def plotMeta(self):
fig, ax = plt.subplots(2,
1,
figsize=(self.config.getPrintToFileWidth(),
self.config.getPrintToFileHeight()))
self.event = Event(self.config, self.fig, self.data.getTitle(),
self.plotType, self.debug)
# For saving the pic we use a generic event object
fig.canvas.mpl_connect('key_press_event', self.event.onEvent)
plt.subplots_adjust(wspace=0, hspace=0)
valueSet = self.data.getMetaDataValues().keys()
results = self.data.getResults()
metaColors = self.config.getMetaColors()
self.colors = assignColors2MetaDataValue(self.data.getMetaDataValues(),
metaColors)
allTargetScores = self.data.getTargetScores()
allNonTargetScores = self.data.getNonTargetScores()
self._plot(ax, allTargetScores, allNonTargetScores)
fig.tight_layout()
if self.config.getPrintToFile():
filename = "%s_%s_%s.%s" % (self.printToFilename, self.plotType,
self.plotType, "png")
print("Writing plot to %s" % filename)
plt.savefig(filename, orientation='landscape', papertype='letter')
else:
plt.show()
def __init__(self, thisData, thisCllrWrapper, thisConfig, thisExpName, thisDebug=True):
self.data = thisData
self.cllr = thisCllrWrapper
self.config = thisConfig
self._printToFilename = thisExpName
self._expName = thisExpName
self.debug = thisDebug
Probability.__init__(self, self.data, self.config, self.debug)
self.plotType = 'eer_plot'
self.fig = None
self.event = None
metaDataValues = self.data.getMetaDataValues()
metaColors = self.config.getMetaColors()
self.colors = assignColors2MetaDataValue(metaDataValues, metaColors)
self.eerData = self.computeProbabilities(self.eerFunc)
self.eerValue = {}
self.score = {}
for thisMetaValue in sorted(self.colors.keys()):
for metaValue, PD, PP, X in self.eerData:
if thisMetaValue == metaValue:
try:
self.eerValue[metaValue], self.score[metaValue] = self.computeEer(PD, PP, X)
print("EER: {:.4f} % at score: {:.4f} and meta value: {}".format(
self.eerValue[metaValue] * 100, self.score[metaValue], metaValue)
)
except Exception as e:
print("Problem computing EER for %s: %s" % (thisMetaValue, e))
else:
self.eerValue[metaValue] *= 100
break
def plot(self):
self.fig = plt.figure(figsize=(self.config.getPrintToFileWidth(), self.config.getPrintToFileHeight()))
self.event = Event(self.config, self.fig, self.data.getTitle(), self.plotType, self.debug)
# For saving the pic we use a generic event object
self.fig.canvas.mpl_connect('key_press_event', self.event.onEvent)
axes = self.fig.add_subplot(111)
metaDataValues = self.data.getMetaDataValues()
metaColors = self.config.getMetaColors()
colors = assignColors2MetaDataValue(metaDataValues, metaColors)
lt = LegendText(self.data, self._cllrObject, colors, self.config, self.config.getShowCllrInDet(),
self.config.getShowMinCllrInDet(), self.config.getShowEerInDet(),
self.config.getShowCountsInDet(),
self._eerObject.eerValue, self.debug)
legendText = lt.make()
eerData = self.computeProbabilities(self.tippetFunc)
for (metaValue, PD, PP, X) in eerData:
pFr, = axes.plot(X, PP, 's-', label="P(pros): %s" % lt.half(legendText[metaValue])[0], color=colors[metaValue])
pFa, = axes.plot(X, PD, 'o-', label="P(def): %s" % lt.half(legendText[metaValue])[1], color=colors[metaValue])
plt.legend()
axes.set_title("Tippett Plot: P(defense) and P(prosecution) for '%s'" % self.data.getTitle())
plt.xlabel('score')
