Hi Yan, I agree, it IS really confusing. Here is the technique for
transforming a column. It is very general because you can make "myConvert"
do whatever you want.
import org.apache.spark.mllib.linalg.Vectors
val df = Seq((0, "[1,3,5]"), (1, "[2,4,6]")).toDF
df.show()
// The columns were named "_1" and "_2"
// Very confusing, because it looks like a Scala wildcard when we refer to
it in code
val myConvert = (x: String) => { Vectors.parse(x) }
val myConvertUDF = udf(myConvert)
val newDf = df.withColumn("parsed", myConvertUDF(col("_2")))
newDf.show()
On Mon, Sep 19, 2016 at 3:29 AM, 颜发才(Yan Facai) <yaf...@gmail.com> wrote:
> Hi, all.
> I find that it's really confuse.
>
> I can use Vectors.parse to create a DataFrame contains Vector type.
>
> scala> val dataVec = Seq((0, Vectors.parse("[1,3,5]")), (1,
> Vectors.parse("[2,4,6]"))).toDF
> dataVec: org.apache.spark.sql.DataFrame = [_1: int, _2: vector]
>
>
> But using map to convert String to Vector throws an error:
>
> scala> val dataStr = Seq((0, "[1,3,5]"), (1, "[2,4,6]")).toDF
> dataStr: org.apache.spark.sql.DataFrame = [_1: int, _2: string]
>
> scala> dataStr.map(row => Vectors.parse(row.getString(1)))
> <console>:30: error: Unable to find encoder for type stored in a
> Dataset. Primitive types (Int, String, etc) and Product types (case
> classes) are supported by importing spark.implicits._ Support for
> serializing other types will be added in future releases.
> dataStr.map(row => Vectors.parse(row.getString(1)))
>
>
> Dose anyone can help me,
> thanks very much!
>
>
>
>
>
>
>
> On Tue, Sep 6, 2016 at 9:58 PM, Peter Figliozzi <pete.figlio...@gmail.com>
> wrote:
>
>> Hi Yan, I think you'll have to map the features column to a new numerical
>> features column.
>>
>> Here's one way to do the individual transform:
>>
>> scala> val x = "[1, 2, 3, 4, 5]"
>> x: String = [1, 2, 3, 4, 5]
>>
>> scala> val y:Array[Int] = x slice(1, x.length - 1) replace(",", "")
>> split(" ") map(_.toInt)
>> y: Array[Int] = Array(1, 2, 3, 4, 5)
>>
>> If you don't know about the Scala command line, just type "scala" in a
>> terminal window. It's a good place to try things out.
>>
>> You can make a function out of this transformation and apply it to your
>> features column to make a new column. Then add this with
>> Dataset.withColumn.
>>
>> See here
>> <http://stackoverflow.com/questions/35227568/applying-function-to-spark-dataframe-column>
>> on how to apply a function to a Column to make a new column.
>>
>> On Tue, Sep 6, 2016 at 1:56 AM, 颜发才(Yan Facai) <yaf...@gmail.com> wrote:
>>
>>> Hi,
>>> I have a csv file like:
>>> uid mid features label
>>> 123 5231 [0, 1, 3, ...] True
>>>
>>> Both "features" and "label" columns are used for GBTClassifier.
>>>
>>> However, when I read the file:
>>> Dataset<Row> samples = sparkSession.read().csv(file);
>>> The type of samples.select("features") is String.
>>>
>>> My question is:
>>> How to map samples.select("features") to Vector or any appropriate type,
>>> so I can use it to train like:
>>> GBTClassifier gbdt = new GBTClassifier()
>>> .setLabelCol("label")
>>> .setFeaturesCol("features")
>>> .setMaxIter(2)
>>> .setMaxDepth(7);
>>>
>>> Thanks.
>>>
>>
>>
>
