sdv 1.0.0rc0

Generate synthetic data for single table, multi table and sequential data

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

Overview

The Synthetic Data Vault (SDV) is a Python library designed to be your one-stop shop for creating tabular synthetic data. The SDV uses a variety of machine learning algorithms to learn patterns from your real data and emulate them in synthetic data.

Features

:brain: Create synthetic data using machine learning. The SDV offers multiple models, ranging from classical statistical methods (GaussianCopula) to deep learning methods (CTGAN). Generate data for single tables, multiple connected tables or sequential tables.

:bar_chart: Evaluate and visualize data. Compare the synthetic data to the real data against a variety of measures. Diagnose problems and generate a quality report to get more insights.

:arrows_counterclockwise: Preprocess, anonymize and define constraints. Control data processing to improve the quality of synthetic data, choose from different types of anonymization and define business rules in the form of logical constraints.

Important Links
Tutorials	Get some hands-on experience with the SDV. Launch the tutorial notebooks and run the code yourself.
:book: Docs	Learn how to use the SDV library with user guides and API references.
:orange_book: Blog	Get more insights about using the SDV, deploying models and our synthetic data community.
Community	Join our Slack workspace for announcements and discussions.
:computer: Website	Check out the SDV website for more information about the project.

Install

The SDV is publicly available under the Business Source License. Install SDV using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device.

pip install sdv

conda install -c pytorch -c conda-forge sdv

Getting Started

Load a demo dataset to get started. This dataset is a single table describing guests staying at a fictional hotel.

from sdv.datasets.demo import download_demo

real_data, metadata = download_demo(
    modality='single_table',
    dataset_name='fake_hotel_guests')

The demo also includes metadata, a description of the dataset, including the data types in each column and the primary key (guest_email).

Synthesizing Data

Next, we can create an SDV synthesizer, an object that you can use to create synthetic data. It learns patterns from the real data and replicates them to generate synthetic data. Let's use the FAST_ML preset synthesizer, which is optimized for performance.

from sdv.lite import SingleTablePreset

synthesizer = SingleTablePreset(metadata, name='FAST_ML')
synthesizer.fit(data=real_data)

And now the synthesizer is ready to create synthetic data!

synthetic_data = synthesizer.sample(num_rows=500)

The synthetic data will have the following properties:

• Sensitive columns are fully anonymized. The email, billing address and credit card number columns contain new data so you don't expose the real values.
• Other columns follow statistical patterns. For example, the proportion of room types, the distribution of check in dates and the correlations between room rate and room type are preserved.
• Keys and other relationships are intact. The primary key (guest email) is unique for each row. If you have multiple tables, the connection between a primary and foreign keys makes sense.

Evaluating Synthetic Data

The SDV library allows you to evaluate the synthetic data by comparing it to the real data. Get started by generating a quality report.

from sdv.evaluation.single_table import evaluate_quality

quality_report = evaluate_quality(
    real_data,
    synthetic_data,
    metadata)

Creating report: 100%|██████████| 4/4 [00:00<00:00, 19.30it/s]
Overall Quality Score: 89.12%
Properties:
Column Shapes: 90.27%
Column Pair Trends: 87.97%

This object computes an overall quality score on a scale of 0 to 100% (100 being the best) as well as detailed breakdowns. For more insights, you can also visualize the synthetic vs. real data.

from sdv.evaluation.single_table import get_column_plot

fig = get_column_plot(
    real_data=real_data,
    synthetic_data=synthetic_data,
    column_name='amenities_fee',
    metadata=metadata
)
    
fig.show()

What's Next?

Using the SDV library, you can synthesize single table, multi table and sequential data. You can also customize the full synthetic data workflow, including preprocessing, anonymization and adding constraints.

To learn more, visit the SDV Demo page.

Credits

Thank you to our team of contributors who have built and maintained the SDV ecosystem over the years!

