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def __getOrangeVariableForFeature(self, feature):
if feature["is_numerical"]:
return orange.FloatVariable(feature["name"])
else:
return orange.EnumVariable(feature["name"])
def createLogRegExampleTable(data, weightID):
setsOfData = []
for at in data.domain.attributes:
# za vsak atribut kreiraj nov newExampleTable newData
# v dataOrig, dataFinal in newData dodaj nov atribut -- continuous variable
if at.varType == orange.VarTypes.Continuous:
atDisc = orange.FloatVariable(at.name + "Disc")
newDomain = orange.Domain(data.domain.attributes+[atDisc,data.domain.classVar])
newDomain.addmetas(data.domain.getmetas())
newData = orange.ExampleTable(newDomain,data)
altData = orange.ExampleTable(newDomain,data)
for i,d in enumerate(newData):
d[atDisc] = 0
d[weightID] = 1*data[i][weightID]
for i,d in enumerate(altData):
d[atDisc] = 1
d[at] = 0
d[weightID] = 0.000001*data[i][weightID]
elif at.varType == orange.VarTypes.Discrete:
# v dataOrig, dataFinal in newData atributu "at" dodaj ee eno vreednost, ki ima vrednost kar ime atributa + "X"
atNew = orange.EnumVariable(at.name, values = at.values + [at.name+"X"])
newDomain = orange.Domain(filter(lambda x: x!=at, data.domain.attributes)+[atNew,data.domain.classVar])
newDomain.addmetas(data.domain.getmetas())
domain = orange.Domain(data.domain.attributes, data.domain.classVar)
domain.addmetas(data.domain.getmetas())
data = orange.ExampleTable(domain, data)
if self.appendPredictions:
cname = self.learnerNames[learnerI]
predVar = type(domain.classVar)("%s(%s)" % (domain.classVar.name, cname.encode("utf-8") if isinstance(cname, unicode) else cname))
if hasattr(domain.classVar, "values"):
predVar.values = domain.classVar.values
predictionsId = orange.newmetaid()
domain.addmeta(predictionsId, predVar)
for i, ex in zip(selectionIndices, data):
ex[predictionsId] = res.results[i].classes[learnerI]
if self.appendProbabilities:
probVars = [orange.FloatVariable("p(%s)" % v) for v in domain.classVar.values]
probIds = [orange.newmetaid() for pv in probVars]
domain.addmetas(dict(zip(probIds, probVars)))
for i, ex in zip(selectionIndices, data):
for id, p in zip(probIds, res.results[i].probabilities[learnerI]):
ex[id] = p
if data is not None:
data.name = self.learnerNames[learnerI]
self.send("Selected Data", data)
def sendList(self, selectedInd):
if self.data and type(self.data[0]) == str:
xAttr=orange.FloatVariable("X")
yAttr=orange.FloatVariable("Y")
nameAttr= orange.StringVariable("name")
if self.selectionOptions == 1:
domain = orange.Domain([xAttr, yAttr, nameAttr])
selection = orange.ExampleTable(domain)
for i in range(len(selectedInd)):
selection.append(list(self.mds.points[selectedInd[i]]) + [self.data[i]])
else:
domain = orange.Domain([nameAttr])
if self.selectionOptions:
domain.addmeta(orange.newmetaid(), xAttr)
domain.addmeta(orange.newmetaid(), yAttr)
selection = orange.ExampleTable(domain)
for i in range(len(selectedInd)):
selection.append([self.data[i]])
if self.selectionOptions:
def applySettings(self):
"""use the setting from the widget, identify the outliers"""
if self.haveInput == 1:
outlier = self.outlier
outlier.setKNN(self.ks[self.k][1])
newdomain = orange.Domain(self.data.domain)
newdomain.addmeta(orange.newmetaid(), orange.FloatVariable("Z score"))
self.newdata = orange.ExampleTable(newdomain, self.data)
zv = outlier.zValues()
for i, el in enumerate(zv):
self.newdata[i]["Z score"] = el
self.send("Data with z-score", self.newdata)
filterout = orange.Filter_values(domain=self.newdata.domain)
filterout["Z score"] = (orange.Filter_values.Greater, eval(self.zscore))
outliers = filterout(self.newdata)
filterin = orange.Filter_values(domain=self.newdata.domain)
filterin["Z score"] = (orange.Filter_values.LessEqual, eval(self.zscore))
inliers = filterin(self.newdata)
import os
import os.path
import glob
import orange
import orngNetwork
atts = []
atts.append(orange.StringVariable("Network Name"))
atts.append(orange.StringVariable("Network File"))
