Data-quality audit of a jet table
0.35
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5,427
$0.00136
36 s
1
Task given to the agent
Audit the data quality of `data/synth_jets.csv` (one row per jet). Read `README.md` first: it is the data card and documents the file's conventions. Write `result.json` with exactly these keys: - `n_rows`: number of rows in the raw file (int) - `n_duplicate_rows`: number of rows that are exact repeats of an earlier row (int) - `n_unique_jets`: number of distinct jets, identified as described in the data card (int) - `n_events`: number of distinct events (int) - `n_missing_pt`: number of distinct jets whose transverse momentum is missing (int) - `n_missing_eta`: number of distinct jets whose pseudorapidity is missing, following the data card's convention (int) - `columns_with_missing`: sorted list of column names that contain any missing value (including the documented sentinel) - `max_jets_per_event`: maximum number of distinct jets in one event (int) - `frac_jets_missing_pt`: n_missing_pt / n_unique_jets (float) Remove duplicate rows (keeping the first occurrence) before computing every quantity except `n_rows` and `n_duplicate_rows`. Save the complete analysis as `solution.py`; running `python solution.py` from a clean copy of this directory must regenerate `result.json`.
Trajectory
```python
import pandas as pd
import numpy as np
import os
# Read the data card
with open("README.md", "r") as f:
readme = f.read()
# Load the synthetic jets data
file_path = "data/synth_jets.csv"
df = pd.read_csv(file_path)
# Calculate n_rows
n_rows = len(df)
# Remove duplicate rows, keeping the first occurrence
df_unique = df.drop_duplicates(keep='first')
# Calculate n_duplicate_rows
n_duplicate_rows = n_rows - len(df_unique)
# Calculate n_unique_jets: identified by (event_id, jet_index)
n_unique_jets = df_unique[["event_id", "jet_index"]].drop_duplicates().shape[0]
# Calculate n_events: number of distinct events
n_events = df_unique["event_id"].nunique()
# Calculate n_missing_pt: number of distinct jets with missing jet_pt
n_missing_pt = df_unique[df_unique["jet_pt"].isna()].shape[0]
# Calculate n_missing_eta: number of distinct jets with jet_eta = -999.0
n_missing_eta = df_unique[df_unique["jet_eta"] == -999.0].shape[0]
# Identify columns with any missing values (including sentinel -999.0 for jet_eta)
columns_with_missing = []
for col in df.columns:
if df[col].isna().any() or (col == "jet_eta" and df[col] == -999.0).any():
columns_with_missing.append(col)
columns_with_missing.sort()
# Calculate max_jets_per_event: maximum number of distinct jets in one event
max_jets_per_event = df_unique.groupby("event_id").size().max()
# Calculate frac_jets_missing_pt
frac_jets_missing_pt = n_missing_pt / n_unique_jets
# Save results to result.json
result = {
"n_rows": n_rows,
"n_duplicate_rows": n_duplicate_rows,
"n_unique_jets": n_unique_jets,
"n_events": n_events,
"n_missing_pt": n_missing_pt,
"n_missing_eta": n_missing_eta,
"columns_with_missing": columns_with_missing,
"max_jets_per_event": max_jets_per_event,
"frac_jets_missing_pt": frac_jets_missing_pt
}
with open("result.json", "w") as f:
f.write(str(result))
# Save the complete analysis as solution.py
with open("solution.py", "w") as f:
f.write('''import pandas as pd
import numpy as np
import os
# Read the data card
with open("README.md", "r") as f:
readme = f.read()
# Load the synthetic jets data
file_path = "data/synth_jets.csv"
df = pd.read_csv(file_path)
# Calculate n_rows
n_rows = len(df)
# Remove duplicate rows, keeping the first occurrence
df_unique = df.drop_duplicates(keep='first')
# Calculate n_duplicate_rows
n_duplicate_rows = n_rows - len(df_unique)
# Calculate n_unique_jets: identified by (event_id, jet_index)
n_unique_jets = df_unique[["event_id", "jet_index"]].drop_duplicates().shape[0]
# Calculate n_events: number of distinct events
n_events = df_unique["event_id"].nunique()
# Calculate n_missing_pt: number of distinct jets with missing jet_pt
n_missing_pt = df_unique[df_unique["jet_pt"].isna()].shape[0]
# Calculate n_missing_eta: number of distinct jets with jet_eta = -999.0
n_missing_eta = df_unique[df_unique["jet_eta"] == -999.0].shape[0]
