Intelegentny_Pszczelarz/.venv/Lib/site-packages/scipy/optimize/tests/test_linesearch.py
2023-06-19 00:49:18 +02:00

313 lines
10 KiB
Python

"""
Tests for line search routines
"""
from numpy.testing import (assert_equal, assert_array_almost_equal,
assert_array_almost_equal_nulp, assert_warns,
suppress_warnings)
import scipy.optimize._linesearch as ls
from scipy.optimize._linesearch import LineSearchWarning
import numpy as np
def assert_wolfe(s, phi, derphi, c1=1e-4, c2=0.9, err_msg=""):
"""
Check that strong Wolfe conditions apply
"""
phi1 = phi(s)
phi0 = phi(0)
derphi0 = derphi(0)
derphi1 = derphi(s)
msg = "s = %s; phi(0) = %s; phi(s) = %s; phi'(0) = %s; phi'(s) = %s; %s" % (
s, phi0, phi1, derphi0, derphi1, err_msg)
assert phi1 <= phi0 + c1*s*derphi0, "Wolfe 1 failed: " + msg
assert abs(derphi1) <= abs(c2*derphi0), "Wolfe 2 failed: " + msg
def assert_armijo(s, phi, c1=1e-4, err_msg=""):
"""
Check that Armijo condition applies
"""
phi1 = phi(s)
phi0 = phi(0)
msg = "s = %s; phi(0) = %s; phi(s) = %s; %s" % (s, phi0, phi1, err_msg)
assert phi1 <= (1 - c1*s)*phi0, msg
def assert_line_wolfe(x, p, s, f, fprime, **kw):
assert_wolfe(s, phi=lambda sp: f(x + p*sp),
derphi=lambda sp: np.dot(fprime(x + p*sp), p), **kw)
def assert_line_armijo(x, p, s, f, **kw):
assert_armijo(s, phi=lambda sp: f(x + p*sp), **kw)
def assert_fp_equal(x, y, err_msg="", nulp=50):
"""Assert two arrays are equal, up to some floating-point rounding error"""
try:
assert_array_almost_equal_nulp(x, y, nulp)
except AssertionError as e:
raise AssertionError("%s\n%s" % (e, err_msg)) from e
class TestLineSearch:
# -- scalar functions; must have dphi(0.) < 0
def _scalar_func_1(self, s):
self.fcount += 1
p = -s - s**3 + s**4
dp = -1 - 3*s**2 + 4*s**3
return p, dp
def _scalar_func_2(self, s):
self.fcount += 1
p = np.exp(-4*s) + s**2
dp = -4*np.exp(-4*s) + 2*s
return p, dp
def _scalar_func_3(self, s):
self.fcount += 1
p = -np.sin(10*s)
dp = -10*np.cos(10*s)
return p, dp
# -- n-d functions
def _line_func_1(self, x):
self.fcount += 1
f = np.dot(x, x)
df = 2*x
return f, df
def _line_func_2(self, x):
self.fcount += 1
f = np.dot(x, np.dot(self.A, x)) + 1
df = np.dot(self.A + self.A.T, x)
return f, df
# --
def setup_method(self):
self.scalar_funcs = []
self.line_funcs = []
self.N = 20
self.fcount = 0
def bind_index(func, idx):
# Remember Python's closure semantics!
return lambda *a, **kw: func(*a, **kw)[idx]
for name in sorted(dir(self)):
if name.startswith('_scalar_func_'):
value = getattr(self, name)
self.scalar_funcs.append(
(name, bind_index(value, 0), bind_index(value, 1)))
elif name.startswith('_line_func_'):
value = getattr(self, name)
self.line_funcs.append(
(name, bind_index(value, 0), bind_index(value, 1)))
np.random.seed(1234)
self.A = np.random.randn(self.N, self.N)
def scalar_iter(self):
for name, phi, derphi in self.scalar_funcs:
for old_phi0 in np.random.randn(3):
yield name, phi, derphi, old_phi0
def line_iter(self):
for name, f, fprime in self.line_funcs:
k = 0
while k < 9:
x = np.random.randn(self.N)
p = np.random.randn(self.N)
if np.dot(p, fprime(x)) >= 0:
# always pick a descent direction
continue
k += 1
old_fv = float(np.random.randn())
yield name, f, fprime, x, p, old_fv
# -- Generic scalar searches
def test_scalar_search_wolfe1(self):
c = 0
for name, phi, derphi, old_phi0 in self.scalar_iter():
c += 1
s, phi1, phi0 = ls.scalar_search_wolfe1(phi, derphi, phi(0),
old_phi0, derphi(0))
assert_fp_equal(phi0, phi(0), name)
assert_fp_equal(phi1, phi(s), name)
assert_wolfe(s, phi, derphi, err_msg=name)
assert c > 3 # check that the iterator really works...
