* allow installation in py 3.6

* test py 3.6

* update got version

* None check

* support latest xgboost version

* Update test_classification.py

* Update 

Exists problems when installing xgb1.6.1 in py3.6

* cleanup

* xgboost version

* remove time_budget_s in test

* remove redundancy

* stop support of python 3.6

Co-authored-by: zsk <shaokunzhang529@gmail.com>
Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>

* update the signature of set_search_properties

* backup & recover global vars for nested tune.run

* ensure recovering global vars before return

* test reproducibility

* cost_attr, _eps and penalty as instance variables

* enable ensemble when using ray

* sanitize config

* init value type match

* bump version to 1.0.6

* add a note about flaml version in notebook

* add note about mismatched ITER_HP

* catch SSLError when accessing OpenML data

* catch errors in autovw test

Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>

* fix checkpoint naming + trial id for non-ray mode, fix the bug in running test mode, delete all the checkpoints in non-ray mode

* finished testing for checkpoint naming, delete checkpoint, ray, max iter = 1

* update doc about scheduler exception

* remove assert

* use zeroshot when no budget is given; custom_hp

* update Getting-Started

* protobuf version

* X_val

* add bs restore test

* use default metric when not provided

* update documentation

* remove print

* period

* remove bs restore test

* Update website/docs/Use-Cases/Task-Oriented-AutoML.md

* fix doc string; enable label transform in automl.score

* set n_jobs in ensemble dict

* catch the ensemble error

* choose n_jobs for stacker

* clarify

* fix conll bug

* update DataCollatorForAuto

* adding label_list comments

* handle non-flaml scheduler in flaml.tune

* revise time budget

* Update website/docs/Use-Cases/Tune-User-Defined-Function.md

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update website/docs/Use-Cases/Tune-User-Defined-Function.md

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update flaml/tune/tune.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* add docstr

* remove random seed

* StopIteration

* StopIteration format

* format

* Update flaml/tune/tune.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* revise docstr

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Bump cross-fetch from 3.1.4 to 3.1.5 in /website

* Bump async from 2.6.3 to 2.6.4 in /website

* cleanup

* refactoring TransformersEstimator to support default and custom_hp

* handling starting_points not in search space

* addressing starting point more than max_iter

* fixing upper < lower bug

* allow evaluated_rewards shorter than points_to_evaluate

* docstr update

* Bump minimist from 1.2.5 to 1.2.6 in /website

* check best_config in test

* handle failing trials

* clarify when to return {}

* skip ensemble in accuracy check

* metric constraint documentation

* update link

* update notebook

* fix a bug in adding 'time_total_s' to result

* use the default multiple factor from config file

* update notebook

* format

* improve test

* revise test budget for macos

* bug fix in adding time_total_s

* increase performance check budget

* revise test

* update notebook

* uncomment test

* remove redundancy

* clear output

* remove n_jobs

* remove constraint in notebook

* increase budget

* revise test

* add python version

* use getattr

* improve code robustness

Co-authored-by: Qingyun Wu <qxw5138@psu.edu>

* fixing bugs in nlp

* version number

* add missing tasks in documentation

* update node-forge version

* adding automl.score

* fixing the metric name in train_with_config

* adding pickle after score

* fixing a bug in automl.pickle

* fixing bug for ner

* removing global var

* adding class for trial counter

* adding notebook

* adding use_ray dict

* updating documentation for nlp

* metric constraints

* revise docstr

* fix docstr

* improve docstr

* Update flaml/automl.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update flaml/automl.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update flaml/automl.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* docstr

Co-authored-by: Qingyun Wu <qxw5138@psu.edu>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* update automl.py
- documentation update

* update test_forecast.py

* update model.py

* update automl_time_series_forecast.ipynb

* update time series forecast website examples

Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com>

* Prepare for release

Co-authored-by: Moe Kayali <t-moekayali@microsoft.com>

* bug fix

* improve doc and code quality

Co-authored-by: Qingyun Wu

* fix a bug when using ray & update ray on aml
When using with_parameters(), the config argument must be the first argument in the trainable function.
* make training function runnable standalone

* bump ray version to 1.10

* init ray in test

* Update setup.py to include hotfixes

Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>

* data in csv

* support ray ObjectRef #365

* use object store to store data when using ray

* make lgbm tuning example a test

* homepage title

* xgboost gpu support

* test xgboost gpu

* test sparse data

* add xgboost test

* remove ray.init to avoid pytest error

* update model.py
- change upper bound for "lags" hyperparameter

* update test_forecast.py
- add a test for a large dataset

* update sample.py
- pre-commit changes

* support 'ts_forecast_classification' task to forecast discrete values

* update test_forecast.py
- add test for forecasting discrete values

* update test_model.py

* pre-commit changes

* remove redundant imports

* getting ride of hf dataset

* max_iter < 2 -> no search

* use_ray in test

* eval_method in ts example

* check sign of constraints

* test metric constraint sign

* replacing val_loss with automl_metric

* console log handler

* version update

* doc

* skippable steps

* notebook update

* constraint sign

* doc for constraints

* bug fix: define-by-run and unflatten_hierarchical

* const

* handle nested space in indexof()

* test grid search

* test suggestion

* model test

* >1 ckpts

* always increase iter count

* log total # iterations

* security patch

* make iter_per_learner consistent

* add sklearn regressors as learners for ts_forecast task

* add direct forecasting strategy
warnings and errors for duplicate rows and missing values

- add preprocess for sklearn time series forecast
 update automl.py
 update test/test_forecast.py

* update model.py and test_forecast.py for cv eval_method

* add "hcrystalball" dependency in setup.py

* update automl.py
- add _validate_ts_data function for abstraction
- include xgb_limitdepth as a learner

* update model.py
- update search space for sklearn ts regressors

* update automl.py and test_forecast.py for numpy array inputs

* add documentations to model.py

* add documentation for removing catboost regressor

* update automl.py
- _validate_ts_data() function

Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com>

* serialize TransformerEstimator

* check has_attr

* custom metric needs trainer

* skip test on mac