tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.cd /app && ls -la && echo "---DATA DIR---" && ls -la data/ 2>/dev/null && echo "---OUTPUTS DIR---" && ls -la outputs/ 2>/dev/null
total 24 drwxrws--- 4 h2tagent h2tagent 4096 Jun 20 17:46 . drwxr-xr-x 28 root root 4096 Jun 20 18:18 .. -rw-rw---- 1 h2tagent h2tagent 8171 Jun 20 17:44 analysis.R drwxrws--- 2 h2tagent h2tagent 4096 Jun 20 17:46 data drwxrws--- 2 h2tagent h2tagent 4096 Jun 20 17:46 outputs ---DATA DIR--- total 598 drwxrws--- 2 h2tagent h2tagent 4096 Jun 20 17:46 . drwxrws--- 4 h2tagent h2tagent 4096 Jun 20 17:46 .. -rw-rw---- 1 h2tagent h2tagent 603612 Jun 20 17:44 coffee_ratings.csv ---OUTPUTS DIR--- total 8 drwxrws--- 2 h2tagent h2tagent 4096 Jun 20 17:46 . drwxrws--- 4 h2tagent h2tagent 4096 Jun 20 17:46 ..
cd /app && cat analysis.R
#!/usr/bin/env Rscript
# Coffee outlier audit -- first draft
# ===================================
# Quick first pass before the deadline. Loaded the CSV, threw mean / sd / IQR
# at each column, used standardised residuals to flag influential lots, and
# took simple country means. Most of this should be good enough but the
# numbers feel off in a few places.
suppressPackageStartupMessages({
library(dplyr)
library(tidyr)
library(readr)
library(stringr)
library(jsonlite)
library(purrr)
})
DATA_PATH <- "/app/data/coffee_ratings.csv"
OUTPUT_DIR <- "/app/outputs"
dir.create(OUTPUT_DIR, showWarnings = FALSE, recursive = TRUE)
raw <- read_csv(DATA_PATH, show_col_types = FALSE)
n_input <- nrow(raw)
dat <- raw %>% filter(total_cup_points > 0)
n_after <- nrow(dat)
dat <- dat %>% mutate(row_id = as.integer(seq_len(n()) - 1L))
# ---- Altitude
alt <- dat$altitude_mean_meters
alt_pos <- alt[!is.na(alt) & alt > 0]
q <- quantile(alt_pos, c(0.25, 0.75))
iqr_alt <- q[[2]] - q[[1]]
lo_m <- q[[1]] - 3 * iqr_alt
hi_m <- q[[2]] + 3 * iqr_alt
flag_alt <- !is.na(alt) & (alt < lo_m | alt > hi_m)
n_flag_alt <- sum(flag_alt)
alt_corrected <- rep(NA_real_, nrow(dat))
unit_corrected <- rep(FALSE, nrow(dat))
n_unit_corrected <- 0L
# ---- Defect counts
defect_summary <- function(x) {
q <- quantile(x, c(0.25, 0.75), na.rm = TRUE)
upper <- q[[2]] + 3 * (q[[2]] - q[[1]])
flagged <- !is.na(x) & x > upper
list(upper_threshold = upper, n = sum(flagged), flag_vec = flagged)
}
d_cat1 <- defect_summary(dat$category_one_defects)
d_cat2 <- defect_summary(dat$category_two_defects)
d_quak <- defect_summary(dat$quakers)
flag_defect <- d_cat1$flag_vec | d_cat2$flag_vec | d_quak$flag_vec
# ---- Multivariate grades (every grade column)
g_all <- c("aroma", "flavor", "aftertaste", "acidity",
"body", "balance", "uniformity", "clean_cup",
"sweetness", "cupper_points")
G <- as.matrix(dat[, g_all])
G_complete <- complete.cases(G)
mu <- colMeans(G[G_complete, ])
S <- cov(G[G_complete, ])
md2 <- rep(NA_real_, nrow(G))
md2[G_complete] <- mahalanobis(G[G_complete, ], mu, S, tol = 1e-30)
md2_thresh <- qchisq(0.999, df = length(g_all))
flag_mv <- !is.na(md2) & md2 > md2_thresh
n_flag_mv <- sum(flag_mv)
# ---- Influence: standardised residuals
fit_df <- dat[G_complete, ]
fit <- lm(total_cup_points ~ aroma + flavor + aftertaste + acidity +
body + balance + cupper_points, data = fit_df)
rstd <- rstandard(fit)
infl_local <- abs(rstd) > 2
n_fit <- length(rstd)
p_fit <- length(coef(fit))
infl_full <- rep(FALSE, nrow(dat))
infl_full[G_complete] <- infl_local
n_high_lev <- sum(infl_full)
n_high_cook <- sum(infl_full)
n_both <- sum(infl_full)
top_idx <- order(-abs(rstd))[1:10]
top10_row_ids <- as.integer(dat$row_id[which(G_complete)[top_idx]])
# ---- Country ranking (raw means)
country_tbl <- dat %>%
filter(!is.na(country_of_origin)) %>%
group_by(country = country_of_origin) %>%
summarise(n = n(),
raw_mean = mean(total_cup_points),
.groups = "drop") %>%
filter(n >= 10) %>%
arrange(desc(raw_mean)) %>%
mutate(raw_rank = rank(-raw_mean, ties.method = "min"),
trimmed_mean = raw_mean,
trimmed_rank = raw_rank,
abs_rank_change = 0L)
n_countries <- nrow(country_tbl)
n_changes_ge_2 <- 0L
max_change <- 0L
# ---- Composite + sensitivity
composite <- flag_alt | flag_mv | infl_full
keep_for_sens <- !composite & G_complete
fit_drop <- lm(total_cup_points ~ aroma + flavor + aftertaste + acidity +
body + balance + cupper_points, data = dat[keep_for_sens, ])
coef_full <- unname(coef(fit)["flavor"])
coef_dropped <- unname(coef(fit_drop)["flavor"])
delta_pct <- 100 * (coef_dropped - coef_full) / coef_full
# ---- Flag interactions
A <- flag_alt; M <- flag_mv; D <- flag_defect; I <- infl_full
total_flags <- as.integer(A) + as.integer(M) + as.integer(D) + as.integer(I)
flag_interactions <- list(
n_with_zero_flags = unbox(as.integer(sum(total_flags == 0L))),
…[truncated]cd /app && head -n 3 data/coffee_ratings.csv && echo "---" && echo "Number of lines:" && wc -l data/coffee_ratings.csv
total_cup_points,species,owner,country_of_origin,farm_name,lot_number,mill,ico_number,company,altitude,region,producer,number_of_bags,bag_weight,in_country_partner,harvest_year,grading_date,owner_1,variety,processing_method,aroma,flavor,aftertaste,acidity,body,balance,uniformity,clean_cup,sweetness,cupper_points,moisture,category_one_defects,quakers,color,category_two_defects,expiration,certification_body,certification_address,certification_contact,unit_of_measurement,altitude_low_meters,altitude_high_meters,altitude_mean_meters 90.58,Arabica,metad plc,Ethiopia,metad plc,NA,metad plc,2014/2015,metad agricultural developmet plc,1950-2200,guji-hambela,METAD PLC,300,60 kg,METAD Agricultural Development plc,2014,"April 4th, 2015",metad plc,NA,Washed / Wet,8.67,8.83,8.67,8.75,8.5,8.42,10,10,10,8.75,0.12,0,0,Green,0,"April 3rd, 2016",METAD Agricultural Development plc,309fcf77415a3661ae83e027f7e5f05dad786e44,19fef5a731de2db57d16da10287413f5f99bc2dd,m,1950,2200,2075 89.92,Arabica,metad plc,Ethiopia,metad plc,NA,metad plc,2014/2015,metad agricultural developmet plc,1950-2200,guji-hambela,METAD PLC,300,60 kg,METAD Agricultural Development plc,2014,"April 4th, 2015",metad plc,Other,Washed / Wet,8.75,8.67,8.5,8.58,8.42,8.42,10,10,10,8.58,0.12,0,0,Green,1,"April 3rd, 2016",METAD Agricultural Development plc,309fcf77415a3661ae83e027f7e5f05dad786e44,19fef5a731de2db57d16da10287413f5f99bc2dd,m,1950,2200,2075 --- Number of lines: 1347 data/coffee_ratings.csv
cd /app && which Rscript && Rscript -e 'cat(R.version.string, "\n"); for (p in c("dplyr","tidyr","readr","stringr","jsonlite","purrr")) cat(p, as.character(packageVersion(p)), "\n")'/usr/local/bin/Rscript R version 4.4.2 (2024-10-31) dplyr 1.1.4 tidyr 1.3.1 readr 2.1.5 stringr 1.5.1 jsonlite 1.9.0 purrr 1.0.4
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
cat("dim:", dim(raw), "\n")
cat("nrow:", nrow(raw), "\n\n")
grades <- c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")
cat("=== Grade column summaries ===\n")
for (g in grades) {
x <- raw[[g]]
cat(sprintf("%-14s min=%.2f q25=%.2f med=%.2f mean=%.3f q75=%.2f max=%.2f sd=%.3f nNA=%d\n",
g, min(x,na.rm=T), quantile(x,.25,na.rm=T), median(x,na.rm=T), mean(x,na.rm=T),
quantile(x,.75,na.rm=T), max(x,na.rm=T), sd(x,na.rm=T), sum(is.na(x))))
}
cat("\ntotal_cup_points: min=",min(raw$total_cup_points),"\n")
cat("Rows with total_cup_points == 0:", sum(raw$total_cup_points==0, na.rm=T), "\n")
'dim: 1339 43 nrow: 1339 === Grade column summaries === aroma min=0.00 q25=7.42 med=7.58 mean=7.567 q75=7.75 max=8.75 sd=0.378 nNA=0 flavor min=0.00 q25=7.33 med=7.58 mean=7.520 q75=7.75 max=8.83 sd=0.398 nNA=0 aftertaste min=0.00 q25=7.25 med=7.42 mean=7.401 q75=7.58 max=8.67 sd=0.404 nNA=0 acidity min=0.00 q25=7.33 med=7.58 mean=7.536 q75=7.75 max=8.75 sd=0.380 nNA=0 body min=0.00 q25=7.33 med=7.50 mean=7.517 q75=7.67 max=8.58 sd=0.370 nNA=0 balance min=0.00 q25=7.33 med=7.50 mean=7.518 q75=7.75 max=8.75 sd=0.409 nNA=0 uniformity min=0.00 q25=10.00 med=10.00 mean=9.835 q75=10.00 max=10.00 sd=0.555 nNA=0 clean_cup min=0.00 q25=10.00 med=10.00 mean=9.835 q75=10.00 max=10.00 sd=0.764 nNA=0 sweetness min=0.00 q25=10.00 med=10.00 mean=9.857 q75=10.00 max=10.00 sd=0.616 nNA=0 cupper_points min=0.00 q25=7.25 med=7.50 mean=7.503 q75=7.75 max=10.00 sd=0.473 nNA=0 total_cup_points: min= 0 Rows with total_cup_points == 0: 1
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
grades <- c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")
# rows where total_cup_points==0
z <- which(raw$total_cup_points == 0)
cat("Row(s) with tcp==0 (1-indexed):", z, "\n")
cat("Their grade values:\n"); print(as.data.frame(raw[z, grades]))
cat("Row sum of grades for that row:", sum(raw[z, grades]), "\n\n")
# Are there other rows with any zero grades?
anyzero <- apply(raw[,grades], 1, function(r) any(r==0))
cat("Rows with ANY zero grade:", sum(anyzero), " -> indices:", which(anyzero), "\n")
allzero <- apply(raw[,grades], 1, function(r) all(r==0))
cat("Rows with ALL zero grades:", sum(allzero), " -> indices:", which(allzero), "\n")
'Row(s) with tcp==0 (1-indexed): 1311 Their grade values: aroma flavor aftertaste acidity body balance uniformity clean_cup sweetness 1 0 0 0 0 0 0 0 0 0 cupper_points 1 0 Row sum of grades for that row: 0 Rows with ANY zero grade: 2 -> indices: 1307 1311 Rows with ALL zero grades: 1 -> indices: 1311
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
grades <- c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")
cat("Row 1307 grades + tcp:\n"); print(as.data.frame(raw[1307, c("total_cup_points",grades)]))
cat("\nRow 1307 is NOT all-zero; it has a zero in one cleanliness col but tcp>0, so it stays.\n")
'Row 1307 grades + tcp: total_cup_points aroma flavor aftertaste acidity body balance uniformity 1 68.33 7.08 6.83 6.25 7.42 7.25 6.75 10 clean_cup sweetness cupper_points 1 0 10 6.75 Row 1307 is NOT all-zero; it has a zero in one cleanliness col but tcp>0, so it stays.
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
dat <- raw %>% filter(total_cup_points > 0)
for (d in c("category_one_defects","category_two_defects","quakers")) {
x <- dat[[d]]
cat("===",d,"=== nNA:",sum(is.na(x)),"\n")
cat(" pct zero:", round(mean(x==0,na.rm=T)*100,1),"%\n")
print(table(x, useNA="ifany"))
cat(" quantiles 90/95/98/99/100:", quantile(x, c(.9,.95,.98,.99,1), na.rm=T), "\n")
q<-quantile(x,c(.25,.75),na.rm=T); cat(" Q1,Q3,IQR:",q[1],q[2],q[2]-q[1]," -> raw Tukey upper(k=3):", q[2]+3*(q[2]-q[1]),"\n\n")
}
'=== category_one_defects === nNA: 0 pct zero: 84.9 % x 0 1 2 3 4 5 6 7 8 9 10 11 12 15 20 23 1136 101 38 18 16 9 3 3 1 1 4 1 1 1 1 1 31 63 2 1 quantiles 90/95/98/99/100: 1 2 5 7.63 63 Q1,Q3,IQR: 0 0 0 -> raw Tukey upper(k=3): 0 === category_two_defects === nNA: 0 pct zero: 27.9 % x 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 373 207 181 134 118 73 44 41 29 23 21 8 13 9 9 6 6 7 2 3 20 21 22 23 24 26 27 28 29 30 31 32 34 38 40 45 47 55 8 2 1 2 1 3 1 1 2 2 1 1 1 1 1 1 1 1 quantiles 90/95/98/99/100: 9 13 20 26.63 55 Q1,Q3,IQR: 0 4 4 -> raw Tukey upper(k=3): 16 === quakers === nNA: 1 pct zero: 93 % x 0 1 2 3 4 5 6 7 8 9 11 <NA> 1243 39 30 5 5 5 4 3 1 1 1 1 quantiles 90/95/98/99/100: 0 1 2 5 11 Q1,Q3,IQR: 0 0 0 -> raw Tukey upper(k=3): 0
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
dat <- raw %>% filter(total_cup_points > 0)
n <- nrow(dat)
for (d in c("category_one_defects","category_two_defects","quakers")) {
x <- dat[[d]]
cat("===",d,"(n nonNA=",sum(!is.na(x)),") ===\n")
# positive-only Tukey k=1.5 and k=3
pos <- x[!is.na(x) & x>0]
for (k in c(1.5,3)) {
q<-quantile(pos,c(.25,.75)); up<-q[2]+k*(q[2]-q[1])
nf<-sum(!is.na(x)&x>up); cat(sprintf(" posTukey k=%.1f: upper=%.2f nflag=%d (%.2f%%)\n",k,up,nf,100*nf/n))
}
# quantile thresholds: flag x > p
for (p in c(.98,.99)) {
th<-quantile(x,p,na.rm=T); nf<-sum(!is.na(x)&x>th)
cat(sprintf(" pctl %.0f: thr=%.2f nflag(>thr)=%d (%.2f%%)\n",p*100,th,nf,100*nf/n))
}
cat("\n")
}
'=== category_one_defects (n nonNA= 1338 ) === posTukey k=1.5: upper=6.00 nflag=17 (1.27%) posTukey k=3.0: upper=9.00 nflag=12 (0.90%) pctl 98: thr=5.00 nflag(>thr)=20 (1.49%) pctl 99: thr=7.63 nflag(>thr)=14 (1.05%) === category_two_defects (n nonNA= 1338 ) === posTukey k=1.5: upper=12.00 nflag=73 (5.46%) posTukey k=3.0: upper=18.00 nflag=34 (2.54%) pctl 98: thr=20.00 nflag(>thr)=23 (1.72%) pctl 99: thr=26.63 nflag(>thr)=14 (1.05%) === quakers (n nonNA= 1337 ) === posTukey k=1.5: upper=6.00 nflag=6 (0.45%) posTukey k=3.0: upper=9.00 nflag=1 (0.07%) pctl 98: thr=2.00 nflag(>thr)=25 (1.87%) pctl 99: thr=5.00 nflag(>thr)=10 (0.75%)
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
dat <- raw %>% filter(total_cup_points > 0)
a <- dat$altitude_mean_meters
cat("altitude_mean_meters: nNA=",sum(is.na(a))," n>0=",sum(!is.na(a)&a>0)," n==0:",sum(!is.na(a)&a==0),"\n")
cat("range:",range(a,na.rm=T),"\n")
cat("quantiles(0,.25,.5,.75,.9,.99,1):",round(quantile(a,c(0,.25,.5,.75,.9,.99,1),na.rm=T),1),"\n\n")
la <- log10(a[!is.na(a)&a>0])
q<-quantile(la,c(.25,.75)); iqr<-q[2]-q[1]
lo<-q[1]-3*iqr; hi<-q[2]+3*iqr
cat("log10 scale: Q1=",q[1]," Q3=",q[2]," IQR=",iqr,"\n")
cat("log10 fence: [",lo,",",hi,"] -> meters [",10^lo,",",10^hi,"]\n")
cat("n flagged (outside meter fence, among a>0):", sum(!is.na(a)&a>0&(a<10^lo|a>10^hi)),"\n")
cat("n with a<=0 or NA:", sum(is.na(a)|a<=0),"\n")
# show the flagged rows with raw altitude text
fl <- which(!is.na(a)&a>0&(a<10^lo|a>10^hi))
cat("\nFlagged rows (mean_m and raw altitude text):\n")
print(dat[fl, c("altitude","altitude_mean_meters","altitude_low_meters","altitude_high_meters","unit_of_measurement")], n=60)
'altitude_mean_meters: nNA= 230 n>0= 1108 n==0: 0 range: 1 190164 quantiles(0,.25,.5,.75,.9,.99,1): 1 1100 1310.6 1600 1799.3 3280 190164 log10 scale: Q1= 3.041393 Q3= 3.20412 IQR= 0.1627273 log10 fence: [ 2.553211 , 3.692302 ] -> meters [ 357.4463 , 4923.817 ] n flagged (outside meter fence, among a>0): 51 n with a<=0 or NA: 230 Flagged rows (mean_m and raw altitude text): # A tibble: 51 × 5 altitude altitude_mean_meters altitude_low_meters altitude_high_meters <chr> <dbl> <dbl> <dbl> 1 1.2 12 12 12 2 1.2 12 12 12 3 -1 1 1 1 4 350 350 350 350 5 170 170 170 170 6 approx. 1'500 … 250. 1 500 7 1 1 1 1 8 175 175 175 175 9 16.8 168 168 168 10 approx.1'500 m… 250. 1 500 11 1 1 1 1 12 350 350 350 350 13 11000 metros 11000 11000 11000 14 518 158. 158. 158. 15 1.65 165 165 165 16 1 1 1 1 17 200 m 200 200 200 18 518 158. 158. 158. 19 1 1 1 1 20 200 m 200 200 200 21 1 1 1 1 22 1 1 1 1 23 1 1 1 1 24 1.3 13 13 13 25 518 158. 158. 158. 26 1 1 1 1 27 1 1 1 1 28 1 1 1 1 29 150 m 150 150 150 30 190164 190164 190164 190164 31 1.3 13 13 13 32 160-200m 180 160 200 33 1.25 125 125 125 34 110公尺 110 110 110 35 1 1 1 1 36 200 200 200 200 37 300 m 300 300 300 38 100 100 100 100 39 1100.00 mosl 110000 110000 110000 40 1.85 185 185 185 41 50 50 50 50 42 280 280 280 280 43 200 m …[truncated]
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr); library(stringr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
dat <- raw %>% filter(total_cup_points > 0)
a <- dat$altitude_mean_meters
la <- log10(a[!is.na(a)&a>0]); q<-quantile(la,c(.25,.75)); iqr<-q[2]-q[1]
lo_m<-10^(q[1]-3*iqr); hi_m<-10^(q[2]+3*iqr)
flag <- !is.na(a) & a>0 & (a<lo_m | a>hi_m)
fl <- which(flag)
first_tok <- function(s){ m<-str_extract(s,"[0-9]+(\\.[0-9]+)?"); as.numeric(m) }
corr <- rep(NA_real_, nrow(dat));
for (i in fl){
tok <- first_tok(dat$altitude[i])
if(is.na(tok)) next
for (cand in c(tok/10, tok/100, tok)){
if(cand>=lo_m && cand<=hi_m){ corr[i]<-cand; break }
}
}
cat("fence m:",round(lo_m,2),round(hi_m,2)," n_flagged:",length(fl)," n_corrected:",sum(!is.na(corr)),"\n\n")
show<-dat[fl,c("altitude","altitude_mean_meters")]; show$token<-sapply(dat$altitude[fl],first_tok); show$corrected<-corr[fl]
print(show,n=60)
'fence m: 357.45 4923.82 n_flagged: 51 n_corrected: 7 # A tibble: 51 × 4 altitude altitude_mean_meters token corrected <chr> <dbl> <dbl> <dbl> 1 1.2 12 1.2 NA 2 1.2 12 1.2 NA 3 -1 1 1 NA 4 350 350 350 NA 5 170 170 170 NA 6 approx. 1'500 metres a.s.l. 250. 1 NA 7 1 1 1 NA 8 175 175 175 NA 9 16.8 168 16.8 NA 10 approx.1'500 metres a.s.l. 250. 1 NA 11 1 1 1 NA 12 350 350 350 NA 13 11000 metros 11000 11000 1100 14 518 158. 518 518 15 1.65 165 1.65 NA 16 1 1 1 NA 17 200 m 200 200 NA 18 518 158. 518 518 19 1 1 1 NA 20 200 m 200 200 NA 21 1 1 1 NA 22 1 1 1 NA 23 1 1 1 NA 24 1.3 13 1.3 NA 25 518 158. 518 518 26 1 1 1 NA 27 1 1 1 NA 28 1 1 1 NA 29 150 m 150 150 NA 30 190164 190164 190164 1902. 31 1.3 13 1.3 NA 32 160-200m 180 160 NA 33 1.25 125 1.25 NA 34 110公尺 110 110 NA 35 1 1 1 NA 36 200 200 200 NA 37 300 m 300 300 NA 38 100 100 100 NA 39 1100.00 mosl 110000 1100 1100 40 1.85 185 1.85 NA 41 50 50 50 NA 42 280 280 280 NA 43 200 m 200 200 NA 44 150 m 150 150 NA 45 250 m 250 250 NA 46 250 m 250 250 NA 47 1901.64 190164 1902. 1902. 48 200 200 200 NA 49 12oo 12 12 NA 50 ~350m 350 350 NA 51 40 40 40 NA
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
dat <- raw %>% filter(total_cup_points > 0)
g7 <- c("aroma","flavor","aftertaste","acidity","body","balance","cupper_points")
G <- as.matrix(dat[,g7]); cc <- complete.cases(G)
cat("complete cases on 7 grades:", sum(cc), "of", nrow(dat), "\n")
mu<-colMeans(G[cc,]); S<-cov(G[cc,])
md2<-rep(NA_real_,nrow(G)); md2[cc]<-mahalanobis(G[cc,],mu,S)
thr<-qchisq(0.999,df=7)
cat("threshold chisq .999 df7:",thr,"\n")
cat("n flagged mv:",sum(!is.na(md2)&md2>thr),"\n")
# check tcp vs sum of 10 grades relationship
s10 <- rowSums(raw[,c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")])
cat("max|tcp - sum10grades|:", max(abs(raw$total_cup_points - s10)), "\n")
'complete cases on 7 grades: 1338 of 1338 threshold chisq .999 df7: 24.32189 n flagged mv: 40 max|tcp - sum10grades|: 0.5
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr); library(stringr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
dat <- raw %>% filter(total_cup_points > 0)
g7 <- c("aroma","flavor","aftertaste","acidity","body","balance","cupper_points")
# altitude corrected
a <- dat$altitude_mean_meters
la <- log10(a[!is.na(a)&a>0]); q<-quantile(la,c(.25,.75)); iqr<-q[2]-q[1]
lo_m<-10^(q[1]-3*iqr); hi_m<-10^(q[2]+3*iqr)
flag <- !is.na(a)&a>0&(a<lo_m|a>hi_m); fl<-which(flag)
ft<-function(s){as.numeric(str_extract(s,"[0-9]+(\\.[0-9]+)?"))}
corr<-rep(NA_real_,nrow(dat))
for(i in fl){tok<-ft(dat$altitude[i]); if(is.na(tok))next; for(cand in c(tok/10,tok/100,tok)){if(cand>=lo_m&&cand<=hi_m){corr[i]<-cand;break}}}
elev <- ifelse(!is.na(corr), corr, a)
logelev <- log10(elev); logelev[!is.finite(logelev)] <- NA
logelev[is.na(logelev)] <- mean(logelev, na.rm=TRUE)
# defects log1p + impute
l1<-function(x){v<-log1p(x); v[!is.finite(v)]<-mean(v[is.finite(v)]); v}
d1<-l1(dat$category_one_defects); d2<-l1(dat$category_two_defects); dq<-l1(dat$quakers)
df <- dat %>% mutate(logelev=logelev, d1=d1,d2=d2,dq=dq)
f_go <- lm(total_cup_points~aroma+flavor+aftertaste+acidity+body+balance+cupper_points, data=df)
f_ga <- lm(total_cup_points~aroma+flavor+aftertaste+acidity+body+balance+cupper_points+logelev, data=df)
f_gd <- lm(total_cup_points~aroma+flavor+aftertaste+acidity+body+balance+cupper_points+d1+d2+dq, data=df)
cat("AIC grades_only:",AIC(f_go),"\n")
cat("AIC grades_altitude:",AIC(f_ga),"\n")
cat("AIC grades_defects:",AIC(f_gd),"\n")
aics<-c(grades_only=AIC(f_go),grades_altitude=AIC(f_ga),grades_defects=AIC(f_gd))
cat("selected:",names(which.min(aics)),"\n")
cat("n obs each:",nobs(f_go),nobs(f_ga),nobs(f_gd),"\n")
'AIC grades_only: 4459.362 AIC grades_altitude: 4461.292 AIC grades_defects: 4418.683 selected: grades_defects n obs each: 1338 1338 1338
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr); library(stringr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
dat <- raw %>% filter(total_cup_points > 0)
l1<-function(x){v<-log1p(x); v[!is.finite(v)]<-mean(v[is.finite(v)]); v}
df <- dat %>% mutate(d1=l1(category_one_defects),d2=l1(category_two_defects),dq=l1(quakers))
fit <- lm(total_cup_points~aroma+flavor+aftertaste+acidity+body+balance+cupper_points+d1+d2+dq, data=df)
n<-nobs(fit); p<-length(coef(fit))
lev<-hatvalues(fit); cook<-cooks.distance(fit)
lev_thr<-2*p/n; cook_thr<-4/n
cat("n:",n," p:",p," lev_thr:",lev_thr," cook_thr:",cook_thr,"\n")
cat("n_high_leverage:",sum(lev>lev_thr)," n_high_cook:",sum(cook>cook_thr)," n_both:",sum(lev>lev_thr & cook>cook_thr),"\n")
top10<-order(cook,decreasing=TRUE)[1:10]
cat("top10 cook row_ids (0-indexed):",top10-1,"\n")
cat("top10 cook values:",round(cook[top10],4),"\n")
'n: 1338 p: 11 lev_thr: 0.01644245 cook_thr: 0.002989537 n_high_leverage: 110 n_high_cook: 66 n_both: 35 top10 cook row_ids (0-indexed): 1309 1308 1333 1295 1306 1260 1307 1302 1291 1259 top10 cook values: 0.3795 0.1501 0.0979 0.0936 0.0765 0.0531 0.0472 0.0457 0.0361 0.0313
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
dat <- raw %>% filter(total_cup_points > 0)
for (tr in c(0.1,0.2)){
ct <- dat %>% filter(!is.na(country_of_origin)) %>% group_by(country=country_of_origin) %>%
summarise(n=n(), raw_mean=mean(total_cup_points), trimmed_mean=mean(total_cup_points,trim=tr), .groups="drop") %>%
filter(n>=10) %>%
mutate(raw_rank=rank(-raw_mean,ties.method="min"), trimmed_rank=rank(-trimmed_mean,ties.method="min"),
chg=abs(raw_rank-trimmed_rank))
cat("=== trim=",tr," n_countries=",nrow(ct)," n_changes>=2:",sum(ct$chg>=2)," max_chg:",max(ct$chg),"\n")
}
# show distribution of n per country
cat("\nCountries with >=10 lots and their counts:\n")
dat %>% filter(!is.na(country_of_origin)) %>% count(country_of_origin) %>% filter(n>=10) %>% arrange(desc(n)) %>% print(n=40)
'=== trim= 0.1 n_countries= 21 n_changes>=2: 2 max_chg: 2 === trim= 0.2 n_countries= 21 n_changes>=2: 2 max_chg: 2 Countries with >=10 lots and their counts: # A tibble: 21 × 2 country_of_origin n <chr> <int> 1 Mexico 236 2 Colombia 183 3 Guatemala 181 4 Brazil 132 5 Taiwan 75 6 United States (Hawaii) 73 7 Honduras 52 8 Costa Rica 51 9 Ethiopia 44 10 Tanzania, United Republic Of 40 11 Uganda 36 12 Thailand 32 13 Nicaragua 26 14 Kenya 25 15 El Salvador 21 16 Indonesia 20 17 China 16 18 India 14 19 Malawi 11 20 Peru 10 21 United States 10
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
dat <- raw %>% filter(total_cup_points > 0)
ct <- dat %>% filter(!is.na(country_of_origin)) %>% group_by(country=country_of_origin) %>%
summarise(n=n(), raw_mean=mean(total_cup_points), trimmed_mean=mean(total_cup_points,trim=0.2), .groups="drop") %>%
filter(n>=10) %>%
mutate(raw_rank=rank(-raw_mean,ties.method="min"), trimmed_rank=rank(-trimmed_mean,ties.method="min"),
chg=abs(raw_rank-trimmed_rank)) %>% arrange(raw_rank)
print(as.data.frame(ct), digits=5)
cat("\nMovers (chg>=2):\n"); print(as.data.frame(ct %>% filter(chg>=2)))
'country n raw_mean trimmed_mean raw_rank trimmed_rank
1 Ethiopia 44 85.484 85.398 1 1
2 United States 10 84.433 85.332 2 2
3 Kenya 25 84.310 84.544 3 3
4 Uganda 36 83.452 83.448 4 4
5 Colombia 183 83.107 83.267 5 5
6 El Salvador 21 83.053 83.059 6 7
7 China 16 82.927 83.267 7 6
8 Costa Rica 51 82.789 83.058 8 8
9 Thailand 32 82.574 82.713 9 10
10 Indonesia 20 82.566 82.760 10 9
11 Peru 10 82.526 82.460 11 12
12 Brazil 132 82.406 82.489 12 11
13 Tanzania, United Republic Of 40 82.370 82.244 13 14
14 Taiwan 75 82.001 81.991 14 16
15 Guatemala 181 81.847 82.309 15 13
16 United States (Hawaii) 73 81.820 82.216 16 15
17 Malawi 11 81.712 81.701 17 17
18 India 14 81.083 81.650 18 18
19 Mexico 236 80.890 81.349 19 20
20 Honduras 52 80.884 81.610 20 19
21 Nicaragua 26 80.458 80.802 21 21
chg
1 0
2 0
3 0
4 0
5 0
6 1
7 1
8 0
9 1
10 1
11 1
12 1
13 1
14 2
15 2
16 1
17 0
18 0
19 1
20 1
21 0
Movers (chg>=2):
country n raw_mean trimmed_mean raw_rank trimmed_rank chg
1 Taiwan 75 82.00133 81.99133 14 16 2
2 Guatemala 181 81.84657 82.30881 15 13 2command: create
path: /app/analysis.R
file_text: #!/usr/bin/env Rscript
# Coffee Quality Outlier Audit -- shape-matched rewrite
# =====================================================
# Each column is audited with a method that matches its distribution instead of
# the draft's one-size-fits-all summaries. See audit_memo.md for rationale.
# Re-running this single script from a clean /app/outputs/ reproduces every
# artifact (outlier_report.json, coffee_ols_selected.rds, outlier_flags.csv,
# audit_memo.md).
suppressPackageStartupMessages({
library(dplyr)
library(tidyr)
library(readr)
library(stringr)
library(jsonlite)
library(purrr)
})
DATA_PATH <- "/app/data/coffee_ratings.csv"
OUTPUT_DIR <- "/app/outputs"
dir.create(OUTPUT_DIR, showWarnings = FALSE, recursive = TRUE)
GRADES7 <- c("aroma", "flavor", "aftertaste", "acidity",
"body", "balance", "cupper_points")
CLEANLINESS <- c("uniformity", "clean_cup", "sweetness")
GRADES10 <- c("aroma", "flavor", "aftertaste", "acidity", "body", "balance",
"uniformity", "clean_cup", "sweetness", "cupper_points")
# ---------------------------------------------------------------------------
# (1) Load + sentinel drop
# ---------------------------------------------------------------------------
raw <- read_csv(DATA_PATH, show_col_types = FALSE)
n_input <- nrow(raw)
# Withdrawn submission: every one of the ten grades recorded as zero.
all_zero_grades <- rowSums(as.matrix(raw[, GRADES10]) == 0) == length(GRADES10)
dat <- raw[!all_zero_grades, , drop = FALSE]
n_after <- nrow(dat)
dat$row_id <- as.integer(seq_len(n_after) - 1L) # 0-indexed post-drop order
# ---------------------------------------------------------------------------
# (2) Altitude -- Tukey fence on log10 scale (k = 3), back-transformed
# ---------------------------------------------------------------------------
alt <- dat$altitude_mean_meters
alt_pos <- alt[!is.na(alt) & alt > 0]
qa <- quantile(log10(alt_pos), c(0.25, 0.75))
iqr_la <- qa[[2]] - qa[[1]]
lo_log <- qa[[1]] - 3 * iqr_la
hi_log <- qa[[2]] + 3 * iqr_la
lo_m <- 10^lo_log
hi_m <- 10^hi_log
flag_alt <- !is.na(alt) & alt > 0 & (alt < lo_m | alt > hi_m)
n_flag_alt <- sum(flag_alt)
# Recover decimal-displacement unit slips from the raw free-text altitude:
# first numeric token, test /10, then /100, then as-is; keep first in-fence hit.
first_token <- function(s) suppressWarnings(as.numeric(str_extract(s, "[0-9]+(\\.[0-9]+)?")))
alt_corrected <- rep(NA_real_, n_after)
for (i in which(flag_alt)) {
tok <- first_token(dat$altitude[i])
if (is.na(tok)) next
for (cand in c(tok / 10, tok / 100, tok)) {
if (is.finite(cand) && cand >= lo_m && cand <= hi_m) {
alt_corrected[i] <- cand
break
}
}
}
n_unit_corrected <- sum(!is.na(alt_corrected))
# ---------------------------------------------------------------------------
# (3) Defects -- empirical upper-tail (98th pct) per zero-inflated count column
# ---------------------------------------------------------------------------
defect_tail <- function(x, p = 0.98) {
thr <- unname(quantile(x, p, na.rm = TRUE))
flagged <- !is.na(x) & x > thr
list(upper_threshold = thr, n = sum(flagged), flag_vec = flagged)
}
d_cat1 <- defect_tail(dat$category_one_defects)
d_cat2 <- defect_tail(dat$category_two_defects)
d_quak <- defect_tail(dat$quakers)
flag_defect <- d_cat1$flag_vec | d_cat2$flag_vec | d_quak$flag_vec
# ---------------------------------------------------------------------------
# (4) Multivariate grades -- classical Mahalanobis on the 7 primary attributes
# ---------------------------------------------------------------------------
G <- as.matrix(dat[, GRADES7])
cc <- complete.cases(G)
mu <- colMeans(G[cc, ])
S <- cov(G[cc, ])
md2 <- rep(NA_real_, n_after)
md2[cc] <- mahalanobis(G[cc, ], mu, S)
md2_thresh <- qchisq(0.999, df = length(GRADES7))
flag_mv <- !is.na(md2) & md2 > md2_thresh
n_flag_mv <- sum(flag_mv)
# -----------------------------------------------
…[truncated]Existing file was successfully overwritten at /app/analysis.R
cd /app && rm -rf outputs && mkdir -p outputs && time Rscript analysis.R
Audit complete: n_input=1339, n_after=1338, selected=grades_defects altitude fence [357.45, 4923.82] m; flagged=51 corrected=7 mv flagged=40; influence both=35; boot reps=500 [stderr] real 0m3.657s user 0m3.240s sys 0m0.280s
cd /app && cat outputs/outlier_report.json
{
"n_input": 1339,
"n_after_zero_score_drop": 1338,
"methods": {
"altitude": "Tukey/IQR fence (k=3) on log10(altitude_mean_meters), back-transformed to meters; flagged rows unit-corrected via power-of-ten (/10,/100,as-is) on the first raw-altitude token.",
"defects": "Per-column empirical upper-tail threshold at the 98th percentile (flag count > threshold); robust to the zero-inflation that collapses raw Tukey fences.",
"multivariate_grades": "Classical Mahalanobis distance (sample mean/covariance) on the 7 primary SCA attributes; flag distance > chi-square 99.9% quantile with df=7.",
"influence": "AIC-selected OLS of total_cup_points on the grades; flag rows with BOTH leverage > 2p/n and Cook's D > 4/n.",
"country_ranking": "Countries with n>=10 ranked by raw mean vs 20% trimmed mean of total_cup_points; count rank moves >= 2 positions."
},
"altitude": {
"lower_bound_m": 357.446289062499,
"upper_bound_m": 4923.8166791886,
"n_flagged": 51,
"n_unit_corrected": 7
},
"defects": {
"category_one_defects": {
"upper_threshold": 5,
"n_flagged": 20
},
"category_two_defects": {
"upper_threshold": 20,
"n_flagged": 23
},
"quakers": {
"upper_threshold": 2,
"n_flagged": 25
}
},
"multivariate_grades": {
"columns_used": ["aroma", "flavor", "aftertaste", "acidity", "body", "balance", "cupper_points"],
"threshold": 24.3218863478569,
"n_flagged": 40
},
"influence": {
"selected_model": "grades_defects",
"aic_grades_only": 4459.36151596946,
"aic_grades_altitude": 4461.29232958899,
"aic_grades_defects": 4418.68272504111,
"leverage_threshold": 0.0164424514200299,
"cook_threshold": 0.00298953662182362,
"n_high_leverage": 110,
"n_high_cook": 66,
"n_both": 35,
"top10_cook_row_ids": [1309, 1308, 1333, 1295, 1306, 1260, 1307, 1302, 1291, 1259]
},
"country_ranking": {
"n_countries_evaluated": 21,
"n_rank_changes_ge_2": 2,
"max_abs_rank_change": 2,
"per_country": [
{
"country": "Ethiopia",
"n": 44,
"raw_mean": 85.4840909090909,
"trimmed_mean": 85.3978571428571,
"raw_rank": 1,
"trimmed_rank": 1
},
{
"country": "United States",
"n": 10,
"raw_mean": 84.433,
"trimmed_mean": 85.3316666666667,
"raw_rank": 2,
"trimmed_rank": 2
},
{
"country": "Kenya",
"n": 25,
"raw_mean": 84.3096,
"trimmed_mean": 84.544,
"raw_rank": 3,
"trimmed_rank": 3
},
{
"country": "Uganda",
"n": 36,
"raw_mean": 83.4519444444445,
"trimmed_mean": 83.4477272727273,
"raw_rank": 4,
"trimmed_rank": 4
},
{
"country": "Colombia",
"n": 183,
"raw_mean": 83.1065573770492,
"trimmed_mean": 83.2674774774775,
"raw_rank": 5,
"trimmed_rank": 5
},
{
"country": "El Salvador",
"n": 21,
"raw_mean": 83.0528571428571,
"trimmed_mean": 83.0592307692308,
"raw_rank": 6,
"trimmed_rank": 7
},
{
"country": "China",
"n": 16,
"raw_mean": 82.9275,
"trimmed_mean": 83.267,
"raw_rank": 7,
"trimmed_rank": 6
},
{
"country": "Costa Rica",
"n": 51,
"raw_mean": 82.7890196078431,
"trimmed_mean": 83.058064516129,
"raw_rank": 8,
"trimmed_rank": 8
},
{
"country": "Thailand",
"n": 32,
"raw_mean": 82.57375,
"trimmed_mean": 82.713,
"raw_rank": 9,
"trimmed_rank": 10
},
{
"country": "Indonesia",
"n": 20,
"raw_mean": 82.5655,
"trimmed_mean": 82.76,
"raw_rank": 10,
"trimmed_rank": 9
},
{
"country": "Peru",
"n": 10,
"raw_mean": 82.526,
…[truncated]cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(jsonlite)})
csv <- read_csv("outputs/outlier_flags.csv", show_col_types = FALSE)
js <- fromJSON("outputs/outlier_report.json", simplifyVector = TRUE)
cat("CSV rows:", nrow(csv), " (expect 1338)\n")
cat("row_id 0..n-1 contiguous:", all(csv$row_id == 0:(nrow(csv)-1)), "\n\n")
# counts vs json
chk <- function(name, a, b) cat(sprintf("%-34s csv=%-5d json=%-5d %s\n", name, a, b, ifelse(a==b,"OK","MISMATCH")))
chk("altitude_outlier_flag", sum(csv$altitude_outlier_flag), js$altitude$n_flagged)
chk("altitude_corrected_m non-NA", sum(!is.na(csv$altitude_corrected_m)), js$altitude$n_unit_corrected)
chk("defect_outlier_flag(any)", sum(csv$defect_outlier_flag), NA)
chk("multivariate flag", sum(csv$multivariate_grade_outlier_flag), js$multivariate_grades$n_flagged)
chk("high_leverage_flag", sum(csv$high_leverage_flag), js$influence$n_high_leverage)
chk("high_cook_flag", sum(csv$high_cook_flag), js$influence$n_high_cook)
chk("both (lev&cook)", sum(csv$high_leverage_flag & csv$high_cook_flag), js$influence$n_both)
# composite definition check
comp_expect <- csv$altitude_outlier_flag | csv$multivariate_grade_outlier_flag | (csv$high_leverage_flag & csv$high_cook_flag)
chk("composite_drop_flag", sum(csv$composite_drop_flag), sum(comp_expect))
cat("composite matches formula rowwise:", all(csv$composite_drop_flag == comp_expect), "\n")
# defect never NA issue
cat("\naltitude_corrected_m only set where altitude_outlier_flag TRUE:",
all(csv$altitude_outlier_flag[!is.na(csv$altitude_corrected_m)]), "\n")
'CSV rows: 1338 (expect 1338) row_id 0..n-1 contiguous: TRUE altitude_outlier_flag csv=51 json=51 OK altitude_corrected_m non-NA csv=7 json=7 OK defect_outlier_flag(any) csv=63 json=NA NA multivariate flag csv=40 json=40 OK high_leverage_flag csv=110 json=110 OK high_cook_flag csv=66 json=66 OK both (lev&cook) csv=35 json=35 OK composite_drop_flag csv=105 json=105 OK composite matches formula rowwise: TRUE altitude_corrected_m only set where altitude_outlier_flag TRUE: TRUE
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(jsonlite)})
csv <- read_csv("outputs/outlier_flags.csv", show_col_types = FALSE)
js <- fromJSON("outputs/outlier_report.json", simplifyVector = TRUE)
fit <- readRDS("outputs/coffee_ols_selected.rds")
cat("class:", class(fit), " formula:", deparse(formula(fit)), "\n")
n <- stats::nobs(fit); p <- length(coef(fit))
cat("n:", n, " p:", p, "\n")
lev <- hatvalues(fit); cook <- cooks.distance(fit)
lev_thr <- 2*p/n; cook_thr <- 4/n
cat("lev_thr match:", isTRUE(all.equal(lev_thr, js$influence$leverage_threshold)),
" cook_thr match:", isTRUE(all.equal(cook_thr, js$influence$cook_threshold)), "\n")
# recompute flags, align by position (fit obs i -> row_id i-1)
hl <- lev > lev_thr; hc <- cook > cook_thr
cat("recomputed high_lev matches CSV:", all(hl == csv$high_leverage_flag), "\n")
cat("recomputed high_cook matches CSV:", all(hc == csv$high_cook_flag), "\n")
top10 <- order(cook, decreasing=TRUE)[1:10] - 1
cat("recomputed top10 cook ids:", top10, "\n")
cat("json top10 cook ids: ", js$influence$top10_cook_row_ids, "\n")
cat("top10 match:", all(top10 == js$influence$top10_cook_row_ids), "\n")
'class: lm formula: total_cup_points ~ aroma + flavor + aftertaste + acidity + body + balance + cupper_points + def1 + def2 + defq n: 1338 p: 11 lev_thr match: TRUE cook_thr match: TRUE recomputed high_lev matches CSV: TRUE recomputed high_cook matches CSV: TRUE recomputed top10 cook ids: 1309 1308 1333 1295 1306 1260 1307 1302 1291 1259 json top10 cook ids: 1309 1308 1333 1295 1306 1260 1307 1302 1291 1259 top10 match: TRUE
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(jsonlite)})
csv <- read_csv("outputs/outlier_flags.csv", show_col_types = FALSE)
js <- fromJSON("outputs/outlier_report.json", simplifyVector = TRUE)
A <- csv$altitude_outlier_flag; M <- csv$multivariate_grade_outlier_flag
D <- csv$defect_outlier_flag; I <- csv$high_leverage_flag & csv$high_cook_flag
tot <- A+M+D+I
fi <- js$flag_interactions
cat("partition sum:", sum(tot==0)+sum(tot==1)+sum(tot==2)+sum(tot==3)+sum(tot==4), "(expect 1338)\n")
ok <- function(lbl,a,b) cat(sprintf("%-22s %-5d vs %-5d %s\n",lbl,a,b,ifelse(a==b,"OK","X")))
ok("zero",sum(tot==0),fi$n_with_zero_flags); ok("one",sum(tot==1),fi$n_with_one_flag)
ok("two",sum(tot==2),fi$n_with_two_flags); ok("three",sum(tot==3),fi$n_with_three_flags)
ok("four",sum(tot==4),fi$n_with_four_flags)
ok("alt_only",sum(A&!M&!I&!D),fi$n_alt_only); ok("mv_only",sum(M&!A&!I&!D),fi$n_mv_only)
ok("infl_only",sum(I&!A&!M&!D),fi$n_influence_only); ok("defect_only",sum(D&!A&!M&!I),fi$n_defect_only)
ok("alt_and_mv",sum(A&M),fi$n_alt_and_mv); ok("alt_and_infl",sum(A&I),fi$n_alt_and_influence)
ok("mv_and_infl",sum(M&I),fi$n_mv_and_influence); ok("alt_mv_infl",sum(A&M&I),fi$n_alt_mv_influence)
# single-flag breakdown should sum to n_with_one_flag
cat("sum of *_only =", sum(A&!M&!I&!D)+sum(M&!A&!I&!D)+sum(I&!A&!M&!D)+sum(D&!A&!M&!I), " vs one-flag", fi$n_with_one_flag, "\n")
'partition sum: 1338 (expect 1338) zero 1186 vs 1186 OK one 122 vs 122 OK two 23 vs 23 OK three 7 vs 7 OK four 0 vs 0 OK alt_only 42 vs 42 OK mv_only 23 vs 23 OK infl_only 10 vs 10 OK defect_only 47 vs 47 OK alt_and_mv 4 vs 4 OK alt_and_infl 5 vs 5 OK mv_and_infl 13 vs 13 OK alt_mv_infl 1 vs 1 OK sum of *_only = 122 vs one-flag 122
cd /app && rm -rf outputs && mkdir outputs && Rscript analysis.R >/dev/null 2>&1 && cp outputs/outlier_report.json /tmp/run1.json && md5sum outputs/outlier_flags.csv | awk '{print "csv1:",$1}'
rm -rf outputs && mkdir outputs && Rscript analysis.R >/dev/null 2>&1 && cp outputs/outlier_report.json /tmp/run2.json && md5sum outputs/outlier_flags.csv | awk '{print "csv2:",$1}'
echo "--- JSON diff (empty = identical) ---"; diff /tmp/run1.json /tmp/run2.json && echo "JSON identical across runs"csv1: 1ae1c22b6ff4fdeca364e121fa4c869b csv2: 1ae1c22b6ff4fdeca364e121fa4c869b --- JSON diff (empty = identical) --- JSON identical across runs
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
dat <- raw %>% filter(total_cup_points > 0)
g7 <- c("aroma","flavor","aftertaste","acidity","body","balance","cupper_points")
g10 <- c(g7,"uniformity","clean_cup","sweetness")
cat("kappa(cov) 7 primary grades:", kappa(cov(as.matrix(dat[,g7]))), "\n")
cat("kappa(cov) all 10 (draft): ", kappa(cov(as.matrix(dat[,g10]))), "\n")
cat("-> draft needed tol=1e-30 because near-constant columns make cov ill-conditioned\n")
'kappa(cov) 7 primary grades: 22.05133 kappa(cov) all 10 (draft): 69.38935 -> draft needed tol=1e-30 because near-constant columns make cov ill-conditioned
command: str_replace
path: /app/analysis.R
old_str: cat(sprintf("Audit complete: n_input=%d, n_after=%d, selected=%s\n",
n_input, n_after, selected_model))
new_str: # ---------------------------------------------------------------------------
# audit_memo.md (reproduced by this script; figures injected from the run)
# ---------------------------------------------------------------------------
movers <- country_tbl %>% filter(abs_rank_change >= 2) %>%
mutate(txt = sprintf("%s (%d\u2192%d)", country, raw_rank, trimmed_rank))
movers_txt <- paste(movers$txt, collapse = ", ")
n_defect_any <- sum(flag_defect)
memo <- sprintf('# Coffee Quality Outlier Audit \u2014 Method Memo
The draft applied identical off-the-shelf summaries to every column. Each block
below says why that fails for the column\'s distribution and the shape-matched
method used instead. All figures come from the current run.
## Data prep
The draft filtered on `total_cup_points > 0`, which happens to remove the one
withdrawn lot but keys on a derived total rather than the documented defect. I
drop the lot whose ten grade cells are *all* exactly zero (a withdrawn
submission) \u2014 the stated rule, and robust even if a genuine lot ever scored a
low-but-positive total. Input n = %d; after the sentinel drop n = %d. A separate
lot with a single zero `clean_cup` but positive grades is correctly retained.
## Altitude
`altitude_mean_meters` is strongly right-skewed (median ~1311 m, max 190164 m).
Tukey fences on the raw meter scale are stretched by the long right tail: they
flag little of value while risking legitimate high farms. I take `log10` of
positive altitudes, build the Tukey fence with k = 3, and back-transform, giving
a fence of [%.2f, %.2f] m that flags %d lots. Many flags are transcription slips
in the free-text `altitude` string. For each flag I parse the first numeric
token and test /10, /100, then as-is, keeping the first candidate inside the
fence; this recovers %d lots (e.g. 190164\u21921901.64, 11000\u21921100). Genuinely tiny
entries ("1", "150", "350") have no in-fence power-of-ten candidate and keep
`altitude_corrected_m = NA`.
## Defects
`category_one_defects`, `category_two_defects`, and `quakers` are mass-at-zero
counts (85%%, 28%%, 93%% zeros). The draft\'s raw Tukey fence is degenerate: with
Q1 = Q3 = 0 the IQR is 0, so it flags *every* nonzero value (~15%% of lots for
category one) \u2014 not "extreme." I instead take each column\'s empirical 98th
percentile as the upper cut and flag counts strictly above it, isolating the
sparse tail: thresholds %g / %g / %g flag %d / %d / %d lots (1.5\u20131.9%% each),
matching the "top 1\u20132%%" brief. A lot is a defect outlier if any column trips
(%d lots).
## Multivariate grades
The draft ran Mahalanobis on all ten grade columns, including `uniformity`,
`clean_cup`, `sweetness` \u2014 near-constant point masses at 10 that force an
ill-conditioned covariance (hence the draft\'s `tol = 1e-30` hack) and let a
single 10\u21920 drop dominate the distance. I restrict to the seven primary SCA
attributes, use the classical mean/covariance Mahalanobis distance, and
threshold at the chi-squared 99.9%% quantile with df = 7 (%.2f). This flags %d
joint outliers on a well-conditioned covariance (condition number ~22 vs ~69).
## Influence
`abs(rstandard) > 2` measures residual size, not influence, and tags ~5%% of any
clean dataset by construction (the draft even reused it for leverage and Cook).
I fit three OLS specs and pick the lowest finite AIC: grades_only (%.1f),
grades_altitude (%.1f), grades_defects (%.1f) \u2014 grades_defects wins. On that fit
I flag high leverage (hat > 2p/n = %.4f) and high Cook\'s D (> 4/n = %.5f)
separately and define influence as BOTH: %d high-leverage, %d high-Cook, %d both.
The saved `lm` reproduces these exactly.
## Country ranking
Raw country means (draft) are dragged by a few very low lots and carry no
robustness check \u2014 the draft reports zero movement by cons
…[truncated]The file /app/analysis.R has been edited successfully.
cd /app && rm -rf outputs && mkdir outputs && Rscript analysis.R && echo "--- artifacts ---" && ls -la outputs/ && echo "--- memo word count ---" && wc -w outputs/audit_memo.md
Audit complete: n_input=1339, n_after=1338, selected=grades_defects altitude fence [357.45, 4923.82] m; flagged=51 corrected=7 mv flagged=40; influence both=35; boot reps=500 --- artifacts --- total 231 drwxr-sr-x 2 h2tagent h2tagent 4096 Jun 20 18:24 . drwxrws--- 4 h2tagent h2tagent 4096 Jun 20 18:24 .. -rw-r--r-- 1 h2tagent h2tagent 4624 Jun 20 18:24 audit_memo.md -rw-r--r-- 1 h2tagent h2tagent 137120 Jun 20 18:24 coffee_ols_selected.rds -rw-r--r-- 1 h2tagent h2tagent 78269 Jun 20 18:24 outlier_flags.csv -rw-r--r-- 1 h2tagent h2tagent 6692 Jun 20 18:24 outlier_report.json --- memo word count --- 744 outputs/audit_memo.md
cd /app && cat outputs/audit_memo.md
# Coffee Quality Outlier Audit , Method Memo
The draft applied identical off-the-shelf summaries to every column. Each block
below says why that fails for the column's distribution and the shape-matched
method used instead. All figures come from the current run.
## Data prep
The draft filtered on `total_cup_points > 0`, which happens to remove the one
withdrawn lot but keys on a derived total rather than the documented defect. I
drop the lot whose ten grade cells are *all* exactly zero (a withdrawn
submission) , the stated rule, and robust even if a genuine lot ever scored a
low-but-positive total. Input n = 1339; after the sentinel drop n = 1338. A separate
lot with a single zero `clean_cup` but positive grades is correctly retained.
## Altitude
`altitude_mean_meters` is strongly right-skewed (median ~1311 m, max 190164 m).
Tukey fences on the raw meter scale are stretched by the long right tail: they
flag little of value while risking legitimate high farms. I take `log10` of
positive altitudes, build the Tukey fence with k = 3, and back-transform, giving
a fence of [357.45, 4923.82] m that flags 51 lots. Many flags are transcription slips
in the free-text `altitude` string. For each flag I parse the first numeric
token and test /10, /100, then as-is, keeping the first candidate inside the
fence; this recovers 7 lots (e.g. 190164→1901.64, 11000→1100). Genuinely tiny
entries ("1", "150", "350") have no in-fence power-of-ten candidate and keep
`altitude_corrected_m = NA`.
## Defects
`category_one_defects`, `category_two_defects`, and `quakers` are mass-at-zero
counts (85%, 28%, 93% zeros). The draft's raw Tukey fence is degenerate: with
Q1 = Q3 = 0 the IQR is 0, so it flags *every* nonzero value (~15% of lots for
category one) , not "extreme." I instead take each column's empirical 98th
percentile as the upper cut and flag counts strictly above it, isolating the
sparse tail: thresholds 5 / 20 / 2 flag 20 / 23 / 25 lots (1.5–1.9% each),
matching the "top 1–2%" brief. A lot is a defect outlier if any column trips
(63 lots).
## Multivariate grades
The draft ran Mahalanobis on all ten grade columns, including `uniformity`,
`clean_cup`, `sweetness` , near-constant point masses at 10 that force an
ill-conditioned covariance (hence the draft's `tol = 1e-30` hack) and let a
single 10→0 drop dominate the distance. I restrict to the seven primary SCA
attributes, use the classical mean/covariance Mahalanobis distance, and
threshold at the chi-squared 99.9% quantile with df = 7 (24.32). This flags 40
joint outliers on a well-conditioned covariance (condition number ~22 vs ~69).
## Influence
`abs(rstandard) > 2` measures residual size, not influence, and tags ~5% of any
clean dataset by construction (the draft even reused it for leverage and Cook).
I fit three OLS specs and pick the lowest finite AIC: grades_only (4459.4),
grades_altitude (4461.3), grades_defects (4418.7) , grades_defects wins. On that fit
I flag high leverage (hat > 2p/n = 0.0164) and high Cook's D (> 4/n = 0.00299)
separately and define influence as BOTH: 110 high-leverage, 66 high-Cook, 35 both.
The saved `lm` reproduces these exactly.
## Country ranking
Raw country means (draft) are dragged by a few very low lots and carry no
robustness check , the draft reports zero movement by construction (trimmed =
raw). I compare the raw mean against a 20% trimmed mean across the 21 countries
with >=10 lots. 2 move >=2 rank positions (max = 2): Taiwan (14→16), Guatemala (15→13), once low-scoring lots
are down-weighted , exactly the robustness the draft skipped.
## Sensitivity
Refitting grades_only before/after the composite drop, the `flavor` coefficient
moves from 2.085 to 1.653, a -20.7% change. Rather than a parametric Wald interval,
I bootstrap the percent change (B = 500, seed 20260512, resampling the 1338
post-drop lots and carrying the *original* composite flags), giving a 95%
percentile CI of [-44.3%, 6.2%] over 500 successful replicates. The interval
spans zero, so
…[truncated]cd /app && echo "=== CSV header ===" && head -1 outputs/outlier_flags.csv && echo && echo "=== sample rows (incl. a corrected + a flagged) ===" && head -4 outputs/outlier_flags.csv && echo "..." && awk -F, 'NR==1 || $5!="NA"' outputs/outlier_flags.csv | head -5
=== CSV header === row_id,country_of_origin,total_cup_points,altitude_outlier_flag,altitude_corrected_m,defect_outlier_flag,multivariate_grade_outlier_flag,high_leverage_flag,high_cook_flag,composite_drop_flag === sample rows (incl. a corrected + a flagged) === row_id,country_of_origin,total_cup_points,altitude_outlier_flag,altitude_corrected_m,defect_outlier_flag,multivariate_grade_outlier_flag,high_leverage_flag,high_cook_flag,composite_drop_flag 0,Ethiopia,90.58,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 1,Ethiopia,89.92,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 2,Guatemala,89.75,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE ... row_id,country_of_origin,total_cup_points,altitude_outlier_flag,altitude_corrected_m,defect_outlier_flag,multivariate_grade_outlier_flag,high_leverage_flag,high_cook_flag,composite_drop_flag 31,"Tanzania, United Republic Of",86.5,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 109,"Tanzania, United Republic Of",84.92,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 201,"Tanzania, United Republic Of",84.25,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 202,"Tanzania, United Republic Of",84.25,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE
cd /app && Rscript -e '
suppressPackageStartupMessages(library(readr))
csv <- read_csv("outputs/outlier_flags.csv", show_col_types = FALSE)
cat("Rows with non-NA altitude_corrected_m (the 7 unit-corrected):\n")
print(as.data.frame(csv[!is.na(csv$altitude_corrected_m), c("row_id","country_of_origin","altitude_corrected_m","altitude_outlier_flag","composite_drop_flag")]))
'Rows with non-NA altitude_corrected_m (the 7 unit-corrected): row_id country_of_origin altitude_corrected_m altitude_outlier_flag 1 543 Brazil 1100.00 TRUE 2 584 Guatemala 518.00 TRUE 3 677 Guatemala 518.00 TRUE 4 833 Guatemala 518.00 TRUE 5 896 Guatemala 1901.64 TRUE 6 1040 Nicaragua 1100.00 TRUE 7 1144 Guatemala 1901.64 TRUE composite_drop_flag 1 TRUE 2 TRUE 3 TRUE 4 TRUE 5 TRUE 6 TRUE 7 TRUE
cd /app && Rscript -e '
suppressPackageStartupMessages(library(jsonlite))
js <- fromJSON("outputs/outlier_report.json", simplifyVector = FALSE)
errs <- character(0)
req <- function(path, cond) if (!isTRUE(cond)) errs <<- c(errs, path)
# top-level
req("n_input", is.numeric(js$n_input) && js$n_input==1339)
req("n_after", is.numeric(js$n_after_zero_score_drop) && js$n_after_zero_score_drop==1338)
for (m in c("altitude","defects","multivariate_grades","influence","country_ranking"))
req(paste0("methods.",m), is.character(js$methods[[m]]) && nchar(js$methods[[m]])>0)
for (k in c("lower_bound_m","upper_bound_m","n_flagged","n_unit_corrected"))
req(paste0("altitude.",k), is.numeric(js$altitude[[k]]))
for (d in c("category_one_defects","category_two_defects","quakers")) {
req(paste0("defects.",d,".upper_threshold"), is.numeric(js$defects[[d]]$upper_threshold))
req(paste0("defects.",d,".n_flagged"), is.numeric(js$defects[[d]]$n_flagged))
}
req("mv.columns_used(len7)", length(js$multivariate_grades$columns_used)==7)
req("mv.threshold", is.numeric(js$multivariate_grades$threshold))
req("mv.n_flagged", is.numeric(js$multivariate_grades$n_flagged))
inf <- js$influence
req("inf.selected_model", inf$selected_model %in% c("grades_only","grades_altitude","grades_defects"))
for (k in c("aic_grades_only","aic_grades_altitude","aic_grades_defects","leverage_threshold","cook_threshold","n_high_leverage","n_high_cook","n_both"))
req(paste0("inf.",k), is.numeric(inf[[k]]))
req("inf.top10(len10)", length(inf$top10_cook_row_ids)==10)
cr <- js$country_ranking
for (k in c("n_countries_evaluated","n_rank_changes_ge_2","max_abs_rank_change"))
req(paste0("cr.",k), is.numeric(cr[[k]]))
req("cr.per_country(len)", length(cr$per_country)==cr$n_countries_evaluated)
pc1 <- cr$per_country[[1]]
for (k in c("country","n","raw_mean","trimmed_mean","raw_rank","trimmed_rank"))
req(paste0("per_country[0].",k), !is.null(pc1[[k]]))
se <- js$sensitivity
for (k in c("coef_flavor_full","coef_flavor_dropped","delta_pct","delta_pct_ci_lower","delta_pct_ci_upper","n_bootstrap_replicates"))
req(paste0("sens.",k), is.numeric(se[[k]]))
fi <- js$flag_interactions
for (k in c("n_with_zero_flags","n_with_one_flag","n_with_two_flags","n_with_three_flags","n_with_four_flags","n_alt_only","n_mv_only","n_influence_only","n_defect_only","n_alt_and_mv","n_alt_and_influence","n_mv_and_influence","n_alt_mv_influence"))
req(paste0("fi.",k), is.numeric(fi[[k]]))
if (length(errs)==0) cat("SCHEMA VALIDATION: ALL KEYS PRESENT & TYPED CORRECTLY\n") else {cat("PROBLEMS:\n"); print(errs)}
'SCHEMA VALIDATION: ALL KEYS PRESENT & TYPED CORRECTLY
cd /app && rm -rf outputs && mkdir outputs && Rscript -e 'options(warn=1); source("analysis.R")' 2>&1 | sed -n '1,40p'Audit complete: n_input=1339, n_after=1338, selected=grades_defects altitude fence [357.45, 4923.82] m; flagged=51 corrected=7 mv flagged=40; influence both=35; boot reps=500
cd /app && rm -rf outputs && mkdir outputs && Rscript analysis.R >/dev/null 2>&1
Rscript -e '
suppressPackageStartupMessages({library(readr); library(jsonlite)})
pass <- TRUE; chk <- function(lbl, cond){ cat(sprintf("[%s] %s\n", ifelse(cond,"PASS","FAIL"), lbl)); if(!cond) pass<<-FALSE }
files <- c("outlier_report.json","coffee_ols_selected.rds","outlier_flags.csv","audit_memo.md")
chk("all 4 artifacts exist", all(file.exists(file.path("outputs",files))))
js<-fromJSON("outputs/outlier_report.json",simplifyVector=TRUE)
csv<-read_csv("outputs/outlier_flags.csv",show_col_types=FALSE)
fit<-readRDS("outputs/coffee_ols_selected.rds")
# core counts
chk("n_input=1339 & n_after=1338", js$n_input==1339 && js$n_after_zero_score_drop==1338)
chk("CSV 1338 rows, contiguous row_id", nrow(csv)==1338 && all(csv$row_id==0:1337))
chk("CSV cols exact/order", identical(names(csv), c("row_id","country_of_origin","total_cup_points","altitude_outlier_flag","altitude_corrected_m","defect_outlier_flag","multivariate_grade_outlier_flag","high_leverage_flag","high_cook_flag","composite_drop_flag")))
# altitude
chk("alt bounds ~[357.45,4923.82]", abs(js$altitude$lower_bound_m-357.45)<0.1 && abs(js$altitude$upper_bound_m-4923.82)<0.1)
chk("alt flagged=51 match CSV", js$altitude$n_flagged==51 && sum(csv$altitude_outlier_flag)==51)
chk("alt corrected=7 match CSV non-NA", js$altitude$n_unit_corrected==7 && sum(!is.na(csv$altitude_corrected_m))==7)
# defects
chk("defect thresholds 5/20/2", js$defects$category_one_defects$upper_threshold==5 && js$defects$category_two_defects$upper_threshold==20 && js$defects$quakers$upper_threshold==2)
chk("defect union in CSV=63", sum(csv$defect_outlier_flag)==63)
# mv
chk("mv df7 thr=24.32, n=40", abs(js$multivariate_grades$threshold-qchisq(.999,7))<1e-6 && js$multivariate_grades$n_flagged==40 && length(js$multivariate_grades$columns_used)==7)
# influence reload reconciliation
n<-nobs(fit); p<-length(coef(fit)); lev<-hatvalues(fit); cook<-cooks.distance(fit)
chk("selected=grades_defects lowest AIC", js$influence$selected_model=="grades_defects" && which.min(c(js$influence$aic_grades_only,js$influence$aic_grades_altitude,js$influence$aic_grades_defects))==3)
chk("reload: high_lev matches CSV", all((lev>2*p/n)==csv$high_leverage_flag))
chk("reload: high_cook matches CSV", all((cook>4/n)==csv$high_cook_flag))
chk("n_both=35 = lev&cook", js$influence$n_both==35 && sum(csv$high_leverage_flag&csv$high_cook_flag)==35)
chk("top10 cook ids reload-match", all((order(cook,decreasing=TRUE)[1:10]-1)==js$influence$top10_cook_row_ids))
# composite
comp <- csv$altitude_outlier_flag | csv$multivariate_grade_outlier_flag | (csv$high_leverage_flag & csv$high_cook_flag)
chk("composite formula matches CSV (defects excluded)", all(comp==csv$composite_drop_flag))
# country
chk("country: 21 eval, 2 changes>=2, max 2", js$country_ranking$n_countries_evaluated==21 && js$country_ranking$n_rank_changes_ge_2==2 && js$country_ranking$max_abs_rank_change==2)
# sensitivity
chk("sensitivity 500 reps, CI brackets delta", js$sensitivity$n_bootstrap_replicates==500 && js$sensitivity$delta_pct_ci_lower < js$sensitivity$delta_pct && js$sensitivity$delta_pct < js$sensitivity$delta_pct_ci_upper)
# flag interactions partition
fi<-js$flag_interactions
chk("flag partition sums to 1338", fi$n_with_zero_flags+fi$n_with_one_flag+fi$n_with_two_flags+fi$n_with_three_flags+fi$n_with_four_flags==1338)
# memo
chk("memo <=900 words & has 8 headings", {w<-length(strsplit(paste(readLines("outputs/audit_memo.md"),collapse=" "),"\\s+")[[1]]); h<-sum(grepl("^## ",readLines("outputs/audit_memo.md"))); w<=900 && h==8})
cat(sprintf("\n==== OVERALL: %s ====\n", ifelse(pass,"ALL CHECKS PASS","SOME CHECKS FAILED")))
'[PASS] all 4 artifacts exist [PASS] n_input=1339 & n_after=1338 [PASS] CSV 1338 rows, contiguous row_id [PASS] CSV cols exact/order [PASS] alt bounds ~[357.45,4923.82] [PASS] alt flagged=51 match CSV [PASS] alt corrected=7 match CSV non-NA [PASS] defect thresholds 5/20/2 [PASS] defect union in CSV=63 [PASS] mv df7 thr=24.32, n=40 [PASS] selected=grades_defects lowest AIC [PASS] reload: high_lev matches CSV [PASS] reload: high_cook matches CSV [PASS] n_both=35 = lev&cook [PASS] top10 cook ids reload-match [PASS] composite formula matches CSV (defects excluded) [PASS] country: 21 eval, 2 changes>=2, max 2 [PASS] sensitivity 500 reps, CI brackets delta [PASS] flag partition sums to 1338 [PASS] memo <=900 words & has 8 headings ==== OVERALL: ALL CHECKS PASS ====
cd /app && echo "=== final outputs/ ===" && ls -la outputs/ && echo && echo "=== analysis.R line count ===" && wc -l analysis.R && echo && echo "=== quick structural grep of the script sections ===" && grep -n "^# ---" analysis.R
=== final outputs/ === total 231 drwxr-sr-x 2 h2tagent h2tagent 4096 Jun 20 18:25 . drwxrws--- 4 h2tagent h2tagent 4096 Jun 20 18:25 .. -rw-r--r-- 1 h2tagent h2tagent 4624 Jun 20 18:25 audit_memo.md -rw-r--r-- 1 h2tagent h2tagent 137120 Jun 20 18:25 coffee_ols_selected.rds -rw-r--r-- 1 h2tagent h2tagent 78269 Jun 20 18:25 outlier_flags.csv -rw-r--r-- 1 h2tagent h2tagent 6692 Jun 20 18:25 outlier_report.json === analysis.R line count === 428 analysis.R === quick structural grep of the script sections === 29:# --------------------------------------------------------------------------- 31:# --------------------------------------------------------------------------- 41:# --------------------------------------------------------------------------- 43:# --------------------------------------------------------------------------- 72:# --------------------------------------------------------------------------- 74:# --------------------------------------------------------------------------- 85:# --------------------------------------------------------------------------- 87:# --------------------------------------------------------------------------- 98:# --------------------------------------------------------------------------- 100:# --------------------------------------------------------------------------- 158:# --------------------------------------------------------------------------- 160:# --------------------------------------------------------------------------- 179:# --------------------------------------------------------------------------- 181:# --------------------------------------------------------------------------- 211:# --------------------------------------------------------------------------- 213:# --------------------------------------------------------------------------- 232:# --------------------------------------------------------------------------- 234:# --------------------------------------------------------------------------- 249:# --------------------------------------------------------------------------- 251:# --------------------------------------------------------------------------- 322:# --------------------------------------------------------------------------- 324:# ---------------------------------------------------------------------------
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: coffee-ratings-outliers # attempt: 1 status: completed raw_score: 0.0 # reward rule: binary: score 0.0 != 1.0 # => reward = 0.0 (FAIL) # # Note: these tasks score via an external hidden grader; no pytest/verifier # block is embedded in the agent transcript. The block below is the last # sizable tool-output captured in the run (supporting evidence). ---------------------------------------------------------------------- === final outputs/ === total 231 drwxr-sr-x 2 h2tagent h2tagent 4096 Jun 20 18:25 . drwxrws--- 4 h2tagent h2tagent 4096 Jun 20 18:25 .. -rw-r--r-- 1 h2tagent h2tagent 4624 Jun 20 18:25 audit_memo.md -rw-r--r-- 1 h2tagent h2tagent 137120 Jun 20 18:25 coffee_ols_selected.rds -rw-r--r-- 1 h2tagent h2tagent 78269 Jun 20 18:25 outlier_flags.csv -rw-r--r-- 1 h2tagent h2tagent 6692 Jun 20 18:25 outlier_report.json === analysis.R line count === 428 analysis.R === quick structural grep of the script sections === 29:# --------------------------------------------------------------------------- 31:# --------------------------------------------------------------------------- 41:# --------------------------------------------------------------------------- 43:# --------------------------------------------------------------------------- 72:# --------------------------------------------------------------------------- 74:# --------------------------------------------------------------------------- 85:# --------------------------------------------------------------------------- 87:# --------------------------------------------------------------------------- 98:# --------------------------------------------------------------------------- 100:# --------------------------------------------------------------------------- 158:# --------------------------------------------------------------------------- 160:# --------------------------------------------------------------------------- 179:# --------------------------------------------------------------------------- 181:# --------------------------------------------------------------------------- 211:# --------------------------------------------------------------------------- 213:# --------------------------------------------------------------------------- 232:# --------------------------------------------------------------------------- 234:# --------------------------------------------------------------------------- 249:# --------------------------------------------------------------------------- 251:# --------------------------------------------------------------------------- 322:# --------------------------------------------------------------------------- 324:# ---------------------------------------------------------------------------
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_d07a97713e254c6b. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_d07a97713e254c6b · verifier authoritative; classifier explanatory.