||
|
|
at least 2). If ``None``, the value of ``min_samples`` is used instead.
|
||
|
|
|
||
|
|
xi : float between 0 and 1, default=0.05
|
||
|
|
Determines the minimum steepness on the reachability plot that
|
||
|
|
constitutes a cluster boundary. For example, an upwards point in the
|
||
|
|
reachability plot is defined by the ratio from one point to its
|
||
|
|
successor being at most 1-xi.
|
||
|
|
|
||
|
|
predecessor_correction : bool, default=True
|
||
|
|
Correct clusters based on the calculated predecessors.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
labels : ndarray of shape (n_samples,)
|
||
|
|
The labels assigned to samples. Points which are not included
|
||
|
|
in any cluster are labeled as -1.
|
||
|
|
|
||
|
|
clusters : ndarray of shape (n_clusters, 2)
|
||
|
|
The list of clusters in the form of ``[start, end]`` in each row, with
|
||
|
|
all indices inclusive. The clusters are ordered according to ``(end,
|
||
|
|
-start)`` (ascending) so that larger clusters encompassing smaller
|
||
|
|
clusters come after such nested smaller clusters. Since ``labels`` does
|
||
|
|
not reflect the hierarchy, usually ``len(clusters) >
|
||
|
|
np.unique(labels)``.
|
||
|
|
"""
|
||
|
|
n_samples = len(reachability)
|
||
|
|
_validate_size(min_samples, n_samples, "min_samples")
|
||
|
|
if min_samples <= 1:
|
||
|
|
min_samples = max(2, int(min_samples * n_samples))
|
||
|
|
if min_cluster_size is None:
|
||
|
|
min_cluster_size = min_samples
|
||
|
|
_validate_size(min_cluster_size, n_samples, "min_cluster_size")
|
||
|
|
if min_cluster_size <= 1:
|
||
|
|
min_cluster_size = max(2, int(min_cluster_size * n_samples))
|
||
|
|
|
||
|
|
clusters = _xi_cluster(
|
||
|
|
reachability[ordering],
|
||
|
|
predecessor[ordering],
|
||
|
|
ordering,
|
||
|
|
xi,
|
||
|
|
min_samples,
|
||
|
|
min_cluster_size,
|
||
|
|
predecessor_correction,
|
||
|
|
)
|
||
|
|
labels = _extract_xi_labels(ordering, clusters)
|
||
|
|
return labels, clusters
|
||
|
|
|
||
|
|
|
||
|
|
def _extend_region(steep_point, xward_point, start, min_samples):
|
||
|
|
"""Extend the area until it's maximal.
|
||
|
|
|
||
|
|
It's the same function for both upward and downward reagions, depending on
|
||
|
|
the given input parameters. Assuming:
|
||
|
|
|
||
|
|
- steep_{upward/downward}: bool array indicating whether a point is a
|
||
|
|
steep {upward/downward};
|
||
|
|
- upward/downward: bool array indicating whether a point is
|
||
|
|
upward/downward;
|
||
|
|
|
||
|
|
To extend an upward reagion, ``steep_point=steep_upward`` and
|
||
|
|
``xward_point=downward`` are expected, and to extend a downward region,
|
||
|
|
``steep_point=steep_downward`` and ``xward_point=upward``.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
steep_point : ndarray of shape (n_samples,), dtype=bool
|
||
|
|
True if the point is steep downward (upward).
|
||
|
|
|
||
|
|
xward_point : ndarray of shape (n_samples,), dtype=bool
|
||
|
|
True if the point is an upward (respectively downward) point.
|
||
|
|
|
||
|
|
start : int
|
||
|
|
The start of the xward region.
|
||
|
|
|
||
|
|
min_samples : int
|
||
|
|
The same as the min_samples given to OPTICS. Up and down steep
|
||
|
|
regions can't have more then ``min_samples`` consecutive non-steep
|
||
|
|
points.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
index : int
|
||
|
|
The current index iterating over all the samples, i.e. where we are up
|
||
|
|
to in our search.
