Machine Learning Software: Design and Practical Use

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Machine Learning Software: Design and Practical Use

Chih-Jen Lin

National Taiwan University eBay Research Labs

Talk at Machine Learning Summer School, Kyoto, August 31,

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Machine Learning Software

Most machine learning works focus on developing algorithms

Researchers didn’t pay much attention to software Recently, some think software is important. For example, “The need for open source software in machine learning” by Sonnenburg et al. (2007) One reasons is for replicating and evaluating research results

However, a good software package is beyond that

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Machine Learning Software (Cont’d)

In this talk, I will share our experiences in developing LIBSVM and LIBLINEAR.

LIBSVM (Chang and Lin, 2011):

One of the most popular SVM packages; cited more than 10, 000 times on Google Scholar

LIBLINEAR (Fan et al., 2008):

A library for large linear classification; popular in Internet companies for document classification and NLP applications

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Machine Learning Software (Cont’d)

This talk will contain two parts:

First, we discuss practical use of SVM as an example to see how users apply a machine learning method Second, we discuss design considerations for a good machine learning package.

The talk is biased toward SVM and logistic regression, but materials are useful for other machine learning methods.

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Outline

1 Practical use of SVM SVM introduction A real example Parameter selection

2 Design of machine learning software Users and their needs

Design considerations

3 Discussion and conclusions

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Outline

1 Practical use of SVM SVM introduction A real example Parameter selection

2 Design of machine learning software Users and their needs

Design considerations

3 Discussion and conclusions

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Outline

1 Practical use of SVM SVM introduction A real example Parameter selection

2 Design of machine learning software Users and their needs

Design considerations

3 Discussion and conclusions

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Support Vector Classification

Training data (xi, yi), i = 1, . . . , l , xi ∈ Rn, yi = ±1 Maximizing the margin (Boser et al., 1992; Cortes and Vapnik, 1995)

minw,b

1

2wTw + C

l

X

i =1

max(1 − yi(wTφ(xi)+ b), 0) High dimensional ( maybe infinite ) feature space

φ(x) = (φ1(x), φ2(x), . . .).

w: maybe infinite variables

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Support Vector Classification (Cont’d)

The dual problem (finite # variables) minα

1

TQα − eTα

subject to 0 ≤ αi ≤ C , i = 1, . . . , l yTα = 0,

where Qij = yiyjφ(xi)Tφ(xj) and e = [1, . . . , 1]T At optimum

w = Pl

i =1αiyiφ(xi)

Kernel: K (xi, xj) ≡ φ(xi)Tφ(xj) ; closed form Example: Gaussian (RBF) kernel: e−γkx−xk2

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Support Vector Classification (Cont’d)

Only xi of αi > 0 used ⇒ support vectors

-0.2 0 0.2 0.4 0.6 0.8 1 1.2

-1.5 -1 -0.5 0 0.5 1

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Status of Support Vector Machines

SVM was introduced with kernels

But ironically, the linear setting also helps SVM to become popular

By linear we mean data are not mapped to a higher dimensional space

There are many differences between using kernel or not

Many people are confused about this; so I will clarify the differences in the next few slides

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Linear and Kernel Classification

Methods such as SVM and logistic regression can used in two ways

Kernel methods: data mapped to a higher dimensional space

x ⇒ φ(x)

φ(xi)Tφ(xj) easily calculated; little control on φ(·) Linear classification + feature engineering:

We have x without mapping. Alternatively, we can say that φ(x) is our x; full control on x or φ(x) We refer to them as kernel and linear classifiers

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Linear and Kernel Classification (Cont’d)

Let’s check the prediction cost wTx + b versus Xl

i =1αiK (xi, x) + b If K (xi, xj) takes O(n), then

O(n) versus O(nl ) Linear is much cheaper

Deciding whether using kernel is cost-effective is still a big issue

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Linear and Kernel Classification (Cont’d)

For certain problems, accuracy by linear is as good as nonlinear

But training and testing are much faster Especially document classification

Number of features (bag-of-words model) very large Recently linear classification is a popular research topic. Sample works in 2005-2008: Joachims (2006); Shalev-Shwartz et al. (2007); Hsieh et al.

