使用逻辑回归进行分类答案

【问题标题】：classification using logistic regression使用逻辑回归进行分类
【发布时间】：2017-09-25 14:44:00
【问题描述】：

我有以下代码，并尝试使用字符串索引器设置标签并使用矢量汇编器设置特征

StructType schema = createStructType(new StructField[]{
                  createStructField("id", IntegerType, false),
                  createStructField("country", StringType, false),
                  createStructField("hour", IntegerType, false),
                  createStructField("clicked", DoubleType, false)
                });

                List<Row> data = Arrays.asList(
                  RowFactory.create(7, "US", 18, 1.0),
                  RowFactory.create(8, "CA", 12, 0.0),
                  RowFactory.create(9, "NZ", 15, 0.0)
                );

                Dataset<Row> dataset = sparkSession.createDataFrame(data, schema);

                StringIndexer indexer = new StringIndexer()
                          .setInputCol("clicked")
                          .setOutputCol("label");
                Dataset<Row> ds = indexer.fit(dataset).transform(dataset);
                VectorAssembler assembler = new VectorAssembler()
                          .setInputCols(new String[]{"id", "country", "hour"})
                          .setOutputCol("features");
                Dataset<Row> finalDS = assembler.transform(ds);

                LogisticRegression lr = new LogisticRegression()
                          .setMaxIter(10)
                          .setRegParam(0.3)
                          .setElasticNetParam(0.8);

                        // Fit the model
                        LogisticRegressionModel lrModel = lr.fit(finalDS);
                        Dataset<Row> output = lrModel.transform(finalDS);
                        output.select("features", "label").show();

当我在 spark 上提交它时，我收到以下错误消息：

 7/04/27 22:34:24 INFO DAGScheduler: Job 0 finished: countByValue at StringIndexer.scala:92, took 1.003742 s
Exception in thread "main" java.lang.IllegalArgumentException: Data type StringType is not supported.
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$transformSchema$1.apply(VectorAssembler.scala:121)
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$transformSchema$1.apply(VectorAssembler.scala:117)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
    at org.apache.spark.ml.feature.VectorAssembler.transformSchema(VectorAssembler.scala:117)
    at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:74)
    at org.apache.spark.ml.feature.VectorAssembler.transform(VectorAssembler.scala:54)

【问题讨论】：

标签： apache-spark machine-learning

【解决方案1】：

VectorAssembler 只接受三种类型的列：

DoubleType - 双标量，可选列元数据。

NumericType - 任意数字。

VectorUDT - 向量列。

了解更多 ->

【讨论】：