View Contributors

Citation

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

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

@inproceedings{
    SDV,
    title={The Synthetic data vault},
    author={Patki, Neha and Wedge, Roy and Veeramachaneni, Kalyan},
    booktitle={IEEE International Conference on Data Science and Advanced Analytics (DSAA)},
    year={2016},
    pages={399-410},
    doi={10.1109/DSAA.2016.49},
    month={Oct}
}

---

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.

Release Notes

0.18.0 - 2023-01-24

This release adds suppport for Python 3.10 and drops support for 3.6.

Maintenance

• Drop support for python 3.6 - Issue #1177 by @amontanez24
• Support for python 3.10 - Issue #939 by @amontanez24
• Support Python >=3.10,<4 - Issue #1000 by @amontanez24

0.17.2 - 2022-12-08

This release fixes a bug in the demo module related to loading the demo data with constraints. It also adds a name to the demo datasets. Finally, it bumps the version of SDMetrics used.

Maintenance

• Upgrade SDMetrics requirement to 0.8.0 - Issue #1125 by @katxiao

New Features

• Provide a name for the default demo datasets - Issue #1124 by @amontanez24

Bugs Fixed

• Cannot load_tabular_demo with metadata - Issue #1123 by @amontanez24

0.17.1 - 2022-09-29

This release bumps the dependency requirements to use the latest version of SDMetrics.

Maintenance

• Patch release: Bump required version for SDMetrics - Issue #1010 by @katxiao

0.17.0 - 2022-09-09

This release updates the code to use RDT version 1.2.0 and greater, so that those new features are now available in SDV. This changes the transformers that are available in SDV models to be those that are in RDT version 1.2.0. As a result, some arguments for initializing models have changed.

Additionally, this release fixes bugs related to loading models with custom constraints. It also fixes a bug that added NaNs to the index of sampled data when using sample_remaining_columns.

Bugs Fixed

• Incorrect rounding in Custom Constraint example - Issue #941 by @amontanez24
• Can't save the model if use the custom constraint - Issue #928 by @pvk-developer
• User Guide code fixes - Issue #983 by @amontanez24
• Index contains NaNs when using sample_remaining_columns - Issue #985 by @amontanez24
• Cannot sample after loading a model with custom constraints: TypeError - Issue #984 by @pvk-developer
• Set HyperTransformer config manually, based on Metadata if given - Issue #982 by @pvk-developer

New Features

• Change default metrics for evaluate - Issue #949 by @fealho

Maintenance

• Update the RDT version to 1.0 - Issue #897 by @pvk-developer

0.16.0 - 2022-07-21

This release brings user friendly improvements and bug fixes on the SDV constraints, to help users generate their synthetic data easily.

Some predefined constraints have been renamed and redefined to be more user friendly & consistent. The custom constraint API has also been updated for usability. The SDV now automatically determines the best handling_strategy to use for each constraint, attempting transform by default and falling back to reject_sampling otherwise. The handling_strategy parameters are no longer included in the API.

Finally, this version of SDV also unifies the parameters for all sampling related methods for all models (including TabularPreset).

Changes to Constraints

• GreatherThan constraint is now separated in two new constraints: Inequality, which is intended to be used between two columns, and ScalarInequality, which is intended to be used between a column and a scalar.

• Between constraint is now separated in two new constraints: Range, which is intended to be used between three columns, and ScalarRange, which is intended to be used between a column and low and high scalar values.

• FixedIncrements a new constraint that makes the data increment by a certain value.

• New create_custom_constraint function available to create custom constraints.

Removed Constraints

• Rounding Rounding is automatically being handled by the rdt.HyperTransformer.
• ColumnFormula the create_custom_constraint takes place over this one and allows more advanced usage for the end users.