(2008)

They focus on large sparse data

There are many other recent papers and software

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Comparison Between Linear and Kernel (Training Time & Testing Accuracy)

Linear RBF Kernel Data set Time Accuracy Time Accuracy

MNIST38 0.1 96.82 38.1 99.70

ijcnn1 1.6 91.81 26.8 98.69

covtype 1.4 76.37 46,695.8 96.11

news20 1.1 96.95 383.2 96.90

real-sim 0.3 97.44 938.3 97.82

yahoo-japan 3.1 92.63 20,955.2 93.31 webspam 25.7 93.35 15,681.8 99.26 Size reasonably large: e.g., yahoo-japan: 140k instances

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Comparison Between Linear and Kernel (Training Time & Testing Accuracy)

Linear RBF Kernel Data set Time Accuracy Time Accuracy

MNIST38 0.1 96.82 38.1 99.70

ijcnn1 1.6 91.81 26.8 98.69

covtype 1.4 76.37 46,695.8 96.11

news20 1.1 96.95 383.2 96.90

real-sim 0.3 97.44 938.3 97.82

yahoo-japan 3.1 92.63 20,955.2 93.31 webspam 25.7 93.35 15,681.8 99.26 Size reasonably large: e.g., yahoo-japan: 140k instances and 830k features

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Comparison Between Linear and Kernel (Training Time & Testing Accuracy)

Linear RBF Kernel Data set Time Accuracy Time Accuracy

MNIST38 0.1 96.82 38.1 99.70

ijcnn1 1.6 91.81 26.8 98.69

covtype 1.4 76.37 46,695.8 96.11

news20 1.1 96.95 383.2 96.90

real-sim 0.3 97.44 938.3 97.82

yahoo-japan 3.1 92.63 20,955.2 93.31 webspam 25.7 93.35 15,681.8 99.26 Size reasonably large: e.g., yahoo-japan: 140k instances

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Summary: Support Vector Machines

The significant differences between linear and kernel motivated us to separately develop LIBSVM and LIBLINEAR

For recent advances of linear classification, you may check a survey by Yuan et al. (2012)

Today I will discuss only the situation of using kernels

Another thing I didn’t mention is online and offline training. We consider only offline training here

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Summary: Support Vector Machines (Cont’d)

So far I only gave a very brief introduction of support vector classification

Today I am not giving a tutorial on SVM. For details of this method, you can check my other tutorial talks (e.g., the one at ACML 2010)

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Outline

1 Practical use of SVM SVM introduction A real example Parameter selection

2 Design of machine learning software Users and their needs

Design considerations

3 Discussion and conclusions

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How People Use SVM?

For most users, what they hope is 1. Prepare training and testing sets 2. Run a package and get good results But things are not that simple

Let’s start with a practical example from a user

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Let’s Try a Practical Example

A problem from astroparticle physics

1 2.61e+01 5.88e+01 -1.89e-01 1.25e+02 1 5.70e+01 2.21e+02 8.60e-02 1.22e+02 1 1.72e+01 1.73e+02 -1.29e-01 1.25e+02 0 2.39e+01 3.89e+01 4.70e-01 1.25e+02 0 2.23e+01 2.26e+01 2.11e-01 1.01e+02 0 1.64e+01 3.92e+01 -9.91e-02 3.24e+01 Training and testing sets available: 3,089 and 4,000 Data available at LIBSVM Data Sets

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The Story Behind this Data Set

User:

I am using libsvm in a astroparticle physics application .. First, let me congratulate you to a really easy to use and nice package. Unfortunately, it gives me astonishingly bad results...

OK. Please send us your data

I am able to get 97% test accuracy. Is that good enough for you ?

User:

You earned a copy of my PhD thesis

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The Story Behind this Data Set (Cont’d)

What we have seen over the years is that

Users expect good results right after using a method If method A doesn’t work, they switch to B

They may inappropriately use most methods they tried

But isn’t it machine learning people’s responsibility to make their methods easily give reasonable results?

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The Story Behind this Data Set (Cont’d)

In my opinion

Machine learning packages should provide some simple and automatic/semi-automatic settings for users

These setting may not be the best, but easily give users some reasonable results

If such settings are not enough, users many need to consult with machine learning experts.

