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Releases: sdv-dev/DeepEcho

v0.6.0 - 2024-04-10

11 Apr 02:19
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This release adds support for Python 3.12!

Maintenance

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

13 Nov 18:26
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This release updates the PAR's model progress bar to show loss values and time elapsed (verbose option).

New Features

v0.4.2 - 2023-07-25

27 Jul 18:12
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This release drops support for Python 3.7 and adds support for Python 3.11.

Maintenance

v0.4.1 - 2023-05-02

02 May 18:09
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This release adds support for Pandas 2.0 and PyTorch 2.0!

Maintenance

v0.4.0 - 2023-01-10

10 Jan 23:20
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This release adds support for python 3.10 and 3.11. It also drops support for python 3.6.

Maintenance

v0.3.0 - 2021-11-15

15 Nov 15:49
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This release adds support for Python 3.9 and updates dependencies to ensure compatibility with the rest of the SDV ecosystem.

  • Add support for Python 3.9 - Issue #41 by @fealho
  • Add pip check to CI workflows internal improvements - Issue #39 by @pvk-developer
  • Add support for pylint>2.7.2 housekeeping - Issue #33 by @fealho
  • Add support for torch>=1.8 housekeeping - Issue #32 by @fealho

v0.2.1 - 2021-10-12

12 Oct 17:43
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This release fixes a bug with how DeepEcho handles NaN values.

v0.2.0 - 2021-02-24

24 Feb 20:25
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Maintenance release to update dependencies and ensure compatibility with the rest
of the SDV ecosystem libraries.

v0.1.4 - 2020-10-16

27 Jan 21:54
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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)

16 Oct 17:20
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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.