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Posted to commits@spark.apache.org by me...@apache.org on 2016/03/24 22:25:14 UTC
spark git commit: [SPARK-13017][DOCS] Replace example code in
mllib-feature-extraction.md using include_example
Repository: spark
Updated Branches:
refs/heads/master 342079dc4 -> d283223a5
[SPARK-13017][DOCS] Replace example code in mllib-feature-extraction.md using include_example
Replace example code in mllib-feature-extraction.md using include_example
https://issues.apache.org/jira/browse/SPARK-13017
The example code in the user guide is embedded in the markdown and hence it is not easy to test. It would be nice to automatically test them. This JIRA is to discuss options to automate example code testing and see what we can do in Spark 1.6.
Goal is to move actual example code to spark/examples and test compilation in Jenkins builds. Then in the markdown, we can reference part of the code to show in the user guide. This requires adding a Jekyll tag that is similar to https://github.com/jekyll/jekyll/blob/master/lib/jekyll/tags/include.rb, e.g., called include_example.
`{% include_example scala/org/apache/spark/examples/mllib/TFIDFExample.scala %}`
Jekyll will find `examples/src/main/scala/org/apache/spark/examples/mllib/TFIDFExample.scala` and pick code blocks marked "example" and replace code block in
`{% highlight %}`
in the markdown.
See more sub-tasks in parent ticket: https://issues.apache.org/jira/browse/SPARK-11337
Author: Xin Ren <ia...@126.com>
Closes #11142 from keypointt/SPARK-13017.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/d283223a
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/d283223a
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/d283223a
Branch: refs/heads/master
Commit: d283223a5a75c53970e72a1016e0b237856b5ea1
Parents: 342079d
Author: Xin Ren <ia...@126.com>
Authored: Thu Mar 24 14:25:10 2016 -0700
Committer: Xiangrui Meng <me...@databricks.com>
Committed: Thu Mar 24 14:25:10 2016 -0700
----------------------------------------------------------------------
docs/mllib-feature-extraction.md | 375 +------------------
.../mllib/JavaChiSqSelectorExample.java | 83 ++++
.../mllib/JavaElementwiseProductExample.java | 78 ++++
.../python/mllib/elementwise_product_example.py | 51 +++
.../src/main/python/mllib/normalizer_example.py | 52 +++
.../python/mllib/standard_scaler_example.py | 55 +++
.../src/main/python/mllib/tf_idf_example.py | 57 +++
.../src/main/python/mllib/word2vec_example.py | 40 ++
.../examples/mllib/ChiSqSelectorExample.scala | 61 +++
.../mllib/ElementwiseProductExample.scala | 56 +++
.../examples/mllib/NormalizerExample.scala | 57 +++
.../spark/examples/mllib/PCAExample.scala | 74 ++++
.../examples/mllib/StandardScalerExample.scala | 62 +++
.../spark/examples/mllib/TFIDFExample.scala | 66 ++++
.../spark/examples/mllib/Word2VecExample.scala | 55 +++
15 files changed, 861 insertions(+), 361 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/docs/mllib-feature-extraction.md
----------------------------------------------------------------------
diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index 7796bac..7a97285 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -58,46 +58,7 @@ Each record could be an iterable of strings or other types.
Refer to the [`HashingTF` Scala docs](api/scala/index.html#org.apache.spark.mllib.feature.HashingTF) for details on the API.
-
-{% highlight scala %}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.SparkContext
-import org.apache.spark.mllib.feature.HashingTF
-import org.apache.spark.mllib.linalg.Vector
-
-val sc: SparkContext = ...
-
-// Load documents (one per line).
-val documents: RDD[Seq[String]] = sc.textFile("...").map(_.split(" ").toSeq)
-
-val hashingTF = new HashingTF()
-val tf: RDD[Vector] = hashingTF.transform(documents)
-{% endhighlight %}
-
-While applying `HashingTF` only needs a single pass to the data, applying `IDF` needs two passes:
-first to compute the IDF vector and second to scale the term frequencies by IDF.
-
-{% highlight scala %}
-import org.apache.spark.mllib.feature.IDF
-
-// ... continue from the previous example
-tf.cache()
-val idf = new IDF().fit(tf)
-val tfidf: RDD[Vector] = idf.transform(tf)
-{% endhighlight %}
-
-`spark.mllib`'s IDF implementation provides an option for ignoring terms which occur in less than a
-minimum number of documents. In such cases, the IDF for these terms is set to 0. This feature
-can be used by passing the `minDocFreq` value to the IDF constructor.
-
-{% highlight scala %}
-import org.apache.spark.mllib.feature.IDF
-
-// ... continue from the previous example
-tf.cache()
-val idf = new IDF(minDocFreq = 2).fit(tf)
-val tfidf: RDD[Vector] = idf.transform(tf)
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/mllib/TFIDFExample.scala %}
</div>
<div data-lang="python" markdown="1">
@@ -109,41 +70,7 @@ Each record could be an iterable of strings or other types.
Refer to the [`HashingTF` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.feature.HashingTF) for details on the API.
-{% highlight python %}
-from pyspark import SparkContext
-from pyspark.mllib.feature import HashingTF
-
-sc = SparkContext()
-
-# Load documents (one per line).
-documents = sc.textFile("...").map(lambda line: line.split(" "))
-
-hashingTF = HashingTF()
-tf = hashingTF.transform(documents)
-{% endhighlight %}
-
-While applying `HashingTF` only needs a single pass to the data, applying `IDF` needs two passes:
-first to compute the IDF vector and second to scale the term frequencies by IDF.