I will illustrate the first point by a procedure we developed for SVM beginners

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Training and Testing

Training the set svmguide1 to obtain svmguide1.model

$./svm-train svmguide1 Testing the set svmguide1.t

$./svm-predict svmguide1.t svmguide1.model out Accuracy = 66.925% (2677/4000)

We see that training and testing accuracy are very different. Training accuracy is almost 100%

$./svm-predict svmguide1 svmguide1.model out Accuracy = 99.7734% (3082/3089)

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Why this Fails

Gaussian kernel is used here

We see that most kernel elements have Kij = e−kxi−xjk2/4

(

= 1 if i = j ,

→ 0 if i 6= j . because some features in large numeric ranges For what kind of data,

K ≈ I ?

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Why this Fails (Cont’d)

If we have training data

φ(x1) = [1, 0, . . . , 0]T ...

φ(xl) = [0, . . . , 0, 1]T then

K = I

Clearly such training data can be correctly separated, but how about testing data?

So overfitting occurs

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Overfitting

See the illustration in the next slide In theory

You can easily achieve 100% training accuracy This is useless

When training and predicting a data, we should Avoid underfitting: small training error

Avoid overfitting: small testing error

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l and s: training; and 4: testing

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Data Scaling

Without scaling, the above overfitting situation may occur

Also, features in greater numeric ranges may dominate

A simple solution is to linearly scale each feature to [0, 1] by:

feature value − min max − min , There are many other scaling methods

Scaling generally helps, but not always

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Data Scaling: Same Factors

A common mistake

$./svm-scale -l -1 -u 1 svmguide1 > svmguide1.scale

$./svm-scale -l -1 -u 1 svmguide1.t > svmguide1.t.scale -l -1 -u 1: scaling to [−1, 1]

We need to use same factors on training and testing

$./svm-scale -s range1 svmguide1 > svmguide1.scale

$./svm-scale -r range1 svmguide1.t > svmguide1.t.scale Later we will give a real example

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After Data Scaling

Train scaled data and then predict

$./svm-train svmguide1.scale

$./svm-predict svmguide1.t.scale svmguide1.scale.model svmguide1.t.predict

Accuracy = 96.15%

Training accuracy is now similar

$./svm-predict svmguide1.scale svmguide1.scale.model o Accuracy = 96.439%

For this experiment, we use parameters C = 1, γ = 0.25,

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Outline

1 Practical use of SVM SVM introduction A real example Parameter selection

2 Design of machine learning software Users and their needs

Design considerations

3 Discussion and conclusions

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Parameters versus Performances

If we use C = 20, γ = 400

$./svm-train -c 20 -g 400 svmguide1.scale

$./svm-predict svmguide1.scale svmguide1.scale.model o Accuracy = 100% (3089/3089)

100% training accuracy but

$./svm-predict svmguide1.t.scale svmguide1.scale.model o Accuracy = 82.7% (3308/4000)

Very bad test accuracy Overfitting happens

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Parameter Selection

For SVM, we may need to select suitable parameters They are C and kernel parameters

Example:

γ of e−γkxi−xjk2 a, b, d of (xTi xj/a + b)d

How to select them so performance is better?

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Performance Evaluation

Available data ⇒ training and validation

Train the training; test the validation to estimate the performance

A common way is k-fold cross validation (CV):

Data randomly separated to k groups

Each time k − 1 as training and one as testing Select parameters/kernels with best CV result There are many other methods to evaluate the performance

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Contour of CV Accuracy

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The good region of parameters is quite large SVM is sensitive to parameters, but not that sensitive

Sometimes default parameters work

but it’s good to select them if time is allowed

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Example of Parameter Selection

Direct training and test

$./svm-train svmguide3

$./svm-predict svmguide3.t svmguide3.model o

→ Accuracy = 2.43902%

After data scaling, accuracy is still low

$./svm-scale -s range3 svmguide3 > svmguide3.scale

$./svm-scale -r range3 svmguide3.t > svmguide3.t.scale

$./svm-train svmguide3.scale

$./svm-predict svmguide3.t.scale svmguide3.scale.model o

→ Accuracy = 12.1951%

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Example of Parameter Selection (Cont’d)

Select parameters by trying a grid of (C , γ) values

$ python grid.py svmguide3.scale

· · ·

128.0 0.125 84.8753

(Best C =128.0, γ=0.125 with five-fold cross-validation rate=84.8753%)

Train and predict using the obtained parameters

$ ./svm-train -c 128 -g 0.125 svmguide3.scale

$ ./svm-predict svmguide3.t.scale svmguide3.scale.model svmguide3.t.predict

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Selecting Kernels

RBF, polynomial, or others?