plt.ylabel('Probability (cumulative distribution function)')
plt.grid()
if self.config.getPrintToFile():
filename = "%s_%s.%s" % (self._printToFilename, self.plotType, "png")
print("Writing plot to %s" % filename)
plt.savefig(filename, orientation='landscape', papertype='letter')
else:
plt.show()
def plot(self):
"""
Cumulative Ranking Plot
"""
self.fig = plt.figure(figsize=(self.config.getPrintToFileWidth(),
self.config.getPrintToFileHeight()))
self.plotType = "ranking_plot"
self.event = Event(self.config, self.fig, self.title, self.plotType,
self.debug)
self.fig.canvas.mpl_connect('key_press_event', self.event.onEvent)
axes = self.fig.add_subplot(111)
metaColors = self.config.getMetaColors()
metaDataValues = self.data.getMetaDataValues()
self.colors = assignColors2MetaDataValue(metaDataValues, metaColors)
self.nrColors = len(self.colors.keys())
xlab = "Rank "
for metaValue in metaDataValues:
ranking, maxRank = self.computeRanking(metaValue)
nr = len(ranking)
x = []
y = []
increment = 1
# range of the scores in 100 steps
for thisRank in range(1, maxRank, increment):
y.append(
float(self.getNrLabels(ranking, thisRank)) / float(nr) *
100.0)
x.append(thisRank)
axes.plot(x,
y,
"o-",
color=self.colors[metaValue],
label=metaValue)
# prepare x-label.
axes.set_xlim(0, maxRank * 1.05)
axes.set_ylim(0, 100)
axes.set_title("Ranking plot for '%s'" % self.title)
xlab += metaValue + ": " + self._mkPercentString(y) + "\n"
#print(metaValue)
# Add labels to the plot.
plt.ylabel('Probability')
# Show the x-label without the last /n.
plt.xlabel(xlab[:-1])
# Add a legend to the plot.
axes.legend(loc=5)
# Add a grid to the plot.
plt.grid()
# If so desired, print the plot to a file.
if self.config.getPrintToFile():
filename = "%s_%s.%s" % (self._printToFilename, self.plotType,
"png")
print("Writing plot to %s" % filename)
plt.savefig(filename, orientation='landscape', papertype='letter')
else:
# Finally: show the plot.
plt.show()
def plot(self):
self.fig = plt.figure(figsize=(self.config.getPrintToFileWidth(),
self.config.getPrintToFileHeight()))
self.event = Event(self.config, self.fig, self.data.getTitle(),
self.plotType, self.debug)
# For saving the pic we use a generic event object
self.fig.canvas.mpl_connect('key_press_event', self.event.onEvent)
axes = self.fig.add_subplot(111)
metaDataValues = self.data.getMetaDataValues()
metaColors = self.config.getMetaColors()
colors = assignColors2MetaDataValue(metaDataValues, metaColors)
lt = LegendText(self.data, self._cllrObject, colors, self.config,
self.config.getShowCllrInDet(),
self.config.getShowMinCllrInDet(),
self.config.getShowEerInDet(),
self.config.getShowCountsInDet(),
self._eerObject.eerValue, self._eerObject.eerScore,
self.debug)
legendText = lt.make()
eerData = self.computeProbabilities(self.tippetFunc)
for (metaValue, PD, PP, X) in eerData:
pFr, = axes.plot(X,
PP,
's-',
label="P(pros): %s" %
lt.half(legendText[metaValue])[0],
color=colors[metaValue])
pFa, = axes.plot(X,
PD,
'o-',
label="P(def): %s" %
lt.half(legendText[metaValue])[1],
color=colors[metaValue])
plt.legend()
axes.set_title("Tippett Plot: P(defense) and P(prosecution) for '%s'" %
self.data.getTitle())
plt.xlabel('score')
plt.ylabel('Probability (cumulative distribution function)')
plt.grid()
if self.config.getPrintToFile():