# Identify columns with any missing values (including sentinel -999.0 for jet_eta)
columns_with_missing = []
for col in df.columns:
if df[col].isna().any() or (col == "jet_eta" and df[col] == -999.0).any():
columns_with_missing.append(col)
columns_with_missing.sort()
# Calculate max_jets_per_event: maximum number of distinct jets in one event
max_jets_per_event = df_unique.groupby("event_id").size().max()
# Calculate frac_jets_missing_pt
frac_jets_missing_pt = n_missing_pt / n_unique_jets
# Save results to result.json
result = {
"n_rows": n_rows,
"n_duplicate_rows": n_duplicate_rows,
"n_unique_jets": n_unique_jets,
"n_events": n_events,
"n_missing_pt": n_missing_pt,
"n_missing_eta": n_missing_eta,
"columns_with_missing": columns_with_missing,
"max_jets_per_event": max_jets_per_event,
"frac_jets_missing_pt": frac_jets_missing_pt
}
with open("result.json", "w") as f:
f.write(str(result))
''')
```
wrote 3895 chars to solution.py
STDERR:
Traceback (most recent call last):
File "/Users/gariyuu/Projects/agenthep/runs/20260906T174829-t1-missing-audit-self_debug-gariyuu-qwen3-8b-r0/workdir/.agenthep_guard.py", line 117, in main
runpy.run_path(SCRIPT, run_name="__main__")
File "<frozen runpy>", line 291, in run_path
File "<frozen runpy>", line 98, in _run_module_code
File "<frozen runpy>", line 88, in _run_code
File "/Users/gariyuu/Projects/agenthep/runs/20260906T174829-t1-missing-audit-self_debug-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 37, in <module>
if df[col].isna().any() or (col == "jet_eta" and df[col] == -999.0).any():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'bool' object has no attribute 'any'
exit code: 1```python
import pandas as pd
import numpy as np
import os
# Read the data card
with open("README.md", "r") as f:
readme = f.read()
# Load the synthetic jets data
file_path = "data/synth_jets.csv"
df = pd.read_csv(file_path)
# Calculate n_rows
n_rows = len(df)
# Remove duplicate rows, keeping the first occurrence
df_unique = df.drop_duplicates(keep='first')
# Calculate n_duplicate_rows
n_duplicate_rows = n_rows - len(df_unique)
# Calculate n_unique_jets: identified by (event_id, jet_index)
n_unique_jets = df_unique[["event_id", "jet_index"]].drop_duplicates().shape[0]
# Calculate n_events: number of distinct events
n_events = df_unique["event_id"].nunique()
# Calculate n_missing_pt: number of distinct jets with missing jet_pt
n_missing_pt = df_unique[df_unique["jet_pt"].isna()].shape[0]
# Calculate n_missing_eta: number of distinct jets with jet_eta = -999.0
n_missing_eta = df_unique[df_unique["jet_eta"] == -999.0].shape[0]
# Identify columns with any missing values (including sentinel -999.0 for jet_eta)
columns_with_missing = []
for col in df.columns:
# Check for NaN values
if df[col].isna().any():
columns_with_missing.append(col)
# Check for sentinel value in jet_eta
elif col == "jet_eta":
if (df[col] == -999.0).any():
columns_with_missing.append(col)
columns_with_missing.sort()
# Calculate max_jets_per_event: maximum number of distinct jets in one event
max_jets_per_event = df_unique.groupby("event_id").size().max()
# Calculate frac_jets_missing_pt
frac_jets_missing_pt = n_missing_pt / n_unique_jets
# Save results to result.json
result = {
"n_rows": n_rows,
"n_duplicate_rows": n_duplicate_rows,
"n_unique_jets": n_unique_jets,
"n_events": n_events,
"n_missing_pt": n_missing_pt,
"n_missing_eta": n_missing_eta,
"columns_with_missing": columns_with_missing,
"max_jets_per_event": max_jets_per_event,
"frac_jets_missing_pt": frac_jets_missing_pt
}
with open("result.json", "w") as f:
f.write(str(result))
# Save the complete analysis as solution.py
with open("solution.py", "w") as f:
f.write('''import pandas as pd
import numpy as np
import os
# Read the data card
with open("README.md", "r") as f:
readme = f.read()
# Load the synthetic jets data
file_path = "data/synth_jets.csv"
df = pd.read_csv(file_path)
# Calculate n_rows
n_rows = len(df)
# Remove duplicate rows, keeping the first occurrence
df_unique = df.drop_duplicates(keep='first')
# Calculate n_duplicate_rows
n_duplicate_rows = n_rows - len(df_unique)
