def __init__(self,parent=None, signalManager = None):
OWWidget.__init__(self, parent, signalManager, "Sieve Multigram", wantGraph = True, wantStatusBar = True)
self.inputs = [("Data", ExampleTable, self.setData), ("Features", AttributeList, self.setShownAttributes)]
self.outputs = []
#add a graph widget
self.graph = OWSieveMultigramGraph(self.mainArea)
self.graph.useAntialiasing = 1
self.mainArea.layout().addWidget(self.graph)
#set default settings
self.graphCanvasColor = str(QColor(Qt.white).name())
self.data = None
self.graph.lineWidth = 3
self.graph.minPearson = 2
self.graph.maxPearson = 10
self.showAllAttributes = 0
# add a settings dialog and initialize its values
self.loadSettings()
#GUI
# add a settings dialog and initialize its values
self.tabs = OWGUI.tabWidget(self.controlArea)
self.GeneralTab = OWGUI.createTabPage(self.tabs, "Main")
self.SettingsTab = OWGUI.createTabPage(self.tabs, "Settings")
#add controls to self.controlArea widget
self.createShowHiddenLists(self.GeneralTab, callback = self.updateGraph)
OWGUI.button(self.GeneralTab, self, "Find Interesting Attr.", callback = self.interestingSubsetSelection, debuggingEnabled = 0)
OWGUI.hSlider(self.SettingsTab, self, 'graph.lineWidth', box = 1, label = "Max line width:", minValue=1, maxValue=10, step=1, callback = self.updateGraph)
residualBox = OWGUI.widgetBox(self.SettingsTab, "Attribute independence (Pearson residuals)")
OWGUI.hSlider(residualBox, self, 'graph.maxPearson', label = "Max residual:", minValue=4, maxValue=12, step=1, callback = self.updateGraph)
OWGUI.hSlider(residualBox, self, 'graph.minPearson', label = "Min residual:", minValue=0, maxValue=4, step=1, callback = self.updateGraph, tooltip = "The minimal absolute residual value that will be shown in graph")
self.SettingsTab.layout().addStretch(100)
self.connect(self.graphButton, SIGNAL("clicked()"), self.graph.saveToFile)
self.resize(800, 600)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self,
parent,
signalManager,
"Sieve Multigram",
wantGraph=True,
wantStatusBar=True)
self.inputs = [("Data", ExampleTable, self.setData),
("Features", AttributeList, self.setShownAttributes)]
self.outputs = []
#add a graph widget
self.graph = OWSieveMultigramGraph(self.mainArea)
self.graph.useAntialiasing = 1
self.mainArea.layout().addWidget(self.graph)
#set default settings
self.graphCanvasColor = str(QColor(Qt.white).name())
self.data = None
self.graph.lineWidth = 3
self.graph.minPearson = 2
self.graph.maxPearson = 10
self.showAllAttributes = 0
# add a settings dialog and initialize its values
self.loadSettings()
#GUI
# add a settings dialog and initialize its values
self.tabs = OWGUI.tabWidget(self.controlArea)
self.GeneralTab = OWGUI.createTabPage(self.tabs, "Main")
self.SettingsTab = OWGUI.createTabPage(self.tabs, "Settings")
#add controls to self.controlArea widget
self.createShowHiddenLists(self.GeneralTab, callback=self.updateGraph)
OWGUI.button(self.GeneralTab,
self,
"Find Interesting Attr.",
callback=self.interestingSubsetSelection,
debuggingEnabled=0)
OWGUI.hSlider(self.SettingsTab,
self,
'graph.lineWidth',
box=1,
label="Max line width:",
minValue=1,
maxValue=10,
step=1,
callback=self.updateGraph)
residualBox = OWGUI.widgetBox(
self.SettingsTab, "Attribute independence (Pearson residuals)")
OWGUI.hSlider(residualBox,
self,
'graph.maxPearson',
label="Max residual:",
minValue=4,
maxValue=12,
step=1,
callback=self.updateGraph)
OWGUI.hSlider(
residualBox,
self,
'graph.minPearson',
label="Min residual:",
minValue=0,
maxValue=4,
step=1,
callback=self.updateGraph,
tooltip=
"The minimal absolute residual value that will be shown in graph")
self.SettingsTab.layout().addStretch(100)
self.connect(self.graphButton, SIGNAL("clicked()"),
self.graph.saveToFile)
self.resize(800, 600)
class OWSieveMultigram(OWVisWidget):
settingsList = [
"graph.lineWidth", "graph.maxPearson", "graph.minPearson",
"showAllAttributes"
]
contextHandlers = {
"":
DomainContextHandler("", [
ContextField("shownAttributes",
DomainContextHandler.RequiredList,
selected="selectedShown",
reservoir="hiddenAttributes")
])
}
