def test_log_params(make_quadratic):
a, b, c, data, _ = make_quadratic
w0 = np.abs(np.random.randn(3))
bounds = [Positive(), Bound(), Positive()]
assert_opt = lambda Ea, Eb, Ec: \
np.allclose((a, b, c), (Ea, Eb, Ec), atol=1e-3, rtol=0)
nmin = logtrick_minimizer(minimize)
res = nmin(qobj,
w0,
args=(data, ),
jac=True,
method='L-BFGS-B',
bounds=bounds)
assert_opt(*res.x)
nsgd = logtrick_sgd(sgd)
res = nsgd(qobj,
w0,
data,
eval_obj=True,
bounds=bounds,
random_state=randstate)
assert_opt(*res.x)
nmin = logtrick_minimizer(minimize)
res = nmin(qfun,
w0,
args=(data, ),
jac=qgrad,
method='L-BFGS-B',
bounds=bounds)
assert_opt(*res.x)
def test_log_params(make_quadratic):
a, b, c, data, _ = make_quadratic
w0 = np.abs(np.random.randn(3))
bounds = [Positive(), Bound(), Positive()]
nmin = logtrick_minimizer(minimize)
res = nmin(qobj, w0, args=(data,), jac=True, bounds=bounds,
method='L-BFGS-B')
Ea_bfgs, Eb_bfgs, Ec_bfgs = res['x']
assert np.allclose((Ea_bfgs, Eb_bfgs, Ec_bfgs), (a, b, c), atol=1e-3,
rtol=0)
nsgd = logtrick_sgd(sgd)
res = nsgd(qobj, w0, data, bounds=bounds, eval_obj=True, gtol=1e-4,
passes=1000, rate=0.95, eta=1e-6)
Ea_sgd, Eb_sgd, Ec_sgd = res['x']
assert np.allclose((Ea_sgd, Eb_sgd, Ec_sgd), (a, b, c), atol=1e-1, rtol=0)
if nlopt_test:
res = nmin(qobj, w0, args=(data, False), jac=False, bounds=bounds,
method='LN_BOBYQA', backend='nlopt')
Ea_bq, Eb_bq, Ec_bq = res['x']
assert np.allclose((Ea_bq, Eb_bq, Ec_bq), (a, b, c), atol=1e-3, rtol=0)
def test_logstruc_params(make_quadratic):
a, b, c, data, _ = make_quadratic
w0 = [np.abs(np.random.randn(2)), np.abs(np.random.randn(1))[0]]
bounds = [Positive(shape=(2,)), Bound()]
nmin = structured_minimizer(logtrick_minimizer(minimize))
res = nmin(qobj_struc, w0, args=(data,), jac=True, bounds=bounds,
method='L-BFGS-B')
(Ea_bfgs, Eb_bfgs), Ec_bfgs = res['x']
assert np.allclose((Ea_bfgs, Eb_bfgs, Ec_bfgs), (a, b, c), atol=1e-2,
rtol=0)
nsgd = structured_sgd(logtrick_sgd(sgd))
res = nsgd(qobj_struc, w0, data, bounds=bounds, eval_obj=True, gtol=1e-4,
passes=1000, rate=0.95, eta=1e-6)
(Ea_sgd, Eb_sgd), Ec_sgd = res['x']
assert np.allclose((Ea_sgd, Eb_sgd, Ec_sgd), (a, b, c), atol=1e-1, rtol=0)
if nlopt_test:
res = nmin(qobj_struc, w0, args=(data, False), jac=False, bounds=None,
method='LN_BOBYQA', backend='nlopt')
(Ea_bq, Eb_bq), Ec_bq = res['x']
assert np.allclose((Ea_bq, Eb_bq, Ec_bq), (a, b, c), atol=1e-3, rtol=0)
def test_logstruc_params(make_quadratic, make_random):
random = make_random
a, b, c, data, _ = make_quadratic
w0 = [Parameter(random.gamma(2, size=(2,)), Positive()),
Parameter(random.randn(), Bound())
]
qobj_struc = lambda w12, w3, data: q_struc(w12, w3, data, qobj)
assert_opt = lambda Eab, Ec: \
np.allclose((a, b, c), (Eab[0], Eab[1], Ec), atol=1e-3, rtol=0)
nmin = structured_minimizer(logtrick_minimizer(minimize))
res = nmin(qobj_struc, w0, args=(data,), jac=True, method='L-BFGS-B')
assert_opt(*res.x)
nsgd = structured_sgd(logtrick_sgd(sgd))
res = nsgd(qobj_struc, w0, data, eval_obj=True, random_state=make_random)
assert_opt(*res.x)
