In this guide we will go through a series of steps that will let you discover functionalities of the CTGAN model, including how to:
CTGAN
Create an instance of CTGAN.
Fit the instance to your data.
Generate synthetic versions of your data.
Use CTGAN to anonymize PII information.
Customize the data transformations to improve the learning process.
Specify hyperparameters to improve the output quality.
The sdv.tabular.CTGAN model is based on the GAN-based Deep Learning data synthesizer which was presented at the NeurIPS 2020 conference by the paper titled Modeling Tabular data using Conditional GAN.
sdv.tabular.CTGAN
Let’s now discover how to learn a dataset and later on generate synthetic data with the same format and statistical properties by using the CTGAN class from SDV.
We will start by loading one of our demo datasets, the student_placements, which contains information about MBA students that applied for placements during the year 2020.
student_placements
Warning
In order to follow this guide you need to have ctgan installed on your system. If you have not done it yet, please install ctgan now by executing the command pip install sdv in a terminal.
ctgan
pip install sdv
In [1]: from sdv.demo import load_tabular_demo In [2]: data = load_tabular_demo('student_placements') In [3]: data.head() Out[3]: student_id gender second_perc high_perc high_spec degree_perc degree_type work_experience experience_years employability_perc mba_spec mba_perc salary placed start_date end_date duration 0 17264 M 67.00 91.00 Commerce 58.00 Sci&Tech False 0 55.0 Mkt&HR 58.80 27000.0 True 2020-07-23 2020-10-12 3.0 1 17265 M 79.33 78.33 Science 77.48 Sci&Tech True 1 86.5 Mkt&Fin 66.28 20000.0 True 2020-01-11 2020-04-09 3.0 2 17266 M 65.00 68.00 Arts 64.00 Comm&Mgmt False 0 75.0 Mkt&Fin 57.80 25000.0 True 2020-01-26 2020-07-13 6.0 3 17267 M 56.00 52.00 Science 52.00 Sci&Tech False 0 66.0 Mkt&HR 59.43 NaN False NaT NaT NaN 4 17268 M 85.80 73.60 Commerce 73.30 Comm&Mgmt False 0 96.8 Mkt&Fin 55.50 42500.0 True 2020-07-04 2020-09-27 3.0
As you can see, this table contains information about students which includes, among other things:
Their id and gender
Their grades and specializations
Their work experience
The salary that they where offered
The duration and dates of their placement
You will notice that there is data with the following characteristics:
There are float, integer, boolean, categorical and datetime values.
There are some variables that have missing data. In particular, all the data related to the placement details is missing in the rows where the student was not placed.
Let us use CTGAN to learn this data and then sample synthetic data about new students to see how well de model captures the characteristics indicated above. In order to do this you will need to:
Import the sdv.tabular.CTGAN class and create an instance of it.
Call its fit method passing our table.
fit
Call its sample method indicating the number of synthetic rows that you want to generate.
sample
In [4]: from sdv.tabular import CTGAN In [5]: model = CTGAN() In [6]: model.fit(data)
Note
Notice that the model fitting process took care of transforming the different fields using the appropriate Reversible Data Transforms to ensure that the data has a format that the underlying CTGANSynthesizer class can handle.
fitting
Once the modeling has finished you are ready to generate new synthetic data by calling the sample method from your model passing the number of rows that we want to generate.
In [7]: new_data = model.sample(200)
This will return a table identical to the one which the model was fitted on, but filled with new data which resembles the original one.
