tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.ls -la /workspace/app/ /workspace/analysis.R 2>&1 | head -30
-rw-rw---- 1 h2tagent h2tagent 2821 Jun 1 08:20 /workspace/analysis.R /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
cd /workspace && Rscript -e '
df <- read.csv("/workspace/app/lgd_workouts_source.csv")
df$lgd <- 1 - df$recovered_amount / df$principal_at_default
cat("n_total:", nrow(df), "\n")
cat("n_resolved:", sum(df$is_resolved), "\n")
cat("naive (all):", mean(df$lgd), "\n")
cat("corrected (resolved only):", mean(df$lgd[df$is_resolved == 1]), "\n")
cat("diff:", mean(df$lgd) - mean(df$lgd[df$is_resolved == 1]), "\n")
cat("grades:", paste(sort(unique(df$grade)), collapse=","), "\n")
cat("purposes:", paste(sort(unique(df$purpose)), collapse=","), "\n")
'
n_total: 6000 n_resolved: 3185 naive (all): 0.677057 corrected (resolved only): 0.5651259 diff: 0.1119311 grades: A,B,C,D,E,F,G purposes: credit_card,debt_consolidation,home_improvement,major_purchase,other,small_business
command: create
path: /workspace/analysis.R
file_text: #!/usr/bin/env Rscript
# LGD analysis , corrected specification.
# - naive LGD: mean(1 - recovered/principal) over ALL rows (biased baseline)
# - corrected LGD: same mean over resolved rows only
# - paired bootstrap (B=1000) on resolved rows for a 95% CI
# - per-grade / per-purpose / panel use the resolved-only population
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 (only meaningful when is_resolved == 1, but we
# compute it everywhere to expose the biased naive baseline).
df <- df %>%
mutate(observed_lgd = 1 - recovered_amount / principal_at_default)
resolved <- df %>% filter(is_resolved == 1)
lgd_res <- resolved$observed_lgd
# ---- headline figures ------------------------------------------------------
naive_lgd <- mean(df$observed_lgd) # biased (full population)
corrected_lgd <- mean(lgd_res) # resolved-only
# ---- paired bootstrap CI on resolved rows ---------------------------------
B <- 1000L
n_res <- length(lgd_res)
set.seed(20260606)
boot_means <- replicate(B, mean(lgd_res[sample.int(n_res, n_res, replace = TRUE)]))
ci_lo <- unname(quantile(boot_means, 0.025))
ci_hi <- unname(quantile(boot_means, 0.975))
# ---- per-grade / per-purpose on resolved-only -----------------------------
lgd_by_grade <- resolved %>%
group_by(grade) %>%
summarise(corrected_lgd = mean(observed_lgd), .groups = "drop") %>%
arrange(grade)
lgd_by_purpose <- resolved %>%
group_by(purpose) %>%
summarise(corrected_lgd = mean(observed_lgd), .groups = "drop") %>%
arrange(purpose)
# ---- grade × purpose panel -------------------------------------------------
naive_panel <- df %>%
group_by(grade, purpose) %>%
summarise(n_total = n(),
n_resolved = sum(is_resolved),
naive_lgd = mean(observed_lgd),
.groups = "drop")
corr_panel <- resolved %>%
group_by(grade, purpose) %>%
summarise(corrected_lgd = mean(observed_lgd), .groups = "drop")
panel <- naive_panel %>%
left_join(corr_panel, by = c("grade", "purpose")) %>%
arrange(grade, purpose)
# Sanity: every group with n_resolved > 0 must have a finite corrected_lgd
stopifnot(all(is.finite(panel$corrected_lgd[panel$n_resolved > 0])))
readr::write_csv(panel, file.path(OUT, "lgd_panel.csv"))
# ---- bias direction --------------------------------------------------------
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$is_resolved)
),
naive_lgd = list(value = naive_lgd),
corrected_lgd = list(value = corrected_lgd),
bootstrap_ci = list(ci_low = ci_lo, ci_high = ci_hi),
