Real-World Machine Learning
Chapter 2
Paul Adamson
December 7, 2016
This file contains R code to accompany Chapter 2 of the book “Real-World Machine Learning”, by Henrik Brink, Joseph W. Richards, and Mark Fetherolf. The code was contributed by Paul Adamson.
NOTE: working directory should be set to this file’s location.
Listing 2.1 Convert categorical features to numerical binary features
Creating dummy variables in R is extremely easy with the model.matrix function. In the below code, when the personData dataframe is created, the maritalstatus variable is of type factor with levels “single” and “married” by default.
personData <- data.frame(person = 1:2,
name = c("Jane Doe", "John Smith"),
age = c(24, 41),
income = c(81200, 121000),
maritalstatus = c("single","married"))
personDataNew <- data.frame(personData[,1:4],
model.matrix(~ maritalstatus - 1,
data = personData))
str(personData)## 'data.frame': 2 obs. of 5 variables:
## $ person : int 1 2
## $ name : Factor w/ 2 levels "Jane Doe","John Smith": 1 2
## $ age : num 24 41
## $ income : num 81200 121000
## $ maritalstatus: Factor w/ 2 levels "married","single": 2 1In the call to model.matrix, the −1 in the model formula ensures that we create a dummy variable for each of the two marital statuses (technically, it suppresses the creation of an intercept).
model.matrix(~ maritalstatus - 1,
data = personData)## maritalstatusmarried maritalstatussingle
## 1 0 1
## 2 1 0
## attr(,"assign")
## [1] 1 1
## attr(,"contrasts")
## attr(,"contrasts")$maritalstatus
## [1] "contr.treatment"The matrix of dummy variables is then joined to the original dataframe (minus the maritalstatus column) with another call to data.frame.
Listing 2.2 Simple feature extraction on Titanic cabins
The packages dplyr and tidyr are excellent for tidying and preprocessing data, including creating new features from existing ones. (Note: plyr will be used later, but we must load it prior to dplyr.)
titanic <- read.csv("../data/titanic.csv",
colClasses = c(
Survived = "factor",
Name = "character",
Ticket = "character",
Cabin = "character"))
titanic$Survived <- revalue(titanic$Survived, c("0"="no", "1"="yes"))
titanicNew <- titanic %>%
separate(Cabin, into = "firstCabin", sep = " ", extra = "drop", remove = FALSE) %>%
separate(firstCabin, into = c("cabinChar", "cabinNum"), sep = 1) %>%
rowwise() %>%
mutate(numCabins = length(unlist(strsplit(Cabin, " "))))
str(titanicNew)## Classes 'rowwise_df', 'tbl_df', 'tbl' and 'data.frame': 891 obs. of 15 variables:
## $ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
## $ Survived : Factor w/ 2 levels "no","yes": 1 2 2 2 1 1 1 1 2 2 ...
## $ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...
## $ Name : chr "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
## $ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
## $ Age : num 22 38 26 35 35 NA 54 2 27 14 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Ticket : chr "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Cabin : chr "" "C85" "" "C123" ...
## $ cabinChar : chr "" "C" "" "C" ...
## $ cabinNum : chr "" "85" "" "123" ...
## $ Embarked : Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...
## $ numCabins : int 0 1 0 1 0 0 1 0 0 0 ...In Listing 2.2, read.csv is used to read in the comma separated value (csv) data file. The colClasses argument is used to specify the correct class for some features. Then, the revalue function changes the levels of the Survived factor variable so that ‘0’ indicates ‘no’ and ‘1’ indicates ‘yes’. The titanicNew dataframe is then created by piping together separate from tidyr and mutate from dplyr. separate does exactly what its name implies: it separates a single character column into multiple columns. mutate is used to add a new feature often (as in this case) by acting on values of another feature.
