fix code block in the MCO section (examples.rst)

docs/source/examples.rst

@@ -1826,17 +1826,17 @@ we will set the number_of_parallel_points parameter to 12, and also limit oursel
     Level lines of the objective function, built with the parameter kernel='rbf'
 
 During the experiment an optimal quality metric value of -0.9469 was obtained with the following combination 
-of parameters: :math:`C = 1.6474∙10^5,  C = 1.6474 \dot 10^5`, gamma = 0.0767, kernel = 'rbf'.
+of parameters: :math:`C = 1.6474∙10^5`, gamma = 0.0767, kernel = 'rbf'.
 
 
 
-Example of solving a multi-criteria optimization problem
+Example of solving a multicriteria optimization problem
 ________________________________________________________
 
 
 If optimization is performed using several criteria, then the solution to the problem becomes more complicated. 
 The problem is that the criteria are usually contradictory: decreasing the value of one of them often leads 
-to an increase in the values of others. Let's consider the work of the iOpt framework when solving a multi-criteria problem. 
+to an increase in the values of others. Let's consider the work of the iOpt framework when solving a multicriteria problem. 
 To do this, we modify the problem statement in Section Tuning support vector machine hyperparameters for a classification problem in machine learning.
 
 Dataset used
@@ -1855,6 +1855,87 @@ Finding optimal parameters using the iOpt framework
 
 Let's launch the iOpt framework to construct the Pareto set.
 We consider two continuous parameters:
+
 #. regularization parameter **C**: [10\ :sup:`1`, 10\ :sup:`6`];
 #. kernel coefficient **gamma**: [10\ :sup:`-7`, 10\ :sup:`-3`].
+
 First, we need to formulate the basic problem as a class inherited from Problem.
+
+.. code-block::
+   :caption: Statement of the problem of multicriteria optimization using the iOpt framework
+
+   import numpy as np
+
+   cs = np.logspace(1, 6, 20)
+   gamms = np.logspace(-7, -3, 20)
+
+   params = {'C': cs, 'gamma': gamms}
+
+   search = GridSearchCV(SVC(), cv=5, param_grid=params, 
+      scoring=lambda model, x, y: f1_score(y, model.predict(x)))
+   search.fit(x, y)
+
+   import numpy as np
+
+   from iOpt.trial import Point
+   from iOpt.trial import FunctionValue
+   from iOpt.trial import Trial
+   from iOpt.problem import Problem
+   from sklearn.model_selection import cross_val_score
+   from sklearn.svm import SVC
+
+   class mco_breast_cancer(Problem):
+
+      def __init__(self, X, y, X_train, y_train):
+         """
+         Class constructor breast_cancer problem.
+         """
+
+         super(mco_breast_cancer, self).__init__()
+
+         self.X = X
+         self.y = y
+         self.X_train = X_train
+         self.y_train = y_train
+
+         self.name = "mco_test1"
+         self.dimension = 2
+         self.number_of_float_variables = 2
+         self.number_of_discrete_variables = 0
+         self.number_of_objectives = 2
+         self.number_of_constraints = 0
+
+         self.float_variable_names = np.ndarray(shape = 
+            (self.number_of_float_variables,), dtype=object)
+
+         for i in range(self.number_of_float_variables):
+            self.float_variable_names[i] = str(i)
+
+         self.lower_bound_of_float_variables = np.array([1, -7],
+            dtype=np.double)
+         self.upper_bound_of_float_variables = np.array([6, -3], 
+            dtype=np.double)
+
+         self.known_optimum = np.ndarray(shape=(1,), dtype=Trial)
+
+      def calculateAllFunction(self, point: Point, function_values: 
+         np.ndarray(shape=(1), dtype=FunctionValue)) -> \
+            np.ndarray(shape=(1), dtype=FunctionValue):
+
+         x = point.float_variables
+
+         svc_c = 10 ** x[0]
+         gamma = 10 ** x[1]
+
+         classifier_obj = SVC(C=svc_c, gamma=gamma)
+         classifier_obj.fit(self.X_train, self.y_train)
+
+         # OBJECTIV 1
+         function_values[0].value = - cross_val_score(classifier_obj, self.X,
+            self.y, n_jobs=4, scoring='precision').mean()
+
+         # OBJECTIV 2
+         function_values[1].value = - cross_val_score(classifier_obj, self.X,
+            self.y, n_jobs=4, scoring='recall').mean()
+
+         return function_values