/usr/lib/python2.7/dist-packages/pymc/Matplot.py is in python-pymc 2.2+ds-1.1.
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Plotting module using matplotlib.
"""
from __future__ import division
# Import matplotlib functions
try:
import matplotlib.gridspec as gridspec
except ImportError:
gridspec = None
import pymc
import os
from pylab import hist, plot as pyplot, xlabel, ylabel, xlim, ylim, savefig, acorr, mlab
from pylab import figure, subplot, subplots_adjust, gca, scatter, axvline, yticks, xticks
from pylab import setp, contourf, cm, title, colorbar, fill, text
from pylab import errorbar
# Import numpy functions
from numpy import arange, log, ravel, rank, swapaxes, concatenate, asarray, ndim
from numpy import mean, std, sort, prod, floor, shape, size, transpose
from numpy import min as nmin, max as nmax, abs
from numpy import append, ones, dtype, indices, array, unique, zeros
from .utils import quantiles as calc_quantiles, hpd as calc_hpd
try:
from scipy import special
except ImportError:
special = None
from . import six
from .six import print_
__all__ = ['func_quantiles', 'func_envelopes', 'func_sd_envelope',
'centered_envelope', 'get_index_list', 'plot', 'histogram', 'trace',
'geweke_plot', 'gof_plot', 'pair_posterior', 'summary_plot']
def get_index_list(shape, j):
"""
index_list = get_index_list(shape, j)
:Arguments:
shape: a tuple
j: an integer
Assumes index j is from a ravelled version of an array
with specified shape, returns the corresponding
non-ravelled index tuple as a list.
"""
r = range(len(shape))
index_list = (r)
for i in r:
if i < len(shape):
prodshape = prod(shape[i+1:])
else:
prodshape=0
index_list[i] = int(floor(j/prodshape))
if index_list[i]>shape[i]:
raise IndexError('Requested index too large')
j %= prodshape
return index_list
def func_quantiles(node, qlist=(.025, .25, .5, .75, .975)):
"""
Returns an array whose ith row is the q[i]th quantile of the
function.
:Arguments:
func_stacks: The samples of the function. func_stacks[i,:]
gives sample i.
qlist: A list or array of the quantiles you would like.
:SeeAlso: func_envelopes, func_hist, weightplot
"""
# For very large objects, this will be rather long.
# Too get the length of the table, use obj.trace.length()
if isinstance(node, pymc.Variable):
func_stacks = node.trace()
else:
func_stacks = node
if any(qlist<0.) or any(qlist>1.):
raise TypeError('The elements of qlist must be between 0 and 1')
func_stacks = func_stacks.copy()
N_samp = shape(func_stacks)[0]
func_len = tuple(shape(func_stacks)[1:])
func_stacks.sort(axis=0)
quants = zeros((len(qlist),func_len),dtype=float)
alphas = 1.-abs(array(qlist)-.5)/.5
for i in range(len(qlist)):
quants[i,] = func_stacks[int(qlist[i]*N_samp),]
return quants, alphas
def func_envelopes(node, CI=(.25, .5, .95)):
"""
func_envelopes(node, CI = (.25, .5, .95))
Returns a list of centered_envelope objects for func_stacks,
each one corresponding to an element of CI, and one
corresponding to mass 0 (the median).
:Arguments:
func_stacks: The samples of the function. func_stacks[i,:]
gives sample i.
CI: A list or array containing the probability masses
the envelopes should enclose.
:Note: The return list of envelopes is sorted from high to low
enclosing probability masses, so they should be plotted in
order.
:SeeAlso: centered_envelope, func_quantiles, func_hist, weightplot
"""
if isinstance(node, pymc.Variable):
func_stacks = asarray(node.trace())
else:
func_stacks = node
func_stacks = func_stacks.copy()
func_stacks.sort(axis=0)
envelopes = []
qsort = sort(CI)
for i in range(len(qsort)):
envelopes.append(centered_envelope(func_stacks, qsort[len(qsort)-i-1]))
envelopes.append(centered_envelope(func_stacks, 0.))
