TINTOlib 0.0.14

Converting tabular data into images

TINTOlib

TINTOlib is a state-of-the-art library that wraps the most important techniques for the construction of Synthetic Images from Sorted Data (also known as Tabular Data).

Features

• Input data formats (2 options):

  • Pandas Dataframe
  • Files with the following format
    • 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.

• Runs on Linux, Windows and macOS systems.

• Compatible with Python 3.7 or higher.

Models

Model	Class	Features	Hyperparameters
TINTO	TINTO()	blur	problem algorithm pixels blur amplification distance steps option seed times verbose
SuperTML	SuperTML()		problem columns font_size image_size verbose
IGTD	IGTD()		problem scale fea_dost_method image_dist_method save_image_size max_step val_step error switch_t min_gain seed verbose
REFINED	REFINED()		problem hcIterations verbose
BarGraph	BarGraph()		problem pixel_width gap verbose
DistanceMatrix	DistanceMatrix()		problem scale verbose
Combination	Combination()		problem pixel_width gap verbose

Documentation

Read the documentation.

Getting Started

You can install Data2Image using Pypi(test):

    pip install TINTOlib

To import a specific model use

    from TINTOlib.tinto import TINTO

Create the model. If you don't set any hyperparameter, the model will use the default values (read documentation).

    model = tinto(blur=True)

To generate the synthetic images use .genereateImages(data,folder) method.

    model.generateImages(data, resultsFolderPath)

License

Data2Image is available under the Apache License 2.0.

Authors

• Borja Reinoso - [e-mail]
• Manuel Castillo-Cara - [e-mail]
• Raúl García-Castro