v1.0.3

Data files needed for zero-shot AutoML are included in this release.
When no search budget is given via time_budget/max_iter, zero-shot automl is used automatically.

What's Changed

• align indent and add missing quotation by @sonichi in #555
• solve issue #542. fix pickle.UnpickingError while blendsearch warm start by @LinWencong in #554
• Documentation, test and bugfix by @qingyun-wu in #556
• Removed cat_hp_cost by @PrajwalBorkar in #559
• Update Tune-User-Defined-Function.md by @sonichi in #562
• use zeroshot when no budget is given; custom_hp by @sonichi in #563
• simplify warmstart in blendsearch by @sonichi in #558
• include .json file in flaml.default package by @sonichi in #565

New Contributors

• @LinWencong made their first contribution in #554
• @PrajwalBorkar made their first contribution in #559

Full Changelog: v1.0.2...v1.0.3

v1.0.2

What's Changed

• docstr cleanup #523: removed lines 259 to 260 in a1c49ca by @elbowgreasel in #524
• refactoring TransformersEstimator to support default and custom_hp by @liususan091219 in #511
• Bump cross-fetch from 3.1.4 to 3.1.5 in /website by @sonichi in #529
• fixing use_ray in automl.py by @liususan091219 in #531
• handle non-flaml scheduler in flaml.tune by @qingyun-wu in #532
• test reproducibility from retrain by @sonichi in #533
• fix the post-processing bug in NER by @liususan091219 in #534
• fixing roberta add_prefix_space bug by @liususan091219 in #546
• choose n_jobs for ensemble according to n_jobs per learner by @sonichi in #551
• Quick-fix by @Qiaochu-Song in #539
• fix indentation in automl.py by @harish445 in #553

New Contributors

• @elbowgreasel made their first contribution in #524
• @Qiaochu-Song made their first contribution in #539
• @harish445 made their first contribution in #553

Full Changelog: v1.0.1...v1.0.2

v1.0.1

What's Changed

• use ffill in forecasting example by @sonichi in #508
• Handling fractional gpu_per_trial for NLP by @liususan091219 in #513
• Fix AttributeError: readonly attribute for Python 3.10.4 by @jayshanker2000 in #518
• max choice is n-1 by @sonichi in #521
• allow evaluated_rewards shorter than points_to_evaluate by @sonichi in #522

New Contributors

• @jayshanker2000 made their first contribution in #518

Full Changelog: v1.0.0...v1.0.1

v1.0.0

What's Changed

• zero-shot AutoML in readme by @sonichi in #474
• update documentation for time series forecasting by @int-chaos in #472
• metric constraints in flaml.automl by @qingyun-wu in #479
• import from lightgbm by @sonichi in #489
• fixing bug for ner by @liususan091219 in #463
• doc update (#490) by @sonichi in #492
• adding evaluation by @liususan091219 in #495
• version number and doc by @sonichi in #497
• fixing a few bugs in nlp by @liususan091219 in #503
• Bug fix and add documentation for metric_constraints by @qingyun-wu in #498
• fixing some bug in NLP by @liususan091219 in #506
• handle failing trials by @sonichi in #505
• Update notebook and test by @qingyun-wu in #507
• Bump minimist from 1.2.5 to 1.2.6 in /website by @sonichi in #502

Full Changelog: v0.10.0...v1.0.0

v0.10.0

This release contains an important new feature: zero-shot AutoML and mete learning. It provides a new way of doing AutoML without tuning. You can now use the existing training API from lightgbm, xgboost etc. while getting the benefit of AutoML in choosing high-performance hyperparameter configurations per task. Recommended for everyone currently using lightgbm, xgboost or random forest, regardless of previous experience in AutoML. This feature also enables continuous improvement of AutoML from historical AutoML experiments.

Other changes can be found below.

