Tab2Img: from tabular data to images

A tool to convert tabular data into images for CNN. Inspired by the DeepInsight paper.

Installation

pip install tab2img

Background

In the paper "DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture" the autors propose a method to convert tabular data into images, in order to utilize the power of convolutional neural network (CNN) for non-image structured data.

The Figure illustrates the main idea: given a training dataset with samples and features, we are required to find a function , where .

There are numerous ways to choose . In this implementation, the features are organized with respect to the correlation vector , where is the target vector. Given and as

the vector express the Pearson correlation coefficient*

where

At this point is sorted from the greatest to the smallest, generating the vector of indices

Eventually, the final tensor is

The function that maps to the right row and column of is

In this case, being a sample, the coefficient is implemented as

Example

from sklearn.datasets import fetch_covtype
from tab2img.converter import Tab2Img

dataset = fetch_covtype()

train = dataset.data
target = dataset.target

model = Tab2Img()
images = model.fit_transform(train, target)