def fit(self, X, y):
self.model = pm.Model()
with self.model:
xi = pm.Bernoulli(
'xi', .05,
shape=X.shape[1]) # inclusion probability for each variable
alpha = pm.Cauchy('alpha', alpha=0, beta=2.0) # Intercept
coeff_shape = pm.Exponential("coeff_shape", lam=0.05)
beta = pm.Cauchy(
'beta', alpha=0, beta=coeff_shape,
shape=X.shape[1]) # Prior for the non-zero coefficients
p = pm.math.dot(
X, xi * beta
) # Deterministic function to map the stochastics to the output
y_obs = pm.Bernoulli('y_obs', invlogit(p + alpha),
observed=y) # Data likelihood
with self.model:
self.trace = pm.sample(4000,
random_seed=4816,
cores=1,
progressbar=False,
chains=1)
return None
def _setup_y(self, y_data, ar, by_run):
''' Sets up y to be a theano shared variable. '''
if 'y' not in self.shared_params:
self.shared_params['y'] = shared(y_data)
with self.model:
n_vols = self.dataset.n_vols
n_runs = int(len(y_data) / n_vols)
for i in range(1, ar + 1):
_pad = shared(np.zeros((i, )))
_trunc = self.shared_params['y'][:-i]
y_shifted = T.concatenate((_pad, _trunc))
weights = np.r_[np.zeros(i), np.ones(n_vols - i)]
# Model an AR term for each run or use just one for all runs
if by_run:
smoother = pm.Cauchy('AR(%d)' % i, alpha=0, beta=1)
_ar = T.repeat(smoother, n_vols) * y_shifted
else:
smoother = pm.Cauchy('AR(%d)' % i, alpha=0, beta=1)
weights = np.tile(weights, n_runs)
_ar = shared(weights) * y_shifted * smoother
self.mu += _ar
sigma = pm.HalfCauchy('sigma_y_obs', beta=2)
y_obs = pm.Normal('Y_obs',
mu=self.mu,
sd=sigma,
observed=self.shared_params['y'])
else:
self.shared_params['y'].set_value(y_data)
def _model_setup(self):
with self._model:
# COSMOLOGY
omega_m = pm.Uniform("OmegaM", lower=0, upper=1.)
# dark energy EOS
w = pm.Normal("w", mu=-1, sd=1)
# My custom distance mod. function to enable
# ADVI and HMC smapling.
dm = distmod_w_flat(omega_m, self._h0, w, self._zcmb)
# PHILIPS PARAMETERS
# M0 is the location parameter for the distribution
# sys_scat is the scale parameter for the M0 distribution
# rather than "unexpalined variance"
M0 = pm.Normal("M0", mu=-19.3, sd=2.)
sys_scat = pm.HalfCauchy('sys_scat', beta=2.5) # Gelman recommendation for variance parameter
M_true = pm.Normal('M_true', M0, sys_scat, shape=self._n_SN)
# following Rubin's Unity model... best idea? not sure
taninv_alpha = pm.Uniform("taninv_alpha", lower=-.2, upper=.3)
taninv_beta = pm.Uniform("taninv_beta", lower=-1.4, upper=1.4)
# Transform variables
alpha = pm.Deterministic('alpha', T.tan(taninv_alpha))
beta = pm.Deterministic('beta', T.tan(taninv_beta))
# Again using Rubin's Unity model.
# After discussion with Rubin, the idea is that
# these parameters are ideally sampled from a Gaussian,
# but we know they are not entirely correct. So instead,
# the Cauchy is less informative around the mean, while
# still having informative tails.
xm = pm.Cauchy('xm', alpha=0, beta=1)
cm = pm.Cauchy('cm', alpha=0, beta=1)
Rx_log = pm.Uniform('Rx_log', lower=-0.5, upper=0.5)
Rc_log = pm.Uniform('Rc_log', lower=-1.5, upper=1.5)
# Transformed variables
Rx = pm.Deterministic("Rx", T.pow(10., Rx_log))
Rc = pm.Deterministic("Rc", T.pow(10., Rc_log))
x_true = pm.Normal('x_true', mu=xm, sd=Rx, shape=self._n_SN)
c_true = pm.Normal('c_true', mu=cm, sd=Rc, shape=self._n_SN)
# Do the correction
mb = pm.Deterministic("mb", M_true + dm - alpha * x_true + beta * c_true)
# Likelihood and measurement error
obsc = pm.Normal("obsc", mu=c_true, sd=self._dcolor, observed=self._color)
obsx = pm.Normal("obsx", mu=x_true, sd=self._dx1, observed=self._x1)
obsm = pm.Normal("obsm", mu=mb, sd=self._dmb_obs, observed=self._mb_obs)
def create_pymc_model(self, min_observations=0, observer_bias=False):
"""Returns a PyMC3 model."""
dfobs = self.obs.data[self.obs.data.observations > min_observations]
with pm.Model() as model:
delta = self.earth_distance_func(dfobs.time.values)
r = self.sun_distance_func(dfobs.time.values)
n = pm.Normal('n', mu=3.49, sigma=1.36) # activity parameter
h = pm.Normal('h', mu=6.66, sigma=1.98) # absolute magnitude
model_mag = comet_magnitude_power_law(h=h, n=n, delta=delta, r=r)
if observer_bias == True:
observers = dfobs.observer.unique()
for obs in observers:
mask = np.array(dfobs.observer.values == obs)
beta = pm.HalfNormal('beta_' + obs, sigma=0.5)
bias = pm.Normal('bias_' + obs, mu=0., sigma=.5)
obsmag = pm.Cauchy('obsmag_' + obs,
alpha=model_mag[mask] + bias,
beta=beta,
observed=dfobs.magnitude[mask])
else:
beta = 0.47 + pm.HalfNormal('beta', sigma=0.02)
obsmag = pm.Cauchy('obsmag',
alpha=model_mag,
beta=beta,
observed=dfobs.magnitude)
self.model = model
return self.model
def fit0(p0, q0):
with pm.Model() as m:
loga = pm.Cauchy('loga', 0, 5)
c = pm.Cauchy('c', 0, 5, testval=-5)
μ0 = pm.Deterministic('μ0', np.exp(loga + c * np.log(p0)))
qval = pm.Poisson('q', μ0, observed=q0)
t = pm.sample()
return t
def fitrep(p0, μ0, n):
qobs = np.random.poisson(np.outer(np.ones(n), μ0))
with pm.Model() as m:
α = pm.Cauchy('α', 0, 5, shape=n)
β = pm.Cauchy('β', 0, 5, shape=n)
μ0rep = np.exp(α + β * (np.log(p0) - logp0mean).reshape(-1, 1)).T
qval = pm.Poisson('q0', μ0rep, observed=qobs)
t = pm.sample()
return t
def fit(p0, q0):
with pm.Model() as m:
α = pm.Cauchy('α', 0, 5)
β = pm.Cauchy('β', 0, 5)
logμ0 = α + β * (np.log(p0) - logp0mean)
μ0 = pm.Deterministic('μ0', np.exp(logμ0))
qval = pm.Poisson('q0', μ0, observed=q0)
t = pm.sample()
return t
def run_breakpoint_model(prices):
with pm.Model() as trend_change_model:
scaling_factor = 10
relative_changes = list(map(lambda p: p.value * scaling_factor,
prices))
dates = list(map(lambda p: p.date, prices))
relative_changes_np = np.array(relative_changes)
date_indexes = np.arange(0, len(prices))
# Prior distributions
trend_change_point = pm.Cauchy('changepoint', alpha=100, beta=2)
early_trend = pm.Normal('early_trend', mu=0.1, sd=0.1)
late_trend = pm.Normal('late_trend', mu=-0.05, sd=0.1)
# Transformed variable
trend = pm.math.switch(trend_change_point >= date_indexes, early_trend,
late_trend)
# Likelyhood
price_change = pm.Normal('price_change',
trend,
observed=relative_changes_np)
samples = pm.sample(draws=1000, tune=1000, cores=1)
# az.plot_trace(samples, var_names=['changepoint', 'early_trend', 'late_trend'])
# trend_change_model.early_trend.summary()#
# trend_change_model.trace['early_trend']
pm.traceplot(samples)
plt.show()
print(pm.summary(samples, kind="stats"))
def _sample_pymc3(cls, dist, size):
"""Sample from PyMC3."""