What's Changed

• Typo on the webpage's Getting Started section by @cammarb in #457
• Bump follow-redirects from 1.14.7 to 1.14.8 in /website by @sonichi in #459
• Docstr update by @qingyun-wu in #460
• update regression metrics in notebooks by @sonichi in #454
• make AutoML.classes_ an array by @sonichi in #467
• Bump prismjs from 1.25.0 to 1.27.0 in /website by @sonichi in #471
• Zero-shot AutoML by @sonichi in #468
• don't init global search with points_to_evaluate unless evaluated_rewards is provided; handle callbacks in fit kwargs by @sonichi in #469

New Contributors

• @cammarb made their first contribution in #457

Full Changelog: v0.9.7...v0.10.0

v0.9.7

What's Changed

• Update Task-Oriented-AutoML.md by @vvijayalakshmi21 in #446
• Update Task-Oriented-AutoML.md by @vvijayalakshmi21 in #447
• Update Tune-User-Defined-Function.md by @vvijayalakshmi21 in #448
• corrected typo in example xgboost documentation by @MichaelMarien in #449
• bump ray version to 1.10 by @sonichi in #450
• fix a bug when using ray & update ray on aml by @sonichi in #455

New Contributors

• @vvijayalakshmi21 made their first contribution in #446

Full Changelog: v0.9.6...v0.9.7

v0.9.6

What's Changed

• reducing AutoConfig.from_pretrained by @liususan091219 in #411
• Set use_ray to True for logging to databricks by @liususan091219 in #414
• Bump nanoid from 3.1.30 to 3.2.0 in /website by @sonichi in #420
• bump version of node-fetch to 3.1.1 in website/ by @sonichi in #423
• Use Ray _BackwardsCompatibleNumpyRng if possible by @Yard1 in #421
• remove FLAML sample size from config by @sonichi in #418
• max_iter < 2 -> no search; sign in metric constraints; test and example for forecasting by @sonichi in #415
• remove redundant imports by @liususan091219 in #426
• Support time series forecasting for discrete target variable by @int-chaos in #416
• homepage update by @sonichi in #425
• fix a broken link in README.md by @m13uz in #439
• adding catch for HTTP error by @liususan091219 in #432
• Change the upper bound for "lags" hyperparameter for sklearn forecast models by @int-chaos in #437
• Gpu support for xgboost by @sonichi in #442
• data in csv by @sonichi in #430
• note about preview feature by @sonichi in #431

New Contributors

• @m13uz made their first contribution in #439

Full Changelog: v0.9.5...v0.9.6

v0.9.5

What's Changed

• fixing load best model at the end by @liususan091219 in #389
• Regression forecast debug by @int-chaos in #391
• set verbose for transformers by @liususan091219 in #392
• Logging multiple checkpoints by @liususan091219 in #394
• postcss version update by @sonichi in #385
• fixing default metric for regression + change verbosity for transformers by @liususan091219 in #397
• fix issues in logging, bug in space.py, constraint sign, and improve code coverage by @sonichi in #388
• moving intermediate_results logging from model.py to huggingface/trainer.py by @liususan091219 in #403
• Update flaml/nlp/README.md by @liususan091219 in #404
• Logo by @qingyun-wu in #399
• update browser icon by @qingyun-wu in #407
• adding logging of training loss by @liususan091219 in #406
• Bump shelljs from 0.8.4 to 0.8.5 in /website by @sonichi in #402
• Sklearn api x by @MichaelMarien in #405

New Contributors

• @MichaelMarien made their first contribution in #405

Full Changelog: v0.9.4...v0.9.5

v0.9.4

This release enables regression models for time series forecasting. It also fixes bugs in nlp tasks, such as serialization of transformer models and automatic metrics.

What's Changed

• citation file by @sonichi in #364
• Fix several issues for nlp tasks by @sonichi in #380
• serialize TransformerEstimator by @sonichi in #381
• Time series forecasting with sklearn regressors by @int-chaos in #362
• fixing auto metric bug by @liususan091219 in #387

Full Changelog: v0.9.3...v0.9.4

v0.9.3

What's Changed

• Finish the Multiple Choice Classification by @oberonbot in #367
• logging by @sonichi in #371
• adding token classification by @liususan091219 and @siddheshshaji in #376

New Contributors

• @oberonbot and @siddheshshaji made their first contribution in #367

Full Changelog: v0.9.2...v0.9.3