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 >>> >> >> >
