microsoft/FLAML v1.1.0 on GitHub

Highlights

Spark is now supported as a new parallel tuning backend.
New tuning capability: targeted tuning with multiple lexicographic objectives. Check out documentation and an example for this new tuning capability.
New metrics: roc_auc_weighted, roc_auc_ovr_weighted, roc_auc_ovo_weighted.
New reproducible learner selection method when time_budget is not specified.
AutoML-related functionaility is moved into a new automl subpackage.

Thanks to all contributors who contributed to this release!

Bump actions/checkout from 2 to 3 by @dependabot in #699
fix dependably alert by @skzhang1 in #818
fix typo by @skzhang1 in #823
install editable package in codespace by @sonichi in #826
skip test_hf_data in py 3.6 by @sonichi in #832
fix typo of output directory by @thinkall in #828
catch TFT logger bugs by @int-chaos in #833
roc_auc_weighted metric addition by @shreyas36 in #827
make performance test reproducible by @sonichi in #837
Refactor into automl subpackage by @markharley in #809
Edit the announcement of AAAI-23 tutorial and the KDD tutorial announcement. by @HangHouCheong in #820
Use get to avoid KeyError by @sonichi in #824
Update doc by @skzhang1 in #843
fix bug related to choice by @sonichi in #848
FAQ about OOM by @sonichi in #849
Update .NET documentation links by @luisquintanilla in #847
Added an info reminding user that if no time_budget and no max_iter is specified, then effectively zero-shot AutoML is used by @jingdong00 in #850
Fix example tune-pytorch where the checkpoint path may be named differently by @jingdong00 in #853
Format errors on the web. by @skzhang1 in #855
Add supporting using Spark as the backend of parallel training by @thinkall in #846
Info and naming by @sonichi in #864

Full Changelog: v1.0.14...v1.1.0