%global _empty_manifest_terminate_build 0
Name: python-ctgan
Version: 0.7.1
Release: 1
Summary: Create tabular synthetic data using a conditional GAN
License: BSL-1.1
URL: https://github.com/sdv-dev/CTGAN
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/63/a6/6ec86f10acc50e5c94aa19aae591b364ee6cfa5f1496b3914cf3f53ad862/ctgan-0.7.1.tar.gz
BuildArch: noarch
Requires: python3-packaging
Requires: python3-rdt
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-torch
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-scikit-learn
Requires: python3-torch
Requires: python3-pip
Requires: python3-bumpversion
Requires: python3-watchdog
Requires: python3-flake8
Requires: python3-isort
Requires: python3-dlint
Requires: python3-flake8-debugger
Requires: python3-flake8-mock
Requires: python3-flake8-mutable
Requires: python3-flake8-absolute-import
Requires: python3-flake8-multiline-containers
Requires: python3-flake8-print
Requires: python3-flake8-quotes
Requires: python3-flake8-fixme
Requires: python3-flake8-expression-complexity
Requires: python3-flake8-eradicate
Requires: python3-flake8-builtins
Requires: python3-flake8-variables-names
Requires: python3-pandas-vet
Requires: python3-flake8-comprehensions
Requires: python3-flake8-docstrings
Requires: python3-flake8-sfs
Requires: python3-flake8-pytest-style
Requires: python3-autoflake
Requires: python3-autopep8
Requires: python3-twine
Requires: python3-wheel
Requires: python3-coverage
Requires: python3-tox
Requires: python3-invoke
Requires: python3-pytest
Requires: python3-pytest-rerunfailures
Requires: python3-pytest-cov
Requires: python3-rundoc
Requires: python3-pytest
Requires: python3-pytest-rerunfailures
Requires: python3-pytest-cov
Requires: python3-rundoc
%description
[The Synthetic Data Vault Project](https://sdv.dev) was first created at MIT's [Data to AI Lab](
https://dai.lids.mit.edu/) in 2016. After 4 years of research and traction with enterprise, we
created [DataCebo](https://datacebo.com) 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](https://sdv.dev/SDV/getting_started/install.html) -- a fully
integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries
for specific needs.
# History
## v0.7.0 - 2023-01-20
This release adds support for python 3.10 and drops support for python 3.6. It also fixes a couple of the most common warnings that were surfacing.
### New Features
* Support Python 3.10 and 3.11 - Issue [#259](https://github.com/sdv-dev/CTGAN/issues/259) by @pvk-developer
### Bugs Fixed
* Fix SettingWithCopyWarning (may be leading to a numerical calculation bug) - Issue [#215](https://github.com/sdv-dev/CTGAN/issues/215) by @amontanez24
* FutureWarning in data_transformer with pandas 1.5.0 - Issue [#246](https://github.com/sdv-dev/CTGAN/issues/246) by @amontanez24
### Maintenance
* CTGAN Package Maintenance Updates - Issue [#257](https://github.com/sdv-dev/CTGAN/issues/257) by @amontanez24
## v0.6.0 - 2022-10-07
This release renames the models in CTGAN. `CTGANSynthesizer` is now called `CTGAN` and `TVAESynthesizer` is now called `TVAE`.
### New Features
* Rename synthesizers - Issue [#243](https://github.com/sdv-dev/CTGAN/issues/243) by @amontanez24
## v0.5.2 - 2022-08-18
This release updates CTGAN to use the latest version of RDT. It also includes performance and robustness updates to the data transformer.
