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5_multipleParameterExploration.Rmd
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---
site: bookdown::bookdown_site
output:
bookdown::gitbook: default
---
# Multiple parameter exploration
```{r 5_setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, message = FALSE, # hide code and messages
cache=TRUE, autodep=TRUE)
source("library/hpcModel.run.R")
source("library/hpcModel.exploration.R")
source("library/hpcModel.plot.R")
require(reshape2)
require(ggplot2)
require(scales)
require(ggpubr)
require(kableExtra)
```
```{r 5_table-functions}
source("library/plotting/printTableRun.R")
source("library/plotting/printTableExperiment.R")
```
```{r 5_default-values}
# this is the 'default' parameter setting (end state is 'fast coevolution'):
# initial populations
initial_population_humans_default = 10
initial_population_plants_default = 10
# number of discrete types
number_types_humans_default = 30
number_types_plants_default = 30
# undirected variation
undirected_variation_humans_default = 0.15
undirected_variation_plants_default = 0.15
# intrinsic growth rate
intrinsic_growth_rate_humans_default = 0.04
intrinsic_growth_rate_plants_default = 0.1
# Utility per capita of individuals of type N
utility_per_capita_type_n_plants_to_humans_default = 1.5
utility_per_capita_type_n_humans_to_plants_default = 1
# Utility per capita of individuals of type 1
utility_per_capita_type_1_plants_to_humans_default = 0.15
utility_per_capita_type_1_humans_to_plants_default = 0
# basic resources:
# population of type N that can be sustained by resources independent of HP relationship
utility_other_to_type_n_humans_default = 10
utility_other_to_type_n_plants_default = 20
# population of type 1 that can be sustained by resources independent of HP relationship
utility_other_to_type_1_humans_default = 80
utility_other_to_type_1_plants_default = 100
# maximum local area to be used by populations (multiplier or scaling effect)
max_area_default = 200
# settings
# simulation flow & data
max_iterations_default = 5000
reltol_exponential_default = 6
coevolution_threshold_default = 0.5
save_trajectories_default = FALSE
messages_default = TRUE
```
```{r 5_setup_plus, include=FALSE}
# this chunk is specific for this exploration
require(DoE.wrapper) # LHS
require(randomForest) # RF
require(party) # classification / regression tree
require(parallel) # use parallelization, it takes 8 min aprox
run.LHS.experiment <- function(experimentSettings, cluster)
{
parallel::parLapply(cluster, 1:nrow(experimentSettings),
function(experimentIndex) {
RESULTS <- hpcModel.run(
initial_population_humans = initial_population_humans_default,
initial_population_plants = initial_population_plants_default,
number_types_humans = experimentSettings[experimentIndex, 1],
number_types_plants = experimentSettings[experimentIndex, 2],
undirected_variation_humans = experimentSettings[experimentIndex, 3],
undirected_variation_plants = experimentSettings[experimentIndex, 4],
intrinsic_growth_rate_humans = experimentSettings[experimentIndex, 5],
intrinsic_growth_rate_plants = experimentSettings[experimentIndex, 6],
utility_per_capita_type_n_plants_to_humans = experimentSettings[experimentIndex, 7],
utility_per_capita_type_n_humans_to_plants = experimentSettings[experimentIndex, 8],
utility_per_capita_type_1_plants_to_humans = experimentSettings[experimentIndex, 9],
utility_per_capita_type_1_humans_to_plants = experimentSettings[experimentIndex, 10],
utility_other_to_type_n_humans = experimentSettings[experimentIndex, 11],
utility_other_to_type_n_plants = experimentSettings[experimentIndex, 12],
utility_other_to_type_1_humans = experimentSettings[experimentIndex, 13],
utility_other_to_type_1_plants = experimentSettings[experimentIndex, 14],
max_area = experimentSettings[experimentIndex, 15],
max_iterations = max_iterations_default,
reltol_exponential = reltol_exponential_default,
save_trajectories = save_trajectories_default,
messages = messages_default
)
return(as.data.frame(RESULTS$END))
})
}
# same, but saving trajectories
run.LHS.experiment2 <- function(experimentSettings, cluster)
{
parallel::parLapply(cluster, 1:nrow(experimentSettings),
function(experimentIndex) {
RESULTS <- hpcModel.run(
initial_population_humans = initial_population_humans_default,
initial_population_plants = initial_population_plants_default,
number_types_humans = experimentSettings[experimentIndex, 1],
number_types_plants = experimentSettings[experimentIndex, 2],
undirected_variation_humans = experimentSettings[experimentIndex, 3],
undirected_variation_plants = experimentSettings[experimentIndex, 4],
intrinsic_growth_rate_humans = experimentSettings[experimentIndex, 5],
intrinsic_growth_rate_plants = experimentSettings[experimentIndex, 6],
