v0.8.0

• This release is the last one which supports Python 3.5. Next release will require Python >= 3.6.
• Trained models can now be transpiled into Haskell and Ruby 🎉
• Various improvements of the code generation runtime:
  • Introduced caching of the interpreter handler names.
  • A string buffer is now used to store generated code.
  • We moved away from using the string.Template.
• The numpy dependency is no longer required at runtime for the generated Python code.
• Improved model support:
  • Enabled multiclass support for XGBoost Random Forest models.
  • Added support of Boosted Random Forest models from the XGBoost package.
  • Added support of GLM models from the statsmodels package.
• Introduced fallback expressions for a variety of functions which rely on simpler language constructs. This should simplify implementation of new interpreters since the number of functions that must be provided by the standard library or by a developer of the given interpreter has been reduced. Note that fallback expressions are optional and can be overridden by a manually written implementation or a corresponding function from the standard library. Among functions for which fallback AST expressions have been introduced are: abs, tanh, sqrt, exp, sigmoid and softmax.

Kudos to @StrikerRUS who's responsible for all these amazing updates 💪