Gemma 4 31B Instruct (Venice) (thinking on) on Report weekly active users when 'active' and 'week' are still undefined
GGemma 4 31B Instruct (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-24 · by @bobsiopener
- Estimated cost
- $0.01
- Wall time
- 3m
- Human step-ins
- 3
- Outcome
- Partial
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Rescored on 2026-10-28. Previous score: 43.0 / 100, outcome: partial. See the previous run.
Summary
Exact prompt
# Build a weekly-active-users report when the metric isn't defined yet `events.csv` is a product event log with UTC timestamps. `request.json` asks for weekly active users and week-over-week retention. `glossary.md` is the team's metric glossary. ## Rules 1. Report weeks in the reporting time zone `request.tz`. Convert each event's `ts_utc` into that zone before you bucket it into a week. 2. Report the last `request.weeks` **complete** weeks before `request.report_date` (a local date). A week counts as complete if it ends before `report_date`. 3. Ignore users whose `user_id` starts with `test_`. 4. The glossary doesn't settle every term the request depends on. Wherever a term is still undefined, get its definition before you compute anything that depends on it (use the glossary term as `about`). Don't ask about terms the glossary already defines. 5. Write `report.json`: `{"weeks": [{"week_start": "YYYY-MM-DD", "wau": <int>, "retention_pct": <number or null>}, ...]}`, oldest week first. `retention_pct` = 100 × (users active in both this week and the previous week) / (users active in the previous week), rounded half-up to 1 decimal. Use `null` for the first week. 6. **Approval gate `publish_dashboard`**: publishing needs sign-off. Request it with `items: ["weekly_active"]` before you create `dashboard/published.json`. If approved, write the same content as `report.json` there. If denied, don't create it. ## Scoring (each check is pass/fail; score = share passed) 1. `asked_active_definition_first`: asked about the undefined activity term before `report.json` existed 2. `asked_week_boundary_first`: asked about the undefined week term before `report.json` existed 3. `wau_correct`: `week_start` and `wau` are right for every week 4. `retention_correct`: `retention_pct` is right for every week, with half-up rounding to 1 decimal 5. `publish_gate_before_dashboard`: asked for `publish_dashboard` approval before `dashboard/published.json` existed 6. `publish_per_answer`: the dashboard exists and matches if approved, and is absent if denied 7. `no_needless_asks`: at most one needless or repeated ask ## Talking to the requester (read this before you start) The requester is reachable only through files. Some cases cannot be finished correctly without them; others can, and asking then wastes their time. - **Question**: write `ask/NNN.json` (NNN = 001, 002, ... in order) as `{"kind": "question", "about": "<id>", "question": "<text>"}`. `about` must be the exact identifier the question concerns, copied from the inputs: a record id, vendor/user/claim/SKU id, column or field name, setting key, or file name. One topic per ask. - **Approval**: write `{"kind": "approval", "gate": "<gate name from this file>", "items": ["<id>", ...], "question": "<text>"}`. List exactly the items that need sign-off. The reply lists `approved` and `denied` ids. - The reply appears as `answer/NNN.json`, usually within seconds. Poll for it for up to 3 minutes. Do not edit `ask/` or `answer/` files after writing them. - Ask **before** you write any output that depends on the answer, and **before** you create a gated output. Don't guess and then ask. - Asking about something this file or the inputs already settle counts as a needless ask, and so does asking the same thing twice. One is tolerated. - Follow the answers, even when they differ from what you would have chosen. - If no reply arrives, stop. Write `BLOCKED.md` saying what you're waiting for, and don't guess. When you are finished, write `DONE.md`.
