[SPARK-1506][MLLIB] Documentation improvements for MLlib 1.0

docs/mllib-clustering.md

@@ -1,19 +1,21 @@
 ---
 layout: global
-title: MLlib - Clustering
+title: <a href="mllib-guide.html">MLlib</a> - Clustering
 ---
 
 * Table of contents
 {:toc}
 
 
-# Clustering
+## Clustering
 
 Clustering is an unsupervised learning problem whereby we aim to group subsets
 of entities with one another based on some notion of similarity.  Clustering is
 often used for exploratory analysis and/or as a component of a hierarchical
 supervised learning pipeline (in which distinct classifiers or regression
-models are trained for each cluster). MLlib supports
+models are trained for each cluster). 
+
+MLlib supports
 [k-means](http://en.wikipedia.org/wiki/K-means_clustering) clustering, one of
 the most commonly used clustering algorithms that clusters the data points into
 predfined number of clusters. The MLlib implementation includes a parallelized
@@ -31,17 +33,14 @@ a given dataset, the algorithm returns the best clustering result).
 * *initializiationSteps* determines the number of steps in the k-means\|\| algorithm.
 * *epsilon* determines the distance threshold within which we consider k-means to have converged. 
 
-Available algorithms for clustering: 
-
-* [KMeans](api/scala/index.html#org.apache.spark.mllib.clustering.KMeans)
-
-
-
-# Usage in Scala
+## Examples
 
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
 Following code snippets can be executed in `spark-shell`.
 
-In the following example after loading and parsing data, we use the KMeans object to cluster the data
+In the following example after loading and parsing data, we use the
+[`KMeans`](api/mllib/index.html#org.apache.spark.mllib.clustering.KMeans) object to cluster the data
 into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within
 Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the
 optimal *k* is usually one where there is an "elbow" in the WSSSE graph.
@@ -63,22 +62,22 @@ val clusters = KMeans.train(parsedData, numClusters, numIterations)
 val WSSSE = clusters.computeCost(parsedData)
 println("Within Set Sum of Squared Errors = " + WSSSE)
 {% endhighlight %}
+</div>
 
-
-# Usage in Java
-
+<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.
+</div>
 
-# Usage in Python
+<div data-lang="python" markdown="1">
 Following examples can be tested in the PySpark shell.
 
-In the following example after loading and parsing data, we use the KMeans object to cluster the data
-into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within
-Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the
-optimal *k* is usually one where there is an "elbow" in the WSSSE graph.
+In the following example after loading and parsing data, we use the KMeans object to cluster the
+data into two clusters. The number of desired clusters is passed to the algorithm. We then compute
+Within Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In
+fact the optimal *k* is usually one where there is an "elbow" in the WSSSE graph.
 
 {% highlight python %}
 from pyspark.mllib.clustering import KMeans
@@ -91,7 +90,7 @@ parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')]))
 
 # Build the model (cluster the data)
 clusters = KMeans.train(parsedData, 2, maxIterations=10,
-        runs=10, initialization_mode="random")
+        runs=10, initializationMode="random")
 
 # Evaluate clustering by computing Within Set Sum of Squared Errors
 def error(point):
@@ -101,7 +100,6 @@ def error(point):
 WSSSE = parsedData.map(lambda point: error(point)).reduce(lambda x, y: x + y)
 print("Within Set Sum of Squared Error = " + str(WSSSE))
 {% endhighlight %}
+</div>
 
-Similarly you can use RidgeRegressionWithSGD and LassoWithSGD and compare training Mean Squared
-Errors.
-
+</div>

docs/mllib-collaborative-filtering.md

@@ -1,57 +1,56 @@
 ---
 layout: global
-title: MLlib - Collaborative Filtering 
+title: <a href="mllib-guide.html">MLlib</a> - Collaborative Filtering 
 ---
 
 * Table of contents
 {:toc}
 
-# Collaborative Filtering 
+## Collaborative filtering 
 
 [Collaborative filtering](http://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering)
 is commonly used for recommender systems.  These techniques aim to fill in the
 missing entries of a user-item association matrix.  MLlib currently supports
 model-based collaborative filtering, in which users and products are described
 by a small set of latent factors that can be used to predict missing entries.
 In particular, we implement the [alternating least squares
-(ALS)](http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf)
+(ALS)](http://dl.acm.org/citation.cfm?id=1608614)
 algorithm to learn these latent factors. The implementation in MLlib has the
 following parameters:
 
-* *numBlocks* is the number of blacks used to parallelize computation (set to -1 to auto-configure). 
+* *numBlocks* is the number of blocks used to parallelize computation (set to -1 to auto-configure).
 * *rank* is the number of latent factors in our model.
 * *iterations* is the number of iterations to run.
 * *lambda* specifies the regularization parameter in ALS.
-* *implicitPrefs* specifies whether to use the *explicit feedback* ALS variant or one adapted for *implicit feedback* data
-* *alpha* is a parameter applicable to the implicit feedback variant of ALS that governs the *baseline* confidence in preference observations
+* *implicitPrefs* specifies whether to use the *explicit feedback* ALS variant or one adapted for
+  *implicit feedback* data.
+* *alpha* is a parameter applicable to the implicit feedback variant of ALS that governs the
+  *baseline* confidence in preference observations.
 
