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 plotELBOtermsForProposal(
curLdict, propLdictList,
xs=None,
ymin=-0.5,
ymax=0.5,
savefilename=None,
**kwargs):
''' Create trace plot of ELBO gain/loss relative to current model.
'''
pylab.figure()
L = len(propLdictList)
if xs is None:
xs = np.arange(0, L)
legendKeys = []
for key in curLdict:
if key.count('_') == 0:
legendKeys.append(key)
for key in legendKeys:
if key.count('total'):
linewidth= 4
alpha = 1
style = '-'
else:
linewidth = 3
alpha = 0.5
style = '--'
ys = np.asarray([propLdictList[i][key] for i in range(L)])
ys -= curLdict[key]
pylab.plot(xs, ys, style,
color=_getLineColorFromELBOKey(key),
linewidth=linewidth,
alpha=alpha,
label=key)
L = L + 1
xlims = np.asarray([-0.75*L, L-0.5])
pylab.xlim(xlims)
pylab.xticks(xs)
pylab.plot(xlims, np.zeros_like(xlims), 'k:')
pylab.xlabel('num proposal steps')
pylab.ylabel('L gain (prop - current)')
pylab.legend(loc='lower left', fontsize=12)
pylab.subplots_adjust(left=0.2)
if savefilename is not None:
pylab.savefig(savefilename, pad_inches=0)
pylab.close('all')
def plotSingleLineAcrossJobsByXVar(jpathPattern,
label='',
xvar=None,
xvals=None,
xlabel=None,
yvar='evidence',
lineStyle='.-',
taskids='all',
lineID=0,
lvar='',
**kwargs):
''' Create line plot in current figure for job matching the pattern
Iterates over each xval in provided list of values.
Each one corresponds to a single saved job.
Post Condition
--------------
Current axes have one line added.
'''
prefixfilepath = os.path.sep.join(jpathPattern.split(os.path.sep)[:-1])
PPListMap = makePPListMapFromJPattern(jpathPattern)
if xvals is None:
xvals = PPListMap[xvar]
xs = np.zeros(len(xvals))
ys = np.zeros(len(xvals))
jpathList = makeListOfJPatternsWithSpecificVals(
PPListMap,
prefixfilepath=prefixfilepath,
key=xvar,
vals=xvals,
**kwargs)
plotargs = copy.deepcopy(DefaultLinePlotKwArgs)
# Plot all tasks as faint points with no connections
for i, jobpath in enumerate(jpathList):
if not os.path.exists(jobpath):
raise ValueError("PATH NOT FOUND: %s" % (jobpath))
x = float(xvals[i])
for key in plotargs:
if key in kwargs:
plotargs[key] = kwargs[key]
plotargs['markeredgecolor'] = plotargs['color']
alltaskids = BNPYArgParser.parse_task_ids(jobpath, taskids)
for tid in alltaskids:
y = loadYValFromDisk(jobpath, tid, yvar=yvar)
pylab.plot(x, y, '.', **plotargs)
# Plot top-ranked tasks as solid points connected by line
for i, jobpath in enumerate(jpathList):
rankTasksForSingleJobOnDisk(os.path.join(jobpath))
x = float(xvals[i])
y = loadYValFromDisk(jobpath, '.best', yvar=yvar)
assert isinstance(x, float)
assert isinstance(y, float)
xs[i] = x
ys[i] = y
plotargs = copy.deepcopy(DefaultLinePlotKwArgs)
for key in plotargs:
if key in kwargs:
plotargs[key] = kwargs[key]
plotargs['markeredgecolor'] = plotargs['color']
plotargs['label'] = label
pylab.plot(xs, ys, lineStyle, **plotargs)
if lineID == 0:
if xlabel is None:
xlabel = xvar
pylab.xlabel(xlabel)
pylab.ylabel(LabelMap[yvar])
def plot_all_tasks_for_job(jobpath, label, taskids=None,
lineType='.-',
spreadLineType='--',
color=None,
yvar='avgLikScore',
xvar='laps',
markersize=10,
linewidth=2,
minLap=0,
showFinalPt=0,
fileSuffix='PredLik.mat',
xjitter=None,
prefix='predlik',
colorID=0,
**kwargs):
''' Create line plot in current figure for each task/run of jobpath
'''
if not os.path.exists(jobpath):
print('PATH NOT FOUND', jobpath)
return None
if not yvar.startswith('avg') and yvar.count('Kactive') == 0:
yvar = 'avg' + yvar