For beginners, use RBF first

Linear kernel: special case of RBF

Accuracy of linear the same as RBF under certain parameters (Keerthi and Lin, 2003)

Polynomial kernel:

(xTi xj/a + b)d

Numerical difficulties: (< 1)d → 0, (> 1)d → ∞ More parameters than RBF

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A Simple Procedure for Beginners

After helping many users, we came up with the following procedure

1. Conduct simple scaling on the data 2. Consider RBF kernel K (x, y) = e−γkx−yk2

3. Use cross-validation to find the best parameter C and γ

4. Use the best C and γ to train the whole training set 5. Test

In LIBSVM, we have a python script easy.py implementing this procedure.

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A Simple Procedure for Beginners (Cont’d)

We proposed this procedure in an “SVM guide”

(Hsu et al., 2003) and implemented it in LIBSVM From research viewpoints, this procedure is not novel. We never thought about submiting our guide somewhere

But this procedure has been tremendously useful.

Now almost the standard thing to do for SVM beginners

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A Real Example of Wrong Scaling

Separately scale each feature of training and testing data to [0, 1]

$ ../svm-scale -l 0 svmguide4 > svmguide4.scale

$ ../svm-scale -l 0 svmguide4.t > svmguide4.t.scale

$ python easy.py svmguide4.scale svmguide4.t.scale Accuracy = 69.2308% (216/312) (classification) The accuracy is low even after parameter selection

$ ../svm-scale -l 0 -s range4 svmguide4 > svmguide4.scale

$ ../svm-scale -r range4 svmguide4.t > svmguide4.t.scale

$ python easy.py svmguide4.scale svmguide4.t.scale Accuracy = 89.4231% (279/312) (classification)

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A Real Example of Wrong Scaling (Cont’d)

With the correct setting, the 10 features in the test data svmguide4.t.scale have the following maximal values:

0.7402, 0.4421, 0.6291, 0.8583, 0.5385, 0.7407, 0.3982, 1.0000, 0.8218, 0.9874

Scaling the test set to [0, 1] generated an erroneous set.

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Outline

1 Practical use of SVM SVM introduction A real example Parameter selection

2 Design of machine learning software Users and their needs

Design considerations

3 Discussion and conclusions

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Outline

1 Practical use of SVM SVM introduction A real example Parameter selection

2 Design of machine learning software Users and their needs

Design considerations

3 Discussion and conclusions

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Users’ Machine Learning Knowledge

When we started developing LIBSVM, we didn’t know who our users are or whether we will get any Very soon we found that many users have zero machine learning knowledge

It is unbelievable that many asked what the difference between training and testing is

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Users’ Machine Learning Knowledge (Cont’d)

A sample mail From:

To: cjlin@csie.ntu.edu.tw Subject: Doubt regarding SVM Date: Sun, 18 Jun 2006 10:04:01 Dear Sir,

sir what is the difference between testing data and training data?

Sometimes we cannot do much for such users.

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Users’ Machine Learning Knowledge (Cont’d)

Fortunately, more people have taken machine learning courses (or attended MLSS)

On the other hand, because users are not machine learning researchers, some automatic or

semi-automatic settings are helpful

The simple procedure discussed earlier is an example Also, your target users affect your design.

For example, we assume LIBLINEAR users are more experienced.

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We are Our Own Users

You may ask why we care non-machine learning users so much

The reason is that we were among them before My background is in optimization. When we started working on SVM, we tried some UCI sets.

We failed to obtain similar accuracy values in papers Through a painful process we learned that scaling may be needed

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We are Our Own Users (Cont’d)

Machine learning researchers sometimes failed to see the difficulties of general users.