filename = "%s_%s.%s" % (self._printToFilename, self.plotType,
"png")
print("Writing plot to %s" % filename)
plt.savefig(filename, orientation='landscape', papertype='letter')
else:
plt.show()
def plot(self):
self.fig = plt.figure(figsize=(self.config.getPrintToFileWidth(),
self.config.getPrintToFileHeight()))
self.event = Event(self.config, self.fig, self.data.getTitle(),
self.plotType, self.debug)
# For saving the pic we use a generic event object
self.fig.canvas.mpl_connect('key_press_event', self.event.onEvent)
axes = self.fig.add_subplot(111)
metaDataValues = self.data.getMetaDataValues()
metaColors = self.config.getMetaColors()
colors = assignColors2MetaDataValue(metaDataValues, metaColors)
lt = LegendText(self.data, self._cllrObject, colors, self.config,
self.config.getShowCllrInDet(),
self.config.getShowMinCllrInDet(),
self.config.getShowEerInDet(),
self.config.getShowCountsInDet(),
self._eerObject.eerValue, self._eerObject.eerScore,
self.debug)
legendText = lt.make()
for metaValue in metaDataValues:
targetScores = self.data.getTargetScores4MetaValue(metaValue)
nonTargetScores = self.data.getNonTargetScores4MetaValue(metaValue)
allScores = targetScores + nonTargetScores
truthValues = np.ones(len(allScores))
truthValues[0:len(targetScores)] = 1
truthValues[len(targetScores):] = 0
fpr, tpr, thresholds = metrics.roc_curve(truthValues,
allScores,
pos_label=1)
bla, = axes.plot(fpr,
tpr,
'x-',
label=legendText[metaValue],
color=colors[metaValue])
#axes.set_title("Receiver Operating Characteristic for '%s'" % self.data.getTitle())
axes.set_title("ROC plot for '%s'" % self.data.getTitle())
plt.xlabel('P(false positive)')
plt.ylabel('P(true positive)')
plt.grid()
plt.legend(loc=5) # Position legend at center right.
if self.config.getPrintToFile():
filename = "%s_%s.%s" % (self._printToFilename, self.plotType,
"png")
print("Writing plot to %s" % filename)
plt.savefig(filename, orientation='landscape', papertype='letter')
else:
plt.show()
def plot(self):
"""
Cumulative Ranking Plot
"""
self.fig = plt.figure(figsize=(self.config.getPrintToFileWidth(), self.config.getPrintToFileHeight()))
self.plotType = "ranking_plot"
self.event = Event(self.config, self.fig, self.title, self.plotType, self.debug)
self.fig.canvas.mpl_connect('key_press_event', self.event.onEvent)
axes = self.fig.add_subplot(111)
metaColors = self.config.getMetaColors()
metaDataValues = self.data.getMetaDataValues()
self.colors = assignColors2MetaDataValue(metaDataValues, metaColors)
self.nrColors = len(self.colors.keys())
xlab = "Rank "
for metaValue in metaDataValues:
ranking, maxRank = self.computeRanking(metaValue)
nr = len(ranking)
x = []
y = []
increment = 1
# range of the scores in 100 steps
for thisRank in range(1, maxRank, increment):
y.append(float(self.getNrLabels(ranking, thisRank)) / float(nr) * 100.0)
x.append(thisRank)
axes.plot(x, y, "o-", color=self.colors[metaValue], label=metaValue)
# prepare x-label.
axes.set_xlim(0, maxRank * 1.05)
axes.set_ylim(0, 100)
axes.set_title("Ranking plot for '%s'" % self.title)
xlab += metaValue + ": " + self._mkPercentString(y) + "\n"
#print(metaValue)
# Add labels to the plot.
plt.ylabel('Probability')
# Show the x-label without the last /n.
plt.xlabel(xlab[:-1])
# Add a legend to the plot.
axes.legend(loc=5)
# Add a grid to the plot.
plt.grid()
