library(ggplot2)

library(stringr)

library(plyr)

library(dplyr)

library(lubridate)

library(reshape2)

library(scales)

library(ggthemes)

library(Metrics)

data <- read.csv("r2plus1d_18_32_2_pretrained_test_predictions.csv", header = FALSE)

str(data)

dataNoAugmentation <- data[data$V2 == 0,]

str(dataNoAugmentation)

dataGlobalAugmentation <- data %>% group_by(V1) %>% summarize(meanPrediction = mean(V3), sdPred = sd(V3))

str(dataGlobalAugmentation)

sizeData <- read.csv("size.csv")

sizeData <- sizeData[sizeData$ComputerSmall == 1,]

str(sizeData)

sizeRelevantFrames <- sizeData[c(1,2)]

sizeRelevantFrames$Frame <- sizeRelevantFrames$Frame - 32

sizeRelevantFrames[sizeRelevantFrames$Frame < 0,]$Frame <- 0

beatByBeat <- merge(sizeRelevantFrames, data, by.x = c("Filename", "Frame"), by.y = c("V1", "V2"))

beatByBeat <- beatByBeat %>% group_by(Filename) %>% summarize(meanPrediction = mean(V3), sdPred = sd(V3))

str(beatByBeat)

### For use, need to specify file directory

fileLocation <- "/Users/davidouyang/Local Medical Data/"

ActualNumbers <- read.csv(paste0(fileLocation, "FileList.csv", sep = ""))

ActualNumbers <- ActualNumbers[c(1,2)]

str(ActualNumbers)

dataNoAugmentation <- merge(dataNoAugmentation, ActualNumbers, by.x = "V1", by.y = "Filename", all.x = TRUE)

dataNoAugmentation$AbsErr <- abs(dataNoAugmentation$V3 - dataNoAugmentation$EF)

str(dataNoAugmentation)

summary(abs(dataNoAugmentation$V3 - dataNoAugmentation$EF))

# Mean of 4.216

rmse(dataNoAugmentation$V3,dataNoAugmentation$EF) 

## 5.56

modelNoAugmentation <- lm(dataNoAugmentation$EF ~ dataNoAugmentation$V3)

summary(modelNoAugmentation)$r.squared

# 0.79475

beatByBeat <- merge(beatByBeat, ActualNumbers, by.x = "Filename", by.y = "Filename", all.x = TRUE)

summary(abs(beatByBeat$meanPrediction - beatByBeat$EF))

# Mean of 4.051697

rmse(beatByBeat$meanPrediction, beatByBeat$EF) 

# 5.325237

modelBeatByBeat <- lm(beatByBeat$EF ~ beatByBeat$meanPrediction)

summary(modelBeatByBeat)$r.squared

# 0.8093174

beatByBeatAnalysis <- merge(sizeRelevantFrames, data, by.x = c("Filename", "Frame"), by.y = c("V1", "V2"))

str(beatByBeatAnalysis)

MAEdata <- data.frame(counter = 1:500)

MAEdata$sample <- -9999

MAEdata$error <- -9999

str(MAEdata)

for (i in 1:500){

samplingBeat <-  sample_n(beatByBeatAnalysis %>% group_by(Filename), 1 + floor((i-1)/100), replace = TRUE) %>% group_by(Filename) %>% dplyr::summarize(meanPred = mean(V3))

samplingBeat <- merge(samplingBeat, ActualNumbers, by.x = "Filename", by.y = "Filename", all.x = TRUE)

samplingBeat$error <- abs(samplingBeat$meanPred - samplingBeat$EF)

MAEdata$sample[i] <-  1 + floor((i-1)/100)

MAEdata$error[i] <- mean(samplingBeat$error )

}

str(MAEdata)

beatBoxPlot <- ggplot(data = MAEdata) + geom_boxplot(aes(x = sample, y = error, group = sample), outlier.shape = NA

) + theme_classic() + theme(legend.position = "none", axis.text.y = element_text( size=7)) + xlab("Number of Sampled Beats") + ylab("Mean Absolute Error") + scale_fill_brewer(palette = "Set1", direction = -1) 

beatBoxPlot