A high performance implementation of HDBSCAN clustering. http://hdbscan.readthedocs.io/en/latest/

Overview
PyPI Version Conda-forge Version Conda-forge downloads License Travis Build Status Test Coverage Docs JOSS article

HDBSCAN

Now a part of scikit-learn-contrib

HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection.

In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster size, is intuitive and easy to select.

HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any).

Based on the paper:
R. Campello, D. Moulavi, and J. Sander, Density-Based Clustering Based on Hierarchical Density Estimates In: Advances in Knowledge Discovery and Data Mining, Springer, pp 160-172. 2013

Documentation, including tutorials, are available on ReadTheDocs at http://hdbscan.readthedocs.io/en/latest/ .

Notebooks comparing HDBSCAN to other clustering algorithms, explaining how HDBSCAN works and comparing performance with other python clustering implementations are available.

How to use HDBSCAN

The hdbscan package inherits from sklearn classes, and thus drops in neatly next to other sklearn clusterers with an identical calling API. Similarly it supports input in a variety of formats: an array (or pandas dataframe, or sparse matrix) of shape (num_samples x num_features); an array (or sparse matrix) giving a distance matrix between samples.

import hdbscan
from sklearn.datasets import make_blobs

data, _ = make_blobs(1000)

clusterer = hdbscan.HDBSCAN(min_cluster_size=10)
cluster_labels = clusterer.fit_predict(data)

Performance

Significant effort has been put into making the hdbscan implementation as fast as possible. It is orders of magnitude faster than the reference implementation in Java, and is currently faster than highly optimized single linkage implementations in C and C++. version 0.7 performance can be seen in this notebook . In particular performance on low dimensional data is better than sklearn's DBSCAN , and via support for caching with joblib, re-clustering with different parameters can be almost free.

Additional functionality

The hdbscan package comes equipped with visualization tools to help you understand your clustering results. After fitting data the clusterer object has attributes for:

  • The condensed cluster hierarchy
  • The robust single linkage cluster hierarchy
  • The reachability distance minimal spanning tree

All of which come equipped with methods for plotting and converting to Pandas or NetworkX for further analysis. See the notebook on how HDBSCAN works for examples and further details.

The clusterer objects also have an attribute providing cluster membership strengths, resulting in optional soft clustering (and no further compute expense). Finally each cluster also receives a persistence score giving the stability of the cluster over the range of distance scales present in the data. This provides a measure of the relative strength of clusters.

Outlier Detection

The HDBSCAN clusterer objects also support the GLOSH outlier detection algorithm. After fitting the clusterer to data the outlier scores can be accessed via the outlier_scores_ attribute. The result is a vector of score values, one for each data point that was fit. Higher scores represent more outlier like objects. Selecting outliers via upper quantiles is often a good approach.

Based on the paper:
R.J.G.B. Campello, D. Moulavi, A. Zimek and J. Sander Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection, ACM Trans. on Knowledge Discovery from Data, Vol 10, 1 (July 2015), 1-51.

Robust single linkage

The hdbscan package also provides support for the robust single linkage clustering algorithm of Chaudhuri and Dasgupta. As with the HDBSCAN implementation this is a high performance version of the algorithm outperforming scipy's standard single linkage implementation. The robust single linkage hierarchy is available as an attribute of the robust single linkage clusterer, again with the ability to plot or export the hierarchy, and to extract flat clusterings at a given cut level and gamma value.

Example usage:

import hdbscan
from sklearn.datasets import make_blobs

data = make_blobs(1000)

clusterer = hdbscan.RobustSingleLinkage(cut=0.125, k=7)
cluster_labels = clusterer.fit_predict(data)
hierarchy = clusterer.cluster_hierarchy_
alt_labels = hierarchy.get_clusters(0.100, 5)
hierarchy.plot()
Based on the paper:
K. Chaudhuri and S. Dasgupta. "Rates of convergence for the cluster tree." In Advances in Neural Information Processing Systems, 2010.

Installing

Easiest install, if you have Anaconda (thanks to conda-forge which is awesome!):

conda install -c conda-forge hdbscan

PyPI install, presuming you have sklearn and all its requirements (numpy and scipy) installed:

pip install hdbscan

If pip is having difficulties pulling the dependencies then we'd suggest installing the dependencies manually using anaconda followed by pulling hdbscan from pip:

conda install cython
conda install numpy scipy
conda install scikit-learn
pip install hdbscan

For a manual install get this package:

wget https://github.com/scikit-learn-contrib/hdbscan/archive/master.zip
unzip master.zip
rm master.zip
cd hdbscan-master

Install the requirements

sudo pip install -r requirements.txt

or

conda install scikit-learn cython

Install the package

python setup.py install

Python Version

The hdbscan library supports both Python 2 and Python 3. However we recommend Python 3 as the better option if it is available to you.

Help and Support

For simple issues you can consult the FAQ in the documentation. If your issue is not suitably resolved there, please check the issues on github. Finally, if no solution is available there feel free to open an issue ; the authors will attempt to respond in a reasonably timely fashion.

Contributing

We welcome contributions in any form! Assistance with documentation, particularly expanding tutorials, is always welcome. To contribute please fork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.

