Expected behavior.
I can combine e.g. NRMSE and PeakPosition as metrics for the fit.
Actual behavior
When using peak position and NRMSE together, I get an error in reduction.py from PyMoo
def _do(self, random_state=None):
rnd = sample_on_unit_simplex(self.n_sample_points, self.n_dim, random_state=random_state, unit_simplex_mapping=self.sampling)
def h(n):
return get_partition_closest_to_points(n, self.n_dim)
H = h(self.n_points)
E = UniformReferenceDirectionFactory(self.n_dim, n_partitions=H).do()
E = E[np.any(E == 0, axis=1)]
# add the edge coordinates
X = np.vstack([E, rnd])
I = select_points_with_maximum_distance(X, self.n_points, selected=list(range((len(E)))))
centroids = X[I].copy()
if self.kmeans:
#centroids = kmeans(X, centroids, self.kmeans_max_iter, self.kmeans_a_tol, 0)
centroids = kmeans(X, centroids, self.kmeans_max_iter, self.kmeans_a_tol, len(E))
return centroids
E consists of the same data points without variation and therefore E = E[np.any(E == 0, axis=1)] is empty and as a consequence the function call to select_points_with_maximum_distance fails
How to produce bug (including a minimal reproducible example)
Unfortunately, I canot share the full example and it is hard for me to reproduce the bug. With data at constant pH, I can use both loss functions and GIEX fits well. If I add about 4 additional runs with varying pH conditions, I can only use either NRMSE or PeakPosition.
I will try to set up a minimal working example which I can share. In the meantime, do you have any ideas what might be the reason?