if not yvar.endswith('Score') and yvar.count('Kactive') == 0:
yvar = yvar + 'Score'
if color is None:
color = Colors[colorID % len(Colors)]
taskids = BNPYArgParser.parse_task_ids(jobpath, taskids)
for tt, taskid in enumerate(taskids):
taskoutpath = os.path.join(jobpath, taskid)
hpaths = glob.glob(os.path.join(taskoutpath, '*' + fileSuffix))
txtpaths = glob.glob(os.path.join(taskoutpath, 'predlik-*.txt'))
ys_hi = None
ys_lo = None
if len(txtpaths) > 0:
if fileSuffix.endswith('.txt'):
suffix = '-' + fileSuffix
else:
suffix = '.txt'
if xvar.count('lap'):
xs = np.loadtxt(
os.path.join(taskoutpath, prefix + '-lapTrain.txt'))
elif xvar.count('K'):
xs = np.loadtxt(os.path.join(taskoutpath, prefix + '-K.txt'))
elif xvar.count('time'):
xs = np.loadtxt(os.path.join(
taskoutpath, prefix + '-timeTrain.txt'))
else:
raise ValueError("Unrecognized xvar: " + xvar)
if yvar.count('Kactive') and not yvar.count('Percentile'):
ys = np.loadtxt(os.path.join(taskoutpath,
prefix + '-' + yvar + 'Percentile50.txt'))
ys_lo = np.loadtxt(os.path.join(taskoutpath,
prefix + '-' + yvar + 'Percentile10.txt'))
ys_hi = np.loadtxt(os.path.join(taskoutpath,
prefix + '-' + yvar + 'Percentile90.txt'))
else:
ys = np.loadtxt(
os.path.join(taskoutpath, prefix + '-' + yvar + suffix))
if minLap > 0 and taskoutpath.count('fix'):
mask = laps > minLap
xs = xs[mask]
ys = ys[mask]
elif len(hpaths) > 0:
hpaths.sort()
basenames = [x.split(os.path.sep)[-1] for x in hpaths]
xs = np.asarray([float(x[3:11]) for x in basenames])
ys = np.zeros_like(xs)
for ii, hpath in enumerate(hpaths):
MatVars = scipy.io.loadmat(hpath)
ys[ii] = float(MatVars['avgPredLL'])
else:
raise ValueError(
'Pred Lik data unavailable for job\n' + taskoutpath)
plotargs = dict(markersize=markersize, linewidth=linewidth, label=None,
color=color, markeredgecolor=color,
)
plotargs.update(kwargs)
if tt == 0:
plotargs['label'] = label
if xjitter is not None:
xs = xs + xjitter
pylab.plot(xs, ys, lineType, **plotargs)
if ys_lo is not None:
del plotargs['label']
pylab.plot(xs, ys_lo, spreadLineType, **plotargs)
pylab.plot(xs, ys_hi, spreadLineType, **plotargs)
if showFinalPt:
pylab.plot(xs[-1], ys[-1], '.', **plotargs)
pylab.xlabel(XLabelMap[xvar])
pylab.ylabel(YLabelMap[yvar])
def plot_all_tasks_for_job(jobpath,
label,
taskids=None,
color=None,
colorID=0,
density=2,
yvar='evidence',
markersize=10,
linewidth=2,
linestyle='-',
drawLineToXMax=None,
showOnlyAfterLap=0,
xvar='laps',
**kwargs):
''' Create line plot in current figure for each task/run of jobpath
'''
if not os.path.exists(jobpath):
if not jobpath.startswith(os.path.sep):
jobpath_tmp = os.path.join(os.environ['BNPYOUTDIR'], jobpath)
if not os.path.exists(jobpath_tmp):
raise ValueError("PATH NOT FOUND: %s" % (jobpath))
jobpath = jobpath_tmp
if color is None:
color = Colors[colorID % len(Colors)]
taskids = BNPYArgParser.parse_task_ids(jobpath, taskids)
if yvar == 'hamming-distance':
yspfile = os.path.join(jobpath, taskids[0], yvar + '-saved-params.txt')
if xvar == 'laps' and os.path.isfile(yspfile):
xvar = 'laps-saved-params'
for tt, taskid in enumerate(taskids):
xs = None
ys = None
laps = None
try:
var_ext = ''
ytxtfile = os.path.join(jobpath, taskid, yvar + '.txt')
if not os.path.isfile(ytxtfile):
var_ext = '-saved-params'
ytxtfile = os.path.join(jobpath, taskid,
yvar + var_ext + '.txt')
ys = np.loadtxt(ytxtfile)
if ytxtfile.count('saved-params'):
laptxtfile = os.path.join(jobpath, taskid,
'laps-saved-params.txt')
else:
laptxtfile = os.path.join(jobpath, taskid, 'laps.txt')
except IOError as e:
