A very simple way to achieve is to generate a random variate on the
driver that describes a mapping of datapoints to samples. Then you
simply join the dataset with this mapping to generate the samples.
This approach requires you to know the size of the dataset in advance,
but has the advantage that you can guarantee the sizes of the samples
and can easily support more involved techniques such as sampling with
replacement.
--sebastian
On 24.06.2015 10:38, Maximilian Alber wrote:
That's not the point. In Machine Learning one often divides a data set X
into f.e. three sets, one for the training, one for the validation, one
for the final testing. The sets are usually created randomly according
to some ratio. Thus it would be important to keep the ratio and to do
the whole process randomly.
Cheers,
Max
On Wed, Jun 24, 2015 at 9:51 AM, Stephan Ewen <se...@apache.org
<mailto:se...@apache.org>> wrote:
If you do "rebalance()", it will redistribute elements round-robin
fashion, which should give you very even partition sizes.
On Tue, Jun 23, 2015 at 11:51 AM, Maximilian Alber
<alber.maximil...@gmail.com <mailto:alber.maximil...@gmail.com>> wrote:
Thank you!
Still I cannot guarantee the size of each partition, or can I?
Something like randomSplit in Spark.
Cheers,
Max
On Mon, Jun 15, 2015 at 5:46 PM, Matthias J. Sax
<mj...@informatik.hu-berlin.de
<mailto:mj...@informatik.hu-berlin.de>> wrote:
Hi,
using partitionCustom, the data distribution depends only on
your
probability distribution. If it is uniform, you should be
fine (ie,
choosing the channel like
> private final Random random = new
Random(System.currentTimeMillis());
> int partition(K key, int numPartitions) {
> return random.nextInt(numPartitions);
> }
should do the trick.
-Matthias
On 06/15/2015 05:41 PM, Maximilian Alber wrote:
> Thanks!
>
> Ok, so for a random shuffle I need partitionCustom. But in that
case the
> data might be out of balance then?
>
> For the splitting. Is there no way to have exact sizes?
>
> Cheers,
> Max
>
> On Mon, Jun 15, 2015 at 2:26 PM, Till Rohrmann <trohrm...@apache.org
<mailto:trohrm...@apache.org>
> <mailto:trohrm...@apache.org <mailto:trohrm...@apache.org>>>
wrote:
>
> Hi Max,
>
> you can always shuffle your elements using the |rebalance|
method.
> What Flink here does is to distribute the elements of each
partition
> among all available TaskManagers. This happens in a
round-robin
> fashion and is thus not completely random.
>
> A different mean is the |partitionCustom| method which allows
you to
> specify for each element to which partition it shall be sent.
You
> would have to specify a |Partitioner| to do this.
>
> For the splitting there is at moment no syntactic sugar. What
you
> can do, though, is to assign each item a split ID and then
use a
> |filter| operation to filter the individual splits. Depending
on you
> split ID distribution you will have differently sized splits.
>
> Cheers,
> Till
>
> On Mon, Jun 15, 2015 at 1:50 PM Maximilian Alber
>alber.maximil...@gmail.com <mailto:alber.maximil...@gmail.com>
> <http://mailto:alber.maximil...@gmail.com> wrote:
>
> Hi Flinksters,
>
> I would like to shuffle my elements in the data
set and then
> split it in two according to some ratio. Each
element in the
> data set has an unique id. Is there a nice way to
do it with the
> flink api?
> (It would be nice to have guaranteed random
shuffling.)
> Thanks!
>
> Cheers,
> Max
>
>
>
>