New Features

• Improve error message for invalid constraints - Issue #801 by @fealho
• Numerical Instability in Constrained GaussianCopula - Issue #806 by @fealho
• Unify sampling params for reject sampling - Issue #809 by @amontanez24
• Split GreaterThan constraint into Inequality and ScalarInequality - Issue #814 by @fealho
• Split Between constraint into Range and ScalarRange - Issue #815 @pvk-developer
• Change columns to column_names in OneHotEncoding and Unique constraints - Issue #816 by @amontanez24
• Update columns parameter in Positive and Negative constraint - Issue #817 by @fealho
• Create FixedIncrements constraint - Issue #818 by @amontanez24
• Improve datetime handling in ScalarInequality and ScalarRange constraints - Issue #819 by @pvk-developer
• Support strict boundaries even when transform strategy is used - Issue #820 by @fealho
• Add create_custom_constraint factory method - Issue #836 by @fealho

Internal Improvements

• Remove handling_strategy parameter - Issue #833 by @amontanez24
• Remove fit_columns_model parameter - Issue #834 by @pvk-developer
• Remove the ColumnFormula constraint - Issue #837 by @amontanez24
• Move table_data.copy to base class of constraints - Issue #845 by @fealho

Bugs Fixed

• Numerical Instability in Constrained GaussianCopula - Issue #801 by @tlranda and @fealho
• Fix error message for FixedIncrements - Issue #865 by @pvk-developer
• Fix constraints with conditional sampling - Issue #866 by @amontanez24
• Fix error message in ScalarInequality - Issue #868 by @pvk-developer
• Cannot use max_tries_per_batch on sample: TypeError: sample() got an unexpected keyword argument 'max_tries_per_batch' - Issue #885 by @amontanez24
• Conditional sampling + batch size: ValueError: Length of values (1) does not match length of index (5) - Issue #886 by @amontanez24
• TabularPreset doesn't support new sampling parameters - Issue #887 by @fealho
• Conditional Sampling: batch_size is being set to None by default? - Issue #889 by @amontanez24
• Conditional sampling using GaussianCopula inefficient when categories are noised - Issue #910 by @amontanez24

Documentation Changes

• Show the API for TabularPreset models - Issue #854 by @katxiao
• Update handling constraints doc - Pull Request #856 by @amontanez24
• Update custom costraints documentation - Pull Request #857 by @pvk-developer

0.15.0 - 2022-05-25

This release improves the speed of the GaussianCopula model by removing logic that previously searched for the appropriate distribution to use. It also fixes a bug that was happening when conditional sampling was used with the TabularPreset.

The rest of the release focuses on making changes to improve constraints including changing the UniqueCombinations constraint to FixedCombinations, making the Unique constraint work with missing values and erroring when null values are seen in the OneHotEncoding constraint.

New Features

• Silence warnings coming from univariate fit in copulas - Issue #769 by @pvk-developer
• Remove parameters related to distribution search and change default - Issue #767 by @fealho
• Update the UniqueCombinations constraint - Issue #793 by @fealho
• Make Unique constraint works with nans - Issue #797 by @fealho
• Error out if nans in OneHotEncoding - Issue #800 by @amontanez24

Bugs Fixed

• Unable to sample conditionally in Tabular_Preset model - Issue #796 by @katxiao

Documentation Changes

• Support GPU computing and progress track? - Issue #478 by @fealho

0.14.1 - 2022-05-03

This release adds a TabularPreset, available in the sdv.lite module, which allows users to easily optimize a tabular model for speed. In this release, we also include bug fixes for sampling with conditions, an unresolved warning, and setting field distributions. Finally, we include documentation updates for sampling and the new TabularPreset.

Bugs Fixed

• Sampling with conditions={column: 0.0} for float columns doesn't work - Issue #525 by @shlomihod and @tssbas
• resolved FutureWarning with Pandas replaced append by concat - Issue #759 by @Deathn0t
• Field distributions bug in CopulaGAN - Issue #747 by @katxiao
• Field distributions bug in GaussianCopula - Issue #746 by @katxiao

New Features

• Set default transformer to categorical_fuzzy - Issue #768 by @amontanez24
• Model nulls normally when tabular preset has constraints - Issue #764 by @katxiao
• Don't modify my metadata object - Issue #754 by @amontanez24
• Presets should be able to handle constraints - Issue #753 by @katxiao
• Change preset optimize_for --> name - Issue #749 by @katxiao
• Create a speed optimized Preset - Issue #716 by @katxiao

Documentation Changes

• Add tabular preset docs - Issue #777 by @katxiao
• sdv.sampling module is missing from the API - Issue #740 by @katxiao

0.14.0 - 2022-03-21

This release updates the sampling API and splits the existing functionality into three methods - sample, sample_conditions, and sample_remaining_columns. We also add support for sampling in batches, displaying a progress bar when sampling with more than one batch, sampling deterministically, and writing the sampled results to an output file. Finally, we include fixes for sampling with conditions and updates to the documentation.

