def test_top_k_dist_warper(self):
input_ids = None
vocab_size = 10
batch_size = 2
# create ramp distribution
ramp_logits = np.broadcast_to(np.arange(vocab_size)[None, :], (batch_size, vocab_size)).copy()
ramp_logits[1:, : vocab_size // 2] = ramp_logits[1:, : vocab_size // 2] + vocab_size
top_k_warp = FlaxTopKLogitsWarper(3)
scores = top_k_warp(input_ids, ramp_logits, cur_len=None)
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0]).tolist(), 7 * [True] + 3 * [False])
self.assertListEqual(jnp.isinf(scores[1]).tolist(), 2 * [True] + 3 * [False] + 5 * [True])
# check special case
length = 5
top_k_warp_safety_check = FlaxTopKLogitsWarper(top_k=1, filter_value=0.0, min_tokens_to_keep=3)
ramp_logits = np.broadcast_to(np.arange(length)[None, :], (batch_size, length)).copy()
scores = top_k_warp_safety_check(input_ids, ramp_logits, cur_len=None)
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1).tolist(), [2, 2])
def _normalize(x, y):
z = jnp.where(jnp.isinf(x), x, x + y)
zz = jnp.where(
lax.abs(x) > lax.abs(y),
x - z + y,
y - z + x,
)
return z, jnp.where(jnp.isinf(z), 0, zz)
def adam(gradval,
params,
eta=0.01,
beta1=0.9,
beta2=0.9,
eps=1e-7,
c=0.01,
R=500,
per=100,
disp=None,
log=False):
params = {k: np.array(v) for k, v in params.items()}
gavg = {k: np.zeros_like(v) for k, v in params.items()}
grms = {k: np.zeros_like(v) for k, v in params.items()}
n = len(params)
if log:
hist = {k: [] for k in params}
# iterate to max
for j in range(R + 1):
val, grad = gradval(params)
# test for nans
vnan = np.isnan(val) or np.isinf(val)
gnan = np.array(
[np.isnan(g).any() or np.isinf(g).any() for g in grad.values()])
if vnan or gnan.any():
print('Encountered nan/inf!')
disp(j, val, params)
summary_naninf(grad)
return params
# clip gradients
gradc = {k: clip2(grad[k], c) for k in params}
# early bias correction
etat = eta * (np.sqrt(1 - beta2**(j + 1))) / (1 - beta1**(j + 1))
for k in params:
gavg[k] += (1 - beta1) * (gradc[k] - gavg[k])
grms[k] += (1 - beta2) * (gradc[k]**2 - grms[k])
params[k] += etat * gavg[k] / (np.sqrt(grms[k]) + eps)
if log:
hist[k].append(params[k])
if disp is not None and j % per == 0:
disp(j, val, params)
if log:
hist = {k: np.stack(v) for k, v in hist.items()}
return hist
return params
def _contains_query(vals, query):
if isinstance(query, tuple):
return map(partial(_contains_query, vals), query)
if np.isnan(query):
if np.any(np.isnan(vals)):
raise FoundValue('NaN')
elif np.isinf(query):
if np.any(np.isinf(vals)):
raise FoundValue('Found Inf')
elif np.isscalar(query):
if np.any(vals == query):
raise FoundValue(str(query))
else:
raise ValueError('Malformed Query: {}'.format(query))
def infer(model, state, unroll = 1, eta = 0.001, jit = True):
init, update, params = adam(eta)
opt = init(state.xs)
window = collections.deque(maxlen=windowlen(5000, unroll))
def step(rng, opt, t):
keys = random.split(rng, unroll)
for i in range(0, unroll):
xs = params(opt)
L, dxs = jax.value_and_grad(energy, argnums=1)(model, xs)
opt = update(t+i, dxs, opt)
return L, opt
if jit:
step = jax.jit(step)
try:
while True:
state.rng, key = random.split(state.rng)
L, opt = step(key, opt, state.t)
if np.isnan(L) or np.isinf(L):
raise Exception("Invalid loss")
state.xs = params(opt)
state.t += unroll
window.append(-L.item())
print("\r[ %e, %.2e iterations " % (statistics.mean(window), state.t), end = "")
except KeyboardInterrupt:
pass
return state
def test_min_length_dist_processor(self):
vocab_size = 20
batch_size = 4
eos_token_id = 0
min_dist_processor = FlaxMinLengthLogitsProcessor(
min_length=10, eos_token_id=eos_token_id)
# check that min length is applied at length 5
input_ids = ids_tensor((batch_size, 20), vocab_size=20)
cur_len = 5
scores = self._get_uniform_logits(batch_size, vocab_size)
scores_before_min_length = min_dist_processor(input_ids,
scores,
cur_len=cur_len)
self.assertListEqual(
scores_before_min_length[:, eos_token_id].tolist(),
4 * [-float("inf")])
# check that min length is not applied anymore at length 15
scores = self._get_uniform_logits(batch_size, vocab_size)
cur_len = 15
scores_before_min_length = min_dist_processor(input_ids,
scores,
cur_len=cur_len)
self.assertFalse(jnp.isinf(scores_before_min_length).any())
def tree_count_infs_nans(tree, psum_axis_name=None):
leaves = jax.tree_leaves(tree)
num_infs = sum(jnp.sum(jnp.isinf(x)) for x in leaves)
num_nans = sum(jnp.sum(jnp.isnan(x)) for x in leaves)
if psum_axis_name:
num_infs, num_nans = jax.lax.psum((num_infs, num_nans),
axis_name=psum_axis_name)
return num_infs, num_nans
def create_model(yT, yC, num_components):
# Cosntants
nC = yC.shape[0]
nT = yT.shape[0]
zC = jnp.isinf(yC).sum().item()
zT = jnp.isinf(yT).sum().item()
yT_finite = yT[jnp.isinf(yT) == False]
yC_finite = yC_finite = yC[jnp.isinf(yC) == False]
K = num_components
