def make_model():
# Construct the prior term
location = pymc.Uniform('location', lower=[0, 0], upper=[1, 1])
# The locations of the sensors
X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]]
# The output of the model
solver = Solver(X=X)
@pymc.deterministic(plot=False)
def model_output(value=None, loc=location):
return solver(loc)
# The hyper-parameters of the noise
alpha = pymc.Exponential('alpha', beta=1.)
beta = pymc.Exponential('beta', beta=1.)
tau = pymc.Gamma('tau', alpha=alpha, beta=beta)
# Load the observed data
data = np.loadtxt('observed_data')
# The observations at the sensor locations
@pymc.stochastic(dtype=float, observed=True)
def sensors(value=data, mu=model_output, tau=tau, gamma=1.):
"""The value of the response at the sensors."""
return gamma * pymc.normal_like(value, mu=mu, tau=tau)
return locals()
def gamma_poisson(x, t):
""" x: number of failures (N vector)
t: operation time, thousands of hours (N vector) """
if x is not None:
N = x.shape
else:
N = num_points
# place an exponential prior on t, for when it is unknown
t = pymc.Exponential('t',
beta=1.0 / 50.0,
value=t,
size=N,
observed=(t is not None))
alpha = pymc.Exponential('alpha', beta=1.0, value=1.0)
beta = pymc.Gamma('beta', alpha=0.1, beta=1.0, value=1.0)
theta = pymc.Gamma('theta', alpha=alpha, beta=beta, size=N)
@pymc.deterministic
def mu(theta=theta, t=t):
return theta * t
x = pymc.Poisson('x', mu=mu, value=x, observed=(x is not None))
return locals()
def three_model_comparison(p_df):
a_n = len(p_df)
t_lam = pm.Uniform('d_lam', 0, 1)
#d_lam = 1.0 / np.mean(p_df)
t_lambda_1 = pm.Exponential("t_lambda_1", t_lam)
#t_lambda_1 = pm.Uniform("t_lambda_1", min(p_df), max(p_df))
t_lambda_2 = pm.Exponential("t_lambda_2", t_lam)
#t_lambda_2 = pm.Uniform("t_lambda_2",min(p_df), max(p_df))
t_lambda_3 = pm.Exponential("t_lambda_3", t_lam)
#t_lambda_2 = pm.Uniform("t_lambda_2",min(p_df), max(p_df))
#tau = pm.DiscreteUniform("tau", lower=min(p_df), upper=max(p_df) )
t_tau_1 = pm.DiscreteUniform("tau1", lower=0, upper=max(p_df) - 1)
t_tau_2 = pm.DiscreteUniform("tau", lower=t_tau_1, upper=max(p_df))
@pm.deterministic
def lambda_(tau_1=t_tau_1,
tau_2=t_tau_2,
lambda_1=t_lambda_1,
lambda_2=t_lambda_2,
lambda_3=t_lambda_3):
out = np.zeros(a_n)
out[:tau_1] = lambda_1 # lambda before tau_1 is lambda1
out[tau_1:tau_2] = lambda_2 # lambda_2 between tau_1 and tau_2
out[tau_2:] = lambda_3 # lambda after (and including) tau is lambda_3
return out
t_obs = pm.Poisson('t_observed', mu=lambda_, value=p_df, observed=True)
t_model = pm.Model(
[t_obs, t_lam, t_lambda_1, t_lambda_2, t_lambda_3, t_tau_1, t_tau_2])
#d_model = pm.Model([d_obs, t_lambda_1, t_lambda_2, tau])
return t_model, t_lam, t_lambda_1, t_lambda_2, t_lambda_3, t_tau_1, t_tau_2
def two_model_comparison(p_df):
a_n = len(p_df)
d_lam = pm.Uniform('d_lam', 0, 1)
#d_lam = 1.0 / np.mean(p_df)
lambda_1 = pm.Exponential("lambda_1", d_lam)
#lambda_1 = pm.Uniform("lambda_1", min(p_df), max(p_df))
lambda_2 = pm.Exponential("lambda_2", d_lam)
#lambda_2 = pm.Uniform("lambda_2",min(p_df), max(p_df))
#tau = pm.DiscreteUniform("tau", lower=min(p_df), upper=max(p_df) )
tau = pm.DiscreteUniform("tau", lower=0, upper=max(p_df))
@pm.deterministic
def lambda_(tau=tau, lambda_1=lambda_1, lambda_2=lambda_2):
out = np.zeros(a_n)
out[:tau] = lambda_1 # lambda before tau is lambda1
out[tau:] = lambda_2 # lambda after (and including) tau is lambda2
return out
d_obs = pm.Poisson('d_observed', mu=lambda_, value=p_df, observed=True)
d_model = pm.Model([d_obs, d_lam, lambda_1, lambda_2, tau])
#d_model = pm.Model([d_obs, lambda_1, lambda_2, tau])
return d_model, d_obs, d_lam, lambda_1, lambda_2, tau
def make_gp_submodel(suffix,
mesh,
africa_val=None,
with_africa_covariate=False):
# The partial sill.
amp = pm.Exponential('amp_%s' % suffix, .1, value=1.)
# The range parameter. Units are RADIANS.
scale = pm.Exponential('scale_%s' % suffix, 1, value=.08)
# 1 radian = the radius of the earth, about 6378.1 km
scale_in_km = scale * 6378.1
# The nugget variance, lower-bounded to preserve mixing.
V = pm.Exponential('V_%s' % suffix, 1, value=1.)
