We're using SparseVector columns in a DataFrame, so they are definitely supported. But maybe for LR some implicit magic is happening inside.
On 7 March 2016 at 23:04, Devin Jones <devin.jo...@columbia.edu> wrote: > I could be wrong but its possible that toDF populates a dataframe which I > understand do not support sparsevectors at the moment. > > If you use the MlLib logistic regression implementation (not ml) you can > pass the RDD[LabeledPoint] data type directly to the learner. > > > http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS > > Only downside is that you can't use the pipeline framework from spark ml. > > Cheers, > Devin > > > > On Mon, Mar 7, 2016 at 4:54 PM, Daniel Siegmann < > daniel.siegm...@teamaol.com> wrote: > >> Yes, it is a SparseVector. Most rows only have a few features, and all >> the rows together only have tens of thousands of features, but the vector >> size is ~ 20 million because that is the largest feature. >> >> On Mon, Mar 7, 2016 at 4:31 PM, Devin Jones <devin.jo...@columbia.edu> >> wrote: >> >>> Hi, >>> >>> Which data structure are you using to train the model? If you haven't >>> tried yet, you should consider the SparseVector >>> >>> >>> http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.SparseVector >>> >>> >>> On Mon, Mar 7, 2016 at 4:03 PM, Daniel Siegmann < >>> daniel.siegm...@teamaol.com> wrote: >>> >>>> I recently tried to a model using >>>> org.apache.spark.ml.classification.LogisticRegression on a data set >>>> where the feature vector size was around ~20 million. It did *not* go >>>> well. It took around 10 hours to train on a substantial cluster. >>>> Additionally, it pulled a lot data back to the driver - I eventually set >>>> --conf >>>> spark.driver.memory=128g --conf spark.driver.maxResultSize=112g when >>>> submitting. >>>> >>>> Attempting the same application on the same cluster with the feature >>>> vector size reduced to 100k took only ~ 9 minutes. Clearly there is an >>>> issue with scaling to large numbers of features. I'm not doing anything >>>> fancy in my app, here's the relevant code: >>>> >>>> val lr = new LogisticRegression().setRegParam(1) >>>> val model = lr.fit(trainingSet.toDF()) >>>> >>>> In comparison, a coworker trained a logistic regression model on her >>>> *laptop* using the Java library liblinear in just a few minutes. >>>> That's with the ~20 million-sized feature vectors. This suggests to me >>>> there is some issue with Spark ML's implementation of logistic regression >>>> which is limiting its scalability. >>>> >>>> Note that my feature vectors are *very* sparse. The maximum feature is >>>> around 20 million, but I think there are only 10's of thousands of >>>> features. >>>> >>>> Has anyone run into this? Any idea where the bottleneck is or how this >>>> problem might be solved? >>>> >>>> One solution of course is to implement some dimensionality reduction. >>>> I'd really like to avoid this, as it's just another thing to deal with - >>>> not so hard to put it into the trainer, but then anything doing scoring >>>> will need the same logic. Unless Spark ML supports this out of the box? An >>>> easy way to save / load a model along with the dimensionality reduction >>>> logic so when transform is called on the model it will handle the >>>> dimensionality reduction transparently? >>>> >>>> Any advice would be appreciated. >>>> >>>> ~Daniel Siegmann >>>> >>> >>> >> >
