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

NOTE: As I've finished my PhD and I left academia, I'm not planning to maintain, nor improve this model. However, if you have questions, feel free to ask in the Issues.

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 perform better. This model integrates two types of conditionality: rejection by sampling and conditional inputs.

Requirements

The current version of the DATGAN works with Python>=3.7 and Tensorflow==2.8.0. Previous versions of Python have not been tested and are thus blocked. Therefore, we recommend the user set up a virtualenv. An installation guide is provided to help with the installation of the library.

Installation

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

Testing the DATGAN

Once you have followed the installation guide, 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 crucial to identify continuous and categorical variables/columns. A general rule of thumb is that any discrete distributions with a large number of categories should be considered continuous and then rounded.

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

This short tutorial will guide you through a series of steps that will help you get started with the most basic usage of DATGAN to generate samples from a given dataset.

While all the methods are explained below, you can use the built-in helper function to get a reminder of the different functions used in this library. Just run the following lines:

import datgan

help(datgan)

1. Load the data and provide info about it

The first step is to load the data, which we will use to fit the DATGAN. We provide the CMAP (Chicago Metropolitan Agency for Planning) dataset in the example folder. It is a travel survey dataset for the city of Chicago. The dataset has already been cleaned compared to the original dataset. It contains 15 columns and 8'929 rows.

You can load this dataset using pandas. You also need to provide the data type for each column. For the moment, only two possibilities are implemented: continuous and categorical. For continuous columns, you can pass more information to the model, such as:

• discrete [mandatory]: Boolean value to indicate if the synthetic value has to be rounded when sampling
• bounds [optional]: Values of the bounds. While sampling synthetic data, all values outside the bounds will be discarded.
• enforce_bounds [optional]: Instead of removing synthetic data generated outside the bounds, we will clip the values to the bounds. (This can be useful in the case of a mixed distribution with a peak of values close to the lower or the upper bound.)
• apply_func [optional]: You can provide a lambda function that will be applied before the encoding step and when sampling the final values. This can help train models on distributions that are more difficult to represent by GMMs, e.g. exponential distributions.

import pandas as pd
import numpy as np

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

data_info = {
    'distance': {
        'type': 'continuous',
        'bounds': [0.0, np.infty],
        'discrete': False,
        'apply_func': (lambda x: np.log(x + 1)),
    },
    'age': {
        'type': 'continuous',
        'bounds': [0, 100],
        'enforce_bounds': True,
        'discrete': True
    },
    'departure_time': {
        'type': 'continuous',
        'bounds': [0, 23.999],
        'discrete': False
    }
}

# For the categorical columns, we can simply add them using a for loop
for c in df.columns:
    if c not in data_info.keys():
        data_info[c] = {'type': 'categorical'}

WARNING: While using a lambda function in the apply_func parameters, we use the pynverse library to compute the inverse of the given function. Therefore, with more complex functions, it is possible to get Warnings while sampling the final synthetic data. This is a normal behavior when pynverse is having trouble computing the inverse of some values.

2. Create a DAG

The second step consists in creating the DAG for the DATGAN. The DAG is created using the library networkx from Python. The only constraint while creating the DAG is that it should not contain any cycle (by definition). In the article, we give more details on creating the DAG. However, this process can still be cumbersome and requires trials and errors to obtain a good DAG. (We highly recommend using a "visualization" tool to create the DAG.)

Below, we provide an example of a DAG for the CMAP dataset. First, the picture shows a visual representation of the DAG. The colors of the nodes show the categories of variables: purple corresponds to individuals, blue to households, and red to trips. The image shows that nodes can be the source and the target of multiple edges. While the direction of the edges do not play an important role, we recommend having as few sink nodes as possible. The DATGAN article provides more details on building the DAG.

The above DAG is represented in the networkx library as the following way:

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")
])

If your DAG contains node that are not connected to any other nodes, you still need to add them. To do this, you can use the add_node method of the DiGraph object.

graph.add_node("single_node")

If you do not know how to create your DAG, it is possible not to provide any DAG to the model. However, in this case, the model will define a linear DAG, i.e. each variable in the dataset is linked to the next one following the order of the columns. It can be helpful to test the model quickly. However, it will reduce the model's performance, as shown in the article.

To help build the DAG and know which edge should be added, we implemented a function named advise that computes Pearson and Spearman correlations and then suggests some links that could be added to the DAG. The suggested links are the ones with the highest correlations.

import datgan

datgan.advise(df, graph, plot_graphs=True)

Results (without the graphs) should look like this:

You might want to add the following edges in your DAG (direction not given here):
  - license <-> education_level
  - license <-> work_status
  - hh_vehicles <-> hh_descr
  - hh_bikes <-> age
  - hh_size <-> age

3. Create a DATGAN instance

The next step is to import DATGAN and create an instance of the model. There are no required parameters for the model. However, we advise you to set up the basic parameters such as the output folder (output), batch size (batch_size), and the number of epochs (num_epochs).

output_folder = './output/'
batch_size = 1116

from datgan import DATGAN

datgan = DATGAN(output=output_folder, batch_size=batch_size, num_epochs=1000)

NOTE: Setting up a suitable batch size is really important. A batch size too big will make the model crash due to memory error, while one that is too small will make the model slower to train. Trials and errors are required depending on your hardware. In addition, it is good to find a batch size such that len(df) % batch_size is as small as possible since the last batch of data is dropped if it is smaller than the batch size.

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, data_info, preprocessed_data_path='./encoded_data')

NOTE 1: If you decide to change any values in the metadata dictionary data_info, you must preprocess the data again. However, since the preprocessing of the continuous variable is random, you will not be able to sample a model trained on a previous preprocessed dataset with the current one. Therefore, we advise not to delete the previous preprocessed dataset.

