def plotDocUsageForProposal(docUsageByUID, savefilename=None, **kwargs):
''' Make trace plot of doc usage for each component.
'''
pylab.figure()
L = 0
maxVal = 0
for k, uid in enumerate(docUsageByUID):
ys = np.asarray(docUsageByUID[uid])
xs = np.arange(0, ys.size)
if k < 6: # only a few labels fit well on a legend
pylab.plot(xs, ys, label=uid)
else:
pylab.plot(xs, ys)
L = np.maximum(L, ys.size)
maxVal = np.maximum(maxVal, ys.max())
# Use big chunk of left-hand side of plot for legend display
xlims = np.asarray([-0.75*L, L-0.5])
pylab.xlim(xlims)
pylab.xticks(np.arange(1, L))
pylab.ylim([0, 1.1*maxVal])
pylab.xlabel('num proposal steps')
pylab.ylabel('num docs using each comp')
pylab.legend(loc='upper left', fontsize=12)
pylab.subplots_adjust(left=0.2)
if savefilename is not None:
pylab.savefig(savefilename, pad_inches=0)
pylab.close('all')
def makePlot(muVals=[(0.01,0), (0.1,0), (1,0), (10,0)], doCorrection=1):
pylab.figure()
xgrid = np.linspace(-8, 8, 2000)
pylab.hold('on')
pylab.plot(xgrid, np.zeros_like(xgrid), ':', alpha=0.2)
for mu1, mu2 in muVals:
ygrid = calcBregDiv_Gauss1D(xgrid, mu1, mu2, doCorrection=doCorrection)
print ygrid.min()
pylab.plot(xgrid, ygrid, label='mu1=% 6.2f mu2=% 6.2f' % (mu1, mu2))
pylab.legend(loc='lower right')
pylab.xlim([xgrid.min(), xgrid.max()])
pylab.ylim([xgrid.min(), xgrid.max()])
pylab.xlabel('x')
if doCorrection:
pylab.ylabel('D(x, \mu) + correction')
else:
pylab.ylabel('D(x, \mu)')
def makePlot(muVals=[0.01, 0.1, 1, 10], B=1e-10, nu=2, justMahalTerm=0):
pylab.figure()
xgrid = np.linspace(0, 8, 2000)
pylab.hold('on')
for mu in muVals:
ygrid = calcBregDiv_ZeroMean(xgrid,
mu,
B=B,
nu=nu,
justMahalTerm=justMahalTerm)
pylab.plot(xgrid, ygrid, linewidth=2, label='\mu=%6.2f' % (mu))
pylab.legend(loc='upper right')
pylab.xlim([-0.1, xgrid.max()])
pylab.ylim([-0.1, xgrid.max()])
pylab.xlabel('x')
pylab.ylabel('D(x, \mu)')
pylab.title('B=%s nu=%s' % (str(B), str(nu)))
def plotJobs(jpaths,
legNames,
styles=None,
density=2,
xvar='laps',
yvar='evidence',
loc='upper right',
xmin=None,
xmax=None,
taskids=None,
savefilename=None,
tickfontsize=None,
bbox_to_anchor=None,
**kwargs):
''' Create line plots for provided jobs.
'''
nLines = len(jpaths)
if nLines == 0:
raise ValueError('Empty job list. Nothing to plot.')
nLeg = len(legNames)
for lineID in range(nLines):
if styles is None:
curStyle = dict(colorID=lineID)
else:
curStyle = styles[lineID]
task_kwargs = dict(**kwargs)
task_kwargs.update(curStyle)
plot_all_tasks_for_job(jpaths[lineID],
legNames[lineID],
xvar=xvar,
yvar=yvar,
taskids=taskids,
density=density,
**task_kwargs)
# Y-axis limit determination
# If we have "enough" data about the run beyond two full passes of dataset,
# we zoom in on the region of data beyond lap 2
if xvar == 'laps' and yvar == 'evidence':
xmax = 0
ymin = np.inf
ymin2 = np.inf
ymax = -np.inf
allRunsHaveXBeyond1 = True
for line in pylab.gca().get_lines():
xd = line.get_xdata()
yd = line.get_ydata()
if xd.size < 3:
allRunsHaveXBeyond1 = False
continue
posLap1 = np.searchsorted(xd, 1.0)
posLap2 = np.searchsorted(xd, 2.0)
if posLap1 < xd.size:
ymin = np.minimum(ymin, yd[posLap1])
ymax = np.maximum(ymax, yd[posLap1:].max())
if posLap2 < xd.size:
ymin2 = np.minimum(ymin2, yd[posLap2])
xmax = np.maximum(xmax, xd.max())
if xd.max() <= 1:
allRunsHaveXBeyond1 = False
if allRunsHaveXBeyond1 and xmax > 1.5:
# If all relevant curves extend beyond x=1, only show that part
xmin = 1.0 - 1e-5
else:
xmin = 0
if allRunsHaveXBeyond1 and ymin2 < ymax:
range1 = ymax - ymin
range2 = ymax - ymin2
if 10 * range2 < range1:
# Y values jump from lap1 to lap2 is enormous,
# so let's just show y values from lap2 onward...
ymin = ymin2
if (not np.allclose(ymax, ymin)) and allRunsHaveXBeyond1:
pylab.ylim([ymin, ymax + 0.1 * (ymax - ymin)])
pylab.xlim([xmin, xmax + .05 * (xmax - xmin)])
if loc is not None and len(jpaths) > 1:
pylab.legend(loc=loc, bbox_to_anchor=bbox_to_anchor)
if tickfontsize is not None:
pylab.tick_params(axis='both', which='major', labelsize=tickfontsize)
if savefilename is not None:
try:
pylab.show(block=False)
except TypeError:
pass # when using IPython notebook
pylab.savefig(savefilename, bbox_inches='tight', pad_inches=0)
else:
try:
pylab.show(block=True)
except TypeError:
pass # when using IPython notebook