* 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>
* 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
* 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
* 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>
* 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
* 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
* 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
* 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>
* query logged runs
* mlflow log when using ray
* key check for newer version of ray #363
* catch importerror
* log and load AutoML model
* retrain if necessary when ensemble fails
