{
"schemaVersion": 1,
"taskId": "yambda-discovery-ranking-v01",
"attemptId": "gemini-3-8-flash-high-basic-fixed-r04-d237f45e",
"taskChecksum": "06d023160277225da99fbf82a38920164f960785fd1fd9068f437996f389d90f",
"agentVersion": "0.61.0",
"model": "gemini-3.8-flash",
"sessionId": "8e1766b7-11bd-455d-94c6-e1a1404b2157",
"steps": [
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"timestamp": "2026-09-29T04:20:15.791Z",
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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 - /task\n- **Directory Structure:**\n\nShowing up to 200 items (files + folders).\n\n/app/\n└───solution/\nShowing up to 200 items (files + folders).\n\n/task/\n├───evaluate_public.py\n├───ranking_evaluation.py\n├───requirements.txt\n└───data/\n ├───candidate_catalog.parquet\n ├───target_users.parquet\n ├───test_eligible_candidates.npz\n ├───test_preference_state.parquet\n ├───train_dislikes.parquet\n ├───train_likes.parquet\n ├───train_listens.parquet\n ├───train_undislikes.parquet\n ├───train_unlikes.parquet\n ├───val_dislikes.parquet\n ├───val_likes.parquet\n ├───val_listens.parquet\n ├───val_undislikes.parquet\n ├───val_unlikes.parquet\n ├───validation_eligible_candidates.npz\n └───validation_preference_state.parquet\n\n\n"
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"timestamp": "2026-09-29T04:20:15.835Z",
"blocks": [
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"timestamp": "2026-09-29T04:20:15.835Z",
"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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"timestamp": "2026-09-29T04:20:21.266Z",
"text": "Reviewing Initial Requirements\n\nI'm currently deep-diving into the core prompt, meticulously examining each mandate and instruction. My focus is on fully grasping the nuances of building this music-discovery ranker for the \"Made for You\" shelf."
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"summary": "Starting exploration of the music discovery ranking task. We will inspect the task files, dataset shapes, evaluation scripts, and available compute resources (CPU/GPU, RAM, libraries).",
"title": "Environment and Dataset Exploration"
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"title": "Environment and Dataset Exploration"
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"output": "Current topic: \"Environment and Dataset Exploration\"\nTopic summary: Starting exploration of the music discovery ranking task. We will inspect the task files, dataset shapes, evaluation scripts, and available compute resources (CPU/GPU, RAM, libraries).\n\nStrategic Intent: Explore the environment, dataset schemas, evaluation script, and baseline setup."
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"timestamp": "2026-09-29T04:20:21.308Z",
"resultDisplay": "## 📂 Topic: **Environment and Dataset Exploration**\n\n**Summary:**\nStarting exploration of the music discovery ranking task. We will inspect the task files, dataset shapes, evaluation scripts, and available compute resources (CPU/GPU, RAM, libraries).\n\n> [!STRATEGY]\n> **Intent:** Explore the environment, dataset schemas, evaluation script, and baseline setup.",
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"timestamp": "2026-09-29T04:20:21.310Z",
"content": "Current topic: \"Environment and Dataset Exploration\"\nTopic summary: Starting exploration of the music discovery ranking task. We will inspect the task files, dataset shapes, evaluation scripts, and available compute resources (CPU/GPU, RAM, libraries).\n\nStrategic Intent: Explore the environment, dataset schemas, evaluation script, and baseline setup.",
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"output": "\nOutput: /usr/bin/bash: line 4: nvidia-smi: command not found\nArchitecture: 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\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 29 04:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 29 04:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 04:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 04:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 04:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 04:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 04:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 04:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 04:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 29 04:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 04:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 04:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 04:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 29 04:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 04:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 04:14 validation_preference_state.parquet\nProcess Group PGID: 1612\n"
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"status": "success",
"timestamp": "2026-09-29T04:20:21.308Z",
"resultDisplay": "/usr/bin/bash: line 4: nvidia-smi: command not found\nArchitecture: 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\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 29 04:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 29 04:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 04:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 04:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 04:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 04:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 04:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 04:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 04:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 29 04:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 04:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 04:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 04:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 29 04:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 04:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 04:14 validation_preference_state.parquet",
"description": "nvidia-smi || lscpu; free -h; ls -lh /task/data",
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},
"result": {
"timestamp": "2026-09-29T04:20:21.310Z",
"content": "\nOutput: /usr/bin/bash: line 4: nvidia-smi: command not found\nArchitecture: 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\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 29 04:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 29 04:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 04:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 04:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 04:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 04:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 04:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 04:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 04:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 29 04:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 04:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 04:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 04:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 29 04:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 04:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 04:14 validation_preference_state.parquet\nProcess Group PGID: 1612\n",
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"callId": "read_file__call_188089",
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},
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"output": "#!/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()\n"
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"content": "#!/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()\n",
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"output": "\"\"\"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 }\n"
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"content": "\"\"\"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 }\n",
"isError": false
},
"responseMessageId": "e135c8a5-2eb0-45ed-a0cb-17c09ea04a3d"
}
]
},
{
"id": 4,
"role": "agent",
"timestamp": "2026-09-29T04:20:32.103Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:20:32.103Z",
"callId": "run_shell_command__call_148647",
"name": "run_shell_command",
"input": {
"command": "cat /task/requirements.txt; python -c \"import torch, lightgbm, implicit, polars, sklearn; print('Imports ok!')\"",
"description": "Check task requirements and installed packages"
},
"recorded": {
"id": "run_shell_command__call_148647",
"name": "run_shell_command",
"args": {
"command": "cat /task/requirements.txt; python -c \"import torch, lightgbm, implicit, polars, sklearn; print('Imports ok!')\"",
"description": "Check task requirements and installed packages"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_148647",
"name": "run_shell_command",
"response": {
"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\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'torch'\nExit Code: 1\nProcess Group PGID: 1618\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:20:32.135Z",
"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\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'torch'",
"description": "cat /task/requirements.txt; python -c \"import torch, lightgbm, implicit, polars, sklearn; print('Imports ok!')\"",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:20:32.136Z",
"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\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'torch'\nExit Code: 1\nProcess Group PGID: 1618\n",
"isError": false
},
"responseMessageId": "84a1bac7-3848-4fc8-ae3a-06a89d9e0b0c"
}
]
},
{
"id": 5,
"role": "agent",
"timestamp": "2026-09-29T04:20:38.317Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:20:38.317Z",
"text": "Reviewing Installed Packages\n\nI've noted the specific versions of libraries like numpy, pandas, and scikit-learn that are currently available. It's also crucial that PyTorch and LightGBM are absent from this setup, as per the task guidelines."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:20:38.317Z",
"callId": "run_shell_command__call_226593",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport psutil, os\nmem = psutil.virtual_memory() if hasattr(psutil, 'virtual_memory') else None\nprint('RAM info:')\nwith open('/proc/meminfo') as f:\n for line in f:\n if 'MemTotal' in line or 'MemAvailable' in line or 'MemFree' in line:\n print(line.strip())\n\"",
"description": "Check system memory via /proc/meminfo"
},
"recorded": {
"id": "run_shell_command__call_226593",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport psutil, os\nmem = psutil.virtual_memory() if hasattr(psutil, 'virtual_memory') else None\nprint('RAM info:')\nwith open('/proc/meminfo') as f:\n for line in f:\n if 'MemTotal' in line or 'MemAvailable' in line or 'MemFree' in line:\n print(line.strip())\n\"",
"description": "Check system memory via /proc/meminfo"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_226593",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'psutil'\nExit Code: 1\nProcess Group PGID: 1622\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:20:38.345Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'psutil'",
"description": "Check system memory via /proc/meminfo",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:20:38.346Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'psutil'\nExit Code: 1\nProcess Group PGID: 1622\n",
"isError": false
},
"responseMessageId": "34412ea8-91c8-473c-8163-1f547a029015"
}
]
},
{
"id": 6,
"role": "agent",
"timestamp": "2026-09-29T04:20:40.344Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:20:40.344Z",
"callId": "run_shell_command__call_344892",
"name": "run_shell_command",
"input": {
"description": "Check available memory from /proc/meminfo",
"command": "grep -E 'MemTotal|MemAvailable|MemFree' /proc/meminfo"
},
"recorded": {
"id": "run_shell_command__call_344892",
"name": "run_shell_command",
"args": {
"description": "Check available memory from /proc/meminfo",
"command": "grep -E 'MemTotal|MemAvailable|MemFree' /proc/meminfo"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_344892",
"name": "run_shell_command",
"response": {
"output": "\nOutput: MemTotal: 15988968 kB\nMemFree: 5513296 kB\nMemAvailable: 14709032 kB\nProcess Group PGID: 1625\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:20:40.359Z",
"resultDisplay": "MemTotal: 15988968 kB\nMemFree: 5513296 kB\nMemAvailable: 14709032 kB",
"description": "grep -E 'MemTotal|MemAvailable|MemFree' /proc/meminfo",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:20:40.360Z",
"content": "\nOutput: MemTotal: 15988968 kB\nMemFree: 5513296 kB\nMemAvailable: 14709032 kB\nProcess Group PGID: 1625\n",
"isError": false
},
"responseMessageId": "a2ca218f-5b42-401c-8f61-849425f66634"
}
]
},
{
"id": 7,
"role": "agent",
"timestamp": "2026-09-29T04:20:47.038Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:20:47.038Z",
"text": "Inspecting Data Files\n\nI'm currently examining the contents of the `/task/data` directory. My focus is on understanding the structure and characteristics of these files, specifically looking at row and column counts, unique user and item identifiers, and timestamp ranges. I'll be using Python for this analysis to quickly gather key metrics."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:20:47.038Z",
"callId": "run_shell_command__call_260706",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nprint('=== Candidate Catalog ===')\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nprint('cat shape:', cat.shape)\nprint('cat cols:', cat.columns.tolist())\nprint(cat.head(3))\n\nprint('\\n=== Target Users ===')\nusers = pd.read_parquet(data_dir / 'target_users.parquet')\nprint('users shape:', users.shape)\nprint('users cols:', users.columns.tolist())\nprint(users.head(3))\n\nprint('\\n=== Validation Candidates ===')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nprint('val_cand keys:', list(val_cand.keys()))\nprint('uids:', len(val_cand['uids']), 'item_ids:', len(val_cand['item_ids']), 'packed shape:', val_cand['packed_eligible'].shape)\n\nprint('\\n=== Test Candidates ===')\ntest_cand = np.load(data_dir / 'test_eligible_candidates.npz')\nprint('test_cand keys:', list(test_cand.keys()))\nprint('uids:', len(test_cand['uids']), 'item_ids:', len(test_cand['item_ids']), 'packed shape:', test_cand['packed_eligible'].shape)\n\nprint('\\n=== Preference states ===')\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\nprint('val_pref shape:', val_pref.shape, val_pref.columns.tolist(), val_pref['state'].value_counts().to_dict() if 'state' in val_pref else '')\nprint(val_pref.head(3))\n\ntest_pref = pd.read_parquet(data_dir / 'test_preference_state.parquet')\nprint('test_pref shape:', test_pref.shape, test_pref.columns.tolist(), test_pref['state'].value_counts().to_dict() if 'state' in test_pref else '')\n\nfor prefix in ['train', 'val']:\n for f in ['listens', 'likes', 'dislikes', 'unlikes', 'undislikes']:\n p = data_dir / f'{prefix}_{f}.parquet'\n if p.exists():\n df = pd.read_parquet(p)\n print(f'{prefix}_{f}: rows={len(df)}, min_ts={df.timestamp.min() if \\\"timestamp\\\" in df else None}, max_ts={df.timestamp.max() if \\\"timestamp\\\" in df else None}')\n\"",
"description": "Inspect dataset files and shapes"
},
"recorded": {
"id": "run_shell_command__call_260706",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nprint('=== Candidate Catalog ===')\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nprint('cat shape:', cat.shape)\nprint('cat cols:', cat.columns.tolist())\nprint(cat.head(3))\n\nprint('\\n=== Target Users ===')\nusers = pd.read_parquet(data_dir / 'target_users.parquet')\nprint('users shape:', users.shape)\nprint('users cols:', users.columns.tolist())\nprint(users.head(3))\n\nprint('\\n=== Validation Candidates ===')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nprint('val_cand keys:', list(val_cand.keys()))\nprint('uids:', len(val_cand['uids']), 'item_ids:', len(val_cand['item_ids']), 'packed shape:', val_cand['packed_eligible'].shape)\n\nprint('\\n=== Test Candidates ===')\ntest_cand = np.load(data_dir / 'test_eligible_candidates.npz')\nprint('test_cand keys:', list(test_cand.keys()))\nprint('uids:', len(test_cand['uids']), 'item_ids:', len(test_cand['item_ids']), 'packed shape:', test_cand['packed_eligible'].shape)\n\nprint('\\n=== Preference states ===')\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\nprint('val_pref shape:', val_pref.shape, val_pref.columns.tolist(), val_pref['state'].value_counts().to_dict() if 'state' in val_pref else '')\nprint(val_pref.head(3))\n\ntest_pref = pd.read_parquet(data_dir / 'test_preference_state.parquet')\nprint('test_pref shape:', test_pref.shape, test_pref.columns.tolist(), test_pref['state'].value_counts().to_dict() if 'state' in test_pref else '')\n\nfor prefix in ['train', 'val']:\n for f in ['listens', 'likes', 'dislikes', 'unlikes', 'undislikes']:\n p = data_dir / f'{prefix}_{f}.parquet'\n if p.exists():\n df = pd.read_parquet(p)\n print(f'{prefix}_{f}: rows={len(df)}, min_ts={df.timestamp.min() if \\\"timestamp\\\" in df else None}, max_ts={df.timestamp.max() if \\\"timestamp\\\" in df else None}')\n\"",
"description": "Inspect dataset files and shapes"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_260706",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Candidate Catalog ===\ncat shape: (30000, 4)\ncat cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\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\n\n=== Target Users ===\nusers shape: (8456, 3)\nusers cols: ['uid', 'recent_positive_listens', 'history_positive_listens']\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n\n=== Validation Candidates ===\nval_cand keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456 item_ids: 30000 packed shape: (8456, 3750)\n\n=== Test Candidates ===\ntest_cand keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456 item_ids: 30000 packed shape: (8456, 3750)\n\n=== Preference states ===\nval_pref shape: (469124, 3) ['uid', 'item_id', 'state'] {'liked': 418435, 'disliked': 50689}\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\ntest_pref shape: (484776, 3) ['uid', 'item_id', 'state'] {'liked': 432659, 'disliked': 52117}\ntrain_listens: rows=43484159, min_ts=0, max_ts=24786795\ntrain_likes: rows=814831, min_ts=80, max_ts=24786790\ntrain_dislikes: rows=101075, min_ts=75, max_ts=24786580\ntrain_unlikes: rows=292665, min_ts=250, max_ts=24786570\ntrain_undislikes: rows=19960, min_ts=15950, max_ts=24785395\nval_listens: rows=1501697, min_ts=24788600, max_ts=25393395\nval_likes: rows=37795, min_ts=24788815, max_ts=25393390\nval_dislikes: rows=3563, min_ts=24789920, max_ts=25393280\nval_unlikes: rows=9346, min_ts=24789005, max_ts=25393385\nval_undislikes: rows=606, min_ts=24810715, max_ts=25393035\nProcess Group PGID: 1628\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:20:48.859Z",
"resultDisplay": "=== Candidate Catalog ===\ncat shape: (30000, 4)\ncat cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\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\n\n=== Target Users ===\nusers shape: (8456, 3)\nusers cols: ['uid', 'recent_positive_listens', 'history_positive_listens']\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n\n=== Validation Candidates ===\nval_cand keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456 item_ids: 30000 packed shape: (8456, 3750)\n\n=== Test Candidates ===\ntest_cand keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456 item_ids: 30000 packed shape: (8456, 3750)\n\n=== Preference states ===\nval_pref shape: (469124, 3) ['uid', 'item_id', 'state'] {'liked': 418435, 'disliked': 50689}\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\ntest_pref shape: (484776, 3) ['uid', 'item_id', 'state'] {'liked': 432659, 'disliked': 52117}\ntrain_listens: rows=43484159, min_ts=0, max_ts=24786795\ntrain_likes: rows=814831, min_ts=80, max_ts=24786790\ntrain_dislikes: rows=101075, min_ts=75, max_ts=24786580\ntrain_unlikes: rows=292665, min_ts=250, max_ts=24786570\ntrain_undislikes: rows=19960, min_ts=15950, max_ts=24785395\nval_listens: rows=1501697, min_ts=24788600, max_ts=25393395\nval_likes: rows=37795, min_ts=24788815, max_ts=25393390\nval_dislikes: rows=3563, min_ts=24789920, max_ts=25393280\nval_unlikes: rows=9346, min_ts=24789005, max_ts=25393385\nval_undislikes: rows=606, min_ts=24810715, max_ts=25393035",
"description": "Inspect dataset files and shapes",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:20:48.859Z",
"content": "\nOutput: === Candidate Catalog ===\ncat shape: (30000, 4)\ncat cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\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\n\n=== Target Users ===\nusers shape: (8456, 3)\nusers cols: ['uid', 'recent_positive_listens', 'history_positive_listens']\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n\n=== Validation Candidates ===\nval_cand keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456 item_ids: 30000 packed shape: (8456, 3750)\n\n=== Test Candidates ===\ntest_cand keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456 item_ids: 30000 packed shape: (8456, 3750)\n\n=== Preference states ===\nval_pref shape: (469124, 3) ['uid', 'item_id', 'state'] {'liked': 418435, 'disliked': 50689}\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\ntest_pref shape: (484776, 3) ['uid', 'item_id', 'state'] {'liked': 432659, 'disliked': 52117}\ntrain_listens: rows=43484159, min_ts=0, max_ts=24786795\ntrain_likes: rows=814831, min_ts=80, max_ts=24786790\ntrain_dislikes: rows=101075, min_ts=75, max_ts=24786580\ntrain_unlikes: rows=292665, min_ts=250, max_ts=24786570\ntrain_undislikes: rows=19960, min_ts=15950, max_ts=24785395\nval_listens: rows=1501697, min_ts=24788600, max_ts=25393395\nval_likes: rows=37795, min_ts=24788815, max_ts=25393390\nval_dislikes: rows=3563, min_ts=24789920, max_ts=25393280\nval_unlikes: rows=9346, min_ts=24789005, max_ts=25393385\nval_undislikes: rows=606, min_ts=24810715, max_ts=25393035\nProcess Group PGID: 1628\n",
"isError": false
},
"responseMessageId": "2db10df4-4f39-4cc1-8b1e-5f43d87ae397"
}
]
},
{
"id": 8,
"role": "agent",
"timestamp": "2026-09-29T04:21:04.419Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:21:04.419Z",
"text": "Analyzing Candidate Data\n\nI've confirmed the candidate catalog contains exactly 30,000 items, aligning perfectly with the 30,000 candidates found in both the validation and test sets. I'm now moving on to examine the target users data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:04.419Z",
"callId": "run_shell_command__call_285319",
"name": "run_shell_command",
"input": {
"description": "Check eligible candidate counts per user",
"command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\npacked = val_cand['packed_eligible']\n# Count bits per user\n# np.unpackbits\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little')\nnum_eligible = unpacked.sum(axis=1)\nprint(f'Eligible items per user in val: min={num_eligible.min()}, mean={num_eligible.mean():.1f}, max={num_eligible.max()}')\n\n# Check test eligible\ntest_cand = np.load(data_dir / 'test_eligible_candidates.npz')\nunpacked_test = np.unpackbits(test_cand['packed_eligible'], axis=1, count=30000, bitorder='little')\nnum_eligible_test = unpacked_test.sum(axis=1)\nprint(f'Eligible items per user in test: min={num_eligible_test.min()}, mean={num_eligible_test.mean():.1f}, max={num_eligible_test.max()}')\n\""
},
"recorded": {
"id": "run_shell_command__call_285319",
"name": "run_shell_command",
"args": {
"description": "Check eligible candidate counts per user",
"command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\npacked = val_cand['packed_eligible']\n# Count bits per user\n# np.unpackbits\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little')\nnum_eligible = unpacked.sum(axis=1)\nprint(f'Eligible items per user in val: min={num_eligible.min()}, mean={num_eligible.mean():.1f}, max={num_eligible.max()}')\n\n# Check test eligible\ntest_cand = np.load(data_dir / 'test_eligible_candidates.npz')\nunpacked_test = np.unpackbits(test_cand['packed_eligible'], axis=1, count=30000, bitorder='little')\nnum_eligible_test = unpacked_test.sum(axis=1)\nprint(f'Eligible items per user in test: min={num_eligible_test.min()}, mean={num_eligible_test.mean():.1f}, max={num_eligible_test.max()}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_285319",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Eligible items per user in val: min=24657, mean=29067.3, max=30000\nEligible items per user in test: min=24552, mean=29041.3, max=30000\nProcess Group PGID: 1648\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:21:05.477Z",
"resultDisplay": "Eligible items per user in val: min=24657, mean=29067.3, max=30000\nEligible items per user in test: min=24552, mean=29041.3, max=30000",
"description": "Check eligible candidate counts per user",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:21:05.478Z",
"content": "\nOutput: Eligible items per user in val: min=24657, mean=29067.3, max=30000\nEligible items per user in test: min=24552, mean=29041.3, max=30000\nProcess Group PGID: 1648\n",
"isError": false
},
"responseMessageId": "d7cffe61-4a4d-4c7d-ac0c-1535d7915857"
}
]
},
{
"id": 9,
"role": "agent",
"timestamp": "2026-09-29T04:21:12.222Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:21:12.222Z",
"text": "Analyzing Eligibility\n\nI've confirmed that the vast majority of items, around 30,000, are eligible for each user, with an average of 29,067. The few exclusions seem to be related to past user interactions, like listening history or explicit feedback. I'm now diving into the specific reasons for these ineligibilities."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:21:12.222Z",
"text": "Refining Filtering Logic\n\nI'm now focusing on how the system filters out items. It appears our eligibility service handles the broad exclusions, like previously consumed or disliked tracks. My current task is to understand the precise mechanics behind these exclusions, as shown in the `validation_truth` function, to ensure optimal ranking of the top 100 eligible tracks per user."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:12.222Z",
"callId": "run_shell_command__call_418637",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\nfrom evaluate_public import validation_truth\ntruth = validation_truth(eligibility, targets)\nprint(f'Users with truth: {len(truth)} / {len(targets)}')\nitems_per_user = [len(v) for v in truth.values()]\nimport numpy as np\nprint(f'Truth items per user: min={np.min(items_per_user)}, mean={np.mean(items_per_user):.2f}, median={np.median(items_per_user)}, max={np.max(items_per_user)}')\ngrades = [g for v in truth.values() for g in v.values()]\nprint(f'Total positive interactions: {len(grades)}, grades count: {pd.Series(grades).value_counts().to_dict()}')\n\"",
"description": "Inspect validation ground truth statistics"
},
"recorded": {
"id": "run_shell_command__call_418637",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\nfrom evaluate_public import validation_truth\ntruth = validation_truth(eligibility, targets)\nprint(f'Users with truth: {len(truth)} / {len(targets)}')\nitems_per_user = [len(v) for v in truth.values()]\nimport numpy as np\nprint(f'Truth items per user: min={np.min(items_per_user)}, mean={np.mean(items_per_user):.2f}, median={np.median(items_per_user)}, max={np.max(items_per_user)}')\ngrades = [g for v in truth.values() for g in v.values()]\nprint(f'Total positive interactions: {len(grades)}, grades count: {pd.Series(grades).value_counts().to_dict()}')\n\"",
"description": "Inspect validation ground truth statistics"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_418637",
"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: 1659\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:21:12.888Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 4, in \nModuleNotFoundError: No module named 'ranking_evaluation'",
"description": "Inspect validation ground truth statistics",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:21:12.889Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 4, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1659\n",
"isError": false
},
"responseMessageId": "54e96c66-8588-4a9c-a1c6-078106d1e771"
}
]
},
{
"id": 10,
"role": "agent",
"timestamp": "2026-09-29T04:21:19.958Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:19.958Z",
"callId": "run_shell_command__call_418727",
"name": "run_shell_command",
"input": {
"description": "Inspect validation ground truth with PYTHONPATH set",
"command": "PYTHONPATH=/task python -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\ntruth = validation_truth(eligibility, targets)\nprint(f'Users with truth: {len(truth)} / {len(targets)}')\nitems_per_user = [len(v) for v in truth.values()]\nimport numpy as np\nprint(f'Truth items per user: min={np.min(items_per_user)}, mean={np.mean(items_per_user):.2f}, median={np.median(items_per_user)}, max={np.max(items_per_user)}')\ngrades = [g for v in truth.values() for g in v.values()]\nprint(f'Total positive interactions: {len(grades)}, grades count: {pd.Series(grades).value_counts().to_dict()}')\n\""
},
"recorded": {
"id": "run_shell_command__call_418727",
"name": "run_shell_command",
"args": {
"description": "Inspect validation ground truth with PYTHONPATH set",
"command": "PYTHONPATH=/task python -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\ntruth = validation_truth(eligibility, targets)\nprint(f'Users with truth: {len(truth)} / {len(targets)}')\nitems_per_user = [len(v) for v in truth.values()]\nimport numpy as np\nprint(f'Truth items per user: min={np.min(items_per_user)}, mean={np.mean(items_per_user):.2f}, median={np.median(items_per_user)}, max={np.max(items_per_user)}')\ngrades = [g for v in truth.values() for g in v.values()]\nprint(f'Total positive interactions: {len(grades)}, grades count: {pd.Series(grades).value_counts().to_dict()}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_418727",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Users with truth: 4263 / 8456\nTruth items per user: min=1, mean=8.86, median=4.0, max=442\nTotal positive interactions: 37769, grades count: {1: 33352, 3: 4417}\nProcess Group PGID: 1670\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:21:21.231Z",
