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Interactive ROC curve
Raw
README.md

This gist has the code to visually display the results of a binary classifier on tennis statistics, using an interactive ROC curve.

Raw
blogtennis.R
library(reshape)
library(ggplot2)
library(glmnet)
tennis <- read.csv('tennis_data_2013.csv')
############
## Clean the data
############
tennis$Result <- ifelse(tennis$Result==1,1,2)
which(names(tennis)=='TPW.1')
which(names(tennis)=='TPW.2')
which(names(tennis)=='FSW.1')
which(names(tennis)=='SSW.1')
which(names(tennis)=='FSW.2')
which(names(tennis)=='SSW.2')
features <- tennis[,-c(1,2,4,5,6,8,9,11,13,22,24,26,35)]
features$Gender <- as.numeric(features$Gender)
features <- features[complete.cases(features),]
feat_diff <- data.frame(V1=matrix(nrow=741,ncol=1))
for(i in 1:10){
feat_diff[,i] <- features[,i+2]-features[,i+12]
names(feat_diff)[i] <- names(features)[i+2]
}
feat_diff$Winner <- features$Result
############
## Men and Women play differently
############
clus2 <- kmeans(scale(features),2)
table(clus2$cluster,features$Gender)
############
## First Serve % is not a good indicator
############
fsp.df <- data.frame(cbind(winner=features$Result,FSP1=features$FSP.1,FSP2=features$FSP.2))
fsp.df$winner<-ifelse(fsp.df$winner==1,'Player 1','Player 2')
ggplot(fsp.df,aes(x=FSP1,y=FSP2,color=winner))+geom_point()+ylim(25,100)+
ylab("Player 2 FSP") +
xlab("Player 1 FSP") +
ggtitle("First Serve %")+
geom_vline(linetype='dotted',xintercept=62)+
geom_hline(linetype='dotted',yintercept=61)
############
## Winners + Unforced Errors are big indicators of who wins
############
wr.df <- data.frame(cbind(r=features$Result,w=features$WNR.1-features$WNR.2))
uf.df <- data.frame(cbind(r=features$Result,u=features$UFE.1-features$UFE.2))
wu.df <- data.frame(cbind(r=features$Result,w=features$WNR.1-features$WNR.2,
u=features$UFE.1-features$UFE.2))
wu.df$winner<-ifelse(wu.df$r==1,'Player 1','Player 2')
ggplot(wu.df,aes(x=w,y=u,color=winner))+geom_point()+
ylim(-60,60) + xlim(-60,60) +
ylab("Unforced Error Difference") +
xlab("Winners Difference") +
ggtitle("Player 1 - Player 2 Stats\nWinners and Unforced Errors")+
geom_vline(linetype='dotted')+
geom_hline(linetype='dotted')
############
## Break Points are most important single variable
############
str(features)
ind <- sample(nrow(features),2/3*nrow(features))
train <- features[ind,]
test <- features[-ind,]
train.y <- train$Result
test.y <- test$Result
train <- scale(train[,-2])
test <- scale(test[,-2])
train.x <- as.matrix(train)
test.x <- as.matrix(test)
cv <- cv.glmnet(train.x,train.y, family='binomial',alpha=1,type.measure='class')
l <- cv$lambda.min
fits <- glmnet(train.x,train.y,family='binomial',alpha=1,nlambda=100)
glm.pred <- predict(fits,newx=test.x,s=l,standarize=FALSE,type='class')
table(test.y,glm.pred)
cos <- coef(fits,l)
vimp <- order(abs(cos),decreasing=TRUE)
num_nonzero <- sum(!abs(cos)==0)
top_vars <- row.names(cos)[vimp][1:num_nonzero]
top_vars
############
## Confusion matrices
############
wu.mod <- with(features,data.frame(cbind(Result,BPC.1,BPC.2)))
wu.fit <- glm(Result-1~.,data=wu.mod,family='binomial')
table(wu.mod$Result,ifelse(predict(wu.fit,type='response')>=0.5,2,1))
