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paper_plots.R
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paper_plots.R
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# "Forecasting Vegetation Health at High Spatial Resolution"
library(dplyr)
load_large_files_on_ornette <- T
saving_data <- baseline <- all_non_scatters <- T
if(load_large_files_on_ornette){
## Forecasting Hold-out Data
rmse <- function(x, y) sqrt( mean( (x - y)^2) )
# install.packages("hexbin")
# Spectral data was the best on training so we used it as the predictor vars in holdout
load_prep <- function(fp, landuse, missing_middle_row = TRUE){
if(missing_middle_row) skip <- 1 else skip <- 0
df <- read.csv(fp,
stringsAsFactors = FALSE, header = TRUE,
skip = skip)
if(missing_middle_row){
# There is one missing row in the exact middle of the data from having
# to split the data into two pieces and bind back together
# to get it from h2o into python, bc the data is too big to do it all at once.
print(df[nrow(df)/2+1,])
df[nrow(df)/2+1,] <- rep(NA, ncol(df))
stopifnot(sum(complete.cases(df)) == nrow(df) - 1)
df <- df[complete.cases(df), ]
}
for(i in c(1,2,4,5,6)) {
df[,i] <- as.numeric(df[,i])
}
landchar <- rep(NA, nrow(df))
df$landuse <- as.integer(df$landuse)
for(i in seq(nrow(df))){
landchar[i] <- landuse[which(landuse$V1 == df$landuse[i]), "V2"]
}
df$landuse <- landchar
df
}
CA_land <- read.delim(textConnection("11 Water
12 Other
21 Urban
22 Urban
23 Urban
24 Urban
31 Other
41 Forest
42 Forest
43 Forest
51 Scrub
52 Scrub
71 Scrub
72 Scrub
73 Lichens
74 Other
81 Agriculture
82 Agriculture
90 Other
95 Other"), header=FALSE, stringsAsFactors=FALSE)
SL_land <- read.delim(textConnection("21 Water
3 Forest
17 Scrub
5 Scrub
4 Agriculture
8 Agriculture
20 Urban
1 Other
0 Other
2 Other
6 Other
7 Other
9 Other
10 Other
11 Other
12 Other
13 Other
14 Other
15 Other
16 Other
18 Other
19 Other"), sep = "", header=FALSE, stringsAsFactors=FALSE)
create_time_measure <- function(df, model_name){
group_by(df, time_period) %>%
summarise(RMSE = rmse(predict, EVI),
Cor = cor(predict, EVI, use = "everything", method = c("pearson")),
Number = n()) %>%
mutate(Model = model_name)
}
create_land_measure <- function(df, model_name){
group_by(df, landuse) %>%
summarise(RMSE = rmse(predict, EVI),
Cor = cor(predict, EVI, use = "everything", method = c("pearson")),
Number = n()) %>%
mutate(Model = model_name)
}
create_both_measure <- function(df, model_name){
group_by(df, time_period, landuse) %>%
summarise(RMSE = rmse(predict, EVI),
Cor = cor(predict, EVI, use = "everything", method = c("pearson")),
Number = n()) %>%
mutate(Model = model_name)
}
create_time_measure_both <- function(dfs, model_names = c("GBM", "Baseline")){
a <- create_time_measure(dfs[[1]], model_name = model_names[1])
b <- create_time_measure(dfs[[2]], model_name = model_names[2])
out <- rbind(a, b)
stopifnot(sum(complete.cases(out)) == nrow(out))
return(out)
}
create_land_measure_both <- function(dfs, model_names = c("GBM", "Baseline")){
a <- create_land_measure(dfs[[1]], model_name = model_names[1])
b <- create_land_measure(dfs[[2]], model_name = model_names[2])
out <- rbind(a, b)
stopifnot(sum(complete.cases(out)) == nrow(out))
return(out)
}
create_both_measure_both <- function(dfs, model_names = c("GBM", "Baseline")){
