{
"schemaVersion": 1,
"taskId": "yambda-discovery-ranking-two-tower-base",
"attemptId": "gemini-3-8-flash-high-base-fixed-r03-3d6fca60",
"taskChecksum": "377e192d12de767139b7c1676e51b87d6c10ecf708fa774aad0eff7b9cfe24a4",
"agentVersion": "0.61.0",
"model": "gemini-3.8-flash",
"sessionId": "db9aeacf-0e46-459f-a908-edb45638eea0",
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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├───.keep\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": "2026-09-29T05:48:06.523Z",
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"timestamp": "2026-09-29T05:48:06.523Z",
"text": "# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in 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 implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed 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 defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt 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- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n limit. Structural validity alone is not sufficient; the final submission is\n also evaluated for ranking quality.\n"
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"blocks": [
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"kind": "reasoning",
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"timestamp": "2026-09-29T05:48:15.637Z",
"text": "Analyzing Requirements\n\nI'm currently dissecting the core requirements for this PyTorch two-tower ranker. My focus is on understanding the nuances of CPU implementation and the specific constraints for the \"Made for You\" music shelf."
},
{
"kind": "reasoning",
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"timestamp": "2026-09-29T05:48:15.637Z",
"text": "Building Neural Towers\n\nI'm focusing on the distinct neural towers now. The user tower needs to learn representations solely from interaction history, eschewing simple ID lookups. Meanwhile, the item tower will generate its vectors independently, setting the stage for efficient dot-product calculation and maximum inner-product search."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:48:15.637Z",
"text": "Processing Data\n\nI'm currently parsing through the provided parquet files, specifically focusing on the validation and test preference states. This data will be crucial for understanding user behavior and evaluating the ranking performance against the defined discovery objectives, including graded NDCG@10."
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"summary": "Starting the project to build a CPU PyTorch two-tower neural network ranker. In this initial phase, I will inspect the data files, directory structure, public evaluation script, and verify python packages and CPU resources.",
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"output": "\nOutput: Architecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 8\nOn-line CPU(s) list: 0-7\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7R13 Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 4\nSocket(s): 1\nStepping: 1\nBogoMIPS: 5300.00\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 36\ndr-xr-xr-x 1 root root 4096 Sep 29 05:45 .\ndrwxr-xr-x 1 root root 4096 Sep 29 05:48 ..\n-r--r--r-- 1 root root 0 Sep 29 05:31 .keep\ndr-xr-xr-x 1 root root 4096 Sep 29 05:32 data\n-r--r--r-- 1 root root 5774 Sep 29 05:31 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 05:31 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 29 05:31 requirements.txt\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 29 05:45 .\ndrwxr-xr-x 1 root root 4096 Sep 29 05:48 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 29 05:45 solution\nProcess Group PGID: 1612\n"
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"status": "success",
"timestamp": "2026-09-29T05:48:15.682Z",
"resultDisplay": "Architecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 8\nOn-line CPU(s) list: 0-7\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7R13 Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 4\nSocket(s): 1\nStepping: 1\nBogoMIPS: 5300.00\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 36\ndr-xr-xr-x 1 root root 4096 Sep 29 05:45 .\ndrwxr-xr-x 1 root root 4096 Sep 29 05:48 ..\n-r--r--r-- 1 root root 0 Sep 29 05:31 .keep\ndr-xr-xr-x 1 root root 4096 Sep 29 05:32 data\n-r--r--r-- 1 root root 5774 Sep 29 05:31 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 05:31 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 29 05:31 requirements.txt\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 29 05:45 .\ndrwxr-xr-x 1 root root 4096 Sep 29 05:48 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 29 05:45 solution",
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"timestamp": "2026-09-29T05:48:15.684Z",
"content": "\nOutput: Architecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 8\nOn-line CPU(s) list: 0-7\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7R13 Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 4\nSocket(s): 1\nStepping: 1\nBogoMIPS: 5300.00\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 36\ndr-xr-xr-x 1 root root 4096 Sep 29 05:45 .\ndrwxr-xr-x 1 root root 4096 Sep 29 05:48 ..\n-r--r--r-- 1 root root 0 Sep 29 05:31 .keep\ndr-xr-xr-x 1 root root 4096 Sep 29 05:32 data\n-r--r--r-- 1 root root 5774 Sep 29 05:31 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 05:31 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 29 05:31 requirements.txt\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 29 05:45 .\ndrwxr-xr-x 1 root root 4096 Sep 29 05:48 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 29 05:45 solution\nProcess Group PGID: 1612\n",
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"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-100 of 158 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 101.\n\n--- FILE CONTENT (truncated) ---\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items."
}
}
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"timestamp": "2026-09-29T05:48:15.682Z",
"resultDisplay": "Read lines 1-100 of 158 from ../task/evaluate_public.py",
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"timestamp": "2026-09-29T05:48:15.684Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-100 of 158 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 101.\n\n--- FILE CONTENT (truncated) ---\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.",
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},
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"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-100 of 232 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 101.\n\n--- FILE CONTENT (truncated) ---\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n"
}
}
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"timestamp": "2026-09-29T05:48:15.682Z",
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"timestamp": "2026-09-29T05:48:15.684Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-100 of 232 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 101.\n\n--- FILE CONTENT (truncated) ---\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n",
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},
{
"id": 4,
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"timestamp": "2026-09-29T05:48:19.904Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:48:19.904Z",
"callId": "read_file__call_27418",
"name": "read_file",
"input": {
"start_line": 101,
"file_path": "/task/evaluate_public.py",
"end_line": 160
},
"recorded": {
"id": "read_file__call_27418",
"name": "read_file",
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"file_path": "/task/evaluate_public.py",
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"id": "read_file__call_27418",
"name": "read_file",
"response": {
"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 101-158 of 158 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 159.\n\n--- FILE CONTENT (truncated) ---\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"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:48:19.911Z",
"resultDisplay": "Read lines 101-158 of 158 from ../task/evaluate_public.py",
"description": "../task/evaluate_public.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:48:19.912Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 101-158 of 158 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 159.\n\n--- FILE CONTENT (truncated) ---\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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"name": "read_file",
"response": {
"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 101-232 of 232 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 233.\n\n--- FILE CONTENT (truncated) ---\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"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:48:19.911Z",
"resultDisplay": "Read lines 101-232 of 232 from ../task/ranking_evaluation.py",
"description": "../task/ranking_evaluation.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:48:19.912Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 101-232 of 232 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 233.\n\n--- FILE CONTENT (truncated) ---\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": "84e799dd-67ef-41be-8f84-fddd5719552a"
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:48:19.904Z",
"callId": "read_file__call_27424",
"name": "read_file",
"input": {
"start_line": 1,
"file_path": "/task/requirements.txt",
"end_line": 100
},
"recorded": {
"id": "read_file__call_27424",
"name": "read_file",
"args": {
"start_line": 1,
"file_path": "/task/requirements.txt",
"end_line": 100
},
"result": [
{
"functionResponse": {
"id": "read_file__call_27424",
"name": "read_file",
"response": {
"output": "# Exact distribution inventory for the Yambda two-tower task family.\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. Python itself is pinned by task.toml and both\n# 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\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\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\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\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\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:48:19.911Z",
"resultDisplay": "",
"description": "../task/requirements.txt",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:48:19.912Z",
"content": "# Exact distribution inventory for the Yambda two-tower task family.\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. Python itself is pinned by task.toml and both\n# 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\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\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\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\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\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
"isError": false
},
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]
},
{
"id": 5,
"role": "agent",
"timestamp": "2026-09-29T05:48:22.760Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:48:22.760Z",
"callId": "run_shell_command__call_337318",
"name": "run_shell_command",
"input": {
"description": "Inspect datasets in /task/data",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nfor p in sorted(data_dir.glob('*')):\n if p.suffix == '.parquet':\n df = pd.read_parquet(p)\n print(f'{p.name}: shape={df.shape}, cols={list(df.columns)}')\n elif p.suffix == '.npz':\n with np.load(p) as a:\n print(f'{p.name}: files={a.files}, uids={len(a[\\\"uids\\\"])}, item_ids={len(a[\\\"item_ids\\\"])}, packed_shape={a[\\\"packed_eligible\\\"].shape}')\n\""
},
"recorded": {
"id": "run_shell_command__call_337318",
"name": "run_shell_command",
"args": {
"description": "Inspect datasets in /task/data",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nfor p in sorted(data_dir.glob('*')):\n if p.suffix == '.parquet':\n df = pd.read_parquet(p)\n print(f'{p.name}: shape={df.shape}, cols={list(df.columns)}')\n elif p.suffix == '.npz':\n with np.load(p) as a:\n print(f'{p.name}: files={a.files}, uids={len(a[\\\"uids\\\"])}, item_ids={len(a[\\\"item_ids\\\"])}, packed_shape={a[\\\"packed_eligible\\\"].shape}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_337318",
"name": "run_shell_command",
"response": {
"output": "\nOutput: candidate_catalog.parquet: shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet: shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_eligible_candidates.npz: files=['uids', 'item_ids', 'packed_eligible'], uids=8456, item_ids=30000, packed_shape=(8456, 3750)\ntest_preference_state.parquet: shape=(484776, 3), cols=['uid', 'item_id', 'state']\ntrain_dislikes.parquet: shape=(101075, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet: shape=(814831, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet: shape=(43484159, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet: shape=(19960, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet: shape=(292665, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet: shape=(3563, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet: shape=(37795, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet: shape=(1501697, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet: shape=(606, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet: shape=(9346, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_eligible_candidates.npz: files=['uids', 'item_ids', 'packed_eligible'], uids=8456, item_ids=30000, packed_shape=(8456, 3750)\nvalidation_preference_state.parquet: shape=(469124, 3), cols=['uid', 'item_id', 'state']\nProcess Group PGID: 1618\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:48:25.342Z",
"resultDisplay": "candidate_catalog.parquet: shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet: shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_eligible_candidates.npz: files=['uids', 'item_ids', 'packed_eligible'], uids=8456, item_ids=30000, packed_shape=(8456, 3750)\ntest_preference_state.parquet: shape=(484776, 3), cols=['uid', 'item_id', 'state']\ntrain_dislikes.parquet: shape=(101075, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet: shape=(814831, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet: shape=(43484159, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet: shape=(19960, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet: shape=(292665, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet: shape=(3563, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet: shape=(37795, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet: shape=(1501697, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet: shape=(606, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet: shape=(9346, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_eligible_candidates.npz: files=['uids', 'item_ids', 'packed_eligible'], uids=8456, item_ids=30000, packed_shape=(8456, 3750)\nvalidation_preference_state.parquet: shape=(469124, 3), cols=['uid', 'item_id', 'state']",
"description": "Inspect datasets in /task/data",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:48:25.343Z",
"content": "\nOutput: candidate_catalog.parquet: shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet: shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_eligible_candidates.npz: files=['uids', 'item_ids', 'packed_eligible'], uids=8456, item_ids=30000, packed_shape=(8456, 3750)\ntest_preference_state.parquet: shape=(484776, 3), cols=['uid', 'item_id', 'state']\ntrain_dislikes.parquet: shape=(101075, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet: shape=(814831, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet: shape=(43484159, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet: shape=(19960, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet: shape=(292665, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet: shape=(3563, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet: shape=(37795, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet: shape=(1501697, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet: shape=(606, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet: shape=(9346, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_eligible_candidates.npz: files=['uids', 'item_ids', 'packed_eligible'], uids=8456, item_ids=30000, packed_shape=(8456, 3750)\nvalidation_preference_state.parquet: shape=(469124, 3), cols=['uid', 'item_id', 'state']\nProcess Group PGID: 1618\n",
"isError": false
},
"responseMessageId": "2f7feddc-401f-4e40-af8e-7674ed4c586c"
}
]
},
{
"id": 6,
"role": "agent",
"timestamp": "2026-09-29T05:48:29.391Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:48:29.391Z",
"text": "Reviewing Data Sizes\n\nI'm currently analyzing the scale of our datasets. The `train_listens` file is particularly massive, containing over 43 million entries. The `candidate_catalog` and `target_users` files are smaller, but `train_likes` and `train_dislikes` are also substantial in their own right."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:48:29.391Z",
"callId": "run_shell_command__call_109472",
"name": "run_shell_command",
"input": {
"description": "Check system resources",
"command": "python3 -c \"\nimport psutil, os\nprint('CPU count:', os.cpu_count())\nprint('RAM GB:', psutil.virtual_memory().total / (1024**3), 'Available GB:', psutil.virtual_memory().available / (1024**3))\n\" && df -h"
},
"recorded": {
"id": "run_shell_command__call_109472",
"name": "run_shell_command",
"args": {
"description": "Check system resources",
"command": "python3 -c \"\nimport psutil, os\nprint('CPU count:', os.cpu_count())\nprint('RAM GB:', psutil.virtual_memory().total / (1024**3), 'Available GB:', psutil.virtual_memory().available / (1024**3))\n\" && df -h"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_109472",
"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: 1638\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:48:29.423Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'psutil'",
"description": "Check system resources",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:48:29.424Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'psutil'\nExit Code: 1\nProcess Group PGID: 1638\n",
"isError": false
},
"responseMessageId": "11457a4b-b5b4-4321-9daf-85b690a4f332"
}
]
},
{
"id": 7,
"role": "agent",
"timestamp": "2026-09-29T05:48:33.838Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:48:33.838Z",
"callId": "run_shell_command__call_365719",
"name": "run_shell_command",
"input": {
"command": "cat /proc/meminfo | grep -E \"MemTotal|MemAvailable|MemFree\" && df -h",
"description": "Check memory and disk space via /proc/meminfo"
},
"recorded": {
"id": "run_shell_command__call_365719",
"name": "run_shell_command",
"args": {
"command": "cat /proc/meminfo | grep -E \"MemTotal|MemAvailable|MemFree\" && df -h",
"description": "Check memory and disk space via /proc/meminfo"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_365719",
"name": "run_shell_command",
"response": {
"output": "\nOutput: MemTotal: 15988964 kB\nMemFree: 2033312 kB\nMemAvailable: 14430976 kB\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 14G 83G 15% /\ntmpfs 64M 0 64M 0% /dev\nshm 64M 0 64M 0% /dev/shm\n/dev/root 96G 14G 83G 15% /etc/hosts\ntmpfs 7.7G 0 7.7G 0% /proc/acpi\ntmpfs 7.7G 0 7.7G 0% /proc/scsi\ntmpfs 7.7G 0 7.7G 0% /sys/firmware\nProcess Group PGID: 1641\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:48:33.854Z",
"resultDisplay": "MemTotal: 15988964 kB\nMemFree: 2033312 kB\nMemAvailable: 14430976 kB\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 14G 83G 15% /\ntmpfs 64M 0 64M 0% /dev\nshm 64M 0 64M 0% /dev/shm\n/dev/root 96G 14G 83G 15% /etc/hosts\ntmpfs 7.7G 0 7.7G 0% /proc/acpi\ntmpfs 7.7G 0 7.7G 0% /proc/scsi\ntmpfs 7.7G 0 7.7G 0% /sys/firmware",
"description": "cat /proc/meminfo | grep -E \"MemTotal|MemAvailable|MemFree\" && df -h",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:48:33.855Z",
"content": "\nOutput: MemTotal: 15988964 kB\nMemFree: 2033312 kB\nMemAvailable: 14430976 kB\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 14G 83G 15% /\ntmpfs 64M 0 64M 0% /dev\nshm 64M 0 64M 0% /dev/shm\n/dev/root 96G 14G 83G 15% /etc/hosts\ntmpfs 7.7G 0 7.7G 0% /proc/acpi\ntmpfs 7.7G 0 7.7G 0% /proc/scsi\ntmpfs 7.7G 0 7.7G 0% /sys/firmware\nProcess Group PGID: 1641\n",
"isError": false
},
"responseMessageId": "22e35a34-21f1-4706-92e1-8a8a1f069334"
}
]
},
{
"id": 8,
"role": "agent",
"timestamp": "2026-09-29T05:48:36.384Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:48:36.384Z",
"text": "Assessing Memory Requirements\n\nI'm currently evaluating memory constraints. With 14.4 GB available RAM and a 43.5 million-row dataset, loading the entire `train_listens.parquet` as a single DataFrame could exceed my capacity. I'll explore alternative loading strategies to manage this."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:48:36.384Z",
"callId": "run_shell_command__call_501493",
"name": "run_shell_command",
"input": {
"command": "ls -lh /task/data/",
"description": "Check file sizes in /task/data"
},
"recorded": {
"id": "run_shell_command__call_501493",
"name": "run_shell_command",
"args": {
"command": "ls -lh /task/data/",
"description": "Check file sizes in /task/data"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_501493",
"name": "run_shell_command",
"response": {
"output": "\nOutput: total 340M\n-r--r--r-- 1 root root 342K Sep 29 05:31 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 29 05:31 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 05:31 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 05:32 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 05:31 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 05:31 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 05:31 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 05:31 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 05:31 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 29 05:31 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 05:31 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 05:31 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 05:31 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 29 05:31 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 05:31 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 05:32 validation_preference_state.parquet\nProcess Group PGID: 1646\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:48:36.398Z",
"resultDisplay": "total 340M\n-r--r--r-- 1 root root 342K Sep 29 05:31 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 29 05:31 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 05:31 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 05:32 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 05:31 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 05:31 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 05:31 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 05:31 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 05:31 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 29 05:31 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 05:31 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 05:31 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 05:31 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 29 05:31 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 05:31 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 05:32 validation_preference_state.parquet",
"description": "ls -lh /task/data/",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:48:36.398Z",
"content": "\nOutput: total 340M\n-r--r--r-- 1 root root 342K Sep 29 05:31 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 29 05:31 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 05:31 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 05:32 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 05:31 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 05:31 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 05:31 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 05:31 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 05:31 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 29 05:31 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 05:31 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 05:31 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 05:31 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 29 05:31 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 05:31 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 05:32 validation_preference_state.parquet\nProcess Group PGID: 1646\n",
"isError": false
},
"responseMessageId": "962f3e33-45aa-49b8-b96b-5860920c8989"
}
]
},
{
"id": 9,
"role": "agent",
"timestamp": "2026-09-29T05:48:41.979Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:48:41.979Z",
"text": "Exploring Data Loading\n\nI'm starting to investigate loading the `train_listens.parquet` file, which is 305MB. My immediate plan is to use `pyarrow` to quickly inspect its schema and row group counts. This will help me understand the data structure before diving deeper and identifying target users."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:48:41.979Z",
"callId": "run_shell_command__call_303990",
"name": "run_shell_command",
"input": {
"description": "Explore datasets schema and content",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nprint('target_users:', len(target_users), 'uids min/max:', target_users.uid.min(), target_users.uid.max())\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('catalog:', len(cat), 'item_ids min/max:', cat.item_id.min(), cat.item_id.max())\n\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('validation_preference_state:', v_pref.head(), v_pref.state.value_counts())\n\nschema = pq.read_schema('/task/data/train_listens.parquet')\nprint('train_listens schema:', schema)\n\nt_users_set = set(target_users.uid)\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('val_listens len:', len(val_listens), 'unique users:', val_listens.uid.nunique(), 'overlap with target_users:', len(set(val_listens.uid) & t_users_set))\nprint('val_listens time range:', val_listens.timestamp.min(), val_listens.timestamp.max())\n\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nprint('val_likes len:', len(val_likes), 'unique users:', val_likes.uid.nunique())\n\""
},
"recorded": {
"id": "run_shell_command__call_303990",
"name": "run_shell_command",
"args": {
"description": "Explore datasets schema and content",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nprint('target_users:', len(target_users), 'uids min/max:', target_users.uid.min(), target_users.uid.max())\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('catalog:', len(cat), 'item_ids min/max:', cat.item_id.min(), cat.item_id.max())\n\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('validation_preference_state:', v_pref.head(), v_pref.state.value_counts())\n\nschema = pq.read_schema('/task/data/train_listens.parquet')\nprint('train_listens schema:', schema)\n\nt_users_set = set(target_users.uid)\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('val_listens len:', len(val_listens), 'unique users:', val_listens.uid.nunique(), 'overlap with target_users:', len(set(val_listens.uid) & t_users_set))\nprint('val_listens time range:', val_listens.timestamp.min(), val_listens.timestamp.max())\n\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nprint('val_likes len:', len(val_likes), 'unique users:', val_likes.uid.nunique())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_303990",
"name": "run_shell_command",
"response": {
"output": "\nOutput: target_users: 8456 uids min/max: 100 1000000\ncatalog: 30000 item_ids min/max: 206 9390294\nvalidation_preference_state: uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\ntrain_listens schema: uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\nval_listens len: 1501697 unique users: 8196 overlap with target_users: 7729\nval_listens time range: 24788600 25393395\nval_likes len: 37795 unique users: 4114\nProcess Group PGID: 1655\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:48:43.723Z",
"resultDisplay": "target_users: 8456 uids min/max: 100 1000000\ncatalog: 30000 item_ids min/max: 206 9390294\nvalidation_preference_state: uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\ntrain_listens schema: uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\nval_listens len: 1501697 unique users: 8196 overlap with target_users: 7729\nval_listens time range: 24788600 25393395\nval_likes len: 37795 unique users: 4114",
"description": "Explore datasets schema and content",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:48:43.724Z",
"content": "\nOutput: target_users: 8456 uids min/max: 100 1000000\ncatalog: 30000 item_ids min/max: 206 9390294\nvalidation_preference_state: uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\ntrain_listens schema: uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\nval_listens len: 1501697 unique users: 8196 overlap with target_users: 7729\nval_listens time range: 24788600 25393395\nval_likes len: 37795 unique users: 4114\nProcess Group PGID: 1655\n",
"isError": false
},
"responseMessageId": "934d0b73-e1ba-4c8e-9345-36efa988043a"
}
]
},
{
"id": 10,
"role": "agent",
"timestamp": "2026-09-29T05:48:49.209Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:48:49.209Z",
"callId": "run_shell_command__call_114472",
"name": "run_shell_command",
"input": {
"description": "Check unique uids in train_listens",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid'])\nuids = table['uid'].to_numpy()\nimport numpy as np\nunique_uids = np.unique(uids)\nprint('train_listens num unique uids:', len(unique_uids))\nimport pandas as pd\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nt_set = set(target_users.uid)\nprint('overlap with target_users:', len(set(unique_uids) & t_set))\n\""
},
"recorded": {
"id": "run_shell_command__call_114472",
"name": "run_shell_command",
"args": {
"description": "Check unique uids in train_listens",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid'])\nuids = table['uid'].to_numpy()\nimport numpy as np\nunique_uids = np.unique(uids)\nprint('train_listens num unique uids:', len(unique_uids))\nimport pandas as pd\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nt_set = set(target_users.uid)\nprint('overlap with target_users:', len(set(unique_uids) & t_set))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_114472",
"name": "run_shell_command",
"response": {
"output": "\nOutput: train_listens num unique uids: 9238\noverlap with target_users: 8456\nProcess Group PGID: 1675\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:48:51.179Z",
"resultDisplay": "train_listens num unique uids: 9238\noverlap with target_users: 8456",
"description": "Check unique uids in train_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:48:51.180Z",
"content": "\nOutput: train_listens num unique uids: 9238\noverlap with target_users: 8456\nProcess Group PGID: 1675\n",
"isError": false
},
"responseMessageId": "6b50e883-20bb-4ae5-85aa-293508434bcd"
}
]
},
{
"id": 11,
"role": "agent",
"timestamp": "2026-09-29T05:48:55.016Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:48:55.016Z",
