scikit-learn-contrib/hdbscan

Introduce sample weighting in HDBSCAN

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#148 opened on Dec 5, 2017

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help wantednew feature

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Description

Like for DBSCAN it would be very useful to also have the possibility to use a "sample_weight" parameter when performing HDBSCAN clustering trough the fit method. This to take into account possible presence of data containing element duplicates.

DBSCAN is just using sample_weight in the following way:

n_neighbors = np.array([np.sum(sample_weight[neighbors]) for neighbors in neighborhoods])

do you know if this is already planned at same time or not?

Contributor guide

Research direction
Inspect how DBSCAN implements sample weight in sklearn and apply similar neighbor weight calculation in HDBSCAN's fit method.
Tech stack
python
Domain
machine learningdata
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
Pythonscikit learn
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
50