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 -3 /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 --- 6001 /workspace/app/lgd_workouts_source.csv
Rscript -e '
df <- readr::read_csv("/workspace/app/lgd_workouts_source.csv", show_col_types=FALSE)
cat("Rows:", nrow(df), "\n")
cat("Resolved:", sum(df$is_resolved), "\n")
cat("Censored:", sum(df$is_resolved==0), "\n")
cat("Grades:", paste(sort(unique(df$grade)), collapse=","), "\n")
cat("Purposes:", paste(sort(unique(df$purpose)), collapse=","), "\n")
df$lgd <- 1 - df$recovered_amount/df$principal_at_default
cat("Naive (all rows) LGD:", mean(df$lgd), "\n")
cat("Corrected (resolved only) LGD:", mean(df$lgd[df$is_resolved==1]), "\n")
'
Rows: 6000 Resolved: 3185 Censored: 2815 Grades: A,B,C,D,E,F,G Purposes: credit_card,debt_consolidation,home_improvement,major_purchase,other,small_business Naive (all rows) LGD: 0.677057 Corrected (resolved only) LGD: 0.5651259
command: create
path: /workspace/analysis.R
file_text: #!/usr/bin/env Rscript
# LGD analysis , corrected version.
# Reads the defaulted-loans workout panel, computes the biased naive LGD
# (mean over ALL rows) vs. the corrected LGD (mean restricted to
# is_resolved == 1), bootstrap CI over resolved loans, grade/purpose
# breakdowns on the resolved subset, and writes metrics + plots.
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 <- readr::read_csv(LGD_PATH, show_col_types = FALSE)
# Per-row observed LGD. For censored rows (is_resolved == 0), recovered_amount
# understates terminal recovery, so 1 - rec/princ is biased UPWARD.
df <- dplyr::mutate(df, observed_lgd = 1 - recovered_amount / principal_at_default)
resolved <- dplyr::filter(df, is_resolved == 1)
# ---- headline metrics -------------------------------------------------------
naive_lgd <- mean(df$observed_lgd) # biased baseline: ALL rows
corrected_lgd <- mean(resolved$observed_lgd) # unbiased: resolved only
# ---- paired bootstrap CI over resolved loans (B = 1000) ---------------------
B <- 1000
resolved_lgd <- resolved$observed_lgd
n_res <- length(resolved_lgd)
boot_means <- numeric(B)
set.seed(20260606)
for (b in seq_len(B)) {
boot_means[b] <- mean(resolved_lgd[sample.int(n_res, n_res, replace = TRUE)])
}
ci_lo <- unname(quantile(boot_means, 0.025))
ci_hi <- unname(quantile(boot_means, 0.975))
# ---- corrected LGD by grade / purpose (resolved only) -----------------------
bg <- resolved %>%
dplyr::group_by(grade) %>%
dplyr::summarise(corrected_lgd = mean(observed_lgd), .groups = "drop") %>%
dplyr::arrange(grade)
bp <- resolved %>%
dplyr::group_by(purpose) %>%
dplyr::summarise(corrected_lgd = mean(observed_lgd), .groups = "drop") %>%
dplyr::arrange(purpose)
# ---- grade x purpose panel --------------------------------------------------
totals <- df %>%
dplyr::group_by(grade, purpose) %>%
dplyr::summarise(
n_total = dplyr::n(),
n_resolved = sum(is_resolved),
naive_lgd = mean(observed_lgd),
.groups = "drop"
)
resolved_panel <- resolved %>%
dplyr::group_by(grade, purpose) %>%
dplyr::summarise(corrected_lgd = mean(observed_lgd), .groups = "drop")
panel <- totals %>%
dplyr::left_join(resolved_panel, by = c("grade", "purpose")) %>%
dplyr::arrange(grade, purpose)
readr::write_csv(panel, file.path(OUT, "lgd_panel.csv"))
# ---- bias assessment --------------------------------------------------------
bias_diff <- naive_lgd - corrected_lgd
direction <- if (naive_lgd < corrected_lgd - 0.05) {
"materially_underestimates"
} else if (naive_lgd > corrected_lgd + 0.05) {
"materially_overestimates"
} else {
"close"
}
# ---- metrics.json -----------------------------------------------------------
metrics <- list(
data_summary = list(
n_total = nrow(df),
n_resolved = sum(df$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_lo, ci_high = 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
)
)
jsonlite::write_json(
metrics, file.path(OUT, "metrics.json"),
auto_unbox = TRUE, digits = 8, pretty = TRUE, na = "null"
)
# ---- insights.md ------------------------------------------------------------
insight <- sprintf(paste0(
"# LGD Read , Lending Club Defaulted Loans Workout Panel\n\n",
"The naive LGD of %.3f (mean of 1 - recovered
…[truncated]Existing file was successfully overwritten at /workspace/analysis.R
cd /workspace && rm -rf output && mkdir -p output && Rscript analysis.R
Naive LGD: 0.6771 Corrected: 0.5651 CI95: [0.5577, 0.5727] Direction: materially_overestimates
ls -la /workspace/output/ && echo "---metrics.json---" && cat /workspace/output/metrics.json
total 60
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 23497 Jun 1 09:34 fig1_lgd_by_grade.png