# If so desired, print the plot to a file.
if self.config.getPrintToFile():
filename = "%s_%s.%s" % (self._printToFilename, self.plotType, "png")
print("Writing plot to %s" % filename)
plt.savefig(filename, orientation='landscape', papertype='letter')
else:
# Finally: show the plot.
plt.show()
def __init__(self,
thisData,
thisCllrWrapper,
thisConfig,
thisExpName,
thisDebug=True):
self.data = thisData
self.cllr = thisCllrWrapper
self.config = thisConfig
self._printToFilename = thisExpName
self._expName = thisExpName
self.debug = thisDebug
Probability.__init__(self, self.data, self.config, self.debug)
self.plotType = 'eer_plot'
self.fig = None
self.event = None
metaDataValues = self.data.getMetaDataValues()
metaColors = self.config.getMetaColors()
self.colors = assignColors2MetaDataValue(metaDataValues, metaColors)
self.eerData = self.computeProbabilities(self.eerFunc)
self.eerValue = {}
self.eerScore = {}
for thisMetaValue in sorted(self.colors.keys()):
for metaValue, PD, PP, X in self.eerData:
if thisMetaValue == metaValue:
try:
self.eerValue[metaValue], self.eerScore[
metaValue] = self.computeEer(PD, PP, X)
print(
"EER: {:.4f} % at score: {:.4f} and meta value: {}"
.format(self.eerValue[metaValue] * 100,
self.eerScore[metaValue], metaValue))
except Exception as e:
print("Problem computing EER for %s: %s" %
(thisMetaValue, e))
else:
self.eerValue[metaValue] *= 100
break
def plot(self):
self.fig = plt.figure(figsize=(self.config.getPrintToFileWidth(), self.config.getPrintToFileHeight()))
self.event = Event(self.config, self.fig, self.data.getTitle(), self.plotType, self.debug)
# For saving the pic we use a generic event object
self.fig.canvas.mpl_connect('key_press_event', self.event.onEvent)
axes = self.fig.add_subplot(111)
metaDataValues = self.data.getMetaDataValues()
metaColors = self.config.getMetaColors()
colors = assignColors2MetaDataValue(metaDataValues, metaColors)
lt = LegendText(self.data, self._cllrObject, colors, self.config, self.config.getShowCllrInDet(),
self.config.getShowMinCllrInDet(), self.config.getShowEerInDet(),
self.config.getShowCountsInDet(),
self._eerObject.eerValue, self.debug)
legendText = lt.make()
for metaValue in metaDataValues:
targetScores = self.data.getTargetScores4MetaValue(metaValue)
nonTargetScores = self.data.getNonTargetScores4MetaValue(metaValue)
allScores = targetScores + nonTargetScores
truthValues = np.ones(len(allScores))
truthValues[0:len(targetScores)] = 1
truthValues[len(targetScores):] = 0
fpr, tpr, thresholds = metrics.roc_curve(truthValues, allScores, pos_label=1)
bla, = axes.plot(fpr, tpr, 'x-', label=legendText[metaValue], color=colors[metaValue])
#axes.set_title("Receiver Operating Characteristic for '%s'" % self.data.getTitle())
axes.set_title("ROC plot for '%s'" % self.data.getTitle())
plt.xlabel('P(false positive)')
plt.ylabel('P(true positive)')
plt.grid()
plt.legend(loc=5) # Position legend at center right.
if self.config.getPrintToFile():
filename = "%s_%s.%s" % (self._printToFilename, self.plotType, "png")
print("Writing plot to %s" % filename)
plt.savefig(filename, orientation='landscape', papertype='letter')
else:
plt.show()
def plotHistogramWithMeta(self):
self.plotType = "histogram_plot"
targetScores = self.data.getTargetScoreValues()
nonTargetScores = self.data.getNonTargetScoreValues()
self.fig = plt.figure(figsize=(self.config.getPrintToFileWidth(),
self.config.getPrintToFileHeight()))
self.event = Event(self.config, self.fig, self.title, self.plotType,
self.debug)
self.fig.canvas.mpl_connect('key_press_event', self.event.onEvent)
suggestedNrBins = max(len(targetScores), len(nonTargetScores))
nrBins = self.config.getNrBins(suggestedNrBins)
