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Posted to reviews@spark.apache.org by tgaloppo <gi...@git.apache.org> on 2015/02/05 18:28:46 UTC
[GitHub] spark pull request: [SPARK-5013] [MLlib] [WIP] Added documentation...
GitHub user tgaloppo opened a pull request:
https://github.com/apache/spark/pull/4401
[SPARK-5013] [MLlib] [WIP] Added documentation and sample data file for GaussianMixture
Simple description and code samples (and sample data) for GaussianMixture
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/tgaloppo/spark spark-5013
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/4401.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #4401
----
commit 3eb41fa1f304d817109d5f56349dfcca49119957
Author: Travis Galoppo <tj...@columbia.edu>
Date: 2015-02-05T17:25:06Z
[SPARK-5013] Added documentation and sample data file for GaussianMixture
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[GitHub] spark pull request: [SPARK-5013] [MLlib] [WIP] Added documentation...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73197096
Did you follow the `docs/README.md`?
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[GitHub] spark pull request: [SPARK-5013] [MLlib] Added documentation and s...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73254122
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[GitHub] spark pull request: [SPARK-5013] [MLlib] [WIP] Added documentation...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73177607
[Test build #26884 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/26884/consoleFull) for PR 4401 at commit [`2368690`](https://github.com/apache/spark/commit/23686904dbea03d10264b00b9fffa797d21d43f0).
* This patch **passes all tests**.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
* `public class GaussianMixtureExample `
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[GitHub] spark pull request: [SPARK-5013] [MLlib] [WIP] Added documentation...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73102200
[Test build #26845 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/26845/consoleFull) for PR 4401 at commit [`3eb41fa`](https://github.com/apache/spark/commit/3eb41fa1f304d817109d5f56349dfcca49119957).
* This patch **passes all tests**.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
* `public class GaussianMixtureExample `
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[GitHub] spark pull request: [SPARK-5013] [MLlib] [WIP] Added documentation...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4401#discussion_r24216731
--- Diff: docs/mllib-clustering.md ---
@@ -168,6 +187,112 @@ print("Within Set Sum of Squared Error = " + str(WSSSE))
</div>
+#### GaussianMixture
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+In the following example after loading and parsing data, we use a
+[`GaussianMixture`](api/scala/index.html#org.apache.spark.mllib.clustering.GaussianMixture)
+object to cluster the data into two clusters. The number of desired clusters is passed
+to the algorithm. We then output the parameters of the mixture model.
+
+{% highlight scala %}
+import org.apache.spark.mllib.clustering.GaussianMixture
+import org.apache.spark.mllib.linalg.Vectors
+
+// Load and parse the data
+val data = sc.textFile("data/mllib/gmm_data.txt")
+val parsedData = data.map(s => Vectors.dense(s.trim.split(' ').map(_.toDouble))).cache()
+
+// Cluster the data into two classes using GaussianMixture
+val gmm = new GaussianMixture().setK(2).run(parsedData)
+
+// output parameters of max-likelihood model
+for (i <- 0 until gmm.k) {
+ println("weight=%f\nmu=%s\nsigma=\n%s\n" format
+ (gmm.weights(i), gmm.gaussians(i).mu, gmm.gaussians(i).sigma))
+}
+
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+All of MLlib's methods use Java-friendly types, so you can import and call them there the same
+way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the
+Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a Scala one by
+calling `.rdd()` on your `JavaRDD` object. A self-contained application example
+that is equivalent to the provided example in Scala is given below:
+
+{% highlight java %}
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.clustering.GaussianMixture;
+import org.apache.spark.mllib.clustering.GaussianMixtureModel;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.SparkConf;
+
+public class GaussianMixtureExample {
+ public static void main(String[] args) {
+ SparkConf conf = new SparkConf().setAppName("GaussianMixture Example");
+ JavaSparkContext sc = new JavaSparkContext(conf);
+
+ // Load and parse data
+ String path = "data/mllib/gmm_data.txt";
+ JavaRDD<String> data = sc.textFile(path);
+ JavaRDD<Vector> parsedData = data.map(
+ new Function<String, Vector>() {
+ public Vector call(String s) {
+ String[] sarray = s.trim().split(" ");
+ double[] values = new double[sarray.length];
+ for (int i = 0; i < sarray.length; i++)
+ values[i] = Double.parseDouble(sarray[i]);
+ return Vectors.dense(values);
+ }
+ }
+ );
+ parsedData.cache();
+
+ // Cluster the data into two classes using GaussianMixture
+ GaussianMixtureModel gmm = new GaussianMixture().setK(2).run(parsedData.rdd());
+
+ // Output the parameters of the mixture model
+ for(int j=0; j<gmm.k(); j++) {
+ System.out.println("weight=%f\nmu=%s\nsigma=\n%s\n",
+ gmm.weights()[j], gmm.gaussians()[j].mu(), gmm.gaussians()[j].sigma());
+ }
+ }
+}
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+In the following example after loading and parsing data, we use a
+[`GaussianMixture`](api/scala/index.html#org.apache.spark.mllib.clustering.GaussianMixture)
+object to cluster the data into two clusters. The number of desired clusters is passed
+to the algorithm. We then output the parameters of the mixture model.