p = numpyro.sample('p', dist.Beta(.5, .5))
gammaC = numpyro.sample('gammaC', dist.Beta(1, 1))
gammaT = numpyro.sample('gammaT', dist.Beta(1, 1))
etaC = numpyro.sample('etaC', dist.Dirichlet(jnp.ones(K) / K))
etaT = numpyro.sample('etaT', dist.Dirichlet(jnp.ones(K) / K))
with numpyro.plate('mixutre_components', K):
nu = numpyro.sample('nu', dist.LogNormal(3.5, 0.5))
mu = numpyro.sample('mu', dist.Normal(0, 3))
sigma = numpyro.sample('sigma', dist.LogNormal(0, .5))
phi = numpyro.sample('phi', dist.Normal(0, 3))
gammaT_star = simulate_data.compute_gamma_T_star(gammaC, gammaT, p)
etaT_star = simulate_data.compute_eta_T_star(etaC, etaT, p, gammaC, gammaT,
gammaT_star)
with numpyro.plate('y_C', nC - zC):
numpyro.sample('finite_obs_C',
Mix(nu[None, :],
mu[None, :],
sigma[None, :],
phi[None, :],
etaC[None, :]), obs=yC_finite[:, None])
with numpyro.plate('y_T', nT - zT):
numpyro.sample('finite_obs_T',
Mix(nu[None, :],
mu[None, :],
sigma[None, :],
phi[None, :],
etaT_star[None, :]), obs=yT_finite[:, None])
numpyro.sample('N_C', dist.Binomial(nC, gammaC), obs=zC)
numpyro.sample('N_T', dist.Binomial(nT, gammaT_star), obs=zT)
def run_hmc_steps(theta, eps, Lmax, key, log_posterior,
log_posterior_grad_theta, diagonal_mass_matrix):
# Diagonal mass matrix: diagonal entries of M (a vector)
inverse_diag_mass = 1. / diagonal_mass_matrix
key, subkey = random.split(key)
# Location-scale transform to get the right variance
# TODO: Check!
phi = random.normal(
subkey, shape=(theta.shape[0], )) * np.sqrt(diagonal_mass_matrix)
start_theta = theta
start_phi = phi
cur_grad = log_posterior_grad_theta(theta)
key, subkey = random.split(key)
L = np_classic.random.randint(1, Lmax)
for cur_l in range(L):
phi = phi + 0.5 * eps * cur_grad
theta = theta + eps * inverse_diag_mass * phi
cur_grad = log_posterior_grad_theta(theta)
phi = phi + 0.5 * eps * cur_grad
# Compute (log) acceptance probability
proposed_log_post = log_posterior(theta)
previous_log_post = log_posterior(start_theta)
proposed_log_phi = np.sum(
norm.logpdf(phi, scale=np.sqrt(diagonal_mass_matrix)))
previous_log_phi = np.sum(
norm.logpdf(start_phi, scale=np.sqrt(diagonal_mass_matrix)))
print(f'Proposed log posterior is: {proposed_log_post}.'
f'Previous was {previous_log_post}.')
if (np.isinf(proposed_log_post) or np.isnan(proposed_log_post)
or np.isneginf(proposed_log_post)):
# Reject
was_accepted = False
new_theta = start_theta
# FIXME: What number to put here?
log_r = -10
return was_accepted, log_r, new_theta
log_r = (proposed_log_post + proposed_log_phi - previous_log_post -
previous_log_phi)
was_accepted, new_theta = acceptance_step(log_r, theta, start_theta, key)
return was_accepted, log_r, new_theta
def __call__(self, controller_state, obs):
state, t = obs.predicted_pressure, obs.time
errs, waveform = controller_state.errs, controller_state.waveform
fwd_targets = controller_state.fwd_targets
target = waveform.at(t)
fwd_t = t + self.fwd_history_len * DEFAULT_DT
if self.fwd_history_len > 0:
fwd_target = jax.lax.cond(fwd_t >= self.horizon * DEFAULT_DT,
lambda x: fwd_targets[-1],
lambda x: waveform.at(fwd_t), None)
if self.normalize:
target_normalized = self.p_normalizer(target).squeeze()
state_normalized = self.p_normalizer(state).squeeze()
next_errs = jnp.roll(errs, shift=-1)
next_errs = next_errs.at[-1].set(target_normalized -
state_normalized)
if self.fwd_history_len > 0:
fwd_target_normalized = self.p_normalizer(fwd_target).squeeze()
next_fwd_targets = jnp.roll(fwd_targets, shift=-1)
next_fwd_targets = next_fwd_targets.at[-1].set(
fwd_target_normalized)
else:
next_fwd_targets = jnp.array([])
else:
next_errs = jnp.roll(errs, shift=-1)
next_errs = next_errs.at[-1].set(target - state)
if self.fwd_history_len > 0:
next_fwd_targets = jnp.roll(fwd_targets, shift=-1)
next_fwd_targets = next_fwd_targets.at[-1].set(fwd_target)
else:
next_fwd_targets = jnp.array([])
controller_state = controller_state.replace(
errs=next_errs, fwd_targets=next_fwd_targets)
decay = self.decay(waveform, t)
def true_func(null_arg):
trajectory = jnp.hstack([next_errs, next_fwd_targets])
u_in = self.model_apply({"params": self.params}, trajectory)
return u_in.squeeze().astype(jnp.float64)
# changed decay compare from None to float(inf) due to cond requirements
u_in = jax.lax.cond(jnp.isinf(decay), true_func,
lambda x: jnp.array(decay), None)
u_in = jax.lax.clamp(0.0, u_in.astype(jnp.float64),
self.clip).squeeze()
# update controller_state
new_dt = jnp.max(
jnp.array([DEFAULT_DT, t - proper_time(controller_state.time)]))
new_time = t
new_steps = controller_state.steps + 1
controller_state = controller_state.replace(time=new_time,
steps=new_steps,
dt=new_dt)
return controller_state, u_in
def log_normal_with_outliers(x, mean, cov, sigma):
"""
Computes log-Normal density with outliers removed.