"text": "Analyzing User Data\n\nI'm currently examining the `train_listens` dataset. My focus is on understanding the scale of user engagement, particularly how many unique items are present and how many of those align with our `candidate_catalog` of 30,000 items."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:48:55.016Z",
"callId": "run_shell_command__call_502261",
"name": "run_shell_command",
"input": {
"description": "Check unique items in train_listens and catalog overlap",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitem_ids = table['item_id'].to_numpy()\nunique_items = np.unique(item_ids)\nprint('train_listens num unique items:', len(unique_items))\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\nprint('cat items in train_listens:', len(cat_set & set(unique_items)))\n\""
},
"recorded": {
"id": "run_shell_command__call_502261",
"name": "run_shell_command",
"args": {
"description": "Check unique items in train_listens and catalog overlap",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitem_ids = table['item_id'].to_numpy()\nunique_items = np.unique(item_ids)\nprint('train_listens num unique items:', len(unique_items))\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\nprint('cat items in train_listens:', len(cat_set & set(unique_items)))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_502261",
"name": "run_shell_command",
"response": {
"output": "\nOutput: train_listens num unique items: 845395\ncat items in train_listens: 30000\nProcess Group PGID: 1697\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:48:57.359Z",
"resultDisplay": "train_listens num unique items: 845395\ncat items in train_listens: 30000",
"description": "Check unique items in train_listens and catalog overlap",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:48:57.360Z",
"content": "\nOutput: train_listens num unique items: 845395\ncat items in train_listens: 30000\nProcess Group PGID: 1697\n",
"isError": false
},
"responseMessageId": "ac1af714-6c9d-46f6-a9e9-cba9efefed71"
}
]
},
{
"id": 12,
"role": "agent",
"timestamp": "2026-09-29T05:49:00.994Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:49:00.994Z",
"callId": "run_shell_command__call_515403",
"name": "run_shell_command",
"input": {
"description": "Check validation ground truth statistics",
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth\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')\ngraded = validation_truth(eligibility, targets)\nprint('Num users with gold labels in val:', len(graded))\ntotal_pos = sum(len(v) for v in graded.values())\ngrade3 = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1 = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f'Total positive labels: {total_pos}, grade 3: {grade3}, grade 1: {grade1}')\n\""
},
"recorded": {
"id": "run_shell_command__call_515403",
"name": "run_shell_command",
"args": {
"description": "Check validation ground truth statistics",
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth\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')\ngraded = validation_truth(eligibility, targets)\nprint('Num users with gold labels in val:', len(graded))\ntotal_pos = sum(len(v) for v in graded.values())\ngrade3 = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1 = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f'Total positive labels: {total_pos}, grade 3: {grade3}, grade 1: {grade1}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_515403",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Num users with gold labels in val: 4263\nTotal positive labels: 37769, grade 3: 4417, grade 1: 33352\nProcess Group PGID: 1718\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:49:03.051Z",
"resultDisplay": "Num users with gold labels in val: 4263\nTotal positive labels: 37769, grade 3: 4417, grade 1: 33352",
"description": "Check validation ground truth statistics",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:49:03.052Z",
"content": "\nOutput: Num users with gold labels in val: 4263\nTotal positive labels: 37769, grade 3: 4417, grade 1: 33352\nProcess Group PGID: 1718\n",
"isError": false
},
"responseMessageId": "e4c3e372-903f-410f-9ff5-419d01ccb26d"
}
]
},
{
"id": 13,
"role": "agent",
"timestamp": "2026-09-29T05:49:07.358Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:07.358Z",
"text": "Evaluating Validation Data\n\nMy focus has shifted to understanding the validation set. I'm reviewing the counts of gold-labeled users and total positive labels, noting the breakdown by grade. I'm also re-examining the specific definition of quality grading based on NDCG@10. My next step is to investigate the `validation_eligible_candidates`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:49:07.358Z",
"callId": "run_shell_command__call_618189",
"name": "run_shell_command",
"input": {
"description": "Check eligible candidate counts per user",
"command": "python3 -c \"\nimport numpy as np\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n packed = a['packed_eligible']\n print('validation packed shape:', packed.shape)\n # unpack bits for all users\n # packed is (8456, 3750) uint8\n unpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little')\n print('unpacked shape:', unpacked.shape)\n counts = unpacked.sum(axis=1)\n print('validation eligible counts per user: min, median, max, mean:', counts.min(), np.median(counts), counts.max(), counts.mean())\n\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n packed = a['packed_eligible']\n unpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little')\n counts = unpacked.sum(axis=1)\n print('test eligible counts per user: min, median, max, mean:', counts.min(), np.median(counts), counts.max(), counts.mean())\n\""
},
"recorded": {
"id": "run_shell_command__call_618189",
"name": "run_shell_command",
"args": {
"description": "Check eligible candidate counts per user",
"command": "python3 -c \"\nimport numpy as np\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n packed = a['packed_eligible']\n print('validation packed shape:', packed.shape)\n # unpack bits for all users\n # packed is (8456, 3750) uint8\n unpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little')\n print('unpacked shape:', unpacked.shape)\n counts = unpacked.sum(axis=1)\n print('validation eligible counts per user: min, median, max, mean:', counts.min(), np.median(counts), counts.max(), counts.mean())\n\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n packed = a['packed_eligible']\n unpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little')\n counts = unpacked.sum(axis=1)\n print('test eligible counts per user: min, median, max, mean:', counts.min(), np.median(counts), counts.max(), counts.mean())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_618189",
"name": "run_shell_command",
"response": {
"output": "\nOutput: validation packed shape: (8456, 3750)\nunpacked shape: (8456, 30000)\nvalidation eligible counts per user: min, median, max, mean: 24657 29242.0 30000 29067.306291390727\ntest eligible counts per user: min, median, max, mean: 24552 29220.0 30000 29041.3178807947\nProcess Group PGID: 1738\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:49:08.152Z",
"resultDisplay": "validation packed shape: (8456, 3750)\nunpacked shape: (8456, 30000)\nvalidation eligible counts per user: min, median, max, mean: 24657 29242.0 30000 29067.306291390727\ntest eligible counts per user: min, median, max, mean: 24552 29220.0 30000 29041.3178807947",
"description": "Check eligible candidate counts per user",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:49:08.153Z",
"content": "\nOutput: validation packed shape: (8456, 3750)\nunpacked shape: (8456, 30000)\nvalidation eligible counts per user: min, median, max, mean: 24657 29242.0 30000 29067.306291390727\ntest eligible counts per user: min, median, max, mean: 24552 29220.0 30000 29041.3178807947\nProcess Group PGID: 1738\n",
"isError": false
},
"responseMessageId": "d7d0ac67-cc21-4484-9411-0c6f9cf67e52"
}
]
},
{
"id": 14,
"role": "agent",
"timestamp": "2026-09-29T05:49:17.498Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:17.498Z",
"text": "Refining Candidate Lists\n\nI'm currently focusing on verifying the item IDs and their order within `candidate_catalog.parquet`. This step is crucial to ensure accuracy after filtering down the initial 30,000 catalog candidates to approximately 29,000 eligible ones per user."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:49:17.498Z",
"callId": "run_shell_command__call_472073",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n val_items = a['item_ids']\n val_uids = a['uids']\n\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n test_items = a['item_ids']\n test_uids = a['uids']\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\n\nprint('cat.item_id equals val_items?', (cat.item_id.to_numpy() == val_items).all())\nprint('val_items equals test_items?', (val_items == test_items).all())\nprint('targets.uid equals val_uids?', (targets.uid.to_numpy() == val_uids).all())\nprint('val_uids equals test_uids?', (val_uids == test_uids).all())\n\"",
"description": "Check alignment of item_ids and uids across datasets"
},
"recorded": {
"id": "run_shell_command__call_472073",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n val_items = a['item_ids']\n val_uids = a['uids']\n\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n test_items = a['item_ids']\n test_uids = a['uids']\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\n\nprint('cat.item_id equals val_items?', (cat.item_id.to_numpy() == val_items).all())\nprint('val_items equals test_items?', (val_items == test_items).all())\nprint('targets.uid equals val_uids?', (targets.uid.to_numpy() == val_uids).all())\nprint('val_uids equals test_uids?', (val_uids == test_uids).all())\n\"",
"description": "Check alignment of item_ids and uids across datasets"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_472073",
"name": "run_shell_command",
"response": {
"output": "\nOutput: cat.item_id equals val_items? True\nval_items equals test_items? True\ntargets.uid equals val_uids? True\nval_uids equals test_uids? True\nProcess Group PGID: 1748\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:49:19.047Z",
"resultDisplay": "cat.item_id equals val_items? True\nval_items equals test_items? True\ntargets.uid equals val_uids? True\nval_uids equals test_uids? True",
"description": "Check alignment of item_ids and uids across datasets",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:49:19.048Z",
"content": "\nOutput: cat.item_id equals val_items? True\nval_items equals test_items? True\ntargets.uid equals val_uids? True\nval_uids equals test_uids? True\nProcess Group PGID: 1748\n",
"isError": false
},
"responseMessageId": "fbcf05b2-47af-45d2-945e-64fa2d7ba85d"
}
]
},
{
"id": 15,
"role": "agent",
"timestamp": "2026-09-29T05:49:23.018Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:23.018Z",
"text": "Confirming Data Integrity\n\nI've confirmed that the `item_id`s, `val_items`, and `test_items` are identical across datasets, as are the user identifiers in `target_users.uid`, `val_uids`, and `test_uids`. This consistency is crucial. My next step is to evaluate the performance of a simple popularity baseline on the public validation set to establish an initial benchmark."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:49:23.018Z",
"callId": "run_shell_command__call_691043",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import score, validation_truth\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')\ngraded = validation_truth(eligibility, targets)\n\n# Generate baseline: for each user, top 100 eligible items in catalog order\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\n\nuids = targets_frame.uid.to_numpy()\npacked = eligibility.packed\nunpacked = np.unpackbits(packed, axis=1, count=len(cat_items), bitorder='little')\n\nranking_rows = []\nranked_dict = {}\nfor i, uid in enumerate(uids):\n mask = unpacked[i]\n eligible_items = cat_items[mask == 1]\n top100 = eligible_items[:100]\n ranked_dict[int(uid)] = top100.tolist()\n\nres = score(ranked_dict, graded)\nprint('Popularity baseline NDCG@10:', res)\n\"",
"description": "Test popularity baseline on validation set"
},
"recorded": {
"id": "run_shell_command__call_691043",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import score, validation_truth\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')\ngraded = validation_truth(eligibility, targets)\n\n# Generate baseline: for each user, top 100 eligible items in catalog order\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\n\nuids = targets_frame.uid.to_numpy()\npacked = eligibility.packed\nunpacked = np.unpackbits(packed, axis=1, count=len(cat_items), bitorder='little')\n\nranking_rows = []\nranked_dict = {}\nfor i, uid in enumerate(uids):\n mask = unpacked[i]\n eligible_items = cat_items[mask == 1]\n top100 = eligible_items[:100]\n ranked_dict[int(uid)] = top100.tolist()\n\nres = score(ranked_dict, graded)\nprint('Popularity baseline NDCG@10:', res)\n\"",
"description": "Test popularity baseline on validation set"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_691043",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Popularity baseline NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nProcess Group PGID: 1765\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:49:25.523Z",
"resultDisplay": "Popularity baseline NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}",
"description": "Test popularity baseline on validation set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:49:25.524Z",
"content": "\nOutput: Popularity baseline NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nProcess Group PGID: 1765\n",
"isError": false
},
"responseMessageId": "3c465f25-b476-4291-b759-fa3f6c8fc2fe"
}
]
},
{
"id": 16,
"role": "agent",
"timestamp": "2026-09-29T05:49:32.070Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:32.070Z",
"text": "Evaluating New Baselines\n\nI'm currently verifying a new popularity baseline, which yielded an NDCG@10 of 0.010710. My next steps involve confirming the accurate saving of parquet rankings and running `evaluate_public.py` on them directly for compatibility checks. I'll also test the evaluation script on a dummy validation file to ensure its integrity."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:49:32.070Z",
"callId": "run_shell_command__call_660846",
"name": "run_shell_command",
"input": {
"description": "Validate baseline with evaluate_public.py",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nuids = targets_frame.uid.to_numpy()\nunpacked = np.unpackbits(eligibility.packed, axis=1, count=len(cat_items), bitorder='little')\n\ndfs = []\nfor i, uid in enumerate(uids):\n mask = unpacked[i]\n eligible_items = cat_items[mask == 1][:100]\n dfs.append(pd.DataFrame({\n 'uid': uid,\n 'item_id': eligible_items,\n 'rank': np.arange(1, 101)\n }))\nsubmission = pd.concat(dfs, ignore_index=True)\nsubmission.to_parquet('/app/solution/validation_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/validation_baseline.parquet"
},
"recorded": {
"id": "run_shell_command__call_660846",
"name": "run_shell_command",
"args": {
"description": "Validate baseline with evaluate_public.py",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nuids = targets_frame.uid.to_numpy()\nunpacked = np.unpackbits(eligibility.packed, axis=1, count=len(cat_items), bitorder='little')\n\ndfs = []\nfor i, uid in enumerate(uids):\n mask = unpacked[i]\n eligible_items = cat_items[mask == 1][:100]\n dfs.append(pd.DataFrame({\n 'uid': uid,\n 'item_id': eligible_items,\n 'rank': np.arange(1, 101)\n }))\nsubmission = pd.concat(dfs, ignore_index=True)\nsubmission.to_parquet('/app/solution/validation_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/validation_baseline.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_660846",
"name": "run_shell_command",
"response": {
"output": "\nOutput: {\n \"model\": \"/app/solution/validation_baseline.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1785\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:49:38.602Z",
"resultDisplay": "{\n \"model\": \"/app/solution/validation_baseline.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}",
"description": "Validate baseline with evaluate_public.py",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:49:38.602Z",
"content": "\nOutput: {\n \"model\": \"/app/solution/validation_baseline.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1785\n",
"isError": false
},
"responseMessageId": "f0ea55d5-c933-4240-a3da-7798359c7804"
}
]
},
{
"id": 17,
"role": "agent",
"timestamp": "2026-09-29T05:49:43.429Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:43.429Z",
"text": "Confirming evaluation completion\n\nI've successfully run the `evaluate_public.py` script on the validation baseline parquet file. I'm now proceeding to generate and validate a baseline test submission to ensure a robust fallback option is in place."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:49:43.429Z",
"callId": "run_shell_command__call_336910",
"name": "run_shell_command",
"input": {
"description": "Generate baseline test submission to guarantee a valid submission exists",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\neligibility = CandidateSet(DATA / 'test_eligible_candidates.npz')\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nuids = targets_frame.uid.to_numpy()\nunpacked = np.unpackbits(eligibility.packed, axis=1, count=len(cat_items), bitorder='little')\n\ndfs = []\nfor i, uid in enumerate(uids):\n mask = unpacked[i]\n eligible_items = cat_items[mask == 1][:100]\n dfs.append(pd.DataFrame({\n 'uid': uid,\n 'item_id': eligible_items,\n 'rank': np.arange(1, 101)\n }))\nsubmission = pd.concat(dfs, ignore_index=True)\ntargets_set = set(targets_frame.uid.astype(int))\nnormalized = validate_ranking(submission, targets_set)\nfor uid, item in normalized[['uid', 'item_id']].itertuples(index=False, name=None):\n if not eligibility.is_eligible(uid, item):\n raise ValueError(f'Item {item} ineligible for user {uid}')\nnormalized.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Baseline submission generated and verified. Shape:', normalized.shape)\n\""
},
"recorded": {
"id": "run_shell_command__call_336910",
"name": "run_shell_command",
"args": {
"description": "Generate baseline test submission to guarantee a valid submission exists",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\neligibility = CandidateSet(DATA / 'test_eligible_candidates.npz')\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nuids = targets_frame.uid.to_numpy()\nunpacked = np.unpackbits(eligibility.packed, axis=1, count=len(cat_items), bitorder='little')\n\ndfs = []\nfor i, uid in enumerate(uids):\n mask = unpacked[i]\n eligible_items = cat_items[mask == 1][:100]\n dfs.append(pd.DataFrame({\n 'uid': uid,\n 'item_id': eligible_items,\n 'rank': np.arange(1, 101)\n }))\nsubmission = pd.concat(dfs, ignore_index=True)\ntargets_set = set(targets_frame.uid.astype(int))\nnormalized = validate_ranking(submission, targets_set)\nfor uid, item in normalized[['uid', 'item_id']].itertuples(index=False, name=None):\n if not eligibility.is_eligible(uid, item):\n raise ValueError(f'Item {item} ineligible for user {uid}')\nnormalized.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Baseline submission generated and verified. Shape:', normalized.shape)\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_336910",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Baseline submission generated and verified. Shape: (845600, 3)\nProcess Group PGID: 1820\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:49:47.586Z",
"resultDisplay": "Baseline submission generated and verified. Shape: (845600, 3)",
"description": "Generate baseline test submission to guarantee a valid submission exists",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:49:47.587Z",
"content": "\nOutput: Baseline submission generated and verified. Shape: (845600, 3)\nProcess Group PGID: 1820\n",
"isError": false
},
"responseMessageId": "d5008d69-df34-495f-be78-bb03615b6a5a"
}
]
},
{
"id": 18,
"role": "agent",
"timestamp": "2026-09-29T05:50:11.373Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:11.372Z",
"text": "Starting Two-Tower Architecture\n\nI've successfully saved our initial baseline submission. Now, my focus shifts to a deep dive into the requirements and architectural design for the two-tower model, starting with a thorough re-reading of the task itself."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:11.372Z",
"text": "Designing Two-Tower Model\n\nI've reviewed the core requirements. My current thoughts center on architecting the two-tower model, ensuring scalability for millions of users and items without a full interaction matrix. The focus is on independent user and item towers, each producing embeddings for offline candidate generation and real-time retrieval."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:11.372Z",
"text": "Analyzing Interaction Data\n\nMy current thinking is focused on parsing the details of the interaction history. I'm specifically examining the provided data fields like `is_organic` and `played_ratio_pct` to understand user engagement nuances. The temporal split of the training and validation windows is also a key consideration for the backtesting strategy."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:11.372Z",
"text": "Validating Ranking Strategies\n\nMy thoughts are now on precisely how to split and utilize the public windows for effective validation. I'm considering how to build and tune a ranker using only the `train_*` data as history, then refitting on the combined `train_*` and `val_*` datasets for the final ranker, ensuring `val_*` events aren't used as predictive features during validation itself. I'm also reviewing the preference state files for both validation and testing."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:50:11.373Z",
"callId": "run_shell_command__call_303434",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Check timestamps\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nprint('train_likes time min, max:', train_likes.timestamp.min(), train_likes.timestamp.max())\nprint('val_likes time min, max:', val_likes.timestamp.min(), val_likes.timestamp.max())\n\n# Cutoff stated: 24786800. 30 min gap = 1800s -> 24788600.\n# val_listens min was 24788600!\nprint('Diff between val start and train cutoff:', 24788600 - 24786800)\n# Val window length: 25393395 - 24788600 = 604795s ~ 7 days (7 * 86400 = 604800s).\n# So hidden test window: 25393400 + 1800 = 25395200 to + 7 days.\n\n# Check preference state\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('val_pref states:', val_pref.state.value_counts())\nprint('test_pref states:', test_pref.state.value_counts())\n\n# Check candidate catalog\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(cat.head(10))\nprint(cat.describe())\n\"",
"description": "Inspect timestamps and preference state"
},
"recorded": {
"id": "run_shell_command__call_303434",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Check timestamps\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nprint('train_likes time min, max:', train_likes.timestamp.min(), train_likes.timestamp.max())\nprint('val_likes time min, max:', val_likes.timestamp.min(), val_likes.timestamp.max())\n\n# Cutoff stated: 24786800. 30 min gap = 1800s -> 24788600.\n# val_listens min was 24788600!\nprint('Diff between val start and train cutoff:', 24788600 - 24786800)\n# Val window length: 25393395 - 24788600 = 604795s ~ 7 days (7 * 86400 = 604800s).\n# So hidden test window: 25393400 + 1800 = 25395200 to + 7 days.\n\n# Check preference state\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('val_pref states:', val_pref.state.value_counts())\nprint('test_pref states:', test_pref.state.value_counts())\n\n# Check candidate catalog\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(cat.head(10))\nprint(cat.describe())\n\"",
"description": "Inspect timestamps and preference state"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_303434",
"name": "run_shell_command",
"response": {
"output": "\nOutput: train_likes time min, max: 80 24786790\nval_likes time min, max: 24788815 25393390\nDiff between val start and train cutoff: 1800\nval_pref states: state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\ntest_pref states: state\nliked 432659\ndisliked 52117\nName: count, dtype: int64\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\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\n item_id ... popularity_rank\ncount 3.000000e+04 ... 30000.000000\nmean 4.698278e+06 ... 15000.500000\nstd 2.700274e+06 ... 8660.398374\nmin 2.060000e+02 ... 1.000000\n25% 2.382368e+06 ... 7500.750000\n50% 4.687992e+06 ... 15000.500000\n75% 7.040711e+06 ... 22500.250000\nmax 9.390294e+06 ... 30000.000000\n\n[8 rows x 4 columns]\nProcess Group PGID: 1843\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:50:13.051Z",
"resultDisplay": "train_likes time min, max: 80 24786790\nval_likes time min, max: 24788815 25393390\nDiff between val start and train cutoff: 1800\nval_pref states: state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\ntest_pref states: state\nliked 432659\ndisliked 52117\nName: count, dtype: int64\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\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\n item_id ... popularity_rank\ncount 3.000000e+04 ... 30000.000000\nmean 4.698278e+06 ... 15000.500000\nstd 2.700274e+06 ... 8660.398374\nmin 2.060000e+02 ... 1.000000\n25% 2.382368e+06 ... 7500.750000\n50% 4.687992e+06 ... 15000.500000\n75% 7.040711e+06 ... 22500.250000\nmax 9.390294e+06 ... 30000.000000\n\n[8 rows x 4 columns]",
"description": "Inspect timestamps and preference state",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:50:13.052Z",
"content": "\nOutput: train_likes time min, max: 80 24786790\nval_likes time min, max: 24788815 25393390\nDiff between val start and train cutoff: 1800\nval_pref states: state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\ntest_pref states: state\nliked 432659\ndisliked 52117\nName: count, dtype: int64\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\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\n item_id ... popularity_rank\ncount 3.000000e+04 ... 30000.000000\nmean 4.698278e+06 ... 15000.500000\nstd 2.700274e+06 ... 8660.398374\nmin 2.060000e+02 ... 1.000000\n25% 2.382368e+06 ... 7500.750000\n50% 4.687992e+06 ... 15000.500000\n75% 7.040711e+06 ... 22500.250000\nmax 9.390294e+06 ... 30000.000000\n\n[8 rows x 4 columns]\nProcess Group PGID: 1843\n",
"isError": false
},
"responseMessageId": "6a8155f1-e494-40e4-aedd-a47a2a47e379"
}
]
},
{
"id": 19,
"role": "agent",
"timestamp": "2026-09-29T05:50:18.440Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:50:18.440Z",
"callId": "run_shell_command__call_226089",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\ntu = pd.read_parquet('/task/data/target_users.parquet')\nprint(tu.head())\nprint(tu.describe())\n\"",
"description": "Inspect target_users.parquet"
},
"recorded": {
"id": "run_shell_command__call_226089",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\ntu = pd.read_parquet('/task/data/target_users.parquet')\nprint(tu.head())\nprint(tu.describe())\n\"",
"description": "Inspect target_users.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_226089",
"name": "run_shell_command",
"response": {
"output": "\nOutput: uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\nProcess Group PGID: 1860\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:50:20.011Z",
"resultDisplay": "uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000",
"description": "python3 -c \"\nimport pandas as pd\ntu = pd.read_parquet('/task/data/target_users.parquet')\nprint(tu.head())\nprint(tu.describe())\n\"",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:50:20.012Z",
"content": "\nOutput: uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\nProcess Group PGID: 1860\n",
"isError": false
},
"responseMessageId": "b9fde14a-bf5b-4fea-83b7-dfdab3f57a42"
}
]
},
{
"id": 20,
"role": "agent",
"timestamp": "2026-09-29T05:50:39.388Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:39.388Z",
"text": "Analyzing Organic Engagement\n\nI'm currently dissecting the definition of \"organic\" within listens and likes, specifically how it relates to the user's immediate behavioral window. My focus is on the two grades provided: grade 3 for organic likes and grade 1 for organically listening to 80% of a track."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:39.388Z",
"text": "Refining Engagement Metrics\n\nI'm currently refining how we define and grade organic engagement. Specifically, I'm examining the criteria for grade 3 (organic likes) and grade 1 (listening to 80% of a track), and considering how these interact. My attention is also drawn to the crucial architectural constraint that user representations must stem from interaction history, not just IDs."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:39.388Z",
"text": "Considering System Architecture\n\nMy thoughts are currently focused on the architectural constraints, particularly how user representations are derived from interaction history, not just IDs. I'm analyzing the implications of pre-computing item embeddings offline for efficient retrieval, avoiding a global user-item matrix."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:39.388Z",
"text": "Integrating Item Embeddings\n\nI'm exploring how to leverage item embeddings within the user tower. Specifically, I'm considering whether these embeddings can share weights with or be derived from those generated by the item tower, as is common in two-tower architectures."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:39.388Z",
"text": "Deriving User Representation\n\nI'm focusing on how user representations must originate from interaction history, not mere IDs. I'm investigating the specific features available for both items and user interactions to understand how to best construct these representations, considering the 30,000 candidate items versus the much larger set of items in user histories."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:50:39.388Z",
"callId": "run_shell_command__call_362128",
"name": "run_shell_command",
"input": {
"description": "Check overlap of interactions with catalog items",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = table.to_pandas()\nprint('Total train_listens:', len(df))\nin_cat = df.item_id.isin(cat_set)\nprint(f'Train listens in catalog: {in_cat.sum()} ({in_cat.mean():.2%})')\n\n# Check likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes_in_cat = likes.item_id.isin(cat_set)\nprint(f'Train likes in catalog: {likes_in_cat.sum()} ({likes_in_cat.mean():.2%})')\n\n# Check validation preference state\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref_in_cat = val_pref.item_id.isin(cat_set)\nprint(f'Validation preference state in catalog: {pref_in_cat.sum()} ({pref_in_cat.mean():.2%})')\n\n# Check target users in train_listens\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ndf_targets = df[df.uid.isin(set(targets.uid))]\nprint(f'Target users listens: {len(df_targets)} ({len(df_targets)/len(df):.2%})')\nin_cat_targets = df_targets.item_id.isin(cat_set)\nprint(f'Target users listens in catalog: {in_cat_targets.sum()} ({in_cat_targets.mean():.2%})')\n\""
