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 <myflying...@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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