@pm.potential
def V_bound(V=V):
if V < .1:
return -np.inf
else:
return 0
# Create the covariance & its evaluation at the data locations.
@pm.deterministic(trace=True, name='C_%s' % suffix)
def C(amp=amp, scale=scale):
return pm.gp.FullRankCovariance(pm.gp.exponential.geo_rad,
amp=amp,
scale=scale)
# Create the mean function
if with_africa_covariate:
beta = pm.Normal('beta_%s' % suffix, 0, .01, value=1)
@pm.deterministic(trace=True, name='M_%s' % suffix)
def M(mesh=mesh, africa_val=africa_val, beta=beta):
M = pm.gp.Mean(retrieve_africa_val,
meshes=[],
africa_vals=[],
beta=beta)
store_africa_val(M, mesh, africa_val)
return M
else:
@pm.deterministic(trace=True, name='M_%s' % suffix)
def M():
return pm.gp.Mean(pm.gp.zero_fn)
# Create the GP submodel
sp_sub = pm.gp.GPSubmodel('sp_sub_%s' % suffix, M, C, mesh)
sp_sub.f_eval.value = sp_sub.f_eval.value - sp_sub.f_eval.value.mean()
return locals()
def param_selector(data):
#Parameters for the hawkes process
#Take the number events/ divided by the number of days
mu = pm.Exponential(
'mu',
len(data) / data[-1] +
(random.choice([0, 1]) * 0.01 * random.random()))
#Alpha is the coefficient for creation - should be an exponential since it's increasingly less likely to be large
alpha = pm.Exponential(
'alpha',
len(data) / data[-1] +
(random.choice([0, 1]) * 0.01 * random.random()))
#We are sure that the impact of one event, if it has one, decreases over time so beta must be positive
beta = pm.Exponential('beta', 1)
return mu, alpha, beta
def __init__(self, observed_frequencies=1.0, observed_power=1.0):
self.observed_frequencies = observed_frequencies
self.observed_power = observed_power
# PyMC definitions
# Define data and stochastics
self.power_law_index = pymc.Uniform('power_law_index',
lower=0.0,
upper=6.0,
doc='power law index')
self.power_law_norm = pymc.Uniform('power_law_norm',
lower=-100.0,
upper=100.0,
doc='power law normalization')
# Model for the power law spectrum
@pymc.deterministic(plot=False)
def fourier_power_spectrum(p=self.power_law_index,
a=self.power_law_norm,
f=self.observed_frequencies):
"""A pure and simple power law model"""
out = rnspectralmodels.power_law(f, [a, p])
return out
self.spectrum = pymc.Exponential('spectrum',
beta=1.0 / fourier_power_spectrum,
value=observed_power,
observed=True)
# MCMC model as a list
self.pymc_model = [self.power_law_index,
self.power_law_norm,
fourier_power_spectrum,
self.spectrum]
def mk_allmodels_bayes(tree, chars, nparam, pi="Equal", dbname=None):
"""
Fit an mk model with nparam parameters distributed about the Q matrix.
"""
if type(chars) == dict:
chars = [chars[l] for l in [n.label for n in tree.leaves()]]
nchar = len(set(chars))
ncell = nchar**2 - nchar
assert nparam <= ncell
minp = pscore(tree, chars)
treelen = sum([n.length for n in tree.descendants()])
### Parameters
# Prior on slowest distribution (beta = 1/mean)
slow = pymc.Exponential("slow", beta=treelen / minp)
paramscales = [None] * (nparam - 1)
for p in range(nparam - 1):
paramscales[p] = pymc.Uniform(name="paramscale_{}".format(str(p)),
lower=2,
upper=20)
### Model
paramset = list(range(nparam + 1))
nonzeros = paramset[1:]
all_mods = list(itertools.product(paramset, repeat=ncell))
all_mods = [
tuple(m) for m in all_mods if all([i in set(m) for i in nonzeros])
]
mod = make_qmat_stoch_mk(all_mods, name="mod")
l = mk.create_likelihood_function_mk(tree=tree,
chars=chars,
Qtype="ARD",
pi=pi,
findmin=False)
Q = np.zeros([nchar, nchar])
mask = np.ones([nchar, nchar], dtype=bool)
mask[np.diag_indices(nchar)] = False
@pymc.potential
def mklik(mod=mod, slow=slow, paramscales=paramscales, name="mklik"):
params = [0.0] * (nparam + 1)
params[1] = slow
for i, s in enumerate(paramscales):
params[2 + i] = params[2 + (i - 1)] * s
Qparams = [params[i] for i in mod]
return l(np.array(Qparams))
if dbname is None:
mod_mcmc = pymc.MCMC(locals(), calc_deviance=True)
else:
mod_mcmc = pymc.MCMC(locals(),
calc_deviance=True,
db="pickle",
dbname=dbname)
mod_mcmc.use_step_method(QmatMetropolis_mk, mod, all_mods)
return mod_mcmc
def _fit_beta_distribution(data, n_iter):
alpha_var = pm.Exponential('alpha', .5)
beta_var = pm.Exponential('beta', .5)
observations = pm.Beta('observations',
alpha_var,
beta_var,
value=data,
observed=True)
model = pm.Model([alpha_var, beta_var, observations])
mcmc = pm.MCMC(model)
mcmc.sample(n_iter)
alphas = mcmc.trace('alpha')[:]
betas = mcmc.trace('beta')[:]
return alphas, betas
def main():
lambda_1 = pm.Exponential("lambda_1", 1) # prior on first behaviour
lambda_2 = pm.Exponential("lambda_2", 1) # prior on second behaviour
tau = pm.DiscreteUniform("tau", lower=0,
upper=10) # prior on behaviour change
print "lambda_1.value = %.3f" % lambda_1.value
print "lambda_2.value = %.3f" % lambda_2.value
print "tau.value = %.3f" % tau.value
print
lambda_1.random(), lambda_2.random(), tau.random()
print "After calling random() on the variables..."