import pymc3
pymc3_rv_map = {
'BetaDistribution': lambda dist:
pymc3.Beta('X', alpha=float(dist.alpha), beta=float(dist.beta)),
'CauchyDistribution': lambda dist:
pymc3.Cauchy('X', alpha=float(dist.x0), beta=float(dist.gamma)),
'ChiSquaredDistribution': lambda dist:
pymc3.ChiSquared('X', nu=float(dist.k)),
'ExponentialDistribution': lambda dist:
pymc3.Exponential('X', lam=float(dist.rate)),
'GammaDistribution': lambda dist:
pymc3.Gamma('X', alpha=float(dist.k), beta=1/float(dist.theta)),
'LogNormalDistribution': lambda dist:
pymc3.Lognormal('X', mu=float(dist.mean), sigma=float(dist.std)),
'NormalDistribution': lambda dist:
pymc3.Normal('X', float(dist.mean), float(dist.std)),
'GaussianInverseDistribution': lambda dist:
pymc3.Wald('X', mu=float(dist.mean), lam=float(dist.shape)),
'ParetoDistribution': lambda dist:
pymc3.Pareto('X', alpha=float(dist.alpha), m=float(dist.xm)),
'UniformDistribution': lambda dist:
pymc3.Uniform('X', lower=float(dist.left), upper=float(dist.right))
}
dist_list = pymc3_rv_map.keys()
if dist.__class__.__name__ not in dist_list:
return None
with pymc3.Model():
pymc3_rv_map[dist.__class__.__name__](dist)
return pymc3.sample(size, chains=1, progressbar=False)[:]['X']
def test_pymc3_convert_dists():
"""Just a basic check that all PyMC3 RVs will convert to and from Theano RVs."""
tt.config.compute_test_value = "ignore"
theano.config.cxx = ""
with pm.Model() as model:
norm_rv = pm.Normal("norm_rv", 0.0, 1.0, observed=1.0)
mvnorm_rv = pm.MvNormal("mvnorm_rv",
np.r_[0.0],
np.c_[1.0],
shape=1,
observed=np.r_[1.0])
cauchy_rv = pm.Cauchy("cauchy_rv", 0.0, 1.0, observed=1.0)
halfcauchy_rv = pm.HalfCauchy("halfcauchy_rv", 1.0, observed=1.0)
uniform_rv = pm.Uniform("uniform_rv", observed=1.0)
gamma_rv = pm.Gamma("gamma_rv", 1.0, 1.0, observed=1.0)
invgamma_rv = pm.InverseGamma("invgamma_rv", 1.0, 1.0, observed=1.0)
exp_rv = pm.Exponential("exp_rv", 1.0, observed=1.0)
halfnormal_rv = pm.HalfNormal("halfnormal_rv", 1.0, observed=1.0)
beta_rv = pm.Beta("beta_rv", 2.0, 2.0, observed=1.0)
binomial_rv = pm.Binomial("binomial_rv", 10, 0.5, observed=5)
dirichlet_rv = pm.Dirichlet("dirichlet_rv",
np.r_[0.1, 0.1],
observed=np.r_[0.1, 0.1])
poisson_rv = pm.Poisson("poisson_rv", 10, observed=5)
bernoulli_rv = pm.Bernoulli("bernoulli_rv", 0.5, observed=0)
betabinomial_rv = pm.BetaBinomial("betabinomial_rv",
0.1,
0.1,
10,
observed=5)
categorical_rv = pm.Categorical("categorical_rv",
np.r_[0.5, 0.5],
observed=1)
multinomial_rv = pm.Multinomial("multinomial_rv",
5,
np.r_[0.5, 0.5],
observed=np.r_[2])
# Convert to a Theano `FunctionGraph`
fgraph = model_graph(model)
rvs_by_name = {
n.owner.inputs[1].name: n.owner.inputs[1]
for n in fgraph.outputs
}
pymc_rv_names = {n.name for n in model.observed_RVs}
assert all(
isinstance(rvs_by_name[n].owner.op, RandomVariable)
for n in pymc_rv_names)
# Now, convert back to a PyMC3 model
pymc_model = graph_model(fgraph)
new_pymc_rv_names = {n.name for n in pymc_model.observed_RVs}
pymc_rv_names == new_pymc_rv_names
def build_model(self, name='normal_model'):
# Define Stochastic variables
with pm.Model(name=name) as self.model:
# Global mean pitch angle
self.mu_phi = pm.Uniform('mu_phi', lower=0, upper=90)
self.sigma_phi = pm.InverseGamma('sigma_phi',
alpha=2,
beta=15,
testval=8)
self.sigma_gal = pm.InverseGamma('sigma_gal',
alpha=2,
beta=15,
testval=8)
# define a mean galaxy pitch angle
self.phi_gal = pm.TruncatedNormal(
'phi_gal',
mu=self.mu_phi,
sd=self.sigma_phi,
lower=0,
upper=90,
shape=len(self.galaxies),
)
# draw arm pitch angles centred around this mean
self.phi_arm = pm.TruncatedNormal(
'phi_arm',
mu=self.phi_gal[self.gal_arm_map],
sd=self.sigma_gal,
lower=0,
upper=90,
shape=len(self.gal_arm_map),
)
# convert to a gradient for a linear fit
self.b = tt.tan(np.pi / 180 * self.phi_arm)
# arm offset parameter
self.c = pm.Cauchy('c',
alpha=0,
beta=10,
shape=self.n_arms,
testval=np.tile(0, self.n_arms))
# radial noise
self.sigma_r = pm.InverseGamma('sigma_r', alpha=2, beta=0.5)
r = pm.Deterministic(
'r',
tt.exp(self.b[self.point_arm_map] * self.data['theta'] +
self.c[self.point_arm_map]))
# likelihood function
self.likelihood = pm.Normal(
'Likelihood',
mu=r,
sigma=self.sigma_r,
observed=self.data['r'],
)
def build_model(self, name=''):
# Define Stochastic variables
with pm.Model(name=name) as self.model:
# Global mean pitch angle
self.phi_gal = pm.Uniform('phi_gal',
lower=0,
upper=90,
shape=len(self.galaxies))
# note we don't model inter-galaxy dispersion here
# intra-galaxy dispersion
self.sigma_gal = pm.InverseGamma('sigma_gal',
alpha=2,
beta=20,
testval=5)
# arm offset parameter
self.c = pm.Cauchy('c',
alpha=0,
beta=10,
shape=self.n_arms,
testval=np.tile(0, self.n_arms))
# radial noise
self.sigma_r = pm.InverseGamma('sigma_r', alpha=2, beta=0.5)
# define prior for Student T degrees of freedom
# self.nu = pm.Uniform('nu', lower=1, upper=100)
# Define Dependent variables
self.phi_arm = pm.TruncatedNormal(
'phi_arm',
mu=self.phi_gal[self.gal_arm_map],
sd=self.sigma_gal,
lower=0,
upper=90,
shape=self.n_arms)
# convert to a gradient for a linear fit
self.b = tt.tan(np.pi / 180 * self.phi_arm)
r = pm.Deterministic(
'r',
tt.exp(self.b[self.data['arm_index'].values] *
self.data['theta'] +
self.c[self.data['arm_index'].values]))
