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TINTOlib

License Python Version Documentation Status Open In Colab

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

Metadata

Release files for TINTOlib 0.0.4

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Source distribution for TINTOlib 0.0.4
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Table of built distributions (wheels) for TINTOlib 0.0.4
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Total release size: 12.2 MB

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