关于spark的mllib学习总结（Java版）

本篇博客主要讲述如何利用spark的mliib构建机器学习模型并预测新的数据，具体的流程以下图所示：

 

加载数据 对于数据的加载或保存，mllib提供了MLUtils包，其做用是Helper methods to load,save and pre-process data used in MLLib.博客中的数据是采用spark中提供的数据sample_libsvm_data.txt，其有一百个数据样本，658个特征。具体的数据形式如图所示: 

加载libsvm 

JavaRDD<LabeledPoint> lpdata = MLUtils.loadLibSVMFile(sc, this.libsvmFile).toJavaRDD();

LabeledPoint数据类型是对应与libsvmfile格式文件, 具体格式为： Lable(double类型），vector(Vector类型） 转化dataFrame数据类型 

JavaRDD<Row> jrow = lpdata.map(new LabeledPointToRow()); StructType schema = new StructType(new StructField[]{ new StructField("label", DataTypes.DoubleType, false, Metadata.empty()), new StructField("features", new VectorUDT(), false, Metadata.empty()), }); SQLContext jsql = new SQLContext(sc); DataFrame df = jsql.createDataFrame(jrow, schema);

DataFrame:DataFrame是一个以命名列方式组织的分布式数据集。在概念上，它跟关系型数据库中的一张表或者1个Python(或者R)中的data frame同样，可是比他们更优化。DataFrame能够根据结构化的数据文件、hive表、外部数据库或者已经存在的RDD构造。 SQLContext:spark sql全部功能的入口是SQLContext类，或者SQLContext的子类。为了建立一个基本的SQLContext，须要一个SparkContext。 特征提取 特征归一化处理 

StandardScaler scaler = new StandardScaler().setInputCol("features").setOutputCol("normFeatures").setWithStd(true); DataFrame scalerDF = scaler.fit(df).transform(df); scaler.save(this.scalerModelPath);

利用卡方统计作特征提取 

ChiSqSelector selector = new ChiSqSelector().setNumTopFeatures(500).setFeaturesCol("normFeatures").setLabelCol("label").setOutputCol("selectedFeatures"); ChiSqSelectorModel chiModel = selector.fit(scalerDF); DataFrame selectedDF = chiModel.transform(scalerDF).select("label", "selectedFeatures"); chiModel.save(this.featureSelectedModelPath);

训练机器学习模型（以SVM为例）

//转化为LabeledPoint数据类型， 训练模型
JavaRDD<Row> selectedrows = selectedDF.javaRDD(); JavaRDD<LabeledPoint> trainset = selectedrows.map(new RowToLabel()); //训练SVM模型, 并保存
int numIteration = 200; SVMModel model = SVMWithSGD.train(trainset.rdd(), numIteration); model.clearThreshold(); model.save(sc, this.mlModelPath); // LabeledPoint数据类型转化为Row
static class LabeledPointToRow implements Function<LabeledPoint, Row> { public Row call(LabeledPoint p) throws Exception { double label = p.label(); Vector vector = p.features(); return RowFactory.create(label, vector); } } //Rows数据类型转化为LabeledPoint
static class RowToLabel implements Function<Row, LabeledPoint> { public LabeledPoint call(Row r) throws Exception { Vector features = r.getAs(1); double label = r.getDouble(0); return new LabeledPoint(label, features); } }

测试新的样本 测试新的样本前，须要将样本作数据的转化和特征提取的工做，全部刚刚训练模型的过程当中，除了保存机器学习模型，还须要保存特征提取的中间模型。具体代码以下:

//初始化spark
SparkConf conf = new SparkConf().setAppName("SVM").setMaster("local"); conf.set("spark.testing.memory", "2147480000"); SparkContext sc = new SparkContext(conf); //加载测试数据
JavaRDD<LabeledPoint> testData = MLUtils.loadLibSVMFile(sc, this.predictDataPath).toJavaRDD(); //转化DataFrame数据类型
JavaRDD<Row> jrow =testData.map(new LabeledPointToRow()); StructType schema = new StructType(new StructField[]{ new StructField("label", DataTypes.DoubleType, false, Metadata.empty()), new StructField("features", new VectorUDT(), false, Metadata.empty()), }); SQLContext jsql = new SQLContext(sc); DataFrame df = jsql.createDataFrame(jrow, schema); //数据规范化
StandardScaler scaler = StandardScaler.load(this.scalerModelPath); DataFrame scalerDF = scaler.fit(df).transform(df); //特征选取
ChiSqSelectorModel chiModel = ChiSqSelectorModel.load( this.featureSelectedModelPath); DataFrame selectedDF = chiModel.transform(scalerDF).select("label", "selectedFeatures");

测试数据集

SVMModel svmmodel = SVMModel.load(sc, this.mlModelPath); JavaRDD<Tuple2<Double, Double>> predictResult = testset.map(new Prediction(svmmodel)) ; predictResult.collect(); static class Prediction implements Function<LabeledPoint, Tuple2<Double , Double>> { SVMModel model; public Prediction(SVMModel model){ this.model = model; } public Tuple2<Double, Double> call(LabeledPoint p) throws Exception { Double score = model.predict(p.features()); return new Tuple2<Double , Double>(score, p.label()); } }

计算准确率

double accuracy = predictResult.filter(new PredictAndScore()).count() * 1.0 / predictResult.count(); System.out.println(accuracy); static class PredictAndScore implements Function<Tuple2<Double, Double>, Boolean> { public Boolean call(Tuple2<Double, Double> t) throws Exception { double score = t._1(); double label = t._2(); System.out.print("score:" + score + ", label:"+ label); if(score >= 0.0 && label >= 0.0) return true; else if(score < 0.0 && label < 0.0) return true; else return false; } }