### Issues closed
* Bump rdt version - Issue [#242](https://github.com/sdv-dev/CTGAN/issues/242) by @katxiao
* Single thread data transform is slow for huge table - Issue [#151](https://github.com/sdv-dev/CTGAN/issues/151) by @mfhbree
* Fix RDT api - Issue [#232](https://github.com/sdv-dev/CTGAN/issues/232) by @pvk-developer
* Update macos to use latest version. - Issue [#237](https://github.com/sdv-dev/CTGAN/issues/237) by @pvk-developer
* Update the RDT version to 1.0 - Issue [#224](https://github.com/sdv-dev/CTGAN/issues/224) by @pvk-developer
* Update slack invite link. - Issue [#222](https://github.com/sdv-dev/CTGAN/issues/222) by @pvk-developer
* robustness fix, when data have less rows than the default number of cl… - Issue [#211](https://github.com/sdv-dev/CTGAN/issues/211) by @Deathn0t
## v0.5.1 - 2022-02-25
This release fixes a bug with the decoder instantiation, and also allows users to set a random state for the model
fitting and sampling.
### Issues closed
* Update self.decoder with correct variable name - Issue [#203](https://github.com/sdv-dev/CTGAN/issues/203) by @tejuafonja
* Add random state - Issue [#204](https://github.com/sdv-dev/CTGAN/issues/204) by @katxiao
## v0.5.0 - 2021-11-18
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](https://github.com/sdv-dev/RDT/releases/tag/v0.6.1)
release.
### Issues closed
* Add support for Python 3.9 - Issue [#177](https://github.com/sdv-dev/CTGAN/issues/177) by @pvk-developer
* Add pip check to CI workflows - Issue [#174](https://github.com/sdv-dev/CTGAN/issues/174) by @pvk-developer
* Typo in `CTGAN` code - Issue [#158](https://github.com/sdv-dev/CTGAN/issues/158) by @ori-katz100 and @fealho
## v0.4.3 - 2021-07-12
Dependency upgrades to ensure compatibility with the rest of the SDV ecosystem.
## v0.4.2 - 2021-04-27
In this release, the way in which the loss function of the TVAE model was computed has been fixed.
In addition, the default value of the `discriminator_decay` has been changed to a more optimal
value. Also some improvements to the tests were added.
### Issues closed
* `TVAE`: loss function - Issue [#143](https://github.com/sdv-dev/CTGAN/issues/143) by @fealho and @DingfanChen
* Set `discriminator_decay` to `1e-6` - Pull request [#145](https://github.com/sdv-dev/CTGAN/pull/145/) by @fealho
* Adds unit tests - Pull requests [#140](https://github.com/sdv-dev/CTGAN/pull/140) by @fealho
## v0.4.1 - 2021-03-30
This release exposes all the hyperparameters which the user may find useful for both `CTGAN`
and `TVAE`. Also `TVAE` can now be fitted on datasets that are shorter than the batch
size and drops the last batch only if the data size is not divisible by the batch size.
### Issues closed
* `TVAE`: Adapt `batch_size` to data size - Issue [#135](https://github.com/sdv-dev/CTGAN/issues/135) by @fealho and @csala
* `ValueError` from `validate_discre_columns` with `uniqueCombinationConstraint` - Issue [133](https://github.com/sdv-dev/CTGAN/issues/133) by @fealho and @MLjungg
## v0.4.0 - 2021-02-24
Maintenance relese to upgrade dependencies to ensure compatibility with the rest
of the SDV libraries.
Also add a validation on the CTGAN `condition_column` and `condition_value` inputs.
### Improvements
* Validate condition_column and condition_value - Issue [#124](https://github.com/sdv-dev/CTGAN/issues/124) by @fealho
## v0.3.1 - 2021-01-27
### Improvements
* Check discrete_columns valid before fitting - [Issue #35](https://github.com/sdv-dev/CTGAN/issues/35) by @fealho
## Bugs fixed
* ValueError: max() arg is an empty sequence - [Issue #115](https://github.com/sdv-dev/CTGAN/issues/115) by @fealho
## v0.3.0 - 2020-12-18
In this release we add a new TVAE model which was presented in the original CTGAN paper.
It also exposes more hyperparameters and moves epochs and log_frequency from fit to the constructor.
