eps_p_f_list = []
N_pos_list = []
# Obtain the spline representation of the log of the Monte Carlo-integrated
# likelihood function at each datapoint. The nodes are at .01,.02,...,.98,.99 .
junk, splreps = age_corr_likelihoods(all_pts, 10000,
np.arange(.01, 1., .01), norun_name)
for i in xrange(len(splreps)):
splreps[i] = list(splreps[i])
# Don't worry, these are just reasonable initial values...
val_now = pm.logit(
np.array(all_pts.PF + 1, dtype=float) / (all_pts.EXAMINED + 2))
if with_stukel:
val_now = pm.stukel_logit(
np.array(all_pts.PF + 1, dtype=float) / (all_pts.EXAMINED + 2),
a1.value, a2.value)
if data_mesh.shape[0] % chunk == 0:
additional_index = 0
else:
additional_index = 1
for i in xrange(0, data_mesh.shape[0] / chunk + additional_index):
this_slice = slice(chunk * i, min((i + 1) * chunk, data_mesh.shape[0]))
# epsilon plus f, given f.
@pm.stochastic(trace=False, dtype=np.float)
def eps_p_f_now(value=val_now[this_slice],
f=f_eval,
init_OK = True
except pm.ZeroProbability, msg:
print "Trying again: %s" % msg
init_OK = False
gc.collect()
# ===========================
# = Create likelihood layer =
# ===========================
eps_p_f_list = []
N_pos_list = []
# Don't worry, these are just reasonable initial values...
if with_stukel:
val_now = pm.stukel_logit((pos + 1.0) / (pos + neg + 2.0), a1.value, a2.value)
else:
val_now = pm.logit((pos + 1.0) / (pos + neg + 2.0))
if data_mesh.shape[0] % chunk == 0:
additional_index = 0
else:
additional_index = 1
for i in xrange(0, data_mesh.shape[0] / chunk + additional_index):
this_slice = slice(chunk * i, min((i + 1) * chunk, data_mesh.shape[0]))
# epsilon plus f, given f.
@pm.stochastic(trace=False, dtype=np.float)
def eps_p_f_now(value=val_now[this_slice], f=sp_sub.f_eval, V=V, sl=this_slice):
# = Create likelihood layer =
# ===========================
eps_p_f_list = []
N_pos_list = []
# Obtain the spline representation of the log of the Monte Carlo-integrated
# likelihood function at each datapoint. The nodes are at .01,.02,...,.98,.99 .
junk, splreps = age_corr_likelihoods(all_pts, 10000, np.arange(.01,1.,.01), norun_name)
for i in xrange(len(splreps)):
splreps[i] = list(splreps[i])
# Don't worry, these are just reasonable initial values...
val_now = pm.logit(np.array(all_pts.PF+1,dtype=float)/(all_pts.EXAMINED+2))
if with_stukel:
val_now = pm.stukel_logit(np.array(all_pts.PF+1,dtype=float)/(all_pts.EXAMINED+2), a1.value, a2.value)
if data_mesh.shape[0] % chunk == 0:
additional_index = 0
else:
additional_index = 1
for i in xrange(0,data_mesh.shape[0] / chunk + additional_index):
this_slice = slice(chunk*i, min((i+1)*chunk, data_mesh.shape[0]))
# epsilon plus f, given f.
@pm.stochastic(trace=False, dtype=np.float)
def eps_p_f_now(value=val_now[this_slice], f=f_eval, V=V, this_slice = this_slice):
return pm.normal_like(value, f[this_slice], 1./V)
eps_p_f_now.__name__ = "eps_p_f%i"%i
def pripred_check(m=m,amp=amp,V=V,a=a):
p_above = scipy.stats.distributions.norm.cdf(m-pm.stukel_logit(threshold_val,*a), 0, np.sqrt(amp**2+V))
if p_above <= max_p_above:
return 0.
else:
return -np.inf
def make_model(lon,lat,input_data,covariate_keys,pos,neg,cpus=1):
"""
This function is required by the generic MBG code.