return envelopes
# FIXME: Not sure of the best way to bring these two into PlotFactory...
class func_sd_envelope(object):
"""
F = func_sd_envelope(func_stacks)
F.display(axes,xlab=None,ylab=None,name=None)
This object plots the mean and +/- 1 sd error bars for
the one or two-dimensional function whose trace
"""
def __init__(self, node, format='pdf', plotpath='', suffix=''):
if isinstance(node, pymc.Variable):
func_stacks = node.trace()
else:
func_stacks = node
self.name = node.__name__
self._format=format
self._plotpath=plotpath
self.suffix=suffix
self.mean = mean(func_stacks,axis=0)
self.std = std(func_stacks, axis=0)
self.lo = self.mean - self.std
self.hi = self.mean + self.std
self.ndim = len(shape(func_stacks))-1
def display(self,axes,xlab=None,ylab=None,name=None,new=True):
if name:
name_str = name
else:
name_str = ''
if self.ndim==1:
if new:
figure()
pyplot(axes,self.lo,'k-.',label=name_str+' mean-sd')
pyplot(axes,self.hi,'k-.',label=name_str+'mean+sd')
pyplot(axes,self.mean,'k-',label=name_str+'mean')
if name:
title(name)
elif self.ndim==2:
if new:
figure(figsize=(14,4))
subplot(1,3,1)
contourf(axes[0],axes[1],self.lo,cmap=cm.bone)
title(name_str+' mean-sd')
if xlab:
xlabel(xlab)
if ylab:
ylabel(ylab)
colorbar()
subplot(1,3,2)
contourf(axes[0],axes[1],self.mean,cmap=cm.bone)
title(name_str+' mean')
if xlab:
xlabel(xlab)
if ylab:
ylabel(ylab)
colorbar()
subplot(1,3,3)
contourf(axes[0],axes[1],self.hi,cmap=cm.bone)
title(name_str+' mean+sd')
if xlab:
xlabel(xlab)
if ylab:
ylabel(ylab)
colorbar()
else:
raise ValueError('Only 1- and 2- dimensional functions can be displayed')
savefig("%s%s%s.%s" % (self._plotpath,self.name,self.suffix,self._format))
class centered_envelope(object):
"""
E = centered_envelope(sorted_func_stack, mass)
An object corresponding to the centered CI envelope
of a function enclosing a particular probability mass.
:Arguments:
sorted_func_stack: The samples of the function, sorted.
if func_stacks[i,:] gives sample i, then
sorted_func_stack is sort(func_stacks,0).
mass: The probability mass enclosed by the CI envelope.
:SeeAlso: func_envelopes
"""
def __init__(self, sorted_func_stack, mass):
if mass<0 or mass>1:
raise ValueError('mass must be between 0 and 1')
N_samp = shape(sorted_func_stack)[0]
self.mass = mass
self.ndim = len(sorted_func_stack.shape)-1
if self.mass == 0:
self.value = sorted_func_stack[int(N_samp*.5),]
else:
quandiff = .5*(1.-self.mass)
self.lo = sorted_func_stack[int(N_samp*quandiff),]
self.hi = sorted_func_stack[int(N_samp*(1.-quandiff)),]
def display(self, xaxis, alpha, new=True):
"""
E.display(xaxis, alpha = .8)
:Arguments: xaxis, alpha
Plots the CI region on the current figure, with respect to
xaxis, at opacity alpha.
:Note: The fill color of the envelope will be self.mass
on the grayscale.
"""
if new:
figure()
if self.ndim == 1:
if self.mass>0.:
x = concatenate((xaxis,xaxis[::-1]))
y = concatenate((self.lo, self.hi[::-1]))
fill(x,y,facecolor='%f' % self.mass,alpha=alpha, label = ('centered CI ' + str(self.mass)))
else:
pyplot(xaxis,self.value,'k-',alpha=alpha, label = ('median'))
else:
if self.mass>0.:
subplot(1,2,1)
contourf(xaxis[0],xaxis[1],self.lo,cmap=cm.bone)
colorbar()
subplot(1,2,2)
contourf(xaxis[0],xaxis[1],self.hi,cmap=cm.bone)
colorbar()
else:
contourf(xaxis[0],xaxis[1],self.value,cmap=cm.bone)
colorbar()
def plotwrapper(f):
"""
This decorator allows for PyMC arguments of various types to be passed to
the plotting functions. It identifies the type of object and locates its
trace(s), then passes the data to the wrapped plotting function.
