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Innovation on
DB Tsai
dbtsai@alpinenow.com
Sung Chung
schung@alpinenow.com
Machine Learning Engineering @AlpineDataLabs
August 14, 2014

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TRADITIONAL
DESKTOP
IN-DATABASE
METHODS
WEB-BASED AND
COLLABORATIVE
SIMPLIFIED CODE-FREE
HADOOP & MPP DATABASE
ONGOING INNOVATION
The Path to Innovation

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The Path to Innovation
Iterative algorithms
scan through the
data each time
With Spark, data is
cached in memory
after first iteration
Quasi-Newton methods
enhance in-memory
benefits
921s
150m
m
rows
97s

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Machine Learning in the Big Data Era
• Hadoop Map Reduce solutions
• MapReduce scales well for batch processing
• Lots of machine learning algorithms are iterative by nature
• There are lots of tricks people do, like training with subsamples of
data, and then average the models. Why have big data if you’re only
approximating.
+ =

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Lightning-fast cluster computing
• Empower users to iterate
through the data by utilizing
the in-memory cache.
• Logistic regression runs up
to 100x faster than Hadoop
M/R in memory.
• We’re able to train exact
models without doing any
approximation.

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Why Alpine supports MLlib?
• MLlib is a Spark subproject providing Machine Learning
primitives.
• It’s built on Apache Spark, a fast and general engine for large-
scale data processing.
• Shipped with Apache Spark since version 0.8
• High quality engineering design and effort
• More than 50 contributors since July 2014
• Alpine is 100% committed to open source to facilitate industry
adoption that are driven by business needs.

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AutoML
• Success of machine learning crucially relies on human machine
learning experts, who select appropriate features, workflows,
paradigms, algorithms, and their hyper-parameters.
• Even the hyper-parameters can be chosen by grid search with
cross-validation, a problem with more than two parameters becomes
very difficult and challenging. It’s a non-convex optimization
problem.
• There is a demand for off-the-shelf machine learning methods that
can be used easily and without expert knowledge.
- AutoML workshop @ ICML’14

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Random Forest
• An ensemble learning method for classification &
regression that operates by constructing a multitude of
decision trees at training time.
• A “black box” without too much tuning and it can
automatically identify the structure, interactions, and
relationships in the data.
• A technique to reduce the variance of single decision
tree predictions by averaging the predictions of many de-
correlated trees.
• De-correlation is achieved through Bagging and / or
randomly selecting features per tree node.
NOTE: Most Kaggle competitions have at least one top
entry that heavily uses Random Forests.

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Sequoia Forest
Why Sequoia Forest?
MLlib already has a decision tree implementation, but it doesn’t support random features and is not
optimized to train on large clusters.
What does Sequoia Forest do?
• Classification and Regression.
• Numerical and Categorical Features.
What’s next?
Gradient Boosting
Where can you find?
https://github.com/AlpineNow/SparkML2
We’re merging back with MLlib and is licensed under the Apache License.
More info: http://spark-summit.org/2014/talk/sequoia-forest-random-forest-of-humongous-trees.

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Spark-1157: L-BFGS Optimizer
• No, its not a blender!

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What is Spark-1157: L-BFGS Optimizer
• Merged in Spark 1.0
• Popular algorithms for parameter estimation in Machine
Learning.
• It’s a quasi-Newton Method.
• Hessian matrix of second derivatives doesn't need to be
evaluated directly.
• Hessian matrix is approximated using gradient evaluations.
• It converges a way faster than the default optimizer in Spark,
Gradient Decent.

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SPARK-2934: LogisticRegressionWithLBFGS
• Merged in Spark 1.1
• Using L-BFGS to train Logistic Regression instead of
default Gradient Descent.
• Users don't have to construct their objective function for
Logistic Regression, and don't have to implement the
whole details.
• Together with SPARK-2979 to minimize the condition
number, the convergence rate is further improved.

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0 5 10 15 20 25 30 35
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
Logistic Regression with a9a Dataset (11M rows, 123 features, 11% non-zero elements)
16 executors in INTEL Xeon E3-1230v3 32GB Memory * 5 nodes Hadoop 2.0.5 alpha cluster
L-BFGS Dense Features
L-BFGS Sparse Features
GD Sparse Features
GD Dense Features
Seconds
Log-Likelihood/NumberofSamplesa9a Dataset Benchmark

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a9a Dataset Benchmark
-1 1 3 5 7 9 11 13 15
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
Logistic Regression with a9a Dataset (11M rows, 123 features, 11% non-zero elements)
16 executors in INTEL Xeon E3-1230v3 32GB Memory * 5 nodes Hadoop 2.0.5 alpha cluster
L-BFGS
GD
Iterations
Log-Likelihood/NumberofSamples

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0 5 10 15 20 25 30
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Logistic Regression with rcv1 Dataset (6.8M rows, 677,399 features, 0.15% non-zero elements)
16 executors in INTEL Xeon E3-1230v3 32GB Memory * 5 nodes Hadoop 2.0.5 alpha cluster
LBFGS Sparse Vector
GD Sparse Vector
Second
Log-Likelihood/NumberofSamples
rcv1 Dataset Benchmark