A new verbose argument has been added to optionally disable unnecessary printing, and a new hyperparameter
called `discriminator_steps` has been added to CTGAN to control the number of optimization steps performed
in the discriminator for each generator epoch.
The code has also been reorganized and cleaned up for better readability and interpretability.
Special thanks to @Baukebrenninkmeijer @fealho @leix28 @csala for the contributions!
### Improvements
* Add TVAE - [Issue #111](https://github.com/sdv-dev/CTGAN/issues/111) by @fealho
* Move `log_frequency` to `__init__` - [Issue #102](https://github.com/sdv-dev/CTGAN/issues/102) by @fealho
* Add discriminator steps hyperparameter - [Issue #101](https://github.com/sdv-dev/CTGAN/issues/101) by @Baukebrenninkmeijer
* Code cleanup / Expose hyperparameters - [Issue #59](https://github.com/sdv-dev/CTGAN/issues/59) by @fealho and @leix28
* Publish to conda repo - [Issue #54](https://github.com/sdv-dev/CTGAN/issues/54) by @fealho
### Bugs fixed
* Fixed NaN != NaN counting bug. - [Issue #100](https://github.com/sdv-dev/CTGAN/issues/100) by @fealho
* Update dependencies and testing - [Issue #90](https://github.com/sdv-dev/CTGAN/issues/90) by @csala
## v0.2.2 - 2020-11-13
In this release we introduce several minor improvements to make CTGAN more versatile and
propertly support new types of data, such as categorical NaN values, as well as conditional
sampling and features to save and load models.
Additionally, the dependency ranges and python versions have been updated to support up
to date runtimes.
Many thanks @fealho @leix28 @csala @oregonpillow and @lurosenb for working on making this release possible!
### Improvements
* Drop Python 3.5 support - [Issue #79](https://github.com/sdv-dev/CTGAN/issues/79) by @fealho
* Support NaN values in categorical variables - [Issue #78](https://github.com/sdv-dev/CTGAN/issues/78) by @fealho
* Sample synthetic data conditioning on a discrete column - [Issue #69](https://github.com/sdv-dev/CTGAN/issues/69) by @leix28
* Support recent versions of pandas - [Issue #57](https://github.com/sdv-dev/CTGAN/issues/57) by @csala
* Easy solution for restoring original dtypes - [Issue #26](https://github.com/sdv-dev/CTGAN/issues/26) by @oregonpillow
### Bugs fixed
* Loss to nan - [Issue #73](https://github.com/sdv-dev/CTGAN/issues/73) by @fealho
* Swapped the sklearn utils testing import statement - [Issue #53](https://github.com/sdv-dev/CTGAN/issues/53) by @lurosenb
## v0.2.1 - 2020-01-27
Minor version including changes to ensure the logs are properly printed and
the option to disable the log transformation to the discrete column frequencies.
Special thanks to @kevinykuo for the contributions!
### Issues Resolved:
* Option to sample from true data frequency instead of logged frequency - [Issue #16](https://github.com/sdv-dev/CTGAN/issues/16) by @kevinykuo
* Flush stdout buffer for epoch updates - [Issue #14](https://github.com/sdv-dev/CTGAN/issues/14) by @kevinykuo
## v0.2.0 - 2019-12-18
Reorganization of the project structure with a new Python API, new Command Line Interface
and increased data format support.
### Issues Resolved:
* Reorganize the project structure - [Issue #10](https://github.com/sdv-dev/CTGAN/issues/10) by @csala
* Move epochs to the fit method - [Issue #5](https://github.com/sdv-dev/CTGAN/issues/5) by @csala
## v0.1.0 - 2019-11-07
First Release - NeurIPS 2019 Version.
