* remove catboost training dir
* close#48
* bs for hierarchical space. close#85
* retrain for hierarchical space
* clean ml (#180)
Co-authored-by: Qingyun Wu <qxw5138@psu.edu>
* support ranking task
* examples
* cv shuffle
* forecast api and implementation cleaner
* period constraints
* delete groups after fit
* 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>
* pickle the AutoML object
* get best model per estimator
* test deberta
* stateless API
* Add Gitter badge (#41)
* prevent divide by zero
* test roberta
* BlendSearchTuner
Co-authored-by: Chi Wang (MSR) <chiw@microsoft.com>
Co-authored-by: The Gitter Badger <badger@gitter.im>
* v0.2.2
separate the HPO part into the module flaml.tune
enhanced implementation of FLOW^2, CFO and BlendSearch
support parallel tuning using ray tune
add support for sample_weight and generic fit arguments
enable mlflow logging
Co-authored-by: Chi Wang (MSR) <chiw@microsoft.com>
Co-authored-by: qingyun-wu <qw2ky@virginia.edu>
