def test_fmin_lbfgs():
def f(x, g, *args):
g[0] = 2 * x
return x ** 2
xmin = fmin_lbfgs(f, 100., line_search='armijo')
assert_array_equal(xmin, [0])
xmin = fmin_lbfgs(f, 100., line_search='strongwolfe')
assert_array_equal(xmin, [0])
def test_fmin_lbfgs():
def f(x, g, *args):
g = [2 * x]
for (i, e) in enumerate(res):
g[i] = e;
return x ** 2
xmin = fmin_lbfgs(f, 100., line_search='armijo')
assert_array_equal(xmin, [0])
xmin = fmin_lbfgs(f, 100., line_search='strongwolfe')
assert_array_equal(xmin, [0])
def test_owl_line_search_warning_explicit(self):
def f(x, g, *args):
g[0] = 2 * x
return x ** 2
with pytest.warns(UserWarning, match="OWL-QN"):
xmin = fmin_lbfgs(f, 100., orthantwise_c=1, line_search='default')
with pytest.warns(UserWarning, match="OWL-QN"):
xmin = fmin_lbfgs(f, 100., orthantwise_c=1, line_search='morethuente')
with pytest.warns(UserWarning, match="OWL-QN"):
xmin = fmin_lbfgs(f, 100., orthantwise_c=1, line_search='armijo')
with pytest.warns(UserWarning, match="OWL-QN"):
xmin = fmin_lbfgs(f, 100., orthantwise_c=1, line_search='strongwolfe')
def test_fmin_lbfgs():
def f(x, g, *args):
g[0] = 2 * x
return x ** 2
xmin = fmin_lbfgs(f, 100.)
assert_array_equal(xmin, [0])
def test_2d():
def f(x, g, f_calls):
#f_calls, = args
assert_equal(x.shape, (2, 2))
assert_equal(g.shape, x.shape)
g[:] = 2 * x
f_calls[0] += 1
return (x ** 2).sum()
def progress(x, g, fx, xnorm, gnorm, step, k, ls, *args):
assert_equal(x.shape, (2, 2))
assert_equal(g.shape, x.shape)
assert_equal(np.sqrt((x ** 2).sum()), xnorm)
assert_equal(np.sqrt((g ** 2).sum()), gnorm)
p_calls[0] += 1
return 0
f_calls = [0]
p_calls = [0]
xmin = fmin_lbfgs(f, [[10., 100.], [44., 55.]], progress, args=[f_calls])
assert_greater(f_calls[0], 0)
assert_greater(p_calls[0], 0)
assert_array_almost_equal(xmin, [[0, 0], [0, 0]])
def test_2d():
def f(x, g, f_calls):
#f_calls, = args
assert x.shape == (2, 2)
assert g.shape == x.shape
g[:] = 2 * x
f_calls[0] += 1
return (x ** 2).sum()
def progress(x, g, fx, xnorm, gnorm, step, k, ls, *args):
assert x.shape == (2, 2)
assert g.shape == x.shape
assert np.sqrt((x ** 2).sum()) == xnorm
assert np.sqrt((g ** 2).sum()) == gnorm
p_calls[0] += 1
return 0
f_calls = [0]
p_calls = [0]
xmin = fmin_lbfgs(f, [[10., 100.], [44., 55.]], progress, args=[f_calls])
assert f_calls[0] > 0
assert p_calls[0] > 0
assert_array_almost_equal(xmin, [[0, 0], [0, 0]])
def test_owl_line_search_default(self):
def f(x, g, *args):
g[0] = 2 * x
return x ** 2
with pytest.warns(UserWarning, match="OWL-QN"):
xmin = fmin_lbfgs(f, 100., orthantwise_c=1)
def test_owl_qn(self):
def f(x, g, *args):
g[0] = 2 * x
return x ** 2
xmin = fmin_lbfgs(f, 100., orthantwise_c=1, line_search='wolfe')
assert_array_equal(xmin, [0])
def lbfgs_modified_logistic_regression(X, y, b=None):
"""Same as modified LR, but solved using lbfgs."""
X, theta, N, M = prepend_and_vars(X)
if b is None:
fix_b, b = False, DEFAULT_B
else:
fix_b, b = True, b
def f(w, g, X, y):
"""Accepts x, and g. Returns value at x, and gradient at g.
"""
b = w[0]
theta = w[1:]
value = np.sum(np.abs(y - (1.0 / (1.0 + (b ** 2) + X.dot(theta)))))
# now fill in the g
ewx = np.exp(-X.dot(theta))
b2ewx = (b * b) + ewx
p = ((y - 1.0) / b2ewx) + (1.0 / (1.0 + b2ewx))
dLdw = (p * ewx).reshape((X.shape[0], 1)) * X
if not fix_b:
w[0] = np.sum(-2 * b * p)
w[1:] = np.sum(dLdw, axis=0)
return value
import lbfgs
w = np.hstack([np.array([b,]), theta])
answer = lbfgs.fmin_lbfgs(f, w, args=(X, y,))
theta, b = answer[1:], answer[0]
return theta, b
def test_owl_wolfe_no_warning(self):
""" This test is an attempt to show that wolfe throws no warnings.
"""
def f(x, g, *args):
g[0] = 2 * x
return x ** 2
with pytest.warns(UserWarning, match="OWL-QN"):
xmin = fmin_lbfgs(f, 100., orthantwise_c=1, line_search='wolfe')
def test_input_validation():
with pytest.raises(TypeError):
fmin_lbfgs([], 1e4)
with pytest.raises(TypeError):
fmin_lbfgs(lambda x: x, 1e4, "ham")
with pytest.raises(TypeError):
fmin_lbfgs(lambda x: x, "spam")
"""Trivial example: minimize x**2 from any start value"""
import lbfgs
import sys
def f(x, g):
"""Returns x**2 and stores its gradient in g[0]"""
x = x[0]
g[0] = 2*x
return x**2
def progress(x, g, f_x, xnorm, gnorm, step, k, ls):
"""Report optimization progress."""
print("x = %8.2g f(x) = %8.2g f'(x) = %8.2g" % (x, f_x, g))
try:
x0 = float(sys.argv[1])
except IndexError:
print("usage: python %s start-value" % sys.argv[0])
sys.exit(1)
print("Minimum found: %f" % lbfgs.fmin_lbfgs(f, x0, progress)[0])
"""Trivial example: minimize x**2 from any start value"""
import lbfgs
import sys
from scipy.optimize import minimize, rosen, rosen_der
import numpy as np
x0 = np.array([1.3, 0.7])
def f(x, g):
g[:] = rosen_der(x)
print "one call"
return rosen(x)
def progress(x, g, f_x, xnorm, gnorm, step, k, ls):
"""Report optimization progress."""
#print("x = %8.2g f(x) = %8.2g f'(x) = %8.2g" % (x, f_x, g))
pass
print("Minimum found", lbfgs.fmin_lbfgs(f, x0, progress))
"""Trivial example: minimize x**2 from any start value"""
import lbfgs
import numpy as np
import sys
def f(x, g):
"""Returns x**2 and stores its gradient in g[0]"""
x = x[0]
g[0] = 2*x
return x**2
x0 = np.asarray([float(sys.argv[1])])
print lbfgs.fmin_lbfgs(f, x0)[0]
