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From mengxr <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-2839][MLlib] Stats Toolkit documentatio...
Date Tue, 26 Aug 2014 04:52:58 GMT
Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/2123#discussion_r16695707
  
    --- Diff: docs/mllib-stats.md ---
    @@ -99,69 +99,336 @@ v = u.map(lambda x: 1.0 + 2.0 * x)
     
     </div>
     
    -## Stratified Sampling 
    +## Correlation Calculation
    +
    +Calculating the correlation between two series of data is a common operation in Statistics.
In MLlib
    +we provide the flexibility to calculate correlation between many series. The supported
correlation
    +methods are currently Pearson's and Spearman's correlation.
    + 
    +<div class="codetabs">
    +<div data-lang="scala" markdown="1">
    +[`Statistics`](api/scala/index.html#org.apache.spark.mllib.stat.Statistics$) provides
methods to 
    +calculate correlations between series. Depending on the type of input, two `RDD[Double]`s
or 
    +an `RDD[Vector]`, the output will be a `Double` or the correlation `Matrix` respectively.
    +
    +{% highlight scala %}
    +import org.apache.spark.SparkContext
    +import org.apache.spark.mllib.linalg._
    +import org.apache.spark.mllib.stat.Statistics
    +
    +val sc: SparkContext = ...
    +
    +val seriesX: RDD[Double] = ... // a series
    +val seriesY: RDD[Double] = ... // must have the same number of partitions and cardinality
as seriesX
    +
    +// compute the correlation using Pearson's method. Enter "spearman" for Spearman's method.
If a 
    +// method is not specified, Pearson's method will be used by default. 
    +val correlation: Double = Statistics.corr(seriesX, seriesY, "pearson")
    +
    +val data: RDD[Vector] = ... // note that each Vector is a row and not a column
    +
    +// calculate the correlation matrix using Pearson's method. Use "spearman" for Spearman's
method.
    +// If a method is not specified, Pearson's method will be used by default. 
    +val correlMatrix: Matrix = Statistics.corr(data, "pearson")
    +
    +{% endhighlight %}
    +</div>
    +
    +<div data-lang="java" markdown="1">
    +[`Statistics`](api/java/org/apache/spark/mllib/stat/Statistics.html) provides methods
to 
    +calculate correlations between series. Depending on the type of input, two `JavaDoubleRDD`s
or 
    +a `JavaRDD<Vector>`, the output will be a `Double` or the correlation `Matrix`
respectively.
    +
    +{% highlight java %}
    +import org.apache.spark.api.java.JavaDoubleRDD;
    +import org.apache.spark.api.java.JavaSparkContext;
    +import org.apache.spark.mllib.linalg.*;
    +import org.apache.spark.mllib.stat.Statistics;
    +
    +JavaSparkContext jsc = ...
    +
    +JavaDoubleRDD seriesX = ... // a series
    +JavaDoubleRDD seriesY = ... // must have the same number of partitions and cardinality
as seriesX
    +
    +// compute the correlation using Pearson's method. Enter "spearman" for Spearman's method.
If a 
    +// method is not specified, Pearson's method will be used by default. 
    +Double correlation = Statistics.corr(seriesX.srdd(), seriesY.srdd(), "pearson");
    +
    +JavaRDD<Vector> data = ... // note that each Vector is a row and not a column
    +
    +// calculate the correlation matrix using Pearson's method. Use "spearman" for Spearman's
method.
    +// If a method is not specified, Pearson's method will be used by default. 
    +Matrix correlMatrix = Statistics.corr(data.rdd(), "pearson");
    +
    +{% endhighlight %}
    +</div>
    +
    +<div data-lang="python" markdown="1">
    +[`Statistics`](api/python/index.html#org.apache.spark.mllib.stat.Statistics$) provides
methods to 
    +calculate correlations between series. Depending on the type of input, two `RDD[Double]`s
or 
    +an `RDD[Vector]`, the output will be a `Double` or the correlation `Matrix` respectively.
    +
    +Support for `RowMatrix` operations in python currently don't exist, but will be added
in future 
    +releases.  
    +
    +{% highlight python %}
    +from pyspark.mllib.stat import Statistics
    +
    +sc = ... # SparkContext
    +
    +seriesX = ... # a series
    +seriesY = ... # must have the same number of partitions and cardinality as seriesX
    +
    +# Compute the correlation using Pearson's method. Enter "spearman" for Spearman's method.
If a 
    +# method is not specified, Pearson's method will be used by default. 
    +print Statistics.corr(seriesX, seriesY, method="pearson")
    +
    +data = ... # an RDD of Vectors
    +# calculate the correlation matrix using Pearson's method. Use "spearman" for Spearman's
method.
    +# If a method is not specified, Pearson's method will be used by default. 
    +print Statistics.corr(data, method="pearson")
    +
    +{% endhighlight %}
    +</div>
    +
    +</div>
    +
    +## Stratified Sampling
    +
    +Unlike the other statistics functions, which reside in MLLib, stratified sampling methods,

    +`sampleByKey` and `sampleByKeyExact`, can be accessed in `PairRDDFunctions` in core,
as stratified 
    +sampling is tightly coupled with the PairRDD data type, and the function signature conforms
to the 
    +other *ByKey* methods in PairRDDFunctions. A separate method for exact sample size support
exists 
    +as it requires significant more resources than the per-stratum simple random sampling
used in 
    +`sampleByKey`.
    +
    +<div class="codetabs">
    +<div data-lang="scala" markdown="1">
    +[`sampleByKeyExact()`](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions) allows
users to
    +sample exactly $\lceil f_k \cdot n_k \rceil \, \forall k \in K$ items, where $f_k$ is
the desired 
    +fraction for key $k$, and $n_k$ is the number of key-value pairs for key $k$. 
    +Sampling without replacement requires one additional pass over the RDD to guarantee sample

    +size, whereas sampling with replacement requires two additional passes.
    +
    +{% highlight scala %}
    +import org.apache.spark.SparkContext
    +import org.apache.spark.rdd.PairRDDFunctions
    +
    +val sc: SparkContext = ...
    +
    +val data = ... // an RDD[(K,V)] of any key value pairs
    +val fractions: Map[K, Double] = ... // specify the exact fraction desired from each key
    +
    +// Get an exact sample from each stratum
    +val sample = data.sampleByKeyExact(withReplacement=false, fractions)
    --- End diff --
    
    add spaces around `=`


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