-
-{% highlight python %}
-from pyspark.mllib.feature import IDF
-
-# ... continue from the previous example
-tf.cache()
-idf = IDF().fit(tf)
-tfidf = idf.transform(tf)
-{% endhighlight %}
-
-`spark.mllib`'s IDF implementation provides an option for ignoring terms which occur in less than a
-minimum number of documents. In such cases, the IDF for these terms is set to 0. This feature
-can be used by passing the `minDocFreq` value to the IDF constructor.
-
-{% highlight python %}
-# ... continue from the previous example
-tf.cache()
-idf = IDF(minDocFreq=2).fit(tf)
-tfidf = idf.transform(tf)
-{% endhighlight %}
+{% include_example python/mllib/tf_idf_example.py %}
</div>
</div>
@@ -192,47 +119,12 @@ Here we assume the extracted file is `text8` and in same directory as you run th
<div data-lang="scala" markdown="1">
Refer to the [`Word2Vec` Scala docs](api/scala/index.html#org.apache.spark.mllib.feature.Word2Vec) for details on the API.
-{% highlight scala %}
-import org.apache.spark._
-import org.apache.spark.rdd._
-import org.apache.spark.SparkContext._
-import org.apache.spark.mllib.feature.{Word2Vec, Word2VecModel}
-
-val input = sc.textFile("text8").map(line => line.split(" ").toSeq)
-
-val word2vec = new Word2Vec()
-
-val model = word2vec.fit(input)
-
-val synonyms = model.findSynonyms("china", 40)
-
-for((synonym, cosineSimilarity) <- synonyms) {
- println(s"$synonym $cosineSimilarity")
-}
-
-// Save and load model
-model.save(sc, "myModelPath")
-val sameModel = Word2VecModel.load(sc, "myModelPath")
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/mllib/Word2VecExample.scala %}
</div>
<div data-lang="python" markdown="1">
Refer to the [`Word2Vec` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.feature.Word2Vec) for more details on the API.
-{% highlight python %}
-from pyspark import SparkContext
-from pyspark.mllib.feature import Word2Vec
-
-sc = SparkContext(appName='Word2Vec')
-inp = sc.textFile("text8_lines").map(lambda row: row.split(" "))
-
-word2vec = Word2Vec()
-model = word2vec.fit(inp)
-
-synonyms = model.findSynonyms('china', 40)
-
-for word, cosine_distance in synonyms:
- print("{}: {}".format(word, cosine_distance))
-{% endhighlight %}
+{% include_example python/mllib/word2vec_example.py %}
</div>
</div>
@@ -277,55 +169,13 @@ so that the new features have unit standard deviation and/or zero mean.
<div data-lang="scala" markdown="1">
Refer to the [`StandardScaler` Scala docs](api/scala/index.html#org.apache.spark.mllib.feature.StandardScaler) for details on the API.
-{% highlight scala %}
-import org.apache.spark.SparkContext._
-import org.apache.spark.mllib.feature.StandardScaler
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.util.MLUtils
-
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-
-val scaler1 = new StandardScaler().fit(data.map(x => x.features))
-val scaler2 = new StandardScaler(withMean = true, withStd = true).fit(data.map(x => x.features))
-// scaler3 is an identical model to scaler2, and will produce identical transformations
-val scaler3 = new StandardScalerModel(scaler2.std, scaler2.mean)
-
-// data1 will be unit variance.
-val data1 = data.map(x => (x.label, scaler1.transform(x.features)))
-
-// Without converting the features into dense vectors, transformation with zero mean will raise
-// exception on sparse vector.
-// data2 will be unit variance and zero mean.
-val data2 = data.map(x => (x.label, scaler2.transform(Vectors.dense(x.features.toArray))))
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/mllib/StandardScalerExample.scala %}
</div>
<div data-lang="python" markdown="1">
Refer to the [`StandardScaler` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.feature.StandardScaler) for more details on the API.
-{% highlight python %}
-from pyspark.mllib.util import MLUtils
-from pyspark.mllib.linalg import Vectors
-from pyspark.mllib.feature import StandardScaler
-
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-label = data.map(lambda x: x.label)
-features = data.map(lambda x: x.features)
-
-scaler1 = StandardScaler().fit(features)
-scaler2 = StandardScaler(withMean=True, withStd=True).fit(features)
-# scaler3 is an identical model to scaler2, and will produce identical transformations
-scaler3 = StandardScalerModel(scaler2.std, scaler2.mean)
-
-
-# data1 will be unit variance.
-data1 = label.zip(scaler1.transform(features))
-
-# Without converting the features into dense vectors, transformation with zero mean will raise
-# exception on sparse vector.
-# data2 will be unit variance and zero mean.
-data2 = label.zip(scaler1.transform(features.map(lambda x: Vectors.dense(x.toArray()))))
-{% endhighlight %}
+{% include_example python/mllib/standard_scaler_example.py %}
</div>
</div>
@@ -355,46 +205,13 @@ with $L^2$ norm, and $L^\infty$ norm.
<div data-lang="scala" markdown="1">
Refer to the [`Normalizer` Scala docs](api/scala/index.html#org.apache.spark.mllib.feature.Normalizer) for details on the API.
-{% highlight scala %}
-import org.apache.spark.SparkContext._
-import org.apache.spark.mllib.feature.Normalizer
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.util.MLUtils
-
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-
-val normalizer1 = new Normalizer()
-val normalizer2 = new Normalizer(p = Double.PositiveInfinity)
-
-// Each sample in data1 will be normalized using $L^2$ norm.