"resultDisplay": "Users with truth: 4263 / 8456\nTruth items per user: min=1, mean=8.86, median=4.0, max=442\nTotal positive interactions: 37769, grades count: {1: 33352, 3: 4417}",
"description": "Inspect validation ground truth with PYTHONPATH set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:21:21.232Z",
"content": "\nOutput: Users with truth: 4263 / 8456\nTruth items per user: min=1, mean=8.86, median=4.0, max=442\nTotal positive interactions: 37769, grades count: {1: 33352, 3: 4417}\nProcess Group PGID: 1670\n",
"isError": false
},
"responseMessageId": "c477719e-662d-4fad-9dee-6fa5a80d8e0e"
}
]
},
{
"id": 11,
"role": "agent",
"timestamp": "2026-09-29T04:21:27.218Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:21:27.218Z",
"text": "Checking Target Users\n\nI'm now focusing on the training data, specifically `train_listens`. My next step is to determine how many of these listens originate from the identified `target_users`. This will provide a crucial measure of overlap."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:27.218Z",
"callId": "run_shell_command__call_169548",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport time\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\nprint(f'Read pq table in {time.time()-t0:.2f}s, num rows = {len(table)}')\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\nuids = table['uid'].to_numpy()\nmask = np.isin(uids, list(targets)) if 'np' in dir() else None\nimport numpy as np\nmask = np.isin(uids, list(targets))\nprint(f'Rows belonging to target users: {mask.sum()} / {len(uids)} ({mask.sum()/len(uids):.1%})')\n\"",
"description": "Benchmark reading train_listens and check target users overlap"
},
"recorded": {
"id": "run_shell_command__call_169548",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport time\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\nprint(f'Read pq table in {time.time()-t0:.2f}s, num rows = {len(table)}')\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\nuids = table['uid'].to_numpy()\nmask = np.isin(uids, list(targets)) if 'np' in dir() else None\nimport numpy as np\nmask = np.isin(uids, list(targets))\nprint(f'Rows belonging to target users: {mask.sum()} / {len(uids)} ({mask.sum()/len(uids):.1%})')\n\"",
"description": "Benchmark reading train_listens and check target users overlap"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_169548",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Read pq table in 0.39s, num rows = 43484159\nRows belonging to target users: 43222297 / 43484159 (99.4%)\nProcess Group PGID: 1690\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:21:28.676Z",
"resultDisplay": "Read pq table in 0.39s, num rows = 43484159\nRows belonging to target users: 43222297 / 43484159 (99.4%)",
"description": "Benchmark reading train_listens and check target users overlap",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:21:28.677Z",
"content": "\nOutput: Read pq table in 0.39s, num rows = 43484159\nRows belonging to target users: 43222297 / 43484159 (99.4%)\nProcess Group PGID: 1690\n",
"isError": false
},
"responseMessageId": "07e57c0e-4b1f-427f-b0fc-3675a77794e0"
}
]
},
{
"id": 12,
"role": "agent",
"timestamp": "2026-09-29T04:21:32.370Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:21:32.370Z",
"text": "Analyzing Data Speed\n\nI've been impressed by PyArrow's speed, reading millions of rows in fractions of a second. Now I'm examining memory usage and the relationship between unique items in `train_listens` and `candidate_catalog.parquet`. My next step is a deeper dive into `candidate_catalog.parquet` itself."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:32.370Z",
"callId": "run_shell_command__call_245251",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog item_ids:', len(catalog['item_id']), 'min:', catalog['item_id'].min(), 'max:', catalog['item_id'].max())\nprint(catalog.head())\n\ntest_cand = np.load('/task/data/test_eligible_candidates.npz')\nprint('Test candidate item_ids equals catalog item_ids?', np.array_equal(test_cand['item_ids'], catalog['item_id'].values))\nval_cand = np.load('/task/data/validation_eligible_candidates.npz')\nprint('Val candidate item_ids equals catalog item_ids?', np.array_equal(val_cand['item_ids'], catalog['item_id'].values))\n\"",
"description": "Verify catalog item IDs match candidate arrays exactly"
},
"recorded": {
"id": "run_shell_command__call_245251",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog item_ids:', len(catalog['item_id']), 'min:', catalog['item_id'].min(), 'max:', catalog['item_id'].max())\nprint(catalog.head())\n\ntest_cand = np.load('/task/data/test_eligible_candidates.npz')\nprint('Test candidate item_ids equals catalog item_ids?', np.array_equal(test_cand['item_ids'], catalog['item_id'].values))\nval_cand = np.load('/task/data/validation_eligible_candidates.npz')\nprint('Val candidate item_ids equals catalog item_ids?', np.array_equal(val_cand['item_ids'], catalog['item_id'].values))\n\"",
"description": "Verify catalog item IDs match candidate arrays exactly"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_245251",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog item_ids: 30000 min: 206 max: 9390294\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\nTest candidate item_ids equals catalog item_ids? True\nVal candidate item_ids equals catalog item_ids? True\nProcess Group PGID: 1712\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:21:33.110Z",
"resultDisplay": "Catalog item_ids: 30000 min: 206 max: 9390294\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\nTest candidate item_ids equals catalog item_ids? True\nVal candidate item_ids equals catalog item_ids? True",
"description": "Verify catalog item IDs match candidate arrays exactly",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:21:33.111Z",
"content": "\nOutput: Catalog item_ids: 30000 min: 206 max: 9390294\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\nTest candidate item_ids equals catalog item_ids? True\nVal candidate item_ids equals catalog item_ids? True\nProcess Group PGID: 1712\n",
"isError": false
},
"responseMessageId": "d08b4416-4e6d-4bf9-a385-5355ab3e7719"
}
]
},
{
"id": 13,
"role": "agent",
"timestamp": "2026-09-29T04:21:45.450Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:21:45.449Z",
"text": "Confirming Catalog Consistency\n\nI've confirmed that the candidate item IDs in both `validation_eligible_candidates.npz` and `test_eligible_candidates.npz` perfectly align with the `candidate_catalog.parquet` entries, in identical order. My next step is to verify the target user data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:45.450Z",
"callId": "run_shell_command__call_245443",
"name": "run_shell_command",
"input": {
"description": "Verify target users match candidate archive uids exactly",
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\n\nusers = pd.read_parquet('/task/data/target_users.parquet')\nval_cand = np.load('/task/data/validation_eligible_candidates.npz')\ntest_cand = np.load('/task/data/test_eligible_candidates.npz')\n\nprint('Val uids equals users uid?', np.array_equal(val_cand['uids'], users['uid'].values))\nprint('Test uids equals users uid?', np.array_equal(test_cand['uids'], users['uid'].values))\n\""
},
"recorded": {
"id": "run_shell_command__call_245443",
"name": "run_shell_command",
"args": {
"description": "Verify target users match candidate archive uids exactly",
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\n\nusers = pd.read_parquet('/task/data/target_users.parquet')\nval_cand = np.load('/task/data/validation_eligible_candidates.npz')\ntest_cand = np.load('/task/data/test_eligible_candidates.npz')\n\nprint('Val uids equals users uid?', np.array_equal(val_cand['uids'], users['uid'].values))\nprint('Test uids equals users uid?', np.array_equal(test_cand['uids'], users['uid'].values))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_245443",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Val uids equals users uid? True\nTest uids equals users uid? True\nProcess Group PGID: 1735\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:21:46.197Z",
"resultDisplay": "Val uids equals users uid? True\nTest uids equals users uid? True",
"description": "Verify target users match candidate archive uids exactly",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:21:46.198Z",
"content": "\nOutput: Val uids equals users uid? True\nTest uids equals users uid? True\nProcess Group PGID: 1735\n",
"isError": false
},
"responseMessageId": "dfe861ce-b607-4213-831b-286c7768f1ba"
}
]
},
{
"id": 14,
"role": "agent",
"timestamp": "2026-09-29T04:21:53.143Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:21:53.143Z",
"text": "Analyzing Data Structure\n\nI've confirmed the precise alignment of 8,456 target users across `target_users.parquet`, `validation_eligible_candidates.npz`, and `test_eligible_candidates.npz`. The candidate item count also matches at 30,000, maintaining the established ordering."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:53.143Z",
"callId": "run_shell_command__call_261162",
"name": "run_shell_command",
"input": {
"description": "Generate baseline popularity ranking for validation",
"command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\n\nt0 = time.time()\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\n\n# catalog is already sorted by popularity rank 1..30000\n# Unpack bits\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little') # (8456, 30000)\n\nrows_uid = []\nrows_item = []\nrows_rank = []\n\nfor r in range(len(uids)):\n eligible_mask = unpacked[r] # bool array of len 30000\n # The item_ids are aligned with catalog\n eligible_items = item_ids[eligible_mask]\n top_100 = eligible_items[:100]\n rows_uid.append(np.full(100, uids[r], dtype=np.int64))\n rows_item.append(top_100)\n rows_rank.append(np.arange(1, 101, dtype=np.int64))\n\ndf = pd.DataFrame({\n 'uid': np.concatenate(rows_uid),\n 'item_id': np.concatenate(rows_item),\n 'rank': np.concatenate(rows_rank)\n})\n\nout_dir = Path('/app/solution')\nout_dir.mkdir(parents=True, exist_ok=True)\nval_path = out_dir / 'val_baseline_pop.parquet'\ndf.to_parquet(val_path, index=False)\nprint(f'Generated baseline in {time.time()-t0:.2f}s, saved to {val_path}')\n\""
},
"recorded": {
"id": "run_shell_command__call_261162",
"name": "run_shell_command",
"args": {
"description": "Generate baseline popularity ranking for validation",
"command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\n\nt0 = time.time()\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\n\n# catalog is already sorted by popularity rank 1..30000\n# Unpack bits\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little') # (8456, 30000)\n\nrows_uid = []\nrows_item = []\nrows_rank = []\n\nfor r in range(len(uids)):\n eligible_mask = unpacked[r] # bool array of len 30000\n # The item_ids are aligned with catalog\n eligible_items = item_ids[eligible_mask]\n top_100 = eligible_items[:100]\n rows_uid.append(np.full(100, uids[r], dtype=np.int64))\n rows_item.append(top_100)\n rows_rank.append(np.arange(1, 101, dtype=np.int64))\n\ndf = pd.DataFrame({\n 'uid': np.concatenate(rows_uid),\n 'item_id': np.concatenate(rows_item),\n 'rank': np.concatenate(rows_rank)\n})\n\nout_dir = Path('/app/solution')\nout_dir.mkdir(parents=True, exist_ok=True)\nval_path = out_dir / 'val_baseline_pop.parquet'\ndf.to_parquet(val_path, index=False)\nprint(f'Generated baseline in {time.time()-t0:.2f}s, saved to {val_path}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_261162",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Generated baseline in 1.09s, saved to /app/solution/val_baseline_pop.parquet\nProcess Group PGID: 1751\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:21:55.024Z",
"resultDisplay": "Generated baseline in 1.09s, saved to /app/solution/val_baseline_pop.parquet",
"description": "Generate baseline popularity ranking for validation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:21:55.024Z",
"content": "\nOutput: Generated baseline in 1.09s, saved to /app/solution/val_baseline_pop.parquet\nProcess Group PGID: 1751\n",
"isError": false
},
"responseMessageId": "6d2218b4-997c-4e72-af68-ea12f721170f"
}
]
},
{
"id": 15,
"role": "agent",
"timestamp": "2026-09-29T04:21:57.673Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:57.673Z",
"callId": "run_shell_command__call_245644",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python /task/evaluate_public.py /app/solution/val_baseline_pop.parquet",
"description": "Evaluate baseline popularity ranking with public evaluator"
},
"recorded": {
"id": "run_shell_command__call_245644",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python /task/evaluate_public.py /app/solution/val_baseline_pop.parquet",
"description": "Evaluate baseline popularity ranking with public evaluator"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_245644",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 153, in validate_ranking\n raise RankingValidationError(\nranking_evaluation.RankingValidationError: submission contains duplicate (uid, item_id) pairs\nExit Code: 1\nProcess Group PGID: 1768\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:21:59.006Z",
"resultDisplay": "Traceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 153, in validate_ranking\n raise RankingValidationError(\nranking_evaluation.RankingValidationError: submission contains duplicate (uid, item_id) pairs",
"description": "PYTHONPATH=/task python /task/evaluate_public.py /app/solution/val_baseline_pop.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:21:59.006Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 153, in validate_ranking\n raise RankingValidationError(\nranking_evaluation.RankingValidationError: submission contains duplicate (uid, item_id) pairs\nExit Code: 1\nProcess Group PGID: 1768\n",
"isError": false
},
"responseMessageId": "f0a0da82-f6e1-4fac-8557-8bc2cee67043"
}
]
},
{
"id": 16,
"role": "agent",
"timestamp": "2026-09-29T04:22:01.750Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:22:01.750Z",
"text": "Investigating Duplicate Items\n\nI'm currently digging into the submission to understand why it flagged \"submission contains duplicate (uid, item_id) pairs.\" I'm specifically examining the first 100 eligible items and the unpacking logic to pinpoint the source of these unexpected duplicates."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:01.750Z",
"callId": "run_shell_command__call_151323",
"name": "run_shell_command",
"input": {
"description": "Inspect duplicates in baseline dataframe",
"command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\ndf = pd.read_parquet('/app/solution/val_baseline_pop.parquet')\nprint('df shape:', df.shape)\nprint('duplicated:', df.duplicated(['uid', 'item_id']).sum())\ndups = df[df.duplicated(['uid', 'item_id'], keep=False)]\nprint(dups.head(10))\n\""
},
"recorded": {
"id": "run_shell_command__call_151323",
"name": "run_shell_command",
"args": {
"description": "Inspect duplicates in baseline dataframe",
"command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\ndf = pd.read_parquet('/app/solution/val_baseline_pop.parquet')\nprint('df shape:', df.shape)\nprint('duplicated:', df.duplicated(['uid', 'item_id']).sum())\ndups = df[df.duplicated(['uid', 'item_id'], keep=False)]\nprint(dups.head(10))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_151323",
"name": "run_shell_command",
"response": {
"output": "\nOutput: df shape: (845600, 3)\nduplicated: 828967\n uid item_id rank\n0 100 6901374 1\n1 100 6901374 2\n2 100 6901374 3\n3 100 6901374 4\n4 100 5862961 5\n5 100 5862961 6\n6 100 6901374 7\n7 100 6901374 8\n8 100 6901374 9\n9 100 6901374 10\nProcess Group PGID: 1788\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:02.589Z",
"resultDisplay": "df shape: (845600, 3)\nduplicated: 828967\n uid item_id rank\n0 100 6901374 1\n1 100 6901374 2\n2 100 6901374 3\n3 100 6901374 4\n4 100 5862961 5\n5 100 5862961 6\n6 100 6901374 7\n7 100 6901374 8\n8 100 6901374 9\n9 100 6901374 10",
"description": "Inspect duplicates in baseline dataframe",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:22:02.589Z",
"content": "\nOutput: df shape: (845600, 3)\nduplicated: 828967\n uid item_id rank\n0 100 6901374 1\n1 100 6901374 2\n2 100 6901374 3\n3 100 6901374 4\n4 100 5862961 5\n5 100 5862961 6\n6 100 6901374 7\n7 100 6901374 8\n8 100 6901374 9\n9 100 6901374 10\nProcess Group PGID: 1788\n",
"isError": false
},
"responseMessageId": "8f10e6a7-f8ed-40eb-a5d8-3cc43334c869"
}
]
},
{
"id": 17,
"role": "agent",
"timestamp": "2026-09-29T04:22:05.989Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:22:05.989Z",
"text": "Clarifying Data Types\n\nI'm currently focusing on understanding the precise data type of `unpacked`. My initial assumption was boolean, but I've now confirmed that `np.unpackbits` yields uint8 values (0s and 1s). This clarification is crucial for correctly indexing `item_ids`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:05.989Z",
"callId": "run_shell_command__call_264178",
"name": "run_shell_command",
"input": {
"description": "Verify numpy indexing behavior between uint8 and bool",
"command": "python -c \"\nimport numpy as np\n\narr = np.array([10, 20, 30])\nmask = np.array([1, 0, 1], dtype=np.uint8)\nprint('With uint8:', arr[mask])\nprint('With bool:', arr[mask.astype(bool)])\n\""
},
"recorded": {
"id": "run_shell_command__call_264178",
"name": "run_shell_command",
"args": {
"description": "Verify numpy indexing behavior between uint8 and bool",
"command": "python -c \"\nimport numpy as np\n\narr = np.array([10, 20, 30])\nmask = np.array([1, 0, 1], dtype=np.uint8)\nprint('With uint8:', arr[mask])\nprint('With bool:', arr[mask.astype(bool)])\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_264178",
"name": "run_shell_command",
"response": {
"output": "\nOutput: With uint8: [20 10 20]\nWith bool: [10 30]\nProcess Group PGID: 1805\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:06.235Z",
"resultDisplay": "With uint8: [20 10 20]\nWith bool: [10 30]",
"description": "Verify numpy indexing behavior between uint8 and bool",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:22:06.236Z",
"content": "\nOutput: With uint8: [20 10 20]\nWith bool: [10 30]\nProcess Group PGID: 1805\n",
"isError": false
},
"responseMessageId": "c286402a-4004-425d-9c72-65706d273c7c"
}
]
},
{
"id": 18,
"role": "agent",
"timestamp": "2026-09-29T04:22:10.903Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:10.903Z",
"callId": "run_shell_command__call_181720",
"name": "run_shell_command",
"input": {
"description": "Regenerate popularity baseline with bool mask and evaluate",
"command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\n\nt0 = time.time()\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\n\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nrows_uid = []\nrows_item = []\nrows_rank = []\n\nfor r in range(len(uids)):\n eligible_mask = unpacked[r] # bool array of len 30000\n eligible_items = item_ids[eligible_mask]\n top_100 = eligible_items[:100]\n rows_uid.append(np.full(100, uids[r], dtype=np.int64))\n rows_item.append(top_100)\n rows_rank.append(np.arange(1, 101, dtype=np.int64))\n\ndf = pd.DataFrame({\n 'uid': np.concatenate(rows_uid),\n 'item_id': np.concatenate(rows_item),\n 'rank': np.concatenate(rows_rank)\n})\n\nout_dir = Path('/app/solution')\nout_dir.mkdir(parents=True, exist_ok=True)\nval_path = out_dir / 'val_baseline_pop.parquet'\ndf.to_parquet(val_path, index=False)\nprint(f'Generated baseline in {time.time()-t0:.2f}s, saved to {val_path}')\n\" && PYTHONPATH=/task python /task/evaluate_public.py /app/solution/val_baseline_pop.parquet"
},
"recorded": {
"id": "run_shell_command__call_181720",
"name": "run_shell_command",
"args": {
"description": "Regenerate popularity baseline with bool mask and evaluate",
"command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\n\nt0 = time.time()\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\n\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nrows_uid = []\nrows_item = []\nrows_rank = []\n\nfor r in range(len(uids)):\n eligible_mask = unpacked[r] # bool array of len 30000\n eligible_items = item_ids[eligible_mask]\n top_100 = eligible_items[:100]\n rows_uid.append(np.full(100, uids[r], dtype=np.int64))\n rows_item.append(top_100)\n rows_rank.append(np.arange(1, 101, dtype=np.int64))\n\ndf = pd.DataFrame({\n 'uid': np.concatenate(rows_uid),\n 'item_id': np.concatenate(rows_item),\n 'rank': np.concatenate(rows_rank)\n})\n\nout_dir = Path('/app/solution')\nout_dir.mkdir(parents=True, exist_ok=True)\nval_path = out_dir / 'val_baseline_pop.parquet'\ndf.to_parquet(val_path, index=False)\nprint(f'Generated baseline in {time.time()-t0:.2f}s, saved to {val_path}')\n\" && PYTHONPATH=/task python /task/evaluate_public.py /app/solution/val_baseline_pop.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_181720",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Generated baseline in 0.96s, saved to /app/solution/val_baseline_pop.parquet\n{\n \"model\": \"/app/solution/val_baseline_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1815\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:15.132Z",
"resultDisplay": "Generated baseline in 0.96s, saved to /app/solution/val_baseline_pop.parquet\n{\n \"model\": \"/app/solution/val_baseline_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}",
"description": "Regenerate popularity baseline with bool mask and evaluate",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:22:15.133Z",
"content": "\nOutput: Generated baseline in 0.96s, saved to /app/solution/val_baseline_pop.parquet\n{\n \"model\": \"/app/solution/val_baseline_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1815\n",
"isError": false
},
"responseMessageId": "cea05911-ef26-42c2-a951-d14ef04d1e5b"
}
]
},
{
"id": 19,
"role": "agent",
"timestamp": "2026-09-29T04:22:21.732Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:22:21.732Z",
"text": "Achieving Baseline Performance\n\nMy recent thoughts have focused on establishing a foundational understanding. I've successfully ran an end-to-end evaluation, confirming that my initial setup achieves a global popularity baseline of NDCG@10 = 0.010710. This is a crucial first step, and all validation checks have passed, ensuring a stable starting point. I'm keeping this best valid submission safe."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:21.732Z",
"callId": "run_shell_command__call_227923",
"name": "run_shell_command",
"input": {
"description": "Generate initial valid submission.parquet and verify invariants",
"command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ndata_dir = Path('/task/data')\ntest_cand = np.load(data_dir / 'test_eligible_candidates.npz')\nuids = test_cand['uids']\nitem_ids = test_cand['item_ids']\npacked = test_cand['packed_eligible']\n\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nrows_uid = []\nrows_item = []\nrows_rank = []\n\nfor r in range(len(uids)):\n eligible_mask = unpacked[r]\n eligible_items = item_ids[eligible_mask]\n top_100 = eligible_items[:100]\n rows_uid.append(np.full(100, uids[r], dtype=np.int64))\n rows_item.append(top_100)\n rows_rank.append(np.arange(1, 101, dtype=np.int64))\n\ndf = pd.DataFrame({\n 'uid': np.concatenate(rows_uid),\n 'item_id': np.concatenate(rows_item),\n 'rank': np.concatenate(rows_rank)\n})\n\nout_path = Path('/app/solution/submission.parquet')\ndf.to_parquet(out_path, index=False)\nprint('Saved baseline submission to', out_path)\n\n# Verify against test invariants\ntargets_frame = pd.read_parquet(data_dir / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(data_dir / 'test_eligible_candidates.npz')\n\nvalidated = validate_ranking(df, targets)\nall_eligible = all(eligibility.is_eligible(uid, item) for uid, item in validated[['uid', 'item_id']].itertuples(index=False, name=None))\nprint('Test submission validation passed:', all_eligible, 'shape:', df.shape)\n\""
},
"recorded": {
"id": "run_shell_command__call_227923",
"name": "run_shell_command",
"args": {
"description": "Generate initial valid submission.parquet and verify invariants",
"command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ndata_dir = Path('/task/data')\ntest_cand = np.load(data_dir / 'test_eligible_candidates.npz')\nuids = test_cand['uids']\nitem_ids = test_cand['item_ids']\npacked = test_cand['packed_eligible']\n\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nrows_uid = []\nrows_item = []\nrows_rank = []\n\nfor r in range(len(uids)):\n eligible_mask = unpacked[r]\n eligible_items = item_ids[eligible_mask]\n top_100 = eligible_items[:100]\n rows_uid.append(np.full(100, uids[r], dtype=np.int64))\n rows_item.append(top_100)\n rows_rank.append(np.arange(1, 101, dtype=np.int64))\n\ndf = pd.DataFrame({\n 'uid': np.concatenate(rows_uid),\n 'item_id': np.concatenate(rows_item),\n 'rank': np.concatenate(rows_rank)\n})\n\nout_path = Path('/app/solution/submission.parquet')\ndf.to_parquet(out_path, index=False)\nprint('Saved baseline submission to', out_path)\n\n# Verify against test invariants\ntargets_frame = pd.read_parquet(data_dir / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(data_dir / 'test_eligible_candidates.npz')\n\nvalidated = validate_ranking(df, targets)\nall_eligible = all(eligibility.is_eligible(uid, item) for uid, item in validated[['uid', 'item_id']].itertuples(index=False, name=None))\nprint('Test submission validation passed:', all_eligible, 'shape:', df.shape)\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_227923",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 5, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1844\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:22.449Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 5, in \nModuleNotFoundError: No module named 'ranking_evaluation'",
"description": "Generate initial valid submission.parquet and verify invariants",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:22:22.449Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 5, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1844\n",
"isError": false
},
"responseMessageId": "bb56fce8-9c6a-4006-8e7e-8f13b8775fd3"
}
]
},
{
"id": 20,
"role": "agent",
"timestamp": "2026-09-29T04:22:27.485Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:27.485Z",
"callId": "run_shell_command__call_262442",
"name": "run_shell_command",
"input": {
"description": "Generate and verify fallback submission.parquet",
"command": "PYTHONPATH=/task python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ndata_dir = Path('/task/data')\ntest_cand = np.load(data_dir / 'test_eligible_candidates.npz')\nuids = test_cand['uids']\nitem_ids = test_cand['item_ids']\npacked = test_cand['packed_eligible']\n\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nrows_uid = []\nrows_item = []\nrows_rank = []\n\nfor r in range(len(uids)):\n eligible_mask = unpacked[r]\n eligible_items = item_ids[eligible_mask]\n top_100 = eligible_items[:100]\n rows_uid.append(np.full(100, uids[r], dtype=np.int64))\n rows_item.append(top_100)\n rows_rank.append(np.arange(1, 101, dtype=np.int64))\n\ndf = pd.DataFrame({\n 'uid': np.concatenate(rows_uid),\n 'item_id': np.concatenate(rows_item),\n 'rank': np.concatenate(rows_rank)\n})\n\nout_path = Path('/app/solution/submission.parquet')\ndf.to_parquet(out_path, index=False)\nprint('Saved baseline submission to', out_path)\n\n# Verify against test invariants\ntargets_frame = pd.read_parquet(data_dir / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(data_dir / 'test_eligible_candidates.npz')\n\nvalidated = validate_ranking(df, targets)\nall_eligible = all(eligibility.is_eligible(uid, item) for uid, item in validated[['uid', 'item_id']].itertuples(index=False, name=None))\nprint('Test submission validation passed:', all_eligible, 'shape:', df.shape)\n\""
},
"recorded": {
"id": "run_shell_command__call_262442",
"name": "run_shell_command",
"args": {
"description": "Generate and verify fallback submission.parquet",
"command": "PYTHONPATH=/task python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ndata_dir = Path('/task/data')\ntest_cand = np.load(data_dir / 'test_eligible_candidates.npz')\nuids = test_cand['uids']\nitem_ids = test_cand['item_ids']\npacked = test_cand['packed_eligible']\n\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nrows_uid = []\nrows_item = []\nrows_rank = []\n\nfor r in range(len(uids)):\n eligible_mask = unpacked[r]\n eligible_items = item_ids[eligible_mask]\n top_100 = eligible_items[:100]\n rows_uid.append(np.full(100, uids[r], dtype=np.int64))\n rows_item.append(top_100)\n rows_rank.append(np.arange(1, 101, dtype=np.int64))\n\ndf = pd.DataFrame({\n 'uid': np.concatenate(rows_uid),\n 'item_id': np.concatenate(rows_item),\n 'rank': np.concatenate(rows_rank)\n})\n\nout_path = Path('/app/solution/submission.parquet')\ndf.to_parquet(out_path, index=False)\nprint('Saved baseline submission to', out_path)\n\n# Verify against test invariants\ntargets_frame = pd.read_parquet(data_dir / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(data_dir / 'test_eligible_candidates.npz')\n\nvalidated = validate_ranking(df, targets)\nall_eligible = all(eligibility.is_eligible(uid, item) for uid, item in validated[['uid', 'item_id']].itertuples(index=False, name=None))\nprint('Test submission validation passed:', all_eligible, 'shape:', df.shape)\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_262442",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Saved baseline submission to /app/solution/submission.parquet\nTest submission validation passed: True shape: (845600, 3)\nProcess Group PGID: 1861\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:30.256Z",
"resultDisplay": "Saved baseline submission to /app/solution/submission.parquet\nTest submission validation passed: True shape: (845600, 3)",
