[BUG] Completely broken on numpy 2.x (wrong vector sent to fitness()) #177
Description
With numpy 2.x, the fitness function is called with the wrong values in the decision vector x.
import pygmo as pg
class sphere_function:
def fitness(self, x):
cost = sum(x * x)
print(f"FITNESS EVAL: x = {x}, cost = {cost}")
return [cost]
def get_bounds(self):
return ([-1] * 3, [1] * 3)
prob = pg.problem(sphere_function())
pop = pg.population(prob, 5)
print(pop)
The example above results in the following. Notice how the population member #0 is [-0.504554, 0.87294, 0.639481] but the fitness function gets [-0.50455375 -0.50455375 -0.50455375].
FITNESS EVAL: x = [-0.50455375 -0.50455375 -0.50455375], cost = 0.7637234652148235
FITNESS EVAL: x = [0.4426457 0.4426457 0.4426457], cost = 0.5878056348538054
FITNESS EVAL: x = [-0.47532954 -0.47532954 -0.47532954], cost = 0.6778145209225833
FITNESS EVAL: x = [-0.29921424 -0.29921424 -0.29921424], cost = 0.2685874763420541
FITNESS EVAL: x = [0.63765334 0.63765334 0.63765334], cost = 1.219805338218631
Problem name: <class '__main__.sphere_function'>
C++ class name: pybind11::object
Global dimension: 3
Integer dimension: 0
Fitness dimension: 1
Number of objectives: 1
Equality constraints dimension: 0
Inequality constraints dimension: 0
Lower bounds: [-1, -1, -1]
Upper bounds: [1, 1, 1]
Has batch fitness evaluation: false
Has gradient: false
User implemented gradient sparsity: false
Has hessians: false
User implemented hessians sparsity: false
Fitness evaluations: 5
Thread safety: none
Population size: 5
List of individuals:
#0:
ID: 14351618211829196000
Decision vector: [-0.504554, 0.87294, 0.639481]
Fitness vector: [0.763723]
#1:
ID: 4233680892357765143
Decision vector: [0.442646, 0.462569, -0.970743]
Fitness vector: [0.587806]
#2:
ID: 457977541108690035
Decision vector: [-0.47533, 0.97677, -0.805264]
Fitness vector: [0.677815]
#3:
ID: 3780462174932513958
Decision vector: [-0.299214, -0.134968, -0.699614]
Fitness vector: [0.268587]
#4:
ID: 12888162120884326344
Decision vector: [0.637653, 0.840588, -0.0665339]
Fitness vector: [1.21981]
Champion decision vector: [-0.299214, -0.134968, -0.699614]
Champion fitness: [0.268587]
On numpy 1.x, we get :
FITNESS EVAL: x = [-0.38570461 -0.2763677 -0.19182114], cost = 0.2619425019598244
FITNESS EVAL: x = [-0.20674582 -0.56690414 -0.68899929], cost = 0.8388441636353365
FITNESS EVAL: x = [-0.57724158 0.56498671 0.34808134], cost = 0.77357844517818
FITNESS EVAL: x = [-0.13960828 0.79237103 0.04465593], cost = 0.6493364798439734
FITNESS EVAL: x = [-0.19441917 0.14853349 0.61230586], cost = 0.4347794724943631
Population size: 5
List of individuals:
#0:
ID: 4942601127234171947
Decision vector: [-0.385705, -0.276368, -0.191821]
Fitness vector: [0.261943]
#1:
ID: 11313699314120481030
Decision vector: [-0.206746, -0.566904, -0.688999]
Fitness vector: [0.838844]
#2:
ID: 8331669631110857304
Decision vector: [-0.577242, 0.564987, 0.348081]
Fitness vector: [0.773578]
#3:
ID: 3796521727361963477
Decision vector: [-0.139608, 0.792371, 0.0446559]
Fitness vector: [0.649336]
#4:
ID: 18100786683321109976
Decision vector: [-0.194419, 0.148533, 0.612306]
Fitness vector: [0.434779]
- OS: ubuntu
- Installation method: uv
- Version: v2.19.5