https://github.com/westlake-ai/dmt-learn
An Explainable Deep Network for Dimension Reduction
https://github.com/westlake-ai/dmt-learn
data-science dimension-reduction python
Last synced: 2 months ago
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An Explainable Deep Network for Dimension Reduction
- Host: GitHub
- URL: https://github.com/westlake-ai/dmt-learn
- Owner: Westlake-AI
- License: mit
- Created: 2024-10-21T07:23:47.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-11-18T03:43:02.000Z (11 months ago)
- Last Synced: 2025-03-19T13:46:34.495Z (7 months ago)
- Topics: data-science, dimension-reduction, python
- Language: Python
- Homepage:
- Size: 15.4 MB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# DMT-EV: An Explainable Deep Network for Dimension Reduction
The code includes the following modules:
* Training
* Inference
* Comparison with t-SNE, UMAP and PCA
## Requirements
* torch>=2.3.1
* torchaudio>=2.3.1
* torchvision>=0.18.1
* pytorch-lightning==2.4.0
## Installation
Create a new conda environment and install torch, torchvision, torchaudio:
```bash
conda create -n DMT python=3.10
conda activate DMT
pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu121
```
Then you can install the package from source or from PyPI.
Install from source:
```bash
pip install -e git+https://github.com/Westlake-AI/DMT-learn.git#egg=dmt-learn
```
Install from PyPI:
```bash
pip install dmt-learn
```
## Running the code
Use the following code to fit the model to the dataset and visualize the results.
```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from dmt import DMT
# Load sample dataset
iris = load_iris()
X = iris.data
y = iris.target
# Perform t-SNE
dmt = DMT(num_fea_aim=100)
X_dmt = dmt.fit_transform(X)
# Plot the result
plt.figure(figsize=(8, 6))
scatter = plt.scatter(X_dmt[:, 0], X_dmt[:, 1], c=y, cmap='viridis')
# Create legend
legend1 = plt.legend(*scatter.legend_elements(), title="Classes")
plt.gca().add_artist(legend1) # Add the legend to the current axes
plt.title('DMT-EV visualization of Iris dataset')
plt.xlabel('DMT-EV Component 1')
plt.ylabel('DMT-EV Component 2')
plt.savefig('dmt.png')
```
You can alse separate the training and inference steps:
```python
dmt.fit(X)
X_dmt = dmt.transform(X)
```
If you want to compare the results with other dimension reduction methods(t-SNE, UMAP), you can use the following code:
```python
dmt.compare(X, "comparison.png")
```
## Example Results
The code is in [example](https://github.com/zangzelin/example_dmtlearn.git).
### Mnist Dataset
### breast_cancer Dataset
### 20News Dataset
### PBMC Dataset
## Cite the paper
```
@article{zang2024dmt,
title={DMT-EV: An Explainable Deep Network for Dimension Reduction},
author={Zang, Zelin and Cheng, Shenghui and Xia, Hanchen and Li, Liangyu and Sun, Yaoting and Xu, Yongjie and Shang, Lei and Sun, Baigui and Li, Stan Z},
journal={IEEE Transactions on Visualization and Computer Graphics},
volume={30},
number={3},
pages={1710--1727},
year={2024},
publisher={IEEE}
}
```
## License
DMT-EV is released under the MIT license.