tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.cd /app && ls -la && echo "---OUTPUTS---" && ls -la outputs/ 2>/dev/null && echo "---DATA---" && ls -la data/
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 ---OUTPUTS--- total 8 drwxrws--- 2 h2tagent h2tagent 4096 Jun 20 17:46 . drwxrws--- 4 h2tagent h2tagent 4096 Jun 20 17:46 .. ---DATA--- 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
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 -1 data/coffee_ratings.csv | tr ',' '\n' | cat -n
1 total_cup_points
2 species
3 owner
4 country_of_origin
5 farm_name
6 lot_number
7 mill
8 ico_number
9 company
10 altitude
11 region
12 producer
13 number_of_bags
14 bag_weight
15 in_country_partner
16 harvest_year
17 grading_date
18 owner_1
19 variety
20 processing_method
21 aroma
22 flavor
23 aftertaste
24 acidity
25 body
26 balance
27 uniformity
28 clean_cup
29 sweetness
30 cupper_points
31 moisture
32 category_one_defects
33 quakers
34 color
35 category_two_defects
36 expiration
37 certification_body
38 certification_address
39 certification_contact
40 unit_of_measurement
41 altitude_low_meters
42 altitude_high_meters
43 altitude_mean_meterscd /app && R --version | head -1 && echo "---PKGS---" && Rscript -e 'for (p in c("dplyr","tidyr","readr","stringr","jsonlite","purrr")) cat(p, as.character(packageVersion(p)), "\n")'R version 4.4.2 (2024-10-31) -- "Pile of Leaves" ---PKGS--- 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("nrow:", nrow(raw), "\n")
grades <- c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")
cat("\n--- Rows with total_cup_points == 0 ---\n")
print(sum(raw$total_cup_points == 0))
cat("\n--- Rows with all 10 grades == 0 ---\n")
allzero <- rowSums(raw[,grades] == 0) == 10
print(sum(allzero))
cat("which:", which(allzero), "\n")
cat("total_cup_points of that row:", raw$total_cup_points[allzero], "\n")
'nrow: 1339 --- Rows with total_cup_points == 0 --- [1] 1 --- Rows with all 10 grades == 0 --- [1] 1 which: 1311 total_cup_points of that row: 0
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")
dat <- raw %>% filter(rowSums(raw[,grades]==0) != 10)
cat("post-drop n:", nrow(dat), "\n\n")
for (g in grades) {
x <- dat[[g]]
cat(sprintf("%-14s mean=%.3f sd=%.3f min=%.2f max=%.2f var=%.4f\n", g, mean(x), sd(x), min(x), max(x), var(x)))
}
'post-drop n: 1338 aroma mean=7.572 sd=0.316 min=5.08 max=8.75 var=0.0998 flavor mean=7.526 sd=0.341 min=6.08 max=8.83 var=0.1165 aftertaste mean=7.407 sd=0.350 min=6.17 max=8.67 var=0.1227 acidity mean=7.541 sd=0.319 min=5.25 max=8.75 var=0.1019 body mean=7.523 sd=0.308 min=5.08 max=8.58 var=0.0947 balance mean=7.524 sd=0.354 min=5.25 max=8.75 var=0.1251 uniformity mean=9.842 sd=0.485 min=6.00 max=10.00 var=0.2354 clean_cup mean=9.842 sd=0.715 min=0.00 max=10.00 var=0.5116 sweetness mean=9.864 sd=0.554 min=1.33 max=10.00 var=0.3071 cupper_points mean=7.509 sd=0.427 min=5.17 max=10.00 var=0.1822
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")
dat <- raw %>% filter(rowSums(raw[,grades]==0) != 10)
alt <- dat$altitude_mean_meters
cat("altitude_mean_meters: NA count =", sum(is.na(alt)), "\n")
cat("non-NA n =", sum(!is.na(alt)), "; >0 n =", sum(!is.na(alt) & alt>0), "\n")
cat("summary:\n"); print(summary(alt))
cat("\ntop 15 altitudes:\n"); print(sort(alt, decreasing=TRUE)[1:15])
cat("\nquantiles of positive:\n")
print(quantile(alt[!is.na(alt)&alt>0], c(0,.01,.05,.25,.5,.75,.95,.99,1)))
'altitude_mean_meters: NA count = 230
non-NA n = 1108 ; >0 n = 1108
summary:
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1 1100 1311 1775 1600 190164 230
top 15 altitudes:
[1] 190164 190164 110000 11000 4287 4001 3850 3845 3825 3800
[11] 3500 3280 3280 3280 3170
quantiles of positive:
0% 1% 5% 25% 50% 75% 95% 99%
1.00 1.77 442.00 1100.00 1310.64 1600.00 1893.00 3280.00
100%
190164.00cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
grades <- c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")
dat <- raw %>% filter(rowSums(raw[,grades]==0) != 10)
alt <- dat$altitude_mean_meters
la <- log10(alt[!is.na(alt) & alt>0])
q <- quantile(la, c(.25,.75))
iqr <- q[2]-q[1]
lo <- q[1]-3*iqr; hi <- q[2]+3*iqr
cat(sprintf("log10 Q1=%.4f Q3=%.4f IQR=%.4f\n", q[1], q[2], iqr))
cat(sprintf("log10 fence [%.4f, %.4f]\n", lo, hi))
cat(sprintf("meters fence [%.4f, %.4f]\n", 10^lo, 10^hi))
flag <- !is.na(alt) & (alt < 10^lo | alt > 10^hi)
cat("n flagged:", sum(flag), "\n")
cat("flagged altitude_mean values:\n")
print(sort(alt[flag]))
'log10 Q1=3.0414 Q3=3.2041 IQR=0.1627 log10 fence [2.5532, 3.6923] meters fence [357.4463, 4923.8167] n flagged: 51 flagged altitude_mean values: [1] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [7] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 [13] 12.0000 12.0000 12.0000 13.0000 13.0000 40.0000 [19] 50.0000 100.0000 110.0000 125.0000 150.0000 150.0000 [25] 157.8864 157.8864 157.8864 165.0000 168.0000 170.0000 [31] 175.0000 180.0000 185.0000 200.0000 200.0000 200.0000 [37] 200.0000 200.0000 250.0000 250.0000 250.5000 250.5000 [43] 280.0000 300.0000 350.0000 350.0000 350.0000 11000.0000 [49] 110000.0000 190164.0000 190164.0000
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr); library(stringr)})
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")
dat <- raw %>% filter(rowSums(raw[,grades]==0) != 10)
alt <- dat$altitude_mean_meters
la <- log10(alt[!is.na(alt) & alt>0])
q <- quantile(la, c(.25,.75)); iqr <- q[2]-q[1]
loM <- 10^(q[1]-3*iqr); hiM <- 10^(q[2]+3*iqr)
flag <- !is.na(alt) & (alt < loM | alt > hiM)
fr <- dat[flag, c("altitude","altitude_mean_meters","unit_of_measurement")]
print(as.data.frame(fr), right=FALSE)
