PySpark PCA: how to convert dataframe rows from multiple columns to a single column DenseVector?

Question

I'd like to perform principle component analysis (PCA), using PySpark (Spark 1.6.2), on numerical data that exists in a Hive table. I'm able to import the Hive table to a Spark dataframe:

>>> from pyspark.sql import HiveContext
>>> hiveContext = HiveContext(sc)
>>> dataframe = hiveContext.sql("SELECT * FROM my_table")
>>> type(dataframe)
<class 'pyspark.sql.dataframe.DataFrame'>
>>> dataframe.columns
['par001', 'par002', 'par003', etc...]
>>> dataframe.collect()
[Row(par001=1.1, par002=5.5, par003=8.2, etc...), Row(par001=0.0, par002=5.7, par003=4.2, etc...), etc...]

There's an excellent StackOverflow post that shows how to perform PCA in PySpark: https://stackoverflow.com/a/33481471/2626491

In the 'test' section of the post, @desertnaut creates a dataframe with just one column (called 'features'):

>>> from pyspark.ml.feature import *
>>> from pyspark.mllib.linalg import Vectors
>>> data = [(Vectors.dense([0.0, 1.0, 0.0, 7.0, 0.0]),),
...          (Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),),
...          (Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),)]
>>> df = sqlContext.createDataFrame(data,["features"])
>>> type(df)
<class 'pyspark.sql.dataframe.DataFrame'>
>>> df.columns
['features']
>>> df.collect()
[Row(features=DenseVector([0.0, 1.0, 0.0, 7.0, 0.0])), Row(features=DenseVector([2.0, 0.0, 3.0, 4.0, 5.0])), Row(features=DenseVector([4.0, 0.0, 0.0, 6.0, 7.0]))]

Each row in @desertnaut's example dataframe contains a DenseVector object, which is then used by the pca function.

Q) How can I convert the dataframe from Hive into a single column dataframe ("features") where each row contains a DenseVector representing all the values from the original row?

Answer 1

You should use a VectorAssembler. If data is similar to this:

from pyspark.sql import Row

data = sc.parallelize([
    Row(par001=1.1, par002=5.5, par003=8.2),
    Row(par001=0.0, par002=5.7, par003=4.2)
]).toDF()

you should import required class:

from pyspark.ml.feature import VectorAssembler

create an instance:

assembler = VectorAssembler(inputCols=data.columns, outputCol="features")

transform and select:

assembler.transform(data).select("features")

You can also use an user defined function. In Spark 1.6 import Vectors and VectorUDT from mllib:

from pyspark.mllib.linalg import Vectors, VectorUDT

and udf from sql.functions:

from pyspark.sql.functions import udf, array

and select:

data.select(
  udf(Vectors.dense, VectorUDT())(*data.columns)
).toDF("features")

This is less verbose but much slower.