R/IRmapping_plan_trimmed.R
In messamat/globalIRmap: What the Package Does (One Line, Title Case)
source('R/IRmapping_packages.R')
source('R/IRmapping_functions.R')
source('R/planutil_functions.R')
rootdir <- find_root(has_dir("src")) #Set root working directory
########################### plan_preprocess ####################################
plan_preprocess <- drake_plan(
#-------------------- Define input and output files ##########################
#Note: for dependency tracking, drake only reads string literals inside file_in and file_out; hence the use of absolute paths
#Tidy evaluation (!!) didn't work for some reason -- can look into it later
path_resdir = file.path(!!rootdir, "results"),
path_GRDCgaugep = file.path(path_resdir, "spatialoutputs.gdb\\grdcstations_cleanjoin"),
path_GRDCgaugedir = file_in(!!file.path(rootdir, "data\\GRDCdat_day")),
path_GSIMgaugep = file.path(path_resdir, "GSIM\\GSIM.gdb\\GSIMstations_cleanriverjoin"),
path_GSIMindicesdir = file.path(!!rootdir, "data\\GSIM\\GSIM_indices"),
path_riveratlas_meta = file_in(
!!file.path(rootdir, "data\\HydroATLAS\\HydroATLAS_metadata_MLMv11.xlsx")),
path_riveratlas_legends = file_in(!!file.path(rootdir, "data\\HydroATLAS\\HydroATLAS_v10_Legends.xlsx")),
path_monthlynetdischarge = file.path(!!rootdir,
"data\\HydroSHEDS\\HS_discharge_monthly.gdb\\Hydrosheds_discharge_monthly"),
path_riveratlas = file_in(!!file.path(rootdir, 'results\\RiverATLAS_v10tab.csv')),
path_riveratlas2 = file_in(!!file.path(rootdir, 'results\\RiverATLAS_v11tab.csv')),
path_bas03 = file.path(!!rootdir, "data\\HydroATLAS\\BasinATLAS_v10.gdb\\BasinATLAS_v10_lev03"),
path_compresdir = file_in(!!file.path(rootdir, 'results\\Comparison_databases')),
path_linkpop = file_in(!!file.path(rootdir, 'results\\worldpop_link_20201220.csv')),
path_frresdir = file.path(path_compresdir, 'france.gdb'),
path_usdatdir = file.path(rootdir, 'data\\Comparison_databases', 'US'),
path_usresdir = file.path(path_compresdir, 'us.gdb'),
path_auresdir = file.path(path_compresdir, 'australia.gdb'),
path_insitudatdir = file.path(rootdir, 'data\\Insitu_databases'),
path_insituresdir = file.path(path_resdir, 'Insitu_databases'),
path_pnwresdir = file.path(path_insituresdir, 'pnw.gdb'),
path_ondedatdir = file.path(path_insitudatdir, 'OndeEau'),
path_onderesdir = file.path(path_insituresdir, 'ondeeau.gdb'),
outpath_gaugep = file_out(!!file.path(rootdir, 'results\\GRDCstations_predbasic800.gpkg')),
outpath_riveratlaspred = file_out(!!file.path(rootdir, 'results\\RiverATLAS_predbasic800.csv')),
outpath_bas03error = file_out(!!file.path(rootdir, 'results\\BasinATLAS_v10_lev03_errors.gpkg')),
path_bufrasdir = file.path(path_resdir, 'bufrasdir')
,
outpath_krigingtif = file_out("E:\\Mathis\\results\\prederror_krigingtest.tif"),
#-------------------- Pre-analysis ------------------------------------------
#monthlydischarge = read_monthlydis(in_path = path_monthlynetdischarge),
gaugep = target(
read_gaugep(inp_GRDCgaugep = path_GRDCgaugep,
inp_GSIMgaugep = path_GSIMgaugep,
#in_monthlydischarge = monthlydischarge,
inp_riveratlas2 = path_riveratlas2
)
,
desc = 'This is the formatted gauging stations geometry'
)
,
GRDCgauged_filenames = read_GRDCgauged_paths(
inp_GRDCgaugedir = path_GRDCgaugedir,
in_gaugep = gaugep),
GSIMgaugedmo_filenames = read_GSIMgauged_paths(
inp_GSIMindicesdir = path_GSIMindicesdir,
in_gaugep = gaugep,
timestep = 'month'),
GSIMgaugedsea_filenames = read_GSIMgauged_paths(
inp_GSIMindicesdir = path_GSIMindicesdir,
in_gaugep = gaugep,
timestep = 'season'),
basemaps = get_basemapswintri()
)
########################### plan_setupdata ####################################
plan_setupdata <- drake_plan(
GRDCgaugestats = future_map(GRDCgauged_filenames, #To map
comp_GRDCdurfreq, #Function to run on each file name
maxgap = 20,
in_gaugep = gaugep,
windowsize = 20,
fullwindow = FALSE,
monthsel = NULL, #Other arguments in function
mdurthresh = 1,
verbose = FALSE,
.progress = TRUE),
GSIMgaugestats = future_map2(GSIMgaugedmo_filenames, #To map
GSIMgaugedsea_filenames,
comp_GSIMdurfreq, #Function to run on each file name
maxgap = 20,
in_gaugep = gaugep,
windowsize = 20,
fullwindow = FALSE,
monthsel = NULL, #Other arguments in function
mdurthresh = 1,
.progress = TRUE),
GSIMplots = plot_GSIM(in_GSIMgaugestats = GSIMgaugestats,
yearthresh = 1800,
inp_resdir = path_resdir,
maxgap = 20,
showmissing = TRUE)
,
gaugestats_analyzed = analyzemerge_gaugeir(in_GRDCgaugestats = GRDCgaugestats,
in_GSIMgaugestats = GSIMgaugestats,
in_gaugep = gaugep,
inp_resdir = path_resdir,
plotseries = FALSE),
gaugestats_format = target(format_gaugestats(in_gaugestats = gaugestats_analyzed$data,