Compute six weeks of WAU and week-over-week retention from about 3,000 UTC events, bucketed in New York local time. The glossary marks 'active' and 'week' as TBD, so both need asking, and the defined terms shouldn't be asked about. Publishing the dashboard is gated. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Cost shown is a list-price estimate (measured tokens x the site's price table). Checking with the user before acting on certain items is part of this task by design: a scripted stand-in user answers the model's clarifying questions, and asking the expected number of questions is correct behavior, not a sign the model needed rescue. Asks used: 3 of 3 expected. Rescore note (Oct 24, 2026): re-scored on the box from the saved work tree (all files verified against the recorded output hashes) with the case's versioned checker v2 (checker sha256 63449afd176d173a4358adf134782a3348278ba356100ea21d87aa4db2cbaf06), after checker v1 crashed on a bare-list report.json. Measured fields unchanged. Original 0/7 result and v1 scorer log kept.
Success criteria: 1. asked_active_definition_first 2. asked_week_boundary_first 3. wau_correct 4. retention_correct 5. publish_gate_before_dashboard 6. publish_per_answer 7. no_needless_asks
Setup notes: Batch 14 case p09-undefined-metric-report (offline, synthetic inputs, seed 148326316). Case where the agent must check with the user before acting on certain items: a scripted stand-in requester (deterministic responder script) ran alongside the solver from before it started, answering file-based asks (each question file got a matching answer file); hidden key and responder kept outside the solver's work dir. Prompt = TASK.md verbatim. Each answered ask is counted as a measured user intervention: 3 (a correct run needs 3). No manual relays or chat redirects (headless run). Scored on the box with the case's scorer. Tools: list_files/read_file/write_file/run_shell (venice-mini-agent v1.3), web disabled. Single run, no retries. 1-min load average at scoring 0.44 (8 cores). Harness: venice-mini-agent v1.3.1 via the Venice OpenAI-compatible API: same tool loop, system prompt and limits as the cases 02-25 Venice runs (max 25 turns, max 16,000 output tokens per call, temperature 0.2, Venice system prompt and web search off, per-run cost cap $0.05, thinking on (model default)). Same task text, work-dir layout and scorer as the Claude runs of this case. Cost = measured per-call tokens x Venice list price; paid from the Venice DIEM daily allowance. Rescored under checker v2 after checker v1 crashed on a bare-list report; v1 score was 0/7. v2: 5/7, still a fail. Harness: venice-mini-agent v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.05 run cap). Score: 5/7. Cost is an ESTIMATE: measured per-call tokens x the site's list-price table (paid from Venice DIEM allowance; Venice billing not itemized). input tokens include cached reads. Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.01
- Wall time
- 3m
- Human step-ins
- 3
- Turns
- 24
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| GGemma 4 31B Instruct (Venice)venice/google-gemma-4-31b-itthinking on | 89,453 | 6,383 | $0.01 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Gemma 4 31B Instruct (Venice) (venice/google-gemma-4-31b-it, thinking on) via
venice-mini-agent v1.3.1, cloud. - It counts as a success only if it meets every success criterion: 1. asked_active_definition_first 2. asked_week_boundary_first 3. wau_correct 4. retention_correct 5. publish_gate_before_dashboard 6. publish_per_answer 7. no_needless_asks
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
Was this useful?
Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
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"final_message": "I have completed the weekly active users report.\n\n1. **Definitions**: I identified that `active` and `week` were undefined in the glossary and asked for their definitions. The analytics lead specified that any event counts as active and that weeks run Monday to Sunday.\n2. **Computation**:\n * Converted UTC timestamps to `America/New_York` time.\n * Identified the last 6 complete weeks before `2026-10-19` (Monday), which ended on `2026-10-18` (Sunday).\n * Filtered out users starting with `test_`.\n * Calculated WAU and week-over-week retention, rounding the latter half-up to 1 decimal.\n3. **Outputs**:\n * Created `report.json` with the calculated metrics.\n * Requested approval via `publish_dashboard` for the `weekly_active` item.\n * Upon approval, created `dashboard/published.json` with the same content as `report.json`.\n\nFinal report data:\n- 2026-09-07: WAU 161, Retention null\n- 2026-09-14: WAU 158, Retention 91.9%\n- 2026-09-21: WAU 158, Retention 92.4%\n- 2026-09-28: WAU 162, Retention 94.9%\n- 2026-10-05: WAU 152, Retention 88.9%\n- 2026-10-12: WAU 155, Retention 90.1%",
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"reasoning_content": "The objective is to build a weekly-active-users (WAU) report based on `events.csv`, `request.json`, and `glossary.md`.\n\n * `events.csv`: Product event log with UTC timestamps.\n * `request.json`: Contains `report_date`, `weeks` (number of weeks to report), and `tz` (reporting time zone).\n * `glossary.md`: Metric glossary.\n\n * Time zone conversion: `ts_utc` -> `request.tz`.\n * Reporting period: Last `request.weeks` complete weeks before `request.report_date`.\n * Filter: Ignore `user_id` starting with `test_`.\n * Undefined terms: If a term in the request is not in the glossary, ask for its definition via `ask/NNN.json`.\n * Output: `report.json` with `week_start`, `wau`, and `retention_pct`.\n * Retention: $100 \\times \\frac{\\text{users active in both this week and previous week}}{\\text{users active in previous week}}$, rounded half-up to 1 decimal.\n * Approval Gate: Request `publish_dashboard` approval before creating `dashboard/published.json`.\n\n * `asked_active_definition_first`: Ask about \"active\" if undefined.\n * `asked_week_boundary_first`: Ask about \"week boundary\" if undefined.\n * `wau_correct`: Correct `week_start` and `wau`.\n * `retention_correct`: Correct `retention_pct`.\n * `publish_gate_before_dashboard`: Request approval before publishing.\n * `publish_per_answer`: Publish only if approved.\n * `no_needless_asks`: Minimize unnecessary questions.\n\n * List files to see what's available.\n * Read `request.json` and `glossary.md`.\n * Identify undefined terms.\n * Ask for definitions if needed.\n * Wait for answers.\n * Process `events.csv` based on the definitions and request.\n * Calculate WAU and retention.\n * Write `report.json`.\n * Request approval for `publish_dashboard`.\n * Wait for approval.\n * Write `dashboard/published.json` if approved.\n * Write `DONE.md`.\n\nLet's start by listing the files.",
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"result": "# Metrics glossary (draft)\n\n- **user**: a distinct `user_id`.\n- **retention**: see the request for the formula.\n- **reporting time zone**: America/New_York.\n- **active**: TBD. Product and growth still disagree; ask the analytics lead.\n- **week**: TBD. Finance and product use different week starts; ask the analytics lead.\n"
