anomaly-detection-ranking

grader verdict: FAIL (exit 0)
============
____________________________ test_grader_validation ____________________________
----------------------------- Captured stdout call -----------------------------
Feedback: results.json not found
Score: 0.0
✓ Grader validation completed. Score: 0.0 written to reward.txt
=========================== short test summary info ============================
PASSED ../tests/test_outputs.py::test_grader_validation
============================== 1 passed in 0.36s ===============================

You are an expert terminal agent. You MUST use the provided tools to complete tasks. Use the terminal tool to run shell commands, read_file to read files, write_file to write files, search_files to search, and patch to edit files. Do NOT write out solutions as text - execute them using the tools. Always start by exploring the environment with terminal commands.

# 🔍 Anomaly Detection & Learning‑to‑Rank Mega‑Task

Produce a single file called `results.json` containing your predictions for **five** sub‑tasks.

Your submission will be graded on the ability to detect anomalies in sensor and network data and to produce high‑quality rankings for movie, news and mixed document collections. A binary score is awarded for each stage; if your metric meets or exceeds the specified threshold, you earn the full weight for that stage, otherwise you receive zero for that stage. The sum of all stage weights is 1.00.

─────────────────────────────────────────────────────────
## 1. Sensor anomaly detection
─────────────────────────────────────────────────────────
You are given two CSV files:

* `sensor_train.csv` contains three columns:
  * `timestamp` — hourly timestamps in ISO format
  * `value` — a univariate sensor reading
  * `label` — ground‑truth anomaly labels (1 = anomaly, 0 = normal)
* `sensor_test.csv` contains only `timestamp` and `value` (labels are hidden)

Your task is to train a point‑anomaly detector on the training data and produce a list of binary labels for each row in `sensor_test.csv`. Store this list (order preserved) under the key `"sensor"` in `results.json`.

**Metric:** F1 score (positive class = 1) • **Pass if:** F1 ≥ 0.85 • **Weight:** 0.20

─────────────────────────────────────────────────────────
## 2. Network anomaly detection
─────────────────────────────────────────────────────────
You receive two CSV files describing network flow records:

* `network_train.csv` has the columns `duration`, `src_bytes`, `dst_bytes` and `label` (1 indicates an anomalous connection).
* `network_test.csv` contains `duration`, `src_bytes` and `dst_bytes` (labels are hidden).

Train a detector on the training set and output one integer (0 or 1) per row in `network_test.csv` predicting whether the connection is anomalous. Save the resulting list as the value of the `"network"` key in `results.json`.

**Metric:** F1 score • **Pass if:** F1 ≥ 0.80 • **Weight:** 0.20

─────────────────────────────────────────────────────────
## 3. Movie ranking
─────────────────────────────────────────────────────────
The movie ranking task asks you to rank a set of movie documents for each query. You are provided with:

* `movie_train.csv` containing `query_id`, `doc_id`, `year` and `relevance`. The `relevance` column encodes how well the document satisfies the query (larger is better).
* `movie_test.csv` containing `query_id`, `doc_id` and `year` (without the `relevance`).

For **each unique** `query_id` in `movie_test.csv`, output an ordered list of `doc_id` values sorted by your predicted relevance (most relevant first). Store these per‑query lists in a dictionary under the key `"movie_rank"` in `results.json`. The expected JSON structure is:

```json
{
  "movie_rank": {
    "q1": ["m1", "m2", ...],
    "q2": [...],
    ...
  }
}
```

**Metric:** Normalized Discounted Cumulative Gain at 10 (NDCG@10) • **Pass if:** NDCG@10 ≥ 0.85 • **Weight:** 0.20

─────────────────────────────────────────────────────────
## 4. News ranking
─────────────────────────────────────────────────────────
This task mirrors the movie ranking task but focuses on news articles. You are given:

* `news_train.csv` with columns `query_id`, `doc_id`, `length` and `relevance`.
* `news_test.csv` with columns `query_id`, `doc_id` and `length`.

For each query in the test set, produce an ordered list of `doc_id` values, sorted by predicted relevance. Place a dictionary mapping each `query_id` to its ranked list under the key `"news_rank"` in `results.json`.