"description": "Generate and verify fallback submission.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:22:30.257Z",
"content": "\nOutput: Saved baseline submission to /app/solution/submission.parquet\nTest submission validation passed: True shape: (845600, 3)\nProcess Group PGID: 1861\n",
"isError": false
},
"responseMessageId": "17af90dc-1f13-47e1-8c10-1647954406fc"
}
]
},
{
"id": 21,
"role": "agent",
"timestamp": "2026-09-29T04:22:35.360Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:22:35.360Z",
"text": "Exploring Data\n\nI've confirmed a solid fallback is ready. My current focus is a deep dive into the data, understanding user signals, and the domain to design effective collaborative filtering and recommendation strategies."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:35.360Z",
"callId": "update_topic__call_228076",
"name": "update_topic",
"input": {
"summary": "We have confirmed the environment, validated the baseline pipeline, and created an initial valid fallback submission. We are now entering the deep analysis and modeling phase. We will examine user and item interaction patterns, explicit feedback (likes/dislikes), completion rates, temporal decay, and evaluate collaborative filtering techniques (item-item CF, BM25/TF-IDF, SVD/ALS, graph walks) on the validation split.",
"strategic_intent": "Analyze data distributions, interaction types, temporal characteristics, and design recommendation algorithms.",
"title": "Interaction Analysis and Model Architecture Research"
},
"recorded": {
"id": "update_topic__call_228076",
"name": "update_topic",
"args": {
"summary": "We have confirmed the environment, validated the baseline pipeline, and created an initial valid fallback submission. We are now entering the deep analysis and modeling phase. We will examine user and item interaction patterns, explicit feedback (likes/dislikes), completion rates, temporal decay, and evaluate collaborative filtering techniques (item-item CF, BM25/TF-IDF, SVD/ALS, graph walks) on the validation split.",
"strategic_intent": "Analyze data distributions, interaction types, temporal characteristics, and design recommendation algorithms.",
"title": "Interaction Analysis and Model Architecture Research"
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_228076",
"name": "update_topic",
"response": {
"output": "Current topic: \"Interaction Analysis and Model Architecture Research\"\nTopic summary: We have confirmed the environment, validated the baseline pipeline, and created an initial valid fallback submission. We are now entering the deep analysis and modeling phase. We will examine user and item interaction patterns, explicit feedback (likes/dislikes), completion rates, temporal decay, and evaluate collaborative filtering techniques (item-item CF, BM25/TF-IDF, SVD/ALS, graph walks) on the validation split.\n\nStrategic Intent: Analyze data distributions, interaction types, temporal characteristics, and design recommendation algorithms."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:35.363Z",
"resultDisplay": "## 📂 Topic: **Interaction Analysis and Model Architecture Research**\n\n**Summary:**\nWe have confirmed the environment, validated the baseline pipeline, and created an initial valid fallback submission. We are now entering the deep analysis and modeling phase. We will examine user and item interaction patterns, explicit feedback (likes/dislikes), completion rates, temporal decay, and evaluate collaborative filtering techniques (item-item CF, BM25/TF-IDF, SVD/ALS, graph walks) on the validation split.\n\n> [!STRATEGY]\n> **Intent:** Analyze data distributions, interaction types, temporal characteristics, and design recommendation algorithms.",
"description": "Update topic to: \"Interaction Analysis and Model Architecture Research\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T04:22:35.364Z",
"content": "Current topic: \"Interaction Analysis and Model Architecture Research\"\nTopic summary: We have confirmed the environment, validated the baseline pipeline, and created an initial valid fallback submission. We are now entering the deep analysis and modeling phase. We will examine user and item interaction patterns, explicit feedback (likes/dislikes), completion rates, temporal decay, and evaluate collaborative filtering techniques (item-item CF, BM25/TF-IDF, SVD/ALS, graph walks) on the validation split.\n\nStrategic Intent: Analyze data distributions, interaction types, temporal characteristics, and design recommendation algorithms.",
"isError": false
},
"responseMessageId": "83b38966-fa7c-45d3-92da-28dcf7f89276"
}
]
},
{
"id": 22,
"role": "agent",
"timestamp": "2026-09-29T04:22:44.239Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:22:44.239Z",
"text": "Defining Relevance Criteria\n\nMy focus has been on dissecting the core of relevance. I'm meticulously examining the provided grades, particularly the nuances between organic liking (grade 3) and sustained listening (grade 1, 80% threshold), and contrasting them with the 'otherwise' condition (grade 0)."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:22:44.239Z",
"text": "Refining Evaluation Metrics\n\nI'm now examining the nuances of graded NDCG@10, specifically how the DCG formula incorporates gains and discounts. I'm also solidifying the validation and test set split strategy, confirming the timestamps and ensuring a proper 30-minute gap."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:44.239Z",
"callId": "run_shell_command__call_217574",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\n\n# Check preference state overlap with eligibility\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nitem_to_col = {item: c for c, item in enumerate(item_ids)}\n\n# Check if any liked/disliked items in val_pref are eligible\neligible_pref_count = 0\nin_catalog_count = 0\nfor uid, item, state in val_pref.itertuples(index=False):\n r = uid_to_row.get(uid)\n c = item_to_col.get(item)\n if c is not None:\n in_catalog_count += 1\n if r is not None and unpacked[r, c]:\n eligible_pref_count += 1\n\nprint(f'Total val_pref rows: {len(val_pref)}')\nprint(f'val_pref items in catalog: {in_catalog_count}')\nprint(f'val_pref items that are eligible: {eligible_pref_count}')\n\"",
"description": "Inspect preference state relationship to candidate eligibility"
},
"recorded": {
"id": "run_shell_command__call_217574",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\n\n# Check preference state overlap with eligibility\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nitem_to_col = {item: c for c, item in enumerate(item_ids)}\n\n# Check if any liked/disliked items in val_pref are eligible\neligible_pref_count = 0\nin_catalog_count = 0\nfor uid, item, state in val_pref.itertuples(index=False):\n r = uid_to_row.get(uid)\n c = item_to_col.get(item)\n if c is not None:\n in_catalog_count += 1\n if r is not None and unpacked[r, c]:\n eligible_pref_count += 1\n\nprint(f'Total val_pref rows: {len(val_pref)}')\nprint(f'val_pref items in catalog: {in_catalog_count}')\nprint(f'val_pref items that are eligible: {eligible_pref_count}')\n\"",
"description": "Inspect preference state relationship to candidate eligibility"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_217574",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total val_pref rows: 469124\nval_pref items in catalog: 469124\nval_pref items that are eligible: 0\nProcess Group PGID: 1877\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:45.719Z",
"resultDisplay": "Total val_pref rows: 469124\nval_pref items in catalog: 469124\nval_pref items that are eligible: 0",
"description": "Inspect preference state relationship to candidate eligibility",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:22:45.720Z",
"content": "\nOutput: Total val_pref rows: 469124\nval_pref items in catalog: 469124\nval_pref items that are eligible: 0\nProcess Group PGID: 1877\n",
"isError": false
},
"responseMessageId": "a2a8270b-acc7-4fa4-b90e-b02e5fe28ebf"
}
]
},
{
"id": 23,
"role": "agent",
"timestamp": "2026-09-29T04:22:50.968Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:22:50.968Z",
"text": "Investigating Eligibility Criteria\n\nI'm digging into why all validation tracks are marked ineligible. It appears every single one, regardless of preference, has been flagged. I'm now focusing on identifying the specific criteria that determine eligibility or ineligibility."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:50.968Z",
"callId": "run_shell_command__call_152061",
"name": "run_shell_command",
"input": {
"description": "Check if previously listened items are eligible",
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nitem_to_col = {item: c for c, item in enumerate(item_ids)}\n\n# Sample 100k train_listens rows\nlistens = pq.read_table(data_dir / 'train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct']).to_pandas()\nprint('Total train_listens:', len(listens))\n\n# Check eligibility of items that appeared in train_listens\nsample = listens.iloc[:100000]\neligible_in_listens = 0\ntotal_checked = 0\nfor uid, item, org, ratio in sample.itertuples(index=False):\n r = uid_to_row.get(uid)\n c = item_to_col.get(item)\n if r is not None and c is not None:\n total_checked += 1\n if unpacked[r, c]:\n eligible_in_listens += 1\n\nprint(f'Checked {total_checked} train_listens pairs: {eligible_in_listens} are eligible ({eligible_in_listens/total_checked:.4f})')\n\""
},
"recorded": {
"id": "run_shell_command__call_152061",
"name": "run_shell_command",
"args": {
"description": "Check if previously listened items are eligible",
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nitem_to_col = {item: c for c, item in enumerate(item_ids)}\n\n# Sample 100k train_listens rows\nlistens = pq.read_table(data_dir / 'train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct']).to_pandas()\nprint('Total train_listens:', len(listens))\n\n# Check eligibility of items that appeared in train_listens\nsample = listens.iloc[:100000]\neligible_in_listens = 0\ntotal_checked = 0\nfor uid, item, org, ratio in sample.itertuples(index=False):\n r = uid_to_row.get(uid)\n c = item_to_col.get(item)\n if r is not None and c is not None:\n total_checked += 1\n if unpacked[r, c]:\n eligible_in_listens += 1\n\nprint(f'Checked {total_checked} train_listens pairs: {eligible_in_listens} are eligible ({eligible_in_listens/total_checked:.4f})')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_152061",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total train_listens: 43484159\nChecked 70840 train_listens pairs: 0 are eligible (0.0000)\nProcess Group PGID: 1894\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:52.408Z",
"resultDisplay": "Total train_listens: 43484159\nChecked 70840 train_listens pairs: 0 are eligible (0.0000)",
"description": "Check if previously listened items are eligible",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:22:52.409Z",
"content": "\nOutput: Total train_listens: 43484159\nChecked 70840 train_listens pairs: 0 are eligible (0.0000)\nProcess Group PGID: 1894\n",
"isError": false
},
"responseMessageId": "3ea41b7f-c043-4202-8362-7e3168241eab"
}
]
},
{
"id": 24,
"role": "agent",
"timestamp": "2026-09-29T04:22:57.593Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:22:57.593Z",
"text": "Discovering New Music\n\nI've analyzed 70,840 train-listen pairs, and it seems none are eligible for this \"Made for You\" shelf. This indicates a strong constraint where any previously interacted-with track is excluded, pushing the focus purely towards true discovery."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:57.593Z",
"callId": "run_shell_command__call_170463",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nitem_to_col = {item: c for c, item in enumerate(item_ids)}\n\n# Let's check target_users\n# Target user history length:\n# In target_users.parquet: 'recent_positive_listens', 'history_positive_listens'\nusers = pd.read_parquet(data_dir / 'target_users.parquet')\nprint('Target users count:', len(users))\nprint('Eligible per user mean:', unpacked.sum(axis=1).mean())\nprint('Ineligible per user mean:', (~unpacked).sum(axis=1).mean())\n\"",
"description": "Inspect mean ineligible tracks per user"
},
"recorded": {
"id": "run_shell_command__call_170463",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nitem_to_col = {item: c for c, item in enumerate(item_ids)}\n\n# Let's check target_users\n# Target user history length:\n# In target_users.parquet: 'recent_positive_listens', 'history_positive_listens'\nusers = pd.read_parquet(data_dir / 'target_users.parquet')\nprint('Target users count:', len(users))\nprint('Eligible per user mean:', unpacked.sum(axis=1).mean())\nprint('Ineligible per user mean:', (~unpacked).sum(axis=1).mean())\n\"",
"description": "Inspect mean ineligible tracks per user"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_170463",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Target users count: 8456\nEligible per user mean: 29067.306291390727\nIneligible per user mean: 932.6937086092715\nProcess Group PGID: 1918\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:58.646Z",
"resultDisplay": "Target users count: 8456\nEligible per user mean: 29067.306291390727\nIneligible per user mean: 932.6937086092715",
"description": "Inspect mean ineligible tracks per user",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:22:58.646Z",
"content": "\nOutput: Target users count: 8456\nEligible per user mean: 29067.306291390727\nIneligible per user mean: 932.6937086092715\nProcess Group PGID: 1918\n",
"isError": false
},
"responseMessageId": "520f7e05-f19a-4f94-a361-3f1db42caa44"
}
]
},
{
"id": 25,
"role": "agent",
"timestamp": "2026-09-29T04:23:05.495Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:23:05.495Z",
"text": "Refining Track Eligibility\n\nI'm currently wrestling with the high volume of ineligible tracks per user, approximately 933, which seem to be directly linked to their past interactions and preferences. This significantly impacts the \"Made for You\" shelf's candidate pool. My focus is on optimizing how we define and manage this eligibility to ensure stronger, more relevant recommendations."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:23:05.495Z",
"text": "Analyzing Discovery Drivers\n\nMy current focus is on understanding what drives organic track discovery. I'm investigating how user interaction history, particularly listen completion rates and organic engagement with candidate tracks, correlates with their preference for similar content. The goal is to refine how we predict future organic discovery by leveraging these insights."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:23:05.495Z",
"callId": "run_shell_command__call_236842",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\n# Check train_listens\nt_listens = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp']).to_pandas()\nuids_in_listens = t_listens['uid'].unique()\ntargets = pd.read_parquet('/task/data/target_users.parquet')['uid'].values\n\nprint('Total unique uids in train_listens:', len(uids_in_listens))\nprint('Target uids in train_listens:', np.isin(targets, uids_in_listens).sum(), 'out of', len(targets))\nprint('Non-target uids in train_listens:', len(set(uids_in_listens) - set(targets)))\n\nmin_ts = t_listens['timestamp'].min()\nmax_ts = t_listens['timestamp'].max()\nprint(f'Timestamp range: {min_ts} to {max_ts} ({(max_ts-min_ts)/86400:.1f} days)')\n\"",
"description": "Check unique users and timestamp range in train_listens"
},
"recorded": {
"id": "run_shell_command__call_236842",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\n# Check train_listens\nt_listens = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp']).to_pandas()\nuids_in_listens = t_listens['uid'].unique()\ntargets = pd.read_parquet('/task/data/target_users.parquet')['uid'].values\n\nprint('Total unique uids in train_listens:', len(uids_in_listens))\nprint('Target uids in train_listens:', np.isin(targets, uids_in_listens).sum(), 'out of', len(targets))\nprint('Non-target uids in train_listens:', len(set(uids_in_listens) - set(targets)))\n\nmin_ts = t_listens['timestamp'].min()\nmax_ts = t_listens['timestamp'].max()\nprint(f'Timestamp range: {min_ts} to {max_ts} ({(max_ts-min_ts)/86400:.1f} days)')\n\"",
"description": "Check unique users and timestamp range in train_listens"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_236842",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total unique uids in train_listens: 9238\nTarget uids in train_listens: 8456 out of 8456\nNon-target uids in train_listens: 782\nTimestamp range: 0 to 24786795 (286.9 days)\nProcess Group PGID: 1934\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:23:06.785Z",
"resultDisplay": "Total unique uids in train_listens: 9238\nTarget uids in train_listens: 8456 out of 8456\nNon-target uids in train_listens: 782\nTimestamp range: 0 to 24786795 (286.9 days)",
"description": "Check unique users and timestamp range in train_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:23:06.785Z",
"content": "\nOutput: Total unique uids in train_listens: 9238\nTarget uids in train_listens: 8456 out of 8456\nNon-target uids in train_listens: 782\nTimestamp range: 0 to 24786795 (286.9 days)\nProcess Group PGID: 1934\n",
"isError": false
},
"responseMessageId": "f5c18bab-f17c-4c2c-8bb7-6108da86bfd5"
}
]
},
{
"id": 26,
"role": "agent",
"timestamp": "2026-09-29T04:23:11.497Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:23:11.497Z",
"text": "Analyzing Item Uniqueness\n\nI'm currently investigating the uniqueness of items within `train_listens`, specifically looking for any data points that fall outside our 30,000 candidate catalog. Concurrently, I'm examining the distribution of items in `val_listens` and `val_likes` to ensure all ground-truth validation items are represented as expected."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:23:11.497Z",
"callId": "run_shell_command__call_182761",
"name": "run_shell_command",
"input": {
"description": "Check item overlap with candidate catalog in train and val",
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat['item_id'])\n\n# Check items in train_listens\nitems_in_train = set(pq.read_table('/task/data/train_listens.parquet', columns=['item_id']).to_pandas()['item_id'].unique())\nprint(f'Unique items in train_listens: {len(items_in_train)}')\nprint(f'Items in train_listens that are in catalog: {len(items_in_train & cat_items)} / {len(cat_items)}')\n\n# Check items in val ground truth\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nval_organic_listens_80 = set(val_listens[(val_listens.is_organic == 1) & (val_listens.played_ratio_pct >= 80)]['item_id'].unique())\nval_organic_likes = set(val_likes[val_likes.is_organic == 1]['item_id'].unique())\nval_pos = val_organic_listens_80 | val_organic_likes\nprint(f'Unique items with positive feedback in val: {len(val_pos)}')\nprint(f'Positive val items in catalog: {len(val_pos & cat_items)} / {len(val_pos)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_182761",
"name": "run_shell_command",
"args": {
"description": "Check item overlap with candidate catalog in train and val",
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat['item_id'])\n\n# Check items in train_listens\nitems_in_train = set(pq.read_table('/task/data/train_listens.parquet', columns=['item_id']).to_pandas()['item_id'].unique())\nprint(f'Unique items in train_listens: {len(items_in_train)}')\nprint(f'Items in train_listens that are in catalog: {len(items_in_train & cat_items)} / {len(cat_items)}')\n\n# Check items in val ground truth\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nval_organic_listens_80 = set(val_listens[(val_listens.is_organic == 1) & (val_listens.played_ratio_pct >= 80)]['item_id'].unique())\nval_organic_likes = set(val_likes[val_likes.is_organic == 1]['item_id'].unique())\nval_pos = val_organic_listens_80 | val_organic_likes\nprint(f'Unique items with positive feedback in val: {len(val_pos)}')\nprint(f'Positive val items in catalog: {len(val_pos & cat_items)} / {len(val_pos)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_182761",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Unique items in train_listens: 845395\nItems in train_listens that are in catalog: 30000 / 30000\nUnique items with positive feedback in val: 93190\nPositive val items in catalog: 25709 / 93190\nProcess Group PGID: 1956\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:23:13.014Z",
"resultDisplay": "Unique items in train_listens: 845395\nItems in train_listens that are in catalog: 30000 / 30000\nUnique items with positive feedback in val: 93190\nPositive val items in catalog: 25709 / 93190",
"description": "Check item overlap with candidate catalog in train and val",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:23:13.015Z",
"content": "\nOutput: Unique items in train_listens: 845395\nItems in train_listens that are in catalog: 30000 / 30000\nUnique items with positive feedback in val: 93190\nPositive val items in catalog: 25709 / 93190\nProcess Group PGID: 1956\n",
"isError": false
},
"responseMessageId": "c404faa5-e768-4c2e-b2c2-7734e05af8ef"
}
]
},
{
"id": 27,
"role": "agent",
"timestamp": "2026-09-29T04:23:23.326Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:23:23.326Z",
"text": "Analyzing Interaction Attributes\n\nMy focus has shifted to dissecting the `train_listens` data. Specifically, I'm examining the attributes of interactions, and I've noted that the `is_organic` attribute is consistently marked as 0. This is a key observation in understanding the nature of these interactions."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:23:23.326Z",
"text": "Exploring Collaborative Filtering\n\nI'm now diving into collaborative filtering techniques suitable for music discovery item ranking, considering the dataset's scale of 8,456 users and 30,000 items. My next step is to evaluate the computational cost of various approaches to score eligible items for each user before selecting the top 100."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:23:23.326Z",
"text": "Evaluating Matrix Factorization\n\nI'm now considering matrix factorization, specifically the computational feasibility of generating an $8456 \\times 30000$ prediction matrix. The $1.01$ GB RAM requirement for this dense representation seems manageable, and I'm exploring the efficiency of computing the $U \\times V^T$ product with $K=10$ latent dimensions."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:23:23.326Z",
"callId": "run_shell_command__call_179000",
"name": "run_shell_command",
"input": {
"description": "Benchmark matrix product and top-100 extraction",
"command": "python -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 128).astype(np.float32)\nV = np.random.randn(30000, 128).astype(np.float32)\n\nt0 = time.time()\nscores = U @ V.T\nt1 = time.time()\nprint(f'Matrix multiplication shape {scores.shape} took {t1-t0:.4f}s')\n\n# And top-100 argpartition\nt0 = time.time()\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nt1 = time.time()\nprint(f'Top 100 argpartition took {t1-t0:.4f}s')\n\""
},
"recorded": {
"id": "run_shell_command__call_179000",
"name": "run_shell_command",
"args": {
"description": "Benchmark matrix product and top-100 extraction",
"command": "python -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 128).astype(np.float32)\nV = np.random.randn(30000, 128).astype(np.float32)\n\nt0 = time.time()\nscores = U @ V.T\nt1 = time.time()\nprint(f'Matrix multiplication shape {scores.shape} took {t1-t0:.4f}s')\n\n# And top-100 argpartition\nt0 = time.time()\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nt1 = time.time()\nprint(f'Top 100 argpartition took {t1-t0:.4f}s')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_179000",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Matrix multiplication shape (8456, 30000) took 0.3894s\nTop 100 argpartition took 0.9599s\nProcess Group PGID: 1978\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:23:25.046Z",
"resultDisplay": "Matrix multiplication shape (8456, 30000) took 0.3894s\nTop 100 argpartition took 0.9599s",
"description": "Benchmark matrix product and top-100 extraction",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:23:25.046Z",
"content": "\nOutput: Matrix multiplication shape (8456, 30000) took 0.3894s\nTop 100 argpartition took 0.9599s\nProcess Group PGID: 1978\n",
"isError": false
},
"responseMessageId": "87080b60-f78e-4c35-9401-76eba6cf4c09"
}
]
},
{
"id": 28,
"role": "agent",
"timestamp": "2026-09-29T04:23:30.886Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:23:30.886Z",
"text": "Accelerating Model Evaluation\n\nMy latest simulations reveal significant performance gains. Matrix multiplications now take a mere 0.39 seconds, and top-100 argpartitions are completed in 0.96 seconds. This efficiency allows for rapid iteration and evaluation of complete models, potentially within seconds. I am now delving into the nuances of the `train_listens`, `train_likes`, and `train_dis` datasets to understand their interrelationships."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:23:30.886Z",
"callId": "run_shell_command__call_219651",
"name": "run_shell_command",
"input": {
"description": "Summary statistics of train_listens on target users and catalog items",
"command": "python -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\nprint('Reading sample of train_listens...')\ntbl = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds', 'timestamp'])\ndf = tbl.to_pandas()\nprint('Number of rows:', len(df))\n\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet')['item_id'])\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\nin_cat = df['item_id'].isin(cat)\nin_targets = df['uid'].isin(targets)\n\nprint(f'Rows with catalog item: {in_cat.sum()} / {len(df)} ({in_cat.sum()/len(df):.1%})')\nprint(f'Rows with target user: {in_targets.sum()} / {len(df)} ({in_targets.sum()/len(df):.1%})')\nboth = in_cat & in_targets\nprint(f'Rows with both: {both.sum()} / {len(df)} ({both.sum()/len(df):.1%})')\n\nsub = df[both]\nprint('Summary of both:')\nprint('is_organic mean:', sub['is_organic'].mean())\nprint('played_ratio_pct percentiles:', np.percentile(sub['played_ratio_pct'], [10, 25, 50, 75, 80, 90]))\nprint('played_ratio_pct >= 80%:', (sub['played_ratio_pct'] >= 80).mean())\nprint('played_ratio_pct >= 50%:', (sub['played_ratio_pct'] >= 50).mean())\n\""
},
"recorded": {
"id": "run_shell_command__call_219651",
"name": "run_shell_command",
"args": {
"description": "Summary statistics of train_listens on target users and catalog items",
"command": "python -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\nprint('Reading sample of train_listens...')\ntbl = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds', 'timestamp'])\ndf = tbl.to_pandas()\nprint('Number of rows:', len(df))\n\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet')['item_id'])\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\nin_cat = df['item_id'].isin(cat)\nin_targets = df['uid'].isin(targets)\n\nprint(f'Rows with catalog item: {in_cat.sum()} / {len(df)} ({in_cat.sum()/len(df):.1%})')\nprint(f'Rows with target user: {in_targets.sum()} / {len(df)} ({in_targets.sum()/len(df):.1%})')\nboth = in_cat & in_targets\nprint(f'Rows with both: {both.sum()} / {len(df)} ({both.sum()/len(df):.1%})')\n\nsub = df[both]\nprint('Summary of both:')\nprint('is_organic mean:', sub['is_organic'].mean())\nprint('played_ratio_pct percentiles:', np.percentile(sub['played_ratio_pct'], [10, 25, 50, 75, 80, 90]))\nprint('played_ratio_pct >= 80%:', (sub['played_ratio_pct'] >= 80).mean())\nprint('played_ratio_pct >= 50%:', (sub['played_ratio_pct'] >= 50).mean())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_219651",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Reading sample of train_listens...\nNumber of rows: 43484159\nRows with catalog item: 31249613 / 43484159 (71.9%)\nRows with target user: 43222297 / 43484159 (99.4%)\nRows with both: 31062540 / 43484159 (71.4%)\nSummary of both:\nis_organic mean: 0.5001908408005269\nplayed_ratio_pct percentiles: [ 1. 8. 100. 100. 100. 100.]\nplayed_ratio_pct >= 80%: 0.6023763349680998\nplayed_ratio_pct >= 50%: 0.6425698928677436\nProcess Group PGID: 1988\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:23:39.191Z",
"resultDisplay": "Reading sample of train_listens...\nNumber of rows: 43484159\nRows with catalog item: 31249613 / 43484159 (71.9%)\nRows with target user: 43222297 / 43484159 (99.4%)\nRows with both: 31062540 / 43484159 (71.4%)\nSummary of both:\nis_organic mean: 0.5001908408005269\nplayed_ratio_pct percentiles: [ 1. 8. 100. 100. 100. 100.]\nplayed_ratio_pct >= 80%: 0.6023763349680998\nplayed_ratio_pct >= 50%: 0.6425698928677436",
"description": "Summary statistics of train_listens on target users and catalog items",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:23:39.192Z",
"content": "\nOutput: Reading sample of train_listens...\nNumber of rows: 43484159\nRows with catalog item: 31249613 / 43484159 (71.9%)\nRows with target user: 43222297 / 43484159 (99.4%)\nRows with both: 31062540 / 43484159 (71.4%)\nSummary of both:\nis_organic mean: 0.5001908408005269\nplayed_ratio_pct percentiles: [ 1. 8. 100. 100. 100. 100.]\nplayed_ratio_pct >= 80%: 0.6023763349680998\nplayed_ratio_pct >= 50%: 0.6425698928677436\nProcess Group PGID: 1988\n",
"isError": false
},
"responseMessageId": "9a2ed2b2-0603-4be8-b084-9dedd3641c4c"
}
]
},
{
"id": 29,
"role": "agent",
"timestamp": "2026-09-29T04:23:46.375Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:23:46.375Z",
"callId": "run_shell_command__call_145567",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\n\nts = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp'])['timestamp'].to_numpy()\nmax_ts = ts.max()\ndiff_days = (max_ts - ts) / 86400.0\n\nfor days in [7, 14, 30, 60, 90, 180]:\n print(f'Listens in last {days:3d} days: {(diff_days <= days).sum()} ({(diff_days <= days).mean():.1%})')\n\"",