"""
def wrapper(pymc_obj, *args, **kwargs):
start = 0
if 'start' in kwargs:
start = kwargs.pop('start')
# Figure out what type of object it is
try:
# First try Model type
for variable in pymc_obj._variables_to_tally:
# Plot object
if variable._plot!=False:
data = pymc_obj.trace(variable.__name__)[start:]
if size(data[-1])>=10 and variable._plot!=True:
continue
elif variable.dtype is dtype('object'):
continue
name = variable.__name__
if args:
name = '%s_%s' % (args[0], variable.__name__)
f(data, name, *args, **kwargs)
return
except AttributeError:
pass
try:
# Then try Trace type
data = pymc_obj()[:]
name = pymc_obj.name
f(data, name, *args, **kwargs)
return
except (AttributeError, TypeError):
pass
try:
# Then try Node type
if pymc_obj._plot!=False:
data = pymc_obj.trace()[start:] # This is deprecated. DH
name = pymc_obj.__name__
f(data, name, *args, **kwargs)
return
except AttributeError:
pass
if type(pymc_obj) == dict:
# Then try dictionary
for i in pymc_obj:
data = pymc_obj[i][start:]
if args:
i = '%s_%s' % (args[0], i)
elif 'name' in kwargs:
i = '%s_%s' % (kwargs.pop('name'), i)
f(data, i, *args, **kwargs)
return
# If others fail, assume that raw data is passed
f(pymc_obj, *args, **kwargs)
wrapper.__doc__ = f.__doc__
wrapper.__name__ = f.__name__
return wrapper
@plotwrapper
def plot(data, name, format='png', suffix='', path='./', common_scale=True, datarange=(None, None),
new=True, last=True, rows=1, num=1, fontmap = None, verbose=1):
"""
Generates summary plots for nodes of a given PyMC object.
:Arguments:
data: PyMC object, trace or array
A trace from an MCMC sample or a PyMC object with one or more traces.
name: string
The name of the object.
format (optional): string
Graphic output format (defaults to png).
suffix (optional): string
Filename suffix.
path (optional): string
Specifies location for saving plots (defaults to local directory).
common_scale (optional): bool
Specifies whether plots of multivariate nodes should be on the same scale
(defaults to True).
"""
if fontmap is None: fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}
# If there is only one data array, go ahead and plot it ...
if rank(data)==1:
if verbose>0:
print_('Plotting', name)
# If new plot, generate new frame
if new:
figure(figsize=(10, 6))
# Call trace
trace(data, name, datarange=datarange, rows=rows*2, columns=2, num=num+3*(num-1), last=last, fontmap=fontmap)
# Call autocorrelation
autocorrelation(data, name, rows=rows*2, columns=2, num=num+3*(num-1)+2, last=last, fontmap=fontmap)
# Call histogram
histogram(data, name, datarange=datarange, rows=rows, columns=2, num=num*2, last=last, fontmap=fontmap)
if last:
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
savefig("%s%s%s.%s" % (path, name, suffix, format))
else:
# ... otherwise plot recursively
tdata = swapaxes(data, 0, 1)
datarange = (None, None)
# Determine common range for plots
if common_scale:
datarange = (nmin(tdata), nmax(tdata))
# How many rows?
_rows = min(4, len(tdata))
for i in range(len(tdata)):
# New plot or adding to existing?
_new = not i % _rows
# Current subplot number
_num = i % _rows + 1
# Final subplot of current figure?
_last = (_num==_rows) or (i==len(tdata)-1)
plot(tdata[i], name+'_'+str(i), format=format, path=path, common_scale=common_scale, datarange=datarange, suffix=suffix, new=_new, last=_last, rows=_rows, num=_num)
@plotwrapper
def histogram(data, name, nbins=None, datarange=(None, None), format='png', suffix='', path='./', rows=1,
columns=1, num=1, last=True, fontmap = None, verbose=1):
# Internal histogram specification for handling nested arrays
try:
if fontmap is None: fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}
# Stand-alone plot or subplot?
standalone = rows==1 and columns==1 and num==1
if standalone:
if verbose>0:
print_('Generating histogram of', name)
figure()
subplot(rows, columns, num)
#Specify number of bins (10 as default)
uniquevals = len(unique(data))
nbins = nbins or uniquevals*(uniquevals<=25) or int(4 + 1.5*log(len(data)))
# Generate histogram
hist(data.tolist(), nbins, histtype='stepfilled')
xlim(datarange)
# Plot options
title('\n\n %s hist'%name, x=0., y=1., ha='left', va='top', fontsize='medium')
ylabel("Frequency", fontsize='x-small')
# Plot vertical lines for median and 95% HPD interval
quant = calc_quantiles(data)
axvline(x=quant[50], linewidth=2, color='black')
for q in calc_hpd(data, 0.05):
axvline(x=q, linewidth=2, color='grey', linestyle='dotted')
# Smaller tick labels
tlabels = gca().get_xticklabels()
setp(tlabels, 'fontsize', fontmap[rows])
tlabels = gca().get_yticklabels()
setp(tlabels, 'fontsize', fontmap[rows])
if standalone:
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
# Save to file
savefig("%s%s%s.%s" % (path, name, suffix, format))
#close()
except OverflowError:
print_('... cannot generate histogram')
@plotwrapper
def trace(data, name, format='png', datarange=(None, None), suffix='', path='./', rows=1, columns=1,
num=1, last=True, fontmap = None, verbose=1):
# Internal plotting specification for handling nested arrays
if fontmap is None: fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}
# Stand-alone plot or subplot?
standalone = rows==1 and columns==1 and num==1
if standalone:
if verbose>0:
print_('Plotting', name)
figure()
subplot(rows, columns, num)
pyplot(data.tolist())
ylim(datarange)
# Plot options
title('\n\n %s trace'%name, x=0., y=1., ha='left', va='top', fontsize='small')
# Smaller tick labels
tlabels = gca().get_xticklabels()
setp(tlabels, 'fontsize', fontmap[rows/2])
tlabels = gca().get_yticklabels()
setp(tlabels, 'fontsize', fontmap[rows/2])
if standalone:
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
# Save to file
savefig("%s%s%s.%s" % (path, name, suffix, format))
#close()
@plotwrapper
def geweke_plot(data, name, format='png', suffix='-diagnostic', path='./', fontmap = None,
verbose=1):
# Generate Geweke (1992) diagnostic plots
if fontmap is None: fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}
# Generate new scatter plot
figure()
x, y = transpose(data)
scatter(x.tolist(), y.tolist())
# Plot options
xlabel('First iteration', fontsize='x-small')
ylabel('Z-score for %s' % name, fontsize='x-small')
# Plot lines at +/- 2 sd from zero
pyplot((nmin(x), nmax(x)), (2, 2), '--')
pyplot((nmin(x), nmax(x)), (-2, -2), '--')
# Set plot bound
ylim(min(-2.5, nmin(y)), max(2.5, nmax(y)))
xlim(0, nmax(x))
# Save to file
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
savefig("%s%s%s.%s" % (path, name, suffix, format))
#close()
@plotwrapper
def discrepancy_plot(data, name='discrepancy', report_p=True, format='png', suffix='-gof', path='./',
fontmap = None, verbose=1):
# Generate goodness-of-fit deviate scatter plot
if verbose>0:
print_('Plotting', name+suffix)
if fontmap is None: fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}
# Generate new scatter plot
figure()
try:
x, y = transpose(data)
except ValueError:
x, y = data
scatter(x, y)
# Plot x=y line
lo = nmin(ravel(data))
hi = nmax(ravel(data))
datarange = hi-lo
lo -= 0.1*datarange
hi += 0.1*datarange
pyplot((lo, hi), (lo, hi))
# Plot options
xlabel('Observed deviates', fontsize='x-small')
ylabel('Simulated deviates', fontsize='x-small')
if report_p:
# Put p-value in legend
count = sum(s>o for o,s in zip(x,y))
text(lo+0.1*datarange, hi-0.1*datarange,
'p=%.3f' % (count/len(x)), horizontalalignment='center',
fontsize=10)
# Save to file
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
savefig("%s%s%s.%s" % (path, name, suffix, format))
#close()
def gof_plot(simdata, trueval, name=None, nbins=None, format='png', suffix='-gof', path='./',
fontmap = None, verbose=1):
"""Plots histogram of replicated data, indicating the location of the observed data"""
if fontmap is None: fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}
try:
if ndim(simdata)==1:
simdata = simdata.trace()
except ValueError:
pass
if ndim(trueval)==1 and ndim(simdata==2):
# Iterate over more than one set of data
for i in range(len(trueval)):
n = name or 'MCMC'
gof_plot(simdata[:,i], trueval[i], '%s[%i]' % (n, i), nbins=nbins, format=format, suffix=suffix, path=path, fontmap=fontmap)
return
if verbose>0:
print_('Plotting', (name or 'MCMC') + suffix)
figure()
#Specify number of bins (10 as default)
uniquevals = len(unique(simdata))
nbins = nbins or uniquevals*(uniquevals<=25) or int(4 + 1.5*log(len(simdata)))
# Generate histogram
hist(simdata, nbins)
# Plot options
xlabel(name or 'Value', fontsize='x-small')
ylabel("Frequency", fontsize='x-small')
# Smaller tick labels
tlabels = gca().get_xticklabels()
setp(tlabels, 'fontsize', fontmap[1])
tlabels = gca().get_yticklabels()
setp(tlabels, 'fontsize', fontmap[1])
# Plot vertical line at location of true data value
axvline(x=trueval, linewidth=2, color='r', linestyle='dotted')
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
# Save to file
savefig("%s%s%s.%s" % (path, name or 'MCMC', suffix, format))
#close()
@plotwrapper
def autocorrelation(data, name, maxlags=100, format='png', suffix='-acf', path='./',
fontmap = None, new=True, last=True, rows=1, columns=1, num=1, verbose=1):
"""
Generate bar plot of the autocorrelation function for a series (usually an MCMC trace).
