move searcher and scheduler into tune (#746)

* move into tune

* correct path

* correct path

* import path

test/nni/flaml_nni_wrap.py

@@ -1,4 +1,4 @@
-from flaml.searcher.blendsearch import BlendSearchTuner as BST
+from flaml.tune.searcher.blendsearch import BlendSearchTuner as BST
 
 
 class BlendSearchTuner(BST):

test/tune/example_scheduler.py

@@ -26,7 +26,7 @@ def easy_objective(use_raytune, config):
 
 def test_tune_scheduler(smoke_test=True, use_ray=True, use_raytune=False):
     import numpy as np
-    from flaml.searcher.blendsearch import BlendSearch
+    from flaml.tune.searcher.blendsearch import BlendSearch
 
     np.random.seed(100)
     easy_objective_custom_tune = partial(easy_objective, use_raytune)

test/tune/test_flaml_raytune_consistency.py

@@ -28,7 +28,7 @@ low_cost_partial_config = {"x": 1}
 
 
 def setup_searcher(searcher_name):
-    from flaml.searcher.blendsearch import BlendSearch, CFO, RandomSearch
+    from flaml.tune.searcher.blendsearch import BlendSearch, CFO, RandomSearch
 
     if "cfo" in searcher_name:
         searcher = CFO(

test/tune/test_record_incumbent.py

@@ -7,7 +7,7 @@ def rosenbrock_function(config: dict):
     funcLoss = 50
     for key, value in config.items():
         if key in ["x1", "x2", "x3", "x4", "x5"]:
-            funcLoss += value ** 2 - 10 * np.cos(2 * np.pi * value)
+            funcLoss += value**2 - 10 * np.cos(2 * np.pi * value)
     if INCUMBENT_RESULT in config.keys():
         print("----------------------------------------------")
         print("incumbent result", config[INCUMBENT_RESULT])
@@ -62,7 +62,7 @@ def test_record_incumbent(method="BlendSearch"):
             use_incumbent_result_in_evaluation=True,
         )
     elif method == "CFOCat":
-        from flaml.searcher.cfo_cat import CFOCat
+        from flaml.tune.searcher.cfo_cat import CFOCat
 
         algo = CFOCat(
             use_incumbent_result_in_evaluation=True,

test/tune/test_reproducibility.py

@@ -26,7 +26,7 @@ def _easy_objective(use_raytune, config):
 
 def test_tune(externally_setup_searcher=False, use_ray=False, use_raytune=False):
     from flaml import tune
-    from flaml.searcher.blendsearch import BlendSearch
+    from flaml.tune.searcher.blendsearch import BlendSearch
 
     easy_objective_custom_tune = partial(_easy_objective, use_raytune)
     search_space = {

test/tune/test_restore.py

@@ -3,7 +3,7 @@ import shutil
 import tempfile
 import unittest
 import numpy as np
-from flaml.searcher.suggestion import ConcurrencyLimiter
+from flaml.tune.searcher.suggestion import ConcurrencyLimiter
 from flaml import tune
 from flaml import CFO
 

test/tune/test_scheduler.py

@@ -1,6 +1,6 @@
 """Require: pip install flaml[test,ray]
 """
-from flaml.scheduler.trial_scheduler import TrialScheduler
+from flaml.tune.scheduler.trial_scheduler import TrialScheduler
 import numpy as np
 from flaml import tune
 

test/tune/test_searcher.py

@@ -32,8 +32,12 @@ def wrong_define_search_space(trial):
 
 
 def test_searcher():
-    from flaml.searcher.suggestion import OptunaSearch, Searcher, ConcurrencyLimiter
-    from flaml.searcher.blendsearch import BlendSearch, CFO, RandomSearch
+    from flaml.tune.searcher.suggestion import (
+        OptunaSearch,
+        Searcher,
+        ConcurrencyLimiter,
+    )
+    from flaml.tune.searcher.blendsearch import BlendSearch, CFO, RandomSearch
     from flaml.tune import sample as flamlsample
 
     searcher = Searcher()
@@ -306,6 +310,6 @@ def test_no_optuna():
     import sys
 
     subprocess.check_call([sys.executable, "-m", "pip", "uninstall", "-y", "optuna"])
-    import flaml.searcher.suggestion
+    import flaml.tune.searcher.suggestion
 
     subprocess.check_call([sys.executable, "-m", "pip", "install", "optuna==2.8.0"])

test/tune/test_space.py

@@ -69,7 +69,7 @@ def test_define_by_run():
 
 
 def test_grid():
-    from flaml.searcher.variant_generator import (
+    from flaml.tune.searcher.variant_generator import (
         generate_variants,
         grid_search,
         TuneError,

test/tune/test_tune.py

@@ -1,6 +1,6 @@
 """Require: pip install flaml[test,ray]
 """
-from flaml.searcher.blendsearch import BlendSearch
+from flaml import BlendSearch
 import time
 import os
 from sklearn.model_selection import train_test_split
@@ -146,7 +146,7 @@ def _test_xgboost(method="BlendSearch"):
                         },
                     )
                 elif "CFOCat" == method:
-                    from flaml.searcher.cfo_cat import CFOCat
+                    from flaml.tune.searcher.cfo_cat import CFOCat
 
                     algo = CFOCat(
                         low_cost_partial_config={