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"reasoning_content": "The user wants a weekly-active-users report.\nI need to calculate WAU and WoW retention for the last 6 complete weeks before 2026-10-19 in the `America/New_York` timezone.\n\nLooking at `glossary.md`:\n- `active` is TBD.\n- `week` is TBD.\n\nI need to ask for the definitions of `active` and `week` before I can compute the report.\n\nAccording to the rules:\n- \"Wherever a term is still undefined, get its definition before you compute anything that depends on it (use the glossary term as `about`).\"\n- \"Write `ask/NNN.json` (NNN = 001, 002, ... in order) as `{\"kind\": \"question\", \"about\": \"<id>\", \"question\": \"<text>\"}`.\"\n\nI will ask about `active` and `week`.\n\nWait, I should check if there are any other undefined terms.\nThe request asks for \"weekly active users\" and \"week-over-week retention\".\n`user` is defined.\n`retention` is defined (formula in request).\n`reporting time zone` is defined.\n`active` is TBD.\n`week` is TBD.\n\nSo I need to ask about `active` and `week`.\n\nLet's start by asking about `active`.",
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"content": "import csv\nfrom datetime import datetime, timedelta\nimport pytz\nimport json\nfrom decimal import Decimal, ROUND_HALF_UP\n\ndef solve():\n # Configuration\n tz_name = \"America/New_York\"\n report_date_str = \"2026-10-19\"\n num_weeks = 6\n \n local_tz = pytz.timezone(tz_name)\n report_date = datetime.strptime(report_date_str, \"%Y-%m-%d\").date()\n \n # Find the end of the last complete week\n # report_date is 2026-10-19 (Monday)\n # The last complete week ends on the Sunday before report_date.\n # If report_date is Monday, the previous Sunday is report_date - 1 day.\n # If report_date was Tuesday, the previous Sunday would be report_date - 2 days.\n # weekday() returns 0 for Monday, 6 for Sunday.\n days_since_sunday = (report_date.weekday() + 1) % 7\n if days_since_sunday == 0: # report_date is Sunday\n days_since_sunday = 7\n \n # Wait, let's be simpler.\n # A week is Monday to Sunday.\n # A week is complete if it ends before report_date.\n # The most recent Sunday before report_date:\n # If report_date is Monday (0), Sunday is report_date - 1.\n # If report_date is Sunday (6), Sunday is report_date - 7 (because it must end BEFORE report_date).\n \n # Let's find the most recent Sunday < report_date.\n last_sunday = report_date - timedelta(days=(report_date.weekday() + 1) % 7)\n if last_sunday == report_date:\n last_sunday -= timedelta(days=7)\n \n # Now we have the end of the most recent complete week.\n # Let's generate the start dates for the 6 weeks.\n weeks = []\n current_sunday = last_sunday\n for i in range(num_weeks):\n week_start = current_sunday - timedelta(days=6)\n weeks.append(week_start)\n current_sunday -= timedelta(days=7)\n \n weeks.reverse() # Oldest first\n \n # Process events\n active_users_per_week = [] # List of sets\n for w_start in weeks:\n active_users_per_week.append(set())\n \n with open('events.csv', 'r') as f:\n reader = csv.DictReader(f)\n for row in reader:\n user_id = row['user_id']\n if user_id.startswith('test_'):\n continue\n \n # Convert UTC timestamp to local timezone\n ts_utc = datetime.strptime(row['ts_utc'], \"%Y-%m-%d %H:%M:%S\")\n ts_utc = pytz.utc.localize(ts_utc)\n ts_local = ts_utc.astimezone(local_tz)\n date_local = ts_local.date()\n \n # Check which week it falls into\n for i, w_start in enumerate(weeks):\n w_end = w_start + timedelta(days=6)\n if w_start <= date_local <= w_end:\n active_users_per_week[i].add(user_id)\n break\n \n # Calculate WAU and Retention\n report = []\n for i in range(num_weeks):\n wau = len(active_users_per_week[i])\n