As users of our own software, we constantly think about difficulties others may face

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Users are Our Teachers

While we criticize users’ lack of machine learning knowledge, they help to point out many useful directions

Example: LIBSVM supported only binary

classification in the beginning. From many users’

requests, we knew the importance of multi-class classification

There are many possible approaches for multi-class SVM. Assume data are in k classes

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Users are Our Teachers (Cont’d)

- One-against-the rest: Train k binary SVMs:

1st class vs. (2, · · · , k)th class 2nd class vs. (1, 3, . . . , k)th class

...

- One-against-one: train k(k − 1)/2 binary SVMs (1, 2), (1, 3), . . . , (1, k), (2, 3), (2, 4), . . . , (k − 1, k) We finished a study in Hsu and Lin (2002), which is now well cited.

Currently LIBSVM supports one-vs-one approach

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Users are Our Teachers (Cont’d)

LIBSVM is among the first SVM software to handle multi-class data.

This helps to attract many users.

Users help to identify useful things for the software They even help to identify important research directions

The paper (Hsu and Lin, 2002) was rejected by many places because it’s a detailed comparison without new algorithms

But from users we knew its results are important.

This paper is now one of my most cited papers

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Outline

1 Practical use of SVM SVM introduction A real example Parameter selection

2 Design of machine learning software Users and their needs

Design considerations

3 Discussion and conclusions

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One or Many Options

Sometimes we received the following requests 1. In addition to “one-vs-one,” could you include other multi-class approaches such as “one-vs-the rest?”

2. Could you extend LIBSVM to support other kernels such as χ2 kernel?

Two extremes in designing a software package 1. One option: reasonably good for most cases 2. Many options: users try options to get best results

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One or Many Options (Cont’d)

From a research viewpoint, we should include everything, so users can play with them

But

more options ⇒ more powerful

⇒ more complicated Some users have no abilities to choose between options

Example: Some need χ2 kernel, but some have no idea what it is

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One or Many Options (Cont’d)

• Users often try all options even if that’s not needed

• Example: LIBLINEAR has the following solvers options:

-s type : set type of solver (default 1)

0 -- L2-regularized logistic regression (primal) ...

7 -- L2-regularized logistic regression (dual)

• Some users told me:

I have tried all solvers, but accuracy is similar

• But wait, doesn’t solvers 0 and 7 always give same accuracy? No need to run both

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One or Many Options (Cont’d)

For LIBSVM, we basically took the “one option”

approach

We are very careful in adding things to LIBSVM However, users do have different needs. For example, some need precision/recall rather than accuracy

We end up with developing another web site

“LIBSVM Tools” to serve users’ special needs

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One or Many Options (Cont’d)

Sample code in LIBSVM tools

- Cross Validation with Different Criteria (AUC, F-score, etc.)

- ROC Curve for Binary SVM - LIBSVM for string data

Not sure if this is the best way, but seems ok so far Another advantage is we can maintain high quality for the core package. Things in LIBSVM Tools are less well maintained.

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Simplicity versus Better Performance

This issue is related to “one or many options”

discussed before

Example: Before, our cross validation (CV) procedure is not stratified

- Results less stable because data of each class not evenly distributed to folds

- We now support stratified CV, but code becomes more complicated

In general, we avoid changes for just marginal improvements

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Simplicity versus Better Performance (Cont’d)

A recent Google research blog “Lessons learned developing a practical large scale machine learning system” by Simon Tong

From the blog, “It is perhaps less academically interesting to design an algorithm that is slightly worse in accuracy, but that has greater ease of use and system reliability. However, in our experience, it is very valuable in practice.”

That is, a complicated method with a slightly higher accuracy may not be useful in practice

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Simplicity versus Better Performance (Cont’d)

Example: LIBSVM uses a grid search to find two parameters C and γ. We may think this is simple and naive

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Simplicity versus Better Performance (Cont’d)

Indeed, we studied loo bound in detail. That is, a function of parameters f (C , γ) is derived to satisfy

leave-one-out error ≤ f (C , γ) Then we solve

min

C ,γ f (C , γ)

Results not very stable because f (C , γ) is only an approximation. Implementation is quite complicated.

