Toybox for intuitive evaluation of dimensionality reduction
Project description
ExDimRed
Examples for Dimensionality Reduction
ExDimRed is a simple visualisation tool, aimed and understanding and evaluating dimensionality reduction methods, using human-intuitive examples.
Examples are 3D point data which are reduced via dimensionality reduction methods to 2D.
Usage
Novel methods, or methods not encluded by default, should follow the standard Scikit-Learn syntax, i.e. have the methods
model.fit_transform(X) to fit to and project input data X.
ExDimRed is accessed as follows:
from sklearn.manifold import Isomap
from exdimred import run
run(Isomap)
This will attempt to generate a pdf of the visualisation in the root directory. If pdf generation fails then a png is generated instead.
Requirements
ExDimRed requires Python >= 3.8 and the packages numpy, matplotlib, scikit-learn, and umap-learn to be installed.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file exdimred-0.0.3.tar.gz.
File metadata
- Download URL: exdimred-0.0.3.tar.gz
- Upload date:
- Size: 17.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.8.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
68279a7f560a9ad69bee9625fa3fa7dccdd757387c40213e7872f2fd1fea5a7e
|
|
| MD5 |
3b65650a2e095cdc235acc01fdd7eb60
|
|
| BLAKE2b-256 |
6762efe865d2a398c5cf3fe38891e76afdf5b45f4df087c0ba4f671714870dd3
|
File details
Details for the file exdimred-0.0.3-py3-none-any.whl.
File metadata
- Download URL: exdimred-0.0.3-py3-none-any.whl
- Upload date:
- Size: 17.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.8.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
d582f688cd6dd9396a01b14dcfedb7f059ea1caf625e74d0a3f9e2b45b5f1c87
|
|
| MD5 |
8c0e165dd1db664607a1fdeba39924e0
|
|
| BLAKE2b-256 |
1e4ef74d9da7b053378d84a6d1763a17ce8373f5a8c05e6681cfd84611008d62
|