**Metric:** NDCG@10 • **Pass if:** NDCG@10 ≥ 0.83 • **Weight:** 0.20

─────────────────────────────────────────────────────────
## 5. Joint movie & news ranking
─────────────────────────────────────────────────────────
The final stage requires ranking a mixture of movie and news documents for each query. Provided files are:

* `both_train.csv` with columns `query_id`, `doc_id`, `score` and
… (2011 more chars)

<think>Let me start by exploring the environment and understanding what data files are available.
</think>

terminal({"command": "ls -la"})

{"output": "total 8\ndrwxr-xr-x 2 root root 4096 Apr 22 04:23 .\ndrwxr-xr-x 1 root root 4096 Apr 22 04:23 ..", "exit_code": 0, "error": null}

<think>The directory appears empty. Let me check for files in different locations.
</think>

terminal({"command": "find . -type f 2>/dev/null; ls -la /home/user"})

{"output": "ls: cannot access '/home/user': No such file or directory", "exit_code": 2, "error": null}

<think>Let me search more thoroughly for CSV files.
</think>

terminal({"command": "find / -name \"*.csv\" 2>/dev/null | head -20; find . -name \"*.csv\" 2>/dev/null | head -20"})

{"output": "/usr/share/distro-info/ubuntu.csv\n/usr/share/distro-info/debian.csv\n/usr/local/lib/python3.11/dist-packages/sklearn/datasets/data/linnerud_physiological.csv\n/usr/local/lib/python3.11/dist-packages/sklearn/datasets/data/wine_data.csv\n/usr/local/lib/python3.11/dist-packages/sklearn/datasets/data/boston_house_prices.csv\n/usr/local/lib/python3.11/dist-packages/sklearn/datasets/data/iris.csv\n/usr/local/lib/python3.11/dist-packages/sklearn/datasets/data/linnerud_exercise.csv\n/usr/local/lib/python3.11/dist-packages/sklearn/datasets/data/breast_cancer.csv\n/usr/local/lib/python3.11/dist-packages/numpy/random/tests/data/mt19937-testset-1.csv\n/usr/local/lib/python3.11/dist-packages/numpy/random/tests/data/sfc64-testset-2.csv\n/usr/local/lib/python3.11/dist-packages/numpy/random/tests/data/pcg64dxsm-testset-1.csv\n/usr/local/lib/python3.11/dist-packages/numpy/random/tests/data/sfc64-testset-1.csv\n/usr/local/lib/python3.11/dist-packages/numpy/random/tests/data/philox-testset-2.csv\n/usr/local/lib/python3.11/dist-packages/numpy/random/tests/data/philox-testset-1.csv\n/usr/local/lib/python3.11/dist-packages/numpy/random/tests/data/mt19937-testset-2.csv\n/usr/local/lib/python3.11/dist-packages/numpy/random/tests/data/pcg64-testset-2.csv\n/usr/local/lib/python3.11/dist-packages/numpy/random/tests/data/pcg64dxsm-testset-2.csv\n/usr/local/lib/python3.11/dist-packages/numpy/random/tests/data/pcg64-testset-1.csv\n/usr/local/lib/python3.11/dist-packages/numpy/_core/tests/data/umath-validation-set-arctan.csv\n/usr/local/lib/python3.11/dist-packages/numpy/_core/tests/data/umath-validation-set-sinh.csv", "exit_code": 0, "error": null}

<think>These are sklearn datasets, not our task files. Let me look for the specific CSV files mentioned in the task: sensor_train.csv, sensor_test.csv, network_train.csv, network_test.csv, movie_train.csv, movie_test.csv, news_train.csv, news_test.csv, both_train.csv, both_test.csv.
</think>

terminal({"command": "find . -name \"*sensor*\" -o -name \"*network*\" -o -name \"*movie*\" -o -name \"*news*\" -o -name \"*both*\" 2>/dev/null | head -30"})

{"output": "", "exit_code": 0, "error": null}

<think>No results. Let me try to find CSV files in other locations.
</think>

terminal({"command": "find /tmp -name \"*.csv\" 2>/dev/null; find /var -name \"*.csv\" 2>/dev/null; find /data -name \"*.csv\" 2>/dev/null; ls -la /tmp"})