-val data1 = data.map(x => (x.label, normalizer1.transform(x.features)))
-
-// Each sample in data2 will be normalized using $L^\infty$ norm.
-val data2 = data.map(x => (x.label, normalizer2.transform(x.features)))
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/mllib/NormalizerExample.scala %}
</div>
<div data-lang="python" markdown="1">
Refer to the [`Normalizer` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.feature.Normalizer) for more details on the API.
-{% highlight python %}
-from pyspark.mllib.util import MLUtils
-from pyspark.mllib.linalg import Vectors
-from pyspark.mllib.feature import Normalizer
-
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-labels = data.map(lambda x: x.label)
-features = data.map(lambda x: x.features)
-
-normalizer1 = Normalizer()
-normalizer2 = Normalizer(p=float("inf"))
-
-# Each sample in data1 will be normalized using $L^2$ norm.
-data1 = labels.zip(normalizer1.transform(features))
-
-# Each sample in data2 will be normalized using $L^\infty$ norm.
-data2 = labels.zip(normalizer2.transform(features))
-{% endhighlight %}
+{% include_example python/mllib/normalizer_example.py %}
</div>
</div>
@@ -435,29 +252,7 @@ The following example shows the basic use of ChiSqSelector. The data set used ha
Refer to the [`ChiSqSelector` Scala docs](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector)
for details on the API.
-{% highlight scala %}
-import org.apache.spark.SparkContext._
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.util.MLUtils
-import org.apache.spark.mllib.feature.ChiSqSelector
-
-// Load some data in libsvm format
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-// Discretize data in 16 equal bins since ChiSqSelector requires categorical features
-// Even though features are doubles, the ChiSqSelector treats each unique value as a category
-val discretizedData = data.map { lp =>
- LabeledPoint(lp.label, Vectors.dense(lp.features.toArray.map { x => (x / 16).floor } ) )
-}
-// Create ChiSqSelector that will select top 50 of 692 features
-val selector = new ChiSqSelector(50)
-// Create ChiSqSelector model (selecting features)
-val transformer = selector.fit(discretizedData)
-// Filter the top 50 features from each feature vector
-val filteredData = discretizedData.map { lp =>
- LabeledPoint(lp.label, transformer.transform(lp.features))
-}
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/mllib/ChiSqSelectorExample.scala %}
</div>
<div data-lang="java" markdown="1">
@@ -465,52 +260,7 @@ val filteredData = discretizedData.map { lp =>
Refer to the [`ChiSqSelector` Java docs](api/java/org/apache/spark/mllib/feature/ChiSqSelector.html)
for details on the API.
-{% highlight java %}
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.api.java.function.Function;
-import org.apache.spark.mllib.feature.ChiSqSelector;
-import org.apache.spark.mllib.feature.ChiSqSelectorModel;
-import org.apache.spark.mllib.linalg.Vectors;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-
-SparkConf sparkConf = new SparkConf().setAppName("JavaChiSqSelector");
-JavaSparkContext sc = new JavaSparkContext(sparkConf);
-JavaRDD<LabeledPoint> points = MLUtils.loadLibSVMFile(sc.sc(),
- "data/mllib/sample_libsvm_data.txt").toJavaRDD().cache();
-
-// Discretize data in 16 equal bins since ChiSqSelector requires categorical features
-// Even though features are doubles, the ChiSqSelector treats each unique value as a category
-JavaRDD<LabeledPoint> discretizedData = points.map(
- new Function<LabeledPoint, LabeledPoint>() {
- @Override
- public LabeledPoint call(LabeledPoint lp) {
- final double[] discretizedFeatures = new double[lp.features().size()];
- for (int i = 0; i < lp.features().size(); ++i) {
- discretizedFeatures[i] = Math.floor(lp.features().apply(i) / 16);
- }
- return new LabeledPoint(lp.label(), Vectors.dense(discretizedFeatures));
- }
- });
-
-// Create ChiSqSelector that will select top 50 of 692 features
-ChiSqSelector selector = new ChiSqSelector(50);
-// Create ChiSqSelector model (selecting features)
-final ChiSqSelectorModel transformer = selector.fit(discretizedData.rdd());
-// Filter the top 50 features from each feature vector
-JavaRDD<LabeledPoint> filteredData = discretizedData.map(
- new Function<LabeledPoint, LabeledPoint>() {
- @Override
- public LabeledPoint call(LabeledPoint lp) {
- return new LabeledPoint(lp.label(), transformer.transform(lp.features()));
- }
- }
-);
-
-sc.stop();
-{% endhighlight %}
+{% include_example java/org/apache/spark/examples/mllib/JavaChiSqSelectorExample.java %}
</div>
</div>
@@ -554,78 +304,19 @@ This example below demonstrates how to transform vectors using a transforming ve
Refer to the [`ElementwiseProduct` Scala docs](api/scala/index.html#org.apache.spark.mllib.feature.ElementwiseProduct) for details on the API.
-{% highlight scala %}
-import org.apache.spark.SparkContext._
-import org.apache.spark.mllib.feature.ElementwiseProduct
-import org.apache.spark.mllib.linalg.Vectors
-
-// Create some vector data; also works for sparse vectors
-val data = sc.parallelize(Array(Vectors.dense(1.0, 2.0, 3.0), Vectors.dense(4.0, 5.0, 6.0)))
-
-val transformingVector = Vectors.dense(0.0, 1.0, 2.0)
-val transformer = new ElementwiseProduct(transformingVector)
-
-// Batch transform and per-row transform give the same results:
-val transformedData = transformer.transform(data)
-val transformedData2 = data.map(x => transformer.transform(x))
-
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/mllib/ElementwiseProductExample.scala %}
</div>
<div data-lang="java" markdown="1">
Refer to the [`ElementwiseProduct` Java docs](api/java/org/apache/spark/mllib/feature/ElementwiseProduct.html) for details on the API.
