{
  "filename": "rollout-2026-01-29T13-23-37-019c09ec-73ab-74d3-8ace-7d0ada6c3231.jsonl",
  "date": "2026-01-29",
  "session_id": "019c09ec-73ab-74d3-8ace-7d0ada6c3231",
  "first_timestamp": "2026-01-29T13:23:37.005Z",
  "last_timestamp": "2026-01-29T13:48:15.357Z",
  "duration_seconds": 1478.4,
  "model": "gpt-5.2-codex",
  "cli_version": "0.92.0",
  "approval_policy": "on-request",
  "effort": "xhigh",
  "raw_event_count": 375,
  "user_messages": [
    {
      "index": 0,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter The writable roots are `/home/ubuntu/nikhil/cold-ranker`, `/tmp`.</permissions instructions>",
      "timestamp": "2026-01-29T13:23:37.005Z"
    },
    {
      "index": 1,
      "text": "# AGENTS.md instructions for /home/ubuntu/nikhil/cold-ranker\n\n<INSTRUCTIONS>\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.\n### Available skills\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)\n### How to use skills\n- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.\n- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.\n- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.\n- How to use a skill (progressive disclosure):\n  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.\n  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.\n  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.\n  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.\n- Coordination and sequencing:\n  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.\n  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.\n- Context hygiene:\n  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.\n  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.\n  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.\n- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.\n</INSTRUCTIONS>",
      "timestamp": "2026-01-29T13:23:37.005Z"
    },
    {
      "index": 2,
      "text": "<environment_context>\n  <cwd>/home/ubuntu/nikhil/cold-ranker</cwd>\n  <shell>bash</shell>\n</environment_context>",
      "timestamp": "2026-01-29T13:23:37.005Z"
    },
    {
      "index": 3,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter The writable roots are `/home/ubuntu/nikhil/cold-ranker`, `/home/ubuntu/nikhil/cold-ranker`, `/tmp`.</permissions instructions>",
      "timestamp": "2026-01-29T13:23:37.074Z"
    },
    {
      "index": 4,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter The writable roots are `/home/ubuntu/nikhil/cold-ranker`, `/home/ubuntu/nikhil/cold-ranker`, `/tmp`.</permissions instructions>",
      "timestamp": "2026-01-29T13:23:37.074Z"
    },
    {
      "index": 5,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/config/config.py\n\n## Open tabs:\n- config.py: col/src/config/config.py\n- model.py: col/src/model/model.py\n- series_meta.py: col/src/data_prep/series_meta.py\n- wandb_utils.py: col/src/utils/wandb_utils.py\n- .gitignore: col/.gitignore\n\n## My request for Codex:\ni want to now generate recommendations for users. the recommendation should be top 50 contents out of all contents in series meta. We should use example of each user with latest \"at\" with meta features and test with all series meta, get top 50 for each of them.\n",
      "timestamp": "2026-01-29T13:23:37.155Z"
    },
    {
      "index": 6,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/recommend.py\n\n## Open tabs:\n- recommend.py: col/src/recommend.py\n- config.py: col/src/config/config.py\n- model.py: col/src/model/model.py\n- series_meta.py: col/src/data_prep/series_meta.py\n- wandb_utils.py: col/src/utils/wandb_utils.py\n\n## My request for Codex:\nsource nikhil/pyG/nik_gpu/bin/activate\nuse this to test\n",
      "timestamp": "2026-01-29T13:40:50.610Z"
    },
    {
      "index": 7,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/config/config.py\n\n## Active selection of the file:\n_cpu_count\n## Open tabs:\n- config.py: col/src/config/config.py\n- recommend.py: col/src/recommend.py\n- model.py: col/src/model/model.py\n- series_meta.py: col/src/data_prep/series_meta.py\n- wandb_utils.py: col/src/utils/wandb_utils.py\n\n## My request for Codex:\ncol/src/driver.py\n\nis my driver file, recommendation should be triggered from this file only not separately, code can be in sepearte file thats fine. Make sure whole code gels togetehre, model is saved and everything is present by driver file before you recommend (via driver file trigger\n",
      "timestamp": "2026-01-29T13:43:09.044Z"
    },
    {
      "index": 8,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/driver.py\n\n## Open tabs:\n- driver.py: col/src/driver.py\n- training.py: col/src/model/training.py\n- recommend.py: col/src/recommend.py\n- config.py: col/src/config/config.py\n- model.py: col/src/model/model.py\n\n## My request for Codex:\nall the env files you introduced have been set?\n",
      "timestamp": "2026-01-29T13:46:49.961Z"
    },
    {
      "index": 9,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/driver.py\n\n## Open tabs:\n- driver.py: col/src/driver.py\n- training.py: col/src/model/training.py\n- recommend.py: col/src/recommend.py\n- config.py: col/src/config/config.py\n- model.py: col/src/model/model.py\n\n## My request for Codex:\nwire them in config with default values\n",
      "timestamp": "2026-01-29T13:47:26.356Z"
    }
  ],
  "assistant_messages": [
    {
      "index": 0,