},
"recorded": {
"id": "run_shell_command__call_362128",
"name": "run_shell_command",
"args": {
"description": "Check overlap of interactions with catalog items",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = table.to_pandas()\nprint('Total train_listens:', len(df))\nin_cat = df.item_id.isin(cat_set)\nprint(f'Train listens in catalog: {in_cat.sum()} ({in_cat.mean():.2%})')\n\n# Check likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes_in_cat = likes.item_id.isin(cat_set)\nprint(f'Train likes in catalog: {likes_in_cat.sum()} ({likes_in_cat.mean():.2%})')\n\n# Check validation preference state\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref_in_cat = val_pref.item_id.isin(cat_set)\nprint(f'Validation preference state in catalog: {pref_in_cat.sum()} ({pref_in_cat.mean():.2%})')\n\n# Check target users in train_listens\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ndf_targets = df[df.uid.isin(set(targets.uid))]\nprint(f'Target users listens: {len(df_targets)} ({len(df_targets)/len(df):.2%})')\nin_cat_targets = df_targets.item_id.isin(cat_set)\nprint(f'Target users listens in catalog: {in_cat_targets.sum()} ({in_cat_targets.mean():.2%})')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_362128",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total train_listens: 43484159\nTrain listens in catalog: 31249613 (71.86%)\nTrain likes in catalog: 554824 (68.09%)\nValidation preference state in catalog: 469124 (100.00%)\nTarget users listens: 43222297 (99.40%)\nTarget users listens in catalog: 31062540 (71.87%)\nProcess Group PGID: 1876\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:50:50.932Z",
"resultDisplay": "Total train_listens: 43484159\nTrain listens in catalog: 31249613 (71.86%)\nTrain likes in catalog: 554824 (68.09%)\nValidation preference state in catalog: 469124 (100.00%)\nTarget users listens: 43222297 (99.40%)\nTarget users listens in catalog: 31062540 (71.87%)",
"description": "Check overlap of interactions with catalog items",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:50:50.933Z",
"content": "\nOutput: Total train_listens: 43484159\nTrain listens in catalog: 31249613 (71.86%)\nTrain likes in catalog: 554824 (68.09%)\nValidation preference state in catalog: 469124 (100.00%)\nTarget users listens: 43222297 (99.40%)\nTarget users listens in catalog: 31062540 (71.87%)\nProcess Group PGID: 1876\n",
"isError": false
},
"responseMessageId": "795cd9ed-c15d-40be-93a0-dc00a0471772"
}
]
},
{
"id": 21,
"role": "agent",
"timestamp": "2026-09-29T05:50:54.438Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:54.438Z",
"text": "Analyzing Catalog Data\n\nIt seems all items in `validation_preference_state` are present in the candidate catalog, which is a promising start. I'll now pivot to examining the `test_preference_state` and the `target_users_listens_in_catalog` figures to see if a similar pattern holds."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:50:54.438Z",
"callId": "run_shell_command__call_715374",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('test_pref in cat:', test_pref.item_id.isin(cat_set).mean())\n\"",
"description": "Check test_pref overlap with catalog"
},
"recorded": {
"id": "run_shell_command__call_715374",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('test_pref in cat:', test_pref.item_id.isin(cat_set).mean())\n\"",
"description": "Check test_pref overlap with catalog"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_715374",
"name": "run_shell_command",
"response": {
"output": "\nOutput: test_pref in cat: 1.0\nProcess Group PGID: 1902\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:50:56.121Z",
"resultDisplay": "test_pref in cat: 1.0",
"description": "Check test_pref overlap with catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:50:56.121Z",
"content": "\nOutput: test_pref in cat: 1.0\nProcess Group PGID: 1902\n",
"isError": false
},
"responseMessageId": "5a228ef1-418a-4c04-b297-9dd9f4faa329"
}
]
},
{
"id": 22,
"role": "agent",
"timestamp": "2026-09-29T05:51:06.329Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:06.329Z",
"text": "Analyzing Preference States\n\nI'm currently examining the contents of `validation_preference_state` and `test_preference_state` to understand their roles. It appears that `test_preference_state` is fully incorporated into the candidate catalog. I'm now delving into the specifics of these states, noting that `validation_preference_state.parquet` contains a substantial 469,124 rows across 8,456 distinct entries."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:51:06.329Z",
"callId": "run_shell_command__call_59660",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\neligible_count = 0\nfor uid, item, state in v_pref.itertuples(index=False):\n if eligibility.is_eligible(uid, item):\n eligible_count += 1\nprint(f'Preference state items eligible: {eligible_count} / {len(v_pref)} ({eligible_count / len(v_pref):.2%})')\n\n# Check by state\nfor s, df in v_pref.groupby('state'):\n el = sum(eligibility.is_eligible(u, i) for u, i in df[['uid', 'item_id']].itertuples(index=False))\n print(f'State {s}: {el} / {len(df)} eligible ({el/len(df):.2%})')\n\"",
"description": "Check preference state eligibility"
},
"recorded": {
"id": "run_shell_command__call_59660",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\neligible_count = 0\nfor uid, item, state in v_pref.itertuples(index=False):\n if eligibility.is_eligible(uid, item):\n eligible_count += 1\nprint(f'Preference state items eligible: {eligible_count} / {len(v_pref)} ({eligible_count / len(v_pref):.2%})')\n\n# Check by state\nfor s, df in v_pref.groupby('state'):\n el = sum(eligibility.is_eligible(u, i) for u, i in df[['uid', 'item_id']].itertuples(index=False))\n print(f'State {s}: {el} / {len(df)} eligible ({el/len(df):.2%})')\n\"",
"description": "Check preference state eligibility"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_59660",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Preference state items eligible: 0 / 469124 (0.00%)\nState disliked: 0 / 50689 eligible (0.00%)\nState liked: 0 / 418435 eligible (0.00%)\nProcess Group PGID: 1926\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:51:09.270Z",
"resultDisplay": "Preference state items eligible: 0 / 469124 (0.00%)\nState disliked: 0 / 50689 eligible (0.00%)\nState liked: 0 / 418435 eligible (0.00%)",
"description": "Check preference state eligibility",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:51:09.271Z",
"content": "\nOutput: Preference state items eligible: 0 / 469124 (0.00%)\nState disliked: 0 / 50689 eligible (0.00%)\nState liked: 0 / 418435 eligible (0.00%)\nProcess Group PGID: 1926\n",
"isError": false
},
"responseMessageId": "9234b103-5012-410d-a038-38cb6f1cd24c"
}
]
},
{
"id": 23,
"role": "agent",
"timestamp": "2026-09-29T05:51:12.102Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:12.102Z",
"text": "Checking Eligibility\n\nMy focus has shifted to verifying the eligibility status of existing user preferences within the `validation_eligible_candidates` set. It appears that every single active preference is currently marked as ineligible, prompting me to extend this check to the `test_preference_state`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:51:12.102Z",
"callId": "run_shell_command__call_455222",
"name": "run_shell_command",
"input": {
"description": "Check test preference state eligibility",
"command": "python3 -c \"\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/test_eligible_candidates.npz')\nt_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\n\neligible_count = sum(eligibility.is_eligible(uid, item) for uid, item in t_pref[['uid', 'item_id']].itertuples(index=False))\nprint(f'Test preference state items eligible: {eligible_count} / {len(t_pref)} ({eligible_count / len(t_pref):.2%})')\n\""
},
"recorded": {
"id": "run_shell_command__call_455222",
"name": "run_shell_command",
"args": {
"description": "Check test preference state eligibility",
"command": "python3 -c \"\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/test_eligible_candidates.npz')\nt_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\n\neligible_count = sum(eligibility.is_eligible(uid, item) for uid, item in t_pref[['uid', 'item_id']].itertuples(index=False))\nprint(f'Test preference state items eligible: {eligible_count} / {len(t_pref)} ({eligible_count / len(t_pref):.2%})')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_455222",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Test preference state items eligible: 0 / 484776 (0.00%)\nProcess Group PGID: 1943\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:51:14.377Z",
"resultDisplay": "Test preference state items eligible: 0 / 484776 (0.00%)",
"description": "Check test preference state eligibility",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:51:14.378Z",
"content": "\nOutput: Test preference state items eligible: 0 / 484776 (0.00%)\nProcess Group PGID: 1943\n",
"isError": false
},
"responseMessageId": "90e1b89a-7028-4b93-bf54-9b9740758ffd"
}
]
},
{
"id": 24,
"role": "agent",
"timestamp": "2026-09-29T05:51:18.813Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:18.813Z",
"text": "Refining Discovery Candidates\n\nMy thinking solidified; it's now clear that actively liked or disliked tracks are excluded from the \"Made for You\" discovery set for each cutoff. This aligns perfectly with the user's established preferences."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:51:18.813Z",
"callId": "run_shell_command__call_64364",
"name": "run_shell_command",
"input": {
"description": "Check why items are ineligible",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\n# Look at user 100\nuid = 100\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nmask = [eligibility.is_eligible(uid, item) for item in cat_items]\nineligible_items = set(cat_items[~np.array(mask)])\nprint(f'User {uid}: {len(ineligible_items)} ineligible items')\n\n# Check user 100's listens\nlistens = pd.read_parquet('/task/data/train_listens.parquet', filters=[('uid', '=', uid)])\nuser_listens_cat = set(listens.item_id) & set(cat_items)\nprint(f'User {uid} listened to {len(user_listens_cat)} catalog items')\nprint(f'How many of those listened items are ineligible? {len(user_listens_cat & ineligible_items)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_64364",
"name": "run_shell_command",
"args": {
"description": "Check why items are ineligible",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\n# Look at user 100\nuid = 100\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nmask = [eligibility.is_eligible(uid, item) for item in cat_items]\nineligible_items = set(cat_items[~np.array(mask)])\nprint(f'User {uid}: {len(ineligible_items)} ineligible items')\n\n# Check user 100's listens\nlistens = pd.read_parquet('/task/data/train_listens.parquet', filters=[('uid', '=', uid)])\nuser_listens_cat = set(listens.item_id) & set(cat_items)\nprint(f'User {uid} listened to {len(user_listens_cat)} catalog items')\nprint(f'How many of those listened items are ineligible? {len(user_listens_cat & ineligible_items)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_64364",
"name": "run_shell_command",
"response": {
"output": "\nOutput: User 100: 682 ineligible items\nUser 100 listened to 682 catalog items\nHow many of those listened items are ineligible? 682\nProcess Group PGID: 1960\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:51:20.530Z",
"resultDisplay": "User 100: 682 ineligible items\nUser 100 listened to 682 catalog items\nHow many of those listened items are ineligible? 682",
"description": "Check why items are ineligible",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:51:20.531Z",
"content": "\nOutput: User 100: 682 ineligible items\nUser 100 listened to 682 catalog items\nHow many of those listened items are ineligible? 682\nProcess Group PGID: 1960\n",
"isError": false
},
"responseMessageId": "6f8deadf-4b22-4cc2-848f-95f4996143da"
}
]
},
{
"id": 25,
"role": "agent",
"timestamp": "2026-09-29T05:51:23.790Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:23.790Z",
"text": "Investigating Filtering Logic\n\nI'm currently examining user interaction data. My initial hypothesis is that our candidate eligibility filters might be overly aggressive, potentially excluding items users have already engaged with or disliked. I'm sampling across 20 users to see if this pattern holds true for a broader user base."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:51:23.790Z",
"callId": "run_shell_command__call_40913",
"name": "run_shell_command",
"input": {
"description": "Check candidate eligibility for sample users",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nsample_uids = targets.uid.iloc[:20].tolist()\n\nfor uid in sample_uids[:5]:\n mask = [eligibility.is_eligible(uid, item) for item in cat_items]\n ineligible_items = set(cat_items[~np.array(mask)])\n listens = pd.read_parquet('/task/data/train_listens.parquet', filters=[('uid', '=', uid)])\n user_listens_cat = set(listens.item_id) & set(cat_items)\n print(f'User {uid}: ineligible={len(ineligible_items)}, listened_in_cat={len(user_listens_cat)}, overlap={len(user_listens_cat & ineligible_items)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_40913",
"name": "run_shell_command",
"args": {
"description": "Check candidate eligibility for sample users",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nsample_uids = targets.uid.iloc[:20].tolist()\n\nfor uid in sample_uids[:5]:\n mask = [eligibility.is_eligible(uid, item) for item in cat_items]\n ineligible_items = set(cat_items[~np.array(mask)])\n listens = pd.read_parquet('/task/data/train_listens.parquet', filters=[('uid', '=', uid)])\n user_listens_cat = set(listens.item_id) & set(cat_items)\n print(f'User {uid}: ineligible={len(ineligible_items)}, listened_in_cat={len(user_listens_cat)}, overlap={len(user_listens_cat & ineligible_items)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_40913",
"name": "run_shell_command",
"response": {
"output": "\nOutput: User 100: ineligible=682, listened_in_cat=682, overlap=682\nUser 200: ineligible=168, listened_in_cat=168, overlap=168\nUser 300: ineligible=69, listened_in_cat=69, overlap=69\nUser 600: ineligible=680, listened_in_cat=680, overlap=680\nUser 700: ineligible=1653, listened_in_cat=1653, overlap=1653\nProcess Group PGID: 1980\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:51:25.654Z",
"resultDisplay": "User 100: ineligible=682, listened_in_cat=682, overlap=682\nUser 200: ineligible=168, listened_in_cat=168, overlap=168\nUser 300: ineligible=69, listened_in_cat=69, overlap=69\nUser 600: ineligible=680, listened_in_cat=680, overlap=680\nUser 700: ineligible=1653, listened_in_cat=1653, overlap=1653",
"description": "Check candidate eligibility for sample users",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:51:25.655Z",
"content": "\nOutput: User 100: ineligible=682, listened_in_cat=682, overlap=682\nUser 200: ineligible=168, listened_in_cat=168, overlap=168\nUser 300: ineligible=69, listened_in_cat=69, overlap=69\nUser 600: ineligible=680, listened_in_cat=680, overlap=680\nUser 700: ineligible=1653, listened_in_cat=1653, overlap=1653\nProcess Group PGID: 1980\n",
"isError": false
},
"responseMessageId": "2b6eec50-a980-4572-86f4-19e24ae55d5e"
}
]
},
{
"id": 26,
"role": "agent",
"timestamp": "2026-09-29T05:51:46.353Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:46.353Z",
"text": "Discovering Unheard Tracks\n\nI'm focusing on identifying music the user hasn't encountered. My current task is to filter out anything previously played or engaged with, ensuring I only surface genuinely new listening experiences that align with their established preferences."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:46.353Z",
"text": "Analyzing Training Data\n\nI'm now examining the available training datasets, specifically looking at `train_listens.parquet`, `train_likes.parquet`, and other preference files. My next step is to understand their structure and how they inform the candidate catalog for recommendation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:46.353Z",
"text": "Designing Efficient Models\n\nMy focus has shifted to the architectural constraints for model design. I'm specifically addressing the requirement for user and item vectors that decompose via dot product, generated by independent neural network towers, with user representations derived from interaction history, not IDs. This design needs to scale to millions of users and items without relying on a global interaction matrix, and I'm keeping the 60-minute training time constraint firmly in mind."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:46.353Z",
"text": "Building Item Embeddings\n\nI'm delving into the architecture of the item tower, considering inputs like item ID embeddings and popularity features. The goal is to process these through an MLP and project them to dimension $D$, potentially with L2 normalization for cosine similarity use."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:46.353Z",
"text": "Synthesizing User Data\n\nI'm now focused on how to represent users based on their historical interactions. My thoughts are on effectively encoding listened items, likes, dislikes, and recency into a user vector, aiming for a representation that captures nuanced preferences without relying on simple ID lookups, and considering how to weight different interaction types."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:46.353Z",
"text": "Processing User Interaction Data\n\nI'm currently exploring standard neural architectures to represent user history. My focus is on weighted pooling of item embeddings from listened, liked, and disliked items, potentially combining these with other behavioral statistics, and then feeding them into an MLP for user vector generation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:46.353Z",
"text": "Refining User Representation\n\nI'm evaluating different methods for user vector generation. My current thoughts are on the trade-offs between standard weighted pooling of item embeddings (factoring in recency and interaction type) followed by an MLP, and more complex approaches like attention mechanisms or Transformers over recent interactions. I'm also checking the average number of historical items per user to inform feasibility."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:51:46.353Z",
"callId": "run_shell_command__call_249211",
"name": "run_shell_command",
"input": {
"description": "Inspect user history statistics",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ntu = pd.read_parquet('/task/data/target_users.parquet')\nuids = set(tu.uid)\n\nprint('Likes in train per target user:', likes[likes.uid.isin(uids)].groupby('uid').size().describe())\nprint('Dislikes in train per target user:', dislikes[dislikes.uid.isin(uids)].groupby('uid').size().describe())\nprint('Pref state per target user:', pref.groupby('uid').size().describe())\n\""
},
"recorded": {
"id": "run_shell_command__call_249211",
"name": "run_shell_command",
"args": {
"description": "Inspect user history statistics",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ntu = pd.read_parquet('/task/data/target_users.parquet')\nuids = set(tu.uid)\n\nprint('Likes in train per target user:', likes[likes.uid.isin(uids)].groupby('uid').size().describe())\nprint('Dislikes in train per target user:', dislikes[dislikes.uid.isin(uids)].groupby('uid').size().describe())\nprint('Pref state per target user:', pref.groupby('uid').size().describe())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_249211",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Likes in train per target user: count 7071.000000\nmean 97.718569\nstd 176.159086\nmin 1.000000\n25% 9.000000\n50% 40.000000\n75% 116.000000\nmax 3479.000000\ndtype: float64\nDislikes in train per target user: count 4977.000000\nmean 16.931083\nstd 89.609589\nmin 1.000000\n25% 2.000000\n50% 4.000000\n75% 9.000000\nmax 2985.000000\ndtype: float64\nPref state per target user: count 7038.000000\nmean 66.655868\nstd 108.686351\nmin 1.000000\n25% 7.000000\n50% 30.000000\n75% 81.000000\nmax 1847.000000\ndtype: float64\nProcess Group PGID: 2000\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:51:48.066Z",
"resultDisplay": "Likes in train per target user: count 7071.000000\nmean 97.718569\nstd 176.159086\nmin 1.000000\n25% 9.000000\n50% 40.000000\n75% 116.000000\nmax 3479.000000\ndtype: float64\nDislikes in train per target user: count 4977.000000\nmean 16.931083\nstd 89.609589\nmin 1.000000\n25% 2.000000\n50% 4.000000\n75% 9.000000\nmax 2985.000000\ndtype: float64\nPref state per target user: count 7038.000000\nmean 66.655868\nstd 108.686351\nmin 1.000000\n25% 7.000000\n50% 30.000000\n75% 81.000000\nmax 1847.000000\ndtype: float64",
"description": "Inspect user history statistics",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:51:48.067Z",
"content": "\nOutput: Likes in train per target user: count 7071.000000\nmean 97.718569\nstd 176.159086\nmin 1.000000\n25% 9.000000\n50% 40.000000\n75% 116.000000\nmax 3479.000000\ndtype: float64\nDislikes in train per target user: count 4977.000000\nmean 16.931083\nstd 89.609589\nmin 1.000000\n25% 2.000000\n50% 4.000000\n75% 9.000000\nmax 2985.000000\ndtype: float64\nPref state per target user: count 7038.000000\nmean 66.655868\nstd 108.686351\nmin 1.000000\n25% 7.000000\n50% 30.000000\n75% 81.000000\nmax 1847.000000\ndtype: float64\nProcess Group PGID: 2000\n",
"isError": false
},
"responseMessageId": "1ddcfbd2-cc2d-4924-862f-0b2ab0a13a8f"
}
]
},
{
"id": 27,
"role": "agent",
"timestamp": "2026-09-29T05:51:54.428Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:54.428Z",
"text": "Analyzing User Listen Data\n\nI'm currently examining the `train_listens` dataset, specifically within `target_users.parquet`, to understand listen counts per user. Initial findings for `recent_positive_listens` show a wide range, with a mean of 1,251 and a max of 19,592. `history_positive_listens` also shows significant numbers, averaging 3,236."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:54.428Z",
"text": "Exploring User Engagement Patterns\n\nI'm refining my understanding of user engagement by analyzing the timestamp cutoff and its implications for \"recent positive listens.\" I'm now shifting focus to correlate user listening history with their validation positive items, specifically exploring how liked tracks relate to artists and co-occurring items, even without direct artist metadata."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:51:54.428Z",
"callId": "run_shell_command__call_286020",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\nprint('Total train listens:', len(df))\nprint('played_ratio_pct >= 80%:', (df.played_ratio_pct >= 80).sum(), f'({(df.played_ratio_pct >= 80).mean():.2%})')\nprint('played_ratio_pct >= 80% & is_organic:', ((df.played_ratio_pct >= 80) & (df.is_organic == 1)).sum(), f'{((df.played_ratio_pct >= 80) & (df.is_organic == 1)).mean():.2%}')\nprint('played_ratio_pct >= 50%:', (df.played_ratio_pct >= 50).sum(), f'({(df.played_ratio_pct >= 50).mean():.2%})')\n\"",
"description": "Check train listens statistics"
},
"recorded": {
"id": "run_shell_command__call_286020",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\nprint('Total train listens:', len(df))\nprint('played_ratio_pct >= 80%:', (df.played_ratio_pct >= 80).sum(), f'({(df.played_ratio_pct >= 80).mean():.2%})')\nprint('played_ratio_pct >= 80% & is_organic:', ((df.played_ratio_pct >= 80) & (df.is_organic == 1)).sum(), f'{((df.played_ratio_pct >= 80) & (df.is_organic == 1)).mean():.2%}')\nprint('played_ratio_pct >= 50%:', (df.played_ratio_pct >= 50).sum(), f'({(df.played_ratio_pct >= 50).mean():.2%})')\n\"",
"description": "Check train listens statistics"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_286020",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total train listens: 43484159\nplayed_ratio_pct >= 80%: 25755197 (59.23%)\nplayed_ratio_pct >= 80% & is_organic: 12177583 28.00%\nplayed_ratio_pct >= 50%: 27500755 (63.24%)\nProcess Group PGID: 2017\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:51:56.327Z",
"resultDisplay": "Total train listens: 43484159\nplayed_ratio_pct >= 80%: 25755197 (59.23%)\nplayed_ratio_pct >= 80% & is_organic: 12177583 28.00%\nplayed_ratio_pct >= 50%: 27500755 (63.24%)",
"description": "Check train listens statistics",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:51:56.328Z",
"content": "\nOutput: Total train listens: 43484159\nplayed_ratio_pct >= 80%: 25755197 (59.23%)\nplayed_ratio_pct >= 80% & is_organic: 12177583 28.00%\nplayed_ratio_pct >= 50%: 27500755 (63.24%)\nProcess Group PGID: 2017\n",
"isError": false
},
"responseMessageId": "7ee67970-54d3-434c-be0e-90a1227e526f"
}
]
},
{
"id": 28,
"role": "agent",
"timestamp": "2026-09-29T05:52:03.019Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:52:03.019Z",
"callId": "run_shell_command__call_369719",
"name": "run_shell_command",
"input": {
"description": "Check strong organic events in catalog",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\nstrong_organic = df[(df.played_ratio_pct >= 80) & (df.is_organic == 1)]\nprint('Strong organic listens in cat:', strong_organic.item_id.isin(cat_set).sum(), f'{strong_organic.item_id.isin(cat_set).mean():.2%}')\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\norganic_likes = likes[likes.is_organic == 1]\nprint('Organic likes in cat:', organic_likes.item_id.isin(cat_set).sum(), f'{organic_likes.item_id.isin(cat_set).mean():.2%}')\n\""
},
"recorded": {
"id": "run_shell_command__call_369719",
"name": "run_shell_command",
"args": {
"description": "Check strong organic events in catalog",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\nstrong_organic = df[(df.played_ratio_pct >= 80) & (df.is_organic == 1)]\nprint('Strong organic listens in cat:', strong_organic.item_id.isin(cat_set).sum(), f'{strong_organic.item_id.isin(cat_set).mean():.2%}')\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\norganic_likes = likes[likes.is_organic == 1]\nprint('Organic likes in cat:', organic_likes.item_id.isin(cat_set).sum(), f'{organic_likes.item_id.isin(cat_set).mean():.2%}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_369719",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Strong organic listens in cat: 8699160 71.44%\nOrganic likes in cat: 301527 65.29%\nProcess Group PGID: 2044\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:52:06.903Z",
"resultDisplay": "Strong organic listens in cat: 8699160 71.44%\nOrganic likes in cat: 301527 65.29%",
"description": "Check strong organic events in catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:52:06.905Z",
"content": "\nOutput: Strong organic listens in cat: 8699160 71.44%\nOrganic likes in cat: 301527 65.29%\nProcess Group PGID: 2044\n",
"isError": false
},
"responseMessageId": "d1bcb649-0b25-426a-aa23-87e0d103bec1"
}
]
},
{
"id": 29,
"role": "agent",
"timestamp": "2026-09-29T05:52:59.844Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:59.844Z",
"text": "Analyzing Validation Data\n\nMy focus has shifted to quantifying the data within `validation_truth`. Currently, I've identified 4,263 users possessing ground truth labels. The total count of positive labels stands at 37,769, with a significant portion, 4,417, categorized as grade 3."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:59.844Z",
"text": "Investigating Model Formulation\n\nI'm now delving into the specifics of the two-tower model. My current task involves understanding its training objective and reconfirming the core requirements, particularly how to avoid a global interaction matrix and ensure offline item embedding computation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:59.844Z",
"text": "Refining Candidate Selection\n\nI'm currently focused on how candidate eligibility is managed, as it's platform-owned. My understanding is that all modeling within this boundary is fair game for exploration. My next step involves solidifying the approach for building and tuning the validation ranker using the designated train and val datasets."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:59.844Z",
"text": "Exploring Candidate Data\n\nI am examining the candidate catalog size and how it relates to user listening history, particularly focusing on how to handle items not present in the main catalog. My next step involves determining the best strategy for mapping these out-of-catalog items."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:59.844Z",
"text": "Estimating Embedding Sizes\n\nMy analysis reveals that item embeddings are remarkably memory-efficient, even with tens of thousands of items. I'm now calculating the size for both 64 and 128 dimensions, considering out-of-catalog tracks and the potential inclusion of top-performing OOC items."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:59.844Z",
"text": "Processing User Input Signals\n\nI am currently analyzing the signals that inform the user tower. My focus is on how to represent a user's listening history, prioritizing recently heard tracks with high completion rates and organic listens. Explicitly liked tracks are also being considered as key inputs."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:59.844Z",
"text": "Formulating User History\n\nI am now focusing on representing user listening history. My approach involves a weighted average of item embeddings, prioritizing recent, high-completion, organic listens while also accounting for dislikes. This will be implemented using a temporal decay factor and completion weights."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:59.844Z",
"text": "Synthesizing User History Representations\n\nI'm now consolidating various user history signals, including listening patterns, explicit likes and dislikes, and recent sequential interactions. These are being combined into a single user representation for the model's user tower, with plans for further refinement using MLPs."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:59.844Z",
"text": "Calculating Latency Impact\n\nI'm now assessing the computational impact of calculating relevance scores. Specifically, I'm examining the latency of matrix multiplication for generating user scores across all catalog items. My goal is to confirm how quickly this operation completes given our embedding dimensions and user/item counts."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:59.844Z",
"text": "Validating Latency Performance\n\nI've confirmed that the latency for calculating relevance scores is well within production requirements. Matrix multiplication operations for generating user scores across all catalog items are completing in under 20 milliseconds on an 8-core AMD EPYC system, easily satisfying the request-time budget."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:59.844Z",
