def calc_p_single(y, theta, mu_single, sigma_single, sigma_multiple, mu_multiple_scalar):
with warnings.catch_warnings():
# I'll log whatever number I want python you can't tell me what to do
warnings.simplefilter("ignore")
mu_multiple = np.log(mu_single + mu_multiple_scalar * sigma_single) + sigma_multiple**2
ln_s = np.log(theta) + utils.normal_lpdf(y, mu_single, sigma_single)
ln_m = np.log(1-theta) + utils.lognormal_lpdf(y, mu_multiple, sigma_multiple)
# FIX BAD SUPPORT.
# This is a BAD MAGIC HACK where we are just going to flip things.
"""
limit = mu_single - 2 * sigma_single
bad_support = (y <= limit) * (ln_m > ln_s)
ln_s_bs = np.copy(ln_s[bad_support])
ln_m_bs = np.copy(ln_m[bad_support])
ln_s[bad_support] = ln_m_bs
ln_m[bad_support] = ln_s_bs
"""
ln_s = np.atleast_1d(ln_s)
ln_m = np.atleast_1d(ln_m)
lp = np.array([ln_s, ln_m]).T
#assert np.all(np.isfinite(lp))
p_single = np.exp(lp[:, 0] - special.logsumexp(lp, axis=1))
return (p_single, ln_s, ln_m)
def calc_p_single(y,
theta,
mu_single,
sigma_single,
sigma_multiple,
mu_multiple_scalar,
N=100):
#check_support(theta, mu_single, sigma_single, sigma_multiple, mu_multiple_scalar, M=M, N=N)
with warnings.catch_warnings():
# I'll log whatever number I want python you can't tell me what to do
warnings.simplefilter("ignore")
mu_multiple = np.log(mu_single + mu_multiple_scalar *
sigma_single) + sigma_multiple**2
sigmoid_weight = get_sigmoid_weight(sigma_single)
sigmoid = 1 / (1 + np.exp(-sigmoid_weight * (y - mu_single)))
ln_s = np.log(theta) + utils.normal_lpdf(y, mu_single, sigma_single)
ln_m = np.log(1 - theta) + utils.lognormal_lpdf(
y, mu_multiple, sigma_multiple)
# Add sigmoid
ln_m += np.log(sigmoid)
ln_s = np.atleast_1d(ln_s)
ln_m = np.atleast_1d(ln_m)
lp = np.array([ln_s, ln_m]).T
p_single = np.exp(lp[:, 0] - special.logsumexp(lp, axis=1))
return (p_single, ln_s, ln_m)
for ii, (theta, mu_single, sigma_single,
sigma_multiple) in enumerate(tqdm(grid[::-1])):
# Do in reverse order because the last edge cases are more problematic.
i = -(ii + 1)
# Check prior.
if not np.isfinite(
ln_prior(theta, mu_single, sigma_single, sigma_multiple)):
# Don't evaluate support at places outside our prior space.
continue
mu_multiple = get_mu_multiple(mu_single, sigma_single,
sigma_multiple, mu_multiple_scalar)
ln_s = np.log(theta) + utils.normal_lpdf(xi, mu_single,
sigma_single)
ln_m = np.log(1 - theta) + utils.lognormal_lpdf(
xi, mu_multiple, sigma_multiple)
# Add sigmoid
sigmoid_weight = (1.0 / sigma_single) * np.log(
(2 * np.pi * sigma_single)**0.5 * np.exp(0.5 * M**2) - 1)
sigmoid = 1 / (1 + np.exp(-sigmoid_weight * (xi - mu_single)))
ln_m = np.log(np.exp(ln_m) * sigmoid)
def plot_it():
fig, axes = plt.subplots(2, sharex=True)
axes[0].plot(xi, ln_s, c="tab:blue")
axes[0].plot(xi, ln_m, c="tab:red")
def check_support(theta,
mu_single,
sigma_single,
sigma_multiple,
mu_multiple_scalar,
M=2,
N=1000,
max_sigma_single_away=10):
max_x = np.max(mu_single + max_sigma_single_away * sigma_single)
