2026-09-06 17:34 UTC · seatbelt sandbox · git unknown

Data-quality audit of a jet table

t1-missing-audittier 1Planner / executorQwen3-8B (gariyuu gateway)✓ strict successtask spec →

Score

artifact 1 · numeric 1 · reproducibility 1 · compliance 1

LLM calls

5 tool calls

Tokens

23,009

18,406 in · 4,603 out

Cost

$0.00425

Wall

1.7 min

ended: submitted

Errors

2 recovered · 0 unsafe

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

observable actions only: assistant text, tool calls, tool results. Hidden reasoning tokens are counted, never shown.

#0 modelplan963→517 tok7.753st+22.894s

### Plan for Data-Quality Audit of `data/synth_jets.csv` 1. **Read the data file** (`data/synth_jets.csv`) to determine the number of rows. - **Computed**: `n_rows` - **Potential issues**: Missing or malformed data, empty file. 2. **Identify and remove duplicate rows** (keeping the first occurrence) to prepare for further analysis. - **Computed**: `n_duplicate_rows` (number of duplicates) - **Potential issues**: Duplicates may not be consistent, leading to incorrect counts. 3. **Determine the number of unique jets** using the `(event_id, jet_index)` pair as the identifier. - **Computed**: `n_unique_jets` - **Potential issues**: Duplicate rows may affect uniqueness, but we already removed them. 4. **Count the number of distinct events** from the `event_id` column. - **Computed**: `n_events` - **Potential issues**: Event IDs may have missing values or invalid formats. 5. **Count the number of jets with missing `jet_pt`** (NaN values). - **Computed**: `n_missing_pt` - **Potential issues**: NaN values may be misinterpreted or not properly handled. 6. **Count the number of jets with the `jet_eta` sentinel value (-999.0)**. - **Computed**: `n_missing_eta` - **Potential issues**: Sentinel values may be misclassified or not properly handled. 7. **Identify columns with any missing values**, including the sentinel value for `jet_eta`. - **Computed**: `columns_with_missing` (sorted list) - **Potential issues**: Missing values may be due to data entry errors or missing data. 8. **Compute the maximum number of distinct jets per event**. - **Computed**: `max_jets_per_event` - **Potential issues**: Events may have inconsistent jet counts or missing data. 9. **Calculate the fraction of jets with missing `jet_pt`** relative to the total number of unique jets. - **Computed**: `frac_jets_missing_pt` - **Potential issues**: Division by zero if there are no unique jets. 10. **Save all computed results** in `result.json`. - **Computed**: All keys as specified - **Potential issues**: File writing errors or incorrect formatting. This plan ensures that all required quantities are computed accurately and consistently, following the guidelines provided in the data card.

#1 modelact1694→36 tok1.536st+24.43s

→ read_file(path, max_chars, offset)

{
 "path": "README.md",
 "max_chars": 3000,
 "offset": 0
}

#2 toolread_fileok0.001st+24.431s

# Data card: `data/synth_jets.csv` (SYNTHETIC)

Simulated jet table for 20,000 events, **one row per jet**, produced by a toy generator with a known seed.
This is synthetic data; it is not a measurement.

| column | meaning | unit |
|---|---|---|
| event_id | event number | – |
| jet_index | index of the jet within its event, 0 = leading (highest-pt) jet | – |
| jet_pt | transverse momentum. **NaN when jet reconstruction failed.** | GeV |
| jet_eta | pseudorapidity. **The value -999.0 is a sentinel meaning "not measured".** | – |
| jet_phi | azimuthal angle | rad |
| jet_e_mev | jet energy. **Note the unit: MeV, not GeV.** | MeV |
| btag_score | b-tagging discriminant in [0, 1] | – |

## Known issues

* A jet is uniquely identified by the pair `(event_id, jet_index)`.
* Because of a bug in the ntuple merger, **some rows are exact duplicates**. Duplicates must be
  removed (keep the first occurrence) before any physics quantity is computed.
* Jets with missing `jet_pt` failed reconstruction and must be excluded from any sum over jet pt.
  Jets with the eta sentinel are otherwise valid (their pt and energy are measured).