"text": "Optimizing Model Objectives\n\nI am now focusing on tailoring the model's training objective. My current thinking is to implement weighted positive sampling, leveraging graded cross-entropy to directly prioritize items with higher discovery relevance, such as organic likes and strong listens. Additionally, I am incorporating popularity debiasing through logQ correction to refine the model's understanding of true user affinity over mere popularity."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:59.844Z",
"text": "Exploring Negative Sampling\n\nI am investigating the inclusion of hard negatives in the model, considering disliked tracks and those skipped early by users as potential negative examples. My current thinking favors InfoNCE over BPR for its superior performance in retrieval and top-K metrics."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:52:59.844Z",
"callId": "run_shell_command__call_551589",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Check train timestamps\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_dislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\n\nprint('train_likes timestamp min, max:', train_likes.timestamp.min(), train_likes.timestamp.max())\nprint('Days in train:', (train_likes.timestamp.max() - train_likes.timestamp.min()) / 86400)\n\n# Check cutoff\ncutoff = 24786800\nprint('Cutoff:', cutoff)\n# Check how many likes in the last 7 days before cutoff (from cutoff - 7*86400 to cutoff)\nw7 = train_likes[train_likes.timestamp >= cutoff - 7 * 86400]\nprint('Likes in last 7 days of train:', len(w7), 'unique users:', w7.uid.nunique())\n\nw30 = train_likes[train_likes.timestamp >= cutoff - 30 * 86400]\nprint('Likes in last 30 days of train:', len(w30), 'unique users:', w30.uid.nunique())\n\"",
"description": "Analyze temporal distribution of training data"
},
"recorded": {
"id": "run_shell_command__call_551589",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Check train timestamps\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_dislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\n\nprint('train_likes timestamp min, max:', train_likes.timestamp.min(), train_likes.timestamp.max())\nprint('Days in train:', (train_likes.timestamp.max() - train_likes.timestamp.min()) / 86400)\n\n# Check cutoff\ncutoff = 24786800\nprint('Cutoff:', cutoff)\n# Check how many likes in the last 7 days before cutoff (from cutoff - 7*86400 to cutoff)\nw7 = train_likes[train_likes.timestamp >= cutoff - 7 * 86400]\nprint('Likes in last 7 days of train:', len(w7), 'unique users:', w7.uid.nunique())\n\nw30 = train_likes[train_likes.timestamp >= cutoff - 30 * 86400]\nprint('Likes in last 30 days of train:', len(w30), 'unique users:', w30.uid.nunique())\n\"",
"description": "Analyze temporal distribution of training data"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_551589",
"name": "run_shell_command",
"response": {
"output": "\nOutput: train_likes timestamp min, max: 80 24786790\nDays in train: 286.8832175925926\nCutoff: 24786800\nLikes in last 7 days of train: 30156 unique users: 3969\nLikes in last 30 days of train: 121098 unique users: 6151\nProcess Group PGID: 2066\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:53:01.424Z",
"resultDisplay": "train_likes timestamp min, max: 80 24786790\nDays in train: 286.8832175925926\nCutoff: 24786800\nLikes in last 7 days of train: 30156 unique users: 3969\nLikes in last 30 days of train: 121098 unique users: 6151",
"description": "Analyze temporal distribution of training data",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:53:01.424Z",
"content": "\nOutput: train_likes timestamp min, max: 80 24786790\nDays in train: 286.8832175925926\nCutoff: 24786800\nLikes in last 7 days of train: 30156 unique users: 3969\nLikes in last 30 days of train: 121098 unique users: 6151\nProcess Group PGID: 2066\n",
"isError": false
},
"responseMessageId": "d305a5c2-f32f-4249-adab-8a9c0614251d"
}
]
},
{
"id": 30,
"role": "agent",
"timestamp": "2026-09-29T05:53:14.941Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:53:14.941Z",
"text": "Analyzing Training Data\n\nI'm reviewing the training data, noting it spans nearly 10 months. Recent analysis of the last week shows around 30,000 likes from nearly 4,000 users, with similar volume observed in the validation set. This consistency is encouraging."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:53:14.941Z",
"text": "Formulating Training Windows\n\nI'm now focusing on how to best construct training examples. My current thought is to leverage multiple temporal windows, using a user's interaction history up to a specific timestamp $T$ to predict their subsequent engagement within a defined future interval. This approach appears quite robust for capturing evolving user behavior."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:53:14.941Z",
"text": "Refining Training Sample Generation\n\nI'm exploring different ways to create training samples. My latest idea is to use a user's interaction history *before* a specific split time to predict their engagement *after* that time. This seems more efficient for capturing longer-term trends. I'm also considering next-item prediction."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:53:14.941Z",
"text": "Expanding Training Slices\n\nI'm considering multiple cutoff points for training, not just one. This will allow me to predict engagement over various preceding periods and explore next-event prediction strategies. I'm also re-evaluating the user tower's dynamic representation calculation from interaction history, as per the documentation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:53:14.941Z",
"text": "Defining Model Architecture\n\nI'm now focusing on the precise structure of the user and item towers, aiming for PyTorch implementations. My current thinking is that the user tower will process historical interaction features, while the item tower will generate item embeddings. The final relevance score will be their dot product."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:53:14.941Z",
"callId": "run_shell_command__call_117513",
"name": "run_shell_command",
"input": {
"description": "Search for tower references in /task",
"command": "grep -rn \"tower\" /task/"
},
"recorded": {
"id": "run_shell_command__call_117513",
"name": "run_shell_command",
"args": {
"description": "Search for tower references in /task",
"command": "grep -rn \"tower\" /task/"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_117513",
"name": "run_shell_command",
"response": {
"output": "\nOutput: /task/requirements.txt:1:# Exact distribution inventory for the Yambda two-tower task family.\nProcess Group PGID: 2091\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:53:15.135Z",
"resultDisplay": "/task/requirements.txt:1:# Exact distribution inventory for the Yambda two-tower task family.",
"description": "grep -rn \"tower\" /task/",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:53:15.136Z",
"content": "\nOutput: /task/requirements.txt:1:# Exact distribution inventory for the Yambda two-tower task family.\nProcess Group PGID: 2091\n",
"isError": false
},
"responseMessageId": "afe4190c-b746-4f6d-a8c2-094e12277f38"
}
]
},
{
"id": 31,
"role": "agent",
"timestamp": "2026-09-29T05:53:25.525Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:53:25.525Z",
"text": "Designing PyTorch Model\n\nI'm focusing on designing the core PyTorch neural two-tower model, as no prior implementations or constraints exist in the `/task` directory. My current effort involves architecting the network's structure and defining its initial components."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:53:25.525Z",
"text": "Analyzing User-Item Interactions\n\nI'm now delving into user history and item data, considering what truly drives effective music recommendations. This involves understanding user preferences for genres, artists, and moods, and recognizing patterns like track co-occurrence and recency of listening, alongside explicit signals like play ratios and dislikes."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:53:25.525Z",
"callId": "run_shell_command__call_58434",
"name": "run_shell_command",
"input": {
"description": "Inspect sample users and their validation gold items",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth\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')\ngraded = validation_truth(eligibility, targets)\n\n# Let's inspect 5 users who have gold labels\ncat = pd.read_parquet(DATA + '/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\nsample_users = list(graded.keys())[:5]\nfor uid in sample_users:\n gold_items = graded[uid]\n likes = pd.read_parquet(DATA + '/train_likes.parquet', filters=[('uid', '=', uid)])\n listens = pd.read_parquet(DATA + '/train_listens.parquet', filters=[('uid', '=', uid)])\n print(f'UID {uid}: gold_count={len(gold_items)}, train_likes={len(likes)}, train_listens={len(listens)}')\n print(' Gold items:', gold_items)\n\""
},
"recorded": {
"id": "run_shell_command__call_58434",
"name": "run_shell_command",
"args": {
"description": "Inspect sample users and their validation gold items",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth\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')\ngraded = validation_truth(eligibility, targets)\n\n# Let's inspect 5 users who have gold labels\ncat = pd.read_parquet(DATA + '/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\nsample_users = list(graded.keys())[:5]\nfor uid in sample_users:\n gold_items = graded[uid]\n likes = pd.read_parquet(DATA + '/train_likes.parquet', filters=[('uid', '=', uid)])\n listens = pd.read_parquet(DATA + '/train_listens.parquet', filters=[('uid', '=', uid)])\n print(f'UID {uid}: gold_count={len(gold_items)}, train_likes={len(likes)}, train_listens={len(listens)}')\n print(' Gold items:', gold_items)\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_58434",
"name": "run_shell_command",
"response": {
"output": "\nOutput: UID 200: gold_count=13, train_likes=0, train_listens=312\n Gold items: {7406680: 1, 694058: 1, 4082738: 1, 6079576: 1, 6006390: 1, 2644025: 1, 4897148: 1, 7930756: 1, 3744896: 1, 3763080: 1, 7481745: 1, 9233959: 1, 9166040: 1}\nUID 800: gold_count=25, train_likes=132, train_listens=11793\n Gold items: {7179437: 1, 1607923: 1, 1816101: 1, 804416: 1, 2650514: 1, 4678741: 1, 2565336: 1, 6803177: 1, 6952637: 1, 4537506: 1, 2766576: 1, 9342289: 1, 9186305: 1, 308692: 1, 5559790: 1, 776584: 1, 388527: 1, 7400303: 1, 6551500: 1, 704566: 1, 843730: 1, 7326035: 1, 4702202: 1, 3531147: 1, 5433005: 1}\nUID 900: gold_count=1, train_likes=0, train_listens=851\n Gold items: {3673625: 1}\nUID 1100: gold_count=20, train_likes=46, train_listens=4217\n Gold items: {4588549: 1, 635564: 1, 1613989: 1, 8681149: 1, 3910656: 1, 1030400: 1, 7387845: 1, 7340717: 1, 6146700: 1, 647839: 1, 7570645: 1, 3891369: 1, 3857830: 1, 5335567: 1, 8971417: 1, 368089: 1, 8653336: 1, 2051915: 1, 6318072: 1, 8078607: 1}\nUID 1400: gold_count=38, train_likes=42, train_listens=11345\n Gold items: {6945311: 1, 2158715: 1, 2194514: 1, 3530629: 1, 2453912: 1, 7767752: 1, 4859530: 1, 3585411: 1, 4500834: 1, 4770069: 1, 3706360: 1, 6083906: 1, 1118882: 1, 8675053: 1, 5551069: 1, 6473581: 1, 2285672: 1, 5933848: 1, 7008219: 1, 5265819: 1, 975128: 1, 3970945: 1, 4345553: 1, 6901050: 1, 6901374: 1, 1851456: 1, 4678741: 1, 4897148: 1, 8420846: 3, 1327266: 1, 9233959: 1, 3203533: 1, 1407989: 1, 6417502: 1, 9016646: 1, 3763080: 1, 9378983: 1, 8669062: 1}\nProcess Group PGID: 2094\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:53:27.704Z",
"resultDisplay": "UID 200: gold_count=13, train_likes=0, train_listens=312\n Gold items: {7406680: 1, 694058: 1, 4082738: 1, 6079576: 1, 6006390: 1, 2644025: 1, 4897148: 1, 7930756: 1, 3744896: 1, 3763080: 1, 7481745: 1, 9233959: 1, 9166040: 1}\nUID 800: gold_count=25, train_likes=132, train_listens=11793\n Gold items: {7179437: 1, 1607923: 1, 1816101: 1, 804416: 1, 2650514: 1, 4678741: 1, 2565336: 1, 6803177: 1, 6952637: 1, 4537506: 1, 2766576: 1, 9342289: 1, 9186305: 1, 308692: 1, 5559790: 1, 776584: 1, 388527: 1, 7400303: 1, 6551500: 1, 704566: 1, 843730: 1, 7326035: 1, 4702202: 1, 3531147: 1, 5433005: 1}\nUID 900: gold_count=1, train_likes=0, train_listens=851\n Gold items: {3673625: 1}\nUID 1100: gold_count=20, train_likes=46, train_listens=4217\n Gold items: {4588549: 1, 635564: 1, 1613989: 1, 8681149: 1, 3910656: 1, 1030400: 1, 7387845: 1, 7340717: 1, 6146700: 1, 647839: 1, 7570645: 1, 3891369: 1, 3857830: 1, 5335567: 1, 8971417: 1, 368089: 1, 8653336: 1, 2051915: 1, 6318072: 1, 8078607: 1}\nUID 1400: gold_count=38, train_likes=42, train_listens=11345\n Gold items: {6945311: 1, 2158715: 1, 2194514: 1, 3530629: 1, 2453912: 1, 7767752: 1, 4859530: 1, 3585411: 1, 4500834: 1, 4770069: 1, 3706360: 1, 6083906: 1, 1118882: 1, 8675053: 1, 5551069: 1, 6473581: 1, 2285672: 1, 5933848: 1, 7008219: 1, 5265819: 1, 975128: 1, 3970945: 1, 4345553: 1, 6901050: 1, 6901374: 1, 1851456: 1, 4678741: 1, 4897148: 1, 8420846: 3, 1327266: 1, 9233959: 1, 3203533: 1, 1407989: 1, 6417502: 1, 9016646: 1, 3763080: 1, 9378983: 1, 8669062: 1}",
"description": "Inspect sample users and their validation gold items",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:53:29.091Z",
"content": "\nOutput: UID 200: gold_count=13, train_likes=0, train_listens=312\n Gold items: {7406680: 1, 694058: 1, 4082738: 1, 6079576: 1, 6006390: 1, 2644025: 1, 4897148: 1, 7930756: 1, 3744896: 1, 3763080: 1, 7481745: 1, 9233959: 1, 9166040: 1}\nUID 800: gold_count=25, train_likes=132, train_listens=11793\n Gold items: {7179437: 1, 1607923: 1, 1816101: 1, 804416: 1, 2650514: 1, 4678741: 1, 2565336: 1, 6803177: 1, 6952637: 1, 4537506: 1, 2766576: 1, 9342289: 1, 9186305: 1, 308692: 1, 5559790: 1, 776584: 1, 388527: 1, 7400303: 1, 6551500: 1, 704566: 1, 843730: 1, 7326035: 1, 4702202: 1, 3531147: 1, 5433005: 1}\nUID 900: gold_count=1, train_likes=0, train_listens=851\n Gold items: {3673625: 1}\nUID 1100: gold_count=20, train_likes=46, train_listens=4217\n Gold items: {4588549: 1, 635564: 1, 1613989: 1, 8681149: 1, 3910656: 1, 1030400: 1, 7387845: 1, 7340717: 1, 6146700: 1, 647839: 1, 7570645: 1, 3891369: 1, 3857830: 1, 5335567: 1, 8971417: 1, 368089: 1, 8653336: 1, 2051915: 1, 6318072: 1, 8078607: 1}\nUID 1400: gold_count=38, train_likes=42, train_listens=11345\n Gold items: {6945311: 1, 2158715: 1, 2194514: 1, 3530629: 1, 2453912: 1, 7767752: 1, 4859530: 1, 3585411: 1, 4500834: 1, 4770069: 1, 3706360: 1, 6083906: 1, 1118882: 1, 8675053: 1, 5551069: 1, 6473581: 1, 2285672: 1, 5933848: 1, 7008219: 1, 5265819: 1, 975128: 1, 3970945: 1, 4345553: 1, 6901050: 1, 6901374: 1, 1851456: 1, 4678741: 1, 4897148: 1, 8420846: 3, 1327266: 1, 9233959: 1, 3203533: 1, 1407989: 1, 6417502: 1, 9016646: 1, 3763080: 1, 9378983: 1, 8669062: 1}\nProcess Group PGID: 2094\n",
"isError": false
},
"responseMessageId": "5da8f12a-a875-4a6c-979c-5083cfa35d76"
}
]
},
{
"id": 32,
"role": "agent",
"timestamp": "2026-09-29T05:53:33.166Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:53:33.166Z",
"text": "Analyzing Gold Items\n\nI'm focusing on our gold items now. Specifically, I'm checking if any of the 38 gold items, like the organically liked item 8420846 with grade 3, are present in `cat_items`. It appears `validation_truth` currently only includes items deemed eligible."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:53:33.166Z",
"callId": "run_shell_command__call_361184",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport time\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read table in {time.time() - t0:.2f}s, rows: {len(table)}')\n\"",
"description": "Benchmark reading train_listens columns"
},
"recorded": {
"id": "run_shell_command__call_361184",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport time\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read table in {time.time() - t0:.2f}s, rows: {len(table)}')\n\"",
"description": "Benchmark reading train_listens columns"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_361184",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Read table in 1.36s, rows: 43484159\nProcess Group PGID: 2114\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:53:35.105Z",
"resultDisplay": "Read table in 1.36s, rows: 43484159",
"description": "Benchmark reading train_listens columns",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:53:35.106Z",
"content": "\nOutput: Read table in 1.36s, rows: 43484159\nProcess Group PGID: 2114\n",
"isError": false
},
"responseMessageId": "818426e4-97d2-40f7-97e1-1ab18ae9565e"
}
]
},
{
"id": 33,
"role": "agent",
"timestamp": "2026-09-29T05:53:56.054Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:53:56.054Z",
"callId": "run_shell_command__call_109762",
"name": "run_shell_command",
"input": {
"description": "Measure memory and numpy conversion speed",
"command": "python3 -c \"\nimport time, pyarrow.parquet as pq, numpy as np\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nuids = table['uid'].to_numpy()\nitems = table['item_id'].to_numpy()\ntimestamps = table['timestamp'].to_numpy()\nratios = table['played_ratio_pct'].to_numpy()\norganics = table['is_organic'].to_numpy()\nprint('Converted to numpy in', round(time.time() - t0, 2), 's')\nprint('Shapes:', uids.shape, 'Memory MB:', (uids.nbytes + items.nbytes + timestamps.nbytes + ratios.nbytes + organics.nbytes) / 1024**2)\n\""
},
"recorded": {
"id": "run_shell_command__call_109762",
"name": "run_shell_command",
"args": {
"description": "Measure memory and numpy conversion speed",
"command": "python3 -c \"\nimport time, pyarrow.parquet as pq, numpy as np\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nuids = table['uid'].to_numpy()\nitems = table['item_id'].to_numpy()\ntimestamps = table['timestamp'].to_numpy()\nratios = table['played_ratio_pct'].to_numpy()\norganics = table['is_organic'].to_numpy()\nprint('Converted to numpy in', round(time.time() - t0, 2), 's')\nprint('Shapes:', uids.shape, 'Memory MB:', (uids.nbytes + items.nbytes + timestamps.nbytes + ratios.nbytes + organics.nbytes) / 1024**2)\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_109762",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Converted to numpy in 1.67 s\nShapes: (43484159,) Memory MB: 622.0458841323853\nProcess Group PGID: 2136\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:53:58.355Z",
"resultDisplay": "Converted to numpy in 1.67 s\nShapes: (43484159,) Memory MB: 622.0458841323853",
"description": "Measure memory and numpy conversion speed",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:53:58.356Z",
"content": "\nOutput: Converted to numpy in 1.67 s\nShapes: (43484159,) Memory MB: 622.0458841323853\nProcess Group PGID: 2136\n",
"isError": false
},
"responseMessageId": "2635eb5c-04da-4898-957c-772a93632e69"
}
]
},
{
"id": 34,
"role": "agent",
"timestamp": "2026-09-29T05:54:19.419Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:54:19.419Z",
"text": "Confirming Memory Availability\n\nI've confirmed the full dataset is a mere 622 MB, loading swiftly in under two seconds. Given my 14.4 GB of RAM, this represents a negligible ~4% usage, assuring ample memory and speed for processing all interaction logs without constraint."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:54:19.419Z",
"text": "Analyzing Model Requirements\n\nMy focus is now on dissecting the core architectural mandates. I'm specifically examining the requirement for independent user and item vector decomposition, ensuring each originates from distinct, trainable PyTorch towers. The emphasis on deriving user representations from interaction history, not ID lookups, is a key constraint being processed."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:54:19.419Z",
"text": "Designing Two-Tower Architecture\n\nI'm now actively designing the two-tower model architecture. My current thoughts center on how to independently decompose user and item vectors using distinct PyTorch towers. I'm also considering how to derive user representations solely from their interaction history, as opposed to relying on ID lookups."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:54:19.419Z",
"text": "Refining Item Tower Inputs\n\nI'm now integrating item catalog data into the item tower design. My thoughts are on how to best leverage item ID alongside tabular features like `positive_listens`, `popularity_rank`, and derived metrics such as like counts, all feeding into the embedding and dense layers to produce the final item vector."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:54:19.419Z",
"text": "Processing User Signals\n\nI'm now consolidating the diverse user interaction signals. My focus is on how to effectively encode sequences of recent listens, incorporating timestamps, track IDs, played ratios, and organic flags. I'm also considering how to integrate explicit likes and dislikes to enrich the user representation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:54:19.419Z",
"text": "Integrating Multi-View User Representations\n\nI'm now merging the distinct user interaction signals into a unified representation. My approach involves combining embeddings from liked and disliked tracks, recent listens with recency decay, strong organic listens, and the last few interactions. This multi-view strategy aims to create a rich user profile."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:54:19.419Z",
"text": "Synthesizing User Representation\n\nI'm now refining the user tower's final representation. My current thinking involves concatenating various signal embeddings, including likes, dislikes, recent and strong listens, and interaction statistics. This combined vector will then be processed through a multi-layer perceptron, potentially with L2 normalization, to produce the final user embedding. I've also considered and confirmed that sharing item embeddings between towers is standard and beneficial for synchronous updates."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:54:19.419Z",
"text": "Validating Vector Dot Product\n\nI've confirmed my current approach precisely meets the requirement for the final relevance score being the dot product of independently computable user and item vectors. The user vector is derived from interaction history, as planned, and the item vectors can indeed be pre-computed offline for efficient retrieval."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:54:19.419Z",
"text": "Formulating Training Strategy\n\nMy current thoughts are on how to effectively train the two-tower model. I'm investigating how to derive target positives from the validation set's criteria, specifically organic likes and significant organic listens within a defined future window. The potential for utilizing multiple temporal slices from the training data is also under consideration."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:54:19.419Z",
"text": "Refining Training Temporal Slices\n\nI'm now focusing on the temporal slicing for training, recognizing how each slice directly mirrors the evaluation task: predicting future organic likes and strong listens based on past history. This setup efficiently utilizes all available data to train the model for real-world prediction."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:54:19.419Z",
"callId": "run_shell_command__call_133017",
"name": "run_shell_command",
"input": {
"description": "Check positive events count per 7-day window in train",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf_listens = table.to_pandas()\ndf_listens = df_listens[(df_listens.is_organic == 1) & (df_listens.played_ratio_pct >= 80) & (df_listens.item_id.isin(cat_set))]\n\ndf_likes = pd.read_parquet('/task/data/train_likes.parquet')\ndf_likes = df_likes[(df_likes.is_organic == 1) & (df_likes.item_id.isin(cat_set))]\n\ncutoff = 24786800\nweek = 7 * 86400\n\nfor w in range(1, 9):\n t_end = cutoff - (w - 1) * week\n t_start = cutoff - w * week\n n_likes = ((df_likes.timestamp >= t_start) & (df_likes.timestamp < t_end)).sum()\n n_listens = ((df_listens.timestamp >= t_start) & (df_listens.timestamp < t_end)).sum()\n print(f'Week -{w}: likes={n_likes}, listens_80={n_listens}, total={n_likes + n_listens}')\n\""
},
"recorded": {
"id": "run_shell_command__call_133017",
"name": "run_shell_command",
"args": {
"description": "Check positive events count per 7-day window in train",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf_listens = table.to_pandas()\ndf_listens = df_listens[(df_listens.is_organic == 1) & (df_listens.played_ratio_pct >= 80) & (df_listens.item_id.isin(cat_set))]\n\ndf_likes = pd.read_parquet('/task/data/train_likes.parquet')\ndf_likes = df_likes[(df_likes.is_organic == 1) & (df_likes.item_id.isin(cat_set))]\n\ncutoff = 24786800\nweek = 7 * 86400\n\nfor w in range(1, 9):\n t_end = cutoff - (w - 1) * week\n t_start = cutoff - w * week\n n_likes = ((df_likes.timestamp >= t_start) & (df_likes.timestamp < t_end)).sum()\n n_listens = ((df_listens.timestamp >= t_start) & (df_listens.timestamp < t_end)).sum()\n print(f'Week -{w}: likes={n_likes}, listens_80={n_listens}, total={n_likes + n_listens}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_133017",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Week -1: likes=11210, listens_80=305188, total=316398\nWeek -2: likes=9918, listens_80=289174, total=299092\nWeek -3: likes=9774, listens_80=287149, total=296923\nWeek -4: likes=10721, listens_80=289244, total=299965\nWeek -5: likes=9641, listens_80=281100, total=290741\nWeek -6: likes=9639, listens_80=270971, total=280610\nWeek -7: likes=12160, listens_80=278744, total=290904\nWeek -8: likes=8709, listens_80=272184, total=280893\nProcess Group PGID: 2164\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:54:25.125Z",
"resultDisplay": "Week -1: likes=11210, listens_80=305188, total=316398\nWeek -2: likes=9918, listens_80=289174, total=299092\nWeek -3: likes=9774, listens_80=287149, total=296923\nWeek -4: likes=10721, listens_80=289244, total=299965\nWeek -5: likes=9641, listens_80=281100, total=290741\nWeek -6: likes=9639, listens_80=270971, total=280610\nWeek -7: likes=12160, listens_80=278744, total=290904\nWeek -8: likes=8709, listens_80=272184, total=280893",
"description": "Check positive events count per 7-day window in train",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:54:25.126Z",
"content": "\nOutput: Week -1: likes=11210, listens_80=305188, total=316398\nWeek -2: likes=9918, listens_80=289174, total=299092\nWeek -3: likes=9774, listens_80=287149, total=296923\nWeek -4: likes=10721, listens_80=289244, total=299965\nWeek -5: likes=9641, listens_80=281100, total=290741\nWeek -6: likes=9639, listens_80=270971, total=280610\nWeek -7: likes=12160, listens_80=278744, total=290904\nWeek -8: likes=8709, listens_80=272184, total=280893\nProcess Group PGID: 2164\n",
"isError": false
},
"responseMessageId": "f9ab6c09-9d94-4952-964f-8f2a6894c379"
}
]
},
{
"id": 35,
"role": "agent",
"timestamp": "2026-09-29T05:54:31.553Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:54:31.553Z",
"text": "Analyzing Engagement Metrics\n\nI'm currently reviewing the organic engagement data from the candidate catalog. It appears there are around 10,000 organic likes and 300,000 strong organic listens weekly. My next step is to cross-reference this with the eligibility conditions established during the validation window."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:54:31.553Z",
"callId": "run_shell_command__call_334222",
"name": "run_shell_command",
"input": {
"description": "Check new discovery items in Week -1",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf = table.to_pandas()\ndf_cat = df[df.item_id.isin(cat_set)]\n\ncutoff = 24786800\nweek = 7 * 86400\nt_split = cutoff - week\n\n# Prior listens before t_split\nprior = df_cat[df_cat.timestamp < t_split]\nprior_user_items = set(zip(prior.uid, prior.item_id))\n\n# Events in Week -1\nw1 = df_cat[(df_cat.timestamp >= t_split) & (df_cat.timestamp < cutoff)]\nw1_strong = w1[(w1.is_organic == 1) & (w1.played_ratio_pct >= 80)]\nw1_pairs = set(zip(w1_strong.uid, w1_strong.item_id))\n\nnew_pairs = w1_pairs - prior_user_items\nprint(f'Week -1 strong pairs: {len(w1_pairs)}, new unlistened pairs: {len(new_pairs)} ({len(new_pairs)/len(w1_pairs):.2%})')\n\n# Check likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes_cat = likes[likes.item_id.isin(cat_set)]\nlikes_w1 = likes_cat[(likes_cat.timestamp >= t_split) & (likes_cat.timestamp < cutoff) & (likes_cat.is_organic == 1)]\nlikes_w1_pairs = set(zip(likes_w1.uid, likes_w1.item_id))\nnew_likes_pairs = likes_w1_pairs - prior_user_items\nprint(f'Week -1 likes pairs: {len(likes_w1_pairs)}, new unlistened likes pairs: {len(new_likes_pairs)} ({len(new_likes_pairs)/len(likes_w1_pairs):.2%})')\n\""
},
"recorded": {
"id": "run_shell_command__call_334222",
"name": "run_shell_command",
"args": {
"description": "Check new discovery items in Week -1",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf = table.to_pandas()\ndf_cat = df[df.item_id.isin(cat_set)]\n\ncutoff = 24786800\nweek = 7 * 86400\nt_split = cutoff - week\n\n# Prior listens before t_split\nprior = df_cat[df_cat.timestamp < t_split]\nprior_user_items = set(zip(prior.uid, prior.item_id))\n\n# Events in Week -1\nw1 = df_cat[(df_cat.timestamp >= t_split) & (df_cat.timestamp < cutoff)]\nw1_strong = w1[(w1.is_organic == 1) & (w1.played_ratio_pct >= 80)]\nw1_pairs = set(zip(w1_strong.uid, w1_strong.item_id))\n\nnew_pairs = w1_pairs - prior_user_items\nprint(f'Week -1 strong pairs: {len(w1_pairs)}, new unlistened pairs: {len(new_pairs)} ({len(new_pairs)/len(w1_pairs):.2%})')\n\n# Check likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes_cat = likes[likes.item_id.isin(cat_set)]\nlikes_w1 = likes_cat[(likes_cat.timestamp >= t_split) & (likes_cat.timestamp < cutoff) & (likes_cat.is_organic == 1)]\nlikes_w1_pairs = set(zip(likes_w1.uid, likes_w1.item_id))\nnew_likes_pairs = likes_w1_pairs - prior_user_items\nprint(f'Week -1 likes pairs: {len(likes_w1_pairs)}, new unlistened likes pairs: {len(new_likes_pairs)} ({len(new_likes_pairs)/len(likes_w1_pairs):.2%})')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_334222",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Week -1 strong pairs: 173200, new unlistened pairs: 39606 (22.87%)\nWeek -1 likes pairs: 10959, new unlistened likes pairs: 8639 (78.83%)\nProcess Group PGID: 2186\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:54:49.872Z",
"resultDisplay": "Week -1 strong pairs: 173200, new unlistened pairs: 39606 (22.87%)\nWeek -1 likes pairs: 10959, new unlistened likes pairs: 8639 (78.83%)",
"description": "Check new discovery items in Week -1",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:54:49.874Z",
"content": "\nOutput: Week -1 strong pairs: 173200, new unlistened pairs: 39606 (22.87%)\nWeek -1 likes pairs: 10959, new unlistened likes pairs: 8639 (78.83%)\nProcess Group PGID: 2186\n",
"isError": false
},
"responseMessageId": "b66718e2-d2af-4b95-9a95-cd0a06a98685"
}
]
},
{
"id": 36,
"role": "agent",
"timestamp": "2026-09-29T05:55:29.505Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:55:29.505Z",
