Skip to main content

Artificial neural network-driven visualization of high-dimensional data using triplets.

Project description

DOI DOI Documentation Status Downloads Build Status

ivis

Implementation of the ivis algorithm as described in the paper Structure-preserving visualisation of high dimensional single-cell datasets. Ivis is designed to reduce dimensionality of very large datasets using a siamese neural network trained on triplets. Both unsupervised and supervised modes are supported.

ivis 10M data points

Installation

Ivis runs on top of TensorFlow. To install the latest ivis release from PyPi running on the CPU TensorFlow package, run:

# TensorFlow 2 packages require a pip version >19.0.
pip install --upgrade pip
pip install ivis[cpu]

If you have CUDA installed and want ivis to use the tensorflow-gpu package, run

pip install ivis[gpu]

Development version can be installed directly from from github:

git clone https://github.com/beringresearch/ivis
cd ivis
pip install -e '.[cpu]'

The following optional dependencies are needed if using the visualization callbacks while training the Ivis model:

  • matplotlib
  • seaborn

Upgrading

Ivis Python package is updated frequently! To upgrade, run:

pip install ivis --upgrade

Features

  • Scalable: ivis is fast and easily extends to millions of observations and thousands of features.
  • Versatile: numpy arrays, sparse matrices, and hdf5 files are supported out of the box. Additionally, both categorical and continuous features are handled well, making it easy to apply ivis to heterogeneous problems including clustering and anomaly detection.
  • Accurate: ivis excels at preserving both local and global features of a dataset. Often, ivis performs better at preserving global structure of the data than t-SNE, making it easy to visualise and interpret high-dimensional datasets.
  • Generalisable: ivis supports addition of new data points to original embeddings via a transform method, making it easy to incorporate ivis into standard sklearn Pipelines.

And many more! See ivis readme for latest additions and examples.

Examples

from ivis import Ivis
from sklearn.preprocessing import MinMaxScaler
from sklearn import datasets

iris = datasets.load_iris()
X = iris.data
X_scaled = MinMaxScaler().fit_transform(X)

model = Ivis(embedding_dims=2, k=15)

embeddings = model.fit_transform(X_scaled)

Copyright 2024 Bering Limited

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ivis-2.0.11.tar.gz (32.4 kB view details)

Uploaded Source

Built Distribution

ivis-2.0.11-py3-none-any.whl (35.5 kB view details)

Uploaded Python 3

File details

Details for the file ivis-2.0.11.tar.gz.

File metadata

  • Download URL: ivis-2.0.11.tar.gz
  • Upload date:
  • Size: 32.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.11.6

File hashes

Hashes for ivis-2.0.11.tar.gz
Algorithm Hash digest
SHA256 44642bd23e4a31ad14e8735d14d2ccdcc3123e060b1355b689c0f5d3ac8cc7e3
MD5 735e99fc2ef309b423d9e4a375c8473a
BLAKE2b-256 08743f2c693155e7f9b8c702e1b2decc02d61924f20a374bcd54b44687d1299e

See more details on using hashes here.

File details

Details for the file ivis-2.0.11-py3-none-any.whl.

File metadata

  • Download URL: ivis-2.0.11-py3-none-any.whl
  • Upload date:
  • Size: 35.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.11.6

File hashes

Hashes for ivis-2.0.11-py3-none-any.whl
Algorithm Hash digest
SHA256 1bfe04b1ffdde2833d49441a1180295647cf982e4ffdca6bdb2e2b7928b6cd24
MD5 d93869c3be1f13d7580b3c6b9c3bbd3e
BLAKE2b-256 4977689c86fc0c8821698477fbb06112bbc5a78b5e499b716bfba3ee8109b3b4

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page