tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.ls -la /workspace/app/ /workspace/
/workspace/: total 19 drwxrws--- 4 h2tagent h2tagent 4096 Jun 1 08:53 . drwxr-xr-x 28 root root 4096 Jun 1 09:33 .. -rw-rw---- 1 h2tagent h2tagent 2821 Jun 1 08:20 analysis.R drwxrws--- 2 h2tagent h2tagent 4096 Jun 1 08:53 app drwxrws--- 2 h2tagent h2tagent 4096 Jun 1 08:53 output /workspace/app/: total 424 drwxrws--- 2 h2tagent h2tagent 4096 Jun 1 08:53 . drwxrws--- 4 h2tagent h2tagent 4096 Jun 1 08:53 .. -rw-rw---- 1 h2tagent h2tagent 1725 Jun 1 08:20 dataset_manifest.json -rw-rw---- 1 h2tagent h2tagent 423783 Jun 1 08:20 lgd_workouts_source.csv
cat /workspace/app/dataset_manifest.json
{
"dataset_name": "Defaulted-loan workout panel for Loss Given Default modelling",
"source": "Calibrated to Lending Club historical loan-level disclosures of defaulted accounts and their post-default workout ledger (https://www.lendingclub.com/info/statistics.action , historical bulk files).",
"license": "Synthetic but matches the Lending Club workout schema for grading.",
"columns": {
"loan_id": "Anonymous loan identifier.",
"grade": "Lending Club grade A-G assigned at origination.",
"purpose": "Borrower-stated loan purpose.",
"home_ownership": "RENT / OWN / MORTGAGE.",
"principal_at_default": "Outstanding principal at the moment of default (USD).",
"default_month": "Months from origination to default.",
"workout_months_observed": "Months of post-default observation in the panel.",
"time_to_resolution": "Months from default to terminal resolution (recovery curve flat-lines).",
"is_resolved": "1 if workout_months_observed >= time_to_resolution; else 0 (right-censored workout).",
"recovered_amount": "Cumulative dollars recovered as of the as-of date (USD).",
"annual_inc": "Borrower annual income at origination."
},
"definitions": {
"lgd": "LGD = 1 - recovered_amount / principal_at_default , only valid when is_resolved == 1.",
"censoring": "Rows with is_resolved == 0 are right-censored: their LGD is incompletely observed. Censored rows have partial recovery, so recovered_amount understates the true terminal recovery , a naive mean of recovered/principal is biased downward, and therefore LGD = 1 - recovered/principal is biased upward (naive overestimates true LGD). Restrict to resolved workouts or model the cure mechanism."
}
}cat /workspace/analysis.R
#!/usr/bin/env Rscript
# LGD analysis , DRAFT. Audit and finish.
suppressPackageStartupMessages({
library(readr); library(dplyr); library(tidyr); library(jsonlite); library(ggplot2)
})
LGD_PATH <- Sys.getenv("LGD_PATH", "/workspace/app/lgd_workouts_source.csv")
OUT <- Sys.getenv("LGD_OUTPUT_DIR", "/workspace/output")
dir.create(OUT, showWarnings = FALSE, recursive = TRUE)
df <- read_csv(LGD_PATH, show_col_types = FALSE)
# Naive LGD over all rows (this is the biased baseline)
df <- df %>% mutate(observed_lgd = 1 - recovered_amount / principal_at_default)
naive_lgd <- mean(df$observed_lgd)
# "corrected" , but the draft uses ALL rows again, just trimming outliers
corrected_lgd <- mean(df$observed_lgd[df$observed_lgd > 0.0 & df$observed_lgd < 1.0])
# bootstrap on the wrong (full) dataset , and only 200 reps
set.seed(20260606)
boot <- replicate(200, mean(sample(df$observed_lgd, replace = TRUE)))
ci_lo <- quantile(boot, 0.025); ci_hi <- quantile(boot, 0.975)
bg <- df %>% group_by(grade) %>% summarise(corrected_lgd = mean(observed_lgd), .groups = "drop")
bp <- df %>% group_by(purpose) %>% summarise(corrected_lgd = mean(observed_lgd), .groups = "drop")
panel <- df %>% group_by(grade, purpose) %>%
summarise(n_total = n(), n_resolved = sum(is_resolved),
naive_lgd = mean(observed_lgd),
corrected_lgd = mean(observed_lgd), .groups = "drop")
write_csv(panel, file.path(OUT, "lgd_panel.csv"))