print "lambda_1.value = %.3f" % lambda_1.value
print "lambda_2.value = %.3f" % lambda_2.value
print "tau.value = %.3f" % tau.value
samples = [lambda_1.random() for i in range(20000)]
plt.hist(samples, bins=70, normed=True, histtype="stepfilled")
plt.title("Prior distribution for $\lambda_1$")
plt.xlim(0, 8)
plt.show()
data = np.array([10, 5])
fixed_variable = pm.Poisson("fxd", 1, value=data, observed=True)
print "value: ", fixed_variable.value
print "calling .random()"
fixed_variable.random()
print "value: ", fixed_variable.value
n_data_points = 5 # in CH1 we had ~70 data points
@pm.deterministic
def lambda_(tau=tau, lambda_1=lambda_1, lambda_2=lambda_2):
out = np.zeros(n_data_points)
out[:tau] = lambda_1 # lambda before tau is lambda1
out[tau:] = lambda_2 # lambda after tau is lambda2
return out
data = np.array([10, 25, 15, 20, 35])
obs = pm.Poisson("obs", lambda_, value=data, observed=True)
model = pm.Model([obs, lambda_, lambda_1, lambda_2, tau])
def make_model(x):
a = pm.Exponential('a', beta=x, value=0.5)
@pm.deterministic
def b(a=a):
return 100 - a
@pm.stochastic
def c(value=0.5, a=a, b=b):
return (value - a)**2 / b
return locals()
def best(group1, group2):
import pymc as pm
group1 = np.random.normal(15, 2, 100)
group2 = np.random.normal(15.3, 2, 100)
# Generate Pooled Data
pooled = np.concatenate((group1, group2))
mu1 = pm.Normal("mu_1", mu=pooled.mean(), tau=1.0 / pooled.var() / 1000.0)
mu2 = pm.Normal("mu_2", mu=pooled.mean(), tau=1.0 / pooled.var() / 1000.0)
sig1 = pm.Uniform("sigma_1",
lower=pooled.var() / 1000.0,
upper=pooled.var() * 1000)
sig2 = pm.Uniform("sigma_2",
lower=pooled.var() / 1000.0,
upper=pooled.var() * 1000)
v = pm.Exponential("nu", beta=1.0 / 29)
t1 = pm.NoncentralT("t_1",
mu=mu1,
lam=1.0 / sig1,
nu=v,
value=group1[:],
observed=True)
t2 = pm.NoncentralT("t_2",
mu=mu2,
lam=1.0 / sig2,
nu=v,
value=group2[:],
observed=True)
model = pm.Model([t1, mu1, sig1, t2, mu2, sig2, v])
# Generate our MCMC object
mcmc = pm.MCMC(model)
mcmc.sample(40000, 10000, 2)
mus1 = mcmc.trace('mu_1')[:]
mus2 = mcmc.trace('mu_2')[:]
sigmas1 = mcmc.trace('sigma_1')[:]
sigmas2 = mcmc.trace('sigma_2')[:]
nus = mcmc.trace('nu')[:]
diff_mus = mus1 - mus2
diff_sigmas = sigmas1 - sigmas2
normality = np.log(nus)
effect_size = (mus1 - mus2) / np.sqrt((sigmas1**2 + sigmas2**2) / 2.)
print("mu_1", mus1.mean())
print("mu_2", mus2.mean())
def make_on_off(n_off, expo_off, n_on, expo_on, mean0):
"""
Make a PyMC model for inferring a Poisson signal rate parameter, `s`, for
'on-off' observations with uncertain background rate, `b`.
Parameters
----------
n_off, n_on : int
Event counts off-source and on-source
expo_off, expo_on : float
Exposures off-source and on-source
mean0 : float
Prior mean for both background and signal rates
"""
# PyMC's exponential dist'n uses beta = 1/scale = 1/mean.
# Here we initialize rates to good guesses.
b_est = float(n_off)/expo_off
s_est = max(float(n_on)/expo_on - b_est, .1*b_est)
b = pymc.Exponential('b', beta=1./mean0, value=b_est)
s = pymc.Exponential('s', beta=1./mean0, value=s_est)
# The expected number of counts on and off source, as deterministic functions.
@pymc.deterministic
def mu_off(b=b):
return b*expo_off
@pymc.deterministic
def mu_on(s=s, b=b):
return (s+b)*expo_on
# Poisson likelihood functions:
off_count = pymc.Poisson('off_count', mu=mu_off, value=n_off, observed=True)
on_count = pymc.Poisson('on_count', mu=mu_on, value=n_on, observed=True)
return locals()
def negative_b_mcm_model(p_df):
n_mu = pm.Normal('n_mu', mu=1650, tau=0.00001)
n_lam = pm.Uniform('n_uni_alpha', 0, 1)
n_alpha = pm.Exponential('n_alpha', beta=n_lam)
n_ob = pm.NegativeBinomial('n_observed',
mu=n_mu,
alpha=n_alpha,
value=p_df,
observed=True)
n_es = pm.NegativeBinomial('n_estimated',
mu=n_mu,
alpha=n_alpha,
observed=False)
n_model = pm.Model([n_mu, n_lam, n_alpha, n_ob, n_es])
return n_mu, n_lam, n_alpha, n_ob, n_es, n_model
def create_mk_model(tree, chars, Qtype, pi):
"""
Create model objects to be passed to pymc.MCMC
Creates Qparams and likelihood function
"""
if type(chars) == dict:
chars = [chars[l] for l in [n.label for n in tree.leaves()]]
nchar = len(set(chars))
if Qtype=="ER":
N = 1
elif Qtype=="Sym":
N = int(binom(nchar, 2))
elif Qtype=="ARD":
N = int((nchar ** 2 - nchar))
else:
ValueError("Qtype must be one of: ER, Sym, ARD")
# Setting a Dirichlet prior with Jeffrey's hyperprior of 1/2
if N != 1:
theta = [1.0/2.0]*N
Qparams_init = pymc.Dirichlet("Qparams_init", theta, value = [0.5])
Qparams_init_full = pymc.CompletedDirichlet("Qparams_init_full", Qparams_init)
else:
Qparams_init_full = [[1.0]]