NOTE 2: In the folder preprocessed_data_path/continuous, you will find PNG files of the continuous distributions. These pictures can be used to make sure that the Variational Gaussian Mixtures (VGM) were correctly trained for each continuous distribution. It can be especially useful if the user is trying to use a lambda function.

5. Fit the model

Once you have a DATGAN instance, you can call the method fit and pass 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 or the path where to save them.

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

NOTE: Depending on the verbose level, the model will show different types of information. The standard level is 1, displaying a progress bar for the whole training process. With level 2, the model will display information at each epoch. With level 0, the model will not print any information.

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.to_csv('./data/synthetic.csv', index=False)

7. Save and load a model (optional)

Fitting the model can take a substantial amount of time. It is, thus, better to avoid fitting a new model every time we need to generate samples. We advise to save checkpoints of the model while it is training. However, if you do not want to do that, the model will always keep the latest checkpoint once it has finished training. You can, thus, load it at any time afterward.

You can simply call the function load with the parameters used while fitting the model to load the model. To save memory, we only save the parameters of the Generator and Discriminator. Therefore, more information is required to load the model.

new_datgan = datgan.load(df, graph, preprocessed_data_path='./encoded_data')

NOTE: This can be replaced by using the function fit once again. Since the model has already been trained, it will just load it.

Conditionality

Conditionality can be used in two different ways. The first one is done after training the DATGAN model. You can provide a dictionary of desired values and passes it to the sampling method. The DATGAN model uses rejection sampling to provide the desired output. The second method consists in training the DATGAN using conditional inputs. To use the second conditionality, the model has to be trained again.

Rejection sampling

In the sample function of the DATGAN, you must provide a dictionary as follows:

• categorical variables: string or list of strings for the desired categories.
• continuous variables: lambda function returning a boolean value.

For example, suppose you want only to generate synthetic data about men under 30 years old. In that case, you can provide the following dictionary to the sample function.

cond_dict = {'age': lambda x: x < 30, 'gender': '0'}

samples = datgan.sample(len(df), cond_dict=cond_dict)
samples.to_csv('./data/synthetic.csv', index=False)

WARNING: Since the DATGAN is using rejection sampling to provide the desired values, you cannot provide a conditional dictionary that is too restrictive. This will significantly slow the sampling process and even make it fail. In some specific cases, you can set the parameter timeout to False if you do not want the sampling process to be stopped.

Conditional inputs

The conditional inputs are columns present in the original dataset. To train the DATGAN model, you need to provide the list of columns you want to consider as conditional inputs. For example, suppose you want the age, gender, and trip purpose to be conditional inputs. In that case, you can initialize the DATGAN model this way:

datgan = DATGAN(output=output_folder,
                batch_size=batch_size,
                num_epochs=1000,
                conditional_inputs=['age', 'gender', 'trip_purpose'])

Suppose the model is initialized in such a way. In that case, it will automatically transform the DAG. Each of the conditional variables is considered a source node in the DAG. It will then train the generator using the values for these variables as inputs. Finally, to sample the synthetic data, you need to provide a pandas.DataFrame or a dict with columns/keys corresponding to the conditional inputs defined in the initialization. You do not need to care about the size of the DataFrame since the model automatically samples values from it. You can, thus, provide any values you want (a subset of the original dataset, unique values, or values generated by another model on another dataset.)

In addition, the rejection sampling conditionality can be used alongside the conditional inputs. You have to provide the dictionary of conditionals alongside the new input data.

Model parameters

Suppose you want to change the default behavior of DATGAN, such as using a different batch size or the total number of epochs. In that case, you can do so by passing additional arguments when creating the DATGAN instance or using its two main functions.

Initialize the model (DATGAN.__init__)

Training the model (DATGAN.fit)

Sampling synthetic data (DATGAN.sample)

When sampling the synthetic data, you can choose between multiple sampling strategies. S means we are using simulation to sample the data, A indicates that the model uses 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.

Tips and tricks

While the DATGAN model will automatically choose its parameters if none are provided, we highly recommend the user to "play" with them. The most important ones are the following:

• loss_function Generally, the WGAN loss function works better on datasets with more categorical columns than continuous. It is the contrary for the WGGP loss. The SGAN loss seems to perform a bit less good than the other two in the specific cases. However, it seems to perform ok in any case.
• g_period This parameter is critical when using either the WGAN or the WGGP loss. Generally, the latter requires a lot more discriminator training than the previous. However, it might be interesting to test different values to see which leads to the best results.
• l2_reg The L2 regularization is "mandatory" for the SGAN loss and "forbidden" for the WGAN loss. The user can test these other configurations, but it will lead to worse results. However, for the WGGP loss, there are no specific rules on whether to apply it or not. Therefore, it might be interesting to test this parameter as well.
• learning_rate It has been fixed depending on the loss function. However, as for any optimization problem, playing with the learning rate to find the optimal value is always important.

Contributing

If you want to participate in the development of this model, you can write a Github issue and give us your suggestions or directly prepare a Pull Request. (However, it would be better first to discuss the content of the PR.)

Acknowledgements

We want to thank the authors of the TGAN model, the authors of the CTGAN model, and the authors of the CTAB-GAN model. These three models have greatly inspired the DATGAN model.

Citation

If you use DATGAN or its evaluation metrics for any of your projects, please cite the following work:

"Lederrey G., Hillel T., Bierlaire M., DATGAN: Integrating Expert Knowledge into Deep Learning for Synthetic Tabular Data, arXiv preprint arXiv:2203.03489 (2022)"

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