"text": "Analyzing New Discovery Data\n\nI'm currently processing approximately 48,000 new positive discovery pairs from last week. This includes both unlistened likes and strong listens, which I'll be correlating against the existing validation ground truth of 37,700 positive labels."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:55:29.505Z",
"text": "Refining User Preference Models\n\nI'm observing a striking numerical correlation between the new positive discoveries and existing validated data, suggesting the discovery engine is effectively predicting novel, well-received tracks. My next steps involve a deeper dive into the candidate eligibility rules to understand how this prediction accuracy is maintained during inference. I'm also beginning to explore the underlying factors driving these user preferences, focusing on acoustic and collaborative filtering insights."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:55:29.505Z",
"text": "Investigating Two-Tower Architecture\n\nI'm analyzing how user history embeddings in the user tower are formed, specifically how they aggregate listened tracks like X, Y, and Z to become proximate to discovered track W's embedding. I'm also considering how explicit likes and dislikes directly influence the scoring of similar items."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:55:29.505Z",
"text": "Verifying Architecture Design\n\nI'm confirming the two-tower architecture adheres to the requirement of producing user and item vectors whose dot product yields the relevance score. I'm also investigating whether unit norm or bias terms are compatible with this core constraint, specifically how interaction history dictates the user tower's output."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:55:29.505Z",
"text": "Incorporating Item Features\n\nI'm actively exploring how to integrate rich item features into the model. My focus is on leveraging readily available data like total plays, listen completion ratios, and like-to-dislike metrics from training logs. This integration aims to enhance the item tower's ability to represent both inherent item characteristics and their associated popularity."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:55:29.505Z",
"text": "Aggregating Historical Data\n\nI'm now focusing on how to aggregate historical item embeddings. Given a user's interaction history, I'm considering different methods to represent this data effectively for the user tower, exploring options for handling out-of-catalog items."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:55:29.505Z",
"text": "Modeling User Embeddings\n\nI'm currently focused on how to aggregate various user interaction signals, like listens and dislikes, into a unified user embedding. My objective is to explore weighted averages, incorporating decay functions and interaction weights, to capture nuanced user preferences and explicitly move away from disliked item embeddings."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:55:29.505Z",
"text": "Exploring Attention Mechanisms\n\nI'm currently investigating the potential of using Attention Pooling, specifically self-attention or multi-head attention, over user history. The computational feasibility appears promising, with self-attention over 100 items projected to take less than a millisecond. I'm also considering learned query attention pooling as an alternative approach."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:55:29.505Z",
"text": "Benchmarking CPU Training Speed\n\nI'm now rigorously evaluating the CPU training speed for my model. I'm prototyping a PyTorch script to benchmark forward and backward passes for both item and user towers, alongside the InfoNCE loss computation. This is to understand computational feasibility with varying batch sizes, embedding dimensions, and catalog sizes."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:55:29.505Z",
"callId": "run_shell_command__call_26547",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\n\ntorch.set_num_threads(8)\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n \n def forward(self, item_ids, dense_features):\n emb = self.item_emb(item_ids)\n d_proj = self.dense_proj(dense_features)\n x = torch.cat([emb, d_proj], dim=-1)\n out = self.mlp(x)\n return F.normalize(out, p=2, dim=-1)\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, num_views, stats_dim, out_dim):\n super().__init__()\n in_dim = embed_dim * num_views + stats_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Linear(embed_dim * 2, out_dim),\n )\n \n def forward(self, view_vectors, stats):\n # view_vectors: (batch_size, num_views * embed_dim)\n x = torch.cat([view_vectors, stats], dim=-1)\n out = self.mlp(x)\n return F.normalize(out, p=2, dim=-1)\n\nnum_items = 30000\nembed_dim = 64\nout_dim = 64\nbatch_size = 512\n\nitem_tower = ItemTower(num_items, embed_dim, 4, out_dim)\nuser_tower = UserTower(embed_dim, 4, 6, out_dim)\n\n# Dummy inputs\nitem_ids = torch.randint(0, num_items, (batch_size,))\ndense_feat = torch.randn(batch_size, 4)\nuser_views = torch.randn(batch_size, 4 * embed_dim)\nuser_stats = torch.randn(batch_size, 6)\n\n# Test timing\nt0 = time.time()\nfor _ in range(100):\n u_vec = user_tower(user_views, user_stats)\n v_vec = item_tower(item_ids, dense_feat)\n \n # In-batch logits: (batch_size, batch_size)\n logits = torch.matmul(u_vec, v_vec.T) / 0.07\n labels = torch.arange(batch_size)\n loss = F.cross_entropy(logits, labels)\n loss.backward()\n\nprint(f'100 batches of size {batch_size} took: {time.time() - t0:.2f}s')\n\"",
"description": "Benchmark basic two-tower forward/backward speed"
},
"recorded": {
"id": "run_shell_command__call_26547",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\n\ntorch.set_num_threads(8)\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n \n def forward(self, item_ids, dense_features):\n emb = self.item_emb(item_ids)\n d_proj = self.dense_proj(dense_features)\n x = torch.cat([emb, d_proj], dim=-1)\n out = self.mlp(x)\n return F.normalize(out, p=2, dim=-1)\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, num_views, stats_dim, out_dim):\n super().__init__()\n in_dim = embed_dim * num_views + stats_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Linear(embed_dim * 2, out_dim),\n )\n \n def forward(self, view_vectors, stats):\n # view_vectors: (batch_size, num_views * embed_dim)\n x = torch.cat([view_vectors, stats], dim=-1)\n out = self.mlp(x)\n return F.normalize(out, p=2, dim=-1)\n\nnum_items = 30000\nembed_dim = 64\nout_dim = 64\nbatch_size = 512\n\nitem_tower = ItemTower(num_items, embed_dim, 4, out_dim)\nuser_tower = UserTower(embed_dim, 4, 6, out_dim)\n\n# Dummy inputs\nitem_ids = torch.randint(0, num_items, (batch_size,))\ndense_feat = torch.randn(batch_size, 4)\nuser_views = torch.randn(batch_size, 4 * embed_dim)\nuser_stats = torch.randn(batch_size, 6)\n\n# Test timing\nt0 = time.time()\nfor _ in range(100):\n u_vec = user_tower(user_views, user_stats)\n v_vec = item_tower(item_ids, dense_feat)\n \n # In-batch logits: (batch_size, batch_size)\n logits = torch.matmul(u_vec, v_vec.T) / 0.07\n labels = torch.arange(batch_size)\n loss = F.cross_entropy(logits, labels)\n loss.backward()\n\nprint(f'100 batches of size {batch_size} took: {time.time() - t0:.2f}s')\n\"",
"description": "Benchmark basic two-tower forward/backward speed"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_26547",
"name": "run_shell_command",
"response": {
"output": "\nOutput: 100 batches of size 512 took: 0.33s\nProcess Group PGID: 2214\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:55:32.902Z",
"resultDisplay": "100 batches of size 512 took: 0.33s",
"description": "Benchmark basic two-tower forward/backward speed",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:55:32.903Z",
"content": "\nOutput: 100 batches of size 512 took: 0.33s\nProcess Group PGID: 2214\n",
"isError": false
},
"responseMessageId": "89c07732-35d2-4e1a-8d85-268a72338630"
}
]
},
{
"id": 37,
"role": "agent",
"timestamp": "2026-09-29T05:57:33.392Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:57:33.392Z",
"text": "Processing Data Quickly\n\nMy recent analysis indicates an impressive processing speed, handling 51,200 training examples in just 0.33 seconds on the CPU. I'm now focused on understanding the specific mapping of user history to input features within the training data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:57:33.392Z",
"callId": "run_shell_command__call_488295",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport time\n\ntorch.set_num_threads(8)\n\nemb_bag = nn.EmbeddingBag(30001, 64, mode='sum')\nbatch_size = 512\nitems_per_user = 50\n\nindices = torch.randint(0, 30000, (batch_size * items_per_user,))\noffsets = torch.arange(0, batch_size * items_per_user, items_per_user)\nweights = torch.rand(batch_size * items_per_user)\n\nt0 = time.time()\nfor _ in range(100):\n out = emb_bag(indices, offsets, per_sample_weights=weights)\n loss = out.sum()\n loss.backward()\n\nprint(f'100 batches of EmbeddingBag took: {time.time() - t0:.2f}s')\n\"",
"description": "Benchmark EmbeddingBag speed on CPU"
},
"recorded": {
"id": "run_shell_command__call_488295",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport time\n\ntorch.set_num_threads(8)\n\nemb_bag = nn.EmbeddingBag(30001, 64, mode='sum')\nbatch_size = 512\nitems_per_user = 50\n\nindices = torch.randint(0, 30000, (batch_size * items_per_user,))\noffsets = torch.arange(0, batch_size * items_per_user, items_per_user)\nweights = torch.rand(batch_size * items_per_user)\n\nt0 = time.time()\nfor _ in range(100):\n out = emb_bag(indices, offsets, per_sample_weights=weights)\n loss = out.sum()\n loss.backward()\n\nprint(f'100 batches of EmbeddingBag took: {time.time() - t0:.2f}s')\n\"",
"description": "Benchmark EmbeddingBag speed on CPU"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_488295",
"name": "run_shell_command",
"response": {
"output": "\nOutput: 100 batches of EmbeddingBag took: 0.36s\nProcess Group PGID: 2250\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:57:36.690Z",
"resultDisplay": "100 batches of EmbeddingBag took: 0.36s",
"description": "Benchmark EmbeddingBag speed on CPU",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:57:36.691Z",
"content": "\nOutput: 100 batches of EmbeddingBag took: 0.36s\nProcess Group PGID: 2250\n",
"isError": false
},
"responseMessageId": "d89911e8-db92-44ef-aacb-a85154e64174"
}
]
},
{
"id": 38,
"role": "agent",
"timestamp": "2026-09-29T05:57:53.520Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:57:53.520Z",
"text": "Optimizing Embedding Computation\n\nMy recent focus has been on the performance of `nn.EmbeddingBag`, which has shown excellent results, processing 100 batches in just 0.36 seconds. This sub-second performance for end-to-end item embedding training and user history pooling on the CPU is a significant breakthrough. My current thoughts are now turning towards architecting the best representation and analyzing the available information."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:57:53.520Z",
"text": "Analyzing Ranking Objectives\n\nMy current focus is on understanding the core ranking task: for each user, predicting the top 100 eligible tracks out of 29,000. I'm evaluating the impact of different interaction signals like organic likes and listens on the graded NDCG@10 metric. Next, I'll explore item-to-item co-occurrence representations."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:57:53.520Z",
"text": "Designing Scalable Towers\n\nI'm currently focused on ensuring our model design avoids constructing a global user-item matrix, which is crucial for scaling to millions of users and items. The architecture must decompose into independent user and item towers, allowing for efficient, real-time retrieval via inner-product search with precomputed item embeddings."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:57:53.520Z",
"text": "Refining User Representation\n\nI'm confirming the feasibility of a dot product score between independently computed user and item embeddings. My current thoughts are on how the user tower will exclusively leverage interaction history, avoiding direct user ID lookups, and how to structure this input for effective PyTorch neural network processing."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:57:53.520Z",
"text": "Structuring Interaction Windows\n\nMy current thoughts are exploring how to structure user interaction history across different time windows to define positive target items. I'm considering using within-session transitions and training with multiple sequential time windows, like the last 12 weeks, for richer representation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:57:53.520Z",
"text": "Evaluating Training Window Efficiency\n\nI'm currently assessing the computational cost of generating user histories and positive target pairs across multiple weekly windows. My focus is on determining the feasibility of using fixed cutoffs for these windows, comparing strategies like using specific historical periods and targeting subsequent days to optimize this process for efficiency."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:57:53.520Z",
"callId": "run_shell_command__call_73311",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_item_to_idx = {item: idx for idx, item in enumerate(cat.item_id)}\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid)\n\n# Read likes and dislikes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\n\n# Read listens\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens_df = table.to_pandas()\n\nprint(f'Loaded all data in {time.time() - t0:.2f}s')\n\"",
"description": "Test loading all interaction data into memory"
},
"recorded": {
"id": "run_shell_command__call_73311",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_item_to_idx = {item: idx for idx, item in enumerate(cat.item_id)}\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid)\n\n# Read likes and dislikes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\n\n# Read listens\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens_df = table.to_pandas()\n\nprint(f'Loaded all data in {time.time() - t0:.2f}s')\n\"",
"description": "Test loading all interaction data into memory"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_73311",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Loaded all data in 0.48s\nProcess Group PGID: 2274\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:57:55.645Z",
"resultDisplay": "Loaded all data in 0.48s",
"description": "Test loading all interaction data into memory",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:57:55.646Z",
"content": "\nOutput: Loaded all data in 0.48s\nProcess Group PGID: 2274\n",
"isError": false
},
"responseMessageId": "2cfb3a3a-3ed5-4f45-a149-0e8332a9bd96"
}
]
},
{
"id": 39,
"role": "agent",
"timestamp": "2026-09-29T05:58:00.411Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:58:00.411Z",
"text": "Loading and Analyzing Data\n\nI've successfully loaded all the data. My current focus is on the rapid aggregation of user histories, specifically examining their interactions up to a designated cutoff point. I'm also beginning to dissect the components of effective user representation, starting with \"liked items.\""
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:58:00.411Z",
"callId": "run_shell_command__call_664095",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\n\n# Tag actions\nlikes['action'] = 'like'\nunlikes['action'] = 'unlike'\ndislikes['action'] = 'dislike'\nundislikes['action'] = 'undislike'\n\nall_actions = pd.concat([likes, unlikes, dislikes, undislikes], ignore_index=True)\nall_actions = all_actions.sort_values('timestamp')\n\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('v_pref shape:', v_pref.shape)\nprint('v_pref unique uids:', v_pref.uid.nunique())\n\"",
"description": "Compare reconstructed preference state with validation_preference_state"
},
"recorded": {
"id": "run_shell_command__call_664095",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\n\n# Tag actions\nlikes['action'] = 'like'\nunlikes['action'] = 'unlike'\ndislikes['action'] = 'dislike'\nundislikes['action'] = 'undislike'\n\nall_actions = pd.concat([likes, unlikes, dislikes, undislikes], ignore_index=True)\nall_actions = all_actions.sort_values('timestamp')\n\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('v_pref shape:', v_pref.shape)\nprint('v_pref unique uids:', v_pref.uid.nunique())\n\"",
"description": "Compare reconstructed preference state with validation_preference_state"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_664095",
"name": "run_shell_command",
"response": {
"output": "\nOutput: v_pref shape: (469124, 3)\nv_pref unique uids: 7038\nProcess Group PGID: 2296\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:58:02.116Z",
"resultDisplay": "v_pref shape: (469124, 3)\nv_pref unique uids: 7038",
"description": "Compare reconstructed preference state with validation_preference_state",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:58:02.117Z",
"content": "\nOutput: v_pref shape: (469124, 3)\nv_pref unique uids: 7038\nProcess Group PGID: 2296\n",
"isError": false
},
"responseMessageId": "df580298-5cf5-4836-85a3-d71961e79e88"
}
]
},
{
"id": 40,
"role": "agent",
"timestamp": "2026-09-29T05:58:15.923Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:58:15.923Z",
"text": "Utilizing Pre-computed Data\n\nI've confirmed the `validation_preference_state` and `test_preference_state` datasets are pre-computed and available, with substantial row counts. This means I can directly leverage `validation_preference_state.parquet` for validation inference, streamlining the process."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:58:15.923Z",
"text": "Leveraging Train Data\n\nI can now directly use `test_preference_state.parquet` for test inference. For earlier training slices, I'll filter `train_likes` and `train_dislikes` by timestamp. I'm currently considering what features are most impactful for the User Tower, starting with average embeddings of liked and disliked items, and recent high-completion listens."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:58:15.923Z",
"text": "Refining User Tower Features\n\nI'm now focusing on refining the User Tower features. I'm exploring incorporating strongly completed listens with exponential recency decay and recent general listens, also weighted by recency and played ratio. Additionally, I'm considering the last 20 listens and several scalar user features like listen counts and organic listen ratios."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:58:15.923Z",
"text": "Investigating Item Tower Inputs\n\nI'm now examining the Item Tower's inputs. I've identified item embeddings, tabular features like `positive_listens` and `popularity_rank`, and a subsequent MLP. A key question is whether the item embeddings used here can be shared with those in the User Tower for pooling."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:58:15.923Z",
"text": "Confirming Model Architecture\n\nI've confirmed the proposed model architecture perfectly aligns with the system's requirements. The user and item vectors are of identical dimensionality, with the score being their dot product, and both vectors are generated by separate, trainable PyTorch towers. This design also supports efficient offline item embedding computation and online retrieval."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:58:15.923Z",
"text": "Training Item Embeddings\n\nI'm currently contemplating the best approach for training item embeddings to maximize NDCG@10. I'm weighing pre-training using session co-occurrences with Word2Vec or similar methods against end-to-end training via PyTorch with AdamW and cosine annealing, or a combination of both."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:58:15.923Z",
"callId": "update_topic__call_227653",
"name": "update_topic",
"input": {
"strategic_intent": "Build and benchmark a prototype two-tower ranker and evaluate on public validation set.",
"title": "Phase 2: Prototype Two-Tower Architecture & Training",
"summary": "Completed Phase 1 exploration of the dataset, candidate eligibility, and architectural requirements. Verified that baseline popularity achieves 0.0107 NDCG@10 and generated a safe test submission baseline. Now moving to Phase 2: building and benchmarking a prototype PyTorch two-tower ranker evaluated via evaluate_public.py."
},
"recorded": {
"id": "update_topic__call_227653",
"name": "update_topic",
"args": {
"strategic_intent": "Build and benchmark a prototype two-tower ranker and evaluate on public validation set.",
"title": "Phase 2: Prototype Two-Tower Architecture & Training",
"summary": "Completed Phase 1 exploration of the dataset, candidate eligibility, and architectural requirements. Verified that baseline popularity achieves 0.0107 NDCG@10 and generated a safe test submission baseline. Now moving to Phase 2: building and benchmarking a prototype PyTorch two-tower ranker evaluated via evaluate_public.py."
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_227653",
"name": "update_topic",
"response": {
"output": "Current topic: \"Phase 2: Prototype Two-Tower Architecture & Training\"\nTopic summary: Completed Phase 1 exploration of the dataset, candidate eligibility, and architectural requirements. Verified that baseline popularity achieves 0.0107 NDCG@10 and generated a safe test submission baseline. Now moving to Phase 2: building and benchmarking a prototype PyTorch two-tower ranker evaluated via evaluate_public.py.\n\nStrategic Intent: Build and benchmark a prototype two-tower ranker and evaluate on public validation set."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:58:15.925Z",
"resultDisplay": "## 📂 Topic: **Phase 2: Prototype Two-Tower Architecture & Training**\n\n**Summary:**\nCompleted Phase 1 exploration of the dataset, candidate eligibility, and architectural requirements. Verified that baseline popularity achieves 0.0107 NDCG@10 and generated a safe test submission baseline. Now moving to Phase 2: building and benchmarking a prototype PyTorch two-tower ranker evaluated via evaluate_public.py.\n\n> [!STRATEGY]\n> **Intent:** Build and benchmark a prototype two-tower ranker and evaluate on public validation set.",
"description": "Update topic to: \"Phase 2: Prototype Two-Tower Architecture & Training\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:58:15.926Z",
"content": "Current topic: \"Phase 2: Prototype Two-Tower Architecture & Training\"\nTopic summary: Completed Phase 1 exploration of the dataset, candidate eligibility, and architectural requirements. Verified that baseline popularity achieves 0.0107 NDCG@10 and generated a safe test submission baseline. Now moving to Phase 2: building and benchmarking a prototype PyTorch two-tower ranker evaluated via evaluate_public.py.\n\nStrategic Intent: Build and benchmark a prototype two-tower ranker and evaluate on public validation set.",
"isError": false
},
"responseMessageId": "3a286359-c00c-4a1b-bdcd-f510e7998377"
}
]
},
{
"id": 41,
"role": "agent",
"timestamp": "2026-09-29T05:58:28.931Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:58:28.931Z",
"text": "Structuring Training Pipeline\n\nI'm currently mapping out the essential components for our training pipeline. My focus is on defining the data preprocessor, specifically outlining its inputs, which will include the `candidate_catalog.parquet` with its 30,000 items and associated popularity and listen data, alongside data for the validation model."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:58:28.931Z",
"text": "Extracting User Features\n\nI'm now focusing on the detailed extraction of user features for the validation set. This involves calculating lists of liked and disliked items, incorporating recent and strong listens with their respective weighting schemes, and compiling key user statistics such as listen counts and mean play ratios. I plan to develop a performant Python script to expedite this process for all target users."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:58:28.931Z",
"callId": "run_shell_command__call_70488",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\nt0 = time.time()\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid)\n\n# Read preference state\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nuser_pref_likes = {u: [] for u in targets.uid}\nuser_pref_dislikes = {u: [] for u in targets.uid}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == 'liked':\n user_pref_likes[int(uid)].append(idx)\n elif state == 'disliked':\n user_pref_dislikes[int(uid)].append(idx)\n\n# Read listens\ncutoff = 24786800\nt_start = cutoff - 60 * 86400 # last 60 days\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_start)])\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(target_uids)]\n\nprint(f'Filtered listens in last 60 days: {len(listens_df)} in {time.time() - t0:.2f}s')\n\"",
"description": "Benchmark feature extraction from listens for last 60 days"
},
"recorded": {
"id": "run_shell_command__call_70488",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\nt0 = time.time()\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid)\n\n# Read preference state\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nuser_pref_likes = {u: [] for u in targets.uid}\nuser_pref_dislikes = {u: [] for u in targets.uid}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == 'liked':\n user_pref_likes[int(uid)].append(idx)\n elif state == 'disliked':\n user_pref_dislikes[int(uid)].append(idx)\n\n# Read listens\ncutoff = 24786800\nt_start = cutoff - 60 * 86400 # last 60 days\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_start)])\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(target_uids)]\n\nprint(f'Filtered listens in last 60 days: {len(listens_df)} in {time.time() - t0:.2f}s')\n\"",
"description": "Benchmark feature extraction from listens for last 60 days"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_70488",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Filtered listens in last 60 days: 11699019 in 1.80s\nProcess Group PGID: 2319\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:58:32.325Z",
"resultDisplay": "Filtered listens in last 60 days: 11699019 in 1.80s",
"description": "Benchmark feature extraction from listens for last 60 days",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:58:32.326Z",
"content": "\nOutput: Filtered listens in last 60 days: 11699019 in 1.80s\nProcess Group PGID: 2319\n",
"isError": false
},
"responseMessageId": "a55f2c89-a37a-49e3-8d00-9891759a6e7e"
}
]
},
{
"id": 42,
"role": "agent",
"timestamp": "2026-09-29T05:59:08.050Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:59:08.050Z",
"text": "Aggregating User Listens\n\nI'm focusing on the aggregation of user listens now, specifically grouping by user and item. This is important because users can listen to the same track multiple times, and I need to account for that."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:59:08.050Z",
"callId": "run_shell_command__call_43372",
"name": "run_shell_command",
"input": {
"description": "Benchmark aggregation speed of user-item weights",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\n\ncutoff = 24786800\nt_start = cutoff - 60 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_start)])\ndf = table.to_pandas()\n\n# Filter only catalog items\ncat_items = set(cat.item_id)\ndf = df[df.item_id.isin(cat_items)]\nprint('Filtered to catalog items:', len(df), f'in {time.time() - t0:.2f}s')\n\n# Map to item idx\ndf['item_idx'] = df.item_id.map(item_to_idx)\n# Compute weights\ndays_ago = (cutoff - df.timestamp) / 86400.0\ndf['weight'] = np.exp(-0.05 * days_ago) * (1.0 + df.is_organic) * (df.played_ratio_pct / 100.0)\n\n# Groupby\nagg = df.groupby(['uid', 'item_idx'])['weight'].sum().reset_index()\nprint('Aggregated rows:', len(agg), f'in {time.time() - t0:.2f}s')\n\""
},
"recorded": {
"id": "run_shell_command__call_43372",
"name": "run_shell_command",
"args": {
"description": "Benchmark aggregation speed of user-item weights",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\n\ncutoff = 24786800\nt_start = cutoff - 60 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_start)])\ndf = table.to_pandas()\n\n# Filter only catalog items\ncat_items = set(cat.item_id)\ndf = df[df.item_id.isin(cat_items)]\nprint('Filtered to catalog items:', len(df), f'in {time.time() - t0:.2f}s')\n\n# Map to item idx\ndf['item_idx'] = df.item_id.map(item_to_idx)\n# Compute weights\ndays_ago = (cutoff - df.timestamp) / 86400.0\ndf['weight'] = np.exp(-0.05 * days_ago) * (1.0 + df.is_organic) * (df.played_ratio_pct / 100.0)\n\n# Groupby\nagg = df.groupby(['uid', 'item_idx'])['weight'].sum().reset_index()\nprint('Aggregated rows:', len(agg), f'in {time.time() - t0:.2f}s')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_43372",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Filtered to catalog items: 8417391 in 1.37s\nAggregated rows: 3406169 in 2.61s\nProcess Group PGID: 2347\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:59:12.249Z",
"resultDisplay": "Filtered to catalog items: 8417391 in 1.37s\nAggregated rows: 3406169 in 2.61s",
"description": "Benchmark aggregation speed of user-item weights",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:59:12.250Z",
"content": "\nOutput: Filtered to catalog items: 8417391 in 1.37s\nAggregated rows: 3406169 in 2.61s\nProcess Group PGID: 2347\n",
"isError": false
},
"responseMessageId": "f4fd000b-5ca1-4824-a49e-099ef466b814"
}
]
},
{
"id": 43,
"role": "agent",
"timestamp": "2026-09-29T05:59:18.710Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:59:18.710Z",
"text": "Aggregating Listen Data\n\nI've successfully aggregated over 8 million listens into user, item, and weight pairs, revealing an average of 400 distinct items per user. My next step is to precisely calculate the item count for each individual user."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:59:18.710Z",
"callId": "run_shell_command__call_187015",
"name": "run_shell_command",
"input": {
"description": "Benchmark EmbeddingBag for all 8456 users",
"command": "python3 -c \"\nimport torch, torch.nn as nn, time\n\ntorch.set_num_threads(8)\nemb_bag = nn.EmbeddingBag(30000, 64, mode='sum')\nindices = torch.randint(0, 30000, (3400000,))\noffsets = torch.linspace(0, 3400000, 8456, dtype=torch.long)[:-1]\nweights = torch.rand(3400000)\n\nt0 = time.time()\nout = emb_bag(indices, offsets, per_sample_weights=weights)\nprint('EmbeddingBag forward for all 8456 users took:', round(time.time() - t0, 4), 's, output shape:', out.shape)\n\""
},
"recorded": {
"id": "run_shell_command__call_187015",
"name": "run_shell_command",
"args": {
"description": "Benchmark EmbeddingBag for all 8456 users",
"command": "python3 -c \"\nimport torch, torch.nn as nn, time\n\ntorch.set_num_threads(8)\nemb_bag = nn.EmbeddingBag(30000, 64, mode='sum')\nindices = torch.randint(0, 30000, (3400000,))\noffsets = torch.linspace(0, 3400000, 8456, dtype=torch.long)[:-1]\nweights = torch.rand(3400000)\n\nt0 = time.time()\nout = emb_bag(indices, offsets, per_sample_weights=weights)\nprint('EmbeddingBag forward for all 8456 users took:', round(time.time() - t0, 4), 's, output shape:', out.shape)\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_187015",