bpc.mod <- data.frame(cbind(train.y,BPC.1=train.x[,7]))
bpc.fit <- glm(train.y-1~.,data=wu.mod,family='binomial')
bpc.pred <- predict(wu.fit,newdata=data.frame(test.x),type='response')
ufe.mod <- data.frame(cbind(train.y,UFE.1=train.x[,6]))
ufe.fit <- glm(train.y-1~.,data=ufe.mod,family='binomial')
ufe.pred <- predict(ufe.fit,newdata=data.frame(test.x),type='response')
wnr.mod <- data.frame(cbind(train.y,WNR.1=train.x[,5]))
wnr.fit <- glm(train.y-1~.,data=wnr.mod,family='binomial')
wnr.pred <- predict(wnr.fit,newdata=data.frame(test.x),type='response')
fsp.mod <- data.frame(cbind(train.y,FSP.1=train.x[,1]))
fsp.fit <- glm(train.y-1~.,data=fsp.mod,family='binomial')
fsp.pred <- predict(fsp.fit,newdata=data.frame(test.x),type='response')
npw.mod <- data.frame(cbind(train.y,NPW.1=train.x[,10]))
npw.fit <- glm(train.y-1~.,data=npw.mod,family='binomial')
npw.pred <- predict(npw.fit,newdata=data.frame(test.x),type='response')
bpc.conf <- table(test.y,ifelse(bpc.pred>0.5,2,1))
wnr.conf <- table(test.y,ifelse(wnr.pred>0.5,2,1))
fsp.conf <- table(test.y,ifelse(fsp.pred>0.5,2,1))
npw.conf <- table(test.y,ifelse(npw.pred>0.5,2,1))
Raw
data.json
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{"X":"0.993","BPC":"1","WNR":"1","FSP":"0.992125984","NPW":"0.984","USELESS":"0.993"},
{"X":"0.994","BPC":"1","WNR":"1","FSP":"0.992125984","NPW":"0.984","USELESS":"0.994"},
{"X":"0.995","BPC":"1","WNR":"1","FSP":"0.992125984","NPW":"0.984","USELESS":"0.995"},
{"X":"0.996","BPC":"1","WNR":"1","FSP":"0.992125984","NPW":"0.984","USELESS":"0.996"},
{"X":"0.997","BPC":"1","WNR":"1","FSP":"0.992125984","NPW":"0.984","USELESS":"0.997"},
{"X":"0.998","BPC":"1","WNR":"1","FSP":"0.992125984","NPW":"0.984","USELESS":"0.998"},
{"X":"0.999","BPC":"1","WNR":"1","FSP":"0.992125984","NPW":"0.984","USELESS":"0.999"},
{"X":"1","BPC":"1","WNR":"1","FSP":"1","NPW":"1","USELESS":"1"}]
Raw
index.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>
body { font: 12px Arial;}
path {
stroke: steelblue;
stroke-width: 3;
fill: none;
}
.axis path,
.axis line {
fill: none;
stroke: grey;
stroke-width: 2;
shape-rendering: crispEdges;
}
.d3-tip {
font-family: Verdana;
background: rgba(0, 0, 0, 0.8);
padding: 8px;
color: #fff;
z-index: 5070;
}
</style>
<body>
<div id="roc"> </div>
<script src="http://d3js.org/d3.v3.js" charset="utf-8"></script>
<script src="http://labratrevenge.com/d3-tip/javascripts/d3.tip.v0.6.3.js"></script>
<script>
var margin = {top: 30, right: 20, bottom: 70, left: 70},
width = 450 - margin.left - margin.right,
height = 350 - margin.top - margin.bottom;
var x = d3.scale.linear().range([0, width]);
var y = d3.scale.linear().range([height, 0]);
var xAxis = d3.svg.axis().scale(x)
.orient("bottom").ticks(5);
var yAxis = d3.svg.axis().scale(y)
.orient("left").ticks(5);
var valueline = d3.svg.line()
.interpolate("basis")
.x(function(d) { return x(d.X); })
.y(function(d) { return y(d.BPC); });
var valueline2 = d3.svg.line()
.interpolate("basis")
.x(function(d) { return x(d.X); })
.y(function(d) { return y(d.WNR); });
var valueline3 = d3.svg.line()
.interpolate("basis")
.x(function(d) { return x(d.X); })
.y(function(d) { return y(d.FSP); });
var valueline4 = d3.svg.line()
.interpolate("basis")
