Metadata-Version: 2.4
Name: spacemap-dr
Version: 0.1.0
Summary: SpaceMAP: Visualizing High-dimensional data by Space Expansion
Author-email: Xinrui Zu <zuxinrui95@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/zuxinrui/SpaceMAP
Project-URL: Source, https://github.com/zuxinrui/SpaceMAP
Keywords: Dimensionality Reduction,t-SNE,UMAP,Representation Learning,ICML
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: C
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: scikit-learn>=0.24
Requires-Dist: numba>=0.55
Requires-Dist: faiss-cpu>=1.7.0
Requires-Dist: scipy>=1.7
Requires-Dist: ipywidgets>=7
Requires-Dist: matplotlib
Requires-Dist: torchvision
Requires-Dist: pynndescent
Dynamic: license-file

![image](image/spacemap-main.png)

# SpaceMAP

SpaceMAP is a dimensionality reduction method utilizing the local and global intrinsic dimensions of the data to better alleviate the 'crowding problem' analytically. 

## Paper

[https://icml.cc/virtual/2022/spotlight/18170](https://icml.cc/virtual/2022/spotlight/18170)

[https://proceedings.mlr.press/v162/zu22a.html](https://proceedings.mlr.press/v162/zu22a.html)


## Hyper-parameters

SpaceMAP has 4 main hyper-parameters: n-near/n-middle and d-local/d-global, which define the intrinsic dimensions and the hierarchical manifold approximation.

- n-near: number of neighbors in the near fields of each data point. (default: 20)
- n-middle: number of neighbors in the middle field of each data point. (default: 1% of the whole dataset)
- d-local: estimated intrinsic dimensions of the near fields of each data point. (default: Auto)
- d-global: estimated intrinsic dimension of the whole dataset. (default: Auto)

# Installation