atts.append(orange.StringVariable("dir"))
atts.append(orange.StringVariable("Item Set"))
atts.append(orange.StringVariable("Edge Set"))
atts.append(orange.FloatVariable("Vertices"))
atts[-1].numberOfDecimals = 0
atts.append(orange.FloatVariable("Edges"))
atts[-1].numberOfDecimals = 0
atts.append(orange.StringVariable("Date"))
atts.append(orange.StringVariable("Description"))
netlist = orange.ExampleTable(orange.Domain(atts, False))
for netFile in glob.glob(os.path.join(os.getcwd(), '*.net')):
net = orngNetwork.Network.read(netFile)
name, ext = os.path.splitext(netFile)
itemFile = ""
if os.path.exists(name + '_items.tab'):
itemFile = name + '_items.tab'
elif os.path.exists(name + '.tab'):
itemFile = name + '.tab'
def sendExampleTable(self, selectedInd):
if self.selectionOptions==0:
self.send("Data", orange.ExampleTable(self.data.getitems(selectedInd)))
else:
xAttr=orange.FloatVariable("X")
yAttr=orange.FloatVariable("Y")
if self.selectionOptions==1:
domain=orange.Domain([xAttr, yAttr]+[v for v in self.data.domain.variables])
domain.addmetas(self.data.domain.getmetas())
else:
domain=orange.Domain(self.data.domain)
domain.addmeta(orange.newmetaid(), xAttr)
domain.addmeta(orange.newmetaid(), yAttr)
selection=orange.ExampleTable(domain)
selection.extend(self.data.getitems(selectedInd))
for i in range(len(selectedInd)):
selection[i][xAttr]=self.mds.points[selectedInd[i]][0]
selection[i][yAttr]=self.mds.points[selectedInd[i]][1]
self.send("Data", selection)
def sendExampleTable(self, selectedInd):
if self.selectionOptions == 0:
self.send("Data", orange.ExampleTable(self.data.getitems(selectedInd)))
else:
xAttr = orange.FloatVariable("X")
yAttr = orange.FloatVariable("Y")
if self.selectionOptions == 1:
domain = orange.Domain([xAttr, yAttr] +
[v for v in self.data.domain.variables])
domain.addmetas(self.data.domain.getmetas())
else:
domain = orange.Domain(self.data.domain)
domain.addmeta(orange.newmetaid(), xAttr)
domain.addmeta(orange.newmetaid(), yAttr)
selection = orange.ExampleTable(domain)
selection.extend(self.data.getitems(selectedInd))
for i in range(len(selectedInd)):
selection[i][xAttr] = self.mds.points[selectedInd[i]][0]
selection[i][yAttr] = self.mds.points[selectedInd[i]][1]
self.send("Data", selection)
def evaluateAttributeOrder(self, attrs, valueOrder, conditions, revert, domain = None):
if not domain:
domain = orange.Domain([orange.FloatVariable("xVar"), orange.FloatVariable("yVar"), self.data.domain.classVar])
self.weightID = orange.newmetaid()
domain.addmeta(self.weightID, orange.FloatVariable("ExampleWeight"))
projData = orange.ExampleTable(domain)
projData.domain.classVar.distributed = True
triedIndices = [0]*(len(attrs))
maxVals = [len(val) for val in valueOrder]
xpos = 0; ypos = 0
while triedIndices[0] < maxVals[0]:
vals = [valueOrder[i][triedIndices[i]] for i in range(len(attrs))]
combVal = reduce(operator.concat, map(operator.concat, [vals[i] for i in revert], ["-"]*len(vals)))[:-1]
if conditions[combVal][0] > 0:
#projData.append([xpos, ypos, conditions[combVal][1]])
projData.append([xpos, ypos, projData.domain.classVar.values[0]])
val = orange.Value(projData.domain.classVar, conditions[combVal][1])
val.svalue = conditions[combVal][2]
def createQTable(cache, data, dimensions, outputAttr = -1, threshold = 0, MQCNotation = False, derivativeAsMeta = False, differencesAsMeta = False, correlationsAsMeta = False, originalAsMeta = False):
nDimensions = len(dimensions)
needQ = outputAttr < 0 or derivativeAsMeta
if needQ:
qVar = createClassVar([cache.attributes[i][0] for i in dimensions], MQCNotation)
if outputAttr >= 0:
classVar = orange.FloatVariable("df/d"+cache.attributes[outputAttr][0])
else:
classVar = qVar
dom = orange.Domain(data.domain.attributes, classVar)
dom.addmetas(data.domain.getmetas())
setattr(dom, "constraintAttributes", [cache.contAttributes[i] for i in dimensions])
if derivativeAsMeta:
derivativeID = orange.newmetaid()
dom.addmeta(derivativeID, qVar)
else:
derivativeID = 0
metaIDs = []
if differencesAsMeta:
for dim in dimensions: