{
"schemaVersion": 1,
"taskId": "yambda-discovery-ranking-v01",
"attemptId": "gemini-3-8-flash-high-basic-r04-b9049e57",
"taskChecksum": "06d023160277225da99fbf82a38920164f960785fd1fd9068f437996f389d90f",
"agentVersion": "0.61.0",
"model": "gemini-3.8-flash",
"sessionId": "12ac898f-c1c7-43a0-8adb-ea8ac16ea308",
"steps": [
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"role": "user",
"timestamp": "2026-09-29T01:22:39.753Z",
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"text": "\nThis is the Gemini CLI. We are setting up the context for our chat.\nToday's date is Tuesday, September 29, 2026 (formatted according to the user's locale).\nMy operating system is: linux\nThe project's temporary directory is: /home/agent/.gemini/tmp/app\n- **Workspace Directories:**\n - /app\n- **Directory Structure:**\n\nShowing up to 200 items (files + folders).\n\n/app/\n└───solution/\n\n\n"
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"text": "# Build a music-discovery ranker\n\nBuild the strongest ranking model you can for the \"Made for You\" shelf of a\nmusic streaming service. Per-user candidate eligibility, cutoff-specific\nexplicit preference state, and a temporal backtest are provided. Candidate\neligibility is platform-owned; all modeling within that candidate boundary is\nin scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model, fitted representation, example\nsubmission, or modeling architecture is supplied. Build and tune the best model\nyou can, then refit the selected approach as described above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that builds the artifact and a concise\n`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission\nsaved throughout the attempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n connection managed by Harbor.\n- You have up to 30 minutes. Improve the model as much as possible within that\n limit. Structural validity alone is not sufficient; the final submission is\n also evaluated for ranking quality.\n"
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"blocks": [
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"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:22:44.243Z",
"text": "Analyzing Prompt & Data\n\nI'm currently dissecting the core prompt, focusing on the \"Made for You\" shelf objective and the specific training data cutoff. My aim is to grasp the environment and inherent constraints to build the most effective ranking model."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:22:44.243Z",
"text": "Exploring Task Environment\n\nI'm now examining the available files and system constraints within the task environment. My focus is on understanding the provided data structures and any limitations that might impact model development and submission."
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"title": "Initial Task and Environment Research",
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"timestamp": "2026-09-29T01:22:44.278Z",
"resultDisplay": "## 📂 Topic: **Initial Task and Environment Research**\n\n**Summary:**\nStarting initial research into the music discovery ranker task. We will inspect the directory structure, available python packages, evaluation code, data sizes, and system hardware to plan our strategy.\n\n> [!STRATEGY]\n> **Intent:** Inspect task files, data schemas, hardware resources, and evaluation script.",
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"timestamp": "2026-09-29T01:22:44.279Z",
"content": "Current topic: \"Initial Task and Environment Research\"\nTopic summary: Starting initial research into the music discovery ranker task. We will inspect the directory structure, available python packages, evaluation code, data sizes, and system hardware to plan our strategy.\n\nStrategic Intent: Inspect task files, data schemas, hardware resources, and evaluation script.",
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"timestamp": "2026-09-29T01:22:44.243Z",
"callId": "run_shell_command__call_72144",
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"command": "ls -la /task && ls -la /task/data",
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"response": {
"output": "\nOutput: total 36\ndr-xr-xr-x 1 root root 4096 Sep 29 01:20 .\ndrwxr-xr-x 1 root root 4096 Sep 29 01:22 ..\ndr-xr-xr-x 1 root root 4096 Sep 29 01:17 data\n-r--r--r-- 1 root root 5774 Sep 29 01:16 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:16 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 29 01:16 requirements.txt\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Sep 29 01:17 .\ndr-xr-xr-x 1 root root 4096 Sep 29 01:20 ..\n-r--r--r-- 1 root root 349352 Sep 29 01:16 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Sep 29 01:16 target_users.parquet\n-r--r--r-- 1 root root 7736341 Sep 29 01:16 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Sep 29 01:17 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Sep 29 01:16 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Sep 29 01:16 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 01:16 train_listens.parquet\n-r--r--r-- 1 root root 155988 Sep 29 01:16 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Sep 29 01:16 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Sep 29 01:16 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Sep 29 01:16 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Sep 29 01:16 val_listens.parquet\n-r--r--r-- 1 root root 7845 Sep 29 01:16 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Sep 29 01:16 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Sep 29 01:16 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Sep 29 01:17 validation_preference_state.parquet\nProcess Group PGID: 1612\n"
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"status": "success",
"timestamp": "2026-09-29T01:22:44.278Z",
"resultDisplay": "total 36\ndr-xr-xr-x 1 root root 4096 Sep 29 01:20 .\ndrwxr-xr-x 1 root root 4096 Sep 29 01:22 ..\ndr-xr-xr-x 1 root root 4096 Sep 29 01:17 data\n-r--r--r-- 1 root root 5774 Sep 29 01:16 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:16 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 29 01:16 requirements.txt\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Sep 29 01:17 .\ndr-xr-xr-x 1 root root 4096 Sep 29 01:20 ..\n-r--r--r-- 1 root root 349352 Sep 29 01:16 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Sep 29 01:16 target_users.parquet\n-r--r--r-- 1 root root 7736341 Sep 29 01:16 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Sep 29 01:17 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Sep 29 01:16 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Sep 29 01:16 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 01:16 train_listens.parquet\n-r--r--r-- 1 root root 155988 Sep 29 01:16 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Sep 29 01:16 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Sep 29 01:16 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Sep 29 01:16 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Sep 29 01:16 val_listens.parquet\n-r--r--r-- 1 root root 7845 Sep 29 01:16 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Sep 29 01:16 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Sep 29 01:16 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Sep 29 01:17 validation_preference_state.parquet",
"description": "ls -la /task && ls -la /task/data",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:22:44.279Z",
"content": "\nOutput: total 36\ndr-xr-xr-x 1 root root 4096 Sep 29 01:20 .\ndrwxr-xr-x 1 root root 4096 Sep 29 01:22 ..\ndr-xr-xr-x 1 root root 4096 Sep 29 01:17 data\n-r--r--r-- 1 root root 5774 Sep 29 01:16 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:16 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 29 01:16 requirements.txt\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Sep 29 01:17 .\ndr-xr-xr-x 1 root root 4096 Sep 29 01:20 ..\n-r--r--r-- 1 root root 349352 Sep 29 01:16 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Sep 29 01:16 target_users.parquet\n-r--r--r-- 1 root root 7736341 Sep 29 01:16 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Sep 29 01:17 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Sep 29 01:16 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Sep 29 01:16 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 01:16 train_listens.parquet\n-r--r--r-- 1 root root 155988 Sep 29 01:16 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Sep 29 01:16 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Sep 29 01:16 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Sep 29 01:16 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Sep 29 01:16 val_listens.parquet\n-r--r--r-- 1 root root 7845 Sep 29 01:16 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Sep 29 01:16 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Sep 29 01:16 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Sep 29 01:17 validation_preference_state.parquet\nProcess Group PGID: 1612\n",
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"role": "agent",
"timestamp": "2026-09-29T01:22:46.258Z",
"blocks": [
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"callId": "run_shell_command__call_108535",
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"command": "lscpu; free -h; nvidia-smi || true",
"description": "Check CPU, memory, and GPU availability"
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"description": "Check CPU, memory, and GPU availability"
},
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"output": "\nOutput: Architecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 8\nOn-line CPU(s) list: 0-7\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7R13 Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 4\nSocket(s): 1\nStepping: 1\nBogoMIPS: 5299.99\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 128 KiB (4 instances)\nL1i cache: 128 KiB (4 instances)\nL2 cache: 2 MiB (4 instances)\nL3 cache: 16 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-7\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Old microcode: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; Safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsa: Mitigation; Clear CPU buffers\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n/usr/bin/bash: line 4: free: command not found\n/usr/bin/bash: line 4: nvidia-smi: command not found\nProcess Group PGID: 1616\n"
}
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],
"status": "success",
"timestamp": "2026-09-29T01:22:46.279Z",
"resultDisplay": "Architecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 8\nOn-line CPU(s) list: 0-7\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7R13 Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 4\nSocket(s): 1\nStepping: 1\nBogoMIPS: 5299.99\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 128 KiB (4 instances)\nL1i cache: 128 KiB (4 instances)\nL2 cache: 2 MiB (4 instances)\nL3 cache: 16 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-7\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Old microcode: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; Safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsa: Mitigation; Clear CPU buffers\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n/usr/bin/bash: line 4: free: command not found\n/usr/bin/bash: line 4: nvidia-smi: command not found",
"description": "lscpu; free -h; nvidia-smi || true",
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"result": {
"timestamp": "2026-09-29T01:22:46.280Z",
"content": "\nOutput: Architecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 8\nOn-line CPU(s) list: 0-7\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7R13 Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 4\nSocket(s): 1\nStepping: 1\nBogoMIPS: 5299.99\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 128 KiB (4 instances)\nL1i cache: 128 KiB (4 instances)\nL2 cache: 2 MiB (4 instances)\nL3 cache: 16 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-7\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Old microcode: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; Safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsa: Mitigation; Clear CPU buffers\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n/usr/bin/bash: line 4: free: command not found\n/usr/bin/bash: line 4: nvidia-smi: command not found\nProcess Group PGID: 1616\n",
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"text": "Checking System Memory\n\nI'm currently examining available system memory to understand resource constraints. My next step is to analyze the `evaluate_public.py` script in detail."
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"output": "\nOutput: MemTotal: 15988968 kB\nMemFree: 5363488 kB\nMemAvailable: 14557848 kB\nBuffers: 196276 kB\nCached: 8873924 kB\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\nProcess Group PGID: 1621\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:22:48.487Z",
"resultDisplay": "MemTotal: 15988968 kB\nMemFree: 5363488 kB\nMemAvailable: 14557848 kB\nBuffers: 196276 kB\nCached: 8873924 kB\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()",
"description": "head -n 5 /proc/meminfo && cat /task/evaluate_public.py",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:22:48.487Z",
"content": "\nOutput: MemTotal: 15988968 kB\nMemFree: 5363488 kB\nMemAvailable: 14557848 kB\nBuffers: 196276 kB\nCached: 8873924 kB\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\nProcess Group PGID: 1621\n",
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"output": "\nOutput: # Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\nProcess Group PGID: 1625\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:22:50.447Z",
"resultDisplay": "# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }",
"description": "cat /task/requirements.txt && cat /task/ranking_evaluation.py",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:22:50.448Z",
"content": "\nOutput: # Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\nProcess Group PGID: 1625\n",
"isError": false
},
"responseMessageId": "28786fd0-9b17-4f1e-8427-189683ac27dc"
}
]
},
{
"id": 7,
"role": "agent",
"timestamp": "2026-09-29T01:22:53.629Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:22:53.629Z",
"text": "Checking Installed Libraries\n\nI'm currently inventorying our available libraries, noting versions for numpy, scipy, pandas, and scikit-learn. My next step is to determine if lightgbm or xgboost are present."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:22:53.629Z",
"callId": "run_shell_command__call_1027536",
"name": "run_shell_command",
"input": {
"description": "Check available ML libraries",
"command": "python3 -c \"\nfor pkg in ['lightgbm', 'xgboost', 'catboost', 'torch', 'implicit', 'numba', 'scipy', 'sklearn']:\n try:\n __import__(pkg)\n print(f'{pkg}: available')\n except ImportError:\n print(f'{pkg}: NOT available')\n\""
},
"recorded": {
"id": "run_shell_command__call_1027536",
"name": "run_shell_command",
"args": {
"description": "Check available ML libraries",
"command": "python3 -c \"\nfor pkg in ['lightgbm', 'xgboost', 'catboost', 'torch', 'implicit', 'numba', 'scipy', 'sklearn']:\n try:\n __import__(pkg)\n print(f'{pkg}: available')\n except ImportError:\n print(f'{pkg}: NOT available')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1027536",
"name": "run_shell_command",
"response": {
"output": "\nOutput: lightgbm: NOT available\nxgboost: NOT available\ncatboost: NOT available\ntorch: NOT available\nimplicit: NOT available\nnumba: NOT available\nscipy: available\nsklearn: available\nProcess Group PGID: 1629\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:22:55.033Z",
"resultDisplay": "lightgbm: NOT available\nxgboost: NOT available\ncatboost: NOT available\ntorch: NOT available\nimplicit: NOT available\nnumba: NOT available\nscipy: available\nsklearn: available",
"description": "Check available ML libraries",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:22:55.034Z",
"content": "\nOutput: lightgbm: NOT available\nxgboost: NOT available\ncatboost: NOT available\ntorch: NOT available\nimplicit: NOT available\nnumba: NOT available\nscipy: available\nsklearn: available\nProcess Group PGID: 1629\n",
"isError": false
},
"responseMessageId": "43eec2df-7a90-427b-96ca-b6605e605478"
}
]
},
{
"id": 8,
"role": "agent",
"timestamp": "2026-09-29T01:22:58.818Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:22:58.818Z",
"text": "Exploring Available Libraries\n\nI'm pleased to confirm that I have access to core scientific computing libraries including `scipy`, `sklearn`, `numpy`, and `pandas`. This is a crucial first step, and I will now focus on how best to leverage these tools to meet the project's objectives within the given constraints and time limit."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:22:58.818Z",
"callId": "run_shell_command__call_132241",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\n\nfor f in sorted(DATA.glob('*.parquet')):\n df = pd.read_parquet(f)\n print(f'{f.name:35s}: shape={df.shape}, cols={list(df.columns)}')\n if 'timestamp' in df.columns:\n print(f' timestamp min={df.timestamp.min()}, max={df.timestamp.max()}')\n\nfor f in sorted(DATA.glob('*.npz')):\n with np.load(f) as z:\n print(f'{f.name:35s}: files={z.files}')\n for k in z.files:\n print(f' {k}: shape={z[k].shape}, dtype={z[k].dtype}')\n\"",
"description": "Inspect dataset shapes, column names, timestamps, and npz contents"
},
"recorded": {
"id": "run_shell_command__call_132241",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\n\nfor f in sorted(DATA.glob('*.parquet')):\n df = pd.read_parquet(f)\n print(f'{f.name:35s}: shape={df.shape}, cols={list(df.columns)}')\n if 'timestamp' in df.columns:\n print(f' timestamp min={df.timestamp.min()}, max={df.timestamp.max()}')\n\nfor f in sorted(DATA.glob('*.npz')):\n with np.load(f) as z:\n print(f'{f.name:35s}: files={z.files}')\n for k in z.files:\n print(f' {k}: shape={z[k].shape}, dtype={z[k].dtype}')\n\"",
"description": "Inspect dataset shapes, column names, timestamps, and npz contents"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_132241",
"name": "run_shell_command",
"response": {
"output": "\nOutput: candidate_catalog.parquet : shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet : shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet : shape=(484776, 3), cols=['uid', 'item_id', 'state']\ntrain_dislikes.parquet : shape=(101075, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=75, max=24786580\ntrain_likes.parquet : shape=(814831, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=80, max=24786790\ntrain_listens.parquet : shape=(43484159, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n timestamp min=0, max=24786795\ntrain_undislikes.parquet : shape=(19960, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=15950, max=24785395\ntrain_unlikes.parquet : shape=(292665, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=250, max=24786570\nval_dislikes.parquet : shape=(3563, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=24789920, max=25393280\nval_likes.parquet : shape=(37795, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=24788815, max=25393390\nval_listens.parquet : shape=(1501697, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n timestamp min=24788600, max=25393395\nval_undislikes.parquet : shape=(606, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=24810715, max=25393035\nval_unlikes.parquet : shape=(9346, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=24789005, max=25393385\nvalidation_preference_state.parquet: shape=(469124, 3), cols=['uid', 'item_id', 'state']\ntest_eligible_candidates.npz : files=['uids', 'item_ids', 'packed_eligible']\n uids: shape=(8456,), dtype=uint32\n item_ids: shape=(30000,), dtype=uint32\n packed_eligible: shape=(8456, 3750), dtype=uint8\nvalidation_eligible_candidates.npz : files=['uids', 'item_ids', 'packed_eligible']\n uids: shape=(8456,), dtype=uint32\n item_ids: shape=(30000,), dtype=uint32\n packed_eligible: shape=(8456, 3750), dtype=uint8\nProcess Group PGID: 1647\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:00.784Z",
"resultDisplay": "candidate_catalog.parquet : shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet : shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet : shape=(484776, 3), cols=['uid', 'item_id', 'state']\ntrain_dislikes.parquet : shape=(101075, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=75, max=24786580\ntrain_likes.parquet : shape=(814831, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=80, max=24786790\ntrain_listens.parquet : shape=(43484159, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n timestamp min=0, max=24786795\ntrain_undislikes.parquet : shape=(19960, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=15950, max=24785395\ntrain_unlikes.parquet : shape=(292665, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=250, max=24786570\nval_dislikes.parquet : shape=(3563, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=24789920, max=25393280\nval_likes.parquet : shape=(37795, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=24788815, max=25393390\nval_listens.parquet : shape=(1501697, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n timestamp min=24788600, max=25393395\nval_undislikes.parquet : shape=(606, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=24810715, max=25393035\nval_unlikes.parquet : shape=(9346, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=24789005, max=25393385\nvalidation_preference_state.parquet: shape=(469124, 3), cols=['uid', 'item_id', 'state']\ntest_eligible_candidates.npz : files=['uids', 'item_ids', 'packed_eligible']\n uids: shape=(8456,), dtype=uint32\n item_ids: shape=(30000,), dtype=uint32\n packed_eligible: shape=(8456, 3750), dtype=uint8\nvalidation_eligible_candidates.npz : files=['uids', 'item_ids', 'packed_eligible']\n uids: shape=(8456,), dtype=uint32\n item_ids: shape=(30000,), dtype=uint32\n packed_eligible: shape=(8456, 3750), dtype=uint8",
"description": "Inspect dataset shapes, column names, timestamps, and npz contents",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:00.785Z",
"content": "\nOutput: candidate_catalog.parquet : shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet : shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet : shape=(484776, 3), cols=['uid', 'item_id', 'state']\ntrain_dislikes.parquet : shape=(101075, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=75, max=24786580\ntrain_likes.parquet : shape=(814831, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=80, max=24786790\ntrain_listens.parquet : shape=(43484159, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n timestamp min=0, max=24786795\ntrain_undislikes.parquet : shape=(19960, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=15950, max=24785395\ntrain_unlikes.parquet : shape=(292665, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=250, max=24786570\nval_dislikes.parquet : shape=(3563, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=24789920, max=25393280\nval_likes.parquet : shape=(37795, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=24788815, max=25393390\nval_listens.parquet : shape=(1501697, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n timestamp min=24788600, max=25393395\nval_undislikes.parquet : shape=(606, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=24810715, max=25393035\nval_unlikes.parquet : shape=(9346, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\n timestamp min=24789005, max=25393385\nvalidation_preference_state.parquet: shape=(469124, 3), cols=['uid', 'item_id', 'state']\ntest_eligible_candidates.npz : files=['uids', 'item_ids', 'packed_eligible']\n uids: shape=(8456,), dtype=uint32\n item_ids: shape=(30000,), dtype=uint32\n packed_eligible: shape=(8456, 3750), dtype=uint8\nvalidation_eligible_candidates.npz : files=['uids', 'item_ids', 'packed_eligible']\n uids: shape=(8456,), dtype=uint32\n item_ids: shape=(30000,), dtype=uint32\n packed_eligible: shape=(8456, 3750), dtype=uint8\nProcess Group PGID: 1647\n",
"isError": false
},
"responseMessageId": "dd81ce66-34f8-42b5-9545-d408521727a9"
}
]
},
{
"id": 9,
"role": "agent",
"timestamp": "2026-09-29T01:23:05.757Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:05.757Z",
"text": "Processing Input Data\n\nI've begun by examining the provided data files. I've noted the total number of users in `target_users.parquet` and the item count along with its ordering and relevant columns from `candidate_catalog.parquet`. My next step is to understand the `validation_` file."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:05.757Z",
"text": "Analyzing Data Sizes\n\nI'm now processing the `.npz` files, noting the user and item counts, and investigating the memory implications of loading the massive `train_listens.parquet` file. I'm also meticulously dissecting the timestamp data to establish precise training, validation, and test windows, which are crucial for the upcoming analysis of target users and candidate eligibility."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:05.757Z",
"callId": "run_shell_command__call_130433",
"name": "run_shell_command",
"input": {
"description": "Check eligible candidates per user statistics",
"command": "python3 -c \"\nimport numpy as np\n\nfor name in ['validation_eligible_candidates.npz', 'test_eligible_candidates.npz']:\n with np.load(f'/task/data/{name}') as z:\n packed = z['packed_eligible']\n # unpackbits\n unpacked = np.unpackbits(packed, axis=1)[:, :30000]\n sums = unpacked.sum(axis=1)\n print(f'{name}: eligible items per user: min={sums.min()}, max={sums.max()}, mean={sums.mean():.1f}, median={np.median(sums)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_130433",
"name": "run_shell_command",
"args": {
"description": "Check eligible candidates per user statistics",
"command": "python3 -c \"\nimport numpy as np\n\nfor name in ['validation_eligible_candidates.npz', 'test_eligible_candidates.npz']:\n with np.load(f'/task/data/{name}') as z:\n packed = z['packed_eligible']\n # unpackbits\n unpacked = np.unpackbits(packed, axis=1)[:, :30000]\n sums = unpacked.sum(axis=1)\n print(f'{name}: eligible items per user: min={sums.min()}, max={sums.max()}, mean={sums.mean():.1f}, median={np.median(sums)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_130433",
"name": "run_shell_command",
"response": {
"output": "\nOutput: validation_eligible_candidates.npz: eligible items per user: min=24657, max=30000, mean=29067.3, median=29242.0\ntest_eligible_candidates.npz: eligible items per user: min=24552, max=30000, mean=29041.3, median=29220.0\nProcess Group PGID: 1667\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:06.416Z",
"resultDisplay": "validation_eligible_candidates.npz: eligible items per user: min=24657, max=30000, mean=29067.3, median=29242.0\ntest_eligible_candidates.npz: eligible items per user: min=24552, max=30000, mean=29041.3, median=29220.0",
"description": "Check eligible candidates per user statistics",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:06.417Z",
"content": "\nOutput: validation_eligible_candidates.npz: eligible items per user: min=24657, max=30000, mean=29067.3, median=29242.0\ntest_eligible_candidates.npz: eligible items per user: min=24552, max=30000, mean=29041.3, median=29220.0\nProcess Group PGID: 1667\n",
"isError": false
},
"responseMessageId": "9a042d1d-75e9-4a79-8f05-14cb1f10b09c"
}
]
},
{
"id": 10,
"role": "agent",
"timestamp": "2026-09-29T01:23:12.084Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:12.084Z",
"text": "Investigating Ineligibility\n\nI'm currently diving into the ~950 ineligible items per user, trying to pinpoint the exact criteria. My current hypothesis is that pre-existing states like 'listened' or 'disliked' might be contributing factors, and I'm examining `validation_preference_state` to confirm this."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:12.084Z",
"callId": "run_shell_command__call_260027",
"name": "run_shell_command",
"input": {
"description": "Check preference state details and relationship with eligibility",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state counts:\\n', pref['state'].value_counts())\nprint('Users in pref:', pref['uid'].nunique())\n\n# Check overlap of preference state with eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids']\n items = z['item_ids']\n packed = z['packed_eligible']\n uid2idx = {u: i for i, u in enumerate(uids)}\n item2idx = {it: i for i, it in enumerate(items)}\n\nliked = pref[pref.state == 'liked']\ndisliked = pref[pref.state == 'disliked']\n\ndef check_elig(df):\n eligs = []\n for u, it in zip(df.uid[:1000], df.item_id[:1000]):\n if u in uid2idx and it in item2idx:\n r = uid2idx[u]\n c = item2idx[it]\n b = (packed[r, c // 8] >> (c % 8)) & 1\n eligs.append(b)\n return np.mean(eligs) if eligs else None\n\nprint('Fraction of liked in eligibility:', check_elig(liked))\nprint('Fraction of disliked in eligibility:', check_elig(disliked))\n\""
},
"recorded": {
"id": "run_shell_command__call_260027",
"name": "run_shell_command",
"args": {
"description": "Check preference state details and relationship with eligibility",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state counts:\\n', pref['state'].value_counts())\nprint('Users in pref:', pref['uid'].nunique())\n\n# Check overlap of preference state with eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids']\n items = z['item_ids']\n packed = z['packed_eligible']\n uid2idx = {u: i for i, u in enumerate(uids)}\n item2idx = {it: i for i, it in enumerate(items)}\n\nliked = pref[pref.state == 'liked']\ndisliked = pref[pref.state == 'disliked']\n\ndef check_elig(df):\n eligs = []\n for u, it in zip(df.uid[:1000], df.item_id[:1000]):\n if u in uid2idx and it in item2idx:\n r = uid2idx[u]\n c = item2idx[it]\n b = (packed[r, c // 8] >> (c % 8)) & 1\n eligs.append(b)\n return np.mean(eligs) if eligs else None\n\nprint('Fraction of liked in eligibility:', check_elig(liked))\nprint('Fraction of disliked in eligibility:', check_elig(disliked))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_260027",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Preference state counts:\n state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\nUsers in pref: 7038\nFraction of liked in eligibility: 0.0\nFraction of disliked in eligibility: 0.0\nProcess Group PGID: 1677\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:13.047Z",
"resultDisplay": "Preference state counts:\n state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\nUsers in pref: 7038\nFraction of liked in eligibility: 0.0\nFraction of disliked in eligibility: 0.0",
"description": "Check preference state details and relationship with eligibility",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:13.048Z",
"content": "\nOutput: Preference state counts:\n state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\nUsers in pref: 7038\nFraction of liked in eligibility: 0.0\nFraction of disliked in eligibility: 0.0\nProcess Group PGID: 1677\n",
"isError": false
},
"responseMessageId": "0a71a0b8-91b3-459d-bcb5-1c5d98018cdc"
}
]
},
{
"id": 11,
"role": "agent",
"timestamp": "2026-09-29T01:23:27.299Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:27.299Z",
"text": "Clarifying Eligibility Rules\n\nI'm examining the platform's ownership of candidate eligibility. Specifically, I'm investigating why `liked` and `disliked` preference states currently register as 0.0 fraction within the eligibility framework, considering all modeling within that boundary is considered in scope for analysis."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:27.299Z",
"callId": "run_shell_command__call_150268",
"name": "run_shell_command",
"input": {
"description": "Check validation truth stats",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Scored users in val:', len(truth))\ntot_items = sum(len(v) for v in truth.values())\ngrade3 = sum(sum(1 for g in v.values() if g == 3) for v in truth.values())\ngrade1 = sum(sum(1 for g in v.values() if g == 1) for v in truth.values())\nprint(f'Total positive user-item pairs in val: {tot_items} (grade 3: {grade3}, grade 1: {grade1})')\n\""
},
"recorded": {
"id": "run_shell_command__call_150268",
"name": "run_shell_command",
"args": {
"description": "Check validation truth stats",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Scored users in val:', len(truth))\ntot_items = sum(len(v) for v in truth.values())\ngrade3 = sum(sum(1 for g in v.values() if g == 3) for v in truth.values())\ngrade1 = sum(sum(1 for g in v.values() if g == 1) for v in truth.values())\nprint(f'Total positive user-item pairs in val: {tot_items} (grade 3: {grade3}, grade 1: {grade1})')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_150268",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 4, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1695\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:28.010Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 4, in \nModuleNotFoundError: No module named 'ranking_evaluation'",
"description": "Check validation truth stats",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:28.011Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 4, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1695\n",
"isError": false
},
"responseMessageId": "56d0c8a9-7474-48a2-92dc-b7b3005a2a37"
}
]
},
{
"id": 12,
"role": "agent",
"timestamp": "2026-09-29T01:23:31.785Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:31.785Z",
"callId": "run_shell_command__call_108736",
"name": "run_shell_command",
"input": {
"description": "Check validation truth stats",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Scored users in val:', len(truth))\ntot_items = sum(len(v) for v in truth.values())\ngrade3 = sum(sum(1 for g in v.values() if g == 3) for v in truth.values())\ngrade1 = sum(sum(1 for g in v.values() if g == 1) for v in truth.values())\nprint(f'Total positive user-item pairs in val: {tot_items} (grade 3: {grade3}, grade 1: {grade1})')\n\""
},
"recorded": {
"id": "run_shell_command__call_108736",
"name": "run_shell_command",
"args": {
"description": "Check validation truth stats",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Scored users in val:', len(truth))\ntot_items = sum(len(v) for v in truth.values())\ngrade3 = sum(sum(1 for g in v.values() if g == 3) for v in truth.values())\ngrade1 = sum(sum(1 for g in v.values() if g == 1) for v in truth.values())\nprint(f'Total positive user-item pairs in val: {tot_items} (grade 3: {grade3}, grade 1: {grade1})')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_108736",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Scored users in val: 4263\nTotal positive user-item pairs in val: 37769 (grade 3: 4417, grade 1: 33352)\nProcess Group PGID: 1706\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:33.058Z",
"resultDisplay": "Scored users in val: 4263\nTotal positive user-item pairs in val: 37769 (grade 3: 4417, grade 1: 33352)",
"description": "Check validation truth stats",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:33.059Z",
"content": "\nOutput: Scored users in val: 4263\nTotal positive user-item pairs in val: 37769 (grade 3: 4417, grade 1: 33352)\nProcess Group PGID: 1706\n",
"isError": false
},
"responseMessageId": "78d4be48-edcf-4395-8ff3-6cb6f1e75810"
}
]
},
{
"id": 13,
"role": "agent",
"timestamp": "2026-09-29T01:23:36.102Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:36.102Z",
"text": "Testing Baseline Performance\n\nI'm currently evaluating a simple global popularity baseline against our validation set. Initial checks indicate that out of 8456 users, 4263 have relevant items, yielding 37,769 positive pairs. I'm scripting a quick comparison to see its performance."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:36.102Z",
"callId": "run_shell_command__call_66381",
"name": "run_shell_command",
"input": {
"description": "Check candidate catalog details",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\n\n# Load candidate catalog\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog columns:', catalog.columns)\nprint(catalog.head())\n\""
},
"recorded": {
"id": "run_shell_command__call_66381",
"name": "run_shell_command",
"args": {
"description": "Check candidate catalog details",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\n\n# Load candidate catalog\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog columns:', catalog.columns)\nprint(catalog.head())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_66381",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog columns: Index(['item_id', 'positive_listens', 'organic_positive_listens',\n 'popularity_rank'],\n dtype='object')\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\nProcess Group PGID: 1726\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:36.859Z",
"resultDisplay": "Catalog columns: Index(['item_id', 'positive_listens', 'organic_positive_listens',\n 'popularity_rank'],\n dtype='object')\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5",
"description": "Check candidate catalog details",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:36.860Z",
"content": "\nOutput: Catalog columns: Index(['item_id', 'positive_listens', 'organic_positive_listens',\n 'popularity_rank'],\n dtype='object')\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\nProcess Group PGID: 1726\n",
"isError": false
},
"responseMessageId": "5ec2531f-08ec-47aa-858f-f552ec59c85c"
}
]
},
{
"id": 14,
"role": "agent",
"timestamp": "2026-09-29T01:23:42.351Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:42.351Z",
"text": "Generating Initial Ranking\n\nI'm currently developing a Python test to establish a baseline ranking. This involves processing a 30,000-item candidate catalog, ordered by popularity, and selecting the top 100 eligible items for each user based on this rank."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:42.351Z",
"callId": "run_shell_command__call_117757",
"name": "run_shell_command",
"input": {
"description": "Evaluate popularity baseline on validation",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import score, validation_truth\n\n# Load validation eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n# catalog items in order of popularity\n# Let's map item_ids in catalog to their column index in candidate npz\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\n# For each user, find top 100 eligible items\nranked = {}\nfor r, uid in enumerate(uids):\n # unpack bits for user r\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n # filter catalog_items where bits[catalog_cols] == 1\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Catalog positive_listens popularity NDCG@10:', score(ranked, truth))\n\n# Now try organic_positive_listens popularity\ncatalog_org = catalog.sort_values('organic_positive_listens', ascending=False)\ncatalog_org_cols = np.array([col_map[item] for item in catalog_org.item_id], dtype=np.int32)\ncatalog_org_items = catalog_org.item_id.to_numpy(dtype=np.int64)\n\nranked_org = {}\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_org_items[bits[catalog_org_cols] == 1]\n ranked_org[uid] = eligible_items[:100].tolist()\n\nprint('Catalog organic_positive_listens popularity NDCG@10:', score(ranked_org, truth))\n\""
},
"recorded": {
"id": "run_shell_command__call_117757",
"name": "run_shell_command",
"args": {
"description": "Evaluate popularity baseline on validation",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import score, validation_truth\n\n# Load validation eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n# catalog items in order of popularity\n# Let's map item_ids in catalog to their column index in candidate npz\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\n# For each user, find top 100 eligible items\nranked = {}\nfor r, uid in enumerate(uids):\n # unpack bits for user r\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n # filter catalog_items where bits[catalog_cols] == 1\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Catalog positive_listens popularity NDCG@10:', score(ranked, truth))\n\n# Now try organic_positive_listens popularity\ncatalog_org = catalog.sort_values('organic_positive_listens', ascending=False)\ncatalog_org_cols = np.array([col_map[item] for item in catalog_org.item_id], dtype=np.int32)\ncatalog_org_items = catalog_org.item_id.to_numpy(dtype=np.int64)\n\nranked_org = {}\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_org_items[bits[catalog_org_cols] == 1]\n ranked_org[uid] = eligible_items[:100].tolist()\n\nprint('Catalog organic_positive_listens popularity NDCG@10:', score(ranked_org, truth))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_117757",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nCatalog organic_positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}\nProcess Group PGID: 1743\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:45.694Z",
"resultDisplay": "Catalog positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nCatalog organic_positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}",
"description": "Evaluate popularity baseline on validation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:45.696Z",
"content": "\nOutput: Catalog positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nCatalog organic_positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}\nProcess Group PGID: 1743\n",
"isError": false
},
"responseMessageId": "a61bd34f-2659-4868-a230-9550fbbef43b"
}
]
},
{
"id": 15,
"role": "agent",
"timestamp": "2026-09-29T01:23:52.336Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:52.336Z",
"text": "Establishing Baseline Performance\n\nMy current focus is on establishing a solid baseline, with an initial NDCG@10 of approximately 1.07%. I'm also prioritizing the immediate generation of a valid submission file for testing to ensure structural integrity from the outset."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:52.336Z",
"callId": "run_shell_command__call_50614",
"name": "run_shell_command",
"input": {
"description": "Generate initial baseline submission and validate it",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\n# Load test eligibility\nwith np.load('/task/data/test_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nrecords = []\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1][:100]\n for rank_idx, item in enumerate(eligible_items, 1):\n records.append((uid, item, rank_idx))\n\nsub_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\nsub_df.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Submission saved! Shape:', sub_df.shape)\n\n# Verify with ranking_evaluation\ntargets = set(uids)\nelig = CandidateSet('/task/data/test_eligible_candidates.npz')\nnorm = validate_ranking(pd.read_parquet('/app/solution/submission.parquet'), targets)\nfor uid, item in norm[['uid', 'item_id']].itertuples(index=False, name=None):\n if not elig.is_eligible(uid, item):\n raise ValueError(f'Item {item} for uid {uid} is not eligible!')\nprint('Submission validation SUCCESSFUL! Exactly 100 rows per user, all ranks 1..100, all items eligible.')\n\""
},
"recorded": {
"id": "run_shell_command__call_50614",
"name": "run_shell_command",
"args": {
"description": "Generate initial baseline submission and validate it",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\n# Load test eligibility\nwith np.load('/task/data/test_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nrecords = []\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1][:100]\n for rank_idx, item in enumerate(eligible_items, 1):\n records.append((uid, item, rank_idx))\n\nsub_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\nsub_df.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Submission saved! Shape:', sub_df.shape)\n\n# Verify with ranking_evaluation\ntargets = set(uids)\nelig = CandidateSet('/task/data/test_eligible_candidates.npz')\nnorm = validate_ranking(pd.read_parquet('/app/solution/submission.parquet'), targets)\nfor uid, item in norm[['uid', 'item_id']].itertuples(index=False, name=None):\n if not elig.is_eligible(uid, item):\n raise ValueError(f'Item {item} for uid {uid} is not eligible!')\nprint('Submission validation SUCCESSFUL! Exactly 100 rows per user, all ranks 1..100, all items eligible.')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_50614",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Submission saved! Shape: (845600, 3)\nSubmission validation SUCCESSFUL! Exactly 100 rows per user, all ranks 1..100, all items eligible.\nProcess Group PGID: 1763\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:55.967Z",
"resultDisplay": "Submission saved! Shape: (845600, 3)\nSubmission validation SUCCESSFUL! Exactly 100 rows per user, all ranks 1..100, all items eligible.",
"description": "Generate initial baseline submission and validate it",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:55.968Z",
"content": "\nOutput: Submission saved! Shape: (845600, 3)\nSubmission validation SUCCESSFUL! Exactly 100 rows per user, all ranks 1..100, all items eligible.\nProcess Group PGID: 1763\n",
"isError": false
},
"responseMessageId": "317e6b10-a0c8-4070-882f-183f983b8dc9"
}
]
},
{
"id": 16,
"role": "agent",
"timestamp": "2026-09-29T01:23:58.528Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:58.528Z",
"callId": "write_file__call_101928",
"name": "write_file",
"input": {
"file_path": "/app/solution/README.md",
"content": "# Music Discovery Ranking Solution\n\n## Overview\nMusic Discovery candidate ranker for target streaming users.\nEvaluated on graded NDCG@10 on organic discovery events (grade 3 for organic likes, grade 1 for organic listens >= 80%).\n\n## Methodology\n- Baseline popularity ranking verified and structurally valid.\n- Active development of personalized recommendation models (collaborative filtering, matrix factorization, item-item similarity, user profile matching, gradient boosting re-ranking).\n"
},
"recorded": {
"id": "write_file__call_101928",
"name": "write_file",
"args": {
"file_path": "/app/solution/README.md",
"content": "# Music Discovery Ranking Solution\n\n## Overview\nMusic Discovery candidate ranker for target streaming users.\nEvaluated on graded NDCG@10 on organic discovery events (grade 3 for organic likes, grade 1 for organic listens >= 80%).\n\n## Methodology\n- Baseline popularity ranking verified and structurally valid.\n- Active development of personalized recommendation models (collaborative filtering, matrix factorization, item-item similarity, user profile matching, gradient boosting re-ranking).\n"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_101928",
"name": "write_file",
"response": {
"output": "Successfully created and wrote to new file: /app/solution/README.md. Here is the updated code:\n# Music Discovery Ranking Solution\n\n## Overview\nMusic Discovery candidate ranker for target streaming users.\nEvaluated on graded NDCG@10 on organic discovery events (grade 3 for organic likes, grade 1 for organic listens >= 80%).\n\n## Methodology\n- Baseline popularity ranking verified and structurally valid.\n- Active development of personalized recommendation models (collaborative filtering, matrix factorization, item-item similarity, user profile matching, gradient boosting re-ranking).\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:58.537Z",
"resultDisplay": {
"fileDiff": "Index: README.md\n===================================================================\n--- README.md\tOriginal\n+++ README.md\tWritten\n@@ -0,0 +1,9 @@\n+# Music Discovery Ranking Solution\n+\n+## Overview\n+Music Discovery candidate ranker for target streaming users.\n+Evaluated on graded NDCG@10 on organic discovery events (grade 3 for organic likes, grade 1 for organic listens >= 80%).\n+\n+## Methodology\n+- Baseline popularity ranking verified and structurally valid.\n+- Active development of personalized recommendation models (collaborative filtering, matrix factorization, item-item similarity, user profile matching, gradient boosting re-ranking).\n",
"fileName": "README.md",
"filePath": "/app/solution/README.md",
"originalContent": "",
"newContent": "# Music Discovery Ranking Solution\n\n## Overview\nMusic Discovery candidate ranker for target streaming users.\nEvaluated on graded NDCG@10 on organic discovery events (grade 3 for organic likes, grade 1 for organic listens >= 80%).\n\n## Methodology\n- Baseline popularity ranking verified and structurally valid.\n- Active development of personalized recommendation models (collaborative filtering, matrix factorization, item-item similarity, user profile matching, gradient boosting re-ranking).\n",
"diffStat": {
"model_added_lines": 9,
"model_removed_lines": 0,
"model_added_chars": 483,
"model_removed_chars": 0,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": true,
"isBuildFile": false
},
"description": "Writing to solution/README.md",
"displayName": "WriteFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T01:23:58.538Z",
"content": "Successfully created and wrote to new file: /app/solution/README.md. Here is the updated code:\n# Music Discovery Ranking Solution\n\n## Overview\nMusic Discovery candidate ranker for target streaming users.\nEvaluated on graded NDCG@10 on organic discovery events (grade 3 for organic likes, grade 1 for organic listens >= 80%).\n\n## Methodology\n- Baseline popularity ranking verified and structurally valid.\n- Active development of personalized recommendation models (collaborative filtering, matrix factorization, item-item similarity, user profile matching, gradient boosting re-ranking).\n",
"isError": false
},
"responseMessageId": "6d161c18-2d19-4262-8753-b7c197fc1e2a"
}
]
},
{
"id": 17,
"role": "agent",
"timestamp": "2026-09-29T01:24:03.432Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:03.432Z",
"text": "Reviewing Data Parameters\n\nI'm currently examining the provided data parameters. My focus is on understanding the training data cutoff and the subsequent validation window, specifically noting the 30-minute gap before the seven-day public validation period begins."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:03.432Z",
"text": "Analyzing Event Structures\n\nI've been dissecting the event data, noting the distinct parquet files for 'listens', 'likes', 'dislikes', 'unlikes', and 'undislikes'. Each contains user, timestamp, and item information, with 'listens' offering additional playback details. I'm particularly focused on how these event types might function as features for the ranking model."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:03.432Z",
"callId": "run_shell_command__call_293847",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nprint('Target users count:', len(target_users))\nprint(target_users.head())\n\nt = pq.read_table('/task/data/train_listens.parquet', columns=['uid'])\nuids = t['uid'].to_numpy()\nunique_uids = set(uids)\nprint('Total unique uids in train_listens:', len(unique_uids))\ntarget_uid_set = set(target_users.uid)\nprint('Target uids in train_listens:', len(target_uid_set.intersection(unique_uids)))\n\"",
"description": "Inspect target users in train_listens"
},
"recorded": {
"id": "run_shell_command__call_293847",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nprint('Target users count:', len(target_users))\nprint(target_users.head())\n\nt = pq.read_table('/task/data/train_listens.parquet', columns=['uid'])\nuids = t['uid'].to_numpy()\nunique_uids = set(uids)\nprint('Total unique uids in train_listens:', len(unique_uids))\ntarget_uid_set = set(target_users.uid)\nprint('Target uids in train_listens:', len(target_uid_set.intersection(unique_uids)))\n\"",
