https://github.com/xhan97/isokernel
A scikit-learn-compatible module for Isolation Kernel with aNNE implemention.
https://github.com/xhan97/isokernel
distribution-similarity isolation-kernel kernel-methods similarity-measures sklearn-compatible
Last synced: 6 months ago
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A scikit-learn-compatible module for Isolation Kernel with aNNE implemention.
- Host: GitHub
- URL: https://github.com/xhan97/isokernel
- Owner: xhan97
- License: apache-2.0
- Created: 2022-06-21T14:21:28.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-11-23T23:38:25.000Z (11 months ago)
- Last Synced: 2024-11-24T00:17:27.730Z (11 months ago)
- Topics: distribution-similarity, isolation-kernel, kernel-methods, similarity-measures, sklearn-compatible
- Language: Python
- Homepage:
- Size: 89.8 KB
- Stars: 7
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
README
.. -*- mode: rst -*-
IsoKernel
======================================================================
IsoKernel - IsoKernel is a python library for Isolation Kernel. It includes several kernel methods using isolation mechanism.
Supported Kernel methods
-----------------------------
* Isolation Kernel (IsoKernel)
* Isolation Distribution Kernel (IsoDisKernel)
Installing
----------
PyPI install, presuming you have an up to date pip.
.. .. code:: bash
.. pip install IsoKernel
For a manual install of the latest code directly from GitHub:
.. code:: bash
pip install git+https://github.com/xhan97/IsoKernel.git
Alternatively download the package, install requirements, and manually run the installer:
.. code:: bash
wget https://codeload.github.com/xhan97/IsoKernel/zip/refs/heads/master
unzip IsoKernel-master.zip
rm IsoKernel-master.zip
cd IsoKernel-master
pip install -r requirements.txt
python setup.py install
How to use IsoKernel
-------------------------
The IsoKernel package inherits from sklearn classes, and thus drops in neatly
next to other sklearn with an identical calling API. Similarly it
supports input in a variety of formats: an array (or pandas dataframe) of shape ``(num_samples x num_features)``.
.. code:: python
from IsoKernel import IsoKernel
from sklearn.datasets import make_blobs
data, _ = make_blobs(1000)
ik = IsoKernel(n_estimators=200, max_samples=16, method="anne") # method can be "anne" or "inne"
ik = ik.fit(data)
# get Isolation Kernel feature for all points in data.
ik.transform(data)
# get pairwise Isolation Kernel similarity for all points in data.
ik.similarity(data)
How to use IsoDisKernel
-------------------------------
Isolation Distributional Kernel is a new way to measure the similarity between two distributions.
It addresses two key issues of kernel mean embedding, where the kernel employed has:
(i) a feature map with intractable dimensionality which leads to high computational cost;
(ii) data independency which leads to poor accuracy.
.. code:: python
from IsoKernel import IsoDisKernel
from sklearn.datasets import make_blobs
data, _ = make_blobs(1000)
idk = IsoDisKernel(n_estimators=200, max_samples=16, method="anne") # method can be "anne" or "inne"
idk = idk.fit(data)
D_i = data[:10]
D_j = data[-10:]
# Directly get the similarity between two distributions
# is_normalize: whether return the normalized similarity matrix ranged of [0,1]. Default: True
sim = idk.similarity(D_i, D_j, is_normalize=True)
# get ik feature of two distributions
ikm_D_i, ikm_D_j = idk.transform(D_i, D_j)
# get kernel mean embedding
kme_D_i = idk.kernel_mean_embedding(ikm_D_i)
kme_D_j = idk.kernel_mean_embedding(ikm_D_j)
# get similarity between two distributions.
sim = idk.kme_similarity(kme_D_i, kme_D_j, is_normalize=True)
Running the Tests
---------------------------
The package tests can be run after installation using the command:
.. code:: bash
pip install pytest
or, if ``pytest`` is installed:
.. code:: bash
pytest IsoKernel/tests
If one or more of the tests fail, please report a bug at https://github.com/xhan97/IsoKernel/issues
Python Version
------------------------
Python 3 is recommend the better option if it is available to you.
Citing
------------------------
If you have used this codebase in a scientific publication and wish to
cite it, please use the following publication (Bibtex format):
.. code:: bibtex
@inproceedings{10.1145/3219819.3219990,
author = {Ting, Kai Ming and Zhu, Yue and Zhou, Zhi-Hua},
title = {Isolation Kernel and Its Effect on SVM},
year = {2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
pages = {2329–2337},
numpages = {9},
location = {London, United Kingdom},
series = {KDD '18}
}
.. code:: bibtex
@inproceedings{ting2020Isolation,
author = {Ting, Kai Ming and Xu, Bi-Cun and Washio, Takashi and Zhou, Zhi-Hua},
title = {Isolation Distributional Kernel: A New Tool for Kernel Based Anomaly Detection},
year = {2020},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
doi = {10.1145/3394486.3403062},
pages = {198-206},
numpages = {9},
series = {KDD '20}
}
.. code:: bibtex
@inproceedings{HZTZL22Streaming,
author = {Han, Xin and Zhu, Ye and Ting, Kai Ming and Zhan, De-Chuan and Li, Gang},
title = {Streaming Hierarchical Clustering Based on Point-Set Kernel},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3534678.3539323},
doi = {10.1145/3534678.3539323},
pages = {525–533},
numpages = {9},
keywords = {streaming data, hierarchical clustering, isolation kernel},
location = {Washington DC, USA},
series = {KDD '22}
}
License
-------
Apache license