Bugs Fixed

• Fix write to file in sampling - Issue #732 by @katxiao
• Conditional sampling doesn't work if the model has a CustomConstraint - Issue #696 by @katxiao

New Features

• Updates to GaussianCopula conditional sampling methods - Issue #729 by @katxiao
• Update conditional sampling errors - Issue #730 by @katxiao
• Enable Batch Sampling + Progress Bar - Issue #693 by @katxiao
• Create sample_remaining_columns() method - Issue #692 by @katxiao
• Create sample_conditions() method - Issue #691 by @katxiao
• Improve sample() method - Issue #690 by @katxiao
• Create Condition object - Issue #689 by @katxiao
• Is it possible to generate data with new set of primary keys? - Issue #686 by @katxiao
• No way to fix the random seed? - Issue #157 by @katxiao
• Can you set a random state for the sdv.tabular.ctgan.CTGAN.sample method? - Issue #515 by @katxiao
• generating different synthetic data while training the model multiple times. - Issue #299 by @katxiao

Documentation Changes

• Typo in the document documentation - Issue #680 by @katxiao

0.13.1 - 2021-12-22

This release adds support for passing tabular constraints to the HMA1 model, and adds more explicit error handling for metric evaluation. It also includes a fix for using categorical columns in the PAR model and documentation updates for metadata and HMA1.

Bugs Fixed

• Categorical column after sequence_index column - Issue #314 by @fealho

New Features

• Support passing tabular constraints to the HMA1 model - Issue #296 by @katxiao
• Metric evaluation error handling metrics - Issue #638 by @katxiao

Documentation Changes

• Make true/false values lowercase in Metadata Schema specification - Issue #664 by @katxiao
• Update docstrings for hma1 methods - Issue #642 by @katxiao

0.13.0 - 2021-11-22

This release makes multiple improvements to different Constraint classes. The Unique constraint can now handle columns with the name index and no longer crashes on subsets of the original data. The Between constraint can now handle columns with nulls properly. The memory of all constraints was also improved.

Various other features and fixes were added. Conditional sampling no longer crashes when the num_rows argument is not provided. Multiple localizations can now be used for PII fields. Scaffolding for integration tests was added and the workflows now run pip check.

Additionally, this release adds support for Python 3.9!

Bugs Fixed

• Gaussian Copula – Memory Issue in Release 0.10.0 - Issue #459 by @xamm
• Applying Unique Constraint errors when calling model.fit() on a subset of data - Issue #610 by @xamm
• Calling sampling with conditions and without num_rows crashes - Issue #614 by @xamm
• Metadata.visualize with path parameter throws AttributeError - Issue #634 by @xamm
• The Unique constraint crashes when the data contains a column called index - Issue #616 by @xamm
• The Unique constraint cannot handle non-default index - Issue #617 by @xamm
• ConstraintsNotMetError when applying Between constraint on datetime columns containing null values - Issue #632 by @katxiao

New Features

• Adds Multi localisations feature for PII fields defined in #308 - PR #609 by @xamm

Housekeeping Tasks

• Support latest version of Faker - Issue #621 by @katxiao
• Add scaffolding for Metadata integration tests - Issue #624 by @katxiao
• Add support for Python 3.9 - Issue #631 by @amontanez24

Internal Improvements

• Add pip check to CI workflows - Issue #626 by @pvk-developer

Documentation Changes

• Anonymizing PII in single table tutorials states address field as e-mail type - Issue #604 by @xamm

Special thanks to @xamm, @katxiao, @pvk-developer and @amontanez24 for all the work that made this release possible!

