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https://issues.apache.org/jira/browse/SPARK-12208?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-12208:
---------------------------------
Labels: bulk-closed (was: )
> 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
> Priority: Minor
> Labels: bulk-closed
>
> 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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