After a couple months hiatus in refactor land, we're excited to release the latest and greatest version of TPOT v0.5. For the past couple months, we worked on heavily refactoring TPOT's code base from a hacky research demo into a more elegant code base that will be easier to maintain in the long run. As an added bonus, TPOT now directly optimizes over and exports to scikit-learn Pipeline objects, so your auto-generated code should be much more readable.
Major changes in v0.5:
- Major refactor: Each operator is defined in a separate class file. Hooray for easier-to-maintain code!
- TPOT now exports directly to scikit-learn Pipelines instead of hacky code.
- Internal representation of individuals now uses scikit-learn pipelines.
- Parameters for each operator have been optimized so TPOT spends less time exploring useless parameters.
- We have removed pandas as a dependency and instead use numpy matrices to store the data.
- TPOT now uses k-fold cross-validation when evaluating pipelines, with a default k = 3. This k parameter can be tuned when creating a new TPOT instance.
- Improved scoring function support: Even though TPOT uses balanced accuracy by default, you can now have TPOT use any of the scoring functions that
cross_val_scoresupports. - Added the scikit-learn Normalizer preprocessor.
- Minor text fixes.