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aimclub · Nov 27, 2023 · 8b12370 · 8b12370
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Showing 2 changed files with 6 additions and 10 deletions.
diff --git a/docs/source/api/tuning.rst b/docs/source/api/tuning.rst
@@ -52,11 +52,6 @@ You can tune all parameters of graph nodes simultaneously using ``SimultaneousTu
 .. note::
    ``IOptTuner`` implements deterministic algorithm.
 
-   For now ``IOptTuner`` can not be constrained by time, so constrain execution by number of iterations.
-
-   Also ``IOptTuner`` can optimise only `continuous` and `discrete` parameters but not `categorical` ones.
-   `Categorical` parameters will be ignored while tuning.
-
    ``IOptTuner`` is implemented using `IOpt library`_. See the `documentation`_ (in Russian) to learn more about
    the optimisation algorithm.
 

diff --git a/golem/core/tuning/iopt_tuner.py b/golem/core/tuning/iopt_tuner.py
@@ -1,7 +1,6 @@
 from copy import deepcopy
 from dataclasses import dataclass, field
 from datetime import timedelta
-from random import choice
 from typing import List, Dict, Generic, Tuple, Any, Optional
 
 import numpy as np
@@ -147,14 +146,15 @@ def _tune(self, graph: DomainGraphForTune, show_progress: bool = True) -> Domain
 
         has_parameters_to_optimize = (len(problem_parameters.discrete_parameters_names) > 0 or
                                       len(problem_parameters.float_parameters_names) > 0)
-        objectives_number = len(ensure_wrapped_in_sequence(self.init_metric))
-        is_multi_objective = objectives_number > 1
-        if self._check_if_tuning_possible(graph, has_parameters_to_optimize):
+        self.objectives_number = len(ensure_wrapped_in_sequence(self.init_metric))
+        is_multi_objective = self.objectives_number > 1
+
+        if self._check_if_tuning_possible(graph, has_parameters_to_optimize, supports_multi_objective=True):
             if initial_parameters:
                 initial_point = Point(**initial_parameters)
                 self.solver_parameters.start_point = initial_point
 
-            problem = GolemProblem(graph, self.objective_evaluate, problem_parameters, objectives_number)
+            problem = GolemProblem(graph, self.objective_evaluate, problem_parameters, self.objectives_number)
             solver = Solver(problem, parameters=self.solver_parameters)
 
             if show_progress:
@@ -177,6 +177,7 @@ def _tune(self, graph: DomainGraphForTune, show_progress: bool = True) -> Domain
                     self.was_tuned = True
         else:
             tuned_graphs = graph
+
         return tuned_graphs
 
     def _get_parameters_for_tune(self, graph: OptGraph) -> Tuple[IOptProblemParameters, dict]: