FeatureWrap

The FeatureWrap method transforms tabular data into synthetic images by encoding features as binary vectors. Categorical data are one-hot encoded, while numerical data are normalized, discretized, and then encoded. These binary vectors are concatenated, padded if necessary, and converted into pixel values ranging from 0 to 255 to create the final image.

Import FeatureWrap

To import FeatureWrap model use:

>>> from TINTOlib.featureWrap import FeatureWrap
>>> model = FeatureWrap()

Hyperparameters & Configuration

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

Parameters	Description	Default value	Valid values
problem	Defines how the images are grouped based on the type of problem.	‘supervised’	[‘supervised’, ‘unsupervised’, ‘regression’]
normalize	Normalizes input data using MinMaxScaler if set to True.	True	[True, False]
verbose	Displays execution details in the terminal if set to True.	False	[True, False]
size	Specifies the width and height of the final image in pixels (rows x columns).	(8, 8)	tuple of two positive integers
bins	Determines the number of bins or intervals used for grouping numeric data.	10	integer > 1
zoom	Sets the multiplication factor for resizing the image relative to its original size.	1	integer > 0

Code example:

>>> model = FeatureWrap(size=[10,10], bins=20)

All the parameters that aren’t expecifically setted will have their default values.

Functions

FeatureWrap has the following functions:

Function	Description	Output
saveHyperparameters(filename)	Allows to save the defined parameters (scale, fea_dost_method, image_dist_method, etc.)	.pkl file with the configuration
loadHyperparameters(filename)	Load TINTO configuration previously saved with saveHyperparameters(filename) filename: .pkl file path
fit(data)	Trains the model on the tabular data and prepares it for image generation. 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/10.1007/978-3-319-70139-4_87

Code Repository: