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Peter Fox

Data Analytics – ITWS-4963/ITWS-6965

Week 6b, February 28, 2014

Weighted kNN, clustering, more plottong, Bayes

Plot tools/ tipshttp://statmethods.net/advgraphs/layout.html

http://flowingdata.com/2014/02/27/how-to-read-histograms-and-use-them-in-r/

pairs, gpairs, scatterplot.matrix, clustergram, etc.

data()

# precip, presidents, iris, swiss, sunspot.month (!), environmental, ethanol, ionosphere

More script fragments in Lab6b_*_2014.R on the web site (escience.rpi.edu/data/DA )

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Weighted KNN?require(kknn)

data(iris)

m <- dim(iris)[1]

val <- sample(1:m, size = round(m/3), replace = FALSE,

prob = rep(1/m, m))

iris.learn <- iris[-val,]

iris.valid <- iris[val,]

iris.kknn <- kknn(Species~., iris.learn, iris.valid, distance = 1,

kernel = "triangular")

summary(iris.kknn)

fit <- fitted(iris.kknn)

table(iris.valid$Species, fit)

pcol <- as.character(as.numeric(iris.valid$Species))

pairs(iris.valid[1:4], pch = pcol, col = c("green3", "red”)[(iris.valid$Species != fit)+1])

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Try Lab6b_8_2014.R

New dataset - ionosphererequire(kknn)

data(ionosphere)

ionosphere.learn <- ionosphere[1:200,]

ionosphere.valid <- ionosphere[-c(1:200),]

fit.kknn <- kknn(class ~ ., ionosphere.learn, ionosphere.valid)

table(ionosphere.valid$class, fit.kknn$fit)

# vary kernel

(fit.train1 <- train.kknn(class ~ ., ionosphere.learn, kmax = 15,

kernel = c("triangular", "rectangular", "epanechnikov", "optimal"), distance = 1))

table(predict(fit.train1, ionosphere.valid), ionosphere.valid$class)

#alter distance

(fit.train2 <- train.kknn(class ~ ., ionosphere.learn, kmax = 15,

kernel = c("triangular", "rectangular", "epanechnikov", "optimal"), distance = 2))

table(predict(fit.train2, ionosphere.valid), ionosphere.valid$class)5

Cluster plottingsource("http://www.r-statistics.com/wp-content/uploads/2012/01/source_https.r.txt") # source code from github

require(RCurl)

require(colorspace)

source_https("https://raw.github.com/talgalili/R-code-snippets/master/clustergram.r")

data(iris)

set.seed(250)

par(cex.lab = 1.5, cex.main = 1.2)

Data <- scale(iris[,-5]) # scaling

clustergram(Data, k.range = 2:8, line.width = 0.004) # line.width - adjust according to Y-scale 6

Clustergram

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Any good?set.seed(500)

Data2 <- scale(iris[,-5])

par(cex.lab = 1.2, cex.main = .7)

par(mfrow = c(3,2))

for(i in 1:6) clustergram(Data2, k.range = 2:8 , line.width = .004, add.center.points = T)

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How can you tell it is good?set.seed(250)

Data <- rbind( cbind(rnorm(100,0, sd = 0.3),rnorm(100,0, sd = 0.3),rnorm(100,0, sd = 0.3)),

cbind(rnorm(100,1, sd = 0.3),rnorm(100,1, sd = 0.3),rnorm(100,1, sd = 0.3)),

cbind(rnorm(100,2, sd = 0.3),rnorm(100,2, sd = 0.3),rnorm(100,2, sd = 0.3)))

clustergram(Data, k.range = 2:5 , line.width = .004, add.center.points = T)

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More complex…set.seed(250)

Data <- rbind( cbind(rnorm(100,1, sd = 0.3),rnorm(100,0, sd = 0.3),rnorm(100,0, sd = 0.3),rnorm(100,0, sd = 0.3)),

cbind(rnorm(100,0, sd = 0.3),rnorm(100,1, sd = 0.3),rnorm(100,0, sd = 0.3),rnorm(100,0, sd = 0.3)),

cbind(rnorm(100,0, sd = 0.3),rnorm(100,1, sd = 0.3),rnorm(100,1, sd = 0.3),rnorm(100,0, sd = 0.3)),

cbind(rnorm(100,0, sd = 0.3),rnorm(100,0, sd = 0.3),rnorm(100,0, sd = 0.3),rnorm(100,1, sd = 0.3)))

clustergram(Data, k.range = 2:8 , line.width = .004, add.center.points = T)

