* 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
* 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
* add consistency test
* test_consistency and format
* add results attribute
* skip when ray is not installed
* Update flaml/tune/analysis.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Qingyun Wu <qxw5138@psu.edu>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* 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 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>
* 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>
* subspace in flow2
* search space and trainable from AutoML
* experimental features: multivariate TPE, grouping, add_evaluated_points
* test experimental features
* readme
* define by run
* set time_budget_s for bs
Co-authored-by: liususan091219 <Xqq630517>
* version
* acl
* test define_by_run_func
* size
* constraints
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* 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>
* api doc for chacha
* update params
* link to paper
* update dataset id
Co-authored-by: Chi Wang (MSR) <chiw@microsoft.com>
Co-authored-by: Qingyun Wu <qiw@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
* 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
* init upload of ChaCha
* remove redundancy
* add back catboost
* improve AutoVW API
* set min_resource_lease in VWOnlineTrial
* docstr
* rename
* docstr
* add docstr
* improve API and documentation
* fix name
* docstr
* naming
* remove max_resource in scheduler
* add TODO in flow2
* remove redundancy in rearcher
* add input type
* adapt code from ray.tune
* move files
* naming
* documentation
* fix import error
* fix format issues
* remove cb in worse than test
* improve _generate_all_comb
* remove ray tune
* naming
* VowpalWabbitTrial
* import error
* import error
* merge test code
* scheduler import
* fix import
* remove
* import, minor bug and version
* Float or Categorical
* fix default
* add test_autovw.py
* add vowpalwabbit and openml
* lint
* reorg
* lint
* indent
* add autovw notebook
* update notebook
* update log msg and autovw notebook
* update autovw notebook
* update autovw notebook
* add available strings for model_select_policy
* string for metric
* Update vw format in flaml/onlineml/trial.py
Co-authored-by: olgavrou <olgavrou@gmail.com>
* make init_config optional
* add _setup_trial_runner and update notebook
* space
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>
Co-authored-by: olgavrou <olgavrou@gmail.com>
* datetime feature engineering added.
* check if datetime in columns moved after drop check. Check if the new columns do not already exist.
* check the drop condition before to add new_column. In transform, check directly if new columns are present in num_column.
* check if new_column is in X.columns.
* fixed lint issue. update version to 0.4.1.