In [8]: new_data.head() Out[8]: student_id gender second_perc high_perc high_spec degree_perc degree_type work_experience experience_years employability_perc mba_spec mba_perc salary placed start_date end_date duration 0 17424 M 61.699746 83.718700 Commerce 70.280676 Sci&Tech False 0 58.433171 Mkt&Fin 70.499948 NaN True 2020-06-15 2020-07-31 6.0 1 17454 F 28.937288 64.358821 Science 65.522645 Comm&Mgmt False 0 95.313676 Mkt&HR 55.380654 60481.489494 True NaT 2020-08-30 NaN 2 17439 F 58.922229 82.018902 Arts 76.902771 Sci&Tech False 0 80.718947 Mkt&Fin 57.571961 NaN True 2020-01-12 NaT 6.0 3 17414 M 73.880056 74.937830 Science 76.265360 Comm&Mgmt False 0 85.179291 Mkt&Fin 48.703399 NaN True 2020-03-27 NaT 12.0 4 17298 M 69.046830 70.492684 Science 86.207639 Sci&Tech False 0 62.793179 Mkt&Fin 59.174083 NaN True 2020-01-14 NaT 6.0
You can control the number of rows by specifying the number of samples in the model.sample(<num_rows>). To test, try model.sample(10000). Note that the original table only had ~200 rows.
samples
model.sample(<num_rows>)
model.sample(10000)
In many scenarios it will be convenient to generate synthetic versions of your data directly in systems that do not have access to the original data source. For example, if you may want to generate testing data on the fly inside a testing environment that does not have access to your production database. In these scenarios, fitting the model with real data every time that you need to generate new data is feasible, so you will need to fit a model in your production environment, save the fitted model into a file, send this file to the testing environment and then load it there to be able to sample from it.
Let’s see how this process works.
Once you have fitted the model, all you need to do is call its save method passing the name of the file in which you want to save the model. Note that the extension of the filename is not relevant, but we will be using the .pkl extension to highlight that the serialization protocol used is pickle.
save
.pkl
In [9]: model.save('my_model.pkl')
This will have created a file called my_model.pkl in the same directory in which you are running SDV.
my_model.pkl
Important
If you inspect the generated file you will notice that its size is much smaller than the size of the data that you used to generate it. This is because the serialized model contains no information about the original data, other than the parameters it needs to generate synthetic versions of it. This means that you can safely share this my_model.pkl file without the risc of disclosing any of your real data!
The file you just generated can be send over to the system where the synthetic data will be generated. Once it is there, you can load it using the CTGAN.load method, and then you are ready to sample new data from the loaded instance:
CTGAN.load
In [10]: loaded = CTGAN.load('my_model.pkl') In [11]: new_data = loaded.sample(200)
Notice that the system where the model is loaded needs to also have sdv and ctgan installed, otherwise it will not be able to load the model and use it.
sdv
One of the first things that you may have noticed when looking that demo data is that there is a student_id column which acts as the primary key of the table, and which is supposed to have unique values. Indeed, if we look at the number of times that each value appears, we see that all of them appear at most once:
student_id
In [12]: data.student_id.value_counts().max() Out[12]: 1
However, if we look at the synthetic data that we generated, we observe that there are some values that appear more than once:
In [13]: new_data[new_data.student_id == new_data.student_id.value_counts().index[0]] Out[13]: student_id gender second_perc high_perc high_spec degree_perc degree_type work_experience experience_years employability_perc mba_spec mba_perc salary placed start_date end_date duration 5 17435 M 73.646986 98.257372 Arts 68.372614 Comm&Mgmt False 0 103.240845 Mkt&Fin 52.049758 29332.138210 True 2020-07-11 NaT 3.0 39 17435 M 64.014901 59.092988 Commerce 51.820853 Comm&Mgmt True 0 77.100064 Mkt&HR 47.358588 29004.950063 True 2020-03-16 2020-04-08 3.0 94 17435 F 43.963257 62.471194 Commerce 60.002170 Comm&Mgmt True 0 102.944376 Mkt&HR 53.425593 NaN True 2020-04-01 2020-07-13 3.0 129 17435 M 60.780645 68.727590 Commerce 75.071070 Sci&Tech True 0 94.857543 Mkt&Fin 45.877561 NaN True NaT NaT 12.0 133 17435 M 72.573262 68.607346 Commerce 81.368647 Others False 0 73.839354 Mkt&HR 51.120447 NaN False 2020-08-13 2020-12-20 3.0
This happens because the model was not notified at any point about the fact that the student_id had to be unique, so when it generates new data it will provoke collisions sooner or later. In order to solve this, we can pass the argument primary_key to our model when we create it, indicating the name of the column that is the index of the table.