#3 modelact2054→1265 tok20.428st+44.86s

→ run_python(code)

import pandas as pd

# Read the data file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Compute n_rows
n_rows = len(df)

# Identify and remove duplicate rows (keeping the first occurrence)
df_unique = df.drop_duplicates(keep='first')

# Compute n_duplicate_rows
n_duplicate_rows = len(df) - len(df_unique)

# Determine the number of unique jets using (event_id, jet_index) as the identifier
n_unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().count()

# Count the number of distinct events
n_events = df_unique['event_id'].nunique()

# Count the number of jets with missing jet_pt (NaN values)
n_missing_pt = df_unique[df_unique['jet_pt'].isna()].shape[0]

# Count the number of jets with the jet_eta sentinel value (-999.0)
n_missing_eta = df_unique[df_unique['jet_eta'] == -999.0].shape[0]

# Identify columns with any missing values, including the sentinel value for jet_eta
columns_with_missing = df_unique.columns[df_unique.isnull().any()].tolist()
# Include jet_eta if it has sentinel values
if -999.0 in df_unique['jet_eta'].values:
    columns_with_missing.append('jet_eta')
# Sort the list
columns_with_missing.sort()

# Compute the maximum number of distinct jets per event
max_jets_per_event = df_unique.groupby('event_id').size().max()

# Calculate the fraction of jets with missing jet_pt relative to the total number of unique jets
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the 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
}

import json
with open('result.json', 'w') as f:
    json.dump(result, f, indent=4)

# Save the complete analysis as solution.py
solution_code = """
import pandas as pd
import json

# Read the data file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Compute n_rows
n_rows = len(df)

# Identify and remove duplicate rows (keeping the first occurrence)
df_unique = df.drop_duplicates(keep='first')

# Compute n_duplicate_rows
n_duplicate_rows = len(df) - len(df_unique)

# Determine the number of unique jets using (event_id, jet_index) as the identifier
n_unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().count()

# Count the number of distinct events
n_events = df_unique['event_id'].nunique()

# Count the number of jets with missing jet_pt (NaN values)
n_missing_pt = df_unique[df_unique['jet_pt'].isna()].shape[0]

# Count the number of jets with the jet_eta sentinel value (-999.0)
n_missing_eta = df_unique[df_unique['jet_eta'] == -999.0].shape[0]

# Identify columns with any missing values, including the sentinel value for jet_eta
columns_with_missing = df_unique.columns[df_unique.isnull().any()].tolist()
# Include jet_eta if it has sentinel values
if -999.0 in df_unique['jet_eta'].values:
    columns_with_missing.append('jet_eta')
# Sort the list
columns_with_missing.sort()

# Compute the maximum number of distinct jets per event
max_jets_per_event = df_unique.groupby('event_id').size().max()

# Calculate the fraction of jets with missing jet_pt relative to the total number of unique jets
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the 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:
    json.dump(result, f, indent=4)
"""

with open('solution.py', 'w') as f:
    f.write(solution_code)

# Print the results
print('n_rows:', n_rows)
print('n_duplicate_rows:', n_duplicate_rows)
print('n_unique_jets:', n_unique_jets)
print('n_events:', n_events)
print('n_missing_pt:', n_missing_pt)
print('n_missing_eta:', n_missing_eta)
print('columns_with_missing:', columns_with_missing)
print('max_jets_per_event:', max_jets_per_event)
print('frac_jets_missing_pt:', frac_jets_missing_pt)

#4 toolrun_pythonTypeError1.054screated: result.jsont+45.914s

STDERR:
Traceback (most recent call last):
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T173433-t1-missing-audit-planner_executor-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/20260906T173433-t1-missing-audit-planner_executor-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 57, in <module>
    json.dump(result, f, indent=4)
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/__init__.py", line 179, in dump
    for chunk in iterable:
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 432, in _iterencode
    yield from _iterencode_dict(o, _current_indent_level)
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 406, in _iterencode_dict
    yield from chunks
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 439, in _iterencode
    o = _default(o)
        ^^^^^^^^^^^
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 180, in default
    raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type int64 is not JSON serializable

exit code: 1
files created: result.json

#5 modelact3940→1269 tok23.227st+69.141s

→ run_python(code)

import pandas as pd

# Read the data file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Compute n_rows
n_rows = len(df)

# Identify and remove duplicate rows (keeping the first occurrence)
df_unique = df.drop_duplicates(keep='first')

# Compute n_duplicate_rows
n_duplicate_rows = len(df) - len(df_unique)

# Determine the number of unique jets using (event_id, jet_index) as the identifier
n_unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().count()

