Examples of Auc

Examples of edu.uci.jforestsx.eval.AUC

121
122
123
124
125
126
127
128
129
130
131
    }
  }

  protected EvaluationMetric getEvaluationMetric(String name) throws Exception {
    if (name.equals("AUC")) {
      return new AUC();
    } else if (name.equals("RMSE")) {
      return new RMSE();
    } else if (name.equals("Accuracy")) {
      return new Accuracy();
    } else if (name.equals("BalancedYoundenIndex")) {
View Full Code Here

Examples of org.apache.mahout.classifier.evaluation.Auc

11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
public class AucExample {

  public static void main(String[] args) throws IOException {
    String inputFile = args[1];
   
    Auc x1 = new Auc();
    OnlineAuc x2 = new GlobalOnlineAuc();
    BufferedReader in = new BufferedReader(new FileReader(inputFile));
    int lineCount = 0;
    String line = in.readLine();
    while (line != null) {
      lineCount++;
      String[] pieces = line.split(",");
      double score = Double.parseDouble(pieces[0]);
      int target = Integer.parseInt(pieces[1]);
      x1.add(target, score);
      x2.addSample(target, score);
      if (lineCount%500 == 0) {
        System.out.printf("%10d\t%10.3f\t%10d\t%.3f\n",
                   lineCount, score, target, x2.auc());
      }
      line = in.readLine();
    }

    System.out.printf("%d lines read\n", lineCount);
    System.out.printf("%10.2f = batch estimate\n", x1.auc());
    System.out.printf("%10.2f = on-line estimate\n", x2.auc()); 
    }
View Full Code Here

Examples of org.apache.mahout.classifier.evaluation.Auc

50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
      if (!showAuc && !showConfusion && !showScores) {
        showAuc = true;
        showConfusion = true;
      }

      Auc collector = new Auc();
      LogisticModelParameters lmp = LogisticModelParameters.loadFrom(new File(modelFile));

      CsvRecordFactory csv = lmp.getCsvRecordFactory();
      OnlineLogisticRegression lr = lmp.createRegression();
      BufferedReader in = TrainLogistic.open(inputFile);
      String line = in.readLine();
      csv.firstLine(line);
      line = in.readLine();
      if (showScores) {
        System.out.printf("\"%s\",\"%s\",\"%s\"\n", "target", "model-output", "log-likelihood");
      }
      while (line != null) {
        Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
        int target = csv.processLine(line, v);
        double score = lr.classifyScalar(v);
        if (showScores) {
          System.out.printf("%d,%.3f,%.6f\n", target, score, lr.logLikelihood(target, v));
        }
        collector.add(target, score);
        line = in.readLine();
      }

      if (showAuc) {
        System.out.printf("AUC = %.2f\n", collector.auc());
      }
      if (showConfusion) {
        Matrix m = collector.confusion();
        System.out.printf("confusion: [[%.1f, %.1f], [%.1f, %.1f]]\n",
            m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
        m = collector.entropy();
        System.out.printf("entropy: [[%.1f, %.1f], [%.1f, %.1f]]\n",
            m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
      }
    }
  }
View Full Code Here

Examples of org.apache.mahout.classifier.evaluation.Auc

66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
      if (!showAuc && !showConfusion && !showScores) {
        showAuc = true;
        showConfusion = true;
      }

      Auc collector = null;
      AdaptiveLogisticModelParameters lmp = AdaptiveLogisticModelParameters
          .loadFromFile(new File(modelFile));
      CsvRecordFactory csv = lmp.getCsvRecordFactory();
      AdaptiveLogisticRegression lr = lmp.createAdaptiveLogisticRegression();     
     
      if (lmp.getTargetCategories().size() <=2 ) {
        collector = new Auc();
      }
     
      OnlineSummarizer slh = new OnlineSummarizer();
      ConfusionMatrix cm = new ConfusionMatrix(lmp.getTargetCategories(), defaultCategory);

      State<Wrapper, CrossFoldLearner> best = lr.getBest();
      if (best == null) {
        output.printf("%s\n",
            "AdaptiveLogisticRegression has not be trained probably.");
        return;
      }
      CrossFoldLearner learner = best.getPayload().getLearner();