Args:
x: RV value
mean: mean of Gaussian
cov: covariance of underlying, minus the obs. covariance
sigma: stddev's of obs. error, inf encodes an outlier.
Returns: a normal density for all points not of inf stddev obs. error.
"""
C = cov / (sigma[:, None] * sigma[None, :]) + jnp.eye(cov.shape[0])
L = jnp.linalg.cholesky(C)
Ls = sigma[:, None] * L
log_det = jnp.sum(jnp.where(jnp.isinf(sigma), 0., jnp.log(jnp.diag(Ls))))
dx = (x - mean)
dx = solve_triangular(L, dx / sigma, lower=True)
maha = dx @ dx
log_likelihood = -0.5 * jnp.sum(~jnp.isinf(sigma)) * jnp.log(2. * jnp.pi) \
- log_det \
- 0.5 * maha
return log_likelihood
def __call__(self, controller_state, obs):
state, t = obs.predicted_pressure, obs.time
errs, waveform = controller_state.errs, controller_state.waveform
target = waveform.at(t)
if self.normalize:
target_normalized = self.p_normalizer(target).squeeze()
state_normalized = self.p_normalizer(state).squeeze()
next_errs = jnp.roll(errs, shift=-1)
next_errs = next_errs.at[-1].set(target_normalized -
state_normalized)
else:
next_errs = jnp.roll(errs, shift=-1)
next_errs = next_errs.at[-1].set(target - state)
controller_state = controller_state.replace(errs=next_errs)
decay = self.decay(waveform, t)
def true_func(null_arg):
trajectory = jnp.expand_dims(next_errs[-self.history_len:],
axis=(0, 1))
input_val = jnp.reshape((trajectory @ self.featurizer),
(1, self.history_len, 1))
u_in = self.model_apply({"params": self.params}, input_val)
return u_in.squeeze().astype(jnp.float32)
# changed decay compare from None to float(inf) due to cond requirements
u_in = jax.lax.cond(jnp.isinf(decay), true_func,
lambda x: jnp.array(decay), None)
# Implementing "leaky" clamp to solve the zero gradient problem
if self.use_leaky_clamp:
u_in = jax.lax.cond(u_in < 0.0, lambda x: x * 0.01, lambda x: x,
u_in)
u_in = jax.lax.cond(u_in > self.clip,
lambda x: self.clip + x * 0.01, lambda x: x,
u_in)
else:
u_in = jax.lax.clamp(0.0, u_in.astype(jnp.float32),
self.clip).squeeze()
# update controller_state
new_dt = jnp.max(
jnp.array([DEFAULT_DT, t - proper_time(controller_state.time)]))
new_time = t
new_steps = controller_state.steps + 1
controller_state = controller_state.replace(time=new_time,
steps=new_steps,
dt=new_dt)
return controller_state, u_in
def annotated_with_grad(flat_theta, summary):
flat_theta = jnp.array(flat_theta)
obj, grad = with_grad(flat_theta)
print(obj, jnp.linalg.norm(grad))
if jnp.isnan(obj) or jnp.isinf(obj) or jnp.any(jnp.isnan(grad)):
import ipdb
problem = reconstruct(flat_theta, summary, jnp.reshape)
ipdb.set_trace()
return np.array(obj).astype(np.float64), np.array(grad).astype(
np.float64)
def loss(params, obs):
diags = model(params, obs)
# obs, _, _, info = env.step(diags)
# norm_res = jax.vmap(lambda x: jnp.linalg.norm(x[1, :], jnp.inf))(obs)
# return jnp.mean(norm_res)
_, _, _, info = env.step(diags)
# assert jnp.allclose(info['residual'] * info['niter'],
# jax.vmap(lambda x, y: x * y)(
# info['residual'], info['niter']))
norm_res = jnp.mean(info['residual'] * info['niter'])
# norm_res = jnp.mean(jnp.array(list(map(
# lambda i: info[0]['residual' + str(i)],
# range(args.batch_size)
# ))))
if jnp.isnan(norm_res):
raise ValueError('encountered NaNs')
if jnp.isinf(norm_res):
raise ValueError('encountered infs')
return norm_res
def test_forced_bos_token_logits_processor(self):
vocab_size = 20
batch_size = 4
bos_token_id = 0
logits_processor = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=bos_token_id)
# check that all scores are -inf except the bos_token_id score
input_ids = ids_tensor((batch_size, 1), vocab_size=20)
cur_len = 1
scores = self._get_uniform_logits(batch_size, vocab_size)
scores = logits_processor(input_ids, scores, cur_len=cur_len)
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]).all())
self.assertListEqual(scores[:, bos_token_id].tolist(), 4 * [0]) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
cur_len = 3
scores = self._get_uniform_logits(batch_size, vocab_size)
scores = logits_processor(input_ids, scores, cur_len=cur_len)
self.assertFalse(jnp.isinf(scores).any())
def test_forced_eos_token_logits_processor(self):
vocab_size = 20
batch_size = 4
eos_token_id = 0
max_length = 5
logits_processor = FlaxForcedEOSTokenLogitsProcessor(max_length=max_length, eos_token_id=eos_token_id)
# check that all scores are -inf except the eos_token_id when max_length is reached
input_ids = ids_tensor((batch_size, 4), vocab_size=20)
cur_len = 4
scores = self._get_uniform_logits(batch_size, vocab_size)
scores = logits_processor(input_ids, scores, cur_len=cur_len)
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]).all())
self.assertListEqual(scores[:, eos_token_id].tolist(), 4 * [0]) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
cur_len = 3
scores = self._get_uniform_logits(batch_size, vocab_size)
scores = logits_processor(input_ids, scores, cur_len=cur_len)
self.assertFalse(jnp.isinf(scores).any())
def metric(points, centers, K):
# N
cluster_id = masked_cluster_id(points, centers, K)
# N,N
dist = jnp.linalg.norm(points[:, None, :] - points[None, :, :], axis=-1)
# N,N
in_group = cluster_id[:, None] == cluster_id[None, :]
in_group_dist, w = jnp.average(dist, weights=in_group, axis=-1, returned=True)
in_group_dist *= w / (w - 1.)
