datgan 1.1.1

Generative adversarial network with integrated expert knowledge for synthesizing tabular data

DATGAN

Directed Acyclic Tabular GAN (DATGAN) for integrating expert knowledge in synthetic tabular data generation.

• License: GPLv3
• Development Status: Alpha
• Homepage: https://github.com/glederrey/DATGAN

The preprint of the article for this model will be available on arXiv by the end of February.

Overview

The DATGAN is a synthesizer for tabular data. It uses LSTM cells to generate synthetic data for continuous and categorical variable types. In addition, a Directed Acyclic Graph (DAG) can be provided to represent the structure between the variables and help the model to perform better.

Requirements

The current version (v1.1.1) of the DATGAN only works with Python 3.7 and in a Jupyter notebook. We, thus, recommend the user to setup a virtualenv.

Installation

We provide a complete installation guide using conda and setting up a virtualenv. Please follow this guide to properly set up everything and make sure that you can use the DATGAN as intended.

Testing the DATGAN

You can clone this repository and use the notebooks provided in the folder example to train the DATGAN and use the evaluation metrics provided in this repository.

Data Format

Input

The DATGAN uses tabular data loaded with the pandas library. This table must follow these rules:

• has no missing values
• has columns of types int, float, str or bool.
• each column contains data of only one type.

NOTE: It is important to identify which columns are considered continuous and which are considered categorical. For example, columns with discrete distributions have to be defined as continuous columns.

Output

The output of the DATGAN is a table of synthetic data with the same columns as the input table and as many rows as requested.

Tutorial

In this short tutorial we will guide you through a series of steps that will help you getting started with the most basic usage of DATGAN in order to generate samples from a given dataset.

NOTE: The following examples are also covered in a Jupyter notebook, which you can execute by running the following commands inside your virtualenv:

pip install jupyter
jupyter notebook example/training.ipynb

1. Load the data

The first step is to load the data wich we will use to fit the DATGAN. In the example, we provide a demo dataset, the CMAP dataset. You can load it using pandas. We also need to define which columns are considered continuous. For this, we simply define a list of str with the name of the variables that we consider as continuous.

import pandas as pd

df = pd.read_csv('./data/CMAP.csv', index_col=False)

continuous_columns = ["distance", "age", "departure_time"]

2. Create a DAG

The second steps consists in creating the DAG for the DATGAN. The DAG is created using the library networkx from Python. If you just want to test the model without any specific DAG, we recommend you to use a linear DAG.

Example of a DAG for the CMAP dataset:

import networkx as nx

graph = nx.DiGraph()
graph.add_edges_from([
    ("age", "license"),
    ("age", "education_level"),
    ("gender", "work_status"),
    ("education_level", "work_status"),
    ("education_level", "hh_income"),
    ("work_status", "hh_income"),
    ("hh_income", "hh_descr"),
    ("hh_income", "hh_size"),
    ("hh_size", "hh_vehicles"),
    ("hh_size", "hh_bikes"),
    ("work_status", "trip_purpose"),
    ("trip_purpose", "departure_time"),
    ("trip_purpose", "distance"),
    ("travel_dow", "choice"),
    ("distance", "choice"),
    ("departure_time", "choice"),
    ("hh_vehicles", "choice"),
    ("hh_bikes", "choice"),
    ("license", "choice"),
    ("education_level", "hh_size"),
    ("work_status", "hh_descr"),
    ("work_status", "hh_size"),
    ("hh_income", "hh_bikes"),
    ("hh_income", "hh_vehicles"),
    ("trip_purpose", "choice")
])

Example of a linear DAG:

import networkx as nx

graph = nx.DiGraph()

list_ = []
for i in range(len(df.columns)-1):
    list_.append((df.columns[i], df.columns[i+1]))
    
graph.add_edges_from(list_)

3. Create a DATGAN instance

The next step is to import DATGAN and create an instance of the model. The only required parameter when instantiating the DATGAN is an output folder. The other parameters are set to their default values.

output_folder = './output/'

from datgan import DATGAN

datgan = datgan = DATGAN(output=output_folder)

4. Preprocess the data (optional)

The fourth step consists in preprocessing the data. This step is optional since it is automatically done in the next step if skipped right now. We propose to do the preprocessing in advance because it usually takes a bit of time. And if you want to try multiple parameters with the DATGAN, you do not have to preprocess the data every time. Therefore, it is possible to do it before fitting the model and saving it somewhere.

datgan.preprocess(df, continuous_columns, preprocessed_data_path='./encoded_data')

5. Fit the model

Once you have a DATGAN instance, you can call the method fit and passing the following parameters:

