forked from OpenGeoHub/spatial-prediction-eml
-
Notifications
You must be signed in to change notification settings - Fork 0
/
mlst_functions.R
executable file
·239 lines (225 loc) · 11.4 KB
/
mlst_functions.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
## spacetime overlay
extract_st <- function(tif, df, date, date.tif.begin, date.tif.end, coords=c("x","y"), crs, format.date="%Y-%m-%d", variable.name){
if(any(!coords %in% colnames(df))){
stop(paste("Coordinate columns", coords, "could not be found"))
}
if(is.character(date) & length(date)==1 & date %in% colnames(df)){
date = as.Date(df[,date], format=format.date, origin="1970-01-01")
} else {
stop(paste("Column name", date, "could not be found in the dataframe"))
}
if(missing(date.tif.end)){
date.tif.end = date.tif.begin
}
sel <- date <= as.Date(date.tif.end, format=format.date, origin="1970-01-01") & date >= as.Date(date.tif.begin, format=format.date, origin="1970-01-01")
if(sum(sel)>0){
pnts = as.matrix(df[sel, coords])
attr(pnts, "dimnames")[[2]] = c("x","y")
df.v = terra::vect(pnts, crs=crs)
if(file.exists(tif)){
ov = terra::extract(terra::rast(tif), df.v)
} else {
ov = matrix(nrow=length(df.v), ncol=2)
}
ov = as.data.frame(ov)
if(missing(variable.name)){
variable.name = tools::file_path_sans_ext(basename(tif))
}
names(ov) = c("ID", variable.name)
ov$row.id = which(sel)
ov$ID = NULL
return(ov)
}
}
## Hyperparameter optimization for component learners of the ensemble model
tune_learners <- function(data, formula, blocking, out.dir="output/", predict.type = "prob", SL.library=c("classif.ranger","classif.xgboost","classif.glmnet"), parallel="multicore", rdesc = mlr::makeResampleDesc("CV", iters = 5L) , xg.skip = FALSE, num.trees = 85){
tv <- all.vars(formula)[1]
r.sel <- stats::complete.cases(data[,all.vars(formula)])
df.s = data[which(r.sel),all.vars(formula)]
l = sapply(df.s[,-c(1)], sd, na.rm = TRUE)
if(length(which(l == 0))>0){
x = which(l==0)+1
message(paste0("The following covariates were removed (sd(x) = 0): ", paste(colnames(df.s)[x], collapse = ", "), "..."), immediate. = TRUE)
df.s = df.s[,-x]
pr.vars = colnames(df.s[-1])
formula <- stats::as.formula(paste(tv, " ~", paste(pr.vars, collapse="+")))
}
rm(l)
gc()
## Set hyperparameter space
## ranger
min.mtry = round(sqrt(length(all.vars(formula)))/3)
max.mtry = ifelse(length(all.vars(formula))>10, length(all.vars(formula))-4, length(all.vars(formula))-1)
discrete_ps <- ParamHelpers::makeParamSet(ParamHelpers::makeDiscreteParam("mtry", values = unique(round(seq(min.mtry, max.mtry, length.out = 5)))))
## xgboost
xg_model_Params = ParamHelpers::makeParamSet(
ParamHelpers::makeDiscreteParam("nrounds", value=c(10)),
ParamHelpers::makeDiscreteParam("max_depth", values=c(5,10)),
ParamHelpers::makeDiscreteParam("eta", values=seq(0.5,1, by = 0.25)),
ParamHelpers::makeDiscreteParam("subsample", value=c(1)),
ParamHelpers::makeDiscreteParam("min_child_weight", values=c(5,10)),
ParamHelpers::makeDiscreteParam("colsample_bytree", values=seq(0.5,1, by = 0.25))
)
## mlr parameters
ctrl = mlr::makeTuneControlGrid()
message(paste0("Using learners: ", paste(SL.library, collapse = ", "), "..."), immediate. = TRUE)
tsk <- mlr::makeClassifTask(data = df.s,
target = tv,
positive = "1",
blocking = blocking[which(r.sel)])
out.rf = paste0(out.dir, "Fagus_sylvatica_rf_model.rds")
if(!file.exists(out.rf)){
## fine-tune mtry
message("Running tuneParams for ranger... ", immediate. = TRUE)
parallelMap::parallelStartSocket(parallel::detectCores())