'altitude altitude_mean_meters unit_of_measurement 1 1.2 12.0000 m 2 1.2 12.0000 m 3 -1 1.0000 m 4 350 350.0000 m 5 170 170.0000 m 6 approx. 1'500 metres a.s.l. 250.5000 m 7 1 1.0000 m 8 175 175.0000 m 9 16.8 168.0000 m 10 approx.1'500 metres a.s.l. 250.5000 m 11 1 1.0000 m 12 350 350.0000 m 13 11000 metros 11000.0000 m 14 518 157.8864 ft 15 1.65 165.0000 m 16 1 1.0000 m 17 200 m 200.0000 m 18 518 157.8864 ft 19 1 1.0000 m 20 200 m 200.0000 m 21 1 1.0000 m 22 1 1.0000 m 23 1 1.0000 m 24 1.3 13.0000 m 25 518 157.8864 ft 26 1 1.0000 m 27 1 1.0000 m 28 1 1.0000 m 29 150 m 150.0000 m 30 190164 190164.0000 m 31 1.3 13.0000 m 32 160-200m 180.0000 m 33 1.25 125.0000 m 34 110公尺 110.0000 m 35 1 1.0000 m 36 200 200.0000 m 37 300 m 300.0000 m 38 100 100.0000 m 39 1100.00 mosl 110000.0000 m 40 1.85 185.0000 m 41 50 50.0000 m 42 280 280.0000 m 43 200 m 200.0000 m 44 150 m 150.0000 m 45 250 m 250.0000 m 46 250 m 250.0000 m 47 1901.64 190164.0000 m 48 200 200.0000 m 49 12oo 12.0000 m 50 ~350m 350.0000 m 51 40 40.0000 m
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr); library(stringr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
grades <- c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")
dat <- raw %>% filter(rowSums(raw[,grades]==0) != 10)
alt <- dat$altitude_mean_meters
la <- log10(alt[!is.na(alt) & alt>0])
q <- quantile(la, c(.25,.75)); iqr <- q[2]-q[1]
loM <- 10^(q[1]-3*iqr); hiM <- 10^(q[2]+3*iqr)
flag <- !is.na(alt) & (alt < loM | alt > hiM)
first_tok <- function(s){ m <- str_extract(s, "[0-9]+([.][0-9]+)?"); as.numeric(m) }
fl_idx <- which(flag)
corr <- rep(NA_real_, length(fl_idx)); toks <- numeric(length(fl_idx))
for (i in seq_along(fl_idx)){
r <- fl_idx[i]; T <- first_tok(dat$altitude[r]); toks[i] <- T
if (is.na(T)) next
for (cand in c(T/10, T/100, T)) if (cand>=loM & cand<=hiM){ corr[i] <- cand; break }
}
res <- data.frame(altitude=dat$altitude[fl_idx], mean_m=alt[fl_idx], token=toks, corrected=corr)
print(res, right=FALSE)
cat("\nn_flagged:", length(fl_idx), " n_corrected:", sum(!is.na(corr)), "\n")
'altitude mean_m token corrected 1 1.2 12.0000 1.20 NA 2 1.2 12.0000 1.20 NA 3 -1 1.0000 1.00 NA 4 350 350.0000 350.00 NA 5 170 170.0000 170.00 NA 6 approx. 1'500 metres a.s.l. 250.5000 1.00 NA 7 1 1.0000 1.00 NA 8 175 175.0000 175.00 NA 9 16.8 168.0000 16.80 NA 10 approx.1'500 metres a.s.l. 250.5000 1.00 NA 11 1 1.0000 1.00 NA 12 350 350.0000 350.00 NA 13 11000 metros 11000.0000 11000.00 1100.00 14 518 157.8864 518.00 518.00 15 1.65 165.0000 1.65 NA 16 1 1.0000 1.00 NA 17 200 m 200.0000 200.00 NA 18 518 157.8864 518.00 518.00 19 1 1.0000 1.00 NA 20 200 m 200.0000 200.00 NA 21 1 1.0000 1.00 NA 22 1 1.0000 1.00 NA 23 1 1.0000 1.00 NA 24 1.3 13.0000 1.30 NA 25 518 157.8864 518.00 518.00 26 1 1.0000 1.00 NA 27 1 1.0000 1.00 NA 28 1 1.0000 1.00 NA 29 150 m 150.0000 150.00 NA 30 190164 190164.0000 190164.00 1901.64 31 1.3 13.0000 1.30 NA 32 160-200m 180.0000 160.00 NA 33 1.25 125.0000 1.25 NA 34 110公尺 110.0000 110.00 NA 35 1 1.0000 1.00 NA 36 200 200.0000 200.00 NA 37 300 m 300.0000 300.00 NA 38 100 100.0000 100.00 NA 39 1100.00 mosl 110000.0000 1100.00 1100.00 40 1.85 185.0000 1.85 NA 41 50 50.0000 50.00 NA 42 280 280.0000 280.00 NA 43 200 m 200.0000 200.00 NA 44 150 m 150.0000 150.00 NA 45 250 m 250.0000 250.00 NA 46 250 m 250.0000 250.00 NA 47 1901.64 190164.0000 1901.64 1901.64 48 200 200.0000 200.00 NA 49 12oo 12.0000 12.00 NA 50 ~350m 350.0000 350.00 NA 51 40 40.0000 40.00 NA n_flagged: 51 n_corrected: 7
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")
dat <- raw %>% filter(rowSums(raw[,grades]==0) != 10)
for (col in c("category_one_defects","category_two_defects","quakers")) {
x <- dat[[col]]
cat("=====", col, "=====\n")
cat("NA:", sum(is.na(x)), " zeros:", sum(x==0, na.rm=TRUE), sprintf("(%.1f%%)", 100*mean(x==0,na.rm=TRUE)), "\n")
cat("Tukey IQR: Q1,Q3 =", quantile(x,c(.25,.75),na.rm=TRUE), "-> both 0, so raw IQR fence = 0\n")
cat("quantiles (.9,.95,.98,.99,.995,1):\n")
print(quantile(x, c(.9,.95,.98,.99,.995,1), na.rm=TRUE))
cat("P98 =", quantile(x,.98,na.rm=TRUE), "; n > P98 =", sum(x > quantile(x,.98,na.rm=TRUE), na.rm=TRUE), "\n")
cat("max:", max(x,na.rm=TRUE), "\n\n")
}
'===== category_one_defects =====
NA: 0 zeros: 1136 (84.9%)
Tukey IQR: Q1,Q3 = 0 0 -> both 0, so raw IQR fence = 0
quantiles (.9,.95,.98,.99,.995,1):
90% 95% 98% 99% 99.5% 100%
1.000 2.000 5.000 7.630 11.315 63.000
P98 = 5 ; n > P98 = 20
max: 63
===== category_two_defects =====
NA: 0 zeros: 373 (27.9%)
Tukey IQR: Q1,Q3 = 0 4 -> both 0, so raw IQR fence = 0
quantiles (.9,.95,.98,.99,.995,1):
90% 95% 98% 99% 99.5% 100%
9.000 13.000 20.000 26.630 31.315 55.000
P98 = 20 ; n > P98 = 23
max: 55
===== quakers =====
NA: 1 zeros: 1243 (93.0%)
Tukey IQR: Q1,Q3 = 0 0 -> both 0, so raw IQR fence = 0
quantiles (.9,.95,.98,.99,.995,1):
90% 95% 98% 99% 99.5% 100%
0 1 2 5 6 11
P98 = 2 ; n > P98 = 25
max: 11cd /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")
dat <- raw %>% filter(rowSums(raw[,grades]==0) != 10)
n <- nrow(dat)
for (col in c("category_one_defects","category_two_defects","quakers")) {
x <- dat[[col]]; xp <- x[!is.na(x) & x > 0]
# positive-only Tukey k=1.5
q <- quantile(xp, c(.25,.75)); up15 <- q[2]+1.5*(q[2]-q[1]); up3 <- q[2]+3*(q[2]-q[1])
p99 <- quantile(x, .99, na.rm=TRUE); p98 <- quantile(x, .98, na.rm=TRUE)
cat(sprintf("%-22s | posTukey1.5 thr=%.2f n=%d (%.1f%%) | posTukey3 thr=%.2f n=%d | P99 thr=%.2f n=%d (%.1f%%) | P98 n=%d\n",
col, up15, sum(x>up15,na.rm=TRUE), 100*sum(x>up15,na.rm=TRUE)/n,
up3, sum(x>up3,na.rm=TRUE),
p99, sum(x>p99,na.rm=TRUE), 100*sum(x>p99,na.rm=TRUE)/n,
sum(x>p98,na.rm=TRUE)))
}
'category_one_defects | posTukey1.5 thr=6.00 n=17 (1.3%) | posTukey3 thr=9.00 n=12 | P99 thr=7.63 n=14 (1.0%) | P98 n=20 category_two_defects | posTukey1.5 thr=12.00 n=73 (5.5%) | posTukey3 thr=18.00 n=34 | P99 thr=26.63 n=14 (1.0%) | P98 n=23 quakers | posTukey1.5 thr=6.00 n=6 (0.4%) | posTukey3 thr=9.00 n=1 | P99 thr=5.00 n=10 (0.7%) | P98 n=25
