add test_constr_opt.py

Author: SimonBlanke

tests/test_api/test_constr_opt.py

@@ -0,0 +1,152 @@
+import numpy as np
+
+from hyperactive.optimizers import HillClimbingOptimizer
+from hyperactive.experiment import BaseExperiment
+from hyperactive.search_config import SearchConfig
+
+
+def test_constr_opt_0():
+    class Experiment(BaseExperiment):
+        def objective_function(self, para):
+            score = -para["x1"] * para["x1"]
+            return score
+
+    experiment = Experiment()
+
+    search_config = SearchConfig(
+        x1=list(np.arange(-15, 15, 1)),
+    )
+
+    def constraint_1(para):
+        print(" para", para)
+
+        return para["x1"] > -5
+
+    constraints_list = [constraint_1]
+
+    hyper = HillClimbingOptimizer()
+    hyper.add_search(
+        experiment,
+        search_config,
+        n_iter=50,
+        constraints=constraints_list,
+    )
+    hyper.run()
+
+    search_data = hyper.search_data(experiment)
+    x0_values = search_data["x1"].values
+
+    print("\n search_data \n", search_data, "\n")
+
+    assert np.all(x0_values > -5)
+
+
+def test_constr_opt_1():
+    class Experiment(BaseExperiment):
+        def objective_function(self, para):
+            score = -(para["x1"] * para["x1"] + para["x2"] * para["x2"])
+            return score
+
+    experiment = Experiment()
+
+    search_config = SearchConfig(
+        x1=list(np.arange(-10, 10, 1)),
+        x2=list(np.arange(-10, 10, 1)),
+    )
+
+    def constraint_1(para):
+        return para["x1"] > -5
+
+    constraints_list = [constraint_1]
+
+    hyper = HillClimbingOptimizer()
+    hyper.add_search(
+        experiment,
+        search_config,
+        n_iter=50,
+        constraints=constraints_list,
+    )
+    hyper.run()
+
+    search_data = hyper.search_data(experiment)
+    x0_values = search_data["x1"].values
+
+    print("\n search_data \n", search_data, "\n")
+
+    assert np.all(x0_values > -5)
+
+
+def test_constr_opt_2():
+    n_iter = 50
+
+    class Experiment(BaseExperiment):
+        def objective_function(self, para):
+            score = -para["x1"] * para["x1"]
+            return score
+
+    experiment = Experiment()
+
+    search_config = SearchConfig(
+        x1=list(np.arange(-10, 10, 0.1)),
+    )
+
+    def constraint_1(para):
+        return para["x1"] > -5
+
+    def constraint_2(para):
+        return para["x1"] < 5
+
+    constraints_list = [constraint_1, constraint_2]
+
+    hyper = HillClimbingOptimizer()
+    hyper.add_search(
+        experiment,
+        search_config,
+        n_iter=50,
+        constraints=constraints_list,
+    )
+    hyper.run()
+
+    search_data = hyper.search_data(experiment)
+    x0_values = search_data["x1"].values
+
+    print("\n search_data \n", search_data, "\n")
+
+    assert np.all(x0_values > -5)
+    assert np.all(x0_values < 5)
+
+    n_new_positions = 0
+    n_new_scores = 0
+
+    n_current_positions = 0
+    n_current_scores = 0
+
+    n_best_positions = 0
+    n_best_scores = 0
+
+    for hyper_optimizer in hyper.opt_pros.values():
+        optimizer = hyper_optimizer.gfo_optimizer
+
+        n_new_positions = n_new_positions + len(optimizer.pos_new_list)
+        n_new_scores = n_new_scores + len(optimizer.score_new_list)
+
+        n_current_positions = n_current_positions + len(optimizer.pos_current_list)
+        n_current_scores = n_current_scores + len(optimizer.score_current_list)
+
+        n_best_positions = n_best_positions + len(optimizer.pos_best_list)
+        n_best_scores = n_best_scores + len(optimizer.score_best_list)
+
+        print("\n  optimizer", optimizer)
+        print("  n_new_positions", optimizer.pos_new_list)
+        print("  n_new_scores", optimizer.score_new_list)
+
+    assert n_new_positions == n_iter
+    assert n_new_scores == n_iter
+
+    assert n_current_positions == n_current_scores
+    assert n_current_positions <= n_new_positions
+
+    assert n_best_positions == n_best_scores
+    assert n_best_positions <= n_new_positions
+
+    assert n_new_positions == n_new_scores