      "text": "Added a recommendation generator that builds a latest-per-user feature snapshot from the interaction shards and scores every series in `series_meta` to produce top‑50 lists, plus a training checkpoint save so there’s a model file to load for inference.\n\nDetails:\n- `col/src/recommend.py` loads latest user rows (by shard order, so “latest at” from the chronological splits), scores all series in batches, and writes JSONL with `user_id`, `series_ids`, and `scores`. It defaults to using `train` then `test` if both exist, and you can override via `--interaction-dir`.\n- `col/src/model/training.py` now saves a checkpoint after training to `../../data/{LANGUAGE_KEY}/model.pt` (override with `MODEL_OUT`).\n\nHow to run (from `col/src`, or pass an absolute `--data-dir`):\n```bash\npython recommend.py --model-path ../../data/bn/model.pt\n# optional knobs:\n# --interaction-dir ../../data/bn/test\n# --top-k 50 --series-batch-size 4096 --max-users 1000\n```\n\nNotes:\n- I didn’t run tests. `python -m py_compile` failed because the default `python` here is Python 2 (f-string syntax error). Use `python3` or your env.\n\nNext steps:\n1) Train or point to an existing checkpoint (or set `MODEL_OUT`) so the recommender has weights.\n2) Run `recommend.py` with your preferred `--interaction-dir` and output path.",
      "timestamp": "2026-01-29T13:39:42.292Z"
    },
    {
      "index": 1,
      "text": "Used the venv you specified and ran a syntax check on `col/src/recommend.py`. It completed without errors. (Only the repeated `.profile` conda warnings showed up.)\n\nWhat test do you want next?\n- If you want a dry run, I can do `python col/src/recommend.py --help` in that venv.\n- If you want a full run, tell me the model path and any args (top‑k, max users, interaction dir).",
      "timestamp": "2026-01-29T13:41:09.610Z"
    },
    {
      "index": 2,
      "text": "Hooked recommendations into the driver flow so training saves a model and the same driver run produces top‑50s using the latest per‑user interaction. Syntax check in your venv passes for the updated files.\n\nWhat changed\n- Driver now orchestrates end‑to‑end: optional data prep → training (saves model) → recommendations. `col/src/driver.py`\n- Training returns the saved model path so the driver can pass it through. `col/src/model/training.py`\n- Recommend module exposes a programmatic `generate_recommendations(...)` API and still supports CLI usage. `col/src/recommend.py`\n- Latest-user extraction now handles int32_min on the preference flag. `col/src/recommend.py`\n\nHow to run via driver\n```bash\nsource nikhil/pyG/nik_gpu/bin/activate\npython col/src/driver.py\n```\n\nOptional env knobs (all via driver)\n- `RUN_DATA_PREP=1` to run data prep before training\n- `MODEL_OUT=/path/to/model.pt` to override model save location\n- `REC_TOP_K=50` (default 50)\n- `REC_SERIES_BATCH_SIZE=4096` (default 4096)\n- `REC_MAX_USERS=1000` to cap users for a quick test\n\nDefault output\n- `data/bn/recommendations_top50.jsonl` (relative to repo root)\n\nIf you want a quick sanity run (no training), tell me the existing model path and I’ll wire a “recommend‑only” flag into `col/src/driver.py`.",
      "timestamp": "2026-01-29T13:45:43.776Z"
    },
    {
      "index": 3,
      "text": "No env files were created or set by me. The driver only reads environment variables at runtime.\n\nThe only new env vars added by my changes are optional:\n- `RUN_DATA_PREP` (default off)\n- `MODEL_OUT` (default `../../data/{LANGUAGE_KEY}/model.pt`)\n- `REC_TOP_K` (default 50)\n- `REC_SERIES_BATCH_SIZE` (default 4096)\n- `REC_MAX_USERS` (default unset)\n\nIf you want, I can add a `.env` loader or wire these into a config file instead of environment variables.",
      "timestamp": "2026-01-29T13:46:55.704Z"
    },
    {
      "index": 4,
      "text": "Wired the runtime knobs into `config.py` with defaults and switched the driver/training to use those constants.\n\nChanges:\n- `col/src/config/config.py` now defines: `RUN_DATA_PREP`, `MODEL_OUT`, `REC_TOP_K`, `REC_SERIES_BATCH_SIZE`, `REC_MAX_USERS` (env overrides still work via config).\n- `col/src/model/training.py` uses `MODEL_OUT` from config.\n- `col/src/driver.py` uses the config constants instead of reading env directly.\n\nIf you want, I can also thread these defaults into `recommend.py`’s CLI help text.",
      "timestamp": "2026-01-29T13:48:15.357Z"
    }
  ],
  "tool_calls": [
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "Exit code: 101\nWall time: 0 seconds\nOutput:\n\nthread 'main' (8577) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\nerror running landlock: Sandbox(LandlockRestrict)\nnote: run with `RUST_BACKTRACE..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Listing workspace files requires escalated permissions due to sandbox landlock er...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncol\ndata\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubunt..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls /home/ubuntu/nikhil/cold-ranker/col\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to inspect repo structure; previous land...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nsrc\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/min..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/config/config.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to rea...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nfrom openai import OpenAI\n\nLANGUAGE=\"bengali\"\nLANGUAGE_KEY=\"bn\"\n\nMODE='test'\n\ndef _env_flag(name: str, default: bool = False) -> bool:\n    value = o..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/model/model.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to read ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nclass CTRModelWithCosineSignals(nn.Module):\n    def __init__(\n        self,\n        in..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/data_prep/series_meta.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Nee...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nfrom utils.utils import chunked\nfrom utils.athena import query_from_athena\nimport pandas as pd\nfrom config.config import BATCH_SIZE, get_openai_client\nfrom tq..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"recommend|inference|predict|score|top\\\" /home/ubuntu/nikhil/cold-ranker/col/src\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justificatio...",