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
grades10 <- c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")
dat <- raw %>% filter(rowSums(raw[,grades10]==0) != 10)
g7 <- c("aroma","flavor","aftertaste","acidity","body","balance","cupper_points")
G <- as.matrix(dat[,g7]); cc <- complete.cases(G)
mu <- colMeans(G[cc,]); S <- cov(G[cc,])
md2 <- mahalanobis(G[cc,], mu, S)
thr <- qchisq(0.999, df=length(g7))
cat("df=",length(g7)," threshold=",thr,"\n")
cat("n complete:",sum(cc)," n flagged MV:", sum(md2>thr),"\n")
# AIC for three models on rows complete on 7 grades
fdf <- dat[cc,]
m1 <- lm(total_cup_points ~ aroma+flavor+aftertaste+acidity+body+balance+cupper_points, data=fdf)
cat("AIC grades_only:", AIC(m1), " n=",nobs(m1)," p=",length(coef(m1)),"\n")
cat("residual sd m1:", summary(m1)$sigma, "\n")
'df= 7 threshold= 24.32189 n complete: 1338 n flagged MV: 40 AIC grades_only: 4459.362 n= 1338 p= 8 residual sd m1: 1.27604
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
grades10 <- c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")
dat <- raw %>% filter(rowSums(raw[,grades10]==0) != 10)
s10 <- rowSums(dat[,grades10])
cat("max abs diff total vs sum10:", max(abs(dat$total_cup_points - s10), na.rm=TRUE), "\n")
'max abs diff total vs sum10: 0.5
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr); library(stringr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
grades10 <- c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")
dat <- raw %>% filter(rowSums(raw[,grades10]==0) != 10)
g7 <- c("aroma","flavor","aftertaste","acidity","body","balance","cupper_points")
# altitude correction
alt <- dat$altitude_mean_meters
la <- log10(alt[!is.na(alt)&alt>0]); q<-quantile(la,c(.25,.75)); iqr<-q[2]-q[1]
loM<-10^(q[1]-3*iqr); hiM<-10^(q[2]+3*iqr)
flag<-!is.na(alt)&(alt<loM|alt>hiM)
first_tok<-function(s){as.numeric(str_extract(s,"[0-9]+([.][0-9]+)?"))}
corr<-rep(NA_real_,nrow(dat))
for(r in which(flag)){T<-first_tok(dat$altitude[r]); if(is.na(T))next; for(cand in c(T/10,T/100,T)) if(cand>=loM&cand<=hiM){corr[r]<-cand;break}}
elev <- ifelse(!is.na(corr), corr, alt)
logelev <- log10(elev); logelev[!is.finite(logelev)] <- NA
G<-as.matrix(dat[,g7]); cc<-complete.cases(G); fdf<-dat[cc,]
# m1
m1<-lm(total_cup_points~aroma+flavor+aftertaste+acidity+body+balance+cupper_points,data=fdf)
# m2 grades_altitude
le<-logelev[cc]; le[!is.finite(le)]<-mean(le[is.finite(le)]); fdf2<-fdf; fdf2$logelev<-le
m2<-lm(total_cup_points~aroma+flavor+aftertaste+acidity+body+balance+cupper_points+logelev,data=fdf2)
# m3 grades_defects
imp<-function(x){x[is.na(x)]<-mean(x,na.rm=TRUE);x}
fdf3<-fdf
fdf3$l1<-log1p(imp(fdf$category_one_defects)); fdf3$l2<-log1p(imp(fdf$category_two_defects)); fdf3$lq<-log1p(imp(fdf$quakers))
m3<-lm(total_cup_points~aroma+flavor+aftertaste+acidity+body+balance+cupper_points+l1+l2+lq,data=fdf3)
cat(sprintf("AIC grades_only = %.4f (n=%d,p=%d)\n",AIC(m1),nobs(m1),length(coef(m1))))
cat(sprintf("AIC grades_altitude = %.4f (n=%d,p=%d)\n",AIC(m2),nobs(m2),length(coef(m2))))
cat(sprintf("AIC grades_defects = %.4f (n=%d,p=%d)\n",AIC(m3),nobs(m3),length(coef(m3))))
' AIC grades_only = 4459.3615 (n=1338,p=8) AIC grades_altitude = 4461.2923 (n=1338,p=9) AIC grades_defects = 4418.6801 (n=1338,p=11)
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
grades10 <- c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")
dat <- raw %>% filter(rowSums(raw[,grades10]==0) != 10)
g7 <- c("aroma","flavor","aftertaste","acidity","body","balance","cupper_points")
G<-as.matrix(dat[,g7]); cat("all complete on 7 grades:", all(complete.cases(G)), "\n")
imp<-function(x){x[is.na(x)]<-mean(x,na.rm=TRUE);x}
fdf<-dat
fdf$l1<-log1p(imp(dat$category_one_defects)); fdf$l2<-log1p(imp(dat$category_two_defects)); fdf$lq<-log1p(imp(dat$quakers))
m3<-lm(total_cup_points~aroma+flavor+aftertaste+acidity+body+balance+cupper_points+l1+l2+lq,data=fdf)
n<-nobs(m3); p<-length(coef(m3))
lev<-hatvalues(m3); ck<-cooks.distance(m3)
lev_thr<-2*p/n; ck_thr<-4/n
hl<-lev>lev_thr; hc<-ck>ck_thr
cat(sprintf("n=%d p=%d lev_thr=%.6f ck_thr=%.6f\n",n,p,lev_thr,ck_thr))
cat(sprintf("n_high_lev=%d n_high_cook=%d n_both=%d\n",sum(hl),sum(hc),sum(hl&hc)))
top10<-order(-ck)[1:10]-1 # 0-indexed
cat("top10 cook row_ids (0-idx):", paste(top10,collapse=","), "\n")
'all complete on 7 grades: TRUE n=1338 p=11 lev_thr=0.016442 ck_thr=0.002990 n_high_lev=110 n_high_cook=66 n_both=35 top10 cook row_ids (0-idx): 1309,1308,1333,1295,1306,1260,1307,1302,1291,1259
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
grades10 <- c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")
dat <- raw %>% filter(rowSums(raw[,grades10]==0) != 10)
ct <- dat %>% filter(!is.na(country_of_origin)) %>%
group_by(country=country_of_origin) %>%
summarise(n=n(), raw_mean=mean(total_cup_points), trimmed_mean=mean(total_cup_points,trim=0.1), .groups="drop") %>%
filter(n>=10)
ct$raw_rank <- rank(-ct$raw_mean, ties.method="min")
ct$trimmed_rank <- rank(-ct$trimmed_mean, ties.method="min")
ct$chg <- abs(ct$raw_rank - ct$trimmed_rank)
ct <- ct %>% arrange(raw_rank)
print(as.data.frame(ct), right=FALSE, digits=5)
cat("\nn_countries:",nrow(ct)," n_changes_ge_2:",sum(ct$chg>=2)," max_change:",max(ct$chg),"\n")
'country n raw_mean trimmed_mean raw_rank trimmed_rank 1 Ethiopia 44 85.484 85.518 1 1 2 United States 10 84.433 84.905 2 2 3 Kenya 25 84.310 84.508 3 3 4 Uganda 36 83.452 83.448 4 4 5 Colombia 183 83.107 83.247 5 5 6 El Salvador 21 83.053 83.109 6 6 7 China 16 82.927 82.971 7 8 8 Costa Rica 51 82.789 83.020 8 7 9 Thailand 32 82.574 82.619 9 10 10 Indonesia 20 82.566 82.774 10 9 11 Peru 10 82.526 82.439 11 12 12 Brazil 132 82.406 82.513 12 11 13 Tanzania, United Republic Of 40 82.370 82.272 13 13 14 Taiwan 75 82.001 81.998 14 16 15 Guatemala 181 81.847 82.168 15 14 16 United States (Hawaii) 73 81.820 82.076 16 15 17 Malawi 11 81.712 81.712 17 17 18 India 14 81.083 81.382 18 19 19 Mexico 236 80.890 81.246 19 20 20 Honduras 52 80.884 81.542 20 18 21 Nicaragua 26 80.458 80.894 21 21 chg 1 0 2 0 3 0 4 0 5 0 6 0 7 1 8 1 9 1 10 1 11 1 12 1 13 0 14 2 15 1 16 1 17 0 18 1 19 1 20 2 21 0 n_countries: 21 n_changes_ge_2: 2 max_change: 2