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news20 Dataset Benchmark
0 10 20 30 40 50 60 70 80
0
0.2
0.4
0.6
0.8
1
1.2
Logistic Regression with news20 Dataset (0.14M rows, 1,355,191 features, 0.034% non-zero elements)
16 executors in INTEL Xeon E3-1230v3 32GB Memory * 5 nodes Hadoop 2.0.5 alpha cluster
LBFGS Sparse Vector
GD Sparse Vector
Second
Log-Likelihood/NumberofSamples

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SPARK-2979: Improve the convergence rate by
standardizing the training features
l Merged in Spark 1.1
l Due to the invariance property of MLEs, the scale of your inputs are
irrelevant.
l However, the optimizer will not be happy with poor condition numbers
which can often be improved by scaling.
l The model is trained in the scaled space, but the coefficients are
converted to original space; as a result, it's transparent to users.
l Without this, some training datasets mixing the columns with different
scales may not be able to converge.
l Scikit and glmnet package also standardize the features before training to
improve the convergence.
l Only enable in Logistic Regression for now.

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SPARK-2272: Transformer
A spark, the soul of a transformer

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SPARK-2272: Transformer
l Merged in Spark 1.1
l MLlib data preprocessing pipeline.
l StandardScaler
- Standardize features by removing the mean and scaling to unit variance.
- RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear
models typically works better with zero mean and unit variance.
l Normalizer
- Normalizes samples individually to unit L^n norm.
- Common operation for text classification or clustering for instance.
- For example, the dot product of two l2-normalized TF-IDF vectors is the
cosine similarity of the vectors.

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StandardScaler

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Normalizer

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l Merged in Spark 1.1
l Online algorithms for computing the mean, variance, min, and max in a streaming
fashion.
l Two online summerier can be merged, so we can use one summerier for one block of
data in map phase, and merge all of them in reduce phase to obtain the global
summarizer.
l A numerically stable one-pass algorithm is implemented to avoid catastrophic cancellation
in naive implementation.
Ref: http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
l Optimized for sparse vector, and the time complexity is O(non-zeors) instead of
O(numCols) for each sample.
SPARK-1969: Online summarizer
Two-pass algorithm Naive algorithm

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l Merged in Spark 1.1
l Floating point math is not exact, and most floating-point numbers end up
being slightly imprecise due to rounding errors.
l Simple values like 0.1 cannot be precisely represented using binary
floating point numbers, and the limited precision of floating point numbers
means that slight changes in the order of operations or the precision of
intermediates can change the result.
l That means that comparing two floats to see if they are equal is usually not
what we want. As long as this imprecision stays small, it can usually be
ignored.
l Scala syntax sugar comparators are implemented using implicit conversion
allowing developers to write unittest easier.
SPARK-2479: MLlib UnitTests

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SPARK-1892: OWL-QN Optimizer
ongoing work
l It extends L-BFGS to handle L2 and L1 regularizations
together
(balanced with alpha as in elastic nets)
l We fixed couple issues #247 in Breeze's OWLQN
implementation, and this work is based on that.
l Blocked by SPARK-2505

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SPARK-2505: Weighted Regularization
ongoing work
l Each components of weights can be penalized differently.
l We can exclude intercept from regularization in this framework.
l Decoupling regularization from the raw gradient update which is
not used in other optimization schemes.
l Allow various update/learning rate schemes (adagrad,
normalized adaptive gradient, etc) to be applied independent of
the regularization
l Smooth and L1 regularization will be handled differently in
optimizer.

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SPARK-2309: Multinomial Logistic Regression
ongoing work
l For K classes multinomial problem, we can generalize it via
K -1 linear models with logist link functions.
l As a result, the weights will have dimension of (K-1)(N + 1)
where N is number of features.
l MLlib interface is designed for one set of paramerters per
model, so it requires some interface design changes.
l Expected to be merged in next release of MLlib, Spark 1.2
Ref: http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297

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l Technology we're using: Scala/Java, Akka, Spray, Hadoop, Spark/SparkSQL, Pig, Sqoop,
Javascripts, D3.js etc.
l Actively involved in the open source community: almost of all our newly developed algorithms
in Spark will be contributed back to MLLib.
l Actively developing on application to/from Spark Yarn communication infrastructure
(application lifecycle, error reporting, progress monitoring and interactive command etc)
l In addition to Spark, we are the maintainer of several open source projects including Chorus,
SBT plugin for JUnit test Listener, and Akka-based R engine.
l Weekly tech/ML talks. Speakers: David Hall (author of Breeze), Heather Miller (student of
Martin Ordersky), H.Y. Li (author of Tachyon), and Jason Lee (student of Trevor Hastie), etc…
l Oraginzes the SF Machine Learning meetup (2k+ members). Speakers: Andrew Ng
(Stanford), Michael Jorden (Berkely), Xiangrui Meng (Databricks), Sandy Ryza (Cloudera),
etc…
We’re open source friendly and tech driven!

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We're hiring!
l Machine Learning Engineer
l Data Scientist
l UI/UX Engineer
l Platform Engineer
l Automation Test Engineer
Shoot me an email at
dbtsai@alpinenow.com

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