# Exponential scaling factor for Qparams
scaling_factor = pymc.Exponential(name="scaling_factor", beta=1.0, value=1.0)
# Scaled Qparams; we would not expect them to necessarily add
# to 1 as would be the case in a Dirichlet distribution
@pymc.deterministic(plot=False)
def Qparams(q=Qparams_init_full, s=scaling_factor):
Qs = np.empty(N)
for i in range(N):
Qs[i] = q[0][i]*s
return Qs
l = mk.create_likelihood_function_mk(tree=tree, chars=chars, Qtype=Qtype,
pi="Equal", findmin=False)
@pymc.potential
def mklik(q = Qparams, name="mklik"):
return l(q)
return locals()
def test_transformed(self):
n = 18
at_bats = 45 * np.ones(n, dtype=int)
hits = np.random.randint(1, 40, size=n, dtype=int)
draws = 50
with pm.Model() as model:
phi = pm.Beta("phi", alpha=1.0, beta=1.0)
kappa_log = pm.Exponential("logkappa", lam=5.0)
kappa = pm.Deterministic("kappa", at.exp(kappa_log))
thetas = pm.Beta("thetas", alpha=phi * kappa, beta=(1.0 - phi) * kappa, size=n)
y = pm.Binomial("y", n=at_bats, p=thetas, observed=hits)
gen = pm.sample_prior_predictive(draws)
assert gen.prior["phi"].shape == (1, draws)
assert gen.prior_predictive["y"].shape == (1, draws, n)
assert "thetas" in gen.prior.data_vars
def make_model():
import cPickle as pickle
with open('reaction_kinetics_data.pickle', 'rb') as fd:
data = pickle.load(fd)
y_obs = data['y_obs']
# The priors for the reaction rates:
k1 = pymc.Lognormal('k1', mu=2, tau=1./(10. ** 2), value=5.)
k2 = pymc.Lognormal('k2', mu=4, tau=1./(10. ** 2), value=5.)
# The noise term
#sigma = pymc.Uninformative('sigma', value=1.)
sigma = pymc.Exponential('sigma', beta=1.)
# The forward model
re_solver = ReactionKineticsSolver()
@pymc.deterministic
def model_output(value=None, k1=k1, k2=k2):
return re_solver(k1, k2)
# The likelihood term
@pymc.stochastic(observed=True)
def output(value=y_obs, mod_out=model_output, sigma=sigma, gamma=1.):
return gamma * pymc.normal_like(y_obs, mu=mod_out, tau=1/sigma ** 2)
return locals()
def make_poisson(n, intvl, mean0):
"""
Make a PyMC model for inferring a Poisson distribution rate parameter,
for a datum consisting of `n` counts observed in an interval of size
`intvl`. The inference will use an exponential prior for the rate,
with prior mean `mean0`.
"""
# PyMC's exponential dist'n uses beta = 1/scale = 1/mean.
# Here we initialize rate to n/intvl.
rate = pymc.Exponential('rate', beta=1./mean0, value=float(n)/intvl)
# The expected number of counts, mu=rate*intvl, is a deterministic function
# of the rate RV (and the constant intvl).
@pymc.deterministic
def mu(rate=rate):
return rate*intvl
# Poisson likelihood function:
count = pymc.Poisson('count', mu=mu, value=n, observed=True)
return locals()
import pymc as pm import numpy as np import matplotlib.pyplot as plt parameter = pm.Exponential( "poisson_param", 1 ) data_generator = pm.Poisson( "data_generator", parameter ) data_plus_one = data_generator + 1 # 'parents' influence another variable # 'children' subject of parent vars parameter.children data_generator.parents data_generator.children # 'value' attribute parameter.value data_generator.value data_plus_one.value # 'stochastic' vars - still random even if parents are known # 'deterministic' vars - not random if parents are known # Initializing variables # * name argument - retrieves posterior dist # * class specific arguments # * size - multivariate indp array of stochastic vars some_var = pm.DiscreteUniform( "discrete_uni_var", 0, 4 ) betas = pm.Uniform( "betas", 0, 1, size=10 ) betas.value
import numpy as np
import pymc as pm
from matplotlib import pyplot as plt
#count_data = np.loadtxt("txtdata.csv")
count_data = np.loadtxt("txtdata_sim.csv")
n_count_data = len(count_data)
print(count_data.mean())
alpha = 1.0 / count_data.mean() # Recall count_data is the
# variable that holds our txt counts
lambda_1 = pm.Exponential("lambda_1", alpha)
lambda_2 = pm.Exponential("lambda_2", alpha)
tau = pm.DiscreteUniform("tau", lower=0, upper=n_count_data)
@pm.deterministic
def lambda_(tau=tau, lambda_1=lambda_1, lambda_2=lambda_2):
out = np.zeros(n_count_data)
out[:tau] = lambda_1 # lambda before tau is lambda1
out[tau:] = lambda_2 # lambda after (and including) tau is lambda2
return out
observation = pm.Poisson("obs", lambda_, value=count_data, observed=True)
model = pm.Model([observation, lambda_1, lambda_2, tau])
def compare_groups(list1, list2):
data = list1 + list2
count_data = np.array(data)
n_count_data = len(count_data)
plt.bar(np.arange(n_count_data), count_data, color="#348ABD")
plt.xlabel("Time (days)")
plt.ylabel("count of text-msgs received")
plt.title(
"Did the viewers' ad viewing increase with the number of ads shown?")
