“值 $ 不是 StringContext 的成员” - 缺少 Scala 插件？答案

【问题标题】：“value $ is not a member of StringContext” - Missing Scala plugin?“值 $ 不是 StringContext 的成员” - 缺少 Scala 插件？
【发布时间】：2017-10-27 19:56:03
【问题描述】：

我正在使用带有 scala 原型的 maven。我收到了这个错误：

“值 $ 不是 StringContext 的成员”

我已经尝试在 pom.xml 中添加一些东西，但没有什么效果很好......

我的代码：

import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.tuning.{ParamGridBuilder, TrainValidationSplit}
// To see less warnings
import org.apache.log4j._
Logger.getLogger("org").setLevel(Level.ERROR)


// Start a simple Spark Session
import org.apache.spark.sql.SparkSession
val spark = SparkSession.builder().getOrCreate()

// Prepare training and test data.
val data = spark.read.option("header","true").option("inferSchema","true").format("csv").load("USA_Housing.csv")

// Check out the Data
data.printSchema()

// See an example of what the data looks like
// by printing out a Row
val colnames = data.columns
val firstrow = data.head(1)(0)
println("\n")
println("Example Data Row")
for(ind <- Range(1,colnames.length)){
  println(colnames(ind))
  println(firstrow(ind))
  println("\n")
}

////////////////////////////////////////////////////
//// Setting Up DataFrame for Machine Learning ////
//////////////////////////////////////////////////

// A few things we need to do before Spark can accept the data!
// It needs to be in the form of two columns
// ("label","features")

// This will allow us to join multiple feature columns
// into a single column of an array of feautre values
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.linalg.Vectors

// Rename Price to label column for naming convention.
// Grab only numerical columns from the data
val df = data.select(data("Price").as("label"),$"Avg Area Income",$"Avg Area House Age",$"Avg Area Number of Rooms",$"Area Population")

// An assembler converts the input values to a vector
// A vector is what the ML algorithm reads to train a model

// Set the input columns from which we are supposed to read the values
// Set the name of the column where the vector will be stored
val assembler = new VectorAssembler().setInputCols(Array("Avg Area Income","Avg Area House Age","Avg Area Number of Rooms","Area Population")).setOutputCol("features")

// Use the assembler to transform our DataFrame to the two columns
val output = assembler.transform(df).select($"label",$"features")


// Create a Linear Regression Model object
val lr = new LinearRegression()

// Fit the model to the data

// Note: Later we will see why we should split
// the data first, but for now we will fit to all the data.
val lrModel = lr.fit(output)

// Print the coefficients and intercept for linear regression
println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}")

// Summarize the model over the training set and print out some metrics!
// Explore this in the spark-shell for more methods to call
val trainingSummary = lrModel.summary

println(s"numIterations: ${trainingSummary.totalIterations}")
println(s"objectiveHistory: ${trainingSummary.objectiveHistory.toList}")

trainingSummary.residuals.show()

println(s"RMSE: ${trainingSummary.rootMeanSquaredError}")
println(s"MSE: ${trainingSummary.meanSquaredError}")
println(s"r2: ${trainingSummary.r2}")

我的 pom.xml 是这样的：

<project xmlns="http://maven.apache.org/POM/4.0.0" 

xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
  <modelVersion>4.0.0</modelVersion>
  <groupId>test</groupId>
  <artifactId>outrotest</artifactId>
  <version>1.0-SNAPSHOT</version>
  <name>${project.artifactId}</name>
  <description>My wonderfull scala app</description>
  <inceptionYear>2015</inceptionYear>
  <licenses>
    <license>
      <name>My License</name>
      <url>http://....</url>
      <distribution>repo</distribution>
    </license>
  </licenses>

  <properties>
    <maven.compiler.source>1.6</maven.compiler.source>
    <maven.compiler.target>1.6</maven.compiler.target>
    <encoding>UTF-8</encoding>
    <scala.version>2.11.5</scala.version>
    <scala.compat.version>2.11</scala.compat.version>
  </properties>

