def plotCompsForJob(jobpath='', taskids=[1], lap=None, **kwargs):
''' Show plot of learned clusters from run(s) saved results on disk
'''
# Verify given absolute path is valid.
jobpath_originalarg = jobpath
if not os.path.isdir(jobpath):
# Fallback: try to prepend BNPYOUTDIR to handle "shortcut" names
jobpath = os.path.join(os.environ['BNPYOUTDIR'], jobpath)
if not os.path.isdir(jobpath):
raise ValueError('Not valid path: ' + jobpath_originalarg)
taskids = BNPYArgParser.parse_task_ids(jobpath, taskids)
for tt, taskid in enumerate(taskids):
if tt == 0 and isinstance(taskid, str):
if taskid.startswith('.'):
rankTasksForSingleJobOnDisk(jobpath)
taskpath = os.path.join(jobpath, str(taskid))
plotCompsForTask(taskpath, lap=lap, **kwargs)
if 'block' in kwargs:
pylab.show(block=kwargs['block'])
def plotSingleJob(
dataset,
jobname,
taskids='1',
lap='final',
sequences=[1],
showELBOInTitle=False,
dispTrue=True,
aspectFactor=4.0,
specialStateIDs=None,
seqNames=None,
cmap='Set1',
maxT=None,
colorManyToOne=False,
):
'''
Returns the array of Data corresponding to a single sequence to display
If dispTrue = True, the true labels will be shown underneath the
estimated labels
'''
# Make sequences zero-indexed
if isinstance(sequences, str):
sequences = np.asarray([int(x) for x in args.sequences.split(',')],
dtype=np.int32)
sequences = np.asarray(sequences, dtype=np.int32)
if np.min(sequences) < 1:
raise ValueError('Sequences need to be one-index.\n' +
'Valid values are 1,2,...N.')
sequences -= 1
# Determine the jobpath and taskids
jobpath = os.path.join(os.path.expandvars('$BNPYOUTDIR'), dataset, jobname)
if isinstance(taskids, str):
if taskids.startswith('.'):
taskids = [taskids]
else:
taskids = BNPYArgParser.parse_task_ids(jobpath, taskids)
elif isinstance(taskids, int):
taskids = [str(taskids)]
datasetPrefFile = os.path.join(jobpath, taskids[0],
'args-DatasetPrefs.txt')
datasetPrefs = dict()
if os.path.exists(datasetPrefFile):
with open(datasetPrefFile, 'r') as f:
for line in f.readlines():
fields = line.strip().split(' ')
if len(fields) != 2:
continue
datasetPrefs[fields[0]] = fields[1]
# Load Data from its python module
Datamod = imp.load_source(
dataset, os.path.expandvars('$BNPYDATADIR/' + dataset + '.py'))
if dataset == 'SpeakerDiar':
if len(sequences) > 1:
raise ValueError(
'Joint modeling of several sequences makes no sense')
Data = Datamod.get_data(meetingNum=sequences[0] + 1, **datasetPrefs)
jobpath = jobpath.replace('SpeakerDiar',
'SpeakerDiar' + str(sequences[0] + 1))
sequences[0] = 0
else:
Data = Datamod.get_data(**datasetPrefs)
# Determine the maximum length among any of the sequences to be plotted
if maxT is None:
Ts = Data.doc_range[sequences + 1] - Data.doc_range[sequences]
maxT = np.max(Ts)
# Define the number of pixels used by vertical space of figure
NUM_STACK = int(np.ceil(maxT / float(aspectFactor)))
if dispTrue:
NUM_STACK /= 2
f, axes = plt.subplots(len(sequences),
len(taskids),
sharex='col',
sharey='row')
# For singleton case, make sure that axes is index-able
if len(sequences) == 1 and len(taskids) == 1:
axes = [axes]
for tt, taskidstr in enumerate(taskids):
if tt == 0 and taskidstr.startswith('.'):
rankTasksForSingleJobOnDisk(jobpath)
path = os.path.join(jobpath, taskidstr) + os.path.sep
# Figure out which lap to use
if lap == 'final':
lapsFile = open(path + 'laps-saved-params.txt')
curLap = lapsFile.readlines()
curLap = float(curLap[-1])
lapsFile.close()
else:
curLap = int(lap)
if showELBOInTitle:
hdists = np.loadtxt(os.path.join(path, 'hamming-distance.txt'))
hlaps = np.loadtxt(os.path.join(path, 'laps-saved-params.txt'))
Keffvals = np.loadtxt(os.path.join(path, 'Keff-saved-params.txt'))
# Determine scalar values to display
loc = np.argmin(np.abs(hlaps - curLap))
hdist = hdists[loc]
Kefffinal = Keffvals[loc]
try:
Kvals = np.loadtxt(os.path.join(path, 'K.txt'))
ELBOscores = np.loadtxt(os.path.join(path, 'evidence.txt'))
laps = np.loadtxt(os.path.join(path, 'laps.txt'))
loc = np.argmin(np.abs(laps - curLap))
ELBO = ELBOscores[loc]
Kfinal = Kvals[loc]
except IOError:
ELBO = 0.0
Kfinal = Kefffinal
# Load in the saved Data from $BNPYOUTDIR
try:
filename = 'Lap%08.3fMAPStateSeqsAligned.mat' % curLap
zHatBySeq = scipy.io.loadmat(path + filename)
key1 = 'zHatBySeqAligned'
key2 = 'zHatBySeq'
if key1 in zHatBySeq:
zHatBySeq = convertStateSeq_MAT2list(zHatBySeq[key1])
elif key2 in zHatBySeq:
zHatBySeq = convertStateSeq_MAT2list(zHatBySeq[key2])
else:
raise IOError
except IOError:
filename = 'Lap%08.3fMAPStateSeqs.mat' % curLap
zHatBySeq = scipy.io.loadmat(path + filename)
zHatBySeq = convertStateSeq_MAT2list(zHatBySeq['zHatBySeq'])
if specialStateIDs is not None:
zHatBySeq = relabelAllSequences(zHatBySeq, specialStateIDs)
# Find maximum number of states we need to display
nSeq = len(zHatBySeq)
Kmax = np.max([zHatBySeq[i].max() for i in xrange(nSeq)])
hasGroundTruth = False
vmin = 0
Kignore = 0
if hasattr(Data, 'TrueParams') and 'Z' in Data.TrueParams:
hasGroundTruth = True
Kmax = np.maximum(Data.TrueParams['Z'].max(), Kmax)
uLabels = np.unique(Data.TrueParams['Z'])
Kignore = np.sum(uLabels < 0)
if Kignore > 0:
for k in range(1, Kignore + 1):
print 'ignoring state %d Ttrue = %d' % (
-k, np.sum(Data.TrueParams['Z'] == -k))
if colorManyToOne:
# For each state in zHat, find best true sequence
Zflat = convertStateSeq_list2flat(zHatBySeq, Data)
ZflatA = -1 * np.ones_like(Zflat)
for uID in np.unique(Zflat):
overlap = np.zeros(uLabels.size)
for ii, trueID in enumerate(uLabels):
overlap[ii] = np.sum(
np.logical_and(Data.TrueParams['Z'] == trueID,
Zflat == uID))
bestii = overlap.argmax()
ZflatA[Zflat == uID] = uLabels[bestii]
zHatBySeq = convertStateSeq_flat2list(ZflatA, Data)
# In case there's only one sequence, make sure it's index-able
for ii, seqNum in enumerate(sequences):
image = np.tile(zHatBySeq[seqNum], (NUM_STACK, 1))
# Add the true labels to the image (if they exist)
if hasGroundTruth and dispTrue:
start = Data.doc_range[seqNum]
stop = Data.doc_range[seqNum + 1]
img_trueZ = np.tile(Data.TrueParams['Z'][start:stop],
(NUM_STACK, 1))
if dispTrue == 2:
image = img_trueZ # Show only true labels
else:
image = np.vstack((image, img_trueZ))
image = image[:, :maxT]
if len(sequences) == 1 or len(taskids) == 1:
cur_ax = axes[ii + tt]
else:
cur_ax = axes[ii, tt]
if hasattr(cmap, 'N'):
vmax = cmap.N
else:
vmax = Kmax
cur_ax.imshow(Kignore + image + .0001,
interpolation='nearest',
vmin=vmin,
vmax=vmax,
cmap=cmap)
if tt == 0:
if seqNames is not None:
h = cur_ax.set_ylabel('%s' % (seqNames[ii]), fontsize=13)
h.set_rotation(0)
elif len(sequences) > 4:
cur_ax.set_ylabel('%d' % (seqNum + 1), fontsize=13)
else:
cur_ax.set_ylabel('Seq. %d' % (seqNum + 1), fontsize=13)
if ii == 0:
if showELBOInTitle:
fmtSpec = "ELBO: %.3f K=%d Keff=%d "
if hdist > 0.01:
fmtSpec += "dist=%.2f"
elif hdist > 0.001:
fmtSpec += "dist=%.3f"
else:
fmtSpec += "dist=%.4f"
title = fmtSpec % (ELBO, Kfinal, Kefffinal, hdist)
cur_ax.set_title(title)
cur_ax.set_xlim([0, maxT])
cur_ax.set_ylim([0, image.shape[0]])
cur_ax.set_yticks([])
# ... end loop over sequences
return axes, zHatBySeq
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])