"description": "Inspect target users in train_listens"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_293847",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Target users count: 8456\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nTotal unique uids in train_listens: 9238\nTarget uids in train_listens: 8456\nProcess Group PGID: 1780\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:06.357Z",
"resultDisplay": "Target users count: 8456\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nTotal unique uids in train_listens: 9238\nTarget uids in train_listens: 8456",
"description": "Inspect target users in train_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:06.358Z",
"content": "\nOutput: Target users count: 8456\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nTotal unique uids in train_listens: 9238\nTarget uids in train_listens: 8456\nProcess Group PGID: 1780\n",
"isError": false
},
"responseMessageId": "992a2865-1b85-4dce-a57e-03132e3a3c6d"
}
]
},
{
"id": 18,
"role": "agent",
"timestamp": "2026-09-29T01:24:09.457Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:09.457Z",
"text": "Analyzing User Data\n\nIt's interesting to note the substantial overlap between our target users and those present in `train_listens`. With 8,456 target users all accounted for, and a candidate pool of 30,000 items, I'm now investigating the unique items within `train_listens` and `candidate_catalog`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:09.457Z",
"callId": "run_shell_command__call_133633",
"name": "run_shell_command",
"input": {
"description": "Inspect unique items in train_listens and candidate catalog",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Candidate catalog unique items:', cat['item_id'].nunique())\nt = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitem_ids = t['item_id'].to_numpy()\nunique_items = set(item_ids)\nprint('Total unique item_ids in train_listens:', len(unique_items))\ncat_item_set = set(cat['item_id'])\nprint('Candidate items in train_listens:', len(cat_item_set.intersection(unique_items)))\n\""
},
"recorded": {
"id": "run_shell_command__call_133633",
"name": "run_shell_command",
"args": {
"description": "Inspect unique items in train_listens and candidate catalog",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Candidate catalog unique items:', cat['item_id'].nunique())\nt = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitem_ids = t['item_id'].to_numpy()\nunique_items = set(item_ids)\nprint('Total unique item_ids in train_listens:', len(unique_items))\ncat_item_set = set(cat['item_id'])\nprint('Candidate items in train_listens:', len(cat_item_set.intersection(unique_items)))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_133633",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Candidate catalog unique items: 30000\nTotal unique item_ids in train_listens: 845395\nCandidate items in train_listens: 30000\nProcess Group PGID: 1802\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:13.382Z",
"resultDisplay": "Candidate catalog unique items: 30000\nTotal unique item_ids in train_listens: 845395\nCandidate items in train_listens: 30000",
"description": "Inspect unique items in train_listens and candidate catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:13.382Z",
"content": "\nOutput: Candidate catalog unique items: 30000\nTotal unique item_ids in train_listens: 845395\nCandidate items in train_listens: 30000\nProcess Group PGID: 1802\n",
"isError": false
},
"responseMessageId": "719806e1-8220-4541-8de5-4e2fc31beced"
}
]
},
{
"id": 19,
"role": "agent",
"timestamp": "2026-09-29T01:24:38.750Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:38.750Z",
"text": "Analyzing Candidate Item Coverage\n\nMy analysis reveals that all 30,000 items in the candidate catalog are present within the training listen data, out of a total of 845,395 unique item IDs. I'm now investigating how target users have interacted with these specific 30,000 items."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:38.750Z",
"callId": "run_shell_command__call_3008164",
"name": "run_shell_command",
"input": {
"description": "Check if validation truth items appeared in user history",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\n# Check validation truth items: did the user listen to or like them in train?\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\n# Load some train data for these users\n# Let's inspect a subset or load train_likes and train_listens\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\n# Check how many (uid, item_id) in truth were in train_pref\ntruth_pairs = [(u, it) for u, items in truth.items() for it in items]\ntruth_df = pd.DataFrame(truth_pairs, columns=['uid', 'item_id'])\nprint(f'Total truth pairs: {len(truth_df)}')\n\npref_merged = pd.merge(truth_df, train_pref, on=['uid', 'item_id'])\nprint(f'Truth pairs in validation_preference_state: {len(pref_merged)}')\n\n# Now let us sample some users to see if their truth items were in train_listens\nsample_users = list(truth.keys())[:100]\nsample_truth = truth_df[truth_df.uid.isin(sample_users)]\nprint(f'Sample truth pairs: {len(sample_truth)}')\n\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('uid', 'in', sample_users)],\n columns=['uid', 'item_id', 'played_ratio_pct'])\nlistens_sample = table.to_pandas()\nprint(f'Listens for sample users: {len(listens_sample)}')\n\nm = pd.merge(sample_truth, listens_sample, on=['uid', 'item_id'])\nprint(f'Sample truth pairs that appeared in train_listens: {len(m)} (unique pairs: {m[[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates().shape[0]}) out of {len(sample_truth)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_3008164",
"name": "run_shell_command",
"args": {
"description": "Check if validation truth items appeared in user history",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\n# Check validation truth items: did the user listen to or like them in train?\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\n# Load some train data for these users\n# Let's inspect a subset or load train_likes and train_listens\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\n# Check how many (uid, item_id) in truth were in train_pref\ntruth_pairs = [(u, it) for u, items in truth.items() for it in items]\ntruth_df = pd.DataFrame(truth_pairs, columns=['uid', 'item_id'])\nprint(f'Total truth pairs: {len(truth_df)}')\n\npref_merged = pd.merge(truth_df, train_pref, on=['uid', 'item_id'])\nprint(f'Truth pairs in validation_preference_state: {len(pref_merged)}')\n\n# Now let us sample some users to see if their truth items were in train_listens\nsample_users = list(truth.keys())[:100]\nsample_truth = truth_df[truth_df.uid.isin(sample_users)]\nprint(f'Sample truth pairs: {len(sample_truth)}')\n\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('uid', 'in', sample_users)],\n columns=['uid', 'item_id', 'played_ratio_pct'])\nlistens_sample = table.to_pandas()\nprint(f'Listens for sample users: {len(listens_sample)}')\n\nm = pd.merge(sample_truth, listens_sample, on=['uid', 'item_id'])\nprint(f'Sample truth pairs that appeared in train_listens: {len(m)} (unique pairs: {m[[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates().shape[0]}) out of {len(sample_truth)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3008164",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total truth pairs: 37769\nTruth pairs in validation_preference_state: 0\nSample truth pairs: 858\nListens for sample users: 591918\nSample truth pairs that appeared in train_listens: 0 (unique pairs: 0) out of 858\nProcess Group PGID: 1823\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:40.123Z",
"resultDisplay": "Total truth pairs: 37769\nTruth pairs in validation_preference_state: 0\nSample truth pairs: 858\nListens for sample users: 591918\nSample truth pairs that appeared in train_listens: 0 (unique pairs: 0) out of 858",
"description": "Check if validation truth items appeared in user history",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:40.125Z",
"content": "\nOutput: Total truth pairs: 37769\nTruth pairs in validation_preference_state: 0\nSample truth pairs: 858\nListens for sample users: 591918\nSample truth pairs that appeared in train_listens: 0 (unique pairs: 0) out of 858\nProcess Group PGID: 1823\n",
"isError": false
},
"responseMessageId": "0da4bd63-cb0d-49df-9aa8-55e527465413"
}
]
},
{
"id": 20,
"role": "agent",
"timestamp": "2026-09-29T01:24:45.135Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:45.135Z",
"text": "Investigating Training Pair Count\n\nI'm currently digging into why zero truth pairs appeared in my training set. I'm planning to broaden my investigation across more users and examine the contents of `validation_eligible_candidates.npz` to understand the underlying issue."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:45.135Z",
"callId": "run_shell_command__call_13192",
"name": "run_shell_command",
"input": {
"description": "Check if listened items in candidate catalog are ever eligible",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\n# Let us check 5 users completely: what is the overlap between their train listens/likes and eligible candidates?\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\nuid2row = {u: i for i, u in enumerate(uids)}\nitem2col = {it: i for i, it in enumerate(item_ids)}\n\nsample_uids = uids[:10].tolist()\n\ntable = pq.read_table('/task/data/train_listens.parquet',\n filters=[('uid', 'in', sample_uids)],\n columns=['uid', 'item_id'])\ndf_listens = table.to_pandas()\ndf_likes = pd.read_parquet('/task/data/train_likes.parquet')\ndf_likes_sample = df_likes[df_likes.uid.isin(sample_uids)]\n\nfor u in sample_uids:\n r = uid2row[u]\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n user_listens = set(df_listens[df_listens.uid == u].item_id)\n user_likes = set(df_likes_sample[df_likes_sample.uid == u].item_id)\n \n # check how many listened items are in candidate catalog\n listened_in_cat = [it for it in user_listens if it in item2col]\n eligible_listened = [it for it in listened_in_cat if bits[item2col[it]] == 1]\n \n print(f'User {u}: listened_in_cat={len(listened_in_cat)}, of which eligible={len(eligible_listened)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_13192",
"name": "run_shell_command",
"args": {
"description": "Check if listened items in candidate catalog are ever eligible",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\n# Let us check 5 users completely: what is the overlap between their train listens/likes and eligible candidates?\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\nuid2row = {u: i for i, u in enumerate(uids)}\nitem2col = {it: i for i, it in enumerate(item_ids)}\n\nsample_uids = uids[:10].tolist()\n\ntable = pq.read_table('/task/data/train_listens.parquet',\n filters=[('uid', 'in', sample_uids)],\n columns=['uid', 'item_id'])\ndf_listens = table.to_pandas()\ndf_likes = pd.read_parquet('/task/data/train_likes.parquet')\ndf_likes_sample = df_likes[df_likes.uid.isin(sample_uids)]\n\nfor u in sample_uids:\n r = uid2row[u]\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n user_listens = set(df_listens[df_listens.uid == u].item_id)\n user_likes = set(df_likes_sample[df_likes_sample.uid == u].item_id)\n \n # check how many listened items are in candidate catalog\n listened_in_cat = [it for it in user_listens if it in item2col]\n eligible_listened = [it for it in listened_in_cat if bits[item2col[it]] == 1]\n \n print(f'User {u}: listened_in_cat={len(listened_in_cat)}, of which eligible={len(eligible_listened)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_13192",
"name": "run_shell_command",
"response": {
"output": "\nOutput: User 100: listened_in_cat=682, of which eligible=0\nUser 200: listened_in_cat=168, of which eligible=0\nUser 300: listened_in_cat=69, of which eligible=0\nUser 600: listened_in_cat=680, of which eligible=0\nUser 700: listened_in_cat=1653, of which eligible=0\nUser 800: listened_in_cat=2524, of which eligible=0\nUser 900: listened_in_cat=334, of which eligible=0\nUser 1000: listened_in_cat=499, of which eligible=0\nUser 1100: listened_in_cat=1440, of which eligible=0\nUser 1400: listened_in_cat=1262, of which eligible=0\nProcess Group PGID: 1849\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:46.093Z",
"resultDisplay": "User 100: listened_in_cat=682, of which eligible=0\nUser 200: listened_in_cat=168, of which eligible=0\nUser 300: listened_in_cat=69, of which eligible=0\nUser 600: listened_in_cat=680, of which eligible=0\nUser 700: listened_in_cat=1653, of which eligible=0\nUser 800: listened_in_cat=2524, of which eligible=0\nUser 900: listened_in_cat=334, of which eligible=0\nUser 1000: listened_in_cat=499, of which eligible=0\nUser 1100: listened_in_cat=1440, of which eligible=0\nUser 1400: listened_in_cat=1262, of which eligible=0",
"description": "Check if listened items in candidate catalog are ever eligible",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:46.094Z",
"content": "\nOutput: User 100: listened_in_cat=682, of which eligible=0\nUser 200: listened_in_cat=168, of which eligible=0\nUser 300: listened_in_cat=69, of which eligible=0\nUser 600: listened_in_cat=680, of which eligible=0\nUser 700: listened_in_cat=1653, of which eligible=0\nUser 800: listened_in_cat=2524, of which eligible=0\nUser 900: listened_in_cat=334, of which eligible=0\nUser 1000: listened_in_cat=499, of which eligible=0\nUser 1100: listened_in_cat=1440, of which eligible=0\nUser 1400: listened_in_cat=1262, of which eligible=0\nProcess Group PGID: 1849\n",
"isError": false
},
"responseMessageId": "a926c3da-e32c-4ff9-bae9-d54490c78312"
}
]
},
{
"id": 21,
"role": "agent",
"timestamp": "2026-09-29T01:25:12.456Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:12.456Z",
"text": "Discovering New Music\n\nI'm focusing on ensuring the recommendations I generate are truly novel. My current thinking prioritizes identifying and surfacing only those tracks the user hasn't encountered before, making this a genuine discovery engine."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:12.456Z",
"text": "Refining Ranking Strategies\n\nMy current focus is on refining the ranking algorithms. I'm exploring implicit feedback collaborative filtering methods, aiming for speed and effectiveness. The goal is to accurately predict organic listening and preference within the next week, considering a large user and item catalog. I'm beginning by analyzing the performance of loading the training data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:12.456Z",
"callId": "run_shell_command__call_2772034",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport time\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nt1 = time.time()\nprint(f'Reading train_listens took {t1 - t0:.2f}s, rows={len(table)}')\n\"",
"description": "Benchmark loading time of train_listens.parquet"
},
"recorded": {
"id": "run_shell_command__call_2772034",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport time\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nt1 = time.time()\nprint(f'Reading train_listens took {t1 - t0:.2f}s, rows={len(table)}')\n\"",
"description": "Benchmark loading time of train_listens.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2772034",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Reading train_listens took 0.71s, rows=43484159\nProcess Group PGID: 1871\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:13.654Z",
"resultDisplay": "Reading train_listens took 0.71s, rows=43484159",
"description": "Benchmark loading time of train_listens.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:13.655Z",
"content": "\nOutput: Reading train_listens took 0.71s, rows=43484159\nProcess Group PGID: 1871\n",
"isError": false
},
"responseMessageId": "e1325b3b-0e3d-4fb0-a5b4-3f5764c61786"
}
]
},
{
"id": 22,
"role": "agent",
"timestamp": "2026-09-29T01:25:19.964Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:19.964Z",
"text": "Analyzing Data Efficiently\n\nI'm impressed by the speed of the data loading, processing 43.48 million rows in under a second. My current focus is on segmenting this data by key metrics: organic status, play ratio percentages, and differentiating between the 30,000 candidate items and the rest, to understand event distribution."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:19.964Z",
"callId": "run_shell_command__call_218225",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ntotal_rows = len(table)\nprint(f'Total rows: {total_rows}')\n\norg = pc.equal(table['is_organic'], 1)\nprint(f'Organic: {pc.sum(org).as_py()} ({pc.mean(org).as_py():.2%})')\n\nplayed_50 = pc.greater_equal(table['played_ratio_pct'], 50)\nprint(f'Played >= 50%: {pc.sum(played_50).as_py()} ({pc.mean(played_50).as_py():.2%})')\n\nplayed_80 = pc.greater_equal(table['played_ratio_pct'], 80)\nprint(f'Played >= 80%: {pc.sum(played_80).as_py()} ({pc.mean(played_80).as_py():.2%})')\n\norg_80 = pc.and_(org, played_80)\nprint(f'Organic & played >= 80%: {pc.sum(org_80).as_py()} ({pc.mean(org_80).as_py():.2%})')\n\n# Check timestamps range\nts = table['timestamp']\nmin_ts, max_ts = pc.min(ts).as_py(), pc.max(ts).as_py()\nprint(f'Timestamps: {min_ts} to {max_ts} (range: {(max_ts - min_ts) / 86400:.1f} days)')\n\"",
"description": "Analyze event statistics and time range in train_listens"
},
"recorded": {
"id": "run_shell_command__call_218225",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ntotal_rows = len(table)\nprint(f'Total rows: {total_rows}')\n\norg = pc.equal(table['is_organic'], 1)\nprint(f'Organic: {pc.sum(org).as_py()} ({pc.mean(org).as_py():.2%})')\n\nplayed_50 = pc.greater_equal(table['played_ratio_pct'], 50)\nprint(f'Played >= 50%: {pc.sum(played_50).as_py()} ({pc.mean(played_50).as_py():.2%})')\n\nplayed_80 = pc.greater_equal(table['played_ratio_pct'], 80)\nprint(f'Played >= 80%: {pc.sum(played_80).as_py()} ({pc.mean(played_80).as_py():.2%})')\n\norg_80 = pc.and_(org, played_80)\nprint(f'Organic & played >= 80%: {pc.sum(org_80).as_py()} ({pc.mean(org_80).as_py():.2%})')\n\n# Check timestamps range\nts = table['timestamp']\nmin_ts, max_ts = pc.min(ts).as_py(), pc.max(ts).as_py()\nprint(f'Timestamps: {min_ts} to {max_ts} (range: {(max_ts - min_ts) / 86400:.1f} days)')\n\"",
"description": "Analyze event statistics and time range in train_listens"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_218225",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total rows: 43484159\nOrganic: 22477622 (51.69%)\nPlayed >= 50%: 27500755 (63.24%)\nPlayed >= 80%: 25755197 (59.23%)\nOrganic & played >= 80%: 12177583 (28.00%)\nTimestamps: 0 to 24786795 (range: 286.9 days)\nProcess Group PGID: 1893\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:21.846Z",
"resultDisplay": "Total rows: 43484159\nOrganic: 22477622 (51.69%)\nPlayed >= 50%: 27500755 (63.24%)\nPlayed >= 80%: 25755197 (59.23%)\nOrganic & played >= 80%: 12177583 (28.00%)\nTimestamps: 0 to 24786795 (range: 286.9 days)",
"description": "Analyze event statistics and time range in train_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:21.847Z",
"content": "\nOutput: Total rows: 43484159\nOrganic: 22477622 (51.69%)\nPlayed >= 50%: 27500755 (63.24%)\nPlayed >= 80%: 25755197 (59.23%)\nOrganic & played >= 80%: 12177583 (28.00%)\nTimestamps: 0 to 24786795 (range: 286.9 days)\nProcess Group PGID: 1893\n",
"isError": false
},
"responseMessageId": "0d8a2e04-c870-43a5-bcd4-2c0de3a88ba8"
}
]
},
{
"id": 23,
"role": "agent",
"timestamp": "2026-09-29T01:25:25.220Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:25.220Z",
"text": "Analyzing User Data\n\nI'm currently examining the `target_users.parquet` file. It contains user identifiers along with their recent and historical positive listening counts, spanning a period of approximately 287 days."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:25.220Z",
"callId": "run_shell_command__call_13457",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Likes:', len(likes), likes.head())\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\nprint('Unlikes:', len(unlikes))\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nprint('Dislikes:', len(dislikes))\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\nprint('Undislikes:', len(undislikes))\n\"",
"description": "Inspect feedback event counts"
},
"recorded": {
"id": "run_shell_command__call_13457",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Likes:', len(likes), likes.head())\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\nprint('Unlikes:', len(unlikes))\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nprint('Dislikes:', len(dislikes))\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\nprint('Undislikes:', len(undislikes))\n\"",
"description": "Inspect feedback event counts"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_13457",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Likes: 814831 uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n3 100 1260005 7371186 0\n4 100 1263935 4943655 0\nUnlikes: 292665\nDislikes: 101075\nUndislikes: 19960\nProcess Group PGID: 1915\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:26.003Z",
"resultDisplay": "Likes: 814831 uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n3 100 1260005 7371186 0\n4 100 1263935 4943655 0\nUnlikes: 292665\nDislikes: 101075\nUndislikes: 19960",
"description": "Inspect feedback event counts",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:26.004Z",
"content": "\nOutput: Likes: 814831 uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n3 100 1260005 7371186 0\n4 100 1263935 4943655 0\nUnlikes: 292665\nDislikes: 101075\nUndislikes: 19960\nProcess Group PGID: 1915\n",
"isError": false
},
"responseMessageId": "3dc0f9f2-22c9-439b-8006-81064261756a"
}
]
},
{
"id": 24,
"role": "agent",
"timestamp": "2026-09-29T01:25:33.301Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:33.301Z",
"text": "Analyzing Validation Data\n\nI'm processing the `validation_preference_state.parquet` file. It contains 469,124 rows representing active preference states at the training cutoff, intended for validation ranking."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:33.301Z",
"text": "Exploring Collaborative Filtering\n\nI'm considering matrix factorization techniques, specifically TruncatedSVD, iALS, and ALS, as potential models for this task. The goal is to leverage collaborative filtering to build effective recommendation systems."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:33.301Z",
"callId": "run_shell_command__call_126409",
"name": "run_shell_command",
"input": {
"description": "Check target user listens in candidate catalog",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ndf = table.to_pandas()\ntarget_users = set(pd.read_parquet('/task/data/target_users.parquet').uid)\ndf_target = df[df.uid.isin(target_users)]\nprint(f'Total target listens: {len(df_target)}')\n\nin_cat = df_target.item_id.isin(cat_set)\nprint(f'Listens in 30k catalog: {in_cat.sum()} ({in_cat.mean():.2%})')\n\""
},
"recorded": {
"id": "run_shell_command__call_126409",
"name": "run_shell_command",
"args": {
"description": "Check target user listens in candidate catalog",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ndf = table.to_pandas()\ntarget_users = set(pd.read_parquet('/task/data/target_users.parquet').uid)\ndf_target = df[df.uid.isin(target_users)]\nprint(f'Total target listens: {len(df_target)}')\n\nin_cat = df_target.item_id.isin(cat_set)\nprint(f'Listens in 30k catalog: {in_cat.sum()} ({in_cat.mean():.2%})')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_126409",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total target listens: 43222297\nListens in 30k catalog: 31062540 (71.87%)\nProcess Group PGID: 1932\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:41.168Z",
"resultDisplay": "Total target listens: 43222297\nListens in 30k catalog: 31062540 (71.87%)",
"description": "Check target user listens in candidate catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:41.169Z",
"content": "\nOutput: Total target listens: 43222297\nListens in 30k catalog: 31062540 (71.87%)\nProcess Group PGID: 1932\n",
"isError": false
},
"responseMessageId": "bdde2c5e-8844-433a-a9bc-af694eb853fb"
}
]
},
{
"id": 25,
"role": "agent",
"timestamp": "2026-09-29T01:25:46.831Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:46.831Z",
"text": "Analyzing User Engagement\n\nMy analysis is honing in on target user engagement. It's clear nearly all listens come from this group, with a significant concentration within the 30,000-item candidate catalog. This suggests a highly engaged core audience."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:46.831Z",
"text": "Forming Interaction Matrix\n\nI'm considering building a User-Item interaction matrix, exploring both dense and sparse formats. The memory requirements for even a large dense matrix appear manageable within our existing RAM, with sparse matrices being even more efficient. This approach seems promising for further analysis."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:46.831Z",
"callId": "run_shell_command__call_2878460",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\n# Check user-item stats\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf = table.to_pandas()\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet').item_id)\ndf_cat = df[df.item_id.isin(cat)]\n\n# Filter to positive listens: played_ratio_pct >= 50 or 80\ndf_pos = df_cat[df_cat.played_ratio_pct >= 50]\nprint(f'Positive listens in catalog: {len(df_pos)}')\nunique_pairs = df_pos.drop_duplicates(subset=['uid', 'item_id'])\nprint(f'Unique user-item positive pairs in catalog: {len(unique_pairs)}')\nprint(f'Average pairs per user: {len(unique_pairs) / 8456:.1f}')\n\"",
"description": "Check unique user-item pairs in catalog"
},
"recorded": {
"id": "run_shell_command__call_2878460",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\n# Check user-item stats\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf = table.to_pandas()\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet').item_id)\ndf_cat = df[df.item_id.isin(cat)]\n\n# Filter to positive listens: played_ratio_pct >= 50 or 80\ndf_pos = df_cat[df_cat.played_ratio_pct >= 50]\nprint(f'Positive listens in catalog: {len(df_pos)}')\nunique_pairs = df_pos.drop_duplicates(subset=['uid', 'item_id'])\nprint(f'Unique user-item positive pairs in catalog: {len(unique_pairs)}')\nprint(f'Average pairs per user: {len(unique_pairs) / 8456:.1f}')\n\"",
"description": "Check unique user-item pairs in catalog"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2878460",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Positive listens in catalog: 20056329\nUnique user-item positive pairs in catalog: 5564926\nAverage pairs per user: 658.1\nProcess Group PGID: 1954\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:53.255Z",
"resultDisplay": "Positive listens in catalog: 20056329\nUnique user-item positive pairs in catalog: 5564926\nAverage pairs per user: 658.1",
"description": "Check unique user-item pairs in catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:53.256Z",
"content": "\nOutput: Positive listens in catalog: 20056329\nUnique user-item positive pairs in catalog: 5564926\nAverage pairs per user: 658.1\nProcess Group PGID: 1954\n",
"isError": false
},
"responseMessageId": "ff4ee13d-278c-43f4-8be2-dd42a3e84ec4"
}
]
},
{
"id": 26,
"role": "agent",
"timestamp": "2026-09-29T01:25:56.294Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:56.294Z",
"text": "Analyzing User Data\n\nMy current focus is on understanding the scale of the user-item interaction data. It appears we have a substantial number of unique catalog items per user, averaging 658, leading to a total of 5.56 million unique user-item pairs. This volume is translating into significant memory considerations for sparse matrix representations."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:56.294Z",
"callId": "run_shell_command__call_87106",
"name": "run_shell_command",
"input": {
"description": "Test evaluate_public.py on submission.parquet",
"command": "python3 /task/evaluate_public.py /app/solution/submission.parquet"
},
"recorded": {
"id": "run_shell_command__call_87106",
"name": "run_shell_command",
"args": {
"description": "Test evaluate_public.py on submission.parquet",
"command": "python3 /task/evaluate_public.py /app/solution/submission.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_87106",
"name": "run_shell_command",
"response": {
"output": "\nOutput: {\n \"model\": \"/app/solution/submission.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1976\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:58.797Z",
"resultDisplay": "{\n \"model\": \"/app/solution/submission.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0,\n \"quality_users\": 4263\n }\n}",
"description": "python3 /task/evaluate_public.py /app/solution/submission.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:58.798Z",
"content": "\nOutput: {\n \"model\": \"/app/solution/submission.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1976\n",
"isError": false
},
"responseMessageId": "4232e35b-dbb0-4f43-a8f2-b069818ba5c4"
}
]
},
{
"id": 27,
"role": "agent",
"timestamp": "2026-09-29T01:26:08.296Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:08.296Z",
"text": "Correcting Evaluation Data\n\nI've identified a critical discrepancy: my submission was evaluated against `test_eligible_candidates.npz`, but the evaluation script `evaluate_public.py` correctly uses `validation_eligible_candidates.npz`. This explains the zero NDCG score and necessitates a re-evaluation using the proper dataset."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:08.296Z",
"text": "Investigating Submission Discrepancies\n\nI've confirmed the public evaluator uses `validation_eligible_candidates.npz`, not `test_eligible_candidates.npz`. The submission's items were all eligible for validation, meaning the issue isn't directly with eligibility checks but likely with how the ranked items align with validation criteria, especially since test eligibility is a subset of validation."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:08.296Z",
"callId": "run_shell_command__call_49033",
"name": "run_shell_command",
"input": {
"description": "Check evaluation of submission.parquet against validation truth",
"command": "python3 -c \"\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import read_ranking, CandidateSet, score_rankings, ranked_items\nfrom evaluate_public import validation_truth\n\nsub = read_ranking('/app/solution/submission.parquet')\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nranked = ranked_items(sub)\nprint('Evaluation of submission.parquet on val truth:', score_rankings(ranked, truth))\n\""
},
"recorded": {
"id": "run_shell_command__call_49033",
"name": "run_shell_command",
"args": {
"description": "Check evaluation of submission.parquet against validation truth",
"command": "python3 -c \"\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import read_ranking, CandidateSet, score_rankings, ranked_items\nfrom evaluate_public import validation_truth\n\nsub = read_ranking('/app/solution/submission.parquet')\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nranked = ranked_items(sub)\nprint('Evaluation of submission.parquet on val truth:', score_rankings(ranked, truth))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_49033",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 8, in \n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^\nAttributeError: 'str' object has no attribute 'suffix'\nExit Code: 1\nProcess Group PGID: 1996\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:09.011Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 8, in \n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^\nAttributeError: 'str' object has no attribute 'suffix'",
"description": "Check evaluation of submission.parquet against validation truth",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:09.011Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 8, in \n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^\nAttributeError: 'str' object has no attribute 'suffix'\nExit Code: 1\nProcess Group PGID: 1996\n",
"isError": false
},
"responseMessageId": "fb38b45f-a51b-41a3-8b6d-e6fda4483a15"
}
]
},
{
"id": 28,
"role": "agent",
"timestamp": "2026-09-29T01:26:13.582Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:13.582Z",
"callId": "run_shell_command__call_116931",
"name": "run_shell_command",
"input": {
"description": "Check score on submission.parquet",
"command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import read_ranking, CandidateSet, score_rankings, ranked_items\nfrom evaluate_public import validation_truth\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nranked = ranked_items(sub)\nprint('Evaluation of submission.parquet on val truth:', score_rankings(ranked, truth))\n\""
},
"recorded": {
"id": "run_shell_command__call_116931",
"name": "run_shell_command",
"args": {
"description": "Check score on submission.parquet",
"command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import read_ranking, CandidateSet, score_rankings, ranked_items\nfrom evaluate_public import validation_truth\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nranked = ranked_items(sub)\nprint('Evaluation of submission.parquet on val truth:', score_rankings(ranked, truth))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_116931",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Evaluation of submission.parquet on val truth: {'quality_ndcg@10': 0.0, 'quality_users': 4263}\nProcess Group PGID: 2007\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:15.314Z",
"resultDisplay": "Evaluation of submission.parquet on val truth: {'quality_ndcg@10': 0.0, 'quality_users': 4263}",
"description": "Check score on submission.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:15.316Z",
"content": "\nOutput: Evaluation of submission.parquet on val truth: {'quality_ndcg@10': 0.0, 'quality_users': 4263}\nProcess Group PGID: 2007\n",
"isError": false
},
"responseMessageId": "b0cf5fa1-7e42-4933-ae8c-4baa1b767145"
}
]
},
{
"id": 29,
"role": "agent",
"timestamp": "2026-09-29T01:26:17.769Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:17.769Z",
"callId": "run_shell_command__call_17933",
"name": "run_shell_command",
"input": {
"description": "Inspect submission.parquet ranking ordering",
"command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import read_ranking, ranked_items\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nprint(sub.head(15))\nprint(sub.dtypes)\n\nordered = sub.sort_values(['uid', 'rank'])\nr = {int(uid): list(map(int, values)) for uid, values in ordered.groupby('uid').item_id}\nprint('User 100 top 10:', r[100][:10])\n\""
},
"recorded": {
"id": "run_shell_command__call_17933",
"name": "run_shell_command",
"args": {
"description": "Inspect submission.parquet ranking ordering",
"command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import read_ranking, ranked_items\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nprint(sub.head(15))\nprint(sub.dtypes)\n\nordered = sub.sort_values(['uid', 'rank'])\nr = {int(uid): list(map(int, values)) for uid, values in ordered.groupby('uid').item_id}\nprint('User 100 top 10:', r[100][:10])\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_17933",
"name": "run_shell_command",
"response": {
"output": "\nOutput: uid item_id rank\n0 100 5862961 1\n1 100 6901374 2\n2 100 5635052 3\n3 100 3542184 4\n4 100 8213481 5\n5 100 906358 6\n6 100 2859641 7\n7 100 3033749 8\n8 100 2651314 9\n9 100 5252571 10\n10 100 5436843 11\n11 100 5927996 12\n12 100 8902934 13\n13 100 5734320 14\n14 100 1464852 15\nuid int64\nitem_id int64\nrank int64\ndtype: object\nUser 100 top 10: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571]\nProcess Group PGID: 2027\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:18.858Z",
"resultDisplay": "uid item_id rank\n0 100 5862961 1\n1 100 6901374 2\n2 100 5635052 3\n3 100 3542184 4\n4 100 8213481 5\n5 100 906358 6\n6 100 2859641 7\n7 100 3033749 8\n8 100 2651314 9\n9 100 5252571 10\n10 100 5436843 11\n11 100 5927996 12\n12 100 8902934 13\n13 100 5734320 14\n14 100 1464852 15\nuid int64\nitem_id int64\nrank int64\ndtype: object\nUser 100 top 10: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571]",
"description": "Inspect submission.parquet ranking ordering",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:18.859Z",
"content": "\nOutput: uid item_id rank\n0 100 5862961 1\n1 100 6901374 2\n2 100 5635052 3\n3 100 3542184 4\n4 100 8213481 5\n5 100 906358 6\n6 100 2859641 7\n7 100 3033749 8\n8 100 2651314 9\n9 100 5252571 10\n10 100 5436843 11\n11 100 5927996 12\n12 100 8902934 13\n13 100 5734320 14\n14 100 1464852 15\nuid int64\nitem_id int64\nrank int64\ndtype: object\nUser 100 top 10: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571]\nProcess Group PGID: 2027\n",
"isError": false
},
"responseMessageId": "ed3ad830-0193-4c65-bf84-6961a9da0e3f"
}
]
},
{
"id": 30,
"role": "agent",
"timestamp": "2026-09-29T01:26:27.305Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:27.305Z",
"text": "Investigating Discrepancies\n\nI'm currently digging into why the popularity baseline calculation yielded different results. It seems there might be a subtle difference in how the data is being processed between the two instances. I'm scrutinizing the earlier script for clues."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:27.305Z",
"text": "Revisiting Candidate Selection\n\nI've identified a potential issue with how user IDs (`uids`) are being handled. It appears the user IDs used for candidate generation might not perfectly align with those in the submission file, leading to discrepancies. I'm now comparing these `uids` directly."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:27.305Z",
"callId": "run_shell_command__call_72460",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\n# Check user 100\nprint('User 100 in truth?', 100 in truth)\nif 100 in truth:\n print('User 100 truth items:', truth[100])\n\n# Check test eligibility vs val eligibility for user 100\nwith np.load('/task/data/validation_eligible_candidates.npz') as val_z, np.load('/task/data/test_eligible_candidates.npz') as test_z:\n val_items = val_z['item_ids']\n val_pack = val_z['packed_eligible']\n test_pack = test_z['packed_eligible']\n \n val_bits_0 = np.unpackbits(val_pack[0], count=len(val_items), bitorder='little')\n test_bits_0 = np.unpackbits(test_pack[0], count=len(val_items), bitorder='little')\n print('Diff between val and test bits for user 0:', np.sum(val_bits_0 != test_bits_0))\n print('Items in val but not test:', np.sum((val_bits_0 == 1) & (test_bits_0 == 0)))\n\"",
"description": "Check user 100 truth and bitmask difference"
},
"recorded": {
"id": "run_shell_command__call_72460",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\n# Check user 100\nprint('User 100 in truth?', 100 in truth)\nif 100 in truth:\n print('User 100 truth items:', truth[100])\n\n# Check test eligibility vs val eligibility for user 100\nwith np.load('/task/data/validation_eligible_candidates.npz') as val_z, np.load('/task/data/test_eligible_candidates.npz') as test_z:\n val_items = val_z['item_ids']\n val_pack = val_z['packed_eligible']\n test_pack = test_z['packed_eligible']\n \n val_bits_0 = np.unpackbits(val_pack[0], count=len(val_items), bitorder='little')\n test_bits_0 = np.unpackbits(test_pack[0], count=len(val_items), bitorder='little')\n print('Diff between val and test bits for user 0:', np.sum(val_bits_0 != test_bits_0))\n print('Items in val but not test:', np.sum((val_bits_0 == 1) & (test_bits_0 == 0)))\n\"",
"description": "Check user 100 truth and bitmask difference"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_72460",
"name": "run_shell_command",
"response": {
"output": "\nOutput: User 100 in truth? False\nDiff between val and test bits for user 0: 24\nItems in val but not test: 24\nProcess Group PGID: 2046\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:28.750Z",
"resultDisplay": "User 100 in truth? False\nDiff between val and test bits for user 0: 24\nItems in val but not test: 24",
"description": "Check user 100 truth and bitmask difference",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:28.751Z",
"content": "\nOutput: User 100 in truth? False\nDiff between val and test bits for user 0: 24\nItems in val but not test: 24\nProcess Group PGID: 2046\n",
"isError": false
},
"responseMessageId": "6e998186-a7e8-420c-ad2c-1f1211543549"
}
]
},
{
"id": 31,
"role": "agent",
"timestamp": "2026-09-29T01:26:32.029Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:32.029Z",
"callId": "run_shell_command__call_85435",
"name": "run_shell_command",
"input": {
"description": "Inspect scores across users for submission.parquet",
"command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, read_ranking, ranked_items, ndcg_at_k\nfrom evaluate_public import validation_truth\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nranked = ranked_items(sub)\n\nscores = [ndcg_at_k(ranked[uid], gold, 10) for uid, gold in truth.items()]\nprint('Number of truth users:', len(truth))\nprint('Number of nonzero scores:', sum(1 for s in scores if s > 0))\nprint('Max score:', max(scores))\nprint('Mean score:', np.mean(scores))\n\""
},
"recorded": {
"id": "run_shell_command__call_85435",
"name": "run_shell_command",
"args": {
"description": "Inspect scores across users for submission.parquet",
"command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, read_ranking, ranked_items, ndcg_at_k\nfrom evaluate_public import validation_truth\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nranked = ranked_items(sub)\n\nscores = [ndcg_at_k(ranked[uid], gold, 10) for uid, gold in truth.items()]\nprint('Number of truth users:', len(truth))\nprint('Number of nonzero scores:', sum(1 for s in scores if s > 0))\nprint('Max score:', max(scores))\nprint('Mean score:', np.mean(scores))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_85435",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Number of truth users: 4263\nNumber of nonzero scores: 0\nMax score: 0.0\nMean score: 0.0\nProcess Group PGID: 2066\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:33.727Z",
"resultDisplay": "Number of truth users: 4263\nNumber of nonzero scores: 0\nMax score: 0.0\nMean score: 0.0",
"description": "Inspect scores across users for submission.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:35.136Z",
"content": "\nOutput: Number of truth users: 4263\nNumber of nonzero scores: 0\nMax score: 0.0\nMean score: 0.0\nProcess Group PGID: 2066\n",
"isError": false
},
"responseMessageId": "56331cce-e054-4e65-8def-71ab8b9ecab7"
}
]
},
{
"id": 32,
"role": "agent",
"timestamp": "2026-09-29T01:26:39.978Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:39.978Z",
"text": "Investigating Catalog Data\n\nI'm re-examining the initial data loading and processing, specifically why non-zero scores were reported as zero. I'm tracing back to the `pd.read_parquet` call and the subsequent handling of \"Catalog positive\" entries to pinpoint the discrepancy."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:39.978Z",
"callId": "run_shell_command__call_62549",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import score, validation_truth\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked = {}\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Catalog positive_listens popularity NDCG@10:', score(ranked, truth))\n\n# Now let us compare ranked[uids[0]][:10] with submission.parquet\nsub = pd.read_parquet('/app/solution/submission.parquet')\nsub_u0 = sub[sub.uid == uids[0]].sort_values('rank').item_id.tolist()[:10]\nprint('ranked u0:', ranked[uids[0]][:10])\nprint('sub_u0: ', sub_u0)\n\"",
"description": "Compare ranked u0 with sub_u0"
},
"recorded": {
"id": "run_shell_command__call_62549",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import score, validation_truth\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked = {}\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Catalog positive_listens popularity NDCG@10:', score(ranked, truth))\n\n# Now let us compare ranked[uids[0]][:10] with submission.parquet\nsub = pd.read_parquet('/app/solution/submission.parquet')\nsub_u0 = sub[sub.uid == uids[0]].sort_values('rank').item_id.tolist()[:10]\nprint('ranked u0:', ranked[uids[0]][:10])\nprint('sub_u0: ', sub_u0)\n\"",
"description": "Compare ranked u0 with sub_u0"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_62549",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nranked u0: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571]\nsub_u0: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571]\nProcess Group PGID: 2086\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:42.428Z",
"resultDisplay": "Catalog positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nranked u0: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571]\nsub_u0: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571]",
"description": "Compare ranked u0 with sub_u0",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:42.430Z",
"content": "\nOutput: Catalog positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nranked u0: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571]\nsub_u0: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571]\nProcess Group PGID: 2086\n",
"isError": false
},
"responseMessageId": "df3fa64e-1198-4b2b-a7f6-ac5092cba2f0"
}
]
},
{
"id": 33,
"role": "agent",
"timestamp": "2026-09-29T01:26:45.414Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:45.414Z",
"text": "Investigating Discrepancy\n\nI'm examining why the ranked lists for `u0` matched, yet the `ndcg_at_k` calculation yielded zero. I'm currently scrutinizing the data types of `uids` in the `z` array to pinpoint the source of this unexpected test result."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:45.414Z",
"callId": "run_shell_command__call_107279",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom ranking_evaluation import read_ranking, ranked_items, score_rankings, CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr = ranked_items(sub)\nprint('type of key in r:', type(list(r.keys())[0]))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nprint('type of key in truth:', type(list(truth.keys())[0]))\nprint('First key in r:', list(r.keys())[0], 'First key in truth:', list(truth.keys())[0])\n\n# Now score_rankings(r, truth)\nprint(score_rankings(r, truth))\n\"",
"description": "Check score_rankings on submission.parquet again"
},
"recorded": {
"id": "run_shell_command__call_107279",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom ranking_evaluation import read_ranking, ranked_items, score_rankings, CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr = ranked_items(sub)\nprint('type of key in r:', type(list(r.keys())[0]))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nprint('type of key in truth:', type(list(truth.keys())[0]))\nprint('First key in r:', list(r.keys())[0], 'First key in truth:', list(truth.keys())[0])\n\n# Now score_rankings(r, truth)\nprint(score_rankings(r, truth))\n\"",
"description": "Check score_rankings on submission.parquet again"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_107279",
"name": "run_shell_command",
"response": {
"output": "\nOutput: type of key in r: \ntype of key in truth: \nFirst key in r: 100 First key in truth: 200\n{'quality_ndcg@10': 0.0, 'quality_users': 4263}\nProcess Group PGID: 2106\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:47.167Z",
"resultDisplay": "type of key in r: \ntype of key in truth: \nFirst key in r: 100 First key in truth: 200\n{'quality_ndcg@10': 0.0, 'quality_users': 4263}",
"description": "Check score_rankings on submission.parquet again",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:47.168Z",
"content": "\nOutput: type of key in r: \ntype of key in truth: \nFirst key in r: 100 First key in truth: 200\n{'quality_ndcg@10': 0.0, 'quality_users': 4263}\nProcess Group PGID: 2106\n",
"isError": false
},
"responseMessageId": "16ec7592-5869-4398-b73d-a4ec0e5cc1a3"
}
]
},
{
"id": 34,
"role": "agent",
"timestamp": "2026-09-29T01:26:51.143Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:51.143Z",
"text": "Investigating Metric Discrepancies\n\nI'm currently analyzing a puzzling difference in scoring metrics. It appears that two functions, `score` and `score_rankings`, are producing distinct results for what I believed were identical inputs. My immediate next step is to inspect the actual rankings (`ranked` and `r`) at a specific index (200) to identify the precise divergence causing this unexpected outcome."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:51.143Z",
"callId": "run_shell_command__call_168563",
"name": "run_shell_command",
"input": {
"description": "Compare ranked_val with r_sub",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import read_ranking, ranked_items, CandidateSet\nfrom pathlib import Path\n\n# Load ranked from first snippet:\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked_val = {}\nfor r_idx, uid in enumerate(uids):\n bits = np.unpackbits(packed[r_idx], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked_val[uid] = eligible_items[:100].tolist()\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr_sub = ranked_items(sub)\n\nprint('ranked_val[200][:10]:', ranked_val[200][:10])\nprint('r_sub[200][:10]: ', r_sub[200][:10])\nprint('Are all users identical between ranked_val and r_sub?')\ndiff_count = 0\nfor u in uids:\n if ranked_val[u][:10] != r_sub[u][:10]:\n diff_count += 1\nprint('Users with diff top 10:', diff_count)\n\""
},
"recorded": {
"id": "run_shell_command__call_168563",
"name": "run_shell_command",
"args": {
"description": "Compare ranked_val with r_sub",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import read_ranking, ranked_items, CandidateSet\nfrom pathlib import Path\n\n# Load ranked from first snippet:\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked_val = {}\nfor r_idx, uid in enumerate(uids):\n bits = np.unpackbits(packed[r_idx], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked_val[uid] = eligible_items[:100].tolist()\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr_sub = ranked_items(sub)\n\nprint('ranked_val[200][:10]:', ranked_val[200][:10])\nprint('r_sub[200][:10]: ', r_sub[200][:10])\nprint('Are all users identical between ranked_val and r_sub?')\ndiff_count = 0\nfor u in uids:\n if ranked_val[u][:10] != r_sub[u][:10]:\n diff_count += 1\nprint('Users with diff top 10:', diff_count)\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_168563",
"name": "run_shell_command",
"response": {
"output": "\nOutput: ranked_val[200][:10]: [5862961, 6901374, 3542184, 9378983, 8213481, 906358, 3033749, 2651314, 5252571, 5436843]\nr_sub[200][:10]: [5862961, 6901374, 3542184, 9378983, 8213481, 906358, 3033749, 2651314, 5252571, 5436843]\nAre all users identical between ranked_val and r_sub?\nUsers with diff top 10: 806\nProcess Group PGID: 2126\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:53.213Z",
"resultDisplay": "ranked_val[200][:10]: [5862961, 6901374, 3542184, 9378983, 8213481, 906358, 3033749, 2651314, 5252571, 5436843]\nr_sub[200][:10]: [5862961, 6901374, 3542184, 9378983, 8213481, 906358, 3033749, 2651314, 5252571, 5436843]\nAre all users identical between ranked_val and r_sub?\nUsers with diff top 10: 806",
"description": "Compare ranked_val with r_sub",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:53.214Z",
"content": "\nOutput: ranked_val[200][:10]: [5862961, 6901374, 3542184, 9378983, 8213481, 906358, 3033749, 2651314, 5252571, 5436843]\nr_sub[200][:10]: [5862961, 6901374, 3542184, 9378983, 8213481, 906358, 3033749, 2651314, 5252571, 5436843]\nAre all users identical between ranked_val and r_sub?\nUsers with diff top 10: 806\nProcess Group PGID: 2126\n",
"isError": false
},
"responseMessageId": "89f88074-75d8-4a5a-9efa-9227be1219c2"
}
]
},
{
"id": 35,
"role": "agent",
"timestamp": "2026-09-29T01:26:57.183Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:57.183Z",
"text": "Analyzing Score Discrepancies\n\nI'm currently scrutinizing a peculiar difference between `ranked_val` and `r_sub` in my evaluation. The `quality_ndcg@10` score, which I expected to be consistent, is showing an unexpected `0.01071`. I'm digging into why `score(ranked_val, truth)` differs from `score(r_sub, truth)` to pinpoint the source of this anomaly."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:57.183Z",
"callId": "run_shell_command__call_2650491",
"name": "run_shell_command",
"input": {
"description": "Evaluate both ranked_val and r_sub on truth",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import read_ranking, ranked_items, score_rankings, CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked_val = {}\nfor r_idx, uid in enumerate(uids):\n bits = np.unpackbits(packed[r_idx], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked_val[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\nprint('Score ranked_val:', score_rankings(ranked_val, truth))\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr_sub = ranked_items(sub)\nprint('Score r_sub: ', score_rankings(r_sub, truth))\n\""
},
"recorded": {
"id": "run_shell_command__call_2650491",
"name": "run_shell_command",
"args": {
"description": "Evaluate both ranked_val and r_sub on truth",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import read_ranking, ranked_items, score_rankings, CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked_val = {}\nfor r_idx, uid in enumerate(uids):\n bits = np.unpackbits(packed[r_idx], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked_val[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\nprint('Score ranked_val:', score_rankings(ranked_val, truth))\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr_sub = ranked_items(sub)\nprint('Score r_sub: ', score_rankings(r_sub, truth))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2650491",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Score ranked_val: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nScore r_sub: {'quality_ndcg@10': 0.0, 'quality_users': 4263}\nProcess Group PGID: 2143\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:59.985Z",
"resultDisplay": "Score ranked_val: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nScore r_sub: {'quality_ndcg@10': 0.0, 'quality_users': 4263}",
"description": "Evaluate both ranked_val and r_sub on truth",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:59.985Z",
"content": "\nOutput: Score ranked_val: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nScore r_sub: {'quality_ndcg@10': 0.0, 'quality_users': 4263}\nProcess Group PGID: 2143\n",
"isError": false
},
"responseMessageId": "cedef41c-654d-4e56-8e76-8a607c368e4d"
}
]
},
{
"id": 36,
"role": "agent",
"timestamp": "2026-09-29T01:27:06.202Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:06.202Z",
"text": "Investigating Zero Values\n\nI'm puzzled by a score of 0.01071 for `ranked_val` while `r_sub` registers as 0.0. I'm delving into the extraction logic for `r_sub` from `sub` and examining the `ranked_items` function within `ranking_evaluation.py` to understand this discrepancy."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:06.202Z",
"callId": "run_shell_command__call_126264",
"name": "run_shell_command",
"input": {
"description": "Debug difference between ranked_val and r_sub",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import read_ranking, ranked_items, CandidateSet, ndcg_at_k\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked_val = {}\nfor r_idx, uid in enumerate(uids):\n bits = np.unpackbits(packed[r_idx], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked_val[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr_sub = ranked_items(sub)\n\n# Find a user where ndcg_at_k(ranked_val[uid], truth[uid], 10) > 0\nfor uid in truth:\n s_val = ndcg_at_k(ranked_val[uid], truth[uid], 10)\n s_sub = ndcg_at_k(r_sub[uid], truth[uid], 10)\n if s_val > 0:\n print(f'User {uid}: val_score={s_val}, sub_score={s_sub}')\n print(' truth:', truth[uid])\n print(' val top 10:', ranked_val[uid][:10])\n print(' sub top 10:', r_sub[uid][:10])\n break\n\""
},
"recorded": {
"id": "run_shell_command__call_126264",
"name": "run_shell_command",
"args": {
"description": "Debug difference between ranked_val and r_sub",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import read_ranking, ranked_items, CandidateSet, ndcg_at_k\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked_val = {}\nfor r_idx, uid in enumerate(uids):\n bits = np.unpackbits(packed[r_idx], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked_val[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr_sub = ranked_items(sub)\n\n# Find a user where ndcg_at_k(ranked_val[uid], truth[uid], 10) > 0\nfor uid in truth:\n s_val = ndcg_at_k(ranked_val[uid], truth[uid], 10)\n s_sub = ndcg_at_k(r_sub[uid], truth[uid], 10)\n if s_val > 0:\n print(f'User {uid}: val_score={s_val}, sub_score={s_sub}')\n print(' truth:', truth[uid])\n print(' val top 10:', ranked_val[uid][:10])\n print(' sub top 10:', r_sub[uid][:10])\n break\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_126264",
"name": "run_shell_command",
"response": {
"output": "\nOutput: User 1400: val_score=0.09653121191524809, sub_score=0.0\n truth: {6945311: 1, 2158715: 1, 2194514: 1, 3530629: 1, 2453912: 1, 7767752: 1, 4859530: 1, 3585411: 1, 4500834: 1, 4770069: 1, 3706360: 1, 6083906: 1, 1118882: 1, 8675053: 1, 5551069: 1, 6473581: 1, 2285672: 1, 5933848: 1, 7008219: 1, 5265819: 1, 975128: 1, 3970945: 1, 4345553: 1, 6901050: 1, 6901374: 1, 1851456: 1, 4678741: 1, 4897148: 1, 8420846: 3, 1327266: 1, 9233959: 1, 3203533: 1, 1407989: 1, 6417502: 1, 9016646: 1, 3763080: 1, 9378983: 1, 8669062: 1}\n val top 10: [5862961, 6901374, 5635052, 3542184, 9378983, 8213481, 906358, 2859641, 3033749, 2651314]\n sub top 10: [5635052, 8213481, 906358, 2859641, 3033749, 2651314, 6853370, 3418493, 5927996, 6155741]\nProcess Group PGID: 2163\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:08.808Z",
"resultDisplay": "User 1400: val_score=0.09653121191524809, sub_score=0.0\n truth: {6945311: 1, 2158715: 1, 2194514: 1, 3530629: 1, 2453912: 1, 7767752: 1, 4859530: 1, 3585411: 1, 4500834: 1, 4770069: 1, 3706360: 1, 6083906: 1, 1118882: 1, 8675053: 1, 5551069: 1, 6473581: 1, 2285672: 1, 5933848: 1, 7008219: 1, 5265819: 1, 975128: 1, 3970945: 1, 4345553: 1, 6901050: 1, 6901374: 1, 1851456: 1, 4678741: 1, 4897148: 1, 8420846: 3, 1327266: 1, 9233959: 1, 3203533: 1, 1407989: 1, 6417502: 1, 9016646: 1, 3763080: 1, 9378983: 1, 8669062: 1}\n val top 10: [5862961, 6901374, 5635052, 3542184, 9378983, 8213481, 906358, 2859641, 3033749, 2651314]\n sub top 10: [5635052, 8213481, 906358, 2859641, 3033749, 2651314, 6853370, 3418493, 5927996, 6155741]",
"description": "Debug difference between ranked_val and r_sub",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:08.809Z",
"content": "\nOutput: User 1400: val_score=0.09653121191524809, sub_score=0.0\n truth: {6945311: 1, 2158715: 1, 2194514: 1, 3530629: 1, 2453912: 1, 7767752: 1, 4859530: 1, 3585411: 1, 4500834: 1, 4770069: 1, 3706360: 1, 6083906: 1, 1118882: 1, 8675053: 1, 5551069: 1, 6473581: 1, 2285672: 1, 5933848: 1, 7008219: 1, 5265819: 1, 975128: 1, 3970945: 1, 4345553: 1, 6901050: 1, 6901374: 1, 1851456: 1, 4678741: 1, 4897148: 1, 8420846: 3, 1327266: 1, 9233959: 1, 3203533: 1, 1407989: 1, 6417502: 1, 9016646: 1, 3763080: 1, 9378983: 1, 8669062: 1}\n val top 10: [5862961, 6901374, 5635052, 3542184, 9378983, 8213481, 906358, 2859641, 3033749, 2651314]\n sub top 10: [5635052, 8213481, 906358, 2859641, 3033749, 2651314, 6853370, 3418493, 5927996, 6155741]\nProcess Group PGID: 2163\n",
"isError": false
},
"responseMessageId": "645e7e29-b6bd-4c30-adf2-7ec5723055b0"
}
]
},
{
"id": 37,
"role": "agent",
"timestamp": "2026-09-29T01:27:14.904Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:14.904Z",
"text": "Confirming Accuracy Now\n\nI'm reviewing the top 10 values and cross-referencing them with the ground truth. It's promising to see several identified values, like 6901374 and 9378983, present in the truth set. I'm specifically noting their original rankings within the top 10 list."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:14.904Z",
"text": "Uncovering Eligibility Logic\n\nI've realized the core issue: items listened to during validation are correctly excluded from the test set's eligible candidates. This explains the zero hits when comparing the submission to validation truth, as the test set was inherently designed to omit these already-heard items."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:14.904Z",
"callId": "run_shell_command__call_82511",
"name": "run_shell_command",
"input": {
"description": "Evaluate val_baseline.parquet with evaluate_public.py",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nrecords = []\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1][:100]\n for rank_idx, item in enumerate(eligible_items, 1):\n records.append((uid, item, rank_idx))\n\ndf = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/app/solution/val_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline.parquet"
},
"recorded": {
"id": "run_shell_command__call_82511",
"name": "run_shell_command",
"args": {
"description": "Evaluate val_baseline.parquet with evaluate_public.py",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nrecords = []\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1][:100]\n for rank_idx, item in enumerate(eligible_items, 1):\n records.append((uid, item, rank_idx))\n\ndf = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/app/solution/val_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_82511",
"name": "run_shell_command",
"response": {
"output": "\nOutput: {\n \"model\": \"/app/solution/val_baseline.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 2183\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:19.964Z",
"resultDisplay": "{\n \"model\": \"/app/solution/val_baseline.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}",
"description": "Evaluate val_baseline.parquet with evaluate_public.py",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:19.965Z",
"content": "\nOutput: {\n \"model\": \"/app/solution/val_baseline.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 2183\n",
"isError": false
},
"responseMessageId": "234ec1ff-22f5-4827-9c97-6e77d00fa69a"
}
]
},
{
"id": 38,
"role": "agent",
"timestamp": "2026-09-29T01:27:22.388Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:22.388Z",
"callId": "run_shell_command__call_137932",
"name": "run_shell_command",
"input": {
"description": "Clean up temporary file",
"command": "rm -f /app/solution/val_baseline.parquet"
},
"recorded": {
"id": "run_shell_command__call_137932",
"name": "run_shell_command",
"args": {
"description": "Clean up temporary file",
"command": "rm -f /app/solution/val_baseline.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_137932",
"name": "run_shell_command",
"response": {
"output": "\nOutput: (empty)\nProcess Group PGID: 2218\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:22.403Z",
"resultDisplay": "",
"description": "rm -f /app/solution/val_baseline.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:22.404Z",
"content": "\nOutput: (empty)\nProcess Group PGID: 2218\n",
"isError": false
},
"responseMessageId": "96a8ade5-7528-4ed9-a567-a2a1803f52d4"
}
]
},
{
"id": 39,
"role": "agent",
"timestamp": "2026-09-29T01:27:30.893Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:30.893Z",
"callId": "update_topic__call_223826",
"name": "update_topic",
"input": {
"strategic_intent": "Design and experiment with personalized recommendation models on validation.",
"summary": "Completed initial exploration and baseline validation. We verified the evaluation pipeline, understanding of eligibility masks, and confirmed candidate catalog baseline NDCG@10 of 0.01071. Now we transition to modeling: researching item-item collaborative filtering, matrix factorization, and user-item interactions to significantly boost discovery NDCG@10.",
"title": "Personalized Recommendation Modeling & Validation"
},
"recorded": {
"id": "update_topic__call_223826",
"name": "update_topic",
"args": {
"strategic_intent": "Design and experiment with personalized recommendation models on validation.",
"summary": "Completed initial exploration and baseline validation. We verified the evaluation pipeline, understanding of eligibility masks, and confirmed candidate catalog baseline NDCG@10 of 0.01071. Now we transition to modeling: researching item-item collaborative filtering, matrix factorization, and user-item interactions to significantly boost discovery NDCG@10.",
"title": "Personalized Recommendation Modeling & Validation"
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_223826",
"name": "update_topic",
"response": {
"output": "Current topic: \"Personalized Recommendation Modeling & Validation\"\nTopic summary: Completed initial exploration and baseline validation. We verified the evaluation pipeline, understanding of eligibility masks, and confirmed candidate catalog baseline NDCG@10 of 0.01071. Now we transition to modeling: researching item-item collaborative filtering, matrix factorization, and user-item interactions to significantly boost discovery NDCG@10.\n\nStrategic Intent: Design and experiment with personalized recommendation models on validation."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:30.895Z",
"resultDisplay": "## 📂 Topic: **Personalized Recommendation Modeling & Validation**\n\n**Summary:**\nCompleted initial exploration and baseline validation. We verified the evaluation pipeline, understanding of eligibility masks, and confirmed candidate catalog baseline NDCG@10 of 0.01071. Now we transition to modeling: researching item-item collaborative filtering, matrix factorization, and user-item interactions to significantly boost discovery NDCG@10.\n\n> [!STRATEGY]\n> **Intent:** Design and experiment with personalized recommendation models on validation.",
"description": "Update topic to: \"Personalized Recommendation Modeling & Validation\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T01:27:30.896Z",
"content": "Current topic: \"Personalized Recommendation Modeling & Validation\"\nTopic summary: Completed initial exploration and baseline validation. We verified the evaluation pipeline, understanding of eligibility masks, and confirmed candidate catalog baseline NDCG@10 of 0.01071. Now we transition to modeling: researching item-item collaborative filtering, matrix factorization, and user-item interactions to significantly boost discovery NDCG@10.\n\nStrategic Intent: Design and experiment with personalized recommendation models on validation.",
"isError": false
},
"responseMessageId": "5fee602f-aece-4ea1-9192-37ac7f136139"
}
]
},
{
"id": 40,
"role": "agent",
"timestamp": "2026-09-29T01:27:44.130Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:44.130Z",
"text": "Analyzing Recommendation Drivers\n\nMy current focus is on dissecting the core components of an effective recommendation engine for our specific scenario. I'm meticulously examining user behavior patterns, particularly their listening history and associated timestamps, to identify key drivers for generating relevant suggestions."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:44.130Z",
"text": "Refining Feature Importance\n\nI'm now delving into how specific user interactions, such as organic listens versus explicit likes and dislikes, will influence recommendation grades. My aim is to quantify their relative impact for NDCG@10, particularly focusing on achieving grade 3 and grade 1 organic outcomes within the week."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:44.130Z",
"text": "Evaluating Matrix Inversion\n\nI'm currently assessing the computational feasibility of inverting a $30000 \\times 30000$ matrix, as required by the EASE model. This large-scale inversion could be a bottleneck, and I'm exploring alternatives or optimizations if it proves impractical."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:44.130Z",
"text": "Exploring Alternative Strategies\n\nI'm shifting focus to computational efficiency. Instead of direct matrix inversion for EASE, I'm now testing item-item similarity methods, including cosine and BM25. I'm also evaluating truncated SVD for latent factor models, as it shows potential for significantly faster computation on validation sets."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:44.130Z",
"callId": "run_shell_command__call_103510",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\nt0 = time.time()\nprint('Loading data...')\n\n# Load target users and candidates\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(item_ids)}\n\n# Load validation eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_col_map = np.array([uid_to_idx[u] for u in val_uids])\nval_item_map = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\nprint(f'Truth loaded ({len(truth)} users). Time: {time.time() - t0:.2f}s')\n\n# Read train listens\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf_listens = table.to_pandas()\nprint(f'Train listens loaded: {len(df_listens)}. Time: {time.time() - t0:.2f}s')\n\n# Filter to target users and catalog items\ndf_listens = df_listens[df_listens.uid.isin(uid_to_idx) & df_listens.item_id.isin(item_to_idx)]\nprint(f'Filtered listens: {len(df_listens)}. Time: {time.time() - t0:.2f}s')\n\"",
"description": "Load and filter train listens in python"
},
"recorded": {
"id": "run_shell_command__call_103510",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\nt0 = time.time()\nprint('Loading data...')\n\n# Load target users and candidates\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(item_ids)}\n\n# Load validation eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_col_map = np.array([uid_to_idx[u] for u in val_uids])\nval_item_map = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\nprint(f'Truth loaded ({len(truth)} users). Time: {time.time() - t0:.2f}s')\n\n# Read train listens\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf_listens = table.to_pandas()\nprint(f'Train listens loaded: {len(df_listens)}. Time: {time.time() - t0:.2f}s')\n\n# Filter to target users and catalog items\ndf_listens = df_listens[df_listens.uid.isin(uid_to_idx) & df_listens.item_id.isin(item_to_idx)]\nprint(f'Filtered listens: {len(df_listens)}. Time: {time.time() - t0:.2f}s')\n\"",
"description": "Load and filter train listens in python"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_103510",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Loading data...\nTruth loaded (4263 users). Time: 0.69s\nTrain listens loaded: 43484159. Time: 1.17s\nFiltered listens: 31062540. Time: 11.08s\nProcess Group PGID: 2221\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:56.794Z",
"resultDisplay": "Loading data...\nTruth loaded (4263 users). Time: 0.69s\nTrain listens loaded: 43484159. Time: 1.17s\nFiltered listens: 31062540. Time: 11.08s",
"description": "Load and filter train listens in python",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:56.795Z",
"content": "\nOutput: Loading data...\nTruth loaded (4263 users). Time: 0.69s\nTrain listens loaded: 43484159. Time: 1.17s\nFiltered listens: 31062540. Time: 11.08s\nProcess Group PGID: 2221\n",
"isError": false
},
"responseMessageId": "5108ea3a-8808-4e89-9034-f345b190e970"
}
]
},
{
"id": 41,
"role": "agent",
"timestamp": "2026-09-29T01:28:00.772Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:00.772Z",
"text": "Optimizing Data Filtering\n\nI'm analyzing the performance of filtering 43 million rows, which currently takes about 10 seconds. I've identified the `df_listens.uid.isin(uid_to_idx)` operation as a key area. Given that the unique UIDs in `train_listens` (9,238) largely overlap with the target users (8,456), a significant portion, approximately 99.4%, of the rows are already relevant. I'm now exploring strategies to further accelerate this process."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:00.772Z",
"callId": "run_shell_command__call_352160",
"name": "run_shell_command",
"input": {
"description": "Benchmark fast PyArrow filtering",
"command": "python3 -c \"\nimport time\nimport numpy as np\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_set = pyarrow.compute.SetLookupOptions(value_set=pyarrow.array(cat.item_id.to_numpy()))\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nt1 = time.time()\nprint(f'Read table: {t1 - t0:.2f}s')\n\n# Filter using pc.is_in\nmask = pc.is_in(table['item_id'], options=item_set)\nt2 = time.time()\nprint(f'pc.is_in took: {t2 - t1:.2f}s, matches: {pc.sum(mask).as_py()}')\n\nfiltered = table.filter(mask)\nt3 = time.time()\nprint(f'Filter table took: {t3 - t2:.2f}s, len={len(filtered)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_352160",
"name": "run_shell_command",
"args": {
"description": "Benchmark fast PyArrow filtering",
"command": "python3 -c \"\nimport time\nimport numpy as np\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_set = pyarrow.compute.SetLookupOptions(value_set=pyarrow.array(cat.item_id.to_numpy()))\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nt1 = time.time()\nprint(f'Read table: {t1 - t0:.2f}s')\n\n# Filter using pc.is_in\nmask = pc.is_in(table['item_id'], options=item_set)\nt2 = time.time()\nprint(f'pc.is_in took: {t2 - t1:.2f}s, matches: {pc.sum(mask).as_py()}')\n\nfiltered = table.filter(mask)\nt3 = time.time()\nprint(f'Filter table took: {t3 - t2:.2f}s, len={len(filtered)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_352160",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 10, in \nNameError: name 'pyarrow' is not defined\nExit Code: 1\nProcess Group PGID: 2250\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:28:01.532Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 10, in \nNameError: name 'pyarrow' is not defined",
"description": "Benchmark fast PyArrow filtering",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:28:01.533Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 10, in \nNameError: name 'pyarrow' is not defined\nExit Code: 1\nProcess Group PGID: 2250\n",
"isError": false
},
"responseMessageId": "76998ce9-f5a8-4e02-a4e1-2dd8c020c09f"
}
]
},
{
"id": 42,
"role": "agent",
"timestamp": "2026-09-29T01:28:09.398Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:09.398Z",
"callId": "run_shell_command__call_19862",
"name": "run_shell_command",
"input": {
"description": "Test fast PyArrow filtering",
"command": "python3 -c \"\nimport time\nimport numpy as np\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat.item_id.to_numpy()))\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nt1 = time.time()\nprint(f'Read table: {t1 - t0:.2f}s')\n\nmask = pc.is_in(table['item_id'], options=item_set)\nt2 = time.time()\nprint(f'pc.is_in took: {t2 - t1:.2f}s, matches: {pc.sum(mask).as_py()}')\n\nfiltered = table.filter(mask)\nt3 = time.time()\nprint(f'Filter table took: {t3 - t2:.2f}s, len={len(filtered)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_19862",
"name": "run_shell_command",
"args": {
"description": "Test fast PyArrow filtering",
"command": "python3 -c \"\nimport time\nimport numpy as np\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat.item_id.to_numpy()))\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nt1 = time.time()\nprint(f'Read table: {t1 - t0:.2f}s')\n\nmask = pc.is_in(table['item_id'], options=item_set)\nt2 = time.time()\nprint(f'pc.is_in took: {t2 - t1:.2f}s, matches: {pc.sum(mask).as_py()}')\n\nfiltered = table.filter(mask)\nt3 = time.time()\nprint(f'Filter table took: {t3 - t2:.2f}s, len={len(filtered)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_19862",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Read table: 0.41s\npc.is_in took: 0.56s, matches: 31249613\nFilter table took: 0.41s, len=31249613\nProcess Group PGID: 2267\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:28:11.622Z",
"resultDisplay": "Read table: 0.41s\npc.is_in took: 0.56s, matches: 31249613\nFilter table took: 0.41s, len=31249613",
"description": "Test fast PyArrow filtering",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:28:11.623Z",
"content": "\nOutput: Read table: 0.41s\npc.is_in took: 0.56s, matches: 31249613\nFilter table took: 0.41s, len=31249613\nProcess Group PGID: 2267\n",
"isError": false
},
"responseMessageId": "31627bbc-f0e0-46e5-88be-66c07ba1e3e3"
}
]
},
{
"id": 43,
"role": "agent",
"timestamp": "2026-09-29T01:28:22.279Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:22.279Z",
"text": "Optimizing Data Processing\n\nI've successfully optimized the data reading and filtering stages, achieving a significant 10x speedup. My next steps involve rigorously testing the construction of the sparse user-item interaction matrix, the SVD computations (both Truncated and randomized), candidate score prediction, and finally, evaluating NDCG@10 on the validation data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:22.279Z",
"callId": "run_shell_command__call_1946240",
"name": "run_shell_command",
"input": {
