Merge pull request #295 from Happy-Algorithms-League/api/rename_min_f…

…itness

Replace min_fitness by termination_fitness

cgp/__init__.py

@@ -6,6 +6,8 @@
 __url__ = "https://happy-algorithms-league.github.io/hal-cgp/"
 __doc__ = f"{__description__} <{__url__}>"
 
+import warnings
+
 from . import ea, local_search, utils
 from .cartesian_graph import CartesianGraph, atomic_operator
 from .genome import Genome
@@ -14,3 +16,5 @@
 from .node import OperatorNode
 from .node_impl import Add, ConstantFloat, Div, IfElse, Mul, Parameter, Pow, Sub
 from .population import Population
+
+warnings.simplefilter("always", DeprecationWarning)

cgp/hl_api.py

@@ -1,3 +1,4 @@
+import warnings
 from typing import Callable, Optional
 
 import numpy as np
@@ -11,7 +12,8 @@ def evolve(
     pop: Population,
     objective: Callable[[IndividualBase], IndividualBase],
     ea: MuPlusLambda,
-    min_fitness: float,
+    min_fitness: float = np.inf,
+    termination_fitness: float = np.inf,
     max_generations: int = np.inf,
     max_objective_calls: int = np.inf,
     print_progress: Optional[bool] = False,
@@ -33,6 +35,10 @@ def evolve(
         `initialize_fitness_parents` and `step` method.
     min_fitness : float
         Minimum fitness at which the evolution is stopped.
+        Warning: This argument is deprecated and will be removed in the 0.4
+        release. Please use `termination_fitness` instead.
+    termination_fitness : float
+        Minimum fitness at which the evolution is terminated
     max_generations : int
         Maximum number of generations.
         Defaults to positive infinity.
@@ -50,6 +56,23 @@ def evolve(
     -------
     None
     """
+    if np.isfinite(min_fitness) and np.isfinite(termination_fitness):
+        raise RuntimeError(
+            "Both `min_fitness` and `termination_fitness` have been set. The "
+            "`min_fitness` argument is deprecated and will be removed in the 0.4 "
+            "release. Please use `termination_fitness` instead."
+        )
+
+    if np.isfinite(min_fitness):
+        warnings.warn(
+            DeprecationWarning(
+                "The `min_fitness` argument is deprecated and "
+                "will be removed in the 0.4 release. Please use "
+                "`termination_fitness` instead."
+            )
+        )
+        termination_fitness = min_fitness
+
     if np.isinf(max_generations) and np.isinf(max_objective_calls):
         raise ValueError("Either max_generations or max_objective_calls must be finite.")
 
@@ -91,7 +114,7 @@ def evolve(
         if callback is not None:
             callback(pop)
 
-        if pop.champion.fitness + 1e-10 >= min_fitness:
+        if pop.champion.fitness + 1e-10 >= termination_fitness:
             break
 
     if print_progress:

examples/example_caching.py

@@ -84,7 +84,7 @@ def objective(individual):
         "levels_back": 2,
         "primitives": (cgp.Add, cgp.Sub, cgp.Mul, cgp.ConstantFloat),
     },
-    "evolve_params": {"max_generations": 200, "min_fitness": -1e-12},
+    "evolve_params": {"max_generations": 200, "termination_fitness": -1e-12},
 }
 
 # %%

examples/example_differential_evo_regression.py

@@ -113,7 +113,7 @@ def objective(individual, seed):
     "k_local_search": 2,
 }
 
-evolve_params = {"max_generations": int(args["--max-generations"]), "min_fitness": 0.0}
+evolve_params = {"max_generations": int(args["--max-generations"]), "termination_fitness": 0.0}
 
 # use an uneven number of gradient steps so they can not easily
 # average out for clipped values

examples/example_evo_regression.py

@@ -136,7 +136,7 @@ def evolution(f_target):
 
     ea_params = {"n_offsprings": 10, "tournament_size": 2, "mutation_rate": 0.03, "n_processes": 2}
 
-    evolve_params = {"max_generations": int(args["--max-generations"]), "min_fitness": 0.0}
+    evolve_params = {"max_generations": int(args["--max-generations"]), "termination_fitness": 0.0}
 
     # create population that will be evolved
     pop = cgp.Population(**population_params, genome_params=genome_params)

examples/example_fec_caching.py

@@ -99,7 +99,7 @@ def objective(individual):
         "levels_back": 2,
         "primitives": (cgp.Add, cgp.Sub, cgp.Mul, cgp.ConstantFloat),
     },
-    "evolve_params": {"max_generations": 200, "min_fitness": -1e-12},
+    "evolve_params": {"max_generations": 200, "termination_fitness": -1e-12},
 }
 