Listing 2.3 Feature normalization
The below code will normalize a feature using the “min-max” method. As an example, the Age feature of the titanic dataframe is normalized and a histogram of the new normalized feature is plotted with ggplot2.
normalizeFeature <- function(data, fMin=-1.0, fMax=1.0){
dMin = min(na.omit(data))
dMax = max(na.omit(data))
factor = (fMax - fMin) / (dMax - dMin)
normalized = fMin + (data - dMin)*factor
normalized
}
titanic$AgeNormalized <- normalizeFeature(titanic$Age)
ggplot(data=titanic, aes(AgeNormalized)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 177 rows containing non-finite values (stat_bin).
Figure 2.12 Mosaic plot for Titanic data: Gender vs. survival
The “Visualizing Categorical Data” (vcd) package provides an excellent set of functions for exploring categorical data, including mosaic plots.
mosaic(
~ Sex + Survived,
data = titanic,
main = "Mosaic plot for Titanic data: Gender vs. survival",
shade = TRUE,
split_vertical = TRUE,
labeling_args = list(
set_varnames = c(
Survived = "Survived?")))
Figure 2.13 Mosaic plot for Titanic data: Passenger class vs. survival
mosaic(
~ Pclass + Survived,
data = titanic,
main = "Mosaic plot for Titanic data: Passenger Class vs. survival",
shade = TRUE,
split_vertical = TRUE,
labeling_args = list(
set_varnames = c(
Pclass = "Passenger class",
Survived = "Survived?")))
Figure 2.14 Box plot for Titanic data: Passenger age vs. survival
The boxplot function is provided as part of the standard graphics package in R. ggplot2 provides a much nicer version.
boxplot(Age ~ Survived,
data = titanic,
xlab = "Survived?",
ylab = "Age\n(years)",
las = 1)
ggplot(titanic, aes(Survived, Age)) +
geom_boxplot() +
xlab("Survived?") +
ylab("Age\n(years)")## Warning: Removed 177 rows containing non-finite values (stat_boxplot).
Figure 2.15 Box plots for Titanic data: Passenger fare versus survival
Plots can be combined in rows and columns using the mfrow graphical parameter set via the par function. (Try help(par) to learn more.)
par(mfrow=c(1,2))
par(mai=c(1,1,.1,.1), las = 1)
boxplot(Fare ~ Survived,
data = titanic,
xlab = "Survived?",
ylab = "Fare Amount")
boxplot(Fare**(1/2) ~ Survived,
data = titanic,
xlab = "Survived?",
ylab = "sqr\n(fare amount)")
Figure 2.16 Density plot for MPG data, by region
The sm.density.compare function from the sm package is useful for comparing a set of univariate density estimates. The first argument is a vector of data, and the second argument is a vector of group labels that correspond to each value. The colfill variable and legend function are used to place a legend on the plot generated by sm.density.compare.
par(mfrow=c(1,1))
auto <- read.csv("../data/auto-mpg.csv",
colClasses = c(origin = "factor"))
auto$origin <- revalue(auto$origin,
c("1\t"="USA", "2\t"="Europe", "3\t"="Asia"))
sm.density.compare(auto$mpg, auto$origin,
xlab="Miles per gallon",
ylab="Density")
title(main="Density plot for MPG data, by region")
colfill<-c(2:(2+length(levels(auto$origin))))
legend(x="topleft",
inset=0.05,
text.width=5,
levels(auto$origin),
fill=colfill)
Figure 2.17 Scatterplots for MPG data
It doesn’t get much simpler than the plot function in R.
par(mfrow=c(1,2), mai=c(1,1,.1,.1), las = 1)
plot(auto$weight, auto$mpg,
xlab = "Vehicle weight",
ylab = "Miles per\ngallon")
plot(auto$modelyear, auto$mpg,
xlab = "Model year",
ylab = "Miles per\ngallon")
Although not in the book, everyone should become familiar with facets via ggplot2.
p <- ggplot(auto, aes(mpg, weight)) +
geom_point()
p + facet_grid(. ~ origin)