Spark MLlib学习（1）-- Pipelines_sparkmllib的pipeline工具类代码

基本概念

DataFrame

机器学习API使用来自Spark SQL的DataFrame作为数据集，它能包括多种数据类型，如文本、特征向量、标签、预测值等。

Transformers

一个Transformers是一个能转化一个DataFrame到另一个DataFrame的算法，例如，一个model可以转化带有特征的DataFrame为一个带有预测值的DataFrame。

Transformers包括特征转化器（feature transformers）和已训练模型（learned models），通常实现方法 transform()，一般通过附加上更多列的方式转化DataFrame为另一个DataFrame。

• 特征转化器：读取DataFrame的一个列，映射为另一个，输出一个新的DataFrame，这个DataFrame附加上新的映射列。
• 已训练模型：读取DataFrame的包含特征向量的列，预测特征向量的标签，输出预测标签作为附加列。

Estimators

一个Estimators能通过一个DataFrame生成一个Transformer，例如，一个机器学习算法是一个Estimators，它能在DataFrame上训练得到model。

通常实现方法fit（）

Pipeline

一个Pipeline链接多个Transformers和Estimators，指定一个机器学习工作流。

例如，一个简单的文本文件处理需要以下步骤：

1. 划分文件的文本为单词
2. 转化单词为特征向量
3. 从特征向量和标签中学习预测模型

这些步骤就是一个机器学习工作流，也就是Pipeline，它包含一系列PipelineStages，并且按一定顺序运行。

例子

Estimator, Transformer, and Param

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.sql.Row
 
// Prepare training data from a list of (label, features) tuples.
val training = 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 an Estimator.
//这是一个逻辑回归实例，是一个Estimator
val lr = new LogisticRegression()
// Print out the parameters, documentation, and any default values.
//打印逻辑回归参数
println(s"LogisticRegression parameters:\n ${lr.explainParams()}\n")
 
// We may set parameters using setter methods.
//设置参数
lr.setMaxIter(10)
  .setRegParam(0.01)
 
// Learn a LogisticRegression model. This uses the parameters stored in lr.
//训练逻辑回归模型
val model1 = lr.fit(training)
// Since model1 is a Model (i.e., a Transformer produced by an Estimator),
// we can view the parameters it used during fit().
// This prints the parameter (name: value) pairs, where names are unique IDs for this
// LogisticRegression instance.
//打印训练model1所用的参数
println(s"Model 1 was fit using parameters: ${model1.parent.extractParamMap}")
 
// We may alternatively specify parameters using a ParamMap,
// which supports several methods for specifying parameters.
//使用ParamMap制定参数
val paramMap = 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.
val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability")  // Change output column name.
val paramMapCombined = paramMap ++ paramMap2
 
// Now learn a new model using the paramMapCombined parameters.
// paramMapCombined overrides all parameters set earlier via lr.set* methods.
val model2 = lr.fit(training, paramMapCombined)
println(s"Model 2 was fit using parameters: ${model2.parent.extractParamMap}")
 
// Prepare test data.
val test = 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 the Transformer.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 the lr.probabilityCol parameter previously.
//用model2做预测
model2.transform(test)
  .select("features", "label", "myProbability", "prediction")
  .collect()
  .foreach { case Row(features: Vector, label: Double, prob: Vector, prediction: Double) =>
    println(s"($features, $label) -> prob=$prob, prediction=$prediction")
  }

Pipeline

import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
import org.apache.spark.ml.linalg.Vector
import org.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.
//配置pipeline，包含三个阶段：tokenizer, hashingTF, and lr.
val tokenizer = new Tokenizer()
  .setInputCol("text")
  .setOutputCol("words")
val hashingTF = new HashingTF()
  .setNumFeatures(1000)
  .setInputCol(tokenizer.getOutputCol)
  .setOutputCol("features")
val lr = new LogisticRegression()
  .setMaxIter(10)
  .setRegParam(0.001)
val pipeline = new Pipeline()
  .setStages(Array(tokenizer, hashingTF, lr))
 
// Fit the pipeline to training documents.
//使用pipeline训练模型
val model = pipeline.fit(training)
 
// Now we can optionally save the fitted pipeline to disk
//保存模型到磁盘
model.write.overwrite().save("/tmp/spark-logistic-regression-model")
 
// We can also save this unfit pipeline to disk
//保存pipeline到磁盘
pipeline.write.overwrite().save("/tmp/unfit-lr-model")
 
// And load it back in during production
//从磁盘加载已保存的model
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 { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>
    println(s"($id, $text) --> prob=$prob, prediction=$prediction")
  }