# Calculate n_unique_jets: identified by (event_id, jet_index)
n_unique_jets = df_unique[["event_id", "jet_index"]].drop_duplicates().shape[0]
# Calculate n_events: number of distinct events
n_events = df_unique["event_id"].nunique()
# Calculate n_missing_pt: number of distinct jets with missing jet_pt
n_missing_pt = df_unique[df_unique["jet_pt"].isna()].shape[0]
# Calculate n_missing_eta: number of distinct jets with jet_eta = -999.0
n_missing_eta = df_unique[df_unique["jet_eta"] == -999.0].shape[0]
# Identify columns with any missing values (including sentinel -999.0 for jet_eta)
columns_with_missing = []
for col in df.columns:
# Check for NaN values
if df[col].isna().any():
columns_with_missing.append(col)
# Check for sentinel value in jet_eta
elif col == "jet_eta":
if (df[col] == -999.0).any():
columns_with_missing.append(col)
columns_with_missing.sort()
# Calculate max_jets_per_event: maximum number of distinct jets in one event
max_jets_per_event = df_unique.groupby("event_id").size().max()
# Calculate frac_jets_missing_pt
frac_jets_missing_pt = n_missing_pt / n_unique_jets
# Save results to result.json
result = {
"n_rows": n_rows,
"n_duplicate_rows": n_duplicate_rows,
"n_unique_jets": n_unique_jets,
"n_events": n_events,
"n_missing_pt": n_missing_pt,
"n_missing_eta": n_missing_eta,
"columns_with_missing": columns_with_missing,
"max_jets_per_event": max_jets_per_event,
"frac_jets_missing_pt": frac_jets_missing_pt
}
with open("result.json", "w") as f:
f.write(str(result))
''')
```
wrote 4155 chars to solution.py
exit code: 0 files created: result.json files modified: solution.py
Final answer
```python
import pandas as pd
import numpy as np
import os
# Read the data card
with open("README.md", "r") as f:
readme = f.read()
# Load the synthetic jets data
file_path = "data/synth_jets.csv"
df = pd.read_csv(file_path)
# Calculate n_rows
n_rows = len(df)
# Remove duplicate rows, keeping the first occurrence
df_unique = df.drop_duplicates(keep='first')
# Calculate n_duplicate_rows
n_duplicate_rows = n_rows - len(df_unique)
# Calculate n_unique_jets: identified by (event_id, jet_index)
n_unique_jets = df_unique[["event_id", "jet_index"]].drop_duplicates().shape[0]
# Calculate n_events: number of distinct events
n_events = df_unique["event_id"].nunique()
# Calculate n_missing_pt: number of distinct jets with missing jet_pt
n_missing_pt = df_unique[df_unique["jet_pt"].isna()].shape[0]
# Calculate n_missing_eta: number of distinct jets with jet_eta = -999.0
n_missing_eta = df_unique[df_unique["jet_eta"] == -999.0].shape[0]
# Identify columns with any missing values (including sentinel -999.0 for jet_eta)
columns_with_missing = []
for col in df.columns:
# Check for NaN values
if df[col].isna().any():
columns_with_missing.append(col)
# Check for sentinel value in jet_eta
elif col == "jet_eta":
if (df[col] == -999.0).any():
columns_with_missing.append(col)
columns_with_missing.sort()
# Calculate max_jets_per_event: maximum number of distinct jets in one event
max_jets_per_event = df_unique.groupby("event_id").size().max()
# Calculate frac_jets_missing_pt
frac_jets_missing_pt = n_missing_pt / n_unique_jets
# Save results to result.json
result = {
"n_rows": n_rows,
"n_duplicate_rows": n_duplicate_rows,
"n_unique_jets": n_unique_jets,
"n_events": n_events,
"n_missing_pt": n_missing_pt,
"n_missing_eta": n_missing_eta,
"columns_with_missing": columns_with_missing,
"max_jets_per_event": max_jets_per_event,
"frac_jets_missing_pt": frac_jets_missing_pt
}
with open("result.json",
Grader verdict
| Check | Result | Category | Weight | Detail | Observed | Expected |
|---|---|---|---|---|---|---|
| result_existscritical | ✗ fail | artifact | 1 | unparseable json: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| solution_exists | ✓ pass | artifact | 1 | ok | ||