# max_K, N, N
out_group = (~in_group) & (jnp.arange(max_K)[:, None, None] == cluster_id[None, None, :])
# max_K, N
out_group_dist = jnp.sum(dist * out_group, axis=-1) / jnp.sum(out_group, axis=-1)
out_group_dist = jnp.where(jnp.isnan(out_group_dist), jnp.inf, out_group_dist)
# N
out_group_dist = jnp.min(out_group_dist, axis=0)
out_group_dist = jnp.where(jnp.isinf(out_group_dist), jnp.max(in_group_dist), out_group_dist)
sillohette = (out_group_dist - in_group_dist) / jnp.maximum(in_group_dist, out_group_dist)
# condition for pos def cov
sillohette = jnp.where(w < points.shape[1], -jnp.inf, sillohette)
return jnp.mean(sillohette), cluster_id
def log_prob(self, value: Array) -> Array:
"""Calculates the log probability of an event.
This implementation differs slightly from the one in TFP, as it returns
`-jnp.inf` on non-integer values instead of returning the log prob of the
floor of the input. In addition, this implementation also returns `-jnp.inf`
on inputs that are outside the support of the distribution (as opposed to
`nan`, like TFP does). On other integer values, both implementations are
identical.
Args:
value: An event.
Returns:
The log probability log P(value).
"""
is_integer = jnp.where(value > jnp.floor(value), False, True)
log_cdf = self.log_cdf(value)
log_cdf_m1 = self.log_cdf(value - 1.)
log_probs = math.log_expbig_minus_expsmall(log_cdf, log_cdf_m1)
return jnp.where(jnp.isinf(log_cdf), -jnp.inf,
jnp.where(is_integer, log_probs, -jnp.inf))
def isinf(self: TensorType) -> TensorType:
return type(self)(np.isinf(self.raw))
def to_array(self, dtype=None):
head, tail = self._tup
if dtype is not None:
head = head.astype(dtype)
tail = tail.astype(dtype)
return head + jnp.where(jnp.isinf(head), 0, tail)
imag = utils.copy_docstring(tf.math.imag,
lambda input, name=None: np.imag(input))
# in_top_k = utils.copy_docstring(
# tf.math.in_top_k,
# lambda targets, predictions, k, name=None: np.in_top_k)
# TODO(b/256095991): Add unit-test.
invert_permutation = utils.copy_docstring(tf.math.invert_permutation,
lambda x, name=None: np.argsort)
is_finite = utils.copy_docstring(tf.math.is_finite,
lambda x, name=None: np.isfinite(x))
is_inf = utils.copy_docstring(tf.math.is_inf, lambda x, name=None: np.isinf(x))
is_nan = utils.copy_docstring(tf.math.is_nan, lambda x, name=None: np.isnan(x))
is_non_decreasing = utils.copy_docstring(
tf.math.is_non_decreasing, lambda x, name=None: np.all(x[1:] >= x[:-1]))
is_strictly_increasing = utils.copy_docstring(
tf.math.is_strictly_increasing,
lambda x, name=None: np.all(x[1:] > x[:-1]))
l2_normalize = utils.copy_docstring(
tf.math.l2_normalize,
lambda x, axis=None, epsilon=1e-12, name=None: ( # pylint: disable=g-long-lambda
np.linalg.norm(x, ord=2, axis=axis, keepdims=True)))
def solve_and_smooth(gain_outliers, phase_obs, times, freqs):
logger.info("Performing solve for tec and const from phases.")
Nd, Na, Nf, Nt = phase_obs.shape
logger.info("Number of nan: {}".format(jnp.sum(jnp.isnan(phase_obs))))
logger.info("Number of inf: {}".format(jnp.sum(jnp.isinf(phase_obs))))
# blocksize chosen to maximise Fisher information, which is 2 for tec+const, and 3 for tec+const+clock
blocksize = 2
remainder = Nt % blocksize
if remainder != 0:
if remainder < Nt:
raise ValueError(
f"Block size {blocksize} too big for number of timesteps {Nt}."