lgd_by_grade = setNames(as.list(lgd_by_grade$corrected_lgd),
lgd_by_grade$grade),
lgd_by_purpose = setNames(as.list(lgd_by_purpose$corrected_lgd),
lgd_by_purpose$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 <- sp
…[truncated]Existing file was successfully overwritten at /workspace/analysis.R
cd /workspace && rm -rf /workspace/output && LGD_OUTPUT_DIR=/workspace/output Rscript /workspace/analysis.R 2>&1 | tail -30
naive=0.6771 corrected=0.5651 diff=0.1119 direction=materially_overestimates
ls -la /workspace/output && echo "---" && cat /workspace/output/metrics.json
total 56
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 23154 Jun 1 09:34 fig1_lgd_by_grade.png
-rw-r--r-- 1 h2tagent h2tagent 18527 Jun 1 09:34 fig2_naive_vs_corrected.png
-rw-r--r-- 1 h2tagent h2tagent 590 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 919 Jun 1 09:34 plot_contract.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": [
{
"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",
"series": [
{
"estimator": "naive",
"lgd": 0.677057
},
{
"estimator": "corrected",
"lgd": 0.56512588
}
]
}
]
}cat /workspace/output/insights.md; echo "---"; head -5 /workspace/output/lgd_panel.csv; echo "..."; tail -5 /workspace/output/lgd_panel.csv
## LGD bias read Naive LGD over all 6000 defaulted workouts is 0.677, while the corrected LGD restricted to the 3185 resolved loans is 0.565. The naive figure therefore **materially_overestimates** the corrected LGD by 0.112 (11.2 percentage points); 95% bootstrap CI on the corrected LGD is [0.558, 0.573]. Right-censored workouts still have recovery cash flows in flight, so including them depresses observed recoveries and inflates the naive LGD mean. Portfolio-grade LGD should be read off the resolved-only panel, with censoring modelled separately rather than pooled into the mean. --- 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
cd /workspace && 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)
"
# Verify panel has no NaN in corrected_lgd where n_resolved>0
Rscript -e '
p <- read.csv("/workspace/output/lgd_panel.csv")
cat("rows:", nrow(p), " groups n_resolved==0:", sum(p$n_resolved == 0), "\n")
bad <- subset(p, n_resolved > 0 & !is.finite(corrected_lgd))
cat("bad rows (n_resolved>0 but corrected NaN):", nrow(bad), "\n")
# check all grade x purpose combos present
cat("unique grades:", length(unique(p$grade)), "unique purposes:", length(unique(p$purpose)), "\n")
'
fig1_lgd_by_grade.png (720, 480) fig2_naive_vs_corrected.png (720, 480) rows: 42 groups n_resolved==0: 0 bad rows (n_resolved>0 but corrected NaN): 0 unique grades: 7 unique purposes: 6
cd /workspace && python3 -c "
import json
m = json.load(open('/workspace/output/metrics.json'))
assert m['data_summary']['n_total'] == 6000
assert m['data_summary']['n_resolved'] == 3185
assert m['data_summary']['n_censored'] == 2815
assert abs(m['data_summary']['share_resolved'] - 3185/6000) < 1e-9
assert 0.67 < m['naive_lgd']['value'] < 0.68
assert 0.56 < m['corrected_lgd']['value'] < 0.57
assert m['bootstrap_ci']['ci_low'] < m['corrected_lgd']['value'] < m['bootstrap_ci']['ci_high']
assert m['bias_assessment']['direction'] == 'materially_overestimates'
assert all(g in m['lgd_by_grade'] for g in list('ABCDEFG'))
assert len(m['lgd_by_purpose']) == 6
print('metrics OK')
pc = json.load(open('/workspace/output/plot_contract.json'))
assert isinstance(pc['figures'], list)
names = [f['name'] for f in pc['figures']]
assert names == ['fig1_lgd_by_grade', 'fig2_naive_vs_corrected']
for f in pc['figures']:
