src/nupic/swarming/ModelRunner.py
def __checkMaturity(self):
    """ Save the current metric value and see if the model's performance has
    'leveled off.' We do this by looking at some number of previous number of
    recordings """

    if self._currentRecordIndex+1 < self._MIN_RECORDS_TO_BE_BEST:
      return

    # If we are already mature, don't need to check anything
    if self._isMature:
      return

    metric = self._getMetrics()[self._optimizedMetricLabel]
    self._metricRegression.addPoint(x=self._currentRecordIndex, y=metric)

   # Perform a linear regression to see if the error is leveled off
    #pctChange = self._metricRegression.getPctChange()
    #if pctChange  is not None and abs(pctChange ) <= self._MATURITY_MAX_CHANGE:
    pctChange, absPctChange = self._metricRegression.getPctChanges()
    if pctChange  is not None and absPctChange <= self._MATURITY_MAX_CHANGE:
      self._jobsDAO.modelSetFields(self._modelID,
                                   {'engMatured':True})

      # TODO: Don't stop if we are currently the best model. Also, if we
      # are still running after maturity, we have to periodically check to
      # see if we are still the best model. As soon we lose to some other
      # model, then we should stop at that point.
      self._cmpReason = ClientJobsDAO.CMPL_REASON_STOPPED
      self._isMature = True

      self._logger.info("Model %d has matured (pctChange=%s, n=%d). \n"\
                        "Scores = %s\n"\
                         "Stopping execution",self._modelID, pctChange,
                                              self._MATURITY_NUM_POINTS,
                                              self._metricRegression._window)
def __checkMaturity(self):
    """ Save the current metric value and see if the model's performance has
    'leveled off.' We do this by looking at some number of previous number of
    recordings """

    if self._currentRecordIndex+1 < self._MIN_RECORDS_TO_BE_BEST:
      return

    # If we are already mature, don't need to check anything
    if self._isMature:
      return

    metric = self._getMetrics()[self._optimizedMetricLabel]
    self._metricRegression.addPoint(x=self._currentRecordIndex, y=metric)

   # Perform a linear regression to see if the error is leveled off
    #pctChange = self._metricRegression.getPctChange()
    #if pctChange  is not None and abs(pctChange ) <= self._MATURITY_MAX_CHANGE:
    pctChange, absPctChange = self._metricRegression.getPctChanges()
    if pctChange  is not None and absPctChange <= self._MATURITY_MAX_CHANGE:
      self._jobsDAO.modelSetFields(self._modelID,
                                   {'engMatured':True})

      # TODO: Don't stop if we are currently the best model. Also, if we
      # are still running after maturity, we have to periodically check to
      # see if we are still the best model. As soon we lose to some other
      # model, then we should stop at that point.
      self._cmpReason = ClientJobsDAO.CMPL_REASON_STOPPED
      self._isMature = True

      self._logger.info("Model %d has matured (pctChange=%s, n=%d). \n"\
                        "Scores = %s\n"\
                         "Stopping execution",self._modelID, pctChange,
                                              self._MATURITY_NUM_POINTS,
                                              self._metricRegression._window)
def __checkMaturity ( self ) : if self . _currentRecordIndex + 1 < self . _MIN_RECORDS_TO_BE_BEST : return # If we are already mature, don't need to check anything if self . _isMature : return metric = self . _getMetrics ( ) [ self . _optimizedMetricLabel ] self . _metricRegression . addPoint ( x = self . _currentRecordIndex , y = metric ) # Perform a linear regression to see if the error is leveled off #pctChange = self._metricRegression.getPctChange() #if pctChange  is not None and abs(pctChange ) <= self._MATURITY_MAX_CHANGE: pctChange , absPctChange = self . _metricRegression . getPctChanges ( ) if pctChange is not None and absPctChange <= self . _MATURITY_MAX_CHANGE : self . _jobsDAO . modelSetFields ( self . _modelID , { 'engMatured' : True } ) # TODO: Don't stop if we are currently the best model. Also, if we # are still running after maturity, we have to periodically check to # see if we are still the best model. As soon we lose to some other # model, then we should stop at that point. self . _cmpReason = ClientJobsDAO . CMPL_REASON_STOPPED self . _isMature = True self . _logger . info ( "Model %d has matured (pctChange=%s, n=%d). \n" "Scores = %s\n" "Stopping execution" , self . _modelID , pctChange , self . _MATURITY_NUM_POINTS , self . _metricRegression . _window )
Save the current metric value and see if the model's performance has
    'leveled off.' We do this by looking at some number of previous number of
    recordings
Save the current metric value and see if the model s performance has leveled off . We do this by looking at some number of previous number of recordings