clean up

flaml/automl.py

@@ -3690,4 +3690,3 @@ class AutoML(BaseEstimator):
                 q += inv[i] / s
                 if p < q:
                     return estimator_list[i]
-                

flaml/tune/searcher/flow2.py

@@ -111,12 +111,18 @@ class FLOW2(Searcher):
         self.lexico_objectives = lexico_objectives
         if self.lexico_objectives is not None:
             if "modes" not in self.lexico_objectives.keys():
-                self.lexico_objectives["modes"] = ["min"]*len(self.lexico_objectives["metrics"])
+                self.lexico_objectives["modes"] = ["min"] * len(
-            for t_metric, t_mode in zip(self.lexico_objectives["metrics"], self.lexico_objectives["modes"]):
+                    self.lexico_objectives["metrics"]
+                )
+            for t_metric, t_mode in zip(
+                self.lexico_objectives["metrics"], self.lexico_objectives["modes"]
+            ):
                 if t_metric not in self.lexico_objectives["tolerances"].keys():
                     self.lexico_objectives["tolerances"][t_metric] = 0
                 if t_metric not in self.lexico_objectives["targets"].keys():
-                    self.lexico_objectives["targets"][t_metric] = -float("inf") if t_mode == "min" else float("inf")
+                    self.lexico_objectives["targets"][t_metric] = (
+                        -float("inf") if t_mode == "min" else float("inf")
+                    )
         self.resource_multiple_factor = (
             resource_multiple_factor or SAMPLE_MULTIPLY_FACTOR
         )
@@ -299,7 +305,7 @@ class FLOW2(Searcher):
             flow2.best_obj = {}
             for k, v in obj.items():
                 flow2.best_obj[k] = (
-                    v * -1
+                    -v
                     if self.lexico_objectives["modes"][
                         self.lexico_objectives["metrics"].index(k)
                     ]
@@ -345,7 +351,9 @@ class FLOW2(Searcher):
             self._init_search()
         return True
 
-    def update_fbest(self,):
+    def update_fbest(
+        self,
+    ):
         obj_initial = self.lexico_objectives["metrics"][0]
         feasible_index = [*range(len(self._histories[obj_initial]))]
         for k_metric in self.lexico_objectives["metrics"]:
@@ -376,10 +384,32 @@ class FLOW2(Searcher):
             for k in self.lexico_objectives["metrics"]:
                 self._histories[k].append(result[k])
             self.update_fbest()
-            for k_metric, k_mode in zip(self.lexico_objectives["metrics"],self.lexico_objectives["modes"]):
+            for k_metric, k_mode in zip(
-                k_target = self.lexico_objectives["targets"][k_metric] if k_mode == "min" else -1*self.lexico_objectives["targets"][k_metric]
+                self.lexico_objectives["metrics"], self.lexico_objectives["modes"]
-                if (result[k_metric] < max([self._f_best[k_metric] + self.lexico_objectives["tolerances"][k_metric], k_target])) and (
+            ):
-                    self.best_obj[k_metric] < max([self._f_best[k_metric] + self.lexico_objectives["tolerances"][k_metric], k_target])
+                k_target = (
+                    self.lexico_objectives["targets"][k_metric]
+                    if k_mode == "min"
+                    else -self.lexico_objectives["targets"][k_metric]
+                )
+                if (
+                    result[k_metric]
+                    < max(
+                        [
+                            self._f_best[k_metric]
+                            + self.lexico_objectives["tolerances"][k_metric],
+                            k_target,
+                        ]
+                    )
+                ) and (
+                    self.best_obj[k_metric]
+                    < max(
+                        [
+                            self._f_best[k_metric]
+                            + self.lexico_objectives["tolerances"][k_metric],
+                            k_target,
+                        ]
+                    )
                 ):
                     continue
                 elif result[k_metric] < self.best_obj[k_metric]:

flaml/tune/searcher/search_thread.py

@@ -150,9 +150,7 @@ class SearchThread:
                     self.obj_best1 = obj
                     self.cost_best = self.cost_last
                     self.best_result = result
-            if (
+            if getattr(self._search_alg, "lexico_objectives", None) is None:
-                getattr(self._search_alg, "lexico_objectives", None) is None
-            ):
                 # TODO: Improve this behavior. When lexico_objectives is provided to CFO,
                 # related variables are not callable.
                 self._update_speed()

flaml/tune/tune.py

@@ -74,13 +74,19 @@ class ExperimentAnalysis(EA):
                 histories[objective].append(
                     results[keys[time_index]][objective]
                     if mode == "min"
-                    else trials[keys[time_index]][objective] * -1
+                    else -trials[keys[time_index]][objective]
                 )
         obj_initial = self.lexico_objectives["metrics"][0]
         feasible_index = [*range(len(histories[obj_initial]))]
-        for k_metric, k_mode in zip(self.lexico_objectives["metrics"],self.lexico_objectives["modes"]):
+        for k_metric, k_mode in zip(
+            self.lexico_objectives["metrics"], self.lexico_objectives["modes"]
+        ):
             k_values = np.array(histories[k_metric])
-            k_target = self.lexico_objectives["targets"][k_metric] * -1 if k_mode == "max" else self.lexico_objectives["targets"][k_metric]
+            k_target = (
+                -self.lexico_objectives["targets"][k_metric]
+                if k_mode == "max"
+                else self.lexico_objectives["targets"][k_metric]
+            )
             f_best[k_metric] = np.min(k_values.take(feasible_index))
             feasible_index_prior = np.where(
                 k_values
@@ -449,8 +455,11 @@ def run(
             logger.setLevel(logging.CRITICAL)
 
     from .searcher.blendsearch import BlendSearch, CFO
-    if lexico_objectives != None:
+
-        logger.warning("If lexico_objectives is not None, search_alg is forced to be CFO")
+    if lexico_objectives is not None:
+        logger.warning(
+            "If lexico_objectives is not None, search_alg is forced to be CFO"
+        )
         search_alg = None
     if search_alg is None:
         flaml_scheduler_resource_attr = (
@@ -469,9 +478,12 @@ def run(
         if lexico_objectives is None:
             try:
                 import optuna as _
+
                 SearchAlgorithm = BlendSearch
                 logger.info(
-                    "Using search algorithm {}.".format(SearchAlgorithm.__class__.__name__)
+                    "Using search algorithm {}.".format(
+                        SearchAlgorithm.__class__.__name__
+                    )
                 )
             except ImportError:
                 SearchAlgorithm = CFO