bias_diff <- naive_lgd - corrected_lgd
direction <- if (abs(bias_diff) < 0.05) "close" else if (bias_diff > 0) "materially_overestimates" else "materially_underestimates"
metrics <- list(
data_summary = list(n_total = nrow(df), n_resolved = sum(df$is_resolved),
n_censored = nrow(df) - sum(df$is_resolved),
share_resolved = mean(df$is_resolved)),
naive_lgd = list(value = naive_lgd),
corrected_lgd = list(value = corrected_lgd),
bootstrap_ci = list(ci_low = unname(ci_lo), ci_high = unname(ci_hi)),
lgd_by_grade = setNames(as.list(bg$corrected_lgd), bg$grade),
lgd_by_purpose = setNames(as.list(bp$corrected_lgd), bp$purpose),
bias_assessment = list(naive_minus_corrected = bias_diff, direction = direction)
)
write_json(metrics, file.path(OUT, "metrics.json"), auto_unbox = TRUE,
digits = 6, pretty = TRUE)
cat(paste0("Naive LGD: ", round(naive_lgd, 3),
"\nCorrected: ", round(corrected_lgd, 3),
"\nDirection: ", direction, "\n"),
file = file.path(OUT, "insights.md"))
write_json(list(figures = list()), file.path(OUT, "plot_contract.json"),
auto_unbox = TRUE, pretty = TRUE)
png(file.path(OUT, "fig1_lgd_by_grade.png"), width = 600, height = 400); plot.new(); dev.off()
png(file.path(OUT, "fig2_naive_vs_corrected.png"), width = 600, height = 400); plot.new(); dev.off()head -5 /workspace/app/lgd_workouts_source.csv; echo '---'; wc -l /workspace/app/lgd_workouts_source.csv
loan_id,grade,purpose,home_ownership,principal_at_default,default_month,workout_months_observed,time_to_resolution,is_resolved,recovered_amount,annual_inc LC0000000,C,small_business,MORTGAGE,9248.9,12,16,8,1,2351.97,37618.14 LC0000001,A,debt_consolidation,RENT,14442.6,10,25,15,1,7783.99,135186.3 LC0000002,E,debt_consolidation,RENT,22782.58,42,21,10,1,5707.52,57938.72 LC0000003,C,debt_consolidation,MORTGAGE,10656.93,23,24,14,1,4049.36,110952.14 --- 6001 /workspace/app/lgd_workouts_source.csv
Rscript -e '
df <- readr::read_csv("/workspace/app/lgd_workouts_source.csv", show_col_types = FALSE)
print(summary(df))
cat("\n--- grades ---\n"); print(table(df$grade))
cat("\n--- purposes ---\n"); print(table(df$purpose))
cat("\n--- is_resolved ---\n"); print(table(df$is_resolved))
cat("\n--- lgd distribution (resolved only) ---\n")
resolved <- df[df$is_resolved==1,]
lgd <- 1 - resolved$recovered_amount/resolved$principal_at_default
print(summary(lgd))
cat("\n--- naive over all ---\n")
print(summary(1 - df$recovered_amount/df$principal_at_default))
'
loan_id grade purpose home_ownership
Length:6000 Length:6000 Length:6000 Length:6000
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
principal_at_default default_month workout_months_observed
Min. : 2505 Min. : 6.00 Min. : 0.00
1st Qu.:10549 1st Qu.:19.00 1st Qu.: 8.00
Median :18464 Median :33.00 Median :18.00
Mean :18621 Mean :32.78 Mean :17.52
3rd Qu.:26828 3rd Qu.:46.00 3rd Qu.:27.00
Max. :34995 Max. :59.00 Max. :35.00
time_to_resolution is_resolved recovered_amount annual_inc
Min. : 1.00 Min. :0.0000 Min. : 0 Min. : 10772
1st Qu.:10.00 1st Qu.:0.0000 1st Qu.: 1824 1st Qu.: 44560
Median :16.00 Median :1.0000 Median : 4529 Median : 59808
Mean :17.37 Mean :0.5308 Mean : 6013 Mean : 66712
3rd Qu.:23.00 3rd Qu.:1.0000 3rd Qu.: 8561 3rd Qu.: 81123
Max. :60.00 Max. :1.0000 Max. :33425 Max. :361202
--- grades ---
A B C D E F G
1140 1607 1389 992 494 242 136
--- purposes ---
credit_card debt_consolidation home_improvement major_purchase
1210 2926 601 341
other small_business
506 416
--- is_resolved ---
0 1
2815 3185
--- lgd distribution (resolved only) ---
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0008458 0.4980549 0.6027807 0.5651259 0.6946984 0.9800002
--- naive over all ---
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0008458 0.5651217 0.6944732 0.6770570 0.8469163 1.0000000command: create
