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Using R for Data Mining Competitions

An GTA R Users Group talk
by Jonathan Lee on 26 September 2012

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Transcript of Using R for Data Mining Competitions

Speed up computation
Bypass memory limitations

Quite easy for independent simulations

Not so simple for non-independent simulations Using for
Data Mining Competitions Case Studies Why Data Mining Competitions? Why R? Jonathan Lee, University of Western Ontario
GTA R Users Group
November 25, 2011
Ryerson University Additional Resources 2007 2009 2008 October 2, 2007 Ready-to-use data
Structured goal
No accountability, no stress
Recognition and prizes ($$)
Publication opportunity
Fun! So Where Are They? Great for You A Bit of History Low-cost way to get innovative solutions
Crowd sourcing
Ownership of solution Great for Companies Benefits open source
de-facto standard Prezi (this presentation software)
Kaggle blog: No Free Hunch R Package Recommendation Engine Tourism Forecasting HIV Viral Load Prediction The Usual Suspects There's a package for that Isn't (insert language here) Faster? Run-time? Perhaps.
Development time? Probably not!

Packages to note: compiler, Rcpp The Prize Not the first data mining competition, but probably the most well-known. 2006 2010 Netflix is an online movie rental service
Can we predict a Netflix user's rating for a movie given previous movie rating data? The Problem The Players The Journey The Data The Goal Data Mining
Competitions Competition
Platforms Individual
Competitions An incomplete list... Do better than Netflix's existing system, Cinematch, by at least 10%
Cinematch uses simple linear models with data conditioning

Grand prize: $1 million USD Anyone could join

Computer scientists, statisticians, programmers etc. 51051 contestants on 41305 teams from 186 countries. Training Set
(~100 million entries) Quiz set (~1 million entries) Test set (~1 million entries) Model tested on quiz set to determine leaderboard ranking
Competition deadline set once 10% improvement is reached At end of competition, models with at least 10% improvement are tested on test set.
Best performing model on test set is declared the winner. All data including answers are provided
Models are fit onto this data There are so many packages that exist for R
Impossible to know all of them -- so which ones should one invest time in learning?
Cinematch for R The Problem Any 3 books from the UseR! book list (postage included)
Winner is determined by highest AUC value on test set The Prize The Data Voluntary sample of 52 R users
~100,000 rows of data for 1865 packages
Each row contains metadata of package and whether or not a user has that package installed. PackageName The name of current R package
DependencyCount Number of other packages that have it as a dependency
SuggestionCount Number of other packages that suggest it
ViewsIncluding Number of views that include it
CorePackage Whether or not it is a core package (binary)
RecommendedPackage Whether or not it is a recommended package (binary)
Maintainer Name and e-mail of package maintainer
PackagesMaintaining How many other packages the maintainer maintains Training set (2/3) Test set (1/3) Add in automatic stepwise model selection...

AUC on test set: 0.9453 A better model Synthesize better predictor variables

Add a little bit of intuition

AUC: 0.9682 An even better model A winning model AUC: Area Under the (ROC) Curve For a binary classification problem, we can get a confusion matrix.

Receiver Operator Characteristic (ROC) space is defined as the True Positive Rate vs the False Positive Rate.

Our predicted values are not binary, but rather a probability between 0 and 1.
By evaluating the ROC at every classification threshold, we get an ROC curve. Positive Negative Positive Negative True Positive False Positive True Negative False Negative Actual Predicted ## Fit model on training data <- glm(Installed ~ (LogDependencyCount +
LogSuggestionCount +
LogImportCount +
ViewsIncluding)^2 +
factor(User) -
LogSuggestionCount:ViewsIncluding -
data =,
family = binomial(link = 'logit'))

#### Predicting the test data
logit.predict <- predict.glm(,, type='response') If a package is installed, assume all dependencies and imports are installed
Assume all core packages are installed Logistic regression (stats package) on predictor variables with some variable transformation

AUC on test set: 0.8178 A simple model ## Fit the model to the training data <- glm(Installed ~ LogDependencyCount +
LogSuggestionCount +
LogImportCount +
LogViewsIncluding +
LogPackagesMaintaining +
CorePackage +
RecommendedPackage +
data =,
family = binomial(link = 'logit'))

## Use model to predict on the test set
logit.predict <- predict.glm(,,type='response') Jonathan Lee

This presentation and slides will be made available on my blog: My Contact from IJCNN Social Network Challenge Predicting edges in an online social network
If you and I are both connected to Bob, what is the probability (p) that I am connected to you? The Problem Free registration for two to the IJCNN 2011 Conference and chance to present your solution. ($950 value)
Winner is determined by highest AUC value on test set The Prize The Data 1,133,547 nodes (users)