-{% highlight java %}
-import java.util.Arrays;
-import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.mllib.feature.ElementwiseProduct;
-import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.linalg.Vectors;
-
-// Create some vector data; also works for sparse vectors
-JavaRDD<Vector> data = sc.parallelize(Arrays.asList(
- Vectors.dense(1.0, 2.0, 3.0), Vectors.dense(4.0, 5.0, 6.0)));
-Vector transformingVector = Vectors.dense(0.0, 1.0, 2.0);
-ElementwiseProduct transformer = new ElementwiseProduct(transformingVector);
-
-// Batch transform and per-row transform give the same results:
-JavaRDD<Vector> transformedData = transformer.transform(data);
-JavaRDD<Vector> transformedData2 = data.map(
- new Function<Vector, Vector>() {
- @Override
- public Vector call(Vector v) {
- return transformer.transform(v);
- }
- }
-);
-
-{% endhighlight %}
+{% include_example java/org/apache/spark/examples/mllib/JavaElementwiseProductExample.java %}
</div>
<div data-lang="python" markdown="1">
Refer to the [`ElementwiseProduct` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.feature.ElementwiseProduct) for more details on the API.
-{% highlight python %}
-from pyspark import SparkContext
-from pyspark.mllib.linalg import Vectors
-from pyspark.mllib.feature import ElementwiseProduct
-
-# Load and parse the data
-sc = SparkContext()
-data = sc.textFile("data/mllib/kmeans_data.txt")
-parsedData = data.map(lambda x: [float(t) for t in x.split(" ")])
-
-# Create weight vector.
-transformingVector = Vectors.dense([0.0, 1.0, 2.0])
-transformer = ElementwiseProduct(transformingVector)
-
-# Batch transform
-transformedData = transformer.transform(parsedData)
-# Single-row transform
-transformedData2 = transformer.transform(parsedData.first())
-
-{% endhighlight %}
+{% include_example python/mllib/elementwise_product_example.py %}
</div>
</div>
@@ -645,44 +336,6 @@ for calculation a [Linear Regression]((mllib-linear-methods.html))
<div data-lang="scala" markdown="1">
Refer to the [`PCA` Scala docs](api/scala/index.html#org.apache.spark.mllib.feature.PCA) for details on the API.
-{% highlight scala %}
-import org.apache.spark.mllib.regression.LinearRegressionWithSGD
-import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.feature.PCA
-
-val data = sc.textFile("data/mllib/ridge-data/lpsa.data").map { line =>
- val parts = line.split(',')
- LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
-}.cache()
-
-val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
-val training = splits(0).cache()
-val test = splits(1)
-
-val pca = new PCA(training.first().features.size/2).fit(data.map(_.features))
-val training_pca = training.map(p => p.copy(features = pca.transform(p.features)))
-val test_pca = test.map(p => p.copy(features = pca.transform(p.features)))
-
-val numIterations = 100
-val model = LinearRegressionWithSGD.train(training, numIterations)
-val model_pca = LinearRegressionWithSGD.train(training_pca, numIterations)
-
-val valuesAndPreds = test.map { point =>
- val score = model.predict(point.features)
- (score, point.label)
-}
-
-val valuesAndPreds_pca = test_pca.map { point =>
- val score = model_pca.predict(point.features)
- (score, point.label)
-}
-
-val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean()
-val MSE_pca = valuesAndPreds_pca.map{case(v, p) => math.pow((v - p), 2)}.mean()
-
-println("Mean Squared Error = " + MSE)
-println("PCA Mean Squared Error = " + MSE_pca)
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/mllib/PCAExample.scala %}
</div>
</div>
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/java/org/apache/spark/examples/mllib/JavaChiSqSelectorExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaChiSqSelectorExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaChiSqSelectorExample.java
new file mode 100644
index 0000000..ad44acb
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaChiSqSelectorExample.java
@@ -0,0 +1,83 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib;
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.VoidFunction;
+// $example on$
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.feature.ChiSqSelector;
+import org.apache.spark.mllib.feature.ChiSqSelectorModel;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+// $example off$
+
+public class JavaChiSqSelectorExample {
+ public static void main(String[] args) {
+
+ SparkConf conf = new SparkConf().setAppName("JavaChiSqSelectorExample");
+ JavaSparkContext jsc = new JavaSparkContext(conf);
+
+ // $example on$
+ JavaRDD<LabeledPoint> points = MLUtils.loadLibSVMFile(jsc.sc(),
+ "data/mllib/sample_libsvm_data.txt").toJavaRDD().cache();
+
+ // Discretize data in 16 equal bins since ChiSqSelector requires categorical features
+ // Although features are doubles, the ChiSqSelector treats each unique value as a category
+ JavaRDD<LabeledPoint> discretizedData = points.map(
+ new Function<LabeledPoint, LabeledPoint>() {
+ @Override
+ public LabeledPoint call(LabeledPoint lp) {
+ final double[] discretizedFeatures = new double[lp.features().size()];
+ for (int i = 0; i < lp.features().size(); ++i) {
+ discretizedFeatures[i] = Math.floor(lp.features().apply(i) / 16);
+ }
+ return new LabeledPoint(lp.label(), Vectors.dense(discretizedFeatures));
+ }
+ }
+ );
+
+ // Create ChiSqSelector that will select top 50 of 692 features
+ ChiSqSelector selector = new ChiSqSelector(50);
