fixed flake8 errors

mango/domain/domain_space.py

@@ -2,7 +2,6 @@
 import math
 import numpy as np
 from collections.abc import Callable
-import warnings
 from itertools import compress
 from functools import cached_property
 import warnings
@@ -272,7 +271,7 @@ def classify_parameters(param_dict: dict) -> (set, set, set, set):
                 try:
                     # this check takes care of numpy ints as well
                     all_int = all(
-                        x == int(x) and type(x) != bool for x in param_dict[par]
+                        x == int(x) and not isinstance(x, bool) for x in param_dict[par]
                     )
                 except (ValueError, TypeError):
                     all_int = False

mango/domain/parameter_sampler.py

@@ -1,5 +1,3 @@
-from abc import ABCMeta, abstractmethod
-
 import warnings
 
 import numpy as np

mango/metatuner.py

@@ -12,8 +12,6 @@
 from tqdm.auto import tqdm
 import random
 
-
-## setting warnings to ignore for now
 import warnings
 
 warnings.filterwarnings("ignore")

mango/optimizer/bayesian_learning.py

@@ -111,7 +111,8 @@ def Get_Upper_Confidence_Bound(self, X):
         return Value
 
     """
-    Returns the most optmal x along with mean value from the domain of x and making sure it is not a Duplicate (depending on closeness)
+    Returns the most optmal x along with mean value from the domain of x and 
+    making sure it is not a Duplicate (depending on closeness)
     used in batch setting: As mean is also returned
     """
 
@@ -128,7 +129,7 @@ def remove_duplicates(self, X, X_Sample, mu, Value):
             # check if x_optimal is in X_Sample
             check_closeness = self.closeness(x_optimal, X_Sample)
 
-            if check_closeness == False:  # No close element to x_optimal in X_Sample
+            if check_closeness is False:  # No close element to x_optimal in X_Sample
                 break
 
                 # we will look for next optimal value to try
@@ -159,7 +160,7 @@ def remove_duplicates_serial(self, X, X_Sample, Value):
             # check if x_optimal is in X_Sample
             check_closeness = self.closeness(x_optimal, X_Sample)
 
-            if check_closeness == False:  # No close element to x_optimal in X_Sample
+            if check_closeness is False:  # No close element to x_optimal in X_Sample
                 break
 
                 # we will look for next optimal value to try
@@ -287,7 +288,7 @@ def remove_duplicates_MetaTuner(self, X, X_Sample, mu, Value, Value_ext):
             # check if x_optimal is in X_Sample
             check_closeness = self.closeness(x_optimal, X_Sample)
 
-            if check_closeness == False:  # No close element to x_optimal in X_Sample
+            if check_closeness is False:  # No close element to x_optimal in X_Sample
                 break
 
                 # we will look for next optimal value to try
@@ -386,7 +387,6 @@ def get_next_batch_MetaTuner(
 
             try:
                 self.surrogate.fit(X_temp, Y_temp)
-
             except:
                 print("*" * 100)
                 print(X_temp)

mango/scheduler.py

@@ -35,7 +35,6 @@ def wrapper(params_batch):
 
 
 def celery(n_jobs, timeout=None):
-    import celery
     from celery import exceptions
 
     def decorator(func):

mango/tuner.py

@@ -246,7 +246,8 @@ def runBayesianOptimizer(self):
 
             if len(Y_next_list) == 0:
                 # no values returned
-                # this is problematic if domain is small and same value is tried again in the next iteration as the optimizer would be stuck
+                # this is problematic if domain is small and same value is tried again
+                # in the next iteration as the optimizer would be stuck
                 continue
 
             Y_next_batch = Y_next_list.reshape(len(Y_next_list), 1)