qf_struc = lambda w12, w3, data: q_struc(w12, w3, data, qfun)
qg_struc = lambda w12, w3, data: q_struc(w12, w3, data, qgrad)
res = nmin(qf_struc, w0, args=(data,), jac=qg_struc, method='L-BFGS-B')
assert_opt(*res.x)
def test_rand_start(make_quadratic, make_random):
random = make_random
a, b, c, data, _ = make_quadratic
w0 = [Parameter(gamma(1), Positive(), shape=(2, )), Parameter(1., Bound())]
qobj_struc = lambda w12, w3, data: q_struc(w12, w3, data, qobj)
assert_opt = lambda Eab, Ec: \
np.allclose((a, b, c), (Eab[0], Eab[1], Ec), atol=1e-3, rtol=0)
nmin = structured_minimizer(logtrick_minimizer(minimize))
res = nmin(qobj_struc,
w0,
args=(data, ),
jac=True,
method='L-BFGS-B',
random_state=random,
nstarts=100)
assert_opt(*res.x)
nsgd = structured_sgd(logtrick_sgd(sgd))
res = nsgd(qobj_struc,
w0,
data,
eval_obj=True,
nstarts=100,
random_state=random)
assert_opt(*res.x)
def test_logstruc_params(make_quadratic, make_random):
random = make_random
a, b, c, data, _ = make_quadratic
w0 = [
Parameter(random.gamma(2, size=(2, )), Positive()),
Parameter(random.randn(), Bound())
]
qobj_struc = lambda w12, w3, data: q_struc(w12, w3, data, qobj)
assert_opt = lambda Eab, Ec: \
np.allclose((a, b, c), (Eab[0], Eab[1], Ec), atol=1e-3, rtol=0)
nmin = structured_minimizer(logtrick_minimizer(minimize))
res = nmin(qobj_struc, w0, args=(data, ), jac=True, method='L-BFGS-B')
assert_opt(*res.x)
nsgd = structured_sgd(logtrick_sgd(sgd))
res = nsgd(qobj_struc, w0, data, eval_obj=True, random_state=make_random)
assert_opt(*res.x)
qf_struc = lambda w12, w3, data: q_struc(w12, w3, data, qfun)
qg_struc = lambda w12, w3, data: q_struc(w12, w3, data, qgrad)
res = nmin(qf_struc, w0, args=(data, ), jac=qg_struc, method='L-BFGS-B')
assert_opt(*res.x)
def test_log_params(make_quadratic, make_random):
random = make_random
a, b, c, data, _ = make_quadratic
w0 = np.abs(random.randn(3))
bounds = [Positive(), Bound(), Positive()]
assert_opt = lambda Ea, Eb, Ec: \
np.allclose((a, b, c), (Ea, Eb, Ec), atol=1e-3, rtol=0)
nmin = logtrick_minimizer(minimize)
res = nmin(qobj, w0, args=(data,), jac=True, method='L-BFGS-B',
bounds=bounds)
assert_opt(*res.x)
nsgd = logtrick_sgd(sgd)
res = nsgd(qobj, w0, data, eval_obj=True, bounds=bounds,
random_state=make_random)
assert_opt(*res.x)
nmin = logtrick_minimizer(minimize)
res = nmin(qfun, w0, args=(data,), jac=qgrad, method='L-BFGS-B',
bounds=bounds)
assert_opt(*res.x)
def test_rand_start(make_quadratic, make_random):
random = make_random
a, b, c, data, _ = make_quadratic
w0 = [
Parameter(gamma(1), Positive(), shape=(2,)),
Parameter(1., Bound())
]
qobj_struc = lambda w12, w3, data: q_struc(w12, w3, data, qobj)
assert_opt = lambda Eab, Ec: \
np.allclose((a, b, c), (Eab[0], Eab[1], Ec), atol=1e-3, rtol=0)
nmin = structured_minimizer(logtrick_minimizer(minimize))
res = nmin(qobj_struc, w0, args=(data,), jac=True, method='L-BFGS-B',
random_state=random, nstarts=100)
assert_opt(*res.x)
nsgd = structured_sgd(logtrick_sgd(sgd))
res = nsgd(qobj_struc, w0, data, eval_obj=True, nstarts=100,
random_state=random)
assert_opt(*res.x)