0.12.1 - 2021-10-12

This release fixes bugs in constraints, metadata behavior, and SDV documentation. Specifically, we added proper handling of data containing null values for constraints and timeseries data, and updated the default metadata detection behavior.

Bugs Fixed

• ValueError: The parameter loc has invalid values - Issue #353 by @fealho
• Gaussian Copula is generating different data with metadata and without metadata - Issue #576 by @katxiao
• Make pomegranate an optional dependency - Issue #567 by @katxiao
• Small wording change for Question Issue Template - Issue #571 by @katxiao
• ConstraintsNotMetError when using GreaterThan constraint with datetime - Issue #590 by @katxiao
• GreaterThan constraint crashing with NaN values - Issue #592 by @katxiao
• Null values in GreaterThan constraint raises error - Issue #589 by @katxiao
• ColumnFormula raises ConstraintsNotMetError when checking NaN values - Issue #593 by @katxiao
• GreaterThan constraint raises TypeError when using datetime - Issue #596 by @katxiao
• Fix repository language - Issue #464 by @fealho
• Update init.py - Issue #578 by @dyuliu
• IndexingError: Unalignable boolean - Issue #446 by @fealho

0.12.0 - 2021-08-17

This release focuses on improving and expanding upon the existing constraints. More specifically, the users can now (1) specify multiple columns in Positive and Negative constraints, (2) use the new Uniqueconstraint and (3) use datetime data with the Between constraint. Additionaly, error messages have been added and updated to provide more useful feedback to the user.

Besides the added features, several bugs regarding the UniqueCombinations and ColumnFormula constraints have been fixed, and an error in the metadata.json for the student_placements dataset was corrected. The release also added documentation for the fit_columns_model which affects the majority of the available constraints.

New Features

• Change default fit_columns_model to False - Issue #550 by @katxiao
• Support multi-column specification for positive and negative constraint - Issue #545 by @sarahmish
• Raise error when multiple constraints can't be enforced - Issue #541 by @amontanez24
• Create Unique Constraint - Issue #532 by @amontanez24
• Passing invalid conditions when using constraints produces unreadable errors - Issue #511 by @katxiao
• Improve error message for ColumnFormula constraint when constraint column used in formula - Issue #508 by @katxiao
• Add datetime functionality to Between constraint - Issue #504 by @katxiao

Bugs Fixed

• UniqueCombinations constraint with handling_strategy = 'transform' yields synthetic data with nan values - Issue #521 by @katxiao and @csala
• UniqueCombinations constraint outputting wrong data type - Issue #510 by @katxiao and @csala
• UniqueCombinations constraint on only one column gets stuck in an infinite loop - Issue #509 by @katxiao
• Conditioning on a non-constraint column using the ColumnFormula constraint - Issue #507 by @katxiao
• Conditioning on the constraint column of the ColumnFormula constraint - Issue #506 by @katxiao
• Update metadata.json for duration of student_placements dataset - Issue #503 by @amontanez24
• Unit test for HMA1 when working with a single child row per parent row - Issue #497 by @pvk-developer
• UniqueCombinations constraint for more than 2 columns - Issue #494 by @katxiao and @csala

Documentation Changes

• Add explanation of fit_columns_model to API docs - Issue #517 by @katxiao

0.11.0 - 2021-07-12

This release primarily addresses bugs and feature requests related to using constraints for the single-table models. Users can now enforce scalar comparison with the existing GreaterThan constraint and apply 5 new constraints: OneHotEncoding, Positive, Negative, Between and Rounding. Additionally, the SDV will now auto-apply constraints for rounding numerical values, and for keeping the data within the observed bounds. All related user guides are updated with the new functionality.

