Added: docs about reproducibility using input parameter seed.

docs/sections/Concepts/index.rst

@@ -12,4 +12,5 @@ even create new Optimizers subclasses inheriting from the ObtimizerBase class.
 
    hyperparam
    score_functions
+   reproducibility
    parallel

docs/sections/Concepts/reproducibility.rst

@@ -0,0 +1,52 @@
+Reproducibility
+===================
+
+Reproducibility is a key aspect of
+scientific research, and more precisely,
+in machine learning. ``MLOptimizer`` provides an
+input parameter ``seed`` that allows to set
+the random seed for:
+
+- The random number generator of the optimizer generating the initial population and the mutations
+- The random number generator of the model on training
+- The random number generator of the data on split
+
+An example of usage is:
+
+.. code-block:: python
+
+    from sklearn.datasets import load_breast_cancer as dataset
+    from sklearn.model_selection import train_test_split
+    from sklearn.tree import DecisionTreeClassifier
+    from sklearn.metrics import accuracy_score
+    from mloptimizer.genoptimizer import TreeOptimizer
+
+    X, y = load_iris(return_X_y=True)
+    population = 2
+    generations = 2
+    seed = 25
+    distinct_seed = 2
+    # It is important to run the optimization
+    # right after the creation of the optimizer
+    optimizer1 = TreeOptimizer(X, y, seed=seed)
+    result1 = optimizer1.optimize_clf(population=population,
+                                      generations=generations)
+    # WARNING: In case the optimizer2 would be created after the optimizer1,
+    # the results would be different
+    optimizer2 = TreeOptimizer(X, y, score_function=target_metric,
+                           eval_function=target_score, seed=seed)
+    result2 = optimizer2.optimize_clf(population=population,
+                                      generations=generations)
+
+    optimizer3 = TreeOptimizer(X, y, score_function=target_metric,
+                           eval_function=target_score, seed=distinct_seed)
+    result3 = optimizer3.optimize_clf(population=population,
+                                      generations=generations)
+    str(result1) == str(result2)
+    str(result1) != str(result3)
+
+.. warning::
+
+    To ensure reproducibility, it is important to run the optimization
+    right after the creation of the optimizer with the seed to ensure no
+    other random number generator has been used in the meantime.