utility_per_capita_type_n_plants_to_humans = experimentSettings[experimentIndex, 7],
utility_per_capita_type_n_humans_to_plants = experimentSettings[experimentIndex, 8],
utility_per_capita_type_1_plants_to_humans = experimentSettings[experimentIndex, 9],
utility_per_capita_type_1_humans_to_plants = experimentSettings[experimentIndex, 10],
utility_other_to_type_n_humans = experimentSettings[experimentIndex, 11],
utility_other_to_type_n_plants = experimentSettings[experimentIndex, 12],
utility_other_to_type_1_humans = experimentSettings[experimentIndex, 13],
utility_other_to_type_1_plants = experimentSettings[experimentIndex, 14],
max_area = experimentSettings[experimentIndex, 15],
max_iterations = max_iterations_default,
reltol_exponential = reltol_exponential_default,
save_trajectories = TRUE,
messages = messages_default
)
return(RESULTS$TRAJECTORIES)
})
}
plotDirectory <- "plots/5_"
dataDirectory <- "data/5_"
# set up color mapping for variable importance plots (variableImportancePlot())
# assuming order to be: n, v, r, mU, U.b and MaxArea
parameterColorMap <- c("black", rep("blue", 4), rep("green", 4), rep("red", 6))
```
```{r 5_plot-functions, include=FALSE}
# this chunk is specific for this exploration
source("library/plotting/bifurcationPlot_twoVariables.R")
source("library/plotting/bifurcationPlot_twoVariables_per_parameter.R")
source("library/plotting/bifurcationPlot_twoVariables_per_parameter_and_scenario.R")
source("library/plotting/plotTrajectoriesPerVariableGroup.R")
source("library/plotting/variableImportancePlot.R")
```
\newpage
## Sampling parameter values with Latin Hypercube Sampling (LHS)
```{r 5_LHS-values}
exp_LHS_parameter_sample_dataFileName <- paste0(dataDirectory, "LHS_parameter_values.RData")
LHS_parameter_names <- c(
'number_types_humans', 'number_types_plants',
'undirected_variation_humans', 'undirected_variation_plants',
'intrinsic_growth_rate_humans', 'intrinsic_growth_rate_plants',
'utility_per_capita_type_n_plants_to_humans', 'utility_per_capita_type_n_humans_to_plants',
'utility_per_capita_type_1_plants_to_humans', 'utility_per_capita_type_1_humans_to_plants',
'utility_other_to_type_n_humans', 'utility_other_to_type_n_plants',
'utility_other_to_type_1_humans', 'utility_other_to_type_1_plants',
'max_area'
)
LHS_parameter_ranges <- list(
number_types_humans = c(3, 50),
number_types_plants = c(3, 50),
undirected_variation_humans = c(0.1, 0.3),
undirected_variation_plants = c(0.1, 0.3),
intrinsic_growth_rate_humans = c(0.01, 0.3),
intrinsic_growth_rate_plants = c(0.01, 0.3),
utility_per_capita_type_n_plants_to_humans = c(0, 3),
utility_per_capita_type_n_humans_to_plants = c(0, 3),
utility_per_capita_type_1_plants_to_humans = c(0, 3),
utility_per_capita_type_1_humans_to_plants = c(0, 3),
utility_other_to_type_n_humans = c(1, 300),
utility_other_to_type_n_plants = c(1, 300),
utility_other_to_type_1_humans = c(1, 300),
utility_other_to_type_1_plants = c(1, 300),
max_area = c(1, 300)
)
```
**parameter** | **value**
------------------ | ----------------------------------
`number_types_humans`, `number_types_plants` | [`r LHS_parameter_ranges$number_types_humans[1]`, `r LHS_parameter_ranges$number_types_humans[2]`], [`r LHS_parameter_ranges$number_types_plants[1]`, `r LHS_parameter_ranges$number_types_plants[2]`]
`undirected_variation_humans`, `undirected_variation_plants` | [`r LHS_parameter_ranges$undirected_variation_humans[1]`, `r LHS_parameter_ranges$undirected_variation_humans[2]`], [`r LHS_parameter_ranges$undirected_variation_plants[1]`, `r LHS_parameter_ranges$undirected_variation_plants[2]`]
`intrinsic_growth_rate_humans`, `intrinsic_growth_rate_plants` | [`r LHS_parameter_ranges$intrinsic_growth_rate_humans[1]`, `r LHS_parameter_ranges$intrinsic_growth_rate_humans[2]`], [`r LHS_parameter_ranges$intrinsic_growth_rate_plants[1]`, `r LHS_parameter_ranges$intrinsic_growth_rate_plants[2]`]
`utility_per_capita_type_n_plants_to_humans`, `utility_per_capita_type_n_humans_to_plants` | [`r LHS_parameter_ranges$utility_per_capita_type_n_plants_to_humans[1]`, `r LHS_parameter_ranges$utility_per_capita_type_n_plants_to_humans[2]`], [`r LHS_parameter_ranges$utility_per_capita_type_n_humans_to_plants[1]`, `r LHS_parameter_ranges$utility_per_capita_type_n_humans_to_plants[2]`]
`utility_per_capita_type_1_plants_to_humans`, `utility_per_capita_type_1_humans_to_plants` | [`r LHS_parameter_ranges$utility_per_capita_type_1_plants_to_humans[1]`, `r LHS_parameter_ranges$utility_per_capita_type_1_plants_to_humans[2]`], [`r LHS_parameter_ranges$utility_per_capita_type_1_humans_to_plants[1]`, `r LHS_parameter_ranges$utility_per_capita_type_1_humans_to_plants[2]`]
`utility_other_to_type_n_humans`, `utility_other_to_type_n_plants` | [`r LHS_parameter_ranges$utility_other_to_type_n_humans[1]`, `r LHS_parameter_ranges$utility_other_to_type_n_humans[2]`], [`r LHS_parameter_ranges$utility_other_to_type_n_plants[1]`, `r LHS_parameter_ranges$utility_other_to_type_n_plants[2]`]
