def learn(X, y, kerneldef, opt_criterion=None, verbose=False, ftol=1e-8,
maxiter=10000):
n, d = X.shape
if opt_criterion is None:
opt_criterion = criterions.negative_log_marginal_likelihood
else:
pass # check type
cov_fn = compose(kerneldef)
# Automatically determine the range
meta = get_meta(kerneldef)
bounds = [Bound(l, h) for l, h in zip(meta.lower_bound, meta.upper_bound)]
theta0 = meta.initial_val
def criterion(*theta):
K = cov_fn(X, X, theta, True) # learn with noise!
factors = np.linalg.svd(K)
value = opt_criterion(y, factors)
if verbose:
log.info("[{0}] {1}".format(value, theta))
return value
# up to here
nmin = structured_minimizer(minimize)
result = nmin(criterion, theta0, backend='scipy',
ftol=ftol, maxiter=maxiter, jac=False,
bounds=bounds, method='L-BFGS-B')
print(result)
return result.x
def test_structured_params(make_quadratic):
a, b, c, data, _ = make_quadratic
w0 = [np.random.randn(2), np.random.randn(1)[0]]
nmin = structured_minimizer(minimize)
res = nmin(qobj_struc, w0, args=(data,), jac=True, bounds=None,
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 = structured_sgd(sgd)
res = nsgd(qobj_struc, w0, data, bounds=None, 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_structured_params(make_quadratic, make_random):
random = make_random
a, b, c, data, _ = make_quadratic
w0 = [Parameter(random.randn(2), Bound()),
Parameter(random.randn(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(minimize)
res = nmin(qobj_struc, w0, args=(data,), jac=True, method='L-BFGS-B')
assert_opt(*res.x)
nsgd = structured_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_structured_params(make_quadratic, make_random):
random = make_random
a, b, c, data, _ = make_quadratic
w0 = [
Parameter(random.randn(2), Bound()),
Parameter(random.randn(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(minimize)
res = nmin(qobj_struc, w0, args=(data, ), jac=True, method='L-BFGS-B')
assert_opt(*res.x)
nsgd = structured_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_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)