plt.xlim(0, n_count_data)
#plt.show()
alpha = 1.0 / count_data.mean() # Recall count_data is the
# variable that holds our txt counts
print alpha
lambda_1 = pm.Exponential("lambda_1", alpha)
lambda_2 = pm.Exponential("lambda_2", alpha)
tau = pm.DiscreteUniform("tau", lower=0, upper=n_count_data)
@pm.deterministic
def lambda_(tau=tau, lambda_1=lambda_1, lambda_2=lambda_2):
out = np.zeros(n_count_data)
out[:tau] = lambda_1 # lambda before tau is lambda1
out[tau:] = lambda_2 # lambda after (and including) tau is lambda2
return out
observation = pm.Poisson("obs", lambda_, value=count_data, observed=True)
model = pm.Model([observation, lambda_1, lambda_2, tau])
mcmc = pm.MCMC(model)
mcmc.sample(40000, 10000, 1)
lambda_1_samples = mcmc.trace('lambda_1')[:]
lambda_2_samples = mcmc.trace('lambda_2')[:]
tau_samples = mcmc.trace('tau')[:]
print tau_samples
# histogram of the samples:
ax = plt.subplot(311)
ax.set_autoscaley_on(False)
plt.hist(lambda_1_samples,
histtype='stepfilled',
bins=30,
alpha=0.85,
label="posterior of $\lambda_1$",
color="#A60628",
normed=True)
plt.legend(loc="upper left")
plt.title(r"""Posterior distributions of the variables
$\lambda_1,\;\lambda_2,\;\tau$""")
plt.xlim([0, 6])
plt.ylim([0, 7])
plt.xlabel("$\lambda_1$ value")
ax = plt.subplot(312)
ax.set_autoscaley_on(False)
plt.hist(lambda_2_samples,
histtype='stepfilled',
bins=30,
alpha=0.85,
label="posterior of $\lambda_2$",
color="#7A68A6",
normed=True)
plt.legend(loc="upper left")
plt.xlim([0, 6])
plt.ylim([0, 7])
plt.xlabel("$\lambda_2$ value")
plt.subplot(313)
w = 1.0 / tau_samples.shape[0] * np.ones_like(tau_samples)
plt.hist(tau_samples,
bins=n_count_data,
alpha=1,
label=r"posterior of $\tau$",
color="#467821",
weights=w,
rwidth=2.)
plt.xticks(np.arange(n_count_data))
plt.legend(loc="upper left")
plt.ylim([0, .75])
plt.xlim([0, len(count_data)])
plt.xlabel(r"$\tau$ (iterations)")
plt.ylabel("probability")
plt.show()
plt.plot(stormsYears, stormsNumbers, '-ok')
plt.xlim(year0, year1)
plt.xlabel("Рік")
plt.ylabel("Кількість штормів")
general.set_grid_to_plot()
plt.savefig(general.folderPath2 + "exp2_storms2.png")
plt.clf()
switchpoint = pm.DiscreteUniform('switchpoint',
lower=0,
upper=len(stormsNumbers) - 1,
doc='Switchpoint[year]')
avg = np.mean(stormsNumbers)
early_mean = pm.Exponential('early_mean', beta=1./avg)
late_mean = pm.Exponential('late_mean', beta=1./avg)
@ pm.deterministic(plot=False)
def rate(s=switchpoint, e=early_mean, l=late_mean):
# Concatenate Poisson means
out = np.zeros(len(stormsNumbers))
out[:s] = e
out[s:] = l
return out
storms = pm.Poisson('storms',
mu=rate,
value=stormsNumbers,
observed=True)
0.23972844, -0.78645389, -0.21687104, -0.2939634, 0.51229013, 0.04626286,
0.18329919, -1.12775839, -1.64187249, 0.33440094, -0.95224695, 0.15650266,
-0.54056102, 0.12240128, -0.95397459, 0.44806432, -1.02955556, 0.31740861,
-0.8762523, 0.47377688, 0.76516415, 0.27890419, -0.07819642, -0.13399348,
0.82877293, 0.22308624, 0.7485783, -0.14700254, -1.03145657, 0.85641097,
0.43396285, 0.47901653, 0.80137086, 0.33566812, 0.71443253, -1.57590815,
-0.24090179, -2.0128344, 0.34503324, 0.12944091, -1.5327008, 0.06363034,
0.21042021, -0.81425636, 0.20209279, -1.48130423, -1.04983523, 0.16001774,
-0.75239072, 0.33427956, -0.10224921, 0.26463561, -1.09374674, -0.72749811,
-0.54892116, -1.89631844, -0.94393545, -0.2521341, 0.26840341, 0.23563219,
0.35333094
])
# Model: the data are truncated-normally distributed with unknown upper bound.
mu = pm.Normal('mu', 0, .01, value=0)
tau = pm.Exponential('tau', .01, value=1)
cutoff = pm.Exponential('cutoff', 1, value=1.3)
D = pm.TruncatedNormal('D',
mu,
tau,
-np.inf,
cutoff,
value=data,
observed=True)
M = pm.MCMC([mu, tau, cutoff, D])
# Use a TruncatedMetropolis step method that will never propose jumps below D's maximum value.
M.use_step_method(TruncatedMetropolis, cutoff, D.value.max(), np.inf)
# Get a handle to the step method handling cutoff to investigate its behavior.
S = M.step_method_dict[cutoff][0]
def mk_multi_bayes(tree, chars,nregime,qidx, pi="Equal" ,seglen=0.02,stepsize=0.05):
"""
Create a Bayesian multi-mk model. User specifies which regime models
to use and the Bayesian model finds the switchpoints.
Args:
tree (Node): Root node of tree.
chars (dict): Dict mapping tip labels to discrete character
states. Character states must be in the form of [0,1,2...]
regime (int): The number of distinct regimes to test. Set to
1 for an Mk model, set to greater than 1 for a multi-regime Mk model.
qidx (np.array): Index specifying the model to test
columns:
0, 1, 2 - index axes of q
3 - index of params
This scheme allows flexible specification of models. E.g.:
Symmetric mk2:
params = [0.2]; qidx = [[0,0,1,0],
[0,1,0,0]]
Asymmetric mk2:
params = [0.2,0.6]; qidx = [[0,0,1,0],
[0,1,0,1]]
NOTE:
The qidx corresponding to the first q matrix (first column 0)
is always the root regime
pi (str or np.array): Option to weight the root node by given values.