%package -n python3-ctgan
Summary: Create tabular synthetic data using a conditional GAN
Provides: python-ctgan
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-ctgan
[The Synthetic Data Vault Project](https://sdv.dev) was first created at MIT's [Data to AI Lab](
https://dai.lids.mit.edu/) in 2016. After 4 years of research and traction with enterprise, we
created [DataCebo](https://datacebo.com) 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](https://sdv.dev/SDV/getting_started/install.html) -- a fully
integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries
for specific needs.
# History
## v0.7.0 - 2023-01-20
This release adds support for python 3.10 and drops support for python 3.6. It also fixes a couple of the most common warnings that were surfacing.
### New Features
* Support Python 3.10 and 3.11 - Issue [#259](https://github.com/sdv-dev/CTGAN/issues/259) by @pvk-developer
### Bugs Fixed
* Fix SettingWithCopyWarning (may be leading to a numerical calculation bug) - Issue [#215](https://github.com/sdv-dev/CTGAN/issues/215) by @amontanez24
* FutureWarning in data_transformer with pandas 1.5.0 - Issue [#246](https://github.com/sdv-dev/CTGAN/issues/246) by @amontanez24
### Maintenance
* CTGAN Package Maintenance Updates - Issue [#257](https://github.com/sdv-dev/CTGAN/issues/257) by @amontanez24
## v0.6.0 - 2022-10-07
This release renames the models in CTGAN. `CTGANSynthesizer` is now called `CTGAN` and `TVAESynthesizer` is now called `TVAE`.
### New Features
* Rename synthesizers - Issue [#243](https://github.com/sdv-dev/CTGAN/issues/243) by @amontanez24
## v0.5.2 - 2022-08-18
This release updates CTGAN to use the latest version of RDT. It also includes performance and robustness updates to the data transformer.
### Issues closed
* Bump rdt version - Issue [#242](https://github.com/sdv-dev/CTGAN/issues/242) by @katxiao
* Single thread data transform is slow for huge table - Issue [#151](https://github.com/sdv-dev/CTGAN/issues/151) by @mfhbree
* Fix RDT api - Issue [#232](https://github.com/sdv-dev/CTGAN/issues/232) by @pvk-developer
* Update macos to use latest version. - Issue [#237](https://github.com/sdv-dev/CTGAN/issues/237) by @pvk-developer
* Update the RDT version to 1.0 - Issue [#224](https://github.com/sdv-dev/CTGAN/issues/224) by @pvk-developer
* Update slack invite link. - Issue [#222](https://github.com/sdv-dev/CTGAN/issues/222) by @pvk-developer
* robustness fix, when data have less rows than the default number of cl… - Issue [#211](https://github.com/sdv-dev/CTGAN/issues/211) by @Deathn0t
## v0.5.1 - 2022-02-25
This release fixes a bug with the decoder instantiation, and also allows users to set a random state for the model
fitting and sampling.
### Issues closed
* Update self.decoder with correct variable name - Issue [#203](https://github.com/sdv-dev/CTGAN/issues/203) by @tejuafonja
* Add random state - Issue [#204](https://github.com/sdv-dev/CTGAN/issues/204) by @katxiao
## v0.5.0 - 2021-11-18
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](https://github.com/sdv-dev/RDT/releases/tag/v0.6.1)
release.
### Issues closed
* Add support for Python 3.9 - Issue [#177](https://github.com/sdv-dev/CTGAN/issues/177) by @pvk-developer
* Add pip check to CI workflows - Issue [#174](https://github.com/sdv-dev/CTGAN/issues/174) by @pvk-developer
* Typo in `CTGAN` code - Issue [#158](https://github.com/sdv-dev/CTGAN/issues/158) by @ori-katz100 and @fealho
## v0.4.3 - 2021-07-12
Dependency upgrades to ensure compatibility with the rest of the SDV ecosystem.
## v0.4.2 - 2021-04-27
In this release, the way in which the loss function of the TVAE model was computed has been fixed.
In addition, the default value of the `discriminator_decay` has been changed to a more optimal
value. Also some improvements to the tests were added.