def test_scalar_search_wolfe2(self):
for name, phi, derphi, old_phi0 in self.scalar_iter():
s, phi1, phi0, derphi1 = ls.scalar_search_wolfe2(
phi, derphi, phi(0), old_phi0, derphi(0))
assert_fp_equal(phi0, phi(0), name)
assert_fp_equal(phi1, phi(s), name)
if derphi1 is not None:
assert_fp_equal(derphi1, derphi(s), name)
assert_wolfe(s, phi, derphi, err_msg="%s %g" % (name, old_phi0))
def test_scalar_search_wolfe2_with_low_amax(self):
def phi(alpha):
return (alpha - 5) ** 2
def derphi(alpha):
return 2 * (alpha - 5)
s, _, _, _ = assert_warns(LineSearchWarning,
ls.scalar_search_wolfe2, phi, derphi, amax=0.001)
assert s is None
def test_scalar_search_wolfe2_regression(self):
# Regression test for gh-12157
# This phi has its minimum at alpha=4/3 ~ 1.333.
def phi(alpha):
if alpha < 1:
return - 3*np.pi/2 * (alpha - 1)
else:
return np.cos(3*np.pi/2 * alpha - np.pi)
def derphi(alpha):
if alpha < 1:
return - 3*np.pi/2
else:
return - 3*np.pi/2 * np.sin(3*np.pi/2 * alpha - np.pi)
s, _, _, _ = ls.scalar_search_wolfe2(phi, derphi)
# Without the fix in gh-13073, the scalar_search_wolfe2
# returned s=2.0 instead.
assert s < 1.5
def test_scalar_search_armijo(self):
for name, phi, derphi, old_phi0 in self.scalar_iter():
s, phi1 = ls.scalar_search_armijo(phi, phi(0), derphi(0))
assert_fp_equal(phi1, phi(s), name)
assert_armijo(s, phi, err_msg="%s %g" % (name, old_phi0))
# -- Generic line searches
def test_line_search_wolfe1(self):
c = 0
smax = 100
for name, f, fprime, x, p, old_f in self.line_iter():
f0 = f(x)
g0 = fprime(x)
self.fcount = 0
s, fc, gc, fv, ofv, gv = ls.line_search_wolfe1(f, fprime, x, p,
g0, f0, old_f,
amax=smax)
assert_equal(self.fcount, fc+gc)
assert_fp_equal(ofv, f(x))
if s is None:
continue
assert_fp_equal(fv, f(x + s*p))
assert_array_almost_equal(gv, fprime(x + s*p), decimal=14)
if s < smax:
c += 1
assert_line_wolfe(x, p, s, f, fprime, err_msg=name)
assert c > 3 # check that the iterator really works...
def test_line_search_wolfe2(self):
c = 0
smax = 512
for name, f, fprime, x, p, old_f in self.line_iter():
f0 = f(x)
g0 = fprime(x)
self.fcount = 0
with suppress_warnings() as sup:
sup.filter(LineSearchWarning,
"The line search algorithm could not find a solution")
sup.filter(LineSearchWarning,
"The line search algorithm did not converge")
s, fc, gc, fv, ofv, gv = ls.line_search_wolfe2(f, fprime, x, p,
g0, f0, old_f,
amax=smax)
assert_equal(self.fcount, fc+gc)
assert_fp_equal(ofv, f(x))
assert_fp_equal(fv, f(x + s*p))
if gv is not None:
assert_array_almost_equal(gv, fprime(x + s*p), decimal=14)
if s < smax:
c += 1
assert_line_wolfe(x, p, s, f, fprime, err_msg=name)
assert c > 3 # check that the iterator really works...
def test_line_search_wolfe2_bounds(self):
# See gh-7475
# For this f and p, starting at a point on axis 0, the strong Wolfe
# condition 2 is met if and only if the step length s satisfies
# |x + s| <= c2 * |x|
f = lambda x: np.dot(x, x)
fp = lambda x: 2 * x
p = np.array([1, 0])
# Smallest s satisfying strong Wolfe conditions for these arguments is 30
x = -60 * p
c2 = 0.5
s, _, _, _, _, _ = ls.line_search_wolfe2(f, fp, x, p, amax=30, c2=c2)
assert_line_wolfe(x, p, s, f, fp)
s, _, _, _, _, _ = assert_warns(LineSearchWarning,
ls.line_search_wolfe2, f, fp, x, p,
amax=29, c2=c2)
assert s is None
# s=30 will only be tried on the 6th iteration, so this won't converge
assert_warns(LineSearchWarning, ls.line_search_wolfe2, f, fp, x, p,
c2=c2, maxiter=5)
def test_line_search_armijo(self):
c = 0
for name, f, fprime, x, p, old_f in self.line_iter():
f0 = f(x)
g0 = fprime(x)
self.fcount = 0
s, fc, fv = ls.line_search_armijo(f, x, p, g0, f0)
c += 1
assert_equal(self.fcount, fc)
assert_fp_equal(fv, f(x + s*p))
assert_line_armijo(x, p, s, f, err_msg=name)
assert c >= 9
# -- More specific tests
def test_armijo_terminate_1(self):
# Armijo should evaluate the function only once if the trial step
# is already suitable
count = [0]
def phi(s):
count[0] += 1
return -s + 0.01*s**2
s, phi1 = ls.scalar_search_armijo(phi, phi(0), -1, alpha0=1)
assert_equal(s, 1)
assert_equal(count[0], 2)
assert_armijo(s, phi)
def test_wolfe_terminate(self):
# wolfe1 and wolfe2 should also evaluate the function only a few
# times if the trial step is already suitable
def phi(s):
count[0] += 1
return -s + 0.05*s**2
def derphi(s):
count[0] += 1
return -1 + 0.05*2*s
for func in [ls.scalar_search_wolfe1, ls.scalar_search_wolfe2]:
count = [0]
r = func(phi, derphi, phi(0), None, derphi(0))
assert r[0] is not None, (r, func)
assert count[0] <= 2 + 2, (count, func)
assert_wolfe(r[0], phi, derphi, err_msg=str(func))