For only two parameters, a simple grid search may be a suitable choice

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Numerical and Optimization Methods

Many classification methods involve numerical and optimization procedures

A key point is that we need to take machine learning properties into the design of your implementation An example in Lieven’s talk yesterday:

L1-regularized least-square regression I will discuss some SVM examples

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Numerical and Optimization Methods (Cont’d)

Let’s consider SVM dual minα

1

TQα − eTα

subject to 0 ≤ αi ≤ C , i = 1, . . . , l yTα = 0

Qij 6= 0, Q : an l by l fully dense matrix 50,000 training points: 50,000 variables:

(50, 0002 × 8/2) bytes = 10GB RAM to store Q Traditional optimization methods:

Newton or gradient cannot be directly applied

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Numerical and Optimization Methods (Cont’d)

One workaround is to work on some variables each time (e.g., Osuna et al., 1997; Joachims, 1998;

Platt, 1998)

Working set B , N = {1, . . . , l }\B fixed Sub-problem at the kth iteration:

minαB

1

2αTBkN)TQBB QBN QNB QNN

 αB αkN



eTB (ekN)TαB αkN



T T k

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Numerical and Optimization Methods (Cont’d)

The new objective function 1

TBQBBαB + (−eB + QBNαkN)TαB + constant Only B columns of Q needed (|B| ≥ 2)

Calculated when used Trade time for space

But is such an approach practical?

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Numerical and Optimization Methods (Cont’d)

Convergence not very fast

But, no need to have very accurate α decision function: Xl

i =1αiK (xi, x) + b Prediction may still be correct with a rough α

Further, in some situations, # support vectors  # training points

Initial α1 = 0, some instances never used

So special properties of SVM did contribute to the viability of this method

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Numerical and Optimization Methods (Cont’d)

An example of training 50,000 instances using LIBSVM

$svm-train -c 16 -g 4 -m 400 22features Total nSV = 3370

Time 79.524s

On a Xeon 2.0G machine

Calculating the whole Q takes more time

#SVs = 3,370  50,000

A good case where some remain at zero all the time

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Numerical and Optimization Methods (Cont’d)

Another example: training kernel and linear SVM should be done by different methods

What’s the difference between kernel and linear?

for linear, K can be written as XXT where

X =

 xT1

...

xTl

 ∈ Rl ×n is the training matrix

Note that n: # features, l : # data

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Numerical and Optimization Methods (Cont’d)

Recall in the kernel case, we need (Qα)i − 1, i ∈ B The cost is O(nl )

Xl

j =1yiyjK (xi, xjj − 1

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Numerical and Optimization Methods (Cont’d)

For linear, if we have w ≡ Xl

j =1yjαjxj then

Xl

j =1yiyjxTi xjαj − 1 = yiwTxi − 1

costs only O(n). Details of maintaining w are not discussed here; the cost is also O(n)

Again, the key is that properties of machine learning

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Numerical Stability

Quality of the numerical programs is important for a machine learning package

Numerical analysts have a high standard on their code, but unfortunately we machine learning people do not

This situation is expected:

If we put efforts on implementing method A and one day method B gives higher accuracy ⇒ Efforts are wasted

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Numerical Stability (Cont’d)

We will give an example to discuss how to improve numerical stability in your implementation

In LIBSVM’s probability outputs, we calculate

− (tilog(pi) + (1 − ti) log(1 − pi)) , where

pi ≡ 1

1 + exp(∆) When ∆ is small, pi ≈ 1

Then 1 − pi is a catastrophic cancellation

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Numerical Stability (Cont’d)

Catastrophic cancellation (Goldberg, 1991): when subtracting two nearby numbers, the relative error can be large so most digits are meaningless.

In a simple C++ program with double precision,

∆ = −64 ⇒ 1 − 1

1 + exp(∆) returns zero but

exp(∆)

1 + exp(∆) gives more accurate result

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Numerical Stability (Cont’d)

Catastrophic cancellation may be resolved by reformulation

Another issue is that log and exp could easily cause an overflow

If ∆ is large ⇒ exp(∆) → ∞ Then

pi = 1

1 + exp(∆) ≈ 0 ⇒ log(pi) → −∞

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We can use the following reformulation log(1 + exp(∆))

= log(exp(∆) · (exp(−∆) + 1))

= ∆ + log(1 + exp(−∆)) When ∆ is large

exp(−∆) ≈ 0 and log(1 + exp(∆)) ≈ ∆ Less like to get overflow because of no exp(∆)

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Numerical Stability (Cont’d)

In summary,

− (ti log pi + (1 − ti) log(1 − pi)) (1)

= (ti − 1)∆ + log (1 + exp(∆)) (2)

= ti(∆) + log (1 + exp(−∆)) (3) We implement (1) with the following rule:

If ∆ ≥ 0 then use (3); Else use (2).