epsilon = 0.01
x = np.atleast_2d(np.linspace(epsilon, max_x, N)).T
# Chose some index.
theta = np.atleast_2d(theta)
mu_single = np.atleast_2d(mu_single)
sigma_single = np.atleast_2d(sigma_single)
sigma_multiple = np.atleast_2d(sigma_multiple)
mu_multiple = np.log(mu_single +
mu_multiple_scalar * sigma_single) + sigma_multiple**2
with warnings.catch_warnings():
# I'll log whatever number I want python you can't tell me what to do
warnings.simplefilter("ignore")
sigmoid_weight = get_sigmoid_weight(sigma_single, M=M)
sigmoid = 1 / (1 + np.exp(-sigmoid_weight * (x - mu_single)))
ln_s = np.log(theta) + utils.normal_lpdf(x, mu_single, sigma_single)
ln_m = np.log(1 - theta) + utils.lognormal_lpdf(
x, mu_multiple, sigma_multiple)
# Add sigmoid
ln_m_truncated = np.log(np.exp(ln_m) * sigmoid)
# Check left hand side.
for i in range(theta.size):
try:
# check ln_single is more than truncated on the LHS
j = x[:, 0].searchsorted(mu_single[0, i])
assert np.all(ln_s[:j, i] > ln_m_truncated[:j, i])
# check that once the ln_m is preferred, that it is always preferred on the RHS
j = np.where(ln_m_truncated[:, i] > ln_s[:, i])[0][0]
assert np.all(ln_m_truncated[:, i][j:] > ln_s[:, i][j:])
except (AssertionError, IndexError):
fig, axes = plt.subplots(2)
axes[0].plot(x, ln_s[:, i], c="tab:blue")
axes[0].plot(x, ln_m[:, i], c="tab:red")
axes[0].plot(x, ln_m_truncated[:, i], c="k")
axes[1].plot(x, np.exp(ln_s[:, i]), c="tab:blue")
axes[1].plot(x, np.exp(ln_m[:, i]), c="tab:red")
axes[1].plot(x, np.exp(ln_m_truncated[:, i]), c="k")
ln_m2 = np.log(1 - theta) + utils.lognormal_lpdf(
x, mu_multiple, 2 * sigma_multiple)
# Add sigmoid
ln_m_truncated2 = np.log(np.exp(ln_m2) * sigmoid)
axes[0].plot(x, ln_m_truncated2[:, i], c="g")
axes[1].plot(x, np.exp(ln_m_truncated2[:, i]), c="g")
raise
"""
index = np.random.choice(N)
fig, axes = plt.subplots(2)
axes[0].plot(x, ln_s[:, index], c="tab:blue")
axes[0].plot(x, ln_m[:, index], c="tab:red")
axes[0].plot(x, ln_m_truncated[:, index], c="k")
axes[1].plot(x, np.exp(ln_s[:, index]), c="tab:blue")
axes[1].plot(x, np.exp(ln_m[:, index]), c="tab:red")
axes[1].plot(x, np.exp(ln_m_truncated[:, index]), c="k")
"""
return True
for k in ("single", "multiple", "ratio_single"):
if k in g: del g[k]
y = sources[predictor_label_name][()][data_indices]
w, w_var = results[f"{model_name}/gp_predictions/theta"][()].T
ln_s = g.create_dataset("single", data=np.nan * np.ones(N))
mu_s, mu_s_var = results[f"{model_name}/gp_predictions/mu_single"][(
)].T
sigma_s, sigma_s_var = results[
f"{model_name}/gp_predictions/sigma_single"][()].T
# Evaluate log-likelihood
ln_s[:] = np.log(w) + utils.normal_lpdf(y, mu_s, sigma_s)
ln_m = g.create_dataset("multiple", data=np.nan * np.ones(N))
sigma_m, sigma_m_var = results[
f"{model_name}/gp_predictions/sigma_multiple"][()].T
if "mu_multiple" in results[f"{model_name}/gp_predictions"]:
mu_m = results[f"{model_name}/gp_predictions/mu_multiple"][()][:,
0]
# TODO: draws in mu_m
else:
scalar = model_config["mu_multiple_scalar"]
mu_m = np.log(mu_s + scalar * sigma_s) + sigma_m**2