### Issues closed
* `TVAE`: loss function - Issue [#143](https://github.com/sdv-dev/CTGAN/issues/143) by @fealho and @DingfanChen
* Set `discriminator_decay` to `1e-6` - Pull request [#145](https://github.com/sdv-dev/CTGAN/pull/145/) by @fealho
* Adds unit tests - Pull requests [#140](https://github.com/sdv-dev/CTGAN/pull/140) by @fealho
## v0.4.1 - 2021-03-30
This release exposes all the hyperparameters which the user may find useful for both `CTGAN`
and `TVAE`. Also `TVAE` can now be fitted on datasets that are shorter than the batch
size and drops the last batch only if the data size is not divisible by the batch size.
### Issues closed
* `TVAE`: Adapt `batch_size` to data size - Issue [#135](https://github.com/sdv-dev/CTGAN/issues/135) by @fealho and @csala
* `ValueError` from `validate_discre_columns` with `uniqueCombinationConstraint` - Issue [133](https://github.com/sdv-dev/CTGAN/issues/133) by @fealho and @MLjungg
## v0.4.0 - 2021-02-24
Maintenance relese to upgrade dependencies to ensure compatibility with the rest
of the SDV libraries.
Also add a validation on the CTGAN `condition_column` and `condition_value` inputs.
### Improvements
* Validate condition_column and condition_value - Issue [#124](https://github.com/sdv-dev/CTGAN/issues/124) by @fealho
## v0.3.1 - 2021-01-27
### Improvements
* Check discrete_columns valid before fitting - [Issue #35](https://github.com/sdv-dev/CTGAN/issues/35) by @fealho
## Bugs fixed
* ValueError: max() arg is an empty sequence - [Issue #115](https://github.com/sdv-dev/CTGAN/issues/115) by @fealho
## v0.3.0 - 2020-12-18
In this release we add a new TVAE model which was presented in the original CTGAN paper.
It also exposes more hyperparameters and moves epochs and log_frequency from fit to the constructor.
A new verbose argument has been added to optionally disable unnecessary printing, and a new hyperparameter
called `discriminator_steps` has been added to CTGAN to control the number of optimization steps performed
in the discriminator for each generator epoch.
The code has also been reorganized and cleaned up for better readability and interpretability.
Special thanks to @Baukebrenninkmeijer @fealho @leix28 @csala for the contributions!
### Improvements
* Add TVAE - [Issue #111](https://github.com/sdv-dev/CTGAN/issues/111) by @fealho
* Move `log_frequency` to `__init__` - [Issue #102](https://github.com/sdv-dev/CTGAN/issues/102) by @fealho
* Add discriminator steps hyperparameter - [Issue #101](https://github.com/sdv-dev/CTGAN/issues/101) by @Baukebrenninkmeijer
* Code cleanup / Expose hyperparameters - [Issue #59](https://github.com/sdv-dev/CTGAN/issues/59) by @fealho and @leix28
* Publish to conda repo - [Issue #54](https://github.com/sdv-dev/CTGAN/issues/54) by @fealho
### Bugs fixed
* Fixed NaN != NaN counting bug. - [Issue #100](https://github.com/sdv-dev/CTGAN/issues/100) by @fealho
* Update dependencies and testing - [Issue #90](https://github.com/sdv-dev/CTGAN/issues/90) by @csala
## v0.2.2 - 2020-11-13
In this release we introduce several minor improvements to make CTGAN more versatile and
propertly support new types of data, such as categorical NaN values, as well as conditional
sampling and features to save and load models.
Additionally, the dependency ranges and python versions have been updated to support up
to date runtimes.
Many thanks @fealho @leix28 @csala @oregonpillow and @lurosenb for working on making this release possible!