path: /workspace/analysis.R
file_text: #!/usr/bin/env Rscript
# LGD analysis , Loss Given Default read on defaulted workouts.
# Naive mean over ALL rows is biased upward (censored rows understate
# eventual recovery). The corrected read restricts to resolved workouts.
suppressPackageStartupMessages({
library(readr)
library(dplyr)
library(tidyr)
library(jsonlite)
library(ggplot2)
})
LGD_PATH <- Sys.getenv("LGD_PATH", "/workspace/app/lgd_workouts_source.csv")
OUT <- Sys.getenv("LGD_OUTPUT_DIR", "/workspace/output")
dir.create(OUT, showWarnings = FALSE, recursive = TRUE)
df <- read_csv(LGD_PATH, show_col_types = FALSE)
# Per-loan observed LGD ratio. Only meaningful as a terminal LGD when
# is_resolved == 1; for censored rows it understates the final recovery
# (hence overstates LGD).
df <- df %>%
mutate(observed_lgd = 1 - recovered_amount / principal_at_default)
resolved <- df %>% filter(is_resolved == 1)
# --- Headline scalars ------------------------------------------------------
naive_lgd <- mean(df$observed_lgd) # biased baseline, all rows
corrected_lgd <- mean(resolved$observed_lgd) # resolved-only population
# --- Paired bootstrap on the resolved rows, B = 1000 -----------------------
B <- 1000L
resolved_lgd <- resolved$observed_lgd
n_res <- length(resolved_lgd)
set.seed(20260606)
boot_means <- numeric(B)
for (b in seq_len(B)) {
idx <- sample.int(n_res, size = n_res, replace = TRUE)
boot_means[b] <- mean(resolved_lgd[idx])
}
ci_low <- unname(quantile(boot_means, 0.025))
ci_high <- unname(quantile(boot_means, 0.975))
# --- Per grade / per purpose (corrected / resolved-only) -------------------
bg <- resolved %>%
group_by(grade) %>%
summarise(corrected_lgd = mean(observed_lgd), .groups = "drop") %>%
arrange(grade)
bp <- resolved %>%
group_by(purpose) %>%
summarise(corrected_lgd = mean(observed_lgd), .groups = "drop") %>%
arrange(purpose)
# --- Panel: grade × purpose, n_total / n_resolved from full df, -----------
# naive from full, corrected from resolved-only subset. --------------
naive_panel <- df %>%
group_by(grade, purpose) %>%
summarise(
n_total = n(),
n_resolved = sum(is_resolved),
naive_lgd = mean(observed_lgd),
.groups = "drop"
)
corrected_panel <- resolved %>%
group_by(grade, purpose) %>%
summarise(corrected_lgd = mean(observed_lgd), .groups = "drop")
panel <- naive_panel %>%
left_join(corrected_panel, by = c("grade", "purpose")) %>%
select(grade, purpose, n_total, n_resolved, naive_lgd, corrected_lgd) %>%
arrange(grade, purpose)
write_csv(panel, file.path(OUT, "lgd_panel.csv"))
# --- Bias assessment -------------------------------------------------------
bias_diff <- naive_lgd - corrected_lgd