)
(gain_outliers, phase_obs) = tree_map(
lambda x: jnp.concatenate(
[x, jnp.repeat(x[..., -1:], remainder, axis=-1)], axis=-1),
(gain_outliers, phase_obs))
Nt = Nt + remainder
times = jnp.concatenate([
times, times[-1] +
jnp.arange(1, 1 + remainder) * jnp.mean(jnp.diff(times))
])
size_dict = dict(d=Nd, a=Na, f=Nf, b=blocksize)
# [Nd*Na*(Nt//blocksize), blocksize, Nf]
gain_outliers = axes_move(gain_outliers, ['d', 'a', 'f', 'tb'],
['dat', 'b', 'f'],
size_dict=size_dict)
phase_obs = axes_move(phase_obs, ['d', 'a', 'f', 'tb'], ['dat', 'b', 'f'],
size_dict=size_dict)
T = Nd * Na * (Nt // blocksize) # Nd * Na * (Nt // blocksize)
keys = random.split(random.PRNGKey(int(1000 * default_timer())), T)
# [Nd*Na*(Nt//blocksize), blocksize], [# Nd*Na*(Nt//blocksize), blocksize]
tec_mean, tec_std, const_mean, const_std, uncert_mean = chunked_pmap(
lambda *args: unconstrained_solve(freqs, *args), keys, phase_obs,
gain_outliers) # Nd*Na*(Nt//blocksize), blocksize
const_weights = 1. / const_std**2
def smooth(y, weights):
y = axes_move(y, ['dat', 'b'], ['da', 'tb'], size_dict=size_dict)
weights = axes_move(weights, ['dat', 'b'], ['da', 'tb'],
size_dict=size_dict)
y = chunked_pmap(
lambda y, weights: poly_smooth(times, y, deg=5, weights=weights),
y, weights)
y = axes_move(y, ['da', 'tb'], ['dat', 'b'], size_dict=size_dict)
return y
logger.info("Smoothing and outlier rejection of const (a weak prior).")
# Nd,Na,Nt/blocksize, blocksize
const_real_mean = smooth(jnp.cos(const_mean),
const_weights) # Nd*Na*(Nt//blocksize), blocksize
const_imag_mean = smooth(jnp.sin(const_mean),
const_weights) # Nd*Na*(Nt//blocksize), blocksize
const_mean_smoothed = jnp.arctan2(
const_imag_mean, const_real_mean) # Nd*Na*(Nt//blocksize), blocksize
# empirically determined uncertainty point where sigma(tec - tec_true) > 6 mTECU
which_reprocess = jnp.any(uncert_mean > 0.,
axis=1) # Nd*Na*(Nt//blocksize)
replace_map = jnp.where(which_reprocess)
logger.info("Performing refined tec-only solve, with fixed const.")
keys = random.split(random.PRNGKey(int(1000 * default_timer())),
jnp.sum(which_reprocess))
# [Nd*Na*(Nt//blocksize), blocksize]
(tec_mean_constrained, tec_std_constrained, const_mean_constrained, const_std_constrained) = \
chunked_pmap(lambda *args: constrained_solve(freqs, *args),
keys,
phase_obs[which_reprocess],
gain_outliers[which_reprocess],
const_mean_smoothed[which_reprocess],
const_std[which_reprocess]
)
tec_mean = tec_mean.at[replace_map].set(tec_mean_constrained)
tec_std = tec_std.at[replace_map].set(tec_std_constrained)
const_std = const_std.at[replace_map].set(const_std_constrained)
const_mean = const_mean.at[replace_map].set(const_mean_constrained)
(tec_mean, tec_std, const_mean,
const_std) = tree_map(lambda x: x.reshape((Nd, Na, Nt)),
(tec_mean, tec_std, const_mean, const_std))
# Nd, Na, Nt
logger.info("Performing outlier detection on tec values.")
tec_est, tec_outliers = detect_tec_outliers(times, tec_mean, tec_std)
tec_std = jnp.where(tec_outliers, jnp.inf, tec_std)
# remove remainder at the end
if remainder != 0:
(tec_mean, tec_std, const_mean,
const_std) = tree_map(lambda x: x[..., :Nt - remainder],
(tec_mean, tec_std, const_mean, const_std))
# compute phase mean with outlier-suppressed tec.
phase_mean = tec_mean[..., None, :] * (
TEC_CONV / freqs[:, None]) + const_mean[..., None, :]
phase_uncert = jnp.sqrt((tec_std[..., None, :] *
(TEC_CONV / freqs[:, None]))**2 +
(const_std[..., None, :])**2)
return phase_mean, phase_uncert, tec_mean, tec_std, tec_outliers, const_mean, const_std
def summary_naninf(d):
for k, v in d.items():
if (na := np.isnan(v)).any():
print(f'{k} nan: {na.sum()}/{na.size}')
if (nf := np.isinf(v)).any():
print(f'{k} inf: {nf.sum()}/{nf.size}')
def single_acceptance(args):
"""Draws a proposal, simulates and compresses, checks distance
A new proposal is drawn from a truncated multivariate normal
distribution whose mean is centred on the parameter to move and
the covariance is set by the population. From this proposed
parameter value a simulation is made and compressed and the
distance from the target is calculated. If this distance is
less than the current position then the proposal is accepted.