+ // Create ChiSqSelector model (selecting features)
+ final ChiSqSelectorModel transformer = selector.fit(discretizedData.rdd());
+ // Filter the top 50 features from each feature vector
+ JavaRDD<LabeledPoint> filteredData = discretizedData.map(
+ new Function<LabeledPoint, LabeledPoint>() {
+ @Override
+ public LabeledPoint call(LabeledPoint lp) {
+ return new LabeledPoint(lp.label(), transformer.transform(lp.features()));
+ }
+ }
+ );
+ // $example off$
+
+ System.out.println("filtered data: ");
+ filteredData.foreach(new VoidFunction<LabeledPoint>() {
+ @Override
+ public void call(LabeledPoint labeledPoint) throws Exception {
+ System.out.println(labeledPoint.toString());
+ }
+ });
+
+ jsc.stop();
+ }
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/java/org/apache/spark/examples/mllib/JavaElementwiseProductExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaElementwiseProductExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaElementwiseProductExample.java
new file mode 100644
index 0000000..c8ce6ab
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaElementwiseProductExample.java
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib;
+
+// $example on$
+import java.util.Arrays;
+// $example off$
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaSparkContext;
+// $example on$
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.feature.ElementwiseProduct;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+// $example off$
+import org.apache.spark.api.java.function.VoidFunction;
+
+public class JavaElementwiseProductExample {
+ public static void main(String[] args) {
+
+ SparkConf conf = new SparkConf().setAppName("JavaElementwiseProductExample");
+ JavaSparkContext jsc = new JavaSparkContext(conf);
+
+ // $example on$
+ // Create some vector data; also works for sparse vectors
+ JavaRDD<Vector> data = jsc.parallelize(Arrays.asList(
+ Vectors.dense(1.0, 2.0, 3.0), Vectors.dense(4.0, 5.0, 6.0)));
+ Vector transformingVector = Vectors.dense(0.0, 1.0, 2.0);
+ final ElementwiseProduct transformer = new ElementwiseProduct(transformingVector);
+
+ // Batch transform and per-row transform give the same results:
+ JavaRDD<Vector> transformedData = transformer.transform(data);
+ JavaRDD<Vector> transformedData2 = data.map(
+ new Function<Vector, Vector>() {
+ @Override
+ public Vector call(Vector v) {
+ return transformer.transform(v);
+ }
+ }
+ );
+ // $example off$
+
+ System.out.println("transformedData: ");
+ transformedData.foreach(new VoidFunction<Vector>() {
+ @Override
+ public void call(Vector vector) throws Exception {
+ System.out.println(vector.toString());
+ }
+ });
+
+ System.out.println("transformedData2: ");
+ transformedData2.foreach(new VoidFunction<Vector>() {
+ @Override
+ public void call(Vector vector) throws Exception {
+ System.out.println(vector.toString());
+ }
+ });
+
+ jsc.stop();
+ }
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/python/mllib/elementwise_product_example.py
----------------------------------------------------------------------
diff --git a/examples/src/main/python/mllib/elementwise_product_example.py b/examples/src/main/python/mllib/elementwise_product_example.py
new file mode 100644
index 0000000..6d8bf6d
--- /dev/null
+++ b/examples/src/main/python/mllib/elementwise_product_example.py
@@ -0,0 +1,51 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import print_function
+
+from pyspark import SparkContext
+# $example on$
+from pyspark.mllib.feature import ElementwiseProduct
+from pyspark.mllib.linalg import Vectors
+# $example off$
+
+if __name__ == "__main__":
+ sc = SparkContext(appName="ElementwiseProductExample") # SparkContext
+
+ # $example on$
+ data = sc.textFile("data/mllib/kmeans_data.txt")
+ parsedData = data.map(lambda x: [float(t) for t in x.split(" ")])
+
+ # Create weight vector.
+ transformingVector = Vectors.dense([0.0, 1.0, 2.0])
+ transformer = ElementwiseProduct(transformingVector)
+
+ # Batch transform
+ transformedData = transformer.transform(parsedData)
+ # Single-row transform
+ transformedData2 = transformer.transform(parsedData.first())
+ # $example off$
+
+ print("transformedData:")
+ for each in transformedData.collect():
+ print(each)
+
+ print("transformedData2:")
+ for each in transformedData2.collect():
+ print(each)
+
+ sc.stop()
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/python/mllib/normalizer_example.py
----------------------------------------------------------------------
diff --git a/examples/src/main/python/mllib/normalizer_example.py b/examples/src/main/python/mllib/normalizer_example.py
new file mode 100644
index 0000000..a4e028c
--- /dev/null
+++ b/examples/src/main/python/mllib/normalizer_example.py
@@ -0,0 +1,52 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import print_function
+
+from pyspark import SparkContext
+# $example on$
+from pyspark.mllib.feature import Normalizer
+from pyspark.mllib.util import MLUtils
+# $example off$
+
+if __name__ == "__main__":
+ sc = SparkContext(appName="NormalizerExample") # SparkContext
+
+ # $example on$
+ data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+ labels = data.map(lambda x: x.label)
+ features = data.map(lambda x: x.features)
+
+ normalizer1 = Normalizer()
+ normalizer2 = Normalizer(p=float("inf"))
+
+ # Each sample in data1 will be normalized using $L^2$ norm.