# likelihood function
self.likelihood = pm.StudentT(
'Likelihood',
mu=r,
sigma=self.sigma_r,
nu=1, #self.nu,
observed=self.data['r'],
)
def model(data):
J = data["J"] # number of schools
y_obs = np.array(data["y"]) # estimated treatment
sigma = np.array(data["sigma"]) # std of estimated effect
with pm3.Model() as pymc_model:
mu = pm3.Normal("mu", mu=0,
sd=5) # hyper-parameter of mean, non-informative prior
tau = pm3.Cauchy("tau", alpha=0, beta=5) # hyper-parameter of sd
theta_trans = pm3.Normal("theta_trans", mu=0, sd=1, shape=J)
theta = mu + tau * theta_trans
y = pm3.Normal("y", mu=theta, sd=sigma, observed=y_obs)
return pymc_model
def bayesianCenter(data):
with pm.Model():
loc = pm.Uniform('location', lower=-1000., upper=1000.)
scale = pm.Uniform('scale', lower=0.01, upper=1000.)
pm.Cauchy('y', alpha=loc, beta=scale, observed=data)
trace = pm.sample(3000, tune=3000, target_accept=0.92)
pm.traceplot(trace)
plt.show()
return np.mean(trace['location'])
def build_model(self, name=''):
# Define Stochastic variables
with pm.Model(name=name) as self.model:
# Global mean pitch angle
self.phi_gal = pm.Uniform('phi_gal',
lower=0,
upper=90,
shape=len(self.galaxies))
# note we don't model inter-galaxy dispersion here
# intra-galaxy dispersion
self.sigma_gal = pm.InverseGamma('sigma_gal',
alpha=2,
beta=20,
testval=5)
# arm offset parameter
self.c = pm.Cauchy('c',
alpha=0,
beta=10,
shape=self.n_arms,
testval=np.tile(0, self.n_arms))
# radial noise
self.sigma_r = pm.InverseGamma('sigma_r', alpha=2, beta=0.5)
# ----- Define Dependent variables -----
# Phi arm is drawn from a truncated normal centred on phi_gal with
# spread sigma_gal
gal_idx = self.gal_arm_map.astype('int32')
self.phi_arm = pm.TruncatedNormal('phi_arm',
mu=self.phi_gal[gal_idx],
sd=self.sigma_gal,
lower=0,
upper=90,
shape=self.n_arms)
# transform to gradient for fitting
self.b = tt.tan(np.pi / 180 * self.phi_arm)
# r = exp(theta * tan(phi) + c)
# do not track this as it uses a lot of memory
arm_idx = self.data['arm_index'].values.astype('int32')
r = tt.exp(self.b[arm_idx] * self.data['theta'] + self.c[arm_idx])
# likelihood function (assume likelihood here)
self.likelihood = pm.Normal(
'Likelihood',
mu=r,
sigma=self.sigma_r,
observed=self.data['r'],
)
def fit(self, X_train, y_train):
#compute the mean, and standard deviation for feature preprocessing (Gelman, 2008)
#print(X_train.shape)
if X_train.ndim == 1:
X_train = X_train.reshape(-1, 1)
self.scaler = StandardScaler()
self.scaler.fit(X_train)
#to transform data to have standard deviation of 0.5 (Gelman, 2008)
self.scaler.var_ *= 4
X_train = self.transform_data(X_train)
samples = 1000
with pm.Model() as _:
# betas
alpha = pm.Cauchy('alpha', 0., 10)
betas = []
for i in range(X_train.shape[1]):
betas.append(pm.Cauchy('beta' + str(i), 0., 2.5))
#beta = pm.Cauchy('beta', 0., 2.5)
# logit
logit_p = alpha # + beta * X_train)
for i in range(X_train.shape[1]):
logit_p += betas[
i] * X_train[:,
i] #.append(pm.Cauchy('beta' + str(i), 0., 2.5))
p = Tht.exp(logit_p) / (1 + Tht.exp(logit_p))
# likelihood
_ = pm.Binomial('likelihood', n=1, p=p, observed=y_train)
# inference
start = pm.find_MAP()
#step = pm.NUTS(scaling = start)
#trace = pm.sample(samples, step, progressbar=False, chains=2, cores=1)
#summary = pm.summary(trace)['mean']
self.b_map = start['alpha']
self.a_map = []
for i in range(X_train.shape[1]):
self.a_map.append(start['beta' + str(i)])
self.a_map = np.array(self.a_map)
def __init__(self,
wave,
flux,
templates,
adegree=None,
mdegree=None,
reddening=False):
with pm.Model() as hierarchical_model:
# Hyperpriors
mu_age = pm.Normal("Age", mu=9, sd=1)
mu_metal = pm.Normal("Metal", mu=0, sd=0.1)
mu_alpha = pm.Normal("Alpha", mu=0.2, sd=.1)
sigma_age = pm.Exponential('SAge', lam=1)
sigma_metal = pm.Exponential('SMetal', lam=.1)
sigma_alpha = pm.Exponential('SAlpha', lam=.1)
ages = pm.Normal("age", mu=mu_age, sd=sigma_age, shape=N)
metals = pm.Normal("metal", mu=mu_metal, sd=sigma_metal, shape=N)
alphas = pm.Normal("alpha", mu=mu_alpha, sd=sigma_alpha, shape=N)
eps = pm.Exponential("eps", lam=1, shape=N)
w = [pm.Deterministic("w_{}".format(i), \
T.exp(-0.5 * T.pow(
(metals[i] - ssps.metals1D) / sigma_metal,
2)) *
T.exp(-0.5 * T.pow(
(ages[i] - ssps.ages1D) / sigma_age, 2)) *
T.exp(
-0.5 * T.pow((alphas[i] - ssps.alphas1D) /
sigma_alpha, 2))) for i in
range(N)]
bestfit = [
pm.math.dot(w[i].T / T.sum(w[i]), ssps.flux) for i in range(N)
]
like = [
pm.Cauchy('like_{}'.format(i),
alpha=bestfit[i],
beta=eps[i],
observed=obs[i]) for i in range(N)
]
with hierarchical_model:
trace = pm.sample(500, tune=500)
vars = [
"Age", "Metal", "Alpha", "SAge", "SMetal", "SAlpha", "age",
"metal", "alpha", "eps"
]
d = dict([(v, trace[v]) for v in vars])
with open(dbname, 'wb') as f:
pickle.dump(d, f)
def mc_estimate_distr(x_obs):
x_obs_p = np.nanpercentile(x_obs, 99)
x_obs_filt = [v for v in x_obs if abs(v) < x_obs_p]
rlim = x_obs_p
with pm.Model() as model:
b = pm.Uniform('b', 0, 5000)
a = pm.Uniform('a', -5000, 5000)
cte = pm.Cauchy('cte', alpha=a, beta=b, observed=x_obs)
x_trace = pm.sample(500, tune=2000)
a_est = np.mean(x_trace['a'])
b_est = np.mean(x_trace['b'])
return a_est, b_est
def test_pymc_convert_dists():
"""Just a basic check that all PyMC3 RVs will convert to and from Theano RVs."""