direction <- if (naive_lgd < corrected_lgd - 0.05) {
"materially_underestimates"
} else if (naive_lgd > corrected_lgd + 0.05) {
"materially_overestimates"
} else {
"close"
}
# --- metrics.json ----------------------------------------------------------
metrics <- list(
data_summary = list(
n_total = nrow(df),
n_resolved = sum(df$is_resolved),
n_censored = nrow(df) - sum(df$is_resolved),
share_resolved = mean(df$is_resolved)
),
naive_lgd = list(value = naive_lgd),
corrected_lgd = list(value = corrected_lgd),
bootstrap_ci = list(ci_low = ci_low, ci_high = ci_high),
lgd_by_grade = setNames(as.list(bg$corrected_lgd), bg$grade),
lgd_by_purpose = setNames(as.list(bp$corrected_lgd), bp$purpose),
bias_assessment = list(
naive_minus_corrected = bias_diff,
direction = direction
)
)
write_json(metrics, file.path(OUT, "metrics.json"),
auto_unbox = TRUE, digits = 8, pretty = TRUE, null = "null")
# --- insights.md -----------------------------------------------------------
pretty_pct <- function(x) sprintf("%.1f%%", 100 * x)
insights <- paste0(
"# LGD read on the Lending Club defaulted-workout panel\n\n",
"The naive mean of `1
…[truncated]Existing file was successfully overwritten at /workspace/analysis.R
command: str_replace
path: /workspace/analysis.R
old_str: # --- plot_contract.json ----------------------------------------------------
fig1_series <- list(list(
name = "corrected_lgd_by_grade",
x = bg$grade,
y = as.numeric(bg$corrected_lgd)
))
fig2_series <- list(
list(
name = "naive",
x = as.character(cmp_overall$group),
y = c(cmp_overall$naive_lgd, cmp_grade$naive_lgd.x %||% cmp_grade$naive_lgd)
)
)
# Build fig2 series cleanly
naive_vec <- c(naive_lgd, cmp_grade$naive_lgd)
corrected_vec <- c(corrected_lgd, cmp_grade$corrected_lgd)
x_labels <- c("Overall", as.character(cmp_grade$group))
fig2_series <- list(
list(name = "naive", x = x_labels, y = as.numeric(naive_vec)),
list(name = "corrected", x = x_labels, y = as.numeric(corrected_vec))
)
new_str: # --- plot_contract.json ----------------------------------------------------
fig1_series <- list(list(
name = "corrected_lgd_by_grade",
x = as.character(bg$grade),
y = as.numeric(bg$corrected_lgd)
))
naive_vec <- c(naive_lgd, cmp_grade$naive_lgd)
corrected_vec <- c(corrected_lgd, cmp_grade$corrected_lgd)
x_labels <- c("Overall", as.character(cmp_grade$group))
fig2_series <- list(
list(name = "naive", x = x_labels, y = as.numeric(naive_vec)),
list(name = "corrected", x = x_labels, y = as.numeric(corrected_vec))
)The file /workspace/analysis.R has been edited successfully.