"description": "Test TruncatedSVD model on validation",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\n# Load validation eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\n\n# Map val_items index to cat_items index\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\n# Read train listens\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\n# Map to matrix indices\n# Only keep uids that are in uid_to_idx\nu_mask = np.isin(u_arr, uids)\nu_arr = u_arr[u_mask]\nit_arr = it_arr[u_mask]\npct_arr = pct_arr[u_mask]\norg_arr = org_arr[u_mask]\nts_arr = ts_arr[u_mask]\n\nu_idx = np.vectorize(uid_to_idx.get)(u_arr)\nit_idx = np.vectorize(item_to_idx.get)(it_arr)\n\n# Weights: base weight for completion + bonus for organic + time decay\n# Max timestamp in train is 24786800\nCUTOFF = 24786800.0\n# Half-life of 60 days (60 * 86400 = 5,184,000s)\ntime_weight = np.exp((ts_arr - CUTOFF) / (60.0 * 86400.0))\n\n# Positive listen weight: >=50%: 1.0, >=80%: 2.0, organic bonus: +1.0\nlisten_weight = np.where(pct_arr >= 80, 2.0, np.where(pct_arr >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr == 1, 1.0, 0.0)\nweights = listen_weight * time_weight\n\nprint(f'Building sparse matrix, elapsed: {time.time() - t0:.2f}s')\nX = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nprint(f'Matrix shape: {X.shape}, nnz: {X.nnz}')\n\n# Add likes data\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.vectorize(uid_to_idx.get)(likes.uid.to_numpy())\nl_it = np.vectorize(item_to_idx.get)(likes.item_id.to_numpy())\nl_ts = likes.timestamp.to_numpy()\nl_org = likes.is_organic.to_numpy()\nl_tw = np.exp((l_ts - CUTOFF) / (60.0 * 86400.0))\nl_w = (np.where(l_org == 1, 5.0, 3.0)) * l_tw\n\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X + X_likes\n\nprint(f'Total matrix nnz: {X.nnz}, elapsed: {time.time() - t0:.2f}s')\n\n# Run SVD\nprint('Computing SVD...')\nsvd = TruncatedSVD(n_components=64, random_state=42)\nU = svd.fit_transform(X) # (8456, 64)\nV = svd.components_ # (64, 30000)\nprint(f'SVD explained variance ratio sum: {svd.explained_variance_ratio_.sum():.4f}, elapsed: {time.time() - t0:.2f}s')\n\n# Predict scores: S = U @ V\n# Let's score target users\nprint('Scoring users and evaluating...')\nranked = {}\n# Process in batches of 500 users\nBATCH_SIZE = 500\nfor start in range(0, len(val_uids), BATCH_SIZE):\n end = min(start + BATCH_SIZE, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_u_idx = [uid_to_idx[u] for u in batch_uids]\n \n # Compute scores for this batch: (batch_size, 30000)\n batch_scores = U[batch_u_idx] @ V\n \n # For each user, get top 100 eligible items\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n # Mask out ineligible items\n user_scores = batch_scores[i].copy()\n # Eligible mask in terms of cat_items:\n # val_items mapped to cat_items:\n # Ineligible items get -inf\n mask = np.ones(len(cat_items), dtype=bool)\n # where bits == 0, mark ineligible\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n \n # Top 100\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n\nres = score(ranked, truth)\nprint(f'TruncatedSVD(64) NDCG@10: {res}')\n\""
},
"recorded": {
"id": "run_shell_command__call_1946240",
"name": "run_shell_command",
"args": {
"description": "Test TruncatedSVD model on validation",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\n# Load validation eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\n\n# Map val_items index to cat_items index\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\n# Read train listens\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\n# Map to matrix indices\n# Only keep uids that are in uid_to_idx\nu_mask = np.isin(u_arr, uids)\nu_arr = u_arr[u_mask]\nit_arr = it_arr[u_mask]\npct_arr = pct_arr[u_mask]\norg_arr = org_arr[u_mask]\nts_arr = ts_arr[u_mask]\n\nu_idx = np.vectorize(uid_to_idx.get)(u_arr)\nit_idx = np.vectorize(item_to_idx.get)(it_arr)\n\n# Weights: base weight for completion + bonus for organic + time decay\n# Max timestamp in train is 24786800\nCUTOFF = 24786800.0\n# Half-life of 60 days (60 * 86400 = 5,184,000s)\ntime_weight = np.exp((ts_arr - CUTOFF) / (60.0 * 86400.0))\n\n# Positive listen weight: >=50%: 1.0, >=80%: 2.0, organic bonus: +1.0\nlisten_weight = np.where(pct_arr >= 80, 2.0, np.where(pct_arr >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr == 1, 1.0, 0.0)\nweights = listen_weight * time_weight\n\nprint(f'Building sparse matrix, elapsed: {time.time() - t0:.2f}s')\nX = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nprint(f'Matrix shape: {X.shape}, nnz: {X.nnz}')\n\n# Add likes data\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.vectorize(uid_to_idx.get)(likes.uid.to_numpy())\nl_it = np.vectorize(item_to_idx.get)(likes.item_id.to_numpy())\nl_ts = likes.timestamp.to_numpy()\nl_org = likes.is_organic.to_numpy()\nl_tw = np.exp((l_ts - CUTOFF) / (60.0 * 86400.0))\nl_w = (np.where(l_org == 1, 5.0, 3.0)) * l_tw\n\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X + X_likes\n\nprint(f'Total matrix nnz: {X.nnz}, elapsed: {time.time() - t0:.2f}s')\n\n# Run SVD\nprint('Computing SVD...')\nsvd = TruncatedSVD(n_components=64, random_state=42)\nU = svd.fit_transform(X) # (8456, 64)\nV = svd.components_ # (64, 30000)\nprint(f'SVD explained variance ratio sum: {svd.explained_variance_ratio_.sum():.4f}, elapsed: {time.time() - t0:.2f}s')\n\n# Predict scores: S = U @ V\n# Let's score target users\nprint('Scoring users and evaluating...')\nranked = {}\n# Process in batches of 500 users\nBATCH_SIZE = 500\nfor start in range(0, len(val_uids), BATCH_SIZE):\n end = min(start + BATCH_SIZE, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_u_idx = [uid_to_idx[u] for u in batch_uids]\n \n # Compute scores for this batch: (batch_size, 30000)\n batch_scores = U[batch_u_idx] @ V\n \n # For each user, get top 100 eligible items\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n # Mask out ineligible items\n user_scores = batch_scores[i].copy()\n # Eligible mask in terms of cat_items:\n # val_items mapped to cat_items:\n # Ineligible items get -inf\n mask = np.ones(len(cat_items), dtype=bool)\n # where bits == 0, mark ineligible\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n \n # Top 100\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n\nres = score(ranked, truth)\nprint(f'TruncatedSVD(64) NDCG@10: {res}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1946240",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Building sparse matrix, elapsed: 12.91s\nMatrix shape: (8456, 30000), nnz: 7868664\nTotal matrix nnz: 7876748, elapsed: 14.76s\nComputing SVD...\nSVD explained variance ratio sum: 0.4563, elapsed: 19.15s\nScoring users and evaluating...\nTruncatedSVD(64) NDCG@10: {'quality_ndcg@10': 0.021702, 'quality_users': 4263}\nProcess Group PGID: 2289\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:28:46.030Z",
"resultDisplay": "Building sparse matrix, elapsed: 12.91s\nMatrix shape: (8456, 30000), nnz: 7868664\nTotal matrix nnz: 7876748, elapsed: 14.76s\nComputing SVD...\nSVD explained variance ratio sum: 0.4563, elapsed: 19.15s\nScoring users and evaluating...\nTruncatedSVD(64) NDCG@10: {'quality_ndcg@10': 0.021702, 'quality_users': 4263}",
"description": "Test TruncatedSVD model on validation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:28:46.031Z",
"content": "\nOutput: Building sparse matrix, elapsed: 12.91s\nMatrix shape: (8456, 30000), nnz: 7868664\nTotal matrix nnz: 7876748, elapsed: 14.76s\nComputing SVD...\nSVD explained variance ratio sum: 0.4563, elapsed: 19.15s\nScoring users and evaluating...\nTruncatedSVD(64) NDCG@10: {'quality_ndcg@10': 0.021702, 'quality_users': 4263}\nProcess Group PGID: 2289\n",
"isError": false
},
"responseMessageId": "1776546f-828f-4af8-b0c0-31a72cea4031"
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]
},
{
"id": 44,
"role": "agent",
"timestamp": "2026-09-29T01:28:56.571Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:56.571Z",
"text": "Improving SVD Performance\n\nMy latest experiments with TruncatedSVD, specifically using 64 components, yielded impressive results, achieving a score of 0.021702, more than double the baseline. This significant improvement was also realized in a remarkably short time, just 19 seconds. I'm now eager to explore the impact of varying the number of components used in the SVD process to further refine these outcomes."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:56.571Z",
"callId": "run_shell_command__call_36342",
"name": "run_shell_command",
"input": {
"description": "Evaluate SVD(128) raw vs cosine",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\n# Fast matrix builder\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\nCUTOFF = 24786800.0\n# Try half-life of 45 days\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (45.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.5, 0.0)\nweights = listen_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (45.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\nX = X + sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n\n# Test SVD with 128 components\nprint('Computing SVD(128)...')\nsvd = TruncatedSVD(n_components=128, random_state=42)\nU = svd.fit_transform(X)\nV = svd.components_\n\ndef eval_preds(U_mat, V_mat, name):\n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_scores = U_mat[[uid_to_idx[u] for u in batch_uids]] @ V_mat\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n print(f'{name} NDCG@10: {score(ranked, truth)}')\n\neval_preds(U, V, 'SVD(128) raw')\n\n# Normalized embeddings (cosine similarity)\nU_norm = U / (np.linalg.norm(U, axis=1, keepdims=True) + 1e-9)\nV_norm = V / (np.linalg.norm(V, axis=0, keepdims=True) + 1e-9)\neval_preds(U_norm, V_norm, 'SVD(128) cosine')\n\""
},
"recorded": {
"id": "run_shell_command__call_36342",
"name": "run_shell_command",
"args": {
"description": "Evaluate SVD(128) raw vs cosine",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\n# Fast matrix builder\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\nCUTOFF = 24786800.0\n# Try half-life of 45 days\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (45.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.5, 0.0)\nweights = listen_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (45.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\nX = X + sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n\n# Test SVD with 128 components\nprint('Computing SVD(128)...')\nsvd = TruncatedSVD(n_components=128, random_state=42)\nU = svd.fit_transform(X)\nV = svd.components_\n\ndef eval_preds(U_mat, V_mat, name):\n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_scores = U_mat[[uid_to_idx[u] for u in batch_uids]] @ V_mat\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n print(f'{name} NDCG@10: {score(ranked, truth)}')\n\neval_preds(U, V, 'SVD(128) raw')\n\n# Normalized embeddings (cosine similarity)\nU_norm = U / (np.linalg.norm(U, axis=1, keepdims=True) + 1e-9)\nV_norm = V / (np.linalg.norm(V, axis=0, keepdims=True) + 1e-9)\neval_preds(U_norm, V_norm, 'SVD(128) cosine')\n\""
},
"result": [
{
"functionResponse": {
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"output": "\nOutput: Computing SVD(128)...\nSVD(128) raw NDCG@10: {'quality_ndcg@10': 0.020271, 'quality_users': 4263}\nSVD(128) cosine NDCG@10: {'quality_ndcg@10': 0.005956, 'quality_users': 4263}\nProcess Group PGID: 2318\n"
}
}
}
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"status": "success",
"timestamp": "2026-09-29T01:29:27.089Z",
"resultDisplay": "Computing SVD(128)...\nSVD(128) raw NDCG@10: {'quality_ndcg@10': 0.020271, 'quality_users': 4263}\nSVD(128) cosine NDCG@10: {'quality_ndcg@10': 0.005956, 'quality_users': 4263}",
"description": "Evaluate SVD(128) raw vs cosine",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:29:27.089Z",
"content": "\nOutput: Computing SVD(128)...\nSVD(128) raw NDCG@10: {'quality_ndcg@10': 0.020271, 'quality_users': 4263}\nSVD(128) cosine NDCG@10: {'quality_ndcg@10': 0.005956, 'quality_users': 4263}\nProcess Group PGID: 2318\n",
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},
{
"id": 45,
"role": "agent",
"timestamp": "2026-09-29T01:29:40.511Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:40.511Z",
"text": "Exploring SVD Performance\n\nI'm observing an intriguing trend where the raw SVD score for 128 dimensions is lower than for 64. This suggests potential overfitting or that a higher dimensionality isn't necessarily improving the raw metric. I'm now turning my attention to item-item collaborative filtering approaches like cosine and BM25 to see how they perform in comparison and if they reveal different insights into item similarity."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:40.511Z",
"text": "Investigating Item-Item CF\n\nI'm delving into the mechanics of item-item collaborative filtering, specifically exploring how TF-IDF or BM25 normalization impacts the item similarity matrix. The current focus is on optimizing the calculation of predicted scores to avoid materializing a massive similarity matrix. I'm evaluating whether computing user-user similarities first offers a more memory-efficient pathway."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:29:40.511Z",
"callId": "run_shell_command__call_123998",
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"input": {
"description": "Test User-User Collaborative Filtering",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\n# Fast load likes + recent listens\n# Let us test loading likes and recent listens\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nCUTOFF = 24786800.0\n# Likes weights\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (60.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\n\n# Let us also load listens from last 90 days\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\n# 90 days before cutoff = CUTOFF - 90 * 86400 = 17010800\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', 17010800)],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (60.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.0, 0.0)\nweights = listen_weight * time_weight\n\nX_listens = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X_listens + X_likes\n\nprint(f'Matrix built, nnz={X.nnz}')\n\n# User-User CF:\n# Normalize rows of X (user vectors)\nX_norm = normalize(X, norm='l2', axis=1) # (8456, 30000)\n\nt0 = time.time()\n# Compute user-user similarity: S_user = X_norm @ X_norm.T (8456 x 8456)\nprint('Computing user-user similarity...')\nS_user = (X_norm @ X_norm.T).toarray()\n# Zero diagonal\nnp.fill_diagonal(S_user, 0.0)\nprint(f'User-user similarity computed: {time.time() - t0:.2f}s, shape={S_user.shape}')\n\n# Power transform on similarities to sharpen neighbors\n# S_user = np.power(np.maximum(S_user, 0), 2.0)\n# Optional: keep top-K neighbors per user\nK = 200\nprint(f'Keeping top-{K} neighbors...')\nfor i in range(len(uids)):\n row = S_user[i]\n if len(row) > K:\n thresh = np.partition(row, -K)[-K]\n row[row < thresh] = 0.0\n\n# Predicted scores: R = S_user @ X\nprint('Computing predicted scores...')\nt1 = time.time()\n# Compute in batches\nranked = {}\nBATCH = 500\nfor start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_indices = [uid_to_idx[u] for u in batch_uids]\n \n # Dense @ Sparse -> Dense (batch_size, 30000)\n batch_scores = S_user[batch_indices] @ X # or S_user @ X.toarray()\n \n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = np.asarray(batch_scores[i]).ravel().copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n\nprint(f'Scoring finished: {time.time() - t1:.2f}s')\nres = score(ranked, truth)\nprint(f'User-User CF (top-{K}) NDCG@10: {res}')\n\""
},
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"name": "run_shell_command",
"args": {
"description": "Test User-User Collaborative Filtering",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\n# Fast load likes + recent listens\n# Let us test loading likes and recent listens\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nCUTOFF = 24786800.0\n# Likes weights\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (60.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\n\n# Let us also load listens from last 90 days\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\n# 90 days before cutoff = CUTOFF - 90 * 86400 = 17010800\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', 17010800)],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (60.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.0, 0.0)\nweights = listen_weight * time_weight\n\nX_listens = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X_listens + X_likes\n\nprint(f'Matrix built, nnz={X.nnz}')\n\n# User-User CF:\n# Normalize rows of X (user vectors)\nX_norm = normalize(X, norm='l2', axis=1) # (8456, 30000)\n\nt0 = time.time()\n# Compute user-user similarity: S_user = X_norm @ X_norm.T (8456 x 8456)\nprint('Computing user-user similarity...')\nS_user = (X_norm @ X_norm.T).toarray()\n# Zero diagonal\nnp.fill_diagonal(S_user, 0.0)\nprint(f'User-user similarity computed: {time.time() - t0:.2f}s, shape={S_user.shape}')\n\n# Power transform on similarities to sharpen neighbors\n# S_user = np.power(np.maximum(S_user, 0), 2.0)\n# Optional: keep top-K neighbors per user\nK = 200\nprint(f'Keeping top-{K} neighbors...')\nfor i in range(len(uids)):\n row = S_user[i]\n if len(row) > K:\n thresh = np.partition(row, -K)[-K]\n row[row < thresh] = 0.0\n\n# Predicted scores: R = S_user @ X\nprint('Computing predicted scores...')\nt1 = time.time()\n# Compute in batches\nranked = {}\nBATCH = 500\nfor start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_indices = [uid_to_idx[u] for u in batch_uids]\n \n # Dense @ Sparse -> Dense (batch_size, 30000)\n batch_scores = S_user[batch_indices] @ X # or S_user @ X.toarray()\n \n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = np.asarray(batch_scores[i]).ravel().copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n\nprint(f'Scoring finished: {time.time() - t1:.2f}s')\nres = score(ranked, truth)\nprint(f'User-User CF (top-{K}) NDCG@10: {res}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_123998",
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"response": {
"output": "\nOutput: Matrix built, nnz=4395173\nComputing user-user similarity...\nUser-user similarity computed: 5.07s, shape=(8456, 8456)\nKeeping top-200 neighbors...\nComputing predicted scores...\nScoring finished: 21.51s\nUser-User CF (top-200) NDCG@10: {'quality_ndcg@10': 0.027614, 'quality_users': 4263}\nProcess Group PGID: 2347\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:30:15.745Z",
"resultDisplay": "Matrix built, nnz=4395173\nComputing user-user similarity...\nUser-user similarity computed: 5.07s, shape=(8456, 8456)\nKeeping top-200 neighbors...\nComputing predicted scores...\nScoring finished: 21.51s\nUser-User CF (top-200) NDCG@10: {'quality_ndcg@10': 0.027614, 'quality_users': 4263}",
"description": "Test User-User Collaborative Filtering",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:30:15.747Z",
"content": "\nOutput: Matrix built, nnz=4395173\nComputing user-user similarity...\nUser-user similarity computed: 5.07s, shape=(8456, 8456)\nKeeping top-200 neighbors...\nComputing predicted scores...\nScoring finished: 21.51s\nUser-User CF (top-200) NDCG@10: {'quality_ndcg@10': 0.027614, 'quality_users': 4263}\nProcess Group PGID: 2347\n",
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},
{
"id": 46,
"role": "agent",
"timestamp": "2026-09-29T01:30:32.281Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:30:32.281Z",
"text": "Analyzing User-User CF Success\n\nI'm thrilled to report a significant leap in our User-User CF performance for top-200 recommendations, hitting an NDCG@10 of 0.027614. This represents almost a threefold improvement over the baseline. My next step is to dissect the underlying reasons for this remarkable effectiveness."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:30:32.281Z",
"text": "Refining Similarity Metrics\n\nI'm exploring ways to enhance our similarity calculations. Specifically, I’m investigating applying power exponents to the similarity matrix and testing various $K$ values. Additionally, I'm considering incorporating IDF weighting for items to better reflect niche taste shared between users, alongside a deeper dive into how different user interaction weights like likes, dislikes, and organic plays impact performance."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:30:32.281Z",
"callId": "run_shell_command__call_1701300",
"name": "run_shell_command",
"input": {
"description": "Grid search alpha and K on User-User CF with IDF",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\n# 1. Load likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (60.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\n\n# 2. Load listens from last 120 days\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (60.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.5, 0.0)\nweights = listen_weight * time_weight\n\nX_listens = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X_listens + X_likes\n\n# Compute IDF for each item\n# Number of users interacting with item\nitem_doc_freq = np.diff(X.tocsc().indptr)\nidf = np.log((len(uids) + 1.0) / (item_doc_freq + 1.0)) + 1.0\n# Apply IDF to X\nX_idf = X.multiply(idf).tocsr()\n\n# Compute user similarities with X_idf\nX_norm = normalize(X_idf, norm='l2', axis=1)\nS_raw = (X_norm @ X_norm.T).toarray()\nnp.fill_diagonal(S_raw, 0.0)\n# Zero negative\nS_raw = np.maximum(S_raw, 0.0)\n\ndef test_config(alpha, K):\n S = np.power(S_raw, alpha)\n for i in range(len(uids)):\n row = S[i]\n thresh = np.partition(row, -K)[-K]\n row[row < thresh] = 0.0\n \n # R = S @ X\n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_indices = [uid_to_idx[u] for u in batch_uids]\n batch_scores = S[batch_indices] @ X\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = np.asarray(batch_scores[i]).ravel().copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'alpha={alpha}, K={K} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor alpha in [1.0, 1.5, 2.0, 2.5]:\n for K in [50, 100, 200]:\n test_config(alpha, K)\n\""
},
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"args": {
"description": "Grid search alpha and K on User-User CF with IDF",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\n# 1. Load likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (60.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\n\n# 2. Load listens from last 120 days\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (60.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.5, 0.0)\nweights = listen_weight * time_weight\n\nX_listens = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X_listens + X_likes\n\n# Compute IDF for each item\n# Number of users interacting with item\nitem_doc_freq = np.diff(X.tocsc().indptr)\nidf = np.log((len(uids) + 1.0) / (item_doc_freq + 1.0)) + 1.0\n# Apply IDF to X\nX_idf = X.multiply(idf).tocsr()\n\n# Compute user similarities with X_idf\nX_norm = normalize(X_idf, norm='l2', axis=1)\nS_raw = (X_norm @ X_norm.T).toarray()\nnp.fill_diagonal(S_raw, 0.0)\n# Zero negative\nS_raw = np.maximum(S_raw, 0.0)\n\ndef test_config(alpha, K):\n S = np.power(S_raw, alpha)\n for i in range(len(uids)):\n row = S[i]\n thresh = np.partition(row, -K)[-K]\n row[row < thresh] = 0.0\n \n # R = S @ X\n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_indices = [uid_to_idx[u] for u in batch_uids]\n batch_scores = S[batch_indices] @ X\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = np.asarray(batch_scores[i]).ravel().copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'alpha={alpha}, K={K} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor alpha in [1.0, 1.5, 2.0, 2.5]:\n for K in [50, 100, 200]:\n test_config(alpha, K)\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1701300",
"name": "run_shell_command",
"response": {
"output": "\nCommand was automatically cancelled because it exceeded the timeout of 5.0 minutes without output. There was no output before it was cancelled.\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:35:32.433Z",
"resultDisplay": "Command was automatically cancelled because it exceeded the timeout of 5.0 minutes without output.",
"description": "Grid search alpha and K on User-User CF with IDF",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:35:33.964Z",
"content": "\nCommand was automatically cancelled because it exceeded the timeout of 5.0 minutes without output. There was no output before it was cancelled.\n",
"isError": false
},
"responseMessageId": "f764d689-30b8-44d7-b4a2-6a223313475a"
}
]
},
{
"id": 47,
"role": "agent",
"timestamp": "2026-09-29T01:35:40.354Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:35:40.354Z",
"text": "Optimizing Calculation Loop\n\nI've been analyzing the computational load from the recent loop execution. It ran through 4 alphas and 3 Ks, totaling 12 iterations. Each iteration involved a significant matrix multiplication for 8456 users across 17 batches, leading to an unexpectedly long processing time exceeding 5 minutes. I'm now focused on identifying specific bottlenecks within this process."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:35:40.354Z",
"callId": "run_shell_command__call_28789",
"name": "run_shell_command",
"input": {
"description": "Benchmark dense float32 GEMM",
"command": "python3 -c \"\nimport time\nimport numpy as np\n\nA = np.random.randn(8456, 8456).astype(np.float32)\nB = np.random.randn(8456, 30000).astype(np.float32)\nt0 = time.time()\nC = A @ B\nprint(f'Dense GEMM (8456x8456 @ 8456x30000) took: {time.time() - t0:.2f}s')\n\""
},
"recorded": {
"id": "run_shell_command__call_28789",
"name": "run_shell_command",
"args": {
"description": "Benchmark dense float32 GEMM",
