Source code for PopPUNK.dbscan

# vim: set fileencoding=<utf-8> :
# Copyright 2018-2023 John Lees and Nick Croucher

'''DBSCAN using hdbscan'''

# universal
import os
import sys
# hdbscan
import hdbscan

[docs] def fitDbScan(X, min_samples, min_cluster_size, cache_out): """Function to fit DBSCAN model as an alternative to the Gaussian Fits the DBSCAN model to the distances using hdbscan Args: X (np.array) n x 2 array of core and accessory distances for n samples min_samples (int) Parameter for DBSCAN clustering 'conservativeness' min_cluster_size (int) Minimum number of points in a cluster for HDBSCAN cache_out (str) Prefix for DBSCAN cache used for refitting Returns: hdb (hdbscan.HDBSCAN) Fitted HDBSCAN to subsampled data labels (list) Cluster assignments of each sample n_clusters (int) Number of clusters used """ # set DBSCAN clustering parameters hdb = hdbscan.HDBSCAN(algorithm='boruvka_balltree', min_samples = min_samples, #core_dist_n_jobs = threads, # may cause error, see #19 memory = cache_out, prediction_data = True, min_cluster_size = min_cluster_size ).fit(X) # Number of clusters in labels, ignoring noise if present. labels = hdb.labels_ n_clusters = len(set(labels)) - (1 if -1 in labels else 0) # return model parameters return hdb, labels, n_clusters
[docs] def evaluate_dbscan_clusters(model): """Evaluate whether fitted dbscan model contains non-overlapping clusters Args: model (DBSCANFit) Fitted model from :func:`` Returns: indistinct (bool) Boolean indicating whether putative within- and between-strain clusters of points overlap """ indistinct = True # calculate ranges of minima and maxima core_minimum_of_between = model.cluster_mins[model.between_label,0] core_maximum_of_within = model.cluster_maxs[model.within_label,0] accessory_minimum_of_between = model.cluster_mins[model.between_label,1] accessory_maximum_of_within = model.cluster_maxs[model.within_label,1] # evaluate whether maxima of cluster nearest origin do not # overlap with minima of cluster furthest from origin if core_minimum_of_between > core_maximum_of_within and \ accessory_minimum_of_between > accessory_maximum_of_within: indistinct = False # return distinctiveness return indistinct
[docs] def findBetweenLabel(assignments, within_cluster): """Identify between-strain links from a DBSCAN model Finds the component containing the largest number of between-strain links, excluding the cluster identified as containing within-strain links. Args: assignments (numpy.array) Sample cluster assignments within_cluster (int) Cluster ID assigned to within-strain assignments, from :func:`~PopPUNK.bgmm.findWithinLabel` Returns: between_cluster (int) The cluster label for the between-strain assignments """ # remove noise and within-strain distance cluster assignment_list = assignments.tolist() assignment_list = list(filter((within_cluster).__ne__, assignment_list)) # remove within-cluster assignment_list = list(filter((-1).__ne__, assignment_list)) # remove noise # identify non-within cluster with most members between_cluster = max(set(assignment_list), key=assignment_list.count) return between_cluster