in_gaugep = gaugep,
yearthresh = 1800)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
,
predvars = target(
selectformat_predvars(inp_riveratlas_meta = path_riveratlas_meta,
in_gaugestats = gaugestats_format)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
,
measures = target(list(classif = msr("classif.bacc"),
regr = msr("regr.mae"))
),
rivernetwork = rformat_network(in_predvars = predvars,
#in_monthlydischarge = monthlydischarge,
inp_riveratlasmeta = path_riveratlas_meta,
inp_riveratlas = path_riveratlas,
inp_riveratlas2 = path_riveratlas2
)
,
netlength_extra = extrapolate_networklength(
inp_riveratlas = path_riveratlas,
min_cutoff = 0.1,
dispred = seq(0.01, 0.09, 0.01),
interactive = F,
grouping_var = 'PFAF_IDclz'
),
#-------------------- set-up tasks -------------------------------------
gauges_div = target(
gaugestats_format[(dis_m3_pyr >= discharge_interval[1]) &
(dis_m3_pyr < discharge_interval[2]),]
,
transform = map(discharge_interval = list(c(0, 10), c(1, Inf)),
.names = c('gaugestats_format_u10',
'gaugestats_format_o1')
)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
,
tasks = target(
create_tasks(in_gaugestats = in_gauges,
in_predvars = predvars,
id_suffix = in_id,
include_discharge = map_includedis),
transform = map(
in_gauges = c(gaugestats_format_u10,
gaugestats_format_o1),
in_id = c('_u10', '_o1'),
map_includedis = c(TRUE, TRUE),
.names = c('tasks_u10', 'tasks_o1')
)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
)
########################### plan_runmodels #########################################
plan_runmodels <- drake_plan(
baselearners = target(
create_baselearners(tasks),
dynamic = map(tasks)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
,
#Subdivide the baselearners dynamic targets
seplearners = target(readd(baselearners, subtarget_list = FALSE)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
),
###when update mlr3, uncomment store_tuning_instance
autotuningset = target(
set_tuning(in_learner = seplearners$lrn_ranger_overp,
in_measure = measures,
nfeatures = length(tasks$classif$feature_names),
insamp_nfolds = 4, insamp_neval = 24,
insamp_nbatch = parallel::detectCores(logical=FALSE)-2
)
# ,
# dynamic = map(seplearners)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
),
resamplingset = target(
set_cvresampling(rsmp_id = 'repeated_cv',
in_task = tasks$classif,
outsamp_nrep = 2,
outsamp_nfolds = 3)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
),
rfresampled_classif = target(
dynamic_resample(in_task = tasks$classif,
in_learner = autotuningset,
in_resampling = resamplingset,
store_models = TRUE,
type = 'classif')
# ,
# dynamic = map(autotuningset)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
),
# rfbm_classif = target(
# combine_bm(in_resampleresults = list(readd(rfresampled_classif)), #, subtarget_list = TRUE), #
# write_qs = T, inp_resdir = path_resdir)
# # ,
# # trigger = trigger(mode = "condition", condition =FALSE)
# ),
selected_learner = target("oversample.classif.ranger"),
tasks_featsel = target(
select_features(
in_bm = rfresampled_classif,
in_lrnid = selected_learner,
in_task = tasks$classif,
pcutoff = 0.05,
inp_resdir = path_resdir
)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
,
resamplingset_featsel = target(
set_cvresampling(rsmp_id = in_strategy,
in_task = tasks$classif,
outsamp_nrep = in_outrep,
outsamp_nfolds = in_outfolds),
transform = map(in_strategy = c('repeated_cv', "repeated-spcv-coords"),
in_outrep = c(2, 1),
in_outfolds = c(3, 40),
.names = c('featsel_cv', 'featsel_spcv'))
# ,
# trigger = trigger(mode = "condition", condition =TRUE)
),
rfresampled_featsel = target(
dynamic_resample(in_task = in_taskfeatsel,
in_learner = autotuningset,
in_resampling = in_resampling,
store_models = store_models,
type = 'classif'),
transform= map(in_taskfeatsel = c(tasks_featsel[[2]]), # tasks_featsel[[2]]),
in_resampling = c(featsel_cv), #featsel_spcv),
store_models = c(TRUE), #FALSE),
.names = c('res_featsel_cv')),# 'res_featsel_spcv'))
# trigger = trigger(mode = "condition", condition =FALSE)
),
rfeval_featsel = target(c(res_featsel_cv) #, res_featsel_spcv), #Cannot use combine as lead to BenchmarkResult directly in the branching
)
,
# rfbm_featsel = target(
# analyze_benchmark(in_bm = rfeval_featsel,
# in_measure = measures)
# # ,
# # trigger = trigger(mode = "condition", condition =FALSE)
# ),
interthresh = target(0.5), #target(rfbm_featsel$interthresh_dt[learner == selected_learner, max(thresh)]),
# Assertion on 'uhash' failed: Must be element of set {'f00f1b58-0316-4828-814f-f30310b47761','1b8bb7dc-69a0-49a2-af2e-f377fb162a5a'}, but is not atomic scalar.