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self,
parent,
signalManager,
"Sieve Multigram",
wantGraph=True,
wantStatusBar=True)
self.inputs = [("Data", ExampleTable, self.setData),
("Features", AttributeList, self.setShownAttributes)]
self.outputs = []
#add a graph widget
self.graph = OWSieveMultigramGraph(self.mainArea)
self.graph.useAntialiasing = 1
self.mainArea.layout().addWidget(self.graph)
#set default settings
self.graphCanvasColor = str(QColor(Qt.white).name())
self.data = None
self.graph.lineWidth = 3
self.graph.minPearson = 2
self.graph.maxPearson = 10
self.showAllAttributes = 0
# add a settings dialog and initialize its values
self.loadSettings()
#GUI
# add a settings dialog and initialize its values
self.tabs = OWGUI.tabWidget(self.controlArea)
self.GeneralTab = OWGUI.createTabPage(self.tabs, "Main")
self.SettingsTab = OWGUI.createTabPage(self.tabs, "Settings")
#add controls to self.controlArea widget
self.createShowHiddenLists(self.GeneralTab, callback=self.updateGraph)
OWGUI.button(self.GeneralTab,
self,
"Find Interesting Attr.",
callback=self.interestingSubsetSelection,
debuggingEnabled=0)
OWGUI.hSlider(self.SettingsTab,
self,
'graph.lineWidth',
box=1,
label="Max line width:",
minValue=1,
maxValue=10,
step=1,
callback=self.updateGraph)
residualBox = OWGUI.widgetBox(
self.SettingsTab, "Attribute independence (Pearson residuals)")
OWGUI.hSlider(residualBox,
self,
'graph.maxPearson',
label="Max residual:",
minValue=4,
maxValue=12,
step=1,
callback=self.updateGraph)
OWGUI.hSlider(
residualBox,
self,
'graph.minPearson',
label="Min residual:",
minValue=0,
maxValue=4,
step=1,
callback=self.updateGraph,
tooltip=
"The minimal absolute residual value that will be shown in graph")
self.SettingsTab.layout().addStretch(100)
self.connect(self.graphButton, SIGNAL("clicked()"),
self.graph.saveToFile)
self.resize(800, 600)
def sendReport(self):
self.reportImage(self.graph.saveToFileDirect, QSize(500, 500))
# receive new data and update all fields
def setData(self, data):
self.closeContext()
self.information(0)
self.error([0, 1])
self.data = None
if data:
data = orange.Preprocessor_dropMissing(data)
if len(data) == 0:
self.error(0, "No examples without missing values")
data = None
if not self.isDataWithClass(data):
self.error(1, "Data is without class")
data = None
if data and data.domain.hasContinuousAttributes():
data = discretizeDomain(data, 1)
self.information(
0,
"Continuous attributes were discretized using entropy discretization."
)
self.data = data
self.computeProbabilities()
self.setShownAttributeList()
self.openContext("", self.data)
self.resetAttrManipulation()
self.updateGraph()
def sendShownAttributes(self):
pass
def updateGraph(self, *args):
self.graph.updateData(self.data, self.getShownAttributeList(),
self.probabilities)
def interestingSubsetSelection(self):
labels = self.getShownAttributeList()
interestingList = []
data = self.data
# create a list of interesting attributes
for attrXindex in range(len(labels)):
attrXName = labels[attrXindex]
for attrYindex in range(attrXindex + 1, len(labels)):
attrYName = labels[attrYindex]
for valXindex in range(len(data.domain[attrXName].values)):
valX = data.domain[attrXName].values[valXindex]
for valYindex in range(len(data.domain[attrYName].values)):
valY = data.domain[attrYName].values[valYindex]
((nameX, countX), (nameY, countY), actual,
sum) = self.probabilities['%s+%s:%s+%s' %
(attrXName, valX, attrYName,
valY)]
expected = float(countX * countY) / float(sum)
if actual == expected == 0: continue
elif expected == 0: pearson = actual / sqrt(actual)
else: pearson = (actual - expected) / sqrt(expected)
if abs(
pearson
) > self.graph.minPearson and attrXName not in interestingList:
interestingList.append(attrXName)
if abs(
pearson
) > self.graph.minPearson and attrYName not in interestingList:
interestingList.append(attrYName)
# remove attributes that are not in interestingList from visible attribute list
self.setShownAttributeList(interestingList)
self.updateGraph()
def computeProbabilities(self):
self.probabilities = {}
if not self.data: return
self.setStatusBarText("Please wait. Computing...")
