The `rec` module

• After a number of issues opened and questions in slack about recommendation algorithms in the library I decided to include a rec module that initially contains a small number of recommendation algorithms. These are:

• Factorisation Machines (FM) and DeepFM
• Field Aware Factorisation Machines (FFM) and DeepFFM
• Extreme Deep Factorisation Machines (xDeepFM)
• Deep Interest Networks (DIN)

We will add more in the near future.

• In addition some bugs were fixed (#232 and #233)

Multiple tabular components

1. Added support to multiple tabular models for different columns (that adds to multiple text and image cols from previous versions)
2. Removed support for FDS and LDS
3. Carries the possibility of saving the optimiser which was added in the version 1.6.2 (short-life and never published)

Patch to limit numpy to version lower than 2.0

This is a quick patch to fix numpy>=1.21.6, <2.0.0

Otherwise, is exactly the same as 1.6.0

Huggingface integration, multi-text and image column support and multi target loss functions

What's Changed

• Huggingface integration by @jrzaurin in #209
• Multi text and image column support by @jrzaurin in #215
• Support for multi target loss functions by @jrzaurin in #215
• README has been almost completely re-written, with drawings of 7 possible architectures (where the boxes/component can be any of the models in the library) and fully runnable examples with a toy dataset that anyone can use as a starting point.

Full Changelog: v1.5.1...v1.6.0

Model Attributes named correctly

Mostly fixed issue #204

Embedding Methods for Numerical Features

Added two new embedding methods for numerical features described in On Embeddings for Numerical Features in Tabular Deep Learning and adjusted all models and functionalities accordingly

The `load_from_folder` module

This release mainly adds the functionality to be able to deal with large datasets via the load_from_folder module.

This module is inspired by the ImageFolder class in the torchvision library but adapted to the needs of our library. See the docs for details.

Flash and Linear Attention mechanisms added to the TabTransformer

1. Added Flash Attention
2. Added Linear Attention
3. Revisited and polished the docs

pytorch-widedeep in the context of recsys

1. Added example scripts and notebooks on how to use the library in the context of recommendation systems using this notebook as example. This is a response to issue #133
2. Used the opportunity to add the movielens 100k dataset to the library, so that now it can be imported from the datasets module
3. Added a simple (not pre-trained) transformer model to to the text component
4. Added citation file
5. Fix a bug regarding the padding index not being 1 when using the fastai transforms

Feature Importance via attention weights

• Added a new functionality to access feature importance via attention weights for all DL models for Tabular data except for the TabPerceiver. This functionality is accessed via the feature_importance attribute in the trainer (computed during training with a sample of observations) and at predict time via de explain method.
• Fix all restore weights capabilities in all forms of training. Such capabilities are present in two callbacks, the EarlyStopping and the ModelCheckpoint Callbacks. Prior to this release there was a bug and the weights were not restored.