added: test about reproducibility using seed

mloptimizer/test/test_genoptimizer/test_optimizers.py

@@ -5,6 +5,7 @@
     GradientBoostingOptimizer, SVCOptimizer, XGBClassifierOptimizer, KerasClassifierOptimizer, \
     CustomXGBClassifierOptimizer, CatBoostClassifierOptimizer, \
     BaseOptimizer
+from mloptimizer.evaluation import kfold_score
 from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, \
     balanced_accuracy_score, precision_score, recall_score, \
     average_precision_score, log_loss, mean_squared_error, mean_absolute_error, \
@@ -39,3 +40,28 @@ def test_get_subclasses():
     ]
     assert all([subclass.__name__ in subclasses_names for subclass in subclasses]) and \
            len(subclasses) == len(subclasses_names)
+
+
+@pytest.mark.parametrize('optimizer',
+                         (TreeOptimizer, ForestOptimizer,
+                          # ExtraTreesOptimizer, GradientBoostingOptimizer,
+                          XGBClassifierOptimizer,
+                          # SVCOptimizer,KerasClassifierOptimizer
+                          ))
+def test_reproducibility(optimizer):
+    X, y = load_iris(return_X_y=True)
+    population = 2
+    generations = 2
+    seed = 25
+    distinct_seed = 2
+    optimizer1 = optimizer(X, y, score_function=balanced_accuracy_score,
+                           eval_function=kfold_score, seed=seed)
+    result1 = optimizer1.optimize_clf(population=population, generations=generations)
+    optimizer2 = optimizer(X, y, score_function=balanced_accuracy_score,
+                           eval_function=kfold_score, seed=seed)
+    result2 = optimizer2.optimize_clf(population=population, generations=generations)
+    optimizer3 = optimizer(X, y, score_function=balanced_accuracy_score,
+                           eval_function=kfold_score, seed=distinct_seed)
+    result3 = optimizer3.optimize_clf(population=population, generations=generations)
+    assert str(result1) == str(result2)
+    assert str(result1) != str(result3)