      BufferedReader in = TrainLogistic.open(inputFile);
      String line = in.readLine();
      csv.firstLine(line);
      line = in.readLine();
      if (showScores) {
        output.printf(Locale.ENGLISH, "\"%s\", \"%s\", \"%s\", \"%s\"\n",
            "target", "model-output", "log-likelihood", "average-likelihood");
      }
      while (line != null) {
        Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
        //TODO: How to avoid extra target values not shown in the training process.
        int target = csv.processLine(line, v);
        double likelihood = learner.logLikelihood(target, v);
        double score = learner.classifyFull(v).maxValue();
       
        slh.add(likelihood);
        cm.addInstance(csv.getTargetString(line), csv.getTargetLabel(target));       
       
        if (showScores) {
          output.printf(Locale.ENGLISH, "%8d, %.12f, %.13f, %.13f\n", target,
              score, learner.logLikelihood(target, v), slh.getMean());
        }
        if (collector != null) {
          collector.add(target, score);
        }
        line = in.readLine();
      }
     
      output.printf(Locale.ENGLISH,"\nLog-likelihood:");
      output.printf(Locale.ENGLISH, "Min=%.2f, Max=%.2f, Mean=%.2f, Median=%.2f\n",
          slh.getMin(), slh.getMax(), slh.getMean(), slh.getMedian());

      if (collector != null) {       
        output.printf(Locale.ENGLISH, "\nAUC = %.2f\n", collector.auc());       
      }
     
      if (showConfusion) {
        output.printf(Locale.ENGLISH, "\n%s\n\n", cm.toString());
       
        if (collector != null){
          Matrix m = collector.entropy();
          output.printf(Locale.ENGLISH,
              "Entropy Matrix: [[%.1f, %.1f], [%.1f, %.1f]]\n", m.get(0, 0),
              m.get(1, 0), m.get(0, 1), m.get(1, 1));
        }       
      }
View Full Code Here

Examples of org.apache.mahout.classifier.evaluation.Auc

45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
      a2.setPolicy(FIFO);

      OnlineAuc a3 = new GlobalOnlineAuc();
      a3.setPolicy(RANDOM);

      Auc a4 = new Auc();

      for (int j = 0; j < 10000; j++) {
        double x = gen.nextGaussian();

        a1.addSample(0, x);
        a2.addSample(0, x);
        a3.addSample(0, x);
        a4.add(0, x);

        x = gen.nextGaussian() + 1;

        a1.addSample(1, x);
        a2.addSample(1, x);
        a3.addSample(1, x);
        a4.add(1, x);
      }

      stats[0].add(a1.auc());
      stats[1].add(a2.auc());
      stats[2].add(a3.auc());
      stats[3].add(a4.auc());
    }
   
    int i = 0;
    for (GlobalOnlineAuc.ReplacementPolicy policy : new GlobalOnlineAuc.ReplacementPolicy[]{FAIR, FIFO, RANDOM, null}) {
      OnlineSummarizer summary = stats[i++];
View Full Code Here

Examples of org.apache.mahout.classifier.evaluation.Auc

68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
      if (!showAuc && !showConfusion && !showScores) {
        showAuc = true;
        showConfusion = true;
      }

      Auc collector = null;
      AdaptiveLogisticModelParameters lmp = AdaptiveLogisticModelParameters
          .loadFromFile(new File(modelFile));
      CsvRecordFactory csv = lmp.getCsvRecordFactory();
      AdaptiveLogisticRegression lr = lmp.createAdaptiveLogisticRegression();     

      if (lmp.getTargetCategories().size() <= 2) {
        collector = new Auc();
      }

      OnlineSummarizer slh = new OnlineSummarizer();
      ConfusionMatrix cm = new ConfusionMatrix(lmp.getTargetCategories(), defaultCategory);

      State<Wrapper, CrossFoldLearner> best = lr.getBest();
      if (best == null) {
        output.println("AdaptiveLogisticRegression has not be trained probably.");
        return;
      }
      CrossFoldLearner learner = best.getPayload().getLearner();