rftuned = target(
selecttrain_rf(in_rf = res_featsel_cv,
in_learnerid = selected_learner,
in_task = "inter_class_featsel")
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
),
# vimp_plot = ggvimp(in_rftuned = rftuned, in_predvars = predvars,
# varnum=20, spatial_rsp = FALSE),
# pd_plot = ggpartialdep(in_rftuned=rftuned,
# in_predvars=predvars,
# colnums=1:27,
# nvariate=1, nodupli = FALSE, ngrid = 20, parallel = F,
# spatial_rsp = FALSE),
table_allbm = target(
tabulate_benchmarks(in_bm, in_bmid, interthresh=0.5),
transform = map(
in_bm = list(
c(rfeval_featsel)),
in_bmid = list('classif2'),
.names = c('tablebm_classif2'))
),
bin_rftunedmisclass = bin_misclass(in_resampleresult = res_featsel_cv, #Replace back to res_featsel_spcv
binvar = 'dis_m3_pyr',
binfunc = 'manual',
binarg = c(0.1, 1, 10, 100, 1000, 10000, 1000000),
interthresh=interthresh,
spatial_rsp=TRUE
),
gpredsdt = target(
make_gaugepreds(in_rftuned = rftuned,
in_res_spcv = res_featsel_cv, #Replace back to res_featsel_spcv
in_gaugestats = gaugestats_format,
in_predvars = predvars,
interthresh = interthresh)
)
)
########################### plan_runsimplemodels_30d ###########################
plan_runsimplemodels_30d <- drake_plan(
tasks_featsel_mdur30 = change_tasktarget(in_task = tasks_featsel[[2]],
in_gaugestats = gaugestats_format,
newmdurthresh = 30),
autotunerdur10 = change_oversamp(in_oversamprf = rftuned$rf_inner,
in_task = tasks_featsel_mdur30),
rftuned_mdur30 = selecttrain_rf(in_rf = autotunerdur10,
in_task = tasks_featsel_mdur30,
insamp_nfolds = 4, insamp_nevals = 25),
gpredsdt_mdur30 = make_gaugepreds(in_rftuned = rftuned_mdur30,
in_gaugestats = gaugestats_format,
in_predvars = predvars,
interthresh = interthresh,
simple = TRUE)
)
########################### plan_getpreds #####################################
plan_getpreds <- drake_plan(
gpredsdt = bind_gaugepreds(list(gpredsdt_u10, gpredsdt_o1),
interthresh = 0.5
)
,
rfpreds_gauges = write_gaugepreds(in_gaugep = gaugep,
in_gpredsdt = gpredsdt,
outp_gaugep = outpath_gaugep
)
,
rfpreds_network = target(
write_netpreds(
in_network = rivernetwork,
in_rftuned = list(rftuned_u10, rftuned_o1),
predcol = 'predbasic800',
discharge_interval = list(c(0, 10), c(1, Inf)),
interthresh = list(interthresh_u10, interthresh_o1),
in_predvars = predvars,
outp_riveratlaspred = outpath_riveratlaspred
)
# ,
# trigger = trigger(mode = "condition", condition =TRUE)
)
)
########################### plan_getpreds_30d #####################################
plan_getpreds_30d <- drake_plan(
gpredsdt_mdur30 = bind_gaugepreds(list(gpredsdt_mdur30_u10,
gpredsdt_mdur30_o1),
interthresh = 0.5
)
,
rfpreds_gauges_mdur30 = write_gaugepreds(
in_gaugep = gaugep,
in_gpredsdt = gpredsdt_mdur30,
outp_gaugep = gsub('[.]gpkg',
'_mdur30.gpkg',
outpath_gaugep)
)
,
rfpreds_network_mdur30 = target(
write_netpreds(
in_network = rivernetwork,
in_rftuned = list(rftuned_mdur30_u10, rftuned_mdur30_o1),
predcol = 'predbasic800_mdur30',
discharge_interval = list(c(0, 10), c(1, Inf)),
interthresh = list(interthresh_u10, interthresh_o1),
in_predvars = predvars,
outp_riveratlaspred = gsub('[.]csv',
'_mdur30.csv',
outpath_riveratlaspred)
)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
)
########################### plan_getoutputs #####################################
plan_getoutputs_1 <- drake_plan(
# gauges_plot = target(
# gggauges(in_gaugepred =rfpreds_gauges,
# in_basemaps = basemaps,
# binarg = c(30, 60, 100),
# binvar = 'totalYears_kept_o1800'
# )
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
