09e47cabaf09c32d402e8527d3804bac5f118ccf,framework/PostProcessors/DataMining.py,DataMining,inputToInternalForHistorySet,#DataMining#Any#,218

Before Change

    
    dataSet = currentInput.asDataset()
    inputDict = {"Features": {}, "parameters": {}, "Labels": {}, "metadata": {}}
    if self.pivotParameter is None and self.metric is not None:
      if not hasattr(self.metric, "pivotParameter"):
        self.raiseAnError(IOError, "HistorySet is provided as input, but this post-processor ", self.name, " can not handle it!")
      self.pivotParameter = self.metric.pivotParameter
    if self.PreProcessor is None and self.metric is None:
      if not currentInput.checkIndexAlignment(indexesToCheck=self.pivotParameter):
        self.raiseAnError(IOError, "The data provided by the DataObject ", currentInput.name, " is not synchronized!")
      // for testing time dependent data mining - time dependent clustering

After Change

    
    dataSet = currentInput.asDataset()
    inputDict = {"Features": {}, "parameters": {}, "Labels": {}, "metadata": {}}
    if self.pivotParameter is None:
      self.pivotParameter = currentInput.indexes[-1]
    if self.PreProcessor is None and self.metric is None:
      if not currentInput.checkIndexAlignment(indexesToCheck=self.pivotParameter):
        self.raiseAnError(IOError, "The data provided by the DataObject ", currentInput.name, " is not synchronized!")
      // for testing time dependent data mining - time dependent clustering

Instances