Introduction

Installation

To use TINTOlib, first install it using PyPi:

(.venv) $ pip install TINTOlib

Import methods

TINTOlib contains state-of-the-art most important methods for the construction of Synthetic Images from Sorted Data (also known as Tabular Data). The following code can be used to import the desired model:

>>> from TINTOlib.tinto import TINTO
>>> from TINTOlib.igtd import IGTD

All the methods requires the same input format and the responses outputs will have the same format.

Available Methods

Method	Class name	Features	Hyperparameters
TINTO	TINTO()	blur	problem, algorithm, pixels, submatrix, blur, amplification, distance, steps, option, random_seed, times, verbose
IGTD	IGTD()		problem, scale, fea_dist_method, image_dist_method, max_step, val_step, error, switch_t, min_gain, zoom, random_seed, verbose
REFINED	REFINED()		problem, n_processors, hcIterations, zoom, random_seed, verbose
BarGraph	BarGraph()		problem, pixel_width, gap, zoom, verbose
DistanceMatrix	DistanceMatrix()		problem, zoom, verbose
Combination	Combination()		problem, zoom, verbose
SuperTML	SuperTML()		problem, columns, font_size, image_size, verbose
FeatureWrap	FeatureWrap()		problem, size, bins, zoom, verbose
BIE	BIE()		problem, precision, zoom, verbose

Input Format

To import the Tabular Data there are 2 options:

Raw CSV path

The CSV file must have the following format:

• 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.

Code example

model=TINTO()
model.generateImages(CsvPath,resultFolderPath)

Pandas Dataframe

Pandas dataframe format can be also used to load the data:

Code example

pandasDf=pd.read_csv(dataPath)
model=TINTO()
model.generateImages(pandasDf,resultFolderPath)

Output Format

The output of the model.generateImages(data,folder)() method are synthetic images grouped in folders depending on their class value.

For example:

If the dataset is composed of 3 different classes, the model.generateImages(data,folder)() method will create 3 folders and each folder will contain one synthetic image for each instance with that class.