### Improvements
* Drop Python 3.5 support - [Issue #79](https://github.com/sdv-dev/CTGAN/issues/79) by @fealho
* Support NaN values in categorical variables - [Issue #78](https://github.com/sdv-dev/CTGAN/issues/78) by @fealho
* Sample synthetic data conditioning on a discrete column - [Issue #69](https://github.com/sdv-dev/CTGAN/issues/69) by @leix28
* Support recent versions of pandas - [Issue #57](https://github.com/sdv-dev/CTGAN/issues/57) by @csala
* Easy solution for restoring original dtypes - [Issue #26](https://github.com/sdv-dev/CTGAN/issues/26) by @oregonpillow
### Bugs fixed
* Loss to nan - [Issue #73](https://github.com/sdv-dev/CTGAN/issues/73) by @fealho
* Swapped the sklearn utils testing import statement - [Issue #53](https://github.com/sdv-dev/CTGAN/issues/53) by @lurosenb
## v0.2.1 - 2020-01-27
Minor version including changes to ensure the logs are properly printed and
the option to disable the log transformation to the discrete column frequencies.
Special thanks to @kevinykuo for the contributions!
### Issues Resolved:
* Option to sample from true data frequency instead of logged frequency - [Issue #16](https://github.com/sdv-dev/CTGAN/issues/16) by @kevinykuo
* Flush stdout buffer for epoch updates - [Issue #14](https://github.com/sdv-dev/CTGAN/issues/14) by @kevinykuo
## v0.2.0 - 2019-12-18
Reorganization of the project structure with a new Python API, new Command Line Interface
and increased data format support.
### Issues Resolved:
* Reorganize the project structure - [Issue #10](https://github.com/sdv-dev/CTGAN/issues/10) by @csala
* Move epochs to the fit method - [Issue #5](https://github.com/sdv-dev/CTGAN/issues/5) by @csala
## v0.1.0 - 2019-11-07
First Release - NeurIPS 2019 Version.
%package help
Summary: Development documents and examples for ctgan
Provides: python3-ctgan-doc
%description help
[The Synthetic Data Vault Project](https://sdv.dev) was first created at MIT's [Data to AI Lab](
https://dai.lids.mit.edu/) in 2016. After 4 years of research and traction with enterprise, we
created [DataCebo](https://datacebo.com) 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](https://sdv.dev/SDV/getting_started/install.html) -- a fully
integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries
for specific needs.
# History
## v0.7.0 - 2023-01-20
This release adds support for python 3.10 and drops support for python 3.6. It also fixes a couple of the most common warnings that were surfacing.
### New Features
* Support Python 3.10 and 3.11 - Issue [#259](https://github.com/sdv-dev/CTGAN/issues/259) by @pvk-developer
### Bugs Fixed
* Fix SettingWithCopyWarning (may be leading to a numerical calculation bug) - Issue [#215](https://github.com/sdv-dev/CTGAN/issues/215) by @amontanez24
* FutureWarning in data_transformer with pandas 1.5.0 - Issue [#246](https://github.com/sdv-dev/CTGAN/issues/246) by @amontanez24
### Maintenance
* CTGAN Package Maintenance Updates - Issue [#257](https://github.com/sdv-dev/CTGAN/issues/257) by @amontanez24
## v0.6.0 - 2022-10-07
This release renames the models in CTGAN. `CTGANSynthesizer` is now called `CTGAN` and `TVAESynthesizer` is now called `TVAE`.
### New Features
* Rename synthesizers - Issue [#243](https://github.com/sdv-dev/CTGAN/issues/243) by @amontanez24
## v0.5.2 - 2022-08-18
This release updates CTGAN to use the latest version of RDT. It also includes performance and robustness updates to the data transformer.