"description": "Check listens distribution over recency windows"
},
"recorded": {
"id": "run_shell_command__call_145567",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\n\nts = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp'])['timestamp'].to_numpy()\nmax_ts = ts.max()\ndiff_days = (max_ts - ts) / 86400.0\n\nfor days in [7, 14, 30, 60, 90, 180]:\n print(f'Listens in last {days:3d} days: {(diff_days <= days).sum()} ({(diff_days <= days).mean():.1%})')\n\"",
"description": "Check listens distribution over recency windows"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_145567",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Listens in last 7 days: 1491707 (3.4%)\nListens in last 14 days: 2920236 (6.7%)\nListens in last 30 days: 6126106 (14.1%)\nListens in last 60 days: 11718765 (26.9%)\nListens in last 90 days: 16769280 (38.6%)\nListens in last 180 days: 30187041 (69.4%)\nProcess Group PGID: 2015\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:23:47.892Z",
"resultDisplay": "Listens in last 7 days: 1491707 (3.4%)\nListens in last 14 days: 2920236 (6.7%)\nListens in last 30 days: 6126106 (14.1%)\nListens in last 60 days: 11718765 (26.9%)\nListens in last 90 days: 16769280 (38.6%)\nListens in last 180 days: 30187041 (69.4%)",
"description": "Check listens distribution over recency windows",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:23:47.893Z",
"content": "\nOutput: Listens in last 7 days: 1491707 (3.4%)\nListens in last 14 days: 2920236 (6.7%)\nListens in last 30 days: 6126106 (14.1%)\nListens in last 60 days: 11718765 (26.9%)\nListens in last 90 days: 16769280 (38.6%)\nListens in last 180 days: 30187041 (69.4%)\nProcess Group PGID: 2015\n",
"isError": false
},
"responseMessageId": "fafc4ff3-031d-4c61-9b5b-f8a1c93676fb"
}
]
},
{
"id": 30,
"role": "agent",
"timestamp": "2026-09-29T04:24:09.602Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:24:09.601Z",
"text": "Analyzing Recent Listen Counts\n\nMy analysis shows that the ~1.5 million listens observed in the last 7 days of training precisely align with the `val_listens` metric. I'm now examining the underlying data structure and the available signals."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:24:09.601Z",
"text": "Exploring ItemCF's Potential\n\nI'm investigating the efficacy of Item-Item Collaborative Filtering, a robust method for implicit feedback. Given its strong performance in similar music streaming contexts, I'm particularly interested in its potential application here."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:24:09.601Z",
"text": "Refining Collaborative Filtering\n\nI'm now focusing on fine-tuning collaborative filtering strategies. Specifically, I'm exploring how to leverage item co-occurrence, emphasizing popularity-normalized similarity metrics like BM25, and developing a mechanism to generate candidate recommendations based on user interaction history. I am also investigating the feasibility of implementing EASE, evaluating memory constraints for large matrix inversions."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:24:09.601Z",
"text": "Evaluating SVD Performance\n\nI'm assessing the computational cost of matrix inversions for methods like EASE, realizing $O(M^3)$ operations might be too slow. However, I've discovered that using TruncatedSVD on the user-item matrix is surprisingly fast, taking only seconds for rank-k approximations."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:24:09.601Z",
"text": "Considering User-User CF\n\nI'm now exploring the potential of user-user collaborative filtering, particularly given the smaller number of users compared to items. This approach, with an $8456 \\times 8456$ similarity matrix, seems computationally feasible and avoids the $O(M^3)$ issues of some other methods."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:24:09.601Z",
"text": "Testing User-User CF Efficiency\n\nI'm now focusing on the computational feasibility of user-user collaborative filtering. It appears that generating predictions with a precomputed similarity matrix is a single matrix multiplication, which is highly efficient, especially with sparse similarity. User-User EASE is also surprisingly quick, with matrix inversion taking only seconds. I'll be testing these ideas and optimizing the construction of the user interaction vector."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:24:09.602Z",
"callId": "run_shell_command__call_366383",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nt0 = time.time()\nprint('Testing data loading...')\n\n# Load catalog and users\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nusers = pd.read_parquet('/task/data/target_users.parquet')\n\nuid_map = {uid: i for i, uid in enumerate(users['uid'])}\nitem_map = {item: j for j, item in enumerate(cat['item_id'])}\n\n# Load preference state\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state rows:', len(val_pref))\n\n# Load train_listens\nlistens = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp']).to_pandas()\nprint(f'Loaded train_listens in {time.time()-t0:.2f}s')\n\n# Filter to target users and catalog items\nmask = listens['uid'].isin(uid_map) & listens['item_id'].isin(item_map)\ndf = listens[mask].copy()\nprint(f'Filtered rows: {len(df)} in {time.time()-t0:.2f}s')\n\"",
"description": "Benchmark filtering train_listens to target users and catalog items"
},
"recorded": {
"id": "run_shell_command__call_366383",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nt0 = time.time()\nprint('Testing data loading...')\n\n# Load catalog and users\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nusers = pd.read_parquet('/task/data/target_users.parquet')\n\nuid_map = {uid: i for i, uid in enumerate(users['uid'])}\nitem_map = {item: j for j, item in enumerate(cat['item_id'])}\n\n# Load preference state\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state rows:', len(val_pref))\n\n# Load train_listens\nlistens = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp']).to_pandas()\nprint(f'Loaded train_listens in {time.time()-t0:.2f}s')\n\n# Filter to target users and catalog items\nmask = listens['uid'].isin(uid_map) & listens['item_id'].isin(item_map)\ndf = listens[mask].copy()\nprint(f'Filtered rows: {len(df)} in {time.time()-t0:.2f}s')\n\"",
"description": "Benchmark filtering train_listens to target users and catalog items"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_366383",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Testing data loading...\nPreference state rows: 469124\nLoaded train_listens in 0.56s\nFiltered rows: 31062540 in 6.92s\nProcess Group PGID: 2037\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:24:17.602Z",
"resultDisplay": "Testing data loading...\nPreference state rows: 469124\nLoaded train_listens in 0.56s\nFiltered rows: 31062540 in 6.92s",
"description": "Benchmark filtering train_listens to target users and catalog items",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:24:17.603Z",
"content": "\nOutput: Testing data loading...\nPreference state rows: 469124\nLoaded train_listens in 0.56s\nFiltered rows: 31062540 in 6.92s\nProcess Group PGID: 2037\n",
"isError": false
},
"responseMessageId": "5964ae00-3c34-4abc-b19e-b83b775b9968"
}
]
},
{
"id": 31,
"role": "agent",
"timestamp": "2026-09-29T04:24:45.736Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:24:45.736Z",
"text": "Analyzing User Behavior\n\nI'm currently analyzing the filtered data, which contains 31 million rows. My next step is to determine the number of unique user-item interactions. I'm also considering the implications of repeated listens, wondering how to best represent that in the data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:24:45.736Z",
"callId": "run_shell_command__call_367071",
"name": "run_shell_command",
"input": {
"description": "Count unique user-item interaction pairs in train_listens",
"command": "python -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ntbl = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\ndf = tbl.to_pandas()\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet')['item_id'])\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\ndf = df[df['uid'].isin(targets) & df['item_id'].isin(cat)]\n\npairs = len(df[['uid', 'item_id']].drop_duplicates())\nprint('Unique (uid, item_id) pairs:', pairs)\n\""
},
"recorded": {
"id": "run_shell_command__call_367071",
"name": "run_shell_command",
"args": {
"description": "Count unique user-item interaction pairs in train_listens",
"command": "python -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ntbl = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\ndf = tbl.to_pandas()\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet')['item_id'])\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\ndf = df[df['uid'].isin(targets) & df['item_id'].isin(cat)]\n\npairs = len(df[['uid', 'item_id']].drop_duplicates())\nprint('Unique (uid, item_id) pairs:', pairs)\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_367071",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Unique (uid, item_id) pairs: 7868664\nProcess Group PGID: 2066\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:24:55.526Z",
"resultDisplay": "Unique (uid, item_id) pairs: 7868664",
"description": "Count unique user-item interaction pairs in train_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:24:57.356Z",
"content": "\nOutput: Unique (uid, item_id) pairs: 7868664\nProcess Group PGID: 2066\n",
"isError": false
},
"responseMessageId": "4da42cc3-6448-46b2-b1d0-8153937faef7"
}
]
},
{
"id": 32,
"role": "agent",
"timestamp": "2026-09-29T04:25:04.560Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:25:04.560Z",
"text": "Analyzing Data Density\n\nMy recent analysis reveals a significant density in the dataset. With 7.87 million unique user-item pairs against 253.68 million potential combinations, the current density stands around 3.1%. This figure is notably high for typical recommendation system benchmarks."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:25:04.560Z",
"text": "Considering Catalog Scope\n\nI'm re-evaluating the scope of items. While the overall density is good, I'm now focused on whether using only catalog items for user representation impacts performance, especially since validation targets are exclusively within the catalog. It seems the majority of listens are also catalog-related."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:25:04.560Z",
"callId": "run_shell_command__call_146554",
"name": "run_shell_command",
"input": {
"description": "Check explicit feedback overlap with catalog",
"command": "python -c \"\nimport pandas as pd\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet')['item_id'])\nfor f in ['likes', 'dislikes', 'unlikes', 'undislikes']:\n df = pd.read_parquet(f'/task/data/train_{f}.parquet')\n in_cat = df['item_id'].isin(cat).mean()\n print(f'train_{f}: {len(df)} rows, {in_cat:.1%} in catalog')\n\""
},
"recorded": {
"id": "run_shell_command__call_146554",
"name": "run_shell_command",
"args": {
"description": "Check explicit feedback overlap with catalog",
"command": "python -c \"\nimport pandas as pd\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet')['item_id'])\nfor f in ['likes', 'dislikes', 'unlikes', 'undislikes']:\n df = pd.read_parquet(f'/task/data/train_{f}.parquet')\n in_cat = df['item_id'].isin(cat).mean()\n print(f'train_{f}: {len(df)} rows, {in_cat:.1%} in catalog')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_146554",
"name": "run_shell_command",
"response": {
"output": "\nOutput: train_likes: 814831 rows, 68.1% in catalog\ntrain_dislikes: 101075 rows, 64.6% in catalog\ntrain_unlikes: 292665 rows, 31.9% in catalog\ntrain_undislikes: 19960 rows, 34.0% in catalog\nProcess Group PGID: 2094\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:25:05.492Z",
"resultDisplay": "train_likes: 814831 rows, 68.1% in catalog\ntrain_dislikes: 101075 rows, 64.6% in catalog\ntrain_unlikes: 292665 rows, 31.9% in catalog\ntrain_undislikes: 19960 rows, 34.0% in catalog",
"description": "Check explicit feedback overlap with catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:25:05.493Z",
"content": "\nOutput: train_likes: 814831 rows, 68.1% in catalog\ntrain_dislikes: 101075 rows, 64.6% in catalog\ntrain_unlikes: 292665 rows, 31.9% in catalog\ntrain_undislikes: 19960 rows, 34.0% in catalog\nProcess Group PGID: 2094\n",
"isError": false
},
"responseMessageId": "a3a0fc4b-a20d-417b-9761-1d246d15caea"
}
]
},
{
"id": 33,
"role": "agent",
"timestamp": "2026-09-29T04:25:18.207Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:25:18.207Z",
"text": "Clarifying Validation Targets\n\nMy current focus is on precisely defining the target labels within the validation set. I've identified that `grade 3` specifically refers to \"organic like,\" while `grade 1` signifies \"organic listen >= 80% completion.\" I'm now cross-referencing these with the training data to ensure consistency."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:25:18.207Z",
"text": "Refining Organic Engagement Logic\n\nI'm exploring how users will encounter and engage with novel items. Specifically, I'm diving into the underlying collaborative filtering mechanisms, both item-item and user-user, to model how a user's existing preferences will lead them to organically listen to or like items they haven't experienced before. This involves examining the mathematical formulations for similarity and prediction."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:25:18.207Z",
"text": "Modeling User Discoveries\n\nI'm delving into collaborative filtering models. Specifically, I'm analyzing how user-item affinities and user-user similarities, derived from training data, can predict new item discoveries. This involves exploring the mathematical underpinnings of item-item and user-user co-occurrence, as well as matrix factorization techniques like SVD."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:25:18.207Z",
"callId": "run_shell_command__call_274516",
"name": "run_shell_command",
"input": {
"description": "Verify in-memory validation evaluation speed",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings, ranked_items, validate_ranking\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\nt0 = time.time()\ngraded = validation_truth(eligibility, targets)\nprint(f'Computed validation_truth in {time.time()-t0:.2f}s')\n\n# Test scoring dummy ranked items\nt0 = time.time()\ndummy_ranked = {uid: list(range(100)) for uid in targets}\nres = score_rankings(dummy_ranked, graded)\nprint(f'Computed score_rankings in {time.time()-t0:.2f}s: {res}')\n\""
},
"recorded": {
"id": "run_shell_command__call_274516",
"name": "run_shell_command",
"args": {
"description": "Verify in-memory validation evaluation speed",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings, ranked_items, validate_ranking\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\nt0 = time.time()\ngraded = validation_truth(eligibility, targets)\nprint(f'Computed validation_truth in {time.time()-t0:.2f}s')\n\n# Test scoring dummy ranked items\nt0 = time.time()\ndummy_ranked = {uid: list(range(100)) for uid in targets}\nres = score_rankings(dummy_ranked, graded)\nprint(f'Computed score_rankings in {time.time()-t0:.2f}s: {res}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_274516",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Computed validation_truth in 0.39s\nComputed score_rankings in 0.09s: {'quality_ndcg@10': 0.0, 'quality_users': 4263}\nProcess Group PGID: 2113\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:25:19.554Z",
"resultDisplay": "Computed validation_truth in 0.39s\nComputed score_rankings in 0.09s: {'quality_ndcg@10': 0.0, 'quality_users': 4263}",
"description": "Verify in-memory validation evaluation speed",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:25:19.555Z",
"content": "\nOutput: Computed validation_truth in 0.39s\nComputed score_rankings in 0.09s: {'quality_ndcg@10': 0.0, 'quality_users': 4263}\nProcess Group PGID: 2113\n",
"isError": false
},
"responseMessageId": "81c1976f-17d0-4d57-b158-189f4f101cc2"
}
]
},
{
"id": 34,
"role": "agent",
"timestamp": "2026-09-29T04:25:33.535Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:25:33.535Z",
"text": "Achieving Faster Evaluations\n\nMy recent focus has been on optimizing the evaluation pipeline. I'm thrilled to report that by analyzing the timings, I've confirmed we can now complete full validation evaluations in under half a second, a significant speedup. Next, I'll be delving into the construction of the interaction matrix, $R$, and exploring the optimal nature of its elements."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:25:33.535Z",
"text": "Analyzing Listen Data\n\nI've been examining the `train_listens` data. I see that a listen with `played_ratio_pct >= 80%` is a definitive positive, but I'm also considering the implications of listens between 50% and 80%, and distinguishing skips below 20%. The recency of listens and organic intent are also key factors I'm exploring for their predictive power."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:25:33.535Z",
"callId": "run_shell_command__call_356574",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\n\nN = len(targets_list) # 8456\nM = len(cat_items) # 30000\n\nprint(f'N={N}, M={M}')\n\n# Load eligibility\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Let us load train_listens with a fast filter\nt0 = time.time()\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\ndf = tbl.to_pandas()\nprint(f'Loaded train_listens in {time.time()-t0:.2f}s')\n\n# Filter to targets and catalog\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)].copy()\nprint(f'Filtered {len(df)} rows in {time.time()-t0:.2f}s')\n\n# Simple interaction weight: played_ratio_pct >= 50\nstrong = df[df.played_ratio_pct >= 50].copy()\n# User and item indices\nu_idx = strong.uid.map(uid_to_idx).to_numpy()\ni_idx = strong.item_id.map(item_to_idx).to_numpy()\n\n# Interaction count or binary\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary: 1 if user listened to item >= 50%\nprint(f'R matrix shape: {R.shape}, nnz: {R.nnz}')\n\n# Let us test SVD\nfrom sklearn.decomposition import TruncatedSVD\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, random_state=42)\nU_svd = svd.fit_transform(R) # (8456, 64)\nV_svd = svd.components_ # (64, 30000)\npred_svd = U_svd @ V_svd # (8456, 30000)\nprint(f'SVD-64 fit & predict in {time.time()-t0:.2f}s')\n\n# Mask ineligible items: set to -1e9\npred_svd[~unpacked] = -1e9\n\n# Get top 100 for each user\nt0 = time.time()\n# argpartition for top 100\ntop100_idx = np.argpartition(-pred_svd, 100, axis=1)[:, :100]\n# sort within top 100\nrow_indices = np.arange(N)[:, None]\ntop100_scores = pred_svd[row_indices, top100_idx]\nsort_order = np.argsort(-top100_scores, axis=1)\nsorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n\nranked = {}\nfor r, uid in enumerate(targets_list):\n ranked[uid] = [cat_items[c] for c in sorted_top100_idx[r]]\n\nres = score_rankings(ranked, graded)\nprint(f'SVD-64 NDCG@10: {res[\\\"quality_ndcg@10\\\"]} (scored {res[\\\"quality_users\\\"]} users)')\n\"",
"description": "Test fast SVD model on validation"
},
"recorded": {
"id": "run_shell_command__call_356574",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\n\nN = len(targets_list) # 8456\nM = len(cat_items) # 30000\n\nprint(f'N={N}, M={M}')\n\n# Load eligibility\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Let us load train_listens with a fast filter\nt0 = time.time()\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\ndf = tbl.to_pandas()\nprint(f'Loaded train_listens in {time.time()-t0:.2f}s')\n\n# Filter to targets and catalog\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)].copy()\nprint(f'Filtered {len(df)} rows in {time.time()-t0:.2f}s')\n\n# Simple interaction weight: played_ratio_pct >= 50\nstrong = df[df.played_ratio_pct >= 50].copy()\n# User and item indices\nu_idx = strong.uid.map(uid_to_idx).to_numpy()\ni_idx = strong.item_id.map(item_to_idx).to_numpy()\n\n# Interaction count or binary\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary: 1 if user listened to item >= 50%\nprint(f'R matrix shape: {R.shape}, nnz: {R.nnz}')\n\n# Let us test SVD\nfrom sklearn.decomposition import TruncatedSVD\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, random_state=42)\nU_svd = svd.fit_transform(R) # (8456, 64)\nV_svd = svd.components_ # (64, 30000)\npred_svd = U_svd @ V_svd # (8456, 30000)\nprint(f'SVD-64 fit & predict in {time.time()-t0:.2f}s')\n\n# Mask ineligible items: set to -1e9\npred_svd[~unpacked] = -1e9\n\n# Get top 100 for each user\nt0 = time.time()\n# argpartition for top 100\ntop100_idx = np.argpartition(-pred_svd, 100, axis=1)[:, :100]\n# sort within top 100\nrow_indices = np.arange(N)[:, None]\ntop100_scores = pred_svd[row_indices, top100_idx]\nsort_order = np.argsort(-top100_scores, axis=1)\nsorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n\nranked = {}\nfor r, uid in enumerate(targets_list):\n ranked[uid] = [cat_items[c] for c in sorted_top100_idx[r]]\n\nres = score_rankings(ranked, graded)\nprint(f'SVD-64 NDCG@10: {res[\\\"quality_ndcg@10\\\"]} (scored {res[\\\"quality_users\\\"]} users)')\n\"",
"description": "Test fast SVD model on validation"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_356574",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 8, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 2133\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:25:34.519Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 8, in \nModuleNotFoundError: No module named 'ranking_evaluation'",
"description": "Test fast SVD model on validation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:25:34.520Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 8, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 2133\n",
"isError": false
},
"responseMessageId": "d375edf1-3e45-42e7-9fd6-81e470c9938b"
}
]
},
{
"id": 35,
"role": "agent",
"timestamp": "2026-09-29T04:25:43.868Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:25:43.868Z",
"callId": "run_shell_command__call_145079",
"name": "run_shell_command",
"input": {
"description": "Run SVD-64 experiment with correct PYTHONPATH",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\n\nN = len(targets_list) # 8456\nM = len(cat_items) # 30000\n\nprint(f'N={N}, M={M}')\n\n# Load eligibility\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Let us load train_listens with a fast filter\nt0 = time.time()\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\ndf = tbl.to_pandas()\nprint(f'Loaded train_listens in {time.time()-t0:.2f}s')\n\n# Filter to targets and catalog\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)].copy()\nprint(f'Filtered {len(df)} rows in {time.time()-t0:.2f}s')\n\n# Simple interaction weight: played_ratio_pct >= 50\nstrong = df[df.played_ratio_pct >= 50].copy()\nu_idx = strong.uid.map(uid_to_idx).to_numpy()\ni_idx = strong.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\nprint(f'R matrix shape: {R.shape}, nnz: {R.nnz}')\n\nfrom sklearn.decomposition import TruncatedSVD\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, random_state=42)\nU_svd = svd.fit_transform(R) # (8456, 64)\nV_svd = svd.components_ # (64, 30000)\npred_svd = U_svd @ V_svd # (8456, 30000)\nprint(f'SVD-64 fit & predict in {time.time()-t0:.2f}s')\n\n# Mask ineligible items: set to -1e9\npred_svd[~unpacked] = -1e9\n\nt0 = time.time()\ntop100_idx = np.argpartition(-pred_svd, 100, axis=1)[:, :100]\nrow_indices = np.arange(N)[:, None]\ntop100_scores = pred_svd[row_indices, top100_idx]\nsort_order = np.argsort(-top100_scores, axis=1)\nsorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n\nranked = {}\nfor r, uid in enumerate(targets_list):\n ranked[uid] = [cat_items[c] for c in sorted_top100_idx[r]]\n\nres = score_rankings(ranked, graded)\nprint(f'SVD-64 NDCG@10: {res[\\\"quality_ndcg@10\\\"]} (scored {res[\\\"quality_users\\\"]} users)')\n\""
},
"recorded": {
"id": "run_shell_command__call_145079",
"name": "run_shell_command",
"args": {
"description": "Run SVD-64 experiment with correct PYTHONPATH",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\n\nN = len(targets_list) # 8456\nM = len(cat_items) # 30000\n\nprint(f'N={N}, M={M}')\n\n# Load eligibility\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Let us load train_listens with a fast filter\nt0 = time.time()\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\ndf = tbl.to_pandas()\nprint(f'Loaded train_listens in {time.time()-t0:.2f}s')\n\n# Filter to targets and catalog\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)].copy()\nprint(f'Filtered {len(df)} rows in {time.time()-t0:.2f}s')\n\n# Simple interaction weight: played_ratio_pct >= 50\nstrong = df[df.played_ratio_pct >= 50].copy()\nu_idx = strong.uid.map(uid_to_idx).to_numpy()\ni_idx = strong.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\nprint(f'R matrix shape: {R.shape}, nnz: {R.nnz}')\n\nfrom sklearn.decomposition import TruncatedSVD\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, random_state=42)\nU_svd = svd.fit_transform(R) # (8456, 64)\nV_svd = svd.components_ # (64, 30000)\npred_svd = U_svd @ V_svd # (8456, 30000)\nprint(f'SVD-64 fit & predict in {time.time()-t0:.2f}s')\n\n# Mask ineligible items: set to -1e9\npred_svd[~unpacked] = -1e9\n\nt0 = time.time()\ntop100_idx = np.argpartition(-pred_svd, 100, axis=1)[:, :100]\nrow_indices = np.arange(N)[:, None]\ntop100_scores = pred_svd[row_indices, top100_idx]\nsort_order = np.argsort(-top100_scores, axis=1)\nsorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n\nranked = {}\nfor r, uid in enumerate(targets_list):\n ranked[uid] = [cat_items[c] for c in sorted_top100_idx[r]]\n\nres = score_rankings(ranked, graded)\nprint(f'SVD-64 NDCG@10: {res[\\\"quality_ndcg@10\\\"]} (scored {res[\\\"quality_users\\\"]} users)')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_145079",
"name": "run_shell_command",
"response": {
"output": "\nOutput: N=8456, M=30000\nLoaded train_listens in 0.47s\nFiltered 31062540 rows in 6.91s\nR matrix shape: (8456, 30000), nnz: 5514505\nSVD-64 fit & predict in 2.52s\nSVD-64 NDCG@10: 0.017121 (scored 4263 users)\nProcess Group PGID: 2151\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:25:59.185Z",
"resultDisplay": "N=8456, M=30000\nLoaded train_listens in 0.47s\nFiltered 31062540 rows in 6.91s\nR matrix shape: (8456, 30000), nnz: 5514505\nSVD-64 fit & predict in 2.52s\nSVD-64 NDCG@10: 0.017121 (scored 4263 users)",
"description": "Run SVD-64 experiment with correct PYTHONPATH",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:25:59.185Z",
"content": "\nOutput: N=8456, M=30000\nLoaded train_listens in 0.47s\nFiltered 31062540 rows in 6.91s\nR matrix shape: (8456, 30000), nnz: 5514505\nSVD-64 fit & predict in 2.52s\nSVD-64 NDCG@10: 0.017121 (scored 4263 users)\nProcess Group PGID: 2151\n",
"isError": false
},
"responseMessageId": "6c148e71-89a7-4a74-9c41-c1b748c16025"
}
]
},
{
"id": 36,
"role": "agent",
"timestamp": "2026-09-29T04:26:15.334Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:26:15.334Z",
"text": "Improving Popularity Models\n\nI'm thrilled by the immediate 60% jump to 0.017121 with SVD-64, achieved in just 2.5 seconds. This initial success is exciting, and my current focus is on understanding the underlying reasons for this rapid improvement. I'm eager to explore other collaborative filtering models and hybrid approaches to further boost performance."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:26:15.334Z",
"text": "Refining Item Similarity\n\nI've discovered a more efficient approach for item-item collaborative filtering. Instead of computing the large item-item similarity matrix directly, I can leverage matrix multiplication properties. By calculating $R R^T$ first, which results in a manageable $N \\times N$ matrix, I can then multiply it by $R$ to get the predictions. This avoids the massive $M \\times M$ matrix computation."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:26:15.334Z",
"callId": "run_shell_command__call_368909",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\n# Normalize items (column L2 normalization: equivalent to cosine similarity between items)\nt0 = time.time()\nR_item_norm = normalize(R, norm='l2', axis=0) # column-normalized\n# user predictions = R_user * (R_item_norm.T * R_item_norm)\n# Associative: = (R_user * R_item_norm.T) * R_item_norm\n# Here R_user can be normalized as well\nR_user_norm = normalize(R, norm='l2', axis=1)\n\n# Step 1: User-Item_norm product: R_user_norm @ R_item_norm.T is (8456, 8456)\nC = R_user_norm @ R_item_norm.T # sparse (8456, 8456)\nprint(f'Computed C = R_u @ R_i.T in {time.time()-t0:.2f}s, nnz={C.nnz}')\n\n# Step 2: C @ R_item_norm is (8456, 30000)\n# But wait, self-recommendations (items the user already listened to) are already ineligible in unpacked!\nt1 = time.time()\npred = (C @ R_item_norm).toarray()\nprint(f'Computed pred in {time.time()-t1:.2f}s')\n\npred[~unpacked] = -1e9\n\ntop100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\nrow_indices = np.arange(N)[:, None]\ntop100_scores = pred[row_indices, top100_idx]\nsort_order = np.argsort(-top100_scores, axis=1)\nsorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n\nranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\nres = score_rankings(ranked, graded)\nprint(f'Item-Item Cosine NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n\"",
"description": "Evaluate Item-Item Cosine via associative multiplication"
},
"recorded": {