"name": "run_shell_command",
"response": {
"output": "\nOutput: EmbeddingBag forward for all 8456 users took: 0.0072 s, output shape: torch.Size([8455, 64])\nProcess Group PGID: 2369\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:59:21.717Z",
"resultDisplay": "EmbeddingBag forward for all 8456 users took: 0.0072 s, output shape: torch.Size([8455, 64])",
"description": "Benchmark EmbeddingBag for all 8456 users",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:59:21.718Z",
"content": "\nOutput: EmbeddingBag forward for all 8456 users took: 0.0072 s, output shape: torch.Size([8455, 64])\nProcess Group PGID: 2369\n",
"isError": false
},
"responseMessageId": "e8dcd3bc-c52a-47bc-8986-7b2bf02fdd06"
}
]
},
{
"id": 44,
"role": "agent",
"timestamp": "2026-09-29T05:59:45.838Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:59:45.838Z",
"text": "Optimizing User History Processing\n\nI'm thrilled to report that user history representation for all 8,456 users is now computed in a mere 7.2 milliseconds. My current focus is on assessing performance with multiple user views, specifically `bag_likes` and `bag_dislikes` embeddings."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:59:45.838Z",
"callId": "run_shell_command__call_441286",
"name": "run_shell_command",
"input": {
"description": "Check environment setup for prototype",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\nimport torch, torch.nn as nn, torch.nn.functional as F\nfrom pathlib import Path\n\ntorch.set_num_threads(8)\n\n# Load catalog\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Targets\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = targets_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\n\nprint('Catalog items:', num_items, 'Target users:', len(target_uids))\n\""
},
"recorded": {
"id": "run_shell_command__call_441286",
"name": "run_shell_command",
"args": {
"description": "Check environment setup for prototype",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\nimport torch, torch.nn as nn, torch.nn.functional as F\nfrom pathlib import Path\n\ntorch.set_num_threads(8)\n\n# Load catalog\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Targets\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = targets_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\n\nprint('Catalog items:', num_items, 'Target users:', len(target_uids))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_441286",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog items: 30000 Target users: 8456\nProcess Group PGID: 2393\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:59:49.852Z",
"resultDisplay": "Catalog items: 30000 Target users: 8456",
"description": "Check environment setup for prototype",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:59:49.852Z",
"content": "\nOutput: Catalog items: 30000 Target users: 8456\nProcess Group PGID: 2393\n",
"isError": false
},
"responseMessageId": "7cf714fd-aa5d-4181-9bef-99fa282c4551"
}
]
},
{
"id": 45,
"role": "agent",
"timestamp": "2026-09-29T06:00:24.305Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:00:24.305Z",
"text": "Developing Feature Extraction\n\nI am now focusing on building out the feature extraction components for the experiment. My current effort is dedicated to designing modular and clean extractors that can efficiently capture user features at a given cutoff time $T$."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:00:24.305Z",
"text": "Refining Preference Extraction\n\nI've realized that for the validation cutoff, the active liked and disliked items are already available in `validation_preference_state.parquet`. For earlier cutoffs, I can efficiently reconstruct this active preference state by processing likes/unlikes and dislikes/undislikes, which takes less than a second. I'll filter listens to a 90-day window preceding the cutoff $T$."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:00:24.305Z",
"callId": "run_shell_command__call_53936",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Load likes and listens\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_to_idx)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = table.to_pandas()\nlistens = listens[listens.item_id.isin(item_to_idx)]\nprint(f'Data loaded in {time.time() - t0:.2f}s')\n\ncutoff = 24786800\nweek = 7 * 86400\n\n# Let's inspect target extraction for week -1: [cutoff - week, cutoff]\nw_end = cutoff\nw_start = cutoff - week\n\n# Prior listens before w_start\nprior = listens[listens.timestamp < w_start]\nprior_user_items = set(zip(prior.uid, prior.item_id))\n\n# Likes in target window\ntarget_likes = likes[(likes.timestamp >= w_start) & (likes.timestamp < w_end) & (likes.is_organic == 1)]\ntarget_listens = listens[(listens.timestamp >= w_start) & (listens.timestamp < w_end) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)]\n\n# Combine into gold targets\ntargets_dict = {}\nfor uid, item in zip(target_listens.uid, target_listens.item_id):\n if (uid, item) not in prior_user_items:\n targets_dict[(uid, item)] = 1\n\nfor uid, item in zip(target_likes.uid, target_likes.item_id):\n if (uid, item) not in prior_user_items:\n targets_dict[(uid, item)] = 3\n\nprint('Target pairs for week -1:', len(targets_dict))\ngrades = list(targets_dict.values())\nprint('Grade 3 count:', grades.count(3), 'Grade 1 count:', grades.count(1))\n\"",
"description": "Verify target pair extraction for week -1"
},
"recorded": {
"id": "run_shell_command__call_53936",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Load likes and listens\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_to_idx)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = table.to_pandas()\nlistens = listens[listens.item_id.isin(item_to_idx)]\nprint(f'Data loaded in {time.time() - t0:.2f}s')\n\ncutoff = 24786800\nweek = 7 * 86400\n\n# Let's inspect target extraction for week -1: [cutoff - week, cutoff]\nw_end = cutoff\nw_start = cutoff - week\n\n# Prior listens before w_start\nprior = listens[listens.timestamp < w_start]\nprior_user_items = set(zip(prior.uid, prior.item_id))\n\n# Likes in target window\ntarget_likes = likes[(likes.timestamp >= w_start) & (likes.timestamp < w_end) & (likes.is_organic == 1)]\ntarget_listens = listens[(listens.timestamp >= w_start) & (listens.timestamp < w_end) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)]\n\n# Combine into gold targets\ntargets_dict = {}\nfor uid, item in zip(target_listens.uid, target_listens.item_id):\n if (uid, item) not in prior_user_items:\n targets_dict[(uid, item)] = 1\n\nfor uid, item in zip(target_likes.uid, target_likes.item_id):\n if (uid, item) not in prior_user_items:\n targets_dict[(uid, item)] = 3\n\nprint('Target pairs for week -1:', len(targets_dict))\ngrades = list(targets_dict.values())\nprint('Grade 3 count:', grades.count(3), 'Grade 1 count:', grades.count(1))\n\"",
"description": "Verify target pair extraction for week -1"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_53936",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Data loaded in 4.08s\nTarget pairs for week -1: 45238\nGrade 3 count: 8639 Grade 1 count: 36599\nProcess Group PGID: 2423\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:00:42.579Z",
"resultDisplay": "Data loaded in 4.08s\nTarget pairs for week -1: 45238\nGrade 3 count: 8639 Grade 1 count: 36599",
"description": "Verify target pair extraction for week -1",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:00:42.580Z",
"content": "\nOutput: Data loaded in 4.08s\nTarget pairs for week -1: 45238\nGrade 3 count: 8639 Grade 1 count: 36599\nProcess Group PGID: 2423\n",
"isError": false
},
"responseMessageId": "c827269a-345e-40bd-947a-fa901dd25c47"
}
]
},
{
"id": 46,
"role": "agent",
"timestamp": "2026-09-29T06:00:53.777Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:00:53.777Z",
"text": "Analyzing Prior Interactions\n\nI'm currently dissecting user interactions preceding `w_start`. My focus is on extracting `user_listens` (considering the last 60-90 days before `w_start`), `user_likes`, and `user_dislikes` to build a comprehensive profile of their engagement."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:00:53.777Z",
"text": "Incorporating Time-Sensitive Data\n\nI'm refining the input by considering `bag_recent_listens` to capture immediate preferences and `bag_strong_listens` for highly engaged listening sessions. This allows for a more nuanced representation of current user interests beyond historical data."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:00:53.777Z",
"text": "Integrating Item and User Embeddings\n\nI'm now focusing on the Item Tower's architecture, detailing its item embeddings and dense features. A key consideration is whether the item embeddings used in my User Tower history pooling can be the same embedding table. This integration would streamline processing and ensure consistency across both towers."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:00:53.777Z",
"text": "Sharing Embedding Tables\n\nI'm exploring the impact of sharing the base item embedding table $E$ between the Item Tower and User Tower. This approach could streamline model architecture and ensure feature consistency, though I'm carefully considering the implications for each tower's distinct trainable components and the User Tower's reliance on history features rather than user IDs."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:00:53.777Z",
"callId": "run_shell_command__call_229564",
"name": "run_shell_command",
"input": {
"description": "Check PyTorch environment",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\nimport torch, torch.nn as nn, torch.nn.functional as F\nfrom pathlib import Path\n\ntorch.set_num_threads(8)\n\n# Check imports\nprint('PyTorch version:', torch.__version__)\nprint('CUDA available:', torch.cuda.is_available())\n\""
},
"recorded": {
"id": "run_shell_command__call_229564",
"name": "run_shell_command",
"args": {
"description": "Check PyTorch environment",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\nimport torch, torch.nn as nn, torch.nn.functional as F\nfrom pathlib import Path\n\ntorch.set_num_threads(8)\n\n# Check imports\nprint('PyTorch version:', torch.__version__)\nprint('CUDA available:', torch.cuda.is_available())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_229564",
"name": "run_shell_command",
"response": {
"output": "\nOutput: PyTorch version: 2.8.0+cpu\nCUDA available: False\nProcess Group PGID: 2445\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:00:57.749Z",
"resultDisplay": "PyTorch version: 2.8.0+cpu\nCUDA available: False",
"description": "Check PyTorch environment",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:00:59.616Z",
"content": "\nOutput: PyTorch version: 2.8.0+cpu\nCUDA available: False\nProcess Group PGID: 2445\n",
"isError": false
},
"responseMessageId": "bddc096b-bf6a-42c4-bfc4-0cfd3c99cb1f"
}
]
},
{
"id": 47,
"role": "agent",
"timestamp": "2026-09-29T06:01:07.721Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:01:07.721Z",
"text": "Developing Prototype Script\n\nI'm focusing on constructing the prototype script, specifically defining the necessary components for `test_prototype.py`. The immediate priority is establishing the feature extraction function, which will take a dataframe of listener data as input."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:01:07.721Z",
"callId": "run_shell_command__call_103420",
"name": "run_shell_command",
"input": {
"description": "Test preparing listens with vectorized mapping",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\n\n# Let's test vectorized extraction for validation cutoff\ncutoff = 24786800\nt0 = time.time()\n\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400)])\nlistens = table.to_pandas()\nlistens = listens[listens.item_id.isin(item_to_idx)]\nlistens['item_idx'] = listens.item_id.map(item_to_idx)\nlistens['days_ago'] = (cutoff - listens.timestamp) / 86400.0\n\nprint(f'Listens prepared in {time.time() - t0:.2f}s, rows: {len(listens)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_103420",
"name": "run_shell_command",
"args": {
"description": "Test preparing listens with vectorized mapping",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\n\n# Let's test vectorized extraction for validation cutoff\ncutoff = 24786800\nt0 = time.time()\n\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400)])\nlistens = table.to_pandas()\nlistens = listens[listens.item_id.isin(item_to_idx)]\nlistens['item_idx'] = listens.item_id.map(item_to_idx)\nlistens['days_ago'] = (cutoff - listens.timestamp) / 86400.0\n\nprint(f'Listens prepared in {time.time() - t0:.2f}s, rows: {len(listens)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_103420",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Listens prepared in 1.88s, rows: 12117965\nProcess Group PGID: 2463\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:01:11.223Z",
"resultDisplay": "Listens prepared in 1.88s, rows: 12117965",
"description": "Test preparing listens with vectorized mapping",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:01:11.223Z",
"content": "\nOutput: Listens prepared in 1.88s, rows: 12117965\nProcess Group PGID: 2463\n",
"isError": false
},
"responseMessageId": "182e7300-e654-42d7-bf2e-47335357da78"
}
]
},
{
"id": 48,
"role": "agent",
"timestamp": "2026-09-29T06:01:19.183Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:01:19.182Z",
"text": "Processing Data Speedily\n\nThe recent performance is astonishing; 12.1 million rows processed in under two seconds. My next focus is on optimizing the aggregation of `listens` per user, specifically by grouping `(uid, item_idx)` to efficiently sum their associated weights."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:01:19.183Z",
"callId": "run_shell_command__call_560638",
"name": "run_shell_command",
"input": {
"description": "Benchmark multi-weight aggregation in pandas",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\ncutoff = 24786800\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400)])\ndf = table.to_pandas()\ndf = df[df.item_id.isin(item_to_idx)]\ndf['item_idx'] = df.item_id.map(item_to_idx)\ndf['days_ago'] = (cutoff - df.timestamp) / 86400.0\n\n# 1. Recent weight (last 14 days)\ndf['w_recent'] = np.where(df.days_ago <= 14, np.exp(-0.1 * df.days_ago) * (1.0 + df.is_organic) * (df.played_ratio_pct / 100.0), 0.0)\n\n# 2. General history weight (90 days)\ndf['w_history'] = np.exp(-0.02 * df.days_ago) * (1.0 + df.is_organic) * (df.played_ratio_pct / 100.0)\n\n# 3. Strong organic (played_ratio >= 80 & is_organic == 1)\ndf['w_strong'] = np.where((df.played_ratio_pct >= 80) & (df.is_organic == 1), np.exp(-0.05 * df.days_ago), 0.0)\n\nprint(f'Computed weights in {time.time() - t0:.2f}s')\n\n# Groupby uid, item_idx\nt1 = time.time()\nagg = df.groupby(['uid', 'item_idx'])[['w_recent', 'w_history', 'w_strong']].sum().reset_index()\nprint(f'Aggregated in {time.time() - t1:.2f}s, unique pairs: {len(agg)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_560638",
"name": "run_shell_command",
"args": {
"description": "Benchmark multi-weight aggregation in pandas",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\ncutoff = 24786800\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400)])\ndf = table.to_pandas()\ndf = df[df.item_id.isin(item_to_idx)]\ndf['item_idx'] = df.item_id.map(item_to_idx)\ndf['days_ago'] = (cutoff - df.timestamp) / 86400.0\n\n# 1. Recent weight (last 14 days)\ndf['w_recent'] = np.where(df.days_ago <= 14, np.exp(-0.1 * df.days_ago) * (1.0 + df.is_organic) * (df.played_ratio_pct / 100.0), 0.0)\n\n# 2. General history weight (90 days)\ndf['w_history'] = np.exp(-0.02 * df.days_ago) * (1.0 + df.is_organic) * (df.played_ratio_pct / 100.0)\n\n# 3. Strong organic (played_ratio >= 80 & is_organic == 1)\ndf['w_strong'] = np.where((df.played_ratio_pct >= 80) & (df.is_organic == 1), np.exp(-0.05 * df.days_ago), 0.0)\n\nprint(f'Computed weights in {time.time() - t0:.2f}s')\n\n# Groupby uid, item_idx\nt1 = time.time()\nagg = df.groupby(['uid', 'item_idx'])[['w_recent', 'w_history', 'w_strong']].sum().reset_index()\nprint(f'Aggregated in {time.time() - t1:.2f}s, unique pairs: {len(agg)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_560638",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Computed weights in 2.29s\nAggregated in 1.51s, unique pairs: 4321941\nProcess Group PGID: 2491\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:01:24.607Z",
"resultDisplay": "Computed weights in 2.29s\nAggregated in 1.51s, unique pairs: 4321941",
"description": "Benchmark multi-weight aggregation in pandas",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:01:24.607Z",
"content": "\nOutput: Computed weights in 2.29s\nAggregated in 1.51s, unique pairs: 4321941\nProcess Group PGID: 2491\n",
"isError": false
},
"responseMessageId": "1c17651a-17f2-4bac-b7f0-ff9eab60f03e"
}
]
},
{
"id": 49,
"role": "agent",
"timestamp": "2026-09-29T06:01:34.873Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:01:34.873Z",
"text": "Processing Data Rapidly\n\nI'm pleased to report significant progress. Feature extraction for 12 million listens is now consistently under 4 seconds. Even processing the likes and dislikes datasets, with their considerably smaller row counts, is remarkably swift, taking less than a tenth of a second. This efficiency is exceeding expectations."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:01:34.873Z",
"text": "Structuring User Embeddings\n\nI'm now focusing on how to effectively feed user interaction data into PyTorch's `nn.EmbeddingBag`. Specifically, I'm examining the structure of recent, history, likes, and dislikes, along with their associated weights. A key consideration is how `EmbeddingBag` handles users with no interactions in a particular category, like dislikes."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:01:34.873Z",
"callId": "run_shell_command__call_291619",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport torch, torch.nn as nn\nemb_bag = nn.EmbeddingBag(10, 4, mode='sum')\nindices = torch.tensor([0, 1, 2], dtype=torch.long)\noffsets = torch.tensor([0, 2, 2, 3], dtype=torch.long) # User 0: [0, 1], User 1: empty, User 2: [2]\nweights = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float)\nout = emb_bag(indices, offsets, per_sample_weights=weights)\nprint('Output shape:', out.shape)\nprint('Row 0:', out[0])\nprint('Row 1 (empty):', out[1])\nprint('Row 2:', out[2])\n\"",
"description": "Verify PyTorch EmbeddingBag empty offset handling"
},
"recorded": {
"id": "run_shell_command__call_291619",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport torch, torch.nn as nn\nemb_bag = nn.EmbeddingBag(10, 4, mode='sum')\nindices = torch.tensor([0, 1, 2], dtype=torch.long)\noffsets = torch.tensor([0, 2, 2, 3], dtype=torch.long) # User 0: [0, 1], User 1: empty, User 2: [2]\nweights = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float)\nout = emb_bag(indices, offsets, per_sample_weights=weights)\nprint('Output shape:', out.shape)\nprint('Row 0:', out[0])\nprint('Row 1 (empty):', out[1])\nprint('Row 2:', out[2])\n\"",
"description": "Verify PyTorch EmbeddingBag empty offset handling"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_291619",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Output shape: torch.Size([4, 4])\nRow 0: tensor([-4.0461, -2.2352, -2.8407, -1.9140], grad_fn=)\nRow 1 (empty): tensor([0., 0., 0., 0.], grad_fn=)\nRow 2: tensor([ 1.7677, -1.5779, 0.4143, 2.0241], grad_fn=)\nProcess Group PGID: 2513\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:01:37.820Z",
"resultDisplay": "Output shape: torch.Size([4, 4])\nRow 0: tensor([-4.0461, -2.2352, -2.8407, -1.9140], grad_fn=)\nRow 1 (empty): tensor([0., 0., 0., 0.], grad_fn=)\nRow 2: tensor([ 1.7677, -1.5779, 0.4143, 2.0241], grad_fn=)",
"description": "Verify PyTorch EmbeddingBag empty offset handling",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:01:37.821Z",
"content": "\nOutput: Output shape: torch.Size([4, 4])\nRow 0: tensor([-4.0461, -2.2352, -2.8407, -1.9140], grad_fn=)\nRow 1 (empty): tensor([0., 0., 0., 0.], grad_fn=)\nRow 2: tensor([ 1.7677, -1.5779, 0.4143, 2.0241], grad_fn=)\nProcess Group PGID: 2513\n",
"isError": false
},
"responseMessageId": "3061f546-fbbe-48f9-b044-d43dfc6bd7a3"
}
]
},
{
"id": 50,
"role": "agent",
"timestamp": "2026-09-29T06:07:31.933Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:07:31.933Z",
"text": "Exploring EmbeddingBag Logic\n\nI'm currently analyzing `EmbeddingBag`'s behavior with empty slices, noting its all-zero output. I've observed how `offsets` with four elements generate four rows, even when the last row is empty, and I'm verifying this."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:07:31.933Z",
"callId": "run_shell_command__call_949851",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport torch, torch.nn as nn\nemb_bag = nn.EmbeddingBag(10, 4, mode='sum')\nindices = torch.tensor([0, 1, 2], dtype=torch.long)\noffsets = torch.tensor([0, 2, 2], dtype=torch.long) # User 0: [0, 1], User 1: empty, User 2: [2]\nweights = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float)\nout = emb_bag(indices, offsets, per_sample_weights=weights)\nprint('Output shape:', out.shape)\nprint('Row 0:', out[0])\nprint('Row 1 (empty):', out[1])\nprint('Row 2:', out[2])\n\"",
"description": "Verify EmbeddingBag with 3 offsets"
},
"recorded": {
"id": "run_shell_command__call_949851",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport torch, torch.nn as nn\nemb_bag = nn.EmbeddingBag(10, 4, mode='sum')\nindices = torch.tensor([0, 1, 2], dtype=torch.long)\noffsets = torch.tensor([0, 2, 2], dtype=torch.long) # User 0: [0, 1], User 1: empty, User 2: [2]\nweights = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float)\nout = emb_bag(indices, offsets, per_sample_weights=weights)\nprint('Output shape:', out.shape)\nprint('Row 0:', out[0])\nprint('Row 1 (empty):', out[1])\nprint('Row 2:', out[2])\n\"",
"description": "Verify EmbeddingBag with 3 offsets"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_949851",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Output shape: torch.Size([3, 4])\nRow 0: tensor([ 1.6807, -2.1469, 1.1582, 1.8539], grad_fn=)\nRow 1 (empty): tensor([0., 0., 0., 0.], grad_fn=)\nRow 2: tensor([-1.9523, -0.4724, 1.8892, 0.2620], grad_fn=)\nProcess Group PGID: 2556\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:07:34.859Z",
"resultDisplay": "Output shape: torch.Size([3, 4])\nRow 0: tensor([ 1.6807, -2.1469, 1.1582, 1.8539], grad_fn=)\nRow 1 (empty): tensor([0., 0., 0., 0.], grad_fn=)\nRow 2: tensor([-1.9523, -0.4724, 1.8892, 0.2620], grad_fn=)",
"description": "Verify EmbeddingBag with 3 offsets",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:07:34.861Z",
"content": "\nOutput: Output shape: torch.Size([3, 4])\nRow 0: tensor([ 1.6807, -2.1469, 1.1582, 1.8539], grad_fn=)\nRow 1 (empty): tensor([0., 0., 0., 0.], grad_fn=)\nRow 2: tensor([-1.9523, -0.4724, 1.8892, 0.2620], grad_fn=)\nProcess Group PGID: 2556\n",
"isError": false
},
"responseMessageId": "d0c9677e-9c88-496a-8c5a-66691b7ee29d"
}
]
},
{
"id": 51,
"role": "agent",
"timestamp": "2026-09-29T06:08:01.250Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:08:01.250Z",
"text": "Exploring EmbeddingBag Sum Mode\n\nI'm currently analyzing the `EmbeddingBag(mode='sum')` functionality. My initial tests with `offsets` confirm the row generation, but I'm now considering the implications of normalization. The inherent sum aggregation will likely lead to significant value disparities between users with vastly different item counts, and I'm exploring strategies to address this bias."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:08:01.250Z",
"text": "Normalizing Pooled Embeddings\n\nI'm now focusing on normalization strategies for the aggregated embeddings. I'm leaning towards applying L2 normalization to each user's pooled view vector to ensure invariance to the number of items. This approach, combined with passing normalized activity counts as dense features for users and items, should effectively mitigate bias and enrich the representations. I'll proceed with Python implementation to test these ideas."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:08:01.250Z",
"text": "Designing User Embeddings\n\nI'm now detailing the architecture for both item and user towers. For items, I'm combining their IDs with dense features and an MLP, ensuring normalized output vectors. The user tower will concatenate normalized pooled embeddings from various listening contexts with user statistics, preparing them for further processing."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:08:01.250Z",
"text": "Validating Training Setup\n\nI'm now designing a self-contained training script to validate the model's trainability. I'm structuring the training data into four distinct slices, each focusing on recent engagement within specific timeframes and incorporating organic likes and strong listens. This approach aims to generate approximately 180,000 training pairs, ensuring each pair includes a target user ID and a positive item index."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:08:01.250Z",
"callId": "run_shell_command__call_244779",
"name": "run_shell_command",
"input": {
"description": "Benchmark slice target extraction",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\n\ncutoff = 24786800\nweek = 7 * 86400\n\nt0 = time.time()\n# Load all likes and listens once\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_to_idx)].copy()\nlikes['item_idx'] = likes.item_id.map(item_to_idx)\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ndislikes = dislikes[dislikes.item_id.isin(item_to_idx)].copy()\ndislikes['item_idx'] = dislikes.item_id.map(item_to_idx)\n\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', cutoff - 120 * 86400)])\nlistens = table.to_pandas()\nlistens = listens[listens.item_id.isin(item_to_idx)].copy()\nlistens['item_idx'] = listens.item_id.map(item_to_idx)\n\nprint(f'Initial load in {time.time() - t0:.2f}s')\n\nfor w in range(1, 3):\n t_w = cutoff - w * week\n t1 = time.time()\n \n # 1. Targets in [t_w, t_w + week]\n # Prior listens before t_w\n prior_listens = listens[listens.timestamp < t_w]\n prior_user_items = set(zip(prior_listens.uid, prior_listens.item_idx))\n \n # Target window\n win_listens = listens[(listens.timestamp >= t_w) & (listens.timestamp < t_w + week)]\n win_likes = likes[(likes.timestamp >= t_w) & (likes.timestamp < t_w + week)]\n \n targets = {}\n # Grade 1\n w_strong = win_listens[(win_listens.is_organic == 1) & (win_listens.played_ratio_pct >= 80)]\n for u, i in zip(w_strong.uid, w_strong.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n # Grade 3\n w_likes = win_likes[win_likes.is_organic == 1]\n for u, i in zip(w_likes.uid, w_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n \n print(f'Slice {w} (cutoff={t_w}): targets={len(targets)} (extracted in {time.time() - t1:.2f}s)')\n\""
},
"recorded": {
"id": "run_shell_command__call_244779",
"name": "run_shell_command",
"args": {
"description": "Benchmark slice target extraction",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\n\ncutoff = 24786800\nweek = 7 * 86400\n\nt0 = time.time()\n# Load all likes and listens once\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_to_idx)].copy()\nlikes['item_idx'] = likes.item_id.map(item_to_idx)\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ndislikes = dislikes[dislikes.item_id.isin(item_to_idx)].copy()\ndislikes['item_idx'] = dislikes.item_id.map(item_to_idx)\n\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', cutoff - 120 * 86400)])\nlistens = table.to_pandas()\nlistens = listens[listens.item_id.isin(item_to_idx)].copy()\nlistens['item_idx'] = listens.item_id.map(item_to_idx)\n\nprint(f'Initial load in {time.time() - t0:.2f}s')\n\nfor w in range(1, 3):\n t_w = cutoff - w * week\n t1 = time.time()\n \n # 1. Targets in [t_w, t_w + week]\n # Prior listens before t_w\n prior_listens = listens[listens.timestamp < t_w]\n prior_user_items = set(zip(prior_listens.uid, prior_listens.item_idx))\n \n # Target window\n win_listens = listens[(listens.timestamp >= t_w) & (listens.timestamp < t_w + week)]\n win_likes = likes[(likes.timestamp >= t_w) & (likes.timestamp < t_w + week)]\n \n targets = {}\n # Grade 1\n w_strong = win_listens[(win_listens.is_organic == 1) & (win_listens.played_ratio_pct >= 80)]\n for u, i in zip(w_strong.uid, w_strong.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n # Grade 3\n w_likes = win_likes[win_likes.is_organic == 1]\n for u, i in zip(w_likes.uid, w_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n \n print(f'Slice {w} (cutoff={t_w}): targets={len(targets)} (extracted in {time.time() - t1:.2f}s)')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_244779",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Initial load in 2.45s\nSlice 1 (cutoff=24182000): targets=51814 (extracted in 4.97s)\nSlice 2 (cutoff=23577200): targets=49902 (extracted in 6.05s)\nProcess Group PGID: 2575\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:08:17.984Z",
"resultDisplay": "Initial load in 2.45s\nSlice 1 (cutoff=24182000): targets=51814 (extracted in 4.97s)\nSlice 2 (cutoff=23577200): targets=49902 (extracted in 6.05s)",
"description": "Benchmark slice target extraction",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:08:17.985Z",
"content": "\nOutput: Initial load in 2.45s\nSlice 1 (cutoff=24182000): targets=51814 (extracted in 4.97s)\nSlice 2 (cutoff=23577200): targets=49902 (extracted in 6.05s)\nProcess Group PGID: 2575\n",
"isError": false
},
"responseMessageId": "99bdcb78-d6bc-4ce7-a724-fc421e4d1c51"
}
]
},
{
"id": 52,
"role": "agent",
"timestamp": "2026-09-29T06:10:37.101Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:10:37.101Z",
"text": "Analyzing Extraction Speeds\n\nThe initial analysis of target extraction shows promising speeds, with over 51,000 targets processed in under 5 seconds for the first slice. I'm now moving to examine user history representation to understand its impact."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:10:37.101Z",
"text": "Exploring User History Efficiency\n\nI'm considering precomputing user representations at a given cutoff. The initial estimate shows that processing ~4,000 active users' history features into pooled tensors or EmbeddingBag inputs is feasible, even with large numbers of historical items per user. This seems efficient."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:10:37.101Z",
"callId": "run_shell_command__call_47590",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\ncutoff = 24786800\n\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', cutoff - 14 * 86400)])\ndf14 = table.to_pandas()\ndf14 = df14[df14.item_id.isin(item_to_idx)]\nprint('Distinct items per user in last 14 days:')\nprint(df14.groupby('uid')['item_id'].nunique().describe())\n\"",
"description": "Check distinct items per user in last 14 days"
},
"recorded": {
"id": "run_shell_command__call_47590",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\ncutoff = 24786800\n\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', cutoff - 14 * 86400)])\ndf14 = table.to_pandas()\ndf14 = df14[df14.item_id.isin(item_to_idx)]\nprint('Distinct items per user in last 14 days:')\nprint(df14.groupby('uid')['item_id'].nunique().describe())\n\"",
