Package org.nd4j.evaluation.classification

Class EvaluationCalibration

    • Field Detail

      • DEFAULT_RELIABILITY_DIAG_NUM_BINS

        public static final int DEFAULT_RELIABILITY_DIAG_NUM_BINS
        See Also:
        Constant Field Values

      • DEFAULT_HISTOGRAM_NUM_BINS

        public static final int DEFAULT_HISTOGRAM_NUM_BINS
        See Also:
        Constant Field Values

      • axis

        protected int axis

    • Constructor Detail

      • EvaluationCalibration

        protected EvaluationCalibration​(int axis,
                                int reliabilityDiagNumBins,
                                int histogramNumBins,
                                boolean excludeEmptyBins)

      • EvaluationCalibration

        public EvaluationCalibration()
        Create an EvaluationCalibration instance with the default number of bins

      • EvaluationCalibration

        public EvaluationCalibration​(int reliabilityDiagNumBins,
                             int histogramNumBins)
        Create an EvaluationCalibration instance with the specified number of bins
        Parameters:
        reliabilityDiagNumBins - Number of bins for the reliability diagram (usually 10)
        histogramNumBins - Number of bins for the histograms

      • EvaluationCalibration

        public EvaluationCalibration​(int reliabilityDiagNumBins,
                             int histogramNumBins,
                             boolean excludeEmptyBins)
        Create an EvaluationCalibration instance with the specified number of bins
        Parameters:
        reliabilityDiagNumBins - Number of bins for the reliability diagram (usually 10)
        histogramNumBins - Number of bins for the histograms
        excludeEmptyBins - For the reliability diagram, whether empty bins should be excluded

    • Method Detail

      • setAxis

        public void setAxis​(int axis)
        Set the axis for evaluation - this is the dimension along which the probability (and label 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()

      • stats

        public String stats()
        Returns:

      • numClasses

        public int numClasses()

      • getReliabilityDiagram

        public ReliabilityDiagram getReliabilityDiagram​(int classIdx)
        Get the reliability diagram for the specified class
        Parameters:
        classIdx - Index of the class to get the reliability diagram for

      • getLabelCountsEachClass

        public int[] getLabelCountsEachClass()
        Returns:
        The number of observed labels for each class. For N classes, be returned array is of length N, with out[i] being the number of labels of class i

      • getPredictionCountsEachClass

        public int[] getPredictionCountsEachClass()
        Returns:
        The number of network predictions for each class. For N classes, be returned array is of length N, with out[i] being the number of predicted values (max probability) for class i

      • getResidualPlotAllClasses

        public Histogram getResidualPlotAllClasses()
        Get the residual plot for all classes combined. The residual plot is defined as a histogram of
        |label_i - prob(class_i | input)| for all classes i and examples.
        In general, small residuals indicate a superior classifier to large residuals.
        Returns:
        Residual plot (histogram) - all predictions/classes

      • getResidualPlot

        public Histogram getResidualPlot​(int labelClassIdx)
        Get the residual plot, only for examples of the specified class.. The residual plot is defined as a histogram of
        |label_i - prob(class_i | input)| for all and examples; for this particular method, only predictions where i == labelClassIdx are included.
        In general, small residuals indicate a superior classifier to large residuals.
        Parameters:
        labelClassIdx - Index of the class to get the residual plot for
        Returns:
        Residual plot (histogram) - all predictions/classes

      • getProbabilityHistogramAllClasses

        public Histogram getProbabilityHistogramAllClasses()
        Return a probability histogram for all predictions/classes.
        Returns:
        Probability histogram

      • getProbabilityHistogram

        public Histogram getProbabilityHistogram​(int labelClassIdx)
        Return a probability histogram of the specified label class index. That is, for label class index i, a histogram of P(class_i | input) is returned, only for those examples that are labelled as class i.
        Parameters:
        labelClassIdx - Index of the label class to get the histogram for
        Returns:
        Probability histogram

      • getValue

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

      • newInstance

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