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0.6.9

What's Changed
* no search when max_iter < 2 by sonichi in https://github.com/microsoft/FLAML/pull/253
* fix bug in subspace identification by sonichi in https://github.com/microsoft/FLAML/pull/257
* Add documentation for warm-start by qingyun-wu in https://github.com/microsoft/FLAML/pull/255


**Full Changelog**: https://github.com/microsoft/FLAML/compare/v0.6.8...v0.6.9

0.6.8

What's Changed
* fix the bug in hierarchical search space (248); make dependency on lgbm and xgboost optional (252) by sonichi in https://github.com/microsoft/FLAML/pull/250
* Add conda forge badge by MichalChromcak in https://github.com/microsoft/FLAML/pull/251

New Contributors
* MichalChromcak made their first contribution in https://github.com/microsoft/FLAML/pull/251

**Full Changelog**: https://github.com/microsoft/FLAML/compare/v0.6.7...v0.6.8

0.6.7

What's Changed
* remove big objects after fit by sonichi in https://github.com/microsoft/FLAML/pull/176
* remove catboost training dir by sonichi in https://github.com/microsoft/FLAML/pull/178
* Forecast v2 by int-chaos in https://github.com/microsoft/FLAML/pull/182
* Fix decide_split_type bug. by gianpDomiziani in https://github.com/microsoft/FLAML/pull/184
* Cleanml by qingyun-wu in https://github.com/microsoft/FLAML/pull/185
* warmstart blendsearch by sonichi in https://github.com/microsoft/FLAML/pull/186
* variable name by sonichi in https://github.com/microsoft/FLAML/pull/187
* notebook example by sonichi in https://github.com/microsoft/FLAML/pull/189
* make flaml work without catboost by sonichi in https://github.com/microsoft/FLAML/pull/197
* package name in setup by sonichi in https://github.com/microsoft/FLAML/pull/198
* clean up forecast notebook by sonichi in https://github.com/microsoft/FLAML/pull/202
* consider num_samples in bs thread priority by sonichi in https://github.com/microsoft/FLAML/pull/207
* accommodate nni usage pattern by sonichi in https://github.com/microsoft/FLAML/pull/209
* random search by sonichi in https://github.com/microsoft/FLAML/pull/213
* add consistency test by qingyun-wu in https://github.com/microsoft/FLAML/pull/216
* set converge flag when no trial can be sampled by sonichi in https://github.com/microsoft/FLAML/pull/217
* seed for hpo method by sonichi in https://github.com/microsoft/FLAML/pull/224
* update config if n_estimators is modified by sonichi in https://github.com/microsoft/FLAML/pull/225
* warning -> info for low cost partial config by sonichi in https://github.com/microsoft/FLAML/pull/231
* Consistent California by cdeil in https://github.com/microsoft/FLAML/pull/245
* Package by sonichi in https://github.com/microsoft/FLAML/pull/244

New Contributors
* cdeil made their first contribution in https://github.com/microsoft/FLAML/pull/245

**Full Changelog**: https://github.com/microsoft/FLAML/compare/v0.6.0...v0.6.7

0.6.6

What's Changed
* remove big objects after fit by sonichi in https://github.com/microsoft/FLAML/pull/176
* remove catboost training dir by sonichi in https://github.com/microsoft/FLAML/pull/178
* Forecast v2 by int-chaos in https://github.com/microsoft/FLAML/pull/182
* Fix decide_split_type bug. by gianpDomiziani in https://github.com/microsoft/FLAML/pull/184
* Cleanml by qingyun-wu in https://github.com/microsoft/FLAML/pull/185
* warmstart blendsearch by sonichi in https://github.com/microsoft/FLAML/pull/186
* variable name by sonichi in https://github.com/microsoft/FLAML/pull/187
* notebook example by sonichi in https://github.com/microsoft/FLAML/pull/189
* make flaml work without catboost by sonichi in https://github.com/microsoft/FLAML/pull/197
* package name in setup by sonichi in https://github.com/microsoft/FLAML/pull/198
* clean up forecast notebook by sonichi in https://github.com/microsoft/FLAML/pull/202
* consider num_samples in bs thread priority by sonichi in https://github.com/microsoft/FLAML/pull/207
* accommodate nni usage pattern by sonichi in https://github.com/microsoft/FLAML/pull/209
* random search by sonichi in https://github.com/microsoft/FLAML/pull/213
* add consistency test by qingyun-wu in https://github.com/microsoft/FLAML/pull/216
* set converge flag when no trial can be sampled by sonichi in https://github.com/microsoft/FLAML/pull/217
* seed for hpo method by sonichi in https://github.com/microsoft/FLAML/pull/224
* update config if n_estimators is modified by sonichi in https://github.com/microsoft/FLAML/pull/225
* warning -> info for low cost partial config by sonichi in https://github.com/microsoft/FLAML/pull/231


**Full Changelog**: https://github.com/microsoft/FLAML/compare/v0.6.0...v0.6.6

0.6.0

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
- sonichi
- qingyun-wu
- int-chaos
- liususan091219
- Yard1
- bnriiitb
- su2umaru
- eduardobull
- sek788432
- ekzhu
- anshumandutt
- yue-msr
- sadtaf
- fzanartu
- dsbyprateekg
- hanhanwu
- PardeepRassani
- gianpDomiziani
- stepthom
- anhnht3
- zzheng93
- flippercy
- luizhemelo
- nabalamu
- lostmygithubaccount
- suryajayaraman

0.5.0

Major update:
* Online automl. For example, we support tuning online machine learning library vowpal wabbit.

Minor updates:
* log best model in mlflow
* utility functions to produce normalized confusion matrix and roc or precision-recall curves for each class in multi-class tasks

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