Using Bayesian Optimization to Tune Machine Learning Models: In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. We will motivate the problem and give example applications. We will also talk about our development of a robust benchmark suite for our algorithms including test selection, metric design, infrastructure architecture, visualization, and comparison to other standard and open source methods. We will discuss how this evaluation framework empowers our research engineers to confidently and quickly make changes to our core optimization engine. We will end with an in-depth example of using these methods to tune the features and hyperparameters of a real world problem and give several real world applications.

1. BAYESIAN GLOBAL OPTIMIZATION
Using Optimal Learning to Tune ML Models
Scott Clark
scott@sigopt.com

2. OUTLINE
1. Why is Tuning ML Models Hard?
2. Standard Tuning Methods
3. Bayesian Global Optimization
4. Comparing Optimizers
5. Real World Examples

4. Machine Learning is
extremely powerful

5. Machine Learning is
extremely powerful
Tuning Machine Learning systems is
extremely non-intuitive

6. https://www.quora.com/What-is-the-most-important-unresolved-problem-in-machine-learning-3
What is the most important unresolved
problem in machine learning?
“...we still don't really know why some configurations of
deep neural networks work in some case and not others,
let alone having a more or less automatic approach to
determining the architectures and the
hyperparameters.”
Xavier Amatriain, VP Engineering at Quora
(former Director of Research at Netflix)

7. Photo: Joe Ross

8. TUNABLE PARAMETERS IN DEEP LEARNING

9. TUNABLE PARAMETERS IN DEEP LEARNING

10. TUNABLE PARAMETERS IN DEEP LEARNING

11. Photo: Tammy Strobel

12. STANDARD METHODS
FOR HYPERPARAMETER SEARCH

13. EXAMPLE: FRANKE FUNCTION

14. Grid Search Random Search

15. Predictive
Models
Predictive
Models
TUNING MACHINE LEARNING MODELS
New parameters
Objective Metric
Better
Models
Big Data

16. BAYESIAN GLOBAL OPTIMIZATION

17. … the challenge of how to collect information as efficiently as
possible, primarily for settings where collecting information is
time consuming and expensive.
Prof. Warren Powell - Princeton
What is the most efficient way to collect information?
Prof. Peter Frazier - Cornell
How do we make the most money, as fast as possible?
Scott Clark - CEO, SigOpt
OPTIMAL LEARNING

18. ● Optimize objective function
○ Loss, Accuracy, Likelihood
● Given parameters
○ Hyperparameters, feature parameters
● Find the best hyperparameters
○ Sample function as few times as possible
○ Training on big data is expensive
BAYESIAN GLOBAL OPTIMIZATION

19. 1. Build Gaussian Process (GP) with points
sampled so far
2. Optimize the fit of the GP (covariance
hyperparameters)
3. Find the point(s) of highest Expected
Improvement within parameter domain
4. Return optimal next best point(s) to sample
HOW DOES IT WORK?

20. GAUSSIAN PROCESSES

21. GAUSSIAN PROCESSES

22. GAUSSIAN PROCESSES

23. GAUSSIAN PROCESSES

24. GAUSSIAN PROCESSES

25. GAUSSIAN PROCESSES

26. GAUSSIAN PROCESSES

27. GAUSSIAN PROCESSES

28. overfit good fit underfit
GAUSSIAN PROCESSES

29. EXPECTED IMPROVEMENT

30. EXPECTED IMPROVEMENT

31. EXPECTED IMPROVEMENT

32. EXPECTED IMPROVEMENT

33. EXPECTED IMPROVEMENT

34. EXPECTED IMPROVEMENT

35. EVALUATING THE OPTIMIZER

36. What is the best value found after optimization
completes?
METRIC: BEST FOUND
BLUE RED
BEST_FOUND 0.7225 0.8949

37. How quickly is optimum found? (area under curve)
METRIC: AUC
BLUE RED
BEST_FOUND 0.9439 0.9435
AUC 0.8299 0.9358

38. ● Optimization functions (eg Branin, Ackeley, Rosenbrock)
● ML datasets (LIBSVM)
BENCHMARK SUITE
TEST FUNCTION TYPE COUNT
Continuous Params 184
Noisy Observations 188
Parallel Observations 45
Integer Params 34
Categorical Params / ML 47
Failure Observations 30
TOTAL 489