### Issues closed
* Bump rdt version - Issue [#242](https://github.com/sdv-dev/CTGAN/issues/242) by @katxiao
* Single thread data transform is slow for huge table - Issue [#151](https://github.com/sdv-dev/CTGAN/issues/151) by @mfhbree
* Fix RDT api - Issue [#232](https://github.com/sdv-dev/CTGAN/issues/232) by @pvk-developer
* Update macos to use latest version. - Issue [#237](https://github.com/sdv-dev/CTGAN/issues/237) by @pvk-developer
* Update the RDT version to 1.0 - Issue [#224](https://github.com/sdv-dev/CTGAN/issues/224) by @pvk-developer
* Update slack invite link. - Issue [#222](https://github.com/sdv-dev/CTGAN/issues/222) by @pvk-developer
* robustness fix, when data have less rows than the default number of cl… - Issue [#211](https://github.com/sdv-dev/CTGAN/issues/211) by @Deathn0t
## v0.5.1 - 2022-02-25
This release fixes a bug with the decoder instantiation, and also allows users to set a random state for the model
fitting and sampling.
### Issues closed
* Update self.decoder with correct variable name - Issue [#203](https://github.com/sdv-dev/CTGAN/issues/203) by @tejuafonja
* Add random state - Issue [#204](https://github.com/sdv-dev/CTGAN/issues/204) by @katxiao
## v0.5.0 - 2021-11-18
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](https://github.com/sdv-dev/RDT/releases/tag/v0.6.1)
release.
### Issues closed
* Add support for Python 3.9 - Issue [#177](https://github.com/sdv-dev/CTGAN/issues/177) by @pvk-developer
* Add pip check to CI workflows - Issue [#174](https://github.com/sdv-dev/CTGAN/issues/174) by @pvk-developer
* Typo in `CTGAN` code - Issue [#158](https://github.com/sdv-dev/CTGAN/issues/158) by @ori-katz100 and @fealho
## v0.4.3 - 2021-07-12
Dependency upgrades to ensure compatibility with the rest of the SDV ecosystem.
## v0.4.2 - 2021-04-27
In this release, the way in which the loss function of the TVAE model was computed has been fixed.
In addition, the default value of the `discriminator_decay` has been changed to a more optimal
value. Also some improvements to the tests were added.
### Issues closed
* `TVAE`: loss function - Issue [#143](https://github.com/sdv-dev/CTGAN/issues/143) by @fealho and @DingfanChen
* Set `discriminator_decay` to `1e-6` - Pull request [#145](https://github.com/sdv-dev/CTGAN/pull/145/) by @fealho
* Adds unit tests - Pull requests [#140](https://github.com/sdv-dev/CTGAN/pull/140) by @fealho
## v0.4.1 - 2021-03-30
This release exposes all the hyperparameters which the user may find useful for both `CTGAN`
and `TVAE`. Also `TVAE` can now be fitted on datasets that are shorter than the batch
size and drops the last batch only if the data size is not divisible by the batch size.
### Issues closed
* `TVAE`: Adapt `batch_size` to data size - Issue [#135](https://github.com/sdv-dev/CTGAN/issues/135) by @fealho and @csala
* `ValueError` from `validate_discre_columns` with `uniqueCombinationConstraint` - Issue [133](https://github.com/sdv-dev/CTGAN/issues/133) by @fealho and @MLjungg
## v0.4.0 - 2021-02-24
Maintenance relese to upgrade dependencies to ensure compatibility with the rest
of the SDV libraries.
Also add a validation on the CTGAN `condition_column` and `condition_value` inputs.
### Improvements
* Validate condition_column and condition_value - Issue [#124](https://github.com/sdv-dev/CTGAN/issues/124) by @fealho
## v0.3.1 - 2021-01-27
### Improvements
* Check discrete_columns valid before fitting - [Issue #35](https://github.com/sdv-dev/CTGAN/issues/35) by @fealho
## Bugs fixed
* ValueError: max() arg is an empty sequence - [Issue #115](https://github.com/sdv-dev/CTGAN/issues/115) by @fealho
## v0.3.0 - 2020-12-18
In this release we add a new TVAE model which was presented in the original CTGAN paper.