      "output_summary": "Exit code: 1\nWall time: 0 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minicon..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"find /home/ubuntu/nikhil/cold-ranker/col/src -maxdepth 3 -type f\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need file list to l...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n/home/ubuntu/nikhil/cold-ranker/col/src/data_prep/mapping.py\n/home/ubuntu/nikhil/cold-ranker/col/src/data_prep/user_meta.py\n/home/ubuntu/nikhil/cold-ranker/co..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/data_prep/user_meta.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pandas as pd\nimport numpy as np\nfrom utils.athena import query_from_athena\nfrom utils.utils import chunked\nimport mysql.connector\nfrom concurrent.futur..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/data_prep/interaction.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Nee...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pandas as pd\nimport numpy as np\nfrom pandarallel import pandarallel\n\ndef get_user_series_interaction(language, language_key):\n    up_df = pd.read_parqu..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/data_prep/data_prep.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pandas as pd\nimport numpy as np\nimport gc\nfrom pandarallel import pandarallel\nfrom data_prep.series_meta import get_series_meta\nfrom data_prep.user_met..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/model/dataset.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to ins...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nimport json\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader, IterableDataset\n\nfrom config.config import EMBEDDING_COLUMNS_TO..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '200,400p' /home/ubuntu/nikhil/cold-ranker/col/src/model/dataset.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Continue ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n    return DataLoader(\n        ds,\n        batch_size = None,\n        num_workers = num_worker_dataloader,\n        pin_memory = pin_memory,\n    )\n/home/ubuntu..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/data_prep/mapping.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport numpy as np\nfrom config.config import CATEGORICAL_COLUMNS, CONTINUOUS_COLUMNS, EMBEDDING_COLUMNS_TO_SIZE\n\nclass Normalizer:\n    def __init__(self, list..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/model/training.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to in...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nimport math\nfrom model.sampler import PopularitySampler\nimport time\nfrom typing import Optional\nfrom typing import Dict, Tuple\nimport numpy as np\nfr..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '200,400p' /home/ubuntu/nikhil/cold-ranker/col/src/model/training.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Continue...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n            positive_series_arr = ints_data[:,0]\n        else:\n            positive_series_arr = np.concatenate([positive_series_arr, ints_data[:,0]])\n    pos..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/driver.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect driver for...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nfrom datetime import datetime\nos.environ[\"AWS_DEFAULT_REGION\"] = \"ap-south-1\"\nos.environ[\"OPENAI_API_KEY\"] = \"sk-proj-qRoswT4wSHS3R4i7ZcIbP3FW5XT0xI..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/utils/utils.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect utils...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pickle\n\ndef chunked(iterable, size):\n    iterable = list(iterable)\n    for i in range(0, len(iterable), size):\n        yield iterable[i:i + size]\n\ndef ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"find /home/ubuntu/nikhil/cold-ranker/data -maxdepth 4 -type f | head -n 50\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect d...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n/home/ubuntu/nikhil/cold-ranker/data/bn/test/shard-00025/float.npy\n/home/ubuntu/nikhil/cold-ranker/data/bn/test/shard-00025/meta.json\n/home/ubuntu/nikhil/cold..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"find /home/ubuntu/nikhil/cold-ranker/data/bn/series_meta -maxdepth 2 -type f | head -n 20\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justificat...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n/home/ubuntu/nikhil/cold-ranker/data/bn/series_meta/shard-00000/float.npy\n/home/ubuntu/nikhil/cold-ranker/data/bn/series_meta/shard-00000/meta.json\n/home/ubun..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls /home/ubuntu/nikhil/cold-ranker/data/bn\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Check available artifacts like mapping pi...