Skip to main content

URF: Unsupervised Random Forest

URF (Unsupervised Random Forest, or Random Forest Clustering) is a python implementation of the paper: Shi, T., & Horvath, S. (2006). Unsupervised learning with random forest predictors. Journal of Computational and Graphical Statistics, 15(1), 118-138.

Prerequisite

conda install -c bioconda pycluster

or:

wget http://bonsai.hgc.jp/~mdehoon/software/cluster/Pycluster-1.54.tar.gz
tar -zxvf Pycluster-1.54.tar.gz
cd Pycluster-1.54
python setup.py install

Installation

pip install URF

Usage

from sklearn.datasets import load_iris
from URF.main import random_forest_cluster, plot_cluster_result
iris = load_iris()
X = iris.data
y = iris.target
print(len(list(set(y))))

clf, prox_mat, cluster_ids = random_forest_cluster(X, k=3, max_depth=20, random_state=0)
plot_cluster_result(prox_mat, cluster_ids, marker=y)

If you encountered an error like

> QXcbConnection: Could not connect to display

then you need to add these codes to the very beginning of your file:

import matplotlib as mpl
mpl.use("Agg")

and you must assign the output file when you call plot_cluster_result, like this:

plot_cluster_result(prox_mat, cluster_ids, marker=y, output="test_123.png")

Metadata

Release files for URF 0.0.5

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for URF 0.0.5
File Size Uploaded Provenance Actions
URF-0.0.5.tar.gz 3.7 kB Without provenance Details for URF-0.0.5.tar.gz

Release files / URF-0.0.5.tar.gz

Download URL URF-0.0.5.tar.gz
Size 3.7 kB
Tags Source
SHA-256 checksum
How to use checksums
2c3cbb794dd284b0387ea28e38c3ed4137ade6488667d7adb2914a551afaf9f1
BLAKE2b-256 checksum
How to use checksums
bac6afc7d111e338acccf917daca888da4d5b36e3af32017ee26e39c7c9b89ae
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Security for URF 0.0.5

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page