Added targets to benchmarks

examples/benchmarks/classification.py

@@ -16,7 +16,6 @@
 from geml.grammars.ruleset_classification import make_grammar
 
 
-
 class ClassificationBenchmark(Benchmark):
 
     def __init__(self, X, y, feature_names):
@@ -34,7 +33,7 @@ def fitness_function(ruleset):
             except ValueError:
                 return -1
 
-        self.problem = SingleObjectiveProblem(minimize=False, fitness_function=fitness_function)
+        self.problem = SingleObjectiveProblem(minimize=False, fitness_function=fitness_function, target=1)
 
     def setup_grammar(self, y, feature_names):
         # Grammar

examples/benchmarks/classification_lexicase.py

@@ -5,7 +5,7 @@
 from examples.benchmarks.benchmark import Benchmark, example_run
 from examples.benchmarks.datasets import get_banknote
 from geneticengine.grammar.grammar import extract_grammar, Grammar
-from geneticengine.problems import LazyMultiObjectiveProblem, Problem
+from geneticengine.problems import MultiObjectiveProblem, Problem
 from geml.common import forward_dataset
 from geml.grammars.ruleset_classification import make_grammar
 
@@ -24,7 +24,12 @@ def fitness_function_lexicase(ruleset):
             except ValueError:
                 return [0 for _ in y]
 
-        self.problem = LazyMultiObjectiveProblem(minimize=False, fitness_function=fitness_function_lexicase, target=1)
+        n_objectives = len(y)
+        self.problem = MultiObjectiveProblem(
+            fitness_function=fitness_function_lexicase,
+            minimize=[False for _ in range(n_objectives)],
+            target=[1 for _ in range(n_objectives)],
+        )
 
     def setup_grammar(self, y, feature_names):
         options = [int(v) for v in np.unique(y)]

examples/benchmarks/game_of_life_vectorial.py

@@ -185,7 +185,7 @@ def fitness_function(i: Condition):
             ypred = [_clf(line) for line in np.rollaxis(X, 0)]
             return f1_score(y, ypred)
 
-        self.problem = SingleObjectiveProblem(minimize=False, fitness_function=fitness_function)
+        self.problem = SingleObjectiveProblem(minimize=False, fitness_function=fitness_function, target=1)
 
     def setup_grammar(self):
         self.grammar = extract_grammar(

examples/benchmarks/regression.py

@@ -33,7 +33,7 @@ def fitness_function(ruleset):
             except ValueError:
                 return -1
 
-        self.problem = SingleObjectiveProblem(minimize=False, fitness_function=fitness_function)
+        self.problem = SingleObjectiveProblem(minimize=False, fitness_function=fitness_function, target=0)
 
     def setup_grammar(self, feature_names):
         # Grammar

examples/benchmarks/regression_lexicase.py

@@ -8,7 +8,7 @@
 from examples.benchmarks.datasets import get_vladislavleva
 from geneticengine.grammar.grammar import extract_grammar
 from geneticengine.grammar.grammar import Grammar
-from geneticengine.problems import LazyMultiObjectiveProblem, Problem
+from geneticengine.problems import MultiObjectiveProblem, Problem
 
 from geml.common import forward_dataset
 from geml.grammars.symbolic_regression import Expression, components, make_var
@@ -61,7 +61,12 @@ def lexicase_fitness_function(n: Expression):
 
             return grouped_errors
 
-        self.problem = LazyMultiObjectiveProblem(minimize=True, fitness_function=lexicase_fitness_function)
+        n_objectives = len(y)
+        self.problem = MultiObjectiveProblem(
+            fitness_function=lexicase_fitness_function,
+            minimize=[True for _ in range(n_objectives)],
+            target=[0 for _ in range(n_objectives)],
+        )
 
     def setup_grammar(self, feature_names):
         # Grammar

examples/benchmarks/santafe.py

@@ -174,7 +174,8 @@ def setup_problem(self, map):
         def fitness_function(i):
             return simulate(i, map)
 
-        self.problem = SingleObjectiveProblem(minimize=False, fitness_function=fitness_function)
+        target_food = map.count("#")
+        self.problem = SingleObjectiveProblem(minimize=False, fitness_function=fitness_function, target=target_food)
 
     def setup_grammar(self):
         self.grammar = extract_grammar([ActionBlock, Action, IfFood, Move, Right, Left], ActionMain)

examples/benchmarks/string_match.py

@@ -1,8 +1,6 @@
 from __future__ import annotations
 
 
-
-
 from examples.benchmarks.benchmark import Benchmark, example_run
 from geml.grammars.letter import Char, Consonant, LetterString, String, Vowel
 from geneticengine.grammar.grammar import extract_grammar
@@ -11,7 +9,6 @@
 from geneticengine.problems import SingleObjectiveProblem
 
 
-
 class StringMatchBenchmark(Benchmark):
     def __init__(self, target: str = "Hellow world!"):
         self.setup_problem(target)
@@ -33,7 +30,7 @@ def fitness_function(individual):
                     fitness -= 1 / (1 + (abs(ord(t_p) - ord(g_p))))
             return fitness
 
-        self.problem = SingleObjectiveProblem(minimize=True, fitness_function=fitness_function)
+        self.problem = SingleObjectiveProblem(minimize=True, fitness_function=fitness_function, target=0)
 
     def setup_grammar(self):
         self.grammar = extract_grammar([LetterString, Char, Vowel, Consonant], String)

examples/benchmarks/vectorialgp.py

@@ -3112,7 +3112,7 @@ def regressor(l):
                 y_pred,
             )
 
-        self.problem = SingleObjectiveProblem(minimize=True, fitness_function=fitness_function)
+        self.problem = SingleObjectiveProblem(minimize=True, fitness_function=fitness_function, target=0)
 
     def setup_grammar(self):
         """See fitness/vectorialgp.py for docs.

geml/classifiers.py

@@ -26,6 +26,9 @@ def get_grammar(self, feature_names: list[str], data, target) -> Grammar:
         Var.to_numpy = lambda s: f"dataset[:,{index_of[s.name]}]"  # type:ignore
         return extract_grammar(components, RuleSet)
 
+    def get_goal(self) -> tuple[bool, float]:
+        return True, 1
+
 
 class GeneticProgrammingClassifier(GeneticEngineClassifier):
 

geml/common.py

@@ -1,4 +1,5 @@
 from abc import abstractmethod
+import abc
 from typing import Any
 
 import numpy as np
@@ -108,6 +109,9 @@ def predict(self, X):
         feature_names, data = self.prepare_inputs(X)
         return forward_dataset(self._best_individual[0], data)
 
+    @abc.abstractmethod
+    def get_goal(self) -> tuple[bool, float]: ...
+
     @_fit_context(prefer_skip_nested_validation=True)
     def fit(self, X, y):
 
@@ -129,7 +133,8 @@ def fitness_function(x: Expression) -> float:
             except ValueError:
                 return -10000000
 
-        problem = SingleObjectiveProblem(fitness_function)
+        minimize, target = self.get_goal()
+        problem = SingleObjectiveProblem(fitness_function, minimize, target)
 
         population_recorder = PopulationRecorder()
 

geml/regressors.py

@@ -42,6 +42,9 @@ def get_grammar(self, feature_names: list[str], data, target) -> Grammar:
         complete_components = components + [Var]
         return extract_grammar(complete_components, Expression)
 
+    def get_goal(self) -> tuple[bool, float]:
+        return False, 0
+
 
 class GeneticProgrammingRegressor(GeneticEngineRegressor):