Spark MLlib Vector only supports data of double type, it's reasonable to throw exception when you creating a Vector with element of unicode type.

2016-05-24 7:27 GMT-07:00 flyinggip <myflyinggip@hotmail.com>:
Hi there,

I notice that there might be a bug in pyspark.mllib.linalg.Vectors when
dealing with a vector with a single element.

Firstly, the 'dense' method says it can also take numpy.array. However the
code uses 'if len(elements) == 1' and when a numpy.array has only one
element its length is undefined and currently if calling dense() on a numpy
array with one element the program crashes. Probably instead of using len()
in the above if, size should be used.

Secondly, after I managed to create a dense-Vectors object with only one
element from unicode, it seems that its behaviour is unpredictable. For
example,

Vectors.dense(unicode("0.1"))

will report an error.

dense_vec = Vectors.dense(unicode("0.1"))

will NOT report any error until you run

dense_vec

to check its value. And the following will be able to create a successful
DataFrame:

mylist = [(0, Vectors.dense(unicode("0.1")))]
myrdd = sc.parallelize(mylist)
mydf = sqlContext.createDataFrame(myrdd, ["X", "Y"])

However if the above unicode value is read from a text file (e.g., a csv
file with 2 columns) then the DataFrame column corresponding to "Y" will be
EMPTY:

raw_data = sc.textFile(filename)
split_data = raw_data.map(lambda line: line.split(','))
parsed_data = split_data.map(lambda line: (int(line[0]),
Vectors.dense(line[1])))
mydf = sqlContext.createDataFrame(parsed_data, ["X", "Y"])

It would be great if someone could share some ideas. Thanks a lot.

f.



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