Package org.apache.mahout.classifier

Examples of org.apache.mahout.classifier.AbstractVectorClassifier

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    CsvRecordFactory csv = lmp.getCsvRecordFactory();
    assertEquals("[1, 2]", Sets.newTreeSet(csv.getTargetCategories()).toString());
    assertEquals("[Intercept Term, x, y]", Sets.newTreeSet(csv.getPredictors()).toString());


    AbstractVectorClassifier model = TrainLogistic.getModel();
    ModelDissector md = new ModelDissector();
    List<String> data = Resources.readLines(Resources.getResource(inputFile), Charsets.UTF_8);
    for (String line : data.subList(1, data.size())) {
      Vector v = new DenseVector(lmp.getNumFeatures());
      csv.getTraceDictionary().clear();
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    CsvRecordFactory csv = lmp.getCsvRecordFactory();
    assertEquals("[1, 2]", Sets.newTreeSet(csv.getTargetCategories()).toString());
    assertEquals("[Intercept Term, x, y]", Sets.newTreeSet(csv.getPredictors()).toString());

    // verify model by building dissector
    AbstractVectorClassifier model = TrainLogistic.getModel();
    List<String> data = Resources.readLines(Resources.getResource("donut.csv"), Charsets.UTF_8);
    Map<String, Double> expectedValues = ImmutableMap.of("x", -0.7, "y", -0.43, "Intercept Term", -0.15);
    verifyModel(lmp, csv, data, model, expectedValues);

    // test saved model
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    trainNaiveBayes.run(new String[] { "--input", inputFile.getAbsolutePath(), "--output", outputDir.getAbsolutePath(),
        "-el", "--tempDir", tempDir.getAbsolutePath() });

    NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);

    AbstractVectorClassifier classifier = new StandardNaiveBayesClassifier(naiveBayesModel);

    assertEquals(2, classifier.numCategories());

    Vector prediction = classifier.classifyFull(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());

    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
  }
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        "-el", "--trainComplementary",
        "--tempDir", tempDir.getAbsolutePath() });

    NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);

    AbstractVectorClassifier classifier = new ComplementaryNaiveBayesClassifier(naiveBayesModel);

    assertEquals(2, classifier.numCategories());

    Vector prediction = classifier.classifyFull(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());

    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
  }
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    CsvRecordFactory csv = lmp.getCsvRecordFactory();
    assertEquals("[1, 2]", Sets.newTreeSet(csv.getTargetCategories()).toString());
    assertEquals("[Intercept Term, x, y]", Sets.newTreeSet(csv.getPredictors()).toString());

    // verify model by building dissector
    AbstractVectorClassifier model = TrainLogistic.getModel();
    List<String> data = Resources.readLines(Resources.getResource("donut.csv"), Charsets.UTF_8);
    Map<String, Double> expectedValues = ImmutableMap.of("x", -0.7, "y", -0.43, "Intercept Term", -0.15);
    verifyModel(lmp, csv, data, model, expectedValues);

    // test saved model
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    trainNaiveBayes.run(new String[] { "--input", inputFile.getAbsolutePath(), "--output", outputDir.getAbsolutePath(),
        "-el", "--tempDir", tempDir.getAbsolutePath() });

    NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);

    AbstractVectorClassifier classifier = new StandardNaiveBayesClassifier(naiveBayesModel);

    assertEquals(2, classifier.numCategories());

    Vector prediction = classifier.classifyFull(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());

    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
  }
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        "-el", "--trainComplementary",
        "--tempDir", tempDir.getAbsolutePath() });

    NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);

    AbstractVectorClassifier classifier = new ComplementaryNaiveBayesClassifier(naiveBayesModel);

    assertEquals(2, classifier.numCategories());

    Vector prediction = classifier.classifyFull(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());

    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
  }
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    CsvRecordFactory csv = lmp.getCsvRecordFactory();
    assertEquals("[1, 2]", Sets.newTreeSet(csv.getTargetCategories()).toString());
    assertEquals("[Intercept Term, x, y]", Sets.newTreeSet(csv.getPredictors()).toString());

    // verify model by building dissector
    AbstractVectorClassifier model = TrainLogistic.getModel();
    List<String> data = Resources.readLines(Resources.getResource("donut.csv"), Charsets.UTF_8);
    Map<String, Double> expectedValues = ImmutableMap.of("x", -0.7, "y", -0.43, "Intercept Term", -0.15);
    verifyModel(lmp, csv, data, model, expectedValues);

    // test saved model
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Related Classes of org.apache.mahout.classifier.AbstractVectorClassifier

  • mia.classifier.ch16.server.Server$ServerWatcher
  • org.apache.mahout.classifier.naivebayes.NaiveBayesTest
  • org.apache.mahout.classifier.sgd.TrainLogisticTest
  • org.apache.mahout.clustering.TestVectorModelClassifier
  • org.apache.mahout.math.DenseMatrix
  • org.apache.mahout.math.DenseVector
  • org.apache.mahout.math.Matrix
  • org.apache.mahout.math.Vector
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