resR.lst <- mlr::tuneParams(mlr::makeLearner("classif.ranger",
num.threads = round(parallel::detectCores()/length(discrete_ps$pars$mtry$values)),
num.trees=num.trees,
predict.type = predict.type),
task = tsk,
resampling = rdesc,
par.set = discrete_ps,
control = ctrl,
measures = list(logloss, setAggregation(logloss, test.sd)))
if(resR.lst$x$mtry >= length(all.vars(formula))){
lrn.rf = mlr::makeLearner("classif.ranger", num.threads = parallel::detectCores(), num.trees=num.trees, importance="impurity", predict.type = predict.type)
} else {
lrn.rf = mlr::makeLearner("classif.ranger", num.threads = parallel::detectCores(), mtry=resR.lst$x$mtry, num.trees=num.trees, importance="impurity", predict.type = predict.type)
}
var.mod1 <- mlr::train(lrn.rf, task = tsk)
parallelMap::parallelStop()
saveRDS(var.mod1, out.rf)
gc()
} else {
var.mod1 = readRDS(out.rf)
}
out.x = paste0(out.dir, "Fagus_sylvatica_xgb_model.rds")
if(!file.exists(out.x)){
## fine-tune xgboost
lrn.xg = mlr::makeLearner("classif.xgboost", par.vals = list(objective ='multi:softprob'))
message("Running tuneParams for xgboost... ", immediate. = TRUE)
parallelMap::parallelStartSocket(parallel::detectCores())
resX.lst = mlr::tuneParams(mlr::makeLearner("classif.xgboost",
predict.type = predict.type),
task = tsk, resampling = rdesc,
par.set = xg_model_Params,
control = ctrl,
measures = list(logloss, setAggregation(logloss, test.sd)))
lrn.xg = mlr::setHyperPars(lrn.xg, par.vals = resX.lst$x)
var.mod2 <- mlr::train(lrn.xg, task = tsk)
parallelMap::parallelStop()
saveRDS(var.mod2, out.x)
gc()
} else {
var.mod2 = readRDS(out.x)
}
out.glm = paste0(out.dir, "Fagus_sylvatica_glm_model.rds")
if(!file.exists(out.glm)){
parallelMap::parallelStartSocket(parallel::detectCores())
var.mod3 = mlr::train(mlr::makeLearner("classif.glmnet", predict.type = predict.type), task = tsk)
saveRDS(var.mod3, out.glm)
parallelMap::parallelStop()
} else {
var.mod3 = readRDS(out.glm)
}
finetuning = list(var.mod1, var.mod2, var.mod3, formula)
saveRDS(finetuning, paste0(out.dir, "Fagus_sylvatica_finetune.rds"))
return(finetuning)
}
## Train spacetime model for predicting species occurrences
train_sp_eml <- function(data, tune_result, blocking, out.dir="output/", predict.type = "prob", SL.library=c("classif.ranger","classif.xgboost","classif.glmnet"), super.learner = "classif.logreg", parallel="multicore", num.trees = 85, xyn = c("easting", "northing"), method = "stack.cv"){
tv <- all.vars(tune_result[[4]][[2]])
r.sel <- stats::complete.cases(data[,all.vars(tune_result[[4]])])
df.s = data[which(r.sel),all.vars(tune_result[[4]])]
l = sapply(df.s[,-c(1)], sd, na.rm = TRUE)
blocking = blocking[which(r.sel)]
## get models
var.mod1 = tune_result[[1]]
var.mod2 = tune_result[[2]]
var.mod3 = tune_result[[3]]
out.eml = paste0(out.dir, "Fagus_sylvatica_eml.rds")
if(!file.exists(out.eml)){
## fit the mlr model:
mlr::configureMlr()
if(parallel=="multicore"){
parallelMap::parallelStartSocket(parallel::detectCores())
}
message(paste0("Using learners: ", paste(SL.library, collapse = ", "), "..."), immediate. = TRUE)
lrns <- lapply(SL.library, mlr::makeLearner)
message("Fitting a spatial learner using 'mlr::makeClassifTask'...", immediate. = TRUE)
tsk <- mlr::makeClassifTask(data = df.s,
target = tv,
positive = "1",
blocking = blocking)
if(any(SL.library %in% "classif.xgboost")){
lrns[[which(SL.library %in% "classif.xgboost")]] = mlr::makeLearner("classif.xgboost", par.vals = list(objective ='multi:softprob'))
}