a <- create_both_measure(dfs[[1]], model_name = model_names[1])
b <- create_both_measure(dfs[[2]], model_name = model_names[2])
out <- rbind(a, b)
stopifnot(sum(complete.cases(out)) == nrow(out))
return(out)
}
sl <- load_prep("/data/john/srilanka/gbm_predicted_holdoutS.csv", SL_land)
if(baseline){
sl_b <- load_prep("/data/john/srilanka/baseline_predicted_holdout.csv", SL_land,
missing_middle_row = FALSE)
colnames(sl_b)[which(colnames(sl_b)=="Pred")] <- "predict"
}
if(saving_data){
time_sl_all <- create_time_measure_both(list(sl, sl_b))
save(time_sl_all, file = "output/time_sl_all.Rda")
land_sl_all <- create_land_measure_both(list(sl, sl_b))
save(land_sl_all, file = "output/land_sl_all.Rda")
both_sl_all <- create_both_measure_both(list(sl, sl_b))
save(both_sl_all, file = "output/both_sl_all.Rda")
}
ca <- load_prep("/data/john/CA/gbm_predicted_holdoutS.csv", CA_land)
if(baseline){
ca_b <- load_prep("/data/john/CA/baseline_predicted_holdout.csv", CA_land,
missing_middle_row = FALSE)
colnames(ca_b)[which(colnames(ca_b)=="Pred")] <- "predict"
}
if(saving_data){
time_ca_all <- create_time_measure_both(list(ca, ca_b))
save(time_ca_all, file = "output/time_ca_all.Rda")
land_ca_all <- create_land_measure_both(list(ca, ca_b))
save(land_ca_all, file = "output/land_ca_all.Rda")
both_ca_all <- create_both_measure_both(list(ca, ca_b))
save(both_ca_all, file = "output/both_ca_all.Rda")
}
nsl <- nrow(sl)
nca <- nrow(ca)
# cor_all <- cor(gbm$predict, gbm$EVI, use = "everything" , method = c("pearson"))
# More Holdout Plots - Large Scatter Plots showing predicted and actual values
source("util_funcs/plot_predicts.R")
library(ggplot2)
# SL
p <- filter(sl, landuse == "Agriculture") %>%
plot_predicts("SL Agricultural Land EVI ")
pdf(paste0("output/paper_plots/scatter_sl.pdf"), width=12, height=12)
print(p)
dev.off()
jpeg(paste0("output/paper_plots/scatter_sl.jpg"), width=12, height=12, units = "in", res=300)
print(p)
dev.off()
if(baseline){
p <- filter(sl_b, landuse == "Agriculture") %>%
plot_predicts("SL Agricultural Land EVI ")
pdf(paste0("output/paper_plots/scatter_sl_b.pdf"), width=12, height=12)
print(p)
dev.off()
jpeg(paste0("output/paper_plots/scatter_sl_b.jpg"), width=12, height=12, units = "in", res=300)
print(p)
dev.off()
}
# CA
p <- filter(ca, landuse == "Agriculture") %>%
plot_predicts("CA Agricultural Land EVI ")
pdf(paste0("output/paper_plots/scatter_ca.pdf"), width=12, height=12)
print(p)
dev.off()
jpeg(paste0("output/paper_plots/scatter_ca.jpg"), width=12, height=12, units = "in", res=300)
print(p)
dev.off()
if(baseline){
p <- filter(ca_b, landuse == "Agriculture") %>%
plot_predicts("CA Agricultural Land EVI ")
pdf(paste0("output/paper_plots/scatter_ca_b.pdf"), width=12, height=12)
print(p)
dev.off()
jpeg(paste0("output/paper_plots/scatter_ca_b.jpg"), width=12, height=12, units = "in", res=300)
print(p)
dev.off()
}
} else {
nsl <- "36,831,863" # nrow(sl)
nca <- "61,681,296" # nrow(ca)
}
if(all_non_scatters){
# If previously saved, can run the next section from the small files in local repo /output directory
load("output/both_ca_all.Rda")
load("output/land_ca_all.Rda")
load("output/time_ca_all.Rda")
load("output/both_sl_all.Rda")
load("output/land_sl_all.Rda")
load("output/time_sl_all.Rda")
titles <- FALSE # No ggtitles for the paper
labels <- TRUE # labels for the CA and SL (A., B.)
