Sean Do you know how to tell decision tree that the "label" is a binary or set some attributes to dataframe to carry number of classes?
Thanks! - Terry On Sun, Sep 6, 2015 at 5:23 PM, Sean Owen <so...@cloudera.com> wrote: > (Sean) > The error suggests that the type is not a binary or nominal attribute > though. I think that's the missing step. A double-valued column need > not be one of these attribute types. > > On Sun, Sep 6, 2015 at 10:14 AM, Terry Hole <hujie.ea...@gmail.com> wrote: > > Hi, Owen, > > > > The dataframe "training" is from a RDD of case class: > RDD[LabeledDocument], > > while the case class is defined as this: > > case class LabeledDocument(id: Long, text: String, label: Double) > > > > So there is already has the default "label" column with "double" type. > > > > I already tried to set the label column for decision tree as this: > > val lr = new > > > DecisionTreeClassifier().setMaxDepth(5).setMaxBins(32).setImpurity("gini").setLabelCol("label") > > It raised the same error. > > > > I also tried to change the "label" to "int" type, it also reported error > > like following stack, I have no idea how to make this work. > > > > java.lang.IllegalArgumentException: requirement failed: Column label > must be > > of type DoubleType but was actually IntegerType. > > at scala.Predef$.require(Predef.scala:233) > > at > > > org.apache.spark.ml.util.SchemaUtils$.checkColumnType(SchemaUtils.scala:37) > > at > > > org.apache.spark.ml.PredictorParams$class.validateAndTransformSchema(Predictor.scala:53) > > at > > > org.apache.spark.ml.Predictor.validateAndTransformSchema(Predictor.scala:71) > > at > > org.apache.spark.ml.Predictor.transformSchema(Predictor.scala:116) > > at > > > org.apache.spark.ml.Pipeline$$anonfun$transformSchema$4.apply(Pipeline.scala:162) > > at > > > org.apache.spark.ml.Pipeline$$anonfun$transformSchema$4.apply(Pipeline.scala:162) > > at > > > scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51) > > at > > > scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60) > > at > > scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:108) > > at > org.apache.spark.ml.Pipeline.transformSchema(Pipeline.scala:162) > > at > > org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:59) > > at org.apache.spark.ml.Pipeline.fit(Pipeline.scala:116) > > at > > > $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:51) > > at > > > $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:56) > > at > > $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:58) > > at > $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:60) > > at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:62) > > at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:64) > > at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:66) > > at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:68) > > at $iwC$$iwC$$iwC$$iwC.<init>(<console>:70) > > at $iwC$$iwC$$iwC.<init>(<console>:72) > > at $iwC$$iwC.<init>(<console>:74) > > at $iwC.<init>(<console>:76) > > at <init>(<console>:78) > > at .<init>(<console>:82) > > at .<clinit>(<console>) > > at .<init>(<console>:7) > > at .<clinit>(<console>) > > at $print(<console>) > > > > Thanks! > > - Terry > > > > On Sun, Sep 6, 2015 at 4:53 PM, Sean Owen <so...@cloudera.com> wrote: > >> > >> I think somewhere alone the line you've not specified your label > >> column -- it's defaulting to "label" and it does not recognize it, or > >> at least not as a binary or nominal attribute. > >> > >> On Sun, Sep 6, 2015 at 5:47 AM, Terry Hole <hujie.ea...@gmail.com> > wrote: > >> > Hi, Experts, > >> > > >> > I followed the guide of spark ml pipe to test DecisionTreeClassifier > on > >> > spark shell with spark 1.4.1, but always meets error like following, > do > >> > you > >> > have any idea how to fix this? > >> > > >> > The error stack: > >> > java.lang.IllegalArgumentException: DecisionTreeClassifier was given > >> > input > >> > with invalid label column label, without the number of classes > >> > specified. > >> > See StringIndexer. > >> > at > >> > > >> > > org.apache.spark.ml.classification.DecisionTreeClassifier.train(DecisionTreeClassifier.scala:71) > >> > at > >> > > >> > > org.apache.spark.ml.classification.DecisionTreeClassifier.train(DecisionTreeClassifier.scala:41) > >> > at org.apache.spark.ml.Predictor.fit(Predictor.scala:90) > >> > at org.apache.spark.ml.Predictor.fit(Predictor.scala:71) > >> > at > >> > org.apache.spark.ml.Pipeline$$anonfun$fit$2.apply(Pipeline.scala:133) > >> > at > >> > org.apache.spark.ml.Pipeline$$anonfun$fit$2.apply(Pipeline.scala:129) > >> > at scala.collection.Iterator$class.foreach(Iterator.scala:727) > >> > at > >> > scala.collection.AbstractIterator.foreach(Iterator.scala:1157) > >> > at > >> > > >> > > scala.collection.IterableViewLike$Transformed$class.foreach(IterableViewLike.scala:42) > >> > at > >> > > >> > > scala.collection.SeqViewLike$AbstractTransformed.foreach(SeqViewLike.scala:43) > >> > at org.apache.spark.ml.Pipeline.fit(Pipeline.scala:129) > >> > at > >> > $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:42) > >> > at > >> > $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:47) > >> > at > $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:49) > >> > at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:51) > >> > at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:53) > >> > at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:55) > >> > at $iwC$$iwC$$iwC$$iwC.<init>(<console>:57) > >> > at $iwC$$iwC$$iwC.<init>(<console>:59) > >> > at $iwC$$iwC.<init>(<console>:61) > >> > at $iwC.<init>(<console>:63) > >> > at <init>(<console>:65) > >> > at .<init>(<console>:69) > >> > at .<clinit>(<console>) > >> > at .<init>(<console>:7) > >> > at .<clinit>(<console>) > >> > at $print(<console>) > >> > > >> > The execute code is: > >> > // Labeled and unlabeled instance types. > >> > // Spark SQL can infer schema from case classes. > >> > case class LabeledDocument(id: Long, text: String, label: Double) > >> > case class Document(id: Long, text: String) > >> > // Prepare training documents, which are labeled. > >> > val training = sc.parallelize(Seq( > >> > LabeledDocument(0L, "a b c d e spark", 1.0), > >> > LabeledDocument(1L, "b d", 0.0), > >> > LabeledDocument(2L, "spark f g h", 1.0), > >> > LabeledDocument(3L, "hadoop mapreduce", 0.0))) > >> > > >> > // Configure an ML pipeline, which consists of three stages: > 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 > >> > > >> > > DecisionTreeClassifier().setMaxDepth(5).setMaxBins(32).setImpurity("gini") > >> > val pipeline = new Pipeline().setStages(Array(tokenizer, hashingTF, > lr)) > >> > > >> > // Error raises from the following line > >> > val model = pipeline.fit(training.toDF) > >> > > >> > > > > > >