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CHANGELOG.rst

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Release Notes

Version 0.4.1 (21.04.2023)

Hotfix for pyspark import in spark criteria.

Version 0.4.0 (21.04.2023)

  • Documentation and usage examples have been substantially reworked and updated.
  • The Designer class and design methods functionality is updated.
    • Empirical design now supports the choice of hypothesis alternative and group ratio parameter
    • Look of resulting tables with calculated parameters is unified for all design methods
    • Changed multiprocessing strategy for bootstrap criterion
  • The Tester class functionality is updated.
    • Spark data support for the Tester class is added. Independent t-test is available now
    • Bootstrap criterion can now return deterministic output using a random_seed parameter
    • Paired bootstrap criterion is now available
    • MHC now is optional and takes into account the number of passed metrics
    • first_errors parameter renamed to first_type_errors
  • pyspark package now is optional and could be installed using pip extras.
  • Fixed a set of bugs.

Version 0.3.0 (15.02.2023)

  • The Designer class and design methods functionality is updated.
    • Theoretical design now supports the choice of hypothesis alternative and group ratio parameter
    • These calculations now use Statsmodels solvers
    • Experimental parameters for binary data can now also be theoretically designed using both the asin variance-stabilizing transformation and the normal approximation
  • All preprocessor classes, except for the Preprocessor, have changed their api and have updated functionality
    • Preprocessing classes now use fit and transform methods to get transformation parameters and apply transformation on pandas tables
    • Fitted classes now can now be saved and loaded from json files
    • Table column names used when fitting class instances are now strictly fixed in instance attributes
  • The Preprocessor class is updated.
    • Added new transformation methods
    • The executed transformation pipeline can now be saved and loaded from a json file. This can be used to store and load the entire experimental data processing pipeline
    • The data handling methods of the class have changed some parameters to match the changes in the classes used
  • The IQRPreprocessor class now is available in ambrosia.preprocessing.
    • It can be used to remove outliers based on quartile and interquartile range estimates
  • The RobustPreprocessor class is updated.
    • It now supports different types of tails for removal: both, right or left
    • For each processed column, a separate alpha portion of the distribution can be passed.
  • The BoxCoxTransformer class now is available in ambrosia.preprocessing
    • It can be used for data distribution normalization.
  • The LogTransformer class now is available in ambrosia.preprocessing
    • It can be used to transform data for variance reduction.
  • The MLVarianceReducer class is updated.
    • Now it can store and load the selected ML model from a single specified path

Version 0.2.0 (22.11.2022)

Library name changed back to ambrosia. Naming conflict in PyPI has been resolved. 0.1.x versions are still available in PyPI under ambrozia name.

Version 0.1.2 (16.11.2022)

Hotfix for Ttest stat criterion absolute effect calculation. Url to main image deleted from docs.

Version 0.1.1 (04.10.2022)

Hotfix for library naming. Library temprorary renamed to ambrozia in PyPI repository due to hidden naming conflict.

Version 0.1.0 (03.10.2022)

First release of Ambrosia package:

  • Added Designer class for experiment parameters design
  • Added Spliiter class for A/B groups split
  • Added Tester class for experiment effect measurement
  • Added various classes for experiment data preprocessing
  • Added A/B testing tools with wide functionality