[jira] [Created] (SPARK-12208) Abstract the examples into a common place

["Timothy Hunter (JIRA)" <ji...@apache.org>]

Timothy Hunter created SPARK-12208:
--------------------------------------

             Summary: Abstract the examples into a common place
                 Key: SPARK-12208
                 URL: https://issues.apache.org/jira/browse/SPARK-12208
             Project: Spark
          Issue Type: Sub-task
          Components: Documentation, MLlib
    Affects Versions: 1.5.2
            Reporter: Timothy Hunter


When we write examples in the code, we put the generation of the data along with the example itself. We typically have either:

{code}
val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
...
{code}

or some more esoteric stuff such as:
{code}
val data = Array(
  (0, 0.1),
  (1, 0.8),
  (2, 0.2)
)
val dataFrame: DataFrame = sqlContext.createDataFrame(data).toDF("label", "feature")
{code}

{code}
val data = Array(
  Vectors.sparse(5, Seq((1, 1.0), (3, 7.0))),
  Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
  Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)
)
val df = sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features")
{code}

I suggest we follow the example of sklearn and standardize all the generation of example data inside a few methods, for example in {{org.apache.spark.ml.examples.ExampleData}}. One reason is that just reading the code is sometimes not enough to figure out what the data is supposed to be. For example when using {{libsvm_data}}, it is unclear what the dataframe columns are. This is something we should comment somewhere.
Also, it would help explaining in one place all the scala idiosyncracies such as using {{Tuple1.apply}} and such.



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