autoembedder 0.1

PyTorch autoencoder with additional embeddings layer for categorical data.

The Autoembedder

Introduction

The Autoembedder is an autoencoder with additional embedding layers for the categorical columns. Its usage is flexible, and hyperparameters like the number of layers can be easily adjusted and tuned. Although primarily designed for Panda's dataframes, it can be easily modified to support other data structures.

Let's get started

training.py is where everything begins. The following arguments can / should be set:

Argument	Type	Required	Default value	Comment
batch_size	int	False	32
drop_last	int	False	1	True/False
pin_memory	int	False	1	True/False
num_workers	int	False	0	0 means that the data will be loaded in the main process
use_mps	int	False	0	Set this to 1 if you want to use the MPS Backend for running on Mac using the M1 GPU. process
model_title	str	False	autoembedder_{datetime}.bin
model_save_path	str	False
n_save_checkpoints	int	False
lr	float	False	0.001
amsgrad	int	False	0	True/False
epochs	int	True
layer_bias	int	False	1	True/False
weight_decay	float	False	0
l1_lambda	float	False	0
xavier_init	int	False	0	True/False
tensorboard_log_path	str	False
train_input_path	str	True
test_input_path	str	True
activation_for_code_layer	int	False	0	True/False, should the layer have an activation
activation_for_final_decoder_layer	int	False	0	True/False, should the final decoder layer have an activation
hidden_layer_representation	str	True		Contains a string representation of a list of list of integers which represents the hidden layer structure. E.g.: "[[64, 32], [32, 16], [16, 8]]" activation
cat_columns	str	False	"[]"	Contains a string representation of a list of list of categorical columns (strings). The columns which use the same encoder should be together in a list. E.g.: "[['a', 'b'], ['c']]".

So, something like this would do it:

$ python3 training.py --epochs 20 \
--train_input_path "path/to/your/train_data" \
--test_input_path "path/to/your/test_data" \
--hidden_layer_representation "[[12, 6], [6, 3]]"

Why additional embedding layers?

The additional embedding layers automatically embed all columns with the Pandas category data type. If categorical columns have another data type, they will not be embedded and will be handled like the continuous columns. Simply encoding the categorical values (e.g., with the usage of a label encoder) decreases the quality of the outcome.