# Count the number of distinct events
n_events = df_unique['event_id'].nunique()

# Count the number of jets with missing jet_pt (NaN values)
n_missing_pt = df_unique[df_unique['jet_pt'].isna()].shape[0]

# Count the number of jets with the jet_eta sentinel value (-999.0)
n_missing_eta = df_unique[df_unique['jet_eta'] == -999.0].shape[0]

# Identify columns with any missing values, including the sentinel value for jet_eta
columns_with_missing = df_unique.columns[df_unique.isnull().any()].tolist()
# Include jet_eta if it has sentinel values
if -999.0 in df_unique['jet_eta'].values:
    columns_with_missing.append('jet_eta')
# Sort the list
columns_with_missing.sort()

# Compute the maximum number of distinct jets per event
max_jets_per_event = df_unique.groupby('event_id').size().max()

# Calculate the fraction of jets with missing jet_pt relative to the total number of unique jets
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the 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
}

import json
with open('result.json', 'w') as f:
    json.dump(result, f, indent=4)

# Save the complete analysis as solution.py
solution_code = """
import pandas as pd
import json

# Read the data file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Compute n_rows
n_rows = len(df)

# Identify and remove duplicate rows (keeping the first occurrence)
df_unique = df.drop_duplicates(keep='first')

# Compute n_duplicate_rows
n_duplicate_rows = len(df) - len(df_unique)

# Determine the number of unique jets using (event_id, jet_index) as the identifier
n_unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().count()

# Count the number of distinct events
n_events = df_unique['event_id'].nunique()

# Count the number of jets with missing jet_pt (NaN values)
n_missing_pt = df_unique[df_unique['jet_pt'].isna()].shape[0]

# Count the number of jets with the jet_eta sentinel value (-999.0)
n_missing_eta = df_unique[df_unique['jet_eta'] == -999.0].shape[0]

# Identify columns with any missing values, including the sentinel value for jet_eta
columns_with_missing = df_unique.columns[df_unique.isnull().any()].tolist()
# Include jet_eta if it has sentinel values
if -999.0 in df_unique['jet_eta'].values:
    columns_with_missing.append('jet_eta')
# Sort the list
columns_with_missing.sort()

# Compute the maximum number of distinct jets per event
max_jets_per_event = df_unique.groupby('event_id').size().max()

# Calculate the fraction of jets with missing jet_pt relative to the total number of unique jets
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the 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:
    json.dump(result, f, indent=4)
"""

with open('solution.py', 'w') as f:
    f.write(solution_code)

# Print the results
print('n_rows:', n_rows)
print('n_duplicate_rows:', n_duplicate_rows)
print('n_unique_jets:', n_unique_jets)
print('n_events:', n_events)
print('n_missing_pt:', n_missing_pt)
print('n_missing_eta:', n_missing_eta)
print('columns_with_missing:', columns_with_missing)
print('max_jets_per_event:', max_jets_per_event)
print('frac_jets_missing_pt:', frac_jets_missing_pt)

#6 toolrun_pythonTypeError0.931st+70.074s

STDERR:
Traceback (most recent call last):
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T173433-t1-missing-audit-planner_executor-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/20260906T173433-t1-missing-audit-planner_executor-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 57, in <module>
    json.dump(result, f, indent=4)
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/__init__.py", line 179, in dump
    for chunk in iterable:
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 432, in _iterencode
    yield from _iterencode_dict(o, _current_indent_level)
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 406, in _iterencode_dict
    yield from chunks
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 439, in _iterencode
    o = _default(o)
        ^^^^^^^^^^^
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 180, in default
    raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type int64 is not JSON serializable

exit code: 1
files modified: result.json

#7 modelact4421→1303 tok27.969st+98.044s

→ run_python(code)

import pandas as pd

# Read the data file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Compute n_rows
n_rows = len(df)

# Identify and remove duplicate rows (keeping the first occurrence)
df_unique = df.drop_duplicates(keep='first')

# Compute n_duplicate_rows
n_duplicate_rows = len(df) - len(df_unique)

# Determine the number of unique jets using (event_id, jet_index) as the identifier
n_unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().count()

# Count the number of distinct events
n_events = df_unique['event_id'].nunique()

# Count the number of jets with missing jet_pt (NaN values)
n_missing_pt = df_unique[df_unique['jet_pt'].isna()].shape[0]