# )
# ,
#
envhist = target(
layout_ggenvhist(in_rivernetwork = rivernetwork,
in_gaugepred = rfpreds_gauges,
in_predvars = predvars)
,
trigger = trigger(mode = "condition", condition =FALSE)
),
bin_finalmisclass = target(
bin_misclass(in_predictions = gpredsdt,
binvar = 'dis_m3_pyr',
binfunc = 'manual',
binarg = c(0.1, 1, 10, 100, 1000, 10000, 1000000),
interthresh=0.5,
rspcol = rspcol
),
map(rspcol = c('IRpredcat_CVnosp', 'IRpredcat_CVsp')
)
)
# ,
#
# basinBACC = target(
# map_basinBACC(in_gaugepred = gpredsdt,
# inp_basin = path_bas03,
# in_rivernetwork = rivpred,
# outp_basinerror = outpath_bas03error,
# spatial_rsp = TRUE)
# # ,
# # trigger = trigger(mode = "condition", condition =FALSE)
# )
)
plan_getoutputs_2 <- drake_plan(
rivpred = target(
netpredformat(in_rivernetwork = rivernetwork,
outp_riveratlaspred = rfpreds_network)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
),
IRESextra = extrapolate_IRES(in_rivpred = rivpred,
in_extranet = netlength_extra,
min_cutoff = 0.1,
valuevar = 'predbasic800cat',
interactive = F),
globaltables = target(
tabulate_globalsummary(outp_riveratlaspred = rfpreds_network,
inp_riveratlas = path_riveratlas,
inp_riveratlas_legends = path_riveratlas_legends,
interthresh = 0.5,
idvars = in_idvars,
castvar = 'dis_m3_pyr',
castvar_num = FALSE,
weightvar = 'LENGTH_KM',
valuevar = 'predbasic800',
valuevarsub = 1,
binfunc = 'manual',
binarg = c(0.1, 1, 10, 100, 1000, 10000, 1000000),
na.rm=T,
tidy = FALSE,
nolake = TRUE,
mincutoff = 0.1),
transform = map(in_idvars = c('gad_id_cmj', 'fmh_cl_cmj',
'tbi_cl_cmj', 'clz_cl_cmj'))
# ,
# trigger = trigger(mode = "condition", condition =TRUE)
),
globaltable_clzextend = extend_globalsummary_clz(
in_IRESextra = IRESextra,
in_globaltable = globaltables_clz_cl_cmj,
inp_riveratlas_legends = path_riveratlas_legends
),
IRpop = compute_IRpop(in_rivpred = rivpred,
inp_linkpop = path_linkpop,
valuevar = 'predbasic800cat'
)
)
########################### plan_compareresults ################################
plan_compareresults <- drake_plan(
fr_plot = compare_fr(inp_frdir = path_frresdir,
in_rivpred = rivpred,
predcol = 'predbasic800cat',
mincutoff = 0.1,
binarg = c(0.1, 1, 10, 100, 1000, 10000, 100000)
)
,
us_plot = compare_us(inp_usresdir = path_usresdir,
inp_usdatdir = path_usdatdir,
in_rivpred = rivpred,
predcol = 'predbasic800cat',
binarg = c(0.1, 1, 10, 100, 1000, 10000, 100000),
mincutoff = 0.1
),
au_plot = compare_au(inp_resdir = path_resdir,
in_rivpred = rivpred,
predcol = 'predbasic800cat',
binarg = c(100, 10^3, 10^4, 10^5, 10^6, 10^7)
),
pnw_plot = qc_pnw(inp_pnwresdir = path_pnwresdir,
in_rivpred = rivpred,
predcol = 'predbasic800cat',
interthresh = 0.5,
mincutoff = 0.1),
onde_plot = qc_onde(inp_ondedatdir = path_ondedatdir,
inp_onderesdir = path_onderesdir,
inp_riveratlas = path_riveratlas,
in_rivpred = rivpred,
predcol = 'predbasic800cat',
interthresh=0.5,
mincutoff = 0.1
)
)
########################### BIND AND BRANCH PLANS ##############################
plan_runmodels_branches_default <- lapply(
c('_u10', '_o1'), function(suffix) { #'_all',
return(
branch_plan(
plan = plan_runmodels,
branch_suffix = suffix,
external_arguments_to_modify = c('tasks', 'gaugestats_format'),
verbose = FALSE)
)
}
)
plan <- bind_plans(plan_preprocess,
plan_setupdata,
plan_runmodels_branches_default,
plan_getpreds,
plan_getoutputs_1,
plan_getoutputs_2
)
messamat/globalIRmap documentation built on July 4, 2021, 10:48 a.m.