# TODO: when is this code needed?
# xs, ys = loadXYFromTopicModelFiles(jobpath, taskid)
try:
if isinstance(xs, np.ndarray) and yvar.count('Keff'):
ys = loadKeffForTask(os.path.join(jobpath, taskid),
**kwargs)
assert xs.size == ys.size
else:
# Heldout metrics
xs, ys = loadXYFromTopicModelSummaryFiles(jobpath,
taskid,
xvar=xvar,
yvar=yvar)
if showOnlyAfterLap and showOnlyAfterLap > 0:
laps, _ = loadXYFromTopicModelSummaryFiles(jobpath,
taskid,
xvar='laps',
yvar=yvar)
except ValueError:
try:
xs, ys = loadXYFromTopicModelSummaryFiles(jobpath, taskid)
except ValueError:
raise e
if yvar == 'hamming-distance' or yvar == 'Keff':
if xvar == 'laps-saved-params':
# fix off-by-one error, if we save an extra dist on final lap
if xs.size == ys.size - 1:
ys = ys[:-1]
elif ys.size == xs.size - 1:
xs = xs[:-1] # fix off-by-one error, if we quit early
elif xs.size != ys.size:
# Try to subsample both time series at laps where they
# intersect
laps_x = np.loadtxt(os.path.join(jobpath, taskid, 'laps.txt'))
laps_y = np.loadtxt(
os.path.join(jobpath, taskid, 'laps-saved-params.txt'))
assert xs.size == laps_x.size
if ys.size == laps_y.size - 1:
laps_y = laps_y[:-1]
xs = xs[np.in1d(laps_x, laps_y)]
ys = ys[np.in1d(laps_y, laps_x)]
if xs.size != ys.size:
raise ValueError('Dimension mismatch. len(xs)=%d, len(ys)=%d' %
(xs.size, ys.size))
# Cleanup laps data. Verify that it is sorted, with no collisions.
if xvar == 'laps':
diff = xs[1:] - xs[:-1]
goodIDs = np.flatnonzero(diff >= 0)
if len(goodIDs) < xs.size - 1:
print(
'WARNING: looks like multiple runs writing to this file!')
print(jobpath)
print('Task: ', taskid)
print(len(goodIDs), xs.size - 1)
xs = np.hstack([xs[goodIDs], xs[-1]])
ys = np.hstack([ys[goodIDs], ys[-1]])
if xvar == 'laps' and yvar == 'evidence':
mask = xs >= 1.0
xs = xs[mask]
ys = ys[mask]
elif showOnlyAfterLap:
# print "Filtering for data recorded at lap >= %s" % (
# showOnlyAfterLap)
if laps is None:
laps = np.loadtxt(laptxtfile)
mask = laps >= showOnlyAfterLap
xs = xs[mask]
ys = ys[mask]
# Force plot density (data points per lap) to desired specification
# This avoids making plots that have huge file sizes,
# due to too much content in the given display space
if xvar == 'laps' and xs.size > 20 and np.sum(xs > 5) > 10:
if (xs[-1] - xs[9]) != 0:
curDensity = (xs.size - 10) / (xs[-1] - xs[9])
else:
curDensity = density
while curDensity > density and xs.size > 11:
# Thin xs and ys data by a factor of 2
# while preserving the first 10 data points
xs = np.hstack([xs[:10], xs[10::2]])
ys = np.hstack([ys[:10], ys[10::2]])
curDensity = (xs.size - 10) / (xs[-1] - xs[9])
plotargs = dict(markersize=markersize,
linewidth=linewidth,
linestyle=linestyle,
label=None,
color=color,
markeredgecolor=color)
for key in kwargs:
if key in plotargs:
plotargs[key] = kwargs[key]
if tt == 0:
plotargs['label'] = label
pylab.plot(xs, ys, **plotargs)
if drawLineToXMax:
xs_dashed = np.asarray([xs[-1], drawLineToXMax])
ys_dashed = np.asarray([ys[-1], ys[-1]])
plotargs['label'] = None
pylab.plot(xs_dashed, ys_dashed, '--', **plotargs)
pylab.xlabel(LabelMap[xvar])
if yvar in LabelMap:
yLabelStr = LabelMap[yvar]
if yvar == 'Keff' and 'effCountThr' in kwargs:
effCountThr = float(kwargs['effCountThr'])
yLabelStr = yLabelStr + ' > %s' % (str(effCountThr))
pylab.ylabel(yLabelStr)