+ data1 = labels.zip(normalizer1.transform(features))
+
+ # Each sample in data2 will be normalized using $L^\infty$ norm.
+ data2 = labels.zip(normalizer2.transform(features))
+ # $example off$
+
+ print("data1:")
+ for each in data1.collect():
+ print(each)
+
+ print("data2:")
+ for each in data2.collect():
+ print(each)
+
+ sc.stop()
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/python/mllib/standard_scaler_example.py
----------------------------------------------------------------------
diff --git a/examples/src/main/python/mllib/standard_scaler_example.py b/examples/src/main/python/mllib/standard_scaler_example.py
new file mode 100644
index 0000000..20a77a4
--- /dev/null
+++ b/examples/src/main/python/mllib/standard_scaler_example.py
@@ -0,0 +1,55 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import print_function
+
+from pyspark import SparkContext
+# $example on$
+from pyspark.mllib.feature import StandardScaler, StandardScalerModel
+from pyspark.mllib.linalg import Vectors
+from pyspark.mllib.util import MLUtils
+# $example off$
+
+if __name__ == "__main__":
+ sc = SparkContext(appName="StandardScalerExample") # SparkContext
+
+ # $example on$
+ data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+ label = data.map(lambda x: x.label)
+ features = data.map(lambda x: x.features)
+
+ scaler1 = StandardScaler().fit(features)
+ scaler2 = StandardScaler(withMean=True, withStd=True).fit(features)
+
+ # data1 will be unit variance.
+ data1 = label.zip(scaler1.transform(features))
+
+ # Without converting the features into dense vectors, transformation with zero mean will raise
+ # exception on sparse vector.
+ # data2 will be unit variance and zero mean.
+ data2 = label.zip(scaler2.transform(features.map(lambda x: Vectors.dense(x.toArray()))))
+ # $example off$
+
+ print("data1:")
+ for each in data1.collect():
+ print(each)
+
+ print("data2:")
+ for each in data2.collect():
+ print(each)
+
+ sc.stop()
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/python/mllib/tf_idf_example.py
----------------------------------------------------------------------
diff --git a/examples/src/main/python/mllib/tf_idf_example.py b/examples/src/main/python/mllib/tf_idf_example.py
new file mode 100644
index 0000000..c4d5333
--- /dev/null
+++ b/examples/src/main/python/mllib/tf_idf_example.py
@@ -0,0 +1,57 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import print_function
+
+from pyspark import SparkContext
+# $example on$
+from pyspark.mllib.feature import HashingTF, IDF
+# $example off$
+
+if __name__ == "__main__":
+ sc = SparkContext(appName="TFIDFExample") # SparkContext
+
+ # $example on$
+ # Load documents (one per line).
+ documents = sc.textFile("data/mllib/kmeans_data.txt").map(lambda line: line.split(" "))
+
+ hashingTF = HashingTF()
+ tf = hashingTF.transform(documents)
+
+ # While applying HashingTF only needs a single pass to the data, applying IDF needs two passes:
+ # First to compute the IDF vector and second to scale the term frequencies by IDF.
+ tf.cache()
+ idf = IDF().fit(tf)
+ tfidf = idf.transform(tf)
+
+ # spark.mllib's IDF implementation provides an option for ignoring terms
+ # which occur in less than a minimum number of documents.
+ # In such cases, the IDF for these terms is set to 0.
+ # This feature can be used by passing the minDocFreq value to the IDF constructor.
+ idfIgnore = IDF(minDocFreq=2).fit(tf)
+ tfidfIgnore = idf.transform(tf)
+ # $example off$
+
+ print("tfidf:")
+ for each in tfidf.collect():
+ print(each)
+
+ print("tfidfIgnore:")
+ for each in tfidfIgnore.collect():
+ print(each)
+
+ sc.stop()
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/python/mllib/word2vec_example.py
----------------------------------------------------------------------
diff --git a/examples/src/main/python/mllib/word2vec_example.py b/examples/src/main/python/mllib/word2vec_example.py
new file mode 100644
index 0000000..ad1090c
--- /dev/null
+++ b/examples/src/main/python/mllib/word2vec_example.py
@@ -0,0 +1,40 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import print_function
+
+from pyspark import SparkContext
+# $example on$
+from pyspark.mllib.feature import Word2Vec
+# $example off$
+
+if __name__ == "__main__":
+ sc = SparkContext(appName="Word2VecExample") # SparkContext
+
+ # $example on$
+ inp = sc.textFile("data/mllib/sample_lda_data.txt").map(lambda row: row.split(" "))
+
+ word2vec = Word2Vec()
+ model = word2vec.fit(inp)
+
+ synonyms = model.findSynonyms('1', 5)
+
+ for word, cosine_distance in synonyms:
+ print("{}: {}".format(word, cosine_distance))
+ # $example off$
+
+ sc.stop()
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/scala/org/apache/spark/examples/mllib/ChiSqSelectorExample.scala
----------------------------------------------------------------------
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/ChiSqSelectorExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/ChiSqSelectorExample.scala
new file mode 100644
index 0000000..5e400b7
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/ChiSqSelectorExample.scala
@@ -0,0 +1,61 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.SparkConf
+import org.apache.spark.SparkContext
+// $example on$
+import org.apache.spark.mllib.feature.ChiSqSelector
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.util.MLUtils
+// $example off$
+
+object ChiSqSelectorExample {
+
+ def main(args: Array[String]): Unit = {
+
+ val conf = new SparkConf().setAppName("ChiSqSelectorExample")
+ val sc = new SparkContext(conf)
+
+ // $example on$
+ // Load some data in libsvm format
+ val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+ // Discretize data in 16 equal bins since ChiSqSelector requires categorical features
+ // Even though features are doubles, the ChiSqSelector treats each unique value as a category
+ val discretizedData = data.map { lp =>
+ LabeledPoint(lp.label, Vectors.dense(lp.features.toArray.map { x => (x / 16).floor }))
+ }
+ // Create ChiSqSelector that will select top 50 of 692 features
+ val selector = new ChiSqSelector(50)
+ // Create ChiSqSelector model (selecting features)
+ val transformer = selector.fit(discretizedData)
+ // Filter the top 50 features from each feature vector
+ val filteredData = discretizedData.map { lp =>
+ LabeledPoint(lp.label, transformer.transform(lp.features))
+ }
+ // $example off$
+
+ println("filtered data: ")
+ filteredData.foreach(x => println(x))
+
+ sc.stop()
+ }
+}
+// scalastyle:on println
