Spark机器学习库现支持两种接口的API:RDD-based和DataFrame-based，Spark官方网站上说，RDD-based APIs在2.0后进入维护模式，主要的机器学习API是spark-ml包中的DataFrame-based API，并将在3.0后完全移除RDD-based API。
在学习了两周Spark MLlib后，准备转向DataFrame-based接口。由于现有的文档资料均是RDD-based接口，于是便去看了看Spark MLlib的源码。DataFrame-based API 包含在org.apache.spark.ml包中，其中主要的类结构如下：
咱先看一个线性回归的例子examples/ml/LinearRegressionExample.scala，其首先定义了一个LinearRegression的对象:
val lir = new LinearRegression()
.setFeaturesCol("features")
.setLabelCol("label")
.setRegParam(params.regParam)
.setElasticNetParam(params.elasticNetParam)
.setMaxIter(params.maxIter)
.setTol(params.tol)
然后，调用fit方法训练数据，得到一个训练好的模型lirModel，它是一个LinearRegressionModel类的对象。
val lirModel = lir.fit(training)
现在，我们大概可以理清MLlib机器学习的流程，和很多单机机器学习库一样，先定义一个模型并设置好参数，然后训练数据，最后返回一个训练好了的模型。
我们现在在源码中去查看LinearRegression和LinearRegressionModel，其类的依赖关系如下：
LinearRegression是一个Predictor，LinearRegressionModel是一个Model，那么Predictor是学习算法，Model是训练得到的模型。除此之外，还有一类继承自Params的类，这是一个表示参数的类。Predictor 和Model 共享一套参数。
现在用Spark MLlib来完成第一个机器学习例子，数据是我之前放在txt文件里的回归数据，一共550多万条，共13列，第一列是Label，后面是Features。分别演示两种接口，先用旧的接口：
1.读取原始数据：
scala> import org.apache.spark.mllib.linalg._
import org.apache.spark.mllib.linalg._
scala> import org.apache.spark.mllib.regression._
import org.apache.spark.mllib.regression._
scala> val raw_data = sc.textFile("data/my/y_x.txt")
raw_data: org.apache.spark.rdd.RDD[String] = data/my/y_x.txt MapPartitionsRDD[1] at textFile at <console>:30
2.转换格式，RDD-based接口以LabeledPoint为输入数据的格式：
scala> val data = raw_data.map{ line =>
| val arr = line.split(' ').map(_.toDouble)
| val label = arr.head
| val features = Vectors.dense(arr.tail)| LabeledPoint(label,features)
| }
data: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[2] at map at <console>:32
3.划分train、test数据集：
scala> val splits = data.randomSplit(Array(0.8, 0.2))
splits: Array[org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint]] = Array(MapPartitionsRDD[3] at randomSplit at <console>:34, MapPartitionsRDD[4] at randomSplit at <console>:34)
scala> val train_set = splits(0).cache
train_set: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[3] at randomSplit at <console>:34
scala> val test_set = splits(1).cache
test_set: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[4] at randomSplit at <console>:34
4.使用LinearRegressionWithSGD.train训练模型：
scala> val lr = LinearRegressionWithSGD.train(train_set,100,0.0001)
warning: there was one deprecation warning; re-run with -deprecation for details
16/08/26 09:20:44 WARN Executor: 1 block locks were not released by TID = 0:
[rdd_3_0]
lr: org.apache.spark.mllib.regression.LinearRegressionModel = org.apache.spark.mllib.regression.LinearRegressionModel: intercept = 0.0, numFeatures = 12
5.模型评估：
scala> val pred_labels = test_set.map(lp => (lp.label, lr.predict(lp.features)))
pred_labels: org.apache.spark.rdd.RDD[(Double, Double)] = MapPartitionsRDD[17] at map at <console>:42
scala> val mse = pred_labels.map{case (p,v) => math.pow(p-v,2)}.mean
mse: Double = 0.05104150735910074
再用新的接口：
1.读取原始数据：
scala> import org.apache.spark.ml.linalg._
import org.apache.spark.ml.linalg._
scala> import org.apache.spark.ml.regression._
import org.apache.spark.ml.regression._
scala> import org.apache.spark.sql._
import org.apache.spark.sql._
scala> val raw_data = spark.read.text("data/my/y_x.txt")
raw_data: org.apache.spark.sql.DataFrame = [value: string]
2.转换数据
scala> val data = raw_data.rdd.map { case Row(line:String) =>
| val arr = line.split(' ').map(_.toDouble)
| val label = arr.head
| val features = Vectors.dense(arr.tail)
| (label,features)
| }
data: org.apache.spark.rdd.RDD[(Double, org.apache.spark.ml.linalg.Vector)] = MapPartitionsRDD[4] at map at <console>:34
3.划分数据集
scala> val splits = data.randomSplit(Array(0.8, 0.2))
splits: Array[org.apache.spark.rdd.RDD[(Double, org.apache.spark.ml.linalg.Vector)]] = Array(MapPartitionsRDD[5] at randomSplit at <console>:36, MapPartitionsRDD[6] at randomSplit at <console>:36)
scala> val train_set = splits(0).toDS.cache
train_set: org.apache.spark.sql.Dataset[(Double, org.apache.spark.ml.linalg.Vector)] = [_1: double, _2: vector]
scala> val test_set = splits(1).toDS.cache
test_set: org.apache.spark.sql.Dataset[(Double, org.apache.spark.ml.linalg.Vector)] = [_1: double, _2: vector]
4.创建LinearRegression对象，并设置模型参数。这里设置类LabelCol和FeaturesCol列，默认为“label”和“features”，而我们的数据是"_1"和”_2“。
scala> val lir = new LinearRegression
lir: org.apache.spark.ml.regression.LinearRegression = linReg_c4e70a01bcd3
scala> lir.setFeaturesCol("_2")
res0: org.apache.spark.ml.regression.LinearRegression = linReg_c4e70a01bcd3
scala> lir.setLabelCol("_1")
res1: org.apache.spark.ml.regression.LinearRegression = linReg_c4e70a01bcd3
5.训练模型
val model = lir.fit(train_set)
16/08/26 09:45:16 WARN Executor: 1 block locks were not released by TID = 0:
[rdd_9_0]
16/08/26 09:45:16 WARN WeightedLeastSquares: regParam is zero, which might cause numerical instability and overfitting.
model: org.apache.spark.ml.regression.LinearRegressionModel = linReg_c4e70a01bcd3
6.模型评估
scala> val res = model.transform(test_set)
res: org.apache.spark.sql.DataFrame = [_1: double, _2: vector ... 1 more field]
scala> import org.apache.spark.ml.evaluation._
import org.apache.spark.ml.evaluation._
scala> val eva = new RegressionEvaluator
eva: org.apache.spark.ml.evaluation.RegressionEvaluator = regEval_8fc6cce63aa9
scala> eva.setLabelCol("_1")
res6: eva.type = regEval_8fc6cce63aa9
scala> eva.setMetricName("mse")
res7: eva.type = regEval_8fc6cce63aa9
scala> eva.evaluate(res)
res8: Double = 0.027933653533088666