ASyH release 1.0.0

The Anonymous Synthesizer for Health Data features now a full pipeline to
produce a synthetic dataset from input data choosing the best fitting SDV
synthesizer model, according to similarity of data distribution in synthetic vs
input data.

The SDV synthesizers are initialised with heuristic settings to prevent over and
underfitting.  In the case of the GaussianCopulaSynthesizer, the best
distribution to fit each numerical data column is determined and finally applied
to initialise the synthesizer to be scored.

The resulting model can be saved to pkl and reused to produce more synthetic
data an demand.

ASyH 1.0.0 is based on SDV-1.0.0.

Optional reporting will give an overview of data fit and correlations between
the synthetic and original input data.

README.md

@@ -1,4 +1,4 @@
-# ASyH - Anonymous Synthesizer for Health Data.
+# ASyH - Anonymous Synthesizer for Health Data (Release 1).
 
 ## Overview
 
@@ -96,3 +96,8 @@ To run the tests set the PYTHONPATH and execute pytest on the 'tests' folder:
 
         export PYTHONPATH=$(pwd)
         pytest tests
+
+## Release History
+| Release | Date |
+| ---: | ---: |
+|1.0.0| 25/05/2023|

pyproject.toml

@@ -3,7 +3,7 @@ requires = ['setuptools', 'wheel']
 
 [project]
 name = 'ASyH'
-version = '0.0.1'
+version = '1.0.0'
 dependencies = [
     'sdv == 1.0.0',
     'openpyxl == 3.1.1',