Converting tabular data into images
Project description
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
-
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
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
tintolib-0.0.6.tar.gz
(7.0 MB
view hashes)