It also exposes more hyperparameters and moves epochs and log_frequency from fit to the constructor.
A new verbose argument has been added to optionally disable unnecessary printing, and a new hyperparameter
called `discriminator_steps` has been added to CTGAN to control the number of optimization steps performed
in the discriminator for each generator epoch.
The code has also been reorganized and cleaned up for better readability and interpretability.
Special thanks to @Baukebrenninkmeijer @fealho @leix28 @csala for the contributions!
### Improvements
* Add TVAE - [Issue #111](https://github.com/sdv-dev/CTGAN/issues/111) by @fealho
* Move `log_frequency` to `__init__` - [Issue #102](https://github.com/sdv-dev/CTGAN/issues/102) by @fealho
* Add discriminator steps hyperparameter - [Issue #101](https://github.com/sdv-dev/CTGAN/issues/101) by @Baukebrenninkmeijer
* Code cleanup / Expose hyperparameters - [Issue #59](https://github.com/sdv-dev/CTGAN/issues/59) by @fealho and @leix28
* Publish to conda repo - [Issue #54](https://github.com/sdv-dev/CTGAN/issues/54) by @fealho
### Bugs fixed
* Fixed NaN != NaN counting bug. - [Issue #100](https://github.com/sdv-dev/CTGAN/issues/100) by @fealho
* Update dependencies and testing - [Issue #90](https://github.com/sdv-dev/CTGAN/issues/90) by @csala
## v0.2.2 - 2020-11-13
In this release we introduce several minor improvements to make CTGAN more versatile and
propertly support new types of data, such as categorical NaN values, as well as conditional
sampling and features to save and load models.
Additionally, the dependency ranges and python versions have been updated to support up
to date runtimes.
Many thanks @fealho @leix28 @csala @oregonpillow and @lurosenb for working on making this release possible!
### Improvements
* Drop Python 3.5 support - [Issue #79](https://github.com/sdv-dev/CTGAN/issues/79) by @fealho
* Support NaN values in categorical variables - [Issue #78](https://github.com/sdv-dev/CTGAN/issues/78) by @fealho
* Sample synthetic data conditioning on a discrete column - [Issue #69](https://github.com/sdv-dev/CTGAN/issues/69) by @leix28
* Support recent versions of pandas - [Issue #57](https://github.com/sdv-dev/CTGAN/issues/57) by @csala
* Easy solution for restoring original dtypes - [Issue #26](https://github.com/sdv-dev/CTGAN/issues/26) by @oregonpillow
### Bugs fixed
* Loss to nan - [Issue #73](https://github.com/sdv-dev/CTGAN/issues/73) by @fealho
* Swapped the sklearn utils testing import statement - [Issue #53](https://github.com/sdv-dev/CTGAN/issues/53) by @lurosenb
## v0.2.1 - 2020-01-27
Minor version including changes to ensure the logs are properly printed and
the option to disable the log transformation to the discrete column frequencies.
Special thanks to @kevinykuo for the contributions!
### Issues Resolved:
* Option to sample from true data frequency instead of logged frequency - [Issue #16](https://github.com/sdv-dev/CTGAN/issues/16) by @kevinykuo
* Flush stdout buffer for epoch updates - [Issue #14](https://github.com/sdv-dev/CTGAN/issues/14) by @kevinykuo
## v0.2.0 - 2019-12-18
Reorganization of the project structure with a new Python API, new Command Line Interface
and increased data format support.
### Issues Resolved:
* Reorganize the project structure - [Issue #10](https://github.com/sdv-dev/CTGAN/issues/10) by @csala
* Move epochs to the fit method - [Issue #5](https://github.com/sdv-dev/CTGAN/issues/5) by @csala
## v0.1.0 - 2019-11-07
First Release - NeurIPS 2019 Version.
%prep
%autosetup -n ctgan-0.7.1
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-ctgan -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot - 0.7.1-1
- Package Spec generated
