MLlib is an Apache Spark component for machine learning. It provides algorithms like classification, regression, clustering, and collaborative filtering. MLlib 1.0 introduced new features like decision trees, sparse data support, and distributed matrices. Future releases will focus on standardizing interfaces, adding more algorithms like NMF and LDA, and better integration with other Spark components. MLlib is developed as an open source project with contributions from many individuals and companies.

1. Recent Developments in
Spark MLlib and Beyond
Xiangrui Meng

2. What is MLlib?

3. What is MLlib?
MLlib is an Apache Spark component focusing on
machine learning:!
• initial contribution from AMPLab, UC Berkeley
• shipped with Spark since version 0.8 (Sep 2013)
• 40 contributors
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4. What is MLlib?
Algorithms:!
• classification: logistic regression, linear support vector machine
(SVM), naive Bayes, classification tree
• regression: generalized linear models (GLMs), regression tree
• collaborative filtering: alternating least squares (ALS), non-negative
matrix factorization (NMF)
• clustering: k-means
• decomposition: singular value decomposition (SVD), principal
component analysis (PCA)
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5. Why MLlib?

6. scikit-learn
Algorithms:!
• classification: SVM, nearest neighbors, random forest, …
• regression: support vector regression (SVR), ridge
regression, Lasso, logistic regression, …!
• clustering: k-means, spectral clustering, …
• decomposition: PCA, non-negative matrix factorization
(NMF), independent component analysis (ICA), …
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7. It is built on Apache Spark, a fast and general engine
for large-scale data processing.
• Run programs up to 100x faster than
Hadoop MapReduce in memory, or
10x faster on disk.
• Write applications quickly in Java, Scala, or Python.
Why MLlib?
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8. Spark philosophy
Make life easy and productive for data scientists:
• well documented, expressive APIs
• powerful domain specific libraries
• easy integration with storage systems
• … and caching to avoid data movement
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9. Word count (Scala)
val counts = sc.textFile("hdfs://...")
.flatMap(line => line.split(" "))
.map(word => (word, 1L))
.reduceByKey(_ + _)
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10. Gradient descent
val points = spark.textFile(...).map(parsePoint).cache()
var w = Vector.zeros(d)
for (i <- 1 to numIterations) {
val gradient = points.map { p =>
(1 / (1 + exp(-p.y * w.dot(p.x)) - 1) * p.y * p.x
).reduce(_ + _)
w -= alpha * gradient
}
w w ↵ ·
nX
i=1
g(w; xi, yi)
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11. K-means (scala)
// Load and parse the data.
val data = sc.textFile("kmeans_data.txt")
val parsedData = data.map(_.split(‘ ').map(_.toDouble)).cache()
!
// Cluster the data into two classes using KMeans.
val clusters = KMeans.train(parsedData, 2, numIterations = 20)
!
// Compute the sum of squared errors.
val cost = clusters.computeCost(parsedData)
println("Sum of squared errors = " + cost)
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12. K-means (python)
# Load and parse the data
data = sc.textFile("kmeans_data.txt")
parsedData = data.map(lambda line:
array([float(x) for x in line.split(' ‘)])).cache()
!
# Build the model (cluster the data)
clusters = KMeans.train(parsedData, 2, maxIterations = 10,
runs = 1, initialization_mode = "kmeans||")
!
# Evaluate clustering by computing the sum of squared errors
def error(point):
center = clusters.centers[clusters.predict(point)]
return sqrt(sum([x**2 for x in (point - center)]))
!
cost = parsedData.map(lambda point: error(point))
.reduce(lambda x, y: x + y)
print("Sum of squared error = " + str(cost))
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13. Dimension reduction
+ k-means
// compute principal components
val points: RDD[Vector] = ...
val mat = RowMatrix(points)
val pc = mat.computePrincipalComponents(20)
!
// project points to a low-dimensional space
val projected = mat.multiply(pc).rows
!
// train a k-means model on the projected data
val model = KMeans.train(projected, 10)
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14. Collaborative filtering
// Load and parse the data
val data = sc.textFile("mllib/data/als/test.data")
val ratings = data.map(_.split(',') match {
case Array(user, item, rate) =>
Rating(user.toInt, item.toInt, rate.toDouble)
})
!
// Build the recommendation model using ALS
val numIterations = 20
val model = ALS.train(ratings, 1, 20, 0.01)
!
// Evaluate the model on rating data
val usersProducts = ratings.map { case Rating(user, product, rate) =>
(user, product)
}
val predictions = model.predict(usersProducts)
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15. Why MLlib?
It ships with Spark as
a standard component.
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16. Community

