* improve max_valid_n and doc
* Update README.md
Co-authored-by: Li Jiang <lijiang1@microsoft.com>
* add support for chatgpt
* notebook
* newline at end of file
* chatgpt notebook
* ChatGPT in Azure
* doc
* math
* warning, timeout, log file name
* handle import error
* doc update; default value
* paper
* doc
* docstr
* eval_func
* prompt and messages
* remove confusing words
* notebook name
---------
Co-authored-by: Li Jiang <lijiang1@microsoft.com>
Co-authored-by: Susan Xueqing Liu <liususan091219@users.noreply.github.com>
* notebook test
* add ipykernel, remove except
* only create dir if not empty
* Stop sequential tuning when result is None
* fix reproducibility of global search
* save gs seed
* use get to avoid KeyError
* test
* make performance test reproducible
* fix test error
* Doc update and disable logging
* document random_state and version
* remove hardcoded budget
* fix test error and dependency; close#777
* iloc
* skip in-search-space check for small max iter
* resolve Pickle Transformer #730
* resolve default config unrecognized #784
* Change definition of init_config
* copy points_to_evaluate
* make test pass
* check learner selector
* categorical choice can be ordered or unordered
* ordered -> order
* move choice into utils
* version comparison
* packaging -> setuptools
* import version
* version_parse
* test order for choice
* 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>
* 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
* 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
if save_best_model_per_estimator is False and retrain_final is True, unfit the model after evaluation in HPO.
retrain if using ray.
update ITER_HP in config after a trial is finished.
change prophet logging level.
example and notebook update.
allow settings to be passed to AutoML constructor. Are you planning to add multi-output-regression capability to FLAML #192 Is multi-tasking allowed? #277 can pass the auotml setting to the constructor instead of requiring a derived class.
remove model_history.
checkpoint bug fix.
* model_history meaning save_best_model_per_estimator
* ITER_HP
* example update
* prophet logging level
* comment update in forecast notebook
* print format improvement
* allow settings to be passed to AutoML constructor
* checkpoint bug fix
* time limit for autohf regression test
* skip slow test on macos
* cleanup before del
* Integrate multivariate time series forecasting, now supports
continuous and categorical variables
- update data.py to transform time series data
- update search space
- update documentations to reflect changes
- update test_forecast.py
- rename 'forecast' task to 'ts_forecast' task
* update automl.py and test_forecast.py
* update forecast notebook
* update README.md and setup.py
* update ml.py and test_forecast.py
- make "ds" and "y" constant variables
* replace constants with constant variables
* bump version to 0.7.0
* update setup.py
- support 'forecast' and 'ts_forecast'
* update automl.py and data.py
- support 'forecast' and 'ts_forecast' tasks
* update config if n_estimators is modified
* prediction as int
* handle the case n_estimators <= 0
* if trained and no budget to train more, return the trained model
* split_type=group for classification & regression
* set converge flag when no trial can be sampled
* require custom_metric to return dict for logging
close#218
* estimate time budget needed
* log info per iteration
* config in result
* value can be float
* pytorch notebook example
* docker, pre-commit
* max_failure (#192); early_stop
* extend starting_points (#196)
Co-authored-by: Chi Wang (MSR) <wang.chi@microsoft.com>
Co-authored-by: Qingyun Wu <qw2ky@virginia.edu>
* non hashable value out of signature
* parallel trials
* add random in _search_parallel
* fix bug in retraining
* check memory constraint before training
* retrain_full
* log custom metric
* retraining budget check
* sample size check before retrain
* remove 'time2eval' from result
* report 'total_search_time' in result
* rename total_search_time to wall_clock_time
* rename train_loss boolean to log_training_metric
* set default train_loss to None
* exclude oom result
* log retrained model
* no subsample
* doc str
* notebook
* predicted value is NaN for sarimax
* version
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Qingyun Wu <qxw5138@psu.edu>
* added 'forecast' task with estimators ['fbprophet', 'arima', 'sarimax']
* update setup.py
* add TimeSeriesSplit to 'regression' and 'classification' task
* add 'time' split_type for 'classification' and 'regression' task
Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com>
* feature importance
* variable name
* Update test/test_split.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* Update test/test_forecast.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* prophet installation fail in windows
* upload flaml_forecast.ipynb
Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com>
* remove extra comma
* exclusive bound
* log file name
* add cost to space
* dataset_format
* add load_openml_dataset test
* docstr
* revise test format
* simplify restore
* order categories
* openml server exception in test
* process space
* add warning
* log format
* reduce n_cpu
* nested space
* hierarchical search space for CFO
* non hierarchical for bs
* unflatten hierarchical config
* connection error
* random sample
* config signature
* check ray version
* preprocess numpy array
* catboost preprocess
* time budget
* seed, verbose, hpo_method
* test cfocat
* shallow copy in flatten_dict
prevent lgbm model duplication
* match estimator name
* quantize and log
* test qloguniform and qrandint
* test qlograndint
* thread.running
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Qingyun Wu <qingyunwu@Qingyuns-MacBook-Pro-2.local>
