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

Preview: http://54.82.240.23:4000/mllib-guide.html

Table of contents:

* Basics
  * Data types
  * Summary statistics
* Classification and regression
  * linear support vector machine (SVM)
  * logistic regression
  * linear linear squares, Lasso, and ridge regression
  * decision tree
  * naive Bayes
* Collaborative Filtering
  * alternating least squares (ALS)
* Clustering
  * k-means
* Dimensionality reduction
  * singular value decomposition (SVD)
  * principal component analysis (PCA)
* Optimization
  * stochastic gradient descent
  * limited-memory BFGS (L-BFGS)

Author: Xiangrui Meng <[email protected]>

Closes #422 from mengxr/mllib-doc and squashes the following commits:

944e3a9 [Xiangrui Meng] merge master
f9fda28 [Xiangrui Meng] minor
9474065 [Xiangrui Meng] add alpha to ALS examples
928e630 [Xiangrui Meng] initialization_mode -> initializationMode
5bbff49 [Xiangrui Meng] add imports to labeled point examples
c17440d [Xiangrui Meng] fix python nb example
28f40dc [Xiangrui Meng] remove localhost:4000
369a4d3 [Xiangrui Meng] Merge branch 'master' into mllib-doc
7dc95cc [Xiangrui Meng] update linear methods
053ad8a [Xiangrui Meng] add links to go back to the main page
abbbf7e [Xiangrui Meng] update ALS argument names
648283e [Xiangrui Meng] level down statistics
14e2287 [Xiangrui Meng] add sample libsvm data and use it in guide
8cd2441 [Xiangrui Meng] minor updates
186ab07 [Xiangrui Meng] update section names
6568d65 [Xiangrui Meng] update toc, level up lr and svm
162ee12 [Xiangrui Meng] rename section names
5c1e1b1 [Xiangrui Meng] minor
8aeaba1 [Xiangrui Meng] wrap long lines
6ce6a6f [Xiangrui Meng] add summary statistics to toc
5760045 [Xiangrui Meng] claim beta
cc604bf [Xiangrui Meng] remove classification and regression
92747b3 [Xiangrui Meng] make section titles consistent
e605dd6 [Xiangrui Meng] add LIBSVM loader
f639674 [Xiangrui Meng] add python section to migration guide
c82ffb4 [Xiangrui Meng] clean optimization
31660eb [Xiangrui Meng] update linear algebra and stat
0a40837 [Xiangrui Meng] first pass over linear methods
1fc8271 [Xiangrui Meng] update toc
906ed0a [Xiangrui Meng] add a python example to naive bayes
5f0a700 [Xiangrui Meng] update collaborative filtering
656d416 [Xiangrui Meng] update mllib-clustering
86e143a [Xiangrui Meng] remove data types section from main page
8d1a128 [Xiangrui Meng] move part of linear algebra to data types and add Java/Python examples
d1b5cbf [Xiangrui Meng] merge master
72e4804 [Xiangrui Meng] one pass over tree guide
64f8995 [Xiangrui Meng] move decision tree guide to a separate file
9fca001 [Xiangrui Meng] add first version of linear algebra guide
53c9552 [Xiangrui Meng] update dependencies
f316ec2 [Xiangrui Meng] add migration guide
f399f6c [Xiangrui Meng] move linear-algebra to dimensionality-reduction
182460f [Xiangrui Meng] add guide for naive Bayes
137fd1d [Xiangrui Meng] re-organize toc
a61e434 [Xiangrui Meng] update mllib's toc

Author: mengxr

docs/mllib-basics.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-classification-regression.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).