Documentation of 'jsat.driftdetectors.ADWIN' Java class
ADWIN
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jsat.driftdetectors

Class ADWIN<V>

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable

    public class ADWIN<V>
extends BaseDriftDetector<V>
    Adaptive Windowing (ADWIN) is an algorithm for detecting changes in an input stream. ADWIN maintains an approximated window of the input history, and works in O(log(n)) time and O(log(n)) memory, where n is the current window size. Whenever a drift is detected and handled, the size of the window will be reduced.

    The window in ADWIN is only for the double values passed when calling addSample(double, java.lang.Object). The object paired with the numeric value will not be compressed and is added to on every update. It is important to control its size using BaseDriftDetector.setMaxHistory(int) when using ADWIN. By default, ADWIN will use a maximum history of 0.

    See: Bifet, A.,&Gavalda, R. (2007). Learning from Time-Changing Data with Adaptive Windowing. In SIAM International Conference on Data Mining.
    See Also:
    Serialized Form

    • Constructor Summary

      Constructors 
      Constructor and Description
      ADWIN(ADWIN<V> toCopy)
      Copy constructor
      ADWIN(double delta)
      Creates a new ADWIN object for detecting changes in the mean value of a stream of inputs.
      ADWIN(double delta, int maxHistory)
      Creates a new ADWIN object for detecting changes in the mean value of a stream of inputs.

    • Method Summary

      All Methods Instance Methods Concrete Methods 
      Modifier and Type Method and Description
      boolean addSample(double value, V obj)
      Adds a new point to the drift detector.
      ADWIN<V> clone() 
      void driftHandled()
      This method should be called once the drift is handled.
      void driftHandled(boolean dropOld)
      This implementation of ADWIN allows for choosing to drop either the old values, as is normal for a drift detector, or to drop the newer values.
      double getDelta()
      Returns the upper bound on false positives
      int getM()
      Returns the accuracy / speed parameter for ADWIN
      double getMean()
      Returns the mean value for all inputs contained in the current window
      double getNewMean()
      Returns the mean value determined for the newer values that we have drifted into.
      double getNewStndDev()
      Returns the standard deviation for the newer values that we have drifted into.
      double getNewVariance()
      Returns the variance for the newer values that we have drifted into.
      double getOldMean()
      Returns the mean value determined for the older values that we have drifted away from.
      double getOldStndDev()
      Returns the standard deviation for the older values that we have drifted away from.
      double getOldVariance()
      Returns the variance for the older values that we have drifted away from.
      double getStndDev()
      Returns the standard deviation for all inputs contained in the current window.
      double getVariance()
      Returns the variance for all inputs contained in the current window
      int getWidnowLength()
      This returns the current "length" of the window, which is the number of items that have been added to the ADWIN object since the last drift, and is reduced when drift occurres.
      void setDelta(double delta)
      Sets the upper bound on the false positive rate for detecting concept drifts
      void setM(int M)
      This parameter controls the trade off of space and accuracy for the sliding window.

      • Methods inherited from class java.lang.Object

        equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    • Constructor Detail

      • ADWIN

        public ADWIN(double delta)
        Creates a new ADWIN object for detecting changes in the mean value of a stream of inputs. It will use a not keep any object history by default.
        Parameters:
        delta - the desired false positive rate

      • ADWIN

        public ADWIN(double delta,
             int maxHistory)
        Creates a new ADWIN object for detecting changes in the mean value of a stream of inputs.
        Parameters:
        delta - the desired false positive rate
        maxHistory - the maximum history of objects to keep

      • ADWIN

        public ADWIN(ADWIN<V> toCopy)
        Copy constructor
        Parameters:
        toCopy - the object to copy

    • Method Detail

      • setDelta

        public void setDelta(double delta)
        Sets the upper bound on the false positive rate for detecting concept drifts
        Parameters:
        delta - the upper bound on false positives in (0,1)

      • getDelta

        public double getDelta()
        Returns the upper bound on false positives
        Returns:
        the upper bound on false positives