7,246,943 edges (contacts)

No additional covariates given

What social network generates this type of graph? Directed graph

0.1 % used as test set A winning model Number of mutual friends

p=1 if within two degrees of separation
p=0 otherwise
AUC on test set: 0.7529 Two Degrees of Separation Inbound nodes
1,133,518 Inbound and Outbound nodes
37,689 Outbound nodes
29 for (i in 1:test.size) {
a <- test$V1[i]
b <- test$V2[i]

# Get all nodes that connect to a
a.list <- c(train$V1[which(train$V2==a)],train$V2[which(train$V1==a)])

# Get all nodes that connect to b
b.list <- c(train$V1[which(train$V2==b)],train$V2[which(train$V1==b)])

# Find number of mutual nodes
mutual[i] <- sum(a.list %in% b.list)
} Can't feasibly find 3+ degrees of separation
Let's put this into a graph (igraph package) and find the degrees of separation

Inverse function of degrees of separation?

AUC on test set: 0.832 n Degrees of Separation library(igraph)
edges <- matrix(c(train$V1,train$V2),ncol=2,byrow=FALSE)
sngraph <- graph.edgelist(edges,directed=TRUE)
get.path.lengths <- function(v1,v2,graph) length(get.shortest.paths(sngraph,v1,v2,mode="all")[[1]])-1
path.lengths <- mapply(get.path.lengths,test$V1,test$V2,MoreArgs=list(graph=sngraph)) Having a graph opens up a lot of possible covariates

After logistic regression...
AUC on test set: 0.8704 More covariates Number of friends
Shortest path
Number of shortest paths etc. Realized the dataset came from
De-anonymized the users (not an easy task!)
Recreated the missing edges (60%)
Used randomForests on standard social network features to fill in the rest

So why not 100% accuracy?
Social networks evolve over time. AUC of 0.9812!
How did they do it? Going Beyond the Data INFORMS 2011 The Heritage Health Prize Predict the number of days a patient will spend in the hospital in the next year given historical claims data.

$3M grand prize + $240k progress
Ends April 2013 Big Data Netflix data - a matrix of about 480,000 members' ratings for about 18,000 movies) was about 65 GB!

Matrix package using sparseMatrix( ) function efficiently stores the data in ~800 MB

irlba package can be used to efficiently do SVD and PCA for large sparse matricies Data Visualization Machine Learning ahaz
elasticnet Model Evaluation Most packages have built in model evaluation tools
ROCR: powerful and easy-to-use package for visualizing classifier performance How can we collaborate?
Code hosting (with version control)
RGoogleDocs package Data Mining is a Team Sport The Elements of Statistical Learning

Stanford's Machine Learning Course Data Mining and
Machine Learning require(tm)

crude <- tm_map(crude, removePunctuation)
crude <- tm_map(crude, function(x)removeWords(x,stopwords()))
tdm <- TermDocumentMatrix(crude)
v <- sort(rowSums(as.matrix(tdm)),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)

wordcloud(d$word,d$freq) wordcloud Some examples from R graph gallery: anaglyph require(anaglyph)

main="Weight vs Miles Per Gallon vs Displacement",
xlab="Weight (lb/1000lb)",
ylab="Miles/US Gallon") by Ian Fellows by Jonathan Lee RnavGraph by Adrian Waddell lattice require(lattice)

histogram( ~ height | voice.part, data = singer,
xlab = "Height (inches)", type = "density",
panel = function(x, ...) {
panel.histogram(x, ...)
panel.mathdensity(dmath = dnorm, col = "black",
args = list(mean=mean(x),sd=sd(x)))
} ) by Deepayan Sarkar ggplot2 require(ggplot2)

ggplot(diamonds, aes(depth, fill = cut))
+ geom_density(alpha = 0.2)
+ xlim(55, 70) by Hadley Wickham rgl by Daniel Adler and Duncan Murdoch Example: Netflix Parallel R Rmpi
parallel (default in version 2.14) Commercial software
built on top of R
Optimized for Multi-processor
Multi-threaded Math libraries
Terabyte file structures and
optimized algorithms for "Big Data"