# Make a normed histogram. It'll be multiplied by 100 later.
if self.type == 'cumulative':
plt.hist(nonTargetScores,
bins=nrBins,
normed=self.config.getNormHist(),
facecolor='red',
alpha=1.0,
histtype='step',
cumulative=-1)
plt.hist(targetScores,
bins=nrBins,
normed=self.config.getNormHist(),
facecolor='green',
alpha=0.7,
histtype='step',
cumulative=True)
plt.title(r"Cumulative histogram for '%s'" % self.title)
else:
if self.config.getShowMetaInHist():
# Split target and non target scores per meta data value
valueSet = self.data.getMetaDataValues().keys()
if self.debug:
print('valueSet:', valueSet)
targetHistData = {}
nonTargetHistData = {}
metaColors = self.config.getMetaColors()
self.colors = assignColors2MetaDataValue(
self.data.getMetaDataValues(), metaColors)
self.nrColors = len(self.colors.keys())
if self.debug:
print('colors:', self.colors)
print('nr colors:', self.nrColors)
for value in valueSet:
targetHistData[value] = []
nonTargetHistData[value] = []
for label in self.data.getTargetLabels():
template = label + self.LABEL_SEPARATOR + value
targetHistData[value] += self.data.getTargetScores(
)[template]
for label in self.data.getNonTargetLabels():
template = label + self.LABEL_SEPARATOR + value
nonTargetHistData[
value] += self.data.getNonTargetScores()[template]
alpha = 1.0
allData = []
allLabels = []
allColors = []
for value in targetHistData:
try:
allColors.append(self.colors[value])
allData.append(targetHistData[value])
allLabels.append(value + ' (target)')
except Exception:
pass
for value in nonTargetHistData:
try:
allColors.append(self.colors[value])
allData.append(nonTargetHistData[value])
allLabels.append(value + ' (non target)')
except Exception:
pass
if self.debug:
print('allColors:', allColors)
try:
if len(self.colors) > 1:
# If we pair colours, this makes it difficult to distinguish between target and non target
# scores for one label.
# plt.hist(allData, bins=nrBins, normed=self.config.getNormHist(), alpha=alpha, label=allLabels, color=allColors)
plt.hist(allData,
bins=nrBins,
normed=self.config.getNormHist(),
alpha=alpha,
label=allLabels)
else:
plt.hist(allData,
bins=nrBins,
normed=self.config.getNormHist(),
alpha=alpha,
label=allLabels)
except Exception:
print("Error: could not plot histogram!")
print("len(allData): %d\nnrBins: %d" %
(len(allData), self.config.getNrBins()))
print("allLabels: %s" % allLabels)
pass
else:
plt.title("Histogram for '%s'" % self.title)
plt.legend()
else:
plt.hist(nonTargetScores,
bins=nrBins,
normed=self.config.getNormHist(),
facecolor='red',
alpha=1.0)
plt.hist(targetScores,
bins=nrBins,
normed=self.config.getNormHist(),
facecolor='green',
alpha=0.7)
plt.title(r"Histogram for '%s'" % self.title)
plt.grid(True)
plt.xlabel('Target and Non Target Scores')
if self.config.getPrintToFile():
filename = "%s_%s_%s.%s" % (self._printToFilename, self.plotType,
self.type, "png")
print("Writing plot to %s" % filename)
plt.savefig(filename, orientation='landscape', papertype='letter')
else:
plt.show()
def plot(self):
self.fig = plt.figure(figsize=(self.config.getPrintToFileWidth(), self.config.getPrintToFileHeight()))
self.event = Event(self.config, self.fig, self._expName, self.plotType, self.debug)
# For saving the pic we use a generic event object
self.fig.canvas.mpl_connect('key_press_event', self.event.onEvent)
axes = self.fig.add_subplot(111)
metaDataValues = self.data.getMetaDataValues()
metaColors = self.config.getMetaColors()
colors = assignColors2MetaDataValue(metaDataValues, metaColors)
points = 100
title = 'DET plot'
figure = plt.gcf()
figure.set_figheight(figure.get_figheight() * 1.3)
desiredTicks = ["0.00001", "0.00002", "0.00005", "0.0001", "0.0002", "0.0005", "0.001", "0.002", "0.005",
"0.01", "0.02", "0.05", "0.1", "0.2", "0.4", "0.6", "0.8", "0.9", "0.95", "0.98", "0.99",
"0.995",
"0.998", "0.999", "0.9995", "0.9998", "0.9999", "0.99995", "0.99998", "0.99999"]
desiredLabels = self.config.getAllowedRates()