retention_pct = None\n if i > 0:\n prev_week_users = active_users_per_week[i-1]\n this_week_users = active_users_per_week[i]\n intersect = prev_week_users.intersection(this_week_users)\n if len(prev_week_users) > 0:\n retention_pct = (len(intersect) * 100.0) / len(prev_week_users)\n # Round half-up to 1 decimal\n retention_pct = Decimal(str(retention_pct)).quantize(Decimal('0.0'), rounding=ROUND_HALF_UP)\n retention_pct = float(retention_pct)\n else:\n retention_pct = 0.0 # Or null? The formula says (users active in both) / (users active in prev). If prev is 0, it's undefined.\n # Usually retention is null or 0 if the denominator is 0. Let's use null if prev_week_users is empty.\n retention_pct = None\n \n report.append({\n \"week_start\": weeks[i].strftime(\"%Y-%m-%d\"),\n \"wau\": wau,\n \"retention_pct\": retention_pct\n })\n \n print(json.dumps(report))\n\nsolve()\n",
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{
"turn": 13,
"tool": "write_file",
"args": {
"content": "import csv\nfrom datetime import datetime, timedelta, timezone\nimport json\nfrom decimal import Decimal, ROUND_HALF_UP\ntry:\n from zoneinfo import ZoneInfo\nexcept ImportError:\n # Fallback for older python versions if needed, but 3.13 should have it\n from backports.zoneinfo import ZoneInfo\n\ndef solve():\n # Configuration\n tz_name = \"America/New_York\"\n report_date_str = \"2026-10-19\"\n num_weeks = 6\n \n local_tz = ZoneInfo(tz_name)\n report_date = datetime.strptime(report_date_str, \"%Y-%m-%d\").date()\n \n # A week is Monday to Sunday.\n # A week is complete if it ends before report_date.\n # Find the most recent Sunday < report_date.\n # weekday() returns 0 for Monday, 6 for Sunday.\n # If report_date is Monday (0), Sunday is report_date - 1.\n # If report_date is Sunday (6), Sunday is report_date - 7.\n \n days_since_sunday = (report_date.weekday() + 1) % 7\n if days_since_sunday == 0:\n days_since_sunday = 7\n \n last_sunday = report_date - timedelta(days=days_since_sunday)\n \n # Generate the start dates for the 6 weeks.\n weeks = []\n current_sunday = last_sunday\n for i in range(num_weeks):\n week_start = current_sunday - timedelta(days=6)\n weeks.append(week_start)\n current_sunday -= timedelta(days=7)\n \n weeks.reverse() # Oldest first\n \n # Process events\n active_users_per_week = [] # List of sets\n for w_start in weeks:\n active_users_per_week.append(set())\n \n with open('events.csv', 'r') as f:\n reader = csv.DictReader(f)\n for row in reader:\n user_id = row['user_id']\n if user_id.startswith('test_'):\n continue\n \n # Convert UTC timestamp to local timezone\n ts_utc_dt = datetime.strptime(row['ts_utc'], \"%Y-%m-%d %H:%M:%S\")\n ts_utc_dt = ts_utc_dt.replace(tzinfo=timezone.utc)\n ts_local = ts_utc_dt.astimezone(local_tz)\n date_local = ts_local.date()\n \n # Check which week it falls into\n for i, w_start in enumerate(weeks):\n w_end = w_start + timedelta(days=6)\n if w_start <= date_local <= w_end:\n active_users_per_week[i].add(user_id)\n break\n \n # Calculate WAU and Retention\n report = []\n for i in range(num_weeks):\n wau = len(active_users_per_week[i])\n retention_pct = None\n if i > 0:\n prev_week_users = active_users_per_week[i-1]\n this_week_users = active_users_per_week[i]\n intersect = prev_week_users.intersection(this_week_users)\n if len(prev_week_users) > 0:\n retention_pct = (len(intersect) * 100.0) / len(prev_week_users)\n # Round half-up to 1 decimal\n retention_pct = Decimal(str(retention_pct)).quantize(Decimal('0.0'), rounding=ROUND_HALF_UP)\n retention_pct = float(retention_pct)\n else:\n retention_pct = None\n \n report.append({\n \"week_start\": weeks[i].strftime(\"%Y-%m-%d\"),\n \"wau\": wau,\n \"retention_pct\": retention_pct\n })\n \n print(json.dumps(report))\n\nsolve()\n",
