BarGraph

The BarGraph method transforms tabular data into grayscale images by representing features as equidistant bars, each scaled according to its normalized value.

Import BarGraph

To import BarGraph model use:

>>> from TINTOlib.bargraph import BarGraph
>>> model = BarGraph()

Hyperparameters & Configuration

When creating the BarGraph class, some parameters can be modified. The parameters are:

Parameters	Description	Default value	Valid values
problem	The type of problem, defining how the images are grouped.	‘classification’	[‘classification’, ‘unsupervised’, ‘regression’]
transformer	Preprocessing transformations like scaling, normalization, etc.	MinMaxScaler()	Scikit Learn transformers or custom implementation inheriting CustomTransformer.
verbose	Show execution details in the terminal.	False	[True, False]
pixel_width	The width of each bar in pixels.	1	integer > 0
gap	The gap between bars in pixels.	0	integer >= 0
zoom	Multiplication factor determining the size of the saved image relative to the original size.	1	integer > 0

Code example:

>>> model = BarGraph(problem='regression', pixel_width=2, gap=1, zoom=2)

All the parameters that aren’t specifically set will have their default values.

Functions

BarGraph has the following functions:

Function	Description	Output
saveHyperparameters(filename)	Allows to save the defined parameters.	.pkl file with the configuration
loadHyperparameters(filename)	Load BarGraph configuration previously saved with saveHyperparameters(filename) filename: .pkl file path
fit(data)	Trains the model on the tabular data. For BarGraph, this step primarily handles setup as the transformation is stateless. data: A path to a CSV file or a Pandas DataFrame containing the features and targets. The target column must be the last column.
transform(data, folder)	Generates and saves synthetic images in a specified folder. Requires the model to be fitted first. data: A path to a CSV file or a Pandas DataFrame containing the features and targets. The target column must be the last column. folder: Path to the folder where the synthetic images will be saved.	Folders with synthetic images
fit_transform(data, folder)	Combines the training and image generation steps. Fits the model to the data and generates synthetic images in one step. data: A path to a CSV file or a Pandas DataFrame containing the features and targets. The target column must be the last column. folder: Path to the folder where the synthetic images will be saved.	Folders with synthetic images

• The model must be fitted before using the transform method. If the model isn’t fitted, a RuntimeError will be raised.

Citation

Paper: https://doi.org/10.1038/s41598-022-26378-6

Code Repository: https://github.com/anuraganands/Non-image-data-classification-with-CNN