tt.config.compute_test_value = 'ignore'
theano.config.cxx = ''
with pm.Model() as model:
norm_rv = pm.Normal('norm_rv', 0.0, 1.0, observed=1.0)
mvnorm_rv = pm.MvNormal('mvnorm_rv',
np.r_[0.0],
np.c_[1.0],
shape=1,
observed=np.r_[1.0])
cauchy_rv = pm.Cauchy('cauchy_rv', 0.0, 1.0, observed=1.0)
halfcauchy_rv = pm.HalfCauchy('halfcauchy_rv', 1.0, observed=1.0)
uniform_rv = pm.Uniform('uniform_rv', observed=1.0)
gamma_rv = pm.Gamma('gamma_rv', 1.0, 1.0, observed=1.0)
invgamma_rv = pm.InverseGamma('invgamma_rv', 1.0, 1.0, observed=1.0)
exp_rv = pm.Exponential('exp_rv', 1.0, observed=1.0)
# Convert to a Theano `FunctionGraph`
fgraph = model_graph(model)
rvs_by_name = {
n.owner.inputs[1].name: n.owner.inputs[1]
for n in fgraph.outputs
}
pymc_rv_names = {n.name for n in model.observed_RVs}
assert all(
isinstance(rvs_by_name[n].owner.op, RandomVariable)
for n in pymc_rv_names)
# Now, convert back to a PyMC3 model
pymc_model = graph_model(fgraph)
new_pymc_rv_names = {n.name for n in pymc_model.observed_RVs}
pymc_rv_names == new_pymc_rv_names
def ReparametrizedCauchy(name, alpha=None, beta=None, shape=1):
"""
Create a reparametrized Cauchy distributed random variable.
Parameters
----------
name : str
Name of the variable.
alpha : float
Mode of Cauchy distribution.
beta : float, > 0
Scale parameter of Cauchy distribution
shape: int or tuple of ints, default 1
Shape of array of variables. If 1, then a single scalar.
Returns
-------
output : pymc3 distribution
Reparametrized Cauchy distribution.
Notes
-----
.. The reparametrization procedure allows the sampler to sample
a Cauchy distribution with alpha = 0 and beta = 1, and then do a
deterministic reparametrization to achieve sampling of the
original desired Cauchy distribution.
"""
# Check inputs
if type(name) != str:
raise RuntimeError('`name` must be a string.')
if alpha is None or beta is None:
raise RuntimeError('`alpha` and `beta` must be provided.')
var_reparam = pm.Cauchy(name + '_reparam', alpha=0, beta=1, shape=shape)
var = pm.Deterministic(name, alpha + var_reparam * beta)
return var
def _model_setup(self):
with self._model:
# COSMOLOGY
omega_m = pm.Uniform("OmegaM", lower=0., upper=1.)
omega_k = pm.Uniform("Omegak", lower=-1., upper=1.)
# My custom distance mod. function to enable
# ADVI and HMC sampling.
# We are going to have to break this into
# four likelihoods
dm_0 = distmod_constant_curve(omega_m, omega_k, self._h0, self._zcmb_survey[0])
dm_1 = distmod_constant_curve(omega_m, omega_k, self._h0, self._zcmb_survey[1])
dm_2 = distmod_constant_curve(omega_m, omega_k, self._h0, self._zcmb_survey[2])
dm_3 = distmod_constant_curve(omega_m, omega_k, self._h0, self._zcmb_survey[3])
# PHILIPS PARAMETERS
# M0 is the location parameter for the distribution
# sys_scat is the scale parameter for the M0 distribution
# rather than "unexpalined variance"
M0 = pm.Uniform("M0", lower=-20., upper=-18.)
sys_scat = pm.HalfCauchy('sys_scat', beta=2.5) # Gelman recommendation for variance parameter
M_true_0 = pm.Normal('M_true_0', M0, sys_scat, shape=self._n_SN_survey[0])
M_true_1 = pm.Normal('M_true_1', M0, sys_scat, shape=self._n_SN_survey[1])
M_true_2 = pm.Normal('M_true_2', M0, sys_scat, shape=self._n_SN_survey[2])
M_true_3 = pm.Normal('M_true_3', M0, sys_scat, shape=self._n_SN_survey[3])
# following Rubin's Unity model... best idea? not sure
taninv_alpha = pm.Uniform("taninv_alpha", lower=-.2, upper=.3)
taninv_beta = pm.Uniform("taninv_beta", lower=-1.4, upper=1.4)
# Transform variables
alpha = pm.Deterministic('alpha', T.tan(taninv_alpha))
beta = pm.Deterministic('beta', T.tan(taninv_beta))
