Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5707#discussion_r32691822
--- Diff: python/pyspark/mllib/util.py ---
@@ -169,6 +170,27 @@ def loadLabeledPoints(sc, path, minPartitions=None):
minPartitions = minPartitions or min(sc.defaultParallelism, 2)
return callMLlibFunc("loadLabeledPoints", sc, path, minPartitions)
+ @staticmethod
+ def appendBias(data):
+ """
+ Returns a new vector with `1.0` (bias) appended to
+ the end of the input vector.
+ """
+ vec = _convert_to_vector(data)
+ if isinstance(vec, SparseVector):
+ return sp.csc_matrix(np.append(vec.toArray(), 1.0))
--- End diff --
Sorry for the slow response around the release! If you look at other uses
of scipy.sparse in MLlib PySpark, we always try to convert to Vector types.
(See e.g. pyspark.mllib.feature.StandardScalerModel)
It is still important to accept scipy types when given them since users
might pass scipy types to MLlib. But you'll need to test for whether scipy is
available before using it. I recommend looking for other uses of scipy in
MLlib PySpark for examples.
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