lrns <- lapply(lrns, mlr::setPredictType, "prob")
lrns[[1]] = mlr::setHyperPars(lrns[[1]], par.vals = mlr::getHyperPars(var.mod1$learner))
lrns[[2]] = mlr::setHyperPars(lrns[[2]], par.vals = mlr::getHyperPars(var.mod2$learner))
lrns[[3]] = mlr::setHyperPars(lrns[[3]], par.vals = list(s = min(var.mod3$learner.model$lambda)))
gc()
init.m <- mlr::makeStackedLearner(base.learners = lrns,
predict.type = predict.type,
method = method,
super.learner = super.learner,
resampling=mlr::makeResampleDesc(method = "CV", blocking.cv=TRUE))
m <- mlr::train(init.m, tsk)
if(parallel=="multicore"){
parallelMap::parallelStop()
}
saveRDS(m, out.eml)
} else {
m = readRDS(out.eml)
}
return(m)
}
## Predict tiles generated using "train_sp_eml"
predict_tiles <- function(input, model, rds.dir="input/", out.dir="output/"){
tile_id = unlist(strsplit(input, split = '[.]'))[1]
year = unlist(strsplit(input, split = '[.]'))[2]
print(paste0(tile_id, ' - reading the data'))
out.files = list()
tmp_folder = file.path(out.dir, tile_id)
dir.create(tmp_folder, recursive =TRUE, showWarnings = FALSE)
out.prob.file = paste0("tile_", tile_id, "_", year, ".tif")
out.md.file = paste0("tile_", tile_id, "_md_", year, ".tif")
out.prob = file.path(tmp_folder, out.prob.file)
out.md = file.path(tmp_folder, out.md.file)
static_data = try(readRDS(paste0(rds.dir, "tile_", tile_id, "_30m_static.rds")))
yearly_data = try(readRDS(paste0(rds.dir, "tile_", tile_id, "_30m_", year, ".rds")))
if(class(static_data) == "try-error" | class(yearly_data) == "try-error"){
message("RDS ", tile_id, " is corrupted or does not exist, skipping to next RDS...", immediate. = TRUE)
return(out.files)
}
yearly_data@data = cbind(yearly_data@data, static_data@data)
print(paste0(tile_id, ' - running predictions'))
probability_map = try(predict(model, newdata=yearly_data@data[,model$features]), silent=T)
if(class(probability_map) == "try-error"){
print(probability_map[1])
message("RDS file ", tile_id, " has NA values, skipping to next RDS...", immediate. = TRUE)
rm(static_data, yearly_data, out.prob, out.md, out.prob.file, out.md.file, probability_map)
gc()
return(out.files)
}
## Get base learners predictions to compute standard deviation and variance
pred = mlr::getStackedBaseLearnerPredictions(model, newdata=yearly_data@data[,model$features])
## setup correction factor
m.train = model$learner.model$super.model$learner.model$data
m.terms = model$learner.model$super.model$learner.model$terms
eml.MSE0 = matrixStats::rowSds(as.matrix(m.train[,all.vars(m.terms)[-1]]), na.rm=TRUE)^2
eml.MSE = deviance(model$learner.model$super.model$learner.model)/df.residual(model$learner.model$super.model$learner.model)
## mass-preservation of MSE
eml.cf = eml.MSE/mean(eml.MSE0, na.rm = TRUE)
rf.sd = sqrt(matrixStats::rowSds(as.matrix(as.data.frame(pred)*100), na.rm=TRUE)^2 * eml.cf)
map <- SpatialPixelsDataFrame(yearly_data@coords, data=as.data.frame(probability_map$data$prob.1*100), grid=yearly_data@grid, proj4string=yearly_data@proj4string)
map@data$md <- rf.sd
colnames(map@data) = c("Prob", "Prd.err")
out.files = append(out.files, out.prob)
out.files = append(out.files, out.md)
print(paste0(tile_id, ' - writing files'))
writeGDAL(map["Prob"], out.prob, drivername="GTiff", type="Byte", mvFlag=0, options=c("COMPRESS=DEFLATE"))
writeGDAL(map["Prd.err"], out.md, drivername="GTiff", type="Byte", mvFlag=0, options=c("COMPRESS=DEFLATE"))
rm(static_data, yearly_data, out.prob, out.md, out.prob.file, out.md.file, probability_map)
return(map)
}