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source('R/IRmapping_packages.R')
source('R/IRmapping_functions.R')
source('R/planutil_functions.R')
rootdir <- find_root(has_dir("src")) #Set root working directory
########################### plan_preprocess ####################################
plan_preprocess <- drake_plan(
#-------------------- Define input and output files ##########################
#Note: for dependency tracking, drake only reads string literals inside file_in and file_out; hence the use of absolute paths
#Tidy evaluation (!!) didn't work for some reason -- can look into it later
path_resdir = file.path(!!rootdir, "results"),
path_GRDCgaugep = file.path(path_resdir, "spatialoutputs.gdb\\grdcstations_cleanjoin"),
path_GRDCgaugedir = file_in(!!file.path(rootdir, "data\\GRDCdat_day")),
path_GSIMgaugep = file.path(path_resdir, "GSIM\\GSIM.gdb\\GSIMstations_cleanriverjoin"),
path_GSIMindicesdir = file.path(!!rootdir, "data\\GSIM\\GSIM_indices"),
path_riveratlas_meta = file_in(
!!file.path(rootdir, "data\\HydroATLAS\\HydroATLAS_metadata_MLMv11.xlsx")),
path_riveratlas_legends = file_in(!!file.path(rootdir, "data\\HydroATLAS\\HydroATLAS_v10_Legends.xlsx")),
path_monthlynetdischarge = file.path(!!rootdir,
"data\\HydroSHEDS\\HS_discharge_monthly.gdb\\Hydrosheds_discharge_monthly"),
path_riveratlas = file_in(!!file.path(rootdir, 'results\\RiverATLAS_v10tab.csv')),
path_riveratlas2 = file_in(!!file.path(rootdir, 'results\\RiverATLAS_v11tab.csv')),
path_bas03 = file.path(!!rootdir, "data\\HydroATLAS\\BasinATLAS_v10.gdb\\BasinATLAS_v10_lev03"),
path_compresdir = file_in(!!file.path(rootdir, 'results\\Comparison_databases')),
path_linkpop = file_in(!!file.path(rootdir, 'results\\worldpop_link_20201220.csv')),
path_frresdir = file.path(path_compresdir, 'france.gdb'),
path_usdatdir = file.path(rootdir, 'data\\Comparison_databases', 'US'),
path_usresdir = file.path(path_compresdir, 'us.gdb'),
path_auresdir = file.path(path_compresdir, 'australia.gdb'),
path_insitudatdir = file.path(rootdir, 'data\\Insitu_databases'),
path_insituresdir = file.path(path_resdir, 'Insitu_databases'),
path_pnwresdir = file.path(path_insituresdir, 'pnw.gdb'),
path_ondedatdir = file.path(path_insitudatdir, 'OndeEau'),
path_onderesdir = file.path(path_insituresdir, 'ondeeau.gdb'),
outpath_gaugep = file_out(!!file.path(rootdir, 'results\\GRDCstations_predbasic800.gpkg')),
outpath_riveratlaspred = file_out(!!file.path(rootdir, 'results\\RiverATLAS_predbasic800.csv')),
outpath_bas03error = file_out(!!file.path(rootdir, 'results\\BasinATLAS_v10_lev03_errors.gpkg')),
path_bufrasdir = file.path(path_resdir, 'bufrasdir')
,
outpath_krigingtif = file_out("E:\\Mathis\\results\\prederror_krigingtest.tif"),
#-------------------- Pre-analysis ------------------------------------------
#monthlydischarge = read_monthlydis(in_path = path_monthlynetdischarge),
gaugep = target(
read_gaugep(inp_GRDCgaugep = path_GRDCgaugep,
inp_GSIMgaugep = path_GSIMgaugep,
#in_monthlydischarge = monthlydischarge,
inp_riveratlas2 = path_riveratlas2
)
,
desc = 'This is the formatted gauging stations geometry'
)
,
GRDCgauged_filenames = read_GRDCgauged_paths(
inp_GRDCgaugedir = path_GRDCgaugedir,
in_gaugep = gaugep),
GSIMgaugedmo_filenames = read_GSIMgauged_paths(
inp_GSIMindicesdir = path_GSIMindicesdir,
in_gaugep = gaugep,
timestep = 'month'),
GSIMgaugedsea_filenames = read_GSIMgauged_paths(
inp_GSIMindicesdir = path_GSIMindicesdir,
in_gaugep = gaugep,
timestep = 'season'),
basemaps = get_basemapswintri()
)
########################### plan_setupdata ####################################
plan_setupdata <- drake_plan(
GRDCgaugestats = future_map(GRDCgauged_filenames, #To map
comp_GRDCdurfreq, #Function to run on each file name
maxgap = 20,
in_gaugep = gaugep,
windowsize = 20,
fullwindow = FALSE,
monthsel = NULL, #Other arguments in function
mdurthresh = 1,
verbose = FALSE,
.progress = TRUE),
GSIMgaugestats = future_map2(GSIMgaugedmo_filenames, #To map
GSIMgaugedsea_filenames,
comp_GSIMdurfreq, #Function to run on each file name
maxgap = 20,
in_gaugep = gaugep,
windowsize = 20,
fullwindow = FALSE,
monthsel = NULL, #Other arguments in function
mdurthresh = 1,
.progress = TRUE),
GSIMplots = plot_GSIM(in_GSIMgaugestats = GSIMgaugestats,
yearthresh = 1800,
inp_resdir = path_resdir,
maxgap = 20,
showmissing = TRUE)
,
gaugestats_analyzed = analyzemerge_gaugeir(in_GRDCgaugestats = GRDCgaugestats,
in_GSIMgaugestats = GSIMgaugestats,
in_gaugep = gaugep,
inp_resdir = path_resdir,
plotseries = FALSE),
gaugestats_format = target(format_gaugestats(in_gaugestats = gaugestats_analyzed$data,
in_gaugep = gaugep,
