Hi Joseph,
Thank you for you feedback. I've managed to define an image type by
following VectorUDT implementation.
I have another question about the definition of a user defined transformer.
The unary tranfromer is private to spark ml. Do you plan
to give a developer api for transformers ?
On Sun, Jan 25, 2015 at 2:26 AM, Joseph Bradley <jos...@databricks.com>
wrote:
> Hi Jao,
>
> You're right that defining serialize and deserialize is the main task in
> implementing a UDT. They are basically translating between your native
> representation (ByteImage) and SQL DataTypes. The sqlType you defined
> looks correct, and you're correct to use a row of length 4. Other than
> that, it should just require copying data to and from SQL Rows. There are
> quite a few examples of that in the codebase; I'd recommend searching based
> on the particular DataTypes you're using.
>
> Are there particular issues you're running into?
>
> Joseph
>
> On Mon, Jan 19, 2015 at 12:59 AM, Jaonary Rabarisoa <jaon...@gmail.com>
> wrote:
>
>> Hi all,
>>
>> I'm trying to implement a pipeline for computer vision based on the
>> latest ML package in spark. The first step of my pipeline is to decode
>> image (jpeg for instance) stored in a parquet file.
>> For this, I begin to create a UserDefinedType that represents a decoded
>> image stored in a array of byte. Here is my first attempt :
>>
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>> *@SQLUserDefinedType(udt = classOf[ByteImageUDT])class ByteImage(channels:
>> Int, width: Int, height: Int, data: Array[Byte])private[spark] class
>> ByteImageUDT extends UserDefinedType[ByteImage] { override def sqlType:
>> StructType = { // type: 0 = sparse, 1 = dense // We only use "values"
>> for dense vectors, and "size", "indices", and "values" for sparse //
>> vectors. The "values" field is nullable because we might want to add binary
>> vectors later, // which uses "size" and "indices", but not "values".
>> StructType(Seq( StructField("channels", IntegerType, nullable = false),
>> StructField("width", IntegerType, nullable = false),
>> StructField("height", IntegerType, nullable = false),
>> StructField("data", BinaryType, nullable = false) } override def
>> serialize(obj: Any): Row = { val row = new GenericMutableRow(4) val
>> img = obj.asInstanceOf[ByteImage]*
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>> *... } override def deserialize(datum: Any): Vector = { *
>>
>> *....*
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>> * } } override def pyUDT: String = "pyspark.mllib.linalg.VectorUDT"
>> override def userClass: Class[Vector] = classOf[Vector]}*
>>
>>
>> I take the VectorUDT as a starting point but there's a lot of thing that I
>> don't really understand. So any help on defining serialize and deserialize
>> methods will be appreciated.
>>
>> Best Regards,
>>
>> Jao
>>
>>
>
