示例#1
0
		/// <summary> Calculates the performance stats for the desired class and return 
		/// results as a set of Instances.
		/// 
		/// </summary>
		/// <param name="classIndex">index of the class of interest.
		/// </param>
		/// <returns> datapoints as a set of instances.
		/// </returns>
		public virtual Instances getCurve(FastVector predictions, int classIndex)
		{
			
			if ((predictions.size() == 0) || (((NominalPrediction) predictions.elementAt(0)).distribution().Length <= classIndex))
			{
				return null;
			}
			
			double totPos = 0, totNeg = 0;
			double[] probs = getProbabilities(predictions, classIndex);
			
			// Get distribution of positive/negatives
			for (int i = 0; i < probs.Length; i++)
			{
				NominalPrediction pred = (NominalPrediction) predictions.elementAt(i);
				if (pred.actual() == weka.classifiers.evaluation.Prediction_Fields.MISSING_VALUE)
				{
					System.Console.Error.WriteLine(GetType().FullName + " Skipping prediction with missing class value");
					continue;
				}
				if (pred.weight() < 0)
				{
					System.Console.Error.WriteLine(GetType().FullName + " Skipping prediction with negative weight");
					continue;
				}
				if (pred.actual() == classIndex)
				{
					totPos += pred.weight();
				}
				else
				{
					totNeg += pred.weight();
				}
			}
			
			Instances insts = makeHeader();
			int[] sorted = Utils.sort(probs);
			TwoClassStats tc = new TwoClassStats(totPos, totNeg, 0, 0);
			double threshold = 0;
			double cumulativePos = 0;
			double cumulativeNeg = 0;
			for (int i = 0; i < sorted.Length; i++)
			{
				
				if ((i == 0) || (probs[sorted[i]] > threshold))
				{
					tc.TruePositive = tc.TruePositive - cumulativePos;
					tc.FalseNegative = tc.FalseNegative + cumulativePos;
					tc.FalsePositive = tc.FalsePositive - cumulativeNeg;
					tc.TrueNegative = tc.TrueNegative + cumulativeNeg;
					threshold = probs[sorted[i]];
					insts.add(makeInstance(tc, threshold));
					cumulativePos = 0;
					cumulativeNeg = 0;
					if (i == sorted.Length - 1)
					{
						break;
					}
				}
				
				NominalPrediction pred = (NominalPrediction) predictions.elementAt(sorted[i]);
				
				if (pred.actual() == weka.classifiers.evaluation.Prediction_Fields.MISSING_VALUE)
				{
					System.Console.Error.WriteLine(GetType().FullName + " Skipping prediction with missing class value");
					continue;
				}
				if (pred.weight() < 0)
				{
					System.Console.Error.WriteLine(GetType().FullName + " Skipping prediction with negative weight");
					continue;
				}
				if (pred.actual() == classIndex)
				{
					cumulativePos += pred.weight();
				}
				else
				{
					cumulativeNeg += pred.weight();
				}
				
				/*
				System.out.println(tc + " " + probs[sorted[i]] 
				+ " " + (pred.actual() == classIndex));
				*/
				/*if ((i != (sorted.length - 1)) &&
				((i == 0) ||  
				(probs[sorted[i]] != probs[sorted[i - 1]]))) {
				insts.add(makeInstance(tc, probs[sorted[i]]));
				}*/
			}
			return insts;
		}
示例#2
0
		private Instance makeInstance(TwoClassStats tc, double prob)
		{
			
			int count = 0;
			double[] vals = new double[11];
			vals[count++] = tc.TruePositive;
			vals[count++] = tc.FalseNegative;
			vals[count++] = tc.FalsePositive;
			vals[count++] = tc.TrueNegative;
			vals[count++] = tc.FalsePositiveRate;
			vals[count++] = tc.TruePositiveRate;
			vals[count++] = tc.Precision;
			vals[count++] = tc.Recall;
			vals[count++] = tc.Fallout;
			vals[count++] = tc.FMeasure;
			vals[count++] = prob;
			return new Instance(1.0, vals);
		}