-rw-r--r-- 1 h2tagent h2tagent 22395 Jun 1 09:34 fig2_naive_vs_corrected.png
-rw-r--r-- 1 h2tagent h2tagent 757 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 1130 Jun 1 09:34 plot_contract.json
---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"
}
}head -5 /workspace/output/lgd_panel.csv
echo "---"
tail -5 /workspace/output/lgd_panel.csv
echo "---"
wc -l /workspace/output/lgd_panel.csv
echo "---"
Rscript -e '
p <- readr::read_csv("/workspace/output/lgd_panel.csv", show_col_types=FALSE)
cat("cols:", paste(names(p), collapse=","), "\n")
cat("rows:", nrow(p), "\n")
cat("rows with n_resolved>0 AND is.na(corrected_lgd):", sum(p$n_resolved>0 & is.na(p$corrected_lgd)), "\n")
cat("rows with n_resolved==0:", sum(p$n_resolved==0), "\n")
cat("groups total:", nrow(p), "min n_total:", min(p$n_total), "\n")
'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 --- G,debt_consolidation,56,32,0.8736372909633625,0.8295847885354636 G,home_improvement,18,8,0.8599317504304652,0.8133579562599889 G,major_purchase,9,6,0.8694264325104171,0.8167843285444563 G,other,11,7,0.871591124141334,0.8562888208527432 G,small_business,11,4,0.98019042346479,0.9772421282080737 --- 43 /workspace/output/lgd_panel.csv --- cols: grade,purpose,n_total,n_resolved,naive_lgd,corrected_lgd rows: 42 rows with n_resolved>0 AND is.na(corrected_lgd): 0 rows with n_resolved==0: 0 groups total: 42 min n_total: 9
cat /workspace/output/plot_contract.json echo "---INSIGHTS---" cat /workspace/output/insights.md
{
"figures": [
{
"name": "fig1_lgd_by_grade",
"title": "Corrected LGD by Grade",
"type": "bar",
"x": "grade",
"y": "corrected_lgd",
"series": [
{
"grade": "A",
"corrected_lgd": 0.43529755
},
{
"grade": "B",
"corrected_lgd": 0.49919225
},
{
"grade": "C",
"corrected_lgd": 0.57150847
},
{
"grade": "D",
"corrected_lgd": 0.65262141
},
{
"grade": "E",
"corrected_lgd": 0.71929622
},
{
"grade": "F",
"corrected_lgd": 0.79746682
},
{
"grade": "G",
"corrected_lgd": 0.84954799
}
]
},
{
"name": "fig2_naive_vs_corrected",
"title": "Naive vs Corrected LGD",
"type": "bar",
"x": "estimate",
"y": "lgd",
"series": [
{
"estimate": "naive",
"lgd": 0.677057
},
{
"estimate": "corrected",
"lgd": 0.56512588
}
]
}
]
}
---INSIGHTS---
# LGD Read , Lending Club Defaulted Loans Workout Panel
The naive LGD of 0.677 (mean of 1 - recovered/principal over all 6000 rows) **materially_overestimates** the corrected LGD of 0.565 (resolved workouts only, n = 3185). The difference naive_minus_corrected = +0.112 is driven by the 2815 right-censored loans whose recoveries are incomplete: their partial `recovered_amount` understates terminal recovery, so `1 - recovered/principal` is biased **upward** on the censored subset and pulls the pooled mean away from the true LGD. A 95% paired bootstrap CI over the resolved population (B = 1000) places corrected LGD in [0.558, 0.573]. Recommended portfolio read: use the corrected value, 0.565, and stratify by grade/purpose per the attached panel.python3 -c "
from PIL import Image
for f in ['fig1_lgd_by_grade','fig2_naive_vs_corrected']:
im = Image.open(f'/workspace/output/{f}.png')
print(f, im.size)
"fig1_lgd_by_grade (840, 540) fig2_naive_vs_corrected (840, 540)
grep -nE 'file\.(copy|rename|link|symlink|append)|system\(|system2\(|shell\(|pipe\(|saveRDS|readRDS|writeBin|readBin|as\.raw|sink\(|^load\(|[^.]load\(' /workspace/analysis.R || echo "NO FORBIDDEN CALLS"NO FORBIDDEN CALLS
cd /workspace && rm -rf output && mkdir output && Rscript analysis.R 2>&1 | tail -4
echo "--- first run CI ---"
python3 -c "import json; d=json.load(open('/workspace/output/metrics.json')); print(d['bootstrap_ci'])"
rm -rf output && mkdir output && Rscript analysis.R > /dev/null 2>&1
echo "--- second run CI (should match) ---"
python3 -c "import json; d=json.load(open('/workspace/output/metrics.json')); print(d['bootstrap_ci'])"Naive LGD: 0.6771
Corrected: 0.5651
CI95: [0.5577, 0.5727]
Direction: materially_overestimates
--- first run CI ---
{'ci_low': 0.55767503, 'ci_high': 0.57265928}
--- second run CI (should match) ---
{'ci_low': 0.55767503, 'ci_high': 0.57265928}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]
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: lending-club-lgd-bias-correction-r # attempt: 2 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). ---------------------------------------------------------------------- <output_image> …[truncated]
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_3ea1300a3b8e4550. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_3ea1300a3b8e4550 · verifier authoritative; classifier explanatory.