`max_area` | [`r LHS_parameter_ranges$MaxArea[1]`, `r LHS_parameter_ranges$MaxArea[2]`]
`utility_other_to_type_1_humans`, `utility_other_to_type_1_plants` | [`r LHS_parameter_ranges$utility_other_to_type_1_humans[1]`, `r LHS_parameter_ranges$utility_other_to_type_1_humans[2]`], [`r LHS_parameter_ranges$utility_other_to_type_1_plants[1]`, `r LHS_parameter_ranges$utility_other_to_type_1_plants[2]`]
Table: Ranges of parameter exploration
```{r 5_LHS, include=FALSE, dependson='5_LHS-values'}
### generate LHS design (strauss method, with 0.2 radious interaction)
if (!file.exists(exp_LHS_parameter_sample_dataFileName)) # you must delete it to create a new file
{
LHS_parameter_values <- DoE.wrapper::lhs.design(
nruns = 10000, # probably should be greater
nfactors = 15,
type = 'strauss',
seed = 777,
factor.names = LHS_parameter_ranges,
digits = c(0, 0, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4),
RND = 0.2
)
# save
save(LHS_parameter_values, file = exp_LHS_parameter_sample_dataFileName)
}
```
```{r 5_LHS-load, include=FALSE}
# load
load(exp_LHS_parameter_sample_dataFileName)
# format LHS_parameter_values as a data frame
LHS_parameter_values <- as.data.frame(LHS_parameter_values)
```
```{r 5LHStablehtml, eval=!params$ispdf, cache=TRUE, dependson='5_LHS-load'}
source("library/plotting/printTableExperiment.R") # sometimes bookdown rendering will act up and ignore the chunks loading functions
printTableExperiment(LHS_parameter_values, caption = 'ACTUAL parameter values')
```
```{r 5LHStablepdf, eval=params$ispdf, cache=TRUE, dependson='5_LHS-load'}
printTableExperiment(LHS_parameter_values, caption = 'ACTUAL parameter values', output = "latex")
```
\newpage
```{r 5_LHS-pairs-plot, cache=TRUE, dependson='5_LHS-load'}
exp_LHS_pairsPlotName = paste0(plotDirectory, "LHS_parameter_values_pairsPlot.png")
plotScale = 10
png(exp_LHS_pairsPlotName,
width = 100 * plotScale, height = 100 * plotScale)
pairs(LHS_parameter_values, pch='·')
invisible(dev.off())
```
```{r 5_LHS-pairs-plot-print, dependson='5_LHS-pairs-plot', out.width = '100%'}
knitr::include_graphics(exp_LHS_pairsPlotName)
```
\newpage
## Experiment overview
```{r 5_run-experiments, include=FALSE}
exp_LHS_runs_dataFileName <- paste0(dataDirectory, "LHS_runs.RData")
### run the model for every setting according to the LHS design
if(!file.exists(exp_LHS_runs_dataFileName)) # you must delete it to create a new file
{
# build cluster
theCluster <- makeCluster(6)
clusterExport(theCluster,
list('hpcModel.run', 'getFitness', 'getCoevolutionCoefficient',
'LHS_parameter_values',
'initial_population_humans_default', 'initial_population_plants_default',
'max_iterations_default', 'reltol_exponential_default',
'save_trajectories_default', 'messages_default'),
envir = environment())
# loop
exp_LHS <- run.LHS.experiment(LHS_parameter_values, theCluster)
# stop cluster and clean
stopCluster(theCluster)
rm(theCluster)
gc()
# bind result
exp_LHS <- do.call('rbind', exp_LHS)
# build object
exp_LHS <- cbind(LHS_parameter_values, exp_LHS)
# save
save(exp_LHS, file = exp_LHS_runs_dataFileName)
}
```
```{r 5_run-experiments-load, include=FALSE, dependson='5_run-experiments'}
# load
load(exp_LHS_runs_dataFileName)
humans_exists <- !is.na(exp_LHS$humans) & exp_LHS$humans > 0
plants_exists <- !is.na(exp_LHS$plants) & exp_LHS$plants > 0
humans_fullEvolved <- exp_LHS$coevolution_coefficient_humans > 0.5 & humans_exists
plants_fullEvolved <- exp_LHS$coevolution_coefficient_plants > 0.5 & plants_exists
humans_mildEvolved <- exp_LHS$coevolution_coefficient_humans > 0 & exp_LHS$coevolution_coefficient_humans <= 0.5 & humans_exists
plants_mildEvolved <- exp_LHS$coevolution_coefficient_plants > 0 & exp_LHS$coevolution_coefficient_plants <= 0.5 & plants_exists
humans_notByFarEvolved <- exp_LHS$coevolution_coefficient_humans < -0.5 & humans_exists
plants_notByFarEvolved <- exp_LHS$coevolution_coefficient_plants < -0.5 & plants_exists
```
### End-states
**_Are there any humans?_**
```{r 5existHtablehtml, eval=!params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(humans_exists), "html")
```
```{r 5existHtablepdf, eval=params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(humans_exists), "latex")
```
**_Are there any plants?_**
```{r 5existPtablehtml, eval=!params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(plants_exists), "html")
```
```{r 5existPtablepdf, eval=params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(plants_exists), "latex")
```
**_Did humans not evolved by far? (coevolution coefficient is less than -0.5)_**
```{r 5notEvolvedHtablehtml, eval=!params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(humans_notByFarEvolved), "html")
```