{"output": "total 12\ndrwxrwxrwt 2 root root   80 Apr 22 04:23 .\ndrwxr-xr-x 1 root root 4096 Apr 22 04:23 ..\n-rw-r--r-- 1 root root    5 Apr 22 04:24 hermes-cwd-d62f13f4bf6e.txt\n-rw-r--r-- 1 root root  250 Apr 22 04:24 hermes-snap-d62f13f4bf6e.sh", "exit_code": 0, "error": null}

<think>Let me look in the current working directory more carefully. Let me try pwd and then look around.
</think>

terminal({"command": "pwd; ls -laR . | head -50"})

{"output": "/app\n.:\ntotal 8\ndrwxr-xr-x 2 root root 4096 Apr 22 04:23 .\ndrwxr-xr-x 1 root root 4096 Apr 22 04:23 ..", "exit_code": 0, "error": null}

<think>Let me search more broadly for any CSV files.
</think>

terminal({"command": "find /app -name \"*.csv\" 2>/dev/null; find /app -type f 2>/dev/null; ls -laR /app"})

{"output": "/app:\ntotal 8\ndrwxr-xr-x 2 root root 4096 Apr 22 04:23 .\ndrwxr-xr-x 1 root root 4096 Apr 22 04:23 ..", "exit_code": 0, "error": null}

<think>The /app directory is empty. Let me check if there are any mounted filesystems or other locations.
</think>

terminal({"command": "mount; df -h; cat /etc/os-release"})

