[tune] refactor tune search space (#10444)

* Added basic functionality and tests

* Feature parity with old tune search space config

* Convert Optuna search spaces

* Introduced quantized values

* Updated Optuna resolving

* Added HyperOpt search space conversion

* Convert search spaces to AxSearch

* Convert search spaces to BayesOpt

* Added basic functionality and tests

* Feature parity with old tune search space config

* Convert Optuna search spaces

* Introduced quantized values

* Updated Optuna resolving

* Added HyperOpt search space conversion

* Convert search spaces to AxSearch

* Convert search spaces to BayesOpt

* Re-factored samplers into domain classes

* Re-added base classes

* Re-factored into list comprehensions

* Added `from_config` classmethod for config conversion

* Applied suggestions from code review

* Removed truncated normal distribution

* Set search properties in tune.run

* Added test for tune.run search properties

* Move sampler initializers to base classes

* Add tune API sampling test, fixed includes, fixed resampling bug

* Add to API docs

* Fix docs

* Update metric and mode only when set. Set default metric and mode to experiment analysis object.

* Fix experiment analysis tests

* Raise error when delimiter is used in the config keys

* Added randint/qrandint to API docs, added additional check in tune.run

* Fix tests

* Fix linting error

* Applied suggestions from code review. Re-aded tune.function for the time being

* Fix sampling tests

* Fix experiment analysis tests

* Fix tests and linting error

* Removed unnecessary default_config attribute from OptunaSearch

* Revert to set AxSearch default metric

* fix-min-max

* fix

* nits

* Added function check, enhanced loguniform error message

* fix-print

* fix

* fix

* Raise if unresolved values are in config and search space is already set

Co-authored-by: Richard Liaw <rliaw@berkeley.edu>

python/ray/util/sgd/tf/examples/tensorflow_train_example.py

@@ -95,8 +95,8 @@ def train_example(num_replicas=1, batch_size=128, use_gpu=False):
 
 def tune_example(num_replicas=1, use_gpu=False):
     config = {
-        "model_creator": tune.function(simple_model),
-        "data_creator": tune.function(simple_dataset),
+        "model_creator": simple_model,
+        "data_creator": simple_dataset,
         "num_replicas": num_replicas,
         "use_gpu": use_gpu,
         "trainer_config": create_config(batch_size=128)