# Again using Rubin's Unity model.
# After discussion with Rubin, the idea is that
# these parameters are ideally sampled from a Gaussian,
# but we know they are not entirely correct. So instead,
# the Cauchy is less informative around the mean, while
# still having informative tails.
xm = pm.Cauchy('xm', alpha=0, beta=1)
cm = pm.Cauchy('cm', alpha=0, beta=1, shape=4)
s = pm.Uniform('s', lower=-2, upper=2, shape=4)
c_shift_0 = cm[0] + s[0] * self._zcmb_survey[0]
c_shift_1 = cm[1] + s[1] * self._zcmb_survey[1]
c_shift_2 = cm[2] + s[2] * self._zcmb_survey[2]
c_shift_3 = cm[3] + s[3] * self._zcmb_survey[3]
Rx_log = pm.Uniform('Rx_log', lower=-0.5, upper=0.5)
Rc_log = pm.Uniform('Rc_log', lower=-1.5, upper=1.5, shape=4)
# Transformed variables
Rx = pm.Deterministic("Rx", T.pow(10., Rx_log))
Rc = pm.Deterministic("Rc", T.pow(10., Rc_log))
x_true_0 = pm.Normal('x_true_0', mu=xm, sd=Rx, shape=self._n_SN_survey[0])
c_true_0 = pm.Normal('c_true_0', mu=c_shift_0, sd=Rc[0], shape=self._n_SN_survey[0])
x_true_1 = pm.Normal('x_true_1', mu=xm, sd=Rx, shape=self._n_SN_survey[1])
c_true_1 = pm.Normal('c_true_1', mu=c_shift_1, sd=Rc[1], shape=self._n_SN_survey[1])
x_true_2 = pm.Normal('x_true_2', mu=xm, sd=Rx, shape=self._n_SN_survey[2])
c_true_2 = pm.Normal('c_true_2', mu=c_shift_2, sd=Rc[2], shape=self._n_SN_survey[2])
x_true_3 = pm.Normal('x_true_3', mu=xm, sd=Rx, shape=self._n_SN_survey[3])
c_true_3 = pm.Normal('c_true_3', mu=c_shift_3, sd=Rc[3], shape=self._n_SN_survey[3])
# Do the correction
mb_0 = pm.Deterministic("mb_0", M_true_0 + dm_0 - alpha * x_true_0 + beta * c_true_0)
mb_1 = pm.Deterministic("mb_1", M_true_1 + dm_1 - alpha * x_true_1 + beta * c_true_1)
mb_2 = pm.Deterministic("mb_2", M_true_2 + dm_2 - alpha * x_true_2 + beta * c_true_2)
mb_3 = pm.Deterministic("mb_3", M_true_3 + dm_3 - alpha * x_true_3 + beta * c_true_3)
# Likelihood and measurement error
obsc_0 = pm.Normal("obsc_0", mu=c_true_0, sd=self._dcolor_survey[0], observed=self._color_survey[0])
obsx_0 = pm.Normal("obsx_0", mu=x_true_0, sd=self._dx1_survey[0], observed=self._x1_survey[0])
obsm_0 = pm.Normal("obsm_0", mu=mb_0, sd=self._dmbObs_survey[0], observed=self._mbObs_survey[0])
obsc_1 = pm.Normal("obsc_1", mu=c_true_1, sd=self._dcolor_survey[1], observed=self._color_survey[1])
obsx_1 = pm.Normal("obsx_1", mu=x_true_1, sd=self._dx1_survey[1], observed=self._x1_survey[1])
obsm_1 = pm.Normal("obsm_1", mu=mb_1, sd=self._dmbObs_survey[1], observed=self._mbObs_survey[1])
obsc_2 = pm.Normal("obsc_2", mu=c_true_2, sd=self._dcolor_survey[2], observed=self._color_survey[2])
obsx_2 = pm.Normal("obsx_2", mu=x_true_2, sd=self._dx1_survey[2], observed=self._x1_survey[2])
obsm_2 = pm.Normal("obsm_2", mu=mb_2, sd=self._dmbObs_survey[2], observed=self._mbObs_survey[2])
obsc_3 = pm.Normal("obsc_3", mu=c_true_3, sd=self._dcolor_survey[3], observed=self._color_survey[3])
obsx_3 = pm.Normal("obsx_3", mu=x_true_3, sd=self._dx1_survey[3], observed=self._x1_survey[3])
obsm_3 = pm.Normal("obsm_3", mu=mb_3, sd=self._dmbObs_survey[3], observed=self._mbObs_survey[3])
plt.plot(x, y, 'o')
plt.xlabel('$x$', fontsize=16)
plt.ylabel('$f(x)$', fontsize=16, rotation=0)
plt.savefig('img801.png')
"""
def gauss_kernel(x, n_knots):
knots = np.linspace(x.min(), x.max(), n_knots)
w = 2
return np.array([np.exp(-(x - k)**2 / w) for k in knots])
n_knots = 5
with pm.Model() as kernel_model:
gamma = pm.Cauchy('gamma', alpha=0, beta=1, shape=n_knots)
sd = pm.Uniform('sd', 0, 10)
mu = pm.math.dot(gamma, gauss_kernel(x, n_knots))
yl = pm.Normal('yl', mu=mu, sd=sd, observed=y)
kernel_trace = pm.sample(5000, njobs=1)
#pm.traceplot(kernel_trace)
#plt.savefig('img802.png')
ppc = pm.sample_ppc(kernel_trace, model=kernel_model, samples=100)
plt.plot(x, ppc['yl'].T, 'ro', alpha=0.1)
plt.plot(x, y, 'bo')
plt.xlabel('$x$', fontsize=16)
plt.ylabel('f(x)', fontsize=16, rotation=0)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.hist(y)
ax1.set_title('Normal distribution returns')
ax2.hist(returns)
ax2.set_title('Real returns')
plt.show()
#%%
# 3. now let's relax the normal distribution assumption: let's fit a Cauchy distribution.
with pm.Model() as model2:
beta = pm.HalfNormal('beta', sd=10.)
pm.Cauchy('returns', alpha=0.0, beta=beta, observed=returns)
mean_field = pm.fit(n=150000,
method='advi',
obj_optimizer=pm.adam(learning_rate=.001))
trace2 = mean_field.sample(draws=10000)
preds2 = pm.sample_ppc(trace2, samples=10000, model=model2)
y2 = np.reshape(np.mean(preds2['returns'], axis=0), [-1])
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.hist(y2)
ax1.set_title('Cauchy distribution returns')
ax2.hist(returns)
def cauchyvar(data):
with pm.Model() as model:
alfa = pm.Uniform('alfa', lower=-1.0, upper=1.0)
beta = pm.Uniform('beta', lower=0.0, upper=200.0)
cauchy = pm.Cauchy('cauchy', alpha=alfa, beta=beta, observed=data)
return model
def main():
"""Download the Rouder et al. (2008) data set, organize it, fit the model, and
plot the traces.