assert isinstance(f['series'], list) and len(f['series']) > 0
print('plot_contract OK')
"
# Check forbidden tokens
grep -nE 'file\\.copy|file\\.rename|file\\.link|file\\.symlink|file\\.append|(^|[^_[:alnum:]])(system|system2|shell|pipe|saveRDS|writeBin|as\\.raw|sink|readRDS|readBin|load)\\(' /workspace/analysis.R || echo "no forbidden tokens"
no forbidden tokens [stderr] Traceback (most recent call last): File "<string>", line 7, in <module> AssertionError grep: Unmatched ( or \(
python3 -c "
import json
m = json.load(open('/workspace/output/metrics.json'))
print(repr(m['data_summary']['share_resolved']), 3185/6000)
print(abs(m['data_summary']['share_resolved'] - 3185/6000))
"0.53083333 0.5308333333333334 3.333333387089965e-09
python3 -c "
import json
m = json.load(open('/workspace/output/metrics.json'))
d = m['data_summary']
assert d['n_total'] == 6000 and d['n_resolved'] == 3185 and d['n_censored'] == 2815
assert abs(d['share_resolved'] - 3185/6000) < 1e-6
assert 0.67 < m['naive_lgd']['value'] < 0.68
assert 0.56 < m['corrected_lgd']['value'] < 0.57
assert m['bootstrap_ci']['ci_low'] < m['corrected_lgd']['value'] < m['bootstrap_ci']['ci_high']
assert m['bias_assessment']['direction'] == 'materially_overestimates'
assert abs(m['bias_assessment']['naive_minus_corrected'] - (m['naive_lgd']['value'] - m['corrected_lgd']['value'])) < 1e-6
assert all(g in m['lgd_by_grade'] for g in list('ABCDEFG'))
assert set(m['lgd_by_purpose']) == {'credit_card','debt_consolidation','home_improvement','major_purchase','other','small_business'}
print('metrics OK')
"
# also re-check forbidden tokens with a saner grep
grep -nE 'file\.copy|file\.rename|file\.link|file\.symlink|file\.append|\\bsystem\\(|\\bsystem2\\(|\\bshell\\(|\\bpipe\\(|\\bsaveRDS\\(|\\bwriteBin\\(|\\bas\.raw\\(|\\bsink\\(|\\breadRDS\\(|\\breadBin\\(|\\bload\\(' /workspace/analysis.R || echo "no forbidden tokens"metrics OK no forbidden tokens [stderr] grep: Unmatched ( or \(
grep -Pn 'file\.copy|file\.rename|file\.link|file\.symlink|file\.append|\bsystem\(|\bsystem2\(|\bshell\(|\bpipe\(|\bsaveRDS\(|\bwriteBin\(|\bas\.raw\(|\bsink\(|\breadRDS\(|\breadBin\(|\bload\(' /workspace/analysis.R && echo FOUND || echo "no forbidden tokens"no forbidden tokens
# Run a second time to confirm idempotent (grader runs twice) rm -rf /workspace/output LGD_OUTPUT_DIR=/workspace/output Rscript /workspace/analysis.R 2>&1 | tail -5 echo "--- second run ---" LGD_OUTPUT_DIR=/workspace/output Rscript /workspace/analysis.R 2>&1 | tail -5 ls /workspace/output
naive=0.6771 corrected=0.5651 diff=0.1119 direction=materially_overestimates --- second run --- naive=0.6771 corrected=0.5651 diff=0.1119 direction=materially_overestimates fig1_lgd_by_grade.png fig2_naive_vs_corrected.png insights.md lgd_panel.csv metrics.json plot_contract.json
command: view path: /workspace/output/fig1_lgd_by_grade.png
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command: view path: /workspace/output/fig2_naive_vs_corrected.png
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No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: lending-club-lgd-bias-correction-r # attempt: 3 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). ---------------------------------------------------------------------- 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…[truncated]
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_aaa014a5dbdb4743. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_aaa014a5dbdb4743 · verifier authoritative; classifier explanatory.