Free for academic users Revolution R More info: library(caret)

model.1 <- rfe(hiv.412[,c(5:617)], as.factor(hiv.412[,618]),
sizes = c(1:10, 15, 30, 60, 90, 120, 150, 200, 250, 300, 350),
rfeControl = rfeControl(functions = rfFuncs , method="cv"))
model.1.prediction <- predict(model.1$fit, hiv.692) Winning Algorithm library(RandomForest)
fin.train.1e.5050 <- randomForest(as.factor(Resp) ~ VL.t0 + rt184 + CD4.t0 + rt215 +
rt98 + rt225 + rt35 + rt151 + rt207 + rt227 + pr12 + pr70 +
rt101 + pr71 + rt69 + rt75 + rt187 + rt203 + rt41 + pr60 +
pr20 + rt43 + rt179 + pr82 + pr46 + pr90 + rt31 + pr30 +
pr10 + rt44 + pr58 + rt121 + pr73 + rt195 + pr63 + rt109 +
pr54 + rt173 + pr88 + rt205 + rt190 + rt77 + pr13 + rt115 +
rt106 + pr32 + pr14 + rt170 + rt4 + pr92 + pr35,
na.action=na.omit, data=yellow.5050, mtry=15) The Problem Single Hidden Layer NN Netflix Prize contest announced. October 8, 2007 Cinematch performance surpassed. October 15, 2007 1% improvement reached. November 13, 2007 BellKor awarded $50,000 progress prize 8.43% improvement
Description of algorithm published December 10, 2008 BellKor in Big Chaos awarded $50,000 progress prize
9.44% improvement
Description of algorithm published June 26, 2009 BellKor's Pragmatic Chaos reaches 10.05% improvement.
"Last call" for final submissions. July 27, 2009 BellKor's Pragmatic Chaos wins grand prize
($1,000,000) September 21, 2009 Accuracy, error rate, true positive rate, false positive rate, true negative rate, false negative rate, sensitivity, specificity, recall, positive predictive value, negative predictive value, precision, fallout, miss, phi correlation coefficient, Matthews correlation coefficient, mutual information, chi square statistic, odds ratio, lift value, precision/recall F measure, ROC convex hull, area under the ROC curve, precision/recall break-even point, calibration error, mean cross-entropy, root mean squared error, SAR measure, expected cost, explicit cost. ROC curves, precision/recall plots, lift charts, cost curves, custom curves by freely selecting one performance measure for the x axis and one for the y axis, handling of data from cross-validation or bootstrapping, curve averaging (vertically, horizontally, or by threshold), standard error bars, box plots, curves that are color-coded by cutoff, printing threshold values on the curve, tight integration with Rs plotting facilities (making it easy to adjust plots or to combine multiple plots), fully customizable, easy to use (only 3 commands). Supported measures Features Check out the MachineLearning Task View on CRAN ElemStatLearn
klaR party
rgenoud lars
pamr rgp
varSelRF Max Lin's 2nd place solution: Ensemble Learning with 4 classifiers Source: Take training data and create an out-of-sample test set 1 Training Data (80%) Test (10%) Test (10%) 2 Train classifiers with training data 3 Use classifiers to predict test data 4 Fit ensemble model using logistic regression on test data (ROCR) x 10-folds 10 =0.9833 logistic regression (stats) latent factor model (ltr) latent Dirichlet allocation on topic (lda) latent Dirichlet allocation on task view (lda) logistic regression
(stats) + intuition logistic regression (stats) latent factor model (ltr) latent Dirichlet allocation on topic (lda) latent Dirichlet allocation on task view (lda) AUC Hidden Layer Input Layer Output Layer The Problem Predict stock price movements at 5-minute intervals
Useful for high-frequency traders
Performance measured by AUC The Data Time series with 609 blind explanatory variables for an unknown stock stock prices
sectoral data
economic data
experts' predictions
indexes Training set: 5922 observations taken at 5-minute intervals.
Test set: 2539 observations taken at 5-minute intervals. V74CA0 V74HA1 V74CD12 V74LA13 V88CA1 V88H12A12 Logisitic Regression Feature selection done to select top two significant variables at lag 12

Further significance noticed at lag 1

2 variables * 4 prices * 4 lags = 32 covariates V74 and V88
Each variable has 4 prices: Open, High, Low, Last

Take lag 0, 1, 12, 13 Model Selection Manual model selection results in logistic regression model of the form... Target ~ V74LASTAdv0
+V88HIGHAdv12 = 0.9908 Artificial Neural Network nnet package to fit a feed-forward neural network with a single hidden layer
Using the covariates from the previous logisitic regression model, we get... AUC = 0.9898 AUC Missing Data Many features had missing data
Solutions? Impute it
impute package (i.e. knn.impute)
Ignore it
Remove it I liked , will I like ? Cole Harris (Team DejaVu)'s Winning Solution True Crowdsourcing Predict the likelihood that an HIV patient's infection will become less severe, given a small dataset and limited clinical information. Chris Raimondi caret package for recursive feature elimination - rfe( )

RandomForest to fit model by Arvind Narayanan, Elaine Shi, Ben Rubinstein, and Yong J Kil Full description: Predicting tourism related time series Miscellaneous
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