Thanks Reza! :-D Naftali
On Wed, Jun 18, 2014 at 1:47 PM, Reza Zadeh <r...@databricks.com> wrote: > Hi Naftali, > > Yes you're right. For now please add a column of ones. We are working on > adding a weighted regularization term, and exposing the scala intercept > option in the python binding. > > Best, > Reza > > > On Mon, Jun 16, 2014 at 12:19 PM, Naftali Harris <naft...@affirm.com> > wrote: > >> Hi everyone, >> >> The Python LogisticRegressionWithSGD does not appear to estimate an >> intercept. When I run the following, the returned weights and intercept >> are both 0.0: >> >> from pyspark import SparkContext >> from pyspark.mllib.regression import LabeledPoint >> from pyspark.mllib.classification import LogisticRegressionWithSGD >> >> def main(): >> sc = SparkContext(appName="NoIntercept") >> >> train = sc.parallelize([LabeledPoint(0, [0]), LabeledPoint(1, [0]), >> LabeledPoint(1, [0])]) >> >> model = LogisticRegressionWithSGD.train(train, iterations=500, >> step=0.1) >> print "Final weights: " + str(model.weights) >> print "Final intercept: " + str(model.intercept) >> >> if __name__ == "__main__": >> main() >> >> >> Of course, one can fit an intercept with the simple expedient of adding a >> column of ones, but that's kind of annoying. Moreover, it looks like the >> scala version has an intercept option. >> >> Am I missing something? Should I just add the column of ones? If I >> submitted a PR doing that, is that the sort of thing you guys would accept? >> >> Thanks! :-) >> >> Naftali >> > >
