Examples with BinaryClassificationMetrics org.apache.spark.mllib.evaluation.BinaryClassificationMetrics used on opensource projects

Example 1 with BinaryClassificationMetrics

use of org.apache.spark.mllib.evaluation.BinaryClassificationMetrics in project mmtf-spark by sbl-sdsc.

the class SparkMultiClassClassifier method fit.

/**
 * Dataset must at least contain the following two columns:
 * label: the class labels
 * features: feature vector
 * @param data
 * @return map with metrics
 */
public Map<String, String> fit(Dataset<Row> data) {
    int classCount = (int) data.select(label).distinct().count();
    StringIndexerModel labelIndexer = new StringIndexer().setInputCol(label).setOutputCol("indexedLabel").fit(data);
    // Split the data into training and test sets (30% held out for testing)
    Dataset<Row>[] splits = data.randomSplit(new double[] { 1.0 - testFraction, testFraction }, seed);
    Dataset<Row> trainingData = splits[0];
    Dataset<Row> testData = splits[1];
    String[] labels = labelIndexer.labels();
    System.out.println();
    System.out.println("Class\tTrain\tTest");
    for (String l : labels) {
        System.out.println(l + "\t" + trainingData.select(label).filter(label + " = '" + l + "'").count() + "\t" + testData.select(label).filter(label + " = '" + l + "'").count());
    }
    // Set input columns
    predictor.setLabelCol("indexedLabel").setFeaturesCol("features");
    // Convert indexed labels back to original labels.
    IndexToString labelConverter = new IndexToString().setInputCol("prediction").setOutputCol("predictedLabel").setLabels(labelIndexer.labels());
    // Chain indexers and forest in a Pipeline
    Pipeline pipeline = new Pipeline().setStages(new PipelineStage[] { labelIndexer, predictor, labelConverter });
    // Train model. This also runs the indexers.
    PipelineModel model = pipeline.fit(trainingData);
    // Make predictions.
    Dataset<Row> predictions = model.transform(testData).cache();
    // Display some sample predictions
    System.out.println();
    System.out.println("Sample predictions: " + predictor.getClass().getSimpleName());
    predictions.sample(false, 0.1, seed).show(25);
    predictions = predictions.withColumnRenamed(label, "stringLabel");
    predictions = predictions.withColumnRenamed("indexedLabel", label);
    // collect metrics
    Dataset<Row> pred = predictions.select("prediction", label);
    Map<String, String> metrics = new LinkedHashMap<>();
    metrics.put("Method", predictor.getClass().getSimpleName());
    if (classCount == 2) {
        BinaryClassificationMetrics b = new BinaryClassificationMetrics(pred);
        metrics.put("AUC", Float.toString((float) b.areaUnderROC()));
    }
    MulticlassMetrics m = new MulticlassMetrics(pred);
    metrics.put("F", Float.toString((float) m.weightedFMeasure()));
    metrics.put("Accuracy", Float.toString((float) m.accuracy()));
    metrics.put("Precision", Float.toString((float) m.weightedPrecision()));
    metrics.put("Recall", Float.toString((float) m.weightedRecall()));
    metrics.put("False Positive Rate", Float.toString((float) m.weightedFalsePositiveRate()));
    metrics.put("True Positive Rate", Float.toString((float) m.weightedTruePositiveRate()));
    metrics.put("", "\nConfusion Matrix\n" + Arrays.toString(labels) + "\n" + m.confusionMatrix().toString());
    return metrics;
}

Example 2 with BinaryClassificationMetrics

use of org.apache.spark.mllib.evaluation.BinaryClassificationMetrics in project java_study by aloyschen.

the class GbdtAndLr method train.