```{r 5notEvolvedHtablepdf, eval=params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(humans_notByFarEvolved), "latex")
```
**_Did plants not evolved by far? (coevolution coefficient is less than -0.5)_**
```{r 5notEvolvedPtablehtml, eval=!params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(plants_notByFarEvolved), "html")
```
```{r 5notEvolvedPtablepdf, eval=params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(plants_notByFarEvolved), "latex")
```
**_Did humans evolved mildly? (coevolution coefficient is positive, but less than 0.5)_**
```{r 5mildEvolvedHtablehtml, eval=!params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(humans_mildEvolved), "html")
```
```{r 5mildEvolvedHtablepdf, eval=params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(humans_mildEvolved), "latex")
```
**_Did plants evolved mildly? (coevolution coefficient is positive, but less than 0.5)_**
```{r 5mildEvolvedPtablehtml, eval=!params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(plants_mildEvolved), "html")
```
```{r 5mildEvolvedPtablepdf, eval=params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(plants_mildEvolved), "latex")
```
**_Did humans evolved fully? (coevolution coefficient is greater than 0.5)_**
```{r 5evolvedHtablehtml, eval=!params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(humans_fullEvolved), "html")
```
```{r 5evolvedHtablepdf, eval=params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(humans_fullEvolved), "latex")
```
**_Did plants evolved fully? (coevolution coefficient is greater than 0.5)_**
```{r 5evolvedPtablehtml, eval=!params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(plants_fullEvolved), "html")
```
```{r 5evolvedPtablepdf, eval=params$ispdf, cache=TRUE, dependson='5_run-experiments-load'}
knitr::kable(table(plants_fullEvolved), "latex")
```
**_Population and output variables_**
```{r 5_variables-plots, include=FALSE, cache=TRUE, dependson=c('5_run-experiments', '5_run-experiments-load', '5_split-scenarios')}
exp_LHS_summaryPlotName = paste0(plotDirectory, "LHS_summary_plot.png")
plotScale = 10
png(exp_LHS_summaryPlotName,
width = 100 * plotScale,
height = 100 * plotScale
)
summaryData1 <- melt(exp_LHS[, c('humans', 'plants',
'coevolution_coefficient_humans', 'coevolution_coefficient_humans',
'dependency_coefficient_humans', 'dependency_coefficient_plants')])
summaryPlot1 <- ggplot(data = summaryData1, aes(value)) +
geom_histogram() +
facet_wrap(.~variable, ncol = 2, scales = "free")
summaryData2 <- melt(exp_LHS[, c('time_end', 'timing_humans', 'timing_plants')])
summaryPlot2 <- ggplot(data = summaryData2, aes(value)) +
geom_histogram() +
facet_wrap(.~variable, scales = "free")
ggpubr::ggarrange(summaryPlot1, summaryPlot2, nrow = 2, heights = c(3, 1))
dev.off()
```
```{r 5_variables-plot-print, dependson='5_variables-plots', out.width = '100%'}
knitr::include_graphics(exp_LHS_summaryPlotName)
```
### Trajectories
```{r 5_exp_LHS_trajectories, include=FALSE}
exp_LHS_trajectories_dataFileName <- paste0(dataDirectory, "LHS_trajectories.RData")
if(!file.exists(exp_LHS_trajectories_dataFileName))
{
set.seed(0)
numberOfTrajectories = 5
LHS_parameter_values_sample <- LHS_parameter_values[
## for sampling random trajectories
#sample(1:nrow(LHS_parameter_values), numberOfTrajectories)
### for selecting one random run of each of the following types:
c(
# No coevolution (not evolved, not mild evolved, but exists)
sample((1:nrow(LHS_parameter_values))[humans_notByFarEvolved & plants_notByFarEvolved], 1),
# Domestication without cultivation
sample((1:nrow(LHS_parameter_values))[humans_notByFarEvolved & plants_fullEvolved], 1),
# Cultivation without domestication
sample((1:nrow(LHS_parameter_values))[humans_fullEvolved & plants_notByFarEvolved], 1),
# Mild coevolution
sample((1:nrow(LHS_parameter_values))[humans_mildEvolved & plants_mildEvolved], 1),
# Coevolution
sample((1:nrow(LHS_parameter_values))[humans_fullEvolved & plants_fullEvolved], 1)
)
,]
# build cluster
theCluster <- makeCluster(6)
clusterExport(theCluster,
list('hpcModel.run', 'getFitness', 'getCoevolutionCoefficient',
'LHS_parameter_values_sample',
'initial_population_humans_default', 'initial_population_plants_default',
'max_iterations_default', 'reltol_exponential_default',
'save_trajectories_default', 'messages_default'),
envir = environment())
# loop
exp_LHS_trajectories_list <- run.LHS.experiment2(LHS_parameter_values_sample, theCluster)
# stop cluster and clean
stopCluster(theCluster)
rm(theCluster)
gc()
# build object
exp_LHS_trajectories <- c()
for (runIndex in 1:numberOfTrajectories)
{
exp_LHS_trajectories <- rbind(
exp_LHS_trajectories,
cbind(
run = rep(runIndex, nrow(exp_LHS_trajectories_list[[runIndex]])),
time = 1:(nrow(exp_LHS_trajectories_list[[runIndex]])),
