public static void main(String[] args) throws IOException {
File tempDBFile = BreastCancerDAO.init(); // setup data
BreastCancerCircuitApp breastCancerAnalysis = new BreastCancerCircuitApp();
breastCancerAnalysis.go();
BreastCancerDAO.release(tempDBFile); // release data resources
}
private void go() throws IOException {
// initialize the ahahNode
double readPeriod = 1E-5; // increase the learning rate from default
double writePeriod = 1E-5;
int numBias = 15;
int numInputs = 70; // max number of inputs/spikes
MemristorType memristorType = MemristorType.AgChalc;
AHaH21Circuit ahahNodeCircuit =
new AHaH21CircuitBuilder()
.numInputs(numInputs)
.numBiasInputs(numBias)
.readPeriod(readPeriod)
.writePeriod(writePeriod)
.memristorType(memristorType)
.build();
// initialize the feature factory. This code converts the raw data to a spiking representation.
BreastCancerSpikeEncoder breastCancerSpikeEncoder = new BreastCancerSpikeEncoder();
SpikeConverter spikeConverter =
new SpikeConverter(); // converts the spike code produced by the feature factory to one that
// can be used by the circuit.
// train the classifier--->
List<BreastCancer> breastCancerTrainingData = BreastCancerDAO.selectTrainData();
System.out.println("Learning...");
for (BreastCancer breastCancer : breastCancerTrainingData) {
String[] topicTrueLabels = new String[1];
topicTrueLabels[0] = breastCancer.getCellClass() + "";
long[] spikes = breastCancerSpikeEncoder.encode(breastCancer); // get the spike representation
Set<Integer> convertedSpikes = spikeConverter.convert(spikes);
int superviseSignal = 0;
if (breastCancer.getCellClass() == 2) {
superviseSignal = 1;
} else {
superviseSignal = -1;
}
System.out.println("Spike Pattern Length = " + convertedSpikes.size());
// System.out.println("Spikes = " + Arrays.toString(convertedSpikes.toArray()));
double y = ahahNodeCircuit.update(convertedSpikes, superviseSignal);
}
System.out.println("Spike Pattern Space = " + breastCancerSpikeEncoder.getSpikePatternSpace());
// Test the classifier
List<BreastCancer> breastCancerTestData = BreastCancerDAO.selectTestData();
System.out.println("Testing...");
Set<String> labels =
new HashSet<String>(); // these are the labels we are evaluating the performance of
labels.add("2");
labels.add("4");
// we are testing the performance for a range of confidence values, from 0 to 1.
int numSteps = 100;
double[] confidenceThresholds = new double[numSteps];
double inc = .061 / numSteps;
for (int i = 0; i < confidenceThresholds.length; i++) {
confidenceThresholds[i] = i * inc;
}
// create evaluators for keeping track of performance
ClassificationEvaluator[] evaluators = new ClassificationEvaluator[numSteps];
for (int i = 0; i < evaluators.length; i++) {
evaluators[i] = new ClassificationEvaluator(labels);
}
// run through the test set
for (BreastCancer breastCancer : breastCancerTestData) {
long[] spikes = breastCancerSpikeEncoder.encode(breastCancer);
Set<Integer> convertedSpikes = spikeConverter.convert(spikes);
double y =
ahahNodeCircuit.update(convertedSpikes, 0); // NOTE: no supervised labels are passed here.
// true labels for evaluation.
Set<String> trueLabels = new HashSet<String>();
trueLabels.add(breastCancer.getCellClass() + "");
for (int i = 0; i < evaluators.length; i++) {
List<String> labelOutput = new ArrayList<String>();
if (y > confidenceThresholds[i]) {
labelOutput.add("2");
} else if (y < -confidenceThresholds[i]) {
labelOutput.add("4");
}
evaluators[i].update(trueLabels, labelOutput);
}
}
// plot results---->
double maxF1 = 0;
int cIdx = 0;
double[] accuracy = new double[numSteps];
double[] precision = new double[numSteps];
double[] recall = new double[numSteps];
double[] f1 = new double[numSteps];
for (int i = 0; i < numSteps; i++) {
accuracy[i] = evaluators[i].getAccuracyMicroAve();
precision[i] = evaluators[i].getPrecisionMicroAve();
recall[i] = evaluators[i].getRecallMicroAve();
f1[i] = evaluators[i].getF1MicroAve();
if (f1[i] > maxF1) { // get the best confidence threshold
maxF1 = f1[i];
cIdx = i;
}
}
Chart chart = new Chart(300, 300, ChartTheme.Matlab);
// chart.setChartTitle("Wisconsin Breast Cancer Benchmark - F1=" + df.format(maxF1) + "
// w/ConfidenceThreshold=" + df.format(confidenceThresholds[cIdx]));
chart.setChartTitle("Breast Cancer - Circuit");
chart.setXAxisTitle("Confidence Threshold");
chart.setYAxisTitle("Score");
chart.getStyleManager().setLegendPosition(LegendPosition.InsideSW);
Series accuracy_series = chart.addSeries("Accuracy", confidenceThresholds, accuracy);
accuracy_series.setMarker(SeriesMarker.NONE);
Series precision_series = chart.addSeries("Precision", confidenceThresholds, precision);
precision_series.setMarker(SeriesMarker.NONE);
Series recall_series = chart.addSeries("Recall", confidenceThresholds, recall);
recall_series.setMarker(SeriesMarker.NONE);
Series f1_series = chart.addSeries("F1", confidenceThresholds, f1);
f1_series.setMarker(SeriesMarker.NONE);
BitmapEncoder.savePNGWithDPI(chart, "./PLOS_AHAH/Figures/Breast_Cancer_Circuit.png", 300);
new SwingWrapper(chart).displayChart();
}