      • setM

        public void setM(int M)
        This parameter controls the trade off of space and accuracy for the sliding window. The larger M becomes, the more accurate the window will be - but at the cost of execution speed.
        Parameters:
        M - the window space constant in [1, ∞)

      • getM

        public int getM()
        Returns the accuracy / speed parameter for ADWIN
        Returns:
        the accuracy / speed parameter

      • addSample

        public boolean addSample(double value,
                         V obj)
        Description copied from class: BaseDriftDetector
        Adds a new point to the drift detector. If an escalation in state occurs, true will be returned. A change of state could be either drift occurring BaseDriftDetector.isDrifting() or a warning state BaseDriftDetector.isWarning(). If the detector was in a warning state and then goes back to normal, false will be returned.

        For binary (true / false) drift detectors, value will be considered false if and only if its value is equal to zero. Any non zero value will be treated as true

        Objects added with the value may or may not be added to the history, the behavior is algorithm dependent. Some may always add it to the history, while others will only begin collecting history once a warning state occurs.
        Specified by:
        addSample in class BaseDriftDetector<V>
        Parameters:
        value - the numeric value to add to the drift detector
        obj - the object associated with this value. It may or may not be stored in the detectors history
        Returns:
        true if a drift has or may be starting.

      • getMean

        public double getMean()
        Returns the mean value for all inputs contained in the current window
        Returns:
        the mean value of the window

      • getVariance

        public double getVariance()
        Returns the variance for all inputs contained in the current window
        Returns:
        the variance for the window

      • getStndDev

        public double getStndDev()
        Returns the standard deviation for all inputs contained in the current window.
        Returns:
        the standard deviation for the window

      • getWidnowLength

        public int getWidnowLength()
        This returns the current "length" of the window, which is the number of items that have been added to the ADWIN object since the last drift, and is reduced when drift occurres.
        Returns:
        the number of items stored implicitly in the window

      • getOldMean

        public double getOldMean()
        Returns the mean value determined for the older values that we have drifted away from.
        If drifting has not occurred or has already been handled, Double.NaN will be returned.
        Returns:
        the mean for the old values.

      • getOldVariance

        public double getOldVariance()
        Returns the variance for the older values that we have drifted away from.
        If drifting has not occurred or has already been handled, Double.NaN will be returned.
        Returns:
        the variance for the old values

      • getOldStndDev

        public double getOldStndDev()
        Returns the standard deviation for the older values that we have drifted away from.
        If drifting has not occurred or has already been handled, Double.NaN will be returned.
        Returns:
        the standard deviation for the old values

      • getNewMean

        public double getNewMean()
        Returns the mean value determined for the newer values that we have drifted into.
        If drifting has not occurred or has already been handled, Double.NaN will be returned.
        Returns:
        the mean for the newer values.

      • getNewVariance

        public double getNewVariance()
        Returns the variance for the newer values that we have drifted into.
        If drifting has not occurred or has already been handled, Double.NaN will be returned.
        Returns:
        the variance for the newer values

      • getNewStndDev

        public double getNewStndDev()
        Returns the standard deviation for the newer values that we have drifted into.
        If drifting has not occurred or has already been handled, Double.NaN will be returned.
        Returns:
        the standard deviation for the newer values

      • driftHandled

        public void driftHandled(boolean dropOld)
        This implementation of ADWIN allows for choosing to drop either the old values, as is normal for a drift detector, or to drop the newer values. Passing true will result in the standard behavior of calling driftHandled().
        If false is passed in to drop the newer values that drifted it is probably that continuing to add new examples will continue to cause detections.
        Parameters:
        dropOld - true to drop the older values out of the window that we drifted away from, or false to drop the newer values and retain the old ones.

      • driftHandled

        public void driftHandled()
        Description copied from class: BaseDriftDetector
        This method should be called once the drift is handled. Once done, this method will clear the flags and prepare the detector to continue tracking drift again.
        By using this method, one can continue to track multiple future drift changes without having to feed the history data (which may be incomplete) into a new detector object.
        Overrides:
        driftHandled in class BaseDriftDetector<V>
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