:Arguments:
data: PyMC object, trace or array
A trace from an MCMC sample or a PyMC object with one or more traces.
name: string
The name of the object.
maxlags (optional): int
The largest discrete value for the autocorrelation to be calculated (defaults to 100).
format (optional): string
Graphic output format (defaults to png).
suffix (optional): string
Filename suffix.
path (optional): string
Specifies location for saving plots (defaults to local directory).
fontmap (optional): dict
Font mapping for plot labels; most users should not specify this.
verbose (optional): int
Level of output verbosity.
"""
# Internal plotting specification for handling nested arrays
if fontmap is None: fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}
# Stand-alone plot or subplot?
standalone = rows==1 and columns==1 and num==1
if standalone:
if verbose>0:
print_('Plotting', name)
figure()
subplot(rows, columns, num)
if ndim(data) == 1:
maxlags = min(len(data)-1, maxlags)
try:
acorr(data, detrend=mlab.detrend_mean, maxlags=maxlags)
except:
print_('Cannot plot autocorrelation for %s' % name)
return
# Set axis bounds
ylim(-.1, 1.1)
xlim(-maxlags, maxlags)
# Plot options
title('\n\n %s acorr'%name, x=0., y=1., ha='left', va='top', fontsize='small')
# Smaller tick labels
tlabels = gca().get_xticklabels()
setp(tlabels, 'fontsize', fontmap[1])
tlabels = gca().get_yticklabels()
setp(tlabels, 'fontsize', fontmap[1])
elif ndim(data) == 2:
# generate acorr plot for each dimension
rows = data.shape[1]
for j in range(rows):
autocorrelation(data[:, j], '%s_%d' % (name, j), maxlags, fontmap=fontmap, rows=rows, columns=1, num=j+1)
else:
raise ValueError('Only 1- and 2- dimensional functions can be displayed')
if standalone:
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
# Save to fiel
savefig("%s%s%s.%s" % (path, name, suffix, format))
#close()
def zplot(pvalue_dict, name='', format='png', path='./', fontmap = None, verbose=1):
"""Plots absolute values of z-scores for model validation output from
diagnostics.validate()."""
if verbose:
print_('\nGenerating model validation plot')
if fontmap is None: fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}
x,y,labels = [],[],[]
for i,var in enumerate(pvalue_dict):
# Get p-values
pvals = pvalue_dict[var]
# Take absolute values of inverse-standard normals
zvals = abs(special.ndtri(pvals))
x = append(x, zvals)
y = append(y, ones(size(zvals))*(i+1))
vname = var
vname += " (%i)" % size(zvals)
labels = append(labels, vname)
# Spawn new figure
figure()
subplot(111)
subplots_adjust(left=0.25, bottom=0.1)
# Plot scores
pyplot(x, y, 'o')
# Set range on axes
ylim(0, size(pvalue_dict)+2)
xlim(xmin=0)
# Tick labels for y-axis
yticks(arange(len(labels)+2), append(append("", labels), ""))
# X label
xlabel("Absolute z transformation of p-values")
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
if name:
name += '-'
savefig("%s%svalidation.%s" % (path, name, format))
def var_str(name, shape):
"""Return a sequence of strings naming the element of the tallyable object.