total = len(self.data)
conts = {}
dc = []
for i in range(len(self.data.domain)):
dc.append(
orange.ContingencyAttrAttr(self.data.domain[i],
self.data.domain[i], self.data))
for i in range(len(self.data.domain)):
if self.data.domain[i].varType == orange.VarTypes.Continuous:
continue # we can only check discrete attributes
cont = dc[i] # distribution of X attribute
vals = []
# compute contingency of x attribute
for key in cont.keys():
sum = 0
try:
for val in cont[key]:
sum += val
except:
pass
vals.append(sum)
conts[self.data.domain[i].name] = (cont, vals)
for attrX in range(len(self.data.domain)):
if self.data.domain[attrX].varType == orange.VarTypes.Continuous:
continue # we can only check discrete attributes
for attrY in range(attrX, len(self.data.domain)):
if self.data.domain[
attrY].varType == orange.VarTypes.Continuous:
continue # we can only check discrete attributes
(contX, valsX) = conts[self.data.domain[attrX].name]
(contY, valsY) = conts[self.data.domain[attrY].name]
# create cartesian product of selected attributes and compute contingency
(cart, profit) = FeatureByCartesianProduct(
self.data,
[self.data.domain[attrX], self.data.domain[attrY]])
tempData = self.data.select(list(self.data.domain) + [cart])
contXY = orange.ContingencyAttrClass(
cart, tempData) # distribution of X attribute
# compute probabilities
for i in range(len(valsX)):
valx = valsX[i]
for j in range(len(valsY)):
valy = valsY[j]
actualCount = 0
try:
for val in contXY['%s-%s' % (contX.keys()[i],
contY.keys()[j])]:
actualCount += val
except:
pass
self.probabilities['%s+%s:%s+%s' % (
self.data.domain[attrX].name, contX.keys()[i],
self.data.domain[attrY].name, contY.keys()[j])] = (
(contX.keys()[i], valx),
(contY.keys()[j], valy), actualCount, total)
self.probabilities['%s+%s:%s+%s' % (
self.data.domain[attrY].name, contY.keys()[j],
self.data.domain[attrX].name, contX.keys()[i])] = (
(contY.keys()[j], valy),
(contX.keys()[i], valx), actualCount, total)
self.setStatusBarText("")
# receive info about which attributes to show
def setShownAttributes(self, list):
self.setShownAttributeList(list)
self.updateGraph()
class OWSieveMultigram(OWVisWidget):
settingsList = ["graph.lineWidth", "graph.maxPearson", "graph.minPearson", "showAllAttributes"]
contextHandlers = {"": DomainContextHandler("", [ContextField("shownAttributes", DomainContextHandler.RequiredList, selected="selectedShown", reservoir="hiddenAttributes")])}
def __init__(self,parent=None, signalManager = None):
OWWidget.__init__(self, parent, signalManager, "Sieve Multigram", wantGraph = True, wantStatusBar = True)
self.inputs = [("Data", ExampleTable, self.setData), ("Features", AttributeList, self.setShownAttributes)]
self.outputs = []
#add a graph widget
self.graph = OWSieveMultigramGraph(self.mainArea)
self.graph.useAntialiasing = 1
self.mainArea.layout().addWidget(self.graph)
#set default settings
self.graphCanvasColor = str(QColor(Qt.white).name())
self.data = None
self.graph.lineWidth = 3
self.graph.minPearson = 2
self.graph.maxPearson = 10
self.showAllAttributes = 0
# add a settings dialog and initialize its values
self.loadSettings()
#GUI
# add a settings dialog and initialize its values
self.tabs = OWGUI.tabWidget(self.controlArea)
self.GeneralTab = OWGUI.createTabPage(self.tabs, "Main")
self.SettingsTab = OWGUI.createTabPage(self.tabs, "Settings")
#add controls to self.controlArea widget
self.createShowHiddenLists(self.GeneralTab, callback = self.updateGraph)
OWGUI.button(self.GeneralTab, self, "Find Interesting Attr.", callback = self.interestingSubsetSelection, debuggingEnabled = 0)
OWGUI.hSlider(self.SettingsTab, self, 'graph.lineWidth', box = 1, label = "Max line width:", minValue=1, maxValue=10, step=1, callback = self.updateGraph)
residualBox = OWGUI.widgetBox(self.SettingsTab, "Attribute independence (Pearson residuals)")
OWGUI.hSlider(residualBox, self, 'graph.maxPearson', label = "Max residual:", minValue=4, maxValue=12, step=1, callback = self.updateGraph)
OWGUI.hSlider(residualBox, self, 'graph.minPearson', label = "Min residual:", minValue=0, maxValue=4, step=1, callback = self.updateGraph, tooltip = "The minimal absolute residual value that will be shown in graph")
self.SettingsTab.layout().addStretch(100)
self.connect(self.graphButton, SIGNAL("clicked()"), self.graph.saveToFile)
self.resize(800, 600)
def sendReport(self):
self.reportImage(self.graph.saveToFileDirect, QSize(500,500))
# receive new data and update all fields
def setData(self, data):
self.closeContext()
self.information(0)
self.error([0, 1])
self.data = None
if data:
data = orange.Preprocessor_dropMissing(data)
if len(data) == 0:
self.error(0, "No examples without missing values")
data = None
if not self.isDataWithClass(data):
self.error(1, "Data is without class")
data = None
if data and data.domain.hasContinuousAttributes():
data = discretizeDomain(data, 1)
self.information(0, "Continuous attributes were discretized using entropy discretization.")