This handles both issues of overflow and catastrophic cancellation

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Legacy Issues

The compatibility between earlier and later versions is an issue

Such legacy issues restrict developers to conduct certain changes.

We face a similar situation. For example, we chose

“one-vs-one” as the multi-class strategy. This decision affects subsequent buildups.

Example: in LIBSVM, multi-class probability outputs must follow the one-vs-one structure. For classes i and j , we obtain

P(x in class i | x in class i or j ),

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Legacy Issues (Cont’d)

Then we need to couple all k2 results (k: the number of classes) and obtain

P(x in class i ), i = 1, . . . , k.

If we further develop multi-label methods, we are restricted to extend from one-versus-one multi-class strategy

What if one day we would like to use a different multi-class method?

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Legacy Issues (Cont’d)

In LIBSVM, we understand this legacy issue in the beginning

Example: we did not make the trained model a public structure

Encapsulation in object-oriented programming Here is the C code to train and test

#include <svm.h>

...

model = svm_train(...);

...

predict_label = svm_predict(model,x);

svm.h includes all public functions and structures

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Legacy Issues (Cont’d)

We decided not to put model structure in svm.h Instead we put it in svm.cpp

User can call

model = svm_train(...);

but cannot directly access a model’s contents int y1 = model.label[1];

We provide functions so users can get some model information

svm_get_svm_type(model);

svm_get_nr_class(model);

svm_get_labels(model, ...);

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Documentation and Support

Any software needs good documents and support I cannot count how many mails my students and I replied. Maybe 20,000 or more.

How to write good documents is an interesting issue Users may not understand what you wrote

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Documentation and Support (Cont’d)

Example: some users asked if LIBSVM supported multi-class classification

I thought it’s well documented in README

Finally I realized that users may not read the whole README.

Instead, some of them check only the “usage”

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Documentation and Support (Cont’d)

They didn’t see “multi-class” in the usage

$ svm-train

Usage: svm-train [options] training_set_file [model_file]

options:

-s svm_type : set type of SVM (default 0) 0 -- C-SVC

1 -- nu-SVC

2 -- one-class SVM 3 -- epsilon-SVR 4 -- nu-SVR

...

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Documentation and Support (Cont’d)

In the next version we will change the usage to -s svm_type : set type of SVM (default 0)

0 -- C-SVC (multi-class classification) 1 -- nu-SVC (multi-class classification) 2 -- one-class SVM

3 -- epsilon-SVR(regression) 4 -- nu-SVR (regression)

I am going to see how many asked “why LIBSVM doesn’t support two-class SVM”

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Outline

1 Practical use of SVM SVM introduction A real example Parameter selection

2 Design of machine learning software Users and their needs

Design considerations

3 Discussion and conclusions

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Software versus Experiment Code

Many researchers now release experiment code used for their papers

Reason: experiments can be reproduced

This is important, but experiment code is different from software

Experiment code often includes messy scripts for various settings in the paper – useful for reviewers Example: to check an implementation trick in a proposed algorithm, need to run with/without the trick

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Software versus Experiment Code (Cont’d)

Software: for general users

One or a few reasonable settings with a suitable interface are enough

Many are now willing to release their experimental code

Basically you clean up the code after finishing a paper

But working on and maintaining high-quality software take much more work

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Software versus Experiment Code (Cont’d)

Reproducibility different from replicability (Drummond, 2009)

Replicability: make sure things work on the sets used in the paper

Reproducibility: ensure that things work in general In my group, we release experiment code for every paper ⇒ for replicability

And carefully select and modify some results to our software ⇒ (hopefully) for reproducibility

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Software versus Experiment Code (Cont’d)

The community now lacks incentives for researchers to work on high quality software

JMLR recently started “open source software”

section (4-page description of the software) This is a positive direction

How to properly evaluate such papers is an issue Some software are very specific on a small problem, but some are more general

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Research versus Software Development

Shouldn’t software be developed by companies?

