faster tests

Author: SimonBlanke

tests/test_empty_output/non_verbose.py

@@ -74,4 +74,4 @@ def ackley_function(para):
 
 for optimizer in optimizers:
     opt = optimizer(search_space)
-    opt.search(ackley_function, n_iter=100, verbosity=False)
+    opt.search(ackley_function, n_iter=10, verbosity=False)

tests/test_empty_output/verbose.py

@@ -22,4 +22,4 @@ def ackley_function(para):
 
 
 opt = RandomSearchOptimizer(search_space)
-opt.search(ackley_function, n_iter=30)
+opt.search(ackley_function, n_iter=10)

tests/test_optimizers/_parametrize.py

@@ -219,3 +219,16 @@
         (ForestOptimizer),
     ],
 )
+
+optimizers_representative = (
+    "Optimizer",
+    [
+        (HillClimbingOptimizer),
+        (RandomSearchOptimizer),
+        (SimulatedAnnealingOptimizer),
+        (ParticleSwarmOptimizer),
+        (EvolutionStrategyOptimizer),
+        (BayesianOptimizer),
+        (TreeStructuredParzenEstimators),
+    ],
+)

tests/test_optimizers/test_best_results.py

@@ -47,7 +47,7 @@ def test_best_results_0(Optimizer, objective):
     opt = Optimizer(search_space, initialize=initialize)
     opt.search(
         objective_function,
-        n_iter=30,
+        n_iter=20,
         memory=False,
         verbosity={"print_results": False, "progress_bar": False},
     )
@@ -68,7 +68,7 @@ def objective_function(para):
     opt = Optimizer(search_space, initialize=initialize)
     opt.search(
         objective_function,
-        n_iter=30,
+        n_iter=20,
         memory=False,
         verbosity={"print_results": False, "progress_bar": False},
     )

tests/test_optimizers/test_constr_opt.py

@@ -1,10 +1,10 @@
 import numpy as np
 import pytest
 
-from ._parametrize import optimizers
+from ._parametrize import optimizers_representative
 
 
-@pytest.mark.parametrize(*optimizers)
+@pytest.mark.parametrize(*optimizers_representative)
 def test_constr_opt_0(Optimizer):
     def objective_function(para):
         score = -para["x1"] * para["x1"]
@@ -30,7 +30,7 @@ def constraint_1(para):
     assert np.all(x0_values > -5)
 
 
-@pytest.mark.parametrize(*optimizers)
+@pytest.mark.parametrize(*optimizers_representative)
 def test_constr_opt_1(Optimizer):
     def objective_function(para):
         score = -(para["x1"] * para["x1"] + para["x2"] * para["x2"])
@@ -47,7 +47,7 @@ def constraint_1(para):
     constraints_list = [constraint_1]
 
     opt = Optimizer(search_space, constraints=constraints_list)
-    opt.search(objective_function, n_iter=50)
+    opt.search(objective_function, n_iter=30)
 
     search_data = opt.search_data
     x0_values = search_data["x1"].values
@@ -57,9 +57,9 @@ def constraint_1(para):
     assert np.all(x0_values > -5)
 
 
-@pytest.mark.parametrize(*optimizers)
+@pytest.mark.parametrize(*optimizers_representative)
 def test_constr_opt_2(Optimizer):
-    n_iter = 50
+    n_iter = 30
 
     def objective_function(para):
         score = -para["x1"] * para["x1"]

tests/test_optimizers/test_inf_nan.py

@@ -57,12 +57,12 @@ def test_inf_nan_0(Optimizer, objective):
     }
 
     if Optimizer.__name__ == "PowellsMethod":
-        opt_para["iters_p_dim"] = 80
+        opt_para["iters_p_dim"] = 40
 
     opt = Optimizer(**opt_para)
     opt.search(
         objective_function,
-        n_iter=80,
+        n_iter=40,
         verbosity={"print_results": False, "progress_bar": False},
     )
 
