tests EDA_multivairate_gaussian

VicentePerezSoloviev · Jun 11, 2020 · 7bd61dc · 7bd61dc
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commit 7bd61dc
Showing 1 changed file with 214 additions and 0 deletions.
diff --git a/EDAspy/tests/test_EDA_multivariate_gaussian.py b/EDAspy/tests/test_EDA_multivariate_gaussian.py
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+from unittest import TestCase
+
+
+class TestEDA_multivariate_gaussian(TestCase):
+    def test_truncation(self):
+        import pandas as pd
+        from EDAspy.optimization.multivariate import EDA_multivariate_gaussian as EDAmg
+
+        def cost_function(dictionary):
+            suma = dictionary['param1'] + dictionary['param2'] + dictionary['param3']
+            if suma < 0:
+                return 999999999
+            return suma
+
+        mus = pd.DataFrame(columns=['param1', 'param2', 'param3'])
+        mus.loc[0] = [10, 8, 5]
+
+        sigma = pd.DataFrame(columns=['param1', 'param2', 'param3'])
+        sigma.loc[0] = 5
+
+        EDA = EDAmg(SIZE_GEN=8, MAX_ITER=20, DEAD_ITER=10, ALPHA=0.5, aim='maximize',
+                    cost_function=cost_function, mus=mus, sigma=sigma)
+
+        gen = pd.DataFrame(columns=sigma.columns)
+
+        individual = {'param1': 0, 'param2': 0, 'param3': 1, 'cost': 1}  # 1
+        gen = gen.append(individual, True)
+        individual = {'param1': 0, 'param2': 1, 'param3': 0, 'cost': 1}  # 2
+        gen = gen.append(individual, True)
+        individual = {'param1': 0, 'param2': 1, 'param3': 1, 'cost': 2}  # 3
+        gen = gen.append(individual, True)
+        individual = {'param1': 1, 'param2': 0, 'param3': 0, 'cost': 1}  # 4
+        gen = gen.append(individual, True)
+        individual = {'param1': 1, 'param2': 0, 'param3': 1, 'cost': 2}  # 5
+        gen = gen.append(individual, True)
+        individual = {'param1': 1, 'param2': 1, 'param3': 0, 'cost': 2}  # 6
+        gen = gen.append(individual, True)
+        individual = {'param1': 1, 'param2': 1, 'param3': 1, 'cost': 3}  # 7
+        gen = gen.append(individual, True)
+        individual = {'param1': 0, 'param2': 0, 'param3': 0, 'cost': 0}  # 8
+        gen = gen.append(individual, True)
+
+        expected_output = pd.DataFrame(columns=sigma.columns)
+
+        individual = {'param1': 1, 'param2': 1, 'param3': 1, 'cost': 3}  # 7
+        expected_output = expected_output.append(individual, True)
+        individual = {'param1': 0, 'param2': 1, 'param3': 1, 'cost': 2}  # 3
+        expected_output = expected_output.append(individual, True)
+        individual = {'param1': 1, 'param2': 0, 'param3': 1, 'cost': 2}  # 5
+        expected_output = expected_output.append(individual, True)
+        individual = {'param1': 1, 'param2': 1, 'param3': 0, 'cost': 2}  # 6
+        expected_output = expected_output.append(individual, True)
+
+        EDA.generation = gen
+        EDA.truncation()
+        EDA.generation = EDA.generation.reset_index()
+        del EDA.generation['index']
+
+        output = expected_output == EDA.generation
+        for col in list(expected_output.columns):
+            assert list(output[col]) == [True, True, True, True], "Should be True"
+        print("EDAspy.optimization.multivariate.EDA_multivariate.truncation test passed")
+
+    def test_check_generation(self):
+        import pandas as pd
+        from EDAspy.optimization.multivariate import EDA_multivariate_gaussian as EDAmg
+
+        def cost_function(dictionary):
+            suma = dictionary['param1'] + dictionary['param2'] + dictionary['param3']
+            if suma < 0:
+                return 999999999
+            return suma
+
+        mus = pd.DataFrame(columns=['param1', 'param2', 'param3'])
+        mus.loc[0] = [10, 8, 5]
+
+        sigma = pd.DataFrame(columns=['param1', 'param2', 'param3'])
+        sigma.loc[0] = 5
+
+        EDA = EDAmg(SIZE_GEN=8, MAX_ITER=20, DEAD_ITER=10, ALPHA=0.5, aim='maximize',
+                    cost_function=cost_function, mus=mus, sigma=sigma)
+
+        gen = pd.DataFrame(columns=sigma.columns)
+
+        individual = {'param1': 0, 'param2': 0, 'param3': 1}  # 1
+        gen = gen.append(individual, True)
+        individual = {'param1': 0, 'param2': 1, 'param3': 0}  # 2
+        gen = gen.append(individual, True)
+        individual = {'param1': 0, 'param2': 1, 'param3': 1}  # 3
+        gen = gen.append(individual, True)
+        individual = {'param1': 1, 'param2': 0, 'param3': 0}  # 4
+        gen = gen.append(individual, True)
+        individual = {'param1': 1, 'param2': 0, 'param3': 1}  # 5
+        gen = gen.append(individual, True)
+        individual = {'param1': 1, 'param2': 1, 'param3': 0}  # 6
+        gen = gen.append(individual, True)
+        individual = {'param1': 1, 'param2': 1, 'param3': 1}  # 7
+        gen = gen.append(individual, True)