Either a string containing the method or an array
of weights. Weights should be given in order.
Accepted methods of weighting root:
Equal: flat prior
Equilibrium: Prior equal to stationary distribution
of Q matrix
Fitzjohn: Root states weighted by how well they
explain the data at the tips.
seglen (float): Size of segments to break tree into. The smaller this
value, the more "fine-grained" the analysis will be. Optional,
defaults to 2% of the root-to-tip length.
stepsize (float): Maximum size of steps for switchpoints to take.
Optional, defaults to 5% of root-to-tip length.
"""
if type(chars) == dict:
data = chars.copy()
chars = [chars[l] for l in [n.label for n in tree.leaves()]]
else:
data = dict(zip([n.label for n in tree.leaves()],chars))
# Preparations
nchar = len(set(chars))
nparam = len(set([n[-1] for n in qidx]))
# This model has 2 components: Q parameters and switchpoints
# They are combined in a custom likelihood function
###########################################################################
# Switchpoint:
###########################################################################
# Modeling the movement of the regime shift(s) is the tricky part
# Regime shifts will only be allowed to happen at a node
seg_map = tree_map(tree,seglen)
switch = [None]*(nregime-1)
for regime in range(nregime-1):
switch[regime]= make_switchpoint_stoch(seg_map, name=str("switch_{}".format(regime)))
###########################################################################
# Qparams:
###########################################################################
# Each Q parameter is an exponential
Qparams = [None] * nparam
for i in range(nparam):
Qparams[i] = pymc.Exponential(name=str("Qparam_{}".format(i)), beta=1.0, value=0.1*(i+1))
###########################################################################
# Likelihood
###########################################################################
# The likelihood function
l = cyexpokit.make_mklnl_func(tree, data,nchar,nregime,qidx)
@pymc.deterministic
def likelihood(q = Qparams, s=switch,name="likelihood"):
return l(np.array(q),np.array([x[0].ni for x in s],dtype=np.intp),np.array([x[1] for x in s]))
@pymc.potential
def multi_mklik(lnl=likelihood):
if not (np.isnan(lnl)):
return lnl
else:
return -np.inf
mod = pymc.MCMC(locals())
for s in switch:
mod.use_step_method(SwitchpointMetropolis, s, tree, seg_map,stepsize=stepsize,seglen=seglen)
return mod
def run_mcmc(gp, img, compare_img, transverse_sigma=1.0, motion_angle=0.0):
"""Estimate PSF using Markov Chain Monte Carlo
gp - Gaussian priors - array of N objects with attributes
a, b, sigma
img - image to apply PSF to
compare_img - comparison image
transverse_sigma - prior
motion_angle - prior
Model a Point Spread Function consisting of the sum of N
collinear Gaussians, blurred in the transverse direction
and the result rotated. Each of the collinear Gauusians
is parameterized by a (amplitude), b (center), and sigma (std. deviation).
The Point Spread Function is applied to the image img
and the result compared with the image compare_img.
"""
print "gp.shape", gp.shape
print "gp", gp
motion_angle = np.deg2rad(motion_angle)
motion_angle = pm.VonMises("motion_angle",
motion_angle,
1.0,
value=motion_angle)
transverse_sigma = pm.Exponential("transverse_sigma",
1.0,
value=transverse_sigma)
N = gp.shape[0]
mixing_coeffs = pm.Exponential("mixing_coeffs", 1.0, size=N)
#mixing_coeffs.set_value(gp['a'])
mixing_coeffs.value = gp['a']
longitudinal_sigmas = pm.Exponential("longitudinal_sigmas", 1.0, size=N)
#longitudinal_sigmas.set_value(gp['sigma'])
longitudinal_sigmas.value = gp['sigma']
b = np.array(sorted(gp['b']), dtype=float)
cut_points = (b[1:] + b[:-1]) * 0.5
long_means = [None] * b.shape[0]
print long_means
left_mean = pm.Gamma("left_mean", 1.0, 2.5 * gp['sigma'][0])
long_means[0] = cut_points[0] - left_mean
right_mean = pm.Gamma("right_mean", 1.0, 2.5 * gp['sigma'][-1])
long_means[-1] = cut_points[-1] + right_mean
for ix in range(1, N - 1):
long_means[ix] = pm.Uniform("mid%d_mean" % ix,
lower=cut_points[ix - 1],
upper=cut_points[ix])
print "cut_points", cut_points
print "long_means", long_means
#longitudinal_means = pm.Normal("longitudinal_means", 0.0, 0.04, size=N)
#longitudinal_means.value = gp['b']
dtype = np.dtype([('a', np.float), ('b', np.float), ('sigma', np.float)])
@pm.deterministic
def psf(mixing_coeffs=mixing_coeffs, longitudinal_sigmas=longitudinal_sigmas, \
longitudinal_means=long_means, transverse_sigma=transverse_sigma, motion_angle=motion_angle):
gp = np.ones((N, ), dtype=dtype)
gp['a'] = mixing_coeffs
gp['b'] = longitudinal_means
