HIVAE (https://arxiv.org/pdf/1807.03653.pdf - by Nazabal, Olmos, Ghahramani, Valera) - extenstion of their implementations as Python library
This repository contains the Modular reimplemenation of the Heterogeneous Incomplete Variational Autoencoder model (HI-VAE)written by Alfredo Nazabal and co-workers. The package provided here is to a large part baseed on this implementation, but adheres to a more pythonic way, omitting the need for supplying parameters via I/O , as well as aligning the modelling more with sklearn. It was written by A. Karwath (email@example.com) and F. Shalaby.
The details of this model are included in this paper.
The package can be installed using pip:
pip install hivae
Once checked out, there are a number of example datasets (Wine, Adult and Diabetes), which can be found in ./hivae/examples/data. To evaluate the package, please use ./hivae/examples/hivae_general_example.py. The example should give a general explaination of how to use the package. More details will folow.
- hivae.py: The main script of the library, it needs to imported to work with the library and is connected to all the other scripts.
- loglik_ models_ missing_normalize.py: In this file, the different likelihood models for the different types of variables considered (real, positive, count, categorical and ordinal) are included.
- model_ HIVAE_inputDropout.py: Contains the HI-VAE with input dropout encoder model.
- model_ HIVAE_factorized.py: Contains the HI-VAE with factorized encoder model
- For questions regarding algorithm --> Alfredo Nazabal: firstname.lastname@example.org
More details regarding the hivae_general_example.py and use of the model (please note that this is under construction)
main_directory: project folder
dataset_name: the name of the database (required)
types_list_d: a dictionary where the key is the dataset name, which contains a list with tuples that indicates the column names, types, the number of dimensions and classes
• count: real values
• cat: categorical 0 or 1
• pos: positive real values
• ordinal: ordinal number
number of dimensions:
• number of possibilities in the categorical variables or 1 in numerical
number of classes:
• number of options (same of number of dimensions for categorical variables)
dataset_path: this is the folder of the csv files
results_path: the output folder for results (currently not used)
network_path: where the models are going to be stored
types_list: the specific type for the dataset you are going to use data_file: the full dataset train_file/ test_file: if the dataset was already splitted
train_data/test_data: pandas dataframes
dim_y: the depth of the network
dim_s/dim_z: dimensions of the embedding
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