17. Spark community
One of the largest open source projects in big data:!
• 190+ developers contributing (40 for MLlib)
• 50+ companies contributing
• 400+ discussions per month on the mailing list
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18. Spark community
The most active project in the Hadoop ecosystem:
JIRA 30 day summary
Spark Map/Reduce YARN
created 296 30 100
resolved 203 28 69
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19. Unification

20. Out for dinner?!
• Search for a restaurant and make a reservation.
• Start navigation.
• Food looks good? Take a photo and share.
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21. Why smartphone?
Out for dinner?!
• Search for a restaurant and make a reservation. (Yellow Pages?)
• Start navigation. (GPS?)
• Food looks good? Take a photo and share. (Camera?)
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22. Why MLlib?
A special-purpose device may be better at one
aspect than a general-purpose device. But the cost
of context switching is high:
• different languages or APIs
• different data formats
• different tuning tricks
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23. Spark SQL + MLlib
// Data can easily be extracted from existing sources,
// such as Apache Hive.
val trainingTable = sql("""
SELECT e.action,
u.age,
u.latitude,
u.longitude
FROM Users u
JOIN Events e
ON u.userId = e.userId""")
!
// Since `sql` returns an RDD, the results of the above
// query can be easily used in MLlib.
val training = trainingTable.map { row =>
val features = Vectors.dense(row(1), row(2), row(3))
LabeledPoint(row(0), features)
}
!
val model = SVMWithSGD.train(training)
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24. Streaming + MLlib
// collect tweets using streaming
!
// train a k-means model
val model: KMmeansModel = ...
!
// apply model to filter tweets
val tweets = TwitterUtils.createStream(ssc, Some(authorizations(0)))
val statuses = tweets.map(_.getText)
val filteredTweets =
statuses.filter(t => model.predict(featurize(t)) == clusterNumber)
!
// print tweets within this particular cluster
filteredTweets.print()
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25. GraphX + MLlib
// assemble link graph
val graph = Graph(pages, links)
val pageRank: RDD[(Long, Double)] = graph.staticPageRank(10).vertices
!
// load page labels (spam or not) and content features
val labelAndFeatures: RDD[(Long, (Double, Seq((Int, Double)))] = ...
val training: RDD[LabeledPoint] =
labelAndFeatures.join(pageRank).map {
case (id, ((label, features), pageRank)) =>
LabeledPoint(label, Vectors.sparse(features ++ (1000, pageRank))
}
!
// train a spam detector using logistic regression
val model = LogisticRegressionWithSGD.train(training)
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26. Why MLlib?
• built on Spark’s lightweight yet powerful APIs
• Spark’s open source community
• seamless integration with Spark’s other components
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• comparable to or even better than other libraries
specialized in large-scale machine learning

27. Why MLlib?
In MLlib’s implementations, we care about!
• scalability
• performance
• user-friendly documentation and APIs
• cost of maintenance
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28. v1.0

29. What’s new in MLlib
• new user guide and code examples
• API stability
• sparse data support
• regression and classification tree
• distributed matrices
• tall-and-skinny PCA and SVD
• L-BFGS
• binary classification model evaluation
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30. MLlib user guide
• v0.9 (link)
• a single giant page
• mixed code snippets in different languages
• v1.0 (link)
• The content is more organized.
• Tabs are used to separate code snippets in different
languages.
• Unified API docs.
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31. Code examples
Code examples that can be used as templates to
build standalone applications are included under the
“examples/” folder with sample datasets:
• binary classification (linear SVM and logistic regression)
• decision tree
• k-means
• linear regression
• naive Bayes
• tall-and-skinny PCA and SVD
• collaborative filtering
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32. Code examples
MovieLensALS: an example app for ALS on MovieLens data.
Usage: MovieLensALS [options] <input>
!
--rank <value>
rank, default: 10
--numIterations <value>
number of iterations, default: 20
--lambda <value>
lambda (smoothing constant), default: 1.0
--kryo
use Kryo serialization
--implicitPrefs
use implicit preference
<input>
input paths to a MovieLens dataset of ratings
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33. API stability
• Following Spark core, MLlib is guaranteeing binary
compatibility for all 1.x releases on stable APIs.
• For changes in experimental and developer APIs,
we will provide migration guide between releases.
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34. Sparse data support

35. “large-scale sparse problems”
Sparse datasets appear almost everywhere in the
world of big data, where the sparsity may come from
many sources, e.g.,
• feature transformation:
one-hot encoding, interaction, and bucketing,
• large feature space: n-grams,
• missing data: rating matrix,
• low-rank structure: images and signals.
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36. Sparsity is almost everywhere
The Netflix Prize:!
• number of users: 480,189
• number of movies: 17,770
• number of observed ratings: 100,480,507
• sparsity = 1.17%
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37. Sparsity is almost everywhere
rcv1.binary (test)!
• number of examples: 677,399
• number of features: 47,236
• sparsity: 0.15%
• storage: 270GB (dense) or 600MB (sparse)
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38. Exploiting sparsity
• In Spark v1.0, MLlib adds support for sparse input
data in Scala, Java, and Python.
• MLlib takes advantage of sparsity in both storage
and computation in
• linear methods (linear SVM, logistic regression, etc)
• naive Bayes,
• k-means,
• summary statistics.
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39. Sparse representation of features
39
dense : 1. 0. 0. 0. 0. 0. 3.
sparse :
8
><
>:
size : 7
indices : 0 6
values : 1. 3.

