https://github.com/kalebers/data_streams_parametric_t-sne
Research for Parametric T-SNE in high to low dimensional data stream, published in 2021 by Kalebe Rodrigues Szlachta and Andre de Macedo Wlodkovski, oriented by Jean Paul Barddal, Computer Science graduation from Pontifical Catholic University of Parana (PUCPR)
https://github.com/kalebers/data_streams_parametric_t-sne
classifier data-analysis data-science data-visualization machinelearning parametric parametric-tsne python tsne-algorithm tsne-visualization
Last synced: 12 days ago
JSON representation
Research for Parametric T-SNE in high to low dimensional data stream, published in 2021 by Kalebe Rodrigues Szlachta and Andre de Macedo Wlodkovski, oriented by Jean Paul Barddal, Computer Science graduation from Pontifical Catholic University of Parana (PUCPR)
- Host: GitHub
- URL: https://github.com/kalebers/data_streams_parametric_t-sne
- Owner: kalebers
- Created: 2021-09-09T11:29:25.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-12-24T10:30:30.000Z (5 months ago)
- Last Synced: 2025-03-31T18:07:39.288Z (about 2 months ago)
- Topics: classifier, data-analysis, data-science, data-visualization, machinelearning, parametric, parametric-tsne, python, tsne-algorithm, tsne-visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 576 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Parametric t-SNE
----------
This research was conducted by the students André de Macedo Wlodkovski @andrewlod and Kalebe Rodrigues Szlachta @kalebers, mentored by professor Jean Paul Barddal @jpbarddal at Pontifical Catholic University of Paraná
for the Computer Science graduation final project.
ENGLISH: Archives referring to the prototype are inside the folder parametric-tsne-keras. To execute, it's necessary to follow the software requirements presented on chapter 4 of the artefact.
Main archives:
- TSNEClassifier.ipynb: file that contains all experiments reffering to the research and implementation of the TSNEClassifier class. Executed on Anaconda environment with Jupyter lab commands.
- parametric_tsne.py: main file for Parametric t-SNE implementation, by Luke Lee.
----------
t-distributed stochastic neighbor embedding, abbreviated as t-SNE, provides the novel method to apply non-linear dimensionality reduction technique that preserves the local structure of original dataset. However, in order to transform newly prepared points, a model must be re-trained with whole dataset. This would be extremely inefficient provided that our previous dataset describes the plausible distribution already. Parametric t-SNE instead gives you an explicit mapping between original data and the embedded points. It is achieved by building a parametric model for prediction and training it using the same loss as t-SNE.
## Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
### Prerequisites
This program was tested under Python 3.6. All necessary packages are contained inside `requirements.txt`.
### Installing
After cloning this repository, install required packages by running the following:
```
pip3 install -r requirements.txt
```
## Running the Tests
`parametric_tsne.py` can be run directly from command-line. See help for details.
```
python3 parametric_tsne.py -h
```
## Deployment
Simply create `ParametricTSNE` instance. The interface was designed similarly to that of scikit-learn estimators.
```python
from parametric_tsne import ParametricTSNE
transformer = ParametricTSNE()
# suppose you have the dataset X
X_new = transformer.fit_transform(X)
# transform new dataset X2 with pre-trained model
X2_new = transformer.transform(X2)
```
## Built With
- [scikit-learn](http://scikit-learn.org/stable/) - Extensive machine learning framework
- [Keras](https://keras.io) - Deep learning framework wrapper that supports TensorFlow, Theano, and CNTK
## Authors
- __Luke Lee__ - Research and implementation - [luke0201](https://github.com/luke0201)
## Acknowledgements
- This project was forked from [zaburo-ch's implementation](https://github.com/zaburo-ch/Parametric-t-SNE-in-Keras).