New Features

• Add OneHotEncoding Constraint - Issue #303 by @fealho
• GreaterThan Constraint should apply to scalars - Issue #410 by @amontanez24
• Improve GreaterThan constraint - Issue #368 by @amontanez24
• Add Non-negative and Positive constraints across multiple columns- Issue #409 by @amontanez24
• Add Between values constraint - Issue #367 by @fealho
• Ensure values fall within the specified range - Issue #423 by @amontanez24
• Add Rounding constraint - Issue #482 by @katxiao
• Add rounding and min/max arguments that are passed down to the NumericalTransformer - Issue #491 by @amontanez24

Bugs Fixed

• GreaterThan constraint between Date columns rasises TypeError - Issue #421 by @amontanez24
• GreaterThan constraint's transform strategy fails on columns that are not float - Issue #448 by @amontanez24
• AttributeError on UniqueCombinations constraint with non-strings - Issue #196 by @katxiao
• Use reject sampling to sample missing columns for constraints - Issue #435 by @amontanez24

Documentation Changes

• Ensure privacy metrics are available in the API docs - Issue #458 by @fealho
• Ensure forumla constraint is called ColumnFormula everywhere in the docs - Issue #449 by @fealho

0.10.1 - 2021-06-10

This release changes the way we sample conditions to not only group by the conditions passed by the user, but also by the transformed conditions that result from them.

Issues resolved

• Conditionally sampling on variable in constraint should have variety for other variables - Issue #440 by @amontanez24

0.10.0 - 2021-05-21

This release improves the constraint functionality by allowing constraints and conditions at the same time. Additional changes were made to update tutorials.

Issues resolved

• Not able to use constraints and conditions in the same time - Issue #379 by @amontanez24
• Update benchmarking user guide for reading private datasets - Issue #427 by @katxiao

0.9.1 - 2021-04-29

This release broadens the constraint functionality by allowing for the ColumnFormula constraint to take lambda functions and returned functions as an input for its formula.

It also improves conditional sampling by ensuring that any id fields generated by the model remain unique throughout the sampled data.

The CTGAN model was improved by adjusting a default parameter to be more mathematically correct.

Additional changes were made to improve tutorials as well as fix fragile tests.

Issues resolved

• Tutorials test sometimes fails - Issue #355 by @fealho
• Duplicate IDs when using reject-sampling - Issue #331 by @amontanez24 and @csala
• discriminator_decay should be initialized at 1e-6 but it's 0 - Issue #401 by @fealho and @YoucefZemmouri
• Tutorial typo - Issue #380 by @fealho
• Request for sdv.constraint.ColumnFormula for a wider range of function - Issue #373 by @amontanez24 and @JetfiRex

0.9.0 - 2021-03-31

This release brings new privacy metrics to the evaluate framework which help to determine if the real data could be obtained or deduced from the synthetic samples. Additionally, now there is a normalized score for the metrics, which stays between 0 and 1.

There are improvements that reduce the usage of memory ram when sampling new data. Also there is a new parameter to control the reject sampling crash, graceful_reject_sampling, which if set to True and if it's not possible to generate all the requested rows, it will just issue a warning and return whatever it was able to generate.

The Metadata object can now be visualized using different combinations of names and details, which can be set to True or False in order to display only the table names with details or without. There is also an improvement on the validation, which now will display all the errors found at the end of the validation instead of only the first one.

This version also exposes all the hyperparameters of the models CTGAN and TVAE to allow a more advanced usage. There is also a fix for the TVAE model on small datasets and it's performance with NaN values has been improved. There is a fix for when using UniqueCombinationConstraint with the transform strategy.

Issues resolved

• Memory Usage Gaussian Copula Trained Model consuming high memory when generating synthetic data - Issue #304 by @pvk-developer and @AnupamaGangadhar
• Add option to visualize metadata with only table names - Issue #347 by @csala
• Add sample parameter to control reject sampling crash - Issue #343 by @fealho
• Verbose metadata validation - Issue #348 by @csala
• Missing the introduction of custom specification for hyperparameters in the TVAE model - Issue #344 by @imkhoa99 and @pvk-developer

0.8.0 - 2021-02-24

This version adds conditional sampling for tabular models by combining a reject-sampling strategy with the native conditional sampling capabilities from the gaussian copulas.

