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OPTICS

OPTICS

Estimate clustering structure from vector array.

OPTICS (Ordering Points To Identify the Clustering Structure), closely related to DBSCAN, finds core sample of high density and expands clusters from them [1]. Unlike DBSCAN, keeps cluster hierarchy for a variable neighborhood radius. Better suited for usage on large datasets than the current sklearn implementation of DBSCAN.

Clusters are then extracted using a DBSCAN-like method (cluster_method = ‘dbscan’) or an automatic technique proposed in [1] (cluster_method = ‘xi’).

This implementation deviates from the original OPTICS by first performing k-nearest-neighborhood searches on all points to identify core sizes, then computing only the distances to unprocessed points when constructing the cluster order. Note that we do not employ a heap to manage the expansion candidates, so the time complexity will be O(n^2).

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new OPTICS(opts?: object): OPTICS;

Parameters

NameTypeDescription
opts?object-
opts.algorithm?"auto" | "ball_tree" | "kd_tree" | "brute"Algorithm used to compute the nearest neighbors: Default Value 'auto'
opts.cluster_method?stringThe extraction method used to extract clusters using the calculated reachability and ordering. Possible values are “xi” and “dbscan”. Default Value 'xi'
opts.eps?numberThe maximum distance between two samples for one to be considered as in the neighborhood of the other. By default it assumes the same value as max\_eps. Used only when cluster\_method='dbscan'.
opts.leaf_size?numberLeaf size passed to BallTree or 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. Default Value 30
opts.max_eps?numberThe 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.
opts.memory?stringUsed to cache the output of the computation of the tree. By default, no caching is done. If a string is given, it is the path to the caching directory.
opts.metric?stringMetric 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: Default Value 'minkowski'
opts.metric_params?anyAdditional keyword arguments for the metric function.
opts.min_cluster_size?anyMinimum 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 undefined, the value of min\_samples is used instead. Used only when cluster\_method='xi'.
opts.min_samples?anyThe number of samples in a neighborhood for a point to be considered as a core point. Also, up and down steep regions can’t have more than 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). Default Value 5
opts.n_jobs?numberThe number of parallel jobs to run for neighbors search. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details.
opts.p?numberParameter for the Minkowski metric from 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. Default Value 2
opts.predecessor_correction?booleanCorrect clusters according to the predecessors calculated by OPTICS [2]. This parameter has minimal effect on most datasets. Used only when cluster\_method='xi'. Default Value true
opts.xi?anyDetermines 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. Used only when cluster\_method='xi'. Default Value 0.05

Returns

OPTICS

Defined in: generated/cluster/OPTICS.ts:29 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/cluster/OPTICS.ts:27 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/cluster/OPTICS.ts:26 (opens in a new tab)

_py

PythonBridge

Defined in: generated/cluster/OPTICS.ts:25 (opens in a new tab)

id

string

Defined in: generated/cluster/OPTICS.ts:22 (opens in a new tab)

opts

any

Defined in: generated/cluster/OPTICS.ts:23 (opens in a new tab)

Accessors

cluster_hierarchy_

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 those smaller ones. Since labels\_ does not reflect the hierarchy, usually len(cluster\_hierarchy\_) > np.unique(optics.labels\_). Please also note that these indices are of the ordering\_, i.e. X\[ordering\_\]\[start:end + 1\] form a cluster. Only available when cluster\_method='xi'.

Signature

cluster_hierarchy_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/cluster/OPTICS.ts:401 (opens in a new tab)

core_distances_

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.

Signature

core_distances_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/cluster/OPTICS.ts:353 (opens in a new tab)

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/cluster/OPTICS.ts:449 (opens in a new tab)

labels_

Cluster labels for each point in the dataset given to fit(). Noisy samples and points which are not included in a leaf cluster of cluster\_hierarchy\_ are labeled as -1.

Signature

labels_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/cluster/OPTICS.ts:285 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/cluster/OPTICS.ts:426 (opens in a new tab)

ordering_

The cluster ordered list of sample indices.

Signature

ordering_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/cluster/OPTICS.ts:330 (opens in a new tab)

predecessor_

Point that a sample was reached from, indexed by object order. Seed points have a predecessor of -1.

Signature

predecessor_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/cluster/OPTICS.ts:378 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/cluster/OPTICS.ts:124 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/cluster/OPTICS.ts:128 (opens in a new tab)

reachability_

Reachability distances per sample, indexed by object order. Use clust.reachability\_\[clust.ordering\_\] to access in cluster order.

Signature

reachability_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/cluster/OPTICS.ts:307 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/cluster/OPTICS.ts:190 (opens in a new tab)

fit()

Perform OPTICS clustering.

Extracts an ordered list of points and reachability distances, and performs initial clustering using max\_eps distance specified at OPTICS object instantiation.

Signature

fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?anyA feature array, or array of distances between samples if metric=’precomputed’. If a sparse matrix is provided, it will be converted into CSR format.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/cluster/OPTICS.ts:209 (opens in a new tab)

fit_predict()

Perform clustering on X and returns cluster labels.

Signature

fit_predict(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Input data.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<ArrayLike>

Defined in: generated/cluster/OPTICS.ts:247 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/cluster/OPTICS.ts:137 (opens in a new tab)