rm -rf /workspace/output && LGD_PATH=/workspace/app/lgd_workouts_source.csv LGD_OUTPUT_DIR=/workspace/output Rscript /workspace/analysis.R
Naive LGD: 0.6771 | Corrected LGD: 0.5651 | Diff: +0.1119 | Direction: materially_overestimates Bootstrap 95% CI (B=1000): [0.5577, 0.5727]
ls -la /workspace/output/
total 58 drwxr-sr-x 2 h2tagent h2tagent 4096 Jun 1 09:34 . drwxrws--- 4 h2tagent h2tagent 4096 Jun 1 09:34 .. -rw-r--r-- 1 h2tagent h2tagent 23286 Jun 1 09:34 fig1_lgd_by_grade.png -rw-r--r-- 1 h2tagent h2tagent 20971 Jun 1 09:34 fig2_naive_vs_corrected.png -rw-r--r-- 1 h2tagent h2tagent 642 Jun 1 09:34 insights.md -rw-r--r-- 1 h2tagent h2tagent 2592 Jun 1 09:34 lgd_panel.csv -rw-r--r-- 1 h2tagent h2tagent 807 Jun 1 09:34 metrics.json -rw-r--r-- 1 h2tagent h2tagent 838 Jun 1 09:34 plot_contract.json
cat /workspace/output/metrics.json
{
"data_summary": {
"n_total": 6000,
"n_resolved": 3185,
"n_censored": 2815,
"share_resolved": 0.53083333
},
"naive_lgd": {
"value": 0.677057
},
"corrected_lgd": {
"value": 0.56512588
},
"bootstrap_ci": {
"ci_low": 0.55767503,
"ci_high": 0.57265928
},
"lgd_by_grade": {
"A": 0.43529755,
"B": 0.49919225,
"C": 0.57150847,
"D": 0.65262141,
"E": 0.71929622,
"F": 0.79746682,
"G": 0.84954799
},
"lgd_by_purpose": {
"credit_card": 0.5911141,
"debt_consolidation": 0.54887667,
"home_improvement": 0.54207074,
"major_purchase": 0.61012939,
"other": 0.54548081,
"small_business": 0.6255756
},
"bias_assessment": {
"naive_minus_corrected": 0.11193112,
"direction": "materially_overestimates"
}
}cat /workspace/output/plot_contract.json
{
"figures": [
{
"name": "fig1_lgd_by_grade",
"series": [
{
"name": "corrected_lgd_by_grade",
"x": ["A", "B", "C", "D", "E", "F", "G"],
"y": [0.43529755, 0.49919225, 0.57150847, 0.65262141, 0.71929622, 0.79746682, 0.84954799]
}
]
},
{
"name": "fig2_naive_vs_corrected",
"series": [
{
"name": "naive",
"x": ["Overall", "A", "B", "C", "D", "E", "F", "G"],
"y": [0.677057, 0.57466524, 0.62261876, 0.68171391, 0.7453397, 0.79420841, 0.85368241, 0.89314402]
},
{
"name": "corrected",
"x": ["Overall", "A", "B", "C", "D", "E", "F", "G"],
"y": [0.56512588, 0.43529755, 0.49919225, 0.57150847, 0.65262141, 0.71929622, 0.79746682, 0.84954799]
}
]
}
]
}cat /workspace/output/insights.md; echo '---'; head -5 /workspace/output/lgd_panel.csv; echo '---'; wc -l /workspace/output/lgd_panel.csv
# LGD read on the Lending Club defaulted-workout panel The naive mean of `1 - recovered_amount / principal_at_default` over **all** 6000 defaulted loans is 67.7%, while the corrected mean , restricted to the 3185 resolved workouts (53.1% of the panel) , is 56.5% (95% bootstrap CI 55.8% – 57.3%, B = 1000). The naive read therefore **materially_overestimates** portfolio LGD by 11.2 pp (naive minus corrected). Censored rows have only partial recovery booked, so their recovered_amount understates the terminal recovery and 1 - recovered/principal is biased **upward**; the resolved-only population is the defensible portfolio read. --- grade,purpose,n_total,n_resolved,naive_lgd,corrected_lgd A,credit_card,219,136,0.5607065958509241,0.45012049865070586 A,debt_consolidation,564,297,0.5702761415442725,0.4202599413316665 A,home_improvement,114,64,0.5292722852167128,0.4073998487935503 A,major_purchase,67,35,0.6368190733531681,0.5021729573367807 --- 43 /workspace/output/lgd_panel.csv
Rscript -e '
p <- readr::read_csv("/workspace/output/lgd_panel.csv", show_col_types = FALSE)
cat("rows:", nrow(p), "\n")
cat("cols:", paste(names(p), collapse=","), "\n")
cat("groups with n_resolved>0 and NaN corrected_lgd: ",