# Replace time periods with nice labels:
x <- c("Jan", " ", "Feb", " ", "Mar", " ", "April", " ", "May", " ", "June", " ", "July",
" ", "Aug", " ", "Sept", " ", "Oct", " ", "Nov", " ", "Dec")
# SL
library(ggplot2)
p <- ggplot(time_sl_all, aes(x = time_period, y = Cor, group = Model, color = Model)) +
geom_point() + geom_line() + theme_classic() +
theme(legend.position=c(0.5, 0.5)) +
scale_x_discrete(breaks=1:23,
labels=x) +
scale_colour_brewer(palette = "Dark2") +
ylab("Correlation between Predicted and Actual") +
xlab("Time Period") + ylim(c(0,1))
if (titles) p <- p + ggtitle(paste0("Correlation Between Predicted and Actual\nin Sri Lanka Holdout Data n=", nsl))
pdf(paste0("output/paper_plots/time_sl.pdf"), width=7, height=7)
print(p)
dev.off()
jpeg(paste0("output/paper_plots/time_sl.jpg"), width=7, height=7, units = "in", res=300)
print(p)
dev.off()
p <- ggplot(land_sl_all,
aes(x = landuse, y = Cor, fill = Number)) +
geom_bar(stat = "identity") + theme_classic() +
theme(legend.position=c(0.15, 0.8)) +
scale_fill_continuous(name = "Number of\nObservations") +
#scale_fill_distiller(name = "Number of\nObservations") +
facet_grid(~Model) + ylim(c(0,1)) +
ylab("Correlation between Predicted and Actual") +
xlab("Land Use")
if (titles) p <- p + ggtitle(paste0("Correlation Between Predicted and Actual\nin Sri Lanka Holdout Data n=", nsl))
if(labels) p <- p + annotation_custom(grid::grobTree(grid::textGrob("B.", x=0.9, y=0.95, hjust=0,
gp=grid::gpar(col="black", fontsize=15, fontface="bold"))))
pdf(paste0("output/paper_plots/land_sl.pdf"), width=7.5, height=7)
print(p)
dev.off()
jpeg(paste0("output/paper_plots/land_sl.jpg"), width=7.5, height=7, units = "in", res=300)
print(p)
dev.off()
p <- both_sl_all %>% filter(landuse!="Other") %>%
ggplot(aes(x = time_period, y = Cor, group = landuse, color = landuse)) +
geom_point() + geom_line() + theme_classic() +
theme(legend.position=c(0.15, 0.85),
legend.title=element_blank()) +
scale_x_discrete(breaks=1:23,
labels=x) +
scale_colour_brewer(palette = "Dark2") +
facet_grid(~Model) +
ylab("Correlation between Predicted and Actual") +
xlab("Time Period") +
ylim(c(min(both_sl_all$Cor),1))
if (titles) p <- p + ggtitle(paste0("Correlation Between Predicted and Actual\nin Sri Lanka Holdout Data n=", nsl))
if(labels) p <- p + annotation_custom(grid::grobTree(grid::textGrob("B.", x=0.9, y=0.95, hjust=0,
gp=grid::gpar(col="black", fontsize=15, fontface="bold"))))
pdf(paste0("output/paper_plots/time_land_sl.pdf"), width=12, height=7)
print(p)
dev.off()
jpeg(paste0("output/paper_plots/time_land_sl.jpg"), width=12, height=7, units = "in", res=300)
print(p)
dev.off()
# Sl specific plot for seasons:
p <- both_sl_all %>% filter(landuse=="Agriculture", Model=="GBM") %>%
ggplot(aes(x = time_period, y = Cor)) +
geom_point() + geom_line() + theme_classic() +
scale_x_discrete(breaks=1:23,
labels=x) +
scale_colour_brewer(palette = "Dark2") +
ylab("Correlation between Predicted and Actual") +
geom_vline(xintercept=c(4,7,16,19), linetype="dotted") +
xlab("Time Period") +
ylim(c(min(both_sl_all$Cor),1)) +
ggtitle(paste0("Correlation Between Predicted and Actual\nin Sri Lanka Holdout Data n=", nsl)) +
annotate("text", x = c(4,7,16,19),
y = c(0.4, 0.5, 0.5, 0.4),
label = c("Maha End", "Yala Start", "Yala End", "Maha Start"),
fontface=c("bold", "plain", "plain", "bold"),
size = c(5))
pdf(paste0("output/paper_plots/seasons_sl.pdf"), width=7, height=7)
print(p)
dev.off()
jpeg(paste0("output/paper_plots/seasons_sl.jpg"), width=7, height=7, units = "in", res=300)