"""
ra = csv2rec(input_data)
if np.any(pos+neg==0):
where_zero = np.where(pos+neg==0)[0]
raise ValueError, 'Pos+neg = 0 in the rows (starting from zero):\n %s'%where_zero
# How many nuggeted field points to handle with each step method
grainsize = 10
# Non-unique data locations
data_mesh = combine_spatial_inputs(lon, lat)
s_hat = (pos+1.)/(pos+neg+2.)
# Uniquify the data locations.
locs = [(lon[0], lat[0])]
fi = [0]
ui = [0]
for i in xrange(1,len(lon)):
# If repeat location, add observation
loc = (lon[i], lat[i])
if loc in locs:
fi.append(locs.index(loc))
# Otherwise, new obs
else:
locs.append(loc)
fi.append(max(fi)+1)
ui.append(i)
fi = np.array(fi)
ti = [np.where(fi == i)[0] for i in xrange(max(fi)+1)]
ui = np.asarray(ui)
lon = np.array(locs)[:,0]
lat = np.array(locs)[:,1]
# Unique data locations
logp_mesh = combine_spatial_inputs(lon,lat)
# Create the mean & its evaluation at the data locations.
@pm.deterministic
def M():
return pm.gp.Mean(mean_fn)
@pm.deterministic
def M_eval(M=M):
return M(logp_mesh)
init_OK = False
while not init_OK:
try:
# The partial sill.
amp = pm.Exponential('amp', .1, value=1.)
a1 = pm.Uninformative('a1',1,observed=True)
a2 = pm.Uninformative('a2',1,observed=True)
p0 = pm.Uniform('p0',0,1,value=.01)
# The range parameters. Units are RADIANS.
# 1 radian = the radius of the earth, about 6378.1 km
scale = pm.Exponential('scale', .1, value=.08)
scale_in_km = scale*6378.1
# This parameter controls the degree of differentiability of the field.
diff_degree = pm.Uniform('diff_degree', .01, 3)
# The nugget variance.
V = pm.Exponential('V', .1, value=1.)
tau = 1./V
# Create the covariance & its evaluation at the data locations.
@pm.deterministic(trace=True)
def C(amp=amp, scale=scale, diff_degree=diff_degree):
"""A covariance function created from the current parameter values."""
eval_fun = CovarianceWithCovariates(pm.gp.cov_funs.matern.geo_rad, input_data, covariate_keys, ui, fac=1.e4, ra=ra)
return pm.gp.FullRankCovariance(eval_fun, amp=amp, scale=scale, diff_degree=diff_degree)
sp_sub = pm.gp.GPSubmodel('sp_sub',M,C,logp_mesh)
# Loop over data clusters
eps_p_f_d = []
s_d = []
data_d = []
for i in xrange(len(pos)/grainsize+1):
sl = slice(i*grainsize,(i+1)*grainsize,None)
# Nuggeted field in this cluster
this_f = sp_sub.f_eval[fi[sl]]
if len(this_f.value)>0:
eps_p_f_d.append(pm.Normal('eps_p_f_%i'%i, this_f, tau, value=pm.stukel_logit(s_hat[sl],a1.value,a2.value),trace=False))
# The allele frequency
s_d.append(pm.Lambda('s_%i'%i,lambda lt=eps_p_f_d[-1]: stukel_invlogit(lt,a1,a2),trace=False))
# The observed allele frequencies
@pm.stochastic(name='data_%i'%i, observed=True)
def d_now(value=pos[sl], p0=p0, n=pos[sl]+neg[sl], p=s_d[-1]):
return zib(value, p0, n, p)
# The field plus the nugget
@pm.deterministic
def eps_p_f(eps_p_fd = eps_p_f_d):
"""Concatenated version of eps_p_f, for postprocessing & Gibbs sampling purposes"""
return np.concatenate(eps_p_fd)
init_OK = True
except pm.ZeroProbability, msg:
print 'Trying again: %s'%msg
init_OK = False
gc.collect()