{"output": "overlay on / type overlay (rw,relatime,lowerdir=/var/lib/docker/overlay2/l/T2V465PBK2J5W2F4WBD7S3G233:/var/lib/docker/overlay2/l/LTVU6U7QPVJYMB3BZLP7VGBDAM:/var/lib/docker/overlay2/l/XDJXD4JK3U7KQJZ2EFPYK5GX7N:/var/lib/docker/overlay2/l/JK7QCZGQHRFJN7FZ275NJ3WCDL:/var/lib/docker/overlay2/l/KMPS2EYRELE6G3BFOK3ZM373J7:/var/lib/docker/overlay2/l/HEMK6CEIFORQYPLD3POJMHXEV3:/var/lib/docker/overlay2/l/OUXOHP32Y2YSSQMNGMILD6A2X4:/var/lib/docker/overlay2/l/LKBRRTH3UYWPJIM654NG3DPDPM:/var/lib/docker/overlay2/l/J5KPH7M7RW4DXZAZRZASZKWZCK:/var/lib/docker/overlay2/l/T24RU3SDJIBXW52VT7ZJHF7KJM:/var/lib/docker/overlay2/l/WR6EXM3XXG5PGB6URRE5ROHYY7:/var/lib/docker/overlay2/l/PUIFGCOV4RSVDP35KG2A5EPUM3:/var/lib/docker/overlay2/l/HBIXKYIR6FVDXLHP4M3ACY5ZTT:/var/lib/docker/overlay2/l/QZJN4E6CT5U7SHB3BWN27FAFGD,upperdir=/var/lib/docker/overlay2/6843d56f203a51d05ec1e7b8e857437adfa1aaa832e4f59b9f7e5f60ce812d95/diff,workdir=/var/lib/docker/overlay2/6843d56f203a51d05ec1e7b8e857437adfa1aaa832e4f59b9f7e5f60ce812d95/work,index=off)\nproc on /proc type proc (rw,nosuid,nodev,noexec,relatime)\ntmpfs on /dev type tmpfs (rw,nosuid,size=65536k,mode=755,inode64)\ndevpts on /dev/pts type devpts (rw,nosuid,noexec,relatime,gid=5,mode=620,ptmxmode=666)\nsysfs on /sys type sysfs (ro,nosuid,nodev,noexec,relatime)\ncgroup on /sys/fs/cgroup type cgroup2 (ro,nosuid,nodev,noexec,relatime,nsdelegate,memory_recursiveprot,memory_hugetlb_accounting)\nmqueue on /dev/mqueue type mqueue (rw,nosuid,nodev,noexec,relatime)\nshm on /dev/shm type tmpfs (rw,nosuid,nodev,noexec,relatime,size=65536k,inode64)\n/dev/nvme0n1p2 on /usr/sbin/docker-init type ext4 (ro,noatime)\ntmpfs on /tmp type tmpfs (rw,nosuid,nodev,noexec,relatime,size=524288k,inode64)\ntmpfs on /run type tmpfs (rw,nosuid,nodev,noexec,relatime,size=65536k,mode=755,inode64)\n/dev/nvme0n1p2 on /root type ext4 (rw,noatime)\n/dev/nvme0n1p2 on /workspace type ext4 (rw,noatime)\ntmpfs on /var/tmp type tmpfs (rw,nosuid,nodev,noexec,relatime,size=262144k,inode64)\n/dev/nvme0n1p2 on /etc/resolv.conf type ext4 (rw,noatime)\n/dev/nvme0n1p2 on /etc/hostname type ext4 (rw,noatime)\n/dev/nvme0n1p2 on /etc/hosts type ext4 (rw,noatime)\n/dev/nvme0n1p2 on /root/.hermes/skills type ext4 (ro,noatime)\n/dev/nvme0n1p2 on /root/.hermes/cache/documents type ext4 (ro,noatime)\n/dev/nvme0n1p2 on /root/.hermes/cache/images type ext4 (ro,noatime)\n/dev/nvme0n1p2 on /root/.hermes/cache/audio type ext4 (ro,noatime)\nproc on /proc/bus type proc (ro,nosuid,nodev,noexec,relatime)\nproc on /proc/fs type proc (ro,nosuid,nodev,noexec,relatime)\nproc on /proc/irq type proc (ro,nosuid,nodev,noexec,relatime)\nproc on /proc/sys type proc (ro,nosuid,nodev,noexec,relatime)\nproc on /proc/sysrq-trigger type proc (ro,nosuid,nodev,noexec,relatime)\ntmpfs on /proc/acpi type tmpfs (ro,relatime,inode64)\ntmpfs on /proc/asound type tmpfs (ro,relatime,inode64)\ntmpfs on /proc/interrupts type tmpfs (rw,nosuid,size=65536k,mode=755,inode64)\ntmpfs on /proc/kcore type tmpfs (rw,nosuid,size=65536k,mode=755,inode64)\ntmpfs on /proc/keys type tmpfs (rw,nosuid,size=65536k,mode=755,inode64)\ntmpfs on /proc/scsi type tmpfs (ro,relatime,inode64)\ntmpfs on /proc/timer_list type tmpfs (rw,nosuid,size=65536k,mode=755,inode64)\ntmpfs on /sys/devices/virtual/powercap type tmpfs (ro,relatime,inode64)\ntmpfs on /sys/firmware type tmpfs (ro,relatime,inode64)\nFilesystem      Size  Used Avail Use% Mounted on\noverlay         1.7T  1.6T   47G  98% /\ntmpfs            64M     0   64M   0% /dev\nshm              64M     0   64M   0% /dev/shm\n/dev/nvme0n1p2  1.7T  1.6T   47G  98% /usr/sbin/docker-init\ntmpfs           512M  8.0K  512M   1% /tmp\ntmpfs            64M     0   64M   0% /run\ntmpfs           256M     0  256M   0% /var/tmp\ntmpfs            63G     0   63G   0% /proc/acpi\ntmpfs            63G     0   63G   0% /proc/asound\ntmpfs            63G     0   63G   0% /proc/scsi\ntmpfs            63G     0   63G   0% /sys/devices/virtual/powercap\ntmpfs            63G     
… (473 more chars)

<think>I see there's a /root/.hermes directory with mounted files. Let me look there for the CSV files.
</think>

terminal({"command": "ls -la /root/.hermes/cache/documents 2>/dev/null; ls -la /root/.hermes/cache 2>/dev/null"})

