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0.4.1 - 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

0.4.0 - 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

0.3.0 - 2021-11-15

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

  • 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

0.2.1 - 2021-10-12

This release fixes a bug with how DeepEcho handles NaN values.

  • Handling NaN's bug - Issue #35 by @fealho

0.2.0 - 2021-02-24

Maintenance release to update dependencies and ensure compatibility with the rest of the SDV ecosystem libraries.

0.1.4 - 2020-10-16

Minor maintenance version to update dependencies and documentation, and also make the demo data loading function parse dates properly.

0.1.3 - 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.

0.1.2 - 2020-09-15

Add BasicGAN Model and additional benchmarking results.

0.1.1 - 2020-08-15

This release includes a few new features to make DeepEcho work on more types of datasets as well as to making it easier to add new datasets to the benchmarking framework.

  • Add segment_size and sequence_index arguments to fit method.
  • Add sequence_length as an optional argument to sample and sample_sequence methods.
  • Update the Dataset storage format to add sequence_index and versioning.
  • Separate the sequence assembling process in its own deepecho.sequences module.
  • Add function make_dataset to create a dataset from a dataframe and just a few column names.
  • Add notebook tutorial to show how to create a datasets and use them.

0.1.0 - 2020-08-11

First release.

Included Features:

  • PARModel
  • Demo dataset and tutorials
  • Benchmarking Framework
  • Support and instructions for benchmarking on a Kubernetes cluster.