def create_optimization_result_nan_inf():
"""
Create a result object containing nan and inf function values
"""
# get result with only numbers
result = create_optimization_result()
# append nan and inf
optimizer_result = pypesto.OptimizerResult(
fval=float('nan'), x=np.array([float('nan'), float('nan')]))
result.optimize_result.append(optimizer_result=optimizer_result)
optimizer_result = pypesto.OptimizerResult(
fval=-float('inf'), x=np.array([-float('inf'), -float('inf')]))
result.optimize_result.append(optimizer_result=optimizer_result)
return result
def create_optimization_result():
# create the pypesto problem
problem = create_problem()
# write some dummy results for optimization
result = pypesto.Result(problem=problem)
for j in range(0, 3):
optimizer_result = pypesto.OptimizerResult(fval=j * 0.01,
x=[j + 0.1, j + 1])
result.optimize_result.append(optimizer_result=optimizer_result)
for j in range(0, 4):
optimizer_result = pypesto.OptimizerResult(
fval=10 + j * 0.01, x=[2.5 + j + 0.1, 2 + j + 1])
result.optimize_result.append(optimizer_result=optimizer_result)
return result
import pypesto
import pypesto.visualize
import numpy as np
import scipy as sp
import unittest
objective = pypesto.Objective(fun=sp.optimize.rosen,
grad=sp.optimize.rosen_der,
hess=sp.optimize.rosen_hess)
lb = -7 * np.ones((1, 2))
ub = 7 * np.ones((1, 2))
problem = pypesto.Problem(objective=objective, lb=lb, ub=ub)
result = pypesto.Result(problem=problem)
for j in range(0, 3):
optimizer_result = pypesto.OptimizerResult(fval=j * 0.01,
x=[j + 0.1, j + 1])
result.optimize_result.append(optimizer_result=optimizer_result)
for j in range(0, 4):
optimizer_result = pypesto.OptimizerResult(fval=10 + j * 0.01,
x=[2.5 + j + 0.1, 2 + j + 1])
result.optimize_result.append(optimizer_result=optimizer_result)
class TestVisualize(unittest.TestCase):
@staticmethod
def test_waterfall():
pypesto.visualize.waterfall(result)
@staticmethod
def test_waterfall_lowlevel():