+
+{% highlight python %}
+from pyspark.mllib.clustering import GaussianMixture
+from numpy import array
+
+# Load and parse the data
+data = sc.textFile("data/mllib/gmm_data.txt")
+parsedData = data.map(lambda line: array([float(x) for x in line.strip().split(' ')]))
+
+# Build the model (cluster the data)
+gmm = new GaussianMixture.train(parsedData, 2)
--- End diff --
remove `new`
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[GitHub] spark pull request: [SPARK-5013] [MLlib] [WIP] Added documentation...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4401#discussion_r24216733
--- Diff: docs/mllib-clustering.md ---
@@ -168,6 +187,112 @@ print("Within Set Sum of Squared Error = " + str(WSSSE))
</div>
+#### GaussianMixture
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+In the following example after loading and parsing data, we use a
+[`GaussianMixture`](api/scala/index.html#org.apache.spark.mllib.clustering.GaussianMixture)
+object to cluster the data into two clusters. The number of desired clusters is passed
+to the algorithm. We then output the parameters of the mixture model.
+
+{% highlight scala %}
+import org.apache.spark.mllib.clustering.GaussianMixture
+import org.apache.spark.mllib.linalg.Vectors
+
+// Load and parse the data
+val data = sc.textFile("data/mllib/gmm_data.txt")
+val parsedData = data.map(s => Vectors.dense(s.trim.split(' ').map(_.toDouble))).cache()
+
+// Cluster the data into two classes using GaussianMixture
+val gmm = new GaussianMixture().setK(2).run(parsedData)
+
+// output parameters of max-likelihood model
+for (i <- 0 until gmm.k) {
+ println("weight=%f\nmu=%s\nsigma=\n%s\n" format
+ (gmm.weights(i), gmm.gaussians(i).mu, gmm.gaussians(i).sigma))
+}
+
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+All of MLlib's methods use Java-friendly types, so you can import and call them there the same
+way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the
+Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a Scala one by
+calling `.rdd()` on your `JavaRDD` object. A self-contained application example
+that is equivalent to the provided example in Scala is given below:
+
+{% highlight java %}
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.clustering.GaussianMixture;
+import org.apache.spark.mllib.clustering.GaussianMixtureModel;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.SparkConf;
+
+public class GaussianMixtureExample {
+ public static void main(String[] args) {
+ SparkConf conf = new SparkConf().setAppName("GaussianMixture Example");
+ JavaSparkContext sc = new JavaSparkContext(conf);
+
+ // Load and parse data
+ String path = "data/mllib/gmm_data.txt";
+ JavaRDD<String> data = sc.textFile(path);
+ JavaRDD<Vector> parsedData = data.map(
+ new Function<String, Vector>() {
+ public Vector call(String s) {
+ String[] sarray = s.trim().split(" ");
+ double[] values = new double[sarray.length];
+ for (int i = 0; i < sarray.length; i++)
+ values[i] = Double.parseDouble(sarray[i]);
+ return Vectors.dense(values);
+ }
+ }
+ );
+ parsedData.cache();
+
+ // Cluster the data into two classes using GaussianMixture
+ GaussianMixtureModel gmm = new GaussianMixture().setK(2).run(parsedData.rdd());
+
+ // Output the parameters of the mixture model
+ for(int j=0; j<gmm.k(); j++) {
+ System.out.println("weight=%f\nmu=%s\nsigma=\n%s\n",
+ gmm.weights()[j], gmm.gaussians()[j].mu(), gmm.gaussians()[j].sigma());
+ }
+ }
+}
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+In the following example after loading and parsing data, we use a
+[`GaussianMixture`](api/scala/index.html#org.apache.spark.mllib.clustering.GaussianMixture)
--- End diff --
This should be linked to the Python doc, e.g., `[NaiveBayes](api/python/pyspark.mllib.classification.NaiveBayes-class.html)`.