• data: the original DataFrame
• graph: the networkx DAG
• continuous_columns: the list of continuous columns
• preprocessed_data_path: the path to the preprocessed data if done in Step 4.

datgan.fit(df, graph, continuous_columns, preprocessed_data_path='./encoded_data')

6. Sample new data

Once the model has been fitted, you can generate new synthetic data by calling the function sample. You have to provide the desired number of samples.

samples = datgan.sample(len(df))
samples.age = np.round(samples.age)
samples.to_csv('./data/CMAP_synthetic.csv', index=False)

In this case, the column age is a discrete distribution. The DATGAN cannot provide such data type for the moment, we, thus, advise you to treat such column as continuous and, then, round the values.

7. Save and load a model

In the steps above we saw that the fitting process can take a lot of time, so we probably would like to avoid having to fit every we want to generate samples. Instead we can fit a model once, save it, and load it every time we want to sample new data.

If we have a fitted model, we can save it by calling it's save method, that only takes as argument the name of the trained model. (the path corresponds to the output of the DATGAN). Similarly, the DATGAN.load allows to load a model stored on disk by passing as argument the path where the model is stored.

datgan.save('trained', force=True)

Bear in mind that in case the file already exists, DATGAN will avoid overwritting it unless the force=True argument is passed.

Once the model is saved, it can be loaded back as a DATGAN instance by using the DATGAN.load method. You need to provide both the output folder, and the name you used to save the model:

new_datgan = DATGAN.load(output_folder, 'trained')

At this point we could use this model instance to generate more samples.

Model parameters

If you want to change the default behavior of DATGAN, such as as different batch_size or max_epochs, you can do so by passing different arguments when creating the instance.

Loading the model

• loss_function (str, default None): Name of the loss function to be used. If not specified, it will choose between 'WGAN' and 'WGGP' depending on the ratio of continuous and categorical columns. Only accepts the values 'SGAN', 'WGAN', and 'WGGP'.
• label_smoothing (str, default 'TS'): Type of label smoothing. Only accepts the values 'TS', 'OS', and 'NO'.
• output (str, default 'output'): Path to store the model and its artifacts.
• gpu (bool, default True): Use the first available GPU if there's one and tensorflow has been built with cuda.
• max_epoch (int, default 100): Number of epochs to use during training.
• batch_size (int, default 500): Size of the batch to feed the model at each step.
• save_checkpoints (bool, default True): Whether to store checkpoints of the model after each training epoch.
• restore_session (bool, default True): Whether continue training from the last checkpoint.
• learning_rate (float, default None): Learning rate. If set to None, the value will be set according to the chosen loss function.
• z_dim (int, default 200): Dimension of the noise vector used as an input to the generator.
• num_gen_rnn (int, default 100): Size of the hidden units in the LSTM cell.
• num_gen_hidden (int, default 50): Size of the hidden layer used on the output of the generator to act as a convolution.
• num_dis_layers (int, default 1): Number of layers for the discriminator.
• num_dis_hidden (int, default 100): Size of the hidden layers in the discriminator.
• noise (float, default 0.2): Upper bound to the gaussian noise added to with the label smoothing. (only used if label_smoothing is set to 'TS' or 'OS')
• l2norm (float, default 0.00001): L2 reguralization coefficient when computing the standard GAN loss.

Sampling synthetic data

When sampling the synthetic data (DATGAN.sample), you can choose between multiple sampling strategies.

• sampling (str, default SS): Type of sampling to use. Only accepts the following values: 'SS', 'SA', 'AS', and 'AA'.

S means we are using simulation to sample the data, A means that we are using argmax. The first letter corresponds to continuous variables and the second to categorical variables. Therefore, SA means we're using simulation for continuous variables and argmax for categorical variables.

Acknowledgements

We would like to thank the authors of the TGAN article, Lei Xu and Kalyan Veeramachaneni, as well as all the contributors of the TGAN Github repository. This model has greatly inspired the ideas behind the DATGAN and we have used their code as a starting point to write our model.

Citing DATGAN

If you use DATGAN or its evaluation metrics, please cite the following work:

Gael Lederrey, Tim Hillel, Michel Bierlaire. 2022. DATGAN: Integrating expert knowledge into deep learning for synthetic tabular data. ArXiv preprint

FULL CITATION COMING SOON!

The original code for this paper can be found in this Github repository: https://github.com/glederrey/SynthPop.

History

1.0

• First release on PyPI.

1.1

• Completed the README with all the details how to use DATGAN.

1.1.1

• README updated and moved installation file to Github.