Hi Hoai-Thu, the issue of private default constructor is unlikely the cause here, since Lance was already able to load/deserialize the model object.
And on that side topic, I wish all serdes libraries would just use constructor.setAccessible(true) by default :-) Most of the time that privacy is not about serdes reflection restrictions. Sent while mobile. Pls excuse typos etc. On Aug 14, 2014 1:58 AM, "Hoai-Thu Vuong" <thuv...@gmail.com> wrote: > A man in this community give me a video: > https://www.youtube.com/watch?v=sPhyePwo7FA. I've got a same question in > this community and other guys helped me to solve this problem. I'm trying > to load MatrixFactorizationModel from object file, but compiler said that, > I can not create object because the constructor is private. To solve this, > I put my new object to same package as MatrixFactorizationModel. Luckly it > works. > > > On Wed, Aug 13, 2014 at 9:20 PM, Christopher Nguyen <c...@adatao.com> > wrote: > >> Lance, some debugging ideas: you might try model.predict(RDD[Vector]) to >> isolate the cause to serialization of the loaded model. And also try to >> serialize the deserialized (loaded) model "manually" to see if that throws >> any visible exceptions. >> >> Sent while mobile. Pls excuse typos etc. >> On Aug 13, 2014 7:03 AM, "lancezhange" <lancezha...@gmail.com> wrote: >> >>> my prediction codes are simple enough as follows: >>> >>> *val labelsAndPredsOnGoodData = goodDataPoints.map { point => >>> val prediction = model.predict(point.features) >>> (point.label, prediction) >>> }* >>> >>> when model is the loaded one, above code just can't work. Can you catch >>> the >>> error? >>> Thanks. >>> >>> PS. i use spark-shell under standalone mode, version 1.0.0 >>> >>> >>> >>> >>> -- >>> View this message in context: >>> http://apache-spark-user-list.1001560.n3.nabble.com/How-to-save-mllib-model-to-hdfs-and-reload-it-tp11953p12035.html >>> Sent from the Apache Spark User List mailing list archive at Nabble.com. >>> >>> --------------------------------------------------------------------- >>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >>> For additional commands, e-mail: user-h...@spark.apache.org >>> >>> > > > -- > Thu. >