It also introduces several upgrades on the HMA1 algorithm that improve data quality and robustness in the multi-table scenarios by making changes in how the parameters of the child tables are aggregated on the parent tables, including a complete rework of how the correlation matrices are modeled and rebuild after sampling.

Issues resolved

• Fix probabilities contain NaN error - Issue #326 by @csala
• Conditional Sampling for tabular models - Issue #316 by @fealho and @csala
• HMA1: LinAlgError: SVD did not converge - Issue #240 by @csala

0.7.0 - 2021-01-27

This release introduces a few changes in the HMA1 relational algorithm to decrease modeling and sampling times, while also ensuring that correlations are properly kept across tables and also adding support for some relational schemas that were not supported before.

A few changes in constraints and tabular models also ensure that situations that produced errors before now work without errors.

Issues resolved

• Fix unique key generation - Issue #306 by @fealho
• Ensure tables that contain nothing but ids can be modeled - Issue #302 by @csala
• Metadata visualization improvements - Issue #301 by @csala
• Multi-parent re-model and re-sample issue - Issue #298 by @csala
• Support datetimes in GreaterThan constraint - Issue #266 by @rollervan
• Support for multiple foreign keys in one table - Issue #185 by @csala

0.6.1 - 2020-12-31

SDMetrics version is updated to include the new Time Series metrics, which have also been added to the API Reference and User Guides documentation. Additionally, a few code has been refactored to reduce external dependencies and a few minor bugs related to single table constraints have been fixed

Issues resolved

• Add timeseries metrics and user guides - Issue #289 by @csala
• Add functions to generate regex ids - Issue #288 by @csala
• Saving a fitted tabular model with UniqueCombinations constraint raises PicklingError - Issue #286 by @csala
• Constraints: handling_strategy='reject_sampling' causes 'ZeroDivisionError: division by zero' - Issue #285 by @csala

0.6.0 - 2020-12-22

This release updates to the latest CTGAN, RDT and SDMetrics libraries to introduce a new TVAE model, multiple new metrics for single table and multi table, and fixes issues in the re-creation of tabular models from a metadata dict.

Issues resolved

• Upgrade to SDMetrics v0.1.0 and add sdv.metrics module - Issue #281 by @csala
• Upgrade to CTGAN 0.3.0 and add TVAE model - Issue #278 by @fealho
• Add dtype_transformers to Table.from_dict - Issue #276 by @csala
• Fix Metadata from_dict behavior - Issue #275 by @csala

0.5.0 - 2020-11-25

This version updates the dependencies and makes a few internal changes in order to ensure that SDV works properly on Windows Systems, making this the first release to be officially supported on Windows.

Apart from this, some more internal changes have been made to solve a few minor issues from the older versions while also improving the processing speed when processing relational datasets with the default parameters.

API breaking changes

• The distribution argument of the GaussianCopula has been renamed to field_distributions.
• The HMA1 and SDV classes now use the categorical_fuzzy transformer by default instead of the one_hot_encoding one.

Issues resolved

• GaussianCopula: rename distribution argument to field_distributions - Issue #237 by @csala
• GaussianCopula: Improve error message if an invalid distribution name is passed - Issue #220 by csala
• Import urllib.request explicitly - Issue #227 by @csala
• TypeError: cannot astype a datetimelike from [datetime64[ns]] to [int32] - Issue #218 by @csala
• Change default categorical transformer to categorical_fuzzy in HMA1 - Issue #214 by @csala
• Integer categoricals being sampled as strings instead of integer values - Issue #194 by @csala

0.4.5 - 2020-10-17

In this version a new family of models for Synthetic Time Series Generation is introduced under the sdv.timeseries sub-package. The new family of models now includes a new class called PAR, which implements a Probabilistic AutoRegressive model.

This version also adds support for composite primary keys and regex based generation of id fields in tabular models and drops Python 3.5 support.

