Package org.nd4j.evaluation.classification

Class ROCBinary

    • Field Detail

      • DEFAULT_STATS_PRECISION

        public static final int DEFAULT_STATS_PRECISION
        See Also:
        Constant Field Values

      • axis

        protected int axis

    • Constructor Detail

      • ROCBinary

        protected ROCBinary​(int axis,
                    int thresholdSteps,
                    boolean rocRemoveRedundantPts,
                    List<String> labels)

      • ROCBinary

        public ROCBinary()

      • ROCBinary

        public ROCBinary​(int thresholdSteps)
        Parameters:
        thresholdSteps - Number of threshold steps to use for the ROC calculation. Set to 0 for exact ROC calculation

      • ROCBinary

        public ROCBinary​(int thresholdSteps,
                 boolean rocRemoveRedundantPts)
        Parameters:
        thresholdSteps - Number of threshold steps to use for the ROC calculation. If set to 0: use exact calculation
        rocRemoveRedundantPts - Usually set to true. If true, remove any redundant points from ROC and P-R curves

    • Method Detail

      • setAxis

        public void setAxis​(int axis)
        Set the axis for evaluation - this is the dimension along which the probability (and label independent binary classes) are present.
        For DL4J, this can be left as the default setting (axis = 1).
        Axis should be set as follows:
        For 2D (OutputLayer), shape [minibatch, numClasses] - axis = 1
        For 3D, RNNs/CNN1D (DL4J RnnOutputLayer), NCW format, shape [minibatch, numClasses, sequenceLength] - axis = 1
        For 3D, RNNs/CNN1D (DL4J RnnOutputLayer), NWC format, shape [minibatch, sequenceLength, numClasses] - axis = 2
        For 4D, CNN2D (DL4J CnnLossLayer), NCHW format, shape [minibatch, channels, height, width] - axis = 1
        For 4D, CNN2D, NHWC format, shape [minibatch, height, width, channels] - axis = 3
        Parameters:
        axis - Axis to use for evaluation

      • getAxis

        public int getAxis()
        Get the axis - see setAxis(int) for details

      • reset

        public void reset()

      • merge

        public void merge​(ROCBinary other)

      • numLabels

        public int numLabels()
        Returns the number of labels - (i.e., size of the prediction/labels arrays) - if known. Returns -1 otherwise

      • getCountActualPositive

        public long getCountActualPositive​(int outputNum)
        Get the actual positive count (accounting for any masking) for the specified output/column
        Parameters:
        outputNum - Index of the output (0 to numLabels()-1)

      • getCountActualNegative

        public long getCountActualNegative​(int outputNum)
        Get the actual negative count (accounting for any masking) for the specified output/column
        Parameters:
        outputNum - Index of the output (0 to numLabels()-1)

      • getROC

        public ROC getROC​(int outputNum)
        Get the ROC object for the specific column
        Parameters:
        outputNum - Column (output number)
        Returns:
        The underlying ROC object for this specific column

      • getRocCurve

        public RocCurve getRocCurve​(int outputNum)
        Get the ROC curve for the specified output
        Parameters:
        outputNum - Number of the output to get the ROC curve for
        Returns:
        ROC curve

      • getPrecisionRecallCurve

        public PrecisionRecallCurve getPrecisionRecallCurve​(int outputNum)
        Get the Precision-Recall curve for the specified output
        Parameters:
        outputNum - Number of the output to get the P-R curve for
        Returns:
        Precision recall curve

      • calculateAverageAuc

        public double calculateAverageAuc()
        Macro-average AUC for all outcomes
        Returns:
        the (macro-)average AUC for all outcomes.

      • calculateAverageAUCPR

        public double calculateAverageAUCPR()
        Returns:
        the (macro-)average AUPRC (area under precision recall curve)

      • calculateAUC

        public double calculateAUC​(int outputNum)
        Calculate the AUC - Area Under (ROC) Curve
        Utilizes trapezoidal integration internally
        Parameters:
        outputNum - Output number to calculate AUC for
        Returns:
        AUC

      • calculateAUCPR

        public double calculateAUCPR​(int outputNum)
        Calculate the AUCPR - Area Under Curve - Precision Recall
        Utilizes trapezoidal integration internally
        Parameters:
        outputNum - Output number to calculate AUCPR for
        Returns:
        AUCPR

      • setLabelNames

        public void setLabelNames​(List<String> labels)
        Set the label names, for printing via stats()

      • stats

        public String stats()
        Returns:

      • stats

        public String stats​(int printPrecision)

      • scoreForMetric

        public double scoreForMetric​(ROCBinary.Metric metric,
                             int idx)

      • getValue

        public double getValue​(IMetric metric)
        Description copied from interface: IEvaluation
        Get the value of a given metric for this evaluation.

      • newInstance

        public ROCBinary newInstance()
        Description copied from interface: IEvaluation
        Get a new instance of this evaluation, with the same configuration but no data.