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@sonichi sonichi released this 24 Aug 01:41
· 662 commits to main since this release
v0.6.0
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Learn about vigilant mode.

In this release, we added support for time series forecasting task and NLP model fine tuning. Also, we have made a large number of feature & performance improvements.

  • data split by 'time' for time-ordered data, and by 'group' for grouped data.
  • support parallel trials and random search in AutoML.fit() API.
  • support warm-start in AutoML.fit() by using previously found start points.
  • support constraints on training/prediction time per model.
  • new optimization metric: ROC_AUC for multi-class classification, MAPE for time series forecasting.
  • utility functions for getting normalized confusion matrices and multi-class ROC or precision-recall curves.
  • automatically retrain models after search by default; options to disable retraining or enforce time limit.
  • CFO supports hierarchical search space and uses points_to_evaluate more effectively.
  • variation of CFO optimized for unordered categorical hps.
  • BlendSearch improved for better performance in parallel setting.
  • memory overhead optimization.
  • search space improvements for random forest and lightgbm.
  • make stacking ensemble work for categorical features.
  • python 3.9 support.
  • experimental support for automated fine-tuning of transformer models from huggingface.
  • experimental support for time series forecasting.
  • warnings to suggest increasing time budget, and warning to inform users there is no performance improvement for a long time.

Minor updates

  • make log file name optional.
  • notebook for time series forecasting.
  • notebook for using AutoML in sklearn pipeline.
  • bug fix when training_function returns a value.
  • support fixed random seeds to improve reproducibility.
  • code coverage improvement.
  • exclusive upper bounds for hyperparameter type randint and lograndint.
  • experimental features in BlendSearch.
  • documentation improvement.
  • bug fixes for multiple logged metrics in cv.
  • adjust epsilon when time per trial is very fast.

Contributors

Contributors

ekzhu, stepthom, and 24 other contributors
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