tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.ls -la /workspace/ /workspace/app/
/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 -n 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 '
library(readr); library(dplyr)
df <- read_csv("/workspace/app/lgd_workouts_source.csv", show_col_types = FALSE)
cat("n_total:", nrow(df), "\n")
cat("n_resolved:", sum(df$is_resolved), "\n")
cat("unique grades:", paste(sort(unique(df$grade)), collapse=", "), "\n")
cat("unique purposes:", paste(sort(unique(df$purpose)), collapse=", "), "\n")
df <- df %>% mutate(observed_lgd = 1 - recovered_amount / principal_at_default)
cat("naive over all:", mean(df$observed_lgd), "\n")
cat("resolved-only mean:", mean(df$observed_lgd[df$is_resolved == 1]), "\n")
'
n_total: 6000
n_resolved: 3185
unique grades: A, B, C, D, E, F, G
unique purposes: credit_card, debt_consolidation, home_improvement, major_purchase, other, small_business
naive over all: 0.677057
resolved-only mean: 0.5651259
[stderr]
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unioncommand: create
path: /workspace/analysis.R
file_text: #!/usr/bin/env Rscript
# LGD analysis on Lending Club defaulted-loan workout panel.
#
# Fixes applied vs the prior draft:
# * "corrected" LGD is now mean(observed_lgd) on is_resolved == 1 rows only
# (the draft used a trimmed mean on the full dataset, which still mixes
# censored loans and is therefore still biased).
# * Bootstrap CI is 1000 paired resamples of the resolved rows only
# (the draft used 200 reps on the full dataset).
# * Per-grade and per-purpose LGDs are computed on the resolved-only
# population.
# * Panel's corrected_lgd is resolved-only per grade*purpose cell; naive_lgd
# stays as the biased full-cell mean, so the two columns differ.
# * plot_contract.json now lists both figures with non-empty series arrays
# mirroring the data drawn on each PNG.
# * insights.md is a short paragraph naming the direction and magnitude.
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)
# LGD = 1 - recovered / principal.
# Censored rows (is_resolved == 0) still have an *observed* LGD but it is
# biased upward because recovered_amount has not finished accruing.
df <- df %>%
mutate(observed_lgd = 1 - recovered_amount / principal_at_default)
resolved <- df %>% filter(is_resolved == 1)
# ---- Aggregate LGDs -------------------------------------------------------
# Naive baseline: full panel mean (spec demands this biased number).
naive_lgd <- mean(df$observed_lgd)
# Corrected: resolved-only.
corrected_lgd <- mean(resolved$observed_lgd)
# ---- Paired bootstrap CI on resolved-only rows ----------------------------
set.seed(20260606)
B <- 1000
resolved_lgd <- resolved$observed_lgd
n_res <- length(resolved_lgd)
boot_means <- numeric(B)
for (b in seq_len(B)) {
idx <- sample.int(n_res, size = n_res, replace = TRUE)
boot_means[b] <- mean(resolved_lgd[idx])
}
ci_low <- unname(quantile(boot_means, 0.025))
ci_high <- unname(quantile(boot_means, 0.975))
# ---- Corrected LGD by grade / purpose -------------------------------------
by_grade <- resolved %>%
group_by(grade) %>%
summarise(corrected_lgd = mean(observed_lgd), .groups = "drop") %>%
arrange(grade)
by_purpose <- resolved %>%
group_by(purpose) %>%
summarise(corrected_lgd = mean(observed_lgd), .groups = "drop") %>%
arrange(purpose)
# ---- Panel: grade x purpose ----------------------------------------------
# naive_lgd , biased full-cell mean (all rows in the cell)
# corrected_lgd , resolved-only mean in the cell; NaN is replaced with NA
# only for cells with zero resolved loans (spec requires
# non-NaN whenever n_resolved > 0).
naive_panel <- df %>%
group_by(grade, purpose) %>%
summarise(n_total = n(),
n_resolved = sum(is_resolved),
naive_lgd = mean(observed_lgd),
.groups = "drop")
corrected_panel <- resolved %>%
group_by(grade, purpose) %>%
summarise(corrected_lgd = mean(observed_lgd), .groups = "drop")
panel <- naive_panel %>%
left_join(corrected_panel, by = c("grade", "purpose")) %>%
arrange(grade, purpose)