 # %%

examples/example_hurdles.py

@@ -132,7 +132,7 @@ def objective_two(individual):
     "hurdle_percentile": [0.5, 0.0],
 }
 
-evolve_params = {"max_generations": int(args["--max-generations"]), "min_fitness": 0.0}
+evolve_params = {"max_generations": int(args["--max-generations"]), "termination_fitness": 0.0}
 
 # %%
 # We create a population that will be evolved

examples/example_local_search_evolution_strategies.py

@@ -101,7 +101,7 @@ def objective(individual, seed):
     "k_local_search": 2,
 }
 
-evolve_params = {"max_generations": int(args["--max-generations"]), "min_fitness": 0.0}
+evolve_params = {"max_generations": int(args["--max-generations"]), "termination_fitness": 0.0}
 
 # restrict the number of steps in the local search; since parameter
 # values are propagated from parents to offsprings, parameter values

examples/example_minimal.py

@@ -83,7 +83,7 @@ def objective(individual):
 
 ea_params = {"n_offsprings": 4, "mutation_rate": 0.03, "n_processes": 2}
 
-evolve_params = {"max_generations": int(args["--max-generations"]), "min_fitness": 0.0}
+evolve_params = {"max_generations": int(args["--max-generations"]), "termination_fitness": 0.0}
 
 # %%
 # We create a population that will be evolved

examples/example_mountain_car.py

@@ -168,7 +168,10 @@ def evolve(seed):
 
     ea_params = {"n_offsprings": 4, "tournament_size": 1, "mutation_rate": 0.04, "n_processes": 4}
 
-    evolve_params = {"max_generations": int(args["--max-generations"]), "min_fitness": 100.0}
+    evolve_params = {
+        "max_generations": int(args["--max-generations"]),
+        "termination_fitness": 100.0,
+    }
 
     pop = cgp.Population(**population_params, genome_params=genome_params)
 

examples/example_multi_genome.py

@@ -84,7 +84,7 @@ def objective(individual):
 
 ea_params = {"n_offsprings": 4, "mutation_rate": 0.03, "n_processes": 1}
 
-evolve_params = {"max_generations": int(args["--max-generations"]), "min_fitness": 0.0}
+evolve_params = {"max_generations": int(args["--max-generations"]), "termination_fitness": 0.0}
 
 # %%
 # We create a population that will be evolved

examples/example_parametrized_nodes.py

@@ -113,7 +113,7 @@ def objective(individual, seed):
 
 ea_params = {"n_offsprings": 4, "tournament_size": 1, "mutation_rate": 0.04, "n_processes": 2}
 
-evolve_params = {"max_generations": int(args["--max-generations"]), "min_fitness": 0.0}
+evolve_params = {"max_generations": int(args["--max-generations"]), "termination_fitness": 0.0}
 
 local_search_params = {"lr": 1e-3, "gradient_steps": 9}
 

examples/example_piecewise_target_function.py

@@ -90,7 +90,7 @@ def objective(individual, rng):
 
 ea_params = {"n_offsprings": 4, "mutation_rate": 0.03, "n_processes": 2}
 
-evolve_params = {"max_generations": int(args["--max-generations"]), "min_fitness": 0.0}
+evolve_params = {"max_generations": int(args["--max-generations"]), "termination_fitness": 0.0}
 
 # create population that will be evolved
 pop = cgp.Population(**population_params, genome_params=genome_params)

examples/example_reorder.py

@@ -93,7 +93,7 @@ def objective(individual):
     "reorder_genome": True,
 }
 
-evolve_params = {"max_generations": int(args["--max-generations"]), "min_fitness": 0.0}
+evolve_params = {"max_generations": int(args["--max-generations"]), "termination_fitness": 0.0}
 
 # %%
 # We create two populations that will be evolved

test/test_hl_api.py

@@ -203,3 +203,19 @@ def test_speedup_parallel_evolve(population_params, genome_params, ea_params):
         else:
             # assert that multiprocessing roughly follows a linear speedup.
             assert T == pytest.approx(T_baseline / n_processes, rel=0.25)
+
+
+def test_min_fitness_deprecation(population_params, genome_params, ea_params):
+    def objective(individual):
+        individual.fitness = 1.0
+        return individual
+
+    pop = cgp.Population(**population_params, genome_params=genome_params)
+    ea = cgp.ea.MuPlusLambda(**ea_params)
+    with pytest.warns(DeprecationWarning):
+        cgp.evolve(pop, objective, ea, min_fitness=2.0, max_generations=10)
+
+    with pytest.raises(RuntimeError):
+        cgp.evolve(
+            pop, objective, ea, min_fitness=2.0, termination_fitness=1.5, max_generations=10
+        )