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• Look at the location of the cluster points on the Y axis. See when they remain stable, when they start flying around, and what happens to them in higher number of clusters (do they re-group together)

• Observe the strands of the datapoints. Even if the clusters centers are not ordered, the lines for each item might (needs more research and thinking) tend to move together – hinting at the real number of clusters

• Run the plot multiple times to observe the stability of the cluster formation (and location)

http://www.r-statistics.com/2010/06/clustergram-visualization-and-diagnostics-for-cluster-analysis-r-code/

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Swiss - pairs

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pairs(~ Fertility + Education + Catholic, data = swiss, subset = Education < 20, main = "Swiss data, Education < 20")

ctree

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require(party)

swiss_ctree <- ctree(Fertility ~ Agriculture + Education + Catholic, data = swiss)

plot(swiss_ctree)

Hierarchical clustering

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> dswiss <- dist(as.matrix(swiss))

> hs <- hclust(dswiss)

> plot(hs)

scatterplotMatrix

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require(lattice); splom(swiss)

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Decision tree (reminder)> str(iris)

'data.frame': 150 obs. of 5 variables:

$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...

$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...

$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...

$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...

$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

> str(swiss)

…

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Beyond plot: pairspairs(iris[1:4], main = "Anderson's Iris Data -- 3 species”, pch = 21, bg = c("red", "green3", "blue")[unclass(iris$Species)])

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Try Lab6b_2_2014.R - USJudgeRatings

Try hclust for iris

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gpairs(iris)

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Try Lab6b_3_2014.R

Better scatterplots

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install.packages("car")

require(car)

scatterplotMatrix(iris)

Try Lab6b_4_2014.R

splom(iris) # default

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Try Lab6b_7_2014.R

splom extra!require(lattice)

super.sym <- trellis.par.get("superpose.symbol")

splom(~iris[1:4], groups = Species, data = iris,

panel = panel.superpose,

key = list(title = "Three Varieties of Iris",

columns = 3,

points = list(pch = super.sym$pch[1:3],

col = super.sym$col[1:3]),

text = list(c("Setosa", "Versicolor", "Virginica"))))

splom(~iris[1:3]|Species, data = iris,

layout=c(2,2), pscales = 0,

varnames = c("Sepal\nLength", "Sepal\nWidth", "Petal\nLength"),

page = function(...) {

ltext(x = seq(.6, .8, length.out = 4),

y = seq(.9, .6, length.out = 4),

labels = c("Three", "Varieties", "of", "Iris"),

cex = 2)

})

parallelplot(~iris[1:4] | Species, iris)

parallelplot(~iris[1:4], iris, groups = Species,

horizontal.axis = FALSE, scales = list(x = list(rot = 90)))

> Lab6b_7_2014.R

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Ctree> iris_ctree <- ctree(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data=iris)

> print(iris_ctree)

Conditional inference tree with 4 terminal nodes

Response: Species

Inputs: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width

Number of observations: 150

1) Petal.Length <= 1.9; criterion = 1, statistic = 140.264

2)* weights = 50

1) Petal.Length > 1.9

3) Petal.Width <= 1.7; criterion = 1, statistic = 67.894

4) Petal.Length <= 4.8; criterion = 0.999, statistic = 13.865

5)* weights = 46

4) Petal.Length > 4.8

6)* weights = 8

3) Petal.Width > 1.7

7)* weights = 46 30

plot(iris_ctree)

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Try Lab6b_5_2014.R> plot(iris_ctree, type="simple”) # try this

Try these on mapmeans, etc.

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Something simpler – kmeans and…

> mapmeans<-data.frame(as.numeric(mapcoord$NEIGHBORHOOD), adduse$GROSS.SQUARE.FEET, adduse$SALE.PRICE, adduse$'querylist$latitude', adduse$'querylist$longitude')

> mapobjnew<-kmeans(mapmeans,5, iter.max=10, nstart=5, algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"))

> fitted(mapobjnew,method=c("centers","classes"))

• Others? 33

Plotting clusters (DIY)library(cluster)

clusplot(mapmeans, mapobj$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)

# Centroid Plot against 1st 2 discriminant functions

#library(fpc)

plotcluster(mapmeans, mapobj$cluster)• dendogram?

library(fpc)• cluster.stats

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Bayes> cl <- kmeans(iris[,1:4], 3)