# Sanity: every n_resolved > 0 cell must have a real corrected_lgd.
stopifnot(all(!is.nan(panel$corrected_lgd[panel$n_resolved > 0])))
stopifnot(all(!is.na (panel$corrected_lgd[panel$n_resolved > 0])))
write_csv(panel, file.path(OUT, "lgd_panel.csv"))
# ---- Bias assessment ------------------------------------------------------
bias_diff <- naive_lgd - corrected_lgd
direction <- if (bias_diff < -0.05) {
"materially_underestimates"
} else if (bias_diff > 0.05) {
"materially_overestimates"
} else {
"close"
}
# ---- metrics.json --------------
…[truncated]Existing file was successfully overwritten at /workspace/analysis.R
grep -nE 'file\.copy|file\.rename|file\.link|file\.symlink|file\.append|system\(|system2\(|shell\(|pipe\(|saveRDS|writeBin|as\.raw|sink\(|readRDS|readBin|load\(' /workspace/analysis.R || echo "OK: no banned calls"OK: no banned calls
rm -rf /workspace/output && mkdir -p /workspace/output && cd /workspace && Rscript analysis.R
<system>Tool ran without output or errors</system>
ls -la /workspace/output/
total 62 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 25712 Jun 1 09:34 fig1_lgd_by_grade.png -rw-r--r-- 1 h2tagent h2tagent 22697 Jun 1 09:34 fig2_naive_vs_corrected.png -rw-r--r-- 1 h2tagent h2tagent 588 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 950 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"
}
}head -n 15 /workspace/output/lgd_panel.csv; echo "---"; wc -l /workspace/output/lgd_panel.csv
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 A,other,90,52,0.5730028339585355,0.4161251086238681 A,small_business,86,44,0.6524850156856171,0.501025517461768 B,credit_card,321,165,0.6650274867660854,0.5529570202359294 B,debt_consolidation,796,431,0.6046227193564916,0.4831736900891306 B,home_improvement,144,72,0.5957294070774534,0.4692244006760461 B,major_purchase,92,47,0.6660533581869653,0.5058966135004026 B,other,147,76,0.6134946055553012,0.4707940973640627 B,small_business,107,62,0.6406465639242245,0.5319930675458743 C,credit_card,278,137,0.6975732212004435,0.5846765821152625 C,debt_consolidation,689,376,0.6700856143560795,0.5627652371775993 --- 43 /workspace/output/lgd_panel.csv
cat /workspace/output/insights.md
Restricting the loss calculation to fully-resolved workouts moves the portfolio LGD from a naive 67.7% (all rows, including right-censored workouts) to a corrected 56.5% (is_resolved == 1 only), with a 95% paired-bootstrap CI of [55.8%, 57.3%] over 3185 resolved loans. The naive estimator therefore materially overestimates true LGD by +11.2 percentage points (naive minus corrected = +0.1119); censored loans have not finished recovering, so their recovered_amount understates terminal recovery and inflates 1 - recovered/principal. Direction classification: 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 (all rows)",
"value": 0.677057
},
{
"estimator": "corrected (resolved only)",
"value": 0.56512588
}
]
}
]
}python3 -c "
from PIL import Image
for f in ['fig1_lgd_by_grade.png', 'fig2_naive_vs_corrected.png']:
im = Image.open(f'/workspace/output/{f}')
print(f, im.size)
"fig1_lgd_by_grade.png (720, 480) fig2_naive_vs_corrected.png (720, 480)
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]
cd /workspace && Rscript analysis.R && md5sum /workspace/output/metrics.json /workspace/output/lgd_panel.csv /workspace/output/plot_contract.json /workspace/output/insights.md
c2a023a9edca7a3a276de4699d328a3e /workspace/output/metrics.json 633a186d045f6490875af4c0ef0e46ad /workspace/output/lgd_panel.csv 34c5410ee5fc0fd051d4b1244d60c1c7 /workspace/output/plot_contract.json 160f318f4d413e4f6fe3532a8a11c751 /workspace/output/insights.md
cd /workspace && Rscript analysis.R && md5sum /workspace/output/metrics.json /workspace/output/lgd_panel.csv /workspace/output/plot_contract.json /workspace/output/insights.md
c2a023a9edca7a3a276de4699d328a3e /workspace/output/metrics.json 633a186d045f6490875af4c0ef0e46ad /workspace/output/lgd_panel.csv 34c5410ee5fc0fd051d4b1244d60c1c7 /workspace/output/plot_contract.json 160f318f4d413e4f6fe3532a8a11c751 /workspace/output/insights.md
rm -rf /tmp/testout && LGD_OUTPUT_DIR=/tmp/testout Rscript /workspace/analysis.R && ls /tmp/testout/ && echo "---" && cat /tmp/testout/metrics.json | python3 -c "import sys,json; m=json.load(sys.stdin); print('keys:', list(m.keys())); print('direction:', m['bias_assessment']['direction']); print('naive_minus_corrected:', m['bias_assessment']['naive_minus_corrected'])"fig1_lgd_by_grade.png fig2_naive_vs_corrected.png insights.md lgd_panel.csv metrics.json plot_contract.json --- keys: ['data_summary', 'naive_lgd', 'corrected_lgd', 'bootstrap_ci', 'lgd_by_grade', 'lgd_by_purpose', 'bias_assessment'] direction: materially_overestimates naive_minus_corrected: 0.11193112
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: lending-club-lgd-bias-correction-r # 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). ---------------------------------------------------------------------- fig1_lgd_by_grade.png fig2_naive_vs_corrected.png insights.md lgd_panel.csv metrics.json plot_contract.json --- keys: ['data_summary', 'naive_lgd', 'corrected_lgd', 'bootstrap_ci', 'lgd_by_grade', 'lgd_by_purpose', 'bias_assessment'] direction: materially_overestimates naive_minus_corrected: 0.11193112
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_e1e0681952ee4ef0. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_e1e0681952ee4ef0 · verifier authoritative; classifier explanatory.