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(dplyr); library(stringr)})
raw <- read_csv("data/coffee_ratings.csv", show_col_types = FALSE)
grades10 <- c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")
dat <- raw %>% filter(rowSums(raw[,grades10]==0) != 10)
g7 <- c("aroma","flavor","aftertaste","acidity","body","balance","cupper_points")
# flags
alt <- dat$altitude_mean_meters
la <- log10(alt[!is.na(alt)&alt>0]); q<-quantile(la,c(.25,.75)); iqr<-q[2]-q[1]
loM<-10^(q[1]-3*iqr); hiM<-10^(q[2]+3*iqr)
flag_alt<-!is.na(alt)&(alt<loM|alt>hiM)
G<-as.matrix(dat[,g7]); mu<-colMeans(G); S<-cov(G); md2<-mahalanobis(G,mu,S)
flag_mv<-md2>qchisq(0.999,df=7)
imp<-function(x){x[is.na(x)]<-mean(x,na.rm=TRUE);x}
fdf<-dat; fdf$l1<-log1p(imp(dat$category_one_defects)); fdf$l2<-log1p(imp(dat$category_two_defects)); fdf$lq<-log1p(imp(dat$quakers))
m3<-lm(total_cup_points~aroma+flavor+aftertaste+acidity+body+balance+cupper_points+l1+l2+lq,data=fdf)
n<-nobs(m3);p<-length(coef(m3)); hl<-hatvalues(m3)>2*p/n; hc<-cooks.distance(m3)>4/n
comp <- flag_alt | flag_mv | (hl & hc)
cat("composite n:", sum(comp), "\n")
f <- total_cup_points~aroma+flavor+aftertaste+acidity+body+balance+cupper_points
mf<-lm(f,data=dat); md<-lm(f,data=dat[!comp,])
cf<-unname(coef(mf)["flavor"]); cd<-unname(coef(md)["flavor"])
cat(sprintf("coef_full=%.6f coef_dropped=%.6f delta_pct=%.6f\n",cf,cd,100*(cd-cf)/cf))
# bootstrap
set.seed(20260512); B<-500; nn<-nrow(dat); deltas<-c()
for(b in 1:B){ idx<-sample.int(nn,nn,replace=TRUE); db<-dat[idx,]; cb<-comp[idx]
dd<-db[!cb,]; if(nrow(dd)<10) next
r<-tryCatch({fb<-lm(f,data=db); dbf<-lm(f,data=dd)
100*(unname(coef(dbf)["flavor"])-unname(coef(fb)["flavor"]))/unname(coef(fb)["flavor"])},error=function(e)NA)
if(is.na(r)||!is.finite(r)) next; deltas<-c(deltas,r)}
ci<-quantile(deltas,c(.025,.975))
cat(sprintf("n_boot=%d CI=[%.4f, %.4f]\n",length(deltas),ci[1],ci[2]))
'composite n: 105 coef_full=2.085398 coef_dropped=1.652728 delta_pct=-20.747640 n_boot=500 CI=[-44.3281, 6.2470]
command: create
path: /app/analysis.R
file_text: #!/usr/bin/env Rscript
# Coffee Quality Outlier Audit -- shape-aware rewrite
# ===================================================
# Each column is audited with a method matched to its distribution instead of
# the draft's off-the-shelf summaries. This single script reproduces every
# artifact in /app/outputs from a clean directory.
suppressPackageStartupMessages({
library(dplyr)
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)
GRADES10 <- c("aroma", "flavor", "aftertaste", "acidity", "body", "balance",
"uniformity", "clean_cup", "sweetness", "cupper_points")
G7 <- c("aroma", "flavor", "aftertaste", "acidity", "body", "balance",
"cupper_points")
## ---------------------------------------------------------------------------
## 1. Sentinel drop: withdrawn lot with every grade == 0
## ---------------------------------------------------------------------------
all_zero <- rowSums(raw[, GRADES10] == 0) == length(GRADES10)
dat <- raw[!all_zero, , 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), then unit-slip recovery
## ---------------------------------------------------------------------------
alt <- dat$altitude_mean_meters
log_alt_pos <- log10(alt[!is.na(alt) & alt > 0])
qa <- quantile(log_alt_pos, c(0.25, 0.75))
iqr_a <- qa[[2]] - qa[[1]]
lo_log <- qa[[1]] - 3 * iqr_a
hi_log <- qa[[2]] + 3 * iqr_a
lo_m <- 10^lo_log
hi_m <- 10^hi_log
flag_alt <- !is.na(alt) & (alt < lo_m | alt > hi_m)
# Recover decimal-displacement unit errors from the raw `altitude` string:
# test the first numeric token under /10, then /100, then as-is; keep the
# first candidate that lands inside the fence.
first_numeric_token <- function(s) as.numeric(str_extract(s, "[0-9]+([.][0-9]+)?"))
alt_corrected <- rep(NA_real_, n_after)
for (r in which(flag_alt)) {
tok <- first_numeric_token(dat$altitude[r])
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[r] <- cand
break
}
}
}
n_flag_alt <- sum(flag_alt)
n_unit_corrected <- sum(!is.na(alt_corrected))
## ---------------------------------------------------------------------------
## 3. Defects: 99th-percentile upper-tail rule for zero-inflated counts
## ---------------------------------------------------------------------------
defect_tail <- function(x) {
thr <- unname(quantile(x, 0.99, na.rm = TRUE)) # sparse upper tail (~top 1%)
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[, G7])
g_complete <- complete.cases(G)
mu <- colMeans(G[g_complete, , drop = FALSE])
S <- cov(G[g_complete, , drop = FALSE])
md2 <- rep(NA_real_, n_after)
md2[g_complete] <- mahalanobis(G[g_complete, , drop = FALSE], mu, S)
md2_thresh <- qchisq(0.999, df = length(G7))
flag_mv <- !is.na(md2) & md2 > md2_thresh
n_flag_mv <- sum(flag_mv)
## ---------------------------------------------------------------------------
## 5. Influence: AIC model selection, then leverage AND Cook's D
## -----------------------------------------------
…[truncated]Existing file was successfully overwritten at /app/analysis.R
cd /app && rm -rf outputs && mkdir -p outputs && Rscript analysis.R && echo "---FILES---" && ls -la outputs/
[stderr] Error: unexpected string constant in: "modes rather than re-flagging the same lots. '" Execution halted
cd /app && sed -i "s/Cook''s/Cook\\\\'s/g" analysis.R && grep -n "Cook" analysis.R
94:## 5. Influence: AIC model selection, then leverage AND Cook's D
147:# Ten largest Cook's D, reported as 0-indexed post-drop row ids.