"description": "Check distinct items per user in last 14 days"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_47590",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Distinct items per user in last 14 days:\ncount 8053.000000\nmean 154.685459\nstd 156.381635\nmin 1.000000\n25% 38.000000\n50% 105.000000\n75% 222.000000\nmax 1626.000000\nName: item_id, dtype: float64\nProcess Group PGID: 2609\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:10:39.311Z",
"resultDisplay": "Distinct items per user in last 14 days:\ncount 8053.000000\nmean 154.685459\nstd 156.381635\nmin 1.000000\n25% 38.000000\n50% 105.000000\n75% 222.000000\nmax 1626.000000\nName: item_id, dtype: float64",
"description": "Check distinct items per user in last 14 days",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:10:39.312Z",
"content": "\nOutput: Distinct items per user in last 14 days:\ncount 8053.000000\nmean 154.685459\nstd 156.381635\nmin 1.000000\n25% 38.000000\n50% 105.000000\n75% 222.000000\nmax 1626.000000\nName: item_id, dtype: float64\nProcess Group PGID: 2609\n",
"isError": false
},
"responseMessageId": "abe4b168-2c25-4b95-97d3-7a3cad1a05a7"
}
]
},
{
"id": 53,
"role": "agent",
"timestamp": "2026-09-29T06:10:54.069Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:10:54.069Z",
"text": "Exploring Item Representation\n\nI'm currently considering how best to represent individual items for recommendation, specifically within the music domain. My focus is on whether co-occurrence patterns between tracks can offer valuable insights beyond simple listening frequency metrics observed over the past fortnight."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:10:54.069Z",
"text": "Building Item Embeddings\n\nMy recent thoughts confirm that items listened to together often share musical affinity. I'm exploring computing an item-item co-occurrence matrix, which, despite its potential sparsity, seems manageable. I'm now evaluating how to derive embeddings from this matrix, specifically considering truncated SVD on PPMI and ensuring it aligns with scaling constraints by avoiding a global user-item matrix."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:10:54.069Z",
"text": "Training Neural Networks Directly\n\nI'm now focusing on training PyTorch neural networks directly for both user and item towers, rather than solely relying on matrix factorization. The plan is to explore contrastive learning, specifically InfoNCE, to learn embeddings from interaction history, avoiding any global user-item matrix construction."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:10:54.069Z",
"text": "Training Embeddings End-to-End\n\nMy latest thinking centers on directly training neural network towers with contrastive learning using InfoNCE, inspired by SimCLR and DSSM. I'm initiating embeddings randomly and plan to empirically validate this approach by developing a Python prototype script to evaluate performance on the validation set."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:10:54.069Z",
"text": "Designing User Embeddings\n\nI'm now refining the user embedding architecture, incorporating historical, liked, and disliked item averages, alongside specific user statistics. This combined representation will then pass through an MLP and be normalized, ready for end-to-end training with contrastive loss."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:10:54.069Z",
"callId": "run_shell_command__call_808015",
"name": "run_shell_command",
"input": {
"description": "Verify data loading for prototype",
"command": "python3 -c \"\nimport sys, os, time\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\ntorch.set_num_threads(8)\n\n# 1. Load catalog & target users\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = targets_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {uid: idx for idx, uid in enumerate(target_uids)}\n\nprint(f'Catalog: {num_items} items, Targets: {len(target_uids)} users')\n\""
},
"recorded": {
"id": "run_shell_command__call_808015",
"name": "run_shell_command",
"args": {
"description": "Verify data loading for prototype",
"command": "python3 -c \"\nimport sys, os, time\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\ntorch.set_num_threads(8)\n\n# 1. Load catalog & target users\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = targets_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {uid: idx for idx, uid in enumerate(target_uids)}\n\nprint(f'Catalog: {num_items} items, Targets: {len(target_uids)} users')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_808015",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog: 30000 items, Targets: 8456 users\nProcess Group PGID: 2637\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:10:58.062Z",
"resultDisplay": "Catalog: 30000 items, Targets: 8456 users",
"description": "Verify data loading for prototype",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:10:58.063Z",
"content": "\nOutput: Catalog: 30000 items, Targets: 8456 users\nProcess Group PGID: 2637\n",
"isError": false
},
"responseMessageId": "49735ee3-0228-43c3-a152-0e63e86180e3"
}
]
},
{
"id": 54,
"role": "agent",
"timestamp": "2026-09-29T06:11:29.956Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:11:29.956Z",
"text": "Analyzing User Engagement Metrics\n\nI'm currently digging into user engagement, specifically focusing on organic positive listens and how they correlate with popularity rank. My computations for total plays, likes, and dislikes from the training data are finalized. I'm also in the process of extracting key user features, such as recent and historical listening habits, along with like data, for validation at cutoff T."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:11:29.956Z",
"callId": "write_file__call_122895",
"name": "write_file",
"input": {
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\nprint(\"Computing item features...\")\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\n# Standardize\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\n# Load listens in the last 120 days before validation cutoff\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n \"\"\"Extract multi-view history features for a set of users at cutoff_time.\"\"\"\n u_set = set(user_ids)\n\n # Filter listens before cutoff_time in last 90 days\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n # Recent (last 14 days)\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n # General history (90 days)\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n # Strong organic\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n # Group by uid, item_idx\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n # Likes and dislikes\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n # User stats\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n # Build bag structures\n recent_indices, recent_offsets, recent_weights = [], [0], []\n history_indices, history_offsets, history_weights = [], [0], []\n strong_indices, strong_offsets, strong_weights = [], [0], []\n like_indices, like_offsets = [], [0]\n dislike_indices, dislike_offsets = [], [0]\n stats_list = []\n\n agg_by_user = {}\n for uid, g in agg.groupby(\"uid\"):\n agg_by_user[uid] = g\n\n for uid in user_ids:\n # Stats\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_list.append([\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ])\n\n if uid in agg_by_user:\n g = agg_by_user[uid]\n # Recent\n mask_r = g.w_recent > 0\n if mask_r.any():\n recent_indices.extend(g.item_idx[mask_r].tolist())\n recent_weights.extend(g.w_recent[mask_r].tolist())\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n # Strong\n mask_s = g.w_strong > 0\n if mask_s.any():\n strong_indices.extend(g.item_idx[mask_s].tolist())\n strong_weights.extend(g.w_strong[mask_s].tolist())\n\n recent_offsets.append(len(recent_indices))\n history_offsets.append(len(history_indices))\n strong_offsets.append(len(strong_indices))\n\n # Likes\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_offsets.append(len(like_indices))\n\n # Dislikes\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_offsets.append(len(dislike_indices))\n\n features = {\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_offsets\": torch.tensor(recent_offsets[:-1], dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_offsets\": torch.tensor(history_offsets[:-1], dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_offsets\": torch.tensor(strong_offsets[:-1], dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_offsets\": torch.tensor(like_offsets[:-1], dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_offsets\": torch.tensor(dislike_offsets[:-1], dtype=torch.long),\n \"user_stats\": torch.tensor(stats_list, dtype=torch.float32),\n }\n return features\n\n\nprint(\"Extracting validation user features...\")\nval_user_features = build_user_features(CUTOFF_VAL, target_uids)\nprint(\"Validation features ready.\")\n\n\ndef extract_slice_training_data(cutoff_time):\n \"\"\"Extract target positive items for users in [cutoff_time, cutoff_time + WEEK_SECONDS].\"\"\"\n w_end = cutoff_time + WEEK_SECONDS\n # Prior listens\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n # Target window\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n # Filter to active users who have at least one target\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = list(user_positives.keys())\n print(f\"Slice cutoff {cutoff_time}: {len(unique_users)} users with {len(targets)} targets\")\n\n # Extract user features for these users at this cutoff\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slice 1 (Week -1)...\")\nslice1_feats, slice1_pairs = extract_slice_training_data(CUTOFF_VAL - WEEK_SECONDS)\nprint(\"Extracting training slice 2 (Week -2)...\")\nslice2_feats, slice2_pairs = extract_slice_training_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.xavier_normal_(self.item_emb.weight)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n emb = self.item_emb(item_ids)\n dense_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([emb, dense_p], dim=-1)\n v = self.mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim # 4 views + dislikes + stats_proj\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool_bag(self, indices, offsets, weights=None):\n if len(indices) == 0:\n return torch.zeros((len(offsets), self.shared_emb.embedding_dim), dtype=torch.float32)\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n # Sum within slices\n # Vectorized sum using torch.scatter_add_\n batch_size = len(offsets)\n counts = torch.diff(torch.cat([offsets, torch.tensor([len(indices)])]))\n user_ids = torch.repeat_interleave(torch.arange(batch_size), counts)\n out = torch.zeros((batch_size, self.shared_emb.embedding_dim), dtype=torch.float32)\n out.index_add_(0, user_ids, embs)\n # Normalize non-zero rows\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def forward_batch(self, user_indices, feats):\n # Slice features for user_indices\n # For efficiency, user_indices indexes into precomputed view matrices\n pass\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n h_rec = self._pool_bag(feats[\"recent_indices\"], feats[\"recent_offsets\"], feats[\"recent_weights\"])\n h_his = self._pool_bag(feats[\"history_indices\"], feats[\"history_offsets\"], feats[\"history_weights\"])\n h_str = self._pool_bag(feats[\"strong_indices\"], feats[\"strong_offsets\"], feats[\"strong_weights\"])\n h_lik = self._pool_bag(feats[\"like_indices\"], feats[\"like_offsets\"])\n h_dis = self._pool_bag(feats[\"dislike_indices\"], feats[\"dislike_offsets\"])\n s_proj = self.stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_proj], dim=-1)\n u = self.mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n\nembed_dim = 64\nout_dim = 64\nitem_tower = ItemTower(num_items, embed_dim, 4, out_dim)\nuser_tower = UserTower(embed_dim, 5, out_dim, item_tower.item_emb)\n\nprint(\"Model architecture initialized.\")\n",
"instruction": "Write train_prototype.py with feature extraction, data loading, and model definition",
"file_path": "/app/solution/train_prototype.py"
},
"recorded": {
"id": "write_file__call_122895",
"name": "write_file",
"args": {
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\nprint(\"Computing item features...\")\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\n# Standardize\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\n# Load listens in the last 120 days before validation cutoff\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n \"\"\"Extract multi-view history features for a set of users at cutoff_time.\"\"\"\n u_set = set(user_ids)\n\n # Filter listens before cutoff_time in last 90 days\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n # Recent (last 14 days)\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n # General history (90 days)\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n # Strong organic\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n # Group by uid, item_idx\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n # Likes and dislikes\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n # User stats\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n # Build bag structures\n recent_indices, recent_offsets, recent_weights = [], [0], []\n history_indices, history_offsets, history_weights = [], [0], []\n strong_indices, strong_offsets, strong_weights = [], [0], []\n like_indices, like_offsets = [], [0]\n dislike_indices, dislike_offsets = [], [0]\n stats_list = []\n\n agg_by_user = {}\n for uid, g in agg.groupby(\"uid\"):\n agg_by_user[uid] = g\n\n for uid in user_ids:\n # Stats\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_list.append([\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ])\n\n if uid in agg_by_user:\n g = agg_by_user[uid]\n # Recent\n mask_r = g.w_recent > 0\n if mask_r.any():\n recent_indices.extend(g.item_idx[mask_r].tolist())\n recent_weights.extend(g.w_recent[mask_r].tolist())\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n # Strong\n mask_s = g.w_strong > 0\n if mask_s.any():\n strong_indices.extend(g.item_idx[mask_s].tolist())\n strong_weights.extend(g.w_strong[mask_s].tolist())\n\n recent_offsets.append(len(recent_indices))\n history_offsets.append(len(history_indices))\n strong_offsets.append(len(strong_indices))\n\n # Likes\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_offsets.append(len(like_indices))\n\n # Dislikes\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_offsets.append(len(dislike_indices))\n\n features = {\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_offsets\": torch.tensor(recent_offsets[:-1], dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_offsets\": torch.tensor(history_offsets[:-1], dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_offsets\": torch.tensor(strong_offsets[:-1], dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_offsets\": torch.tensor(like_offsets[:-1], dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_offsets\": torch.tensor(dislike_offsets[:-1], dtype=torch.long),\n \"user_stats\": torch.tensor(stats_list, dtype=torch.float32),\n }\n return features\n\n\nprint(\"Extracting validation user features...\")\nval_user_features = build_user_features(CUTOFF_VAL, target_uids)\nprint(\"Validation features ready.\")\n\n\ndef extract_slice_training_data(cutoff_time):\n \"\"\"Extract target positive items for users in [cutoff_time, cutoff_time + WEEK_SECONDS].\"\"\"\n w_end = cutoff_time + WEEK_SECONDS\n # Prior listens\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n # Target window\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n # Filter to active users who have at least one target\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = list(user_positives.keys())\n print(f\"Slice cutoff {cutoff_time}: {len(unique_users)} users with {len(targets)} targets\")\n\n # Extract user features for these users at this cutoff\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slice 1 (Week -1)...\")\nslice1_feats, slice1_pairs = extract_slice_training_data(CUTOFF_VAL - WEEK_SECONDS)\nprint(\"Extracting training slice 2 (Week -2)...\")\nslice2_feats, slice2_pairs = extract_slice_training_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.xavier_normal_(self.item_emb.weight)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n emb = self.item_emb(item_ids)\n dense_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([emb, dense_p], dim=-1)\n v = self.mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim # 4 views + dislikes + stats_proj\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool_bag(self, indices, offsets, weights=None):\n if len(indices) == 0:\n return torch.zeros((len(offsets), self.shared_emb.embedding_dim), dtype=torch.float32)\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n # Sum within slices\n # Vectorized sum using torch.scatter_add_\n batch_size = len(offsets)\n counts = torch.diff(torch.cat([offsets, torch.tensor([len(indices)])]))\n user_ids = torch.repeat_interleave(torch.arange(batch_size), counts)\n out = torch.zeros((batch_size, self.shared_emb.embedding_dim), dtype=torch.float32)\n out.index_add_(0, user_ids, embs)\n # Normalize non-zero rows\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def forward_batch(self, user_indices, feats):\n # Slice features for user_indices\n # For efficiency, user_indices indexes into precomputed view matrices\n pass\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n h_rec = self._pool_bag(feats[\"recent_indices\"], feats[\"recent_offsets\"], feats[\"recent_weights\"])\n h_his = self._pool_bag(feats[\"history_indices\"], feats[\"history_offsets\"], feats[\"history_weights\"])\n h_str = self._pool_bag(feats[\"strong_indices\"], feats[\"strong_offsets\"], feats[\"strong_weights\"])\n h_lik = self._pool_bag(feats[\"like_indices\"], feats[\"like_offsets\"])\n h_dis = self._pool_bag(feats[\"dislike_indices\"], feats[\"dislike_offsets\"])\n s_proj = self.stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_proj], dim=-1)\n u = self.mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n\nembed_dim = 64\nout_dim = 64\nitem_tower = ItemTower(num_items, embed_dim, 4, out_dim)\nuser_tower = UserTower(embed_dim, 5, out_dim, item_tower.item_emb)\n\nprint(\"Model architecture initialized.\")\n",
"instruction": "Write train_prototype.py with feature extraction, data loading, and model definition",
"file_path": "/app/solution/train_prototype.py"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_122895",
"name": "write_file",
"response": {
"output": "Successfully created and wrote to new file: /app/solution/train_prototype.py. Here is the updated code:\nimport os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\nprint(\"Computing item features...\")\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\n# Standardize\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\n# Load listens in the last 120 days before validation cutoff\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n \"\"\"Extract multi-view history features for a set of users at cutoff_time.\"\"\"\n u_set = set(user_ids)\n\n # Filter listens before cutoff_time in last 90 days\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n # Recent (last 14 days)\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n # General history (90 days)\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n # Strong organic\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n # Group by uid, item_idx\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n # Likes and dislikes\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n # User stats\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n # Build bag structures\n recent_indices, recent_offsets, recent_weights = [], [0], []\n history_indices, history_offsets, history_weights = [], [0], []\n strong_indices, strong_offsets, strong_weights = [], [0], []\n like_indices, like_offsets = [], [0]\n dislike_indices, dislike_offsets = [], [0]\n stats_list = []\n\n agg_by_user = {}\n for uid, g in agg.groupby(\"uid\"):\n agg_by_user[uid] = g\n\n for uid in user_ids:\n # Stats\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_list.append([\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ])\n\n if uid in agg_by_user:\n g = agg_by_user[uid]\n # Recent\n mask_r = g.w_recent > 0\n if mask_r.any():\n recent_indices.extend(g.item_idx[mask_r].tolist())\n recent_weights.extend(g.w_recent[mask_r].tolist())\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n # Strong\n mask_s = g.w_strong > 0\n if mask_s.any():\n strong_indices.extend(g.item_idx[mask_s].tolist())\n strong_weights.extend(g.w_strong[mask_s].tolist())\n\n recent_offsets.append(len(recent_indices))\n history_offsets.append(len(history_indices))\n strong_offsets.append(len(strong_indices))\n\n # Likes\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_offsets.append(len(like_indices))\n\n # Dislikes\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_offsets.append(len(dislike_indices))\n\n features = {\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_offsets\": torch.tensor(recent_offsets[:-1], dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_offsets\": torch.tensor(history_offsets[:-1], dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_offsets\": torch.tensor(strong_offsets[:-1], dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_offsets\": torch.tensor(like_offsets[:-1], dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_offsets\": torch.tensor(dislike_offsets[:-1], dtype=torch.long),\n \"user_stats\": torch.tensor(stats_list, dtype=torch.float32),\n }\n return features\n\n\nprint(\"Extracting validation user features...\")\nval_user_features = build_user_features(CUTOFF_VAL, target_uids)\nprint(\"Validation features ready.\")\n\n\ndef extract_slice_training_data(cutoff_time):\n \"\"\"Extract target positive items for users in [cutoff_time, cutoff_time + WEEK_SECONDS].\"\"\"\n w_end = cutoff_time + WEEK_SECONDS\n # Prior listens\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n # Target window\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n # Filter to active users who have at least one target\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = list(user_positives.keys())\n print(f\"Slice cutoff {cutoff_time}: {len(unique_users)} users with {len(targets)} targets\")\n\n # Extract user features for these users at this cutoff\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slice 1 (Week -1)...\")\nslice1_feats, slice1_pairs = extract_slice_training_data(CUTOFF_VAL - WEEK_SECONDS)\nprint(\"Extracting training slice 2 (Week -2)...\")\nslice2_feats, slice2_pairs = extract_slice_training_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.xavier_normal_(self.item_emb.weight)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n emb = self.item_emb(item_ids)\n dense_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([emb, dense_p], dim=-1)\n v = self.mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim # 4 views + dislikes + stats_proj\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool_bag(self, indices, offsets, weights=None):\n if len(indices) == 0:\n return torch.zeros((len(offsets), self.shared_emb.embedding_dim), dtype=torch.float32)\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n # Sum within slices\n # Vectorized sum using torch.scatter_add_\n batch_size = len(offsets)\n counts = torch.diff(torch.cat([offsets, torch.tensor([len(indices)])]))\n user_ids = torch.repeat_interleave(torch.arange(batch_size), counts)\n out = torch.zeros((batch_size, self.shared_emb.embedding_dim), dtype=torch.float32)\n out.index_add_(0, user_ids, embs)\n # Normalize non-zero rows\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def forward_batch(self, user_indices, feats):\n # Slice features for user_indices\n # For efficiency, user_indices indexes into precomputed view matrices\n pass\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n h_rec = self._pool_bag(feats[\"recent_indices\"], feats[\"recent_offsets\"], feats[\"recent_weights\"])\n h_his = self._pool_bag(feats[\"history_indices\"], feats[\"history_offsets\"], feats[\"history_weights\"])\n h_str = self._pool_bag(feats[\"strong_indices\"], feats[\"strong_offsets\"], feats[\"strong_weights\"])\n h_lik = self._pool_bag(feats[\"like_indices\"], feats[\"like_offsets\"])\n h_dis = self._pool_bag(feats[\"dislike_indices\"], feats[\"dislike_offsets\"])\n s_proj = self.stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_proj], dim=-1)\n u = self.mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n\nembed_dim = 64\nout_dim = 64\nitem_tower = ItemTower(num_items, embed_dim, 4, out_dim)\nuser_tower = UserTower(embed_dim, 5, out_dim, item_tower.item_emb)\n\nprint(\"Model architecture initialized.\")\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:11:29.971Z",
"resultDisplay": {
"fileDiff": "Index: train_prototype.py\n===================================================================\n--- train_prototype.py\tOriginal\n+++ train_prototype.py\tWritten\n@@ -0,0 +1,347 @@\n+import os\n+import sys\n+import time\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import pyarrow.parquet as pq\n+import torch\n+import torch.nn as nn\n+import torch.nn.functional as F\n+\n+sys.path.append(\"/task\")\n+from evaluate_public import score, validation_truth\n+from ranking_evaluation import CandidateSet\n+\n+torch.set_num_threads(8)\n+\n+DATA_DIR = Path(\"/task/data\")\n+OUT_DIR = Path(\"/app/solution\")\n+OUT_DIR.mkdir(parents=True, exist_ok=True)\n+\n+CUTOFF_VAL = 24786800\n+WEEK_SECONDS = 7 * 86400\n+\n+print(\"Loading metadata...\")\n+cat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\n+cat_items = cat_df.item_id.to_numpy()\n+item_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\n+num_items = len(cat_items)\n+\n+target_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\n+target_uids = target_users_df.uid.to_numpy()\n+target_uid_set = set(target_uids)\n+uid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\n+num_targets = len(target_uids)\n+\n+# Item features\n+print(\"Computing item features...\")\n+cat_features = np.zeros((num_items, 4), dtype=np.float32)\n+cat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\n+cat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\n+cat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\n+cat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\n+# Standardize\n+for c in range(4):\n+ mean = cat_features[:, c].mean()\n+ std = cat_features[:, c].std() + 1e-6\n+ cat_features[:, c] = (cat_features[:, c] - mean) / std\n+\n+item_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n+\n+print(\"Loading raw events...\")\n+t0 = time.time()\n+likes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\n+likes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\n+likes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n+\n+dislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\n+dislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\n+dislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n+\n+# Load listens in the last 120 days before validation cutoff\n+min_listen_time = CUTOFF_VAL - 120 * 86400\n+table = pq.read_table(\n+ DATA_DIR / \"train_listens.parquet\",\n+ columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n+ filters=[(\"timestamp\", \">=\", min_listen_time)],\n+)\n+listens_df = table.to_pandas()\n+listens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\n+listens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n+print(f\"Events loaded in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n+\n+\n+def build_user_features(cutoff_time, user_ids):\n+ \"\"\"Extract multi-view history features for a set of users at cutoff_time.\"\"\"\n+ u_set = set(user_ids)\n+\n+ # Filter listens before cutoff_time in last 90 days\n+ t_min = cutoff_time - 90 * 86400\n+ sub_listens = listens_df[\n+ (listens_df.timestamp >= t_min)\n+ & (listens_df.timestamp < cutoff_time)\n+ & (listens_df.uid.isin(u_set))\n+ ].copy()\n+\n+ sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n+\n+ # Recent (last 14 days)\n+ sub_listens[\"w_recent\"] = np.where(\n+ sub_listens.days_ago <= 14.0,\n+ np.exp(-0.1 * sub_listens.days_ago)\n+ * (1.0 + sub_listens.is_organic)\n+ * (sub_listens.played_ratio_pct / 100.0),\n+ 0.0,\n+ )\n+\n+ # General history (90 days)\n+ sub_listens[\"w_history\"] = (\n+ np.exp(-0.02 * sub_listens.days_ago)\n+ * (1.0 + sub_listens.is_organic)\n+ * (sub_listens.played_ratio_pct / 100.0)\n+ )\n+\n+ # Strong organic\n+ sub_listens[\"w_strong\"] = np.where(\n+ (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n+ np.exp(-0.05 * sub_listens.days_ago),\n+ 0.0,\n+ )\n+\n+ # Group by uid, item_idx\n+ agg = (\n+ sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n+ .sum()\n+ .reset_index()\n+ )\n+\n+ # Likes and dislikes\n+ sub_likes = likes_df[\n+ (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n+ ]\n+ likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n+\n+ sub_dislikes = dislikes_df[\n+ (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n+ ]\n+ dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n+\n+ # User stats\n+ uid_counts = sub_listens.groupby(\"uid\").agg(\n+ n_listens=(\"item_idx\", \"count\"),\n+ mean_ratio=(\"played_ratio_pct\", \"mean\"),\n+ organic_ratio=(\"is_organic\", \"mean\"),\n+ ).to_dict(orient=\"index\")\n+\n+ # Build bag structures\n+ recent_indices, recent_offsets, recent_weights = [], [0], []\n+ history_indices, history_offsets, history_weights = [], [0], []\n+ strong_indices, strong_offsets, strong_weights = [], [0], []\n+ like_indices, like_offsets = [], [0]\n+ dislike_indices, dislike_offsets = [], [0]\n+ stats_list = []\n+\n+ agg_by_user = {}\n+ for uid, g in agg.groupby(\"uid\"):\n+ agg_by_user[uid] = g\n+\n+ for uid in user_ids:\n+ # Stats\n+ st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n+ n_likes = len(likes_grouped.get(uid, []))\n+ n_dislikes = len(dislikes_grouped.get(uid, []))\n+ stats_list.append([\n+ np.log1p(st[\"n_listens\"]),\n+ st[\"mean_ratio\"] / 100.0,\n+ st[\"organic_ratio\"],\n+ np.log1p(n_likes),\n+ np.log1p(n_dislikes),\n+ ])\n+\n+ if uid in agg_by_user:\n+ g = agg_by_user[uid]\n+ # Recent\n+ mask_r = g.w_recent > 0\n+ if mask_r.any():\n+ recent_indices.extend(g.item_idx[mask_r].tolist())\n+ recent_weights.extend(g.w_recent[mask_r].tolist())\n+ # History\n+ history_indices.extend(g.item_idx.tolist())\n+ history_weights.extend(g.w_history.tolist())\n+ # Strong\n+ mask_s = g.w_strong > 0\n+ if mask_s.any():\n+ strong_indices.extend(g.item_idx[mask_s].tolist())\n+ strong_weights.extend(g.w_strong[mask_s].tolist())\n+\n+ recent_offsets.append(len(recent_indices))\n+ history_offsets.append(len(history_indices))\n+ strong_offsets.append(len(strong_indices))\n+\n+ # Likes\n+ l_items = likes_grouped.get(uid, [])\n+ like_indices.extend(l_items)\n+ like_offsets.append(len(like_indices))\n+\n+ # Dislikes\n+ d_items = dislikes_grouped.get(uid, [])\n+ dislike_indices.extend(d_items)\n+ dislike_offsets.append(len(dislike_indices))\n+\n+ features = {\n+ \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n+ \"recent_offsets\": torch.tensor(recent_offsets[:-1], dtype=torch.long),\n+ \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n+ \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n+ \"history_offsets\": torch.tensor(history_offsets[:-1], dtype=torch.long),\n+ \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n+ \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n+ \"strong_offsets\": torch.tensor(strong_offsets[:-1], dtype=torch.long),\n+ \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n+ \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n+ \"like_offsets\": torch.tensor(like_offsets[:-1], dtype=torch.long),\n+ \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n+ \"dislike_offsets\": torch.tensor(dislike_offsets[:-1], dtype=torch.long),\n+ \"user_stats\": torch.tensor(stats_list, dtype=torch.float32),\n+ }\n+ return features\n+\n+\n+print(\"Extracting validation user features...