exp_LHS_trajectories_list[[runIndex]][,c('humans', 'plants', 'coevolution_coefficient_humans', 'coevolution_coefficient_plants')]
)
)
}
exp_LHS_trajectories <- exp_LHS_trajectories[!is.na(exp_LHS_trajectories$humans) & exp_LHS_trajectories$humans > 0,]
rm(exp_LHS_trajectories_list)
# save
save(exp_LHS_trajectories, file = exp_LHS_trajectories_dataFileName)
}
```
```{r 5_overview-trajectories-plot, dependson=c('5_plot-functions', '5_exp_LHS_trajectories'), include=FALSE, cache=TRUE}
load(exp_LHS_trajectories_dataFileName)
source("library/plotting/plotTrajectoriesPerVariableGroup.R")
exp_LHS_trajectoriesOverviewPlotName = paste0(plotDirectory, "LHS_trajectoriesOverview.png")
plotScale = 10
png(exp_LHS_trajectoriesOverviewPlotName,
width = 100 * plotScale, height = 50 * plotScale)
plotTrajectoriesPerVariableGroup(exp_LHS_trajectories,
plot1_variables = c('humans', 'plants'),
plot2_variables = c('coevolution_coefficient_humans', 'coevolution_coefficient_plants'),
plot1_title = 'Populations',
plot2_title = 'Coevolution coefficients',
plot1_colors = c('blue', 'red'),
plot2_colors = c('blue', 'red'),
plot_legend_labels = c('Humans', 'Humans', 'Plants', 'Plants'),
plot_legend_line_x_positions = c(35, 39, 57, 61),
plot_legend_label_x_positions = c(40, 40, 62, 62),
plot1_mark_y_zero = FALSE,
plot2_mark_y_zero = TRUE,
plot_scale = plotScale)
dev.off()
```
```{r 5_overview-trajectories-plot-print, dependson='5_overview-trajectories-plot', out.width = '100%'}
knitr::include_graphics(exp_LHS_trajectoriesOverviewPlotName)
```
\newpage
```{r 5_overview-endstates-plot, include=FALSE, cache=TRUE}
exp_LHS_coevoEndStatePlotName = paste0(plotDirectory, "LHS_coevo_end_state.png")
plotScale = 10
png(exp_LHS_coevoEndStatePlotName,
width = 50 * plotScale, height = 50 * plotScale)
ggplot(data = exp_LHS, aes(x = coevolution_coefficient_humans, y = coevolution_coefficient_plants)) +
geom_point(alpha = 0.3) +
geom_hline(yintercept = 0) + geom_vline(xintercept = 0) +
geom_hline(yintercept = -1) + geom_vline(xintercept = -1) +
geom_hline(yintercept = 1) + geom_vline(xintercept = 1)
dev.off()
```
```{r 5_overview-endstates-plot-print, dependson='5_overview-endstates-plot', out.width = '100%'}
knitr::include_graphics(exp_LHS_coevoEndStatePlotName)
```
\newpage
## Random forest
### Optimisation
```{r 5_RF_tunning.preparation, include=FALSE}
set.seed(777)
exp_LHS_r <- exp_LHS[plants_exists,]
exp_LHS_r <- exp_LHS_r[sample(1:nrow(exp_LHS_r), nrow(exp_LHS_r)),]
exp_LHS_spl <- split(as.data.frame(exp_LHS_r),
f = factor(ceiling(seq_along(1:nrow(exp_LHS_r)) / 1000)))
Grid <- expand.grid(mtry = c(2:10), ntree = seq(500, 2000, 500))
```
```{r 5_RF_tunning_humans, include=FALSE}
exp_RF_coevolution_coefficient_humans_tunning_dataFileName <- paste0(dataDirectory, "RF_coevolution_coefficient_humans_tunning.RData")
if (!file.exists(exp_RF_coevolution_coefficient_humans_tunning_dataFileName)) # you must delete it to create a new file
{
# cluster and loop
theCluster <- makeCluster(6)
clusterExport(theCluster,
list('randomForest', 'Grid', 'LHS_parameter_names', 'exp_LHS_spl'),
envir = environment())
RF_coevolution_coefficient_humans_tunning <- parSapply(theCluster, 1:nrow(Grid), function(rowInGrid) {
aggregateStatisticPerGridRow <- c()
for (i in 1:10) {
exp_LHS_leftOneOut <- do.call(rbind, exp_LHS_spl[-i])
RF_coevolution_coefficient_humans <- randomForest(
as.formula(paste0("coevolution_coefficient_humans ~ ", paste(LHS_parameter_names, collapse = '+'))),
data = exp_LHS_leftOneOut,
mtry = Grid[rowInGrid, 1],
ntree = Grid[rowInGrid, 2]
)
aggregateStatistic_coevolution_coefficient_humans <-
mean((predict(RF_coevolution_coefficient_humans, exp_LHS_spl[[i]][, 1:15]) - exp_LHS_spl[[i]]$coevolution_coefficient_humans) ^ 2)
rm(RF_coevolution_coefficient_humans)
gc()
aggregateStatisticPerGridRow[i] <- aggregateStatistic_coevolution_coefficient_humans
}
return(mean(aggregateStatisticPerGridRow))
})
RF_coevolution_coefficient_humans_tunning <- cbind(Grid, meanPredict = RF_coevolution_coefficient_humans_tunning)
# save
save(RF_coevolution_coefficient_humans_tunning, file = exp_RF_coevolution_coefficient_humans_tunning_dataFileName)
# stop cluster and clean
stopCluster(theCluster)
rm(theCluster)
}
```
```{r 5_RF_coevolution_coefficient_humans_tunning-load, include=FALSE}
# load
load(exp_RF_coevolution_coefficient_humans_tunning_dataFileName)
```
```{r 5_RF_tunning_plants, include=FALSE, eval=FALSE}
exp_RF_coevolution_coefficient_plants_tunning_dataFileName <- paste0(dataDirectory, "RF_coevolution_coefficient_plants_tunning.RData")
if (!file.exists(exp_RF_coevolution_coefficient_plants_tunning_dataFileName)) # you must delete to create a new file
{
# cluster and loop
theCluster <- makeCluster(6)
clusterExport(theCluster,
list('randomForest', 'Grid', 'LHS_parameter_names', 'exp_LHS_spl'),
envir = environment())
