Pipelines中的主要概念
MLlib 提供的API可以通过Pipelines将多个复杂的机器学习算法结合成单个pipeline或者单个工作流。这个概念和scikit-learn里的概念类似，根据官方的说法是，此抽象概念的设计灵感来自于scikit-learn。
· DataFrame:通过Spark SQL 组件里的DataFrame作为机器学习的数据集。支持多种数据类型.比如 DataFrame 可以将文本，数据库等外部数据源划分为不同的列,包含特征向量, 特征值等。
· Transformer: 一个 Transformer可以将一个DataFrame 转换成另一个DataFrame. 比如, 一个机器学习模型可以将带有特征值的DataFrame转换为一个带有模型预测结果数据的DataFrame.
· Estimator:通过 DataFrame数据集进行训练 产生一个机器学习模型的算法。
· Pipeline:联合多个 Transformer和 Estimator构成一个机器学习工作流。
· Parameter: 所有Transformer和 Estimator指定参数的共享API。
DataFrame
DataFrame里广泛运用的数据结构，可以包含向量，文本，图片，以及结构化数据。DataFrame通过Spark SQL支持多种数据源。
工作流程如图所示：
机器学习中Pipleline流程图
正如图中所示，Pipeline有三个阶段，每个阶段要么是Transformer ，要么就是Estimator，这些阶段按照一定的顺序执行，执行的过程中，通过圆柱体代表的DataFrame类型的Raw text产生一个新的Words(DataFrame类型)，最后建立了一个LogisticRegressionModel。图中的Tokenizer,HashingTF都是Transformer,而LogisticRegressionModel是Estimator 。
在Transformer 阶段，主要调用transform()方法进行计算。
在Estimator阶段，主要调用fit()方法进行计算。
DAG Pipelines：多个阶段形成一个pipeline,同理，DAG Pipelines就是多个pipeline组成的一个有向无环图。
运行时检查：数据结构DataFrame中可以有各种各样的数据，但是在编译的时候不会检查数据的数据类型，而是在运行的时候才根据DataFrame的Schema来检查数据类型。
唯一ID标识:Pipeline的每一个阶段（stage）都通过id来进行唯一的标识，同一个相同的实列，比如HashingTF不会插入到同一个Pipeline俩次，因为每一个stage都有自身的唯一的ID来进行标识。
保存和读取pipeline
代码案例：
Estimator, Transformer, 以及 Param综合案例
importorg.apache.spark.ml.classification.LogisticRegression
importorg.apache.spark.ml.linalg.{Vector,Vectors}
importorg.apache.spark.ml.param.ParamMap
importorg.apache.spark.sql.Row
// Prepare training data from a list of (label, features)tuples.
valtraining=spark.createDataFrame(Seq(
(1.0,Vectors.dense(0.0,1.1,0.1)),
(0.0,Vectors.dense(2.0,1.0,-1.0)),
(0.0,Vectors.dense(2.0,1.3,1.0)),
(1.0,Vectors.dense(0.0,1.2,-0.5))
)).toDF("label","features")
// Create a LogisticRegression instance. This instance is anEstimator.
vallr=newLogisticRegression()
// Print out the parameters, documentation, and any defaultvalues.
println("LogisticRegressionparameters:\n"+lr.explainParams()+"\n")
// We may set parameters using setter methods.
lr.setMaxIter(10)
.setRegParam(0.01)
// Learn a LogisticRegression model. This uses the parametersstored in lr.
valmodel1=lr.fit(training)
// Since model1 is a Model (i.e., a Transformer produced byan Estimator),
// we can view the parameters it used during fit().
// This prints the parameter (name: value) pairs, where namesare unique IDs for this
// LogisticRegression instance.
println("Model 1 was fit usingparameters: "+model1.parent.extractParamMap)
// We may alternatively specify parameters using a ParamMap,
// which supports several methods for specifying parameters.
valparamMap=ParamMap(lr.maxIter->20)
.put(lr.maxIter,30) // Specify 1 Param. This overwrites the original maxIter.
.put(lr.regParam->0.1,lr.threshold->0.55) // Specify multiple Params.
// One can also combine ParamMaps.
valparamMap2=ParamMap(lr.probabilityCol->"myProbability") // Change output column name.
valparamMapCombined=paramMap++paramMap2
// Now learn a new model using the paramMapCombinedparameters.
// paramMapCombined overrides all parameters set earlier vialr.set* methods.
valmodel2=lr.fit(training,paramMapCombined)
println("Model 2 was fit usingparameters: "+model2.parent.extractParamMap)
// Prepare test data.
valtest=spark.createDataFrame(Seq(
(1.0,Vectors.dense(-1.0,1.5,1.3)),
(0.0,Vectors.dense(3.0,2.0,-0.1)),
(1.0,Vectors.dense(0.0,2.2,-1.5))
)).toDF("label","features")
// Make predictions on test data using theTransformer.transform() method.
// LogisticRegression.transform will only use the 'features'column.
// Note that model2.transform() outputs a 'myProbability'column instead of the usual
// 'probability' column since we renamed thelr.probabilityCol parameter previously.
model2.transform(test)
.select("features","label","myProbability","prediction")
.collect()
.foreach{caseRow(features:Vector,label:Double,prob:Vector,prediction:Double)=>
println(s"($features, $label) -> prob=$prob, prediction=$prediction")
}
Pipeline单独的案例代码
importorg.apache.spark.ml.{Pipeline,PipelineModel}importorg.apache.spark.ml.classification.LogisticRegression
importorg.apache.spark.ml.feature.{HashingTF,Tokenizer}importorg.apache.spark.ml.linalg.Vector
importorg.apache.spark.sql.Row
// Prepare training documents from a list of (id, text, label) tuples.
val training = spark.createDataFrame(Seq(
(0L,"a b c d e spark",1.0),
(1L,"b d",0.0),
(2L,"spark f g h",1.0),
(3L,"hadoop mapreduce",0.0)
)).toDF("id","text","label")// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
val tokenizer =newTokenizer()
.setInputCol("text") .setOutputCol("words")val hashingTF =newHashingTF()
.setNumFeatures(1000)
.setInputCol(tokenizer.getOutputCol)
.setOutputCol("features")val lr =newLogisticRegression()
.setMaxIter(10)
.setRegParam(0.001)
val pipeline =newPipeline()
.setStages(Array(tokenizer, hashingTF, lr))
// Fit the pipeline to training documents.
val model = pipeline.fit(training)
// Now we can optionally save the fitted pipeline to disk
model.write.overwrite().save("/tmp/spark-logistic-regression-model")// We can also save this unfit pipeline to disk
pipeline.write.overwrite().save("/tmp/unfit-lr-model")// And load it back in during production
val sameModel =PipelineModel.load("/tmp/spark-logistic-regression-model")// Prepare test documents, which are unlabeled (id, text) tuples.
val test = spark.createDataFrame(Seq(
(4L,"spark i j k"),
(5L,"l m n"),
(6L,"spark hadoop spark"),
(7L,"apache hadoop")
)).toDF("id","text")// Make predictions on test documents.
model.transform(test)
.select("id","text","probability","prediction").collect()
.foreach{caseRow(id:Long, text:String, prob:Vector, prediction:Double)=>println(s"($id, $text) --> prob=$prob, prediction=$prediction")
}