> table(cl$cluster, iris[,5])

setosa versicolor virginica

2 0 2 36

1 0 48 14

3 50 0 0

#

> m <- naiveBayes(iris[,1:4], iris[,5])

> table(predict(m, iris[,1:4]), iris[,5])

setosa versicolor virginica

setosa 50 0 0

versicolor 0 47 3

virginica 0 3 47 35

pairs(iris[1:4],main="Iris Data (red=setosa,green=versicolor,blue=virginica)", pch=21, bg=c("red","green3","blue")[unclass(iris$Species)])

Digging into irisclassifier<-naiveBayes(iris[,1:4], iris[,5])

table(predict(classifier, iris[,-5]), iris[,5], dnn=list('predicted','actual'))

classifier$apriori

classifier$tables$Petal.Length

plot(function(x) dnorm(x, 1.462, 0.1736640), 0, 8, col="red", main="Petal length distribution for the 3 different species")

curve(dnorm(x, 4.260, 0.4699110), add=TRUE, col="blue")

curve(dnorm(x, 5.552, 0.5518947 ), add=TRUE, col = "green") 36

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Using a contingency table> data(Titanic)

> mdl <- naiveBayes(Survived ~ ., data = Titanic)

> mdl

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Naive Bayes Classifier for Discrete PredictorsCall: naiveBayes.formula(formula = Survived ~ ., data = Titanic)A-priori probabilities:Survived No Yes 0.676965 0.323035 Conditional probabilities: ClassSurvived 1st 2nd 3rd Crew No 0.08187919 0.11208054 0.35436242 0.45167785 Yes 0.28551336 0.16596343 0.25035162 0.29817159 SexSurvived Male Female No 0.91543624 0.08456376 Yes 0.51617440 0.48382560 AgeSurvived Child Adult No 0.03489933 0.96510067 Yes 0.08016878 0.91983122 Try Lab6b_9_2014.R

http://www.ugrad.stat.ubc.ca/R/library/mlbench/html/HouseVotes84.html

require(mlbench)

data(HouseVotes84)

model <- naiveBayes(Class ~ ., data = HouseVotes84)

predict(model, HouseVotes84[1:10,-1])

predict(model, HouseVotes84[1:10,-1], type = "raw")

pred <- predict(model, HouseVotes84[,-1])

table(pred, HouseVotes84$Class) 39

Exercise for you> data(HairEyeColor)

> mosaicplot(HairEyeColor)

> margin.table(HairEyeColor,3)

Sex

Male Female

279 313

> margin.table(HairEyeColor,c(1,3))

Sex

Hair Male Female

Black 56 52

Brown 143 143

Red 34 37

Blond 46 81

How would you construct a naïve Bayes classifier and test it? 40

Assignment 5• Project proposals…

• Let’s look at it

• Assignment 4 - how is it going – assume you all start after today?

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Assignment 6 preview• Your term projects should fall within the scope of a data analytics

problem of the type you have worked with in class/ labs, or know of yourself – the bigger the data the better. This means that the work must go beyond just making lots of figures. You should develop the project to indicate you are thinking of and exploring the relationships and distributions within your data. Start with a hypothesis, think of a way to model and use the hypothesis, find or collect the necessary data, and do both preliminary analysis, detailed modeling and summary (interpretation). – Note: You do not have to come up with a positive result, i.e. disproving the hypothesis

is just as good. Please use the section numbering below for your written submission for this assignment.

• Introduction (2%)• Data Description (3%)• Analysis (8%)• Model Development (8%)• Conclusions and Discussion (4%)• Oral presentation (5%) (10 mins)

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Assignments to come• Term project (6). Due ~ week 13/ 14 – early May. 30% (25%

written, 5% oral; individual). Available after spring break.

• Assignment 7: Predictive and Prescriptive Analytics. Due ~ week 10. 15% (15% written; individual);

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Admin info (keep/ print this slide)• Class: ITWS-4963/ITWS 6965• Hours: 12:00pm-1:50pm Tuesday/ Friday• Location: SAGE 3101• Instructor: Peter Fox• Instructor contact: pfox@cs.rpi.edu, 518.276.4862 (do not

leave a msg)• Contact hours: Monday** 3:00-4:00pm (or by email appt)• Contact location: Winslow 2120 (sometimes Lally 207A

announced by email)• TA: Lakshmi Chenicheri chenil@rpi.edu • Web site: http://tw.rpi.edu/web/courses/DataAnalytics/2014

– Schedule, lectures, syllabus, reading, assignments, etc.

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