"""
# load the data
a = "https://raw.githubusercontent.com/PerceptionCognitionLab/"
b = "data0/master/wmPNAS2008/lk2clean.csv"
df = pd.read_csv(urlopen(a + b), index_col=0)
# compress into "binomial" format
data = []
for (subj, N), _df in df.groupby(["sub", "N"]):
data.append({
"subj": subj,
"M": N,
"H": _df[_df.ischange.astype(bool)].resp.sum(),
"D": _df.ischange.sum(),
"F": _df[(1 - _df.ischange).astype(bool)].resp.sum(),
"S": (1 - _df.ischange).sum(),
})
data = pd.DataFrame(data)
subjects = data.subj.unique()
# create a design matrix to map subjects to rows in data
X = np.asarray(dmatrix("0 + C(subj)", data))
# create model
with pm.Model():
# capacity
mu = pm.Cauchy(name=r"$\mu_{(\kappa)}$", alpha=0, beta=5)
de = pm.Normal(name=r"$\delta_{\kappa)}$",
mu=0,
sigma=1,
shape=len(subjects))
si = pm.HalfCauchy(name=r"$\sigma_{(\kappa)}$", beta=5)
x = pm.Deterministic(r"$\kappa$", mu + de * si)
x = pm.Deterministic(r"$k$", tt.largest(x, tt.zeros(len(subjects))))
k = pm.math.dot(X, x)
# guesses "same"
mu = pm.Cauchy(name=r"$\mu_{(\gamma)}$", alpha=0, beta=5)
de = pm.Normal(name=r"$\delta_{\gamma)}$",
mu=0,
sigma=1,
shape=len(subjects))
si = pm.HalfCauchy(name=r"$\sigma_{(\gamma)}$", beta=5)
x = pm.Deterministic(r"$\gamma$", mu + de * si)
x = pm.Deterministic(r"$g$", pm.math.sigmoid(x))
g = pm.math.dot(X, x)
# does not lapse
mu = pm.Cauchy(name=r"$\mu_{(\zeta)}$", alpha=0, beta=5)
de = pm.Normal(name=r"$\delta_{\zeta)}$",
mu=0,
sigma=1,
shape=len(subjects))
si = pm.HalfCauchy(name=r"$\sigma_{(\zeta)}$", beta=5)
x = pm.Deterministic(r"$\zeta$", mu + de * si)
x = pm.Deterministic(r"$z$", pm.math.sigmoid(x))
z = pm.math.dot(X, x)
# probabilities
q = tt.smallest(k / data.M, tt.ones(len(data)))
h = (1 - z) * g + z * q + z * (1 - q) * g
f = (1 - z) * g + z * (1 - q) * g
# responses
pm.Binomial(name="$H$", p=h, n=data.D, observed=data.H)
pm.Binomial(name="$F$", p=f, n=data.S, observed=data.F)
# sample and plot
trace = pm.sample(draws=5000, tune=2000, chains=2)
pm.traceplot(trace, compact=True)
plt.savefig("../../assets/images/wm-cap.png",
bbox_inches=0,
transparent=True)
data = pd.read_csv('{}_r{}_{}C_{}_{}_growth.csv'.format(
DATE, RUN_NUMBER, TEMP, CARBON, OPERATOR))
data = data[(data['absorbance'] >= 0.1) & (data['absorbance'] <= 0.8)]
with pm.Model() as model:
a0 = pm.HalfNormal('a0', sd=1)
lam = pm.HalfFlat('lambda')
gamma = mwc.bayes.Jeffreys('gamma', lower=1E-9, upper=100)
# Compute the expected value.
time = data['elapsed_time_min'].values
mu = np.log(a0) + time * lam
# Define the likelihood and sample.
like = pm.Cauchy('like',
mu,
gamma,
observed=np.log(data['absorbance'].values))
trace = pm.sample(tune=5000, draws=5000)
trace_df = mwc.stats.trace_to_dataframe(trace, model)
stats = mwc.stats.compute_statistics(trace_df)
# %% Compute the best fit and credible region
modes = {}
hpds = {}
grouped = stats.groupby('parameter')
for g, d in grouped:
modes[g] = d['mode'].values[0]
hpds[g] = [d[['hpd_min', 'hpd_max']].values]
time_range = np.linspace(data['elapsed_time_min'].min(),
data['elapsed_time_min'].max(), 500)
# ----------------------------------------------------------------------
# Draw the sample from a Cauchy distribution
np.random.seed(44)
mu_0 = 0
gamma_0 = 2
xi = cauchy(mu_0, gamma_0).rvs(10)
# ----------------------------------------------------------------------
# Set up and run MCMC:
with pm.Model():
mu = pm.Uniform('mu', -5, 5)
log_gamma = pm.Uniform('log_gamma', -10, 10)
# set up our observed variable x
x = pm.Cauchy('x', mu, np.exp(log_gamma), observed=xi)
trace = pm.sample(draws=12000, tune=1000, cores=1)
# compute histogram of results to plot below
L_MCMC, mu_bins, gamma_bins = np.histogram2d(trace['mu'],
np.exp(trace['log_gamma']),
bins=(np.linspace(-5, 5, 41),
np.linspace(0, 5, 41)))
L_MCMC[L_MCMC == 0] = 1E-16 # prevents zero-division errors
# ----------------------------------------------------------------------
# Compute likelihood analytically for comparison
mu = np.linspace(-5, 5, 70)
gamma = np.linspace(0.1, 5, 70)
logL = cauchy_logL(xi, gamma[:, np.newaxis], mu)
# In[2]:
# http://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-93-1179
# * Shots are fired isotropically from a point and hit a position sensitive detector
# * There is no scattering
# * y is fixed to be 1 away
# In[3]:
# generate some data
with pm.Model() as model:
x = pm.Cauchy(name='x', alpha=0, beta=1)
trace = pm.sample(20000)
pm.traceplot(trace)
sampledat = trace['x']
# In[4]:
trace.varnames, trace['x']
plt.hist(sampledat, 200, normed=True);
plt.yscale('log');
# In[5]:
np.random.randint(0, len(sampledat), 10)
# Load and trim the data to start at 0D = 0.1
data = pd.read_csv('{}_growth.csv'.format(BASE_NAME))
data = data[(data['OD_600'] >= 0.1) & (data['OD_600'] <= 0.8)]
with pm.Model() as model:
a0 = pm.HalfNormal('a0', sd=1)
lam = pm.HalfFlat('lambda')
gamma = mwc.bayes.Jeffreys('gamma', lower=1E-9, upper=100)
# Compute the expected value.
time = data['elapsed_time_min'].values
mu = np.log(a0) + time * lam
# Define the likelihood and sample.
like = pm.Cauchy('like', mu, gamma, observed=np.log(data['OD_600'].values))
trace = pm.sample(tune=5000, draws=5000)
trace_df = mwc.stats.trace_to_dataframe(trace, model)
stats = mwc.stats.compute_statistics(trace_df)
# %% Compute the best fit and credible region
modes = {}
hpds = {}
grouped = stats.groupby('parameter')
for g, d in grouped:
modes[g] = d['mode'].values[0]
hpds[g] = [d[['hpd_min', 'hpd_max']].values]
time_range = np.linspace(data['elapsed_time_min'].min(),
data['elapsed_time_min'].max(), 500)
best_fit = modes['a0'] * np.exp(modes['lambda'] * time_range)
def rwfmm(functional_data,
static_data,
Y,
func_coef_sd='prior',
method='nuts',
robust=False,
func_coef_sd_hypersd=0.1,
coefficient_prior='flat',
include_random_effect=True,
variable_func_scale=True,
time_rescale_func=False,
sampler_kwargs={
'init': 'adapt_diag',
'chains': 1,
'tune': 500,
'draws': 500
},
return_model_only=False,
n_spline_knots=20,
func_coef_type='random_walk',
spline_degree=3,
spline_coef_sd='prior',
spline_coef_hyper_sd=2.,
spline_coef_prior='random_walk',
spline_rw_sd=1.,
average_every_n=1,
spline_rw_hyper_sd=1.,
poly_order=4):
'''
Fits a functional mixed model with a random-walk model of
the functional coefficient. A range of different priors is available for
the model coefficients.