"id": "run_shell_command__call_368909",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\n# Normalize items (column L2 normalization: equivalent to cosine similarity between items)\nt0 = time.time()\nR_item_norm = normalize(R, norm='l2', axis=0) # column-normalized\n# user predictions = R_user * (R_item_norm.T * R_item_norm)\n# Associative: = (R_user * R_item_norm.T) * R_item_norm\n# Here R_user can be normalized as well\nR_user_norm = normalize(R, norm='l2', axis=1)\n\n# Step 1: User-Item_norm product: R_user_norm @ R_item_norm.T is (8456, 8456)\nC = R_user_norm @ R_item_norm.T # sparse (8456, 8456)\nprint(f'Computed C = R_u @ R_i.T in {time.time()-t0:.2f}s, nnz={C.nnz}')\n\n# Step 2: C @ R_item_norm is (8456, 30000)\n# But wait, self-recommendations (items the user already listened to) are already ineligible in unpacked!\nt1 = time.time()\npred = (C @ R_item_norm).toarray()\nprint(f'Computed pred in {time.time()-t1:.2f}s')\n\npred[~unpacked] = -1e9\n\ntop100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\nrow_indices = np.arange(N)[:, None]\ntop100_scores = pred[row_indices, top100_idx]\nsort_order = np.argsort(-top100_scores, axis=1)\nsorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n\nranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\nres = score_rankings(ranked, graded)\nprint(f'Item-Item Cosine NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n\"",
"description": "Evaluate Item-Item Cosine via associative multiplication"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_368909",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Computed C = R_u @ R_i.T in 5.86s, nnz=63275517\nComputed pred in 92.31s\nItem-Item Cosine NDCG@10: 0.01249\nProcess Group PGID: 2186\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:28:04.521Z",
"resultDisplay": "Computed C = R_u @ R_i.T in 5.86s, nnz=63275517\nComputed pred in 92.31s\nItem-Item Cosine NDCG@10: 0.01249",
"description": "Evaluate Item-Item Cosine via associative multiplication",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:28:04.522Z",
"content": "\nOutput: Computed C = R_u @ R_i.T in 5.86s, nnz=63275517\nComputed pred in 92.31s\nItem-Item Cosine NDCG@10: 0.01249\nProcess Group PGID: 2186\n",
"isError": false
},
"responseMessageId": "5e264bd5-c9f5-42da-b08e-7ee4ea660f44"
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]
},
{
"id": 37,
"role": "agent",
"timestamp": "2026-09-29T04:28:16.919Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:28:16.919Z",
"text": "Calculating Prediction\n\nI've just finished computing the prediction. The sheer density of matrix $C$, at nearly 90%, made this step quite time-consuming, taking 92 seconds. The resulting score is 0.01249."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:28:16.919Z",
"text": "Refining SVD Approach\n\nMy recent exploration has focused on improving the SVD prediction. I'm investigating the impact of tuning components and weighting schemes, considering options like TF-IDF or log frequency. I'm also experimenting with different numbers of SVD components and normalizing user/item vectors. Additionally, I'm delving into powering singular values, specifically exploring the PureSVD approach ($\\alpha=0$) as it appears to mitigate overemphasis on popular items."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:28:16.919Z",
"callId": "run_shell_command__call_229443",
"name": "run_shell_command",
"input": {
"description": "Test SVD rank k and singular value power alpha",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\nprint(f'R constructed: shape={R.shape}, nnz={R.nnz}')\n\n# Compute SVD with k=256\nt0 = time.time()\nU, S, Vt = svds(R, k=256)\n# svds returns ascending singular values, sort descending\nidx = np.argsort(-S)\nU = U[:, idx]\nS = S[idx]\nVt = Vt[idx, :]\nprint(f'Computed svds(k=256) in {time.time()-t0:.2f}s')\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\nfor k in [64, 128, 192, 256]:\n for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]:\n t0 = time.time()\n # pred = (U[:, :k] * (S[:k]**alpha)) @ Vt[:k, :]\n U_k = U[:, :k] * (S[:k]**alpha)\n pred = U_k @ Vt[:k, :]\n score = eval_pred(pred)\n print(f'k={k:3d}, alpha={alpha:.2f} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\""
},
"recorded": {
"id": "run_shell_command__call_229443",
"name": "run_shell_command",
"args": {
"description": "Test SVD rank k and singular value power alpha",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\nprint(f'R constructed: shape={R.shape}, nnz={R.nnz}')\n\n# Compute SVD with k=256\nt0 = time.time()\nU, S, Vt = svds(R, k=256)\n# svds returns ascending singular values, sort descending\nidx = np.argsort(-S)\nU = U[:, idx]\nS = S[idx]\nVt = Vt[idx, :]\nprint(f'Computed svds(k=256) in {time.time()-t0:.2f}s')\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\nfor k in [64, 128, 192, 256]:\n for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]:\n t0 = time.time()\n # pred = (U[:, :k] * (S[:k]**alpha)) @ Vt[:k, :]\n U_k = U[:, :k] * (S[:k]**alpha)\n pred = U_k @ Vt[:k, :]\n score = eval_pred(pred)\n print(f'k={k:3d}, alpha={alpha:.2f} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_229443",
"name": "run_shell_command",
"response": {
"output": "\nOutput: R constructed: shape=(8456, 30000), nnz=5514505\nComputed svds(k=256) in 21.37s\nk= 64, alpha=0.00 -> NDCG@10 = 0.016787 (1.83s)\nk= 64, alpha=0.25 -> NDCG@10 = 0.016857 (1.73s)\nk= 64, alpha=0.50 -> NDCG@10 = 0.017075 (1.68s)\nk= 64, alpha=0.75 -> NDCG@10 = 0.017572 (1.72s)\nk= 64, alpha=1.00 -> NDCG@10 = 0.017077 (1.70s)\nk=128, alpha=0.00 -> NDCG@10 = 0.019034 (1.85s)\nk=128, alpha=0.25 -> NDCG@10 = 0.019406 (1.89s)\nk=128, alpha=0.50 -> NDCG@10 = 0.019274 (1.84s)\nk=128, alpha=0.75 -> NDCG@10 = 0.019152 (1.90s)\nk=128, alpha=1.00 -> NDCG@10 = 0.018916 (1.89s)\nk=192, alpha=0.00 -> NDCG@10 = 0.018039 (2.01s)\nk=192, alpha=0.25 -> NDCG@10 = 0.018478 (1.99s)\nk=192, alpha=0.50 -> NDCG@10 = 0.018716 (2.00s)\nk=192, alpha=0.75 -> NDCG@10 = 0.019405 (2.01s)\nk=192, alpha=1.00 -> NDCG@10 = 0.019673 (2.01s)\nk=256, alpha=0.00 -> NDCG@10 = 0.018581 (2.15s)\nk=256, alpha=0.25 -> NDCG@10 = 0.018655 (2.14s)\nk=256, alpha=0.50 -> NDCG@10 = 0.018902 (2.17s)\nk=256, alpha=0.75 -> NDCG@10 = 0.018778 (2.14s)\nk=256, alpha=1.00 -> NDCG@10 = 0.019073 (2.18s)\nProcess Group PGID: 2225\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:29:26.300Z",
"resultDisplay": "R constructed: shape=(8456, 30000), nnz=5514505\nComputed svds(k=256) in 21.37s\nk= 64, alpha=0.00 -> NDCG@10 = 0.016787 (1.83s)\nk= 64, alpha=0.25 -> NDCG@10 = 0.016857 (1.73s)\nk= 64, alpha=0.50 -> NDCG@10 = 0.017075 (1.68s)\nk= 64, alpha=0.75 -> NDCG@10 = 0.017572 (1.72s)\nk= 64, alpha=1.00 -> NDCG@10 = 0.017077 (1.70s)\nk=128, alpha=0.00 -> NDCG@10 = 0.019034 (1.85s)\nk=128, alpha=0.25 -> NDCG@10 = 0.019406 (1.89s)\nk=128, alpha=0.50 -> NDCG@10 = 0.019274 (1.84s)\nk=128, alpha=0.75 -> NDCG@10 = 0.019152 (1.90s)\nk=128, alpha=1.00 -> NDCG@10 = 0.018916 (1.89s)\nk=192, alpha=0.00 -> NDCG@10 = 0.018039 (2.01s)\nk=192, alpha=0.25 -> NDCG@10 = 0.018478 (1.99s)\nk=192, alpha=0.50 -> NDCG@10 = 0.018716 (2.00s)\nk=192, alpha=0.75 -> NDCG@10 = 0.019405 (2.01s)\nk=192, alpha=1.00 -> NDCG@10 = 0.019673 (2.01s)\nk=256, alpha=0.00 -> NDCG@10 = 0.018581 (2.15s)\nk=256, alpha=0.25 -> NDCG@10 = 0.018655 (2.14s)\nk=256, alpha=0.50 -> NDCG@10 = 0.018902 (2.17s)\nk=256, alpha=0.75 -> NDCG@10 = 0.018778 (2.14s)\nk=256, alpha=1.00 -> NDCG@10 = 0.019073 (2.18s)",
"description": "Test SVD rank k and singular value power alpha",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:29:26.301Z",
"content": "\nOutput: R constructed: shape=(8456, 30000), nnz=5514505\nComputed svds(k=256) in 21.37s\nk= 64, alpha=0.00 -> NDCG@10 = 0.016787 (1.83s)\nk= 64, alpha=0.25 -> NDCG@10 = 0.016857 (1.73s)\nk= 64, alpha=0.50 -> NDCG@10 = 0.017075 (1.68s)\nk= 64, alpha=0.75 -> NDCG@10 = 0.017572 (1.72s)\nk= 64, alpha=1.00 -> NDCG@10 = 0.017077 (1.70s)\nk=128, alpha=0.00 -> NDCG@10 = 0.019034 (1.85s)\nk=128, alpha=0.25 -> NDCG@10 = 0.019406 (1.89s)\nk=128, alpha=0.50 -> NDCG@10 = 0.019274 (1.84s)\nk=128, alpha=0.75 -> NDCG@10 = 0.019152 (1.90s)\nk=128, alpha=1.00 -> NDCG@10 = 0.018916 (1.89s)\nk=192, alpha=0.00 -> NDCG@10 = 0.018039 (2.01s)\nk=192, alpha=0.25 -> NDCG@10 = 0.018478 (1.99s)\nk=192, alpha=0.50 -> NDCG@10 = 0.018716 (2.00s)\nk=192, alpha=0.75 -> NDCG@10 = 0.019405 (2.01s)\nk=192, alpha=1.00 -> NDCG@10 = 0.019673 (2.01s)\nk=256, alpha=0.00 -> NDCG@10 = 0.018581 (2.15s)\nk=256, alpha=0.25 -> NDCG@10 = 0.018655 (2.14s)\nk=256, alpha=0.50 -> NDCG@10 = 0.018902 (2.17s)\nk=256, alpha=0.75 -> NDCG@10 = 0.018778 (2.14s)\nk=256, alpha=1.00 -> NDCG@10 = 0.019073 (2.18s)\nProcess Group PGID: 2225\n",
"isError": false
},
"responseMessageId": "f8a0a729-ea8d-4d14-9d06-e09e3a74b96c"
}
]
},
{
"id": 38,
"role": "agent",
"timestamp": "2026-09-29T04:29:39.693Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:29:39.693Z",
"text": "Analyzing SVD Performance\n\nI'm seeing promising initial SVD results, with $k=128, \\alpha=0.25$ slightly outperforming $k=192, \\alpha=1.00$ on the simple binary played ratio matrix. The next step is integrating interaction rec data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:29:39.693Z",
"callId": "run_shell_command__call_153634",
"name": "run_shell_command",
"input": {
"description": "Test User-User k-NN CF with various neighborhood sizes",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Test User-User Cosine with Top-K neighbors\nt0 = time.time()\nR_norm = normalize(R, norm='l2', axis=1) # (8456, 30000)\n# Compute user-user similarity: W = R_norm @ R_norm.T\n# Since N=8456, W is (8456, 8456)\nW = (R_norm @ R_norm.T).toarray()\nprint(f'Computed user similarity W (8456x8456) in {time.time()-t0:.2f}s')\n\n# Set diagonal of W to 0\nnp.fill_diagonal(W, 0)\n\nfor top_k_users in [50, 100, 200, 500, 1000]:\n t0 = time.time()\n # Zero out all except top_k_users per row\n # Use partition to find threshold\n kth = W.shape[1] - top_k_users\n # partition along columns\n thresh = np.partition(W, kth, axis=1)[:, kth:kth+1]\n W_k = np.where(W >= thresh, W, 0.0).astype(np.float32)\n # Convert W_k to csr_matrix for fast multiplication with sparse R\n W_k_sparse = sp.csr_matrix(W_k)\n pred_user = (W_k_sparse @ R).toarray()\n score = eval_pred(pred_user)\n print(f'User-User k-NN k={top_k_users:4d} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\""
},
"recorded": {
"id": "run_shell_command__call_153634",
"name": "run_shell_command",
"args": {
"description": "Test User-User k-NN CF with various neighborhood sizes",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Test User-User Cosine with Top-K neighbors\nt0 = time.time()\nR_norm = normalize(R, norm='l2', axis=1) # (8456, 30000)\n# Compute user-user similarity: W = R_norm @ R_norm.T\n# Since N=8456, W is (8456, 8456)\nW = (R_norm @ R_norm.T).toarray()\nprint(f'Computed user similarity W (8456x8456) in {time.time()-t0:.2f}s')\n\n# Set diagonal of W to 0\nnp.fill_diagonal(W, 0)\n\nfor top_k_users in [50, 100, 200, 500, 1000]:\n t0 = time.time()\n # Zero out all except top_k_users per row\n # Use partition to find threshold\n kth = W.shape[1] - top_k_users\n # partition along columns\n thresh = np.partition(W, kth, axis=1)[:, kth:kth+1]\n W_k = np.where(W >= thresh, W, 0.0).astype(np.float32)\n # Convert W_k to csr_matrix for fast multiplication with sparse R\n W_k_sparse = sp.csr_matrix(W_k)\n pred_user = (W_k_sparse @ R).toarray()\n score = eval_pred(pred_user)\n print(f'User-User k-NN k={top_k_users:4d} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_153634",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Computed user similarity W (8456x8456) in 5.89s\nUser-User k-NN k= 50 -> NDCG@10 = 0.016141 (5.22s)\nUser-User k-NN k= 100 -> NDCG@10 = 0.015614 (6.71s)\nUser-User k-NN k= 200 -> NDCG@10 = 0.015026 (9.26s)\nUser-User k-NN k= 500 -> NDCG@10 = 0.014031 (15.11s)\nUser-User k-NN k=1000 -> NDCG@10 = 0.013844 (23.06s)\nProcess Group PGID: 2264\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:30:54.621Z",
"resultDisplay": "Computed user similarity W (8456x8456) in 5.89s\nUser-User k-NN k= 50 -> NDCG@10 = 0.016141 (5.22s)\nUser-User k-NN k= 100 -> NDCG@10 = 0.015614 (6.71s)\nUser-User k-NN k= 200 -> NDCG@10 = 0.015026 (9.26s)\nUser-User k-NN k= 500 -> NDCG@10 = 0.014031 (15.11s)\nUser-User k-NN k=1000 -> NDCG@10 = 0.013844 (23.06s)",
"description": "Test User-User k-NN CF with various neighborhood sizes",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:30:54.621Z",
"content": "\nOutput: Computed user similarity W (8456x8456) in 5.89s\nUser-User k-NN k= 50 -> NDCG@10 = 0.016141 (5.22s)\nUser-User k-NN k= 100 -> NDCG@10 = 0.015614 (6.71s)\nUser-User k-NN k= 200 -> NDCG@10 = 0.015026 (9.26s)\nUser-User k-NN k= 500 -> NDCG@10 = 0.014031 (15.11s)\nUser-User k-NN k=1000 -> NDCG@10 = 0.013844 (23.06s)\nProcess Group PGID: 2264\n",
"isError": false
},
"responseMessageId": "c6dccfeb-2a8f-4020-8f9b-bafe84ea6701"
}
]
},
{
"id": 39,
"role": "agent",
"timestamp": "2026-09-29T04:31:37.931Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:31:37.931Z",
"text": "Exploring User-User kNN\n\nMy current focus is on refining User-User kNN. I've observed a promising result with k=50 yielding an accuracy of 0.016141. I'm now investigating how BM25/TF-IDF item weighting might further enhance these User-User calculations, particularly in how to appropriately weigh co-occurrences of shared listening habits."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:31:37.931Z",
"callId": "run_shell_command__call_157174",
"name": "run_shell_command",
"input": {
"description": "Test IDF weighting with SVD",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Item popularity n_j\nn_j = np.array(R.sum(axis=0)).flatten()\n# IDF\nidf = np.log((N + 1.0) / (n_j + 1.0))\n# Let us test SVD on IDF-weighted R\nfor p in [0.25, 0.5, 0.75, 1.0]:\n R_idf = R.multiply(idf**p).tocsr()\n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx], S[idx], Vt[idx, :]\n for alpha in [0.5, 0.75, 1.0]:\n pred = (U * (S**alpha)) @ Vt\n print(f'IDF p={p:.2f}, alpha={alpha:.2f} -> SVD NDCG@10 = {eval_pred(pred):.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_157174",
"name": "run_shell_command",
"args": {
"description": "Test IDF weighting with SVD",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Item popularity n_j\nn_j = np.array(R.sum(axis=0)).flatten()\n# IDF\nidf = np.log((N + 1.0) / (n_j + 1.0))\n# Let us test SVD on IDF-weighted R\nfor p in [0.25, 0.5, 0.75, 1.0]:\n R_idf = R.multiply(idf**p).tocsr()\n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx], S[idx], Vt[idx, :]\n for alpha in [0.5, 0.75, 1.0]:\n pred = (U * (S**alpha)) @ Vt\n print(f'IDF p={p:.2f}, alpha={alpha:.2f} -> SVD NDCG@10 = {eval_pred(pred):.6f}')\n\""
},
"result": [
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"functionResponse": {
"id": "run_shell_command__call_157174",
"name": "run_shell_command",
"response": {
"output": "\nOutput: IDF p=0.25, alpha=0.50 -> SVD NDCG@10 = 0.019336\nIDF p=0.25, alpha=0.75 -> SVD NDCG@10 = 0.019684\nIDF p=0.25, alpha=1.00 -> SVD NDCG@10 = 0.019117\nIDF p=0.50, alpha=0.50 -> SVD NDCG@10 = 0.019292\nIDF p=0.50, alpha=0.75 -> SVD NDCG@10 = 0.019479\nIDF p=0.50, alpha=1.00 -> SVD NDCG@10 = 0.019935\nIDF p=0.75, alpha=0.50 -> SVD NDCG@10 = 0.018273\nIDF p=0.75, alpha=0.75 -> SVD NDCG@10 = 0.018423\nIDF p=0.75, alpha=1.00 -> SVD NDCG@10 = 0.018897\nIDF p=1.00, alpha=0.50 -> SVD NDCG@10 = 0.016926\nIDF p=1.00, alpha=0.75 -> SVD NDCG@10 = 0.017248\nIDF p=1.00, alpha=1.00 -> SVD NDCG@10 = 0.017685\nProcess Group PGID: 2309\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:33:12.079Z",
"resultDisplay": "IDF p=0.25, alpha=0.50 -> SVD NDCG@10 = 0.019336\nIDF p=0.25, alpha=0.75 -> SVD NDCG@10 = 0.019684\nIDF p=0.25, alpha=1.00 -> SVD NDCG@10 = 0.019117\nIDF p=0.50, alpha=0.50 -> SVD NDCG@10 = 0.019292\nIDF p=0.50, alpha=0.75 -> SVD NDCG@10 = 0.019479\nIDF p=0.50, alpha=1.00 -> SVD NDCG@10 = 0.019935\nIDF p=0.75, alpha=0.50 -> SVD NDCG@10 = 0.018273\nIDF p=0.75, alpha=0.75 -> SVD NDCG@10 = 0.018423\nIDF p=0.75, alpha=1.00 -> SVD NDCG@10 = 0.018897\nIDF p=1.00, alpha=0.50 -> SVD NDCG@10 = 0.016926\nIDF p=1.00, alpha=0.75 -> SVD NDCG@10 = 0.017248\nIDF p=1.00, alpha=1.00 -> SVD NDCG@10 = 0.017685",
"description": "Test IDF weighting with SVD",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:33:12.081Z",
"content": "\nOutput: IDF p=0.25, alpha=0.50 -> SVD NDCG@10 = 0.019336\nIDF p=0.25, alpha=0.75 -> SVD NDCG@10 = 0.019684\nIDF p=0.25, alpha=1.00 -> SVD NDCG@10 = 0.019117\nIDF p=0.50, alpha=0.50 -> SVD NDCG@10 = 0.019292\nIDF p=0.50, alpha=0.75 -> SVD NDCG@10 = 0.019479\nIDF p=0.50, alpha=1.00 -> SVD NDCG@10 = 0.019935\nIDF p=0.75, alpha=0.50 -> SVD NDCG@10 = 0.018273\nIDF p=0.75, alpha=0.75 -> SVD NDCG@10 = 0.018423\nIDF p=0.75, alpha=1.00 -> SVD NDCG@10 = 0.018897\nIDF p=1.00, alpha=0.50 -> SVD NDCG@10 = 0.016926\nIDF p=1.00, alpha=0.75 -> SVD NDCG@10 = 0.017248\nIDF p=1.00, alpha=1.00 -> SVD NDCG@10 = 0.017685\nProcess Group PGID: 2309\n",
"isError": false
},
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]
},
{
"id": 40,
"role": "agent",
"timestamp": "2026-09-29T04:33:22.179Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:33:22.179Z",
"text": "Exploring User-User CF\n\nI'm now investigating the impact of IDF weighting on our User-User collaborative filtering approach. My current focus is on how applying IDF to user vectors, specifically in the similarity calculation, influences the overall performance metrics."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:33:22.179Z",
"callId": "run_shell_command__call_218952",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\nn_j = np.array(R.sum(axis=0)).flatten()\nidf = np.log((N + 1.0) / (n_j + 1.0))\n\nfor p in [0.5, 1.0]:\n R_idf = R.multiply(idf**p).tocsr()\n R_norm = normalize(R_idf, norm='l2', axis=1)\n W = (R_norm @ R_norm.T).toarray()\n np.fill_diagonal(W, 0)\n for top_k in [30, 50, 100]:\n kth = W.shape[1] - top_k\n thresh = np.partition(W, kth, axis=1)[:, kth:kth+1]\n W_k = np.where(W >= thresh, W, 0.0).astype(np.float32)\n # Power weights\n for power in [1.0, 2.0]:\n W_kp = sp.csr_matrix(W_k**power)\n pred = (W_kp @ R).toarray()\n print(f'User-User IDF p={p}, k={top_k}, power={power} -> NDCG@10 = {eval_pred(pred):.6f}')\n\"",
"description": "Test User-User k-NN with IDF weighting and power"
},
"recorded": {
"id": "run_shell_command__call_218952",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\nn_j = np.array(R.sum(axis=0)).flatten()\nidf = np.log((N + 1.0) / (n_j + 1.0))\n\nfor p in [0.5, 1.0]:\n R_idf = R.multiply(idf**p).tocsr()\n R_norm = normalize(R_idf, norm='l2', axis=1)\n W = (R_norm @ R_norm.T).toarray()\n np.fill_diagonal(W, 0)\n for top_k in [30, 50, 100]:\n kth = W.shape[1] - top_k\n thresh = np.partition(W, kth, axis=1)[:, kth:kth+1]\n W_k = np.where(W >= thresh, W, 0.0).astype(np.float32)\n # Power weights\n for power in [1.0, 2.0]:\n W_kp = sp.csr_matrix(W_k**power)\n pred = (W_kp @ R).toarray()\n print(f'User-User IDF p={p}, k={top_k}, power={power} -> NDCG@10 = {eval_pred(pred):.6f}')\n\"",
"description": "Test User-User k-NN with IDF weighting and power"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_218952",
"name": "run_shell_command",
"response": {
"output": "\nOutput: User-User IDF p=0.5, k=30, power=1.0 -> NDCG@10 = 0.017023\nUser-User IDF p=0.5, k=30, power=2.0 -> NDCG@10 = 0.017023\nUser-User IDF p=0.5, k=50, power=1.0 -> NDCG@10 = 0.016660\nUser-User IDF p=0.5, k=50, power=2.0 -> NDCG@10 = 0.016718\nUser-User IDF p=0.5, k=100, power=1.0 -> NDCG@10 = 0.016198\nUser-User IDF p=0.5, k=100, power=2.0 -> NDCG@10 = 0.016450\nUser-User IDF p=1.0, k=30, power=1.0 -> NDCG@10 = 0.016281\nUser-User IDF p=1.0, k=30, power=2.0 -> NDCG@10 = 0.016289\nUser-User IDF p=1.0, k=50, power=1.0 -> NDCG@10 = 0.017501\nUser-User IDF p=1.0, k=50, power=2.0 -> NDCG@10 = 0.017175\nUser-User IDF p=1.0, k=100, power=1.0 -> NDCG@10 = 0.016475\nUser-User IDF p=1.0, k=100, power=2.0 -> NDCG@10 = 0.016862\nProcess Group PGID: 2348\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:34:45.003Z",
"resultDisplay": "User-User IDF p=0.5, k=30, power=1.0 -> NDCG@10 = 0.017023\nUser-User IDF p=0.5, k=30, power=2.0 -> NDCG@10 = 0.017023\nUser-User IDF p=0.5, k=50, power=1.0 -> NDCG@10 = 0.016660\nUser-User IDF p=0.5, k=50, power=2.0 -> NDCG@10 = 0.016718\nUser-User IDF p=0.5, k=100, power=1.0 -> NDCG@10 = 0.016198\nUser-User IDF p=0.5, k=100, power=2.0 -> NDCG@10 = 0.016450\nUser-User IDF p=1.0, k=30, power=1.0 -> NDCG@10 = 0.016281\nUser-User IDF p=1.0, k=30, power=2.0 -> NDCG@10 = 0.016289\nUser-User IDF p=1.0, k=50, power=1.0 -> NDCG@10 = 0.017501\nUser-User IDF p=1.0, k=50, power=2.0 -> NDCG@10 = 0.017175\nUser-User IDF p=1.0, k=100, power=1.0 -> NDCG@10 = 0.016475\nUser-User IDF p=1.0, k=100, power=2.0 -> NDCG@10 = 0.016862",
"description": "Test User-User k-NN with IDF weighting and power",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:34:45.004Z",
"content": "\nOutput: User-User IDF p=0.5, k=30, power=1.0 -> NDCG@10 = 0.017023\nUser-User IDF p=0.5, k=30, power=2.0 -> NDCG@10 = 0.017023\nUser-User IDF p=0.5, k=50, power=1.0 -> NDCG@10 = 0.016660\nUser-User IDF p=0.5, k=50, power=2.0 -> NDCG@10 = 0.016718\nUser-User IDF p=0.5, k=100, power=1.0 -> NDCG@10 = 0.016198\nUser-User IDF p=0.5, k=100, power=2.0 -> NDCG@10 = 0.016450\nUser-User IDF p=1.0, k=30, power=1.0 -> NDCG@10 = 0.016281\nUser-User IDF p=1.0, k=30, power=2.0 -> NDCG@10 = 0.016289\nUser-User IDF p=1.0, k=50, power=1.0 -> NDCG@10 = 0.017501\nUser-User IDF p=1.0, k=50, power=2.0 -> NDCG@10 = 0.017175\nUser-User IDF p=1.0, k=100, power=1.0 -> NDCG@10 = 0.016475\nUser-User IDF p=1.0, k=100, power=2.0 -> NDCG@10 = 0.016862\nProcess Group PGID: 2348\n",
"isError": false
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"responseMessageId": "9bcde017-952f-4d10-bfed-bd62e71c7b5b"
}
]
},
{
"id": 41,
"role": "agent",
"timestamp": "2026-09-29T04:34:52.359Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:34:52.359Z",
"text": "Exploring User-EASE\n\nMy current focus is on implementing the User-EASE model, following its definition from Steck's 2019 WWW paper. I've successfully integrated SVD and User-User k-NN, and now I'm diving into the mathematical formulation of EASE, specifically the matrix operations involving G and P."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:34:52.359Z",
"callId": "run_shell_command__call_273424",
"name": "run_shell_command",
"input": {
"description": "Test User-EASE model on validation",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Compute Gram matrix G = R @ R.T\nt0 = time.time()\nG = (R @ R.T).toarray().astype(np.float32)\nprint(f'G computed in {time.time()-t0:.2f}s')\n\nfor reg in [50.0, 100.0, 200.0, 500.0, 1000.0, 2000.0]:\n t0 = time.time()\n G_reg = G.copy()\n diag_indices = np.diag_indices_from(G_reg)\n G_reg[diag_indices] += reg\n P = np.linalg.inv(G_reg)\n P = -P / np.diag(P)[:, None]\n P[diag_indices] = 0.0\n \n # Compute pred = P @ R\n pred = P @ R\n score = eval_pred(pred)\n print(f'User-EASE reg={reg:6.1f} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\""
},
"recorded": {
"id": "run_shell_command__call_273424",
"name": "run_shell_command",
"args": {
"description": "Test User-EASE model on validation",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Compute Gram matrix G = R @ R.T\nt0 = time.time()\nG = (R @ R.T).toarray().astype(np.float32)\nprint(f'G computed in {time.time()-t0:.2f}s')\n\nfor reg in [50.0, 100.0, 200.0, 500.0, 1000.0, 2000.0]:\n t0 = time.time()\n G_reg = G.copy()\n diag_indices = np.diag_indices_from(G_reg)\n G_reg[diag_indices] += reg\n P = np.linalg.inv(G_reg)\n P = -P / np.diag(P)[:, None]\n P[diag_indices] = 0.0\n \n # Compute pred = P @ R\n pred = P @ R\n score = eval_pred(pred)\n print(f'User-EASE reg={reg:6.1f} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_273424",
"name": "run_shell_command",
"response": {
"output": "\nOutput: G computed in 5.96s\nUser-EASE reg= 50.0 -> NDCG@10 = 0.016856 (29.47s)\nUser-EASE reg= 100.0 -> NDCG@10 = 0.018053 (29.40s)\nUser-EASE reg= 200.0 -> NDCG@10 = 0.019040 (29.48s)\nUser-EASE reg= 500.0 -> NDCG@10 = 0.019749 (29.44s)\nUser-EASE reg=1000.0 -> NDCG@10 = 0.019765 (29.79s)\nUser-EASE reg=2000.0 -> NDCG@10 = 0.020333 (29.42s)\nProcess Group PGID: 2393\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:38:04.958Z",
"resultDisplay": "G computed in 5.96s\nUser-EASE reg= 50.0 -> NDCG@10 = 0.016856 (29.47s)\nUser-EASE reg= 100.0 -> NDCG@10 = 0.018053 (29.40s)\nUser-EASE reg= 200.0 -> NDCG@10 = 0.019040 (29.48s)\nUser-EASE reg= 500.0 -> NDCG@10 = 0.019749 (29.44s)\nUser-EASE reg=1000.0 -> NDCG@10 = 0.019765 (29.79s)\nUser-EASE reg=2000.0 -> NDCG@10 = 0.020333 (29.42s)",
"description": "Test User-EASE model on validation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:38:04.959Z",
"content": "\nOutput: G computed in 5.96s\nUser-EASE reg= 50.0 -> NDCG@10 = 0.016856 (29.47s)\nUser-EASE reg= 100.0 -> NDCG@10 = 0.018053 (29.40s)\nUser-EASE reg= 200.0 -> NDCG@10 = 0.019040 (29.48s)\nUser-EASE reg= 500.0 -> NDCG@10 = 0.019749 (29.44s)\nUser-EASE reg=1000.0 -> NDCG@10 = 0.019765 (29.79s)\nUser-EASE reg=2000.0 -> NDCG@10 = 0.020333 (29.42s)\nProcess Group PGID: 2393\n",
"isError": false
},
"responseMessageId": "97307d96-277b-44ab-942b-08348b8ed7a0"
}
]
},
{
"id": 42,
"role": "agent",
"timestamp": "2026-09-29T04:38:54.006Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:38:54.006Z",
"text": "Exploring Higher Regularization\n\nI'm excited by the recent jump in NDCG@10 to 0.020333 with reg=2000. I'm now investigating even greater regularization values (3000, 5000, 10000) and exploring the impact of incorporating recency decay, both exponential and time-weighted, to further refine performance."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:38:54.006Z",
"callId": "run_shell_command__call_285646",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport numpy as np\nimport scipy.linalg.lapack as lapack\nimport time\n\nA = np.random.randn(8456, 8456).astype(np.float64)\nG = A.T @ A + 1000.0 * np.eye(8456)\n\nt0 = time.time()\nc, info = lapack.dpotrf(G, lower=False)\ninv, info = lapack.dpotri(c, lower=False)\n# dpotri only fills upper triangle, fill lower\ninv = np.triu(inv) + np.triu(inv, 1).T\nprint(f'Cholesky inversion (dpotri) took: {time.time()-t0:.2f}s')\n\nt0 = time.time()\ninv2 = np.linalg.inv(G)\nprint(f'np.linalg.inv took: {time.time()-t0:.2f}s')\n\"",
"description": "Benchmark Cholesky inversion vs np.linalg.inv"
},
"recorded": {
"id": "run_shell_command__call_285646",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport numpy as np\nimport scipy.linalg.lapack as lapack\nimport time\n\nA = np.random.randn(8456, 8456).astype(np.float64)\nG = A.T @ A + 1000.0 * np.eye(8456)\n\nt0 = time.time()\nc, info = lapack.dpotrf(G, lower=False)\ninv, info = lapack.dpotri(c, lower=False)\n# dpotri only fills upper triangle, fill lower\ninv = np.triu(inv) + np.triu(inv, 1).T\nprint(f'Cholesky inversion (dpotri) took: {time.time()-t0:.2f}s')\n\nt0 = time.time()\ninv2 = np.linalg.inv(G)\nprint(f'np.linalg.inv took: {time.time()-t0:.2f}s')\n\"",
"description": "Benchmark Cholesky inversion vs np.linalg.inv"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_285646",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Cholesky inversion (dpotri) took: 7.74s\nnp.linalg.inv took: 16.32s\nProcess Group PGID: 2438\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:39:26.688Z",
"resultDisplay": "Cholesky inversion (dpotri) took: 7.74s\nnp.linalg.inv took: 16.32s",
"description": "Benchmark Cholesky inversion vs np.linalg.inv",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:39:26.689Z",
"content": "\nOutput: Cholesky inversion (dpotri) took: 7.74s\nnp.linalg.inv took: 16.32s\nProcess Group PGID: 2438\n",
"isError": false
},
"responseMessageId": "c0de15fd-5f9a-4efa-980e-d63fedf754ac"
}
]
},
{
"id": 43,
"role": "agent",
"timestamp": "2026-09-29T04:39:46.790Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:39:46.789Z",
"text": "Optimizing Cholesky Inversion\n\nI've significantly reduced Cholesky inversion time from 16.32s to 7.74s by leveraging `dpotrf` and `dpotri`. My next focus is to dissect the feature set and weights in R that contribute most to accuracy, starting with an in-depth analysis of `played_ratio_pct` and its impact when `played_ratio_pct >= 80`."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:39:46.789Z",
"text": "Investigating Feature Weighting\n\nI'm now exploring the influence of `played_ratio_pct` categories on model performance, assigning differential weights to segments like `>= 80%` and `50-80%`. I'm also considering boosts for organic listens and investigating time-based decay mechanisms. Explicit likes and dislikes are also on my radar."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:39:46.790Z",
"callId": "run_shell_command__call_274819",
"name": "run_shell_command",
"input": {
"description": "Test temporal decay half-life on SVD performance",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\nimport scipy.linalg.lapack as lapack\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Load listens\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\nlistens = tbl.to_pandas()\nlistens = listens[listens.uid.isin(targets_set) & listens.item_id.isin(cat_set)].copy()\n\n# Load preference state\nval_pref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\nprint(f'Listens: {len(listens)}, val_pref: {len(val_pref)}')\n\ncutoff_ts = 24786800.0\n\ndef build_R(half_life_days=60.0, organic_boost=1.5, like_weight=3.0, dislike_weight=-2.0, min_played=50.0):\n # Filter listens\n sub = listens[listens.played_ratio_pct >= min_played].copy()\n \n # Recency weight\n if half_life_days is not None and half_life_days > 0:\n age_days = (cutoff_ts - sub.timestamp) / 86400.0\n time_w = 0.5 ** (age_days / half_life_days)\n else:\n time_w = 1.0\n \n # Played ratio weight: >= 80 gets 1.0, 50-80 gets 0.5\n ratio_w = np.where(sub.played_ratio_pct >= 80, 1.0, 0.5)\n \n # Organic boost\n org_w = np.where(sub.is_organic == 1, organic_boost, 1.0)\n \n w = time_w * ratio_w * org_w\n sub['weight'] = w\n \n # Group by (uid, item_id) summing weights\n agg = sub.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n \n # Combine with preferences\n pref_w = np.where(val_pref.state == 'liked', like_weight, dislike_weight)\n pref_df = pd.DataFrame({'uid': val_pref.uid, 'item_id': val_pref.item_id, 'weight': pref_w})\n \n comb = pd.concat([agg, pref_df], ignore_index=True)\n comb_agg = comb.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n \n u_idx = comb_agg.uid.map(uid_to_idx).to_numpy()\n i_idx = comb_agg.item_id.map(item_to_idx).to_numpy()\n vals = comb_agg.weight.to_numpy(dtype=np.float32)\n \n R = sp.csr_matrix((vals, (u_idx, i_idx)), shape=(N, M))\n return R\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Test SVD with half_life = 60, 90, None\nfor hl in [30.0, 60.0, 90.0, None]:\n t0 = time.time()\n R = build_R(half_life_days=hl)\n U, S, Vt = svds(R, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx], S[idx], Vt[idx, :]\n pred = (U * (S**0.75)) @ Vt\n score = eval_pred(pred)\n print(f'Half-life {hl} -> SVD-192 NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\""
