(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)
>> >
>> >
>
>
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