Parameters
----------
functional_data : 4D Numpy array
Data inputs for functional covariates with expected shape (S,V,T,F)
where S denotes the number of subjects, V denotes the number of
visits or repeated observations for each subject, T denotes the
dimension of the functional data (i.e. number of timesteps)
and F denotes the number of functional coefficients.
static_data: 3D Numpy array
Data inputs for static (i.e. non-functional) covariates which are
constant for each subject/visits combination.
This array is expected to have the shape (S,V,C) where
C denotes the number of static covariates.
Y: 3D Numpy array
Responses for the functional regression. This array is expected to
have the same dimensions as static_dataself.
tune: int
Number of tuning steps used in MCMC
draws: int
Number of post-tuning draws sampled.
chains: int
Number of MCMC chains used.
func_coef_sd: float or string
The standard deviation of the Gaussian random walk for all
functional coefficients. If set to "prior", then this quantity
will also be treated as a parameter that needs to be estimated.
method: string
Designates the method to be ued to fit the model.
This must be one of "nuts", "mh" or one of the approximate inference
methods at https://docs.pymc.io/api/inference.html#variational.
n_iter_approx: int
Number of optimization iterations to be used if the model fitting
method is an approximate inference method.
robust: bool
Determines whether a normal error model or a robust Student-T error
model is assumed for the residuals.
func_coef_sd_hypersd: float
If func_coef_sd is set to "prior", then this parameter sets the
standard deviation of the half-normal distribution over the
functional coefficient standard deviation (func_coef_sd). Note that
in this case, each functional coefficient gets its own standard
deviation drawn from the same prior defined by this parameter.
coefficient_prior: string
Determines the prior placed on the static covariate coefficients as
well as the mean (a.k.a. the level) of the functional coefficient.
The options are "flat","normal","horseshoe","finnish_horseshoe".
include_random_effect: bool
Determines whether or not a per-subject random intercept is included.
variable_func_scale : bool
Determines whether or not to allow the functional coefficients be
multiplied by a positive number. This can lead to identifiability issues
if a weak prior is specified on the functional coefficient evolution
variance.
time_rescale_func : bool
If true, divides the functional coefficient by T. This can help make
the coefficient more interpretable.
sampler_kwargs: dict
Any additional arguments to be passed to pm.sample().
return_model_only: bool
If true, returns only the model object without sampling. This can be
helpful for debugging.
func_coef_type : string
One of 'constant','random_walk', 'bspline_recursive', 'natural_spline',
'linear','bspline_design' or 'polynomial'.
This determines how the functional coefficient will be parameterized. If it
is 'random_walk', then the coefficient will be computed as the cumulative
sum of many small normally-distributed jumps whose standard deviation
is controlled by 'func_coef_sd'. Alternatively, if one of the bspline
options is used, then the functional coefficient will be a bspline. The option
'bspline_recursive' builds the coefficient using the de Boor algorithm
while the options 'bspline_design' and 'natural_spline' build a design
matrix using patsy's functionality and then estimates the coefficients
linking that matrix to the functional coefficients. Using 'polynomial'
specifies the functional coefficient as a polynomial of order 'poly_order'.
'linear' makes the functional coefficient a linear function of the function
domain.
poly_order : int
The degree of the polynomial used if the functional coefficient type is
set to 'polynomial'.
n_spline_knots : int
In the event that the functional coefficient is one of the bspline choices,
then this controls how many knots or breakpoints the spline has. In general,
higher numbers for this value are required for higher spline orders.
spline_degree : int
The order of the spline if the functional coefficient is parameterized as a
bspline. This is also the order of the polynomial for each spline section
plus 1. Set this equal to 4 for cubic polynomial approximations in the spline.
spline_coef_sd : float
The standard deviation of the normal prior on the spline coefficients.
spline_coef_prior : string
One of 'normal', 'flat', or 'random_walk'. This controls how the
bspline coefficients are smoothed.
spline_rw_sd : string or float
Either 'prior' or a float. This controls how much the spline coefficients
are allowed to jump when using a random walk for the spline coefficient
prior.
spline_rw_hyper_sd : float
If 'spline_rw_sd' is set to 'prior', this is the standard deviation
of the half-Normal prior on the spline random walk jump standard
deviation.
average_every_n : int
This is used to average every n measurements of the functional data
together. For example, if the functional data corresponds to 96 hourly
timesteps' worth of data, setting this to 4 would take the 24 hour average
and reduce the size of T from 96 to 24. The default setting of 1 leaves
the data unchanged.
Returns
-------
trace: pymc3 Trace
Samples produced either via MCMC or approximate inference during
fitting.
model: pymc3 Model
The model object describing the RWFMM.
'''
with pm.Model() as model:
S, V, T, F = functional_data.shape
_, _, C = static_data.shape
#functional_data = np.mean(functional_data.reshape(-1, average_every_n), axis=1)
# We want to make sure the two data arrays agree in the number of
# subjects (S) and visits (V).
assert static_data.shape[0:2] == functional_data.shape[0:2]
# Total number of functional and static coefficients.
# This does not include the random-walk jumps.
n_covariates = F + C
if include_random_effect:
random_effect_mean = pm.Flat('random_effect_mean')
random_effect_sd = pm.HalfCauchy('random_effect_sd', beta=1.)
random_effect_unscaled = pm.Normal('random_effect_unscaled',
shape=[S, 1])
random_effect = pm.Deterministic(
'random_effect',
random_effect_unscaled * random_effect_sd + random_effect_mean)
else:
random_effect = 0.
if coefficient_prior == 'flat':
coef = pm.Flat('coef', shape=n_covariates)
elif coefficient_prior == 'normal':
coef_sd = pm.HalfCauchy('coef_sd', beta=1.)