},
"recorded": {
"id": "run_shell_command__call_274819",
"name": "run_shell_command",
"args": {
"description": "Test temporal decay half-life on SVD performance",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\nimport scipy.linalg.lapack as lapack\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Load listens\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\nlistens = tbl.to_pandas()\nlistens = listens[listens.uid.isin(targets_set) & listens.item_id.isin(cat_set)].copy()\n\n# Load preference state\nval_pref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\nprint(f'Listens: {len(listens)}, val_pref: {len(val_pref)}')\n\ncutoff_ts = 24786800.0\n\ndef build_R(half_life_days=60.0, organic_boost=1.5, like_weight=3.0, dislike_weight=-2.0, min_played=50.0):\n # Filter listens\n sub = listens[listens.played_ratio_pct >= min_played].copy()\n \n # Recency weight\n if half_life_days is not None and half_life_days > 0:\n age_days = (cutoff_ts - sub.timestamp) / 86400.0\n time_w = 0.5 ** (age_days / half_life_days)\n else:\n time_w = 1.0\n \n # Played ratio weight: >= 80 gets 1.0, 50-80 gets 0.5\n ratio_w = np.where(sub.played_ratio_pct >= 80, 1.0, 0.5)\n \n # Organic boost\n org_w = np.where(sub.is_organic == 1, organic_boost, 1.0)\n \n w = time_w * ratio_w * org_w\n sub['weight'] = w\n \n # Group by (uid, item_id) summing weights\n agg = sub.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n \n # Combine with preferences\n pref_w = np.where(val_pref.state == 'liked', like_weight, dislike_weight)\n pref_df = pd.DataFrame({'uid': val_pref.uid, 'item_id': val_pref.item_id, 'weight': pref_w})\n \n comb = pd.concat([agg, pref_df], ignore_index=True)\n comb_agg = comb.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n \n u_idx = comb_agg.uid.map(uid_to_idx).to_numpy()\n i_idx = comb_agg.item_id.map(item_to_idx).to_numpy()\n vals = comb_agg.weight.to_numpy(dtype=np.float32)\n \n R = sp.csr_matrix((vals, (u_idx, i_idx)), shape=(N, M))\n return R\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Test SVD with half_life = 60, 90, None\nfor hl in [30.0, 60.0, 90.0, None]:\n t0 = time.time()\n R = build_R(half_life_days=hl)\n U, S, Vt = svds(R, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx], S[idx], Vt[idx, :]\n pred = (U * (S**0.75)) @ Vt\n score = eval_pred(pred)\n print(f'Half-life {hl} -> SVD-192 NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_274819",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Listens: 31062540, val_pref: 469124\nHalf-life 30.0 -> SVD-192 NDCG@10 = 0.017831 (19.27s)\nHalf-life 60.0 -> SVD-192 NDCG@10 = 0.016437 (18.61s)\nHalf-life 90.0 -> SVD-192 NDCG@10 = 0.014718 (18.60s)\nHalf-life None -> SVD-192 NDCG@10 = 0.011525 (18.16s)\nProcess Group PGID: 2461\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:41:10.066Z",
"resultDisplay": "Listens: 31062540, val_pref: 469124\nHalf-life 30.0 -> SVD-192 NDCG@10 = 0.017831 (19.27s)\nHalf-life 60.0 -> SVD-192 NDCG@10 = 0.016437 (18.61s)\nHalf-life 90.0 -> SVD-192 NDCG@10 = 0.014718 (18.60s)\nHalf-life None -> SVD-192 NDCG@10 = 0.011525 (18.16s)",
"description": "Test temporal decay half-life on SVD performance",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:41:10.068Z",
"content": "\nOutput: Listens: 31062540, val_pref: 469124\nHalf-life 30.0 -> SVD-192 NDCG@10 = 0.017831 (19.27s)\nHalf-life 60.0 -> SVD-192 NDCG@10 = 0.016437 (18.61s)\nHalf-life 90.0 -> SVD-192 NDCG@10 = 0.014718 (18.60s)\nHalf-life None -> SVD-192 NDCG@10 = 0.011525 (18.16s)\nProcess Group PGID: 2461\n",
"isError": false
},
"responseMessageId": "e2a17202-fbc8-4746-bf6d-71726389f2f3"
}
]
},
{
"id": 44,
"role": "agent",
"timestamp": "2026-09-29T04:42:29.383Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:42:29.383Z",
"text": "Analyzing Weighting Schemes\n\nI'm currently investigating why summing weights yielded a lower NDCG@10 (0.0199) than anticipated, especially when compared to simpler binary or log-frequency approaches. My focus is on understanding the nuances of this observed performance drop."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:42:29.383Z",
"callId": "run_shell_command__call_279708",
"name": "run_shell_command",
"input": {
"description": "Test log1p and recency transforms vs binary on SVD",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Load listens\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\nlistens = tbl.to_pandas()\nlistens = listens[listens.uid.isin(targets_set) & listens.item_id.isin(cat_set)].copy()\n\nval_pref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\ncutoff_ts = 24786800.0\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Let us test log1p and capping\nfor tf in ['binary', 'log1p', 'log1p_recency']:\n if tf == 'binary':\n sub = listens[listens.played_ratio_pct >= 50]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data)\n elif tf == 'log1p':\n sub = listens[listens.played_ratio_pct >= 50]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.log1p(R.data)\n elif tf == 'log1p_recency':\n sub = listens[listens.played_ratio_pct >= 50].copy()\n age_days = (cutoff_ts - sub.timestamp) / 86400.0\n time_w = 0.5 ** (age_days / 60.0)\n sub['w'] = time_w\n agg = sub.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n u_idx = agg.uid.map(uid_to_idx).to_numpy()\n i_idx = agg.item_id.map(item_to_idx).to_numpy()\n vals = np.log1p(agg.w.to_numpy(dtype=np.float32))\n R = sp.csr_matrix((vals, (u_idx, i_idx)), shape=(N, M))\n \n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n R_idf = R.multiply(np.sqrt(idf)).tocsr()\n \n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx], S[idx], Vt[idx, :]\n pred = (U * S) @ Vt\n score = eval_pred(pred)\n print(f'Transform {tf:15s} -> SVD NDCG@10 = {score:.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_279708",
"name": "run_shell_command",
"args": {
"description": "Test log1p and recency transforms vs binary on SVD",
"command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Load listens\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\nlistens = tbl.to_pandas()\nlistens = listens[listens.uid.isin(targets_set) & listens.item_id.isin(cat_set)].copy()\n\nval_pref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\ncutoff_ts = 24786800.0\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Let us test log1p and capping\nfor tf in ['binary', 'log1p', 'log1p_recency']:\n if tf == 'binary':\n sub = listens[listens.played_ratio_pct >= 50]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data)\n elif tf == 'log1p':\n sub = listens[listens.played_ratio_pct >= 50]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.log1p(R.data)\n elif tf == 'log1p_recency':\n sub = listens[listens.played_ratio_pct >= 50].copy()\n age_days = (cutoff_ts - sub.timestamp) / 86400.0\n time_w = 0.5 ** (age_days / 60.0)\n sub['w'] = time_w\n agg = sub.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n u_idx = agg.uid.map(uid_to_idx).to_numpy()\n i_idx = agg.item_id.map(item_to_idx).to_numpy()\n vals = np.log1p(agg.w.to_numpy(dtype=np.float32))\n R = sp.csr_matrix((vals, (u_idx, i_idx)), shape=(N, M))\n \n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n R_idf = R.multiply(np.sqrt(idf)).tocsr()\n \n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx], S[idx], Vt[idx, :]\n pred = (U * S) @ Vt\n score = eval_pred(pred)\n print(f'Transform {tf:15s} -> SVD NDCG@10 = {score:.6f}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_279708",
"name": "run_shell_command",
"response": {
"output": "\nOutput: /usr/bin/bash: line 91: 2510 Killed PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Load listens\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\nlistens = tbl.to_pandas()\nlistens = listens[listens.uid.isin(targets_set) & listens.item_id.isin(cat_set)].copy()\n\nval_pref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\ncutoff_ts = 24786800.0\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Let us test log1p and capping\nfor tf in ['binary', 'log1p', 'log1p_recency']:\n if tf == 'binary':\n sub = listens[listens.played_ratio_pct >= 50]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data)\n elif tf == 'log1p':\n sub = listens[listens.played_ratio_pct >= 50]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.log1p(R.data)\n elif tf == 'log1p_recency':\n sub = listens[listens.played_ratio_pct >= 50].copy()\n age_days = (cutoff_ts - sub.timestamp) / 86400.0\n time_w = 0.5 ** (age_days / 60.0)\n sub['w'] = time_w\n agg = sub.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n u_idx = agg.uid.map(uid_to_idx).to_numpy()\n i_idx = agg.item_id.map(item_to_idx).to_numpy()\n vals = np.log1p(agg.w.to_numpy(dtype=np.float32))\n R = sp.csr_matrix((vals, (u_idx, i_idx)), shape=(N, M))\n \n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n R_idf = R.multiply(np.sqrt(idf)).tocsr()\n \n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx], S[idx], Vt[idx, :]\n pred = (U * S) @ Vt\n score = eval_pred(pred)\n print(f'Transform {tf:15s} -> SVD NDCG@10 = {score:.6f}')\n\"\nExit Code: 137\nProcess Group PGID: 2508\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:43:15.363Z",
"resultDisplay": "/usr/bin/bash: line 91: 2510 Killed PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Load listens\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\nlistens = tbl.to_pandas()\nlistens = listens[listens.uid.isin(targets_set) & listens.item_id.isin(cat_set)].copy()\n\nval_pref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\ncutoff_ts = 24786800.0\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Let us test log1p and capping\nfor tf in ['binary', 'log1p', 'log1p_recency']:\n if tf == 'binary':\n sub = listens[listens.played_ratio_pct >= 50]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data)\n elif tf == 'log1p':\n sub = listens[listens.played_ratio_pct >= 50]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.log1p(R.data)\n elif tf == 'log1p_recency':\n sub = listens[listens.played_ratio_pct >= 50].copy()\n age_days = (cutoff_ts - sub.timestamp) / 86400.0\n time_w = 0.5 ** (age_days / 60.0)\n sub['w'] = time_w\n agg = sub.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n u_idx = agg.uid.map(uid_to_idx).to_numpy()\n i_idx = agg.item_id.map(item_to_idx).to_numpy()\n vals = np.log1p(agg.w.to_numpy(dtype=np.float32))\n R = sp.csr_matrix((vals, (u_idx, i_idx)), shape=(N, M))\n \n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n R_idf = R.multiply(np.sqrt(idf)).tocsr()\n \n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx], S[idx], Vt[idx, :]\n pred = (U * S) @ Vt\n score = eval_pred(pred)\n print(f'Transform {tf:15s} -> SVD NDCG@10 = {score:.6f}')\n\"",
"description": "Test log1p and recency transforms vs binary on SVD",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:43:15.367Z",
"content": "\nOutput: /usr/bin/bash: line 91: 2510 Killed PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Load listens\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\nlistens = tbl.to_pandas()\nlistens = listens[listens.uid.isin(targets_set) & listens.item_id.isin(cat_set)].copy()\n\nval_pref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\ncutoff_ts = 24786800.0\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Let us test log1p and capping\nfor tf in ['binary', 'log1p', 'log1p_recency']:\n if tf == 'binary':\n sub = listens[listens.played_ratio_pct >= 50]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data)\n elif tf == 'log1p':\n sub = listens[listens.played_ratio_pct >= 50]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.log1p(R.data)\n elif tf == 'log1p_recency':\n sub = listens[listens.played_ratio_pct >= 50].copy()\n age_days = (cutoff_ts - sub.timestamp) / 86400.0\n time_w = 0.5 ** (age_days / 60.0)\n sub['w'] = time_w\n agg = sub.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n u_idx = agg.uid.map(uid_to_idx).to_numpy()\n i_idx = agg.item_id.map(item_to_idx).to_numpy()\n vals = np.log1p(agg.w.to_numpy(dtype=np.float32))\n R = sp.csr_matrix((vals, (u_idx, i_idx)), shape=(N, M))\n \n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n R_idf = R.multiply(np.sqrt(idf)).tocsr()\n \n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx], S[idx], Vt[idx, :]\n pred = (U * S) @ Vt\n score = eval_pred(pred)\n print(f'Transform {tf:15s} -> SVD NDCG@10 = {score:.6f}')\n\"\nExit Code: 137\nProcess Group PGID: 2508\n",
"isError": false
},
"responseMessageId": "de4ecc5a-aa18-4878-a709-8e67e8b97379"
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]
},
{
"id": 45,
"role": "agent",
"timestamp": "2026-09-29T04:43:22.268Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:43:22.268Z",
"text": "Analyzing Memory Usage\n\nI've identified a significant memory bottleneck within the loop where `(U * S) @ Vt` is computed. The resulting float64 array of shape $(8456, 30000)$ consumes approximately 2 GB. This, coupled with the prior loading of `tbl.to_pandas()`, likely triggered the Out Of Memory error."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:43:22.268Z",
"callId": "run_shell_command__call_262545",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport gc\nimport pyarrow.compute as pc\nimport pyarrow.parquet as pq\nimport numpy as np\n\n# PyArrow filter at read time!\ncat_arr = pq.read_table('/task/data/candidate_catalog.parquet', columns=['item_id'])['item_id']\n# Filter: played_ratio_pct >= 50\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'],\n filters=[('played_ratio_pct', '>=', 50)])\nprint('Filtered table rows:', len(table))\n\"",
"description": "Check pyarrow filtered read memory efficiency"
},
"recorded": {
"id": "run_shell_command__call_262545",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport gc\nimport pyarrow.compute as pc\nimport pyarrow.parquet as pq\nimport numpy as np\n\n# PyArrow filter at read time!\ncat_arr = pq.read_table('/task/data/candidate_catalog.parquet', columns=['item_id'])['item_id']\n# Filter: played_ratio_pct >= 50\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'],\n filters=[('played_ratio_pct', '>=', 50)])\nprint('Filtered table rows:', len(table))\n\"",
"description": "Check pyarrow filtered read memory efficiency"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_262545",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Filtered table rows: 27500755\nProcess Group PGID: 2537\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:43:23.520Z",
"resultDisplay": "Filtered table rows: 27500755",
"description": "Check pyarrow filtered read memory efficiency",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:43:23.521Z",
"content": "\nOutput: Filtered table rows: 27500755\nProcess Group PGID: 2537\n",
"isError": false
},
"responseMessageId": "aac551a0-cb8c-48ac-8f72-a9a9d5b39a16"
}
]
},
{
"id": 46,
"role": "agent",
"timestamp": "2026-09-29T04:43:27.131Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:43:27.131Z",
"text": "Exploring Data Filtration\n\nI'm currently investigating the impact of applying more stringent filtration criteria. Specifically, I'm evaluating how a `played_ratio_pct` threshold of 80% affects the dataset size, building upon the observation that a 50% threshold significantly reduced the data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:43:27.131Z",
"callId": "run_shell_command__call_249863",
"name": "run_shell_command",
"input": {
"command": "python -c \"\nimport pyarrow.parquet as pq\n\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'],\n filters=[('played_ratio_pct', '>=', 80)])\nprint('Filtered played_ratio_pct >= 80 rows:', len(table))\n\"",
"description": "Check rows count with played_ratio_pct >= 80"
},
"recorded": {
"id": "run_shell_command__call_249863",
"name": "run_shell_command",
"args": {
"command": "python -c \"\nimport pyarrow.parquet as pq\n\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'],\n filters=[('played_ratio_pct', '>=', 80)])\nprint('Filtered played_ratio_pct >= 80 rows:', len(table))\n\"",
"description": "Check rows count with played_ratio_pct >= 80"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_249863",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Filtered played_ratio_pct >= 80 rows: 25755197\nProcess Group PGID: 2559\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:43:28.400Z",
"resultDisplay": "Filtered played_ratio_pct >= 80 rows: 25755197",
"description": "Check rows count with played_ratio_pct >= 80",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:43:34.743Z",
"content": "\nOutput: Filtered played_ratio_pct >= 80 rows: 25755197\nProcess Group PGID: 2559\n",
"isError": false
},
"responseMessageId": "50396c16-7fdc-4f07-a6ef-1723d32d1059"
}
]
},
{
"id": 47,
"role": "agent",
"timestamp": "2026-09-29T04:44:02.154Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:44:02.154Z",
"text": "Analyzing Time Windows\n\nI'm currently focused on defining and analyzing specific time windows within the training data. My immediate task is to determine the number of data rows corresponding to the last 90, 60, and 30 days, leveraging the maximum timestamp identified earlier."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:44:02.154Z",
"callId": "run_shell_command__call_257744",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python -c \"\nimport gc, time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Test different history windows: 30 days, 60 days, 90 days, 180 days, all\nmax_ts = 24786795\nfor days in [30, 60, 90, 180, None]:\n t0 = time.time()\n min_ts = (max_ts - days * 86400) if days is not None else 0\n filters = [('played_ratio_pct', '>=', 50)]\n if days is not None:\n filters.append(('timestamp', '>=', min_ts))\n \n table = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id'],\n filters=filters)\n df = table.to_pandas()\n df = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)]\n u_idx = df.uid.map(uid_to_idx).to_numpy()\n i_idx = df.item_id.map(item_to_idx).to_numpy()\n del table, df\n gc.collect()\n \n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data) # binary\n \n # SVD\n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n R_idf = R.multiply(np.sqrt(idf)).tocsr()\n \n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n pred = (U * S) @ Vt\n score = eval_pred(pred)\n del R, R_idf, U, S, Vt, pred\n gc.collect()\n print(f'Window {str(days):>4s} days -> SVD-192 NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\"",
"description": "Test history window length on SVD performance"
},
"recorded": {
"id": "run_shell_command__call_257744",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python -c \"\nimport gc, time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Test different history windows: 30 days, 60 days, 90 days, 180 days, all\nmax_ts = 24786795\nfor days in [30, 60, 90, 180, None]:\n t0 = time.time()\n min_ts = (max_ts - days * 86400) if days is not None else 0\n filters = [('played_ratio_pct', '>=', 50)]\n if days is not None:\n filters.append(('timestamp', '>=', min_ts))\n \n table = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id'],\n filters=filters)\n df = table.to_pandas()\n df = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)]\n u_idx = df.uid.map(uid_to_idx).to_numpy()\n i_idx = df.item_id.map(item_to_idx).to_numpy()\n del table, df\n gc.collect()\n \n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data) # binary\n \n # SVD\n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n R_idf = R.multiply(np.sqrt(idf)).tocsr()\n \n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n pred = (U * S) @ Vt\n score = eval_pred(pred)\n del R, R_idf, U, S, Vt, pred\n gc.collect()\n print(f'Window {str(days):>4s} days -> SVD-192 NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\"",
"description": "Test history window length on SVD performance"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_257744",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Window 30 days -> SVD-192 NDCG@10 = 0.032118 (8.79s)\nWindow 60 days -> SVD-192 NDCG@10 = 0.029739 (11.43s)\nWindow 90 days -> SVD-192 NDCG@10 = 0.026048 (13.55s)\nWindow 180 days -> SVD-192 NDCG@10 = 0.020980 (18.60s)\nWindow None days -> SVD-192 NDCG@10 = 0.019935 (22.80s)\nProcess Group PGID: 2587\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:45:19.046Z",
"resultDisplay": "Window 30 days -> SVD-192 NDCG@10 = 0.032118 (8.79s)\nWindow 60 days -> SVD-192 NDCG@10 = 0.029739 (11.43s)\nWindow 90 days -> SVD-192 NDCG@10 = 0.026048 (13.55s)\nWindow 180 days -> SVD-192 NDCG@10 = 0.020980 (18.60s)\nWindow None days -> SVD-192 NDCG@10 = 0.019935 (22.80s)",
"description": "Test history window length on SVD performance",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:45:19.047Z",
"content": "\nOutput: Window 30 days -> SVD-192 NDCG@10 = 0.032118 (8.79s)\nWindow 60 days -> SVD-192 NDCG@10 = 0.029739 (11.43s)\nWindow 90 days -> SVD-192 NDCG@10 = 0.026048 (13.55s)\nWindow 180 days -> SVD-192 NDCG@10 = 0.020980 (18.60s)\nWindow None days -> SVD-192 NDCG@10 = 0.019935 (22.80s)\nProcess Group PGID: 2587\n",
"isError": false
},
"responseMessageId": "62683ff3-e6b0-4bf2-8de1-eca7fdbdd90a"
}
]
},
{
"id": 48,
"role": "agent",
"timestamp": "2026-09-29T04:45:29.360Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:45:29.360Z",
"text": "Observing Score Progression\n\nI'm noticing a distinct upward trend in the score as I look at shorter, more recent time windows. The gains are quite substantial, especially when comparing the last 30 days to the overall period and even a baseline."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:45:29.360Z",
"callId": "run_shell_command__call_226465",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python -c \"\nimport gc, time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\nmax_ts = 24786795\nfor days in [7, 10, 14, 21, 28, 35, 45]:\n t0 = time.time()\n min_ts = max_ts - days * 86400\n filters = [('played_ratio_pct', '>=', 50), ('timestamp', '>=', min_ts)]\n table = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id'],\n filters=filters)\n df = table.to_pandas()\n df = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)]\n u_idx = df.uid.map(uid_to_idx).to_numpy()\n i_idx = df.item_id.map(item_to_idx).to_numpy()\n del table, df\n gc.collect()\n \n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data) # binary\n \n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n R_idf = R.multiply(np.sqrt(idf)).tocsr()\n \n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n pred = (U * S) @ Vt\n score = eval_pred(pred)\n del R, R_idf, U, S, Vt, pred\n gc.collect()\n print(f'Window {days:2d} days -> SVD-192 NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\"",
"description": "Fine-grained test of recent history window on SVD"
},
"recorded": {
"id": "run_shell_command__call_226465",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python -c \"\nimport gc, time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\nmax_ts = 24786795\nfor days in [7, 10, 14, 21, 28, 35, 45]:\n t0 = time.time()\n min_ts = max_ts - days * 86400\n filters = [('played_ratio_pct', '>=', 50), ('timestamp', '>=', min_ts)]\n table = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id'],\n filters=filters)\n df = table.to_pandas()\n df = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)]\n u_idx = df.uid.map(uid_to_idx).to_numpy()\n i_idx = df.item_id.map(item_to_idx).to_numpy()\n del table, df\n gc.collect()\n \n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data) # binary\n \n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n R_idf = R.multiply(np.sqrt(idf)).tocsr()\n \n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n pred = (U * S) @ Vt\n score = eval_pred(pred)\n del R, R_idf, U, S, Vt, pred\n gc.collect()\n print(f'Window {days:2d} days -> SVD-192 NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\"",
"description": "Fine-grained test of recent history window on SVD"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_226465",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Window 7 days -> SVD-192 NDCG@10 = 0.030192 (6.43s)\nWindow 10 days -> SVD-192 NDCG@10 = 0.032551 (6.78s)\nWindow 14 days -> SVD-192 NDCG@10 = 0.032901 (7.28s)\nWindow 21 days -> SVD-192 NDCG@10 = 0.032399 (8.30s)\nWindow 28 days -> SVD-192 NDCG@10 = 0.032255 (8.41s)\nWindow 35 days -> SVD-192 NDCG@10 = 0.032803 (9.04s)\nWindow 45 days -> SVD-192 NDCG@10 = 0.031721 (9.95s)\nProcess Group PGID: 2626\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:46:27.268Z",
"resultDisplay": "Window 7 days -> SVD-192 NDCG@10 = 0.030192 (6.43s)\nWindow 10 days -> SVD-192 NDCG@10 = 0.032551 (6.78s)\nWindow 14 days -> SVD-192 NDCG@10 = 0.032901 (7.28s)\nWindow 21 days -> SVD-192 NDCG@10 = 0.032399 (8.30s)\nWindow 28 days -> SVD-192 NDCG@10 = 0.032255 (8.41s)\nWindow 35 days -> SVD-192 NDCG@10 = 0.032803 (9.04s)\nWindow 45 days -> SVD-192 NDCG@10 = 0.031721 (9.95s)",
"description": "Fine-grained test of recent history window on SVD",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:46:27.269Z",
"content": "\nOutput: Window 7 days -> SVD-192 NDCG@10 = 0.030192 (6.43s)\nWindow 10 days -> SVD-192 NDCG@10 = 0.032551 (6.78s)\nWindow 14 days -> SVD-192 NDCG@10 = 0.032901 (7.28s)\nWindow 21 days -> SVD-192 NDCG@10 = 0.032399 (8.30s)\nWindow 28 days -> SVD-192 NDCG@10 = 0.032255 (8.41s)\nWindow 35 days -> SVD-192 NDCG@10 = 0.032803 (9.04s)\nWindow 45 days -> SVD-192 NDCG@10 = 0.031721 (9.95s)\nProcess Group PGID: 2626\n",
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},
{
"id": 49,
"role": "agent",
"timestamp": "2026-09-29T04:46:36.747Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:46:36.747Z",
"text": "Analyzing User-EASE Impact\n\nI'm now investigating how User-EASE performs across 14, 21, and 30-day windows. This follows a promising 0.032901 score at 14 days, building on previous work with binary R yielding 0.02033."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:46:36.747Z",
"callId": "run_shell_command__call_378063",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python -c \"\nimport gc, time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nimport scipy.linalg.lapack as lapack\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\nmax_ts = 24786795\ndays = 14\nmin_ts = max_ts - days * 86400\nfilters = [('played_ratio_pct', '>=', 50), ('timestamp', '>=', min_ts)]\ntable = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id'],\n filters=filters)\ndf = table.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\ndel table, df\ngc.collect()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\nt0 = time.time()\nG = (R @ R.T).toarray().astype(np.float64)\nprint(f'G computed in {time.time()-t0:.2f}s')\n\nfor reg in [100.0, 300.0, 500.0, 1000.0, 2000.0, 5000.0]:\n t0 = time.time()\n G_reg = G.copy()\n diag_indices = np.diag_indices_from(G_reg)\n G_reg[diag_indices] += reg\n c, info = lapack.dpotrf(G_reg, lower=False)\n inv, info = lapack.dpotri(c, lower=False)\n inv = np.triu(inv) + np.triu(inv, 1).T\n \n P = -inv / np.diag(inv)[:, None]\n P[diag_indices] = 0.0\n \n pred = (P.astype(np.float32) @ R).astype(np.float32)\n score = eval_pred(pred)\n print(f'User-EASE 14d reg={reg:6.1f} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\"",
"description": "Test User-EASE on 14-day history window"
},
"recorded": {
"id": "run_shell_command__call_378063",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python -c \"\nimport gc, time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nimport scipy.linalg.lapack as lapack\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\nmax_ts = 24786795\ndays = 14\nmin_ts = max_ts - days * 86400\nfilters = [('played_ratio_pct', '>=', 50), ('timestamp', '>=', min_ts)]\ntable = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id'],\n filters=filters)\ndf = table.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\ndel table, df\ngc.collect()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\nt0 = time.time()\nG = (R @ R.T).toarray().astype(np.float64)\nprint(f'G computed in {time.time()-t0:.2f}s')\n\nfor reg in [100.0, 300.0, 500.0, 1000.0, 2000.0, 5000.0]:\n t0 = time.time()\n G_reg = G.copy()\n diag_indices = np.diag_indices_from(G_reg)\n G_reg[diag_indices] += reg\n c, info = lapack.dpotrf(G_reg, lower=False)\n inv, info = lapack.dpotri(c, lower=False)\n inv = np.triu(inv) + np.triu(inv, 1).T\n \n P = -inv / np.diag(inv)[:, None]\n P[diag_indices] = 0.0\n \n pred = (P.astype(np.float32) @ R).astype(np.float32)\n score = eval_pred(pred)\n print(f'User-EASE 14d reg={reg:6.1f} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\"",
"description": "Test User-EASE on 14-day history window"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_378063",
"name": "run_shell_command",
"response": {
"output": "\nOutput: /usr/bin/bash: line 81: 2665 Killed PYTHONPATH=/task python -c \"\nimport gc, time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nimport scipy.linalg.lapack as lapack\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\nmax_ts = 24786795\ndays = 14\nmin_ts = max_ts - days * 86400\nfilters = [('played_ratio_pct', '>=', 50), ('timestamp', '>=', min_ts)]\ntable = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id'],\n filters=filters)\ndf = table.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\ndel table, df\ngc.collect()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\nt0 = time.time()\nG = (R @ R.T).toarray().astype(np.float64)\nprint(f'G computed in {time.time()-t0:.2f}s')\n\nfor reg in [100.0, 300.0, 500.0, 1000.0, 2000.0, 5000.0]:\n t0 = time.time()\n G_reg = G.copy()\n diag_indices = np.diag_indices_from(G_reg)\n G_reg[diag_indices] += reg\n c, info = lapack.dpotrf(G_reg, lower=False)\n inv, info = lapack.dpotri(c, lower=False)\n inv = np.triu(inv) + np.triu(inv, 1).T\n \n P = -inv / np.diag(inv)[:, None]\n P[diag_indices] = 0.0\n \n pred = (P.astype(np.float32) @ R).astype(np.float32)\n score = eval_pred(pred)\n print(f'User-EASE 14d reg={reg:6.1f} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\"\nExit Code: 137\nProcess Group PGID: 2663\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:46:51.641Z",
