Release v1.2.0

Release v1.2.0

This update enhances the preprocessing and embedding layers in the mambular package, introducing several key improvements:

• Feature-Specific Preprocessing: The Preprocessor class now includes a feature preprocessing dictionary, enabling different preprocessing strategies for each feature.
• Support for Unstructured Data: The model can now handle a combination of tabular features and unstructured data, such as images and text.
• Latent Representation Generation: It is now possible to generate latent representations of the input data, improving downstream modeling and interpretability.

These changes enhance flexibility and extend mambular's capabilities to more diverse data modalities.

Preprocessing improvements:

• mambular/preprocessing/preprocessor.py: Added feature_preprocessing parameter to allow custom preprocessing techniques for individual columns. Updated the fit method to use this parameter for both numerical and categorical features. [1] [2] [3] [4] [5]

Embedding layer updates:

• mambular/arch_utils/layer_utils/embedding_layer.py: Modified the forward method to handle different dimensions of categorical embeddings and ensure they are properly processed. [1] [2]

Allow unstructured data as inputs:

• mambular/arch_utils/layer_utils/embedding_layer.py: Modified the forward method to handle num_features, cat_features and pre-embedded unstructured data. [1] [2]

Get latent representation of tables

• mambular/base_models/basemodel.py: Updated the encode method to accept a single data parameter instead of separate num_features and cat_features parameters. [1] [2]