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/scala/org/apache/spark/examples/mllib/ElementwiseProductExample.scala
----------------------------------------------------------------------
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/ElementwiseProductExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/ElementwiseProductExample.scala
new file mode 100644
index 0000000..1e4e354
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/ElementwiseProductExample.scala
@@ -0,0 +1,56 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.SparkConf
+import org.apache.spark.SparkContext
+// $example on$
+import org.apache.spark.mllib.feature.ElementwiseProduct
+import org.apache.spark.mllib.linalg.Vectors
+// $example off$
+
+object ElementwiseProductExample {
+
+ def main(args: Array[String]): Unit = {
+
+ val conf = new SparkConf().setAppName("ElementwiseProductExample")
+ val sc = new SparkContext(conf)
+
+ // $example on$
+ // Create some vector data; also works for sparse vectors
+ val data = sc.parallelize(Array(Vectors.dense(1.0, 2.0, 3.0), Vectors.dense(4.0, 5.0, 6.0)))
+
+ val transformingVector = Vectors.dense(0.0, 1.0, 2.0)
+ val transformer = new ElementwiseProduct(transformingVector)
+
+ // Batch transform and per-row transform give the same results:
+ val transformedData = transformer.transform(data)
+ val transformedData2 = data.map(x => transformer.transform(x))
+ // $example off$
+
+ println("transformedData: ")
+ transformedData.foreach(x => println(x))
+
+ println("transformedData2: ")
+ transformedData2.foreach(x => println(x))
+
+ sc.stop()
+ }
+}
+// scalastyle:on println
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/scala/org/apache/spark/examples/mllib/NormalizerExample.scala
----------------------------------------------------------------------
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/NormalizerExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/NormalizerExample.scala
new file mode 100644
index 0000000..b3a9604
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/NormalizerExample.scala
@@ -0,0 +1,57 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.SparkConf
+import org.apache.spark.SparkContext
+// $example on$
+import org.apache.spark.mllib.feature.Normalizer
+import org.apache.spark.mllib.util.MLUtils
+// $example off$
+
+object NormalizerExample {
+
+ def main(args: Array[String]): Unit = {
+
+ val conf = new SparkConf().setAppName("NormalizerExample")
+ val sc = new SparkContext(conf)
+
+ // $example on$
+ val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+
+ val normalizer1 = new Normalizer()
+ val normalizer2 = new Normalizer(p = Double.PositiveInfinity)
+
+ // Each sample in data1 will be normalized using $L^2$ norm.
+ val data1 = data.map(x => (x.label, normalizer1.transform(x.features)))
+
+ // Each sample in data2 will be normalized using $L^\infty$ norm.
+ val data2 = data.map(x => (x.label, normalizer2.transform(x.features)))
+ // $example off$
+
+ println("data1: ")
+ data1.foreach(x => println(x))
+
+ println("data2: ")
+ data2.foreach(x => println(x))
+
+ sc.stop()
+ }
+}
+// scalastyle:on println
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala
----------------------------------------------------------------------
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala
new file mode 100644
index 0000000..f7a8136
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala
@@ -0,0 +1,74 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.SparkConf
+import org.apache.spark.SparkContext
+// $example on$
+import org.apache.spark.mllib.feature.PCA
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.regression.{LabeledPoint, LinearRegressionWithSGD}
+// $example off$
+
+object PCAExample {
+
+ def main(args: Array[String]): Unit = {
+
+ val conf = new SparkConf().setAppName("PCAExample")
+ val sc = new SparkContext(conf)
+
+ // $example on$
+ val data = sc.textFile("data/mllib/ridge-data/lpsa.data").map { line =>
+ val parts = line.split(',')
+ LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
+ }.cache()
+
+ val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
+ val training = splits(0).cache()
+ val test = splits(1)
+
+ val pca = new PCA(training.first().features.size / 2).fit(data.map(_.features))
+ val training_pca = training.map(p => p.copy(features = pca.transform(p.features)))
+ val test_pca = test.map(p => p.copy(features = pca.transform(p.features)))
+
+ val numIterations = 100
+ val model = LinearRegressionWithSGD.train(training, numIterations)
+ val model_pca = LinearRegressionWithSGD.train(training_pca, numIterations)
+
+ val valuesAndPreds = test.map { point =>
+ val score = model.predict(point.features)
+ (score, point.label)
+ }
+
+ val valuesAndPreds_pca = test_pca.map { point =>
+ val score = model_pca.predict(point.features)
+ (score, point.label)
+ }
+
+ val MSE = valuesAndPreds.map { case (v, p) => math.pow((v - p), 2) }.mean()
+ val MSE_pca = valuesAndPreds_pca.map { case (v, p) => math.pow((v - p), 2) }.mean()
+
+ println("Mean Squared Error = " + MSE)
+ println("PCA Mean Squared Error = " + MSE_pca)
+ // $example off$
+
+ sc.stop()
+ }
+}
+// scalastyle:on println
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/scala/org/apache/spark/examples/mllib/StandardScalerExample.scala
----------------------------------------------------------------------
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/StandardScalerExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/StandardScalerExample.scala
new file mode 100644
index 0000000..fc0aa1b
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/StandardScalerExample.scala
@@ -0,0 +1,62 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.SparkConf
+import org.apache.spark.SparkContext
+// $example on$
+import org.apache.spark.mllib.feature.{StandardScaler, StandardScalerModel}
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.MLUtils
+// $example off$
+
+object StandardScalerExample {
+
+ def main(args: Array[String]): Unit = {
+
+ val conf = new SparkConf().setAppName("StandardScalerExample")
+ val sc = new SparkContext(conf)
+
+ // $example on$
+ val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+
+ val scaler1 = new StandardScaler().fit(data.map(x => x.features))
+ val scaler2 = new StandardScaler(withMean = true, withStd = true).fit(data.map(x => x.features))
+ // scaler3 is an identical model to scaler2, and will produce identical transformations
+ val scaler3 = new StandardScalerModel(scaler2.std, scaler2.mean)
+
+ // data1 will be unit variance.