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[GitHub] spark pull request: [SPARK-5013] [MLlib] [WIP] Added documentation...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73165600
@tgaloppo Thanks for working on the user guide! I saw this is still marked as `WIP`. Are you going to add more content?
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[GitHub] spark pull request: [SPARK-5013] [MLlib] Added documentation and s...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73242351
[Test build #26915 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/26915/consoleFull) for PR 4401 at commit [`c9ff9a5`](https://github.com/apache/spark/commit/c9ff9a5536a3141baa785b87a7fce05543b90c84).
* This patch merges cleanly.
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[GitHub] spark pull request: [SPARK-5013] [MLlib] [WIP] Added documentation...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73177613
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[GitHub] spark pull request: [SPARK-5013] [MLlib] [WIP] Added documentation...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73171464
[Test build #26884 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/26884/consoleFull) for PR 4401 at commit [`2368690`](https://github.com/apache/spark/commit/23686904dbea03d10264b00b9fffa797d21d43f0).
* This patch merges cleanly.
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[GitHub] spark pull request: [SPARK-5013] [MLlib] Added documentation and s...
Posted by tgaloppo <gi...@git.apache.org>.
Github user tgaloppo commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73243150
@mengxr I was able to build the docs (I had to do a clean build on my source tree for some reason). I have checked all links (that I added, anyway). I also updated mllib-guide.md to include Gaussian mixture (and power iteration, since it was also missing) in the list of clustering techniques.
I've removed the WIP tag; I will not add any more content, unless requested.
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[GitHub] spark pull request: [SPARK-5013] [MLlib] Added documentation and s...
Posted by asfgit <gi...@git.apache.org>.
Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/4401
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[GitHub] spark pull request: [SPARK-5013] [MLlib] [WIP] Added documentation...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73089327
[Test build #26845 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/26845/consoleFull) for PR 4401 at commit [`3eb41fa`](https://github.com/apache/spark/commit/3eb41fa1f304d817109d5f56349dfcca49119957).
* This patch merges cleanly.
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[GitHub] spark pull request: [SPARK-5013] [MLlib] [WIP] Added documentation...
Posted by tgaloppo <gi...@git.apache.org>.
Github user tgaloppo commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73171324
@mengxr I have made the fixes you pointed out. I am having trouble building the API docs so I can not verify that the link to the python GaussianMixture class resolves properly.
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[GitHub] spark pull request: [SPARK-5013] [MLlib] Added documentation and s...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73287334
LGTM. Merged into master and branch-1.3! Thanks for working on the user guide!
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[GitHub] spark pull request: [SPARK-5013] [MLlib] [WIP] Added documentation...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73102218
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[GitHub] spark pull request: [SPARK-5013] [MLlib] Added documentation and s...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73254105
[Test build #26915 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/26915/consoleFull) for PR 4401 at commit [`c9ff9a5`](https://github.com/apache/spark/commit/c9ff9a5536a3141baa785b87a7fce05543b90c84).
* This patch **passes all tests**.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
* `public class GaussianMixtureExample `
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[GitHub] spark pull request: [SPARK-5013] [MLlib] [WIP] Added documentation...
Posted by tgaloppo <gi...@git.apache.org>.
Github user tgaloppo commented on the pull request:
https://github.com/apache/spark/pull/4401#issuecomment-73166676
@mengxr I was considering adding a graphic of the test data with the recovered 2-d gaussians... I'm not sure if it would really be beneficial or not.
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