Model building with the iris data set for Big Data

August 14, 2014
By

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

by Joseph Rickert

I think we can be sure that when American botanist Edgar Anderson meticulously collected data on three species of iris in the early 1930s he had no idea that these data would produce a computational storm that would persist well into the 21st century. The calculations started, presumably by hand, when R. A. Fisher selected this data set to illustrate the techniques described in his 1936 paper on discriminant analysis. However, they really got going in the early 1970s when the pattern recognition and machine learn community began using it to test new algorithms or illustrate fundamental principles. (The earliest reference I could find was: Gates, G.W. "The Reduced Nearest Neighbor Rule". IEEE Transactions on Information Theory, May 1972, 431-433.) Since then, the data set (or one of its variations) has been used to test hundreds, if not thousands, of machine learning algorithms. The UCI Machine Learning Repository, which contains what is probably the “official” iris data set, lists over 200 papers referencing the iris data.

So why has the iris data set become so popular? Like most success stories, randomness undoubtedly plays a huge part. However, Fisher’s selecting it to illustrate a discrimination algorithm brought it to peoples attention, and the fact that the data set contains three classes, only one of which is linearly separable from the other two, makes it interesting.

For similar reasons, the airlines data set used in the 2009 ASA Sections on Statistical Computing and Statistical Graphics Data expo has gained a prominent place in the machine learning world and is well on its way to becoming the “iris data set for big data”. It shows up in all kinds of places. (In addition to this blog, it made its way into the RHIPE documentation and figures in several college course modeling efforts.)

Some key features of the airlines data set are:

  • It is big enough to exceed the memory of most desktop machines. (The version of the airlines data set used for the competition contained just over 123 million records with twenty-nine variables.
  • The data set contains several different types of variables. (Some of the categorical variables have hundreds of levels.)
  • There are interesting things to learn from the data set. (This exercise from Kane and Emerson for example)
  • The data set is tidy, but not clean, making it an attractive tool to practice big data wrangling. (The AirTime variable ranges from -3,818 minutes to 3,508 minutes)

An additional, really nice feature of the airlines data set is that it keeps getting bigger! RITA, The Research and Innovative Technology Administration Bureau of Transportation Statistics continues to collect data which can be downloaded in .csv files. For your convenience, we have a 143M+ record version of the data set Revolution Analytics test data site  which contains all of the RITA records from 1987 through the end of 2012 available for download.

The following analysis from Revolution Analytics’ Sue Ranney uses this large version of the airlines data set and illustrates how a good model, driven with enough data, can reveal surprising features of a data set.

# Fit a Tweedie GLM
tm <- system.time(
glmOut <- rxGlm(ArrDelayMinutes~Origin:Dest + UniqueCarrier + F(Year) + 
	DayOfWeek:F(CRSDepTime) , 
	data = airData,  family = rxTweedie(var.power =1.15),
	cube = TRUE, blocksPerRead = 30) 
)
tm
 
# Build a dataframe for three airlines: Delta (DL), Alaska (AK), HA
airVarInfo <- rxGetVarInfo(airData)
predData <- data.frame(
  UniqueCarrier = factor(rep(c( "DL", "AS", "HA"), times = 168), levels = airVarInfo$UniqueCarrier$levels),
  Year = as.integer(rep(2012, times = 504)),
  DayOfWeek = factor(rep(c("Mon", "Tues", "Wed", "Thur", "Fri", "Sat", "Sun"), times = 72), levels = airVarInfo$DayOfWeek$levels),
  CRSDepTime = rep(0:23, each = 21),
  Origin = factor(rep("SEA", times = 504), levels = airVarInfo$Origin$levels),
  Dest = factor(rep("HNL", times = 504), levels = airVarInfo$Dest$levels)
)
# Use the model to predict the arrival day for the three airlines and plot
predDataOut <- rxPredict(glmOut, data = predData, 
	outData = predData, type = "response")
 
rxLinePlot(ArrDelayMinutes_Pred~CRSDepTime|UniqueCarrier, groups = DayOfWeek, 
	data = predDataOut, layout = c(3,1), 
	title = "Expected Delay: Seattle to Honolulu by Departure Time, Day of Week, and Airline",
	xTitle = "Scheduled Departure Time", yTitle = "Expected Delay")

Here, rxGLM()fits a Tweedie Generalized Linear model that looks at arrival delay as a function of the interaction between origin and destination airports, carriers, year, and the interaction between days of the week and scheduled departure time. This function kicks off a considerable amount of number crunching as Origin is a factor variable with 373 levels and Dest, also a factor, has 377 levels. The F() function makes Year and CrsDepTIme factors "on the fly" as the model is being fit. The resulting model ends up with 140,852 coefficients 8,626 of which are not NA. The calculation takes 12.6 minutes to run on a 5 node (4 cores and 16GB of RAM per node) IBM Platform LFS cluster.

The rest of the code uses the model to predict arrival delay for three airlines and plots the fitted values by day of the week and departure time. 

Arrival_delay

It looks like Saturday is the best time to fly for these airlines. Note that none of the sturcture revealed in these curves was put into the model in the sense that there are no polynomial terms in the model.

It will take a few minutes to download the zip file with the 143M airlines records, but please do, and let us know how your modeling efforts go.

 

 

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