scala Spark 多类分类示例

Spark Multiclass Classification Example

提问by deniswsrosa

Do you guys know where can I find examples of multiclass classification in Spark. I spent a lot of time searching in books and in the web, and so far I just know that it is possible since the latest version according the documentation.

你们知道我在哪里可以找到 Spark 中多类分类的例子吗？我花了很多时间在书籍和网络上搜索，到目前为止，我只知道根据文档的最新版本是可能的。

回答by zero323

ML

机器学习

(Recommended in Spark 2.0+)

（推荐在 Spark 2.0+ 中）

We'll use the same data as in the MLlib below. There are two basic options. If Estimatorsupports multilclass classification out-of-the-box (for example random forest) you can use it directly:

我们将使用与下面 MLlib 中相同的数据。有两个基本选项。如果Estimator支持开箱即用的多类分类（例如随机森林），您可以直接使用它：

val trainRawDf = trainRaw.toDF

import org.apache.spark.ml.feature.{Tokenizer, CountVectorizer, StringIndexer}
import org.apache.spark.ml.Pipeline

import org.apache.spark.ml.classification.RandomForestClassifier

val transformers = Array(
  new StringIndexer().setInputCol("group").setOutputCol("label"),
  new Tokenizer().setInputCol("text").setOutputCol("tokens"),
  new CountVectorizer().setInputCol("tokens").setOutputCol("features")
)


val rf = new RandomForestClassifier() 
  .setLabelCol("label")
  .setFeaturesCol("features")

val model = new Pipeline().setStages(transformers :+ rf).fit(trainRawDf)

model.transform(trainRawDf)

If model supports only binary classification (logistic regression) and extends o.a.s.ml.classification.Classifieryou can use one-vs-rest strategy:

如果模型仅支持二元分类（逻辑回归）并扩展o.a.s.ml.classification.Classifier，则可以使用一对一策略：

import org.apache.spark.ml.classification.OneVsRest
import org.apache.spark.ml.classification.LogisticRegression

val lr = new LogisticRegression() 
  .setLabelCol("label")
  .setFeaturesCol("features")

val ovr = new OneVsRest().setClassifier(lr)

val ovrModel = new Pipeline().setStages(transformers :+ ovr).fit(trainRawDf)

MLLib

机器学习库

According to the official documentationat this moment (MLlib 1.6.0) following methods support multiclass classification:

根据目前官方文档（MLlib 1.6.0）以下方法支持多类分类：

• logistic regression,
• decision trees,
• random forests,
• naive Bayes

• 逻辑回归，
• 决策树，
• 随机森林，
• 朴素贝叶斯

At least some of the examples use multiclass classification:

至少有一些示例使用多类分类：

• Naive Bayes example- 3 classes
• Logistic regression- 10 classes for classifier although only 2 in the example data

• 朴素贝叶斯示例- 3 个类
• 逻辑回归- 分类器有 10 个类别，尽管示例数据中只有 2 个

General framework, ignoring method specific arguments, is pretty much the same as for all the other methods in MLlib. You have to pre-processes your input to create either data frame with columns representing labeland features:

忽略方法特定参数的通用框架与 MLlib 中的所有其他方法几乎相同。您必须预处理您的输入以创建具有代表label和 的列的任一数据框features：

root
 |-- label: double (nullable = true)
 |-- features: vector (nullable = true)

or RDD[LabeledPoint].

或RDD[LabeledPoint]。

Spark provides broad range of useful tools designed to facilitate this process including Feature Extractorsand Feature Transformersand pipelines.

Spark 提供了广泛的有用工具，旨在促进这一过程，包括特征提取器和特征转换器和管道。

You'll find a rather naive example of using Random Forest below.

你会在下面找到一个使用随机森林的相当幼稚的例子。

First lets import required packages and create dummy data:

首先让我们导入所需的包并创建虚拟数据：

import sqlContext.implicits._
import org.apache.spark.ml.feature.{HashingTF, Tokenizer} 
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.tree.model.RandomForestModel
import org.apache.spark.mllib.linalg.{Vectors, Vector}
import org.apache.spark.mllib.evaluation.MulticlassMetrics
import org.apache.spark.sql.Row
import org.apache.spark.rdd.RDD

case class LabeledRecord(group: String, text: String)

val trainRaw = sc.parallelize(
    LabeledRecord("foo", "foo v a y b  foo") ::
    LabeledRecord("bar", "x bar y bar v") ::
    LabeledRecord("bar", "x a y bar z") ::
    LabeledRecord("foobar", "foo v b bar z") ::
    LabeledRecord("foo", "foo x") ::
    LabeledRecord("foobar", "z y x foo a b bar v") ::
    Nil
)

Now let's define required transformers and process train Dataset:

现在让我们定义所需的变压器和工艺流程Dataset：

// Tokenizer to process text fields
val tokenizer = new Tokenizer()
    .setInputCol("text")
    .setOutputCol("words")

// HashingTF to convert tokens to the feature vector
val hashingTF = new HashingTF()
    .setInputCol("words")
    .setOutputCol("features")
    .setNumFeatures(10)

// Indexer to convert String labels to Double
val indexer = new StringIndexer()
    .setInputCol("group")
    .setOutputCol("label")
    .fit(trainRaw.toDF)


def transfom(rdd: RDD[LabeledRecord]) = {
    val tokenized = tokenizer.transform(rdd.toDF)
    val hashed = hashingTF.transform(tokenized)
    val indexed = indexer.transform(hashed)
    indexed
        .select($"label", $"features")
        .map{case Row(label: Double, features: Vector) =>
            LabeledPoint(label, features)}
}

val train: RDD[LabeledPoint] = transfom(trainRaw)

Please note that indexeris "fitted" on the train data. It simply means that categorical values used as the labels are converted to doubles. To use classifier on a new data you have to transform it first using this indexer.

请注意，它indexer是“拟合”在火车数据上的。它只是意味着用作标签的分类值被转换为doubles. 要在新数据上使用分类器，您必须先使用 this 对其进行转换indexer。

Next we can train RF model:

接下来我们可以训练 RF 模型：

val numClasses = 3
val categoricalFeaturesInfo = Map[Int, Int]()
val numTrees = 10
val featureSubsetStrategy = "auto"
val impurity = "gini"
val maxDepth = 4
val maxBins = 16

val model = RandomForest.trainClassifier(
    train, numClasses, categoricalFeaturesInfo, 
    numTrees, featureSubsetStrategy, impurity,
    maxDepth, maxBins
)

and finally test it:

最后测试一下：

val testRaw = sc.parallelize(
    LabeledRecord("foo", "foo  foo z z z") ::
    LabeledRecord("bar", "z bar y y v") ::
    LabeledRecord("bar", "a a  bar a z") ::
    LabeledRecord("foobar", "foo v b bar z") ::
    LabeledRecord("foobar", "a foo a bar") ::
    Nil
)

val test: RDD[LabeledPoint] = transfom(testRaw)

val predsAndLabs = test.map(lp => (model.predict(lp.features), lp.label))
val metrics = new MulticlassMetrics(predsAndLabs)

metrics.precision
metrics.recall

回答by chelBert

Are you using Spark 1.6 rather than Spark 2.1? I think the problem is that in spark 2.1 the transform method returns a dataset, which can be implicitly converted to a typed RDD, where as prior to that, it returns a data frame or row.

您使用的是 Spark 1.6 而不是 Spark 2.1？我认为问题在于，在 spark 2.1 中，transform 方法返回一个数据集，该数据集可以隐式转换为类型化 RDD，而在此之前，它返回一个数据框或行。

Try as a diagnostic specifying the return type of the transform function as RDD[LabeledPoint] and see if you get the same error.

尝试作为将转换函数的返回类型指定为 RDD[LabeledPoint] 的诊断，看看是否得到相同的错误。