UltraFastWWS/src/experiments/ScalabilityTrainSize.java at main · ChangWeiTan/UltraFastWWS · GitHub

1
2
3
4
5
6
7
8
9
10
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
39
40
41
42
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
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
138
139
140
package experiments;

import application.Application;
import classifiers.TimeSeriesClassifier;
import datasets.DatasetLoader;
import datasets.Sequences;
import datasets.TimeSeriesDatasets;
import results.ClassificationResults;
import results.TrainingClassificationResults;

import java.util.Objects;

import static application.Application.extractArguments;
import static utils.GenericTools.doTimeNs;
import static utils.GenericTools.println;

public class ScalabilityTrainSize {
    static String moduleName = "FASTEREE";
    private static final String[] testArgs = new String[]{
            "-problem=SITS1M_fold1",
            "-classifier=UltraFastWWSearch", // see classifiers in TimeSeriesClassifier.java
            "-paramId=99",
            "-cpu=2",
            "-verbose=0",
            "-iter=0",
            "-eval=0",
            "-trainSize=0",
    };

    public static void main(String[] args) throws Exception {
        final long startTime = System.nanoTime();
//        args = testArgs;
        extractArguments(args);
        if (Application.problem.equals(""))
            Application.problem = "SITS1M_fold1";

        Application.printSummary(moduleName);

        switch (Application.problem) {
            case "all":
                for (String problem : TimeSeriesDatasets.largeDatasets)
                    if (Application.scalabilityTrainRatio == 0) {
                        if (problem.contains("SITS"))
                            for (int i = 1; i <= 10; i++) {
                                singleRun(problem, 1.0 * i / 100);
                            }
                        else
                            for (int i = 1; i <= 10; i++) {
                                singleRun(problem, 1.0 * i / 10);
                            }
                    } else {
                        singleRun(problem, Application.scalabilityTrainRatio);
                    }
                break;
            default:
                if (Application.scalabilityTrainRatio == 0) {
                    if (Application.problem.contains("SITS"))
                        for (int i = 1; i <= 10; i++) {
                            singleRun(Application.problem, 1.0 * i / 100);
                        }
                    else
                        for (int i = 1; i <= 10; i++) {
                            singleRun(Application.problem, 1.0 * i / 10);
                        }
                } else {
                    singleRun(Application.problem, Application.scalabilityTrainRatio);
                }
                break;
        }
        final long endTime = System.nanoTime();
        println("[" + moduleName + "] Total time taken " + doTimeNs(endTime - startTime));
    }

    private static void singleRun(String problem, double ratio) throws Exception {
        String outputPath = Objects.requireNonNullElseGet(Application.outputPath, () -> System.getProperty("user.dir") + "/outputs/scalability_size/");
        if (Application.paramId > 0)
            outputPath = outputPath +
                    Application.classifierName + "_" +
                    Application.paramId + "/" +
                    Application.iteration + "/" +
                    problem;
        else
            outputPath = outputPath +
                    Application.classifierName + "/" +
                    Application.iteration + "/" +
                    problem;

        println("[" + moduleName + "] Problem: " + problem + " -- " + ratio);

        DatasetLoader loader = new DatasetLoader();
        Sequences trainData = loader.readUCRTrain(problem, Application.datasetPath, Application.znorm);

        if (Application.iteration == 0) {
            trainData.shuffle(0);
        }

        trainData = trainData.stratifySubset(ratio);
        if (Application.verbose > 1) {
            trainData.summary();
        } else if (Application.verbose == 1) {
            println("[" + moduleName + "] Number of instances: " + trainData.size());
        }

        TimeSeriesClassifier classifier = Application.initTSC(trainData);
        if (Application.verbose > 1)
            println(classifier);

        TrainingClassificationResults trainingResults = classifier.fit(trainData);
        trainingResults.problem = problem;
        if (Application.verbose > 1)
            println("[" + moduleName + "]" + trainingResults);

        if (Application.doEvaluation) {
            Sequences testData = loader.readUCRTest(problem, Application.datasetPath, Application.znorm);
            if (Application.iteration == 0) {
                testData.shuffle(0);
            }

            ClassificationResults classificationResults = classifier.evaluate(testData);
            classificationResults.problem = problem;
            if (Application.verbose > 1)
                println("[" + moduleName + "]" + classificationResults);

            double totalTime = trainingResults.elapsedTimeNanoSeconds + classificationResults.elapsedTimeNanoSeconds;
            if (Application.verbose > 1)
                println("[" + moduleName + "] Total time taken " + totalTime);

            Application.saveResults(
                    outputPath,
                    trainingResults,
                    classificationResults,
                    "results_" + ratio + ".csv");
        } else {
            Application.saveResults(
                    outputPath,
                    trainingResults,
                    "results_" + ratio + ".csv");
        }
    }
}