.x(function(d) { return x(d.X); })
.y(function(d) { return y(d.NPW); });
var valueline5 = d3.svg.line()
.interpolate("linear")
.x(function(d) { return x(d.X); })
.y(function(d) { return y(d.USELESS); });
var tip = d3.tip()
.attr('class', 'd3-tip')
.style("visibility","visible")
.offset([-20, 0])
.html(function(d) {
return "Break Points";
});
var tip2 = d3.tip()
.attr('class', 'd3-tip')
.style("visibility","visible")
.offset([-20, 0])
.html(function(d) {
return "Winners";
});
var tip3 = d3.tip()
.attr('class', 'd3-tip')
.style("visibility","visible")
.offset([-20, 0])
.html(function(d) {
return "First Serve %";
});
var tip4 = d3.tip()
.attr('class', 'd3-tip')
.style("visibility","visible")
.offset([-20, 0])
.html(function(d) {
return "Net Points Won";
});
var tip5 = d3.tip()
.attr('class', 'd3-tip')
.style("visibility","visible")
.offset([-20, 0])
.html(function(d) {
return "Useless model";
});
var svg = d3.select("#roc")
.append("svg")
.attr("width", width + margin.left + margin.right)
.attr("height", height + margin.top + margin.bottom)
.append("g")
.attr("transform",
"translate(" + margin.left + "," + margin.top + ")");
d3.json('data.json',function(error,data){
x.domain([0, 1]);
y.domain([0, 1]);
svg.call(tip);
svg.call(tip2);
svg.call(tip3);
svg.call(tip4);
svg.call(tip5);
svg.append("path")
.attr("class", "line")
.attr("d", valueline(data))
.on('mouseover', tip.show)
.on("mousemove", function(){return tip.style("top", (event.pageY-10)+"px").
style("left",(event.pageX+10)+"px");})
.on('mouseout', tip.hide);
svg.append("path")
.style("stroke", "red")
.attr("d", valueline2(data))
.on('mouseover', tip2.show)
.on("mousemove", function(){return tip2.style("top", (event.pageY-10)+"px").
style("left",(event.pageX+10)+"px");})
.on('mouseout', tip2.hide);
svg.append("path")
.style("stroke", "green")
.attr("d", valueline3(data))
.on('mouseover', tip3.show)
.on("mousemove", function(){return tip3.style("top", (event.pageY-10)+"px").
style("left",(event.pageX+10)+"px");})
.on('mouseout', tip3.hide);
svg.append("path")
.style("stroke", "purple")
.attr("d", valueline4(data))
.on('mouseover', tip4.show)
.on("mousemove", function(){return tip4.style("top", (event.pageY-10)+"px").
style("left",(event.pageX+10)+"px");})
.on('mouseout', tip4.hide);
svg.append("path")
.style("stroke", "black")
.attr("d", valueline5(data))
.on('mouseover', tip5.show)
.on("mousemove", function(){return tip5.style("top", (event.pageY-10)+"px").
style("left",(event.pageX+10)+"px");})
.on('mouseout', tip5.hide);
svg.append("g")
.attr("class", "x axis")
.attr("transform", "translate(0," + height + ")")
.call(xAxis)
.append("text")
.attr("x", width / 2)
.attr("y", 40 )
.style("text-anchor", "middle")
.text("1 - False Negative Rate");
svg.append("g")
.attr("class", "y axis")
.call(yAxis)
.append("text")
.attr("transform", "rotate(-90)")
.attr("y", 0 - margin.left + 10 )
.attr("x", 0 - height/1.6 )
.style("font-size","12px")
.style("text-anchor", "left")
.text("True Positive Rate");
svg.append("text")
.attr("x", (width / 2))
.attr("y", 0 - (margin.top / 2))
.attr("text-anchor", "middle")
.style("font-size", "16px")
.text("ROC Curve for Tennis Stats");
});
</script>
</body>
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