      BufferedReader in = TrainLogistic.open(inputFile);
      String line = in.readLine();
      csv.firstLine(line);
      line = in.readLine();
      if (showScores) {
        output.println("\"target\", \"model-output\", \"log-likelihood\", \"average-likelihood\"");
      }
      while (line != null) {
        Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
        //TODO: How to avoid extra target values not shown in the training process.
        int target = csv.processLine(line, v);
        double likelihood = learner.logLikelihood(target, v);
        double score = learner.classifyFull(v).maxValue();

        slh.add(likelihood);
        cm.addInstance(csv.getTargetString(line), csv.getTargetLabel(target));       

        if (showScores) {
          output.printf(Locale.ENGLISH, "%8d, %.12f, %.13f, %.13f%n", target,
              score, learner.logLikelihood(target, v), slh.getMean());
        }
        if (collector != null) {
          collector.add(target, score);
        }
        line = in.readLine();
      }

      output.printf(Locale.ENGLISH,"\nLog-likelihood:");
      output.printf(Locale.ENGLISH, "Min=%.2f, Max=%.2f, Mean=%.2f, Median=%.2f%n",
          slh.getMin(), slh.getMax(), slh.getMean(), slh.getMedian());

      if (collector != null) {       
        output.printf(Locale.ENGLISH, "%nAUC = %.2f%n", collector.auc());
      }

      if (showConfusion) {
        output.printf(Locale.ENGLISH, "%n%s%n%n", cm.toString());

        if (collector != null) {
          Matrix m = collector.entropy();
          output.printf(Locale.ENGLISH,
              "Entropy Matrix: [[%.1f, %.1f], [%.1f, %.1f]]%n", m.get(0, 0),
              m.get(1, 0), m.get(0, 1), m.get(1, 1));
        }       
      }
View Full Code Here

Examples of org.apache.mahout.classifier.evaluation.Auc

57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
      if (!showAuc && !showConfusion && !showScores) {
        showAuc = true;
        showConfusion = true;
      }

      Auc collector = new Auc();
      LogisticModelParameters lmp = LogisticModelParameters.loadFrom(new File(modelFile));

      CsvRecordFactory csv = lmp.getCsvRecordFactory();
      OnlineLogisticRegression lr = lmp.createRegression();
      BufferedReader in = TrainLogistic.open(inputFile);
      String line = in.readLine();
      csv.firstLine(line);
      line = in.readLine();
      if (showScores) {
        output.println("\"target\",\"model-output\",\"log-likelihood\"");
      }
      while (line != null) {
        Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
        int target = csv.processLine(line, v);

        double score = lr.classifyScalar(v);
        if (showScores) {
          output.printf(Locale.ENGLISH, "%d,%.3f,%.6f%n", target, score, lr.logLikelihood(target, v));
        }
        collector.add(target, score);
        line = in.readLine();
      }

      if (showAuc) {
        output.printf(Locale.ENGLISH, "AUC = %.2f%n", collector.auc());
      }
      if (showConfusion) {
        Matrix m = collector.confusion();
        output.printf(Locale.ENGLISH, "confusion: [[%.1f, %.1f], [%.1f, %.1f]]%n",
          m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
        m = collector.entropy();
        output.printf(Locale.ENGLISH, "entropy: [[%.1f, %.1f], [%.1f, %.1f]]%n",
          m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
      }
    }
  }
View Full Code Here

Examples of org.apache.mahout.classifier.evaluation.Auc

43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
      a2.setPolicy(GlobalOnlineAuc.ReplacementPolicy.FIFO);

      OnlineAuc a3 = new GlobalOnlineAuc();
      a3.setPolicy(GlobalOnlineAuc.ReplacementPolicy.RANDOM);

      Auc a4 = new Auc();

      for (int j = 0; j < 10000; j++) {
        double x = gen.nextGaussian();

        a1.addSample(0, x);
        a2.addSample(0, x);
        a3.addSample(0, x);
        a4.add(0, x);

        x = gen.nextGaussian() + 1;

        a1.addSample(1, x);
        a2.addSample(1, x);
        a3.addSample(1, x);
        a4.add(1, x);
      }

      stats[0].add(a1.auc());
      stats[1].add(a2.auc());
      stats[2].add(a3.auc());
      stats[3].add(a4.auc());
    }
   
    int i = 0;
    for (GlobalOnlineAuc.ReplacementPolicy policy : new GlobalOnlineAuc.ReplacementPolicy[] {
                                                      GlobalOnlineAuc.ReplacementPolicy.FAIR,
View Full Code Here

Examples of org.apache.mahout.classifier.evaluation.Auc

53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
      if (!showAuc && !showConfusion && !showScores) {
        showAuc = true;
        showConfusion = true;
      }

      Auc collector = new Auc();
      LogisticModelParameters lmp = LogisticModelParameters.loadFrom(new File(modelFile));

