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java.lang.Objectgov.sandia.cognition.util.AbstractCloneableSerializable
gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic
@PublicationReference(author="Wikipedia", title="Receiver operating characteristic", type=WebPage, year=2009, url="http://en.wikipedia.org/wiki/Receiver_operating_characteristic") public class ReceiverOperatingCharacteristic
Class that describes a Receiver Operating Characteristic (usually called an "ROC Curve"). This is a function that describes the performance of a classification system where the x-axis is the FalsePositiveRate and the y-axis is the TruePositiveRate. Both axes are on the interval [0,1]. A typical ROC curve has a logarithm-shaped plot, ideally it looks like a capital Gamma letter. An ROC curve also has an associated group of statistics with it from a Mann-Whitney U-test, which gives the probability that the classifier is essentially randomly "guessing." We create ROC curves by calling the method: ReceiverOperatingCharacteristic.create(data)
Nested Class Summary | |
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static class |
ReceiverOperatingCharacteristic.DataPoint
Contains information about a datapoint on an ROC curve |
static class |
ReceiverOperatingCharacteristic.Statistic
Contains useful statistics derived from a ROC curve |
Method Summary | |
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ReceiverOperatingCharacteristic |
clone()
This makes public the clone method on the Object class and
removes the exception that it throws. |
ReceiverOperatingCharacteristic.Statistic |
computeStatistics()
Computes useful statistical information associated with the ROC curve |
static ReceiverOperatingCharacteristic |
create(Collection<? extends InputOutputPair<Double,Boolean>> data)
Creates an ROC curve based on the scored data with target information |
static ReceiverOperatingCharacteristic |
createFromTargetEstimatePairs(Collection<? extends Pair<Boolean,? extends Number>> data)
Creates an ROC curve based on the scored data with target information. |
Double |
evaluate(Double input)
Evaluates the "pessimistic" value of the truePositiveRate for a given falsePositiveRate. |
ArrayList<ReceiverOperatingCharacteristic.DataPoint> |
getSortedROCData()
Getter for sortedROCData |
MannWhitneyUConfidence.Statistic |
getUtest()
Getter for Utest |
protected void |
setSortedROCData(ArrayList<ReceiverOperatingCharacteristic.DataPoint> sortedROCData)
Setter for srtedROCData |
void |
setUtest(MannWhitneyUConfidence.Statistic Utest)
Setter for Utest |
Methods inherited from class java.lang.Object |
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equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Method Detail |
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public ReceiverOperatingCharacteristic clone()
AbstractCloneableSerializable
Object
class and
removes the exception that it throws. Its default behavior is to
automatically create a clone of the exact type of object that the
clone is called on and to copy all primitives but to keep all references,
which means it is a shallow copy.
Extensions of this class may want to override this method (but call
super.clone()
to implement a "smart copy". That is, to target
the most common use case for creating a copy of the object. Because of
the default behavior being a shallow copy, extending classes only need
to handle fields that need to have a deeper copy (or those that need to
be reset). Some of the methods in ObjectUtil
may be helpful in
implementing a custom clone method.
Note: The contract of this method is that you must use
super.clone()
as the basis for your implementation.
clone
in interface CloneableSerializable
clone
in class AbstractCloneableSerializable
public Double evaluate(Double input)
evaluate
in interface Evaluator<Double,Double>
input
- falsePositiveRate from which to estimate the truePositiveRate
public ArrayList<ReceiverOperatingCharacteristic.DataPoint> getSortedROCData()
protected void setSortedROCData(ArrayList<ReceiverOperatingCharacteristic.DataPoint> sortedROCData)
sortedROCData
- Sorted data containing a ConfusionMatrix at each point, sorted in an
ascending order along the abscissa (x-axis), which is FalsePositiveRatepublic static ReceiverOperatingCharacteristic createFromTargetEstimatePairs(Collection<? extends Pair<Boolean,? extends Number>> data)
data
- Collection of target/estimate-score pairs. The second element in
the pair is an estimated score, the first is a flag to determine
which group the score belongs to. For example:
{(true, 1.0), (false, 0.9)}
means that data1=1.0 and data2=0.9 and so forth. This is useful
for computing that classified data partitions data better than
chance.
public static ReceiverOperatingCharacteristic create(Collection<? extends InputOutputPair<Double,Boolean>> data)
data
- Collection of estimate-score/target pairs. The second element in the
Pair is an estimated score, the first is a flag to determine which
group the score belongs to. For example {<1.0,true>, <0.9,false> means
that data1=1.0 and data2=0.9 and so forth. This is useful for computing
that classified data partitions data better than chance.
public ReceiverOperatingCharacteristic.Statistic computeStatistics()
public MannWhitneyUConfidence.Statistic getUtest()
public void setUtest(MannWhitneyUConfidence.Statistic Utest)
Utest
- Results from conducting a U-test on the underlying classification data,
the null hypothesis determines if the classifier can reliably separate
the classes, not just chance
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