"resultDisplay": "/usr/bin/bash: line 81: 2665 Killed PYTHONPATH=/task python -c \"\nimport gc, time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nimport scipy.linalg.lapack as lapack\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\nmax_ts = 24786795\ndays = 14\nmin_ts = max_ts - days * 86400\nfilters = [('played_ratio_pct', '>=', 50), ('timestamp', '>=', min_ts)]\ntable = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id'],\n filters=filters)\ndf = table.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\ndel table, df\ngc.collect()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\nt0 = time.time()\nG = (R @ R.T).toarray().astype(np.float64)\nprint(f'G computed in {time.time()-t0:.2f}s')\n\nfor reg in [100.0, 300.0, 500.0, 1000.0, 2000.0, 5000.0]:\n t0 = time.time()\n G_reg = G.copy()\n diag_indices = np.diag_indices_from(G_reg)\n G_reg[diag_indices] += reg\n c, info = lapack.dpotrf(G_reg, lower=False)\n inv, info = lapack.dpotri(c, lower=False)\n inv = np.triu(inv) + np.triu(inv, 1).T\n \n P = -inv / np.diag(inv)[:, None]\n P[diag_indices] = 0.0\n \n pred = (P.astype(np.float32) @ R).astype(np.float32)\n score = eval_pred(pred)\n print(f'User-EASE 14d reg={reg:6.1f} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\"",
"description": "Test User-EASE on 14-day history window",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:46:51.643Z",
"content": "\nOutput: /usr/bin/bash: line 81: 2665 Killed PYTHONPATH=/task python -c \"\nimport gc, time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nimport scipy.linalg.lapack as lapack\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ndef eval_pred(pred):\n pred_c = pred.copy()\n pred_c[~unpacked] = -1e9\n top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred_c[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n return score_rankings(ranked, graded)['quality_ndcg@10']\n\nmax_ts = 24786795\ndays = 14\nmin_ts = max_ts - days * 86400\nfilters = [('played_ratio_pct', '>=', 50), ('timestamp', '>=', min_ts)]\ntable = pq.read_table(DATA / 'train_listens.parquet', \n columns=['uid', 'item_id'],\n filters=filters)\ndf = table.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\ndel table, df\ngc.collect()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\nt0 = time.time()\nG = (R @ R.T).toarray().astype(np.float64)\nprint(f'G computed in {time.time()-t0:.2f}s')\n\nfor reg in [100.0, 300.0, 500.0, 1000.0, 2000.0, 5000.0]:\n t0 = time.time()\n G_reg = G.copy()\n diag_indices = np.diag_indices_from(G_reg)\n G_reg[diag_indices] += reg\n c, info = lapack.dpotrf(G_reg, lower=False)\n inv, info = lapack.dpotri(c, lower=False)\n inv = np.triu(inv) + np.triu(inv, 1).T\n \n P = -inv / np.diag(inv)[:, None]\n P[diag_indices] = 0.0\n \n pred = (P.astype(np.float32) @ R).astype(np.float32)\n score = eval_pred(pred)\n print(f'User-EASE 14d reg={reg:6.1f} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\"\nExit Code: 137\nProcess Group PGID: 2663\n",
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]
},
{
"id": 50,
"role": "agent",
"timestamp": "2026-09-29T04:47:04.280Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:47:04.280Z",
"text": "Analyzing Matrix Multiplication\n\nI'm currently dissecting the memory consumption and computational cost of matrix multiplication, specifically focusing on the `P @ R` operation. My initial analysis indicates significant memory overhead associated with creating the intermediate dense matrix `P`."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:47:04.280Z",
"text": "Optimizing SVD Usage\n\nI've identified that the dense @ sparse multiplication in SciPy creates significant temporary memory structures, whereas SVD is far more efficient. My current focus is on refining the SVD approach and exploring further modeling innovations, specifically considering how to incorporate explicit preference states."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:47:04.280Z",
"text": "Incorporating Engagement Metrics\n\nI'm now exploring how to integrate explicit user feedback, like likes and dislikes, into the model. I'm also investigating the impact of weighting organic listens higher and considering different approaches to completion rates, including negative weights for skips. The current focus is on refining time decay models, specifically exploring exponential decay with a 14-day half-life."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:47:04.280Z",
"text": "Refining Interaction Weighting\n\nI'm now experimenting with combining recency, organic listens, and completion rates into a single weight. I'm also exploring sublinear transformations like log or square root to prevent any single interaction from dominating, and investigating IDF powers and user normalization for SVD."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:47:04.280Z",
"callId": "update_topic__call_253613",
"name": "update_topic",
"input": {
"title": "Systematic Optimization of the Latent Factor Ranker",
"summary": "We discovered that recency gating dramatically improved validation NDCG@10 from 0.0107 to over 0.0329. We are now running systematic ablation experiments to optimize: 1) explicit preferences (likes/dislikes), 2) completion and organic weighting with saturation, 3) row and column normalization, 4) SVD dimensionality and singular value powers, and 5) catalog popularity blending.",
"strategic_intent": "Run systematic experiments optimizing interaction weighting, explicit preferences, IDF scaling, rank K, and popularity blending."
},
"recorded": {
"id": "update_topic__call_253613",
"name": "update_topic",
"args": {
"title": "Systematic Optimization of the Latent Factor Ranker",
"summary": "We discovered that recency gating dramatically improved validation NDCG@10 from 0.0107 to over 0.0329. We are now running systematic ablation experiments to optimize: 1) explicit preferences (likes/dislikes), 2) completion and organic weighting with saturation, 3) row and column normalization, 4) SVD dimensionality and singular value powers, and 5) catalog popularity blending.",
"strategic_intent": "Run systematic experiments optimizing interaction weighting, explicit preferences, IDF scaling, rank K, and popularity blending."
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_253613",
"name": "update_topic",
"response": {
"output": "Current topic: \"Systematic Optimization of the Latent Factor Ranker\"\nTopic summary: We discovered that recency gating dramatically improved validation NDCG@10 from 0.0107 to over 0.0329. We are now running systematic ablation experiments to optimize: 1) explicit preferences (likes/dislikes), 2) completion and organic weighting with saturation, 3) row and column normalization, 4) SVD dimensionality and singular value powers, and 5) catalog popularity blending.\n\nStrategic Intent: Run systematic experiments optimizing interaction weighting, explicit preferences, IDF scaling, rank K, and popularity blending."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:47:04.284Z",
"resultDisplay": "## 📂 Topic: **Systematic Optimization of the Latent Factor Ranker**\n\n**Summary:**\nWe discovered that recency gating dramatically improved validation NDCG@10 from 0.0107 to over 0.0329. We are now running systematic ablation experiments to optimize: 1) explicit preferences (likes/dislikes), 2) completion and organic weighting with saturation, 3) row and column normalization, 4) SVD dimensionality and singular value powers, and 5) catalog popularity blending.\n\n> [!STRATEGY]\n> **Intent:** Run systematic experiments optimizing interaction weighting, explicit preferences, IDF scaling, rank K, and popularity blending.",
"description": "Update topic to: \"Systematic Optimization of the Latent Factor Ranker\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T04:47:04.286Z",
"content": "Current topic: \"Systematic Optimization of the Latent Factor Ranker\"\nTopic summary: We discovered that recency gating dramatically improved validation NDCG@10 from 0.0107 to over 0.0329. We are now running systematic ablation experiments to optimize: 1) explicit preferences (likes/dislikes), 2) completion and organic weighting with saturation, 3) row and column normalization, 4) SVD dimensionality and singular value powers, and 5) catalog popularity blending.\n\nStrategic Intent: Run systematic experiments optimizing interaction weighting, explicit preferences, IDF scaling, rank K, and popularity blending.",
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},
{
"id": 51,
"role": "agent",
"timestamp": "2026-09-29T04:47:15.915Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:47:15.915Z",
"text": "Designing Modular Experiment Script\n\nI'm focusing on architecting a robust, modular Python script for experiments. Key features include secure data loading, efficient ablation studies, precise NDCG@10 metric recording, and transparent result reporting. I'm also preparing to implement and execute the `/app/solution/experiment_svd.py` script."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:47:15.915Z",
"callId": "write_file__call_210849",
"name": "write_file",
"input": {
"file_path": "/app/solution/experiment_svd.py",
"content": "import gc\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport scipy.sparse as sp\nfrom scipy.sparse.linalg import svds\nfrom sklearn.preprocessing import normalize\n\n# Make sure /task is in sys.path\nif \"/task\" not in sys.path:\n sys.path.insert(0, \"/task\")\n\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\n\nprint(\"Loading metadata...\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\nunpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets_set)\n\nprint(f\"Loaded: N={N} users, M={M} items. Ground truth users: {len(graded)}\")\n\ndef evaluate_predictions(pred_matrix: np.ndarray) -> float:\n \"\"\"Helper to evaluate NDCG@10 from a dense (N, M) prediction matrix.\"\"\"\n pred = pred_matrix.copy()\n pred[~unpacked] = -1e9\n \n top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n \n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n res = score_rankings(ranked, graded)\n return res[\"quality_ndcg@10\"]\n\n# Load listens in the last 45 days\ncutoff_ts = 24786795\nmin_ts_45d = cutoff_ts - 45 * 86400\n\nprint(\"Reading recent listens (last 45 days)...\")\ntbl = pq.read_table(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n filters=[(\"timestamp\", \">=\", min_ts_45d)]\n)\nlistens_df = tbl.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\ndel tbl\ngc.collect()\nprint(f\"Loaded {len(listens_df)} listens.\")\n\n# Load validation preference state\nval_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\nprint(f\"Loaded {len(val_pref)} preferences.\")\n\n# Experiment 1: Window size and IDF scaling on binary listens\nprint(\"\\n--- Experiment 1: Window size vs IDF scaling (Binary) ---\")\nfor days in [10, 14, 18, 24, 30]:\n cur_min_ts = cutoff_ts - days * 86400\n sub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n \n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data) # binary\n \n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n \n for idf_pow in [0.3, 0.5, 0.7]:\n R_idf = R.multiply(idf**idf_pow).tocsr()\n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n pred = (U * S) @ Vt\n score = evaluate_predictions(pred)\n print(f\"Days={days:2d}, idf_pow={idf_pow:.1f} -> NDCG@10 = {score:.6f}\")\n del R_idf, U, S, Vt, pred\n gc.collect()\n",
"instruction": "Create experiment_svd.py to test window sizes and IDF scaling"
},
"recorded": {
"id": "write_file__call_210849",
"name": "write_file",
"args": {
"file_path": "/app/solution/experiment_svd.py",
"content": "import gc\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport scipy.sparse as sp\nfrom scipy.sparse.linalg import svds\nfrom sklearn.preprocessing import normalize\n\n# Make sure /task is in sys.path\nif \"/task\" not in sys.path:\n sys.path.insert(0, \"/task\")\n\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\n\nprint(\"Loading metadata...\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\nunpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets_set)\n\nprint(f\"Loaded: N={N} users, M={M} items. Ground truth users: {len(graded)}\")\n\ndef evaluate_predictions(pred_matrix: np.ndarray) -> float:\n \"\"\"Helper to evaluate NDCG@10 from a dense (N, M) prediction matrix.\"\"\"\n pred = pred_matrix.copy()\n pred[~unpacked] = -1e9\n \n top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n \n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n res = score_rankings(ranked, graded)\n return res[\"quality_ndcg@10\"]\n\n# Load listens in the last 45 days\ncutoff_ts = 24786795\nmin_ts_45d = cutoff_ts - 45 * 86400\n\nprint(\"Reading recent listens (last 45 days)...\")\ntbl = pq.read_table(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n filters=[(\"timestamp\", \">=\", min_ts_45d)]\n)\nlistens_df = tbl.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\ndel tbl\ngc.collect()\nprint(f\"Loaded {len(listens_df)} listens.\")\n\n# Load validation preference state\nval_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\nprint(f\"Loaded {len(val_pref)} preferences.\")\n\n# Experiment 1: Window size and IDF scaling on binary listens\nprint(\"\\n--- Experiment 1: Window size vs IDF scaling (Binary) ---\")\nfor days in [10, 14, 18, 24, 30]:\n cur_min_ts = cutoff_ts - days * 86400\n sub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n \n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data) # binary\n \n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n \n for idf_pow in [0.3, 0.5, 0.7]:\n R_idf = R.multiply(idf**idf_pow).tocsr()\n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n pred = (U * S) @ Vt\n score = evaluate_predictions(pred)\n print(f\"Days={days:2d}, idf_pow={idf_pow:.1f} -> NDCG@10 = {score:.6f}\")\n del R_idf, U, S, Vt, pred\n gc.collect()\n",
"instruction": "Create experiment_svd.py to test window sizes and IDF scaling"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_210849",
"name": "write_file",
"response": {
"output": "Successfully created and wrote to new file: /app/solution/experiment_svd.py. Here is the updated code:\nimport gc\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport scipy.sparse as sp\nfrom scipy.sparse.linalg import svds\nfrom sklearn.preprocessing import normalize\n\n# Make sure /task is in sys.path\nif \"/task\" not in sys.path:\n sys.path.insert(0, \"/task\")\n\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\n\nprint(\"Loading metadata...\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\nunpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets_set)\n\nprint(f\"Loaded: N={N} users, M={M} items. Ground truth users: {len(graded)}\")\n\ndef evaluate_predictions(pred_matrix: np.ndarray) -> float:\n \"\"\"Helper to evaluate NDCG@10 from a dense (N, M) prediction matrix.\"\"\"\n pred = pred_matrix.copy()\n pred[~unpacked] = -1e9\n \n top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n \n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n res = score_rankings(ranked, graded)\n return res[\"quality_ndcg@10\"]\n\n# Load listens in the last 45 days\ncutoff_ts = 24786795\nmin_ts_45d = cutoff_ts - 45 * 86400\n\nprint(\"Reading recent listens (last 45 days)...\")\ntbl = pq.read_table(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n filters=[(\"timestamp\", \">=\", min_ts_45d)]\n)\nlistens_df = tbl.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\ndel tbl\ngc.collect()\nprint(f\"Loaded {len(listens_df)} listens.\")\n\n# Load validation preference state\nval_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\nprint(f\"Loaded {len(val_pref)} preferences.\")\n\n# Experiment 1: Window size and IDF scaling on binary listens\nprint(\"\\n--- Experiment 1: Window size vs IDF scaling (Binary) ---\")\nfor days in [10, 14, 18, 24, 30]:\n cur_min_ts = cutoff_ts - days * 86400\n sub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n \n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data) # binary\n \n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n \n for idf_pow in [0.3, 0.5, 0.7]:\n R_idf = R.multiply(idf**idf_pow).tocsr()\n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n pred = (U * S) @ Vt\n score = evaluate_predictions(pred)\n print(f\"Days={days:2d}, idf_pow={idf_pow:.1f} -> NDCG@10 = {score:.6f}\")\n del R_idf, U, S, Vt, pred\n gc.collect()\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:47:15.930Z",
"resultDisplay": {
"fileDiff": "Index: experiment_svd.py\n===================================================================\n--- experiment_svd.py\tOriginal\n+++ experiment_svd.py\tWritten\n@@ -0,0 +1,100 @@\n+import gc\n+import sys\n+import time\n+from pathlib import Path\n+import numpy as np\n+import pandas as pd\n+import pyarrow.parquet as pq\n+import scipy.sparse as sp\n+from scipy.sparse.linalg import svds\n+from sklearn.preprocessing import normalize\n+\n+# Make sure /task is in sys.path\n+if \"/task\" not in sys.path:\n+ sys.path.insert(0, \"/task\")\n+\n+from ranking_evaluation import CandidateSet, score_rankings\n+from evaluate_public import validation_truth\n+\n+DATA = Path(\"/task/data\")\n+\n+print(\"Loading metadata...\")\n+targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n+targets_list = targets_frame.uid.astype(int).tolist()\n+targets_set = set(targets_list)\n+cat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+cat_items = cat_frame.item_id.astype(int).tolist()\n+cat_set = set(cat_items)\n+\n+uid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\n+item_to_idx = {item: j for j, item in enumerate(cat_items)}\n+N, M = len(targets_list), len(cat_items)\n+\n+val_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\n+unpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n+\n+eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n+graded = validation_truth(eligibility, targets_set)\n+\n+print(f\"Loaded: N={N} users, M={M} items. Ground truth users: {len(graded)}\")\n+\n+def evaluate_predictions(pred_matrix: np.ndarray) -> float:\n+ \"\"\"Helper to evaluate NDCG@10 from a dense (N, M) prediction matrix.\"\"\"\n+ pred = pred_matrix.copy()\n+ pred[~unpacked] = -1e9\n+ \n+ top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n+ row_indices = np.arange(N)[:, None]\n+ top100_scores = pred[row_indices, top100_idx]\n+ sort_order = np.argsort(-top100_scores, axis=1)\n+ sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n+ \n+ ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n+ res = score_rankings(ranked, graded)\n+ return res[\"quality_ndcg@10\"]\n+\n+# Load listens in the last 45 days\n+cutoff_ts = 24786795\n+min_ts_45d = cutoff_ts - 45 * 86400\n+\n+print(\"Reading recent listens (last 45 days)...\")\n+tbl = pq.read_table(\n+ DATA / \"train_listens.parquet\",\n+ columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n+ filters=[(\"timestamp\", \">=\", min_ts_45d)]\n+)\n+listens_df = tbl.to_pandas()\n+listens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\n+del tbl\n+gc.collect()\n+print(f\"Loaded {len(listens_df)} listens.\")\n+\n+# Load validation preference state\n+val_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n+val_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n+print(f\"Loaded {len(val_pref)} preferences.\")\n+\n+# Experiment 1: Window size and IDF scaling on binary listens\n+print(\"\\n--- Experiment 1: Window size vs IDF scaling (Binary) ---\")\n+for days in [10, 14, 18, 24, 30]:\n+ cur_min_ts = cutoff_ts - days * 86400\n+ sub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\n+ u_idx = sub.uid.map(uid_to_idx).to_numpy()\n+ i_idx = sub.item_id.map(item_to_idx).to_numpy()\n+ \n+ R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n+ R.data = np.ones_like(R.data) # binary\n+ \n+ n_j = np.array(R.sum(axis=0)).flatten()\n+ idf = np.log((N + 1.0) / (n_j + 1.0))\n+ \n+ for idf_pow in [0.3, 0.5, 0.7]:\n+ R_idf = R.multiply(idf**idf_pow).tocsr()\n+ U, S, Vt = svds(R_idf, k=192)\n+ idx = np.argsort(-S)\n+ U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n+ pred = (U * S) @ Vt\n+ score = evaluate_predictions(pred)\n+ print(f\"Days={days:2d}, idf_pow={idf_pow:.1f} -> NDCG@10 = {score:.6f}\")\n+ del R_idf, U, S, Vt, pred\n+ gc.collect()\n",
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"filePath": "/app/solution/experiment_svd.py",
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"newContent": "import gc\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport scipy.sparse as sp\nfrom scipy.sparse.linalg import svds\nfrom sklearn.preprocessing import normalize\n\n# Make sure /task is in sys.path\nif \"/task\" not in sys.path:\n sys.path.insert(0, \"/task\")\n\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\n\nprint(\"Loading metadata...\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\nunpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets_set)\n\nprint(f\"Loaded: N={N} users, M={M} items. Ground truth users: {len(graded)}\")\n\ndef evaluate_predictions(pred_matrix: np.ndarray) -> float:\n \"\"\"Helper to evaluate NDCG@10 from a dense (N, M) prediction matrix.\"\"\"\n pred = pred_matrix.copy()\n pred[~unpacked] = -1e9\n \n top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n \n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n res = score_rankings(ranked, graded)\n return res[\"quality_ndcg@10\"]\n\n# Load listens in the last 45 days\ncutoff_ts = 24786795\nmin_ts_45d = cutoff_ts - 45 * 86400\n\nprint(\"Reading recent listens (last 45 days)...\")\ntbl = pq.read_table(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n filters=[(\"timestamp\", \">=\", min_ts_45d)]\n)\nlistens_df = tbl.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\ndel tbl\ngc.collect()\nprint(f\"Loaded {len(listens_df)} listens.\")\n\n# Load validation preference state\nval_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\nprint(f\"Loaded {len(val_pref)} preferences.\")\n\n# Experiment 1: Window size and IDF scaling on binary listens\nprint(\"\\n--- Experiment 1: Window size vs IDF scaling (Binary) ---\")\nfor days in [10, 14, 18, 24, 30]:\n cur_min_ts = cutoff_ts - days * 86400\n sub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n \n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data) # binary\n \n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n \n for idf_pow in [0.3, 0.5, 0.7]:\n R_idf = R.multiply(idf**idf_pow).tocsr()\n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n pred = (U * S) @ Vt\n score = evaluate_predictions(pred)\n print(f\"Days={days:2d}, idf_pow={idf_pow:.1f} -> NDCG@10 = {score:.6f}\")\n del R_idf, U, S, Vt, pred\n gc.collect()\n",
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"timestamp": "2026-09-29T04:47:15.931Z",
"content": "Successfully created and wrote to new file: /app/solution/experiment_svd.py. Here is the updated code:\nimport gc\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport scipy.sparse as sp\nfrom scipy.sparse.linalg import svds\nfrom sklearn.preprocessing import normalize\n\n# Make sure /task is in sys.path\nif \"/task\" not in sys.path:\n sys.path.insert(0, \"/task\")\n\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\n\nprint(\"Loading metadata...\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\nunpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets_set)\n\nprint(f\"Loaded: N={N} users, M={M} items. Ground truth users: {len(graded)}\")\n\ndef evaluate_predictions(pred_matrix: np.ndarray) -> float:\n \"\"\"Helper to evaluate NDCG@10 from a dense (N, M) prediction matrix.\"\"\"\n pred = pred_matrix.copy()\n pred[~unpacked] = -1e9\n \n top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n \n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n res = score_rankings(ranked, graded)\n return res[\"quality_ndcg@10\"]\n\n# Load listens in the last 45 days\ncutoff_ts = 24786795\nmin_ts_45d = cutoff_ts - 45 * 86400\n\nprint(\"Reading recent listens (last 45 days)...\")\ntbl = pq.read_table(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n filters=[(\"timestamp\", \">=\", min_ts_45d)]\n)\nlistens_df = tbl.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\ndel tbl\ngc.collect()\nprint(f\"Loaded {len(listens_df)} listens.\")\n\n# Load validation preference state\nval_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\nprint(f\"Loaded {len(val_pref)} preferences.\")\n\n# Experiment 1: Window size and IDF scaling on binary listens\nprint(\"\\n--- Experiment 1: Window size vs IDF scaling (Binary) ---\")\nfor days in [10, 14, 18, 24, 30]:\n cur_min_ts = cutoff_ts - days * 86400\n sub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\n u_idx = sub.uid.map(uid_to_idx).to_numpy()\n i_idx = sub.item_id.map(item_to_idx).to_numpy()\n \n R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n R.data = np.ones_like(R.data) # binary\n \n n_j = np.array(R.sum(axis=0)).flatten()\n idf = np.log((N + 1.0) / (n_j + 1.0))\n \n for idf_pow in [0.3, 0.5, 0.7]:\n R_idf = R.multiply(idf**idf_pow).tocsr()\n U, S, Vt = svds(R_idf, k=192)\n idx = np.argsort(-S)\n U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n pred = (U * S) @ Vt\n score = evaluate_predictions(pred)\n print(f\"Days={days:2d}, idf_pow={idf_pow:.1f} -> NDCG@10 = {score:.6f}\")\n del R_idf, U, S, Vt, pred\n gc.collect()\n",
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{
"id": 52,
"role": "agent",
"timestamp": "2026-09-29T04:47:18.472Z",
"blocks": [
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"kind": "tool",
"timestamp": "2026-09-29T04:47:18.472Z",
"callId": "run_shell_command__call_230445",
"name": "run_shell_command",
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"command": "PYTHONPATH=/task python /app/solution/experiment_svd.py",
"description": "Run experiment 1 to compare days and idf_pow"
},
"recorded": {
"id": "run_shell_command__call_230445",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python /app/solution/experiment_svd.py",
"description": "Run experiment 1 to compare days and idf_pow"
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"output": "\nOutput: Loading metadata...\nLoaded: N=8456 users, M=30000 items. Ground truth users: 4263\nReading recent listens (last 45 days)...\nLoaded 6409037 listens.\nLoaded 469124 preferences.\n\n--- Experiment 1: Window size vs IDF scaling (Binary) ---\nDays=10, idf_pow=0.3 -> NDCG@10 = 0.032113\nDays=10, idf_pow=0.5 -> NDCG@10 = 0.032619\nDays=10, idf_pow=0.7 -> NDCG@10 = 0.032033\nDays=14, idf_pow=0.3 -> NDCG@10 = 0.032767\nDays=14, idf_pow=0.5 -> NDCG@10 = 0.032901\nDays=14, idf_pow=0.7 -> NDCG@10 = 0.032594\nDays=18, idf_pow=0.3 -> NDCG@10 = 0.031356\nDays=18, idf_pow=0.5 -> NDCG@10 = 0.032168\nDays=18, idf_pow=0.7 -> NDCG@10 = 0.031984\nDays=24, idf_pow=0.3 -> NDCG@10 = 0.032113\nDays=24, idf_pow=0.5 -> NDCG@10 = 0.032704\nDays=24, idf_pow=0.7 -> NDCG@10 = 0.032900\nDays=30, idf_pow=0.3 -> NDCG@10 = 0.031707\nDays=30, idf_pow=0.5 -> NDCG@10 = 0.032118\nDays=30, idf_pow=0.7 -> NDCG@10 = 0.032051\nProcess Group PGID: 2694\n"
}
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],
"status": "success",
"timestamp": "2026-09-29T04:49:09.498Z",
"resultDisplay": "Loading metadata...\nLoaded: N=8456 users, M=30000 items. Ground truth users: 4263\nReading recent listens (last 45 days)...\nLoaded 6409037 listens.\nLoaded 469124 preferences.\n\n--- Experiment 1: Window size vs IDF scaling (Binary) ---\nDays=10, idf_pow=0.3 -> NDCG@10 = 0.032113\nDays=10, idf_pow=0.5 -> NDCG@10 = 0.032619\nDays=10, idf_pow=0.7 -> NDCG@10 = 0.032033\nDays=14, idf_pow=0.3 -> NDCG@10 = 0.032767\nDays=14, idf_pow=0.5 -> NDCG@10 = 0.032901\nDays=14, idf_pow=0.7 -> NDCG@10 = 0.032594\nDays=18, idf_pow=0.3 -> NDCG@10 = 0.031356\nDays=18, idf_pow=0.5 -> NDCG@10 = 0.032168\nDays=18, idf_pow=0.7 -> NDCG@10 = 0.031984\nDays=24, idf_pow=0.3 -> NDCG@10 = 0.032113\nDays=24, idf_pow=0.5 -> NDCG@10 = 0.032704\nDays=24, idf_pow=0.7 -> NDCG@10 = 0.032900\nDays=30, idf_pow=0.3 -> NDCG@10 = 0.031707\nDays=30, idf_pow=0.5 -> NDCG@10 = 0.032118\nDays=30, idf_pow=0.7 -> NDCG@10 = 0.032051",
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"timestamp": "2026-09-29T04:49:09.500Z",
"content": "\nOutput: Loading metadata...\nLoaded: N=8456 users, M=30000 items. Ground truth users: 4263\nReading recent listens (last 45 days)...\nLoaded 6409037 listens.\nLoaded 469124 preferences.\n\n--- Experiment 1: Window size vs IDF scaling (Binary) ---\nDays=10, idf_pow=0.3 -> NDCG@10 = 0.032113\nDays=10, idf_pow=0.5 -> NDCG@10 = 0.032619\nDays=10, idf_pow=0.7 -> NDCG@10 = 0.032033\nDays=14, idf_pow=0.3 -> NDCG@10 = 0.032767\nDays=14, idf_pow=0.5 -> NDCG@10 = 0.032901\nDays=14, idf_pow=0.7 -> NDCG@10 = 0.032594\nDays=18, idf_pow=0.3 -> NDCG@10 = 0.031356\nDays=18, idf_pow=0.5 -> NDCG@10 = 0.032168\nDays=18, idf_pow=0.7 -> NDCG@10 = 0.031984\nDays=24, idf_pow=0.3 -> NDCG@10 = 0.032113\nDays=24, idf_pow=0.5 -> NDCG@10 = 0.032704\nDays=24, idf_pow=0.7 -> NDCG@10 = 0.032900\nDays=30, idf_pow=0.3 -> NDCG@10 = 0.031707\nDays=30, idf_pow=0.5 -> NDCG@10 = 0.032118\nDays=30, idf_pow=0.7 -> NDCG@10 = 0.032051\nProcess Group PGID: 2694\n",
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},
{
"id": 53,
"role": "agent",
"timestamp": "2026-09-29T04:49:34.295Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:49:34.295Z",
"text": "Exploring Preference Integration\n\nI'm now investigating how incorporating explicit user preferences, specifically likes and dislikes captured in `val_pref`, might influence NDCG@10, building on the recent NDCG@10 score of 0.032901 derived from `idf_pow=0.5` over 14 days. My current focus is on the impact of these accumulated preferences."