# Count the number of jets with the jet_eta sentinel value (-999.0)
n_missing_eta = df_unique[df_unique['jet_eta'] == -999.0].shape[0]

# Identify columns with any missing values, including the sentinel value for jet_eta
columns_with_missing = df_unique.columns[df_unique.isnull().any()].tolist()
# Include jet_eta if it has sentinel values
if -999.0 in df_unique['jet_eta'].values:
    columns_with_missing.append('jet_eta')
# Sort the list
columns_with_missing.sort()

# Compute the maximum number of distinct jets per event
max_jets_per_event = df_unique.groupby('event_id').size().max()

# Calculate the fraction of jets with missing jet_pt relative to the total number of unique jets
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the results to result.json
result = {
    'n_rows': int(n_rows),
    'n_duplicate_rows': int(n_duplicate_rows),
    'n_unique_jets': int(n_unique_jets),
    'n_events': int(n_events),
    'n_missing_pt': int(n_missing_pt),
    'n_missing_eta': int(n_missing_eta),
    'columns_with_missing': columns_with_missing,
    'max_jets_per_event': int(max_jets_per_event),
    'frac_jets_missing_pt': float(frac_jets_missing_pt)
}

import json
with open('result.json', 'w') as f:
    json.dump(result, f, indent=4)

# Save the complete analysis as solution.py
solution_code = """
import pandas as pd
import json

# Read the data file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Compute n_rows
n_rows = len(df)

# Identify and remove duplicate rows (keeping the first occurrence)
df_unique = df.drop_duplicates(keep='first')

# Compute n_duplicate_rows
n_duplicate_rows = len(df) - len(df_unique)

# Determine the number of unique jets using (event_id, jet_index) as the identifier
n_unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().count()

# Count the number of distinct events
n_events = df_unique['event_id'].nunique()

# Count the number of jets with missing jet_pt (NaN values)
n_missing_pt = df_unique[df_unique['jet_pt'].isna()].shape[0]

# Count the number of jets with the jet_eta sentinel value (-999.0)
n_missing_eta = df_unique[df_unique['jet_eta'] == -999.0].shape[0]

# Identify columns with any missing values, including the sentinel value for jet_eta
columns_with_missing = df_unique.columns[df_unique.isnull().any()].tolist()
# Include jet_eta if it has sentinel values
if -999.0 in df_unique['jet_eta'].values:
    columns_with_missing.append('jet_eta')
# Sort the list
columns_with_missing.sort()

# Compute the maximum number of distinct jets per event
max_jets_per_event = df_unique.groupby('event_id').size().max()

# Calculate the fraction of jets with missing jet_pt relative to the total number of unique jets
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the results to result.json
result = {
    'n_rows': int(n_rows),
    'n_duplicate_rows': int(n_duplicate_rows),
    'n_unique_jets': int(n_unique_jets),
    'n_events': int(n_events),
    'n_missing_pt': int(n_missing_pt),
    'n_missing_eta': int(n_missing_eta),
    'columns_with_missing': columns_with_missing,
    'max_jets_per_event': int(max_jets_per_event),
    'frac_jets_missing_pt': float(frac_jets_missing_pt)
}

with open('result.json', 'w') as f:
    json.dump(result, f, indent=4)
"""

with open('solution.py', 'w') as f:
    f.write(solution_code)

# Print the results
print('n_rows:', n_rows)
print('n_duplicate_rows:', n_duplicate_rows)
print('n_unique_jets:', n_unique_jets)
print('n_events:', n_events)
print('n_missing_pt:', n_missing_pt)
print('n_missing_eta:', n_missing_eta)
print('columns_with_missing:', columns_with_missing)
print('max_jets_per_event:', max_jets_per_event)
print('frac_jets_missing_pt:', frac_jets_missing_pt)

#8 toolrun_pythonok0.771screated: solution.pyt+98.815s

STDOUT:
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

exit code: 0
files created: solution.py
files modified: result.json

#9 modelact5334→213 tok5.926st+104.741s

→ submit(summary, assumptions)

{
 "summary": "Completed data-quality audit of `data/synth_jets.csv`. Key results: 65779 rows, 1915 duplicates, 63864 unique jets, 20000 events, 1277 jets missing PT, 957 jets with missing ETA, columns with missing values: ['jet_eta', 'jet_pt'], max jets per event: 6, fraction of jets missing PT: 0.01999561568332707.",
 "assumptions": [
  "The data card specifies that -999.0 is the sentinel for missing jet_eta.",
  "Duplicate rows are identified by exact repetition.",
  "Missing values in other columns are handled using pandas' isnull() function.",
  "The (event_id, jet_index) pair uniquely identifies jets.",
  "The fraction of jets missing PT is calculated relative to the total number of unique jets."
 ]
}