208:## 8. Flag interactions (influence = leverage AND Cook)
260: influence = unbox("Lowest-finite-AIC OLS among grades_only/grades_altitude/grades_defects; flag leverage>2p/n AND Cook's D>4/n on the selected fit."),
377:(hat > 2p/n = %.4f) and high Cook\'s D (> 4/n = %.4f) separately and require
378:**both**: %d high-leverage, %d high-Cook, %d influential. n=%d, p=%d.
389:Composite drop = altitude OR multivariate OR (leverage AND Cook); defect
400:{altitude, multivariate, defect, influence=leverage&Cook} by how many fire percd /app && rm -rf outputs && mkdir -p outputs && Rscript analysis.R && echo "---FILES---" && ls -la outputs/
Audit complete: n_input=1339 n_after=1338 selected=grades_defects composite=105 ---FILES--- total 230 drwxr-sr-x 2 h2tagent h2tagent 4096 Jun 20 18:22 . drwxrws--- 4 h2tagent h2tagent 4096 Jun 20 18:22 .. -rw-r--r-- 1 h2tagent h2tagent 4132 Jun 20 18:22 audit_memo.md -rw-r--r-- 1 h2tagent h2tagent 137152 Jun 20 18:22 coffee_ols_selected.rds -rw-r--r-- 1 h2tagent h2tagent 78297 Jun 20 18:22 outlier_flags.csv -rw-r--r-- 1 h2tagent h2tagent 6776 Jun 20 18:22 outlier_report.json
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) over positive values, bounds back-transformed to meters; flagged rows retried for decimal-displacement slips on the first numeric token (/10, /100, as-is).",
"defects": "Per-column 99th-percentile upper-tail rule for mass-at-zero counts (flag value > P99); a row is a defect outlier if any of the three columns trips.",
"multivariate_grades": "Classical Mahalanobis distance (sample mean/covariance) on the 7 primary SCA attributes vs chi-squared 99.9% cutoff with df=7.",
"influence": "Lowest-finite-AIC OLS among grades_only/grades_altitude/grades_defects; flag leverage>2p/n AND Cook's D>4/n on the selected fit.",
"country_ranking": "Countries with n>=10 ranked by raw mean vs 10% trimmed mean of total_cup_points; count rank moves >=2."
},
"altitude": {
"lower_bound_m": 357.446289062499,
"upper_bound_m": 4923.8166791886,
"n_flagged": 51,
"n_unit_corrected": 7
},
"defects": {
"category_one_defects": {
"upper_threshold": 7.62999999999988,
"n_flagged": 14
},
"category_two_defects": {
"upper_threshold": 26.6299999999999,
"n_flagged": 14
},
"quakers": {
"upper_threshold": 5,
"n_flagged": 10
}
},
"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": 4418.68007429542,
"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": 85.5177777777778,
"raw_rank": 1,
"trimmed_rank": 1
},
{
"country": "United States",
"n": 10,
"raw_mean": 84.433,
"trimmed_mean": 84.905,
"raw_rank": 2,
"trimmed_rank": 2
},
{
"country": "Kenya",
"n": 25,
"raw_mean": 84.3096,
"trimmed_mean": 84.507619047619,
"raw_rank": 3,
"trimmed_rank": 3
},
{
"country": "Uganda",
"n": 36,
"raw_mean": 83.4519444444445,
"trimmed_mean": 83.448,
"raw_rank": 4,
"trimmed_rank": 4
},
{
"country": "Colombia",
"n": 183,
"raw_mean": 83.1065573770492,
"trimmed_mean": 83.2466666666667,
"raw_rank": 5,
"trimmed_rank": 5
},
{
"country": "El Salvador",
"n": 21,
"raw_mean": 83.0528571428571,
"trimmed_mean": 83.1094117647059,
"raw_rank": 6,
"trimmed_rank": 6
},
{
"country": "China",
"n": 16,
"raw_mean": 82.9275,
"trimmed_mean": 82.9707142857143,
"raw_rank": 7,
"trimmed_rank": 8
},
{
"country": "Costa Rica",
"n": 51,
"raw_mean": 82.7890196078431,
"trimmed_mean": 83.02,
"raw_rank": 8,
"trimmed_rank": 7
},
{
"country": "Thailand",
"n": 32,
"raw_mean": 82.57375,
"trimmed_mean": 82.6192307692308,
"raw_rank": 9,
"trimmed_rank": 10
},
{
"country": "Indonesia",
"n": 20,
"raw_mean": 82.5655,
"trimmed_mean": 82.77375,
"raw_rank": 10,
"trimmed_rank": 9
},
{
"country": "Pe
…[truncated]cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(jsonlite)})
j <- fromJSON("outputs/outlier_report.json")
csv <- read_csv("outputs/outlier_flags.csv", show_col_types = FALSE)
cat("=== CSV shape ===\n"); cat("nrow:", nrow(csv), " (expect", j$n_after_zero_score_drop, ")\n")
cat("row_id 0..n-1 ordered:", all(csv$row_id == 0:(nrow(csv)-1)), "\n\n")
cat("=== Reload rds and reconcile influence ===\n")
fit <- readRDS("outputs/coffee_ols_selected.rds")
cat("class:", class(fit), " nobs:", nobs(fit), " p:", length(coef(fit)), "\n")
n <- nobs(fit); p <- length(coef(fit))
lev <- hatvalues(fit); ck <- cooks.distance(fit)
hl <- lev > 2*p/n; hc <- ck > 4/n
cat("recomputed n_high_lev:", sum(hl), " vs json:", j$influence$n_high_leverage, "\n")
cat("recomputed n_high_cook:", sum(hc), " vs json:", j$influence$n_high_cook, "\n")
cat("recomputed n_both:", sum(hl&hc), " vs json:", j$influence$n_both, "\n")
# csv reconcile (all rows complete so fit order == csv order)
cat("csv high_leverage sum:", sum(csv$high_leverage_flag), " high_cook sum:", sum(csv$high_cook_flag), "\n")
cat("match hl vec:", all(hl == csv$high_leverage_flag), " match hc vec:", all(hc == csv$high_cook_flag), "\n")
top10 <- order(-ck)[1:10]-1
cat("recomputed top10 cook ids:", paste(top10,collapse=","), "\n")
cat("json top10 cook ids: ", paste(j$influence$top10_cook_row_ids,collapse=","), "\n\n")
cat("=== Defect/alt/mv CSV vs JSON counts ===\n")
cat("alt flag csv:", sum(csv$altitude_outlier_flag), " json:", j$altitude$n_flagged, "\n")
cat("alt corrected non-NA csv:", sum(!is.na(csv$altitude_corrected_m)), " json:", j$altitude$n_unit_corrected, "\n")
cat("defect flag csv:", sum(csv$defect_outlier_flag), "\n")
cat("mv flag csv:", sum(csv$multivariate_grade_outlier_flag), " json:", j$multivariate_grades$n_flagged, "\n")
cat("composite csv:", sum(csv$composite_drop_flag), "\n")
'=== CSV shape === nrow: 1338 (expect 1338 ) row_id 0..n-1 ordered: TRUE === Reload rds and reconcile influence === class: lm nobs: 1338 p: 11 recomputed n_high_lev: 110 vs json: 110 recomputed n_high_cook: 66 vs json: 66 recomputed n_both: 35 vs json: 35 csv high_leverage sum: 110 high_cook sum: 66 match hl vec: TRUE match hc vec: TRUE recomputed top10 cook ids: 1309,1308,1333,1295,1306,1260,1307,1302,1291,1259 json top10 cook ids: 1309,1308,1333,1295,1306,1260,1307,1302,1291,1259 === Defect/alt/mv CSV vs JSON counts === alt flag csv: 51 json: 51 alt corrected non-NA csv: 7 json: 7 defect flag csv: 35 mv flag csv: 40 json: 40 composite csv: 105
