Pakistan Conflict Modeling

Introduction

The purpose of this document is to explore different modeling approaches with the compiled Pakistan Conflict data set. The .rmd file “pak_conflict_urban_20230325.Rmd” hosts the code for data wrangling.

##Set working directory
##r code is the same level as data
##data has the following subfolders - conflict, IPUMS, NASA HiMAT, population, Suicide, and Water Basins

setwd("~/Desktop/University of Utah PhD /Research/r_code")

## Load applicable libraries

library(mapview) #mapping package
library(raster) #raster data manipulation (Climate Data)
library(RColorBrewer) #color palettes for visualization
library(sf) #simple features for spatial data
library(tmap) #mapping package
library(viridis) #color palette for visualization
library(ncdf4) #working with netCDF files (Climate Data)
library(leaflet) #basemaps for mapview
library(ggplot2) #better figures
library(ggcorrplot) #Load the correlation plot package
library(plotly) #interactive figures
library(maps) #mapping 
library(kableExtra) #creating better tables and outputs
library(dplyr) #count and data functions
library(reshape2) ## Package used to reformat data - wide to long
library(tidyverse) ##Formatting dataframes, merge, and join
library(stargazer) ##Formatting model outputs to tables
library(pscl) ##Used to calculate pseudo r^2 values for log regression models (poisson)
library(janitor) ##Used to count/provide summaries for dataframes
library(jtools) ##Used to produce aesthetically pleasing model output tables
library(huxtable) ##Used in conjunction with jtools to export model outputs
library(flextable) ##Needed to knit. linked to the janitor library
library(geomerge) ##Merges conducts a series of spatial joins to combine geospatial data ##Andrew Linke recommendation!!
library(tidyr) ##reshaping data formats long/wide
library(lubridate) ##Helps dealing with date/time ##Needed for geomerge
library(kohonen) #self organizing maps
library(ggpubr) ##publication ready tables
library(stats) ##Stats functions
library(lme4) ##linear mixed effects model

## Read in the "wrangled" long format dataframe from "pak_conflict_urban_20230325.Rmd"

con.df = read.csv("../data/conflict/complete_dataframe_long_20230405.csv")

## Check
#head(con.df)
#names(con.df)

## Drop the X column
con.df = con.df[,-1]

## Check
names(con.df)

##  [1] "districts"  "longitude"  "latitude"   "year"       "pop"       
##  [6] "spei"       "dep.index"  "built"      "temp.jan"   "temp.apr"  
## [11] "temp.jul"   "temp.oct"   "total.con"  "protests"   "battles"   
## [16] "expl"       "riots"      "vac"        "sd"         "precip.jan"
## [21] "precip.apr" "precip.jul" "precip.oct"

## Remove the coordinates from the dataframe

## Add new variables/features that are ratios/normalized for populations. 
## **Note** Need to impute the 2022 population before calculating these ratios!!!!

## Impute the population for 2022 - completed this in the data wrangling script


con.df = con.df %>% 
  mutate(total.con.percapita = total.con/pop) %>% 
  mutate(protests.percapita = protests/pop) %>% 
  mutate(battles.percapita = battles/pop) %>% 
  mutate(expl.percapita = expl/pop) %>% 
  mutate(riots.percapita = protests/pop) %>% 
  mutate(vac.percapita = vac/pop) %>% 
  mutate(sd.percapita = sd/pop) 


## Check
head(con.df)

districts	longitude	latitude	year	pop	spei	dep.index	built	temp.jan	temp.apr	temp.jul	temp.oct	total.con	protests	battles	expl	riots	vac	sd	precip.jan	precip.apr	precip.jul	precip.oct	total.con.percapita	protests.percapita	battles.percapita	expl.percapita	riots.percapita	vac.percapita	sd.percapita
Abbottabad	73.3	34.1	2010	1204572	0.7	73	1.09	12.2	22.3	26.2	21	24	17	0	0	7	0	0	0.898	1.95	7.32	0.414	1.99e-05	1.41e-05	0	0	1.41e-05	0	0
Abbottabad	73.3	34.1	2011	1225491	1.33	73	1.09	8.94	18.5	25.5	20	10	10	3	7	2	2	0	0.704	3.55	3.02	1.59	8.16e-06	8.16e-06	2.45e-06	5.71e-06	8.16e-06	1.63e-06	0
Abbottabad	73.3	34.1	2012	1244756	-0.197	73	1.09	6.42	18.5	28.1	18.9	12	15	1	5	0	3	0	1.71	2.92	2.39	0.71	9.64e-06	1.21e-05	8.03e-07	4.02e-06	1.21e-05	2.41e-06	0
Abbottabad	73.3	34.1	2013	1264224	0.965	73	1.09	8.77	18.8	26.5	20.8	12	16	1	3	2	2	0	1.01	2.75	3.18	0.892	9.49e-06	1.27e-05	7.91e-07	2.37e-06	1.27e-05	1.58e-06	0
Abbottabad	73.3	34.1	2014	1282908	0.726	73	1.09	9.73	18.1	26.2	19.2	6	14	4	4	1	1	0	0.676	1.93	3.76	1.28	4.68e-06	1.09e-05	3.12e-06	3.12e-06	1.09e-05	7.79e-07	0
Abbottabad	73.3	34.1	2015	1302026	1.69	73	1.09	9.84	19.4	25	19.7	10	11	7	2	1	3	0	1.32	3.29	6.28	1.66	7.68e-06	8.45e-06	5.38e-06	1.54e-06	8.45e-06	2.3e-06	0

## Check for NAs

summary(con.df)

##   districts           longitude        latitude          year     
##  Length:2080        Min.   :63.05   Min.   :24.24   Min.   :2010  
##  Class :character   1st Qu.:68.42   1st Qu.:28.48   1st Qu.:2013  
##  Mode  :character   Median :71.29   Median :31.31   Median :2016  
##                     Mean   :70.74   Mean   :31.03   Mean   :2016  
##                     3rd Qu.:73.21   3rd Qu.:33.95   3rd Qu.:2019  
##                     Max.   :76.72   Max.   :36.58   Max.   :2022  
##       pop               spei            dep.index         built        
##  Min.   :  19632   Min.   :-2.49610   Min.   :32.88   Min.   : 0.0000  
##  1st Qu.: 210256   1st Qu.:-0.75322   1st Qu.:76.79   1st Qu.: 0.0151  
##  Median : 754223   Median : 0.12693   Median :82.27   Median : 0.2054  
##  Mean   :1299273   Mean   : 0.02997   Mean   :79.82   Mean   : 2.3783  
##  3rd Qu.:1781504   3rd Qu.: 0.75218   3rd Qu.:85.37   3rd Qu.: 1.9565  
##  Max.   :9503689   Max.   : 2.14594   Max.   :93.93   Max.   :88.2998  
##     temp.jan          temp.apr         temp.jul          temp.oct     
##  Min.   :-25.803   Min.   :-12.98   Min.   : 0.2799   Min.   :-13.01  
##  1st Qu.:  8.874   1st Qu.: 20.87   1st Qu.:27.9947   1st Qu.: 21.30  
##  Median : 15.583   Median : 29.36   Median :32.9042   Median : 28.23  
##  Mean   : 11.637   Mean   : 25.27   Mean   :30.6372   Mean   : 24.32  
##  3rd Qu.: 19.630   3rd Qu.: 34.33   3rd Qu.:36.3789   3rd Qu.: 31.99  
##  Max.   : 26.985   Max.   : 41.04   Max.   :42.7885   Max.   : 37.27  
##    total.con         protests         battles            expl        
##  Min.   :  0.00   Min.   :  0.00   Min.   : 0.000   Min.   :  0.000  
##  1st Qu.:  4.00   1st Qu.:  1.00   1st Qu.: 0.000   1st Qu.:  0.000  
##  Median : 13.00   Median :  6.00   Median : 0.000   Median :  0.000  
##  Mean   : 38.25   Mean   : 25.47   Mean   : 2.631   Mean   :  2.806  
##  3rd Qu.: 38.00   3rd Qu.: 20.25   3rd Qu.: 2.000   3rd Qu.:  2.000  
##  Max.   :914.00   Max.   :497.00   Max.   :80.000   Max.   :130.000  
##      riots             vac               sd            precip.jan      
##  Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   :0.000222  
##  1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.:0.060707  
##  Median : 0.000   Median : 0.000   Median : 0.0000   Median :0.354124  
##  Mean   : 1.652   Mean   : 1.826   Mean   : 0.3601   Mean   :0.640124  
##  3rd Qu.: 1.000   3rd Qu.: 2.000   3rd Qu.: 0.0000   3rd Qu.:0.887848  
##  Max.   :54.000   Max.   :98.000   Max.   :13.0000   Max.   :5.402182  
##    precip.apr         precip.jul          precip.oct        total.con.percapita
##  Min.   :0.000358   Min.   : 0.000232   Min.   :0.0002222   Min.   :0.000e+00  
##  1st Qu.:0.134405   1st Qu.: 0.573645   1st Qu.:0.0295241   1st Qu.:5.484e-06  
##  Median :0.537943   Median : 1.576505   Median :0.1878518   Median :2.132e-05  
##  Mean   :0.891818   Mean   : 2.054646   Mean   :0.4022050   Mean   :4.223e-05  
##  3rd Qu.:1.378782   3rd Qu.: 3.003253   3rd Qu.:0.6553435   3rd Qu.:5.221e-05  
##  Max.   :5.318427   Max.   :10.785484   Max.   :2.9658391   Max.   :6.662e-04  
##  protests.percapita  battles.percapita   expl.percapita     
##  Min.   :0.000e+00   Min.   :0.000e+00   Min.   :0.000e+00  
##  1st Qu.:1.481e-06   1st Qu.:0.000e+00   1st Qu.:0.000e+00  
##  Median :6.634e-06   Median :0.000e+00   Median :0.000e+00  
##  Mean   :2.387e-05   Mean   :2.700e-06   Mean   :3.058e-06  
##  3rd Qu.:2.698e-05   3rd Qu.:2.524e-06   3rd Qu.:2.405e-06  
##  Max.   :4.359e-04   Max.   :2.410e-04   Max.   :2.869e-04  
##  riots.percapita     vac.percapita        sd.percapita      
##  Min.   :0.000e+00   Min.   :0.000e+00   Min.   :0.000e+00  
##  1st Qu.:1.481e-06   1st Qu.:0.000e+00   1st Qu.:0.000e+00  
##  Median :6.634e-06   Median :0.000e+00   Median :0.000e+00  
##  Mean   :2.387e-05   Mean   :1.816e-06   Mean   :3.603e-07  
##  3rd Qu.:2.698e-05   3rd Qu.:1.558e-06   3rd Qu.:0.000e+00  
##  Max.   :4.359e-04   Max.   :4.947e-05   Max.   :1.929e-05

## Missing SPEI values for 2022
## For testing sake imputing just the median

#con.df$spei[is.na(con.df$spei)] = median(con.df$spei, na.rm = TRUE)

## Create a histogram of the features

h1 = con.df %>% 
  gghistogram(x = "pop")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h2 = con.df %>% 
  gghistogram(x = "spei")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h3 = con.df %>% 
  gghistogram(x = "dep.index")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h4 = con.df %>% 
  gghistogram(x = "built")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h5 = con.df %>% 
  gghistogram(x = "total.con")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

## Visualize 
ggarrange(h1, h2, h3, h4, h5)

## SPEI is normally distributed

## Create a histogram of the features

h6 = con.df %>% 
  gghistogram(x = "total.con.percapita")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h7 = con.df %>% 
  gghistogram(x = "protests.percapita")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h8 = con.df %>% 
  gghistogram(x = "battles.percapita")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h9 = con.df %>% 
  gghistogram(x = "expl.percapita")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h10 = con.df %>% 
  gghistogram(x = "riots.percapita")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h11 = con.df %>% 
  gghistogram(x = "vac.percapita")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h12 = con.df %>% 
  gghistogram(x = "sd.percapita")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

## Visualize 
ggarrange(h6, h7, h8, h9, h10, h11, h12)

## Create a histogram of the features

h13 = con.df %>% 
  gghistogram(x = "temp.jan")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h14 = con.df %>% 
  gghistogram(x = "temp.apr")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h15 = con.df %>% 
  gghistogram(x = "temp.jul")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h16 = con.df %>% 
  gghistogram(x = "temp.oct")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h17 = con.df %>% 
  gghistogram(x = "precip.jan")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h18 = con.df %>% 
  gghistogram(x = "precip.apr")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h19 = con.df %>% 
  gghistogram(x = "precip.jul")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h20 = con.df %>% 
  gghistogram(x = "precip.oct")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

## Visualize 
ggarrange(h13, h14, h15, h16, h17, h18, h19, h20)

Note:

A few of our variables/features are skewed so we will log transform them and scale them before our analysis.

## SPEI Creates NaNs of you try to log transform - plus it is normally distributed
## log transforming 0 results in NaNs
## Reminder scale() in R converts to z-scores
## Maybe min/max scaling is the way?

var.names = c("pop", "dep.index", "built", "total.con", "protests", "battles", "expl", "riots", "vac", "sd", "total.con.percapita", "protests.percapita", "battles.percapita", "expl.percapita", "riots.percapita", "vac.percapita", "sd.percapita", "temp.jan", "temp.apr", "temp.jul", "temp.oct", "precip.jan", "precip.apr", "precip.jul", "precip.oct")


## Create a helper function to log transform
## Courtesy of Simon

## inverse hyperbolic sin transformation
# This should work with -ve and +ve values (and zeros!). In R you can do this with:

ihs <- function(x) {
 y <- log(x + sqrt(x^2 + 1))
  return(y)
}

## back transform with
hs = function(x) {
  0.5*exp(-x)*(exp(2*x) - 1)
}

## Create a new data frame with the log transformed and scaled data

con.df.scale = con.df %>% 
  mutate_at(var.names, ihs)

 

## Check
head(con.df.scale)

districts	longitude	latitude	year	pop	spei	dep.index	built	temp.jan	temp.apr	temp.jul	temp.oct	total.con	protests	battles	expl	riots	vac	sd	precip.jan	precip.apr	precip.jul	precip.oct	total.con.percapita	protests.percapita	battles.percapita	expl.percapita	riots.percapita	vac.percapita	sd.percapita
Abbottabad	73.3	34.1	2010	14.7	0.7	4.98	0.943	3.2	3.8	3.96	3.74	3.87	3.53	0	0	2.64	0	0	0.808	1.42	2.69	0.403	1.99e-05	1.41e-05	0	0	1.41e-05	0	0
Abbottabad	73.3	34.1	2011	14.7	1.33	4.98	0.943	2.89	3.61	3.93	3.69	3	3	1.82	2.64	1.44	1.44	0	0.656	1.98	1.82	1.24	8.16e-06	8.16e-06	2.45e-06	5.71e-06	8.16e-06	1.63e-06	0
Abbottabad	73.3	34.1	2012	14.7	-0.197	4.98	0.943	2.56	3.61	4.03	3.63	3.18	3.4	0.881	2.31	0	1.82	0	1.31	1.79	1.6	0.661	9.64e-06	1.21e-05	8.03e-07	4.02e-06	1.21e-05	2.41e-06	0
Abbottabad	73.3	34.1	2013	14.7	0.965	4.98	0.943	2.87	3.63	3.97	3.73	3.18	3.47	0.881	1.82	1.44	1.44	0	0.889	1.74	1.87	0.803	9.49e-06	1.27e-05	7.91e-07	2.37e-06	1.27e-05	1.58e-06	0
Abbottabad	73.3	34.1	2014	14.8	0.726	4.98	0.943	2.97	3.59	3.96	3.65	2.49	3.33	2.09	2.09	0.881	0.881	0	0.633	1.41	2.04	1.06	4.68e-06	1.09e-05	3.12e-06	3.12e-06	1.09e-05	7.79e-07	0
Abbottabad	73.3	34.1	2015	14.8	1.69	4.98	0.943	2.98	3.66	3.91	3.67	3	3.09	2.64	1.44	0.881	1.82	0	1.09	1.91	2.54	1.28	7.68e-06	8.45e-06	5.38e-06	1.54e-06	8.45e-06	2.3e-06	0

## Check a few variables to see if the variables are still skewed

h.precip = con.df.scale %>% 
  gghistogram(x = "precip.oct")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h.temp = con.df.scale %>% 
  gghistogram(x = "temp.apr")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

h.pop = con.df.scale %>% 
  gghistogram(x = "pop")

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

ggarrange(h.precip, h.temp, h.pop)

Note: The IHS transformation did not really take out the skew of our variables

## This code is from Simon and can be used for data transformations

## modified log transformation
#log(x+1)
#means that a zero remains a zero. But if your values are quite large you can get a weird spike at some distance from the rest of the data

#log(x + (min(x) / 2))
# this avoids the weird spike

## inverse hyperbolic sin transformation
# This should work with -ve and +ve values (and zeros!). In R you can do this with:

#ihs <- function(x) {
 #y <- log(x + sqrt(x^2 + 1))
  #return(y)
#}

## back transform with
#hs = function(x) {
  #0.5*exp(-x)*(exp(2*x) - 1)
#}

## Find the NAs
#test.df.nas = test.df %>% 
  #filter_all(any_vars(. %in% c(NA)))

## Check
#test.df.nas

## Use minmax scaling

## Define the maxs
maxs = apply(con.df.scale[ , c(var.names, "spei")], 2, max)

## Define the mins
mins = apply(con.df.scale[ , c(var.names, "spei")], 2, min)

## Build a dataframe with the scaled features
scaled.features = as.data.frame(scale(con.df.scale[ , c(var.names, "spei")],
                                      center = mins,
                                      scale = maxs-mins))


## Rebuild the complete dataframe (without the lat/long)
con.df.scale.v2 = data.frame(districts = con.df$districts, year = con.df$year, scaled.features)


## Check
summary(con.df.scale.v2)

##   districts              year           pop           dep.index     
##  Length:2080        Min.   :2010   Min.   :0.0000   Min.   :0.0000  
##  Class :character   1st Qu.:2013   1st Qu.:0.3835   1st Qu.:0.8080  
##  Mode  :character   Median :2016   Median :0.5902   Median :0.8737  
##                     Mean   :2016   Mean   :0.5593   Mean   :0.8358  
##                     3rd Qu.:2019   3rd Qu.:0.7292   3rd Qu.:0.9089  
##                     Max.   :2022   Max.   :1.0000   Max.   :1.0000  
##      built            total.con         protests         battles      
##  Min.   :0.000000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.002919   1st Qu.:0.2789   1st Qu.:0.1277   1st Qu.:0.0000  
##  Median :0.039431   Median :0.4340   Median :0.3610   Median :0.0000  
##  Mean   :0.144928   Mean   :0.4218   Mean   :0.3589   Mean   :0.1740  
##  3rd Qu.:0.275218   3rd Qu.:0.5766   3rd Qu.:0.5363   3rd Qu.:0.2844  
##  Max.   :1.000000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##       expl            riots             vac               sd         
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.00000  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.00000  
##  Median :0.0000   Median :0.0000   Median :0.0000   Median :0.00000  
##  Mean   :0.1557   Mean   :0.1472   Mean   :0.1358   Mean   :0.07182  
##  3rd Qu.:0.2596   3rd Qu.:0.1882   3rd Qu.:0.2735   3rd Qu.:0.00000  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.00000  
##  total.con.percapita protests.percapita battles.percapita expl.percapita    
##  Min.   :0.000000    Min.   :0.000000   Min.   :0.00000   Min.   :0.000000  
##  1st Qu.:0.008231    1st Qu.:0.003398   1st Qu.:0.00000   1st Qu.:0.000000  
##  Median :0.032007    Median :0.015218   Median :0.00000   Median :0.000000  
##  Mean   :0.063380    Mean   :0.054759   Mean   :0.01120   Mean   :0.010661  
##  3rd Qu.:0.078362    3rd Qu.:0.061887   3rd Qu.:0.01047   3rd Qu.:0.008383  
##  Max.   :1.000000    Max.   :1.000000   Max.   :1.00000   Max.   :1.000000  
##  riots.percapita    vac.percapita      sd.percapita        temp.jan     
##  Min.   :0.000000   Min.   :0.00000   Min.   :0.00000   Min.   :0.0000  
##  1st Qu.:0.003398   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.8602  
##  Median :0.015218   Median :0.00000   Median :0.00000   Median :0.9309  
##  Mean   :0.054759   Mean   :0.03670   Mean   :0.01868   Mean   :0.7983  
##  3rd Qu.:0.061887   3rd Qu.:0.03148   3rd Qu.:0.00000   3rd Qu.:0.9599  
##  Max.   :1.000000   Max.   :1.00000   Max.   :1.00000   Max.   :1.0000  
##     temp.apr         temp.jul         temp.oct        precip.jan     
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.00000  
##  1st Qu.:0.9118   1st Qu.:0.8984   1st Qu.:0.9262   1st Qu.:0.02531  
##  Median :0.9563   Median :0.9371   Median :0.9633   Median :0.14525  
##  Mean   :0.8686   Mean   :0.9039   Mean   :0.8913   Mean   :0.21185  
##  3rd Qu.:0.9767   3rd Qu.:0.9611   3rd Qu.:0.9798   3rd Qu.:0.33481  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.00000  
##    precip.apr        precip.jul       precip.oct           spei       
##  Min.   :0.00000   Min.   :0.0000   Min.   :0.00000   Min.   :0.0000  
##  1st Qu.:0.05633   1st Qu.:0.1776   1st Qu.:0.01621   1st Qu.:0.3755  
##  Median :0.21686   Median :0.4023   Median :0.10321   Median :0.5651  
##  Mean   :0.28535   Mean   :0.3957   Mean   :0.19612   Mean   :0.5442  
##  3rd Qu.:0.47424   3rd Qu.:0.5920   3rd Qu.:0.34054   3rd Qu.:0.6998  
##  Max.   :1.00000   Max.   :1.0000   Max.   :1.00000   Max.   :1.0000