#10 toolsubmitok0st+104.741s

submitted

Final answer

Completed data-quality audit of `data/synth_jets.csv`. Key results: 65779 rows, 1915 duplicates, 63864 unique jets, 20000 events, 1277 jets missing PT, 957 jets with missing ETA, columns with missing values: ['jet_eta', 'jet_pt'], max jets per event: 6, fraction of jets missing PT: 0.01999561568332707.

The data card specifies that -999.0 is the sentinel for missing jet_eta.
Duplicate rows are identified by exact repetition.
Missing values in other columns are handled using pandas' isnull() function.
The (event_id, jet_index) pair uniquely identifies jets.
The fraction of jets missing PT is calculated relative to the total number of unique jets.

Grader verdict

13 / 13 checks passed

Check	Result	Category	Weight	Detail	Observed	Expected
result_existscritical	✓ pass	artifact	1	ok
solution_exists	✓ pass	artifact	1	ok
n_rows	✓ pass	numeric	1	match	65779	65779
n_dupcritical	✓ pass	numeric	2	match	1915	1915
n_uniquecritical	✓ pass	numeric	2	match	63864	63864
n_events	✓ pass	numeric	1	match	20000	20000
n_missing_pt	✓ pass	numeric	2	match	1277	1277
n_missing_etacritical	✓ pass	numeric	3	match	957	957
cols_missing	✓ pass	numeric	2	match	["jet_eta","jet_pt"]	["jet_eta","jet_pt"]
max_jets	✓ pass	numeric	1	match	6	6
frac_missing	✓ pass	numeric	1	match	0.01999561568332707	0.01999561568332707
reruns	✓ pass	reproducibility	1	reproduced exactly	{"ok":true,"artifacts_reproduced":true,"output_matches":true,"exit_code":0}
not_hardcoded	✓ pass	compliance	1	no expected values hard-coded	[]

Reproducibility rerun

script executes

✓ pass

artifacts reproduced

✓ pass

output matches submission

✓ exact

dependencies resolvable

✓ pass

seed controlled

no randomness used

detail

reproduced exactly

Artifacts

result.json: run_python · solution.py: run_python

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 json

# Read the data file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Compute n_rows
n_rows = len(df)

# Identify and remove duplicate rows (keeping the first occurrence)
df_unique = df.drop_duplicates(keep='first')

# Compute n_duplicate_rows
n_duplicate_rows = len(df) - len(df_unique)

# Determine the number of unique jets using (event_id, jet_index) as the identifier
n_unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().count()

# Count the number of distinct events
n_events = df_unique['event_id'].nunique()

# Count the number of jets with missing jet_pt (NaN values)
n_missing_pt = df_unique[df_unique['jet_pt'].isna()].shape[0]

# Count the number of jets with the jet_eta sentinel value (-999.0)
n_missing_eta = df_unique[df_unique['jet_eta'] == -999.0].shape[0]

# Identify columns with any missing values, including the sentinel value for jet_eta
columns_with_missing = df_unique.columns[df_unique.isnull().any()].tolist()
# Include jet_eta if it has sentinel values
if -999.0 in df_unique['jet_eta'].values:
    columns_with_missing.append('jet_eta')
# Sort the list
columns_with_missing.sort()

# Compute the maximum number of distinct jets per event
max_jets_per_event = df_unique.groupby('event_id').size().max()

# Calculate the fraction of jets with missing jet_pt relative to the total number of unique jets
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the results to result.json
result = {
    'n_rows': int(n_rows),
    'n_duplicate_rows': int(n_duplicate_rows),
    'n_unique_jets': int(n_unique_jets),
    'n_events': int(n_events),
    'n_missing_pt': int(n_missing_pt),
    'n_missing_eta': int(n_missing_eta),
    'columns_with_missing': columns_with_missing,
    'max_jets_per_event': int(max_jets_per_event),
    'frac_jets_missing_pt': float(frac_jets_missing_pt)
}

with open('result.json', 'w') as f:
    json.dump(result, f, indent=4)

Run metadata

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