{"output": "total 8\ndrwxr-xr-x 2 1000 1000 4096 Mar  9 06:28 .\ndrwxr-xr-x 5 root root 4096 Apr 22 04:23 ..\ntotal 20\ndrwxr-xr-x 5 root root 4096 Apr 22 04:23 .\ndrwxr-xr-x 4 root root 4096 Apr 22 04:23 ..\ndrwxr-xr-x 2 1000 1000 4096 Mar  9 05:13 audio\ndrwxr-xr-x 2 1000 1000 4096 Mar  9 06:28 documents\ndrwxr-xr-x 2 1000 1000 4096 Mar 10 00:09 images", "exit_code": 0, "error": null}

<think>Let me explore the documents folder.
</think>

terminal({"command": "ls -la /root/.hermes/cache/documents; find /root/.hermes/cache/documents -type f 2>/dev/null | head -50"})

{"output": "total 8\ndrwxr-xr-x 2 1000 1000 4096 Mar  9 06:28 .\ndrwxr-xr-x 5 root root 4096 Apr 22 04:23 ..", "exit_code": 0, "error": null}

<think>Empty. Let me look at the images folder.
</think>

terminal({"command": "ls -laR /root/.hermes; find /root/.hermes -type f 2>/dev/null | head -50"})

{"output": "/root/.hermes:\ntotal 16\ndrwxr-xr-x  4 root root 4096 Apr 22 04:23 .\ndrwxr-xr-x  3 1000 1000 4096 Apr 22 04:23 ..\ndrwxr-xr-x  5 root root 4096 Apr 22 04:23 cache\ndrwxr-xr-x 29 1000 1000 4096 Apr 22 04:22 skills\n\n/root/.hermes/cache:\ntotal 20\ndrwxr-xr-x 5 root root 4096 Apr 22 04:23 .\ndrwxr-xr-x 4 root root 4096 Apr 22 04:23 ..\ndrwxr-xr-x 2 1000 1000 4096 Mar  9 05:13 audio\ndrwxr-xr-x 2 1000 1000 4096 Mar  9 06:28 documents\ndrwxr-xr-x 2 1000 1000 4096 Mar 10 00:09 images\n\n/root/.hermes/cache/audio:\ntotal 8\ndrwxr-xr-x 2 1000 1000 4096 Mar  9 05:13 .\ndrwxr-xr-x 5 root root 4096 Apr 22 04:23 ..\n\n/root/.hermes/cache/documents:\ntotal 8\ndrwxr-xr-x 2 1000 1000 4096 Mar  9 06:28 .\ndrwxr-xr-x 5 root root 4096 Apr 22 04:23 ..\n\n/root/.hermes/cache/images:\ntotal 220\ndrwxr-xr-x 2 1000 1000   4096 Mar 10 00:09 .\ndrwxr-xr-x 5 root root   4096 Apr 22 04:23 ..\n-rw-r--r-- 1 1000 1000 163530 Mar  9 06:28 img_31a3e44a41f7.jpg\n-rw-r--r-- 1 1000 1000  52466 Mar 10 00:09 img_f4f8d4607cfb.jpg\n\n/root/.hermes/skills:\ntotal 120\ndrwxr-xr-x 29 1000 1000 4096 Apr 22 04:22 .\ndrwxr-xr-x  4 root root 4096 Apr 22 04:23 ..\n-rw-------  1 1000 1000 3323 Apr 22 04:22 .bundled_manifest\ndrwxr-xr-x  6 1000 1000 4096 Apr 20 23:52 apple\ndrwxr-xr-x  6 1000 1000 4096 Apr 20 23:52 autonomous-ai-agents\ndrwxr-xr-x 12 1000 1000 4096 Apr 20 23:52 creative\ndrwxr-xr-x  3 1000 1000 4096 Apr 20 23:52 data-science\ndrwxr-xr-x  3 1000 1000 4096 Apr 20 23:52 devops\ndrwxr-xr-x  3 1000 1000 4096 Mar  9 05:13 diagramming\ndrwxr-xr-x  4 1000 1000 4096 Apr 