def results(self, dres):
self.closeContext()
self.targetClass = None
self.classifiersQLB.clear()
self.removeGraphs()
self.classCombo.clear()
self.dres = dres
if dres and dres.test_type != TEST_TYPE_SINGLE:
self.warning(
0,
"Calibration plot is supported only for single-target prediction problems."
)
return
self.warning(0, None)
self.graphs = []
if self.dres <> None:
self.numberOfClasses = len(self.dres.classValues)
## one graph for each class
for i in range(self.numberOfClasses):
graph = singleClassCalibrationPlotGraph(self.mainArea)
graph.hide()
self.graphs.append(graph)
self.classCombo.addItem(self.dres.classValues[i])
## classifiersQLB
self.classifierColor = []
self.numberOfClassifiers = self.dres.numberOfLearners
if self.numberOfClassifiers > 1:
allCforHSV = self.numberOfClassifiers - 1
else:
allCforHSV = self.numberOfClassifiers
for i in range(self.numberOfClassifiers):
newColor = QColor()
newColor.setHsv(i * 255 / allCforHSV, 255, 255)
self.classifierColor.append(newColor)
self.calcAllClassGraphs()
## update graphics
## classifiersQLB
for i in range(self.numberOfClassifiers):
newColor = self.classifierColor[i]
self.classifiersQLB.addItem(
QListWidgetItem(ColorPixmap(newColor),
self.dres.classifierNames[i]))
self.classifiersQLB.selectAll()
else:
self.numberOfClasses = 0
self.classifierColor = None
self.targetClass = None ## no results, no target
if not self.targetClass:
self.targetClass = 0
self.openContext("", self.dres)
self.target()
def updatellb(self):
self.blockSelectionChanges = 1
self.llb.clear()
colors = ColorPaletteHSV(len(self.learners))
for (i, lt) in enumerate(self.learners):
l = lt[1]
item = QListWidgetItem(ColorPixmap(colors[i]), l.name)
self.llb.addItem(item)
item.setSelected(l.isSelected)
l.color = colors[i]
self.blockSelectionChanges = 0
def results(self, dres):
self.closeContext()
self.targeClass = None
self.classifiersQLB.clear()
self.removeGraphs()
self.classCombo.clear()
self.dres = dres
self.graphs = []
if self.dres != None:
self.numberOfClasses = len(self.dres.classValues)
## one graph for each class
for i in range(self.numberOfClasses):
graph = singleClassCalibrationPlotGraph(self.mainArea)
graph.hide()
self.graphs.append(graph)
self.classCombo.addItem(self.dres.classValues[i])
## classifiersQLB
self.classifierColor = []
self.numberOfClassifiers = self.dres.numberOfLearners
if self.numberOfClassifiers > 1:
allCforHSV = self.numberOfClassifiers - 1
else:
allCforHSV = self.numberOfClassifiers
for i in range(self.numberOfClassifiers):
newColor = QColor()
newColor.setHsv(i * 255 / allCforHSV, 255, 255)
self.classifierColor.append(newColor)
self.calcAllClassGraphs()
## update graphics
## classifiersQLB
for i in range(self.numberOfClassifiers):
newColor = self.classifierColor[i]
self.classifiersQLB.addItem(
QListWidgetItem(ColorPixmap(newColor),
self.dres.classifierNames[i]))
self.classifiersQLB.selectAll()
else:
self.numberOfClasses = 0
self.classifierColor = None
self.targetClass = None ## no results, no target
if not self.targetClass:
self.targetClass = 0
self.openContext("", self.dres)
self.target()
def setOutcomeNames(self, list):
"Sets the outcome target names."
