Just found that you can specify number of features when loading libsvm
source:
val df = spark.read.option("numFeatures", "100").format("libsvm")
Liang-Chi Hsieh wrote
> As the libsvm format can't specify number of features, and looks like
> NaiveBayes doesn't have such parameter, if your
As the libsvm format can't specify number of features, and looks like
NaiveBayes doesn't have such parameter, if your training/testing data is
sparse, the number of features inferred from the data files can be
inconsistent.
We may need to fix this.
Before a fixing going into NaiveBayes,
Hi Jinhong,
Based on the error message, your second collection of vectors has a
dimension of 804202, while the dimension of your training vectors
was 144109. So please make sure your test dataset are of the same dimension
as the training data.
>From the test dataset you posted, the vector
GraphFrame is just a Graph Analytics/Query Engine, not a Graph Engine which
GraphX used to be.
And I'm sorry to say, it doesn’t fit most scenarioes at all in fact.
Enzo, I don’t think there is any roadmap of Graph libraries for Spark for
now.
*Andy*
On Tue, Mar 14, 2017 at 7:28 AM, Tim Hunter