-## Explicit vs Implicit Feedback
+### Explicit vs. implicit feedback
 
 The standard approach to matrix factorization based collaborative filtering treats 
 the entries in the user-item matrix as *explicit* preferences given by the user to the item.
 
-It is common in many real-world use cases to only have access to *implicit feedback* 
-(e.g. views, clicks, purchases, likes, shares etc.). The approach used in MLlib to deal with 
-such data is taken from 
-[Collaborative Filtering for Implicit Feedback Datasets](http://www2.research.att.com/~yifanhu/PUB/cf.pdf).
-Essentially instead of trying to model the matrix of ratings directly, this approach treats the data as 
-a combination of binary preferences and *confidence values*. The ratings are then related 
-to the level of confidence in observed user preferences, rather than explicit ratings given to items. 
-The model then tries to find latent factors that can be used to predict the expected preference of a user
-for an item. 
+It is common in many real-world use cases to only have access to *implicit feedback* (e.g. views,
+clicks, purchases, likes, shares etc.). The approach used in MLlib to deal with such data is taken
+from
+[Collaborative Filtering for Implicit Feedback Datasets](http://dx.doi.org/10.1109/ICDM.2008.22).
+Essentially instead of trying to model the matrix of ratings directly, this approach treats the data
+as a combination of binary preferences and *confidence values*. The ratings are then related to the
+level of confidence in observed user preferences, rather than explicit ratings given to items.  The
+model then tries to find latent factors that can be used to predict the expected preference of a
+user for an item.
 
-Available algorithms for collaborative filtering: 
+## Examples
 
-* [ALS](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS)
-
-
-# Usage in Scala
-
-Following code snippets can be executed in `spark-shell`.
+<div class="codetabs">
 
+<div data-lang="scala" markdown="1">
 In the following example we load rating data. Each row consists of a user, a product and a rating.
-We use the default ALS.train() method which assumes ratings are explicit. We evaluate the recommendation
-model by measuring the Mean Squared Error of rating prediction.
+We use the default [ALS.train()](api/mllib/index.html#org.apache.spark.mllib.recommendation.ALS$) 
+method which assumes ratings are explicit. We evaluate the
+recommendation model by measuring the Mean Squared Error of rating prediction.
 
 {% highlight scala %}
 import org.apache.spark.mllib.recommendation.ALS
@@ -64,8 +63,9 @@ val ratings = data.map(_.split(',') match {
 })
 
 // Build the recommendation model using ALS
+val rank = 10
 val numIterations = 20
-val model = ALS.train(ratings, 1, 20, 0.01)
+val model = ALS.train(ratings, rank, numIterations, 0.01)
 
 // Evaluate the model on rating data
 val usersProducts = ratings.map{ case Rating(user, product, rate)  => (user, product)}
@@ -85,19 +85,19 @@ If the rating matrix is derived from other source of information (i.e., it is in
 other signals), you can use the trainImplicit method to get better results.
 
 {% highlight scala %}
-val model = ALS.trainImplicit(ratings, 1, 20, 0.01)
+val alpha = 0.01
+val model = ALS.trainImplicit(ratings, rank, numIterations, alpha)
 {% endhighlight %}
+</div>
 
-# Usage in Java
-
+<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.
+</div>
 
-# Usage in Python
-Following examples can be tested in the PySpark shell.
-
+<div data-lang="python" markdown="1">
 In the following example we load rating data. Each row consists of a user, a product and a rating.
 We use the default ALS.train() method which assumes ratings are explicit. We evaluate the
 recommendation by measuring the Mean Squared Error of rating prediction.
@@ -111,7 +111,9 @@ data = sc.textFile("mllib/data/als/test.data")
 ratings = data.map(lambda line: array([float(x) for x in line.split(',')]))
 
 # Build the recommendation model using Alternating Least Squares
-model = ALS.train(ratings, 1, 20)
+rank = 10
+numIterations = 20
+model = ALS.train(ratings, rank, numIterations)
 
 # Evaluate the model on training data
 testdata = ratings.map(lambda p: (int(p[0]), int(p[1])))
@@ -126,5 +128,13 @@ signals), you can use the trainImplicit method to get better results.
 
 {% highlight python %}
 # Build the recommendation model using Alternating Least Squares based on implicit ratings
-model = ALS.trainImplicit(ratings, 1, 20)
+model = ALS.trainImplicit(ratings, rank, numIterations, alpha = 0.01)
 {% endhighlight %}
+</div>
+
+</div>
+
+## Tutorial
+
+[AMP Camp](http://ampcamp.berkeley.edu/) provides a hands-on tutorial for
+[personalized movie recommendation with MLlib](http://ampcamp.berkeley.edu/big-data-mini-course/movie-recommendation-with-mllib.html).