+ val data1 = data.map(x => (x.label, scaler1.transform(x.features)))
+
+ // Without converting the features into dense vectors, transformation with zero mean will raise
+ // exception on sparse vector.
+ // data2 will be unit variance and zero mean.
+ val data2 = data.map(x => (x.label, scaler2.transform(Vectors.dense(x.features.toArray))))
+ // $example off$
+
+ println("data1: ")
+ data1.foreach(x => println(x))
+
+ println("data2: ")
+ data2.foreach(x => println(x))
+
+ sc.stop()
+ }
+}
+// scalastyle:on println
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/scala/org/apache/spark/examples/mllib/TFIDFExample.scala
----------------------------------------------------------------------
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/TFIDFExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/TFIDFExample.scala
new file mode 100644
index 0000000..a5bdcd8
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/TFIDFExample.scala
@@ -0,0 +1,66 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.SparkConf
+import org.apache.spark.SparkContext
+// $example on$
+import org.apache.spark.mllib.feature.{HashingTF, IDF}
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.rdd.RDD
+// $example off$
+
+object TFIDFExample {
+
+ def main(args: Array[String]): Unit = {
+
+ val conf = new SparkConf().setAppName("TFIDFExample")
+ val sc = new SparkContext(conf)
+
+ // $example on$
+ // Load documents (one per line).
+ val documents: RDD[Seq[String]] = sc.textFile("data/mllib/kmeans_data.txt")
+ .map(_.split(" ").toSeq)
+
+ val hashingTF = new HashingTF()
+ val tf: RDD[Vector] = hashingTF.transform(documents)
+
+ // While applying HashingTF only needs a single pass to the data, applying IDF needs two passes:
+ // First to compute the IDF vector and second to scale the term frequencies by IDF.
+ tf.cache()
+ val idf = new IDF().fit(tf)
+ val tfidf: RDD[Vector] = idf.transform(tf)
+
+ // spark.mllib IDF implementation provides an option for ignoring terms which occur in less than
+ // a minimum number of documents. In such cases, the IDF for these terms is set to 0.
+ // This feature can be used by passing the minDocFreq value to the IDF constructor.
+ val idfIgnore = new IDF(minDocFreq = 2).fit(tf)
+ val tfidfIgnore: RDD[Vector] = idfIgnore.transform(tf)
+ // $example off$
+
+ println("tfidf: ")
+ tfidf.foreach(x => println(x))
+
+ println("tfidfIgnore: ")
+ tfidfIgnore.foreach(x => println(x))
+
+ sc.stop()
+ }
+}
+// scalastyle:on println
http://git-wip-us.apache.org/repos/asf/spark/blob/d283223a/examples/src/main/scala/org/apache/spark/examples/mllib/Word2VecExample.scala
----------------------------------------------------------------------
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/Word2VecExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/Word2VecExample.scala
new file mode 100644
index 0000000..ea794c7
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/Word2VecExample.scala
@@ -0,0 +1,55 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.SparkConf
+import org.apache.spark.SparkContext
+// $example on$
+import org.apache.spark.mllib.feature.{Word2Vec, Word2VecModel}
+// $example off$
+
+object Word2VecExample {
+
+ def main(args: Array[String]): Unit = {
+
+ val conf = new SparkConf().setAppName("Word2VecExample")
+ val sc = new SparkContext(conf)
+
+ // $example on$
+ val input = sc.textFile("data/mllib/sample_lda_data.txt").map(line => line.split(" ").toSeq)
+
+ val word2vec = new Word2Vec()
+
+ val model = word2vec.fit(input)
+
+ val synonyms = model.findSynonyms("1", 5)
+
+ for((synonym, cosineSimilarity) <- synonyms) {
+ println(s"$synonym $cosineSimilarity")
+ }
+
+ // Save and load model
+ model.save(sc, "myModelPath")
+ val sameModel = Word2VecModel.load(sc, "myModelPath")
+ // $example off$
+
+ sc.stop()
+ }
+}
+// scalastyle:on println
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