39. ● On-demand cluster
in AWS for parallel
eval function
optimization
● Full eval consists of
~10000 optimizations,
taking ~4 hours
INFRASTRUCTURE

40. VIZ TOOL : BEST SEEN TRACES

41. METRICS: STOCHASTICITY
● Run each 20
times
● Mann-Whitney
U test for
significance

42. RANKING OPTIMIZERS
● Alternate methods exist for black box optimization :
Spearmint, TPE, SMAC, PSO, RND Search, Grid Search
● Important to understand / track method
performance disparity on high-level categories of
functions
● For a given test function, want a partial ranking
(allowing for ties) of method performance

43. RANKING OPTIMIZERS
● First, Mann-Whitney U
tests using
BEST_FOUND
● Tied results then
partially ranked using
AUC
● Any remaining ties, stay
as ties for final ranking

44. RANKING AGGREGATION
● Aggregate partial rankings across all eval functions
using Borda count (sum of methods ranked lower)

45. SHORT RESULTS SUMMARY

46. SIGOPT SERVICE

47. Predictive
Models
Predictive
Models
HOW DOES SIGOPT INTEGRATE?
New parameters
Objective Metric
Better
Models
Big Data

48. SIMPLIFIED MANAGEMENT
Before SigOpt

49. DISTRIBUTED MODEL TRAINING
● SigOpt serves as an
AWS-ready distributed
scheduler for training
models across workers
● Each worker accesses the
SigOpt API for the latest
parameters to try
● Enables distributed training
of non-distributed
algorithms

50. INTEGRATIONS
REST API

51. Questions?
scott@sigopt.com
@DrScottClark
https://sigopt.com
@SigOpt

52. SHORT EXAMPLES

53. EXAMPLE: LOAN DATA
Loan
Applications
Default
Prediction
with tunable
ML parameters
● Income
● Credit Score
● Loan Amount
New parameters
Prediction Accuracy
Better
Accuracy

54. COMPARATIVE PERFORMANCE
Accuracy
Grid Search
Random Search
AUC
.698
.690
.683
.675
$1,000
100 hrs
$10,000
1,000 hrs
$100,000
10,000 hrs
Cost
● Better: 22%
fewer bad loans
vs baseline
● Faster/Cheaper:
100x less time
and AWS cost
than standard
tuning methods

55. EXAMPLE: ALGORITHMIC TRADING
Market Data
Trading
Strategy
with tunable
weights and
thresholds
● Closing Prices
● Day of Week
● Market Volatility
New parameters
Expected Revenue
Higher
Returns

56. COMPARATIVE PERFORMANCE
Standard Method
Expert
● Better: 200%
Higher model
returns than
expert
● Faster/Cheaper:
10x faster than
standard
methods

57. 1. SigOpt Live Demo
2. More Examples
a. Text Classification
b. Unsupervised + Supervised
c. Neural Nets with TensorFlow
ADDITIONAL TOPICS

58. AUTOMATICALLY TUNING TEXT
SENTIMENT CLASSIFIER

59. ● Automatically tune text sentiment classifier
● Amazon product review dataset (35K labels)
eg : “This DVD is great. It brings back all the memories of the holidays as a young child.”
● Logistic regression is generally a good place to start
PROBLEM

60. ● Maximize mean of k-fold cross-validation accuracies
● k = 5 folds, train and valid randomly split 70%, 30%
OBJECTIVE FUNCTION

61. ● n-gram vocabulary selection parameters
● (min_n_gram, ngram_offset) determine which n-grams
● (log_min_df, df_offset) filter for n-grams within df range
TEXT FEATURE PARAMETERS
Original Text “SigOpt optimizes any complicated system”
1-grams { “SigOpt”, “optimizes”, “any”, “complicated”, “system”}
2-grams { “SigOpt_optimizes”, “optimizes_any”, “any_complicated” … }
3-grams { “SigOpt_optimizes_any”, “optimizes_any_complicated” … }

62. ● Logistic regression error cost parameters
M = number of training examples
θ = vector of weights the algorithm will learn for each n-gram in vocabulary
yi
- training data label : {-1, 1} for our two class problem
xi
- training data input vector: BOW vectors described in previous section
α - weight of regularization term (log_reg_coef in our experiment)
ρ - weight of l1 norm term (l1_coef in our experiment)
ERROR COST PARAMETERS