gp['sigma'] = longitudinal_sigmas
motion_angle_deg = np.rad2deg(motion_angle)
if True:
print "gp: a", mixing_coeffs
print " b", longitudinal_means
print " s", longitudinal_sigmas
print "tr-sigma", transverse_sigma, "angle=", motion_angle_deg
return generate_sum_gauss(gp, transverse_sigma, motion_angle_deg)
@pm.deterministic
def image_fitness(psf=psf, img=img, compare_img=compare_img):
img_convolved = ndimage.convolve(img, psf)
img_diff = img_convolved.astype(int) - compare_img
return img_diff.std()
if False:
trial_psf = generate_sum_gauss(gp,
2.0,
50.0,
plot_unrot_kernel=True,
plot_rot_kernel=True,
verbose=True)
print "trial_psf", trial_psf.min(), trial_psf.mean(), trial_psf.max(
), trial_psf.std()
obs_psf = pm.Uniform("obs_psf",
lower=-1.0,
upper=1.0,
doc="Point Spread Function",
value=trial_psf,
observed=True,
verbose=False)
print "image_fitness value started at", image_fitness.value
known_fitness = pm.Exponential("fitness",
image_fitness + 0.001,
value=0.669,
observed=True)
#mcmc = pm.MCMC([motion_angle, transverse_sigma, mixing_coeffs, longitudinal_sigmas, longitudinal_means, image_fitness, known_fitness], verbose=2)
mcmc = pm.MCMC([
motion_angle, transverse_sigma, mixing_coeffs, longitudinal_sigmas,
image_fitness, known_fitness, left_mean, right_mean
] + long_means,
verbose=2)
pm.graph.dag(mcmc, format='png')
plt.show()
#mcmc.sample(20000, 1000)
mcmc.sample(2000)
motion_angle_samples = mcmc.trace("motion_angle")[:]
transverse_sigma_samples = mcmc.trace("transverse_sigma")[:]
image_fitness_samples = mcmc.trace("image_fitness")[:]
best_fit = np.percentile(image_fitness_samples, 1.0)
best_fit_selection = image_fitness_samples < best_fit
print mcmc.db.trace_names
for k in [k for k in mcmc.stats().keys() if k != "known_fitness"]:
#samples = mcmc.trace(k)[:]
samples = mcmc.trace(k).gettrace()
print samples.shape
selected_samples = samples[best_fit_selection]
print k, samples.mean(axis=0), samples.std(axis=0), \
selected_samples.mean(axis=0), selected_samples.std(axis=0)
ax = plt.subplot(211)
plt.hist(motion_angle_samples,
histtype='stepfilled',
bins=25,
alpha=0.85,
label="posterior of $p_\\theta$",
color="#A60628",
normed=True)
plt.legend(loc="upper right")
plt.title("Posterior distributions of $p_\\theta$, $p_\\sigma$")
ax = plt.subplot(212)
plt.hist(transverse_sigma_samples,
histtype='stepfilled',
bins=25,
alpha=0.85,
label="posterior of $p_\\sigma$",
color="#467821",
normed=True)
plt.legend(loc="upper right")
plt.show()
for k, v in mcmc.stats().iteritems():
print k, v
# deprecated? use discrepancy... print mcmc.goodness()
mcmc.write_csv("out.csv")
pm.Matplot.plot(mcmc)
plt.show()
def time_drug_evaluation(self):
# fmt: off
drug = np.array([
101, 100, 102, 104, 102, 97, 105, 105, 98, 101, 100, 123, 105, 103,
100, 95, 102, 106, 109, 102, 82, 102, 100, 102, 102, 101, 102, 102,
103, 103, 97, 97, 103, 101, 97, 104, 96, 103, 124, 101, 101, 100,
101, 101, 104, 100, 101
])
placebo = np.array([
99, 101, 100, 101, 102, 100, 97, 101, 104, 101, 102, 102, 100, 105,
88, 101, 100, 104, 100, 100, 100, 101, 102, 103, 97, 101, 101, 100,
101, 99, 101, 100, 100, 101, 100, 99, 101, 100, 102, 99, 100, 99
])
# fmt: on
y = pd.DataFrame({
"value":
np.r_[drug, placebo],
"group":
np.r_[["drug"] * len(drug), ["placebo"] * len(placebo)],
})
y_mean = y.value.mean()
y_std = y.value.std() * 2
sigma_low = 1
sigma_high = 10
with pm.Model():
group1_mean = pm.Normal("group1_mean", y_mean, sd=y_std)
group2_mean = pm.Normal("group2_mean", y_mean, sd=y_std)
group1_std = pm.Uniform("group1_std",
lower=sigma_low,
upper=sigma_high)
group2_std = pm.Uniform("group2_std",
lower=sigma_low,
upper=sigma_high)
lambda_1 = group1_std**-2
lambda_2 = group2_std**-2
nu = pm.Exponential("ν_minus_one", 1 / 29.0) + 1
pm.StudentT("drug",
nu=nu,
mu=group1_mean,
lam=lambda_1,
observed=drug)
pm.StudentT("placebo",
nu=nu,
mu=group2_mean,
lam=lambda_2,
observed=placebo)
diff_of_means = pm.Deterministic("difference of means",
group1_mean - group2_mean)
pm.Deterministic("difference of stds", group1_std - group2_std)
pm.Deterministic(
"effect size", diff_of_means / np.sqrt(
(group1_std**2 + group2_std**2) / 2))
pm.sample(draws=20000,
cores=4,
chains=4,
progressbar=False,
compute_convergence_checks=False)
def rn_model_load(analysis_frequencies, analysis_power):
# __all__ = ['analysis_power', 'analysis_frequencies', 'power_law_index',
# 'power_law_norm', 'power_law_spectrum', 'spectrum']
estimate = rn_utils.do_simple_fit(analysis_frequencies, analysis_power)
c_estimate = estimate[0]
m_estimate = estimate[1]
# Define data and stochastics
@pymc.stochastic