Citing

If you have used this codebase in a scientific publication and wish to cite it, please use the Journal of Open Source Software article.

L. McInnes, J. Healy, S. Astels, hdbscan: Hierarchical density based clustering In: Journal of Open Source Software, The Open Journal, volume 2, number 11. 2017

Licensing

The hdbscan package is 3-clause BSD licensed. Enjoy.

Owner
Leland McInnes
Leland McInnes
An interactive GUI for WhiteboxTools in a Jupyter-based environment

whiteboxgui An interactive GUI for WhiteboxTools in a Jupyter-based environment GitHub repo: https://github.com/giswqs/whiteboxgui Documentation: http

Qiusheng Wu 105 Dec 15, 2022
Visualize your pandas data with one-line code

PandasEcharts 简介 基于pandas和pyecharts的可视化工具 安装 pip 安装 $ pip install pandasecharts 源码安装 $ git clone https://github.com/gamersover/pandasecharts $ cd pand

陈华杰 2 Apr 13, 2022
Movies-chart - A CLI app gets the top 250 movies of all time from imdb.com and the top 100 movies from rottentomatoes.com

movies-chart This CLI app gets the top 250 movies of all time from imdb.com and

3 Feb 17, 2022
Standardized plots and visualizations in Python

Standardized plots and visualizations in Python pltviz is a Python package for standardized visualization. Routine and novel plotting approaches are f

Andrew Tavis McAllister 0 Jul 09, 2022
Fast scatter density plots for Matplotlib

About Plotting millions of points can be slow. Real slow... 😴 So why not use density maps? ⚡ The mpl-scatter-density mini-package provides functional

Thomas Robitaille 473 Dec 12, 2022
Open-source demos hosted on Dash Gallery

Dash Sample Apps This repository hosts the code for over 100 open-source Dash apps written in Python or R. They can serve as a starting point for your

Plotly 2.7k Jan 07, 2023
Calendar heatmaps from Pandas time series data

Note: See MarvinT/calmap for the maintained version of the project. That is also the version that gets published to PyPI and it has received several f

Martijn Vermaat 195 Dec 22, 2022
trade bot connected to binance API/ websocket.,, include dashboard in plotly dash to visualize trades and balances

Crypto trade bot 1. What it is Trading bot connected to Binance API. This project made for fun. So ... Do not use to trade live before you have backte

G 3 Oct 07, 2022
finds grocery stores and stuff next to route (gpx)

Route-Report Route report is a command-line utility that can be used to locate points-of-interest near your planned route (gpx). The results are based

Clemens Mosig 5 Oct 10, 2022
Here I plotted data for the average test scores across schools and class sizes across school districts.

HW_02 Here I plotted data for the average test scores across schools and class sizes across school districts. Average Test Score by Race This graph re

7 Oct 27, 2021
An intuitive library to add plotting functionality to scikit-learn objects.

Welcome to Scikit-plot Single line functions for detailed visualizations The quickest and easiest way to go from analysis... ...to this. Scikit-plot i

Reiichiro Nakano 2.3k Dec 31, 2022
JSNAPY example: Validate NAT policies

JSNAPY example: Validate NAT policies Overview This example will show how to use JSNAPy to make sure the expected NAT policy matches are taking place.

Calvin Remsburg 1 Jan 07, 2022
A little logger for machine learning research

Blinker Blinker provides a fast dispatching system that allows any number of interested parties to subscribe to events, or "signals". Signal receivers

Reinforcement Learning Working Group 27 Dec 03, 2022
Shaded 😎 quantile plots

shadyquant 😎 This python package allows you to quantile and plot lines where you have multiple samples, typically for visualizing uncertainty. Your d

Mehrad Ansari 13 Sep 29, 2022
A gui application to visualize various sorting algorithms using pure python.

Sorting Algorithm Visualizer A gui application to visualize various sorting algorithms using pure python. Language : Python 3 Libraries required Tkint

Rajarshi Banerjee 19 Nov 30, 2022
HM02: Visualizing Interesting Datasets

HM02: Visualizing Interesting Datasets This is a homework assignment for CSCI 40 class at Claremont McKenna College. Go to the project page to learn m

Qiaoling Chen 11 Oct 26, 2021
Parse Robinhood 1099 Tax Document from PDF into CSV

Robinhood 1099 Parser This project converts Robinhood Securities 1099 tax document from PDF to CSV file. This tool will be helpful for those who need

Keun Tae (Kevin) Park 52 Jun 10, 2022
Sky attention heatmap of submissions to astrometry.net

astroheat Installation Requires Python 3.6+, Tested with Python 3.9.5 Install library dependencies pip install -r requirements.txt The program require

4 Jun 20, 2022
Visualize and compare datasets, target values and associations, with one line of code.

In-depth EDA (target analysis, comparison, feature analysis, correlation) in two lines of code! Sweetviz is an open-source Python library that generat

Francois Bertrand 2.3k Jan 05, 2023
An open-source tool for visual and modular block programing in python

PyFlow PyFlow is an open-source tool for modular visual programing in python ! Although for now the tool is in Beta and features are coming in bit by

1.1k Jan 06, 2023