public void testRule(NumericalRule rule, int minSupport, float minAccuracy) throws SQLException {
// System.out.println("!!!"+rule.getRuleString()+"!!!");
String prediction = rule.getHead().getConstant();
int positivesCount = 0;
int negativesCount = 0;
String sparql = createTestQuery(rule);
// System.out.println(sparql);
long t = System.currentTimeMillis();
ResultSet rs = (ResultSet) qh.executeQuery(sparql);
// System.out.println("Executed in "+(System.currentTimeMillis()-t));
float overallAcc =
((float)
ArrayTools.sum(
ArrayTools.multiply(
rule.getAccuracyDistribution(), rule.getSupportDistribution())))
/ ((float) ArrayTools.sum(rule.getSupportDistribution()));
float[] acc =
ArrayTools.getAccuraciesWithMinSupport(
rule.getSupportDistribution(), rule.getBodySupportDistribution(), minSupport);
String ranges = "";
for (int i = 0; i < acc.length; i++) {
if (acc[i] >= minAccuracy) ranges += "+";
else ranges += "-";
}
// System.out.println(ranges);
// ArrayTools.print(acc);
float lastNum = Float.NaN;
while (rs.next()) {
float num = rs.getFloat(1);
String observation = rs.getString(2).replaceAll("\"", "");
int count = rs.getInt(3);
int bucket = histogram.getBucket(num);
if (acc[bucket] >= minAccuracy) {
if (prediction.equals(observation)) positivesCount += count;
else negativesCount += count;
}
lastNum = num;
}
int coveringCount = positivesCount + negativesCount;
float predictionAccuracy = ((float) positivesCount) / (((float) (coveringCount)));
// System.out.println(rule.getRuleString() + "\n[" + predictionAccuracy + "," + coveringCount +
// "] = ("+positivesCount+"/"+negativesCount+")");
rule.observedConfidence = predictionAccuracy;
rule.observedPositives = positivesCount;
rule.observedNegatives = negativesCount;
rule.observedOverallConfidence = overallAcc;
}