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(jsonlite); library(dplyr)})
j <- fromJSON("outputs/outlier_report.json")
csv <- read_csv("outputs/outlier_flags.csv", show_col_types = FALSE)
cat("=== composite == alt | mv | (lev & cook) ? ===\n")
comp_check <- csv$altitude_outlier_flag | csv$multivariate_grade_outlier_flag | (csv$high_leverage_flag & csv$high_cook_flag)
cat(all(comp_check == csv$composite_drop_flag), " (defects excluded)\n")
cat("composite count:", sum(csv$composite_drop_flag), "\n\n")
cat("=== corrected altitudes within fence [lo,hi] ===\n")
lo <- j$altitude$lower_bound_m; hi <- j$altitude$upper_bound_m
cv <- csv$altitude_corrected_m[!is.na(csv$altitude_corrected_m)]
cat("all in-bounds:", all(cv>=lo & cv<=hi), "\n")
cat("values:", paste(round(cv,3),collapse=", "), "\n")
cat("all corrected rows are altitude-flagged:", all(csv$altitude_outlier_flag[!is.na(csv$altitude_corrected_m)]), "\n\n")
cat("=== defect flag = value > P99 per column ===\n")
raw <- read_csv("data/coffee_ratings.csv", show_col_types=FALSE)
g10 <- c("aroma","flavor","aftertaste","acidity","body","balance","uniformity","clean_cup","sweetness","cupper_points")
dat <- raw %>% filter(rowSums(raw[,g10]==0)!=10)
for(col in c("category_one_defects","category_two_defects","quakers")){
thr <- j$defects[[col]]$upper_threshold
x <- dat[[col]]; fl <- !is.na(x) & x>thr
cat(sprintf("%-22s thr=%.4f n=%d (json %d) match=%s\n", col, thr, sum(fl), j$defects[[col]]$n_flagged, sum(fl)==j$defects[[col]]$n_flagged))
}
cat("\n=== flag interaction arithmetic ===\n")
fi <- j$flag_interactions
cat("sum 0..4 flags:", fi$n_with_zero_flags+fi$n_with_one_flag+fi$n_with_two_flags+fi$n_with_three_flags+fi$n_with_four_flags, "(expect 1338)\n")
A<-csv$altitude_outlier_flag; M<-csv$multivariate_grade_outlier_flag; D<-csv$defect_outlier_flag; I<-csv$high_leverage_flag & csv$high_cook_flag
cat("defect total:", sum(D), " defect_only:", sum(D & !A & !M & !I), "(json", fi$n_defect_only,")\n")
cat("mv_and_influence:", sum(M&I), "(json", fi$n_mv_and_influence,")\n")
cat("alt_mv_influence:", sum(A&M&I), "(json", fi$n_alt_mv_influence,")\n")
'=== composite == alt | mv | (lev & cook) ? === TRUE (defects excluded) composite count: 105 === corrected altitudes within fence [lo,hi] === all in-bounds: TRUE values: 1100, 518, 518, 518, 1901.64, 1100, 1901.64 all corrected rows are altitude-flagged: TRUE === defect flag = value > P99 per column === category_one_defects thr=7.6300 n=14 (json 14) match=TRUE category_two_defects thr=26.6300 n=14 (json 14) match=TRUE quakers thr=5.0000 n=10 (json 10) match=TRUE === flag interaction arithmetic === sum 0..4 flags: 1338 (expect 1338) defect total: 35 defect_only: 25 (json 25 ) mv_and_influence: 13 (json 13 ) alt_mv_influence: 1 (json 1 )
cd /app && echo "=== HEADINGS ===" && grep -n '^#' outputs/audit_memo.md && echo "" && echo "=== WORD COUNT ===" && wc -w < outputs/audit_memo.md && echo "" && echo "=== leftover format specifiers (should be none) ===" && grep -nE '%[0-9.]*[dfs]|%%' outputs/audit_memo.md || echo "none found"
=== HEADINGS === 1:# Coffee Quality Outlier Audit -- Method Memo 6:## Data prep 15:## Altitude 27:## Defects 36:## Multivariate grades 47:## Influence 56:## Country ranking 63:## Sensitivity 73:## Composite flags === WORD COUNT === 637 === leftover format specifiers (should be none) === none found
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 this column shape and what replaced it.
## Data prep
One withdrawn lot carries every one of the ten grades as 0 (a sentinel, not a
real 0-point cup). The draft filtered on `total_cup_points > 0`, which happens
to remove it but encodes the wrong intent. I drop on the explicit condition
"all ten grades == 0" so the rule survives future data where a genuine lot
could score low. Input = 1339 lots; after the drop = 1338 lots. Row ids are
0-indexed in post-drop input order and reused everywhere downstream.
## Altitude
`altitude_mean_meters` is strongly right-skewed (median ~1310 m, max 190164 m),
so a meter-scale Tukey fence is dominated by the long tail and misplaces the
bounds. I compute the Tukey fence with k=3 on `log10` of the positive values,
then back-transform: **[357.4, 4923.8] m**. 51 rows fall outside. Many low flags
are decimal-displacement typos in the raw `altitude` string, so for each flagged
row I take the first numeric token and try /10, /100, then as-is, keeping the
first candidate inside the fence. That recovers **7** lots (e.g. "11000
metros" -> 1100; "190164"/"1901.64" -> 1901.64; 518 ft strings -> 518);
unrecoverable rows keep `altitude_corrected_m = NA`.
## Defects
`category_one_defects`, `category_two_defects` and `quakers` are mass-at-zero
(85%, 28%, 93% zeros) with a thin upper tail. A raw Tukey fence has Q1=Q3=0,
so IQR=0 and the "upper fence" collapses to 0 -- the draft flags every nonzero
lot (~15-70%), which is not "extreme". I instead flag counts strictly above the
per-column 99th percentile (thresholds 7.63 / 26.63 / 5), which isolates the
genuine ~top 1% tail. A lot is a defect outlier if any column trips.
## Multivariate grades
The draft ran Mahalanobis on all ten grades, including `uniformity`,
`clean_cup` and `sweetness` -- near-constant columns pinned at 10 (variance
0.24/0.51/0.31 with heavy point mass). They inflate the covariance condition
number and make distances reflect cleanliness ties rather than joint flavor
structure. I restrict to the seven primary SCA attributes, use the classical
sample mean/covariance Mahalanobis distance, and threshold at the chi-squared
99.9% quantile with df=7 (24.322). That flags 40 joint outliers. (The task
specifies the classical estimator rather than a high-breakdown MCD.)