primary_key
In [14]: model = CTGAN( ....: primary_key='student_id' ....: ) ....: In [15]: model.fit(data) In [16]: new_data = model.sample(200) In [17]: new_data.head() Out[17]: student_id gender second_perc high_perc high_spec degree_perc degree_type work_experience experience_years employability_perc mba_spec mba_perc salary placed start_date end_date duration 0 0 F 65.303279 55.046971 Commerce 65.994968 Comm&Mgmt True 0 101.689991 Mkt&Fin 83.747836 28776.045186 False NaT 2020-08-29 6.0 1 1 F 88.865623 84.548175 Commerce 72.082708 Comm&Mgmt True 0 63.221083 Mkt&Fin 57.549107 NaN True 2020-07-24 2020-06-28 12.0 2 2 F 74.502908 60.371752 Commerce 71.002056 Sci&Tech True 0 59.251831 Mkt&Fin 69.141486 NaN True 2020-03-03 2020-07-10 6.0 3 3 M 74.579937 76.262574 Commerce 70.327636 Comm&Mgmt True 0 83.870629 Mkt&HR 68.849868 NaN True NaT 2020-03-14 NaN 4 4 M 54.085977 74.655591 Commerce 69.084811 Comm&Mgmt True 0 56.246279 Mkt&Fin 64.962192 23254.883178 False 2020-05-29 2020-08-19 12.0
As a result, the model will learn that this column must be unique and generate a unique sequence of values for the column:
In [18]: new_data.student_id.value_counts().max() Out[18]: 1
There will be many cases where the data will contain Personally Identifiable Information which we cannot disclose. In these cases, we will want our Tabular Models to replace the information within these fields with fake, simulated data that looks similar to the real one but does not contain any of the original values.
Let’s load a new dataset that contains a PII field, the student_placements_pii demo, and try to generate synthetic versions of it that do not contain any of the PII fields.
student_placements_pii
The student_placements_pii dataset is a modified version of the student_placements dataset with one new field, address, which contains PII information about the students. Notice that this additional address field has been simulated and does not correspond to data from the real users.
address
In [19]: data_pii = load_tabular_demo('student_placements_pii') In [20]: data_pii.head() Out[20]: student_id address gender second_perc high_perc high_spec degree_perc degree_type work_experience experience_years employability_perc mba_spec mba_perc salary placed start_date end_date duration 0 17264 70304 Baker Turnpike\nEricborough, MS 15086 M 67.00 91.00 Commerce 58.00 Sci&Tech False 0 55.0 Mkt&HR 58.80 27000.0 True 2020-07-23 2020-10-12 3.0 1 17265 805 Herrera Avenue Apt. 134\nMaryview, NJ 36510 M 79.33 78.33 Science 77.48 Sci&Tech True 1 86.5 Mkt&Fin 66.28 20000.0 True 2020-01-11 2020-04-09 3.0 2 17266 3702 Bradley Island\nNorth Victor, FL 12268 M 65.00 68.00 Arts 64.00 Comm&Mgmt False 0 75.0 Mkt&Fin 57.80 25000.0 True 2020-01-26 2020-07-13 6.0 3 17267 Unit 0879 Box 3878\nDPO AP 42663 M 56.00 52.00 Science 52.00 Sci&Tech False 0 66.0 Mkt&HR 59.43 NaN False NaT NaT NaN 4 17268 96493 Kelly Canyon Apt. 145\nEast Steven, NC 3... M 85.80 73.60 Commerce 73.30 Comm&Mgmt False 0 96.8 Mkt&Fin 55.50 42500.0 True 2020-07-04 2020-09-27 3.0
If we use our tabular model on this new data we will see how the synthetic data that it generates discloses the addresses from the real students:
In [21]: model = CTGAN( ....: primary_key='student_id', ....: ) ....: In [22]: model.fit(data_pii) In [23]: new_data_pii = model.sample(200) In [24]: new_data_pii.head() Out[24]: student_id address gender second_perc high_perc high_spec degree_perc degree_type work_experience experience_years employability_perc mba_spec mba_perc salary placed start_date end_date duration 0 0 4210 Baker Stravenue\nNorth Christopher, WI 90462 F 40.886854 68.071539 Arts 67.089470 Comm&Mgmt True 0 60.705155 Mkt&Fin 46.366971 NaN True 2020-01-16 2020-09-04 6.0 1 1 Unit 3091 Box 7338\nDPO AP 23696 F 46.628696 92.569276 Science 61.523067 Comm&Mgmt False 0 45.885736 Mkt&Fin 54.871093 NaN False NaT NaT NaN 2 2 1471 Gill Manors Suite 595\nNorth Ryan, CT 04194 F 74.529631 64.216288 Science 49.221393 Sci&Tech False 0 63.180622 Mkt&HR 55.591053 NaN True 2020-07-16 2020-07-06 3.0 3 3 162 Schmidt Harbor Apt. 653\nNew Paula, MI 52655 M 80.530458 69.236880 Commerce 58.604563 Comm&Mgmt False 0 78.980979 Mkt&HR 49.765651 NaN True 2020-01-23 2020-12-13 NaN 4 4 793 Rebecca Isle Apt. 327\nSouth Nicoleport, H... F 60.506434 74.467124 Commerce 62.781454 Comm&Mgmt True 0 60.934057 Mkt&Fin 52.172007 30160.800528 True 2020-03-22 2020-09-07 3.0
More specifically, we can see how all the addresses that have been generated actually come from the original dataset:
In [25]: new_data_pii.address.isin(data_pii.address).sum() Out[25]: 200
In order to solve this, we can pass an additional argument anonymize_fields to our model when we create the instance. This anonymize_fields argument will need to be a dictionary that contains:
anonymize_fields
The name of the field that we want to anonymize.
The category of the field that we want to use when we generate fake values for it.
The list complete list of possible categories can be seen in the Faker Providers page, and it contains a huge list of concepts such as:
name
country
city
ssn
credit_card_number
credit_card_expire
credit_card_security_code
email
telephone
…
In this case, since the field is an e-mail address, we will pass a dictionary indicating the category address
In [26]: model = CTGAN( ....: primary_key='student_id', ....: anonymize_fields={ ....: 'address': 'address' ....: } ....: ) ....: In [27]: model.fit(data_pii)
As a result, we can see how the real address values have been replaced by other fake addresses:
In [28]: new_data_pii = model.sample(200) In [29]: new_data_pii.head() Out[29]: student_id address gender second_perc high_perc high_spec degree_perc degree_type work_experience experience_years employability_perc mba_spec mba_perc salary placed start_date end_date duration 0 0 7770 Mueller Expressway Apt. 608\nEast Valerie... F 40.127671 70.349477 Science 78.356026 Others False 0 90.463599 Mkt&Fin 58.109020 NaN True 2020-02-01 2020-08-23 NaN 1 1 7546 Rebecca Bridge Suite 038\nNew Zachary, UT... M 68.775723 66.996412 Science 75.208394 Comm&Mgmt False 1 67.194478 Mkt&Fin 69.472567 NaN True 2020-01-13 NaT 6.0 2 2 78754 Hill Mall\nNew Christopher, WA 61576 F 46.535746 50.574402 Science 84.250139 Comm&Mgmt False 0 58.740641 Mkt&HR 68.646390 112284.187131 False NaT 2020-09-03 NaN 3 3 2670 Dennis Estate Suite 606\nWest Kenneth, CO... F 43.120468 76.221681 Commerce 71.135291 Comm&Mgmt False 0 55.511173 Mkt&Fin 71.048466 NaN True 2020-01-25 2020-11-27 3.0 4 4 503 Leslie Passage\nJohnsonmouth, IA 88767 F 54.257690 97.344343 Science 75.519884 Others True 0 46.282516 Mkt&Fin 58.324091 41612.662088 True 2019-12-22 2020-09-03 12.0
Which means that none of the original addresses can be found in the sampled data:
In [30]: data_pii.address.isin(new_data_pii.address).sum() Out[30]: 0
Now that we have discovered the basics, let’s go over a few more advanced usage examples and see the different arguments that we can pass to our CTGAN Model in order to customize it to our needs.