},
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"kind": "tool",
"timestamp": "2026-09-29T04:49:34.295Z",
"callId": "write_file__call_206873",
"name": "write_file",
"input": {
"content": "import gc\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport scipy.sparse as sp\nfrom scipy.sparse.linalg import svds\n\nif \"/task\" not in sys.path:\n sys.path.insert(0, \"/task\")\n\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\n\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\nunpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets_set)\n\ncutoff_ts = 24786795\nmin_ts_21d = cutoff_ts - 21 * 86400\n\ntbl = pq.read_table(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n filters=[(\"timestamp\", \">=\", min_ts_21d)]\n)\nlistens_df = tbl.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\ndel tbl\ngc.collect()\n\nval_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\ndef evaluate_predictions(pred_matrix: np.ndarray) -> float:\n pred = pred_matrix.copy()\n pred[~unpacked] = -1e9\n \n top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n \n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n res = score_rankings(ranked, graded)\n return res[\"quality_ndcg@10\"]\n\n# Baseline matrix (14 days, binary)\ncur_min_ts = cutoff_ts - 14 * 86400\nsub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\nu_idx = sub.uid.map(uid_to_idx).to_numpy()\ni_idx = sub.item_id.map(item_to_idx).to_numpy()\n\nR_base = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR_base.data = np.ones_like(R_base.data)\n\nn_j = np.array(R_base.sum(axis=0)).flatten()\nidf = np.log((N + 1.0) / (n_j + 1.0))\nR_idf = R_base.multiply(np.sqrt(idf)).tocsr()\n\nU, S, Vt = svds(R_idf, k=192)\nidx = np.argsort(-S)\nU, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\npred_base = (U * S) @ Vt\nprint(f\"Base SVD-192 (14d binary, sqrt idf): NDCG@10 = {evaluate_predictions(pred_base):.6f}\")\n\n# Test Popularity Blend\nprint(\"\\n--- Popularity Blending ---\")\n# Organic positive listens from catalog\norg_pop = np.log1p(cat_frame.organic_positive_listens.to_numpy(dtype=np.float32))\n# Normalize org_pop to same standard deviation or range\norg_pop_norm = (org_pop - org_pop.mean()) / org_pop.std()\npred_std = pred_base.std()\n\nfor beta in [0.01, 0.03, 0.05, 0.1, 0.2, 0.5]:\n blended = pred_base + (beta * pred_std) * org_pop_norm[None, :]\n print(f\"Beta {beta:.2f} (organic pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n\n# Also test positive_listens popularity\npos_pop = np.log1p(cat_frame.positive_listens.to_numpy(dtype=np.float32))\npos_pop_norm = (pos_pop - pos_pop.mean()) / pos_pop.std()\nfor beta in [0.01, 0.03, 0.05, 0.1, 0.2]:\n blended = pred_base + (beta * pred_std) * pos_pop_norm[None, :]\n print(f\"Beta {beta:.2f} (total pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n",
"instruction": "Create experiment_blend.py to test popularity blending",
"file_path": "/app/solution/experiment_blend.py"
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"content": "import gc\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport scipy.sparse as sp\nfrom scipy.sparse.linalg import svds\n\nif \"/task\" not in sys.path:\n sys.path.insert(0, \"/task\")\n\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\n\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\nunpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets_set)\n\ncutoff_ts = 24786795\nmin_ts_21d = cutoff_ts - 21 * 86400\n\ntbl = pq.read_table(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n filters=[(\"timestamp\", \">=\", min_ts_21d)]\n)\nlistens_df = tbl.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\ndel tbl\ngc.collect()\n\nval_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\ndef evaluate_predictions(pred_matrix: np.ndarray) -> float:\n pred = pred_matrix.copy()\n pred[~unpacked] = -1e9\n \n top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n \n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n res = score_rankings(ranked, graded)\n return res[\"quality_ndcg@10\"]\n\n# Baseline matrix (14 days, binary)\ncur_min_ts = cutoff_ts - 14 * 86400\nsub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\nu_idx = sub.uid.map(uid_to_idx).to_numpy()\ni_idx = sub.item_id.map(item_to_idx).to_numpy()\n\nR_base = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR_base.data = np.ones_like(R_base.data)\n\nn_j = np.array(R_base.sum(axis=0)).flatten()\nidf = np.log((N + 1.0) / (n_j + 1.0))\nR_idf = R_base.multiply(np.sqrt(idf)).tocsr()\n\nU, S, Vt = svds(R_idf, k=192)\nidx = np.argsort(-S)\nU, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\npred_base = (U * S) @ Vt\nprint(f\"Base SVD-192 (14d binary, sqrt idf): NDCG@10 = {evaluate_predictions(pred_base):.6f}\")\n\n# Test Popularity Blend\nprint(\"\\n--- Popularity Blending ---\")\n# Organic positive listens from catalog\norg_pop = np.log1p(cat_frame.organic_positive_listens.to_numpy(dtype=np.float32))\n# Normalize org_pop to same standard deviation or range\norg_pop_norm = (org_pop - org_pop.mean()) / org_pop.std()\npred_std = pred_base.std()\n\nfor beta in [0.01, 0.03, 0.05, 0.1, 0.2, 0.5]:\n blended = pred_base + (beta * pred_std) * org_pop_norm[None, :]\n print(f\"Beta {beta:.2f} (organic pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n\n# Also test positive_listens popularity\npos_pop = np.log1p(cat_frame.positive_listens.to_numpy(dtype=np.float32))\npos_pop_norm = (pos_pop - pos_pop.mean()) / pos_pop.std()\nfor beta in [0.01, 0.03, 0.05, 0.1, 0.2]:\n blended = pred_base + (beta * pred_std) * pos_pop_norm[None, :]\n print(f\"Beta {beta:.2f} (total pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n",
"instruction": "Create experiment_blend.py to test popularity blending",
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"output": "Successfully created and wrote to new file: /app/solution/experiment_blend.py. Here is the updated code:\nimport gc\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport scipy.sparse as sp\nfrom scipy.sparse.linalg import svds\n\nif \"/task\" not in sys.path:\n sys.path.insert(0, \"/task\")\n\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\n\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\nunpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets_set)\n\ncutoff_ts = 24786795\nmin_ts_21d = cutoff_ts - 21 * 86400\n\ntbl = pq.read_table(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n filters=[(\"timestamp\", \">=\", min_ts_21d)]\n)\nlistens_df = tbl.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\ndel tbl\ngc.collect()\n\nval_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\ndef evaluate_predictions(pred_matrix: np.ndarray) -> float:\n pred = pred_matrix.copy()\n pred[~unpacked] = -1e9\n \n top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n \n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n res = score_rankings(ranked, graded)\n return res[\"quality_ndcg@10\"]\n\n# Baseline matrix (14 days, binary)\ncur_min_ts = cutoff_ts - 14 * 86400\nsub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\nu_idx = sub.uid.map(uid_to_idx).to_numpy()\ni_idx = sub.item_id.map(item_to_idx).to_numpy()\n\nR_base = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR_base.data = np.ones_like(R_base.data)\n\nn_j = np.array(R_base.sum(axis=0)).flatten()\nidf = np.log((N + 1.0) / (n_j + 1.0))\nR_idf = R_base.multiply(np.sqrt(idf)).tocsr()\n\nU, S, Vt = svds(R_idf, k=192)\nidx = np.argsort(-S)\nU, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\npred_base = (U * S) @ Vt\nprint(f\"Base SVD-192 (14d binary, sqrt idf): NDCG@10 = {evaluate_predictions(pred_base):.6f}\")\n\n# Test Popularity Blend\nprint(\"\\n--- Popularity Blending ---\")\n# Organic positive listens from catalog\norg_pop = np.log1p(cat_frame.organic_positive_listens.to_numpy(dtype=np.float32))\n# Normalize org_pop to same standard deviation or range\norg_pop_norm = (org_pop - org_pop.mean()) / org_pop.std()\npred_std = pred_base.std()\n\nfor beta in [0.01, 0.03, 0.05, 0.1, 0.2, 0.5]:\n blended = pred_base + (beta * pred_std) * org_pop_norm[None, :]\n print(f\"Beta {beta:.2f} (organic pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n\n# Also test positive_listens popularity\npos_pop = np.log1p(cat_frame.positive_listens.to_numpy(dtype=np.float32))\npos_pop_norm = (pos_pop - pos_pop.mean()) / pos_pop.std()\nfor beta in [0.01, 0.03, 0.05, 0.1, 0.2]:\n blended = pred_base + (beta * pred_std) * pos_pop_norm[None, :]\n print(f\"Beta {beta:.2f} (total pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n"
}
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],
"status": "success",
"timestamp": "2026-09-29T04:49:34.303Z",
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"fileDiff": "Index: experiment_blend.py\n===================================================================\n--- experiment_blend.py\tOriginal\n+++ experiment_blend.py\tWritten\n@@ -0,0 +1,102 @@\n+import gc\n+import sys\n+import time\n+from pathlib import Path\n+import numpy as np\n+import pandas as pd\n+import pyarrow.parquet as pq\n+import scipy.sparse as sp\n+from scipy.sparse.linalg import svds\n+\n+if \"/task\" not in sys.path:\n+ sys.path.insert(0, \"/task\")\n+\n+from ranking_evaluation import CandidateSet, score_rankings\n+from evaluate_public import validation_truth\n+\n+DATA = Path(\"/task/data\")\n+\n+targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n+targets_list = targets_frame.uid.astype(int).tolist()\n+targets_set = set(targets_list)\n+cat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+cat_items = cat_frame.item_id.astype(int).tolist()\n+cat_set = set(cat_items)\n+\n+uid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\n+item_to_idx = {item: j for j, item in enumerate(cat_items)}\n+N, M = len(targets_list), len(cat_items)\n+\n+val_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\n+unpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n+\n+eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n+graded = validation_truth(eligibility, targets_set)\n+\n+cutoff_ts = 24786795\n+min_ts_21d = cutoff_ts - 21 * 86400\n+\n+tbl = pq.read_table(\n+ DATA / \"train_listens.parquet\",\n+ columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n+ filters=[(\"timestamp\", \">=\", min_ts_21d)]\n+)\n+listens_df = tbl.to_pandas()\n+listens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\n+del tbl\n+gc.collect()\n+\n+val_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n+val_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n+\n+def evaluate_predictions(pred_matrix: np.ndarray) -> float:\n+ pred = pred_matrix.copy()\n+ pred[~unpacked] = -1e9\n+ \n+ top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n+ row_indices = np.arange(N)[:, None]\n+ top100_scores = pred[row_indices, top100_idx]\n+ sort_order = np.argsort(-top100_scores, axis=1)\n+ sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n+ \n+ ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n+ res = score_rankings(ranked, graded)\n+ return res[\"quality_ndcg@10\"]\n+\n+# Baseline matrix (14 days, binary)\n+cur_min_ts = cutoff_ts - 14 * 86400\n+sub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\n+u_idx = sub.uid.map(uid_to_idx).to_numpy()\n+i_idx = sub.item_id.map(item_to_idx).to_numpy()\n+\n+R_base = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n+R_base.data = np.ones_like(R_base.data)\n+\n+n_j = np.array(R_base.sum(axis=0)).flatten()\n+idf = np.log((N + 1.0) / (n_j + 1.0))\n+R_idf = R_base.multiply(np.sqrt(idf)).tocsr()\n+\n+U, S, Vt = svds(R_idf, k=192)\n+idx = np.argsort(-S)\n+U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n+pred_base = (U * S) @ Vt\n+print(f\"Base SVD-192 (14d binary, sqrt idf): NDCG@10 = {evaluate_predictions(pred_base):.6f}\")\n+\n+# Test Popularity Blend\n+print(\"\\n--- Popularity Blending ---\")\n+# Organic positive listens from catalog\n+org_pop = np.log1p(cat_frame.organic_positive_listens.to_numpy(dtype=np.float32))\n+# Normalize org_pop to same standard deviation or range\n+org_pop_norm = (org_pop - org_pop.mean()) / org_pop.std()\n+pred_std = pred_base.std()\n+\n+for beta in [0.01, 0.03, 0.05, 0.1, 0.2, 0.5]:\n+ blended = pred_base + (beta * pred_std) * org_pop_norm[None, :]\n+ print(f\"Beta {beta:.2f} (organic pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n+\n+# Also test positive_listens popularity\n+pos_pop = np.log1p(cat_frame.positive_listens.to_numpy(dtype=np.float32))\n+pos_pop_norm = (pos_pop - pos_pop.mean()) / pos_pop.std()\n+for beta in [0.01, 0.03, 0.05, 0.1, 0.2]:\n+ blended = pred_base + (beta * pred_std) * pos_pop_norm[None, :]\n+ print(f\"Beta {beta:.2f} (total pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n",
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"newContent": "import gc\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport scipy.sparse as sp\nfrom scipy.sparse.linalg import svds\n\nif \"/task\" not in sys.path:\n sys.path.insert(0, \"/task\")\n\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\n\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\nunpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets_set)\n\ncutoff_ts = 24786795\nmin_ts_21d = cutoff_ts - 21 * 86400\n\ntbl = pq.read_table(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n filters=[(\"timestamp\", \">=\", min_ts_21d)]\n)\nlistens_df = tbl.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\ndel tbl\ngc.collect()\n\nval_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\ndef evaluate_predictions(pred_matrix: np.ndarray) -> float:\n pred = pred_matrix.copy()\n pred[~unpacked] = -1e9\n \n top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n \n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n res = score_rankings(ranked, graded)\n return res[\"quality_ndcg@10\"]\n\n# Baseline matrix (14 days, binary)\ncur_min_ts = cutoff_ts - 14 * 86400\nsub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\nu_idx = sub.uid.map(uid_to_idx).to_numpy()\ni_idx = sub.item_id.map(item_to_idx).to_numpy()\n\nR_base = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR_base.data = np.ones_like(R_base.data)\n\nn_j = np.array(R_base.sum(axis=0)).flatten()\nidf = np.log((N + 1.0) / (n_j + 1.0))\nR_idf = R_base.multiply(np.sqrt(idf)).tocsr()\n\nU, S, Vt = svds(R_idf, k=192)\nidx = np.argsort(-S)\nU, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\npred_base = (U * S) @ Vt\nprint(f\"Base SVD-192 (14d binary, sqrt idf): NDCG@10 = {evaluate_predictions(pred_base):.6f}\")\n\n# Test Popularity Blend\nprint(\"\\n--- Popularity Blending ---\")\n# Organic positive listens from catalog\norg_pop = np.log1p(cat_frame.organic_positive_listens.to_numpy(dtype=np.float32))\n# Normalize org_pop to same standard deviation or range\norg_pop_norm = (org_pop - org_pop.mean()) / org_pop.std()\npred_std = pred_base.std()\n\nfor beta in [0.01, 0.03, 0.05, 0.1, 0.2, 0.5]:\n blended = pred_base + (beta * pred_std) * org_pop_norm[None, :]\n print(f\"Beta {beta:.2f} (organic pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n\n# Also test positive_listens popularity\npos_pop = np.log1p(cat_frame.positive_listens.to_numpy(dtype=np.float32))\npos_pop_norm = (pos_pop - pos_pop.mean()) / pos_pop.std()\nfor beta in [0.01, 0.03, 0.05, 0.1, 0.2]:\n blended = pred_base + (beta * pred_std) * pos_pop_norm[None, :]\n print(f\"Beta {beta:.2f} (total pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n",
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"timestamp": "2026-09-29T04:49:34.305Z",
"content": "Successfully created and wrote to new file: /app/solution/experiment_blend.py. Here is the updated code:\nimport gc\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport scipy.sparse as sp\nfrom scipy.sparse.linalg import svds\n\nif \"/task\" not in sys.path:\n sys.path.insert(0, \"/task\")\n\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\n\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\nunpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets_set)\n\ncutoff_ts = 24786795\nmin_ts_21d = cutoff_ts - 21 * 86400\n\ntbl = pq.read_table(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n filters=[(\"timestamp\", \">=\", min_ts_21d)]\n)\nlistens_df = tbl.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\ndel tbl\ngc.collect()\n\nval_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\ndef evaluate_predictions(pred_matrix: np.ndarray) -> float:\n pred = pred_matrix.copy()\n pred[~unpacked] = -1e9\n \n top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n row_indices = np.arange(N)[:, None]\n top100_scores = pred[row_indices, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n \n ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n res = score_rankings(ranked, graded)\n return res[\"quality_ndcg@10\"]\n\n# Baseline matrix (14 days, binary)\ncur_min_ts = cutoff_ts - 14 * 86400\nsub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\nu_idx = sub.uid.map(uid_to_idx).to_numpy()\ni_idx = sub.item_id.map(item_to_idx).to_numpy()\n\nR_base = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR_base.data = np.ones_like(R_base.data)\n\nn_j = np.array(R_base.sum(axis=0)).flatten()\nidf = np.log((N + 1.0) / (n_j + 1.0))\nR_idf = R_base.multiply(np.sqrt(idf)).tocsr()\n\nU, S, Vt = svds(R_idf, k=192)\nidx = np.argsort(-S)\nU, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\npred_base = (U * S) @ Vt\nprint(f\"Base SVD-192 (14d binary, sqrt idf): NDCG@10 = {evaluate_predictions(pred_base):.6f}\")\n\n# Test Popularity Blend\nprint(\"\\n--- Popularity Blending ---\")\n# Organic positive listens from catalog\norg_pop = np.log1p(cat_frame.organic_positive_listens.to_numpy(dtype=np.float32))\n# Normalize org_pop to same standard deviation or range\norg_pop_norm = (org_pop - org_pop.mean()) / org_pop.std()\npred_std = pred_base.std()\n\nfor beta in [0.01, 0.03, 0.05, 0.1, 0.2, 0.5]:\n blended = pred_base + (beta * pred_std) * org_pop_norm[None, :]\n print(f\"Beta {beta:.2f} (organic pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n\n# Also test positive_listens popularity\npos_pop = np.log1p(cat_frame.positive_listens.to_numpy(dtype=np.float32))\npos_pop_norm = (pos_pop - pos_pop.mean()) / pos_pop.std()\nfor beta in [0.01, 0.03, 0.05, 0.1, 0.2]:\n blended = pred_base + (beta * pred_std) * pos_pop_norm[None, :]\n print(f\"Beta {beta:.2f} (total pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n",
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"output": "\nOutput: Base SVD-192 (14d binary, sqrt idf): NDCG@10 = 0.032901\n\n--- Popularity Blending ---\nBeta 0.01 (organic pop) -> NDCG@10 = 0.033837\nBeta 0.03 (organic pop) -> NDCG@10 = 0.033782\nBeta 0.05 (organic pop) -> NDCG@10 = 0.033740\nBeta 0.10 (organic pop) -> NDCG@10 = 0.033658\nBeta 0.20 (organic pop) -> NDCG@10 = 0.034038\nBeta 0.50 (organic pop) -> NDCG@10 = 0.034051\nBeta 0.01 (total pop) -> NDCG@10 = 0.033729\nBeta 0.03 (total pop) -> NDCG@10 = 0.033665\nBeta 0.05 (total pop) -> NDCG@10 = 0.033651\nBeta 0.10 (total pop) -> NDCG@10 = 0.033480\nBeta 0.20 (total pop) -> NDCG@10 = 0.033505\nProcess Group PGID: 2735\n"
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"timestamp": "2026-09-29T04:50:03.912Z",
"content": "\nOutput: Base SVD-192 (14d binary, sqrt idf): NDCG@10 = 0.032901\n\n--- Popularity Blending ---\nBeta 0.01 (organic pop) -> NDCG@10 = 0.033837\nBeta 0.03 (organic pop) -> NDCG@10 = 0.033782\nBeta 0.05 (organic pop) -> NDCG@10 = 0.033740\nBeta 0.10 (organic pop) -> NDCG@10 = 0.033658\nBeta 0.20 (organic pop) -> NDCG@10 = 0.034038\nBeta 0.50 (organic pop) -> NDCG@10 = 0.034051\nBeta 0.01 (total pop) -> NDCG@10 = 0.033729\nBeta 0.03 (total pop) -> NDCG@10 = 0.033665\nBeta 0.05 (total pop) -> NDCG@10 = 0.033651\nBeta 0.10 (total pop) -> NDCG@10 = 0.033480\nBeta 0.20 (total pop) -> NDCG@10 = 0.033505\nProcess Group PGID: 2735\n",
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