RF_coevolution_coefficient_plants_tunning <- parSapply(theCluster, 1:nrow(Grid), function(rowInGrid) {
aggregateStatisticPerGridRow <- c()
for (i in 1:10) {
exp_LHS_leftOneOut <- do.call(rbind, exp_LHS_spl[-i])
RF_coevolution_coefficient_plants <- randomForest(
as.formula(paste0("coevolution_coefficient_plants ~ ", paste(LHS_parameter_names, collapse = '+'))),
data = exp_LHS_leftOneOut,
mtry = Grid[rowInGrid, 1],
ntree = Grid[rowInGrid, 2]
)
aggregateStatistic_coevolution_coefficient_plants <-
mean((predict(RF_coevolution_coefficient_plants, exp_LHS_spl[[i]][, 1:15]) - exp_LHS_spl[[i]]$coevolution_coefficient_plants) ^ 2)
rm(RF_coevolution_coefficient_plants)
gc()
aggregateStatisticPerGridRow[i] <- aggregateStatistic_coevolution_coefficient_plants
}
return(mean(aggregateStatisticPerGridRow))
})
RF_coevolution_coefficient_plants_tunning <- cbind(Grid, meanPredict = RF_coevolution_coefficient_plants_tunning)
# save
save(RF_coevolution_coefficient_plants_tunning, file = exp_RF_coevolution_coefficient_plants_tunning_dataFileName)
}
```
```{r 5_RF_coevolution_coefficient_plants_tunning-load, include=FALSE}
# load
load(exp_RF_coevolution_coefficient_plants_tunning_dataFileName)
```
```{r 5_RF_coevo_tunning-plot, dependson=c('5_RF_coevolution_coefficient_humans_tunning-load', '5_RF_coevolution_coefficient_humans_tunning-load'), out.width = '100%'}
temporaryData <- cbind(
output.var = c(
rep('coevolution_coefficient_humans', nrow(RF_coevolution_coefficient_humans_tunning)),
rep('coevolution_coefficient_plants', nrow(RF_coevolution_coefficient_plants_tunning))
),
rbind(RF_coevolution_coefficient_humans_tunning, RF_coevolution_coefficient_plants_tunning)
)
exp_LHS_coevo_optimisationPlotName = paste0(plotDirectory, "LHS_coevo_optimisationPlot.png")
plotScale = 2
png(exp_LHS_coevo_optimisationPlotName,
width = 320 * plotScale, height = 180 * plotScale)
ggplot(temporaryData, aes(x = ntree, y = mtry)) +
geom_tile(aes(fill = meanPredict), colour = "white") +
scale_fill_gradient(low = "white", high = "black") +
geom_text(aes(label = round(meanPredict, 5)), color = "steelblue", size = plotScale * 2.5) +
facet_wrap(.~output.var) + theme_bw()
dev.off()
```
```{r 5_RF_coevo_tunning-plot-print, dependson='5_RF-coevo-plots', out.width = '100%'}
knitr::include_graphics(paste0(exp_LHS_coevo_optimisationPlotName))
```
\newpage
### Coevolution coefficients
Only using those runs with any humans (coevolution_coefficient_humans) and plants (coevolution_coefficient_humans) at the end-state.
```{r 5_RF-coevolution_coefficient_humans, include=FALSE, cache=TRUE}
exp_RF_coevolution_coefficient_humans_dataFileName <- paste0(dataDirectory, "RF_coevolution_coefficient_humans.RData")
if(!file.exists(exp_RF_coevolution_coefficient_humans_dataFileName))
{
RF_coevolution_coefficient_humans <-
randomForest::randomForest(
as.formula(paste0("coevolution_coefficient_humans ~ ", paste(LHS_parameter_names, collapse = '+'))),
data = exp_LHS[humans_exists,],
mtry = 5,
ntree = 1000,
importance = T
)
# save
save(RF_coevolution_coefficient_humans, file = exp_RF_coevolution_coefficient_humans_dataFileName)
}
```
```{r 5_RF_coevolution_coefficient_plants, include=FALSE, cache=TRUE}
exp_RF_coevolution_coefficient_plants_dataFileName <- paste0(dataDirectory, "RF_coevolution_coefficient_plants.RData")
if(!file.exists(exp_RF_coevolution_coefficient_plants_dataFileName))
{
RF_coevolution_coefficient_plants <-
randomForest::randomForest(
as.formula(paste0("coevolution_coefficient_plants ~ ", paste(LHS_parameter_names, collapse = '+'))),
data = exp_LHS[plants_exists,],
mtry = 5,
ntree = 1000,
importance = T
)
# save
save(RF_coevolution_coefficient_plants, file = exp_RF_coevolution_coefficient_plants_dataFileName)
}
```
```{r 5_RF_coevo-load, include=FALSE}
# load
load(exp_RF_coevolution_coefficient_humans_dataFileName)
load(exp_RF_coevolution_coefficient_plants_dataFileName)
```
```{r 5_RF-coevo-plots, include=FALSE, cache=TRUE, dependson=c('5_plot-functions', '5_RF_coevo-load')}
source("library/plotting/variableImportancePlot.R") # sometimes bookdown rendering will act up and ignore the chunks loading functions
exp_LHS_coevo_variableImportancePlotName = paste0(plotDirectory, "LHS_coevo_variableImportancePlot.png")
plotScale = 2
png(exp_LHS_coevo_variableImportancePlotName,
width = 960 * plotScale, height = 380 * plotScale)
variableImportancePlotPair(RF_coevolution_coefficient_humans,
RF_coevolution_coefficient_plants,
plot_scale = plotScale,
color_variable_map = parameterColorMap)
dev.off()
```
```{r 5_RF-coevo-plot-print, dependson='5_RF-coevo-plots', out.width = '100%'}
knitr::include_graphics(paste0(exp_LHS_coevo_variableImportancePlotName))
```
\newpage
### Dependency coefficients
Only using those runs with any humans (dependency_coefficient_humans) and plants (dependency_coefficient_plants) at the end-state.
