关于spark的mllib学习总结(Java版)

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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,具体的流程如下图所示:
基本流程

加载数据

对于数据的加载或保存,mllib提供了MLUtils包,其作用是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:
数据格式

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一样,但是比他们更优化。DataFrame可以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所有功能的入口是SQLContext类,或者SQLContext的子类。为了创建一个基本的SQLContext,需要一个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);

利用卡方统计做特征提取

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Rowstatic 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LabeledPointstatic 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,需要将样本做数据的转化和特征提取的工作,所有刚刚训练模型的过程中,除了保存机器学习模型,还需要保存特征提取的中间模型。具体代码如下:

//初始化sparkSparkConf 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;        }    }

具体的代码,放在我的github上:https://github.com/Quincy1994/MachineLearning/

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