## Influence
`abs(rstandard) > 2` measures outlyingness in y, not *influence*, and ignores
the design. I fit three OLS specs on rows complete in the seven grades 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 = 0.0030) separately and require
**both**: 110 high-leverage, 66 high-Cook, 35 influential. n=1338, p=11.
## Country ranking
Raw per-country means have no robustness check, so one odd lot can move a
country. For countries with n>=10 I compare the raw mean against a 10% trimmed
mean of `total_cup_points` and rank both. 21 countries qualify; 2 move >=2
positions (max shift 2), showing which rankings are fragile to tail lots.
## Sensitivity
Composite drop = altitude OR multivariate OR (leverage AND Cook); defect
outliers are excluded by design. Refitting grades_only moves the `flavor`
coefficient from 2.0854 to 1.6527 (-20.75%). A percentile bootstrap (seed 20260512,
B=500, resampling post-drop lots and carrying the original composite flags,
skipping replicates with <10 dropped-subset rows or failed fits) gives a 95%
CI of [-44.33%, 6.25%] over 500 successful replicates -- a genuine resampling
interval, not a parametric Wald substitute.
## Composite flags
Finally I cross-tabulate the four independent flag families
{altitude, multivariate, defect, influence=leverage&Cook} by how many fire per
lot plus their pairwise/triple intersections. Most lots carry zero f
…[truncated]cd /app && rm -rf outputs && mkdir outputs && Rscript analysis.R >/dev/null 2>&1 && cp outputs/outlier_report.json /tmp/run1.json && cp outputs/outlier_flags.csv /tmp/run1.csv && rm -rf outputs && mkdir outputs && Rscript analysis.R >/dev/null 2>&1 && diff /tmp/run1.json outputs/outlier_report.json && echo "JSON identical across runs" && diff /tmp/run1.csv outputs/outlier_flags.csv && echo "CSV identical across runs"
JSON identical across runs CSV identical across runs
cd /app && Rscript -e '
library(jsonlite)
j <- fromJSON("outputs/outlier_report.json", simplifyVector=FALSE)
errs <- c()
req_top <- c("n_input","n_after_zero_score_drop","methods","altitude","defects","multivariate_grades","influence","country_ranking","sensitivity","flag_interactions")
for(k in req_top) if(is.null(j[[k]])) errs<-c(errs,paste("missing top:",k))
for(k in c("altitude","defects","multivariate_grades","influence","country_ranking")) if(is.null(j$methods[[k]])) errs<-c(errs,paste("missing methods:",k))
for(k in c("lower_bound_m","upper_bound_m","n_flagged","n_unit_corrected")) if(is.null(j$altitude[[k]])) errs<-c(errs,paste("missing altitude:",k))
for(col in c("category_one_defects","category_two_defects","quakers")){ for(k in c("upper_threshold","n_flagged")) if(is.null(j$defects[[col]][[k]])) errs<-c(errs,paste("missing defects:",col,k)) }
for(k in c("columns_used","threshold","n_flagged")) if(is.null(j$multivariate_grades[[k]])) errs<-c(errs,paste("missing mv:",k))
for(k in c("selected_model","aic_grades_only","aic_grades_altitude","aic_grades_defects","leverage_threshold","cook_threshold","n_high_leverage","n_high_cook","n_both","top10_cook_row_ids")) if(is.null(j$influence[[k]])) errs<-c(errs,paste("missing influence:",k))
for(k in c("n_countries_evaluated","n_rank_changes_ge_2","max_abs_rank_change","per_country")) if(is.null(j$country_ranking[[k]])) errs<-c(errs,paste("missing country:",k))
for(k in c("coef_flavor_full","coef_flavor_dropped","delta_pct","delta_pct_ci_lower","delta_pct_ci_upper","n_bootstrap_replicates")) if(is.null(j$sensitivity[[k]])) errs<-c(errs,paste("missing sens:",k))
fik <- 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")
for(k in fik) if(is.null(j$flag_interactions[[k]])) errs<-c(errs,paste("missing fi:",k))
# type checks
stopifnot(is.numeric(j$n_input), length(j$influence$top10_cook_row_ids)==10, length(j$multivariate_grades$columns_used)==7)
cat("selected_model valid:", j$influence$selected_model %in% c("grades_only","grades_altitude","grades_defects"), "\n")
cat("top10 length:", length(j$influence$top10_cook_row_ids), " mv cols:", length(j$multivariate_grades$columns_used), "\n")
cat("per_country rows:", length(j$country_ranking$per_country), "\n")
pc1 <- j$country_ranking$per_country[[1]]
cat("per_country[1] keys:", paste(names(pc1),collapse=","), "\n")
if(length(errs)==0) cat("\nSCHEMA OK: all required keys present\n") else { cat("\nERRORS:\n"); print(errs) }
'selected_model valid: TRUE top10 length: 10 mv cols: 7 per_country rows: 21 per_country[1] keys: country,n,raw_mean,trimmed_mean,raw_rank,trimmed_rank SCHEMA OK: all required keys present
cd /app && Rscript -e '
library(jsonlite)
j <- fromJSON("outputs/outlier_report.json")
pc <- j$country_ranking$per_country
# re-derive ranks from reported means
rr <- rank(-pc$raw_mean, ties.method="min"); tr <- rank(-pc$trimmed_mean, ties.method="min")
cat("raw_rank consistent:", all(rr==pc$raw_rank), " trimmed_rank consistent:", all(tr==pc$trimmed_rank), "\n")
chg <- abs(pc$raw_rank - pc$trimmed_rank)
cat("n_rank_changes_ge_2 re-derived:", sum(chg>=2), " json:", j$country_ranking$n_rank_changes_ge_2, "\n")
cat("max_abs_rank_change re-derived:", max(chg), " json:", j$country_ranking$max_abs_rank_change, "\n")
cat("all n>=10:", all(pc$n>=10), "\n")
cat("movers (>=2):", paste(pc$country[chg>=2], collapse=", "), "\n")
' && echo "--- fresh-session rds load ---" && env -i Rscript -e '
fit <- readRDS("/app/outputs/coffee_ols_selected.rds")
cat("reloaded class:", class(fit), "terms:", length(attr(terms(fit),"term.labels")), "predictors\n")
cat("cooks+hat computable:", is.numeric(cooks.distance(fit)) && is.numeric(hatvalues(fit)), "\n")
cat("formula:", deparse(formula(fit)), "\n")'raw_rank consistent: TRUE trimmed_rank consistent: TRUE n_rank_changes_ge_2 re-derived: 2 json: 2 max_abs_rank_change re-derived: 2 json: 2 all n>=10: TRUE movers (>=2): Taiwan, Honduras --- fresh-session rds load --- [stderr] env: ‘Rscript’: No such file or directory
cd /tmp && Rscript --vanilla -e '
fit <- readRDS("/app/outputs/coffee_ols_selected.rds")
cat("reloaded class:", class(fit), "\n")
cat("n predictors:", length(attr(terms(fit),"term.labels")), "\n")
cat("formula:", deparse(formula(fit)), "\n")
cd <- cooks.distance(fit); hv <- hatvalues(fit)
cat("cooks+hat computable:", is.numeric(cd) && is.numeric(hv) && length(cd)==1338, "\n")
cat("model frame rows:", nrow(model.frame(fit)), " cols:", ncol(model.frame(fit)), "\n")
cat("response in frame:", "total_cup_points" %in% names(model.frame(fit)), "\n")
'reloaded class: lm n predictors: 10 formula: total_cup_points ~ aroma + flavor + aftertaste + acidity + body + balance + cupper_points + ld1 + ld2 + ldq cooks+hat computable: TRUE model frame rows: 1338 cols: 11 response in frame: TRUE
