fix optimize mode bug

docs/en_US/CommunitySharings/HpoComparision.md

@@ -100,7 +100,7 @@ The total search space is 1,204,224, we set the number of maximum trial to 1000.
 | HyperBand     |0.415550|0.415977|0.417186|
 | GP            |0.414353|0.418563|0.420263|
 | GP            |0.414395|0.418006|0.420431|
-| GP            |0.416807|0.418095|0.419767|
+| GP            |0.412943|0.416566|0.418443|
 
 For Metis, there are about 300 trials because it runs slowly due to its high time complexity O(n^3) in Gaussian Process.
 

src/sdk/pynni/nni/gp_tuner/gp_tuner.py

@@ -29,7 +29,7 @@ from sklearn.gaussian_process.kernels import Matern
 from sklearn.gaussian_process import GaussianProcessRegressor
 
 from nni.tuner import Tuner
-from nni.utils import extract_scalar_reward
+from nni.utils import OptimizeMode, extract_scalar_reward
 
 from .target_space import TargetSpace
 from .util import UtilityFunction, acq_max
@@ -134,7 +134,7 @@ class GPTuner(Tuner):
             if value is dict, it should have "default" key.
         """
         value = extract_scalar_reward(value)
-        if self.optimize_mode == 'minimize':
+        if self.optimize_mode == OptimizeMode.Minimize.value:
             value = -value
 
         logger.info("Received trial result.")

src/sdk/pynni/nni/gp_tuner/util.py

@@ -82,14 +82,14 @@ def acq_max(f_acq, gp, y_max, bounds, space, num_warmup, num_starting_points):
 
     # Warm up with random points
     x_tries = [space.random_sample()
-               for _ in range(int(num_warmup)]
+               for _ in range(int(num_warmup))]
     ys = f_acq(x_tries, gp=gp, y_max=y_max)
     x_max = x_tries[ys.argmax()]
     max_acq = ys.max()
 
 
     # Explore the parameter space more throughly
-    x_seeds = [space.random_sample() for _ in range(int(num_starting_points)]
+    x_seeds = [space.random_sample() for _ in range(int(num_starting_points))]
 
     bounds_minmax = np.array(
         [[bound['_value'][0], bound['_value'][-1]] for bound in bounds])

src/sdk/pynni/nni/metis_tuner/metis_tuner.py

@@ -216,7 +216,7 @@ class MetisTuner(Tuner):
             if value is dict, it should have "default" key.
         """
         value = extract_scalar_reward(value)
-        if self.optimize_mode == OptimizeMode.Maximize:
+        if self.optimize_mode == OptimizeMode.Maximize.value:
             value = -value
 
         logger.info("Received trial result.")