* warning -> info for low cost partial config
#195, #110
* when n_estimators < 0, use trained_estimator's
* log debug info
* test random seed
* remove "objective"; avoid ZeroDivisionError
* hp config to estimator params
* check type of searcher
* default n_jobs
* try import
* Update searchalgo_auto.py
* CLASSIFICATION
* auto_augment flag
* min_sample_size
* make catboost optional
* 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>
* increase test coverage
* use define by run only when needed
* warmstart bs
* classification -> binary, multi
* warm start with evaluated rewards
* data transformer; resource attr for gs
* BlendSearchTuner bug fix and unittest
* bug fix
* docstr and import
* task type
* 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>
* add starting point in fit
* add estimator best config
* add test
* add doc string
* when there are multiple points_to_evaluate in CFO, use the best one to start local search; after that use low cost partial config as the start point; then, remove the points whose performance is worse than the converged, and start local search from the remaining ones ordered by their performance.
Co-authored-by: Qingyun Wu <qingyunwu@Qingyuns-MacBook-Pro-2.local>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* pickle the AutoML object
* get best model per estimator
* test deberta
* stateless API
* pickle the AutoML object
* get best model per estimator
* test deberta
* stateless API
* prevent divide by zero
* test roberta
* BlendSearchTuner
* sync
* version number
* update gitignore
* delta time
* reindex columns when dropping int-indexed columns
* add seed
* add seed in Args
* merge
* stabilize SearchThread speed
* add seed
* fix import
* use except
* add restore test for CFO
* remove test_restore
* remove inspect
* remove print
* change to SearchThread._esp
* add _eps lower bound
* _eps in SearchThread
* add test_restore
* 1<<32
Co-authored-by: Chi Wang (MSR) <chiw@microsoft.com>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Qingyun Wu <qiw@microsoft.com>
* pickle the AutoML object
* get best model per estimator
* test deberta
* stateless API
* prevent divide by zero
* test roberta
* BlendSearchTuner
* delta time
* reindex columns when dropping int-indexed columns
* test drop columns and small training data
* param set for ensemble builder
* fillna on copy
Co-authored-by: Chi Wang (MSR) <chiw@microsoft.com>
* xgboost notebook
* finetuning notebook
* finetuning test
* experimental nni support
* support nested search space
* log file name
* record training_iteration
* eps
* reset times
* std set to default step size if 0
* 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>