Parameters
----------
args : tuple
see loop variable in `single_iteration`
Returns
-------
bool:
True if proposal not accepted and number of attempts to get
an accepted proposal not yet reached
Todo
----
Parallel sampling is currently commented out
"""
(rng, loc, scale, summ, dis, draws, accepted,
acceptance_counter) = args
rng, key = jax.random.split(rng)
proposed, summaries = self.get_samples(
key,
None,
dist=tmvn(loc,
scale,
self.prior.low,
self.prior.high,
max_counter=max_samples))
distances = np.squeeze(
self.distance_measure(np.expand_dims(summaries, 0), target,
F))
# if n_parallel_simulations is not None:
# min_distance_index = np.argmin(distances)
# min_distance = distances[min_distance_index]
# closer = np.less(min_distance, ϵ)
# loc = jax.lax.cond(
# closer,
# lambda _ : proposed[min_distance_index],
# lambda _ : loc,
# None)
# summ = jax.lax.cond(
# closer,
# lambda _ : summaries[min_distance_index],
# lambda _ : summ,
# None)
# dis = jax.lax.cond(
# closer,
# lambda _ : distances[min_distance_index],
# lambda _ : dis,
# None)
# iteration_draws = n_parallel_simulations \
# - np.isinf(distances).sum()
# draws += iteration_draws
# accepted = closer.sum()
# else:
closer = np.less(distances, np.min(dis))
loc = jax.lax.cond(closer, lambda _: proposed, lambda _: loc,
None)
summ = jax.lax.cond(closer, lambda _: summaries,
lambda _: summ, None)
dis = jax.lax.cond(closer, lambda _: distances, lambda _: dis,
None)
iteration_draws = 1 - np.isinf(distances).sum()
draws += iteration_draws
accepted = closer.sum()
return (rng, loc, scale, summ, dis, draws, accepted,
acceptance_counter + 1)
def main(data_dir, working_dir, obs_num, ncpu, plot_results):
os.environ['XLA_FLAGS'] = f"--xla_force_host_platform_device_count={ncpu}"
logger.info("Performing data smoothing via tec+const+clock inference.")
dds4_h5parm = os.path.join(data_dir,
'L{}_DDS4_full_merged.h5'.format(obs_num))
dds5_h5parm = os.path.join(working_dir,
'L{}_DDS5_full_merged.h5'.format(obs_num))
linked_dds5_h5parm = os.path.join(
data_dir, 'L{}_DDS5_full_merged.h5'.format(obs_num))
logger.info("Looking for {}".format(dds4_h5parm))
link_overwrite(dds5_h5parm, linked_dds5_h5parm)
prepare_soltabs(dds4_h5parm, dds5_h5parm)
gain_outliers, phase_obs, amp, times, freqs = get_data(
solution_file=dds4_h5parm)
phase_mean, phase_uncert, tec_mean, tec_std, tec_outliers, const_mean, const_std = \
solve_and_smooth(gain_outliers, phase_obs, times, freqs)
# exit(0)
logger.info("Storing smoothed phase, amplitudes, tec, const, and clock")
with DataPack(dds5_h5parm, readonly=False) as h:
h.current_solset = 'sol000'
# h.select(pol=slice(0, 1, 1), ant=slice(50, 51), dir=slice(0, None, 1), time=slice(0, 100, 1))
h.select(pol=slice(0, 1, 1))
h.phase = np.asarray(phase_mean)[None, ...]
h.weights_phase = np.asarray(phase_uncert)[None, ...]
h.amplitude = np.asarray(amp)[None, ...]
h.tec = np.asarray(tec_mean)[None, ...]
h.tec_outliers = np.asarray(tec_outliers)[None, ...]
h.weights_tec = np.asarray(tec_std)[None, ...]
h.const = np.asarray(const_mean)[None, ...]
axes = h.axes_phase
patch_names, _ = h.get_directions(axes['dir'])
antenna_labels, _ = h.get_antennas(axes['ant'])
if plot_results:
diagnostic_data_dir = os.path.join(working_dir, 'diagnostic')
os.makedirs(diagnostic_data_dir, exist_ok=True)
logger.info("Plotting results.")
data_plot_dir = os.path.join(working_dir, 'data_plots')
os.makedirs(data_plot_dir, exist_ok=True)
Nd, Na, Nf, Nt = phase_mean.shape
for ia in range(Na):
for id in range(Nd):
fig, axs = plt.subplots(3, 1, sharex=True)
axs[0].plot(times, tec_mean[id, ia, :], c='black', label='tec')
ylim = axs[0].get_ylim()
axs[0].vlines(times[tec_outliers[id, ia, :]],
*ylim,
colors='red',
label='outliers',
alpha=0.5)
axs[0].set_ylim(*ylim)
axs[1].plot(times,
const_mean[id, ia, :],
c='black',
label='const')
axs[1].fill_between(
times,
const_mean[id, ia, :] - const_std[id, ia, :],
const_mean[id, ia, :] + const_std[id, ia, :],
color='black',
alpha=0.2)
ylim = axs[1].get_ylim()
axs[1].vlines(times[tec_outliers[id, ia, :]],
*ylim,
colors='red',
label='outliers',
alpha=0.5)
axs[1].set_ylim(*ylim)
axs[2].plot(times,
tec_std[id, ia, :],
c='black',
label='tec_std')
ylim = axs[2].get_ylim()
axs[2].vlines(times[tec_outliers[id, ia, :]],
*ylim,
colors='red',
label='outliers',
alpha=0.5)
axs[2].set_ylim(*ylim)
axs[0].legend()
axs[1].legend()
axs[2].legend()
axs[0].set_ylabel("DTEC [mTECU]")
axs[1].set_ylabel("const [rad]")
axs[2].set_ylabel("DTEC uncert [mTECU]")
axs[2].set_xlabel("time [s]")
fig.savefig(
os.path.join(
data_plot_dir,
'solutions_ant{:02d}_dir{:02d}.png'.format(ia, id)))
plt.close("all")
fig, axs = plt.subplots(4, 1, sharex=True, sharey=True)
# phase data with input outliers
# phase posterior with tec outliers
# dphase with no outliers
# phase uncertainty
axs[0].imshow(phase_obs[id, ia, :, :],
vmin=-jnp.pi,
vmax=jnp.pi,
cmap='twilight',
aspect='auto',
origin='lower',
interpolation='nearest')
axs[0].imshow(jnp.where(gain_outliers[id, ia, :, :], 1.,
jnp.nan),
vmin=0.,
vmax=1.,
cmap='bone',
aspect='auto',
origin='lower',
interpolation='nearest')
add_colorbar_to_axes(axs[0],
"twilight",
vmin=-jnp.pi,
vmax=jnp.pi)
axs[1].imshow(phase_mean[id, ia, :, :],
vmin=-jnp.pi,
vmax=jnp.pi,
cmap='twilight',
aspect='auto',
origin='lower',
interpolation='nearest')
axs[1].imshow(jnp.where(jnp.isinf(phase_uncert[id, ia, :, :]),
1., jnp.nan),
vmin=0.,
vmax=1.,
cmap='bone',
aspect='auto',
origin='lower',
interpolation='nearest')
add_colorbar_to_axes(axs[1],
"twilight",
vmin=-jnp.pi,
vmax=jnp.pi)
dphase = wrap(wrap(phase_mean) - phase_obs)
vmin = -0.5
vmax = 0.5
axs[2].imshow(dphase[id, ia, :, :],
vmin=vmin,
vmax=vmax,
cmap='PuOr',
aspect='auto',
origin='lower',
interpolation='nearest')
add_colorbar_to_axes(axs[2], "PuOr", vmin=vmin, vmax=vmax)
vmin = 0.