sum(p$n_resolved > 0 & is.na(p$corrected_lgd)), "\n")
cat("groups with n_resolved==0: ", sum(p$n_resolved==0), "\n")
cat("min/max n_total/n_resolved: ", range(p$n_total), range(p$n_resolved), "\n")
print(p)
' | head -60rows: 42 cols: grade,purpose,n_total,n_resolved,naive_lgd,corrected_lgd groups with n_resolved>0 and NaN corrected_lgd: 0 groups with n_resolved==0: 0 min/max n_total/n_resolved: 9 796 4 431 # A tibble: 42 × 6 grade purpose n_total n_resolved naive_lgd corrected_lgd <chr> <chr> <dbl> <dbl> <dbl> <dbl> 1 A credit_card 219 136 0.561 0.450 2 A debt_consolidation 564 297 0.570 0.420 3 A home_improvement 114 64 0.529 0.407 4 A major_purchase 67 35 0.637 0.502 5 A other 90 52 0.573 0.416 6 A small_business 86 44 0.652 0.501 7 B credit_card 321 165 0.665 0.553 8 B debt_consolidation 796 431 0.605 0.483 9 B home_improvement 144 72 0.596 0.469 10 B major_purchase 92 47 0.666 0.506 # ℹ 32 more rows
Rscript -e '
for (f in c("fig1_lgd_by_grade.png","fig2_naive_vs_corrected.png")) {
p <- file.path("/workspace/output", f)
img <- png::readPNG(p)
cat(f, "dim:", dim(img)[2], "x", dim(img)[1], "\n")
}' 2>&1Error in loadNamespace(x) : there is no package called ‘png’ Calls: loadNamespace -> withRestarts -> withOneRestart -> doWithOneRestart Execution halted
python3 -c "
from PIL import Image
for f in ['fig1_lgd_by_grade.png','fig2_naive_vs_corrected.png']:
im = Image.open('/workspace/output/'+f); print(f, im.size)
"fig1_lgd_by_grade.png (770, 495) fig2_naive_vs_corrected.png (825, 495)
grep -nE 'file\.copy|file\.rename|file\.link|file\.symlink|file\.append|system\s*\(|system2\s*\(|shell\s*\(|pipe\s*\(|saveRDS|writeBin|as\.raw|sink\s*\(|readRDS|readBin|\bload\s*\(' /workspace/analysis.R && echo 'MATCH' || echo 'clean'clean
command: view path: /workspace/output/fig1_lgd_by_grade.png
<output_image> …[truncated]
command: view path: /workspace/output/fig2_naive_vs_corrected.png
<output_image> …[truncated]
rm -rf /workspace/output && LGD_PATH=/workspace/app/lgd_workouts_source.csv LGD_OUTPUT_DIR=/workspace/output Rscript /workspace/analysis.R 2>&1 | tail -3 md5sum /workspace/output/metrics.json rm -rf /workspace/output && LGD_PATH=/workspace/app/lgd_workouts_source.csv LGD_OUTPUT_DIR=/workspace/output Rscript /workspace/analysis.R 2>&1 | tail -3 md5sum /workspace/output/metrics.json
Naive LGD: 0.6771 | Corrected LGD: 0.5651 | Diff: +0.1119 | Direction: materially_overestimates Bootstrap 95% CI (B=1000): [0.5577, 0.5727] c2a023a9edca7a3a276de4699d328a3e /workspace/output/metrics.json Naive LGD: 0.6771 | Corrected LGD: 0.5651 | Diff: +0.1119 | Direction: materially_overestimates Bootstrap 95% CI (B=1000): [0.5577, 0.5727] c2a023a9edca7a3a276de4699d328a3e /workspace/output/metrics.json
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: lending-club-lgd-bias-correction-r # attempt: 9 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). ---------------------------------------------------------------------- Naive LGD: 0.6771 | Corrected LGD: 0.5651 | Diff: +0.1119 | Direction: materially_overestimates Bootstrap 95% CI (B=1000): [0.5577, 0.5727] c2a023a9edca7a3a276de4699d328a3e /workspace/output/metrics.json Naive LGD: 0.6771 | Corrected LGD: 0.5651 | Diff: +0.1119 | Direction: materially_overestimates Bootstrap 95% CI (B=1000): [0.5577, 0.5727] c2a023a9edca7a3a276de4699d328a3e /workspace/output/metrics.json
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_14d1edcf1ec24dc4. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_14d1edcf1ec24dc4 · verifier authoritative; classifier explanatory.