/*
    * 获取GBDT模型组合后的特征输入到Lr模型中，训练LR模型
    * @param Path: 训练数据路径
     */
public void train(String Path) {
    JavaSparkContext jsc = getSc();
    ArrayList<ArrayList<Integer>> treeLeafArray = new ArrayList<>();
    Dataset<Row> all_data = Preprocessing(jsc, Path);
    JavaRDD<LabeledPoint> gbdt_data_labelpoint = load_gbdt_data(all_data);
    GradientBoostedTreesModel gbdt = train_gbdt(jsc, gbdt_data_labelpoint);
    DecisionTreeModel[] decisionTreeModels = gbdt.trees();
    // 获取GBDT每棵树的叶子索引
    for (int i = 0; i < this.maxIter; i++) {
        treeLeafArray.add(getLeafNodes(decisionTreeModels[i].topNode()));
    // System.out.println("叶子索引");
    // System.out.println(treeLeafArray.get(i));
    }
    JavaRDD<LabeledPoint> CombineFeatures = all_data.toJavaRDD().map(line -> {
        double[] newvaluesDouble;
        double[] features = new double[24];
        // 将dataset中每列特征值放入DenseVector中
        for (Integer i = 6; i < 18; i++) {
            org.apache.spark.mllib.linalg.DenseVector den = null;
            if (line.get(i) instanceof org.apache.spark.ml.linalg.Vector) {
                den = (DenseVector) Vectors.fromML((org.apache.spark.ml.linalg.DenseVector) line.get(i));
                features[i - 6] = den.toArray()[0];
            } else {
                features[i - 6] = Double.parseDouble(line.get(i).toString());
            }
        }
        DenseVector numerical_vector = new DenseVector(features);
        ArrayList<Double> newvaluesArray = new ArrayList<>();
        for (int i = 0; i < this.maxIter; i++) {
            int treePredict = predictModify(decisionTreeModels[i].topNode(), numerical_vector);
            int len = treeLeafArray.get(i).size();
            ArrayList<Double> treeArray = new ArrayList<>(len);
            // 数组所有值初始化为0，落在的叶子节点至为1
            for (int j = 0; j < len; j++) treeArray.add(j, 0d);
            treeArray.set(treeLeafArray.get(i).indexOf(treePredict), 1d);
            newvaluesArray.addAll(treeArray);
        }
        for (int i = 18; i < 29; i++) {
            SparseVector onehot_data = (SparseVector) Vectors.fromML((org.apache.spark.ml.linalg.SparseVector) line.get(i));
            DenseVector cat_data = onehot_data.toDense();
            for (int j = 0; j < cat_data.size(); j++) {
                newvaluesArray.add(cat_data.apply(j));
            }
        }
        newvaluesDouble = newvaluesArray.stream().mapToDouble(Double::doubleValue).toArray();
        DenseVector newdenseVector = new DenseVector(newvaluesDouble);
        return (new LabeledPoint(Double.valueOf(line.get(1).toString()), newdenseVector));
    });
    JavaRDD<LabeledPoint>[] splitsLR = CombineFeatures.randomSplit(new double[] { 0.7, 0.3 });
    JavaRDD<LabeledPoint> trainingDataLR = splitsLR[0];
    JavaRDD<LabeledPoint> testDataLR = splitsLR[1];
    System.out.println("Start train LR");
    LogisticRegressionModel LR = new LogisticRegressionWithLBFGS().setNumClasses(2).run(trainingDataLR.rdd()).clearThreshold();
    System.out.println("modelLR.weights().size():" + LR.weights().size());
    JavaPairRDD<Object, Object> test_LR = testDataLR.mapToPair((PairFunction<LabeledPoint, Object, Object>) labeledPoint -> {
        Tuple2<Object, Object> tuple2 = new Tuple2<>(LR.predict(labeledPoint.features()), labeledPoint.label());
        return tuple2;
    });
    BinaryClassificationMetrics test_metrics = new BinaryClassificationMetrics(test_LR.rdd());
    double test_auc = test_metrics.areaUnderROC();
    System.out.println("test data auc_score:" + test_auc);
    JavaPairRDD<Object, Object> train_LR = trainingDataLR.mapToPair((PairFunction<LabeledPoint, Object, Object>) labeledPoint -> {
        Tuple2<Object, Object> tuple2 = new Tuple2<>(LR.predict(labeledPoint.features()), labeledPoint.label());
        return tuple2;
    });
    BinaryClassificationMetrics train_metrics = new BinaryClassificationMetrics(train_LR.rdd());
    double train_auc = train_metrics.areaUnderROC();
    System.out.println("train data auc_score:" + train_auc);
    // 不同阈值下的精确度排序，取前十个输出
    JavaRDD<Tuple2<Object, Object>> precision = train_metrics.precisionByThreshold().toJavaRDD();
    JavaPairRDD<Object, Object> temp = JavaPairRDD.fromJavaRDD(precision);
    JavaPairRDD<Object, Object> swap = temp.mapToPair(Tuple2::swap);
    JavaPairRDD<Object, Object> precision_sort = swap.sortByKey(false);
    System.out.println("Precision by threshold: (Precision, Threshold)");
    for (int i = 0; i < 10; i++) {
        System.out.println(precision_sort.take(10).toArray()[i]);
    }
}