microsoft/FLAML v0.6.0 on GitHub

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