```{r 5_RF-dependency_coefficient_humans, include=FALSE, cache=TRUE}
exp_RF_dependency_coefficient_humans_dataFileName <- paste0(dataDirectory, "RF_dependency_coefficient_humans.RData")
if(!file.exists(exp_RF_dependency_coefficient_humans_dataFileName))
{
RF_dependency_coefficient_humans <-
randomForest::randomForest(
as.formula(paste0("dependency_coefficient_humans ~ ", paste(LHS_parameter_names, collapse = '+'))),
data = exp_LHS[humans_exists,],
mtry = 5,
ntree = 1000,
importance = T
)
# save
save(RF_dependency_coefficient_humans, file = exp_RF_dependency_coefficient_humans_dataFileName)
}
```
```{r 5_RF-dependency_coefficient_plants, include=FALSE, cache=TRUE}
exp_RF_dependency_coefficient_plants_dataFileName <- paste0(dataDirectory, "RF_dependency_coefficient_plants.RData")
if(!file.exists(exp_RF_dependency_coefficient_plants_dataFileName))
{
RF_dependency_coefficient_plants <-
randomForest::randomForest(
as.formula(paste0("dependency_coefficient_plants ~ ", paste(LHS_parameter_names, collapse = '+'))),
data = exp_LHS[plants_exists,],
mtry = 5,
ntree = 1000,
importance = T
)
# save
save(RF_dependency_coefficient_plants, file = exp_RF_dependency_coefficient_plants_dataFileName)
}
```
```{r 5_RF_depend-load, include=FALSE}
# load
load(exp_RF_dependency_coefficient_humans_dataFileName)
load(exp_RF_dependency_coefficient_plants_dataFileName)
```
```{r 5_RF-depend-plots, include=FALSE, cache=TRUE, dependson=c('5_plot-functions', '5_RF_depend-load')}
exp_LHS_dependency_coefficient_variableImportancePlotName = paste0(plotDirectory, "LHS_dependency_coefficient_variableImportancePlot.png")
plotScale = 2
png(exp_LHS_dependency_coefficient_variableImportancePlotName, width = 960 * plotScale, height = 360 * plotScale)
variableImportancePlotPair(RF_dependency_coefficient_humans,
RF_dependency_coefficient_plants,
plot_scale = plotScale,
color_variable_map = parameterColorMap)
dev.off()
```
```{r 5_RF-dependency_coefficient_humans-plot-print, dependson='5_RF-depend-plots', out.width = '100%'}
knitr::include_graphics(exp_LHS_dependency_coefficient_variableImportancePlotName)
```
### Timings
Only using those runs with successful human (`timing_humans`) and plant (`timing_.P_plants`) evolution.
```{r 5_RF-timing.H, include=FALSE, cache=TRUE}
exp_RF_timing_humans_dataFileName <- paste0(dataDirectory, "RF_timing_humans.RData")
if(!file.exists(exp_RF_timing_humans_dataFileName))
{
RF_timing_humans <-
randomForest::randomForest(
as.formula(paste0("timing_humans ~ ", paste(LHS_parameter_names, collapse = '+'))),
data = exp_LHS[humans_fullEvolved,], # filter out cases where humans did not evolved towards n
mtry = 5,
ntree = 1000,
importance = T
)
# save
save(RF_timing_humans, file = exp_RF_timing_humans_dataFileName)
}
```
```{r 5_RF-timing.P, include=FALSE, cache=TRUE}
exp_RF_timing_plants_dataFileName <- paste0(dataDirectory, "RF_timing_plants.RData")
if(!file.exists(exp_RF_timing_plants_dataFileName))
{
RF_timing_plants <-
randomForest::randomForest(
as.formula(paste0("timing_plants ~ ", paste(LHS_parameter_names, collapse = '+'))),
data = exp_LHS[plants_fullEvolved,], # filter out cases where plants did not evolved towards n
mtry = 5,
ntree = 1000,
importance = T
)
# save
save(RF_timing_plants, file = exp_RF_timing_plants_dataFileName)
}
```
```{r 5_RF_timing-load, include=FALSE}
# load
load(exp_RF_timing_humans_dataFileName)
load(exp_RF_timing_plants_dataFileName)
```
```{r 5_RF-timing-plots, include=FALSE, cache=TRUE, dependson=c('5_plot-functions', '5_RF_timing-load')}
exp_LHS_timing_variableImportancePlotName = paste0(plotDirectory, "LHS_timing_variableImportancePlot.png")
plotScale = 2
png(exp_LHS_timing_variableImportancePlotName, width = 960 * plotScale, height = 360 * plotScale)
variableImportancePlotPair(RF_timing_humans,
RF_timing_plants,
plot_scale = plotScale,
color_variable_map = parameterColorMap)
dev.off()
```
```{r 5_RF-timing_humans-plot-print, dependson='5_RF-timing-plots', out.width = '100%'}
knitr::include_graphics(exp_LHS_timing_variableImportancePlotName)
```
\newpage
## Visualisation of parameter effect
### The effect on coevolution coefficients of the utility per capita of type n humans to plants ($\bar{U}_{H_{n}P}$)
\sectionmark{Visualising effect of $\bar{U}_{H_{n}P}$}
