{"id":21231806,"url":"https://github.com/kalebers/data_streams_parametric_t-sne","last_synced_at":"2026-02-11T16:02:27.285Z","repository":{"id":197983980,"uuid":"404696116","full_name":"kalebers/Data_Streams_Parametric_T-SNE","owner":"kalebers","description":"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)","archived":false,"fork":false,"pushed_at":"2024-12-24T10:30:30.000Z","size":590,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-31T18:07:39.288Z","etag":null,"topics":["classifier","data-analysis","data-science","data-visualization","machinelearning","parametric","parametric-tsne","python","tsne-algorithm","tsne-visualization"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kalebers.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-09-09T11:29:25.000Z","updated_at":"2024-12-24T10:30:34.000Z","dependencies_parsed_at":null,"dependency_job_id":"e13b5903-bc1d-46a6-9934-e9403b0337e6","html_url":"https://github.com/kalebers/Data_Streams_Parametric_T-SNE","commit_stats":null,"previous_names":["kalebers/data_streams_parametric_t-sne"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kalebers%2FData_Streams_Parametric_T-SNE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kalebers%2FData_Streams_Parametric_T-SNE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kalebers%2FData_Streams_Parametric_T-SNE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kalebers%2FData_Streams_Parametric_T-SNE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kalebers","download_url":"https://codeload.github.com/kalebers/Data_Streams_Parametric_T-SNE/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253149383,"owners_count":21861717,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["classifier","data-analysis","data-science","data-visualization","machinelearning","parametric","parametric-tsne","python","tsne-algorithm","tsne-visualization"],"created_at":"2024-11-20T23:48:25.394Z","updated_at":"2026-02-11T16:02:27.250Z","avatar_url":"https://github.com/kalebers.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Parametric t-SNE  \r\n----------\r\nThis 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\r\nfor the Computer Science graduation final project. \r\n\r\nENGLISH: 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.\r\n\r\nMain archives: \r\n- 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.\r\n  \r\n- parametric_tsne.py: main file for Parametric t-SNE implementation, by Luke Lee.\r\n\r\n----------\r\n\r\nt-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.\r\n\r\n## Getting Started\r\n\r\nThese 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.\r\n\r\n### Prerequisites\r\n\r\nThis program was tested under Python 3.6. All necessary packages are contained inside `requirements.txt`.\r\n\r\n### Installing\r\n\r\nAfter cloning this repository, install required packages by running the following:\r\n\r\n```\r\npip3 install -r requirements.txt\r\n```\r\n\r\n## Running the Tests\r\n\r\n`parametric_tsne.py` can be run directly from command-line. See help for details.\r\n\r\n```\r\npython3 parametric_tsne.py -h\r\n```\r\n\r\n## Deployment\r\n\r\nSimply create `ParametricTSNE` instance. The interface was designed similarly to that of scikit-learn estimators.\r\n\r\n```python\r\nfrom parametric_tsne import ParametricTSNE\r\n\r\ntransformer = ParametricTSNE()\r\n\r\n# suppose you have the dataset X\r\nX_new = transformer.fit_transform(X)\r\n\r\n# transform new dataset X2 with pre-trained model\r\nX2_new = transformer.transform(X2)\r\n```\r\n\r\n## Built With\r\n\r\n- [scikit-learn](http://scikit-learn.org/stable/) - Extensive machine learning framework\r\n\r\n- [Keras](https://keras.io) - Deep learning framework wrapper that supports TensorFlow, Theano, and CNTK\r\n\r\n## Authors\r\n\r\n- __Luke Lee__ - Research and implementation - [luke0201](https://github.com/luke0201)\r\n\r\n## Acknowledgements\r\n\r\n- This project was forked from [zaburo-ch's implementation](https://github.com/zaburo-ch/Parametric-t-SNE-in-Keras).\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkalebers%2Fdata_streams_parametric_t-sne","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkalebers%2Fdata_streams_parametric_t-sne","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkalebers%2Fdata_streams_parametric_t-sne/lists"}