@@ -76,7 +76,7 @@ def test_inf_nan_1(Optimizer, objective):
     opt = Optimizer(search_space, initialize=initialize)
     opt.search(
         objective_function,
-        n_iter=50,
+        n_iter=40,
         memory=False,
         verbosity={"print_results": False, "progress_bar": False},
     )

tests/test_optimizers/test_initializers.py

@@ -1,7 +1,7 @@
 import pytest
 import numpy as np
 
-from ._parametrize import optimizers, optimizers_2
+from ._parametrize import optimizers, optimizers_2, optimizers_representative
 
 
 def objective_function(para):
@@ -101,23 +101,23 @@ def test_initialize_grid_1(Optimizer):
     assert abs(opt.best_score) - 1 < 0.001
 
 
-@pytest.mark.parametrize(*optimizers)
-@pytest.mark.parametrize(*optimizers_2)
+@pytest.mark.parametrize("Optimizer", optimizers_representative[1])
+@pytest.mark.parametrize("Optimizer2", optimizers_representative[1])
 def test_initialize_warm_start_twoOpt_0(Optimizer, Optimizer2):
     opt1 = Optimizer(search_space)
     opt1.search(objective_function, n_iter=1)
 
     opt2 = Optimizer2(search_space, initialize={"warm_start": [opt1.best_para]})
-    opt2.search(objective_function, n_iter=20)
+    opt2.search(objective_function, n_iter=15)
 
     assert opt1.best_score <= opt2.best_score
 
 
-@pytest.mark.parametrize(*optimizers)
-@pytest.mark.parametrize(*optimizers_2)
+@pytest.mark.parametrize("Optimizer", optimizers_representative[1])
+@pytest.mark.parametrize("Optimizer2", optimizers_representative[1])
 def test_initialize_warm_start_twoOpt_1(Optimizer, Optimizer2):
     opt1 = Optimizer(search_space)
-    opt1.search(objective_function, n_iter=20)
+    opt1.search(objective_function, n_iter=15)
 
     opt2 = Optimizer2(search_space, initialize={"warm_start": [opt1.best_para]})
     opt2.search(objective_function, n_iter=1)

tests/test_optimizers/test_large_search_space.py

@@ -49,7 +49,7 @@ def test_large_search_space_2(Optimizer):
 def test_large_search_space_3(Optimizer):
 
     search_space = {}
-    for i in range(10):
+    for i in range(5):
         key = "x" + str(i)
         search_space[key] = np.arange(0, 10)
 

tests/test_optimizers/test_results.py

@@ -1,10 +1,10 @@
 import pytest
 import numpy as np
 
-from ._parametrize import optimizers
+from ._parametrize import optimizers_representative
 
 
-@pytest.mark.parametrize(*optimizers)
+@pytest.mark.parametrize(*optimizers_representative)
 def test_results_0(Optimizer):
     search_space = {"x1": np.arange(-10, 1, 1)}
 
@@ -17,7 +17,7 @@ def objective_function(para):
     opt = Optimizer(search_space, initialize=initialize)
     opt.search(
         objective_function,
-        n_iter=30,
+        n_iter=20,
         memory=False,
         verbosity={"print_results": False, "progress_bar": False},
     )
@@ -28,7 +28,7 @@ def objective_function(para):
     assert results_set.issubset(search_space_set)
 
 
-@pytest.mark.parametrize(*optimizers)
+@pytest.mark.parametrize(*optimizers_representative)
 def test_results_1(Optimizer):
     search_space = {"x1": np.arange(-10, 1, 1), "x2": np.arange(-10, 1, 1)}
 
@@ -41,7 +41,7 @@ def objective_function(para):
     opt = Optimizer(search_space, initialize=initialize)
     opt.search(
         objective_function,
-        n_iter=50,
+        n_iter=30,
         memory=False,
         verbosity={"print_results": False, "progress_bar": False},
     )

tests/test_optimizers/test_search_step.py

@@ -16,7 +16,7 @@ def objective_function(para):
 
 @pytest.mark.parametrize(*optimizers)
 def test_search_step_0(Optimizer):
-    n_iter = 100
+    n_iter = 30
 
     opt = Optimizer(search_space)