yearthresh = 1800)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
,
predvars = target(
selectformat_predvars(inp_riveratlas_meta = path_riveratlas_meta,
in_gaugestats = gaugestats_format)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
,
measures = target(list(classif = msr("classif.bacc"),
regr = msr("regr.mae"))
),
rivernetwork = rformat_network(in_predvars = predvars,
#in_monthlydischarge = monthlydischarge,
inp_riveratlasmeta = path_riveratlas_meta,
inp_riveratlas = path_riveratlas,
inp_riveratlas2 = path_riveratlas2
)
,
netlength_extra = extrapolate_networklength(
inp_riveratlas = path_riveratlas,
min_cutoff = 0.1,
dispred = seq(0.01, 0.09, 0.01),
interactive = F,
grouping_var = 'PFAF_IDclz'
),
#-------------------- set-up tasks -------------------------------------
gauges_div = target(
gaugestats_format[(dis_m3_pyr >= discharge_interval[1]) &
(dis_m3_pyr < discharge_interval[2]),]
,
transform = map(discharge_interval = list(c(0, 10), c(1, Inf)),
.names = c('gaugestats_format_u10',
'gaugestats_format_o1')
)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
,
tasks = target(
create_tasks(in_gaugestats = in_gauges,
in_predvars = predvars,
id_suffix = in_id,
include_discharge = map_includedis),
transform = map(
in_gauges = c(gaugestats_format_u10,
gaugestats_format_o1),
in_id = c('_u10', '_o1'),
map_includedis = c(TRUE, TRUE),
.names = c('tasks_u10', 'tasks_o1')
)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
)
########################### plan_runmodels #########################################
plan_runmodels <- drake_plan(
baselearners = target(
create_baselearners(tasks),
dynamic = map(tasks)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
,
#Subdivide the baselearners dynamic targets
seplearners = target(readd(baselearners, subtarget_list = FALSE)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
),
###when update mlr3, uncomment store_tuning_instance
autotuningset = target(
set_tuning(in_learner = seplearners$lrn_ranger_overp,
in_measure = measures,
nfeatures = length(tasks$classif$feature_names),
insamp_nfolds = 4, insamp_neval = 24,
insamp_nbatch = parallel::detectCores(logical=FALSE)-2
)
# ,
# dynamic = map(seplearners)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
),
resamplingset = target(
set_cvresampling(rsmp_id = 'repeated_cv',
in_task = tasks$classif,
outsamp_nrep = 2,
outsamp_nfolds = 3)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
),
rfresampled_classif = target(
dynamic_resample(in_task = tasks$classif,
in_learner = autotuningset,
in_resampling = resamplingset,
store_models = TRUE,
type = 'classif')
# ,
# dynamic = map(autotuningset)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
),
# rfbm_classif = target(
# combine_bm(in_resampleresults = list(readd(rfresampled_classif)), #, subtarget_list = TRUE), #
# write_qs = T, inp_resdir = path_resdir)
# # ,
# # trigger = trigger(mode = "condition", condition =FALSE)
# ),
selected_learner = target("oversample.classif.ranger"),
tasks_featsel = target(
select_features(
in_bm = rfresampled_classif,
in_lrnid = selected_learner,
in_task = tasks$classif,
pcutoff = 0.05,
inp_resdir = path_resdir
)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
,
resamplingset_featsel = target(
set_cvresampling(rsmp_id = in_strategy,
in_task = tasks$classif,
outsamp_nrep = in_outrep,
outsamp_nfolds = in_outfolds),
transform = map(in_strategy = c('repeated_cv', "repeated-spcv-coords"),
in_outrep = c(2, 1),
in_outfolds = c(3, 40),
.names = c('featsel_cv', 'featsel_spcv'))
# ,
# trigger = trigger(mode = "condition", condition =TRUE)
),
rfresampled_featsel = target(
dynamic_resample(in_task = in_taskfeatsel,
in_learner = autotuningset,
in_resampling = in_resampling,
store_models = store_models,
type = 'classif'),
transform= map(in_taskfeatsel = c(tasks_featsel[[2]]), # tasks_featsel[[2]]),
in_resampling = c(featsel_cv), #featsel_spcv),
store_models = c(TRUE), #FALSE),
.names = c('res_featsel_cv')),# 'res_featsel_spcv'))
# trigger = trigger(mode = "condition", condition =FALSE)
),
rfeval_featsel = target(c(res_featsel_cv) #, res_featsel_spcv), #Cannot use combine as lead to BenchmarkResult directly in the branching
)
,
# rfbm_featsel = target(
# analyze_benchmark(in_bm = rfeval_featsel,
# in_measure = measures)
# # ,
# # trigger = trigger(mode = "condition", condition =FALSE)
# ),
interthresh = target(0.5), #target(rfbm_featsel$interthresh_dt[learner == selected_learner, max(thresh)]),
# Assertion on 'uhash' failed: Must be element of set {'f00f1b58-0316-4828-814f-f30310b47761','1b8bb7dc-69a0-49a2-af2e-f377fb162a5a'}, but is not atomic scalar.
