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Metrics for Synthetic Data Generation Projects

Project description


This repository is part of The Synthetic Data Vault Project, a project from DataCebo.

Development Status PyPI Shield Downloads Tests Coverage Status

Overview

The SDMetrics library provides a set of dataset-agnostic tools for evaluating the quality of a synthetic database by comparing it to the real database that it is modeled after.

Important Links
:computer: Website Check out the SDV Website for more information about the project.
:orange_book: SDV Blog Regular publshing of useful content about Synthetic Data Generation.
:book: Documentation Quickstarts, User and Development Guides, and API Reference.
:octocat: Repository The link to the Github Repository of this library.
:scroll: License The entire ecosystem is published under the MIT License.
:keyboard: Development Status This software is in its Pre-Alpha stage.
Community Join our Slack Workspace for announcements and discussions.
Tutorials Run the SDV Tutorials in a Binder environment.

Features

It supports multiple data modalities:

  • Single Columns: Compare 1 dimensional numpy arrays representing individual columns.
  • Column Pairs: Compare how columns in a pandas.DataFrame relate to each other, in groups of 2.
  • Single Table: Compare an entire table, represented as a pandas.DataFrame.
  • Multi Table: Compare multi-table and relational datasets represented as a python dict with multiple tables passed as pandas.DataFrames.
  • Time Series: Compare tables representing ordered sequences of events.

It includes a variety of metrics such as:

  • Statistical metrics which use statistical tests to compare the distributions of the real and synthetic distributions.
  • Detection metrics which use machine learning to try to distinguish between real and synthetic data.
  • Efficacy metrics which compare the performance of machine learning models when run on the synthetic and real data.
  • Bayesian Network and Gaussian Mixture metrics which learn the distribution of the real data and evaluate the likelihood of the synthetic data belonging to the learned distribution.
  • Privacy metrics which evaluate whether the synthetic data is leaking information about the real data.

Install

SDMetrics is part of the SDV project and is automatically installed alongside it. For details about this process please visit the SDV Installation Guide

Optionally, SDMetrics can also be installed as a standalone library using the following commands:

Using pip:

pip install sdmetrics

Using conda:

conda install -c conda-forge -c pytorch sdmetrics

For more installation options please visit the SDMetrics installation Guide

Usage

SDMetrics is included as part of the framework offered by SDV to evaluate the quality of your synthetic dataset. For more details about how to use it please visit the corresponding User Guide:

Standalone usage

SDMetrics can also be used as a standalone library to run metrics individually.

In this short example we show how to use it to evaluate a toy multi-table dataset and its synthetic replica by running all the compatible multi-table metrics on it:

import sdmetrics

# Load the demo data, which includes:
# - A dict containing the real tables as pandas.DataFrames.
# - A dict containing the synthetic clones of the real data.
# - A dict containing metadata about the tables.
real_data, synthetic_data, metadata = sdmetrics.load_demo()

# Obtain the list of multi table metrics, which is returned as a dict
# containing the metric names and the corresponding metric classes.
metrics = sdmetrics.multi_table.MultiTableMetric.get_subclasses()

# Run all the compatible metrics and get a report
sdmetrics.compute_metrics(metrics, real_data, synthetic_data, metadata=metadata)

The output will be a table with all the details about the executed metrics and their score:

metric name score min_value max_value goal
CSTest Chi-Squared 0.76651 0 1 MAXIMIZE
KSComplement Complement to Kolmogorov-Smirnov D statistic 0.75 0 1 MAXIMIZE
LogisticDetection LogisticRegression Detection 0.882716 0 1 MAXIMIZE
SVCDetection SVC Detection 0.833333 0 1 MAXIMIZE
BNLikelihood BayesianNetwork Likelihood nan 0 1 MAXIMIZE
BNLogLikelihood BayesianNetwork Log Likelihood nan -inf 0 MAXIMIZE
LogisticParentChildDetection LogisticRegression Detection 0.619444 0 1 MAXIMIZE
SVCParentChildDetection SVC Detection 0.916667 0 1 MAXIMIZE

What's next?

If you want to read more about each individual metric, please visit the following folders:




The Synthetic Data Vault Project was first created at MIT's Data to AI Lab in 2016. After 4 years of research and traction with enterprise, we created DataCebo in 2020 with the goal of growing the project. Today, DataCebo is the proud developer of SDV, the largest ecosystem for synthetic data generation & evaluation. It is home to multiple libraries that support synthetic data, including:

  • 🔄 Data discovery & transformation. Reverse the transforms to reproduce realistic data.
  • 🧠 Multiple machine learning models -- ranging from Copulas to Deep Learning -- to create tabular, multi table and time series data.
  • 📊 Measuring quality and privacy of synthetic data, and comparing different synthetic data generation models.

Get started using the SDV package -- a fully integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries for specific needs.

History

v0.6.0 - 2022-08-12

This release removes SDMetric's dependency on the RDT library, and also introduces new quality and diagnostic metrics. Additionally, we introduce a new compute_breakdown method that returns a breakdown of metric results.

