def distributionForInstance(self,instance:Instance)->List[float]:
dist=[0]*instance.numClasses()
if instance.classAttribute().type() == Attribute.NOMINAL:
classification=self.classifyInstance(instance)
if Utils.isMissingValue(classification):
return dist
else:
dist[int(classification)]=1.0
return dist
elif instance.classAttribute().type() == Attribute.NUMERIC or instance.classAttribute().type() == Attribute.DATE:
dist[0]=self.classifyInstance(instance)
return dist
return dist
def classifyInstance(self,instance:Instance):
dist=self.distributionForInstance(instance)
if dist is None:
raise Exception("Null distribution predicted")
if instance.classAttribute().type() == Attribute.NOMINAL:
max=maxIndex=0
for i in range(len(dist)):
if dist[i]>max:
maxIndex=i
max=dist[i]
if max > 0:
return maxIndex
return Utils.missingValue()
elif instance.classAttribute().type() == Attribute.NUMERIC or instance.classAttribute().type() == Attribute.DATE:
return dist[0]
return Utils.missingValue()
def process(self, toPredict: Instance, classifier: Classifier,
evaluation: Evaluation):
probActual = probNext = pred = 0
classMissing = copy.deepcopy(toPredict)
classMissing.setDataset(toPredict.dataset())
if toPredict.classAttribute().isNominal():
#返回分类预测的概率分布
preds = classifier.distributionForInstance(classMissing)
#若概率全部为0，则表示不属于任何一类
val = 0
if sum(preds) == 0:
pred = Utils.missingValue()
probActual = Utils.missingValue()
else:
#分类结果为概率最大的一项下标
pred = Utils.maxIndex(preds)
if not Utils.isMissingValue(toPredict.classIndex()):
#如果值不缺失，表示非预测样本，不做修改
if not Utils.isMissingValue(toPredict.classValue()):
val = int(toPredict.classValue())
probActual = preds[val]
else:
probActual = preds[Utils.maxIndex(preds)]
for i in range(toPredict.classAttribute().numValues()):
if i != val and preds[i] > probNext:
probNext = preds[i]
evaluation.evaluationForSingleInstance(preds, toPredict, True)
else:
#单项评估
pred = evaluation.evaluateModelOnceAndRecordPrediction(
classifier, toPredict)
if not self.m_SaveForVisualization:
return
#保存可视化数据
if self.m_PlotInstances is not None:
isNominal = toPredict.classAttribute().isNominal()
values = [0] * self.m_PlotInstances.numAttributes()
i = 0
while i < self.m_PlotInstances.numAttributes():
#预测值前的所有值照原来的拷贝
if i < toPredict.classIndex():
values[i] = toPredict.value(i)
elif i == toPredict.classIndex():
if isNominal:
#首选结果与备选结果的差值
values[i] = probActual - probNext
#预测结果
values[i + 1] = pred
#原始值
values[i + 2] = toPredict.value(i)
i += 2
else:
values[i] = pred
values[i + 1] = toPredict.value(i)
i += 1
else:
if isNominal:
values[i] = toPredict.value(i - 2)
else:
values[i] = toPredict.value(i - 1)
i += 1
# print("============")
# for m in values:
# print("val:",m)
# print("============")
self.m_PlotInstances.add(Instance(1.0, values))
if toPredict.classAttribute().isNominal():
if toPredict.isMissing(
toPredict.classIndex()) or Utils.isMissingValue(pred):
self.m_PlotShapes.append(Plot2D.MISSING_SHAPE)
elif pred != toPredict.classValue():
self.m_PlotShapes.append(Plot2D.ERROR_SHAPE)
else:
self.m_PlotShapes.append(Plot2D.CONST_AUTOMATIC_SHAPE)
if self.m_pointSizeProportionalToMargin:
self.m_PlotSizes.append(probActual - probNext)
else:
sizeAdj = 0
if pred != toPredict.classValue():
sizeAdj = 1
self.m_PlotSizes.append(Plot2D.DEFAULT_SHAPE_SIZE.value +
sizeAdj)
else:
errd = None
if not toPredict.isMissing(toPredict.classIndex(
)) and not Utils.isMissingValue(pred):
errd = pred - toPredict.classValue()
self.m_PlotShapes.append(Plot2D.CONST_AUTOMATIC_SHAPE)
else:
self.m_PlotShapes.append(Plot2D.MISSING_SHAPE)
self.m_PlotSizes.append(errd)