rftuned = target(
selecttrain_rf(in_rf = res_featsel_cv,
in_learnerid = selected_learner,
in_task = "inter_class_featsel")
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
),
# vimp_plot = ggvimp(in_rftuned = rftuned, in_predvars = predvars,
# varnum=20, spatial_rsp = FALSE),
# pd_plot = ggpartialdep(in_rftuned=rftuned,
# in_predvars=predvars,
# colnums=1:27,
# nvariate=1, nodupli = FALSE, ngrid = 20, parallel = F,
# spatial_rsp = FALSE),
table_allbm = target(
tabulate_benchmarks(in_bm, in_bmid, interthresh=0.5),
transform = map(
in_bm = list(
c(rfeval_featsel)),
in_bmid = list('classif2'),
.names = c('tablebm_classif2'))
),
bin_rftunedmisclass = bin_misclass(in_resampleresult = res_featsel_cv, #Replace back to res_featsel_spcv
binvar = 'dis_m3_pyr',
binfunc = 'manual',
binarg = c(0.1, 1, 10, 100, 1000, 10000, 1000000),
interthresh=interthresh,
spatial_rsp=TRUE
),
gpredsdt = target(
make_gaugepreds(in_rftuned = rftuned,
in_res_spcv = res_featsel_cv, #Replace back to res_featsel_spcv
in_gaugestats = gaugestats_format,
in_predvars = predvars,
interthresh = interthresh)
)
)
########################### plan_runsimplemodels_30d ###########################
plan_runsimplemodels_30d <- drake_plan(
tasks_featsel_mdur30 = change_tasktarget(in_task = tasks_featsel[[2]],
in_gaugestats = gaugestats_format,
newmdurthresh = 30),
autotunerdur10 = change_oversamp(in_oversamprf = rftuned$rf_inner,
in_task = tasks_featsel_mdur30),
rftuned_mdur30 = selecttrain_rf(in_rf = autotunerdur10,
in_task = tasks_featsel_mdur30,
insamp_nfolds = 4, insamp_nevals = 25),
gpredsdt_mdur30 = make_gaugepreds(in_rftuned = rftuned_mdur30,
in_gaugestats = gaugestats_format,
in_predvars = predvars,
interthresh = interthresh,
simple = TRUE)
)
########################### plan_getpreds #####################################
plan_getpreds <- drake_plan(
gpredsdt = bind_gaugepreds(list(gpredsdt_u10, gpredsdt_o1),
interthresh = 0.5
)
,
rfpreds_gauges = write_gaugepreds(in_gaugep = gaugep,
in_gpredsdt = gpredsdt,
outp_gaugep = outpath_gaugep
)
,
rfpreds_network = target(
write_netpreds(
in_network = rivernetwork,
in_rftuned = list(rftuned_u10, rftuned_o1),
predcol = 'predbasic800',
discharge_interval = list(c(0, 10), c(1, Inf)),
interthresh = list(interthresh_u10, interthresh_o1),
in_predvars = predvars,
outp_riveratlaspred = outpath_riveratlaspred
)
# ,
# trigger = trigger(mode = "condition", condition =TRUE)
)
)
########################### plan_getpreds_30d #####################################
plan_getpreds_30d <- drake_plan(
gpredsdt_mdur30 = bind_gaugepreds(list(gpredsdt_mdur30_u10,
gpredsdt_mdur30_o1),
interthresh = 0.5
)
,
rfpreds_gauges_mdur30 = write_gaugepreds(
in_gaugep = gaugep,
in_gpredsdt = gpredsdt_mdur30,
outp_gaugep = gsub('[.]gpkg',
'_mdur30.gpkg',
outpath_gaugep)
)
,
rfpreds_network_mdur30 = target(
write_netpreds(
in_network = rivernetwork,
in_rftuned = list(rftuned_mdur30_u10, rftuned_mdur30_o1),
predcol = 'predbasic800_mdur30',
discharge_interval = list(c(0, 10), c(1, Inf)),
interthresh = list(interthresh_u10, interthresh_o1),
in_predvars = predvars,
outp_riveratlaspred = gsub('[.]csv',
'_mdur30.csv',
outpath_riveratlaspred)
)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
)
)
########################### plan_getoutputs #####################################
plan_getoutputs_1 <- drake_plan(
# gauges_plot = target(
# gggauges(in_gaugepred =rfpreds_gauges,
# in_basemaps = basemaps,
# binarg = c(30, 60, 100),
# binvar = 'totalYears_kept_o1800'
# )
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