New Features

  • Handle null values correctly - Issue #194 by @katxiao
  • Add wrapper classes for new single and multi table metrics - Issue #169 by @katxiao
  • Add CorrelationSimilarity metric - Issue #143 by @katxiao
  • Add CardinalityShapeSimilarity metric - Issue #160 by @katxiao
  • Add CardinalityStatisticSimilarity metric - Issue #145 by @katxiao
  • Add ContingencySimilarity Metric - Issue #159 by @katxiao
  • Add TVComplement metric - Issue #142 by @katxiao
  • Add MissingValueSimilarity metric - Issue #139 by @katxiao
  • Add CategoryCoverage metric - Issue #140 by @katxiao
  • Add compute breakdown column for single column - Issue #152 by @katxiao
  • Add BoundaryAdherence metric - Issue #138 by @katxiao
  • Get KSComplement Score Breakdown - Issue #130 by @katxiao
  • Add StatisticSimilarity Metric - Issue #137 by @katxiao
  • New features for KSTest.compute - Issue #129 by @amontanez24

Internal Improvements

  • Add integration tests and fixes - Issue #183 by @katxiao
  • Remove rdt hypertransformer dependency in timeseries metrics - Issue #176 by @katxiao
  • Replace rdt LabelEncoder with sklearn - Issue #178 by @katxiao
  • Remove rdt as a dependency - Issue #182 by @katxiao
  • Use sklearn's OneHotEncoder instead of rdt - Issue #170 by @katxiao
  • Remove KSTestExtended - Issue #180 by @katxiao
  • Remove TSFClassifierEfficacy and TSFCDetection metrics - Issue #171 by @katxiao
  • Update the default tags for a feature request - Issue #172 by @katxiao
  • Bump github macos version - Issue #174 by @katxiao
  • Fix pydocstyle to check sdmetrics - Issue #153 by @pvk-developer
  • Update the RDT version to 1.0 - Issue #150 by @pvk-developer
  • Update slack invite link - Issue #132 by @pvk-developer

v0.5.0 - 2022-05-11

This release fixes an error where the relational KSTest crashes if a table doesn't have numerical columns. It also includes some housekeeping, updating the pomegranate and copulas version requirements.

Issues closed

  • Cap pomegranate to <0.14.7 - Issue #116 by @csala
  • Relational KSTest crashes with IncomputableMetricError if a table doesn't have numerical columns - Issue #109 by @katxiao

v0.4.1 - 2021-12-09

This release improves the handling of metric errors, and updates the default transformer behavior used in SDMetrics.

Issues closed

  • Report metric errors from compute_metrics - Issue #107 by @katxiao
  • Specify default categorical transformers - Issue #105 by @katxiao

v0.4.0 - 2021-11-16

This release adds support for Python 3.9 and updates dependencies to ensure compatibility with the rest of the SDV ecosystem, and upgrades to the latests RDT release.

Issues closed

  • Replace sktime for pyts - Issue #103 by @pvk-developer
  • Add support for Python 3.9 - Issue #102 by @pvk-developer
  • Increase code style lint - Issue #80 by @fealho
  • Add pip check to CI workflows - Issue #79 by @pvk-developer
  • Upgrade dependency ranges - Issue #69 by @katxiao

v0.3.2 - 2021-08-16

This release makes pomegranate an optional dependency.

Issues closed

  • Make pomegranate an optional dependency - Issue #63 by @fealho

v0.3.1 - 2021-07-12

This release fixes a bug to make the privacy metrics available in the API docs. It also updates dependencies to ensure compatibility with the rest of the SDV ecosystem.

Issues closed

  • CategoricalSVM not being imported - Issue #65 by @csala

v0.3.0 - 2021-03-30

This release includes privacy metrics to evaluate if the real data could be obtained or deduced from the synthetic samples. Additionally all the metrics have a normalize method which takes the raw_score generated by the metric and returns a value between 0 and 1.

Issues closed

  • Add normalize method to metrics - Issue #51 by @csala and @fealho
  • Implement privacy metrics - Issue #36 by @ZhuofanXie and @fealho

v0.2.0 - 2021-02-24

Dependency upgrades to ensure compatibility with the rest of the SDV ecosystem.

v0.1.3 - 2021-02-13

Updates the required dependecies to facilitate a conda release.

Issues closed

  • Upgrade sktime - Issue #49 by @fealho

v0.1.2 - 2021-01-27

Big fixing release that addresses several minor errors.

Issues closed

  • More splits than classes - Issue #46 by @fealho
  • Scipy 1.6.0 causes an AttributeError - Issue #44 by @fealho
  • Time series metrics fails with variable length timeseries - Issue #42 by @fealho
  • ParentChildDetection metrics KeyError - Issue #39 by @csala

v0.1.1 - 2020-12-30

This version adds Time Series Detection and Efficacy metrics, as well as a fix to ensure that Single Table binary classification efficacy metrics work well with binary targets which are not boolean.

Issues closed

  • Timeseries efficacy metrics - Issue #35 by @csala
  • Timeseries detection metrics - Issue #34 by @csala
  • Ensure binary classification targets are bool - Issue #33 by @csala

v0.1.0 - 2020-12-18

This release introduces a new project organization and API, with metrics grouped by data modality, with a common API:

  • Single Column
  • Column Pair
  • Single Table
  • Multi Table
  • Time Series

Within each data modality, different families of metrics have been implemented:

  • Statistical
  • Detection
  • Bayesian Network and Gaussian Mixture Likelihood
  • Machine Learning Efficacy

v0.0.4 - 2020-11-27

Patch release to relax dependencies and avoid conflicts when using the latest SDV version.

v0.0.3 - 2020-11-20

Fix error on detection metrics when input data contains infinity or NaN values.

Issues closed

  • ValueError: Input contains infinity or a value too large for dtype('float64') - Issue #11 by @csala

v0.0.2 - 2020-08-08

Add support for Python 3.8 and a broader range of dependencies.

v0.0.1 - 2020-06-26

First release to PyPI.

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