  <dependencies>
    <dependency>
      <groupId>org.scala-lang</groupId>
      <artifactId>scala-library</artifactId>
      <version>${scala.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-mllib_2.11</artifactId>
      <version>2.0.1</version>
    </dependency>
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_2.11</artifactId>
      <version>2.0.1</version>
    </dependency>
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql_2.11</artifactId>
      <version>2.0.2</version>
    </dependency>
    <dependency>
      <groupId>com.databricks</groupId>
      <artifactId>spark-csv_2.11</artifactId>
      <version>1.5.0</version>
    </dependency>

    <!-- Test -->
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.11</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.specs2</groupId>
      <artifactId>specs2-junit_${scala.compat.version}</artifactId>
      <version>2.4.16</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.specs2</groupId>
      <artifactId>specs2-core_${scala.compat.version}</artifactId>
      <version>2.4.16</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.scalatest</groupId>
      <artifactId>scalatest_${scala.compat.version}</artifactId>
      <version>2.2.4</version>
      <scope>test</scope>
    </dependency>
  </dependencies>

  <build>
    <sourceDirectory>src/main/scala</sourceDirectory>
    <testSourceDirectory>src/test/scala</testSourceDirectory>
    <plugins>
      <plugin>
        <!-- see http://davidb.github.com/scala-maven-plugin -->
        <groupId>net.alchim31.maven</groupId>
        <artifactId>scala-maven-plugin</artifactId>
        <version>3.2.0</version>
        <executions>
          <execution>
            <goals>
              <goal>compile</goal>
              <goal>testCompile</goal>
            </goals>
            <configuration>
              <args>
                <!--<arg>-make:transitive</arg>-->
                <arg>-dependencyfile</arg>
                <arg>${project.build.directory}/.scala_dependencies</arg>
              </args>
            </configuration>
          </execution>
        </executions>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-surefire-plugin</artifactId>
        <version>2.18.1</version>
        <configuration>
          <useFile>false</useFile>
          <disableXmlReport>true</disableXmlReport>
          <!-- If you have classpath issue like NoDefClassError,... -->
          <!-- useManifestOnlyJar>false</useManifestOnlyJar -->
          <includes>
            <include>**/*Test.*</include>
            <include>**/*Suite.*</include>
          </includes>
        </configuration>
      </plugin>
    </plugins>
  </build>
</project>

我不知道如何解决它。有人知道吗？

【问题讨论】：

您是否尝试导入import sqlContext.implicits._？
是的，但它不起作用。它继续出现同样的错误：“value $ is not a member of StringContext”
您需要从 pom.xml 中删除 spark-csv，因为这会导致运行时错误
你能隔离错误代码吗？
我必须在我使用 $ 的同一个子函数中导入 spark.sqlContext.implicits._，只是在 main() 中导入隐式不起作用。

标签： scala apache-spark

【解决方案1】：

添加这个..它会工作

val spark = SparkSession.builder().getOrCreate()    
import spark.implicits._ // << add this

【讨论】：

【解决方案2】：

您可以使用col 函数，而是像这样导入它：

import org.apache.spark.sql.functions.col

然后把$"column"改成col("column")

希望对你有帮助

【讨论】：

这对我来说效果更好，因为我的 spark 对象不在我需要进行的函数调用的范围内，因为我正在编写对 dataFrame 对象起作用的函数。

【解决方案3】：

@Apurva's answer 最初对我有用，因为错误从 IntelliJ 消失了
但后来在sbt compile阶段导致"Could not find implicit value for spark"

我发现了一个奇怪的解决方法，方法是从SparkSession 导入spark.implicits._，引用自DataFrame 而不是getOrCreate 获得的getOrCreate

import df.sparkSession.implicits._

其中df 是DataFrame

这可能是因为我的代码被放置在一个接收implicit val spark: SparkSession 参数的case class 中；但我不太确定为什么这个修复对我有用

【讨论】：

【解决方案4】：

我使用的是火花 1.6。上面的答案很好，但不幸的是在 1.6 中不起作用

我解决它的方法是使用 df.col("column-name")

val df = df_mid
         .withColumn("dt", date_format(df_mid.col("timestamp"), "yyyy-MM-dd"))
         .filter("dt != 'null'")

【讨论】：