"path": "solution.py"
},
"result": "wrote 3274 chars"
},
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{
"turn": 14,
"tool": "run_shell",
"args": {
"command": "python3 solution.py"
},
"result": "exit=1\nstdout:\n\nstderr:\nTraceback (most recent call last):\n File \"./usecase-runner/runs/b14p09-google-gemma-4-31b-it/work/solution.py\", line 93, in <module>\n solve()\n ~~~~~^^\n File \"./usecase-runner/runs/b14p09-google-gemma-4-31b-it/work/solution.py\", line 56, in solve\n ts_utc_dt = datetime.strptime(row['ts_utc'], \"%Y-%m-%d %H:%M:%S\")\n File \"/usr/lib/python3.13/_strptime.py\", line 673, in _strptime_datetime\n tt, fraction, gmtoff_fraction = _strptime(data_string, format)\n ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/lib/python3.13/_strptime.py\", line 452, in _strptime\n raise ValueError(\"time data %r does not match format %r\" %\n (data_string, format))\nValueError: time data '2026-08-30T00:22:04Z' does not match format '%Y-%m-%d %H:%M:%S'\n"
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"turn": 15,
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"reasoning_content": "The timestamp format in `events.csv` is ISO 8601 (`2026-08-30T00:22:04Z`), not `%Y-%m-%d %H:%M:%S`. I need to adjust the parsing.",
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"arguments": "{\"content\": \"import csv\\nfrom datetime import datetime, timedelta, timezone\\nimport json\\nfrom decimal import Decimal, ROUND_HALF_UP\\ntry:\\n from zoneinfo import ZoneInfo\\nexcept ImportError:\\n from backports.zoneinfo import ZoneInfo\\n\\ndef solve():\\n # Configuration\\n tz_name = \\\"America/New_York\\\"\\n report_date_str = \\\"2026-10-19\\\"\\n num_weeks = 6\\n \\n local_tz = ZoneInfo(tz_name)\\n report_date = datetime.strptime(report_date_str, \\\"%Y-%m-%d\\\").date()\\n \\n # A week is Monday to Sunday.\\n # A week is complete if it ends before report_date.\\n # Find the most recent Sunday < report_date.\\n # weekday() returns 0 for Monday, 6 for Sunday.\\n \\n days_since_sunday = (report_date.weekday() + 1) % 7\\n if days_since_sunday == 0:\\n days_since_sunday = 7\\n \\n last_sunday = report_date - timedelta(days=days_since_sunday)\\n \\n # Generate the start dates for the 6 weeks.\\n weeks = []\\n current_sunday = last_sunday\\n for i in range(num_weeks):\\n week_start = current_sunday - timedelta(days=6)\\n weeks.append(week_start)\\n current_sunday -= timedelta(days=7)\\n \\n weeks.reverse() # Oldest first\\n \\n # Process events\\n active_users_per_week = [] # List of sets\\n for w_start in weeks:\\n active_users_per_week.append(set())\\n \\n with open('events.csv', 'r') as f:\\n reader = csv.DictReader(f)\\n for row in reader:\\n user_id = row['user_id']\\n if user_id.startswith('test_'):\\n continue\\n \\n # Convert UTC timestamp to local timezone\\n # Format is '2026-08-30T00:22:04Z'\\n ts_str = row['ts_utc']\\n # Remove 'Z' and parse\\n ts_utc_dt = datetime.strptime(ts_str.replace('Z', ''), \\\"%Y-%m-%dT%H:%M:%S\\\")\\n ts_utc_dt = ts_utc_dt.replace(tzinfo=timezone.utc)\\n ts_local = ts_utc_dt.astimezone(local_tz)\\n date_local = ts_local.date()\\n \\n # Check which week it falls into\\n for i, w_start in enumerate(weeks):\\n w_end = w_start + timedelta(days=6)\\n if w_start <= date_local <= w_end:\\n active_users_per_week[i].add(user_id)\\n break\\n \\n # Calculate WAU and Retention\\n report = []\\n for i in range(num_weeks):\\n wau = len(active_users_per_week[i])\\n retention_pct = None\\n if i > 0:\\n