Fix ExperimentAnalysis usage for Ray 2.7 (#272)

Ray 2.7 has some breaking API changes for `tune.ExperimentAnalysis`, which `tune-sklearn` depends on to construct the output. This PR fixes the API usage.

---------

Signed-off-by: Justin Yu <justinvyu@anyscale.com>

tests/test_randomizedsearch.py

@@ -156,7 +156,7 @@ def test_local_dir(self):
             parameter_grid,
             early_stopping=scheduler,
             max_iters=10,
-            local_dir="./test-result")
+            local_dir=os.path.abspath("./test-result"))
         tune_search.fit(x, y)
 
         self.assertTrue(len(os.listdir("./test-result")) != 0)
@@ -290,7 +290,7 @@ def test_warm_start_detection(self):
             parameter_grid,
             n_jobs=1,
             max_iters=10,
-            local_dir="./test-result")
+            local_dir=os.path.abspath("./test-result"))
         self.assertEqual(tune_search.early_stop_type,
                          EarlyStopping.NO_EARLY_STOP)
 
@@ -301,7 +301,7 @@ def test_warm_start_detection(self):
             parameter_grid,
             n_jobs=1,
             max_iters=10,
-            local_dir="./test-result")
+            local_dir=os.path.abspath("./test-result"))
         self.assertEqual(tune_search2.early_stop_type,
                          EarlyStopping.NO_EARLY_STOP)
 
@@ -312,7 +312,7 @@ def test_warm_start_detection(self):
             parameter_grid,
             n_jobs=1,
             max_iters=10,
-            local_dir="./test-result")
+            local_dir=os.path.abspath("./test-result"))
 
         self.assertEqual(tune_search3.early_stop_type,
                          EarlyStopping.NO_EARLY_STOP)
@@ -323,7 +323,7 @@ def test_warm_start_detection(self):
             early_stopping=True,
             n_jobs=1,
             max_iters=10,
-            local_dir="./test-result")
+            local_dir=os.path.abspath("./test-result"))
         self.assertEqual(tune_search4.early_stop_type,
                          EarlyStopping.WARM_START_ITER)
 
@@ -334,7 +334,7 @@ def test_warm_start_detection(self):
             early_stopping=True,
             n_jobs=1,
             max_iters=10,
-            local_dir="./test-result")
+            local_dir=os.path.abspath("./test-result"))
         self.assertEqual(tune_search5.early_stop_type,
                          EarlyStopping.WARM_START_ENSEMBLE)
 
@@ -349,7 +349,7 @@ def test_warm_start_error(self):
             n_jobs=1,
             early_stopping=False,
             max_iters=10,
-            local_dir="./test-result")
+            local_dir=os.path.abspath("./test-result"))
         self.assertFalse(tune_search._can_early_stop())
         with self.assertRaises(ValueError):
             tune_search = TuneSearchCV(
@@ -358,7 +358,7 @@ def test_warm_start_error(self):
                 n_jobs=1,
                 early_stopping=True,
                 max_iters=10,
-                local_dir="./test-result")
+                local_dir=os.path.abspath("./test-result"))
 
         from sklearn.linear_model import LogisticRegression
         clf = LogisticRegression()
@@ -370,7 +370,7 @@ def test_warm_start_error(self):
                 early_stopping=True,
                 n_jobs=1,
                 max_iters=10,
-                local_dir="./test-result")
+                local_dir=os.path.abspath("./test-result"))
 
         from sklearn.ensemble import RandomForestClassifier
         clf = RandomForestClassifier()
@@ -382,21 +382,24 @@ def test_warm_start_error(self):
                 early_stopping=True,
                 n_jobs=1,
                 max_iters=10,
-                local_dir="./test-result")
+                local_dir=os.path.abspath("./test-result"))
 
     def test_warn_reduce_maxiters(self):
         parameter_grid = {"alpha": Real(1e-4, 1e-1, prior="log-uniform")}
         from sklearn.ensemble import RandomForestClassifier
         clf = RandomForestClassifier(max_depth=2, random_state=0)
         with self.assertWarnsRegex(UserWarning, "max_iters is set"):
             TuneSearchCV(
-                clf, parameter_grid, max_iters=10, local_dir="./test-result")
+                clf,
+                parameter_grid,
+                max_iters=10,
+                local_dir=os.path.abspath("./test-result"))
         with self.assertWarnsRegex(UserWarning, "max_iters is set"):
             TuneSearchCV(
                 SGDClassifier(),
                 parameter_grid,
                 max_iters=10,
-                local_dir="./test-result")
+                local_dir=os.path.abspath("./test-result"))
 
     def test_warn_early_stop(self):
         X, y = make_classification(
@@ -893,9 +896,9 @@ def testHyperoptPointsToEvaluate(self):
         from ray.tune.search.hyperopt import HyperOptSearch
         # Skip test if category conversion is not available
         if not hasattr(HyperOptSearch, "_convert_categories_to_indices"):
-            self.skipTest(f"The current version of Ray does not support the "
-                          f"`points_to_evaluate` argument for search method "
-                          f"`hyperopt`. Skipping test.")
+            self.skipTest("The current version of Ray does not support the "
+                          "`points_to_evaluate` argument for search method "
+                          "`hyperopt`. Skipping test.")
             return
         self._test_points_to_evaluate("hyperopt")
 

tune_sklearn/_trainable.py

@@ -319,8 +319,8 @@ def _train(self):
                         return_train_score=self.return_train_score,
                         error_score=self.error_score)
             except ValueError as e:
-                if ("It is very likely that your"
-                        "model is misconfigured") not in str(e):
+                if ("It is very likely that your model is misconfigured" not in
+                        str(e)):
                     raise e
                 fit_failed = True
 