colors = ColorPaletteGenerator()
self.outcomes = [(ColorPixmap(c), l) for c, l in zip(colors, list)]
self.visibleOutcomes = range(len(list))
def test_results(self, dres):
self.FPcostList = []
self.FNcostList = []
self.pvalueList = []
self.closeContext()
if not dres:
self.targetClass = None
self.classCombo.clear()
self.testSetsQLB.clear()
self.classifiersQLB.clear()
self.removeGraphs()
self.openContext("", dres)
return
self.dres = dres
self.classifiersQLB.clear()
self.testSetsQLB.clear()
self.removeGraphs()
self.classCombo.clear()
self.defaultPerfLinePValues = []
if self.dres != None:
## classQLB
self.numberOfClasses = len(self.dres.classValues)
self.graphs = []
for i in range(self.numberOfClasses):
self.FPcostList.append(500)
self.FNcostList.append(500)
graph = singleClassROCgraph(
self.mainArea, "",
"Predicted class: " + self.dres.classValues[i])
self.graphs.append(graph)
self.classCombo.addItem(self.dres.classValues[i])
## classifiersQLB
self.classifierColor = []
self.numberOfClassifiers = self.dres.numberOfLearners
if self.numberOfClassifiers > 1:
allCforHSV = self.numberOfClassifiers - 1
else:
allCforHSV = self.numberOfClassifiers
for i in range(self.numberOfClassifiers):
newColor = QColor()
newColor.setHsv(i * 255 / allCforHSV, 255, 255)
self.classifierColor.append(newColor)
## testSetsQLB
self.dresSplitByIterations = orngStat.splitByIterations(self.dres)
self.numberOfIterations = len(self.dresSplitByIterations)
self.calcAllClassGraphs()
## classifiersQLB
for i in range(self.numberOfClassifiers):
newColor = self.classifierColor[i]
self.classifiersQLB.addItem(
QListWidgetItem(ColorPixmap(newColor),
self.dres.classifierNames[i]))
self.classifiersQLB.selectAll()
## testSetsQLB
self.testSetsQLB.addItems(
[str(i) for i in range(self.numberOfIterations)])
self.testSetsQLB.selectAll()
## calculate default pvalues
reminder = self.maxp
for f in orngStat.classProbabilitiesFromRes(self.dres):
v = int(round(f * self.maxp))
reminder -= v
if reminder < 0:
v = v + reminder
self.defaultPerfLinePValues.append(v)
self.pvalueList.append(v)
self.targetClass = 0 ## select first target
self.target()
else:
self.classifierColor = None
self.openContext("", self.dres)
self.performanceTabCosts.setEnabled(self.AveragingMethod == 'merge')
self.setDefaultPValues()
def results(self, dres):
self.closeContext()
self.FPcostList = []
self.FNcostList = []
self.pvalueList = []
self.classCombo.clear()
self.removeGraphs()
self.testSetsQLB.clear()
self.classifiersQLB.clear()
self.warning([0, 1])
if dres is not None and dres.class_values is None:
self.warning(1, "Lift Curve cannot be used for regression results.")
dres = None
self.dres = dres
if not dres:
self.targetClass = None
self.openContext("", dres)
return
if dres and dres.test_type != TEST_TYPE_SINGLE:
self.warning(0, "Lift curve is supported only for single-target prediction problems.")
return
self.defaultPerfLinePValues = []
if self.dres <> None:
## classQLB
self.numberOfClasses = len(self.dres.classValues)
self.graphs = []
for i in range(self.numberOfClasses):
self.FPcostList.append( 500)
self.FNcostList.append( 500)
graph = singleClassLiftCurveGraph(self.mainArea, "", "Predicted class: " + self.dres.classValues[i])
self.graphs.append( graph )
self.classCombo.addItem(self.dres.classValues[i])
## classifiersQLB
self.classifierColor = []
self.numberOfClassifiers = self.dres.numberOfLearners
if self.numberOfClassifiers > 1:
allCforHSV = self.numberOfClassifiers - 1
else:
allCforHSV = self.numberOfClassifiers
for i in range(self.numberOfClassifiers):
newColor = QColor()
newColor.setHsv(i*255/allCforHSV, 255, 255)
self.classifierColor.append( newColor )
## testSetsQLB
self.dresSplitByIterations = orngStat.splitByIterations(self.dres)
self.numberOfIterations = len(self.dresSplitByIterations)
self.calcAllClassGraphs()
## classifiersQLB
for i in range(self.numberOfClassifiers):
newColor = self.classifierColor[i]
self.classifiersQLB.addItem(QListWidgetItem(ColorPixmap(newColor), self.dres.classifierNames[i]))
self.classifiersQLB.selectAll()
## testSetsQLB
self.testSetsQLB.addItems([str(i) for i in range(self.numberOfIterations)])
self.testSetsQLB.selectAll()
## calculate default pvalues
reminder = self.maxp
for f in orngStat.classProbabilitiesFromRes(self.dres):
v = int(round(f * self.maxp))
reminder -= v
if reminder < 0:
v = v+reminder
self.defaultPerfLinePValues.append(v)
self.pvalueList.append( v)
self.targetClass = 0 # select first class as default target
self.openContext("", self.dres)
# Update target class and selected classifiers from
# context settings
self.target()
self.classifiersSelectionChange()
else:
self.classifierColor = None
self.performanceTabCosts.setEnabled(1)
self.setDefaultPValues()