sdv 0.2.1

Automated Generative Modeling and Sampling

An open source project from Data to AI Lab at MIT.

SDV - Synthetic Data Vault

• License: MIT
• Documentation: https://HDI-Project.github.io/SDV
• Homepage: https://github.com/HDI-Project/SDV

Overview

The Synthetic Data Vault (SDV) is a tool that allows users to statistically model an entire multi-table, relational dataset. Users can then use the statistical model to generate a synthetic dataset. Synthetic data can be used to supplement, augment and in some cases replace real data when training machine learning models. Additionally, it enables the testing of machine learning or other data dependent software systems without the risk of exposure that comes with data disclosure. Underneath the hood it uses a unique hierarchical generative modeling and recursive sampling techniques.

Install

Requirements

SDV has been developed and tested on Python 3.5, 3.6 and 3.7

Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid interfering with other software installed in the system where SDV is run.

Install with pip

The easiest and recommended way to install SDV is using pip:

pip install sdv

This will pull and install the latest stable release from PyPi.

If you want to install from source or contribute to the project please read the Contributing Guide.

Quickstart

In this short tutorial we will guide you through a series of steps that will help you getting started using SDV.

1. Model the dataset using SDV

To model a multi table, relational dataset, we follow two steps. In the first step, we will load the data and configures the meta data. In the second step, we will use the sdv API to fit and save a hierarchical model. We will cover these two steps in this section using an example dataset.

Step 1: Load example data

SDV comes with a toy dataset to play with, which can be loaded using the sdv.load_demo function:

from sdv import load_demo

metadata, tables = load_demo(metadata=True)

This will return two objects:

1. A Metadata object with all the information that SDV needs to know about the dataset.

For more details about how to build the Metadata for your own dataset, please refer to the Metadata section of the documentation.

2. A dictionary containing three pandas.DataFrames with the tables described in the metadata object.

The returned objects contain the following information:

{
    'users':
            user_id country gender  age
          0        0     USA      M   34
          1        1      UK      F   23
          2        2      ES   None   44
          3        3      UK      M   22
          4        4     USA      F   54
          5        5      DE      M   57
          6        6      BG      F   45
          7        7      ES   None   41
          8        8      FR      F   23
          9        9      UK   None   30,
  'sessions':
          session_id  user_id  device       os
          0           0        0  mobile  android
          1           1        1  tablet      ios
          2           2        1  tablet  android
          3           3        2  mobile  android
          4           4        4  mobile      ios
          5           5        5  mobile  android
          6           6        6  mobile      ios
          7           7        6  tablet      ios
          8           8        6  mobile      ios
          9           9        8  tablet      ios,
  'transactions':
          transaction_id  session_id           timestamp  amount  approved
          0               0           0 2019-01-01 12:34:32   100.0      True
          1               1           0 2019-01-01 12:42:21    55.3      True
          2               2           1 2019-01-07 17:23:11    79.5      True
          3               3           3 2019-01-10 11:08:57   112.1     False
          4               4           5 2019-01-10 21:54:08   110.0     False
          5               5           5 2019-01-11 11:21:20    76.3      True
          6               6           7 2019-01-22 14:44:10    89.5      True
          7               7           8 2019-01-23 10:14:09   132.1     False
          8               8           9 2019-01-27 16:09:17    68.0      True
          9               9           9 2019-01-29 12:10:48    99.9      True
}

2. Fit a model using the SDV API.

First, we build a hierarchical statistical model of the data using SDV. For this we will create an instance of the sdv.SDV class and use its fit method.

During this process, SDV will traverse across all the tables in your dataset following the primary key-foreign key relationships and learn the probability distributions of the values in the columns.

from sdv import SDV

sdv = SDV()
sdv.fit(metadata, tables)

Once the modeling has finished, you can save your fitted SDV instance for later usage using the save method of your instance.

sdv.save('path/to/sdv.pkl')

The generated pkl file will not include any of the original data in it, so it can be safely sent to where the synthetic data will be generated without any privacy concerns.

2. Sample data from the fitted model

In order to sample data from the fitted model, we will first need to load it from its pkl file. Note that you can skip this step if you are running all the steps sequentially within the same python session.

sdv = SDV.load('path/to/sdv.pkl')

After loading the instance, we can sample synthetic data using its sample_all method, passing the number of rows that we want to generate.

samples = sdv.sample_all(5)

The output will be a dictionary with the same structure as the original tables dict, but filled with synthetic data instead of the real one.

Note that only the parent tables of your dataset will have the specified number of rows, as the number of child rows that each row in the parent table has is also sampled following the original distribution of your dataset.

Join out community

1. If you would like to see more usage examples, please have a look at the examples folder or the repository. Please contact us if you have a usage example that you would want to share with the community.
2. Please head to the Contributing Guide for more details about this process.
3. If you have any doubts, feature requests or detect an error, please open an issue on github
4. Also do not forget to check the project documentation site!

Citation

If you use SDV for your research, please consider citing the following paper:

Neha Patki, Roy Wedge, Kalyan Veeramachaneni. The Synthetic Data Vault. IEEE DSAA 2016.

@inproceedings{
    7796926,
    author={N. {Patki} and R. {Wedge} and K. {Veeramachaneni}},
    booktitle={2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)},
    title={The Synthetic Data Vault},
    year={2016},
    volume={},
    number={},
    pages={399-410},
    keywords={data analysis;relational databases;synthetic data vault;SDV;generative model;relational database;multivariate modelling;predictive model;data analysis;data science;Data models;Databases;Computational modeling;Predictive models;Hidden Markov models;Numerical models;Synthetic data generation;crowd sourcing;data science;predictive modeling},
    doi={10.1109/DSAA.2016.49},
    ISSN={},
    month={Oct}
}

History

0.2.1 - 2019-11-25

New Features

• Methods to generate Metadata from DataFrames - Issue #126 by @csala @JDTheRipperPC

0.2.0 - 2019-10-11

New Features

• compatibility with rdt issue 72 - Issue #120 by @csala @JDTheRipperPC

General Improvements

• Error docstring sampler.__fill_text_columns - Issue #144 by @JDTheRipperPC
• Reach 90% coverage - Issue #112 by @JDTheRipperPC
• Review unittests - Issue #111 by @JDTheRipperPC

Bugs Fixed

• Time required for sample_all function? - Issue #118 by @csala @JDTheRipperPC

0.1.2 - 2019-09-18

New Features

• Add option to model the amount of child rows - Issue 93 by @ManuelAlvarezC

General Improvements

• Add Evaluation Metrics - Issue 52 by @ManuelAlvarezC

• Ensure unicity on primary keys on different calls - Issue 63 by @ManuelAlvarezC

Bugs fixed

• executing readme: 'not supported between instances of 'int' and 'NoneType' - Issue 104 by @csala

0.1.1 - Anonymization of data

• Add warnings when trying to model an unsupported dataset structure. GH#73
• Add option to anonymize data. GH#51
• Add support for modeling data with different distributions, when using GaussianMultivariate model. GH#68
• Add support for VineCopulas as a model. GH#71
• Improve GaussianMultivariate parameter sampling, avoiding warnings and unvalid parameters. GH#58
• Fix issue that caused that sampled categorical values sometimes got numerical values mixed. GH#81
• Improve the validation of extensions. GH#69
• Update examples. GH#61
• Replaced Table class with a NamedTuple. GH#92
• Fix inconsistent dependencies and add upper bound to dependencies. GH#96
• Fix error when merging extension in Modeler.CPA when running examples. GH#86

0.1.0 - First Release

• First release on PyPI.