A part from the common Tabular Model arguments, CTGAN has a number of additional hyperparameters that control its learning behavior and can impact on the performance of the model, both in terms of quality of the generated data and computational time.
epochs and batch_size: these arguments control the number of iterations that the model will perform to optimize its parameters, as well as the number of samples used in each step. Its default values are 300 and 500 respectively, and batch_size needs to always be a value which is multiple of 10.
epochs
batch_size
300
500
10
These hyperparameters have a very direct effect in time the training process lasts but also on the performance of the data, so for new datasets, you might want to start by setting a low value on both of them to see how long the training process takes on your data and later on increase the number to acceptable values in order to improve the performance.
log_frequency: Whether to use log frequency of categorical levels in conditional sampling. It defaults to True. This argument affects how the model processes the frequencies of the categorical values that are used to condition the rest of the values. In some cases, changing it to False could lead to better performance.
log_frequency
True
False
embedding_dim (int): Size of the random sample passed to the Generator. Defaults to 128.
embedding_dim
generator_dim (tuple or list of ints): Size of the output samples for each one of the Residuals. A Resiudal Layer will be created for each one of the values provided. Defaults to (256, 256).
generator_dim
discriminator_dim (tuple or list of ints): Size of the output samples for each one of the Discriminator Layers. A Linear Layer will be created for each one of the values provided. Defaults to (256, 256).
discriminator_dim
generator_lr (float): Learning rate for the generator. Defaults to 2e-4.
generator_lr
generator_decay (float): Generator weight decay for the Adam Optimizer. Defaults to 1e-6.
generator_decay
discriminator_lr (float): Learning rate for the discriminator. Defaults to 2e-4.
discriminator_lr
discriminator_decay (float): Discriminator weight decay for the Adam Optimizer. Defaults to 1e-6.
discriminator_decay
discriminator_steps (int): Number of discriminator updates to do for each generator update. From the WGAN paper: https://arxiv.org/abs/1701.07875. WGAN paper default is 5. Default used is 1 to match original CTGAN implementation.
discriminator_steps
verbose: Whether to print fit progress on stdout. Defaults to False.
verbose
Notice that the value that you set on the batch_size argument must always be a multiple of 10!
As an example, we will try to fit the CTGAN model slightly increasing the number of epochs, reducing the batch_size, adding one additional layer to the models involved and using a smaller wright decay.
Before we start, we will evaluate the quality of the previously generated data using the sdv.evaluation.evaluate function
sdv.evaluation.evaluate
In [31]: from sdv.evaluation import evaluate In [32]: evaluate(new_data, data) Out[32]: 0.5230034621786793
Afterwards, we create a new instance of the CTGAN model with the hyperparameter values that we want to use
In [33]: model = CTGAN( ....: primary_key='student_id', ....: epochs=500, ....: batch_size=100, ....: generator_dim=(256, 256, 256), ....: discriminator_dim=(256, 256, 256) ....: ) ....:
And fit to our data.
In [34]: model.fit(data)
Finally, we are ready to generate new data and evaluate the results.
In [35]: new_data = model.sample(len(data)) In [36]: evaluate(new_data, data) Out[36]: 0.5190417791199542
As we can see, in this case these modifications changed the obtained results slightly, but they did neither introduce dramatic changes in the performance.
If you look closely at the data you may notice that some properties were not completely captured by the model. For example, you may have seen that sometimes the model produces an experience_years number greater than 0 while also indicating that work_experience is False. These type of properties are what we call Constraints and can also be handled using SDV. For further details about them please visit the Handling Constraints guide.
experience_years
0
work_experience
Constraints
SDV
A very common question when someone starts using SDV to generate synthetic data is: “How good is the data that I just generated?”
In order to answer this question, SDV has a collection of metrics and tools that allow you to compare the real that you provided and the synthetic data that you generated using SDV or any other tool.
You can read more about this in the Synthetic Data Evaluation guide.