      CsvRecordFactory csv = lmp.getCsvRecordFactory();
      OnlineLogisticRegression lr = lmp.createRegression();
      BufferedReader in = TrainLogistic.open(inputFile);
      String line = in.readLine();
      csv.firstLine(line);
      line = in.readLine();
      if (showScores) {
        output.printf(Locale.ENGLISH, "\"%s\",\"%s\",\"%s\"\n", "target", "model-output", "log-likelihood");
      }
      while (line != null) {
        Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
        int target = csv.processLine(line, v);
        double score = lr.classifyScalar(v);
        if (showScores) {
          output.printf(Locale.ENGLISH, "%d,%.3f,%.6f\n", target, score, lr.logLikelihood(target, v));
        }
        collector.add(target, score);
        line = in.readLine();
      }

      if (showAuc) {
        output.printf(Locale.ENGLISH, "AUC = %.2f\n", collector.auc());
      }
      if (showConfusion) {
        Matrix m = collector.confusion();
        output.printf(Locale.ENGLISH, "confusion: [[%.1f, %.1f], [%.1f, %.1f]]\n",
          m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
        m = collector.entropy();
        output.printf(Locale.ENGLISH, "entropy: [[%.1f, %.1f], [%.1f, %.1f]]\n",
          m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
      }
    }
  }
View Full Code Here

Examples of org.apache.mahout.classifier.evaluation.Auc

66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
      if (!showAuc && !showConfusion && !showScores) {
        showAuc = true;
        showConfusion = true;
      }

      Auc collector = null;
      AdaptiveLogisticModelParameters lmp = AdaptiveLogisticModelParameters
          .loadFromFile(new File(modelFile));
      CsvRecordFactory csv = lmp.getCsvRecordFactory();
      AdaptiveLogisticRegression lr = lmp.createAdaptiveLogisticRegression();     
     
      if (lmp.getTargetCategories().size() <=2 ) {
        collector = new Auc();
      }
     
      OnlineSummarizer slh = new OnlineSummarizer();
      ConfusionMatrix cm = new ConfusionMatrix(lmp.getTargetCategories(), defaultCategory);

      State<Wrapper, CrossFoldLearner> best = lr.getBest();
      if (best == null) {
        output.printf("%s\n",
            "AdaptiveLogisticRegression has not be trained probably.");
        return;
      }
      CrossFoldLearner learner = best.getPayload().getLearner();

      BufferedReader in = TrainLogistic.open(inputFile);
      String line = in.readLine();
      csv.firstLine(line);
      line = in.readLine();
      if (showScores) {
        output.printf(Locale.ENGLISH, "\"%s\", \"%s\", \"%s\", \"%s\"\n",
            "target", "model-output", "log-likelihood", "average-likelihood");
      }
      while (line != null) {
        Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
        //TODO: How to avoid extra target values not shown in the training process.
        int target = csv.processLine(line, v);
        double likelihood = learner.logLikelihood(target, v);
        double score = learner.classifyFull(v).maxValue();
       
        slh.add(likelihood);
        cm.addInstance(csv.getTargetString(line), csv.getTargetLabel(target));       
       
        if (showScores) {
          output.printf(Locale.ENGLISH, "%8d, %.12f, %.13f, %.13f\n", target,
              score, learner.logLikelihood(target, v), slh.getMean());
        }
        if (collector != null) {
          collector.add(target, score);
        }
        line = in.readLine();
      }
     
      output.printf(Locale.ENGLISH,"\nLog-likelihood:");
      output.printf(Locale.ENGLISH, "Min=%.2f, Max=%.2f, Mean=%.2f, Median=%.2f\n",
          slh.getMin(), slh.getMax(), slh.getMean(), slh.getMedian());

      if (collector != null) {       
        output.printf(Locale.ENGLISH, "\nAUC = %.2f\n", collector.auc());       
      }
     
      if (showConfusion) {
        output.printf(Locale.ENGLISH, "\n%s\n\n", cm.toString());
       
        if (collector != null) {
          Matrix m = collector.entropy();
          output.printf(Locale.ENGLISH,
              "Entropy Matrix: [[%.1f, %.1f], [%.1f, %.1f]]\n", m.get(0, 0),
              m.get(1, 0), m.get(0, 1), m.get(1, 1));
        }       
      }
View Full Code Here
TOP
Copyright © 2018 www.massapi.com. All rights reserved.
All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.