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A package for automatic clustering hyperparameter optmization

Project description

Hypercluster

A package for clustering optimization with sklearn.

Requirements:

pandas
numpy
scipy
matplotlib
seaborn
scikit-learn
hdbscan

Optional: snakemake

Install

With pip:

pip install hypercluster

or with conda:

conda install hypercluster
# or
conda install -c conda-forge -c bioconda hypercluster

If you are having problems installing with conda, try changing your channel priority. Priority of conda-forge > bioconda > defaults is recommended. To check channel priority: conda config --get channels It should look like:

--add channels 'defaults'   # lowest priority
--add channels 'bioconda'
--add channels 'conda-forge'   # highest priority

If it doesn't look like that, try:

conda config --add channels bioconda
conda config --add channels conda-forge

Docs

https://hypercluster.readthedocs.io/en/latest/index.html

Examples

https://github.com/liliblu/hypercluster/tree/dev/examples

Quickstart example

import pandas as pd
from sklearn.datasets import make_blobs
import hypercluster

data, labels = make_blobs()
data = pd.DataFrame(data)
labels = pd.Series(labels, index=data.index, name='labels')

# With a single clustering algorithm
clusterer = hypercluster.AutoClusterer()
clusterer.fit(data).evaluate(
  methods = hypercluster.constants.need_ground_truth+hypercluster.constants.inherent_metrics, 
  gold_standard = labels
  )

clusterer.visualize_evaluations()

# With a range of algorithms

clusterer = hypercluster.MultiAutoClusterer()
clusterer.fit(data).evaluate(
  methods = hypercluster.constants.need_ground_truth+hypercluster.constants.inherent_metrics, 
  gold_standard = labels
  )

clusterer.visualize_evaluations()

Project details


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