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	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]
size	The width and height of the final image, in pixels (rows x columns).	(8, 8)	tuple of two positive integers
bins	The number of bins or intervals used for grouping numeric data.	10	integer > 1
zoom	Multiplication factor determining the size of the saved image relative to the original size.	1	integer > 0

Code example:

>>> model = FeatureWrap(size=(10, 10), bins=20, verbose=True)

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

Functions

FeatureWrap has the following functions:

Function	Description	Output
saveHyperparameters(filename)	Allows to save the defined parameters.	.pkl file with the configuration
loadHyperparameters(filename)	Load FeatureWrap configuration previously saved with saveHyperparameters(filename) filename: .pkl file path
fit(data)	Trains the model on the tabular data. For FeatureWrap, this step prepares the transformer but the primary logic is applied during transformation. 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: https://github.com/oeg-upm/TINTO