cd /app && echo "=== HEADER ===" && head -1 outputs/outlier_flags.csv && echo "" && echo "=== column count ===" && head -1 outputs/outlier_flags.csv | tr ',' '\n' | wc -l && echo "" && echo "=== rows with a corrected altitude ===" && awk -F, 'NR==1 || $5!="NA"' outputs/outlier_flags.csv | head -20 && echo "" && echo "=== dtype sanity: flag columns are TRUE/FALSE ===" && Rscript -e 'x<-read.csv("outputs/outlier_flags.csv"); cat("logical cols:", sapply(x[,c(4,6,7,8,9,10)], is.logical), "\n"); cat("NA count in corrected:", sum(is.na(x$altitude_corrected_m)), "non-NA:", sum(!is.na(x$altitude_corrected_m)), "\n")'=== 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 === column count === 10 === rows with a corrected altitude === 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 221,"Tanzania, United Republic Of",84.17,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 357,"Tanzania, United Republic Of",83.5,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 385,"Tanzania, United Republic Of",83.42,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 402,"Tanzania, United Republic Of",83.33,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 442,"Tanzania, United Republic Of",83.17,TRUE,NA,FALSE,FALSE,FALSE,FALSE,TRUE 454,"Tanzania, United Republic Of",83.17,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 462,"Tanzania, United Republic Of",83.17,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 543,Brazil,82.92,TRUE,1100,FALSE,FALSE,FALSE,FALSE,TRUE 572,"Tanzania, United Republic Of",82.83,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 584,Guatemala,82.75,TRUE,518,FALSE,FALSE,FALSE,FALSE,TRUE 615,"Tanzania, United Republic Of",82.67,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 616,"Tanzania, United Republic Of",82.67,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 641,"Tanzania, United Republic Of",82.58,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 659,"Tanzania, United Republic Of",82.5,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE 660,"Tanzania, United Republic Of",82.5,FALSE,NA,FALSE,FALSE,FALSE,FALSE,FALSE === dtype sanity: flag columns are TRUE/FALSE === logical cols: TRUE TRUE TRUE TRUE TRUE TRUE NA count in corrected: 1331 non-NA: 7
cd /app && Rscript -e ' library(jsonlite) # test how unbox(NA_real_) serializes with na="null" x <- list(a = unbox(NA_real_), b = unbox(3.5)) cat(toJSON(x, na="null", auto_unbox=FALSE), "\n") '
{"a":null,"b":3.5}command: str_replace path: /app/analysis.R old_str: aic_or_null <- function(x) if (is.finite(x)) unbox(as.numeric(x)) else NULL new_str: aic_or_null <- function(x) if (is.finite(x)) unbox(as.numeric(x)) else unbox(NA_real_)
The file /app/analysis.R has been edited successfully.
cd /app && rm -rf outputs && mkdir -p outputs && Rscript analysis.R && echo "" && echo "=== DELIVERABLES ===" && ls -la outputs/
Audit complete: n_input=1339 n_after=1338 selected=grades_defects composite=105 === DELIVERABLES === total 230 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 4132 Jun 20 18:24 audit_memo.md -rw-r--r-- 1 h2tagent h2tagent 137152 Jun 20 18:24 coffee_ols_selected.rds -rw-r--r-- 1 h2tagent h2tagent 78297 Jun 20 18:24 outlier_flags.csv -rw-r--r-- 1 h2tagent h2tagent 6776 Jun 20 18:24 outlier_report.json
cd /app && Rscript -e '
suppressPackageStartupMessages({library(readr); library(jsonlite)})
j <- fromJSON("outputs/outlier_report.json"); csv <- read_csv("outputs/outlier_flags.csv", show_col_types=FALSE)
fit <- readRDS("outputs/coffee_ols_selected.rds")
n<-nobs(fit); p<-length(coef(fit)); hl<-hatvalues(fit)>2*p/n; hc<-cooks.distance(fit)>4/n
pass <- c(
"n_input=1339" = j$n_input==1339,
"n_after=1338" = j$n_after_zero_score_drop==1338,
"csv rows=1338" = nrow(csv)==1338,
"row_id ordered" = all(csv$row_id==0:1337),
"alt flags match" = sum(csv$altitude_outlier_flag)==j$altitude$n_flagged,
"alt corrected=7" = sum(!is.na(csv$altitude_corrected_m))==j$altitude$n_unit_corrected,
"mv flags match" = sum(csv$multivariate_grade_outlier_flag)==j$multivariate_grades$n_flagged,
"mv cols=7" = length(j$multivariate_grades$columns_used)==7,
"selected=grades_defects" = j$influence$selected_model=="grades_defects",
"lev reproduces" = all(hl==csv$high_leverage_flag),
"cook reproduces" = all(hc==csv$high_cook_flag),
"n_both match" = sum(hl&hc)==j$influence$n_both,
"top10 len=10" = length(j$influence$top10_cook_row_ids)==10,
"composite formula" = all(csv$composite_drop_flag==(csv$altitude_outlier_flag|csv$multivariate_grade_outlier_flag|(csv$high_leverage_flag&csv$high_cook_flag))),
"defects not in composite" = !any(csv$composite_drop_flag & csv$defect_outlier_flag & !(csv$altitude_outlier_flag|csv$multivariate_grade_outlier_flag|(csv$high_leverage_flag&csv$high_cook_flag))),
"countries=21" = j$country_ranking$n_countries_evaluated==21,
"flag counts sum 1338" = (j$flag_interactions$n_with_zero_flags+j$flag_interactions$n_with_one_flag+j$flag_interactions$n_with_two_flags+j$flag_interactions$n_with_three_flags+j$flag_interactions$n_with_four_flags)==1338,
"bootstrap=500" = j$sensitivity$n_bootstrap_replicates==500,
"delta in CI" = j$sensitivity$delta_pct>=j$sensitivity$delta_pct_ci_lower && j$sensitivity$delta_pct<=j$sensitivity$delta_pct_ci_upper,
"3 finite AICs" = all(is.finite(c(j$influence$aic_grades_only,j$influence$aic_grades_altitude,j$influence$aic_grades_defects)))
)
for(nm in names(pass)) cat(sprintf("[%s] %s\n", ifelse(pass[[nm]],"PASS","FAIL"), nm))
cat("\nALL PASS:", all(unlist(pass)), "\n")
'[PASS] n_input=1339 [PASS] n_after=1338 [PASS] csv rows=1338 [PASS] row_id ordered [PASS] alt flags match [PASS] alt corrected=7 [PASS] mv flags match [PASS] mv cols=7 [PASS] selected=grades_defects [PASS] lev reproduces [PASS] cook reproduces [PASS] n_both match [PASS] top10 len=10 [PASS] composite formula [PASS] defects not in composite [PASS] countries=21 [PASS] flag counts sum 1338 [PASS] bootstrap=500 [PASS] delta in CI [PASS] 3 finite AICs ALL PASS: TRUE
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: coffee-ratings-outliers # attempt: 5 status: completed raw_score: 1.0 # reward rule: binary: score 1.0 == 1.0 # => reward = 1.0 (PASS) # # 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). ---------------------------------------------------------------------- [PASS] n_input=1339 [PASS] n_after=1338 [PASS] csv rows=1338 [PASS] row_id ordered [PASS] alt flags match [PASS] alt corrected=7 [PASS] mv flags match [PASS] mv cols=7 [PASS] selected=grades_defects [PASS] lev reproduces [PASS] cook reproduces [PASS] n_both match [PASS] top10 len=10 [PASS] composite formula [PASS] defects not in composite [PASS] countries=21 [PASS] flag counts sum 1338 [PASS] bootstrap=500 [PASS] delta in CI [PASS] 3 finite AICs ALL PASS: TRUE
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_7ae4ae530c8d4fee. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_7ae4ae530c8d4fee · verifier authoritative; classifier explanatory.