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncat_columns_to_mapper.pkl\ncon_columns_to_normalizer.pkl\nseries_meta\ntest\ntrain\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh:..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"find /home/ubuntu/nikhil/cold-ranker -maxdepth 4 -type f -name \\\"*.pt\\\" -o -name \\\"*.pth\\\"\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justifica...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minicon..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/temp.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Check temp.py for an...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pandas as pd\nimport pyarrow.parquet as pq\n\npq_file = pq.ParquetFile(\"bn_all_data.parquet\")\n\nsampled_chunks = []\n\nfor batch in pq_file.iter_batches(batc..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/model/sampler.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect sam...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport numpy as np\n\n\nclass PopularitySampler:\n    def __init__(self, vocab_arr, positive_series_arr, seed=None):\n        values, counts = np.unique(positive_s..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"cat /home/ubuntu/nikhil/cold-ranker/data/bn/series_meta/shard-00000/meta.json\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspec...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n{\"n_rows\": 11793, \"int_cols\": [\"target_series_id\", \"target_author_id\"], \"float_cols\": [\"target_rating_count\", \"target_average_rating\", \"target_genre_embedding..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"cat /home/ubuntu/nikhil/cold-ranker/data/bn/test/shard-00000/meta.json\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect test ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n{\"n_rows\": 10000, \"int_cols\": [\"target_series_id\", \"user_id\", \"gender\", \"if_user_genre_pref_embedding_present\"], \"float_cols\": [\"age_days\", \"user_genre_pref_e..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"python -m py_compile /home/ubuntu/nikhil/cold-ranker/col/src/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Quick synt...",
      "output_summary": "Exit code: 1\nWall time: 0.1 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minic..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '250,310p' /home/ubuntu/nikhil/cold-ranker/col/src/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect synta...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n    else:\n        interaction_dirs = []\n        train_dir = os.path.join(data_dir, \"train\")\n        test_dir = os.path.join(data_dir, \"test\")\n        if os.pa..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '310,360p' /home/ubuntu/nikhil/cold-ranker/col/src/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect rest ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n\n            original_user_id = user_mapper.idx_to_val_dict.get(user_id, user_id)\n            original_series_ids = [\n                series_mapper.idx_to_val..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"nl -ba /home/ubuntu/nikhil/cold-ranker/col/src/recommend.py | sed -n '260,290p'\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Show...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n   260\t        raise ValueError(\"No interaction directories found.\")\n   261\t\n   262\t    device = args.device or (\"cuda\" if torch.cuda.is_available() else \"cpu..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"nl -ba /home/ubuntu/nikhil/cold-ranker/col/src/recommend.py | sed -n '200,260p'\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Chec...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n   200\t                target_author_id,\n   201\t                user_genre_b,\n   202\t                read_genre_b,\n   203\t                target_genre_embeddi..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"python - <<'PY'\\nwith open('/home/ubuntu/nikhil/cold-ranker/col/src/recommend.py','r',encoding='utf-8') as f:\\n    lines=f.readlines()\\nfor i in [275,276,277,278]:\\n    print(i+1,repr(line...",
      "output_summary": "Exit code: 1\nWall time: 0.1 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minic..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"bash -lc \\\"source nikhil/pyG/nik_gpu/bin/activate && python -m py_compile /home/ubuntu/nikhil/cold-ranker/col/src/recommend.py\\\"\",\"workdir\":\"/home/ubuntu\",\"sandbox_permissions\":\"require_es...",
      "output_summary": "Exit code: 0\nWall time: 0.1 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minic..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' /home/ubuntu/nikhil/cold-ranker/col/src/driver.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Review updated dri...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nfrom datetime import datetime\nfrom config.config import LANGUAGE_KEY\nos.environ[\"AWS_DEFAULT_REGION\"] = \"ap-south-1\"\nos.environ[\"OPENAI_API_KEY\"] = ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"bash -lc \\\"source nikhil/pyG/nik_gpu/bin/activate && python -m py_compile /home/ubuntu/nikhil/cold-ranker/col/src/recommend.py /home/ubuntu/nikhil/cold-ranker/col/src/model/training.py /ho...",
      "output_summary": "Exit code: 0\nWall time: 0.1 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minic..."
    }
  ],
  "turn_count": 10,
  "files_touched": [],
  "summary_from_context": "auto",
  "metrics": {
    "outcome": "resolved",
    "session_type": "bug-fix",
    "iteration_count": 10,
    "struggle_score": 4,
    "prompt_specificity": 4,
    "time_of_day": "afternoon"
  }
}