Numpy 2.0.0 breaks pymoo 0.6.1.1 #606

pymoo/algorithms/moo/age.py

@@ -167,7 +167,7 @@ def survival_score(self, front, ideal_point):
         p = self.compute_geometry(front, extreme, n)
 
         nn = np.linalg.norm(front, p, axis=1)
-        distances = self.pairwise_distances(front, p) / nn[:, None]
+        distances = self.pairwise_distances(front, p) / (nn[:, None])
 
         neighbors = 2
         remaining = np.arange(m)

pymoo/gradient/__init__.py

@@ -3,7 +3,7 @@
 
 GRADIENT_MODULE = "pymoo.gradient.toolbox"
 
-TOOLBOX = "autograd.numpy"
+TOOLBOX = "numpy"
 
 
 def activate(name):
@@ -17,3 +17,5 @@ def activate(name):
 
 def deactivate():
     activate("numpy")
+
+

pymoo/gradient/grad_autograd.py

@@ -1,9 +1,33 @@
 import warnings
-
-import autograd.numpy as anp
 import numpy as np
-from autograd.core import VJPNode, vspace, backward_pass
-from autograd.tracer import new_box, isbox
+
+try:
+    import autograd.numpy as anp
+    from autograd import value_and_grad
+    from autograd.core import VJPNode, backward_pass
+    from autograd.tracer import new_box, isbox
+except:
+    print("autograd only supports numpy < 2.0.0 versions.")
+
+
+def value_and_grad(*args, **kwargs):
+    return value_and_grad(*args, **kwargs)
+
+
+def log(*args, **kwargs):
+    return anp.log(*args, **kwargs)
+
+
+def sqrt(*args, **kwargs):
+    return anp.sqrt(*args, **kwargs)
+
+
+def row_stack(*args, **kwargs):
+    return anp.row_stack(*args, **kwargs)
+
+
+def triu_indices(*args, **kwargs):
+    return anp.triu_indices(*args, **kwargs)
 
 
 def run_and_trace(f, x):

pymoo/util/ref_dirs/energy.py

@@ -1,7 +1,6 @@
 import numpy as np
-import pymoo.gradient.toolbox as anp
-
 
+from pymoo.gradient.grad_autograd import triu_indices, sqrt, log
 from pymoo.util.ref_dirs.construction import ConstructionBasedReferenceDirectionFactory
 from pymoo.util.ref_dirs.misc import project_onto_sum_equals_zero_plane, project_onto_unit_simplex_recursive
 from pymoo.util.ref_dirs.optimizer import Adam
@@ -280,9 +279,9 @@ def squared_dist(A, B):
 
 
 def calc_potential_energy(A, d):
-    i, j = anp.triu_indices(len(A), 1)
-    D = anp.sqrt(squared_dist(A, A)[i, j])
-    energy = anp.log((1 / D ** d).mean())
+    i, j = triu_indices(len(A), 1)
+    D = sqrt(squared_dist(A, A)[i, j])
+    energy = log((1 / D ** d).mean())
     return energy
 
 

pymoo/util/ref_dirs/energy_layer.py

@@ -1,7 +1,8 @@
-import autograd.numpy as anp
+
+
 import numpy as np
-from autograd import value_and_grad
 
+from pymoo.gradient.grad_autograd import value_and_grad, triu_indices, row_stack
 from pymoo.util.normalization import normalize
 from pymoo.util.ref_dirs.energy import squared_dist
 from pymoo.util.ref_dirs.optimizer import Adam
@@ -102,14 +103,14 @@ def get_points(X, scalings):
     vals = []
     for i in range(len(X)):
         vals.append(scale_reference_directions(X[i], scalings[i]))
-    X = anp.row_stack(vals)
+    X = row_stack(vals)
     return X
 
 
 def calc_potential_energy(scalings, X):
     X = get_points(X, scalings)
 
-    i, j = anp.triu_indices(len(X), 1)
+    i, j = triu_indices(len(X), 1)
     D = squared_dist(X, X)[i, j]
 
     if np.any(D < 1e-12):