For this assignment I analyzed the provided data to determine what activity an individual perform. To do this I made use of caret and randomForest, this allowed me to generate correct answers for each of the 20 test data cases provided in this assignment. I made use of a seed value for consistent results.
library(Hmisc)## Loading required package: grid
## Loading required package: lattice
## Loading required package: survival
## Loading required package: splines
## Loading required package: Formula
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## Attaching package: 'Hmisc'
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## The following objects are masked from 'package:base':
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## format.pval, round.POSIXt, trunc.POSIXt, unitslibrary(caret)## Loading required package: ggplot2
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## Attaching package: 'caret'
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## The following object is masked from 'package:survival':
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## clusterlibrary(randomForest)## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
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## Attaching package: 'randomForest'
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## The following object is masked from 'package:Hmisc':
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## combinelibrary(foreach)
library(doParallel)## Loading required package: iterators
## Loading required package: parallelset.seed(2048)
options(warn=-1)First, I loaded the data both from the provided training and test data provided by COURSERA. Some values contained a “#DIV/0!” that I replaced with an NA value.
training_data <- read.csv("pml-training.csv", na.strings=c("#DIV/0!") )
evaluation_data <- read.csv("pml-testing.csv", na.strings=c("#DIV/0!") )I also casted all columns 8 to the end to be numeric.
for(i in c(8:ncol(training_data)-1)) {training_data[,i] = as.numeric(as.character(training_data[,i]))}
for(i in c(8:ncol(evaluation_data)-1)) {evaluation_data[,i] = as.numeric(as.character(evaluation_data[,i]))}Some columns were mostly blank. These did not contribute well to the prediction. I chose a feature set that only included complete columns. We also remove user name, timestamps and windows.
Determine and display out feature set.
feature_set <- colnames(training_data[colSums(is.na(training_data)) == 0])[-(1:7)]
model_data <- training_data[feature_set]
feature_set## [1] "roll_belt" "pitch_belt" "yaw_belt"
## [4] "total_accel_belt" "gyros_belt_x" "gyros_belt_y"
## [7] "gyros_belt_z" "accel_belt_x" "accel_belt_y"
## [10] "accel_belt_z" "magnet_belt_x" "magnet_belt_y"
## [13] "magnet_belt_z" "roll_arm" "pitch_arm"
## [16] "yaw_arm" "total_accel_arm" "gyros_arm_x"
## [19] "gyros_arm_y" "gyros_arm_z" "accel_arm_x"
## [22] "accel_arm_y" "accel_arm_z" "magnet_arm_x"
## [25] "magnet_arm_y" "magnet_arm_z" "roll_dumbbell"
## [28] "pitch_dumbbell" "yaw_dumbbell" "total_accel_dumbbell"
## [31] "gyros_dumbbell_x" "gyros_dumbbell_y" "gyros_dumbbell_z"
## [34] "accel_dumbbell_x" "accel_dumbbell_y" "accel_dumbbell_z"
## [37] "magnet_dumbbell_x" "magnet_dumbbell_y" "magnet_dumbbell_z"
## [40] "roll_forearm" "pitch_forearm" "yaw_forearm"
## [43] "total_accel_forearm" "gyros_forearm_x" "gyros_forearm_y"
## [46] "gyros_forearm_z" "accel_forearm_x" "accel_forearm_y"
## [49] "accel_forearm_z" "magnet_forearm_x" "magnet_forearm_y"
## [52] "magnet_forearm_z" "classe"We now have the model data built from our feature set.
idx <- createDataPartition(y=model_data$classe, p=0.75, list=FALSE )
training <- model_data[idx,]
testing <- model_data[-idx,]We now build 5 random forests with 150 trees each. We make use of parallel processing to build this model. I found several examples of how to perform parallel processing with random forests in R, this provided a great speedup.
registerDoParallel()
x <- training[-ncol(training)]
y <- training$classe
rf <- foreach(ntree=rep(150, 6), .combine=randomForest::combine, .packages='randomForest') %dopar% {
randomForest(x, y, ntree=ntree)
}Provide error reports for both training and test data.
predictions1 <- predict(rf, newdata=training)
confusionMatrix(predictions1,training$classe)## Confusion Matrix and Statistics
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## Reference
## Prediction A B C D E
## A 4185 0 0 0 0
## B 0 2848 0 0 0
## C 0 0 2567 0 0
## D 0 0 0 2412 0
## E 0 0 0 0 2706
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## Overall Statistics
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## Accuracy : 1
## 95% CI : (1, 1)
## No Information Rate : 0.284
## P-Value [Acc > NIR] : <2e-16
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## Kappa : 1
## Mcnemar's Test P-Value : NA
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## Statistics by Class:
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## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 1.000 1.000 1.000 1.000 1.000
## Specificity 1.000 1.000 1.000 1.000 1.000
## Pos Pred Value 1.000 1.000 1.000 1.000 1.000
## Neg Pred Value 1.000 1.000 1.000 1.000 1.000
## Prevalence 0.284 0.194 0.174 0.164 0.184
## Detection Rate 0.284 0.194 0.174 0.164 0.184
## Detection Prevalence 0.284 0.194 0.174 0.164 0.184
## Balanced Accuracy 1.000 1.000 1.000 1.000 1.000predictions2 <- predict(rf, newdata=testing)
confusionMatrix(predictions2,testing$classe)## Confusion Matrix and Statistics
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## Reference
## Prediction A B C D E
## A 1395 5 0 0 0
## B 0 941 6 0 0
## C 0 3 848 8 2
## D 0 0 1 796 1
## E 0 0 0 0 898
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## Overall Statistics
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## Accuracy : 0.995
## 95% CI : (0.992, 0.997)
## No Information Rate : 0.284
## P-Value [Acc > NIR] : <2e-16
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## Kappa : 0.993
## Mcnemar's Test P-Value : NA
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## Statistics by Class:
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## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 1.000 0.992 0.992 0.990 0.997
## Specificity 0.999 0.998 0.997 1.000 1.000
## Pos Pred Value 0.996 0.994 0.985 0.997 1.000
## Neg Pred Value 1.000 0.998 0.998 0.998 0.999
## Prevalence 0.284 0.194 0.174 0.164 0.184
## Detection Rate 0.284 0.192 0.173 0.162 0.183
## Detection Prevalence 0.285 0.193 0.176 0.163 0.183
## Balanced Accuracy 0.999 0.995 0.994 0.995 0.998As can be seen from the confusion matrix this model is very accurate. I did experiment with PCA and other models, but did not get as good of accuracy. Because my test data was around 99% accurate I expected nearly all of the submitted test cases to be correct. It turned out they were all correct.
Prepare the submission. (using COURSERA provided code)
pml_write_files = function(x){
n = length(x)
for(i in 1:n){
filename = paste0("problem_id_",i,".txt")
write.table(x[i],file=filename,quote=FALSE,row.names=FALSE,col.names=FALSE)
}
}
x <- evaluation_data
x <- x[feature_set[feature_set!='classe']]
answers <- predict(rf, newdata=x)
answers## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## B A B A A E D B A A B C B A E E A B B B
## Levels: A B C D Epml_write_files(answers)