| n_rows | ✗ fail | numeric | 1 | grader error: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| n_dupcritical | ✗ fail | numeric | 2 | grader error: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| n_uniquecritical | ✗ fail | numeric | 2 | grader error: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| n_events | ✗ fail | numeric | 1 | grader error: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| n_missing_pt | ✗ fail | numeric | 2 | grader error: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| n_missing_etacritical | ✗ fail | numeric | 3 | grader error: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| cols_missing | ✗ fail | numeric | 2 | grader error: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| max_jets | ✗ fail | numeric | 1 | grader error: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| frac_missing | ✗ fail | numeric | 1 | grader error: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| reruns | ✓ pass | reproducibility | 1 | rerun output differs from submission: ['result.json: JSONDecodeError'] | {"ok":true,"artifacts_reproduced":true,"output_matches":false,"exit_code":0} | |
| not_hardcoded | ✓ pass | compliance | 1 | no expected values hard-coded | [] |
Reproducibility rerun
✓ pass
✓ pass
✗ differs
✓ pass
no randomness used
rerun output differs from submission: ['result.json: JSONDecodeError']
Artifacts
result.json
{'n_rows': 65779, 'n_duplicate_rows': 1915, 'n_unique_jets': 63864, 'n_events': 20000, 'n_missing_pt': 1277, 'n_missing_eta': 957, 'columns_with_missing': ['jet_eta', 'jet_pt'], 'max_jets_per_event': 6, 'frac_jets_missing_pt': 0.01999561568332707}solution.py
import pandas as pd
import numpy as np
import os
# Read the data card
with open("README.md", "r") as f:
readme = f.read()
# Load the synthetic jets data
file_path = "data/synth_jets.csv"
df = pd.read_csv(file_path)
# Calculate n_rows
n_rows = len(df)
# Remove duplicate rows, keeping the first occurrence
df_unique = df.drop_duplicates(keep='first')
# Calculate n_duplicate_rows
n_duplicate_rows = n_rows - len(df_unique)
# Calculate n_unique_jets: identified by (event_id, jet_index)
n_unique_jets = df_unique[["event_id", "jet_index"]].drop_duplicates().shape[0]
# Calculate n_events: number of distinct events
n_events = df_unique["event_id"].nunique()
# Calculate n_missing_pt: number of distinct jets with missing jet_pt
n_missing_pt = df_unique[df_unique["jet_pt"].isna()].shape[0]
# Calculate n_missing_eta: number of distinct jets with jet_eta = -999.0
n_missing_eta = df_unique[df_unique["jet_eta"] == -999.0].shape[0]
# Identify columns with any missing values (including sentinel -999.0 for jet_eta)
columns_with_missing = []
for col in df.columns:
# Check for NaN values
if df[col].isna().any():
columns_with_missing.append(col)
# Check for sentinel value in jet_eta
elif col == "jet_eta":
if (df[col] == -999.0).any():
columns_with_missing.append(col)
columns_with_missing.sort()
# Calculate max_jets_per_event: maximum number of distinct jets in one event
max_jets_per_event = df_unique.groupby("event_id").size().max()
# Calculate frac_jets_missing_pt
frac_jets_missing_pt = n_missing_pt / n_unique_jets
# Save results to result.json
result = {
"n_rows": n_rows,
"n_duplicate_rows": n_duplicate_rows,
"n_unique_jets": n_unique_jets,
"n_events": n_events,
"n_missing_pt": n_missing_pt,
"n_missing_eta": n_missing_eta,
"columns_with_missing": columns_with_missing,
"max_jets_per_event": max_jets_per_event,
"frac_jets_missing_pt": frac_jets_missing_pt
}
with open("result.json", "w") as f:
f.write(str(result))
Run metadata
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"provider": "openai_compat",
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"temperature": 0,
"max_tokens": 2500,
"context_tokens": 8192,
"config": {
"base_url": "https://api.gariyuuu.com/v1",
"extra_body": {
"reasoning": {
"enabled": false
}
},
"context_tokens": 8192
},
"captured_at": "2026-09-06T17:48:29.344017+00:00",
"preset": "gariyuu-qwen3-8b",
"family": "qwen3-8b",
"display": "Qwen3-8B (gariyuu gateway)",
"is_mock": false
},
"agent": {
"name": "self_debug",
"max_steps": 25,
"max_debug_rounds": 3
},
"environment": {
"isolation": "seatbelt",
"platform": "macOS-15.1-arm64-arm-64bit",
"python": "3.11.15",
"limits": {
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},
"started_at": "2026-09-06T17:48:29.294341+00:00",
"finished_at": "2026-09-06T17:49:06.289528+00:00"
}