# Available styles: please note that we plot up to the number of styles
# available. So, for coloured plots, we can go up to 6 lines in a single
# plot. For gray scaled ones, up to 12. If you need more plots just extend the
# list bellow.
limits = ('0.1', str(self.config.getMaxFalseRejectionRate()), '0.1', str(self.config.getMaxFalseAcceptRate()))
# Check limits.
for k in limits:
if k not in desiredLabels:
raise (SyntaxError, 'Unsupported limit %s. Please use one of %s' % (k, desiredLabels))
lt = LegendText(self.data, self._cllrObject, colors, self.config, self.config.getShowCllrInDet(),
self.config.getShowMinCllrInDet(), self.config.getShowEerInDet(),
self.config.getShowCountsInDet(),
self._eerObject.eerValue, self.debug)
legendText = lt.make()
for metaValue in metaDataValues:
negatives = [np.array([k for k in self.data.getNonTargetScores4MetaValue(metaValue)], dtype='float64')]
positives = [np.array([k for k in self.data.getTargetScores4MetaValue(metaValue)], dtype='float64')]
for neg, pos in zip(negatives, positives):
ppfar, ppfrr = self._evalDET(neg, pos, points)
plt.plot(ppfar, ppfrr, label=legendText[metaValue], color=colors[metaValue])
fr_minIndex = desiredLabels.index(limits[0])
fr_maxIndex = desiredLabels.index(limits[1])
fa_minIndex = desiredLabels.index(limits[2])
fa_maxIndex = desiredLabels.index(limits[3])
# Convert into DET scale
pticks = [self.__ppndf__(float(v)) for v in desiredTicks]
# Plot diagonal line to facilitate reading the EER-value(s) from the plot.
if self.config.getShowDiagonalInDet():
plt.plot(pticks, pticks, color='red', lw=1.0, linestyle="-.")
ax = plt.gca()
plt.axis([pticks[fa_minIndex], pticks[fa_maxIndex], pticks[fr_minIndex], pticks[fr_maxIndex]])
ax.set_yticks(pticks[fr_minIndex:fr_maxIndex])
ax.set_yticklabels(desiredLabels[fr_minIndex:fr_maxIndex], size='x-small', rotation='horizontal')
ax.set_xticks(pticks[fa_minIndex:fa_maxIndex])
ax.set_xticklabels(desiredLabels[fa_minIndex:fa_maxIndex], size='x-small', rotation='vertical')
if title:
plt.title("DET plot for '" + self.data.getTitle() + "'")
plt.grid(True)
plt.ylabel('False Rejection Rate [%]')
plt.xlabel('False Acceptance Rate [%]')
plt.legend(loc=1)
if self.config.getPrintToFile():
filename = "%s_%s.%s" % (self._printToFilename, self.plotType, "png")
print("Writing plot to %s" % filename)
plt.savefig(filename, orientation='landscape', papertype='letter')
else:
plt.show()
def plot(self):
self.fig = plt.figure(figsize=(self.config.getPrintToFileWidth(),
self.config.getPrintToFileHeight()))
self.event = Event(self.config, self.fig, self._expName, self.plotType,
self.debug)
# For saving the pic we use a generic event object
self.fig.canvas.mpl_connect('key_press_event', self.event.onEvent)
axes = self.fig.add_subplot(111)
metaDataValues = self.data.getMetaDataValues()
metaColors = self.config.getMetaColors()
colors = assignColors2MetaDataValue(metaDataValues, metaColors)
points = 100
title = 'DET plot'
figure = plt.gcf()
figure.set_figheight(figure.get_figheight() * 1.3)
desiredTicks = [
"0.00001", "0.00002", "0.00005", "0.0001", "0.0002", "0.0005",
"0.001", "0.002", "0.005", "0.01", "0.02", "0.05", "0.1", "0.2",
"0.4", "0.6", "0.8", "0.9", "0.95", "0.98", "0.99", "0.995",
"0.998", "0.999", "0.9995", "0.9998", "0.9999", "0.99995",
"0.99998", "0.99999"
]
desiredLabels = self.config.getAllowedRates()
