You are right... my code example doesn't work :)
I actually do want a decision tree per user. So, for 1 million users, I
want 1 million trees. We're training against time series data, so there are
still quite a few data points per users. My previous message where I
mentioned RDDs with no length was, I think, a result of the way the random
partitioning worked (I was partitioning into N groups where N was the
number of users... total).
Given this, I'm thinking the mlllib is not designed for this particular
case? It appears optimized for training across large datasets. I was just
hoping to leverage it since creating my feature sets for the users was
already in Spark.
On Mon, Jan 12, 2015 at 5:05 PM, Sean Owen so...@cloudera.com wrote:
A model partitioned by users?
I mean that if you have a million users surely you don't mean to build a
million models. There would be little data per user right? Sounds like you
have 0 sometimes.
You would typically be generalizing across users not examining them in
isolation. Models are built on thousands or millions of data points.
I assumed you were subsetting for cross validation in which case we are
talking about making more like say 10 models. You usually take random
subsets. But it might be as fine to subset as a function of a user ID if
you like. Or maybe you do have some reason for segregating users and
modeling them differently (e.g. different geographies or something).
Your code doesn't work as is since you are using RDDs inside RDDs. But I
am also not sure you should do what it looks like you are trying to do.
On Jan 13, 2015 12:32 AM, Josh Buffum jbuf...@gmail.com wrote:
Sean,
Thanks for the response. Is there some subtle difference between one
model partitioned by N users or N models per each 1 user? I think I'm
missing something with your question.
Looping through the RDD filtering one user at a time would certainly give
me the response that I am hoping for (i.e a map of user = decisiontree),
however, that seems like it would yield poor performance? The userIDs are
not integers, so I either need to iterator through some in-memory array of
them (could be quite large) or have some distributed lookup table. Neither
seem great.
I tried the random split thing. I wonder if I did something wrong there,
but some of the splits got RDDs with 0 tuples and some got RDDs with 1
tuple. I guess that's to be expected with some random distribution?
However, that won't work for me since it breaks the one tree per user
thing. I guess I could randomly distribute user IDs and then do the scan
everything and filter step...
How bad of an idea is it to do:
data.groupByKey.map( kvp = {
val (key, data) = kvp
val tree = DecisionTree.train( sc.makeRDD(data), ... )
(key, tree)
})
Is there a way I could tell spark not to distribute the RDD created by
sc.makeRDD(data) but just to deal with it on whatever spark worker is
handling kvp? Does that question make sense?
Thanks!
Josh
On Sun, Jan 11, 2015 at 4:12 AM, Sean Owen so...@cloudera.com wrote:
You just mean you want to divide the data set into N subsets, and do
that dividing by user, not make one model per user right?
I suppose you could filter the source RDD N times, and build a model
for each resulting subset. This can be parallelized on the driver. For
example let's say you divide into N subsets depending on the value of
the user ID modulo N:
val N = ...
(0 until N).par.map(d = DecisionTree.train(data.filter(_.userID % N
== d), ...))
data should be cache()-ed here of course.
However it may be faster and more principled to take random subsets
directly:
data.randomSplit(Array.fill(N)(1.0 / N)).par.map(subset =
DecisionTree.train(subset, ...))
On Sun, Jan 11, 2015 at 1:53 AM, Josh Buffum jbuf...@gmail.com wrote:
I've got a data set of activity by user. For each user, I'd like to
train a
decision tree model. I currently have the feature creation step
implemented
in Spark and would naturally like to use mllib's decision tree model.
However, it looks like the decision tree model expects the whole RDD
and
will train a single tree.
Can I split the RDD by user (i.e. groupByKey) and then call the
DecisionTree.trainClassifer in a reduce() or aggregate function to
create a
RDD[DecisionTreeModels]? Maybe train the model with an in-memory
dataset
instead of an RDD? Call sc.parallelize on the Iterable values in a
groupBy
to create a mini-RDD?
Has anyone else tried something like this with success?
Thanks!