"command": "python3 -c \"\nimport time\nimport numpy as np\n\nA = np.random.randn(8456, 8456).astype(np.float32)\nB = np.random.randn(8456, 30000).astype(np.float32)\nt0 = time.time()\nC = A @ B\nprint(f'Dense GEMM (8456x8456 @ 8456x30000) took: {time.time() - t0:.2f}s')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_28789",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Dense GEMM (8456x8456 @ 8456x30000) took: 19.82s\nProcess Group PGID: 2406\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:36:06.596Z",
"resultDisplay": "Dense GEMM (8456x8456 @ 8456x30000) took: 19.82s",
"description": "Benchmark dense float32 GEMM",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:36:06.597Z",
"content": "\nOutput: Dense GEMM (8456x8456 @ 8456x30000) took: 19.82s\nProcess Group PGID: 2406\n",
"isError": false
},
"responseMessageId": "a3719a75-d279-4180-a6a3-675b9dc9acd2"
}
]
},
{
"id": 48,
"role": "agent",
"timestamp": "2026-09-29T01:36:22.789Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:36:22.789Z",
"text": "Calculating Validation Users\n\nI've identified that dense GEMM took 19.82 seconds and realized we only need to score validation or target users. A key question now is how many users actually fall into the validation set. It appears we only need to score 4263 users, or a subset for NDCG@10 evaluation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:36:22.789Z",
"text": "Refining Computation Times\n\nI've realized that the bulk of the computation isn't the user scoring itself, but rather the matrix operations. SVD(64) is surprisingly fast, taking only 4 seconds for decomposition, and scoring all users is a mere 0.02 seconds. Now I'm exploring why SVD yielded 0.021702 and how to improve matrix factorization performance beyond 0.030."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:36:22.789Z",
"text": "Testing Transformations\n\nI'm now prioritizing testing TF-IDF transformations before SVD, as this approach is known to significantly improve recommendations. I'll also be investigating the impact of PPMI, explicit preference states, distinguishing organic from non-organic interactions, exploring different rank dimensions, and incorporating item popularity priors."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:36:22.789Z",
"callId": "run_shell_command__call_148619",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\n# 1. Load likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (60.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\n\n# 2. Load listens from last 120 days\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (60.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.5, 0.0)\nweights = listen_weight * time_weight\n\nX_listens = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X_listens + X_likes\n\nprint(f'Data prepared in {time.time() - t0:.2f}s, X shape={X.shape}, nnz={X.nnz}')\n\ndef evaluate_model(pred_fn, name):\n ranked = {}\n BATCH = 1000\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = pred_fn(batch_idx) # (batch_size, 30000)\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'{name} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n return res[\\\"quality_ndcg@10\\\"]\n\n# 1. Raw SVD\nfor n_comp in [64, 100]:\n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n evaluate_model(lambda b_idx, U=U, V=V: U[b_idx] @ V, f'Raw SVD({n_comp})')\n\n# 2. Log1p(X) + SVD\nX_log = X.copy()\nX_log.data = np.log1p(X_log.data)\nfor n_comp in [64, 100]:\n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X_log)\n V = svd.components_\n evaluate_model(lambda b_idx, U=U, V=V: U[b_idx] @ V, f'Log1p SVD({n_comp})')\n\n# 3. TF-IDF / BM25 + SVD\nitem_df = np.diff(X.tocsc().indptr)\nidf = np.log((len(uids) + 1.0) / (item_df + 1.0)) + 1.0\nX_tfidf = X_log.multiply(idf).tocsr()\nfor n_comp in [64, 100, 150]:\n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X_tfidf)\n V = svd.components_\n evaluate_model(lambda b_idx, U=U, V=V: U[b_idx] @ V, f'TFIDF SVD({n_comp})')\n\"",
"description": "Test various SVD transformations and dimensions"
},
"recorded": {
"id": "run_shell_command__call_148619",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\n# 1. Load likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (60.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\n\n# 2. Load listens from last 120 days\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (60.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.5, 0.0)\nweights = listen_weight * time_weight\n\nX_listens = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X_listens + X_likes\n\nprint(f'Data prepared in {time.time() - t0:.2f}s, X shape={X.shape}, nnz={X.nnz}')\n\ndef evaluate_model(pred_fn, name):\n ranked = {}\n BATCH = 1000\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = pred_fn(batch_idx) # (batch_size, 30000)\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'{name} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n return res[\\\"quality_ndcg@10\\\"]\n\n# 1. Raw SVD\nfor n_comp in [64, 100]:\n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n evaluate_model(lambda b_idx, U=U, V=V: U[b_idx] @ V, f'Raw SVD({n_comp})')\n\n# 2. Log1p(X) + SVD\nX_log = X.copy()\nX_log.data = np.log1p(X_log.data)\nfor n_comp in [64, 100]:\n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X_log)\n V = svd.components_\n evaluate_model(lambda b_idx, U=U, V=V: U[b_idx] @ V, f'Log1p SVD({n_comp})')\n\n# 3. TF-IDF / BM25 + SVD\nitem_df = np.diff(X.tocsc().indptr)\nidf = np.log((len(uids) + 1.0) / (item_df + 1.0)) + 1.0\nX_tfidf = X_log.multiply(idf).tocsr()\nfor n_comp in [64, 100, 150]:\n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X_tfidf)\n V = svd.components_\n evaluate_model(lambda b_idx, U=U, V=V: U[b_idx] @ V, f'TFIDF SVD({n_comp})')\n\"",
"description": "Test various SVD transformations and dimensions"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_148619",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Data prepared in 8.27s, X shape=(8456, 30000), nnz=5114107\nRaw SVD(64) -> NDCG@10 = 0.021653\nRaw SVD(100) -> NDCG@10 = 0.019723\nLog1p SVD(64) -> NDCG@10 = 0.032032\nLog1p SVD(100) -> NDCG@10 = 0.031072\nTFIDF SVD(64) -> NDCG@10 = 0.029062\nTFIDF SVD(100) -> NDCG@10 = 0.029837\nTFIDF SVD(150) -> NDCG@10 = 0.030893\nProcess Group PGID: 2416\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:37:17.797Z",
"resultDisplay": "Data prepared in 8.27s, X shape=(8456, 30000), nnz=5114107\nRaw SVD(64) -> NDCG@10 = 0.021653\nRaw SVD(100) -> NDCG@10 = 0.019723\nLog1p SVD(64) -> NDCG@10 = 0.032032\nLog1p SVD(100) -> NDCG@10 = 0.031072\nTFIDF SVD(64) -> NDCG@10 = 0.029062\nTFIDF SVD(100) -> NDCG@10 = 0.029837\nTFIDF SVD(150) -> NDCG@10 = 0.030893",
"description": "Test various SVD transformations and dimensions",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:37:17.798Z",
"content": "\nOutput: Data prepared in 8.27s, X shape=(8456, 30000), nnz=5114107\nRaw SVD(64) -> NDCG@10 = 0.021653\nRaw SVD(100) -> NDCG@10 = 0.019723\nLog1p SVD(64) -> NDCG@10 = 0.032032\nLog1p SVD(100) -> NDCG@10 = 0.031072\nTFIDF SVD(64) -> NDCG@10 = 0.029062\nTFIDF SVD(100) -> NDCG@10 = 0.029837\nTFIDF SVD(150) -> NDCG@10 = 0.030893\nProcess Group PGID: 2416\n",
"isError": false
},
"responseMessageId": "62b53a62-411f-482a-bdd9-2db191003236"
}
]
},
{
"id": 49,
"role": "agent",
"timestamp": "2026-09-29T01:37:23.378Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:37:23.378Z",
"text": "Improving Recommendation Performance\n\nMy current focus is on dramatically enhancing recommendation efficacy. I've just observed a substantial improvement with `Log1p SVD(64)`, achieving an NDCG@10 of 0.032032, a significant jump from baseline methods and completed in a remarkably short time."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:37:23.378Z",
"text": "Exploring Transform Options\n\nI am now investigating alternative power transformations beyond `log1p`, specifically exploring square root and scaled log transforms. Simultaneously, I'm refining SVD component counts around 48, 64, and 80. Additionally, I'm considering adjustments to the listening window and time decay parameters, along with the impact of organic listen weighting. The explicit preference state data is also being re-examined to ensure all relevant user interactions are captured."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:37:23.378Z",
"callId": "run_shell_command__call_4457",
"name": "run_shell_command",
"input": {
"description": "Check preference state overlap with train_likes and candidate catalog",
"command": "python3 -c \"\nimport pandas as pd\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\npref_cat = pref[pref.item_id.isin(cat_set)]\nprint(f'Preference state in catalog: total={len(pref_cat)}, liked={(pref_cat.state==\\\"liked\\\").sum()}, disliked={(pref_cat.state==\\\"disliked\\\").sum()}')\n\n# Check overlap with train_likes\npref_liked_pairs = set(zip(pref_cat[pref_cat.state == 'liked'].uid, pref_cat[pref_cat.state == 'liked'].item_id))\ntrain_likes_pairs = set(zip(likes.uid, likes.item_id))\nprint(f'Liked in pref but not in train_likes: {len(pref_liked_pairs - train_likes_pairs)}')\nprint(f'Disliked in pref: {len(pref_cat[pref_cat.state == \\\"disliked\\\"])}')\n\""
},
"recorded": {
"id": "run_shell_command__call_4457",
"name": "run_shell_command",
"args": {
"description": "Check preference state overlap with train_likes and candidate catalog",
"command": "python3 -c \"\nimport pandas as pd\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\npref_cat = pref[pref.item_id.isin(cat_set)]\nprint(f'Preference state in catalog: total={len(pref_cat)}, liked={(pref_cat.state==\\\"liked\\\").sum()}, disliked={(pref_cat.state==\\\"disliked\\\").sum()}')\n\n# Check overlap with train_likes\npref_liked_pairs = set(zip(pref_cat[pref_cat.state == 'liked'].uid, pref_cat[pref_cat.state == 'liked'].item_id))\ntrain_likes_pairs = set(zip(likes.uid, likes.item_id))\nprint(f'Liked in pref but not in train_likes: {len(pref_liked_pairs - train_likes_pairs)}')\nprint(f'Disliked in pref: {len(pref_cat[pref_cat.state == \\\"disliked\\\"])}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4457",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Preference state in catalog: total=469124, liked=418435, disliked=50689\nLiked in pref but not in train_likes: 0\nDisliked in pref: 50689\nProcess Group PGID: 2445\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:37:25.470Z",
"resultDisplay": "Preference state in catalog: total=469124, liked=418435, disliked=50689\nLiked in pref but not in train_likes: 0\nDisliked in pref: 50689",
"description": "Check preference state overlap with train_likes and candidate catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:37:25.471Z",
"content": "\nOutput: Preference state in catalog: total=469124, liked=418435, disliked=50689\nLiked in pref but not in train_likes: 0\nDisliked in pref: 50689\nProcess Group PGID: 2445\n",
"isError": false
},
"responseMessageId": "06a07dfb-9f97-46f3-8295-b4f0c7855e66"
}
]
},
{
"id": 50,
"role": "agent",
"timestamp": "2026-09-29T01:37:41.747Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:37:41.747Z",
"text": "Analyzing Preference Data\n\nI'm currently processing a significant volume of user preference data, specifically noting the 50,689 disliked tracks within `validation_preference_state`. My focus is on how this past preference impacts eligibility for new recommendations, as candidates must not be currently liked or disliked."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:37:41.747Z",
"text": "Incorporating Active Preferences\n\nMy thoughts are now deeply focused on integrating the active preference state. I'm considering assigning significant positive weight to actively liked tracks and substantial negative weight to actively disliked ones. SVD's capacity to handle negative entries is a key consideration, and I'm preparing to test various parameter combinations for active likes/dislikes, time decay, SVD components, and data transformations."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:37:41.747Z",
"callId": "run_shell_command__call_204147",
"name": "run_shell_command",
"input": {
"description": "Test half-life and dislike weights with SVD",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\n# Preference state:\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\n# Train likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\n# Train listens from last 180 days\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=45.0, org_weight=2.0, dis_weight=2.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n # completion weight\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n # organic bonus\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n # Active preferences (at cutoff, so time weight = 1.0)\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n # Apply log1p on positive part\n X.data = np.log1p(X.data)\n \n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef evaluate_svd(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n ranked = {}\n BATCH = 1000\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor hl in [30.0, 45.0, 60.0]:\n for dis in [0.0, 1.0, 3.0]:\n X = build_matrix(half_life_days=hl, dis_weight=dis)\n ndcg = evaluate_svd(X, n_components=64)\n print(f'hl={hl:4.1f}, dis={dis:3.1f}, n_comp=64 -> NDCG@10 = {ndcg:.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_204147",
"name": "run_shell_command",
"args": {
"description": "Test half-life and dislike weights with SVD",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\n# Preference state:\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\n# Train likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\n# Train listens from last 180 days\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=45.0, org_weight=2.0, dis_weight=2.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n # completion weight\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n # organic bonus\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n # Active preferences (at cutoff, so time weight = 1.0)\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n # Apply log1p on positive part\n X.data = np.log1p(X.data)\n \n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef evaluate_svd(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n ranked = {}\n BATCH = 1000\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor hl in [30.0, 45.0, 60.0]:\n for dis in [0.0, 1.0, 3.0]:\n X = build_matrix(half_life_days=hl, dis_weight=dis)\n ndcg = evaluate_svd(X, n_components=64)\n print(f'hl={hl:4.1f}, dis={dis:3.1f}, n_comp=64 -> NDCG@10 = {ndcg:.6f}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_204147",
"name": "run_shell_command",
"response": {
"output": "\nOutput: hl=30.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.035042\nhl=30.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.035127\nhl=30.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.034566\nhl=45.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.033303\nhl=45.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.033362\nhl=45.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.032999\nhl=60.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.031963\nhl=60.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.031900\nhl=60.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.031689\nProcess Group PGID: 2463\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:39:00.343Z",
"resultDisplay": "hl=30.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.035042\nhl=30.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.035127\nhl=30.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.034566\nhl=45.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.033303\nhl=45.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.033362\nhl=45.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.032999\nhl=60.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.031963\nhl=60.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.031900\nhl=60.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.031689",
"description": "Test half-life and dislike weights with SVD",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:39:00.344Z",
"content": "\nOutput: hl=30.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.035042\nhl=30.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.035127\nhl=30.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.034566\nhl=45.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.033303\nhl=45.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.033362\nhl=45.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.032999\nhl=60.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.031963\nhl=60.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.031900\nhl=60.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.031689\nProcess Group PGID: 2463\n",
"isError": false
},
"responseMessageId": "a4cf93f7-dee5-4a43-ba5b-1bf45dcf3d69"
}
]
},
{
"id": 51,
"role": "agent",
"timestamp": "2026-09-29T01:39:15.112Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:39:15.112Z",
"text": "Testing Half-Life Impacts\n\nI've observed that a shorter half-life significantly boosts NDCG@10 performance, with `hl=30` achieving `0.0351`. I'm now exploring even shorter durations like 20, 14, and 25 days to pinpoint the optimal recency setting."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:39:15.112Z",
"callId": "run_shell_command__call_12067",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=20.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef evaluate_svd(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n ranked = {}\n BATCH = 1000\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor hl in [14.0, 21.0, 28.0]:\n X = build_matrix(half_life_days=hl, dis_weight=1.0)\n for n_comp in [48, 64, 80]:\n ndcg = evaluate_svd(X, n_components=n_comp)\n print(f'hl={hl:4.1f}, n_comp={n_comp:2d} -> NDCG@10 = {ndcg:.6f}')\n\"",
"description": "Tune half-life and components around sweet spot"
},
"recorded": {
"id": "run_shell_command__call_12067",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=20.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef evaluate_svd(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n ranked = {}\n BATCH = 1000\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor hl in [14.0, 21.0, 28.0]:\n X = build_matrix(half_life_days=hl, dis_weight=1.0)\n for n_comp in [48, 64, 80]:\n ndcg = evaluate_svd(X, n_components=n_comp)\n print(f'hl={hl:4.1f}, n_comp={n_comp:2d} -> NDCG@10 = {ndcg:.6f}')\n\"",
"description": "Tune half-life and components around sweet spot"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_12067",
"name": "run_shell_command",
"response": {
"output": "\nOutput: hl=14.0, n_comp=48 -> NDCG@10 = 0.037210\nhl=14.0, n_comp=64 -> NDCG@10 = 0.037352\nhl=14.0, n_comp=80 -> NDCG@10 = 0.037183\nhl=21.0, n_comp=48 -> NDCG@10 = 0.036251\nhl=21.0, n_comp=64 -> NDCG@10 = 0.036416\nhl=21.0, n_comp=80 -> NDCG@10 = 0.036135\nhl=28.0, n_comp=48 -> NDCG@10 = 0.035093\nhl=28.0, n_comp=64 -> NDCG@10 = 0.035299\nhl=28.0, n_comp=80 -> NDCG@10 = 0.035341\nProcess Group PGID: 2492\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:40:23.642Z",
"resultDisplay": "hl=14.0, n_comp=48 -> NDCG@10 = 0.037210\nhl=14.0, n_comp=64 -> NDCG@10 = 0.037352\nhl=14.0, n_comp=80 -> NDCG@10 = 0.037183\nhl=21.0, n_comp=48 -> NDCG@10 = 0.036251\nhl=21.0, n_comp=64 -> NDCG@10 = 0.036416\nhl=21.0, n_comp=80 -> NDCG@10 = 0.036135\nhl=28.0, n_comp=48 -> NDCG@10 = 0.035093\nhl=28.0, n_comp=64 -> NDCG@10 = 0.035299\nhl=28.0, n_comp=80 -> NDCG@10 = 0.035341",
"description": "Tune half-life and components around sweet spot",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:40:23.644Z",
"content": "\nOutput: hl=14.0, n_comp=48 -> NDCG@10 = 0.037210\nhl=14.0, n_comp=64 -> NDCG@10 = 0.037352\nhl=14.0, n_comp=80 -> NDCG@10 = 0.037183\nhl=21.0, n_comp=48 -> NDCG@10 = 0.036251\nhl=21.0, n_comp=64 -> NDCG@10 = 0.036416\nhl=21.0, n_comp=80 -> NDCG@10 = 0.036135\nhl=28.0, n_comp=48 -> NDCG@10 = 0.035093\nhl=28.0, n_comp=64 -> NDCG@10 = 0.035299\nhl=28.0, n_comp=80 -> NDCG@10 = 0.035341\nProcess Group PGID: 2492\n",
"isError": false
},
"responseMessageId": "e4e3e908-0f51-4705-92ea-f882af8d0b7c"
}
]
},
{
"id": 52,
"role": "agent",
"timestamp": "2026-09-29T01:40:39.607Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:40:39.607Z",
"text": "Refining Parameters and Tastes\n\nI've achieved a score of 0.037352 with `hl=14.0, n_comp=64`. I'm now investigating whether 7 or 10 days provides superior results and exploring how to best integrate short-term and medium-term user taste components within the recommender system."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:40:39.607Z",
"callId": "run_shell_command__call_342484",
"name": "run_shell_command",
"input": {
"description": "Test fine-grained half-life values",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=10.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef get_scores(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n return U @ V\n\nfor hl in [5.0, 7.0, 10.0, 14.0]:\n X = build_matrix(half_life_days=hl, dis_weight=1.0)\n scores_mat = get_scores(X, 64)\n # Evaluate\n ranked = {}\n for i, (r_idx, uid) in enumerate(zip(range(len(val_uids)), val_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = scores_mat[uid_to_idx[uid]].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'hl={hl:4.1f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_342484",
"name": "run_shell_command",
"args": {
"description": "Test fine-grained half-life values",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=10.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef get_scores(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n return U @ V\n\nfor hl in [5.0, 7.0, 10.0, 14.0]:\n X = build_matrix(half_life_days=hl, dis_weight=1.0)\n scores_mat = get_scores(X, 64)\n # Evaluate\n ranked = {}\n for i, (r_idx, uid) in enumerate(zip(range(len(val_uids)), val_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = scores_mat[uid_to_idx[uid]].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'hl={hl:4.1f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_342484",
"name": "run_shell_command",
"response": {
"output": "\nOutput: hl= 5.0 -> NDCG@10 = 0.037882\nhl= 7.0 -> NDCG@10 = 0.039199\nhl=10.0 -> NDCG@10 = 0.039379\nhl=14.0 -> NDCG@10 = 0.037352\nProcess Group PGID: 2521\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:41:19.652Z",
"resultDisplay": "hl= 5.0 -> NDCG@10 = 0.037882\nhl= 7.0 -> NDCG@10 = 0.039199\nhl=10.0 -> NDCG@10 = 0.039379\nhl=14.0 -> NDCG@10 = 0.037352",
"description": "Test fine-grained half-life values",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:41:19.653Z",
"content": "\nOutput: hl= 5.0 -> NDCG@10 = 0.037882\nhl= 7.0 -> NDCG@10 = 0.039199\nhl=10.0 -> NDCG@10 = 0.039379\nhl=14.0 -> NDCG@10 = 0.037352\nProcess Group PGID: 2521\n",
"isError": false
},
"responseMessageId": "ac7702a7-de03-452e-a0ee-9e67e9523585"
}
]
},
{
"id": 53,
"role": "agent",
"timestamp": "2026-09-29T01:41:33.326Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:41:33.326Z",
"text": "Refining the algorithm\n\nI've achieved a `hl=10.0` score of 0.039379, nearing 0.040 which is nearly four times the baseline. I'm now exploring combinations of `hl` values (9.0, 10.0, 11.0) and integrating short-term recency (`hl=10.0`), medium-term preferences (`hl=40.0`), and item popularity priors."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:41:33.326Z",
"callId": "run_shell_command__call_32912",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=10.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef get_scores(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n return U @ V\n\nscores_10 = get_scores(build_matrix(10.0), 64)\nscores_35 = get_scores(build_matrix(35.0), 64)\n\n# Normalize scores per user by std dev\nscores_10_norm = scores_10 / (np.std(scores_10, axis=1, keepdims=True) + 1e-9)\nscores_35_norm = scores_35 / (np.std(scores_35, axis=1, keepdims=True) + 1e-9)\n\ndef test_blend(w1, w2):\n blended = w1 * scores_10_norm + w2 * scores_35_norm\n ranked = {}\n for i, (r_idx, uid) in enumerate(zip(range(len(val_uids)), val_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = blended[uid_to_idx[uid]].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'w1={w1:.2f}, w2={w2:.2f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor w2 in [0.0, 0.1, 0.2, 0.3, 0.5]:\n test_blend(1.0, w2)\n\"",
"description": "Test multi-timescale blend (short + medium)"
},
"recorded": {
"id": "run_shell_command__call_32912",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=10.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef get_scores(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n return U @ V\n\nscores_10 = get_scores(build_matrix(10.0), 64)\nscores_35 = get_scores(build_matrix(35.0), 64)\n\n# Normalize scores per user by std dev\nscores_10_norm = scores_10 / (np.std(scores_10, axis=1, keepdims=True) + 1e-9)\nscores_35_norm = scores_35 / (np.std(scores_35, axis=1, keepdims=True) + 1e-9)\n\ndef test_blend(w1, w2):\n blended = w1 * scores_10_norm + w2 * scores_35_norm\n ranked = {}\n for i, (r_idx, uid) in enumerate(zip(range(len(val_uids)), val_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = blended[uid_to_idx[uid]].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'w1={w1:.2f}, w2={w2:.2f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor w2 in [0.0, 0.1, 0.2, 0.3, 0.5]:\n test_blend(1.0, w2)\n\"",
"description": "Test multi-timescale blend (short + medium)"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_32912",
"name": "run_shell_command",
"response": {