vmax = 0.8
axs[3].imshow(phase_uncert[id, ia, :, :],
vmin=vmin,
vmax=vmax,
cmap='PuOr',
aspect='auto',
origin='lower',
interpolation='nearest')
add_colorbar_to_axes(axs[3], "PuOr", vmin=vmin, vmax=vmax)
axs[0].set_ylabel("freq [MHz]")
axs[1].set_ylabel("freq [MHz]")
axs[2].set_ylabel("freq [MHz]")
axs[3].set_ylabel("freq [MHz]")
axs[3].set_xlabel("time [s]")
axs[0].set_title("phase data [rad]")
axs[1].set_title("phase model [rad]")
axs[2].set_title("phase diff. [rad]")
axs[3].set_title("phase uncert [rad]")
fig.savefig(
os.path.join(
data_plot_dir,
'data_comparison_ant{:02d}_dir{:02d}.png'.format(
ia, id)))
plt.close("all")
# exit(0)
d = os.path.join(working_dir, 'tec_plots')
animate_datapack(dds5_h5parm,
d,
num_processes=(ncpu * 2) // 3,
vmin=-60,
vmax=60.,
observable='tec',
phase_wrap=False,
plot_crosses=False,
plot_facet_idx=True,
labels_in_radec=True,
per_timestep_scale=True,
solset='sol000',
cmap=plt.cm.PuOr)
# os.makedirs(d, exist_ok=True)
# DatapackPlotter(dds5_h5parm).plot(
# fignames=[os.path.join(d, "fig-{:04d}.png".format(j)) for j in range(Nt)],
# vmin=-60,
# vmax=60., observable='tec', phase_wrap=False, plot_crosses=False,
# plot_facet_idx=True, labels_in_radec=True, per_timestep_scale=True,
# solset='sol000', cmap=plt.cm.PuOr)
# make_animation(d, prefix='fig', fps=4)
d = os.path.join(working_dir, 'const_plots')
animate_datapack(dds5_h5parm,
d,
num_processes=(ncpu * 2) // 3,
vmin=-np.pi,
vmax=np.pi,
observable='const',
phase_wrap=False,
plot_crosses=False,
plot_facet_idx=True,
labels_in_radec=True,
per_timestep_scale=True,
solset='sol000',
cmap=plt.cm.PuOr)
# os.makedirs(d, exist_ok=True)
# DatapackPlotter(dds5_h5parm).plot(
# fignames=[os.path.join(d, "fig-{:04d}.png".format(j)) for j in range(Nt)],
# vmin=-np.pi,
# vmax=np.pi, observable='const', phase_wrap=False, plot_crosses=False,
# plot_facet_idx=True, labels_in_radec=True, per_timestep_scale=True,
# solset='sol000', cmap=plt.cm.PuOr)
# make_animation(d, prefix='fig', fps=4)
d = os.path.join(working_dir, 'clock_plots')
animate_datapack(dds5_h5parm,
d,
num_processes=(ncpu * 2) // 3,
vmin=None,
vmax=None,
observable='clock',
phase_wrap=False,
plot_crosses=False,
plot_facet_idx=True,
labels_in_radec=True,
per_timestep_scale=True,
solset='sol000',
cmap=plt.cm.PuOr)
# os.makedirs(d, exist_ok=True)
# DatapackPlotter(dds5_h5parm).plot(
# fignames=[os.path.join(d, "fig-{:04d}.png".format(j)) for j in range(Nt)],
# vmin=None,
# vmax=None,
# observable='clock', phase_wrap=False, plot_crosses=False,
# plot_facet_idx=True, labels_in_radec=True, per_timestep_scale=True,
# solset='sol000', cmap=plt.cm.PuOr)
# make_animation(d, prefix='fig', fps=4)
d = os.path.join(working_dir, 'amplitude_plots')
animate_datapack(dds5_h5parm,
d,
num_processes=(ncpu * 2) // 3,
log_scale=True,
observable='amplitude',
phase_wrap=False,
plot_crosses=False,
plot_facet_idx=True,
labels_in_radec=True,
per_timestep_scale=True,
solset='sol000',
cmap=plt.cm.PuOr)
def has_inf_or_nan(x):
return jnp.isinf(x).any() or jnp.isnan(x).any()
def isinf(self, a):
return jnp.isinf(a)
def isinf(x):
if isinstance(x, JaxArray):
return JaxArray(jnp.isinf(x.value))
else:
return jnp.isinf(x)
def linesearch(cost_and_grad, x, d, f0, df0, g0, aold=None, dfold=None, fold=None, ls_pars=None):
if ls_pars is None:
ls_pars = LineSearchParameter()
# Wolfe conditions
def wolfe_one(ai, fi):
return fi > f0 + ls_pars.ls_suff_decr * ai * df0
def wolfe_two(dfi):
return jnp.abs(dfi) <= - ls_pars.ls_curvature * df0
state = _LineSearchState(
done=False,
failed=False,
i=1,
a_i1=0.,
phi_i1=f0,
dphi_i1=df0,
nfev=0,
ngev=0,
a_star=0,
phi_star=f0,
dphi_star=df0,
g_star=g0,
)
if ls_pars.ls_verbosity >= 1:
print('\tStarting linesearch...')