## Great - No NAs, everything is scaled
## Interpretation note - how does this affect spei??
## Scaling year?

Self Organizing Map

Conflict Only

## Build the SOM grid
## rule of thumb to initializing the grid is:
## pick a high number (5*sqrt(n)) where n is the number of observations
## In this case, 5*sqrt(2080) 228 nodes

## Initialize a grid of 300 nodes
## Second run - 225 nodes
som_grid = somgrid(xdim = 15,
                   ydim = 15, 
                   topo = "hexagonal")

## SOMs require a matrix object to run

## Conflict variable


## Convert the dataframe to a matrix
con.mat = as.matrix(con.df.scale.v2[ ,6:12])

## Pass this to train the SOM function
## Set seed to reproduce results
set.seed(1)
con.som = som(con.mat, som_grid,
              rlen = 1000)

## Check to see if the algorithm has converged
## If not you may need to run more iterations (rlen =) argument

plot(con.som,
     type = "changes")

## Converged after 1000 runs

SOM Plots

## Plot the distance between nodes
## Used to check for outliers

plot(con.som, type = "dist")

## Potential outliers? Upper left corner?

## Check the number of observations per clusters
## Used to look for empty nodes

plot(con.som, type = "counts")

## Perhaps a smaller grid is necessary?
## several empty clusters

## Plot the codebook vectors
## These are the weights associated with each feature and can be used to interpret the organization of the SOM


plot(con.som, type = "code")

## Warning in par(opar): argument 1 does not name a graphical parameter

Feature Plots

## Define a Palette
heat.pal = function(n, pal= "Spectral") {
  rev(brewer.pal(n, pal))
}


## Use viridis for SOM
library(viridis)

## Test
plot(con.som,
     type="property",
     property = con.som$codes[[1]][,"total.con"], 
     palette.name=heat.pal, 
     ncolors=9,
     main = "Total Conflict",
     shape = "straight")

## Feature plots

plot(con.som, 
     type = "property", 
     property = con.som$codes[[1]][,"total.con"],
     main = "Total Conflict",
     palette.name = turbo)

## Feature plots

plot(con.som, 
     type = "property", 
     property = con.som$codes[[1]][,"battles"],
     main = "Battles",
     palette.name = turbo)

## Feature plots

plot(con.som, 
     type = "property", 
     property = con.som$codes[[1]][,"protests"],
     main = "protests",
     palette.name = turbo)

## Feature plots

plot(con.som, 
     type = "property", 
     property = con.som$codes[[1]][,"expl"],
     main = "Explosions",
     palette.name = turbo)

## Feature plots

plot(con.som, 
     type = "property", 
     property = con.som$codes[[1]][,"riots"],
     main = "Riots",
     palette.name = turbo)

## Feature plots

plot(con.som, 
     type = "property", 
     property = con.som$codes[[1]][,"vac"],
     main = "Violence Against Civilians",
     palette.name = turbo)

## Feature plots

plot(con.som, 
     type = "property", 
     property = con.som$codes[[1]][,"sd"],
     main = "Strategic Development",
     palette.name = turbo)

Cluster Analysis

## For kmeans - calculate the optimal number of clusters
library(cluster)

## 
## Attaching package: 'cluster'

## The following object is masked from 'package:maps':
## 
##     votes.repub

library(fpc) ##For the Calin-Hara index

source("../r_code/scripts/conflict_cluster_num_script.R")

## Warning: did not converge in 10 iterations

## Visualize

plot(1:20,ch.out, type = 'b', lwd = 2,
     xlab = "N Groups", ylab = "C", main = "Calinski-Harabasz index")

## 2 seems like the CH solution although 5/6 may be better?

## Lets start with hierarchical clustering 

## First calculate the dissimilarity matrix between the values (codes) of each SOM nodes.
dist_codes = dist(con.som$codes[[1]])


## Use cutree and hclust to form the hierarchical clusters (start with 6)
con_cluster = cutree(hclust(dist_codes), 6)

## Visualize the SOM grid with the clusters

## Color Palette
my.pal = brewer.pal(n = 6,
                    "Dark2")


## Visualize
plot(con.som, type="mapping", 
     bgcol = my.pal[con_cluster], 
     main = "Clusters", 
     pch = '.') 
add.cluster.boundaries(con.som, con_cluster)

## Contiguous Mapping of the Clusters

## Calculate the distance between nodes
dist_grid = unit.distances(som_grid)

## Multiply the distance/dissimilarity matrix together
dist_adj <- as.matrix(dist_codes) * dist_grid

## Repeat the clustering
clust_adj = hclust(as.dist(dist_adj), 'ward.D2')
som_cluster_adj = cutree(clust_adj, 6)

## Re-plot the the clusters

plot(con.som, 
     type="mapping", 
     bgcol = my.pal[som_cluster_adj], 
     main = "Clusters", 
     pch = '.') 
add.cluster.boundaries(con.som, som_cluster_adj)

Cluster Values

## Extract a vector with the node assignments for each observation

nodeByObs = con.som$unit.classif

## Assign and attach the node assignments (as a factor) to the dataframe we ran the SOM with

con.df.scale.v2$som.clust = as.factor(som_cluster_adj[nodeByObs])


## Add Cluster Assignments to original dataframe 
## Do this using merge?

con.df.clus = merge(con.df, con.df.scale.v2, by = "districts")


## Check
head(con.df.clus)

districts	longitude	latitude	year.x	pop.x	spei.x	dep.index.x	built.x	temp.jan.x	temp.apr.x	temp.jul.x	temp.oct.x	total.con.x	protests.x	battles.x	expl.x	riots.x	vac.x	sd.x	precip.jan.x	precip.apr.x	precip.jul.x	precip.oct.x	total.con.percapita.x	protests.percapita.x	battles.percapita.x	expl.percapita.x	riots.percapita.x	vac.percapita.x	sd.percapita.x	year.y	pop.y	dep.index.y	built.y	total.con.y	protests.y	battles.y	expl.y	riots.y	vac.y	sd.y	total.con.percapita.y	protests.percapita.y	battles.percapita.y	expl.percapita.y	riots.percapita.y	vac.percapita.y	sd.percapita.y	temp.jan.y	temp.apr.y	temp.jul.y	temp.oct.y	precip.jan.y	precip.apr.y	precip.jul.y	precip.oct.y	spei.y	som.clust
Abbottabad	73.3	34.1	2010	1204572	0.7	73	1.09	12.2	22.3	26.2	21	24	17	0	0	7	0	0	0.898	1.95	7.32	0.414	1.99e-05	1.41e-05	0	0	1.41e-05	0	0	2010	0.666	0.76	0.182	0.515	0.511	0	0	0.565	0	0	0.0299	0.0324	0	0	0.0324	0	0	0.9	0.92	0.883	0.924	0.338	0.599	0.875	0.223	0.689	1
Abbottabad	73.3	34.1	2010	1204572	0.7	73	1.09	12.2	22.3	26.2	21	24	17	0	0	7	0	0	0.898	1.95	7.32	0.414	1.99e-05	1.41e-05	0	0	1.41e-05	0	0	2011	0.669	0.76	0.182	0.399	0.434	0.358	0.476	0.308	0.274	0	0.0122	0.0187	0.0102	0.0199	0.0187	0.033	0	0.861	0.896	0.876	0.918	0.274	0.834	0.594	0.687	0.824	3
Abbottabad	73.3	34.1	2010	1204572	0.7	73	1.09	12.2	22.3	26.2	21	24	17	0	0	7	0	0	0.898	1.95	7.32	0.414	1.99e-05	1.41e-05	0	0	1.41e-05	0	0	2012	0.671	0.76	0.182	0.423	0.493	0.174	0.416	0	0.345	0	0.0145	0.0276	0.00333	0.014	0.0276	0.0487	0	0.82	0.896	0.9	0.911	0.548	0.755	0.522	0.366	0.495	2
Abbottabad	73.3	34.1	2010	1204572	0.7	73	1.09	12.2	22.3	26.2	21	24	17	0	0	7	0	0	0.898	1.95	7.32	0.414	1.99e-05	1.41e-05	0	0	1.41e-05	0	0	2013	0.674	0.76	0.182	0.423	0.502	0.174	0.327	0.308	0.274	0	0.0142	0.029	0.00328	0.00827	0.029	0.032	0	0.859	0.898	0.885	0.923	0.372	0.731	0.609	0.444	0.746	2
Abbottabad	73.3	34.1	2010	1204572	0.7	73	1.09	12.2	22.3	26.2	21	24	17	0	0	7	0	0	0.898	1.95	7.32	0.414	1.99e-05	1.41e-05	0	0	1.41e-05	0	0	2014	0.676	0.76	0.182	0.332	0.483	0.413	0.377	0.188	0.167	0	0.00702	0.025	0.0129	0.0109	0.025	0.0158	0	0.872	0.894	0.882	0.912	0.265	0.595	0.662	0.589	0.694	5
Abbottabad	73.3	34.1	2010	1204572	0.7	73	1.09	12.2	22.3	26.2	21	24	17	0	0	7	0	0	0.898	1.95	7.32	0.414	1.99e-05	1.41e-05	0	0	1.41e-05	0	0	2015	0.678	0.76	0.182	0.399	0.448	0.521	0.26	0.188	0.345	0	0.0115	0.0194	0.0223	0.00535	0.0194	0.0466	0	0.873	0.902	0.872	0.916	0.456	0.804	0.825	0.708	0.903	2

## Aggregate Values

cluster.vals <- aggregate(con.df.clus[ ,13:19], 
                         by = list(con.df.clus$som.clust), mean)

## Coerce into a table for a better figure
library(data.table)

## 
## Attaching package: 'data.table'

## The following objects are masked from 'package:lubridate':
## 
##     hour, isoweek, mday, minute, month, quarter, second, wday, week,
##     yday, year

## The following object is masked from 'package:purrr':
## 
##     transpose

## The following objects are masked from 'package:reshape2':
## 
##     dcast, melt

## The following objects are masked from 'package:dplyr':
## 
##     between, first, last

## The following object is masked from 'package:raster':
## 
##     shift

library(kableExtra)

con.clus.table = setDT(cluster.vals)

con.clus.pub = con.clus.table %>% 
  kable() %>% 
  kable_material_dark() #%>% 
  #scroll_box(width = "600px", height= "600px" )

con.clus.pub

Group.1	total.con.x	protests.x	battles.x	expl.x	riots.x	vac.x	sd.x
1	6.805668	2.197144	0.2317814	0.2895861	0.1654296	0.1634053	0.0330634
2	37.735708	23.134092	2.7369051	2.8874588	1.5552230	1.7680335	0.3112840
3	65.391738	40.687678	4.3180199	4.3383191	2.7054843	2.7478632	0.5683761
4	12.096830	3.428682	0.3681871	0.4253962	0.2218786	0.2526092	0.0620410
5	25.680336	12.513500	1.3970963	1.4465104	0.7997962	1.0328579	0.1910341
6	172.660228	141.665747	13.9458434	15.0386340	9.0131080	9.9858572	1.9786133

Conflict Trajectories

## Establish the id of the city
city_id = which(con.df.scale.v2$districts == "Quetta")

## Pull out the classifications for the city
city_nodes <- con.som$unit.classif[city_id]

## Pull out the coordinates for the city
city_crds <- som_grid$pts[city_nodes, ]


## Plot

plot(con.som, 
     type="mapping", 
     bgcol = my.pal[som_cluster_adj], 
     main = "Clusters", pch = '', 
     keepMargins = TRUE) 
#add.cluster.boundaries(gap_som, som_cluster_adj)

lines(city_crds, lwd=2)

## Year labels

years <- ifelse(con.df.scale.v2$year[city_id] %% 2 == 0, as.character(con.df.scale.v2$year[city_id]), "")
text(jitter(city_crds), labels = years, cex = 0.75)

Full SOM Build

## Build the SOM grid
## rule of thumb to initializing the grid is:
## pick a high number (5*sqrt(n)) where n is the number of observations
## In this case, 5*sqrt(2080) 228 nodes

## Initialize a grid of  225 nodes
som_grid_full = somgrid(xdim = 15,
                   ydim = 15, 
                   topo = "hexagonal")

## SOMs require a matrix object to run

## Full Variables

## Recreate the con.df.scale.v2 dataframe - drop the last column "som.clust" from the previous SOM

con.df.scale.full = subset(con.df.scale.v2,
                           select = -som.clust)

## Check
str(con.df.scale.full)

## 'data.frame':    2080 obs. of  28 variables:
##  $ districts          : chr  "Abbottabad" "Abbottabad" "Abbottabad" "Abbottabad" ...
##  $ year               : int  2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 ...
##  $ pop                : num  0.666 0.669 0.671 0.674 0.676 ...
##  $ dep.index          : num  0.76 0.76 0.76 0.76 0.76 ...
##  $ built              : num  0.182 0.182 0.182 0.182 0.182 ...
##  $ total.con          : num  0.515 0.399 0.423 0.423 0.332 ...
##  $ protests           : num  0.511 0.434 0.493 0.502 0.483 ...
##  $ battles            : num  0 0.358 0.174 0.174 0.413 ...
##  $ expl               : num  0 0.476 0.416 0.327 0.377 ...
##  $ riots              : num  0.565 0.308 0 0.308 0.188 ...
##  $ vac                : num  0 0.274 0.345 0.274 0.167 ...
##  $ sd                 : num  0 0 0 0 0 ...
##  $ total.con.percapita: num  0.02991 0.01225 0.01447 0.01425 0.00702 ...
##  $ protests.percapita : num  0.0324 0.0187 0.0276 0.029 0.025 ...
##  $ battles.percapita  : num  0 0.01016 0.00333 0.00328 0.01294 ...
##  $ expl.percapita     : num  0 0.01991 0.014 0.00827 0.01087 ...
##  $ riots.percapita    : num  0.0324 0.0187 0.0276 0.029 0.025 ...
##  $ vac.percapita      : num  0 0.033 0.0487 0.032 0.0158 ...
##  $ sd.percapita       : num  0 0 0 0 0 ...
##  $ temp.jan           : num  0.9 0.861 0.82 0.859 0.872 ...
##  $ temp.apr           : num  0.92 0.896 0.896 0.898 0.894 ...
##  $ temp.jul           : num  0.883 0.876 0.9 0.885 0.882 ...
##  $ temp.oct           : num  0.924 0.918 0.911 0.923 0.912 ...
##  $ precip.jan         : num  0.338 0.274 0.548 0.372 0.265 ...
##  $ precip.apr         : num  0.599 0.834 0.755 0.731 0.595 ...
##  $ precip.jul         : num  0.875 0.594 0.522 0.609 0.662 ...
##  $ precip.oct         : num  0.223 0.687 0.366 0.444 0.589 ...
##  $ spei               : num  0.689 0.824 0.495 0.746 0.694 ...

## Good: som.clust removed

## Convert the dataframe to a matrix
## All rows except for the district names
con.mat.full = as.matrix(con.df.scale.full[ ,3:28])

## Pass this to train the SOM function
set.seed(2)
con.som.full = som(con.mat.full, som_grid_full,
              rlen = 1000)

## Check to see if the algorithm has converged
## If not you may need to run more iterations (rlen =) argument

plot(con.som.full,
     type = "changes")

## Converged after 900 runs

## Plot the distance between nodes
## Used to check for outliers

plot(con.som.full, type = "dist")

## Potential outliers? 

## Check the number of observations per clusters
## Used to look for empty nodes

plot(con.som.full, type = "counts")

## Perhaps a smaller grid is necessary?
## several empty clusters

## Plot the codebook vectors
## These are the weights associated with each feature and can be used to interpret the organization of the SOM


plot(con.som.full, type = "code")

## Warning in par(opar): argument 1 does not name a graphical parameter

## Too many parameters

Full SOM Property Plots

## Define a helper function to plot


prop.plot = function(x){
  plot(con.som.full,
     type="property",
     property = con.som.full$codes[[1]][, x], 
     palette.name=heat.pal, 
     ncolors=9,
     main = x,
     shape = "straight")
}

## Define a loop to plot all of the variables
## prop.plot is the helper function
## This plot the relative weights? in the SOM?

lapply(names(con.df.scale.full[,3:28]), prop.plot)

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Full SOM Cluster Build

## Lets start with hierarchical clustering 

## First calculate the dissimilarity matrix between the values (codes) of each SOM nodes.
dist_codes_full = dist(con.som.full$codes[[1]])


## Use cutree and hclust to form the hierarchical clusters (start with 6)
con_cluster_full = cutree(hclust(dist_codes_full), 6)

## Visualize the SOM grid with the clusters

## Color Palette
my.pal = brewer.pal(n = 6,
                    "Dark2")


## Visualize
plot(con.som.full, type="mapping", 
     bgcol = my.pal[con_cluster_full], 
     main = "Clusters", 
     pch = '.') 
add.cluster.boundaries(con.som.full, con_cluster_full)

##Resembles the year

## Contiguous Mapping of the Clusters

## Calculate the distance between nodes
dist_grid_full = unit.distances(som_grid_full)

## Multiply the distance/dissimilarity matrix together
dist_adj_full <- as.matrix(dist_codes_full) * dist_grid_full

## Repeat the clustering
clust_adj_full = hclust(as.dist(dist_adj_full), 'ward.D2')
som_cluster_adj_full = cutree(clust_adj_full, 6)

## Re-plot the the clusters

plot(con.som.full, 
     type="mapping", 
     bgcol = my.pal[som_cluster_adj_full], 
     main = "Clusters", 
     pch = '.') 
add.cluster.boundaries(con.som.full, som_cluster_adj_full)

## Define the same color palette
clus.pal = function(n, pal="Dark2") {
  brewer.pal(n, pal)
}

## Plot the clusters Values

plot(con.som.full, 
     type="property", 
     property = som_cluster_adj_full,
     palette.name = clus.pal, 
     main = "Clusters",
     shape = "straight") 
add.cluster.boundaries(con.som.full, som_cluster_adj_full)

Full Cluster Values

## Extract a vector with the node assignments for each observation

nodeByObs.full = con.som.full$unit.classif

## Assign and attach the node assignments (as a factor) to the dataframe we ran the SOM with

con.df.scale.full$som.clust = as.factor(som_cluster_adj_full[nodeByObs.full])


## Add Cluster Assignments to original dataframe 
## Do this using merge?

con.df.clus.full = merge(con.df, con.df.scale.full, by = c("districts", "year"))


## Check
head(con.df.clus.full)

districts	year	longitude	latitude	pop.x	spei.x	dep.index.x	built.x	temp.jan.x	temp.apr.x	temp.jul.x	temp.oct.x	total.con.x	protests.x	battles.x	expl.x	riots.x	vac.x	sd.x	precip.jan.x	precip.apr.x	precip.jul.x	precip.oct.x	total.con.percapita.x	protests.percapita.x	battles.percapita.x	expl.percapita.x	riots.percapita.x	vac.percapita.x	sd.percapita.x	pop.y	dep.index.y	built.y	total.con.y	protests.y	battles.y	expl.y	riots.y	vac.y	sd.y	total.con.percapita.y	protests.percapita.y	battles.percapita.y	expl.percapita.y	riots.percapita.y	vac.percapita.y	sd.percapita.y	temp.jan.y	temp.apr.y	temp.jul.y	temp.oct.y	precip.jan.y	precip.apr.y	precip.jul.y	precip.oct.y	spei.y	som.clust
Abbottabad	2010	73.3	34.1	1204572	0.7	73	1.09	12.2	22.3	26.2	21	24	17	0	0	7	0	0	0.898	1.95	7.32	0.414	1.99e-05	1.41e-05	0	0	1.41e-05	0	0	0.666	0.76	0.182	0.515	0.511	0	0	0.565	0	0	0.0299	0.0324	0	0	0.0324	0	0	0.9	0.92	0.883	0.924	0.338	0.599	0.875	0.223	0.689	5
Abbottabad	2011	73.3	34.1	1225491	1.33	73	1.09	8.94	18.5	25.5	20	10	10	3	7	2	2	0	0.704	3.55	3.02	1.59	8.16e-06	8.16e-06	2.45e-06	5.71e-06	8.16e-06	1.63e-06	0	0.669	0.76	0.182	0.399	0.434	0.358	0.476	0.308	0.274	0	0.0122	0.0187	0.0102	0.0199	0.0187	0.033	0	0.861	0.896	0.876	0.918	0.274	0.834	0.594	0.687	0.824	2
Abbottabad	2012	73.3	34.1	1244756	-0.197	73	1.09	6.42	18.5	28.1	18.9	12	15	1	5	0	3	0	1.71	2.92	2.39	0.71	9.64e-06	1.21e-05	8.03e-07	4.02e-06	1.21e-05	2.41e-06	0	0.671	0.76	0.182	0.423	0.493	0.174	0.416	0	0.345	0	0.0145	0.0276	0.00333	0.014	0.0276	0.0487	0	0.82	0.896	0.9	0.911	0.548	0.755	0.522	0.366	0.495	3
Abbottabad	2013	73.3	34.1	1264224	0.965	73	1.09	8.77	18.8	26.5	20.8	12	16	1	3	2	2	0	1.01	2.75	3.18	0.892	9.49e-06	1.27e-05	7.91e-07	2.37e-06	1.27e-05	1.58e-06	0	0.674	0.76	0.182	0.423	0.502	0.174	0.327	0.308	0.274	0	0.0142	0.029	0.00328	0.00827	0.029	0.032	0	0.859	0.898	0.885	0.923	0.372	0.731	0.609	0.444	0.746	3
Abbottabad	2014	73.3	34.1	1282908	0.726	73	1.09	9.73	18.1	26.2	19.2	6	14	4	4	1	1	0	0.676	1.93	3.76	1.28	4.68e-06	1.09e-05	3.12e-06	3.12e-06	1.09e-05	7.79e-07	0	0.676	0.76	0.182	0.332	0.483	0.413	0.377	0.188	0.167	0	0.00702	0.025	0.0129	0.0109	0.025	0.0158	0	0.872	0.894	0.882	0.912	0.265	0.595	0.662	0.589	0.694	2
Abbottabad	2015	73.3	34.1	1302026	1.69	73	1.09	9.84	19.4	25	19.7	10	11	7	2	1	3	0	1.32	3.29	6.28	1.66	7.68e-06	8.45e-06	5.38e-06	1.54e-06	8.45e-06	2.3e-06	0	0.678	0.76	0.182	0.399	0.448	0.521	0.26	0.188	0.345	0	0.0115	0.0194	0.0223	0.00535	0.0194	0.0466	0	0.873	0.902	0.872	0.916	0.456	0.804	0.825	0.708	0.903	2

dim(con.df.clus.full)

## [1] 2080   57

## Aggregate Values

cluster.vals.full <- aggregate(con.df.clus.full[ ,5:29], 
                         by = list(con.df.clus.full$som.clust), mean)

## Coerce into a table for a better figure
library(data.table)
library(kableExtra)

con.clus.table.full = setDT(cluster.vals.full)

con.clus.table.full.v2 = con.clus.table.full[,-c("protests.x", "battles.x", "expl.x", "riots.x", "vac.x", "sd.x")]

names(con.clus.table.full.v2)[names(con.clus.table.full.v2) == "Group.1"] = "Cluster"

con.clus.pub.full = con.clus.table.full.v2 %>% 
  kable() %>% 
  kable_paper() #%>% 
  #scroll_box(width = "600px", height= "600px" )

con.clus.pub.full

Cluster	pop.x	spei.x	dep.index.x	built.x	temp.jan.x	temp.apr.x	temp.jul.x	temp.oct.x	total.con.x	precip.jan.x	precip.apr.x	precip.jul.x	precip.oct.x	total.con.percapita.x	protests.percapita.x	battles.percapita.x	expl.percapita.x	riots.percapita.x	vac.percapita.x
1	1862586.9	-0.8589532	74.67794	5.6399143	21.312248	35.0242466	36.99635	32.575415	207.710000	0.1004824	0.1713311	0.5910805	0.0646402	0.0001334	1.12e-04	1.23e-05	1.28e-05	1.12e-04	8.7e-06
2	1954990.1	0.5019426	74.70898	2.4533313	13.908260	26.6192127	31.50128	25.794464	77.052830	0.5625011	1.2631208	3.2409398	0.6193169	0.0000554	5.55e-05	8.60e-06	1.02e-05	5.55e-05	4.8e-06
3	337374.1	0.2435646	82.17123	0.0986478	-12.329593	0.3980489	14.46612	2.673755	10.096096	1.3760635	1.8949987	2.6191618	0.9858153	0.0000392	8.30e-06	8.00e-07	7.00e-07	8.30e-06	5.0e-07
4	547831.4	0.5219010	79.37733	0.4974029	9.399342	21.6125169	27.79365	22.074089	9.539419	1.3262499	1.9688702	3.7248382	0.6111632	0.0000221	8.50e-06	1.00e-06	1.00e-06	8.50e-06	6.0e-07
5	2181412.4	0.1675284	77.64140	4.7324281	18.909462	33.2900267	35.23407	31.090108	37.668011	0.3342582	0.3474250	1.7145165	0.2085646	0.0000287	1.71e-05	1.30e-06	1.30e-06	1.71e-05	8.0e-07
6	329504.2	-0.7967339	86.90302	0.1489662	15.513768	29.9659053	35.13414	28.088534	11.775819	0.3672614	0.3503926	0.7814579	0.0888275	0.0000506	1.56e-05	1.60e-06	2.40e-06	1.56e-05	1.8e-06

Full City Trajectories

## Establish the id of the city
city_id = which(con.df.scale.full$districts == "East Karachi")

## Pull out the classifications for the city
city_nodes <- con.som.full$unit.classif[city_id]

## Pull out the coordinates for the city
city_crds <- som_grid_full$pts[city_nodes, ]


## Plot

plot(con.som.full, 
     type="mapping", 
     bgcol = my.pal[som_cluster_adj_full], 
     main = "Clusters", pch = '', 
     keepMargins = TRUE,
     shape = "straight") 
#add.cluster.boundaries(gap_som, som_cluster_adj)

lines(city_crds, lwd=2)

## Year labels

years <- ifelse(con.df.scale.full$year[city_id] %% 2 == 0, as.character(con.df.scale.full$year[city_id]), "")
text(jitter(city_crds), labels = years, cex = 0.75)

## Establish the id of the city
city_id = which(con.df.scale.full$districts == "Lahore")

## Pull out the classifications for the city
city_nodes <- con.som.full$unit.classif[city_id]

## Pull out the coordinates for the city
city_crds <- som_grid_full$pts[city_nodes, ]


## Plot

plot(con.som.full, 
     type="mapping", 
     bgcol = my.pal[som_cluster_adj_full], 
     main = "Clusters", pch = '', 
     keepMargins = TRUE,
     shape = "straight") 
#add.cluster.boundaries(gap_som, som_cluster_adj)

lines(city_crds, lwd=2)

## Year labels

years <- ifelse(con.df.scale.full$year[city_id] %% 2 == 0, as.character(con.df.scale.full$year[city_id]), "")
text(jitter(city_crds), labels = years, cex = 0.75)

## Establish the id of the city
city_id = which(con.df.scale.full$districts == "Peshawar")

## Pull out the classifications for the city
city_nodes <- con.som.full$unit.classif[city_id]

## Pull out the coordinates for the city
city_crds <- som_grid_full$pts[city_nodes, ]


## Plot

plot(con.som.full, 
     type="mapping", 
     bgcol = my.pal[som_cluster_adj_full], 
     main = "Clusters", pch = '', 
     keepMargins = TRUE,
     shape = "straight") 
#add.cluster.boundaries(gap_som, som_cluster_adj)

lines(city_crds, lwd=2)

## Year labels

years <- ifelse(con.df.scale.full$year[city_id] %% 1 == 0, as.character(con.df.scale.full$year[city_id]), "")
text(jitter(city_crds), labels = years, cex = 0.75)

## Establish the id of the city
city_id = which(con.df.scale.full$districts == "Quetta")

## Pull out the classifications for the city
city_nodes <- con.som.full$unit.classif[city_id]

## Pull out the coordinates for the city
city_crds <- som_grid_full$pts[city_nodes, ]


## Plot

plot(con.som.full, 
     type="mapping", 
     bgcol = my.pal[som_cluster_adj_full], 
     main = "Clusters", pch = '', 
     keepMargins = TRUE,
     shape = "straight") 
#add.cluster.boundaries(gap_som, som_cluster_adj)

lines(city_crds, lwd=2)

## Year labels

years <- ifelse(con.df.scale.full$year[city_id] %% 2 == 0, as.character(con.df.scale.full$year[city_id]), "")
text(jitter(city_crds), labels = years, cex = 0.75)

Data Exploration

Data Read

## Read in the spatial conflict dataframe
con.df.sf = st_read("../data/conflict/complete_data_20230411.shp")

## Reading layer `complete_data_20230411' from data source 
##   `/Users/davidleydet/Desktop/University of Utah PhD /Research/data/conflict/complete_data_20230411.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 2080 features and 23 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 60.8786 ymin: 23.69468 xmax: 77.83397 ymax: 37.08942
## Geodetic CRS:  WGS 84

## Check
head(con.df.sf)

districts	longitude	latitude	year	pop	spei	dep_index	built	temp_jan	temp_apr	temp_jul	temp_oct	precip_jan	precip_apr	precip_jul	precip_oct	total_con	protests	battles	expl	riots	vac	sd	geometry
Abbottabad	73.3	34.1	2010	1.2e+06	0.7	73	1.09	12.2	22.3	26.2	21	0.898	1.95	7.32	0.414	24	17	0	0	7	0	0	list(list(c(73.3980410830001, 73.3985407750001, 73.399154464, 73.3994059830001, 73.400542557, 73.4019308100001, 73.402948063, 73.404160527, 73.4046113730001, 73.4060988420001, 73.4097135380001, 73.410326786, 73.414153328, 73.430540604, 73.435373997, 73.444976468, 73.4533223550001, 73.4563076540001, 73.464706683, 73.464117651, 73.4639413200001, 73.4622645850001, 73.464994897, 73.4702664380001, 73.4794306750001, 73.4800004530001, 73.483323676, 73.4877634650001, 73.487975856, 73.488310254, 73.488058143, 73.487197032, 73.487057189, 73.48664697, 73.487312526, 73.4880384960001, 73.4935915070001, 73.493765376, 73.4943085570001, 73.4944054950001, 73.495817951, 73.496981148, 73.501387332, 73.506102153, 73.5066973340001, 73.506814499, 73.507218649, 73.508234481, 73.5094287210001, 73.5120075290001, 73.511291504, 73.506706238, 73.504302979, 73.503219604, 73.502296448, 73.500892639, 73.500190735, 73.498779297, 73.4975891110001, 73.4965591430001, 73.495536804, 73.494293213, 73.492683411, 73.4905700680001, 73.488380432, 73.4869613650001, 73.485603333, 73.4828872680001, 73.480026245, 73.4786071780001, 73.476722717, 73.475418091, 73.4745635990001, 73.4731979370001, 73.4719031650001, 73.471763611, 73.471443176, 73.471473694, 73.4715423580001, 73.470069885, 73.469566345, 73.468696594, 73.4683685300001, 73.4650802610001, 73.4639968870001, 73.4652099610001, 73.4647064210001, 73.463912964, 73.46321106, 73.4619712830001, 73.4607696530001, 73.460327148, 73.4598236080001, 73.4587783810001, 73.45765686, 73.4566726680001, 73.455886841, 73.4548721310001, 73.454334303, 73.453849792, 73.4530029300001, 73.45211792, 73.4509429930001, 73.45010376, 73.4490127560001, 73.447853088, 73.446800232, 73.4452362060001, 73.4437332150001, 73.4433364870001, 73.4423980710001, 73.4420928960001, 73.4415893550001, 73.440696716, 73.44052887, 73.4406661990001, 73.440299988, 73.4394836430001, 73.4385528560001, 73.4374389650001, 73.4365768430001, 73.4353866580001, 73.4339675900001, 73.4324798580001, 73.4318237300001, 73.4312973020001, 73.43031311, 73.428848267, 73.427200317, 73.4254989620001, 73.424430847, 73.423629761, 73.423080444, 73.422767639, 73.4235992430001, 73.4241409300001, 73.4235763550001, 73.422599792, 73.420883179, 73.419403076, 73.417671204, 73.4161834720001, 73.41620636, 73.4174728390001, 73.4181671140001, 73.417823792, 73.41481781, 73.4144668580001, 73.4147491460001, 73.4148864750001, 73.414222717, 73.4131774900001, 73.4112396240001, 73.409370422, 73.408363342, 73.406967163, 73.406013489, 73.4054183960001, 73.4046630860001, 73.40411377, 73.402633667, 73.401351929, 73.398033142, 73.3967971800001, 73.3934190680001, 73.392562866, 73.3921966550001, 73.391777039, 73.3908233640001, 73.389709473, 73.38848877, 73.3873367310001, 73.3854370120001, 73.383712769, 73.3819274900001, 73.3799896240001, 73.3787002560001, 73.378196716, 73.3781661990001, 73.378158569, 73.3764038090001, 73.3742828370001, 73.3743769330001, 73.376266479, 73.3775329590001, 73.3795013430001, 73.3821868900001, 73.3840484620001, 73.385498047, 73.3857421870001, 73.3858184810001, 73.385482788, 73.3851013180001, 73.3846664430001, 73.382156372, 73.3809661870001, 73.3793563840001, 73.37840271, 73.377159119, 73.3750686650001, 73.3736419680001, 73.371490479, 73.3695068360001, 73.368392944, 73.3678588870001, 73.3666498160001, 73.36613235, 73.3641967770001, 73.3629531860001, 73.3599929810001, 73.358177185, 73.356773376, 73.355937365, 73.3530197140001, 73.352058411, 73.3491516110001, 73.3476715090001, 73.346626282, 73.346626282, 73.347640991, 73.3482894900001, 73.34828186, 73.3480834960001, 73.3480072020001, 73.3475418090001, 73.346817017, 73.344787598, 73.3424530030001, 73.3412704470001, 73.3402862550001, 73.3392410280001, 73.3382034300001, 73.337356567, 73.33618927, 73.335327148, 73.334716797, 73.333830541, 73.3330230710001, 73.3319931030001, 73.330291748, 73.3279724120001, 73.327529907, 73.3283081050001, 73.329559326, 73.330680847, 73.3311614990001, 73.331016541, 73.330538038, 73.3297576900001, 73.328842163, 73.3278732300001, 73.3266372680001, 73.3230667110001, 73.3208312990001, 73.318908691, 73.3173599240001, 73.31778717, 73.319206238, 73.319587708, 73.3189773560001, 73.3182373050001, 73.317352295, 73.3167190550001, 73.3156661990001, 73.31477356, 73.311431885, 73.309951782, 73.3080291750001, 73.3069000240001, 73.3041610720001, 73.3020935060001, 73.2999725340001, 73.298210144, 73.296539307, 73.2958883030001, 73.2950897220001, 73.293426514, 73.291297913, 73.289627075, 73.2877426150001, 73.2855834960001, 73.2823562620001, 73.2754355710001, 73.2673110960001, 73.265968323, 73.2651290890001, 73.2635879520001, 73.262718201, 73.2612228390001, 73.2593994060001, 73.2573928830001, 73.2568664550001, 73.2552337650001, 73.2538833620001, 73.252296448, 73.2508621220001, 73.2473526, 73.2458114620001, 73.244613647, 73.2433624270001, 73.242607117, 73.241577148, 73.241027832, 73.2388534550001, 73.2380523680001, 73.237039767, 73.2367019650001, 73.2359466550001, 73.232688904, 73.231042008, 73.230079651, 73.2280197140001, 73.2259597780001, 73.22403717, 73.2211837770001, 73.219787598, 73.2184066770001, 73.2164916990001, 73.213172913, 73.211616516, 73.2102966310001, 73.2093370000001, 73.2088700040001, 73.2072000030001, 73.204579999, 73.2039012700001, 73.203300002, 73.202909998, 73.203010003, 73.2035300040001, 73.204050005, 73.2046967410001, 73.2056700040001, 73.2061438580001, 73.206500002, 73.206903191, 73.2090640590001, 73.2100800000001, 73.210870001, 73.213599431, 73.215029996, 73.2147199990001, 73.2140128070001, 73.213192365, 73.212740001, 73.212571166, 73.212439997, 73.211800004, 73.210929997, 73.2106346740001, 73.210060002, 73.2097700030001, 73.2097700030001, 73.210135, 73.211179998, 73.211870005, 73.2121816090001, 73.2129099950001, 73.213319998, 73.212734915, 73.2122099950001, 73.211750002, 73.211980004, 73.211980004, 73.211690005, 73.211030673, 73.2102299960001, 73.209009995, 73.208238098, 73.206580869, 73.2043705070001, 73.203129995, 73.201320003, 73.200525574, 73.1994599970001, 73.1975399950001, 73.195730003, 73.1952699980001, 73.195591505, 73.195730003, 73.195568651, 73.1955, 73.1955540530001, 73.1956037150001, 73.195730003, 73.1956107560001, 73.195559996, 73.1955, 73.1945100010001, 73.193990967, 73.1932007240001, 73.1923500030001, 73.1914131610001, 73.1906600030001, 73.190079994, 73.190079994, 73.1903250710001, 73.1901999980001, 73.1906940270001, 73.191418516, 73.1917699940001, 73.192261833, 73.1925300030001, 73.19363, 73.194884088, 73.195609999, 73.197249996, 73.198536689, 73.199417268, 73.201300863, 73.201975787, 73.204189107, 73.204759999, 73.2055130140001, 73.207422201, 73.208957581, 73.209880002, 73.210639998, 73.211629998, 73.2124272660001, 73.2129700030001, 73.213582729, 73.214193336, 73.2148299980001, 73.2152599990001, 73.2154099950001, 73.2150617840001, 73.2144800030001, 73.214190005, 73.213670004, 73.213670004, 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Abbottabad	73.3	34.1	2012	1.24e+06	-0.197	73	1.09	6.42	18.5	28.1	18.9	1.71	2.92	2.39	0.71	12	15	1	5	0	3	0	list(list(c(73.3980410830001, 73.3985407750001, 73.399154464, 73.3994059830001, 73.400542557, 73.4019308100001, 73.402948063, 73.404160527, 73.4046113730001, 73.4060988420001, 73.4097135380001, 73.410326786, 73.414153328, 73.430540604, 73.435373997, 73.444976468, 73.4533223550001, 73.4563076540001, 73.464706683, 73.464117651, 73.4639413200001, 73.4622645850001, 73.464994897, 73.4702664380001, 73.4794306750001, 73.4800004530001, 73.483323676, 73.4877634650001, 73.487975856, 73.488310254, 73.488058143, 73.487197032, 73.487057189, 73.48664697, 73.487312526, 73.4880384960001, 73.4935915070001, 73.493765376, 73.4943085570001, 73.4944054950001, 73.495817951, 73.496981148, 73.501387332, 73.506102153, 73.5066973340001, 73.506814499, 73.507218649, 73.508234481, 73.5094287210001, 73.5120075290001, 73.511291504, 73.506706238, 73.504302979, 73.503219604, 73.502296448, 73.500892639, 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## Add Ratios
## Why the change to _ instead of total"."con? Shapefile nuance?

con.df.sf = con.df.sf %>% 
  mutate(total_con_percapita = total_con/pop) %>% 
  mutate(protests_percapita = protests/pop) %>% 
  mutate(battles_percapita = battles/pop) %>% 
  mutate(expl_percapita = expl/pop) %>% 
  mutate(riots_percapita = protests/pop) %>% 
  mutate(vac_percapita = vac/pop) %>% 
  mutate(sd_percapita = sd/pop) 


## Check
head(con.df.sf)

districts	longitude	latitude	year	pop	spei	dep_index	built	temp_jan	temp_apr	temp_jul	temp_oct	precip_jan	precip_apr	precip_jul	precip_oct	total_con	protests	battles	expl	riots	vac	sd	geometry	total_con_percapita	protests_percapita	battles_percapita	expl_percapita	riots_percapita	vac_percapita	sd_percapita
Abbottabad	73.3	34.1	2010	1.2e+06	0.7	73	1.09	12.2	22.3	26.2	21	0.898	1.95	7.32	0.414	24	17	0	0	7	0	0	list(list(c(73.3980410830001, 73.3985407750001, 73.399154464, 73.3994059830001, 73.400542557, 73.4019308100001, 73.402948063, 73.404160527, 73.4046113730001, 73.4060988420001, 73.4097135380001, 73.410326786, 73.414153328, 73.430540604, 73.435373997, 73.444976468, 73.4533223550001, 73.4563076540001, 73.464706683, 73.464117651, 73.4639413200001, 73.4622645850001, 73.464994897, 73.4702664380001, 73.4794306750001, 73.4800004530001, 73.483323676, 73.4877634650001, 73.487975856, 73.488310254, 73.488058143, 73.487197032, 73.487057189, 73.48664697, 73.487312526, 73.4880384960001, 73.4935915070001, 73.493765376, 73.4943085570001, 73.4944054950001, 73.495817951, 73.496981148, 73.501387332, 73.506102153, 73.5066973340001, 73.506814499, 73.507218649, 73.508234481, 73.5094287210001, 73.5120075290001, 73.511291504, 73.506706238, 73.504302979, 73.503219604, 73.502296448, 73.500892639, 73.500190735, 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## Merge the clusters to the shape file
con.df.sf$som.clust = con.df.scale.full$som.clust

## Visualize the som clusters

tm_shape(con.df.sf) +
  tm_borders() +
  tm_facets(by = "year") +
  tm_fill("som.clust",
          palette = my.pal,
          title = "Cluster")

Visualizations

## Visualize the geometry
tm_shape(con.df.sf) + 
  tm_borders()

## Good

## Check some of the variables
## Reminder - yearly data
quick.pal = brewer.pal(n = 9,
                       "RdPu")

tm_shape(con.df.sf) +
  tm_borders() +
  tm_facets(by = "year") +
  tm_fill("pop",
          palette = quick.pal)

## Check some of the variables
quick.pal = brewer.pal(n = 9,
                       "YlOrRd")

tm_shape(con.df.sf) +
  tm_borders() +
  tm_facets(by = "year") +
  tm_fill("total_con",
          palette = quick.pal)

## Check some of the variables
quick.pal = brewer.pal(n = 11,
                       "RdYlBu")

tm_shape(con.df.sf) +
  tm_borders() +
  tm_facets(by = "year") +
  tm_fill("spei",
          palette = quick.pal)

## Variable(s) "spei" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.

## Check some of the variables
quick.pal = brewer.pal(n = 9,
                       "YlOrRd")

tm_shape(con.df.sf) +
  tm_borders() +
  tm_facets(by = "year") +
  tm_fill("total_con_percapita",
          palette = quick.pal)

Linear Model Build

Intro

## Plot
plot(total_con ~ year,
     data = con.df.sf)

## Plot
plot(total_con_percapita ~ year,
     data = con.df.sf)

## Visualize the Correlation between features
##con.df.sf.sub = subset(con.df.sf, select = -c(districts, longitude, latitude, geometry))

##Use the orignal dataframe
con.df.sub = subset(con.df, select = -c(districts, longitude, latitude))

ggcorrplot(cor(con.df.sub), 
           method = "square",
           type = "lower",
           lab = TRUE,
           colors = c("blue", "darksalmon", "firebrick"))

           #p.mat = cor_pmat(pak.model.df)) #x out non-significant p-values

## Is y normal? 
## y - conflict (both count and ratio)

hist(con.df$total.con)

## Left skewed
## Log transform fixes this, although a glm may be a better/more flexible approach

## histograms for all variables

hist(log(con.df$total.con.percapita))

## Transform y's
#con.df$log.total.con = log(con.df$total.con)

## percapita total con
#con.df$log.total.con.percapita = log(con.df$total.con.percapita)

## Set districts as a factor
con.df$districts = factor(con.df$districts)

Intercept Model

## Intercept model

fit0 = glm(total.con ~ 1,
           family = poisson(link = "log"),
           data = con.df)

## Check
summary(fit0)

## 
## Call:
## glm(formula = total.con ~ 1, family = poisson(link = "log"), 
##     data = con.df)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -8.746  -7.101  -4.737  -0.040  63.641  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.644030   0.003545    1028   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 154485  on 2079  degrees of freedom
## Residual deviance: 154485  on 2079  degrees of freedom
## AIC: 163278
## 
## Number of Fisher Scoring iterations: 6

## Transform coefficients
exp(coef(fit0))

## (Intercept) 
##    38.24567

Note: The average number of conflicts per district by year is approximately 38.2456731

City GLM

## Visual Check of the mean total conflicts by district
## Need to subset
city.names = c("East Karachi", "Lahore", "Peshawar", "Quetta")

city.con.df = con.df %>% 
  filter(districts %in% city.names)

## Check
str(city.con.df)

## 'data.frame':    52 obs. of  30 variables:
##  $ districts          : Factor w/ 160 levels "Abbottabad","Astore",..: 31 31 31 31 31 31 31 31 31 31 ...
##  $ longitude          : num  67.1 67.1 67.1 67.1 67.1 ...
##  $ latitude           : num  24.9 24.9 24.9 24.9 24.9 ...
##  $ year               : int  2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 ...
##  $ pop                : int  2617842 2685587 2723651 2758193 2790141 4686459 4686459 5083349 5155070 5672524 ...
##  $ spei               : num  0.575 0.186 -0.911 -0.277 -1.063 ...
##  $ dep.index          : num  32.9 32.9 32.9 32.9 32.9 ...
##  $ built              : num  37.5 37.5 37.5 37.5 37.5 ...
##  $ temp.jan           : num  25.2 24.1 22.9 24.3 23.2 ...
##  $ temp.apr           : num  32.6 32.6 31.8 31.1 31.8 ...
##  $ temp.jul           : num  31.3 31.1 30.9 30.7 31.2 ...
##  $ temp.oct           : num  31.6 30.5 31.7 31.7 32.5 ...
##  $ total.con          : int  462 485 848 914 481 390 267 166 190 168 ...
##  $ protests           : int  272 301 294 300 246 296 347 373 368 356 ...
##  $ battles            : int  27 31 28 40 69 57 41 37 18 25 ...
##  $ expl               : int  6 62 52 56 72 48 35 22 15 12 ...
##  $ riots              : int  54 27 26 25 22 27 10 12 33 33 ...
##  $ vac                : int  98 38 58 38 49 26 18 14 24 30 ...
##  $ sd                 : int  5 3 4 3 4 8 11 4 4 6 ...
##  $ precip.jan         : num  0.000764 0.010238 0.090503 0.016059 0.000222 ...
##  $ precip.apr         : num  0.0055 0.0655 0.0837 0.2485 0.0887 ...
##  $ precip.jul         : num  1.964 0.925 0.25 0.691 0.463 ...
##  $ precip.oct         : num  0.0308 0.0693 0.0537 0.1878 0.0729 ...
##  $ total.con.percapita: num  0.000176 0.000181 0.000311 0.000331 0.000172 ...
##  $ protests.percapita : num  1.04e-04 1.12e-04 1.08e-04 1.09e-04 8.82e-05 ...
##  $ battles.percapita  : num  1.03e-05 1.15e-05 1.03e-05 1.45e-05 2.47e-05 ...
##  $ expl.percapita     : num  2.29e-06 2.31e-05 1.91e-05 2.03e-05 2.58e-05 ...
##  $ riots.percapita    : num  1.04e-04 1.12e-04 1.08e-04 1.09e-04 8.82e-05 ...
##  $ vac.percapita      : num  3.74e-05 1.41e-05 2.13e-05 1.38e-05 1.76e-05 ...
##  $ sd.percapita       : num  1.91e-06 1.12e-06 1.47e-06 1.09e-06 1.43e-06 ...

City Visualizations

pplot = ggplot(data = city.con.df, aes(x = districts, y = total.con)) +
                        geom_point(aes(color = districts)) +
                        labs(title = "Total Conflict") +
                        xlab("Location") +
                        ylab("Number of Conflicts") +
                        facet_wrap(facets = "year")

pplot

bplot = ggplot(data = city.con.df, aes(x = districts, y = total.con)) +
                        geom_boxplot(aes(fill = districts))+
                        labs(title = "Annual Total Conflict Events (2010 - 2022)") +
                        xlab("Location") +
                        ylab("Number of Conflicts") +
                        labs(fill = "District")

bplot

bplot.percapita = ggplot(data = city.con.df, aes(x = districts, y = total.con.percapita)) +
                        geom_boxplot(aes(fill = districts))+
                        labs(title = "Annual Total Conflict Per Capita (2010 - 2022)") +
                        xlab("Location") +
                        ylab("Total Conflict per capita") +
                        labs(fill = "District")

bplot.percapita

bplot = ggplot(data = city.con.df, aes(x = districts, y = pop/1000000)) +
                        geom_boxplot(aes(fill = districts))+
                        labs(title = "Population") +
                        xlab("Location") +
                        ylab("Population (millions)")

bplot

## Split the events into violent/non-violent
city.con.df$violent.events = city.con.df$battles + city.con.df$expl + city.con.df$riots + city.con.df$vac

city.con.df$nonviolent.events = city.con.df$protests + city.con.df$sd

## Check
str(city.con.df)

## 'data.frame':    52 obs. of  32 variables:
##  $ districts          : Factor w/ 160 levels "Abbottabad","Astore",..: 31 31 31 31 31 31 31 31 31 31 ...
##  $ longitude          : num  67.1 67.1 67.1 67.1 67.1 ...
##  $ latitude           : num  24.9 24.9 24.9 24.9 24.9 ...
##  $ year               : int  2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 ...
##  $ pop                : int  2617842 2685587 2723651 2758193 2790141 4686459 4686459 5083349 5155070 5672524 ...
##  $ spei               : num  0.575 0.186 -0.911 -0.277 -1.063 ...
##  $ dep.index          : num  32.9 32.9 32.9 32.9 32.9 ...
##  $ built              : num  37.5 37.5 37.5 37.5 37.5 ...
##  $ temp.jan           : num  25.2 24.1 22.9 24.3 23.2 ...
##  $ temp.apr           : num  32.6 32.6 31.8 31.1 31.8 ...
##  $ temp.jul           : num  31.3 31.1 30.9 30.7 31.2 ...
##  $ temp.oct           : num  31.6 30.5 31.7 31.7 32.5 ...
##  $ total.con          : int  462 485 848 914 481 390 267 166 190 168 ...
##  $ protests           : int  272 301 294 300 246 296 347 373 368 356 ...
##  $ battles            : int  27 31 28 40 69 57 41 37 18 25 ...
##  $ expl               : int  6 62 52 56 72 48 35 22 15 12 ...
##  $ riots              : int  54 27 26 25 22 27 10 12 33 33 ...
##  $ vac                : int  98 38 58 38 49 26 18 14 24 30 ...
##  $ sd                 : int  5 3 4 3 4 8 11 4 4 6 ...
##  $ precip.jan         : num  0.000764 0.010238 0.090503 0.016059 0.000222 ...
##  $ precip.apr         : num  0.0055 0.0655 0.0837 0.2485 0.0887 ...
##  $ precip.jul         : num  1.964 0.925 0.25 0.691 0.463 ...
##  $ precip.oct         : num  0.0308 0.0693 0.0537 0.1878 0.0729 ...
##  $ total.con.percapita: num  0.000176 0.000181 0.000311 0.000331 0.000172 ...
##  $ protests.percapita : num  1.04e-04 1.12e-04 1.08e-04 1.09e-04 8.82e-05 ...
##  $ battles.percapita  : num  1.03e-05 1.15e-05 1.03e-05 1.45e-05 2.47e-05 ...
##  $ expl.percapita     : num  2.29e-06 2.31e-05 1.91e-05 2.03e-05 2.58e-05 ...
##  $ riots.percapita    : num  1.04e-04 1.12e-04 1.08e-04 1.09e-04 8.82e-05 ...
##  $ vac.percapita      : num  3.74e-05 1.41e-05 2.13e-05 1.38e-05 1.76e-05 ...
##  $ sd.percapita       : num  1.91e-06 1.12e-06 1.47e-06 1.09e-06 1.43e-06 ...
##  $ violent.events     : int  185 158 164 159 212 158 104 85 90 100 ...
##  $ nonviolent.events  : int  277 304 298 303 250 304 358 377 372 362 ...

## Visualize
bplot.viol = ggplot(data = city.con.df, aes(x = districts, y = violent.events)) +
                        geom_boxplot(aes(fill = districts))+
                        labs(title = "Violent Events") +
                        xlab("Location") +
                        ylab("Number of Events") +
                        labs(fill = "Districts")

## Non-violent events
bplot.nonviol = ggplot(data = city.con.df, aes(x = districts, y = nonviolent.events)) +
                        geom_boxplot(aes(fill = districts))+
                        labs(title = "Non-violent Events") +
                        xlab("Location") +
                        ylab("Number of Events") +
                        labs(fill = "Districts")


ggarrange(bplot.viol, bplot.nonviol,
         ncol = 2,
         nrow = 1,
         common.legend = TRUE,
         legend = "right")

## Violent Events

vio.aov = aov(violent.events ~ districts,
              data = city.con.df)

summary(vio.aov)

##             Df Sum Sq Mean Sq F value   Pr(>F)    
## districts    3  46351   15450   11.41 9.15e-06 ***
## Residuals   48  65022    1355                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Violent Events

nonvio.aov = aov(nonviolent.events ~ districts,
              data = city.con.df)

summary(nonvio.aov)

##             Df Sum Sq Mean Sq F value Pr(>F)    
## districts    3 401539  133846   98.81 <2e-16 ***
## Residuals   48  65022    1355                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

library(xtable)

## Neat ANOVA tables
v.anova.table = xtable(vio.aov)
nv.anova.table = xtable(nonvio.aov)


## kableextra
kable(v.anova.table, caption = "Violent Events ANOVA") %>% 
  kable_classic()

Violent Events ANOVA

	Df	Sum Sq	Mean Sq	F value	Pr(>F)
districts	3	46350.54	15450.179	11.40556	9.1e-06
Residuals	48	65021.69	1354.619	NA	NA

kable(nv.anova.table, caption = "Non-Violent Events ANOVA") %>% 
  kable_classic()

Non-Violent Events ANOVA

	Df	Sum Sq	Mean Sq	F value	Pr(>F)
districts	3	401539.29	133846.429	98.80747	0
Residuals	48	65021.69	1354.619	NA	NA

## Stacked record of conflict by city

city.time.plot = ggplot(data = city.con.df, aes(x = year, y = total.con, col = districts)) +
  geom_line() +
  geom_point() +
  geom_smooth(method = "lm") +
  xlab("Year") +
  ylab("Total Number of Conflicts") +
  labs(col = "Districts") +
  scale_x_continuous(breaks = c(seq(from = 2010, to = 2022, by = 1))) +
  facet_wrap(~districts, scales = "free")


city.time.plot

## `geom_smooth()` using formula = 'y ~ x'

## Stacked record of conflict by city

spei.time.plot = ggplot(data = con.df, aes(x = year, y = spei)) +
  geom_line() +
  geom_point() +
  geom_smooth(method = "lm") +
  xlab("Year") +
  ylab("SPEI") +
  scale_x_continuous(breaks = c(seq(from = 2010, to = 2022, by = 1)))


spei.time.plot

## `geom_smooth()` using formula = 'y ~ x'

## Visualize the trend

bplot.dep = ggplot(data = city.con.df, aes(x = districts, y = dep.index)) +
                        geom_point(aes(col = districts))+
                        labs(title = "Deprivation Index") +
                        xlab("Location") +
                        ylab("Deprivation Index") +
                        labs(col = "Districts")

## Non-violent events
bplot.built = ggplot(data = city.con.df, aes(x = districts, y = built)) +
                        geom_point(aes(col = districts))+
                        labs(title = "Built") +
                        xlab("Location") +
                        ylab("Built") +
                        labs(col = "Districts")


ggarrange(bplot.dep, bplot.built,
         ncol = 2,
         nrow = 1,
         common.legend = TRUE,
         legend = "right")

GLMs

## Use the original data con.df
##Convert district to factor
con.df$districts = factor(con.df$districts)

## Poisson Distribution
tc.glm.1 = glmer(total.con ~ 1 + (1|districts),
                 family = poisson,
                 data = con.df)

summary(tc.glm.1)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: total.con ~ 1 + (1 | districts)
##    Data: con.df
## 
##      AIC      BIC   logLik deviance df.resid 
##  34779.6  34790.9 -17387.8  34775.6     2078 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -15.528  -1.868  -0.690   1.410  26.583 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  districts (Intercept) 2.186    1.478   
## Number of obs: 2080, groups:  districts, 160
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   2.6902     0.1173   22.93   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Population becomes an offset - conflict will scale with population
## Climate variables highly correlated
#offset(log(pop)) + spei + dep.index + built + temp.jul + precip.apr + (1|districts),

## Full model
##Covert year to numeric
con.df$year = as.numeric(con.df$year)

## Poisson Distribution
tc.glm.2 = glmer(total.con ~ offset(log(pop)) + year + spei + dep.index + built + temp.jul + precip.apr + (1|districts),
                 family = poisson,
                 data = con.df)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0102097 (tol = 0.002, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?

summary(tc.glm.2)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: total.con ~ offset(log(pop)) + year + spei + dep.index + built +  
##     temp.jul + precip.apr + (1 | districts)
##    Data: con.df
## 
##      AIC      BIC   logLik deviance df.resid 
##  36926.8  36972.0 -18455.4  36910.8     2072 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -22.235  -1.709  -0.537   1.440  47.708 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  districts (Intercept) 1.451    1.205   
## Number of obs: 2080, groups:  districts, 160
## 
## Fixed effects:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -85.610643   2.365431 -36.192  < 2e-16 ***
## year          0.036358   0.001002  36.277  < 2e-16 ***
## spei          0.054146   0.004869  11.120  < 2e-16 ***
## dep.index     0.013807   0.014559   0.948    0.343    
## built        -0.007269   0.016231  -0.448    0.654    
## temp.jul      0.014406   0.002409   5.981 2.22e-09 ***
## precip.apr    0.088932   0.010180   8.736  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) year   spei   dp.ndx built  tmp.jl
## year       -0.863                                   
## spei       -0.030  0.030                            
## dep.index  -0.505  0.003  0.009                     
## built      -0.373  0.001  0.005  0.725              
## temp.jul   -0.195  0.201  0.017 -0.015 -0.022       
## precip.apr -0.148  0.162 -0.162  0.019  0.027 -0.116
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0102097 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
## Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?

## Population becomes an offset - conflict will scale with population
## Climate variables highly correlated
#

## Scale the covariates 
new.df = con.df.scale.v2

#Add the total.con unscaled
new.df$total.con = con.df$total.con

## Check
str(new.df)

## 'data.frame':    2080 obs. of  29 variables:
##  $ districts          : chr  "Abbottabad" "Abbottabad" "Abbottabad" "Abbottabad" ...
##  $ year               : int  2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 ...
##  $ pop                : num  0.666 0.669 0.671 0.674 0.676 ...
##  $ dep.index          : num  0.76 0.76 0.76 0.76 0.76 ...
##  $ built              : num  0.182 0.182 0.182 0.182 0.182 ...
##  $ total.con          : int  24 10 12 12 6 10 9 19 20 33 ...
##  $ protests           : num  0.511 0.434 0.493 0.502 0.483 ...
##  $ battles            : num  0 0.358 0.174 0.174 0.413 ...
##  $ expl               : num  0 0.476 0.416 0.327 0.377 ...
##  $ riots              : num  0.565 0.308 0 0.308 0.188 ...
##  $ vac                : num  0 0.274 0.345 0.274 0.167 ...
##  $ sd                 : num  0 0 0 0 0 ...
##  $ total.con.percapita: num  0.02991 0.01225 0.01447 0.01425 0.00702 ...
##  $ protests.percapita : num  0.0324 0.0187 0.0276 0.029 0.025 ...
##  $ battles.percapita  : num  0 0.01016 0.00333 0.00328 0.01294 ...
##  $ expl.percapita     : num  0 0.01991 0.014 0.00827 0.01087 ...
##  $ riots.percapita    : num  0.0324 0.0187 0.0276 0.029 0.025 ...
##  $ vac.percapita      : num  0 0.033 0.0487 0.032 0.0158 ...
##  $ sd.percapita       : num  0 0 0 0 0 ...
##  $ temp.jan           : num  0.9 0.861 0.82 0.859 0.872 ...
##  $ temp.apr           : num  0.92 0.896 0.896 0.898 0.894 ...
##  $ temp.jul           : num  0.883 0.876 0.9 0.885 0.882 ...
##  $ temp.oct           : num  0.924 0.918 0.911 0.923 0.912 ...
##  $ precip.jan         : num  0.338 0.274 0.548 0.372 0.265 ...
##  $ precip.apr         : num  0.599 0.834 0.755 0.731 0.595 ...
##  $ precip.jul         : num  0.875 0.594 0.522 0.609 0.662 ...
##  $ precip.oct         : num  0.223 0.687 0.366 0.444 0.589 ...
##  $ spei               : num  0.689 0.824 0.495 0.746 0.694 ...
##  $ som.clust          : Factor w/ 6 levels "1","2","3","4",..: 1 3 2 2 5 2 3 5 3 2 ...

## Convert district to factor and year to numeric
new.df$districts = factor(new.df$districts)
new.df$year = as.numeric(new.df$year)

## Re run model
tc.glm.3 = glmer(total.con ~ dep.index + offset(pop) + spei + built + temp.jul + precip.apr + temp.jan + precip.jul + precip.oct + temp.oct + temp.apr + (1|districts),
                 family = poisson,
                 data = new.df)

summary(tc.glm.3)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: total.con ~ dep.index + offset(pop) + spei + built + temp.jul +  
##     precip.apr + temp.jan + precip.jul + precip.oct + temp.oct +  
##     temp.apr + (1 | districts)
##    Data: new.df
## 
##      AIC      BIC   logLik deviance df.resid 
##  34077.6  34145.3 -17026.8  34053.6     2068 
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -16.6267  -1.8160  -0.6257   1.3546  29.2749 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  districts (Intercept) 1.457    1.207   
## Number of obs: 2080, groups:  districts, 160
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  2.96154    1.04133   2.844 0.004455 ** 
## dep.index   -4.16793    1.09608  -3.803 0.000143 ***
## spei         0.29754    0.02422  12.285  < 2e-16 ***
## built       -1.51490    0.80945  -1.872 0.061275 .  
## temp.jul     0.23201    0.41133   0.564 0.572728    
## precip.apr   0.07543    0.04202   1.795 0.072652 .  
## temp.jan    -2.14980    0.15148 -14.192  < 2e-16 ***
## precip.jul  -0.09427    0.02791  -3.378 0.000731 ***
## precip.oct   0.10532    0.03312   3.180 0.001475 ** 
## temp.oct    -1.82790    0.21945  -8.330  < 2e-16 ***
## temp.apr     6.70572    0.29212  22.956  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dp.ndx spei   built  tmp.jl prcp.p tmp.jn prcp.j prcp.c
## dep.index  -0.916                                                        
## spei       -0.005 -0.029                                                 
## built      -0.722  0.790 -0.052                                          
## temp.jul   -0.224 -0.072 -0.045 -0.097                                   
## precip.apr -0.048  0.009 -0.144  0.004 -0.054                            
## temp.jan    0.046 -0.056  0.112 -0.100 -0.059 -0.072                     
## precip.jul -0.144 -0.015 -0.136 -0.018  0.655  0.080 -0.057              
## precip.oct -0.018  0.001  0.049 -0.006 -0.095 -0.291 -0.156 -0.114       
## temp.oct   -0.061 -0.027 -0.125 -0.054 -0.103 -0.006 -0.139 -0.032  0.207
## temp.apr   -0.066 -0.063  0.217 -0.115 -0.156  0.246 -0.216 -0.290  0.117
##            tmp.ct
## dep.index        
## spei             
## built            
## temp.jul         
## precip.apr       
## temp.jan         
## precip.jul       
## precip.oct       
## temp.oct         
## temp.apr   -0.177

## offset(log(pop)) + + spei + dep.index + built + temp.jul + precip.apr
## 1st Run - model failed to converge
## year and precip.jan are the issues

## Reminder that this are transformed and scaled
exp(fixef(tc.glm.3))

##  (Intercept)    dep.index         spei        built     temp.jul   precip.apr 
##  19.32763850   0.01548427   1.34654262   0.21983095   1.26112818   1.07834749 
##     temp.jan   precip.jul   precip.oct     temp.oct     temp.apr 
##   0.11650692   0.91003747   1.11106579   0.16075029 817.06840202

## Need help on interpretation!!
##These are scaled and transformed

## Re run the intercept model for comparison

tc.glm.0.new = glmer(total.con ~ 1 + (1|districts),
                 family = poisson,
                 data = new.df)

summary(tc.glm.0.new)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: total.con ~ 1 + (1 | districts)
##    Data: new.df
## 
##      AIC      BIC   logLik deviance df.resid 
##  34779.6  34790.9 -17387.8  34775.6     2078 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -15.528  -1.868  -0.690   1.410  26.583 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  districts (Intercept) 2.186    1.478   
## Number of obs: 2080, groups:  districts, 160
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   2.6902     0.1173   22.93   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Compare the two glms
anova(tc.glm.3, tc.glm.0.new)

npar	AIC	BIC	logLik	deviance	Chisq	Df	Pr(>Chisq)
2	3.48e+04	3.48e+04	-1.74e+04	3.48e+04
12	3.41e+04	3.41e+04	-1.7e+04	3.41e+04	722	10	1.17e-148

#library(moments) ##Skewness

#var.names2 = var.names
#log.trans = function(x){
  #log(x + 1)
#}

#con.df.x = con.df %>% 
  #mutate_at(var.names, log.trans)

## Re run model
#tc.glm.x = glmer(total.con ~ 1 + dep.index + offset(pop) + spei + built + temp.jul + precip.apr + temp.jan + precip.jul + precip.oct + temp.oct + temp.apr + (1|districts),
                 #family = poisson,
                 #ata = con.df)

#summary(tc.glm.x)

# dep.index + offset(pop) + spei + built + temp.jul + precip.apr + temp.jan + precip.jul + precip.oct + temp.oct + temp.apr +

Spatial Model Build

## Check 
class(con.df.sf)

## [1] "sf"         "data.frame"

## Variables
str(con.df.sf)

## Classes 'sf' and 'data.frame':   2080 obs. of  32 variables:
##  $ districts          : chr  "Abbottabad" "Abbottabad" "Abbottabad" "Abbottabad" ...
##  $ longitude          : num  73.3 73.3 73.3 73.3 73.3 ...
##  $ latitude           : num  34.1 34.1 34.1 34.1 34.1 ...
##  $ year               : chr  "2010" "2011" "2012" "2013" ...
##  $ pop                : num  1204572 1225491 1244756 1264224 1282908 ...
##  $ spei               : num  0.7 1.327 -0.197 0.965 0.726 ...
##  $ dep_index          : num  73 73 73 73 73 ...
##  $ built              : num  1.09 1.09 1.09 1.09 1.09 ...
##  $ temp_jan           : num  12.23 8.94 6.42 8.77 9.73 ...
##  $ temp_apr           : num  22.3 18.5 18.5 18.8 18.1 ...
##  $ temp_jul           : num  26.2 25.5 28.1 26.5 26.2 ...
##  $ temp_oct           : num  21 20 18.9 20.8 19.2 ...
##  $ precip_jan         : num  0.898 0.704 1.714 1.011 0.676 ...
##  $ precip_apr         : num  1.95 3.55 2.92 2.75 1.93 ...
##  $ precip_jul         : num  7.32 3.02 2.39 3.18 3.76 ...
##  $ precip_oct         : num  0.414 1.587 0.71 0.892 1.276 ...
##  $ total_con          : int  24 10 12 12 6 10 9 19 20 33 ...
##  $ protests           : int  17 10 15 16 14 11 13 17 15 19 ...
##  $ battles            : int  0 3 1 1 4 7 3 1 1 0 ...
##  $ expl               : int  0 7 5 3 4 2 4 1 1 2 ...
##  $ riots              : int  7 2 0 2 1 1 2 3 5 0 ...
##  $ vac                : int  0 2 3 2 1 3 1 1 2 3 ...
##  $ sd                 : int  0 0 0 0 0 0 1 1 0 0 ...
##  $ geometry           :sfc_MULTIPOLYGON of length 2080; first list element: List of 1
##   ..$ :List of 1
##   .. ..$ : num [1:1765, 1:2] 73.4 73.4 73.4 73.4 73.4 ...
##   ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
##  $ total_con_percapita: num  1.99e-05 8.16e-06 9.64e-06 9.49e-06 4.68e-06 ...
##  $ protests_percapita : num  1.41e-05 8.16e-06 1.21e-05 1.27e-05 1.09e-05 ...
##  $ battles_percapita  : num  0.00 2.45e-06 8.03e-07 7.91e-07 3.12e-06 ...
##  $ expl_percapita     : num  0.00 5.71e-06 4.02e-06 2.37e-06 3.12e-06 ...
##  $ riots_percapita    : num  1.41e-05 8.16e-06 1.21e-05 1.27e-05 1.09e-05 ...
##  $ vac_percapita      : num  0.00 1.63e-06 2.41e-06 1.58e-06 7.79e-07 ...
##  $ sd_percapita       : num  0 0 0 0 0 ...
##  $ som.clust          : Factor w/ 6 levels "1","2","3","4",..: 5 2 3 3 2 2 4 4 3 3 ...
##  - attr(*, "sf_column")= chr "geometry"
##  - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
##   ..- attr(*, "names")= chr [1:31] "districts" "longitude" "latitude" "year" ...

## Need to extract one years worth of data
## 2012 to start
con.df.sf.2012 = con.df.sf %>% 
  filter(year %in% "2012")

## Extract Centroids
pak.coords = st_centroid(st_geometry(con.df.sf.2012))

## Check
plot(st_geometry(con.df.sf.2012),
     reset = FALSE)
plot(pak.coords, pch = 16, col = 2, add =TRUE)

library(spdep)
library(spatialreg)

## Build neighborhood structure
pak1_nb = poly2nb(st_geometry(con.df.sf.2012))

## Check
plot(st_geometry(con.df.sf.2012),
     reset = FALSE)
plot(pak1_nb, pak.coords, lwd = 1.5, col = 2, add =TRUE)

## Generate the spatial weights
## Row standardization

pak1_lw_w = nb2listw(pak1_nb, style = "W")

## Check for skew in the con.df.sf outcome variable
hist(log(con.df.sf.2012$total_con + 1))

## Create a log + 1 transformed variable
con.df.sf.2012$log.total.con = log(con.df.sf.2012$total_con + 1)

## Visualize 
#plot(con.df.sf["log.total.con"])

## Visually there appears to be autocorrelation

Random Forest Build

Initial Model

## Load ranger package
library(ranger)
library(mlr3verse)

## Loading required package: mlr3

## 
## Attaching package: 'mlr3'

## The following object is masked from 'package:raster':
## 
##     resample

library(mlr3tuning)

## Loading required package: paradox

## Transform total.con to log
con.df$log.total.con = ihs(con.df$total.con)

## Define the task
task_totalcon = TaskRegr$new(id = "total_con",
                             backend = con.df,
                             target = "log.total.con")

## Task details
task_totalcon$col_roles

## $feature
##  [1] "battles"             "battles.percapita"   "built"              
##  [4] "dep.index"           "districts"           "expl"               
##  [7] "expl.percapita"      "latitude"            "longitude"          
## [10] "pop"                 "precip.apr"          "precip.jan"         
## [13] "precip.jul"          "precip.oct"          "protests"           
## [16] "protests.percapita"  "riots"               "riots.percapita"    
## [19] "sd"                  "sd.percapita"        "spei"               
## [22] "temp.apr"            "temp.jan"            "temp.jul"           
## [25] "temp.oct"            "total.con"           "total.con.percapita"
## [28] "vac"                 "vac.percapita"       "year"               
## 
## $target
## [1] "log.total.con"
## 
## $name
## character(0)
## 
## $order
## character(0)
## 
## $stratum
## character(0)
## 
## $group
## character(0)
## 
## $weight
## character(0)

## Exclude Features
## lat, long, and percapitas
## Exclude conflict
task_totalcon$col_roles$feature = setdiff(task_totalcon$col_roles$feature,
                                          c("latitude", "longitude", "total.con.percapita", "battles.percapita", "protests.percapita", "riots.percapita", "vac.percapita", "sd.percapita", "expl.percapita", "total.con", "protests", "battles", "riots", "vac", "sd", "expl"))


## Check
task_totalcon$col_roles

## $feature
##  [1] "built"      "dep.index"  "districts"  "pop"        "precip.apr"
##  [6] "precip.jan" "precip.jul" "precip.oct" "spei"       "temp.apr"  
## [11] "temp.jan"   "temp.jul"   "temp.oct"   "year"      
## 
## $target
## [1] "log.total.con"
## 
## $name
## character(0)
## 
## $order
## character(0)
## 
## $stratum
## character(0)
## 
## $group
## character(0)
## 
## $weight
## character(0)

## Define the performance measure
## Reminder - Regression task 

measure = msr("regr.rmse")

## Define the learner
lrn_rf = lrn("regr.ranger",
             predict_type = "response",
             importance = "permutation" )

## Define the resampling method
## 1st Run - Simple holdout 0.8
resamp_hout = rsmp("holdout",
                   ratio = 0.8)


## Instantiate the resampling method
resamp_hout$instantiate(task_totalcon)

## Run the resampler/model
##tc = total conflict

tc_rf = resample(task = task_totalcon,
                 learner = lrn_rf,
                 resampling = resamp_hout,
                 store_models = TRUE)

## INFO  [11:57:18.337] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)

## Check the performace measures
tc_rf$score(measure)

task	task_id	learner	learner_id	resampling	resampling_id	iteration	prediction	regr.rmse
<environment>	total_con	<environment>	regr.ranger	<environment>	holdout	1	<environment>	0.779

## Initial visualization of the model
## Rpart for visualization
library(rpart)
library(rpart.plot)

## Visualize
## Can't run it with a random forest
## Needs to be a CART
#prp(tc_rf$learners[[1]]$model,
    #extra = 106,
    #roundint = FALSE)

Tuning the Model

## Check the Parameters 
lrn_rf$param_set

## <ParamSet>
##                               id    class lower upper nlevels        default
##  1:                        alpha ParamDbl  -Inf   Inf     Inf            0.5
##  2:       always.split.variables ParamUty    NA    NA     Inf <NoDefault[3]>
##  3:                      holdout ParamLgl    NA    NA       2          FALSE
##  4:                   importance ParamFct    NA    NA       4 <NoDefault[3]>
##  5:                   keep.inbag ParamLgl    NA    NA       2          FALSE
##  6:                    max.depth ParamInt     0   Inf     Inf               
##  7:                min.node.size ParamInt     1   Inf     Inf              5
##  8:                     min.prop ParamDbl  -Inf   Inf     Inf            0.1
##  9:                      minprop ParamDbl  -Inf   Inf     Inf            0.1
## 10:                         mtry ParamInt     1   Inf     Inf <NoDefault[3]>
## 11:                   mtry.ratio ParamDbl     0     1     Inf <NoDefault[3]>
## 12:            num.random.splits ParamInt     1   Inf     Inf              1
## 13:                  num.threads ParamInt     1   Inf     Inf              1
## 14:                    num.trees ParamInt     1   Inf     Inf            500
## 15:                    oob.error ParamLgl    NA    NA       2           TRUE
## 16:                     quantreg ParamLgl    NA    NA       2          FALSE
## 17:        regularization.factor ParamUty    NA    NA     Inf              1
## 18:      regularization.usedepth ParamLgl    NA    NA       2          FALSE
## 19:                      replace ParamLgl    NA    NA       2           TRUE
## 20:    respect.unordered.factors ParamFct    NA    NA       3         ignore
## 21:              sample.fraction ParamDbl     0     1     Inf <NoDefault[3]>
## 22:                  save.memory ParamLgl    NA    NA       2          FALSE
## 23: scale.permutation.importance ParamLgl    NA    NA       2          FALSE
## 24:                    se.method ParamFct    NA    NA       2        infjack
## 25:                         seed ParamInt  -Inf   Inf     Inf               
## 26:         split.select.weights ParamUty    NA    NA     Inf               
## 27:                    splitrule ParamFct    NA    NA       3       variance
## 28:                      verbose ParamLgl    NA    NA       2           TRUE
## 29:                 write.forest ParamLgl    NA    NA       2           TRUE
##                               id    class lower upper nlevels        default
##        parents       value
##  1:  splitrule            
##  2:                       
##  3:                       
##  4:            permutation
##  5:                       
##  6:                       
##  7:                       
##  8:                       
##  9:  splitrule            
## 10:                       
## 11:                       
## 12:  splitrule            
## 13:                      1
## 14:                       
## 15:                       
## 16:                       
## 17:                       
## 18:                       
## 19:                       
## 20:                       
## 21:                       
## 22:                       
## 23: importance            
## 24:                       
## 25:                       
## 26:                       
## 27:                       
## 28:                       
## 29:                       
##        parents       value

## Tune the number of variables per split (mtry)
## Tune the number of trees built (numtrees)

tune_ps = ParamSet$new(list(
  ParamInt$new("mtry", lower = 1, upper = 8),
  ParamInt$new("num.trees", lower = 100, upper = 1000)
))

## Limit the number of iterations/evaluations to 50
evals = trm("evals",
            n_evals = 50)

## Define the tuner to grid search with 10 steps between the lower and upper bounds
tuner = tnr("grid_search",
            resolution = 10)

## Set up the nested resampling for tuning
## Build the resample for the inner
resampling_inner = rsmp("holdout",
                        ratio = 0.8)

## Build the resample for the outer (in this case 3-fold)
resampling_outer = rsmp("cv", 
                        folds = 3)


## Set up the autotuner

at_rf = AutoTuner$new(learner = lrn_rf,
                      resampling = resampling_inner,
                      measure = measure,
                      search_space = tune_ps,
                      terminator = evals,
                      tuner = tuner)

## Tuning the paramaters using the parameter set/solution

tc_rf.2 = resample(task = task_totalcon,
                 learner = at_rf,
                 resampling = resampling_outer,
                 store_models = TRUE)

## INFO  [11:57:19.566] [mlr3] Applying learner 'regr.ranger.tuned' on task 'total_con' (iter 1/3)
## INFO  [11:57:19.603] [bbotk] Starting to optimize 2 parameter(s) with '<TunerGridSearch>' and '<TerminatorEvals> [n_evals=50, k=0]'
## INFO  [11:57:19.605] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:19.617] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:19.619] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:20.808] [mlr3] Finished benchmark
## INFO  [11:57:20.820] [bbotk] Result of batch 1:
## INFO  [11:57:20.821] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:20.821] [bbotk]     2      1000 0.8732468        0      0            1.184
## INFO  [11:57:20.821] [bbotk]                                 uhash
## INFO  [11:57:20.821] [bbotk]  6df88bdd-1c7b-4b20-a738-f545f376dc8b
## INFO  [11:57:20.822] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:20.831] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:20.834] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:22.256] [mlr3] Finished benchmark
## INFO  [11:57:22.267] [bbotk] Result of batch 2:
## INFO  [11:57:22.268] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:22.268] [bbotk]     4       900 0.8228984        0      0            1.418
## INFO  [11:57:22.268] [bbotk]                                 uhash
## INFO  [11:57:22.268] [bbotk]  3ef1ae80-afb2-49df-a870-0d75feaaa90e
## INFO  [11:57:22.269] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:22.289] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:22.292] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:23.962] [mlr3] Finished benchmark
## INFO  [11:57:23.972] [bbotk] Result of batch 3:
## INFO  [11:57:23.973] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:23.973] [bbotk]     7       800  0.807133        0      0            1.666
## INFO  [11:57:23.973] [bbotk]                                 uhash
## INFO  [11:57:23.973] [bbotk]  da9c61b1-f870-417a-a3b2-ecb5c69293e7
## INFO  [11:57:23.973] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:23.983] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:23.986] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:25.086] [mlr3] Finished benchmark
## INFO  [11:57:25.097] [bbotk] Result of batch 4:
## INFO  [11:57:25.098] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:25.098] [bbotk]     3       800 0.8432881        0      0            1.096
## INFO  [11:57:25.098] [bbotk]                                 uhash
## INFO  [11:57:25.098] [bbotk]  f0ba7491-6a60-463a-8385-12cd68864a0a
## INFO  [11:57:25.098] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:25.108] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:25.111] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:25.942] [mlr3] Finished benchmark
## INFO  [11:57:25.954] [bbotk] Result of batch 5:
## INFO  [11:57:25.955] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:25.955] [bbotk]     3       600  0.838263        0      0            0.827
## INFO  [11:57:25.955] [bbotk]                                 uhash
## INFO  [11:57:25.955] [bbotk]  3c653e8a-6a84-4a4c-9bbe-f72569057046
## INFO  [11:57:25.956] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:25.966] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:25.969] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:26.325] [mlr3] Finished benchmark
## INFO  [11:57:26.335] [bbotk] Result of batch 6:
## INFO  [11:57:26.336] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:26.336] [bbotk]     5       200 0.8223107        0      0            0.353
## INFO  [11:57:26.336] [bbotk]                                 uhash
## INFO  [11:57:26.336] [bbotk]  69b57dda-ec20-450b-8fa6-aaf5e6d14261
## INFO  [11:57:26.336] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:26.346] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:26.349] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:28.083] [mlr3] Finished benchmark
## INFO  [11:57:28.094] [bbotk] Result of batch 7:
## INFO  [11:57:28.095] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:28.095] [bbotk]     6       900  0.809787        0      0            1.732
## INFO  [11:57:28.095] [bbotk]                                 uhash
## INFO  [11:57:28.095] [bbotk]  b17c2761-e947-407f-a137-be12c7cece88
## INFO  [11:57:28.096] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:28.115] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:28.117] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:28.839] [mlr3] Finished benchmark
## INFO  [11:57:28.849] [bbotk] Result of batch 8:
## INFO  [11:57:28.850] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:28.850] [bbotk]     2       600 0.8737958        0      0            0.717
## INFO  [11:57:28.850] [bbotk]                                 uhash
## INFO  [11:57:28.850] [bbotk]  1e9c07cd-af63-4e64-a1b3-c9cd4e9463b5
## INFO  [11:57:28.850] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:28.860] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:28.863] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:29.389] [mlr3] Finished benchmark
## INFO  [11:57:29.400] [bbotk] Result of batch 9:
## INFO  [11:57:29.400] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:29.400] [bbotk]     5       300  0.810393        0      0            0.523
## INFO  [11:57:29.400] [bbotk]                                 uhash
## INFO  [11:57:29.400] [bbotk]  21358f50-9180-44b5-9c7f-952c72a31984
## INFO  [11:57:29.401] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:29.411] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:29.414] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:29.613] [mlr3] Finished benchmark
## INFO  [11:57:29.625] [bbotk] Result of batch 10:
## INFO  [11:57:29.626] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:29.626] [bbotk]     6       100  0.819131        0      0            0.196
## INFO  [11:57:29.626] [bbotk]                                 uhash
## INFO  [11:57:29.626] [bbotk]  32e1796d-120d-4ce9-879a-c57f127a8cb8
## INFO  [11:57:29.627] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:29.637] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:29.640] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:30.896] [mlr3] Finished benchmark
## INFO  [11:57:30.907] [bbotk] Result of batch 11:
## INFO  [11:57:30.908] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:30.908] [bbotk]     4       800 0.8221486        0      0            1.253
## INFO  [11:57:30.908] [bbotk]                                 uhash
## INFO  [11:57:30.908] [bbotk]  de9f2932-4e18-47ba-b816-50f81edde9a3
## INFO  [11:57:30.908] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:30.918] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:30.920] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:32.934] [mlr3] Finished benchmark
## INFO  [11:57:32.944] [bbotk] Result of batch 12:
## INFO  [11:57:32.945] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:32.945] [bbotk]     8       900 0.8008724        0      0             2.01
## INFO  [11:57:32.945] [bbotk]                                 uhash
## INFO  [11:57:32.945] [bbotk]  f9f3d8e8-6c9f-4134-94ab-1dc8de654b74
## INFO  [11:57:32.946] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:32.966] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:32.969] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:34.010] [mlr3] Finished benchmark
## INFO  [11:57:34.020] [bbotk] Result of batch 13:
## INFO  [11:57:34.021] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:34.021] [bbotk]     5       600 0.8134351        0      0            1.037
## INFO  [11:57:34.021] [bbotk]                                 uhash
## INFO  [11:57:34.021] [bbotk]  e74155ef-48ca-40af-a14f-434e103d2d4a
## INFO  [11:57:34.022] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:34.031] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:34.034] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:35.934] [mlr3] Finished benchmark
## INFO  [11:57:35.945] [bbotk] Result of batch 14:
## INFO  [11:57:35.946] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:35.946] [bbotk]     6      1000 0.8044866        0      0            1.896
## INFO  [11:57:35.946] [bbotk]                                 uhash
## INFO  [11:57:35.946] [bbotk]  3229d62e-2e2b-4ca7-8b87-762039fda562
## INFO  [11:57:35.947] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:35.957] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:35.960] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:37.047] [mlr3] Finished benchmark
## INFO  [11:57:37.057] [bbotk] Result of batch 15:
## INFO  [11:57:37.058] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:37.058] [bbotk]     4       700 0.8266195        0      0            1.083
## INFO  [11:57:37.058] [bbotk]                                 uhash
## INFO  [11:57:37.058] [bbotk]  f42b684a-ac7e-4b77-877f-10c1ac973eeb
## INFO  [11:57:37.059] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:37.068] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:37.071] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:38.420] [mlr3] Finished benchmark
## INFO  [11:57:38.431] [bbotk] Result of batch 16:
## INFO  [11:57:38.432] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:38.432] [bbotk]     8       600 0.8050944        0      0            1.345
## INFO  [11:57:38.432] [bbotk]                                 uhash
## INFO  [11:57:38.432] [bbotk]  76252482-0c31-40dd-a791-913a33accef1
## INFO  [11:57:38.433] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:38.442] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:38.445] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:39.351] [mlr3] Finished benchmark
## INFO  [11:57:39.361] [bbotk] Result of batch 17:
## INFO  [11:57:39.362] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:39.362] [bbotk]     8       400 0.8094588        0      0            0.901
## INFO  [11:57:39.362] [bbotk]                                 uhash
## INFO  [11:57:39.362] [bbotk]  938b0af9-35b9-43d0-b2d1-f65752d21e87
## INFO  [11:57:39.362] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:39.381] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:39.384] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:40.084] [mlr3] Finished benchmark
## INFO  [11:57:40.095] [bbotk] Result of batch 18:
## INFO  [11:57:40.096] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:40.096] [bbotk]     1       700 0.9599837        0      0            0.696
## INFO  [11:57:40.096] [bbotk]                                 uhash
## INFO  [11:57:40.096] [bbotk]  8b8446a8-df46-4ce2-9ba5-dcc7ad00aa2d
## INFO  [11:57:40.096] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:40.106] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:40.109] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:41.102] [mlr3] Finished benchmark
## INFO  [11:57:41.112] [bbotk] Result of batch 19:
## INFO  [11:57:41.113] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:41.113] [bbotk]     1      1000 0.9542121        0      0            0.988
## INFO  [11:57:41.113] [bbotk]                                 uhash
## INFO  [11:57:41.113] [bbotk]  c6e41504-32db-429e-baa0-903ff16ee21d
## INFO  [11:57:41.114] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:41.123] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:41.126] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:41.903] [mlr3] Finished benchmark
## INFO  [11:57:41.914] [bbotk] Result of batch 20:
## INFO  [11:57:41.914] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:41.914] [bbotk]     4       500 0.8170365        0      0            0.773
## INFO  [11:57:41.914] [bbotk]                                 uhash
## INFO  [11:57:41.914] [bbotk]  e540bf33-b713-4cb5-a539-6c35a1fb05d1
## INFO  [11:57:41.915] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:41.925] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:41.927] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:42.528] [mlr3] Finished benchmark
## INFO  [11:57:42.539] [bbotk] Result of batch 21:
## INFO  [11:57:42.540] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:42.540] [bbotk]     1       600 0.9523506        0      0            0.598
## INFO  [11:57:42.540] [bbotk]                                 uhash
## INFO  [11:57:42.540] [bbotk]  c1951bbd-5ae0-4a3f-b161-3f2f0e15879e
## INFO  [11:57:42.540] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:42.550] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:42.553] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:43.236] [mlr3] Finished benchmark
## INFO  [11:57:43.246] [bbotk] Result of batch 22:
## INFO  [11:57:43.247] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:43.247] [bbotk]     8       300 0.8036688        0      0            0.678
## INFO  [11:57:43.247] [bbotk]                                 uhash
## INFO  [11:57:43.247] [bbotk]  9763d52d-d0af-46d1-96d1-0a10d16a79ff
## INFO  [11:57:43.247] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:43.267] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:43.270] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:44.609] [mlr3] Finished benchmark
## INFO  [11:57:44.619] [bbotk] Result of batch 23:
## INFO  [11:57:44.620] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:44.620] [bbotk]     6       700 0.8092876        0      0            1.334
## INFO  [11:57:44.620] [bbotk]                                 uhash
## INFO  [11:57:44.620] [bbotk]  a1429951-5f33-406d-81c6-7f42dffeb719
## INFO  [11:57:44.620] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:44.630] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:44.633] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:46.019] [mlr3] Finished benchmark
## INFO  [11:57:46.030] [bbotk] Result of batch 24:
## INFO  [11:57:46.031] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:46.031] [bbotk]     5       800 0.8165429        0      0            1.383
## INFO  [11:57:46.031] [bbotk]                                 uhash
## INFO  [11:57:46.031] [bbotk]  b1e8d00d-f65c-4227-aa11-0fbf756a77db
## INFO  [11:57:46.031] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:46.041] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:46.044] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:47.004] [mlr3] Finished benchmark
## INFO  [11:57:47.014] [bbotk] Result of batch 25:
## INFO  [11:57:47.015] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:47.015] [bbotk]     3       700 0.8436984        0      0            0.957
## INFO  [11:57:47.015] [bbotk]                                 uhash
## INFO  [11:57:47.015] [bbotk]  a4119afd-383a-48d7-823d-84c75c4b21ea
## INFO  [11:57:47.016] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:47.025] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:47.028] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:48.754] [mlr3] Finished benchmark
## INFO  [11:57:48.765] [bbotk] Result of batch 26:
## INFO  [11:57:48.765] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:48.765] [bbotk]     5      1000 0.8128171        0      0            1.722
## INFO  [11:57:48.765] [bbotk]                                 uhash
## INFO  [11:57:48.765] [bbotk]  e54bab24-5588-440d-91ef-76af9774ace7
## INFO  [11:57:48.766] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:48.776] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:48.779] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:48.984] [mlr3] Finished benchmark
## INFO  [11:57:48.994] [bbotk] Result of batch 27:
## INFO  [11:57:48.995] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:48.995] [bbotk]     1       200 0.9559873        0      0            0.201
## INFO  [11:57:48.995] [bbotk]                                 uhash
## INFO  [11:57:48.995] [bbotk]  4b49adea-9978-4d75-bded-16738e9fa987
## INFO  [11:57:48.995] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:49.015] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:49.018] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:49.232] [mlr3] Finished benchmark
## INFO  [11:57:49.243] [bbotk] Result of batch 28:
## INFO  [11:57:49.244] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:49.244] [bbotk]     7       100 0.8021352        0      0             0.21
## INFO  [11:57:49.244] [bbotk]                                 uhash
## INFO  [11:57:49.244] [bbotk]  8c8a8be9-1065-4ee2-a5fb-205586c04741
## INFO  [11:57:49.244] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:49.254] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:49.256] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:50.499] [mlr3] Finished benchmark
## INFO  [11:57:50.510] [bbotk] Result of batch 29:
## INFO  [11:57:50.511] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:50.511] [bbotk]     3       900 0.8474551        0      0            1.239
## INFO  [11:57:50.511] [bbotk]                                 uhash
## INFO  [11:57:50.511] [bbotk]  81584e86-10b7-44f7-9429-aa6803fc3c51
## INFO  [11:57:50.511] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:50.521] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:50.524] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:50.838] [mlr3] Finished benchmark
## INFO  [11:57:50.849] [bbotk] Result of batch 30:
## INFO  [11:57:50.850] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:50.850] [bbotk]     4       200 0.8260061        0      0            0.311
## INFO  [11:57:50.850] [bbotk]                                 uhash
## INFO  [11:57:50.850] [bbotk]  908e50f9-5839-4f90-b1f9-79f2e6ffe0dd
## INFO  [11:57:50.850] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:50.860] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:50.863] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:50.987] [mlr3] Finished benchmark
## INFO  [11:57:50.998] [bbotk] Result of batch 31:
## INFO  [11:57:50.999] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:50.999] [bbotk]     2       100 0.8774656        0      0             0.12
## INFO  [11:57:50.999] [bbotk]                                 uhash
## INFO  [11:57:50.999] [bbotk]  851f3c3a-c33d-4eaf-885a-026e5833f87a
## INFO  [11:57:50.999] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:51.009] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:51.011] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:51.315] [mlr3] Finished benchmark
## INFO  [11:57:51.325] [bbotk] Result of batch 32:
## INFO  [11:57:51.326] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:51.326] [bbotk]     1       300 0.9528277        0      0            0.299
## INFO  [11:57:51.326] [bbotk]                                 uhash
## INFO  [11:57:51.326] [bbotk]  0cc92340-ab0f-4b26-8692-3e1f148962e2
## INFO  [11:57:51.326] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:51.345] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:51.348] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:51.454] [mlr3] Finished benchmark
## INFO  [11:57:51.465] [bbotk] Result of batch 33:
## INFO  [11:57:51.465] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:51.465] [bbotk]     1       100  0.952116        0      0            0.103
## INFO  [11:57:51.465] [bbotk]                                 uhash
## INFO  [11:57:51.465] [bbotk]  8997bfff-eafc-47a5-9b6c-accb0670b00c
## INFO  [11:57:51.466] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:51.476] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:51.479] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:51.897] [mlr3] Finished benchmark
## INFO  [11:57:51.908] [bbotk] Result of batch 34:
## INFO  [11:57:51.909] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:51.909] [bbotk]     3       300  0.849469        0      0            0.414
## INFO  [11:57:51.909] [bbotk]                                 uhash
## INFO  [11:57:51.909] [bbotk]  08ee064b-fd4c-4f50-b348-cac98966117b
## INFO  [11:57:51.909] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:51.919] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:51.922] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:54.192] [mlr3] Finished benchmark
## INFO  [11:57:54.203] [bbotk] Result of batch 35:
## INFO  [11:57:54.203] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:54.203] [bbotk]     8      1000 0.7988514        0      0            2.266
## INFO  [11:57:54.203] [bbotk]                                 uhash
## INFO  [11:57:54.203] [bbotk]  ec1ad606-a48c-402d-aaec-986e431d986c
## INFO  [11:57:54.204] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:54.214] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:54.216] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:54.640] [mlr3] Finished benchmark
## INFO  [11:57:54.651] [bbotk] Result of batch 36:
## INFO  [11:57:54.652] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:54.652] [bbotk]     7       200 0.8020706        0      0             0.42
## INFO  [11:57:54.652] [bbotk]                                 uhash
## INFO  [11:57:54.652] [bbotk]  80a54028-e348-40f6-a42d-5c06fbf8067a
## INFO  [11:57:54.652] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:54.662] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:54.665] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:55.146] [mlr3] Finished benchmark
## INFO  [11:57:55.157] [bbotk] Result of batch 37:
## INFO  [11:57:55.158] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:55.158] [bbotk]     2       400 0.8770853        0      0            0.477
## INFO  [11:57:55.158] [bbotk]                                 uhash
## INFO  [11:57:55.158] [bbotk]  5429ae55-39e5-4467-b25d-e8724d1267da
## INFO  [11:57:55.158] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:55.177] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:55.180] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:55.589] [mlr3] Finished benchmark
## INFO  [11:57:55.600] [bbotk] Result of batch 38:
## INFO  [11:57:55.600] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:55.600] [bbotk]     1       400 0.9506275        0      0            0.405
## INFO  [11:57:55.600] [bbotk]                                 uhash
## INFO  [11:57:55.600] [bbotk]  0fae0463-0f26-4dbe-ad75-358b7d90a38a
## INFO  [11:57:55.601] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:55.611] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:55.614] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:56.221] [mlr3] Finished benchmark
## INFO  [11:57:56.231] [bbotk] Result of batch 39:
## INFO  [11:57:56.232] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:56.232] [bbotk]     2       500 0.8712632        0      0            0.602
## INFO  [11:57:56.232] [bbotk]                                 uhash
## INFO  [11:57:56.232] [bbotk]  1a676db6-1aa6-4ee3-992d-189b06f4d920
## INFO  [11:57:56.233] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:56.243] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:56.245] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:56.936] [mlr3] Finished benchmark
## INFO  [11:57:56.947] [bbotk] Result of batch 40:
## INFO  [11:57:56.947] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:56.947] [bbotk]     3       500 0.8429602        0      0            0.686
## INFO  [11:57:56.947] [bbotk]                                 uhash
## INFO  [11:57:56.947] [bbotk]  15a14f83-5b2e-4470-99bc-53a6998e4ace
## INFO  [11:57:56.948] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:56.958] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:56.961] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:57.915] [mlr3] Finished benchmark
## INFO  [11:57:57.926] [bbotk] Result of batch 41:
## INFO  [11:57:57.927] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:57.927] [bbotk]     2       800 0.8725347        0      0             0.95
## INFO  [11:57:57.927] [bbotk]                                 uhash
## INFO  [11:57:57.927] [bbotk]  c43739aa-e1a3-489b-b212-8eaf65a6b586
## INFO  [11:57:57.927] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:57.937] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:57.940] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:57:58.784] [mlr3] Finished benchmark
## INFO  [11:57:58.795] [bbotk] Result of batch 42:
## INFO  [11:57:58.796] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:57:58.796] [bbotk]     7       400 0.7991818        0      0            0.841
## INFO  [11:57:58.796] [bbotk]                                 uhash
## INFO  [11:57:58.796] [bbotk]  282cfd00-0f28-463b-a848-0ff07f9e4154
## INFO  [11:57:58.796] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:57:58.815] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:57:58.818] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:00.713] [mlr3] Finished benchmark
## INFO  [11:58:00.723] [bbotk] Result of batch 43:
## INFO  [11:58:00.724] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:00.724] [bbotk]     7       900 0.8065042        0      0            1.891
## INFO  [11:58:00.724] [bbotk]                                 uhash
## INFO  [11:58:00.724] [bbotk]  fc7f0f2e-0781-470c-aaf5-333fd9d28808
## INFO  [11:58:00.725] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:00.734] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:00.737] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:01.291] [mlr3] Finished benchmark
## INFO  [11:58:01.302] [bbotk] Result of batch 44:
## INFO  [11:58:01.303] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:01.303] [bbotk]     3       400 0.8384336        0      0             0.55
## INFO  [11:58:01.303] [bbotk]                                 uhash
## INFO  [11:58:01.303] [bbotk]  f3f66273-ed3b-4aaa-b4dc-509b2958366d
## INFO  [11:58:01.303] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:01.313] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:01.316] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:02.018] [mlr3] Finished benchmark
## INFO  [11:58:02.030] [bbotk] Result of batch 45:
## INFO  [11:58:02.031] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:02.031] [bbotk]     5       400 0.8108746        0      0            0.699
## INFO  [11:58:02.031] [bbotk]                                 uhash
## INFO  [11:58:02.031] [bbotk]  6c26c260-a31b-4e53-8809-b944a15d4bf6
## INFO  [11:58:02.031] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:02.041] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:02.044] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:03.257] [mlr3] Finished benchmark
## INFO  [11:58:03.268] [bbotk] Result of batch 46:
## INFO  [11:58:03.268] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:03.268] [bbotk]     5       700 0.8126134        0      0            1.209
## INFO  [11:58:03.268] [bbotk]                                 uhash
## INFO  [11:58:03.268] [bbotk]  9803fe0e-bfd9-4151-8a13-5bd7b4714ca9
## INFO  [11:58:03.269] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:03.279] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:03.281] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:03.665] [mlr3] Finished benchmark
## INFO  [11:58:03.684] [bbotk] Result of batch 47:
## INFO  [11:58:03.685] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:03.685] [bbotk]     6       200 0.8089984        0      0             0.38
## INFO  [11:58:03.685] [bbotk]                                 uhash
## INFO  [11:58:03.685] [bbotk]  59b60cfc-350d-401e-9cae-c572733dbb20
## INFO  [11:58:03.686] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:03.696] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:03.699] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:04.567] [mlr3] Finished benchmark
## INFO  [11:58:04.578] [bbotk] Result of batch 48:
## INFO  [11:58:04.579] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:04.579] [bbotk]     5       500 0.8155667        0      0            0.865
## INFO  [11:58:04.579] [bbotk]                                 uhash
## INFO  [11:58:04.579] [bbotk]  6c91539c-6b4b-44c0-9920-b77cf994b1df
## INFO  [11:58:04.579] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:04.589] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:04.592] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:06.133] [mlr3] Finished benchmark
## INFO  [11:58:06.146] [bbotk] Result of batch 49:
## INFO  [11:58:06.147] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:06.147] [bbotk]     6       800 0.8100991        0      0            1.538
## INFO  [11:58:06.147] [bbotk]                                 uhash
## INFO  [11:58:06.147] [bbotk]  33018e31-27fb-4763-bf27-e166b6348d2e
## INFO  [11:58:06.147] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:06.158] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:06.160] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:07.103] [mlr3] Finished benchmark
## INFO  [11:58:07.114] [bbotk] Result of batch 50:
## INFO  [11:58:07.115] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:07.115] [bbotk]     4       600  0.828019        0      0             0.94
## INFO  [11:58:07.115] [bbotk]                                 uhash
## INFO  [11:58:07.115] [bbotk]  cfbc02b2-5106-49e8-9f76-ff9248ed48dd
## INFO  [11:58:07.120] [bbotk] Finished optimizing after 50 evaluation(s)
## INFO  [11:58:07.120] [bbotk] Result:
## INFO  [11:58:07.121] [bbotk]  mtry num.trees learner_param_vals  x_domain regr.rmse
## INFO  [11:58:07.121] [bbotk]     8      1000          <list[4]> <list[2]> 0.7988514
## INFO  [11:58:10.114] [mlr3] Applying learner 'regr.ranger.tuned' on task 'total_con' (iter 2/3)
## INFO  [11:58:10.138] [bbotk] Starting to optimize 2 parameter(s) with '<TunerGridSearch>' and '<TerminatorEvals> [n_evals=50, k=0]'
## INFO  [11:58:10.139] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:10.158] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:10.161] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:10.993] [mlr3] Finished benchmark
## INFO  [11:58:11.003] [bbotk] Result of batch 1:
## INFO  [11:58:11.004] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:11.004] [bbotk]     2       700 0.8317178        0      0            0.828
## INFO  [11:58:11.004] [bbotk]                                 uhash
## INFO  [11:58:11.004] [bbotk]  d4552b12-3849-4f74-b271-81f1ec12e47e
## INFO  [11:58:11.004] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:11.014] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:11.017] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:11.420] [mlr3] Finished benchmark
## INFO  [11:58:11.432] [bbotk] Result of batch 2:
## INFO  [11:58:11.432] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:11.432] [bbotk]     1       400 0.9022382        0      0            0.399
## INFO  [11:58:11.432] [bbotk]                                 uhash
## INFO  [11:58:11.432] [bbotk]  4cfb58c9-4ebb-4ff8-9e6d-b4b498df997a
## INFO  [11:58:11.433] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:11.443] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:11.446] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:12.798] [mlr3] Finished benchmark
## INFO  [11:58:12.809] [bbotk] Result of batch 3:
## INFO  [11:58:12.809] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:12.809] [bbotk]     8       600 0.7546694        0      0            1.349
## INFO  [11:58:12.809] [bbotk]                                 uhash
## INFO  [11:58:12.809] [bbotk]  ba4e4c78-512c-4562-bc39-36bcd8a78982
## INFO  [11:58:12.810] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:12.820] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:12.823] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:13.002] [mlr3] Finished benchmark
## INFO  [11:58:13.013] [bbotk] Result of batch 4:
## INFO  [11:58:13.014] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:13.014] [bbotk]     5       100 0.7802346        0      0            0.176
## INFO  [11:58:13.014] [bbotk]                                 uhash
## INFO  [11:58:13.014] [bbotk]  e4b50c8e-a17e-42c4-bc7b-3e7438f2bc53
## INFO  [11:58:13.014] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:13.024] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:13.026] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:13.711] [mlr3] Finished benchmark
## INFO  [11:58:13.730] [bbotk] Result of batch 5:
## INFO  [11:58:13.731] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:13.731] [bbotk]     8       300 0.7630187        0      0            0.681
## INFO  [11:58:13.731] [bbotk]                                 uhash
## INFO  [11:58:13.731] [bbotk]  d75f770b-d97e-4ca1-8b74-a20b111175ea
## INFO  [11:58:13.731] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:13.741] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:13.744] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:13.905] [mlr3] Finished benchmark
## INFO  [11:58:13.915] [bbotk] Result of batch 6:
## INFO  [11:58:13.916] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:13.916] [bbotk]     4       100 0.7717546        0      0            0.157
## INFO  [11:58:13.916] [bbotk]                                 uhash
## INFO  [11:58:13.916] [bbotk]  086b7416-0964-4aa1-9e1c-becba591d92d
## INFO  [11:58:13.917] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:13.926] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:13.929] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:14.448] [mlr3] Finished benchmark
## INFO  [11:58:14.459] [bbotk] Result of batch 7:
## INFO  [11:58:14.459] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:14.459] [bbotk]     5       300 0.7701657        0      0            0.515
## INFO  [11:58:14.459] [bbotk]                                 uhash
## INFO  [11:58:14.459] [bbotk]  d9426650-0bae-4fc5-96d7-e12a9504f974
## INFO  [11:58:14.460] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:14.470] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:14.472] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:15.412] [mlr3] Finished benchmark
## INFO  [11:58:15.423] [bbotk] Result of batch 8:
## INFO  [11:58:15.424] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:15.424] [bbotk]     4       600 0.7798742        0      0            0.936
## INFO  [11:58:15.424] [bbotk]                                 uhash
## INFO  [11:58:15.424] [bbotk]  b75ef212-56a5-49e6-b970-641586a8f369
## INFO  [11:58:15.424] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:15.436] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:15.439] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:17.086] [mlr3] Finished benchmark
## INFO  [11:58:17.097] [bbotk] Result of batch 9:
## INFO  [11:58:17.098] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:17.098] [bbotk]     7       800 0.7558616        0      0            1.644
## INFO  [11:58:17.098] [bbotk]                                 uhash
## INFO  [11:58:17.098] [bbotk]  b7b322c0-1a44-4725-9236-b105fb548ffa
## INFO  [11:58:17.098] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:17.108] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:17.110] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:18.370] [mlr3] Finished benchmark
## INFO  [11:58:18.383] [bbotk] Result of batch 10:
## INFO  [11:58:18.383] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:18.383] [bbotk]     7       600  0.761385        0      0            1.245
## INFO  [11:58:18.383] [bbotk]                                 uhash
## INFO  [11:58:18.383] [bbotk]  47008679-114a-4d6a-b9ab-a44f72bfb340
## INFO  [11:58:18.384] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:18.394] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:18.397] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:19.355] [mlr3] Finished benchmark
## INFO  [11:58:19.366] [bbotk] Result of batch 11:
## INFO  [11:58:19.366] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:19.366] [bbotk]     2       800 0.8258718        0      0            0.953
## INFO  [11:58:19.366] [bbotk]                                 uhash
## INFO  [11:58:19.366] [bbotk]  649bc49d-985f-460b-a43e-d86f753e73e4
## INFO  [11:58:19.367] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:19.377] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:19.380] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:19.855] [mlr3] Finished benchmark
## INFO  [11:58:19.867] [bbotk] Result of batch 12:
## INFO  [11:58:19.868] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:19.868] [bbotk]     4       300   0.78672        0      0            0.471
## INFO  [11:58:19.868] [bbotk]                                 uhash
## INFO  [11:58:19.868] [bbotk]  669a0158-4bbc-4b3a-801b-aeec5778323a
## INFO  [11:58:19.868] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:19.879] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:19.882] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:21.020] [mlr3] Finished benchmark
## INFO  [11:58:21.032] [bbotk] Result of batch 13:
## INFO  [11:58:21.032] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:21.032] [bbotk]     6       600 0.7638263        0      0            1.134
## INFO  [11:58:21.032] [bbotk]                                 uhash
## INFO  [11:58:21.032] [bbotk]  1faa3a65-7503-4894-8217-1978ced4122e
## INFO  [11:58:21.033] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:21.043] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:21.045] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:21.323] [mlr3] Finished benchmark
## INFO  [11:58:21.333] [bbotk] Result of batch 14:
## INFO  [11:58:21.334] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:21.334] [bbotk]     3       200 0.7947557        0      0            0.273
## INFO  [11:58:21.334] [bbotk]                                 uhash
## INFO  [11:58:21.334] [bbotk]  ac99b614-9a46-4b5c-85c3-aceac0badc00
## INFO  [11:58:21.335] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:21.352] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:21.355] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:22.046] [mlr3] Finished benchmark
## INFO  [11:58:22.056] [bbotk] Result of batch 15:
## INFO  [11:58:22.057] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:22.057] [bbotk]     5       400 0.7678981        0      0            0.686
## INFO  [11:58:22.057] [bbotk]                                 uhash
## INFO  [11:58:22.057] [bbotk]  8311e297-1619-45cc-b7db-f04f0fe4e33e
## INFO  [11:58:22.058] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:22.068] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:22.070] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:22.483] [mlr3] Finished benchmark
## INFO  [11:58:22.494] [bbotk] Result of batch 16:
## INFO  [11:58:22.495] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:22.495] [bbotk]     3       300 0.7983365        0      0             0.41
## INFO  [11:58:22.495] [bbotk]                                 uhash
## INFO  [11:58:22.495] [bbotk]  cbf4b704-27bb-4e65-9a79-50dbe331b79e
## INFO  [11:58:22.495] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:22.505] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:22.508] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:23.505] [mlr3] Finished benchmark
## INFO  [11:58:23.516] [bbotk] Result of batch 17:
## INFO  [11:58:23.516] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:23.516] [bbotk]     1      1000 0.8970296        0      0            0.993
## INFO  [11:58:23.516] [bbotk]                                 uhash
## INFO  [11:58:23.516] [bbotk]  c07f7593-85a8-48db-b0fc-429e95aea327
## INFO  [11:58:23.517] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:23.527] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:23.530] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:25.062] [mlr3] Finished benchmark
## INFO  [11:58:25.073] [bbotk] Result of batch 18:
## INFO  [11:58:25.073] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:25.073] [bbotk]     4      1000 0.7834622        0      0            1.529
## INFO  [11:58:25.073] [bbotk]                                 uhash
## INFO  [11:58:25.073] [bbotk]  9fd5917a-2c85-45df-8441-4681135a1a2a
## INFO  [11:58:25.074] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:25.084] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:25.087] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:27.137] [mlr3] Finished benchmark
## INFO  [11:58:27.147] [bbotk] Result of batch 19:
## INFO  [11:58:27.148] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:27.148] [bbotk]     7      1000   0.75798        0      0            2.046
## INFO  [11:58:27.148] [bbotk]                                 uhash
## INFO  [11:58:27.148] [bbotk]  df578c01-a0b5-47c6-9964-ffcbd864dad2
## INFO  [11:58:27.149] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:27.169] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:27.172] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:27.744] [mlr3] Finished benchmark
## INFO  [11:58:27.755] [bbotk] Result of batch 20:
## INFO  [11:58:27.756] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:27.756] [bbotk]     6       300  0.768809        0      0            0.568
## INFO  [11:58:27.756] [bbotk]                                 uhash
## INFO  [11:58:27.756] [bbotk]  009b1dda-9b44-413c-bd9c-13389fb6d856
## INFO  [11:58:27.756] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:27.766] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:27.769] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:29.213] [mlr3] Finished benchmark
## INFO  [11:58:29.224] [bbotk] Result of batch 21:
## INFO  [11:58:29.225] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:29.225] [bbotk]     7       700 0.7550753        0      0            1.441
## INFO  [11:58:29.225] [bbotk]                                 uhash
## INFO  [11:58:29.225] [bbotk]  6e3739b0-1234-4992-b37c-49b054e0e0bc
## INFO  [11:58:29.225] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:29.235] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:29.238] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:30.199] [mlr3] Finished benchmark
## INFO  [11:58:30.210] [bbotk] Result of batch 22:
## INFO  [11:58:30.211] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:30.211] [bbotk]     3       700 0.7998518        0      0            0.958
## INFO  [11:58:30.211] [bbotk]                                 uhash
## INFO  [11:58:30.211] [bbotk]  4b11c510-2f4e-4609-900d-d8e8ca883c1b
## INFO  [11:58:30.212] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:30.222] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:30.224] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:30.643] [mlr3] Finished benchmark
## INFO  [11:58:30.654] [bbotk] Result of batch 23:
## INFO  [11:58:30.654] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:30.654] [bbotk]     7       200 0.7624997        0      0            0.415
## INFO  [11:58:30.654] [bbotk]                                 uhash
## INFO  [11:58:30.654] [bbotk]  6a200396-5b2d-4c4e-9de2-2fdb9a695c70
## INFO  [11:58:30.655] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:30.665] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:30.667] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:31.763] [mlr3] Finished benchmark
## INFO  [11:58:31.840] [bbotk] Result of batch 24:
## INFO  [11:58:31.841] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:31.841] [bbotk]     3       800 0.7960154        0      0            1.092
## INFO  [11:58:31.841] [bbotk]                                 uhash
## INFO  [11:58:31.841] [bbotk]  4507219c-e9a6-47f9-994d-aed9d534e5ee
## INFO  [11:58:31.842] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:31.856] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:31.859] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:32.461] [mlr3] Finished benchmark
## INFO  [11:58:32.472] [bbotk] Result of batch 25:
## INFO  [11:58:32.472] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:32.472] [bbotk]     1       600 0.9018282        0      0            0.598
## INFO  [11:58:32.472] [bbotk]                                 uhash
## INFO  [11:58:32.472] [bbotk]  d822c695-3afc-486f-9cfa-a446542c3663
## INFO  [11:58:32.473] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:32.483] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:32.486] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:33.311] [mlr3] Finished benchmark
## INFO  [11:58:33.321] [bbotk] Result of batch 26:
## INFO  [11:58:33.322] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:33.322] [bbotk]     7       400 0.7618804        0      0            0.821
## INFO  [11:58:33.322] [bbotk]                                 uhash
## INFO  [11:58:33.322] [bbotk]  e617e143-7d83-4dfd-add9-a6209c329d59
## INFO  [11:58:33.323] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:33.333] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:33.336] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:33.685] [mlr3] Finished benchmark
## INFO  [11:58:33.695] [bbotk] Result of batch 27:
## INFO  [11:58:33.696] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:33.696] [bbotk]     5       200 0.7717828        0      0            0.345
## INFO  [11:58:33.696] [bbotk]                                 uhash
## INFO  [11:58:33.696] [bbotk]  d0cc8a4b-cf68-499d-b8e5-761e1b6b63cb
## INFO  [11:58:33.697] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:33.707] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:33.710] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:35.412] [mlr3] Finished benchmark
## INFO  [11:58:35.422] [bbotk] Result of batch 28:
## INFO  [11:58:35.423] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:35.423] [bbotk]     6       900 0.7672989        0      0            1.697
## INFO  [11:58:35.423] [bbotk]                                 uhash
## INFO  [11:58:35.423] [bbotk]  7fee3709-0dcb-412e-87fc-4498f90b40af
## INFO  [11:58:35.424] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:35.434] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:35.437] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:37.149] [mlr3] Finished benchmark
## INFO  [11:58:37.160] [bbotk] Result of batch 29:
## INFO  [11:58:37.161] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:37.161] [bbotk]     5      1000 0.7671374        0      0            1.709
## INFO  [11:58:37.161] [bbotk]                                 uhash
## INFO  [11:58:37.161] [bbotk]  e6124273-0c38-462b-898b-f282214a68da
## INFO  [11:58:37.162] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:37.185] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:37.189] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:37.385] [mlr3] Finished benchmark
## INFO  [11:58:37.395] [bbotk] Result of batch 30:
## INFO  [11:58:37.396] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:37.396] [bbotk]     6       100 0.7581125        0      0            0.191
## INFO  [11:58:37.396] [bbotk]                                 uhash
## INFO  [11:58:37.396] [bbotk]  1aa75c18-7cc9-4391-bc3a-cc35b10bfcf3
## INFO  [11:58:37.397] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:37.407] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:37.409] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:38.305] [mlr3] Finished benchmark
## INFO  [11:58:38.315] [bbotk] Result of batch 31:
## INFO  [11:58:38.316] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:38.316] [bbotk]     1       900 0.9058328        0      0            0.891
## INFO  [11:58:38.316] [bbotk]                                 uhash
## INFO  [11:58:38.316] [bbotk]  9fa4e639-a26e-4903-835f-79cbd03b06ed
## INFO  [11:58:38.317] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:38.327] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:38.329] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:40.210] [mlr3] Finished benchmark
## INFO  [11:58:40.221] [bbotk] Result of batch 32:
## INFO  [11:58:40.222] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:40.222] [bbotk]     6      1000 0.7605343        0      0            1.877
## INFO  [11:58:40.222] [bbotk]                                 uhash
## INFO  [11:58:40.222] [bbotk]  a2b247d7-588c-40a9-aa87-48e61a2ffe9c
## INFO  [11:58:40.222] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:40.232] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:40.235] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:41.093] [mlr3] Finished benchmark
## INFO  [11:58:41.104] [bbotk] Result of batch 33:
## INFO  [11:58:41.105] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:41.105] [bbotk]     5       500 0.7662215        0      0            0.854
## INFO  [11:58:41.105] [bbotk]                                 uhash
## INFO  [11:58:41.105] [bbotk]  ccc106e9-6eea-4c4b-b3b8-e2b8a067e6b0
## INFO  [11:58:41.105] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:41.115] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:41.118] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:41.498] [mlr3] Finished benchmark
## INFO  [11:58:41.509] [bbotk] Result of batch 34:
## INFO  [11:58:41.509] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:41.509] [bbotk]     6       200 0.7539971        0      0            0.376
## INFO  [11:58:41.509] [bbotk]                                 uhash
## INFO  [11:58:41.509] [bbotk]  1fa2e7af-133e-403f-b649-b6742fdf7713
## INFO  [11:58:41.510] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:41.528] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:41.531] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:42.770] [mlr3] Finished benchmark
## INFO  [11:58:42.785] [bbotk] Result of batch 35:
## INFO  [11:58:42.786] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:42.786] [bbotk]     3       900 0.7994883        0      0            1.234
## INFO  [11:58:42.786] [bbotk]                                 uhash
## INFO  [11:58:42.786] [bbotk]  70f9119b-57ff-49b3-9148-2176485da9bc
## INFO  [11:58:42.786] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:42.797] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:42.799] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:44.341] [mlr3] Finished benchmark
## INFO  [11:58:44.353] [bbotk] Result of batch 36:
## INFO  [11:58:44.354] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:44.354] [bbotk]     5       900 0.7675102        0      0            1.538
## INFO  [11:58:44.354] [bbotk]                                 uhash
## INFO  [11:58:44.354] [bbotk]  5251071e-d8d4-4895-b623-78f504f2c60f
## INFO  [11:58:44.354] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:44.364] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:44.367] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:46.144] [mlr3] Finished benchmark
## INFO  [11:58:46.156] [bbotk] Result of batch 37:
## INFO  [11:58:46.157] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:46.157] [bbotk]     8       800 0.7537794        0      0            1.774
## INFO  [11:58:46.157] [bbotk]                                 uhash
## INFO  [11:58:46.157] [bbotk]  5efb8dbf-1344-4652-b76e-de825a86618f
## INFO  [11:58:46.157] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:46.167] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:46.170] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:46.649] [mlr3] Finished benchmark
## INFO  [11:58:46.660] [bbotk] Result of batch 38:
## INFO  [11:58:46.661] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:46.661] [bbotk]     2       400 0.8315601        0      0            0.476
## INFO  [11:58:46.661] [bbotk]                                 uhash
## INFO  [11:58:46.661] [bbotk]  dc39576e-56b8-4936-8f1d-a255abe32a77
## INFO  [11:58:46.662] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:46.672] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:46.675] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:47.788] [mlr3] Finished benchmark
## INFO  [11:58:47.799] [bbotk] Result of batch 39:
## INFO  [11:58:47.800] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:47.800] [bbotk]     8       500  0.753134        0      0             1.11
## INFO  [11:58:47.800] [bbotk]                                 uhash
## INFO  [11:58:47.800] [bbotk]  49b677fc-a29b-4a19-b0cf-76790e38d81c
## INFO  [11:58:47.800] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:47.810] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:47.813] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:49.378] [mlr3] Finished benchmark
## INFO  [11:58:49.395] [bbotk] Result of batch 40:
## INFO  [11:58:49.396] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:49.396] [bbotk]     8       700 0.7538395        0      0            1.561
## INFO  [11:58:49.396] [bbotk]                                 uhash
## INFO  [11:58:49.396] [bbotk]  73b48cd4-5d56-47a8-b5f5-314bb0249cb3
## INFO  [11:58:49.397] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:49.408] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:49.411] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:49.861] [mlr3] Finished benchmark
## INFO  [11:58:49.872] [bbotk] Result of batch 41:
## INFO  [11:58:49.872] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:49.872] [bbotk]     8       200 0.7573453        0      0            0.447
## INFO  [11:58:49.872] [bbotk]                                 uhash
## INFO  [11:58:49.872] [bbotk]  3f1b8247-adfd-441c-b489-4afb525f1619
## INFO  [11:58:49.873] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:49.883] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:49.886] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:50.188] [mlr3] Finished benchmark
## INFO  [11:58:50.198] [bbotk] Result of batch 42:
## INFO  [11:58:50.199] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:50.199] [bbotk]     1       300 0.8995974        0      0            0.298
## INFO  [11:58:50.199] [bbotk]                                 uhash
## INFO  [11:58:50.199] [bbotk]  f88e6348-ec45-4ba4-b2d3-a3982c5cc8d9
## INFO  [11:58:50.200] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:50.210] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:50.213] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:50.526] [mlr3] Finished benchmark
## INFO  [11:58:50.537] [bbotk] Result of batch 43:
## INFO  [11:58:50.538] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:50.538] [bbotk]     4       200 0.7896456        0      0             0.31
## INFO  [11:58:50.538] [bbotk]                                 uhash
## INFO  [11:58:50.538] [bbotk]  a850727f-5c43-4779-a862-d01db3fb6eb1
## INFO  [11:58:50.538] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:50.548] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:50.551] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:51.250] [mlr3] Finished benchmark
## INFO  [11:58:51.261] [bbotk] Result of batch 44:
## INFO  [11:58:51.262] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:51.262] [bbotk]     1       700 0.9040158        0      0            0.695
## INFO  [11:58:51.262] [bbotk]                                 uhash
## INFO  [11:58:51.262] [bbotk]  477c795d-2ea8-4491-beca-06e3535c713a
## INFO  [11:58:51.263] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:51.273] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:51.276] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:51.977] [mlr3] Finished benchmark
## INFO  [11:58:51.998] [bbotk] Result of batch 45:
## INFO  [11:58:51.999] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:51.999] [bbotk]     3       500 0.7982769        0      0            0.697
## INFO  [11:58:51.999] [bbotk]                                 uhash
## INFO  [11:58:51.999] [bbotk]  6543fe79-f937-4b20-8322-674660a23e1b
## INFO  [11:58:52.000] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:52.016] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:52.019] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:53.067] [mlr3] Finished benchmark
## INFO  [11:58:53.079] [bbotk] Result of batch 46:
## INFO  [11:58:53.079] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:53.079] [bbotk]     5       600 0.7714905        0      0            1.044
## INFO  [11:58:53.079] [bbotk]                                 uhash
## INFO  [11:58:53.079] [bbotk]  2ee05b59-8fcf-4119-99c1-bd6617996581
## INFO  [11:58:53.080] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:53.091] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:53.093] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:54.000] [mlr3] Finished benchmark
## INFO  [11:58:54.011] [bbotk] Result of batch 47:
## INFO  [11:58:54.012] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:54.012] [bbotk]     8       400 0.7571002        0      0            0.902
## INFO  [11:58:54.012] [bbotk]                                 uhash
## INFO  [11:58:54.012] [bbotk]  8aef8c63-aa5e-43b6-98ff-20e1fd7d8ae2
## INFO  [11:58:54.013] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:54.022] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:54.025] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:54.256] [mlr3] Finished benchmark
## INFO  [11:58:54.267] [bbotk] Result of batch 48:
## INFO  [11:58:54.267] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:54.267] [bbotk]     8       100 0.7604713        0      0            0.225
## INFO  [11:58:54.267] [bbotk]                                 uhash
## INFO  [11:58:54.267] [bbotk]  364bd988-ed6d-40bf-bf29-c883068bf37f
## INFO  [11:58:54.268] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:54.278] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:54.281] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:54.388] [mlr3] Finished benchmark
## INFO  [11:58:54.399] [bbotk] Result of batch 49:
## INFO  [11:58:54.400] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:54.400] [bbotk]     1       100 0.9130186        0      0            0.104
## INFO  [11:58:54.400] [bbotk]                                 uhash
## INFO  [11:58:54.400] [bbotk]  8eed351a-1ed2-4ade-bb40-dcd546f1a8d3
## INFO  [11:58:54.401] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:54.411] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:54.414] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:55.014] [mlr3] Finished benchmark
## INFO  [11:58:55.026] [bbotk] Result of batch 50:
## INFO  [11:58:55.027] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:55.027] [bbotk]     2       500 0.8281358        0      0            0.597
## INFO  [11:58:55.027] [bbotk]                                 uhash
## INFO  [11:58:55.027] [bbotk]  62dd2e72-5f61-407e-8274-9c1431c224c4
## INFO  [11:58:55.036] [bbotk] Finished optimizing after 50 evaluation(s)
## INFO  [11:58:55.037] [bbotk] Result:
## INFO  [11:58:55.037] [bbotk]  mtry num.trees learner_param_vals  x_domain regr.rmse
## INFO  [11:58:55.037] [bbotk]     8       500          <list[4]> <list[2]>  0.753134
## INFO  [11:58:56.553] [mlr3] Applying learner 'regr.ranger.tuned' on task 'total_con' (iter 3/3)
## INFO  [11:58:56.577] [bbotk] Starting to optimize 2 parameter(s) with '<TunerGridSearch>' and '<TerminatorEvals> [n_evals=50, k=0]'
## INFO  [11:58:56.578] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:56.588] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:56.591] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:57.831] [mlr3] Finished benchmark
## INFO  [11:58:57.841] [bbotk] Result of batch 1:
## INFO  [11:58:57.841] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:57.841] [bbotk]     7       600 0.8095253        0      0            1.235
## INFO  [11:58:57.841] [bbotk]                                 uhash
## INFO  [11:58:57.841] [bbotk]  f437f4d0-6695-48d4-9f4a-be7d77899c3c
## INFO  [11:58:57.842] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:57.852] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:57.855] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:58.000] [mlr3] Finished benchmark
## INFO  [11:58:58.012] [bbotk] Result of batch 2:
## INFO  [11:58:58.013] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:58.013] [bbotk]     3       100 0.8526854        0      0            0.141
## INFO  [11:58:58.013] [bbotk]                                 uhash
## INFO  [11:58:58.013] [bbotk]  1a50f1f8-532f-4aeb-9c1b-eab28da6320a
## INFO  [11:58:58.014] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:58.025] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:58.027] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:58:58.985] [mlr3] Finished benchmark
## INFO  [11:58:58.996] [bbotk] Result of batch 3:
## INFO  [11:58:58.997] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:58:58.997] [bbotk]     6       500 0.8051755        0      0            0.953
## INFO  [11:58:58.997] [bbotk]                                 uhash
## INFO  [11:58:58.997] [bbotk]  0ef48cb1-e8e6-46dd-a601-4213489b3bfe
## INFO  [11:58:58.997] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:58:59.008] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:58:59.019] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:00.101] [mlr3] Finished benchmark
## INFO  [11:59:00.117] [bbotk] Result of batch 4:
## INFO  [11:59:00.117] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:00.117] [bbotk]     2       900 0.8795428        0      0            1.077
## INFO  [11:59:00.117] [bbotk]                                 uhash
## INFO  [11:59:00.117] [bbotk]  817ec372-a517-4577-b40b-87e936e1c2b1
## INFO  [11:59:00.118] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:00.128] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:00.131] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:01.508] [mlr3] Finished benchmark
## INFO  [11:59:01.519] [bbotk] Result of batch 5:
## INFO  [11:59:01.520] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:01.520] [bbotk]     5       800 0.8138172        0      0            1.372
## INFO  [11:59:01.520] [bbotk]                                 uhash
## INFO  [11:59:01.520] [bbotk]  24069108-c4d3-45bd-b4a1-1a4383c2a542
## INFO  [11:59:01.520] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:01.531] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:01.533] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:02.598] [mlr3] Finished benchmark
## INFO  [11:59:02.610] [bbotk] Result of batch 6:
## INFO  [11:59:02.611] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:02.611] [bbotk]     7       500  0.807036        0      0            1.061
## INFO  [11:59:02.611] [bbotk]                                 uhash
## INFO  [11:59:02.611] [bbotk]  eb13127e-cdb0-4834-911e-be07c8f7a64d
## INFO  [11:59:02.612] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:02.622] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:02.625] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:03.034] [mlr3] Finished benchmark
## INFO  [11:59:03.046] [bbotk] Result of batch 7:
## INFO  [11:59:03.047] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:03.047] [bbotk]     1       400 0.9640679        0      0            0.405
## INFO  [11:59:03.047] [bbotk]                                 uhash
## INFO  [11:59:03.047] [bbotk]  71a47f4b-6c0f-41c4-b658-cdbcf1ba9e58
## INFO  [11:59:03.048] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:03.058] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:03.061] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:04.878] [mlr3] Finished benchmark
## INFO  [11:59:04.889] [bbotk] Result of batch 8:
## INFO  [11:59:04.890] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:04.890] [bbotk]     8       800 0.8027351        0      0            1.813
## INFO  [11:59:04.890] [bbotk]                                 uhash
## INFO  [11:59:04.890] [bbotk]  d0089224-69d8-4af0-bb71-06d02d597cbf
## INFO  [11:59:04.891] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:04.900] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:04.903] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:05.576] [mlr3] Finished benchmark
## INFO  [11:59:05.597] [bbotk] Result of batch 9:
## INFO  [11:59:05.599] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:05.599] [bbotk]     7       300 0.8085369        0      0            0.667
## INFO  [11:59:05.599] [bbotk]                                 uhash
## INFO  [11:59:05.599] [bbotk]  1a678398-46bf-4886-adde-d53c87f31813
## INFO  [11:59:05.600] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:05.613] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:05.616] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:06.114] [mlr3] Finished benchmark
## INFO  [11:59:06.126] [bbotk] Result of batch 10:
## INFO  [11:59:06.127] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:06.127] [bbotk]     4       300 0.8231903        0      0            0.494
## INFO  [11:59:06.127] [bbotk]                                 uhash
## INFO  [11:59:06.127] [bbotk]  5557ad82-30a2-4b88-bbeb-a076152ed41b
## INFO  [11:59:06.128] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:06.139] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:06.142] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:07.103] [mlr3] Finished benchmark
## INFO  [11:59:07.114] [bbotk] Result of batch 11:
## INFO  [11:59:07.115] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:07.115] [bbotk]     2       800 0.8785604        0      0            0.957
## INFO  [11:59:07.115] [bbotk]                                 uhash
## INFO  [11:59:07.115] [bbotk]  711035c5-364e-492b-af5b-edf9bf39e63f
## INFO  [11:59:07.115] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:07.125] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:07.128] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:08.677] [mlr3] Finished benchmark
## INFO  [11:59:08.688] [bbotk] Result of batch 12:
## INFO  [11:59:08.689] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:08.689] [bbotk]     5       900 0.8089606        0      0            1.545
## INFO  [11:59:08.689] [bbotk]                                 uhash
## INFO  [11:59:08.689] [bbotk]  7cd71972-3aaf-413f-8f00-eccc2a197b20
## INFO  [11:59:08.689] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:08.699] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:08.702] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:09.378] [mlr3] Finished benchmark
## INFO  [11:59:09.390] [bbotk] Result of batch 13:
## INFO  [11:59:09.391] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:09.391] [bbotk]     8       300 0.8035548        0      0            0.672
## INFO  [11:59:09.391] [bbotk]                                 uhash
## INFO  [11:59:09.391] [bbotk]  b7432f81-9e53-4c46-a0be-fa541afff2e2
## INFO  [11:59:09.391] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:09.402] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:09.405] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:10.114] [mlr3] Finished benchmark
## INFO  [11:59:10.132] [bbotk] Result of batch 14:
## INFO  [11:59:10.134] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:10.134] [bbotk]     3       500 0.8505515        0      0            0.704
## INFO  [11:59:10.134] [bbotk]                                 uhash
## INFO  [11:59:10.134] [bbotk]  0e51d59a-2e29-41bc-ae30-3a38f965ca5c
## INFO  [11:59:10.135] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:10.149] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:10.152] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:10.355] [mlr3] Finished benchmark
## INFO  [11:59:10.371] [bbotk] Result of batch 15:
## INFO  [11:59:10.373] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:10.373] [bbotk]     6       100 0.8028508        0      0            0.198
## INFO  [11:59:10.373] [bbotk]                                 uhash
## INFO  [11:59:10.373] [bbotk]  63fd53eb-bfe6-465a-831c-882b7d130e8c
## INFO  [11:59:10.374] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:10.385] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:10.387] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:11.659] [mlr3] Finished benchmark
## INFO  [11:59:11.670] [bbotk] Result of batch 16:
## INFO  [11:59:11.671] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:11.671] [bbotk]     3       900 0.8416965        0      0            1.267
## INFO  [11:59:11.671] [bbotk]                                 uhash
## INFO  [11:59:11.671] [bbotk]  ec91f772-a97d-41fa-a281-f98592b33a06
## INFO  [11:59:11.671] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:11.682] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:11.684] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:13.404] [mlr3] Finished benchmark
## INFO  [11:59:13.417] [bbotk] Result of batch 17:
## INFO  [11:59:13.418] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:13.418] [bbotk]     6       900  0.804443        0      0            1.715
## INFO  [11:59:13.418] [bbotk]                                 uhash
## INFO  [11:59:13.418] [bbotk]  7b383cc2-4f53-4e8a-8e4f-3ed7406db1ff
## INFO  [11:59:13.419] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:13.430] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:13.432] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:14.041] [mlr3] Finished benchmark
## INFO  [11:59:14.052] [bbotk] Result of batch 18:
## INFO  [11:59:14.052] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:14.052] [bbotk]     2       500 0.8834893        0      0            0.604
## INFO  [11:59:14.052] [bbotk]                                 uhash
## INFO  [11:59:14.052] [bbotk]  b300aaac-a77a-4fff-8bbc-e2cd4835f949
## INFO  [11:59:14.053] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:14.063] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:14.066] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:15.522] [mlr3] Finished benchmark
## INFO  [11:59:15.547] [bbotk] Result of batch 19:
## INFO  [11:59:15.548] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:15.548] [bbotk]     7       700 0.8054803        0      0            1.452
## INFO  [11:59:15.548] [bbotk]                                 uhash
## INFO  [11:59:15.548] [bbotk]  be13808e-5293-44f8-b526-240ec08c6201
## INFO  [11:59:15.549] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:15.561] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:15.563] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:16.391] [mlr3] Finished benchmark
## INFO  [11:59:16.402] [bbotk] Result of batch 20:
## INFO  [11:59:16.403] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:16.403] [bbotk]     7       400 0.8066212        0      0            0.824
## INFO  [11:59:16.403] [bbotk]                                 uhash
## INFO  [11:59:16.403] [bbotk]  eac69335-8bcc-4c33-9a35-70d245467a52
## INFO  [11:59:16.403] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:16.413] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:16.416] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:16.620] [mlr3] Finished benchmark
## INFO  [11:59:16.631] [bbotk] Result of batch 21:
## INFO  [11:59:16.632] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:16.632] [bbotk]     1       200 0.9637405        0      0            0.201
## INFO  [11:59:16.632] [bbotk]                                 uhash
## INFO  [11:59:16.632] [bbotk]  6fc5c385-2412-453f-867c-55ae1b98da40
## INFO  [11:59:16.632] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:16.642] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:16.645] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:17.061] [mlr3] Finished benchmark
## INFO  [11:59:17.072] [bbotk] Result of batch 22:
## INFO  [11:59:17.073] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:17.073] [bbotk]     7       200 0.8005345        0      0            0.413
## INFO  [11:59:17.073] [bbotk]                                 uhash
## INFO  [11:59:17.073] [bbotk]  d1da63dc-5a12-45d7-92d7-d8dae8f111bb
## INFO  [11:59:17.073] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:17.083] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:17.086] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:19.142] [mlr3] Finished benchmark
## INFO  [11:59:19.153] [bbotk] Result of batch 23:
## INFO  [11:59:19.154] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:19.154] [bbotk]     7      1000 0.8059895        0      0            2.054
## INFO  [11:59:19.154] [bbotk]                                 uhash
## INFO  [11:59:19.154] [bbotk]  95ae6411-7471-4ece-b7bb-1f2883399ec8
## INFO  [11:59:19.155] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:19.164] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:19.167] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:19.863] [mlr3] Finished benchmark
## INFO  [11:59:19.887] [bbotk] Result of batch 24:
## INFO  [11:59:19.888] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:19.888] [bbotk]     1       700 0.9552524        0      0            0.692
## INFO  [11:59:19.888] [bbotk]                                 uhash
## INFO  [11:59:19.888] [bbotk]  e645aef6-bd1c-4c2c-9314-eb4ef39ed282
## INFO  [11:59:19.889] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:19.901] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:19.903] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:20.383] [mlr3] Finished benchmark
## INFO  [11:59:20.394] [bbotk] Result of batch 25:
## INFO  [11:59:20.395] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:20.395] [bbotk]     2       400 0.8855727        0      0            0.476
## INFO  [11:59:20.395] [bbotk]                                 uhash
## INFO  [11:59:20.395] [bbotk]  c00a1dfe-f9bd-4a45-93a1-eca5136a481a
## INFO  [11:59:20.396] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:20.406] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:20.408] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:21.749] [mlr3] Finished benchmark
## INFO  [11:59:21.760] [bbotk] Result of batch 26:
## INFO  [11:59:21.761] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:21.761] [bbotk]     8       600  0.803112        0      0            1.337
## INFO  [11:59:21.761] [bbotk]                                 uhash
## INFO  [11:59:21.761] [bbotk]  e08d405d-13b8-4b2c-b75c-5dba0e258330
## INFO  [11:59:21.761] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:21.771] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:21.774] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:22.703] [mlr3] Finished benchmark
## INFO  [11:59:22.714] [bbotk] Result of batch 27:
## INFO  [11:59:22.715] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:22.715] [bbotk]     4       600 0.8215165        0      0            0.925
## INFO  [11:59:22.715] [bbotk]                                 uhash
## INFO  [11:59:22.715] [bbotk]  9c7fceb9-1a37-40b8-ad6b-cdaf2de95941
## INFO  [11:59:22.716] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:22.726] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:22.728] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:23.248] [mlr3] Finished benchmark
## INFO  [11:59:23.259] [bbotk] Result of batch 28:
## INFO  [11:59:23.259] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:23.259] [bbotk]     5       300 0.8190783        0      0            0.516
## INFO  [11:59:23.259] [bbotk]                                 uhash
## INFO  [11:59:23.259] [bbotk]  75b9b397-a73b-44fb-8a91-5c0f9909d08e
## INFO  [11:59:23.260] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:23.270] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:23.272] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:23.551] [mlr3] Finished benchmark
## INFO  [11:59:23.575] [bbotk] Result of batch 29:
## INFO  [11:59:23.576] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:23.576] [bbotk]     3       200 0.8439689        0      0            0.275
## INFO  [11:59:23.576] [bbotk]                                 uhash
## INFO  [11:59:23.576] [bbotk]  aa4b847d-36b8-467b-b274-a1dd650979f5
## INFO  [11:59:23.577] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:23.589] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:23.592] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:24.091] [mlr3] Finished benchmark
## INFO  [11:59:24.102] [bbotk] Result of batch 30:
## INFO  [11:59:24.103] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:24.103] [bbotk]     1       500 0.9658757        0      0            0.495
## INFO  [11:59:24.103] [bbotk]                                 uhash
## INFO  [11:59:24.103] [bbotk]  4267013e-bcb6-4eae-9753-01426503c209
## INFO  [11:59:24.104] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:24.113] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:24.116] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:24.807] [mlr3] Finished benchmark
## INFO  [11:59:24.818] [bbotk] Result of batch 31:
## INFO  [11:59:24.819] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:24.819] [bbotk]     5       400 0.8089013        0      0            0.687
## INFO  [11:59:24.819] [bbotk]                                 uhash
## INFO  [11:59:24.819] [bbotk]  fa27b4fa-ef24-48c8-99e2-4602d3ac4d79
## INFO  [11:59:24.819] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:24.829] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:24.832] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:26.563] [mlr3] Finished benchmark
## INFO  [11:59:26.574] [bbotk] Result of batch 32:
## INFO  [11:59:26.575] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:26.575] [bbotk]     5      1000 0.8098292        0      0            1.728
## INFO  [11:59:26.575] [bbotk]                                 uhash
## INFO  [11:59:26.575] [bbotk]  021aeaf6-e9dc-4347-96b4-a1cb952ebb52
## INFO  [11:59:26.576] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:26.586] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:26.589] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:26.976] [mlr3] Finished benchmark
## INFO  [11:59:26.987] [bbotk] Result of batch 33:
## INFO  [11:59:26.988] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:26.988] [bbotk]     6       200 0.8058583        0      0            0.384
## INFO  [11:59:26.988] [bbotk]                                 uhash
## INFO  [11:59:26.988] [bbotk]  785493d4-ab7d-476a-804a-a1bf48bc7a09
## INFO  [11:59:26.988] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:26.998] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:27.001] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:28.572] [mlr3] Finished benchmark
## INFO  [11:59:28.588] [bbotk] Result of batch 34:
## INFO  [11:59:28.590] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:28.590] [bbotk]     8       700 0.8001454        0      0            1.567
## INFO  [11:59:28.590] [bbotk]                                 uhash
## INFO  [11:59:28.590] [bbotk]  73934568-5689-46ef-8dfe-35668c9f7a16
## INFO  [11:59:28.590] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:28.601] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:28.604] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:29.444] [mlr3] Finished benchmark
## INFO  [11:59:29.455] [bbotk] Result of batch 35:
## INFO  [11:59:29.455] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:29.455] [bbotk]     3       600 0.8394363        0      0            0.835
## INFO  [11:59:29.455] [bbotk]                                 uhash
## INFO  [11:59:29.455] [bbotk]  bd145900-f750-45ff-bcb6-d93184570cbd
## INFO  [11:59:29.456] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:29.467] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:29.469] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:30.672] [mlr3] Finished benchmark
## INFO  [11:59:30.683] [bbotk] Result of batch 36:
## INFO  [11:59:30.683] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:30.683] [bbotk]     5       700 0.8120535        0      0            1.198
## INFO  [11:59:30.683] [bbotk]                                 uhash
## INFO  [11:59:30.683] [bbotk]  27b97497-00c9-4e35-937e-779efe2633ad
## INFO  [11:59:30.684] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:30.694] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:30.696] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:31.455] [mlr3] Finished benchmark
## INFO  [11:59:31.466] [bbotk] Result of batch 37:
## INFO  [11:59:31.467] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:31.467] [bbotk]     6       400  0.806146        0      0            0.756
## INFO  [11:59:31.467] [bbotk]                                 uhash
## INFO  [11:59:31.467] [bbotk]  d21aea9f-3ee3-43ea-918f-6bb0dc1aed84
## INFO  [11:59:31.467] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:31.478] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:31.480] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:33.028] [mlr3] Finished benchmark
## INFO  [11:59:33.039] [bbotk] Result of batch 38:
## INFO  [11:59:33.040] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:33.040] [bbotk]     4      1000 0.8256474        0      0            1.544
## INFO  [11:59:33.040] [bbotk]                                 uhash
## INFO  [11:59:33.040] [bbotk]  cba27025-3a3d-41ec-820b-268db4a19ca9
## INFO  [11:59:33.041] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:33.050] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:33.053] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:33.412] [mlr3] Finished benchmark
## INFO  [11:59:33.428] [bbotk] Result of batch 39:
## INFO  [11:59:33.429] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:33.429] [bbotk]     5       200 0.8264025        0      0            0.354
## INFO  [11:59:33.429] [bbotk]                                 uhash
## INFO  [11:59:33.429] [bbotk]  f48fffc8-7c0a-4586-bbf1-0f446c0cf7b4
## INFO  [11:59:33.430] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:33.440] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:33.443] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:33.859] [mlr3] Finished benchmark
## INFO  [11:59:33.870] [bbotk] Result of batch 40:
## INFO  [11:59:33.871] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:33.871] [bbotk]     3       300 0.8411996        0      0            0.413
## INFO  [11:59:33.871] [bbotk]                                 uhash
## INFO  [11:59:33.871] [bbotk]  b058648f-2241-4c2c-b7c9-b0ae01fc4d0a
## INFO  [11:59:33.872] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:33.881] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:33.884] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:35.279] [mlr3] Finished benchmark
## INFO  [11:59:35.290] [bbotk] Result of batch 41:
## INFO  [11:59:35.291] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:35.291] [bbotk]     4       900 0.8259767        0      0            1.391
## INFO  [11:59:35.291] [bbotk]                                 uhash
## INFO  [11:59:35.291] [bbotk]  46d87c58-78e0-48de-a478-6204741e760d
## INFO  [11:59:35.291] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:35.301] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:35.304] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:36.145] [mlr3] Finished benchmark
## INFO  [11:59:36.156] [bbotk] Result of batch 42:
## INFO  [11:59:36.157] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:36.157] [bbotk]     2       700 0.8805729        0      0            0.838
## INFO  [11:59:36.157] [bbotk]                                 uhash
## INFO  [11:59:36.157] [bbotk]  fe62eace-d0d8-400f-98f1-e79026bec58d
## INFO  [11:59:36.158] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:36.168] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:36.170] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:36.357] [mlr3] Finished benchmark
## INFO  [11:59:36.369] [bbotk] Result of batch 43:
## INFO  [11:59:36.370] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:36.370] [bbotk]     5       100 0.8209825        0      0            0.183
## INFO  [11:59:36.370] [bbotk]                                 uhash
## INFO  [11:59:36.370] [bbotk]  51322f5c-ff2c-48a7-8777-226aaab28fed
## INFO  [11:59:36.371] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:36.382] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:36.393] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:38.333] [mlr3] Finished benchmark
## INFO  [11:59:38.349] [bbotk] Result of batch 44:
## INFO  [11:59:38.350] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:38.350] [bbotk]     7       900 0.8045132        0      0            1.934
## INFO  [11:59:38.350] [bbotk]                                 uhash
## INFO  [11:59:38.350] [bbotk]  d8ca46cf-e621-4a2f-8dfd-dbd977060f5d
## INFO  [11:59:38.351] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:38.362] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:38.364] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:39.725] [mlr3] Finished benchmark
## INFO  [11:59:39.736] [bbotk] Result of batch 45:
## INFO  [11:59:39.737] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:39.737] [bbotk]     6       700 0.8039676        0      0            1.356
## INFO  [11:59:39.737] [bbotk]                                 uhash
## INFO  [11:59:39.737] [bbotk]  21ad122f-4b2f-4b83-a2c6-71ec34f64430
## INFO  [11:59:39.738] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:39.749] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:39.752] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:41.286] [mlr3] Finished benchmark
## INFO  [11:59:41.297] [bbotk] Result of batch 46:
## INFO  [11:59:41.298] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:41.298] [bbotk]     6       800 0.8052058        0      0            1.531
## INFO  [11:59:41.298] [bbotk]                                 uhash
## INFO  [11:59:41.298] [bbotk]  b602e6c8-6070-4eec-bd0d-f8079ee3a2bd
## INFO  [11:59:41.298] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:41.308] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:41.311] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:42.417] [mlr3] Finished benchmark
## INFO  [11:59:42.428] [bbotk] Result of batch 47:
## INFO  [11:59:42.429] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:42.429] [bbotk]     3       800 0.8474695        0      0            1.102
## INFO  [11:59:42.429] [bbotk]                                 uhash
## INFO  [11:59:42.429] [bbotk]  c6333858-7a32-4644-afca-8043b6231bfc
## INFO  [11:59:42.430] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:42.440] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:42.442] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:42.684] [mlr3] Finished benchmark
## INFO  [11:59:42.695] [bbotk] Result of batch 48:
## INFO  [11:59:42.696] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:42.696] [bbotk]     2       200 0.8802479        0      0            0.238
## INFO  [11:59:42.696] [bbotk]                                 uhash
## INFO  [11:59:42.696] [bbotk]  c2f6c006-a55f-4d5d-9d59-37f5de8015ce
## INFO  [11:59:42.696] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:42.721] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:42.725] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:42.941] [mlr3] Finished benchmark
## INFO  [11:59:42.953] [bbotk] Result of batch 49:
## INFO  [11:59:42.954] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:42.954] [bbotk]     7       100 0.8099799        0      0            0.211
## INFO  [11:59:42.954] [bbotk]                                 uhash
## INFO  [11:59:42.954] [bbotk]  55ab0518-4917-4e02-9baf-d1c7ad037458
## INFO  [11:59:42.954] [bbotk] Evaluating 1 configuration(s)
## INFO  [11:59:42.964] [mlr3] Running benchmark with 1 resampling iterations
## INFO  [11:59:42.967] [mlr3] Applying learner 'regr.ranger' on task 'total_con' (iter 1/1)
## INFO  [11:59:43.832] [mlr3] Finished benchmark
## INFO  [11:59:43.843] [bbotk] Result of batch 50:
## INFO  [11:59:43.844] [bbotk]  mtry num.trees regr.rmse warnings errors runtime_learners
## INFO  [11:59:43.844] [bbotk]     5       500 0.8146161        0      0            0.861
## INFO  [11:59:43.844] [bbotk]                                 uhash
## INFO  [11:59:43.844] [bbotk]  0de5d487-8b2d-46cc-bef5-c5f966efc488
## INFO  [11:59:43.846] [bbotk] Finished optimizing after 50 evaluation(s)
## INFO  [11:59:43.846] [bbotk] Result:
## INFO  [11:59:43.847] [bbotk]  mtry num.trees learner_param_vals  x_domain regr.rmse
## INFO  [11:59:43.847] [bbotk]     8       700          <list[4]> <list[2]> 0.8001454

## Check the performance measure for each of the three model runs
tc_rf.2$score(measure)

task	task_id	learner	learner_id	resampling	resampling_id	iteration	prediction	regr.rmse
<environment>	total_con	<environment>	regr.ranger.tuned	<environment>	cv	1	<environment>	0.767
<environment>	total_con	<environment>	regr.ranger.tuned	<environment>	cv	2	<environment>	0.801
<environment>	total_con	<environment>	regr.ranger.tuned	<environment>	cv	3	<environment>	0.774

## Check the aggregate performance measure
tc_rf.2$aggregate(measure)

## regr.rmse 
## 0.7807724

## Check the variable importance for the first model
library(vip)

## 
## Attaching package: 'vip'

## The following object is masked from 'package:utils':
## 
##     vi

vip(object = tc_rf.2$learners[[1]]$model$learner$model)

## Check the variable importance for the first model
library(vip)
vip(object = tc_rf.2$learners[[2]]$model$learner$model)