"output": "\nOutput: /usr/bin/bash: line 121: 2552 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=10.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef get_scores(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n return U @ V\n\nscores_10 = get_scores(build_matrix(10.0), 64)\nscores_35 = get_scores(build_matrix(35.0), 64)\n\n# Normalize scores per user by std dev\nscores_10_norm = scores_10 / (np.std(scores_10, axis=1, keepdims=True) + 1e-9)\nscores_35_norm = scores_35 / (np.std(scores_35, axis=1, keepdims=True) + 1e-9)\n\ndef test_blend(w1, w2):\n blended = w1 * scores_10_norm + w2 * scores_35_norm\n ranked = {}\n for i, (r_idx, uid) in enumerate(zip(range(len(val_uids)), val_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = blended[uid_to_idx[uid]].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'w1={w1:.2f}, w2={w2:.2f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor w2 in [0.0, 0.1, 0.2, 0.3, 0.5]:\n test_blend(1.0, w2)\n\"\nExit Code: 137\nProcess Group PGID: 2550\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:41:58.317Z",
"resultDisplay": "/usr/bin/bash: line 121: 2552 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=10.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef get_scores(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n return U @ V\n\nscores_10 = get_scores(build_matrix(10.0), 64)\nscores_35 = get_scores(build_matrix(35.0), 64)\n\n# Normalize scores per user by std dev\nscores_10_norm = scores_10 / (np.std(scores_10, axis=1, keepdims=True) + 1e-9)\nscores_35_norm = scores_35 / (np.std(scores_35, axis=1, keepdims=True) + 1e-9)\n\ndef test_blend(w1, w2):\n blended = w1 * scores_10_norm + w2 * scores_35_norm\n ranked = {}\n for i, (r_idx, uid) in enumerate(zip(range(len(val_uids)), val_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = blended[uid_to_idx[uid]].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'w1={w1:.2f}, w2={w2:.2f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor w2 in [0.0, 0.1, 0.2, 0.3, 0.5]:\n test_blend(1.0, w2)\n\"",
"description": "Test multi-timescale blend (short + medium)",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:41:58.337Z",
"content": "\nOutput: /usr/bin/bash: line 121: 2552 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=10.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef get_scores(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n return U @ V\n\nscores_10 = get_scores(build_matrix(10.0), 64)\nscores_35 = get_scores(build_matrix(35.0), 64)\n\n# Normalize scores per user by std dev\nscores_10_norm = scores_10 / (np.std(scores_10, axis=1, keepdims=True) + 1e-9)\nscores_35_norm = scores_35 / (np.std(scores_35, axis=1, keepdims=True) + 1e-9)\n\ndef test_blend(w1, w2):\n blended = w1 * scores_10_norm + w2 * scores_35_norm\n ranked = {}\n for i, (r_idx, uid) in enumerate(zip(range(len(val_uids)), val_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = blended[uid_to_idx[uid]].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'w1={w1:.2f}, w2={w2:.2f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor w2 in [0.0, 0.1, 0.2, 0.3, 0.5]:\n test_blend(1.0, w2)\n\"\nExit Code: 137\nProcess Group PGID: 2550\n",
"isError": false
},
"responseMessageId": "8cbb7f8e-6c39-4fd5-b35e-1fe508482981"
}
]
},
{
"id": 54,
"role": "agent",
"timestamp": "2026-09-29T01:42:12.379Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:42:12.379Z",
"text": "Analyzing Memory Leaks\n\nI'm encountering an \"Out of Memory\" error (exit code 137). It appears that intermediate matrix multiplications, specifically `scores_10 = U @ V` and `scores_35 = U @ V`, each consuming approximately 2GB of memory, are the primary culprits. I'm also seeing significant memory usage from `scores_10_norm`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:42:12.379Z",
"callId": "run_shell_command__call_2580864",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\n# Clean up arrow table\ndel table, filtered\ngc.collect()\n\ndef fit_svd(half_life_days=10.0, n_components=64, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, 2.0, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n return U, V\n\nprint('Fitting model 1 (hl=10)...')\nU1, V1 = fit_svd(10.0, 64)\nprint('Fitting model 2 (hl=35)...')\nU2, V2 = fit_svd(35.0, 64)\n\ndef evaluate_blend(w1, w2):\n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n \n s1 = U1[batch_idx] @ V1\n s2 = U2[batch_idx] @ V2\n # Normalize each per row\n s1 = s1 / (np.std(s1, axis=1, keepdims=True) + 1e-9)\n s2 = s2 / (np.std(s2, axis=1, keepdims=True) + 1e-9)\n batch_scores = w1 * s1 + w2 * s2\n \n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'w1={w1:.2f}, w2={w2:.2f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor w2 in [0.0, 0.15, 0.25, 0.35, 0.5]:\n evaluate_blend(1.0, w2)\n\"",
"description": "Test multi-timescale blend with safe memory footprint"
},
"recorded": {
"id": "run_shell_command__call_2580864",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\n# Clean up arrow table\ndel table, filtered\ngc.collect()\n\ndef fit_svd(half_life_days=10.0, n_components=64, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, 2.0, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n return U, V\n\nprint('Fitting model 1 (hl=10)...')\nU1, V1 = fit_svd(10.0, 64)\nprint('Fitting model 2 (hl=35)...')\nU2, V2 = fit_svd(35.0, 64)\n\ndef evaluate_blend(w1, w2):\n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n \n s1 = U1[batch_idx] @ V1\n s2 = U2[batch_idx] @ V2\n # Normalize each per row\n s1 = s1 / (np.std(s1, axis=1, keepdims=True) + 1e-9)\n s2 = s2 / (np.std(s2, axis=1, keepdims=True) + 1e-9)\n batch_scores = w1 * s1 + w2 * s2\n \n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'w1={w1:.2f}, w2={w2:.2f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor w2 in [0.0, 0.15, 0.25, 0.35, 0.5]:\n evaluate_blend(1.0, w2)\n\"",
"description": "Test multi-timescale blend with safe memory footprint"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2580864",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Fitting model 1 (hl=10)...\nFitting model 2 (hl=35)...\nw1=1.00, w2=0.00 -> NDCG@10 = 0.039379\nw1=1.00, w2=0.15 -> NDCG@10 = 0.038790\nw1=1.00, w2=0.25 -> NDCG@10 = 0.038580\nw1=1.00, w2=0.35 -> NDCG@10 = 0.038450\nw1=1.00, w2=0.50 -> NDCG@10 = 0.038535\nProcess Group PGID: 2579\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:42:49.014Z",
"resultDisplay": "Fitting model 1 (hl=10)...\nFitting model 2 (hl=35)...\nw1=1.00, w2=0.00 -> NDCG@10 = 0.039379\nw1=1.00, w2=0.15 -> NDCG@10 = 0.038790\nw1=1.00, w2=0.25 -> NDCG@10 = 0.038580\nw1=1.00, w2=0.35 -> NDCG@10 = 0.038450\nw1=1.00, w2=0.50 -> NDCG@10 = 0.038535",
"description": "Test multi-timescale blend with safe memory footprint",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:42:49.014Z",
"content": "\nOutput: Fitting model 1 (hl=10)...\nFitting model 2 (hl=35)...\nw1=1.00, w2=0.00 -> NDCG@10 = 0.039379\nw1=1.00, w2=0.15 -> NDCG@10 = 0.038790\nw1=1.00, w2=0.25 -> NDCG@10 = 0.038580\nw1=1.00, w2=0.35 -> NDCG@10 = 0.038450\nw1=1.00, w2=0.50 -> NDCG@10 = 0.038535\nProcess Group PGID: 2579\n",
"isError": false
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"responseMessageId": "a62e3564-d599-4abe-8173-ec332a000589"
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]
},
{
"id": 55,
"role": "agent",
"timestamp": "2026-09-29T01:43:03.327Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:43:03.327Z",
"text": "Exploring SVD Components\n\nI'm currently investigating the impact of increasing SVD component counts while keeping `hl=10.0` constant. My focus is on how these higher counts influence NDCG@10, and I'm also considering the roles of `org_weight` and likes weight in our objective."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:43:03.327Z",
"callId": "run_shell_command__call_308041",
"name": "run_shell_command",
"input": {
"description": "Test n_components and popularity gamma",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\n# Popularity feature\norg_pop = cat.organic_positive_listens.to_numpy(dtype=np.float32)\nlog_org_pop = np.log1p(org_pop)\nlog_org_pop = (log_org_pop - log_org_pop.mean()) / (log_org_pop.std() + 1e-9)\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef build_and_evaluate(half_life_days=10.0, n_components=64, org_weight=2.0, pop_gamma=0.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n \n batch_scores = U[batch_idx] @ V\n if pop_gamma > 0:\n batch_scores = batch_scores + pop_gamma * log_org_pop\n \n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor n_c in [64, 96, 128]:\n for gamma in [0.0, 0.05, 0.1]:\n ndcg = build_and_evaluate(half_life_days=10.0, n_components=n_c, pop_gamma=gamma)\n print(f'n_comp={n_c:3d}, gamma={gamma:4.2f} -> NDCG@10 = {ndcg:.6f}')\n\""
},
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"name": "run_shell_command",
"args": {
"description": "Test n_components and popularity gamma",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\n# Popularity feature\norg_pop = cat.organic_positive_listens.to_numpy(dtype=np.float32)\nlog_org_pop = np.log1p(org_pop)\nlog_org_pop = (log_org_pop - log_org_pop.mean()) / (log_org_pop.std() + 1e-9)\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef build_and_evaluate(half_life_days=10.0, n_components=64, org_weight=2.0, pop_gamma=0.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n \n batch_scores = U[batch_idx] @ V\n if pop_gamma > 0:\n batch_scores = batch_scores + pop_gamma * log_org_pop\n \n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor n_c in [64, 96, 128]:\n for gamma in [0.0, 0.05, 0.1]:\n ndcg = build_and_evaluate(half_life_days=10.0, n_components=n_c, pop_gamma=gamma)\n print(f'n_comp={n_c:3d}, gamma={gamma:4.2f} -> NDCG@10 = {ndcg:.6f}')\n\""
},
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"output": "\nOutput: n_comp= 64, gamma=0.00 -> NDCG@10 = 0.039379\nn_comp= 64, gamma=0.05 -> NDCG@10 = 0.033986\nn_comp= 64, gamma=0.10 -> NDCG@10 = 0.030370\nn_comp= 96, gamma=0.00 -> NDCG@10 = 0.038020\nn_comp= 96, gamma=0.05 -> NDCG@10 = 0.034237\nn_comp= 96, gamma=0.10 -> NDCG@10 = 0.030247\nn_comp=128, gamma=0.00 -> NDCG@10 = 0.038432\nn_comp=128, gamma=0.05 -> NDCG@10 = 0.034034\nn_comp=128, gamma=0.10 -> NDCG@10 = 0.030205\nProcess Group PGID: 2608\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:44:25.681Z",
"resultDisplay": "n_comp= 64, gamma=0.00 -> NDCG@10 = 0.039379\nn_comp= 64, gamma=0.05 -> NDCG@10 = 0.033986\nn_comp= 64, gamma=0.10 -> NDCG@10 = 0.030370\nn_comp= 96, gamma=0.00 -> NDCG@10 = 0.038020\nn_comp= 96, gamma=0.05 -> NDCG@10 = 0.034237\nn_comp= 96, gamma=0.10 -> NDCG@10 = 0.030247\nn_comp=128, gamma=0.00 -> NDCG@10 = 0.038432\nn_comp=128, gamma=0.05 -> NDCG@10 = 0.034034\nn_comp=128, gamma=0.10 -> NDCG@10 = 0.030205",
"description": "Test n_components and popularity gamma",
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"result": {
"timestamp": "2026-09-29T01:44:25.682Z",
"content": "\nOutput: n_comp= 64, gamma=0.00 -> NDCG@10 = 0.039379\nn_comp= 64, gamma=0.05 -> NDCG@10 = 0.033986\nn_comp= 64, gamma=0.10 -> NDCG@10 = 0.030370\nn_comp= 96, gamma=0.00 -> NDCG@10 = 0.038020\nn_comp= 96, gamma=0.05 -> NDCG@10 = 0.034237\nn_comp= 96, gamma=0.10 -> NDCG@10 = 0.030247\nn_comp=128, gamma=0.00 -> NDCG@10 = 0.038432\nn_comp=128, gamma=0.05 -> NDCG@10 = 0.034034\nn_comp=128, gamma=0.10 -> NDCG@10 = 0.030205\nProcess Group PGID: 2608\n",
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},
{
"id": 56,
"role": "agent",
"timestamp": "2026-09-29T01:44:39.777Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:44:39.777Z",
"text": "Refining Popularity Logic\n\nI've discovered the popularity bonus is counterproductive, likely because SVD inherently captures item affinities. Including global popularity seems to dilute individual user preferences by boosting generic tracks. My next step is to investigate further, starting with the `half` parameter."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:44:39.777Z",
"text": "Exploring Parameter Variations\n\nI'm currently investigating the impact of different `life_days` values on model performance, specifically testing integers between 8.0 and 12.0. Concurrently, I'm evaluating alternative power transformations beyond `log1p`, like `log1p(c*X)` with varying `c` and `power(X, p)` with `p` as 0.4 or 0.5."
},
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"timestamp": "2026-09-29T01:44:39.777Z",
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"input": {
"description": "Test fine half-lives and power transformations",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef run_test(half_life_days, transform_type, param, n_comp=64):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, 2.0, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n if transform_type == 'log1p':\n X.data = np.log1p(param * X.data)\n elif transform_type == 'power':\n X.data = np.power(X.data, param)\n \n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor hl in [8.0, 9.0, 10.0, 11.0, 12.0]:\n ndcg = run_test(hl, 'log1p', 1.0, 64)\n print(f'hl={hl:4.1f}, log1p(1.0) -> NDCG@10 = {ndcg:.6f}')\n\nfor p in [0.3, 0.4, 0.5, 0.6]:\n ndcg = run_test(9.0, 'power', p, 64)\n print(f'hl=9.0, power({p:.1f}) -> NDCG@10 = {ndcg:.6f}')\n\""
},
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"id": "run_shell_command__call_156118",
"name": "run_shell_command",
"args": {
"description": "Test fine half-lives and power transformations",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef run_test(half_life_days, transform_type, param, n_comp=64):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, 2.0, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n if transform_type == 'log1p':\n X.data = np.log1p(param * X.data)\n elif transform_type == 'power':\n X.data = np.power(X.data, param)\n \n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor hl in [8.0, 9.0, 10.0, 11.0, 12.0]:\n ndcg = run_test(hl, 'log1p', 1.0, 64)\n print(f'hl={hl:4.1f}, log1p(1.0) -> NDCG@10 = {ndcg:.6f}')\n\nfor p in [0.3, 0.4, 0.5, 0.6]:\n ndcg = run_test(9.0, 'power', p, 64)\n print(f'hl=9.0, power({p:.1f}) -> NDCG@10 = {ndcg:.6f}')\n\""
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"name": "run_shell_command",
"response": {
"output": "\nOutput: hl= 8.0, log1p(1.0) -> NDCG@10 = 0.039347\nhl= 9.0, log1p(1.0) -> NDCG@10 = 0.039269\nhl=10.0, log1p(1.0) -> NDCG@10 = 0.039379\nhl=11.0, log1p(1.0) -> NDCG@10 = 0.038408\nhl=12.0, log1p(1.0) -> NDCG@10 = 0.037942\nhl=9.0, power(0.3) -> NDCG@10 = 0.037865\nhl=9.0, power(0.4) -> NDCG@10 = 0.038516\nhl=9.0, power(0.5) -> NDCG@10 = 0.039264\nhl=9.0, power(0.6) -> NDCG@10 = 0.037893\nProcess Group PGID: 2637\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:45:52.377Z",
"resultDisplay": "hl= 8.0, log1p(1.0) -> NDCG@10 = 0.039347\nhl= 9.0, log1p(1.0) -> NDCG@10 = 0.039269\nhl=10.0, log1p(1.0) -> NDCG@10 = 0.039379\nhl=11.0, log1p(1.0) -> NDCG@10 = 0.038408\nhl=12.0, log1p(1.0) -> NDCG@10 = 0.037942\nhl=9.0, power(0.3) -> NDCG@10 = 0.037865\nhl=9.0, power(0.4) -> NDCG@10 = 0.038516\nhl=9.0, power(0.5) -> NDCG@10 = 0.039264\nhl=9.0, power(0.6) -> NDCG@10 = 0.037893",
"description": "Test fine half-lives and power transformations",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:45:52.379Z",
"content": "\nOutput: hl= 8.0, log1p(1.0) -> NDCG@10 = 0.039347\nhl= 9.0, log1p(1.0) -> NDCG@10 = 0.039269\nhl=10.0, log1p(1.0) -> NDCG@10 = 0.039379\nhl=11.0, log1p(1.0) -> NDCG@10 = 0.038408\nhl=12.0, log1p(1.0) -> NDCG@10 = 0.037942\nhl=9.0, power(0.3) -> NDCG@10 = 0.037865\nhl=9.0, power(0.4) -> NDCG@10 = 0.038516\nhl=9.0, power(0.5) -> NDCG@10 = 0.039264\nhl=9.0, power(0.6) -> NDCG@10 = 0.037893\nProcess Group PGID: 2637\n",
"isError": false
},
"responseMessageId": "2aab6651-d64e-4713-8d86-49c0b010ea6d"
}
]
},
{
"id": 57,
"role": "agent",
"timestamp": "2026-09-29T01:48:41.837Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:48:41.837Z",
"text": "Exploring Unlikes/Undislikes\n\nI'm currently investigating the impact of unlikes and undislikes. My initial thought was to directly add these, but I've paused to confirm if `validation_preference_state.parquet` already accounts for them, as the \"active explicit\" label suggests."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:48:41.837Z",
"callId": "run_shell_command__call_298371",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef test_weights(org_listen_bonus, org_like_w, pref_like_w):\n tau = 10.0 * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_listen_bonus, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, org_like_w, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), pref_like_w), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=64, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor org_b in [1.0, 2.0, 3.0, 4.0]:\n for org_l in [6.0, 10.0]:\n for pref_l in [3.0, 5.0, 8.0]:\n ndcg = test_weights(org_b, org_l, pref_l)\n print(f'org_b={org_b:.1f}, org_l={org_l:.1f}, pref_l={pref_l:.1f} -> NDCG@10 = {ndcg:.6f}')\n\"",
"description": "Test interaction event weights grid"
},
"recorded": {
"id": "run_shell_command__call_298371",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef test_weights(org_listen_bonus, org_like_w, pref_like_w):\n tau = 10.0 * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_listen_bonus, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, org_like_w, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), pref_like_w), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=64, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor org_b in [1.0, 2.0, 3.0, 4.0]:\n for org_l in [6.0, 10.0]:\n for pref_l in [3.0, 5.0, 8.0]:\n ndcg = test_weights(org_b, org_l, pref_l)\n print(f'org_b={org_b:.1f}, org_l={org_l:.1f}, pref_l={pref_l:.1f} -> NDCG@10 = {ndcg:.6f}')\n\"",
"description": "Test interaction event weights grid"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_298371",
"name": "run_shell_command",
"response": {
"output": "\nOutput: org_b=1.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038661\norg_b=1.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038202\norg_b=1.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.036613\norg_b=1.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038810\norg_b=1.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038652\norg_b=1.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.036688\norg_b=2.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038739\norg_b=2.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.039379\norg_b=2.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.037763\norg_b=2.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038921\norg_b=2.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.039163\norg_b=2.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.037839\norg_b=3.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038768\norg_b=3.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038589\norg_b=3.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.038598\norg_b=3.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038939\norg_b=3.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038756\norg_b=3.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.038819\norg_b=4.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.039789\norg_b=4.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038557\norg_b=4.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.039228\norg_b=4.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.039756\norg_b=4.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038666\norg_b=4.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.039095\nProcess Group PGID: 2666\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:51:39.983Z",
"resultDisplay": "org_b=1.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038661\norg_b=1.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038202\norg_b=1.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.036613\norg_b=1.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038810\norg_b=1.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038652\norg_b=1.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.036688\norg_b=2.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038739\norg_b=2.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.039379\norg_b=2.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.037763\norg_b=2.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038921\norg_b=2.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.039163\norg_b=2.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.037839\norg_b=3.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038768\norg_b=3.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038589\norg_b=3.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.038598\norg_b=3.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038939\norg_b=3.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038756\norg_b=3.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.038819\norg_b=4.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.039789\norg_b=4.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038557\norg_b=4.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.039228\norg_b=4.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.039756\norg_b=4.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038666\norg_b=4.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.039095",
"description": "Test interaction event weights grid",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:51:39.985Z",
"content": "\nOutput: org_b=1.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038661\norg_b=1.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038202\norg_b=1.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.036613\norg_b=1.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038810\norg_b=1.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038652\norg_b=1.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.036688\norg_b=2.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038739\norg_b=2.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.039379\norg_b=2.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.037763\norg_b=2.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038921\norg_b=2.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.039163\norg_b=2.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.037839\norg_b=3.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038768\norg_b=3.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038589\norg_b=3.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.038598\norg_b=3.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038939\norg_b=3.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038756\norg_b=3.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.038819\norg_b=4.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.039789\norg_b=4.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038557\norg_b=4.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.039228\norg_b=4.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.039756\norg_b=4.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038666\norg_b=4.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.039095\nProcess Group PGID: 2666\n",
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},
{
"id": 58,
"role": "agent",
"timestamp": "2026-09-29T01:51:51.064Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:51:51.064Z",
"text": "Testing Organic Boost\n\nMy focus is on optimizing the 'org_b' parameter to maximize NDCG@10. I've observed that increasing it from 4.0 to 5.0 and then to 6.0 shows promise. My next step is to thoroughly evaluate these higher values, as organic listens are the critical metric."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:51:51.064Z",
"callId": "run_shell_command__call_24098",
"name": "run_shell_command",
"input": {
"description": "Test higher organic listen bonus",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef test_weights(org_listen_bonus):\n tau = 10.0 * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_listen_bonus, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 3.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=64, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor ob in [3.0, 4.0, 5.0, 6.0, 8.0]:\n ndcg = test_weights(ob)\n print(f'org_listen_bonus={ob:.1f} -> NDCG@10 = {ndcg:.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_24098",
"name": "run_shell_command",
"args": {
"description": "Test higher organic listen bonus",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef test_weights(org_listen_bonus):\n tau = 10.0 * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_listen_bonus, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 3.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=64, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor ob in [3.0, 4.0, 5.0, 6.0, 8.0]:\n ndcg = test_weights(ob)\n print(f'org_listen_bonus={ob:.1f} -> NDCG@10 = {ndcg:.6f}')\n\""
},
"result": [
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"id": "run_shell_command__call_24098",
"name": "run_shell_command",
"response": {
"output": "\nOutput: org_listen_bonus=3.0 -> NDCG@10 = 0.038768\norg_listen_bonus=4.0 -> NDCG@10 = 0.039789\norg_listen_bonus=5.0 -> NDCG@10 = 0.039753\norg_listen_bonus=6.0 -> NDCG@10 = 0.039323\norg_listen_bonus=8.0 -> NDCG@10 = 0.039805\nProcess Group PGID: 2695\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:52:35.738Z",
"resultDisplay": "org_listen_bonus=3.0 -> NDCG@10 = 0.038768\norg_listen_bonus=4.0 -> NDCG@10 = 0.039789\norg_listen_bonus=5.0 -> NDCG@10 = 0.039753\norg_listen_bonus=6.0 -> NDCG@10 = 0.039323\norg_listen_bonus=8.0 -> NDCG@10 = 0.039805",
"description": "Test higher organic listen bonus",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:52:35.739Z",
"content": "\nOutput: org_listen_bonus=3.0 -> NDCG@10 = 0.038768\norg_listen_bonus=4.0 -> NDCG@10 = 0.039789\norg_listen_bonus=5.0 -> NDCG@10 = 0.039753\norg_listen_bonus=6.0 -> NDCG@10 = 0.039323\norg_listen_bonus=8.0 -> NDCG@10 = 0.039805\nProcess Group PGID: 2695\n",
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