```{r 5_overview-collapsed-plot, include=FALSE, cache=TRUE, dependson=c('5_plot-functions', '5_run-experiments-load') }
source("library/plotting/bifurcationPlot_twoVariables.R")
exp_LHS_coevolution_coefficients_bifurcationPlot_twoVariables_plotName = paste0(plotDirectory, "LHS_coevolution_coefficients_bifurcationPlot_twoVariables.png")
plotScale = 10
#svg('plots/5_collapsed.svg', width=10, height=10)
png(exp_LHS_coevolution_coefficients_bifurcationPlot_twoVariables_plotName,
width = 100 * plotScale, height = 100 * plotScale)
bifurcationPlot_twoVariables(exp_LHS,
parameter_x = 'utility_per_capita_type_n_humans_to_plants',
variable_1 = 'coevolution_coefficient_humans',
variable_2 = 'coevolution_coefficient_plants',
axis_x_label = '',
axis_y_label = 'coevolution coefficient',
variable_1_label = 'Humans',
variable_2_label = 'Plants',
plot_scale = plotScale)
dev.off()
```
```{r 5_overview-collapsed-plot-print, dependson='5_overview-collapsed-plot', out.width = '100%'}
knitr::include_graphics(exp_LHS_coevolution_coefficients_bifurcationPlot_twoVariables_plotName)
```
\newpage
### All parameters
**_Coevolution coefficients_**
```{r 5_scenarios-coevo-plots, include=FALSE, cache=TRUE, dependson=c('5_plot-functions', '5_run-experiments', '5_run-experiments-load', '5_split-scenarios')}
source("library/plotting/bifurcationPlot_twoVariables_per_parameter.R")
exp_LHS_coevolution_coefficients_bifurcationPlot_twoVariables_per_parameter_plotName = paste0(plotDirectory, "LHS_coevolution_coefficients_bifurcationPlot_twoVariables_per_parameter.png")
plotScale = 10
png(exp_LHS_coevolution_coefficients_bifurcationPlot_twoVariables_per_parameter_plotName,
width = 200 * plotScale,
height = 200 * plotScale
)
bifurcationPlot_twoVariables_per_parameter(exp_LHS,
LHS_parameter_names,
variable_1 = 'coevolution_coefficient_humans',
variable_2 = 'coevolution_coefficient_plants',
axis_x_label = 'parameter values',
axis_y_label = 'coevolution coefficient',
variable_1_label = 'Humans',
variable_2_label = 'Plants',
facet_wrap_number_of_rows = 4,
plot_scale = plotScale)
dev.off()
```
```{r 5_scenarios-coevo-plot-print, dependson='5_scenarios-humansImprove-coevo-plot', out.width = '100%'}
knitr::include_graphics(exp_LHS_coevolution_coefficients_bifurcationPlot_twoVariables_per_parameter_plotName)
```
**_Dependency coefficients_**
```{r 5_scenarios-depend-plots, include=FALSE, cache=TRUE, dependson=c('5_plot-functions', '5_run-experiments', '5_run-experiments-load', '5_split-scenarios')}
source("library/plotting/bifurcationPlot_twoVariables_per_parameter.R")
exp_LHS_dependency_coefficients_bifurcationPlot_twoVariables_per_parameter_plotName = paste0(plotDirectory, "LHS_dependency_coefficients_bifurcationPlot_twoVariables_per_parameter.png")
plotScale = 10
png(exp_LHS_dependency_coefficients_bifurcationPlot_twoVariables_per_parameter_plotName,
width = 200 * plotScale,
height = 200 * plotScale
)
bifurcationPlot_twoVariables_per_parameter(exp_LHS,
LHS_parameter_names,
variable_1 = 'dependency_coefficient_humans',
variable_2 = 'dependency_coefficient_plants',
axis_x_label = 'parameter values',
axis_y_label = 'dependency coefficient',
variable_1_label = 'Humans',
variable_2_label = 'Plants',
plot_scale = plotScale,
facet_wrap_number_of_rows = 4,
axis_y_log = TRUE)
dev.off()
```
```{r 5_scenarios-depend-plot-print, dependson='5_scenarios-humansImprove-depend-plot', out.width = '100%'}
knitr::include_graphics(exp_LHS_dependency_coefficients_bifurcationPlot_twoVariables_per_parameter_plotName)
```
**_Timings_**
```{r 5_scenarios-timing-plots, include=FALSE, cache=TRUE, dependson=c('5_plot-functions', '5_run-experiments', '5_run-experiments-load', '5_split-scenarios')}
exp_LHS_timing_bifurcationPlot_twoVariables_per_parameter_plotName = paste0(plotDirectory, "LHS_timing_bifurcationPlot_twoVariables_per_parameter.png")
plotScale = 10
png(exp_LHS_timing_bifurcationPlot_twoVariables_per_parameter_plotName,
width = 200 * plotScale,
height = 200 * plotScale
)
bifurcationPlot_twoVariables_per_parameter(exp_LHS,
LHS_parameter_names,
variable_1 = 'timing_humans',
variable_2 = 'timing_plants',
axis_x_label = 'parameter values',
axis_y_label = 'timing of change',
variable_1_label = 'Humans',
variable_2_label = 'Plants',
plot_scale = plotScale,
facet_wrap_number_of_rows = 4,