Releases: sdv-dev/DeepEcho
v0.6.0 - 2024-04-10
This release adds support for Python 3.12!
Maintenance
- Support Python 3.12 - Issue #85 by @fealho
- Transition from using setup.py to pyproject.toml to specify project metadata - Issue #86 by @R-Palazzo
- Remove bumpversion and use bump-my-version - Issue #87 by @R-Palazzo
- Add dependency checker - Issue #96 by @lajohn4747
- Add bandit workflow - Issue #98 by @R-Palazzo
Bugs Fixed
- Fix make check candidate - Issue #91 by @R-Palazzo
- Fix minimum version workflow when pointing to github branch - Issue #99 by @R-Palazzo
v0.5.0 - 2023-11-13
This release updates the PAR's model progress bar to show loss values and time elapsed (verbose option).
New Features
- Update progress bar for PAR fitting - Issue #80 by @frances-h
v0.4.2 - 2023-07-25
This release drops support for Python 3.7 and adds support for Python 3.11.
Maintenance
- Add support for Python 3.11 - Issue #74 by @fealho
- Drop support for Python 3.7 - Issue #75 by @R-Palazzo
v0.4.1 - 2023-05-02
This release adds support for Pandas 2.0 and PyTorch 2.0!
Maintenance
- Remove upper bound for pandas - Issue #69 by @frances-h
- Upgrade to Torch 2.0 - Issue #70 by @frances-h
v0.4.0 - 2023-01-10
This release adds support for python 3.10 and 3.11. It also drops support for python 3.6.
Maintenance
- Support Python 3.10 and 3.11 - Issue #63 by @pvk-developer
- DeepEcho Package Maintenance Updates - Issue #62 by @pvk-developer
v0.3.0 - 2021-11-15
This release adds support for Python 3.9 and updates dependencies to ensure compatibility with the rest of the SDV ecosystem.
v0.2.1 - 2021-10-12
v0.2.0 - 2021-02-24
Maintenance release to update dependencies and ensure compatibility with the rest
of the SDV ecosystem libraries.
v0.1.4 - 2020-10-16
Minor maintenance version to update dependencies and documentation, and
also make the demo data loading function parse dates properly.
v0.1.3 (2020-10-16)
This version includes several minor improvements to the PAR model and the
way the sequences are generated:
- Sequences can now be generated without dropping the sequence index.
- The PAR model learns the min and max length of the sequence from the input data.
- NaN values are properly supported for both categorical and numerical columns.
- NaN values are generated for numerical columns only if there were NaNs in the input data.
- Constant columns can now be modeled.