# Available styles: please note that we plot up to the number of styles
# available. So, for coloured plots, we can go up to 6 lines in a single
# plot. For gray scaled ones, up to 12. If you need more plots just extend the
# list bellow.
limits = ('0.1', str(self.config.getMaxFalseRejectionRate()), '0.1',
str(self.config.getMaxFalseAcceptRate()))
# Check limits.
for k in limits:
if k not in desiredLabels:
raise (SyntaxError,
'Unsupported limit %s. Please use one of %s' %
(k, desiredLabels))
lt = LegendText(self.data, self._cllrObject, colors, self.config,
self.config.getShowCllrInDet(),
self.config.getShowMinCllrInDet(),
self.config.getShowEerInDet(),
self.config.getShowCountsInDet(),
self._eerObject.eerValue, self._eerObject.eerScore,
self.debug)
legendText = lt.make()
for metaValue in metaDataValues:
negatives = [
np.array([
k
for k in self.data.getNonTargetScores4MetaValue(metaValue)
],
dtype='float64')
]
positives = [
np.array([
k for k in self.data.getTargetScores4MetaValue(metaValue)
],
dtype='float64')
]
for neg, pos in zip(negatives, positives):
ppfar, ppfrr = self._evalDET(neg, pos, points)
plt.plot(ppfar,
ppfrr,
label=legendText[metaValue],
color=colors[metaValue])
fr_minIndex = desiredLabels.index(limits[0])
fr_maxIndex = desiredLabels.index(limits[1])
fa_minIndex = desiredLabels.index(limits[2])
fa_maxIndex = desiredLabels.index(limits[3])
# Convert into DET scale
pticks = [self.__ppndf__(float(v)) for v in desiredTicks]
# Plot diagonal line to facilitate reading the EER-value(s) from the plot.
if self.config.getShowDiagonalInDet():
plt.plot(pticks, pticks, color='red', lw=1.0, linestyle="-.")
ax = plt.gca()
plt.axis([
pticks[fa_minIndex], pticks[fa_maxIndex], pticks[fr_minIndex],
pticks[fr_maxIndex]
])
ax.set_yticks(pticks[fr_minIndex:fr_maxIndex])
ax.set_yticklabels(desiredLabels[fr_minIndex:fr_maxIndex],
size='x-small',
rotation='horizontal')
ax.set_xticks(pticks[fa_minIndex:fa_maxIndex])
ax.set_xticklabels(desiredLabels[fa_minIndex:fa_maxIndex],
size='x-small',
rotation='vertical')
if title:
plt.title("DET plot for '" + self.data.getTitle() + "'")
plt.grid(True)
plt.ylabel('False Rejection Rate [%]')
plt.xlabel('False Acceptance Rate [%]')
plt.legend(loc=1)
if self.config.getPrintToFile():
filename = "%s_%s.%s" % (self._printToFilename, self.plotType,
"png")
print("Writing plot to %s" % filename)
plt.savefig(filename, orientation='landscape', papertype='letter')
else:
plt.show()