BIE
The Binary Image Encoding (BIE) method converts numeric values into black-and-white images by using the binary floating-point representation of numbers according to the IEEE 754 standard. Numeric values are encoded into binary, with each bit translated into image pixels: 0s as black (0) and 1s as white (255).

Import BIE
To import the BIE model, use:
>>> from TINTOlib.bie import BIE
>>> model = BIE()
Hyperparameters & Configuration
When creating the BIE
class, some parameters can be modified. The parameters are:
Parameters |
Description |
Default value |
Valid values |
---|---|---|---|
|
Defines how images are grouped based on the type of problem, affecting how data is interpreted and visualized. |
‘supervised’ |
[‘supervised’, ‘unsupervised’, ‘regression’] |
|
If set to True, applies MinMaxScaler to normalize input data, ensuring consistent feature scaling across transformations. |
True |
[True, False] |
|
Enables the display of detailed execution processes in the terminal, aiding in debugging and optimization. |
False |
[True, False] |
|
Specifies the precision of the binary encoding used in image generation, impacting the granularity of data representation. |
32 |
[32, 64] |
|
Sets the multiplication factor for scaling the output image relative to its original dimensions, useful for enhancing visual clarity or details. |
1 |
integer > 0 |
Code example:
>>> model = BIE(problem='regression', precision=64, zoom=2)
All the parameters that aren’t expecifically setted will have their default values.
Functions
BIE has the following functions:
Function |
Description |
Output |
---|---|---|
|
Allows to save the defined parameters (scale, fea_dost_method, image_dist_method, etc.) |
.pkl file with the configuration |
|
Load TINTO configuration previously saved with
|
|
|
Trains the model on the tabular data and prepares it for image generation.
|
|
|
Generates and saves synthetic images in a specified folder. Requires the model to be fitted first.
|
Folders with synthetic images |
|
Combines the training and image generation steps. Fits the model to the data and generates synthetic images in one step.
|
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://ieeexplore.ieee.org/document/10278393
Code Repository: https://jds-online.org/journal/JDS/article/1360/file/12976