if (aold is not None) and (dfold is not None):
alpha_0 = aold * dfold / df0
initial_step_length = alpha_0
initial_step_length = jnp.where(alpha_0 > ls_pars.ls_initial_step,
ls_pars.ls_initial_step,
alpha_0)
elif (fold is not None) and (~jnp.isinf(fold)):
candidate = 1.01 * 2 * jnp.abs((f0 - fold) / df0)
candidate = jnp.where(candidate < 1e-8, 1e-8, candidate)
initial_step_length = jnp.where(candidate > 1.2 * ls_pars.ls_initial_step,
ls_pars.ls_initial_step,
candidate)
if ls_pars.ls_verbosity >= 3:
print(f'\tcandidate: {candidate:.2e}, accepted: {initial_step_length:.2e}')
else:
initial_step_length = ls_pars.ls_initial_step
while ((~state.done) & (state.i <= ls_pars.ls_maxiter) & (~state.failed)):
# no amax in this version, we just double as in scipy.
# unlike original algorithm we do our choice at the start of this loop
ai = jnp.where(
state.i == 1,
initial_step_length,
state.a_i1 * ls_pars.ls_optimism
)
fi, gri, dfi = cost_and_grad(ai)
state = state._replace(
nfev=state.nfev + 1,
ngev=state.ngev + 1
)
# if dfi > 0:
# state._replace(
# failed=True,
# )
# break
while jnp.isnan(fi):
ai = ai / 10.
fi, gri, dfi = cost_and_grad(ai)
state = state._replace(
nfev=state.nfev + 1,
ngev=state.ngev + 1
)
if ls_pars.ls_verbosity >= 2:
print("\titer: {}\n\t\talpha: {:.2e} "
"f(alpha): {:.5e}".format(state.i, ai, fi))
if wolfe_one(ai, fi) or ((fi > state.phi_i1) and state.i > 1):
if ls_pars.ls_verbosity >= 2:
print('\t\tEntering zoom1...')
zoom1 = _zoom(cost_and_grad, wolfe_one, wolfe_two,
state.a_i1, state.phi_i1, state.dphi_i1, ai, fi, dfi,
gri, ls_pars)
state = state._replace(
done=(zoom1.done or state.done),
failed=(zoom1.failed or state.failed),
a_star=zoom1.a_star,
phi_star=zoom1.phi_star,
dphi_star=zoom1.dphi_star,
g_star=zoom1.g_star,
nfev=state.nfev + zoom1.nfev,
ngev=state.ngev + zoom1.ngev
)
elif wolfe_two(dfi):
if ls_pars.ls_verbosity >= 2:
print('\t\tWolfe two condition met, stopping')
state = state._replace(
done=(True or state.done),
a_star=ai,
phi_star=fi,
dphi_star=dfi,
g_star=gri
)
elif dfi >= 0:
if ls_pars.ls_verbosity >= 2:
print('\t\tEntering zoom2')
zoom2 = _zoom(cost_and_grad, wolfe_one, wolfe_two,
ai, fi, dfi, state.a_i1, state.phi_i1, state.dphi_i1,
gri, ls_pars)
state = state._replace(
done=(zoom2.done or state.done),
failed=(zoom2.failed or state.failed),
a_star=zoom2.a_star,
phi_star=zoom2.phi_star,
dphi_star=zoom2.dphi_star,
g_star=zoom2.g_star,
nfev=state.nfev + zoom2.nfev,
ngev=state.ngev + zoom2.ngev
)
state = state._replace(
i=state.i + 1,
a_i1=ai,
phi_i1=fi,
dphi_i1=dfi)
status = jnp.where(
state.failed,
jnp.array(2), # zoom failed
jnp.where(
state.i > ls_pars.ls_maxiter,
jnp.array(1), # maxiter reached
jnp.array(0), # passed (should be)
),
)
result = _LineSearchResult(
failed=state.failed,
nit=state.i - 1, # because iterations started at 1
nfev=state.nfev,
ngev=state.ngev,
k=state.i,
a_k=state.a_i1 if status==1 else state.a_star,
f_k=state.phi_star,
g_k=state.g_star,
status=status,
)
if ls_pars.ls_verbosity >= 1:
print('\tLinesearch {}, alpha star = {:.2e}'.format(
'failed' if state.failed else 'done', result.a_k))
return result
def check():
if np.isnan(lls[-1]) or np.isinf(lls[-1]):
raise Exception("LL check failed.")