Two issues

1 Business models of machine learning software

2 Research problems in developing software

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Research versus Software Development (Cont’d)

Business model

It is unclear to me what a good model should be Machine learning software are basically “research”

software

They are often called by some bigger packages

For example, LIBSVM and LIBLINEAR are called by Weka and Rapidminer through interfaces

These data mining packages are open sourced and their business is mainly on consulting

Should we on the machine learning side use a similar way?

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Research versus Software Development (Cont’d)

Research issues

A good machine learning package involves more than the core machine learning algorithms

There are many other research issues - Numerical algorithms and their stability

- Parameter tuning, feature generation, and user interfaces

- Serious comparisons and system issues

These issues need researchers rather than engineers Currently we lack a system to encourage machine

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Machine Learning and Its Practical Use

I just mentioned that a machine learning package involves more than algorithms

Here is an interesting conversation between me and a friend

Me: Your company has system A for large-scale training and now also has system B. What are their differences?

My friend: A uses ... algorithms and B uses ...

Me: But these algorithms are related

My friend: Yes, but you know sometimes algorithms are the least important thing

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Machine Learning and Its Practical Use (Cont’d)

As machine learning researchers, of course we think algorithms are important

But we must realize that in industry machine learning is often only part of a big project

Moreover, it rarely is the most important component We academic machine learning people must try to know where machine learning is useful. Then we can develop better algorithms and software

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Conclusions

From my experience, developing machine learning software is very interesting.

In particular, you get to know how your research work is used

Through software, we have seen different application areas of machine learning

We should encourage more researchers to develop high quality machine learning software

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Acknowledgments

All users have greatly helped us to make improvements

Without them we cannot get this far

We also thank all our past group members

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References I

B. E. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pages 144–152. ACM Press, 1992.

C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

C. Cortes and V. Vapnik. Support-vector network. Machine Learning, 20:273–297, 1995.

C. Drummond. Replicability is not reproducibility: Nor is it good science. In Proceedings of the Evaluation Methods for Machine Learning Workshop at the 26th ICML, 2009.

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9:1871–1874, 2008. URL http://www.csie.ntu.edu.tw/~cjlin/papers/liblinear.pdf.

D. Goldberg. What every computer scientist should know about floating-point arithmetic.

ACM Computing Surveys, 23(1):5–48, 1991.

C.-J. Hsieh, K.-W. Chang, C.-J. Lin, S. S. Keerthi, and S. Sundararajan. A dual coordinate descent method for large-scale linear SVM. In Proceedings of the Twenty Fifth International Conference on Machine Learning (ICML), 2008. URL

http://www.csie.ntu.edu.tw/~cjlin/papers/cddual.pdf.

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References II

C.-W. Hsu and C.-J. Lin. A comparison of methods for multi-class support vector machines.

IEEE Transactions on Neural Networks, 13(2):415–425, 2002.

C.-W. Hsu, C.-C. Chang, and C.-J. Lin. A practical guide to support vector classification.

Technical report, Department of Computer Science, National Taiwan University, 2003.

URL http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.

T. Joachims. Making large-scale SVM learning practical. In B. Sch¨olkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods – Support Vector Learning, pages 169–184, Cambridge, MA, 1998. MIT Press.

T. Joachims. Training linear SVMs in linear time. In Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006.

S. S. Keerthi and C.-J. Lin. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation, 15(7):1667–1689, 2003.

E. Osuna, R. Freund, and F. Girosi. Training support vector machines: An application to face detection. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 130–136, 1997.

J. C. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Sch¨olkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, Cambridge, MA, 1998. MIT Press.

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References III

S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: primal estimated sub-gradient solver for SVM. In Proceedings of the Twenty Fourth International Conference on Machine Learning (ICML), 2007.

S. Sonnenburg, M. Braun, C. Ong, S. Bengio, L. Bottou, G. Holmes, Y. LeCun, K. M¨uller, F. Pereira, C. Rasmussen, et al. The need for open source software in machine learning.

Journal of Machine Learning Research, 8:2443–2466, 2007.

G.-X. Yuan, C.-H. Ho, and C.-J. Lin. Recent advances of large-scale linear classification.

Proceedings of IEEE, 2012. URL

http://www.csie.ntu.edu.tw/~cjlin/papers/survey-linear.pdf. To appear.

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