40. Exploiting sparsity in k-means
Training set:!
• number of examples: 12 million
• number of features: 500
• sparsity: 10%
!
Not only did we save 40GB of storage by switching to the sparse
format, but we also received a 4x speedup.
dense sparse
storage 47GB 7GB
time 240s 58s
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41. Implementation of sparse k-means
Algorithm:!
• For each point, find its closest center.
• Update cluster centers.
li = arg min
j
kxi cjk2
2
cj =
P
i,li=j xj
P
i,li=j 1
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42. Implementation of sparse k-means
The points are usually sparse, but the centers are most likely to be
dense. Computing the distance takes O(d) time. So the time
complexity is O(n d k) per iteration. We don’t take any advantage of
sparsity on the running time. However, we have
kx ck2
2 = kxk2
2 + kck2
2 2hx, ci
Computing the inner product only needs non-zero elements. So we
can cache the norms of the points and of the centers, and then only
need the inner products to obtain the distances. This reduce the
running time to O(nnz k + d k) per iteration.
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43. Exploiting sparsity in linear methods
The essential part of the computation in a gradient-
based method is computing the sum of gradients. For
linear methods, the gradient is sparse if the feature
vector is sparse. In our implementation, the total
computation cost is linear on the input sparsity.
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44. Acknowledgement
For sparse data support, special thanks go to!
• David Hall (breeze),
• Sam Halliday (netlib-java),
• and many others who joined the discussion.
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45. Decision tree

46. Decision tree
• a major feature introduced in MLlib v1.0
• supports both classification and regression
• supports both continuous features and categorical
features
• scales well on massive datasets
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47. Decision tree
Come to to learn more:!
• Scalable distributed decision trees in Spark MLlib
• Manish Made, Hirakendu Das,
Evan Sparks, Ameet Talwalkar
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48. More features to explore
• distributed matrices
• tall-and-skinny PCA and SVD
• L-BFGS
• binary classification model evaluation
48

49. Beyond v1.0

50. Next release (v1.1)
Spark has a 3-month release cycle:!
• July 25: cut-off for new features
• August 1: QA period starts
• August 15+: RC’s and voting
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51. MLlib roadmap for v1.1
• Standardize interfaces!
• problem / formulation / algorithm / parameter set / model
• towards building an ecosystem
• Train multiple models in parallel!
• large-scale vs. medium-scale
• Statistical analysis!
• more descriptive statistics
• more sampling and bootstrapping
• hypothesis testing
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52. MLlib roadmap for v1.1
• Learning algorithms!
• Non-negative matrix factorization (NMF)
• Sparse SVD
• Multiclass support in decision tree
• Latent Dirichlet allocation (LDA)
• …
• Optimization algorithms!
• Alternating direction method of multipliers (ADMM)
• Accelerated gradient methods
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53. Beyond v1.1?
What to expect from the community (and maybe you):!
• scalable implementations of well known and well
understood machine learning algorithms
• user-friendly documentation and consistent APIs
• better integration with Spark SQL, Streaming, and GraphX
• addressing practical machine learning pipelines
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54. Contributors
Ameet Talwalkar, Andrew Tulloch, Chen Chao, Nan Zhu, DB Tsai,
Evan Sparks, Frank Dai, Ginger Smith, Henry Saputra, Holden
Karau, Hossein Falaki, Jey Kottalam, Cheng Lian, Marek
Kolodziej, Mark Hamstra, Martin Jaggi, Martin Weindel, Matei
Zaharia, Nick Pentreath, Patrick Wendell, Prashant Sharma,
Reynold Xin, Reza Zadeh, Sandy Ryza, Sean Owen, Shivaram
Venkataraman, Tor Myklebust, Xiangrui Meng, Xinghao Pan,
Xusen Yin, Jerry Shao, Sandeep Singh, Ryan LeCompte
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55. Interested?
• : http://spark-summit.org
• Website: http://spark.apache.org
• Tutorials: http://ampcamp.berkeley.edu
• Github: https://github.com/apache/spark
• Mailing lists: user@spark.apache.org
dev@spark.apache.org
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