+        individual = {'param1': 0, 'param2': 0, 'param3': 0}  # 8
+        gen = gen.append(individual, True)
+
+        expected_output = [1, 1, 2, 1, 2, 2, 3, 0]
+
+        EDA.generation = gen
+        EDA.check_generation()
+        real_output = list(EDA.generation['cost'].values)
+
+        assert real_output == expected_output, "Should be True"
+        print("EDAspy.optimization.multivariate.EDA_multivariate.check_generation test passed")
+
+    def test_update_vector(self):
+        import pandas as pd
+        from EDAspy.optimization.multivariate import EDA_multivariate_gaussian as EDAmg
+
+        def cost_function(dictionary):
+            suma = dictionary['param1'] + dictionary['param2'] + dictionary['param3']
+            if suma < 0:
+                return 999999999
+            return suma
+
+        mus = pd.DataFrame(columns=['param1', 'param2', 'param3'])
+        mus.loc[0] = [10, 8, 5]
+
+        sigma = pd.DataFrame(columns=['param1', 'param2', 'param3'])
+        sigma.loc[0] = 5
+
+        EDA = EDAmg(SIZE_GEN=8, MAX_ITER=20, DEAD_ITER=10, ALPHA=0.5, aim='maximize',
+                    cost_function=cost_function, mus=mus, sigma=sigma)
+
+        gen = pd.DataFrame(columns=sigma.columns)
+
+        individual = {'param1': 1, 'param2': 1, 'param3': 1, 'cost': 3}  # 7
+        gen = gen.append(individual, True)
+        individual = {'param1': 0, 'param2': 1, 'param3': 1, 'cost': 2}  # 3
+        gen = gen.append(individual, True)
+        individual = {'param1': 1, 'param2': 0, 'param3': 1, 'cost': 2}  # 5
+        gen = gen.append(individual, True)
+        individual = {'param1': 1, 'param2': 1, 'param3': 0, 'cost': 2}  # 6
+        gen = gen.append(individual, True)
+
+        EDA.generation = gen
+        EDA.update_vector()
+
+        expected_sigma = pd.DataFrame(columns=sigma.columns)
+        expected_sigma['vars'] = sigma.columns
+        expected_sigma = expected_sigma.set_index('vars')
+        expected_sigma.loc['param1'] = [1.0000, -0.0833, -0.0833]
+        expected_sigma.loc['param2'] = [-0.0833, 1.0000, -0.0833]
+        expected_sigma.loc['param3'] = [-0.0833, -0.0833, 1.0000]
+
+        for var in list(sigma.columns):
+            for varj in list(sigma.columns):
+                # print(float(expected_sigma.round(decimals=4).loc[var, var]),
+                # float(EDA.sigma.round(decimals=4).loc[var, var]))
+
+                assert float(expected_sigma.round(decimals=4).loc[var, varj]) == \
+                       float(EDA.sigma.round(decimals=4).loc[var, varj]), "Should be True"
+
+        expected_mus = pd.DataFrame(columns=sigma.columns)
+        expected_mus.loc[0] = 0.75
+
+        for var in list(sigma.columns):
+            assert float(expected_mus[var]) == float(EDA.mus[var]), "Should be True"
+        print("EDAspy.optimization.multivariate.EDA_multivariate.update_vector test passed")
+
+    def test_compare_costs(self):
+        import pandas as pd
+        from EDAspy.optimization.multivariate import EDA_multivariate_gaussian as EDAmg
+
+        def cost_function(dictionary):
+            suma = dictionary['param1'] + dictionary['param2'] + dictionary['param3']
+            if suma < 0:
+                return 999999999
+            return suma
+
+        mus = pd.DataFrame(columns=['param1', 'param2', 'param3'])
+        mus.loc[0] = [10, 8, 5]
+
+        sigma = pd.DataFrame(columns=['param1', 'param2', 'param3'])
+        sigma.loc[0] = 5
+
+        EDA = EDAmg(SIZE_GEN=8, MAX_ITER=20, DEAD_ITER=10, ALPHA=0.5, aim='minimize',
+                    cost_function=cost_function, mus=mus, sigma=sigma)
+
+        EDA.best_mae_global = 10
+        EDA.__compare_costs__(8)
+
+        assert EDA.__compare_costs__(8) is True, "Should be True"
+        print("EDAspy.optimization.multivariate.EDA_multivariate.__compare_costs__ test passed")
+
+    def test_check_individual(self):
+        import pandas as pd
+        from EDAspy.optimization.multivariate import EDA_multivariate_gaussian as EDAmg
+
+        def cost_function(dictionary):
+            suma = dictionary['param1'] + dictionary['param2'] + dictionary['param3']
+            if suma < 0:
+                return 999999999
+            return suma
+
+        mus = pd.DataFrame(columns=['param1', 'param2', 'param3'])
+        mus.loc[0] = [10, 8, 5]
+
+        sigma = pd.DataFrame(columns=['param1', 'param2', 'param3'])
+        sigma.loc[0] = 5
+
+        EDA = EDAmg(SIZE_GEN=8, MAX_ITER=20, DEAD_ITER=10, ALPHA=0.5, aim='minimize',
+                    cost_function=cost_function, mus=mus, sigma=sigma)
+
+        individual = {'param1': 5.1, 'param2': 1.0, 'param3': 6.9}
+        assert EDA.__check_individual__(individual) == 13.0, "Should be 13.0"
+        print("EDAspy.optimization.multivariate.EDA_multivariate._check_individual_ test passed")
+
+