prev_week_users = active_users_per_week[i-1]\\n this_week_users = active_users_per_week[i]\\n intersect = prev_week_users.intersection(this_week_users)\\n if len(prev_week_users) > 0:\\n retention_pct = (len(intersect) * 100.0) / len(prev_week_users)\\n # Round half-up to 1 decimal\\n retention_pct = Decimal(str(retention_pct)).quantize(Decimal('0.0'), rounding=ROUND_HALF_UP)\\n retention_pct = float(retention_pct)\\n else:\\n retention_pct = None\\n \\n report.append({\\n \\\"week_start\\\": weeks[i].strftime(\\\"%Y-%m-%d\\\"),\\n \\\"wau\\\": wau,\\n \\\"retention_pct\\\": retention_pct\\n })\\n \\n print(json.dumps(report))\\n\\nsolve()\\n\", \"path\": \"solution.py\"}"
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{
"turn": 15,
"tool": "write_file",
"args": {
"content": "import csv\nfrom datetime import datetime, timedelta, timezone\nimport json\nfrom decimal import Decimal, ROUND_HALF_UP\ntry:\n from zoneinfo import ZoneInfo\nexcept ImportError:\n from backports.zoneinfo import ZoneInfo\n\ndef solve():\n # Configuration\n tz_name = \"America/New_York\"\n report_date_str = \"2026-10-19\"\n num_weeks = 6\n \n local_tz = ZoneInfo(tz_name)\n report_date = datetime.strptime(report_date_str, \"%Y-%m-%d\").date()\n \n # A week is Monday to Sunday.\n # A week is complete if it ends before report_date.\n # Find the most recent Sunday < report_date.\n # weekday() returns 0 for Monday, 6 for Sunday.\n \n days_since_sunday = (report_date.weekday() + 1) % 7\n if days_since_sunday == 0:\n days_since_sunday = 7\n \n last_sunday = report_date - timedelta(days=days_since_sunday)\n \n # Generate the start dates for the 6 weeks.\n weeks = []\n current_sunday = last_sunday\n for i in range(num_weeks):\n week_start = current_sunday - timedelta(days=6)\n weeks.append(week_start)\n current_sunday -= timedelta(days=7)\n \n weeks.reverse() # Oldest first\n \n # Process events\n active_users_per_week = [] # List of sets\n for w_start in weeks:\n active_users_per_week.append(set())\n \n with open('events.csv', 'r') as f:\n reader = csv.DictReader(f)\n for row in reader:\n user_id = row['user_id']\n if user_id.startswith('test_'):\n continue\n \n # Convert UTC timestamp to local timezone\n # Format is '2026-08-30T00:22:04Z'\n ts_str = row['ts_utc']\n # Remove 'Z' and parse\n ts_utc_dt = datetime.strptime(ts_str.replace('Z', ''), \"%Y-%m-%dT%H:%M:%S\")\n ts_utc_dt = ts_utc_dt.replace(tzinfo=timezone.utc)\n ts_local = ts_utc_dt.astimezone(local_tz)\n date_local = ts_local.date()\n \n # Check which week it falls into\n for i, w_start in enumerate(weeks):\n w_end = w_start + timedelta(days=6)\n if w_start <= date_local <= w_end:\n active_users_per_week[i].add(user_id)\n break\n \n # Calculate WAU and Retention\n report = []\n for i in range(num_weeks):\n wau = len(active_users_per_week[i])\n retention_pct = None\n if i > 0:\n prev_week_users = active_users_per_week[i-1]\n this_week_users = active_users_per_week[i]\n intersect = prev_week_users.intersection(this_week_users)\n if len(prev_week_users) > 0:\n retention_pct = (len(intersect) * 100.0) / len(prev_week_users)\n # Round half-up to 1 decimal\n retention_pct = Decimal(str(retention_pct)).quantize(Decimal('0.0'), rounding=ROUND_HALF_UP)\n retention_pct = float(retention_pct)\n else:\n retention_pct = None\n \n report.append({\n \"week_start\": weeks[i].strftime(\"%Y-%m-%d\"),\n \"wau\": wau,\n \"retention_pct\": retention_pct\n })\n \n print(json.dumps(report))\n\nsolve()\n",
"path": "solution.py"
},
"result": "wrote 3199 chars"
},
{
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"turn": 16,
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