Issues resolved

• Drop python 3.5 support - Issue #204 by @csala
• Support composite primary keys in tabular models - Issue #207 by @csala
• Add the option to generate string id fields based on regex on tabular models - Issue #208 by @csala
• Synthetic Time Series - Issue #142 by @csala

0.4.4 - 2020-10-06

This version adds a new tabular model based on combining the CTGAN model with the reversible transformation applied in the GaussianCopula model that converts random variables with arbitrary distributions to new random variables with standard normal distribution.

The reversible transformation is handled by the GaussianCopulaTransformer recently added to RDT.

Issues resolved

• Add CopulaGAN Model - Issue #202 by @csala

0.4.3 - 2020-09-28

This release moves the models and algorithms related to generation of synthetic relational data to a new sdv.relational subpackage (Issue #198)

As part of the change, also the old sdv.models have been removed and now relational model is based on the recently introduced sdv.tabular models.

0.4.2 - 2020-09-19

In this release the sdv.evaluation module has been reworked to include 4 different metrics and in all cases return a normalized score between 0 and 1.

Included metrics are:

• cstest
• kstest
• logistic_detection
• svc_detection

0.4.1 - 2020-09-07

This release fixes a couple of minor issues and introduces an important rework of the User Guides section of the documentation.

Issues fixed

• Error Message: "make sure the Graphviz executables are on your systems' PATH" - Issue #182 by @csala
• Anonymization mappings leak - Issue #187 by @csala

0.4.0 - 2020-08-08

In this release SDV gets new documentation, new tutorials, improvements to the Tabular API and broader python and dependency support.

Complete list of changes:

• New Documentation site based on the pydata-sphinx-theme.
• New User Guides and Notebook tutorials.
• New Developer Guides section within the docs with details about the SDV architecture, the ecosystem libraries and how to extend and contribute to the project.
• Improved API for the Tabular models with focus on ease of use.
• Support for Python 3.8 and the newest versions of pandas, scipy and scikit-learn.
• New Slack Workspace for development discussions and community support.

0.3.6 - 2020-07-23

This release introduces a new concept of Constraints, which allow the user to define special relationships between columns that will not be handled via modeling.

This is done via a new sdv.constraints subpackage which defines some well-known pre-defined constraints, as well as a generic framework that allows the user to customize the constraints to their needs as much as necessary.

New Features

• Support for Constraints - Issue #169 by @csala

0.3.5 - 2020-07-09

This release introduces a new subpackage sdv.tabular with models designed specifically for single table modeling, while still providing all the usual conveniences from SDV, such as:

• Seamless multi-type support
• Missing data handling
• PII anonymization

Currently implemented models are:

• GaussianCopula: Multivariate distributions modeled using copula functions. This is stronger version, with more marginal distributions and options, than the one used to model multi-table datasets.
• CTGAN: GAN-based data synthesizer that can generate synthetic tabular data with high fidelity.

0.3.4 - 2020-07-04

New Features

• Support for Multiple Parents - Issue #162 by @csala
• Sample by default the same number of rows as in the original table - Issue #163 by @csala

General Improvements

• Add benchmark - Issue #165 by @csala

0.3.3 - 2020-06-26

General Improvements

• Use SDMetrics for evaluation - Issue #159 by @csala

0.3.2 - 2020-02-03

General Improvements

• Improve metadata visualization - Issue #151 by @csala @JDTheRipperPC

0.3.1 - 2020-01-22

New Features

• Add Metadata Validation - Issue #134 by @csala @JDTheRipperPC

• Add Metadata Visualization - Issue #135 by @JDTheRipperPC

General Improvements

• Add path to metadata JSON - Issue #143 by @JDTheRipperPC

• Use new Copulas and RDT versions - Issue #147 by @csala @JDTheRipperPC

0.3.0 - 2019-12-23

New Features

• Create sdv.models subpackage - Issue #141 by @JDTheRipperPC

0.2.2 - 2019-12-10

New Features

• Adapt evaluation to the different data types - Issue #128 by @csala @JDTheRipperPC

• Extend load_demo functionality to load other datasets - Issue #136 by @JDTheRipperPC

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.