Re: How VectorIndexer works in Spark ML pipelines

[Jorge Sánchez]

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 :

> 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
>
>
>
>
>
>
>
>

How VectorIndexer works in Spark ML pipelines

[VISHNU SUBRAMANIAN]

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