coef = pm.Normal('coef', sd=coef_sd, shape=[n_covariates])
elif coefficient_prior == 'cauchy':
coef_sd = pm.HalfCauchy('coef_sd', beta=1.0)
coef = pm.Cauchy('coef',
alpha=0.,
beta=coef_sd,
shape=[n_covariates])
elif coefficient_prior == 'horseshoe':
loc_shrink = pm.HalfCauchy('loc_shrink',
beta=1,
shape=[n_covariates])
glob_shrink = pm.HalfCauchy('glob_shrink', beta=1)
coef = pm.Normal('coef',
sd=(loc_shrink * glob_shrink),
shape=[n_covariates])
# Implemented per Piironnen and Vehtari '18
elif coefficient_prior == 'finnish_horseshoe':
loc_shrink = pm.HalfCauchy('loc_shrink',
beta=1,
shape=[n_covariates])
glob_shrink = pm.HalfCauchy('glob_shrink', beta=1)
# In order to get some of the values within the prior calculations,
# we need to know the variance of the predictors.
static_var = np.var(static_data, axis=(0, 1))
func_var = np.var(functional_data, axis=(0, 1, 2))
variances = np.concatenate([static_var, func_var])
nu_c = pm.Gamma('nu_c', alpha=2.0, beta=0.1)
c = pm.InverseGamma('c',
alpha=nu_c / 2,
beta=nu_c * variances / 2,
shape=[n_covariates])
regularized_loc_shrink = c * loc_shrink**2 / (
c + glob_shrink**2 * loc_shrink**2)
coef = pm.Normal('coef',
sd=(regularized_loc_shrink * glob_shrink**2)**0.5,
shape=[n_covariates])
if func_coef_type == 'constant':
func_coef = pm.Deterministic('func_coef',
tt.zeros([T, F]) + coef[C:])
elif func_coef_type == 'random_walk':
if func_coef_sd == 'prior':
func_coef_sd = pm.HalfNormal('func_coef_sd',
sd=func_coef_sd_hypersd,
shape=F)
# The 'jumps' are the small deviations about the mean of the functional
# coefficient.
if variable_func_scale:
log_scale = pm.Normal('log_scale', shape=F)
else:
log_scale = 0.0
jumps = pm.Normal('jumps', sd=func_coef_sd, shape=(T, F))
random_walks = tt.cumsum(jumps,
axis=0) * tt.exp(log_scale) + coef[C:]
func_coef = pm.Deterministic('func_coef', random_walks)
elif (func_coef_type == 'natural_spline'
or func_coef_type == 'bspline_design'):
x = np.arange(T)
# The -1 in the design matrix creation is to make sure that there
# is no constant term which would be made superfluous by 'coef'
# which is added to the functional coefficient later.
if func_coef_type == 'natural_spline':
spline_basis = p.dmatrix(
"cr(x, df = {0}) - 1".format(n_spline_knots), {
"x": x
},
return_type='dataframe').values
elif func_coef_type == 'bspline_design':
spline_basis = p.dmatrix(
"bs(x, df = {0}) - 1".format(n_spline_knots), {
"x": x
},
return_type='dataframe').values
# If this produces a curve which is too spiky or rapidly-varying,
# then a smoothing prior such as a Gaussian random walk could
# instead be used here.
if spline_coef_prior == 'normal':
spline_coef = pm.Normal('spline_coef',
sd=spline_coef_sd,
shape=[n_spline_knots, F])
elif spline_coef_prior == 'flat':
spline_coef = pm.Flat('spline_coef', shape=[n_spline_knots, F])
elif spline_coef_prior == 'random_walk':
if spline_rw_sd == 'prior':
spline_rw_sd = pm.HalfNormal('spline_rw_sd',
sd=spline_rw_hyper_sd,
shape=F)
spline_jumps = pm.Normal('spline_jumps',
shape=[n_spline_knots, F])
spline_coef = pm.Deterministic(
'spline_coef',
tt.cumsum(spline_jumps * spline_rw_sd, axis=0))
# This inner product sums over the spline coefficients
func_coef = pm.Deterministic(
'func_coef',
(tt.tensordot(spline_basis, spline_coef, axes=[[1], [0]]) +
coef[C:]))
# This is deprecated - it is missing some priors.
elif func_coef_type == 'bspline_recursive':
n_spline_coefficients = spline_degree + n_spline_knots + 1
spline_coef = pm.Normal('spline_coef',
sd=spline_coef_sd,
shape=[n_spline_coefficients, F])
x = np.linspace(-4, 4, T)
func_coefs = []
for f in range(F):
func_coefs.append(
utilities.bspline(spline_coef[:, f], spline_degree,
n_spline_knots, x))
func_coef = pm.Deterministic('func_coef',
(tt.stack(func_coefs, axis=1)))
elif func_coef_type == 'polynomial':
poly_basis = np.zeros([T, poly_order])
for i in range(1, poly_order + 1):
poly_basis[:, i - 1] = np.arange(T)**i
poly_coef = pm.Flat('poly_coef', shape=[poly_order, F])
func_coef = pm.Deterministic(
'func_coef',
tt.tensordot(poly_basis, poly_coef, axes=[[1], [0]]) +
coef[C:])
elif func_coef_type == 'linear':
linear_basis = np.zeros([T, F])
for i in range(F):
linear_basis[:, i] = np.arange(T)
linear_coef = pm.Flat('linear_coef', [F])
func_coef = pm.Deterministic('func_coef',
linear_basis * linear_coef + coef[C:])
else:
raise ValueError('Functional coefficient type not recognized.""')
# This is the additive term in y_hat that comes from the functional
# part of the model.
func_contrib = tt.tensordot(functional_data,
func_coef,
axes=[[2, 3], [0, 1]])
if time_rescale_func:
func_contrib = func_contrib / T
# The part of y_hat that comes from the static covariates
static_contrib = tt.tensordot(static_data, coef[0:C], axes=[2, 0])
noise_sd = pm.HalfCauchy('noise_sd', beta=1.0)
# y_hat is the predictive mean.
y_hat = pm.Deterministic('y_hat',
static_contrib + func_contrib + random_effect)
#y_hat = pm.Deterministic('y_hat', static_contrib +func_contrib )
# If the robust error option is used, then a gamma-Student-T distribution
# is placed on the residuals.
if robust:
DOF = pm.Gamma('DOF', alpha=2, beta=0.1)
response = pm.StudentT('response',
mu=y_hat,
sd=noise_sd,
nu=DOF,
observed=Y)
else:
response = pm.Normal('response', mu=y_hat, sd=noise_sd, observed=Y)
if return_model_only:
return model
# NUTS is the default PyMC3 sampler and is what we recommend for fitting.
if method == 'nuts':
trace = pm.sample(**sampler_kwargs)
# Metropolis-Hastings does poorly with lots of correlated parameters,
# so this fitting method should only be used if T is small or you are
# fitting a scalarized model.
elif method == 'mh':
trace = pm.sample(step=pm.Metropolis(), **sampler_kwargs)
# There are a number of approximate inference methods available, but
# none of them gave results that were close to what we got with MCMC.
else:
approx = pm.fit(method=method, **sampler_kwargs)
trace = approx.sample(draws)
return trace, model