20 23:45 dogfood\ndrwxr-xr-x  3 1000 1000 4096 Mar  9 05:13 domain\ndrwxr-xr-x  3 1000 1000 4096 Apr 20 23:52 email\ndrwxr-xr-x  3 1000 1000 4096 Mar  9 05:13 feeds\ndrwxr-xr-x  4 1000 1000 4096 Apr 20 23:52 gaming\ndrwxr-xr-x  3 1000 1000 4096 Mar  9 05:13 gifs\ndrwxr-xr-x  8 1000 1000 4096 Apr 20 23:52 github\ndrwxr-xr-x  2 1000 1000 4096 Apr 20 23:52 inference-sh\ndrwxr-xr-x  3 1000 1000 4096 Mar  9 05:13 market-data\ndrwxr-xr-x  4 1000 1000 4096 Apr 20 23:52 mcp\ndrwxr-xr-x  3 1000 1000 4096 Apr 20 23:52 media\ndrwxr-xr-x 49 1000 1000 4096 Apr 20 23:52 mlops\ndrwxr-xr-x  4 1000 1000 4096 Mar  9 05:13 music-creation\ndrwxr-xr-x  3 1000 1000 4096 Mar  9 05:13 note-taking\ndrwxr-xr-x  3 1000 1000 4096 Mar  9 05:08 ocr-and-documents\ndrwxr-xr-x  9 1000 1000 4096 Apr 20 23:52 productivity\ndrwxr-xr-x  3 1000 1000 4096 Apr 20 23:52 red-teaming\ndrwxr-xr-x  6 1000 1000 4096 Apr 20 23:52 research\ndrwxr-xr-x  3 1000 1000 4096 Apr 20 23:52 smart-home\ndrwxr-xr-x  3 1000 1000 4096 Apr 20 23:52 social-media\ndrwxr-xr-x  8 1000 1000 4096 Apr 20 23:52 software-development\n\n/root/.hermes/skills/apple:\ntotal 28\ndrwxr-xr-x  6 1000 1000 4096 Apr 20 23:52 .\ndrwxr-xr-x 29 1000 1000 4096 Apr 22 04:22 ..\n-rw-r--r--  1 1000 1000  152 Mar  9 05:08 DESCRIPTION.md\ndrwxr-xr-x  2 1000 1000 4096 Apr 20 23:45 apple-notes\ndrwxr-xr-x  2 1000 1000 4096 Apr 20 23:45 apple-reminders\ndrwxr-xr-x  2 1000 1000 4096 Mar  9 05:08 findmy\ndrwxr-xr-x  2 1000 1000 4096 Apr 20 23:45 imessage\n\n/root/.hermes/skills/apple/apple-notes:\ntotal 12\ndrwxr-xr-x 2 1000 1000 4096 Apr 20 23:45 .\ndrwxr-xr-x 6 1000 1000 4096 Apr 20 23:52 ..\n-rw-r--r-- 1 1000 1000 2187 Apr 20 23:45 SKILL.md\n\n/root/.hermes/skills/apple/apple-reminders:\ntotal 12\ndrwxr-xr-x 2 1000 1000 4096 Apr 20 23:45 .\ndrwxr-xr-x 6 1000 1000 4096 Apr 20 23:52 ..\n-rw-r--r-- 1 1000 1000 2548 Apr 20 23:45 SKILL.md\n\n/root/.hermes/skills/apple/findmy:\ntotal 12\ndrwxr-xr-x 2 1000 1000 4096 Mar  9 05:08 .\ndrwxr-xr-x 6 1000 1000 4096 Apr 20 23:52 ..\n-rw-r--r-- 1 1000 1000 3748 Mar  9 05:08 SKILL.md\n\n/root/.hermes/skills/apple/imessage:\ntotal 12\ndrwxr-xr-x 2 1000 1000 4096 Apr 20 23:45 .\ndrwxr-xr-x 6 1000 1000 4096 Apr 20 23:52 ..\n-rw-r--r-- 1 1000 1000 2442 Apr 20 23:45 SKILL.md\n\n/root/.hermes/skills/autonomous-ai-agents:\ntotal 28\ndrwxr-xr-x  6 1000 1000 4096 Apr 20 23:52 .\ndrwxr-xr-x 29 1000 1000 4096 Apr 22 04:22 ..\n-rw-r--r--  1 1000 1000  2
… (47310 more chars)