power_law_index = pymc.Uniform('power_law_index',
value=m_estimate,
lower=0.0,
upper=m_estimate + 2,
doc='power law index')
@pymc.stochastic
power_law_norm = pymc.Uniform('power_law_norm',
value=c_estimate,
lower=c_estimate * 0.8,
upper=c_estimate * 1.2,
doc='power law normalization')
# Model for the power law spectrum
@pymc.deterministic(plot=False)
def power_law_spectrum(p=power_law_index,
a=power_law_norm,
f=analysis_frequencies):
"""A pure and simple power law model"""
out = a * (f ** (-p))
return out
#@pymc.deterministic(plot=False)
#def power_law_spectrum_with_constant(p=power_law_index, a=power_law_norm,
# c=constant, f=frequencies):
# """Simple power law with a constant"""
# out = empty(frequencies)
# out = c + a/(f**p)
# return out
#@pymc.deterministic(plot=False)
#def broken_power_law_spectrum(p2=power_law_index_above,
# p1=power_law_index_below,
# bf=break_frequency,
# a=power_law_norm,
# f=analysis_frequencies):
# """A broken power law model"""
# out = np.empty(len(f))
# out[f < bf] = a * (f[f < bf] ** (-p1))
# out[f > bf] = a * (f[f >= bf] ** (-p2)) * bf ** (p2 - p1)
# return out
# This is the PyMC model we will use: fits the model defined in
# beta=1.0 / model to the power law spectrum we are analyzing
# value=analysis_power
spectrum = pymc.Exponential('spectrum',
beta=1.0 / power_law_spectrum,
value=analysis_power,
observed=True)
return locals()
for v in range(len(gap)):
ret.append(chunk[v] + gap[v])
return ret
return predict
predictions = [
mc.Deterministic(eval=chunkPrediction(i),
name='chunk%sPrediction' % i,
parents={'gap': gaps[i]},
doc='chunk %s prediction' % i)
for i in range(len(chunks) - 1)
]
noise = mc.Exponential('noise', 1, 1)
observations = [
mc.Normal('chunk%sObservation' % i,
predictions[i - 1],
noise,
value=chunks[i - 1]['mouth'],
observed=True) for i in range(1, len(chunks))
]
@mc.deterministic
def mouthChanges(early=mouthFactor[0], late=mouthFactor[1]):
return late - early
def bayes_ttest(groups=None, N=40, show=False):
"""
Run a Bayesian t-test on sample or true data.
"""
if groups is None: # Generate some data
group1, group2 = gen_data(N=40)
elif len(groups) != 2:
print('T-test requires only 2 groups, not %i' % len(groups))
return None
else:
group1, group2 = groups
pooled = np.concatenate((group1, group2)) # Pooled data
# Establish priors
mu1 = pm.Normal("mu_1", mu=pooled.mean(), tau=1.0 / pooled.var() / N)
mu2 = pm.Normal("mu_2", mu=pooled.mean(), tau=1.0 / pooled.var() / N)
sig1 = pm.Uniform("sigma_1",
lower=pooled.var() / 1000.0,
upper=pooled.var() * 1000)
sig2 = pm.Uniform("sigma_2",
lower=pooled.var() / 1000.0,
upper=pooled.var() * 1000)
v = pm.Exponential("nu", beta=1.0 / 29)
# Set up posterior distribution
t1 = pm.NoncentralT("t_1",
mu=mu1,
lam=1.0 / sig1,
nu=v,
value=group1,
observed=True)
t2 = pm.NoncentralT("t_1",
mu=mu2,
lam=1.0 / sig2,
nu=v,
value=group2,
observed=True)
# Generate the model
model = pm.Model([t1, mu1, sig1, t2, mu2, sig2, v]) # Push priors
mcmc = pm.MCMC(model) # Generate MCMC object
mcmc.sample(40000, 10000, 2) # Run MCMC sampler # "trace"
# Get the numerical results
mus1 = mcmc.trace('mu_1')[:]
mus2 = mcmc.trace('mu_2')[:]
sigmas1 = mcmc.trace('sigma_1')[:]
sigmas2 = mcmc.trace('sigma_2')[:]
nus = mcmc.trace('nu')[:]
diff_mus = mus1 - mus2 # Difference in mus
diff_sigmas = sigmas1 - sigmas2
normality = np.log(nus)
effect_size = (mus1 - mus2) / np.sqrt((sigmas1**2 + sigmas2**2) / 2.)
print('\n Group 1 mu: %.4f\n Group 2 mu: %.4f\n Effect size: %.4f' %
(mus1.mean(), mus2.mean(), effect_size.mean()))
if show: # Plot some basic metrics if desired
from pymc.Matplot import plot as mcplot
# mcplot(mcmc) # This plots 5 graphs, only useful as a benchmark.
# Finally, what can this tell is about the null hypothesis?
# Split distribution
fig2 = plt.figure()
ax2 = fig2.add_subplot(121)
minx = min(min(mus1), min(mus2))
maxx = max(max(mus1), max(mus2))
xs = np.linspace(minx, maxx, 1000)
gkde1 = stats.gaussian_kde(mus1)
gkde2 = stats.gaussian_kde(mus2)
ax2.plot(xs, gkde1(xs), label='$\mu_1$')
ax2.plot(xs, gkde2(xs), label='$\mu_2$')
ax2.set_title('$\mu_1$ vs $\mu_2$')
ax2.legend()
# Difference of mus
ax3 = fig2.add_subplot(122)
minx = min(diff_mus)
maxx = max(diff_mus)
xs = np.linspace(minx, maxx, 1000)
gkde = stats.gaussian_kde(diff_mus)
ax3.plot(xs, gkde(xs), label='$\mu_1-\mu_2$')
ax3.legend()
ax3.axvline(0, color='#000000', alpha=0.3, linestyle='--')
ax3.set_title('$\mu_1-\mu_2$')
plt.show()
return
def exponential_beta(n=2):
with pm.Model() as model:
pm.Beta("x", 3, 1, size=n, transform=None)
pm.Exponential("y", 1, size=n, transform=None)
return model.compute_initial_point(), model, None
