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).

Constructors

constructor()

Signature

new OPTICS(opts?: object): OPTICS;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.algorithm?`	`"auto"` \| `"ball_tree"` \| `"kd_tree"` \| `"brute"`	Algorithm used to compute the nearest neighbors: `Default Value` `'auto'`
`opts.cluster_method?`	`string`	The extraction method used to extract clusters using the calculated reachability and ordering. Possible values are “xi” and “dbscan”. `Default Value` `'xi'`
`opts.eps?`	`number`	The 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?`	`number`	Leaf 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?`	`number`	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.
`opts.memory?`	`string`	Used 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?`	`string`	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: `Default Value` `'minkowski'`
`opts.metric_params?`	`any`	Additional keyword arguments for the metric function.
`opts.min_cluster_size?`	`any`	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 `undefined`, the value of `min\_samples` is used instead. Used only when `cluster\_method='xi'`.
`opts.min_samples?`	`any`	The 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?`	`number`	The 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?`	`number`	Parameter 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?`	`boolean`	Correct 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?`	`any`	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. Used only when `cluster\_method='xi'`. `Default Value` `0.05`

Returns

OPTICS

Defined in: generated/cluster/OPTICS.ts:29 (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

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`	A 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?`	`any`	Not 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

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Input data.
`opts.y?`	`any`	Not used, present for API consistency by convention.

Returns

Promise<ArrayLike>

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

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.routing?`	`any`	A `MetadataRequest` encapsulating routing information.

Returns

Promise<any>

Defined in: generated/cluster/OPTICS.ts:287 (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

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/cluster/OPTICS.ts:137 (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:436 (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:388 (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:484 (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.