|
||
|
|
|
||
|
|
end : int
|
||
|
|
The end of the region, which can be behind the index. The region
|
||
|
|
includes the ``end`` index.
|
||
|
|
"""
|
||
|
|
n_samples = len(steep_point)
|
||
|
|
non_xward_points = 0
|
||
|
|
index = start
|
||
|
|
end = start
|
||
|
|
# find a maximal area
|
||
|
|
while index < n_samples:
|
||
|
|
if steep_point[index]:
|
||
|
|
non_xward_points = 0
|
||
|
|
end = index
|
||
|
|
elif not xward_point[index]:
|
||
|
|
# it's not a steep point, but still goes up.
|
||
|
|
non_xward_points += 1
|
||
|
|
# region should include no more than min_samples consecutive
|
||
|
|
# non steep xward points.
|
||
|
|
if non_xward_points > min_samples:
|
||
|
|
break
|
||
|
|
else:
|
||
|
|
return end
|
||
|
|
index += 1
|
||
|
|
return end
|
||
|
|
|
||
|
|
|
||
|
|
def _update_filter_sdas(sdas, mib, xi_complement, reachability_plot):
|
||
|
|
"""Update steep down areas (SDAs) using the new maximum in between (mib)
|
||
|
|
value, and the given complement of xi, i.e. ``1 - xi``.
|
||
|
|
"""
|
||
|
|
if np.isinf(mib):
|
||
|
|
return []
|
||
|
|
res = [
|
||
|
|
sda for sda in sdas if mib <= reachability_plot[sda["start"]] * xi_complement
|
||
|
|
]
|
||
|
|
for sda in res:
|
||
|
|
sda["mib"] = max(sda["mib"], mib)
|
||
|
|
return res
|
||
|
|
|
||
|
|
|
||
|
|
def _correct_predecessor(reachability_plot, predecessor_plot, ordering, s, e):
|
||
|
|
"""Correct for predecessors.
|
||
|
|
|
||
|
|
Applies Algorithm 2 of [1]_.
|
||
|
|
|
||
|
|
Input parameters are ordered by the computer OPTICS ordering.
|
||
|
|
|
||
|
|
.. [1] Schubert, Erich, Michael Gertz.
|
||
|
|
"Improving the Cluster Structure Extracted from OPTICS Plots." Proc. of
|
||
|
|
the Conference "Lernen, Wissen, Daten, Analysen" (LWDA) (2018): 318-329.
|
||
|
|
"""
|
||
|
|
while s < e:
|
||
|
|
if reachability_plot[s] > reachability_plot[e]:
|
||
|
|
return s, e
|
||
|
|
p_e = ordering[predecessor_plot[e]]
|
||
|
|
for i in range(s, e):
|
||
|
|
if p_e == ordering[i]:
|
||
|
|
return s, e
|
||
|
|
e -= 1
|
||
|
|
return None, None
|
||
|
|
|
||
|
|
|
||
|
|
def _xi_cluster(
|
||
|
|
reachability_plot,
|
||
|
|
predecessor_plot,
|
||
|
|
ordering,
|
||
|
|
xi,
|
||
|
|
min_samples,
|
||
|
|
min_cluster_size,
|
||
|
|
predecessor_correction,
|
||
|
|
):
|
||
|
|
"""Automatically extract clusters according to the Xi-steep method.
|
||
|
|
|
||
|
|
This is rouphly an implementation of Figure 19 of the OPTICS paper.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
reachability_plot : array-like of shape (n_samples,)
|
||
|
|
The reachability plot, i.e. reachability ordered according to
|
||
|
|
the calculated ordering, all computed by OPTICS.
|
||
|
|
|
||
|
|
predecessor_plot : array-like of shape (n_samples,)
|
||
|
|
Predecessors ordered according to the calculated ordering.