self.data = data
self.computeProbabilities()
self.setShownAttributeList()
self.openContext("", self.data)
self.resetAttrManipulation()
self.updateGraph()
def sendShownAttributes(self):
pass
def updateGraph(self, *args):
self.graph.updateData(self.data, self.getShownAttributeList(), self.probabilities)
def interestingSubsetSelection(self):
labels = self.getShownAttributeList()
interestingList = []
data = self.data
# create a list of interesting attributes
for attrXindex in range(len(labels)):
attrXName = labels[attrXindex]
for attrYindex in range(attrXindex+1, len(labels)):
attrYName = labels[attrYindex]
for valXindex in range(len(data.domain[attrXName].values)):
valX = data.domain[attrXName].values[valXindex]
for valYindex in range(len(data.domain[attrYName].values)):
valY = data.domain[attrYName].values[valYindex]
((nameX, countX),(nameY, countY), actual, sum) = self.probabilities['%s+%s:%s+%s' %(attrXName, valX, attrYName, valY)]
expected = float(countX*countY)/float(sum)
if actual == expected == 0: continue
elif expected == 0: pearson = actual/sqrt(actual)
else: pearson = (actual - expected) / sqrt(expected)
if abs(pearson) > self.graph.minPearson and attrXName not in interestingList: interestingList.append(attrXName)
if abs(pearson) > self.graph.minPearson and attrYName not in interestingList: interestingList.append(attrYName)
# remove attributes that are not in interestingList from visible attribute list
self.setShownAttributeList(interestingList)
self.updateGraph()
def computeProbabilities(self):
self.probabilities = {}
if not self.data: return
self.setStatusBarText("Please wait. Computing...")
total = len(self.data)
conts = {}
dc = []
for i in range(len(self.data.domain)):
dc.append(orange.ContingencyAttrAttr(self.data.domain[i], self.data.domain[i], self.data))
for i in range(len(self.data.domain)):
if self.data.domain[i].varType == orange.VarTypes.Continuous: continue # we can only check discrete attributes
cont = dc[i] # distribution of X attribute
vals = []
# compute contingency of x attribute
for key in cont.keys():
sum = 0
try:
for val in cont[key]: sum += val
except: pass
vals.append(sum)
conts[self.data.domain[i].name] = (cont, vals)
for attrX in range(len(self.data.domain)):
if self.data.domain[attrX].varType == orange.VarTypes.Continuous: continue # we can only check discrete attributes
for attrY in range(attrX, len(self.data.domain)):
if self.data.domain[attrY].varType == orange.VarTypes.Continuous: continue # we can only check discrete attributes
(contX, valsX) = conts[self.data.domain[attrX].name]
(contY, valsY) = conts[self.data.domain[attrY].name]
# create cartesian product of selected attributes and compute contingency
(cart, profit) = FeatureByCartesianProduct(self.data, [self.data.domain[attrX], self.data.domain[attrY]])
tempData = self.data.select(list(self.data.domain) + [cart])
contXY = orange.ContingencyAttrClass(cart, tempData) # distribution of X attribute
# compute probabilities
for i in range(len(valsX)):
valx = valsX[i]
for j in range(len(valsY)):
valy = valsY[j]
actualCount = 0
try:
for val in contXY['%s-%s' %(contX.keys()[i], contY.keys()[j])]: actualCount += val
except: pass
self.probabilities['%s+%s:%s+%s' %(self.data.domain[attrX].name, contX.keys()[i], self.data.domain[attrY].name, contY.keys()[j])] = ((contX.keys()[i], valx), (contY.keys()[j], valy), actualCount, total)
self.probabilities['%s+%s:%s+%s' %(self.data.domain[attrY].name, contY.keys()[j], self.data.domain[attrX].name, contX.keys()[i])] = ((contY.keys()[j], valy), (contX.keys()[i], valx), actualCount, total)
self.setStatusBarText("")
# receive info about which attributes to show
def setShownAttributes(self, list):
self.setShownAttributeList(list)
self.updateGraph()