# )
# ,
#
envhist = target(
layout_ggenvhist(in_rivernetwork = rivernetwork,
in_gaugepred = rfpreds_gauges,
in_predvars = predvars)
,
trigger = trigger(mode = "condition", condition =FALSE)
),
bin_finalmisclass = target(
bin_misclass(in_predictions = gpredsdt,
binvar = 'dis_m3_pyr',
binfunc = 'manual',
binarg = c(0.1, 1, 10, 100, 1000, 10000, 1000000),
interthresh=0.5,
rspcol = rspcol
),
map(rspcol = c('IRpredcat_CVnosp', 'IRpredcat_CVsp')
)
)
# ,
#
# basinBACC = target(
# map_basinBACC(in_gaugepred = gpredsdt,
# inp_basin = path_bas03,
# in_rivernetwork = rivpred,
# outp_basinerror = outpath_bas03error,
# spatial_rsp = TRUE)
# # ,
# # trigger = trigger(mode = "condition", condition =FALSE)
# )
)
plan_getoutputs_2 <- drake_plan(
rivpred = target(
netpredformat(in_rivernetwork = rivernetwork,
outp_riveratlaspred = rfpreds_network)
# ,
# trigger = trigger(mode = "condition", condition =FALSE)
),
IRESextra = extrapolate_IRES(in_rivpred = rivpred,
in_extranet = netlength_extra,
min_cutoff = 0.1,
valuevar = 'predbasic800cat',
interactive = F),
globaltables = target(
tabulate_globalsummary(outp_riveratlaspred = rfpreds_network,
inp_riveratlas = path_riveratlas,
inp_riveratlas_legends = path_riveratlas_legends,
interthresh = 0.5,
idvars = in_idvars,
castvar = 'dis_m3_pyr',
castvar_num = FALSE,
weightvar = 'LENGTH_KM',
valuevar = 'predbasic800',
valuevarsub = 1,
binfunc = 'manual',
binarg = c(0.1, 1, 10, 100, 1000, 10000, 1000000),
na.rm=T,
tidy = FALSE,
nolake = TRUE,
mincutoff = 0.1),
transform = map(in_idvars = c('gad_id_cmj', 'fmh_cl_cmj',
'tbi_cl_cmj', 'clz_cl_cmj'))
# ,
# trigger = trigger(mode = "condition", condition =TRUE)
),
globaltable_clzextend = extend_globalsummary_clz(
in_IRESextra = IRESextra,
in_globaltable = globaltables_clz_cl_cmj,
inp_riveratlas_legends = path_riveratlas_legends
),
IRpop = compute_IRpop(in_rivpred = rivpred,
inp_linkpop = path_linkpop,
valuevar = 'predbasic800cat'
)
)
########################### plan_compareresults ################################
plan_compareresults <- drake_plan(
fr_plot = compare_fr(inp_frdir = path_frresdir,
in_rivpred = rivpred,
predcol = 'predbasic800cat',
mincutoff = 0.1,
binarg = c(0.1, 1, 10, 100, 1000, 10000, 100000)
)
,
us_plot = compare_us(inp_usresdir = path_usresdir,
inp_usdatdir = path_usdatdir,
in_rivpred = rivpred,
predcol = 'predbasic800cat',
binarg = c(0.1, 1, 10, 100, 1000, 10000, 100000),
mincutoff = 0.1
),
au_plot = compare_au(inp_resdir = path_resdir,
in_rivpred = rivpred,
predcol = 'predbasic800cat',
binarg = c(100, 10^3, 10^4, 10^5, 10^6, 10^7)
),
pnw_plot = qc_pnw(inp_pnwresdir = path_pnwresdir,
in_rivpred = rivpred,
predcol = 'predbasic800cat',
interthresh = 0.5,
mincutoff = 0.1),
onde_plot = qc_onde(inp_ondedatdir = path_ondedatdir,
inp_onderesdir = path_onderesdir,
inp_riveratlas = path_riveratlas,
in_rivpred = rivpred,
predcol = 'predbasic800cat',
interthresh=0.5,
mincutoff = 0.1
)
)
########################### BIND AND BRANCH PLANS ##############################
plan_runmodels_branches_default <- lapply(
c('_u10', '_o1'), function(suffix) { #'_all',
return(
branch_plan(
plan = plan_runmodels,
branch_suffix = suffix,
external_arguments_to_modify = c('tasks', 'gaugestats_format'),
verbose = FALSE)
)
}
)
plan <- bind_plans(plan_preprocess,
plan_setupdata,
plan_runmodels_branches_default,
plan_getpreds,
plan_getoutputs_1,
plan_getoutputs_2
)
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