|
||
|
|
|
||
|
|
xi : float, between 0 and 1
|
||
|
|
Determines the minimum steepness on the reachability plot that
|
||
|
|
constitutes a cluster boundary. For example, an upwards point in the
|
||
|
|
reachability plot is defined by the ratio from one point to its
|
||
|
|
successor being at most 1-xi.
|
||
|
|
|
||
|
|
min_samples : int > 1
|
||
|
|
The same as the min_samples given to OPTICS. Up and down steep regions
|
||
|
|
can't have more then ``min_samples`` consecutive non-steep points.
|
||
|
|
|
||
|
|
min_cluster_size : int > 1
|
||
|
|
Minimum number of samples in an OPTICS cluster.
|
||
|
|
|
||
|
|
predecessor_correction : bool
|
||
|
|
Correct clusters based on the calculated predecessors.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
clusters : ndarray of shape (n_clusters, 2)
|
||
|
|
The list of clusters in the form of [start, end] in each row, with all
|
||
|
|
indices inclusive. The clusters are ordered in a way that larger
|
||
|
|
clusters encompassing smaller clusters come after those smaller
|
||
|
|
clusters.
|
||
|
|
"""
|
||
|
|
|
||
|
|
# Our implementation adds an inf to the end of reachability plot
|
||
|
|
# this helps to find potential clusters at the end of the
|
||
|
|
# reachability plot even if there's no upward region at the end of it.
|
||
|
|
reachability_plot = np.hstack((reachability_plot, np.inf))
|
||
|
|
|
||
|
|
xi_complement = 1 - xi
|
||
|
|
sdas = [] # steep down areas, introduced in section 4.3.2 of the paper
|
||
|
|
clusters = []
|
||
|
|
index = 0
|
||
|
|
mib = 0.0 # maximum in between, section 4.3.2
|
||
|
|
|
||
|
|
# Our implementation corrects a mistake in the original
|
||
|
|
# paper, i.e., in Definition 9 steep downward point,
|
||
|
|
# r(p) * (1 - x1) <= r(p + 1) should be
|
||
|
|
# r(p) * (1 - x1) >= r(p + 1)
|
||
|
|
with np.errstate(invalid="ignore"):
|
||
|
|
ratio = reachability_plot[:-1] / reachability_plot[1:]
|
||
|
|
steep_upward = ratio <= xi_complement
|
||
|
|
steep_downward = ratio >= 1 / xi_complement
|
||
|
|
downward = ratio > 1
|
||
|
|
upward = ratio < 1
|
||
|
|
|
||
|
|
# the following loop is almost exactly as Figure 19 of the paper.
|
||
|
|
# it jumps over the areas which are not either steep down or up areas
|
||
|
|
for steep_index in iter(np.flatnonzero(steep_upward | steep_downward)):
|
||
|
|
# just continue if steep_index has been a part of a discovered xward
|
||
|
|
# area.
|
||
|
|
if steep_index < index:
|
||
|
|
continue
|
||
|
|
|
||
|
|
mib = max(mib, np.max(reachability_plot[index : steep_index + 1]))
|
||
|
|
|
||
|
|
# steep downward areas
|
||
|
|
if steep_downward[steep_index]:
|
||
|
|
sdas = _update_filter_sdas(sdas, mib, xi_complement, reachability_plot)
|
||
|
|
D_start = steep_index
|
||
|
|
D_end = _extend_region(steep_downward, upward, D_start, min_samples)
|
||
|
|
D = {"start": D_start, "end": D_end, "mib": 0.0}
|
||
|
|
sdas.append(D)
|
||
|
|
index = D_end + 1
|
||
|
|
mib = reachability_plot[index]
|
||
|
|
|
||
|
|
# steep upward areas
|
||
|
|
else:
|
||
|
|
sdas = _update_filter_sdas(sdas, mib, xi_complement, reachability_plot)
|
||
|
|
U_start = steep_index
|
||
|
|
U_end = _extend_region(steep_upward, downward, U_start, min_samples)
|
||
|
|
index = U_end + 1
|
||
|
|
mib = reachability_plot[index]
|
||
|
|
|
||
|
|
U_clusters = []
|
||
|
|
for D in sdas:
|
||
|
|
c_start = D["start"]
|
||
|
|
c_end = U_end
|
||
|
|
|
||
|
|
# line (**), sc2*
|
||
|
|
if reachability_plot[c_end + 1] * xi_complement < D["mib"]:
|
||
|
|
continue
|
||
|
|
|
||
|
|
# Definition 11: criterion 4
|
||
|
|
D_max = reachability_plot[D["start"]]
|
||
|
|
if D_max * xi_complement >= reachability_plot[c_end + 1]:
|
||
|
|
# Find the first index from the left side which is almost
|
||
|
|
# at the same level as the end of the detected cluster.
|
||
|
|
while (
|
||
|
|
reachability_plot[c_start + 1] > reachability_plot[c_end + 1]
|
||
|
|
and c_start < D["end"]
|
||
|
|
):
|
||
|
|
c_start += 1
|
||
|
|
elif reachability_plot[c_end + 1] * xi_complement >= D_max:
|
||
|
|
# Find the first index from the right side which is almost
|
||
|
|
# at the same level as the beginning of the detected
|
||
|
|
# cluster.
|
||
|
|
# Our implementation corrects a mistake in the original
|
||
|
|
# paper, i.e., in Definition 11 4c, r(x) < r(sD) should be
|
||
|
|
# r(x) > r(sD).
|
||
|
|
while reachability_plot[c_end - 1] > D_max and c_end > U_start:
|
||
|
|
c_end -= 1
|
||
|
|
|
||
|
|
# predecessor correction
|
||
|
|
if predecessor_correction:
|
||
|
|
c_start, c_end = _correct_predecessor(
|
||
|
|
reachability_plot, predecessor_plot, ordering, c_start, c_end
|
||
|
|
)
|
||
|
|
if c_start is None:
|
||
|
|
continue
|
||
|
|
|
||
|
|
# Definition 11: criterion 3.a
|
||
|
|
if c_end - c_start + 1 < min_cluster_size:
|
||
|
|
continue
|
||
|
|
|
||
|
|
# Definition 11: criterion 1
|
||
|
|
if c_start > D["end"]:
|
||
|
|
continue
|
||
|
|
|
||
|
|
# Definition 11: criterion 2
|
||
|
|
if c_end < U_start:
|
||
|
|
continue
|
||
|
|
|
||
|
|
U_clusters.append((c_start, c_end))
|
||
|
|
|
||
|
|
# add smaller clusters first.
|
||
|
|
U_clusters.reverse()
|
||
|
|
clusters.extend(U_clusters)
|
||
|
|
|
||
|
|
return np.array(clusters)
|
||
|
|
|
||
|
|
|
||
|
|
def _extract_xi_labels(ordering, clusters):
|
||
|
|
"""Extracts the labels from the clusters returned by `_xi_cluster`.
|
||
|
|
We rely on the fact that clusters are stored
|
||
|
|
with the smaller clusters coming before the larger ones.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
ordering : array-like of shape (n_samples,)
|
||
|
|
The ordering of points calculated by OPTICS
|
||
|
|
|
||
|
|
clusters : array-like of shape (n_clusters, 2)
|
||
|
|
List of clusters i.e. (start, end) tuples,
|
||
|
|
as returned by `_xi_cluster`.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
labels : ndarray of shape (n_samples,)
|
||
|
|
"""
|
||
|
|
|
||
|
|
labels = np.full(len(ordering), -1, dtype=int)
|
||
|
|
label = 0
|
||
|
|
for c in clusters:
|
||
|
|
if not np.any(labels[c[0] : (c[1] + 1)] != -1):
|
||
|
|
labels[c[0] : (c[1] + 1)] = label
|
||
|
|
label += 1
|
||
|
|
labels[ordering] = labels.copy()
|
||
|
|
return labels
|