:Example:
>>> var_str('theta', (4,))
['theta[1]', 'theta[2]', 'theta[3]', 'theta[4]']
"""
size = prod(shape)
ind = (indices(shape) + 1).reshape(-1, size)
names = ['['+','.join(map(str, i))+']' for i in zip(*ind)]
# if len(name)>12:
# name = '\n'.join(name.split('_'))
# name += '\n'
names[0] = '%s %s' % (name, names[0])
return names
def summary_plot(pymc_obj, name='model', format='png', suffix='-summary', path='./',
alpha=0.05, quartiles=True, hpd=True, rhat=True, main=None, xlab=None, x_range=None,
custom_labels=None, chain_spacing=0.05, vline_pos=0):
"""
Model summary plot
Generates a "forest plot" of 100*(1-alpha)% credible intervals for either the
set of nodes in a given model, or a specified set of nodes.
:Arguments:
pymc_obj: PyMC object, trace or array
A trace from an MCMC sample or a PyMC object with one or more traces.
name (optional): string
The name of the object.
format (optional): string
Graphic output format (defaults to png).
suffix (optional): string
Filename suffix.
path (optional): string
Specifies location for saving plots (defaults to local directory).
alpha (optional): float
Alpha value for (1-alpha)*100% credible intervals (defaults to 0.05).
quartiles (optional): bool
Flag for plotting the interquartile range, in addition to the
(1-alpha)*100% intervals (defaults to True).
hpd (optional): bool
Flag for plotting the highest probability density (HPD) interval
instead of the central (1-alpha)*100% interval (defaults to True).
rhat (optional): bool
Flag for plotting Gelman-Rubin statistics. Requires 2 or more
chains (defaults to True).
main (optional): string
Title for main plot. Passing False results in titles being
suppressed; passing False (default) results in default titles.
xlab (optional): string
Label for x-axis. Defaults to no label
x_range (optional): list or tuple
Range for x-axis. Defaults to matplotlib's best guess.
custom_labels (optional): list
User-defined labels for each node. If not provided, the node
__name__ attributes are used.
chain_spacing (optional): float
Plot spacing between chains (defaults to 0.05).
vline_pos (optional): numeric
Location of vertical reference line (defaults to 0).
"""
if not gridspec:
print_('\nYour installation of matplotlib is not recent enough to support summary_plot; this function is disabled until matplotlib is updated.')
return
# Quantiles to be calculated
quantiles = [100*alpha/2, 50, 100*(1-alpha/2)]
if quartiles:
quantiles = [100*alpha/2, 25, 50, 75, 100*(1-alpha/2)]
# Range for x-axis
plotrange = None
# Number of chains
chains = None
# Gridspec
gs = None
# Subplots
interval_plot = None
rhat_plot = None
try:
# First try Model type
vars = pymc_obj._variables_to_tally
except AttributeError:
try:
# Try a database object
vars = pymc_obj._traces
except AttributeError:
# Assume an iterable
vars = pymc_obj
# Empty list for y-axis labels
labels = []
# Counter for current variable
var = 1
# Make sure there is something to print
if all([v._plot==False for v in vars]):
print_('No variables to plot')
return
for variable in vars:
# If plot flag is off, do not print
if variable._plot==False:
continue
# Extract name
varname = variable.__name__
# Retrieve trace(s)
i = 0
traces = []
while True:
try:
#traces.append(pymc_obj.trace(varname, chain=i)[:])
traces.append(variable.trace(chain=i))
i+=1
except (KeyError, IndexError):
break
chains = len(traces)
if gs is None:
# Initialize plot
if rhat and chains>1:
gs = gridspec.GridSpec(1, 2, width_ratios=[3,1])
else:
gs = gridspec.GridSpec(1, 1)
# Subplot for confidence intervals
interval_plot = subplot(gs[0])
# Get quantiles
data = [calc_quantiles(d, quantiles) for d in traces]
if hpd:
# Substitute HPD interval
for i,d in enumerate(traces):
hpd_interval = calc_hpd(d, alpha).T
data[i][quantiles[0]] = hpd_interval[0]
data[i][quantiles[-1]] = hpd_interval[1]
data = [[d[q] for q in quantiles] for d in data]
# Ensure x-axis contains range of current interval
if plotrange:
plotrange = [min(plotrange[0], nmin(data)), max(plotrange[1], nmax(data))]
else:
plotrange = [nmin(data), nmax(data)]
try:
# First try missing-value stochastic
value = variable.get_stoch_value()
except AttributeError:
# All other variable types
value = variable.value
# Number of elements in current variable
k = size(value)
# Append variable name(s) to list
if k>1:
names = var_str(varname, shape(value))
labels += names
else:
labels.append(varname)
#labels.append('\n'.join(varname.split('_')))
# Add spacing for each chain, if more than one
e = [0] + [(chain_spacing * ((i+2)/2))*(-1)**i for i in range(chains-1)]
# Loop over chains
for j,quants in enumerate(data):
# Deal with multivariate nodes
if k>1:
for i,q in enumerate(transpose(quants)):
# Y coordinate with jitter
y = -(var+i) + e[j]
if quartiles:
# Plot median
pyplot(q[2], y, 'bo', markersize=4)
# Plot quartile interval
errorbar(x=(q[1],q[3]), y=(y,y), linewidth=2, color="blue")
else:
# Plot median
pyplot(q[1], y, 'bo', markersize=4)
# Plot outer interval
errorbar(x=(q[0],q[-1]), y=(y,y), linewidth=1, color="blue")
else:
# Y coordinate with jitter
y = -var + e[j]
if quartiles:
# Plot median
pyplot(quants[2], y, 'bo', markersize=4)
# Plot quartile interval
errorbar(x=(quants[1],quants[3]), y=(y,y), linewidth=2, color="blue")
else:
# Plot median
pyplot(quants[1], y, 'bo', markersize=4)
# Plot outer interval
errorbar(x=(quants[0],quants[-1]), y=(y,y), linewidth=1, color="blue")
# Increment index
var += k
if custom_labels is not None:
labels = custom_labels
# Update margins
left_margin = max([len(x) for x in labels])*0.015
gs.update(left=left_margin, right=0.95, top=0.9, bottom=0.05)
# Define range of y-axis
ylim(-var+0.5, -0.5)
datarange = plotrange[1] - plotrange[0]
xlim(plotrange[0] - 0.05*datarange, plotrange[1] + 0.05*datarange)
# Add variable labels
yticks([-(l+1) for l in range(len(labels))], labels)
# Add title
if main is not False:
plot_title = main or str(int((1-alpha)*100)) + "% Credible Intervals"
title(plot_title)
# Add x-axis label
if xlab is not None:
xlabel(xlab)
# Constrain to specified range
if x_range is not None:
xlim(*x_range)
# Remove ticklines on y-axes
for ticks in interval_plot.yaxis.get_major_ticks():
ticks.tick1On = False
ticks.tick2On = False
for loc, spine in six.iteritems(interval_plot.spines):
if loc in ['bottom','top']:
pass
#spine.set_position(('outward',10)) # outward by 10 points
elif loc in ['left','right']:
spine.set_color('none') # don't draw spine
# Reference line
axvline(vline_pos, color='k', linestyle='--')
# Genenerate Gelman-Rubin plot
if rhat and chains>1:
from .diagnostics import gelman_rubin
# If there are multiple chains, calculate R-hat
rhat_plot = subplot(gs[1])
if main is not False:
title("R-hat")
# Set x range
xlim(0.9,2.1)
# X axis labels
xticks((1.0,1.5,2.0), ("1", "1.5", "2+"))
yticks([-(l+1) for l in range(len(labels))], "")
# Calculate diagnostic
try:
R = gelman_rubin(pymc_obj)
except ValueError:
R = {}
for variable in vars:
R[variable.__name__] = gelman_rubin(variable)
i = 1
for variable in vars:
if variable._plot==False:
continue
# Extract name
varname = variable.__name__
try:
value = variable.get_stoch_value()
except AttributeError:
value = variable.value
k = size(value)
if k>1:
pyplot([min(r, 2) for r in R[varname]], [-(j+i) for j in range(k)], 'bo', markersize=4)
else:
pyplot(min(R[varname], 2), -i, 'bo', markersize=4)
i += k
# Define range of y-axis
ylim(-i+0.5, -0.5)
# Remove ticklines on y-axes
for ticks in rhat_plot.yaxis.get_major_ticks():
ticks.tick1On = False
ticks.tick2On = False
for loc, spine in six.iteritems(rhat_plot.spines):
if loc in ['bottom','top']:
pass
#spine.set_position(('outward',10)) # outward by 10 points
elif loc in ['left','right']:
spine.set_color('none') # don't draw spine
savefig("%s%s%s.%s" % (path, name, suffix, format))
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