tinto-prueba

TINTO: Software to convert Tidy Data into Image for Classification with 2-Dimensional Convolutional Neural Networks

https://github.com/oeg-upm/tinto

Science Score: 49.0%

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    Found 7 DOI reference(s) in README
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    Low similarity (16.6%) to scientific vocabulary

Keywords

convolutional-neural-network deep-neural-networks python python-script synthetic-data tabular-data tidy-data tidy-data-into-images
Last synced: 6 months ago · JSON representation

Repository

TINTO: Software to convert Tidy Data into Image for Classification with 2-Dimensional Convolutional Neural Networks

Basic Info
  • Host: GitHub
  • Owner: oeg-upm
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 112 MB
Statistics
  • Stars: 5
  • Watchers: 3
  • Forks: 3
  • Open Issues: 0
  • Releases: 3
Topics
convolutional-neural-network deep-neural-networks python python-script synthetic-data tabular-data tidy-data tidy-data-into-images
Created about 3 years ago · Last pushed 9 months ago
Metadata Files
Readme Contributing License Citation

README.md

TINTO: Converting Tidy Data into Image for Classification with 2D CNNs

License DOI Python Version Open In Colab Ask DeepWiki

TINTO Logo


Important Notice

This repository contains the original implementation of TINTO, an engine for converting tabular (tidy) data into synthetic images using dimensionality reduction and convolution techniques.

We strongly recommend using the updated library TINTOlib, which includes:

  • The original TINTO method
  • Several additional methods such as IGTD, REFINED, BarGraph, DistanceMatrix, Combination, FeatureWrap, SuperTML, and BIE
  • A much more user-friendly and flexible interface
  • Complete and regularly updated documentation
  • A free course with examples, notebooks, and video tutorials

TINTOlib is under active development and continues to receive improvements.

For new projects and applications, we highly recommend switching to TINTOlib.


Overview

TINTO is a Python engine to transform Tidy Data (aka tabular data) into synthetic images, enabling CNN-based classification on non-visual datasets.


Explore with DeepWiki

TINTO has a dedicated page in DeepWiki, where you can browse semantic documentation, use cases, FAQs, and more.

Ask DeepWiki


Key Features

  • Input: Tidy CSV files (target as last column)
  • Supports binary and multi-class classification
  • Dimensionality reduction: PCA and t-SNE
  • Output: grayscale synthetic images with/without blurring
  • Customizable image size and overlap strategies
  • Python 3.7+ compatible

Quick Demo

https://user-images.githubusercontent.com/102165947/217485660-ca7e936a-e9bb-48a3-aaa4-3ba003bac36d.mp4


Installation & Usage

bash pip install -r requirements.txt python tinto.py -h # view all available options

Example - No Blurring:

bash python tinto.py iris.csv iris_images

Example - With Blurring + t-SNE:

bash python tinto.py iris.csv iris_images_tSNE -B -alg t-SNE -oB maximum -px 30 -sB 5


Example Dataset Format (Iris)

| sepal length | sepal width | petal length | petal width | target | | ------------ | ----------- | ------------ | ----------- | ------ | | 4.9 | 3.0 | 1.4 | 0.2 | 1 | | 7.0 | 3.2 | 4.7 | 1.4 | 2 | | 6.3 | 3.3 | 6.0 | 2.5 | 3 |


Output Examples

TINTO pixel TINTO blurring


Citation

If you use TINTO, please cite:

bib @article{softwarex_TINTO, title = {TINTO: Converting Tidy Data into Image for Classification with 2-Dimensional Convolutional Neural Networks}, journal = {SoftwareX}, author = {Manuel Castillo-Cara and Reewos Talla-Chumpitaz and Ral Garca-Castro and Luis Orozco-Barbosa}, volume = {22}, pages = {101391}, year = {2023}, issn = {2352-7110}, doi = {https://doi.org/10.1016/j.softx.2023.101391} }

And for indoor localisation use-case:

bib @article{inffus_TINTO, title = {A novel deep learning approach using blurring image techniques for Bluetooth-based indoor localisation}, journal = {Information Fusion}, author = {Reewos Talla-Chumpitaz and Manuel Castillo-Cara and Luis Orozco-Barbosa and Ral Garca-Castro}, volume = {91}, pages = {173-186}, year = {2023}, doi = {https://doi.org/10.1016/j.inffus.2022.10.011} }


Colab Tutorial

Learn how to load the generated images into CNNs:

Open In Colab


Authors & Contributors

OEG UPM UNED UCLM


License

TINTO is released under the Apache License 2.0.

Owner

  • Name: Ontology Engineering Group (UPM)
  • Login: oeg-upm
  • Kind: organization
  • Email: oeg-dev@delicias.dia.fi.upm.es
  • Location: Boadilla del Monte, Madrid, Spain

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  • Total versions: 3
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pypi.org: tinto-prueba

TINTO package test

  • Homepage: https://github.com/oeg-upm/TINTO
  • Documentation: https://tinto-prueba.readthedocs.io/
  • License: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
  • Latest release: 0.0.2
    published almost 3 years ago
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  • Downloads: 10 Last month
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pypi.org: tintonera-prueba

TINTO package test

  • Homepage: https://github.com/oeg-upm/TINTO
  • Documentation: https://tintonera-prueba.readthedocs.io/
  • License: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. 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Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
  • Latest release: 0.0.1
    published almost 3 years ago
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 4 Last month
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Downloads: 18.0%
Average: 22.2%
Forks count: 23.2%
Dependent repos count: 30.6%
Stargazers count: 32.3%
Maintainers (1)
Last synced: 6 months ago