コード例 #1
0
class LogisticRegressionWidget(QWidget, Ui_Widget):
  def __init__(self, plugin, parent=None):
    QWidget.__init__(self, parent)
    self.setupUi(self)

    self.plugin = plugin
    self.inputs = plugin.inputs

    self.settings = QSettings("NextGIS", "MOLUSCE")

    self.btnFitModel.clicked.connect(self.fitModel)

    self.manageGui()

  def manageGui(self):
    self.spnNeighbourhood.setValue(self.settings.value("ui/LR/neighborhood", 1).toInt()[0])

  def fitModel(self):
    self.settings.setValue("ui/LR/neighborhood", self.spnNeighbourhood.value())

    self.model = LR(ns=self.spnNeighbourhood.value())

    self.model.setTrainingData(self.inputs["initial"],
                               self.inputs["factors"].values(),
                               self.inputs["final"],
                               mode=self.inputs["samplingMode"],
                               samples=self.plugin.spnSamplesCount.value()
                              )

    self.model.train()

    # populate table
    self.showCoefficients()

    self.inputs["model"] = self.model

  def showCoefficients(self):
    fm = self.model.getIntercept()
    coef = self.model.getCoef()

    self.tblCoefficients.clear()
    self.tblCoefficients.setColumnCount(len(fm))
    self.tblCoefficients.setRowCount(len(coef[0]) + 1)

    for i in xrange(len(fm)):
      item = QTableWidgetItem(unicode(fm[i]))
      self.tblCoefficients.setItem(0, i, item)
      for j in xrange(len(coef[i])):
        item = QTableWidgetItem(unicode(coef[i][j]))
        self.tblCoefficients.setItem(j + 1, i, item)

    self.tblCoefficients.resizeRowsToContents()
    self.tblCoefficients.resizeColumnsToContents()
コード例 #2
0
  def fitModel(self):
    self.settings.setValue("ui/LR/neighborhood", self.spnNeighbourhood.value())

    self.model = LR(ns=self.spnNeighbourhood.value())

    self.model.setTrainingData(self.inputs["initial"],
                               self.inputs["factors"].values(),
                               self.inputs["final"],
                               mode=self.inputs["samplingMode"],
                               samples=self.plugin.spnSamplesCount.value()
                              )

    self.model.train()

    # populate table
    self.showCoefficients()

    self.inputs["model"] = self.model
コード例 #3
0
  def startFitModel(self):
    if not utils.checkInputRasters(self.inputs):
      QMessageBox.warning(self.plugin,
                          self.tr("Missed input data"),
                          self.tr("Initial or final raster is not set. Please specify input data and try again")
                         )
      return

    if not utils.checkFactors(self.inputs):
      QMessageBox.warning(self.plugin,
                          self.tr("Missed input data"),
                          self.tr("Factors rasters is not set. Please specify them and try again")
                         )
      return

    if not utils.checkChangeMap(self.inputs):
      QMessageBox.warning(self.plugin,
                          self.tr("Missed input data"),
                          self.tr("Change map raster is not set. Please create it try again")
                         )
      return

    self.settings.setValue("ui/LR/neighborhood", self.spnNeighbourhood.value())

    self.plugin.logMessage(self.tr("Init LR model"))

    model = LR(ns=self.spnNeighbourhood.value())
    self.inputs["model"] = model
    model.setMaxIter(self.spnMaxIterations.value())

    model.setState(self.inputs["initial"])
    model.setFactors(self.inputs["factors"].values())
    model.setOutput(self.inputs["changeMap"])
    model.setMode(self.inputs["samplingMode"],)
    model.setSamples(self.plugin.spnSamplesCount.value())

    self.plugin.logMessage(self.tr("Set training data"))
    model.moveToThread(self.plugin.workThread)
    self.plugin.workThread.started.connect(model.startTrain)
    self.plugin.setProgressRange("Train LR model", 0)
    model.finished.connect(self.__trainFinished)
    model.errorReport.connect(self.plugin.logErrorReport)
    model.finished.connect(self.plugin.workThread.quit)
    self.plugin.workThread.start()