Vishnu,

VectorIndexer will add metadata regarding which features are categorical and what are continuous depending on the threshold, if there are more different unique values than the MaxCategories parameter, they will be treated as continuous. That will help the learning algorithms as they will be treated differently.
From the data I can see you have more than one Vector in the features column? Try using some Vectors with only two different values.

Regards.

2015-10-15 10:14 GMT+01:00 VISHNU SUBRAMANIAN <johnfedrickenator@gmail.com>:
HI All,

I am trying to use the VectorIndexer (FeatureExtraction) technique available from the Spark ML Pipelines. 

I ran the example in the documentation . 

val featureIndexer = new VectorIndexer()
  .setInputCol("features")
  .setOutputCol("indexedFeatures")
  .setMaxCategories(4)
  .fit(data)

And then I wanted to see what output it generates.

After performing transform on the data set , the output looks like below.

scala> predictions.select("indexedFeatures").take(1).foreach(println)

[(692,[124,125,126,127,151,152,153,154,155,179,180,181,182,183,208,209,210,211,235,236,237,238,239,263,264,265,266,267,268,292,293,294,295,296,321,322,323,324,349,350,351,352,377,378,379,380,405,406,407,408,433,434,435,436,461,462,463,464,489,490,491,492,493,517,518,519,520,521,545,546,547,548,549,574,575,576,577,578,602,603,604,605,606,630,631,632,633,634,658,659,660,661,662],[145.0,255.0,211.0,31.0,32.0,237.0,253.0,252.0,71.0,11.0,175.0,253.0,252.0,71.0,144.0,253.0,252.0,71.0,16.0,191.0,253.0,252.0,71.0,26.0,221.0,253.0,252.0,124.0,31.0,125.0,253.0,252.0,252.0,108.0,253.0,252.0,252.0,108.0,255.0,253.0,253.0,108.0,253.0,252.0,252.0,108.0,253.0,252.0,252.0,108.0,253.0,252.0,252.0,108.0,255.0,253.0,253.0,170.0,253.0,252.0,252.0,252.0,42.0,149.0,252.0,252.0,252.0,144.0,109.0,252.0,252.0,252.0,144.0,218.0,253.0,253.0,255.0,35.0,175.0,252.0,252.0,253.0,35.0,73.0,252.0,252.0,253.0,35.0,31.0,211.0,252.0,253.0,35.0])]


scala> predictions.select("features").take(1).foreach(println)

[(692,[124,125,126,127,151,152,153,154,155,179,180,181,182,183,208,209,210,211,235,236,237,238,239,263,264,265,266,267,268,292,293,294,295,296,321,322,323,324,349,350,351,352,377,378,379,380,405,406,407,408,433,434,435,436,461,462,463,464,489,490,491,492,493,517,518,519,520,521,545,546,547,548,549,574,575,576,577,578,602,603,604,605,606,630,631,632,633,634,658,659,660,661,662],[145.0,255.0,211.0,31.0,32.0,237.0,253.0,252.0,71.0,11.0,175.0,253.0,252.0,71.0,144.0,253.0,252.0,71.0,16.0,191.0,253.0,252.0,71.0,26.0,221.0,253.0,252.0,124.0,31.0,125.0,253.0,252.0,252.0,108.0,253.0,252.0,252.0,108.0,255.0,253.0,253.0,108.0,253.0,252.0,252.0,108.0,253.0,252.0,252.0,108.0,253.0,252.0,252.0,108.0,255.0,253.0,253.0,170.0,253.0,252.0,252.0,252.0,42.0,149.0,252.0,252.0,252.0,144.0,109.0,252.0,252.0,252.0,144.0,218.0,253.0,253.0,255.0,35.0,175.0,252.0,252.0,253.0,35.0,73.0,252.0,252.0,253.0,35.0,31.0,211.0,252.0,253.0,35.0])]

I can,t understand what is happening. I tried with simple data sets also , but similar result.

Please help.

Thanks,

Vishnu