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"""
Unit tests for trust-region optimization routines.
To run it in its simplest form::
nosetests test_optimize.py
"""
from __future__ import division, print_function, absolute_import
import numpy as np
from scipy.optimize import (minimize, rosen, rosen_der, rosen_hess,
rosen_hess_prod)
from numpy.testing import (TestCase, assert_, assert_equal, assert_allclose,
run_module_suite)
class Accumulator:
""" This is for testing callbacks."""
def __init__(self):
self.count = 0
self.accum = None
def __call__(self, x):
self.count += 1
if self.accum is None:
self.accum = np.array(x)
else:
self.accum += x
class TestTrustRegionSolvers(TestCase):
def setUp(self):
self.x_opt = [1.0, 1.0]
self.easy_guess = [2.0, 2.0]
self.hard_guess = [-1.2, 1.0]
def test_dogleg_accuracy(self):
# test the accuracy and the return_all option
x0 = self.hard_guess
r = minimize(rosen, x0, jac=rosen_der, hess=rosen_hess, tol=1e-8,
method='dogleg', options={'return_all': True},)
assert_allclose(x0, r['allvecs'][0])
assert_allclose(r['x'], r['allvecs'][-1])
assert_allclose(r['x'], self.x_opt)
def test_dogleg_callback(self):
# test the callback mechanism and the maxiter and return_all options
accumulator = Accumulator()
maxiter = 5
r = minimize(rosen, self.hard_guess, jac=rosen_der, hess=rosen_hess,
callback=accumulator, method='dogleg',
options={'return_all': True, 'maxiter': maxiter},)
assert_equal(accumulator.count, maxiter)
assert_equal(len(r['allvecs']), maxiter+1)
assert_allclose(r['x'], r['allvecs'][-1])
assert_allclose(sum(r['allvecs'][1:]), accumulator.accum)
def test_solver_concordance(self):
# Assert that dogleg uses fewer iterations than ncg on the Rosenbrock
# test function, although this does not necessarily mean
# that dogleg is faster or better than ncg even for this function
# and especially not for other test functions.
f = rosen
g = rosen_der
h = rosen_hess
for x0 in (self.easy_guess, self.hard_guess):
r_dogleg = minimize(f, x0, jac=g, hess=h, tol=1e-8,
method='dogleg', options={'return_all': True})
r_trust_ncg = minimize(f, x0, jac=g, hess=h, tol=1e-8,
method='trust-ncg',
options={'return_all': True})
r_ncg = minimize(f, x0, jac=g, hess=h, tol=1e-8,
method='newton-cg', options={'return_all': True})
assert_allclose(self.x_opt, r_dogleg['x'])
assert_allclose(self.x_opt, r_trust_ncg['x'])
assert_allclose(self.x_opt, r_ncg['x'])
assert_(len(r_dogleg['allvecs']) < len(r_ncg['allvecs']))
def test_trust_ncg_hessp(self):
for x0 in (self.easy_guess, self.hard_guess):
r = minimize(rosen, x0, jac=rosen_der, hessp=rosen_hess_prod,
tol=1e-8, method='trust-ncg')
assert_allclose(self.x_opt, r['x'])
if __name__ == '__main__':
run_module_suite()
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