Metadata-Version: 2.1
Name: hivae
Version: 0.12
Summary: HIVAE (https://arxiv.org/pdf/1807.03653.pdf - by Nazabal, Olmos, Ghahramani, Valera) - extenstion of their implementations as Python library
Home-page: https://github.com/gkoutos-group/hivae/
Author: Andreas Karwath
Author-email: a.karwath@bham.ac.uk
License: MIT
Description: # hivae
        
        This repository contains the Modular reimplemenation of the Heterogeneous Incomplete Variational Autoencoder model (HI-VAE)written by Alfredo Nazabal (anazabal@turing.ac.uk) 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.
        
        
        The details of this model are included in this [paper](https://arxiv.org/abs/1807.03653). 
        
        ## Install
        
        The package can be installed using pip:
        
        ```pip install hivae```
        
        
        ## Examples
        
        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.
        
        
        ## Files description
        
        * **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
        
        ## Contact
        
        * **For questions regarding algorithm --> Alfredo Nazabal**: anazabal@turing.ac.uk
        
        ## 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 
        
        types:
        
        •	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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