print(p)
dev.off()
# CA
p <- ggplot(time_ca_all, aes(x = time_period, y = Cor, group = Model, color = Model)) +
geom_point() + geom_line() + theme_classic() +
theme(legend.position=c(0.5, 0.2)) +
scale_x_discrete(breaks=1:23,
labels=x) +
scale_colour_brewer(palette = "Dark2") +
ylab("Correlation between Predicted and Actual") +
xlab("Time Period") + ylim(c(0,1))
if (titles) p <- p + ggtitle(paste0("Correlation Between Predicted and Actual\nin CA Holdout Data n=", nca))
pdf(paste0("output/paper_plots/time_ca.pdf"), width=7, height=7)
print(p)
dev.off()
jpeg(paste0("output/paper_plots/time_ca.jpg"), width=7, height=7, units = "in", res=300)
print(p)
dev.off()
p <- ggplot(land_ca_all,
aes(x = landuse, y = Cor, fill = Number)) +
geom_bar(stat = "identity") + theme_classic() +
theme(legend.position=c(0.15, 0.8)) +
scale_fill_continuous(name = "Number of\nObservations") +
facet_grid(~Model) + ylim(c(0,1)) +
ylab("Correlation between Predicted and Actual") +
xlab("Land Use")
if (titles) p <- p + ggtitle(paste0("Correlation Between Predicted and Actual\nin CA Holdout Data n=", nca))
if(labels) p <- p + annotation_custom(grid::grobTree(grid::textGrob("A.", x=0.9, y=0.95, hjust=0,
gp=grid::gpar(col="black", fontsize=15, fontface="bold"))))
pdf(paste0("output/paper_plots/land_ca.pdf"), width=7.5, height=7)
print(p)
dev.off()
jpeg(paste0("output/paper_plots/land_ca.jpg"), width=7.5, height=7, units = "in", res=300)
print(p)
dev.off()
p <- both_ca_all %>% filter(landuse!="Other") %>%
ggplot(aes(x = time_period, y = Cor, group = landuse, color = landuse)) +
geom_point() + geom_line() + theme_classic() +
theme(legend.position=c(0.15, 0.85),
legend.title=element_blank()) +
scale_x_discrete(breaks=1:23,
labels=x) +
scale_colour_brewer(palette = "Dark2") +
ylim(c(0,1)) + # ylim(c(min(both_ca_all$Cor),1)) +
facet_grid(~Model) +
ylab("Correlation between Predicted and Actual") +
xlab("Time Period")
if (titles) p <- p + ggtitle(paste0("Correlation Between Predicted and Actual\nin CA Holdout Data n=", nca))
if(labels) p <- p + annotation_custom(grid::grobTree(grid::textGrob("A.", x=0.93, y=0.95, hjust=0,
gp=grid::gpar(col="black", fontsize=15, fontface="bold"))))
pdf(paste0("output/paper_plots/time_land_ca.pdf"), width=12, height=7)
print(p)
dev.off()
jpeg(paste0("output/paper_plots/time_land_ca.jpg"), width=12, height=7, units = "in", res=300)
print(p)
dev.off()
# Missing data over time
cam <- read.csv("output/missing_data/ca_mean.csv", header = FALSE)[,1]
cas <- read.csv("output/missing_data/ca_std.csv", header = FALSE)[,1]
slm <- read.csv("output/missing_data/sl_mean.csv", header = FALSE)[,1]
sls <- read.csv("output/missing_data/sl_std.csv", header = FALSE)[,1]
ca_missing <- data.frame(Mean = cam, SD = cas, Location = "CA",
Time = 1:23)
sl_missing <- data.frame(Mean = slm, SD = sls, Location = "SL",
Time = 1:23)
p <- rbind(ca_missing, sl_missing) %>%
mutate(min = Mean - SD, max = Mean + SD) %>%
ggplot(aes(Time, Mean, group =1)) +
geom_line() +
scale_x_discrete(breaks=1:23,
labels=x) +
geom_ribbon(aes(ymin=min, ymax=max), alpha=0.3) +
ylim(0,100) + ylab("Mean and SD of Percent of Pixels with Missing Data") +
facet_grid(~Location) +
theme_classic()
if (titles) p <- p + ggtitle("Percent of Pixels with Missing Data Over 23 Periods of the Year")
pdf(paste0("output/paper_plots/missing_data.pdf"), width=7, height=5)
print(p)
dev.off()
jpeg(paste0("output/paper_plots/missing_data.jpg"), width=7, height=5, units = "in", res=300)
print(p)
dev.off()
}