@@ -367,9 +367,9 @@ def _train(self):
 
         return ret
 
-    def save_checkpoint(self, checkpoint_dir):
+    def save_checkpoint(self, checkpoint_dir: str):
         # forward-compatbility
-        return self._save(checkpoint_dir)
+        self._save(checkpoint_dir)
 
     def _save(self, checkpoint_dir):
         """Creates a checkpoint in ``checkpoint_dir``, creating a pickle file.
@@ -387,21 +387,21 @@ def _save(self, checkpoint_dir):
                 cpickle.dump(self.estimator_list, f)
         except Exception:
             warnings.warn("Unable to save estimator.", category=RuntimeWarning)
-        return path
 
-    def load_checkpoint(self, checkpoint):
+    def load_checkpoint(self, checkpoint_dir: str):
         # forward-compatbility
-        return self._restore(checkpoint)
+        self._restore(checkpoint_dir)
 
-    def _restore(self, checkpoint):
+    def _restore(self, checkpoint_dir):
         """Loads a checkpoint created from `save`.
 
         Args:
             checkpoint (str): file path to pickled checkpoint file.
 
         """
+        path = os.path.join(checkpoint_dir, "checkpoint")
         try:
-            with open(checkpoint, "rb") as f:
+            with open(path, "rb") as f:
                 self.estimator_list = cpickle.load(f)
         except Exception:
             warnings.warn("No estimator restored", category=RuntimeWarning)

tune_sklearn/tune_basesearch.py

@@ -327,7 +327,7 @@ def __init__(self,
                  verbose=0,
                  error_score="raise",
                  return_train_score=False,
-                 local_dir="~/ray_results",
+                 local_dir=None,
                  name=None,
                  max_iters=1,
                  use_gpu=False,
@@ -773,32 +773,31 @@ def _format_results(self, n_splits, out):
         trials = [
             trial for trial in out.trials if trial.status == Trial.TERMINATED
         ]
-        trial_dirs = [trial.logdir for trial in trials]
-        # The result dtaframes are indexed by their trial logdir
-        trial_dfs = out.fetch_trial_dataframes()
+        trial_dfs = out.trial_dataframes
+        trial_ids = list(trial_dfs)
 
         # Try to find a template df to use for trials that did not return
         # any results. These trials should copy the structure and fill it
         # with NaNs so that the later reshape actions work.
         template_df = None
-        fix_trial_dirs = []  # Holds trial dirs with no results
-        for trial_dir in trial_dirs:
-            if trial_dir in trial_dfs and template_df is None:
-                template_df = trial_dfs[trial_dir]
-            elif trial_dir not in trial_dfs:
-                fix_trial_dirs.append(trial_dir)
+        fix_trial_ids = []  # Holds trial_ids with no results
+        for trial_id, trial_df in trial_dfs.items():
+            if template_df is None and not trial_df.empty:
+                template_df = trial_df
+            elif trial_df.empty:
+                fix_trial_ids.append(trial_id)
 
         # Create NaN dataframes for trials without results
-        if fix_trial_dirs:
+        if fix_trial_ids:
             if template_df is None:
                 # No trial returned any results
                 return {}
-            for trial_dir in fix_trial_dirs:
+            for trial_id in fix_trial_ids:
                 trial_df = pd.DataFrame().reindex_like(template_df)
-                trial_dfs[trial_dir] = trial_df
+                trial_dfs[trial_id] = trial_df
 
         # Keep right order
-        dfs = [trial_dfs[trial_dir] for trial_dir in trial_dirs]
+        dfs = [trial_dfs[trial_id] for trial_id in trial_ids]
         finished = [df.iloc[[-1]] for df in dfs]
         test_scores = {}
         train_scores = {}

tune_sklearn/tune_gridsearch.py

@@ -157,7 +157,7 @@ def __init__(self,
                  verbose=0,
                  error_score="raise",
                  return_train_score=False,
-                 local_dir="~/ray_results",
+                 local_dir=None,
                  name=None,
                  max_iters=1,
                  use_gpu=False,

tune_sklearn/tune_search.py

@@ -316,7 +316,7 @@ def __init__(self,
                  random_state=None,
                  error_score=np.nan,
                  return_train_score=False,
-                 local_dir="~/ray_results",
+                 local_dir=None,
                  name=None,
                  max_iters=1,
                  search_optimization="random",