63. ● 50 line python snippet to train
and tune classifier with SigOpt
● 20 lines to define 6 parameter
experiment and run
optimization loop using SigOpt
PYTHON CODE

64. ● E[f (λ)] after 20 runs, each run consisting of 60 function
evaluations
● For Grid Search : 64 evenly spaced parameter
configurations (order shuffled randomly)
● SigOpt statistical significance over grid and rnd
(p = 0.0001, Mann-Whitney U test)
PERFORMANCE
SigOpt Rnd. Search Grid Search No Tuning (Baseline)
Best Found 0.8760 (+5.72%) 0.8673 (+4.67%) 0.8680 (+4.76%) 0.8286

65. EXPLOITING UNLABELLED DATA

66. ● Classify house number
digits with lack of
labelled data
● Challenging digit
variations, image clutter
with neighboring digits
PROBLEM

67. ● In general we’ll search for an optimized ML pipeline
OBJECTIVE

68. ● Transform image patches into vectors of centroid
distances, then pool to form final representation
● SigOpt optimizes selection of w, pool_r, K
UNSUPERVISED MODEL PARAMS

69. ● Whitening transform often useful as image data
pre-processing step, expose εZCA
to SigOpt
UNSUPERVISED MODEL PARAMS

70. ● Tune sparsity of centroid
distance transform
● SigOpt optimizes threshold
(active_p) selection
UNSUPERVISED MODEL PARAMS

71. ● learning rate
number of trees
tree parameters
(max_depth,
sub_sample_sz),
exposed to SigOpt
SUPERVISED MODEL PARAMS

72. METRIC OPTIMIZATION

73. ● 20 optimization runs, each run consisting of 90 / 40
function evaluations for Unsup / Raw feature settings
● Optimized single CV fold on training set, ACC reported
on test set as hold out
PERFORMANCE
SigOpt
(Unsup Feats)
Rnd Search
(Unsup Feats)
SigOpt
(Raw Feats)
Rnd Search
(Raw Feats)
No Tuning RF
(Raw Feats)
Hold Out
ACC
0.8708 (+51.4%) 0.8583 0.6844 0.6739 0.5751

74. EFFICIENTLY BUILDING CONVNETS

75. ● Classify house numbers
with more training data and
more sophisticated model
PROBLEM

76. ● TensorFlow makes it easier to design DNN architectures,
but what structure works best on a given dataset?
CONVNET STRUCTURE

77. ● Per parameter
adaptive SGD variants
like RMSProp and
Adagrad seem to
work best
● Still require careful
selection of learning
rate (α), momentum
(β), decay (γ) terms
STOCHASTIC GRADIENT DESCENT

78. ● Comparison of several RMSProp SGD parametrizations
● Not obvious which configurations will work best on a
given dataset without experimentation
STOCHASTIC GRADIENT DESCENT

79. METRIC OPTIMIZATION

80. ● Avg Hold out accuracy after 5 optimization runs
consisting of 80 objective evaluations
● Optimized single 80/20 CV fold on training set, ACC
reported on test set as hold out
PERFORMANCE
SigOpt
(TensorFlow CNN)
Rnd Search
(TensorFlow CNN)
No Tuning
(sklearn RF)
No Tuning
(TensorFlow CNN)
Hold Out
ACC
0.8130 (+315.2%) 0.5690 0.5278 0.1958

81. COST ANALYSIS
Model Performance
(CV Acc. threshold)
Random
Search Cost
SigOpt
Cost
SigOpt Cost
Savings
Potential Savings In
Production (50 GPUs)
87 % $275 $42 84% $12,530
85 % $195 $23 88% $8,750
80 % $46 $21 55% $1,340
70 % $29 $21 27% $400

82. https://sigopt.com/getstarted
Try it yourself!

83. MORE EXAMPLES
Automatically Tuning Text Classifiers (with code)
A short example using SigOpt and scikit-learn to build and tune a text
sentiment classifier.
Tuning Machine Learning Models (with code)
A comparison of different hyperparameter optimization methods.
Using Model Tuning to Beat Vegas (with code)
Using SigOpt to tune a model for predicting basketball scores.
Learn more about the technology behind SigOpt at
https://sigopt.com/research