TINTO is an engine that constructs Synthetic Images from Tidy Data (also knows as Tabular Data).

Citing TINTO: If you used TINTO in your work, please cite the INFFUS Paper:

@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 Raúl García-Castro},
    volume = {91},
    pages = {173-186},
    year = {2023},
    issn = {1566-2535},
    doi = {https://doi.org/10.1016/j.inffus.2022.10.011}
}

Main Features

• Supports all CSV data in Tidy Data format.
• For now, the algorithm converts tabular data for binary and multi-class classification problems into machine learning.
• Input data formats:
  • Tabular files: The input data must be in CSV, taking into account the Tidy Data format.
  • Tidy Data: The target (variable to be predicted) should be set as the last column of the dataset. Therefore, the first columns will be the features.
  • All data must be in numerical form. TINTO does not accept data in string or any other non-numeric format.
• Two dimensionality reduction algorithms are used in image creation, PCA and t-SNE from the Scikit-learn Python library.
• The synthetic images to be created will be in black and white, i.e. in 1 channel.
• The synthetic image dimensions can be set as a parameter when creating them.
• The synthetic images can be created using characteristic pixels or blurring painting technique (expressing an overlap of pixels as the maximum or average).
• Runs on Linux, Windows and macOS systems.
• Compatible with Python 3.7 or higher.

Video Documentation

Getting Started

TINTO is easy to use in terminal:

Fist, it is important to install all previus libraries

    pip install -r requirements.txt

To run the engine via command line and see all the arguments you just need to execute the following:

    python tinto.py -h

The default parameter are the following:

• Dimensional Reduction Algorithm (-alg): Select the dimensionality reduction algorithm to be used for image creation. The PCA** or t-SNE algorithms can be chosen. By default, use the PCA** algorithm.
• Image size (-px): 20x20 pixels
• Blurring (-B): for default is False, i.e., it do not use Blurring technique and create de images with characteristic pixels
• Amplification (-aB): Only if Blurring is True. It is the blurring amplification and for default is PI number, i.e., 3.141592653589793 aprox.
• Blurring distance (-dB): Only if Blurring is True. It is Blurring distance and for default is 0.1 (10%).
• Blurring steps (-sB): Only if Blurring is True. It is Blurring steps and for default is 4, i.e., expand 4 pixels the blurring.
• Blurring option (-oB): Only if Blurring is True. It is the Blurring option and for default is mean, i.e., if two pixels are overlaping, calculate the average number of this two overlaping pixels.
• Save Configuration (-sC): Save the configurarion in a pikle object. It is False for default.
• Load Configuration (-lC): Load the configurarion in a pikle object. It is False for default.
• Seed (-sd): Set a seed for the random numbers. It is 20 for default.
• _t_SNE times replication (-tt): It is only used when t-SNE is used. It is t-SNE times replication and for defaultd is 4.
• Verbose (-v). Show in terminal the execution. For default is False.

Previous considerations

Please note that the following considerations must be taken into account before running the script:

• Data must be in CSV with the default separator, i.e., commas.
• Only create images when we have data for a binary or multi-class classification problem.
• The last column should be the targer (variable to predict).
• The first columns will be the characteristics.
• All variables must be in numerical format.
• The script takes by default the first row as the name of each feature, therefore, the different features must be named.
• Each sample (row) of the dataset will correspond to an image.

For example, the following table shows a classic example of the IRIS CSV dataset as it should look like for the run:

Simple example without Blurring

The following example shows how to create 20x20 images with characteristic pixels, i.e. without blurring.

    python tinto.py "iris.csv" "iris_images"

The images are created with the following considerations regarding the parameters used:

• python: to launch the Python script
• tinto.py: the name of the script
• iris.csv: the dataset to use. In this example, the IRIS dataset is used.
• iris/: the folder where the images will be saved.

Also, as no other parameters are indicated, you will choose the following parameters which are set by default:

• Image size: 20x20 pixels
• Blurring: No blurring will be used.
• Seed: with the seed set to 20.

Within the folder named "iris/" we can find subfolders with numbers where each number corresponds to the target used. For example, for the dataset iris.csv we will have three subfolders named "1/", "2/" and "3/". The following Figure shows an image created according to the example seen.

More specific example

The following example shows how to create with blurring with a more especific parameters.

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

The images are created with the following considerations regarding the parameters used:

• Blurring (-B): Create the images with blurring technique.
• Dimensional Reduction Algorithm (-alg): t-SNE is used.
• Blurring option (-oB): Create de images with maximum value of overlaping pixel
• Image size (-px): 30x30 pixels
• Blurring steps (-sB): Expand 5 pixels the blurring.

License

TINTO is available under the Apache License 2.0.

Authors

• Manuel Castillo-Cara - jcastillo@fi.upm.es
• Raúl García-Castro

Ontology Engineering Group, Universidad Politécnica de Madrid.

Contributors

See the full list of contributors here.