\")\n+val_user_features = build_user_features(CUTOFF_VAL, target_uids)\n+print(\"Validation features ready.\")\n+\n+\n+def extract_slice_training_data(cutoff_time):\n+ \"\"\"Extract target positive items for users in [cutoff_time, cutoff_time + WEEK_SECONDS].\"\"\"\n+ w_end = cutoff_time + WEEK_SECONDS\n+ # Prior listens\n+ prior = listens_df[listens_df.timestamp < cutoff_time]\n+ prior_user_items = set(zip(prior.uid, prior.item_idx))\n+\n+ # Target window\n+ win_listens = listens_df[\n+ (listens_df.timestamp >= cutoff_time)\n+ & (listens_df.timestamp < w_end)\n+ & (listens_df.is_organic == 1)\n+ & (listens_df.played_ratio_pct >= 80)\n+ ]\n+ win_likes = likes_df[\n+ (likes_df.timestamp >= cutoff_time)\n+ & (likes_df.timestamp < w_end)\n+ & (likes_df.is_organic == 1)\n+ ]\n+\n+ targets = {}\n+ for u, i in zip(win_listens.uid, win_listens.item_idx):\n+ if (u, i) not in prior_user_items:\n+ targets[(u, i)] = 1\n+ for u, i in zip(win_likes.uid, win_likes.item_idx):\n+ if (u, i) not in prior_user_items:\n+ targets[(u, i)] = 3\n+\n+ # Filter to active users who have at least one target\n+ user_positives = {}\n+ for (u, i), grade in targets.items():\n+ user_positives.setdefault(u, []).append((i, grade))\n+\n+ unique_users = list(user_positives.keys())\n+ print(f\"Slice cutoff {cutoff_time}: {len(unique_users)} users with {len(targets)} targets\")\n+\n+ # Extract user features for these users at this cutoff\n+ user_feats = build_user_features(cutoff_time, unique_users)\n+ user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n+\n+ pairs = []\n+ for u, items in user_positives.items():\n+ u_idx = user_to_idx[u]\n+ for item_idx, grade in items:\n+ pairs.append((u_idx, item_idx, float(grade)))\n+\n+ return user_feats, pairs\n+\n+\n+print(\"Extracting training slice 1 (Week -1)...\")\n+slice1_feats, slice1_pairs = extract_slice_training_data(CUTOFF_VAL - WEEK_SECONDS)\n+print(\"Extracting training slice 2 (Week -2)...\")\n+slice2_feats, slice2_pairs = extract_slice_training_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\n+\n+\n+class ItemTower(nn.Module):\n+ def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n+ super().__init__()\n+ self.item_emb = nn.Embedding(num_items, embed_dim)\n+ nn.init.xavier_normal_(self.item_emb.weight)\n+ self.dense_proj = nn.Linear(dense_dim, embed_dim)\n+ self.mlp = nn.Sequential(\n+ nn.Linear(embed_dim * 2, embed_dim),\n+ nn.LayerNorm(embed_dim),\n+ nn.GELU(),\n+ nn.Linear(embed_dim, out_dim),\n+ )\n+\n+ def forward(self, item_ids, dense_features):\n+ emb = self.item_emb(item_ids)\n+ dense_p = self.dense_proj(dense_features[item_ids])\n+ x = torch.cat([emb, dense_p], dim=-1)\n+ v = self.mlp(x)\n+ return F.normalize(v, p=2, dim=-1)\n+\n+\n+class UserTower(nn.Module):\n+ def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n+ super().__init__()\n+ self.shared_emb = shared_item_emb\n+ self.stats_proj = nn.Linear(stats_dim, embed_dim)\n+ in_dim = embed_dim * 5 + embed_dim # 4 views + dislikes + stats_proj\n+ self.mlp = nn.Sequential(\n+ nn.Linear(in_dim, embed_dim * 2),\n+ nn.LayerNorm(embed_dim * 2),\n+ nn.GELU(),\n+ nn.Dropout(0.1),\n+ nn.Linear(embed_dim * 2, out_dim),\n+ )\n+\n+ def _pool_bag(self, indices, offsets, weights=None):\n+ if len(indices) == 0:\n+ return torch.zeros((len(offsets), self.shared_emb.embedding_dim), dtype=torch.float32)\n+ embs = self.shared_emb(indices)\n+ if weights is not None:\n+ embs = embs * weights.unsqueeze(-1)\n+ # Sum within slices\n+ # Vectorized sum using torch.scatter_add_\n+ batch_size = len(offsets)\n+ counts = torch.diff(torch.cat([offsets, torch.tensor([len(indices)])]))\n+ user_ids = torch.repeat_interleave(torch.arange(batch_size), counts)\n+ out = torch.zeros((batch_size, self.shared_emb.embedding_dim), dtype=torch.float32)\n+ out.index_add_(0, user_ids, embs)\n+ # Normalize non-zero rows\n+ norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n+ return out / norms\n+\n+ def forward_batch(self, user_indices, feats):\n+ # Slice features for user_indices\n+ # For efficiency, user_indices indexes into precomputed view matrices\n+ pass\n+\n+ def compute_all_user_vectors(self, feats):\n+ with torch.no_grad():\n+ h_rec = self._pool_bag(feats[\"recent_indices\"], feats[\"recent_offsets\"], feats[\"recent_weights\"])\n+ h_his = self._pool_bag(feats[\"history_indices\"], feats[\"history_offsets\"], feats[\"history_weights\"])\n+ h_str = self._pool_bag(feats[\"strong_indices\"], feats[\"strong_offsets\"], feats[\"strong_weights\"])\n+ h_lik = self._pool_bag(feats[\"like_indices\"], feats[\"like_offsets\"])\n+ h_dis = self._pool_bag(feats[\"dislike_indices\"], feats[\"dislike_offsets\"])\n+ s_proj = self.stats_proj(feats[\"user_stats\"])\n+ x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_proj], dim=-1)\n+ u = self.mlp(x)\n+ return F.normalize(u, p=2, dim=-1)\n+\n+\n+embed_dim = 64\n+out_dim = 64\n+item_tower = ItemTower(num_items, embed_dim, 4, out_dim)\n+user_tower = UserTower(embed_dim, 5, out_dim, item_tower.item_emb)\n+\n+print(\"Model architecture initialized.\")\n",
"fileName": "train_prototype.py",
"filePath": "/app/solution/train_prototype.py",
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"newContent": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\nprint(\"Computing item features...\")\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\n# Standardize\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\n# Load listens in the last 120 days before validation cutoff\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n \"\"\"Extract multi-view history features for a set of users at cutoff_time.\"\"\"\n u_set = set(user_ids)\n\n # Filter listens before cutoff_time in last 90 days\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n # Recent (last 14 days)\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n # General history (90 days)\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n # Strong organic\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n # Group by uid, item_idx\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n # Likes and dislikes\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n # User stats\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n # Build bag structures\n recent_indices, recent_offsets, recent_weights = [], [0], []\n history_indices, history_offsets, history_weights = [], [0], []\n strong_indices, strong_offsets, strong_weights = [], [0], []\n like_indices, like_offsets = [], [0]\n dislike_indices, dislike_offsets = [], [0]\n stats_list = []\n\n agg_by_user = {}\n for uid, g in agg.groupby(\"uid\"):\n agg_by_user[uid] = g\n\n for uid in user_ids:\n # Stats\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_list.append([\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ])\n\n if uid in agg_by_user:\n g = agg_by_user[uid]\n # Recent\n mask_r = g.w_recent > 0\n if mask_r.any():\n recent_indices.extend(g.item_idx[mask_r].tolist())\n recent_weights.extend(g.w_recent[mask_r].tolist())\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n # Strong\n mask_s = g.w_strong > 0\n if mask_s.any():\n strong_indices.extend(g.item_idx[mask_s].tolist())\n strong_weights.extend(g.w_strong[mask_s].tolist())\n\n recent_offsets.append(len(recent_indices))\n history_offsets.append(len(history_indices))\n strong_offsets.append(len(strong_indices))\n\n # Likes\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_offsets.append(len(like_indices))\n\n # Dislikes\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_offsets.append(len(dislike_indices))\n\n features = {\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_offsets\": torch.tensor(recent_offsets[:-1], dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_offsets\": torch.tensor(history_offsets[:-1], dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_offsets\": torch.tensor(strong_offsets[:-1], dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_offsets\": torch.tensor(like_offsets[:-1], dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_offsets\": torch.tensor(dislike_offsets[:-1], dtype=torch.long),\n \"user_stats\": torch.tensor(stats_list, dtype=torch.float32),\n }\n return features\n\n\nprint(\"Extracting validation user features...\")\nval_user_features = build_user_features(CUTOFF_VAL, target_uids)\nprint(\"Validation features ready.\")\n\n\ndef extract_slice_training_data(cutoff_time):\n \"\"\"Extract target positive items for users in [cutoff_time, cutoff_time + WEEK_SECONDS].\"\"\"\n w_end = cutoff_time + WEEK_SECONDS\n # Prior listens\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n # Target window\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n # Filter to active users who have at least one target\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = list(user_positives.keys())\n print(f\"Slice cutoff {cutoff_time}: {len(unique_users)} users with {len(targets)} targets\")\n\n # Extract user features for these users at this cutoff\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slice 1 (Week -1)...\")\nslice1_feats, slice1_pairs = extract_slice_training_data(CUTOFF_VAL - WEEK_SECONDS)\nprint(\"Extracting training slice 2 (Week -2)...\")\nslice2_feats, slice2_pairs = extract_slice_training_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.xavier_normal_(self.item_emb.weight)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n emb = self.item_emb(item_ids)\n dense_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([emb, dense_p], dim=-1)\n v = self.mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim # 4 views + dislikes + stats_proj\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool_bag(self, indices, offsets, weights=None):\n if len(indices) == 0:\n return torch.zeros((len(offsets), self.shared_emb.embedding_dim), dtype=torch.float32)\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n # Sum within slices\n # Vectorized sum using torch.scatter_add_\n batch_size = len(offsets)\n counts = torch.diff(torch.cat([offsets, torch.tensor([len(indices)])]))\n user_ids = torch.repeat_interleave(torch.arange(batch_size), counts)\n out = torch.zeros((batch_size, self.shared_emb.embedding_dim), dtype=torch.float32)\n out.index_add_(0, user_ids, embs)\n # Normalize non-zero rows\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def forward_batch(self, user_indices, feats):\n # Slice features for user_indices\n # For efficiency, user_indices indexes into precomputed view matrices\n pass\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n h_rec = self._pool_bag(feats[\"recent_indices\"], feats[\"recent_offsets\"], feats[\"recent_weights\"])\n h_his = self._pool_bag(feats[\"history_indices\"], feats[\"history_offsets\"], feats[\"history_weights\"])\n h_str = self._pool_bag(feats[\"strong_indices\"], feats[\"strong_offsets\"], feats[\"strong_weights\"])\n h_lik = self._pool_bag(feats[\"like_indices\"], feats[\"like_offsets\"])\n h_dis = self._pool_bag(feats[\"dislike_indices\"], feats[\"dislike_offsets\"])\n s_proj = self.stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_proj], dim=-1)\n u = self.mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n\nembed_dim = 64\nout_dim = 64\nitem_tower = ItemTower(num_items, embed_dim, 4, out_dim)\nuser_tower = UserTower(embed_dim, 5, out_dim, item_tower.item_emb)\n\nprint(\"Model architecture initialized.\")\n",
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"timestamp": "2026-09-29T06:11:29.972Z",
"content": "Successfully created and wrote to new file: /app/solution/train_prototype.py. Here is the updated code:\nimport os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\nprint(\"Computing item features...\")\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\n# Standardize\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\n# Load listens in the last 120 days before validation cutoff\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n \"\"\"Extract multi-view history features for a set of users at cutoff_time.\"\"\"\n u_set = set(user_ids)\n\n # Filter listens before cutoff_time in last 90 days\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n # Recent (last 14 days)\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n # General history (90 days)\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n # Strong organic\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n # Group by uid, item_idx\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n # Likes and dislikes\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n # User stats\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n # Build bag structures\n recent_indices, recent_offsets, recent_weights = [], [0], []\n history_indices, history_offsets, history_weights = [], [0], []\n strong_indices, strong_offsets, strong_weights = [], [0], []\n like_indices, like_offsets = [], [0]\n dislike_indices, dislike_offsets = [], [0]\n stats_list = []\n\n agg_by_user = {}\n for uid, g in agg.groupby(\"uid\"):\n agg_by_user[uid] = g\n\n for uid in user_ids:\n # Stats\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_list.append([\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ])\n\n if uid in agg_by_user:\n g = agg_by_user[uid]\n # Recent\n mask_r = g.w_recent > 0\n if mask_r.any():\n recent_indices.extend(g.item_idx[mask_r].tolist())\n recent_weights.extend(g.w_recent[mask_r].tolist())\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n # Strong\n mask_s = g.w_strong > 0\n if mask_s.any():\n strong_indices.extend(g.item_idx[mask_s].tolist())\n strong_weights.extend(g.w_strong[mask_s].tolist())\n\n recent_offsets.append(len(recent_indices))\n history_offsets.append(len(history_indices))\n strong_offsets.append(len(strong_indices))\n\n # Likes\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_offsets.append(len(like_indices))\n\n # Dislikes\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_offsets.append(len(dislike_indices))\n\n features = {\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_offsets\": torch.tensor(recent_offsets[:-1], dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_offsets\": torch.tensor(history_offsets[:-1], dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_offsets\": torch.tensor(strong_offsets[:-1], dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_offsets\": torch.tensor(like_offsets[:-1], dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_offsets\": torch.tensor(dislike_offsets[:-1], dtype=torch.long),\n \"user_stats\": torch.tensor(stats_list, dtype=torch.float32),\n }\n return features\n\n\nprint(\"Extracting validation user features...\")\nval_user_features = build_user_features(CUTOFF_VAL, target_uids)\nprint(\"Validation features ready.\")\n\n\ndef extract_slice_training_data(cutoff_time):\n \"\"\"Extract target positive items for users in [cutoff_time, cutoff_time + WEEK_SECONDS].\"\"\"\n w_end = cutoff_time + WEEK_SECONDS\n # Prior listens\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n # Target window\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n # Filter to active users who have at least one target\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = list(user_positives.keys())\n print(f\"Slice cutoff {cutoff_time}: {len(unique_users)} users with {len(targets)} targets\")\n\n # Extract user features for these users at this cutoff\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slice 1 (Week -1)...\")\nslice1_feats, slice1_pairs = extract_slice_training_data(CUTOFF_VAL - WEEK_SECONDS)\nprint(\"Extracting training slice 2 (Week -2)...\")\nslice2_feats, slice2_pairs = extract_slice_training_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.xavier_normal_(self.item_emb.weight)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n emb = self.item_emb(item_ids)\n dense_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([emb, dense_p], dim=-1)\n v = self.mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim # 4 views + dislikes + stats_proj\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool_bag(self, indices, offsets, weights=None):\n if len(indices) == 0:\n return torch.zeros((len(offsets), self.shared_emb.embedding_dim), dtype=torch.float32)\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n # Sum within slices\n # Vectorized sum using torch.scatter_add_\n batch_size = len(offsets)\n counts = torch.diff(torch.cat([offsets, torch.tensor([len(indices)])]))\n user_ids = torch.repeat_interleave(torch.arange(batch_size), counts)\n out = torch.zeros((batch_size, self.shared_emb.embedding_dim), dtype=torch.float32)\n out.index_add_(0, user_ids, embs)\n # Normalize non-zero rows\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def forward_batch(self, user_indices, feats):\n # Slice features for user_indices\n # For efficiency, user_indices indexes into precomputed view matrices\n pass\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n h_rec = self._pool_bag(feats[\"recent_indices\"], feats[\"recent_offsets\"], feats[\"recent_weights\"])\n h_his = self._pool_bag(feats[\"history_indices\"], feats[\"history_offsets\"], feats[\"history_weights\"])\n h_str = self._pool_bag(feats[\"strong_indices\"], feats[\"strong_offsets\"], feats[\"strong_weights\"])\n h_lik = self._pool_bag(feats[\"like_indices\"], feats[\"like_offsets\"])\n h_dis = self._pool_bag(feats[\"dislike_indices\"], feats[\"dislike_offsets\"])\n s_proj = self.stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_proj], dim=-1)\n u = self.mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n\nembed_dim = 64\nout_dim = 64\nitem_tower = ItemTower(num_items, embed_dim, 4, out_dim)\nuser_tower = UserTower(embed_dim, 5, out_dim, item_tower.item_emb)\n\nprint(\"Model architecture initialized.\")\n",
"isError": false
},
"responseMessageId": "35ea0551-1aa2-4e5a-8f7c-3b498ccc454f"
}
]
},
{
"id": 55,
"role": "agent",
"timestamp": "2026-09-29T06:12:42.462Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:12:42.462Z",
"text": "Exploring UserTower Precomputation\n\nI'm currently investigating the feasibility of precomputing pooled view vectors within `UserTower` during training, specifically considering the scenario with $N$ users in a slice. This aims to optimize computational efficiency by calculating $\\mathbf{h}_{\\text{rec}}, \\mathbf{h}_{\\text{his}}, \\mathbf{h}_{\\text{str}}, \\mathbf{h}_{\\text{lik}}$ beforehand."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:12:42.462Z",
"callId": "run_shell_command__call_124893",
"name": "run_shell_command",
"input": {
"description": "Test index_add autograd gradient flow",
"command": "python3 -c \"\nimport torch\nimport torch.nn as nn\n\nemb = nn.Embedding(10, 4)\nindices = torch.tensor([0, 1, 2, 3], dtype=torch.long)\ne = emb(indices)\nuser_ids = torch.tensor([0, 0, 1, 1], dtype=torch.long)\nout = torch.zeros((2, 4), requires_grad=False)\nout = torch.index_add(out, 0, user_ids, e)\nloss = out.sum()\nloss.backward()\nprint('emb grad:', emb.weight.grad)\n\""
},
"recorded": {
"id": "run_shell_command__call_124893",
"name": "run_shell_command",
"args": {
"description": "Test index_add autograd gradient flow",
"command": "python3 -c \"\nimport torch\nimport torch.nn as nn\n\nemb = nn.Embedding(10, 4)\nindices = torch.tensor([0, 1, 2, 3], dtype=torch.long)\ne = emb(indices)\nuser_ids = torch.tensor([0, 0, 1, 1], dtype=torch.long)\nout = torch.zeros((2, 4), requires_grad=False)\nout = torch.index_add(out, 0, user_ids, e)\nloss = out.sum()\nloss.backward()\nprint('emb grad:', emb.weight.grad)\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_124893",
"name": "run_shell_command",
"response": {
"output": "\nOutput: emb grad: tensor([[1., 1., 1., 1.],\n [1., 1., 1., 1.],\n [1., 1., 1., 1.],\n [1., 1., 1., 1.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.]])\nProcess Group PGID: 2673\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:12:45.456Z",
"resultDisplay": "emb grad: tensor([[1., 1., 1., 1.],\n [1., 1., 1., 1.],\n [1., 1., 1., 1.],\n [1., 1., 1., 1.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.]])",
"description": "Test index_add autograd gradient flow",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:12:45.458Z",
"content": "\nOutput: emb grad: tensor([[1., 1., 1., 1.],\n [1., 1., 1., 1.],\n [1., 1., 1., 1.],\n [1., 1., 1., 1.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.],\n [0., 0., 0., 0.]])\nProcess Group PGID: 2673\n",
"isError": false
},
"responseMessageId": "acb0a40a-3757-4357-8db0-05defad85c77"
}
]
},
{
"id": 56,
"role": "agent",
"timestamp": "2026-09-29T06:15:24.541Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T06:15:24.541Z",
"callId": "run_shell_command__call_233165",
"name": "run_shell_command",
"input": {
"description": "Benchmark full index_add forward and backward",
"command": "python3 -c \"\nimport torch, torch.nn as nn, time\n\ntorch.set_num_threads(8)\nemb = nn.Embedding(30000, 64)\nindices = torch.randint(0, 30000, (4000000,))\nuser_ids = torch.randint(0, 8456, (4000000,))\nweights = torch.rand(4000000)\n\nt0 = time.time()\ne = emb(indices) * weights.unsqueeze(-1)\nout = torch.zeros((8456, 64))\nout = torch.index_add(out, 0, user_ids, e)\nloss = out.sum()\nloss.backward()\nprint('Full index_add forward + backward took:', round(time.time() - t0, 3), 's')\n\""
},
"recorded": {
"id": "run_shell_command__call_233165",
"name": "run_shell_command",
"args": {
"description": "Benchmark full index_add forward and backward",
"command": "python3 -c \"\nimport torch, torch.nn as nn, time\n\ntorch.set_num_threads(8)\nemb = nn.Embedding(30000, 64)\nindices = torch.randint(0, 30000, (4000000,))\nuser_ids = torch.randint(0, 8456, (4000000,))\nweights = torch.rand(4000000)\n\nt0 = time.time()\ne = emb(indices) * weights.unsqueeze(-1)\nout = torch.zeros((8456, 64))\nout = torch.index_add(out, 0, user_ids, e)\nloss = out.sum()\nloss.backward()\nprint('Full index_add forward + backward took:', round(time.time() - t0, 3), 's')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_233165",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Full index_add forward + backward took: 0.915 s\nProcess Group PGID: 2695\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:15:28.516Z",
"resultDisplay": "Full index_add forward + backward took: 0.915 s",
"description": "Benchmark full index_add forward and backward",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:15:28.517Z",
"content": "\nOutput: Full index_add forward + backward took: 0.915 s\nProcess Group PGID: 2695\n",
"isError": false
},
"responseMessageId": "80bbfb8a-460a-4d61-a6b4-ea0deb59da73"
}
]
},
{
"id": 57,
"role": "agent",
"timestamp": "2026-09-29T06:16:05.471Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:16:05.471Z",
"text": "Optimizing Memory Usage\n\nI'm exploring ways to optimize memory. We're seeing incredible speed, under a second for processing all history, and I'm realizing we don't need this on every mini-batch. I'm now considering computing user pooled representations per epoch or sub-batch to further enhance efficiency."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:16:05.471Z",
"text": "Refining User Representations\n\nI'm considering how to best integrate user pooled representations. Computing them per epoch seems efficient, allowing a fast $O(1)$ lookup during training. This approach will update user and item tower parameters, with item embeddings refreshing user representations for subsequent epochs."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:16:05.471Z",
"text": "Testing Hyperparameters\n\nMy focus has shifted to hyperparameter tuning. I'm now investigating the impact of temperature scaling ($\\tau$) and the inclusion of popularity bias within the InfoNCE loss function to refine gradient sharpness and overall ranking performance. I'm also experimenting with using both in-batch and random catalog items as negative samples."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:16:05.471Z",
"callId": "write_file__call_221957",
"name": "write_file",
"input": {
"instruction": "Create prototype_eval.py to run full train and eval loop",
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_data = []\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n \n # Pack for torch\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n # Recent\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n # Strong\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n features = {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n return features\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slices...\")\nslice1_feats, slice1_pairs = extract_slice_data(CUTOFF_VAL - WEEK_SECONDS)\nslice2_feats, slice2_pairs = extract_slice_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\nslice3_feats, slice3_pairs = extract_slice_data(CUTOFF_VAL - 3 * WEEK_SECONDS)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\n\n\nclass TwoTowerModel(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, stats_dim, out_dim):\n super().__init__()\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n # Shared item embeddings\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n\n # Item Tower\n self.item_dense_proj = nn.Linear(dense_dim, embed_dim)\n self.item_mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n # User Tower\n self.user_stats_proj = nn.Linear(stats_dim, embed_dim)\n user_in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.user_mlp = nn.Sequential(\n nn.Linear(user_in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.item_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_all_user_vectors(self, feats):\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user representations for this slice\n all_user_vecs = model.compute_all_user_vectors(feats)\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = all_user_vecs[b_u]\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Evaluate validation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Dot product scores: (8456, 30000)\n # In chunks of 1000 users to keep memory small\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n # (chunk_size, 30000)\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n # Mask out ineligibles\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n # Top 100 per user\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n # Sort within top 100\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n\n print(\n f\"Epoch {epoch}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={val_score['quality_ndcg@10']:.6f} \"\n f\"({time.time() - t_ep:.1f}s)\"\n )\n",
"file_path": "/app/solution/prototype_eval.py"
},
"recorded": {
"id": "write_file__call_221957",
"name": "write_file",
"args": {
"instruction": "Create prototype_eval.py to run full train and eval loop",
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_data = []\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n \n # Pack for torch\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n # Recent\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n # Strong\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n features = {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n return features\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slices...\")\nslice1_feats, slice1_pairs = extract_slice_data(CUTOFF_VAL - WEEK_SECONDS)\nslice2_feats, slice2_pairs = extract_slice_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\nslice3_feats, slice3_pairs = extract_slice_data(CUTOFF_VAL - 3 * WEEK_SECONDS)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\n\n\nclass TwoTowerModel(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, stats_dim, out_dim):\n super().__init__()\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n # Shared item embeddings\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n\n # Item Tower\n self.item_dense_proj = nn.Linear(dense_dim, embed_dim)\n self.item_mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n # User Tower\n self.user_stats_proj = nn.Linear(stats_dim, embed_dim)\n user_in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.user_mlp = nn.Sequential(\n nn.Linear(user_in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.item_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_all_user_vectors(self, feats):\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user representations for this slice\n all_user_vecs = model.compute_all_user_vectors(feats)\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = all_user_vecs[b_u]\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Evaluate validation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Dot product scores: (8456, 30000)\n # In chunks of 1000 users to keep memory small\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n # (chunk_size, 30000)\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n # Mask out ineligibles\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n # Top 100 per user\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n # Sort within top 100\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n\n print(\n f\"Epoch {epoch}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={val_score['quality_ndcg@10']:.6f} \"\n f\"({time.time() - t_ep:.1f}s)\"\n )\n",
"file_path": "/app/solution/prototype_eval.py"
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"result": [
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"output": "Successfully created and wrote to new file: /app/solution/prototype_eval.py. Here is the updated code:\nimport os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_data = []\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n \n # Pack for torch\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n # Recent\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n # Strong\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n features = {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n return features\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slices...\")\nslice1_feats, slice1_pairs = extract_slice_data(CUTOFF_VAL - WEEK_SECONDS)\nslice2_feats, slice2_pairs = extract_slice_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\nslice3_feats, slice3_pairs = extract_slice_data(CUTOFF_VAL - 3 * WEEK_SECONDS)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\n\n\nclass TwoTowerModel(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, stats_dim, out_dim):\n super().__init__()\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n # Shared item embeddings\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n\n # Item Tower\n self.item_dense_proj = nn.Linear(dense_dim, embed_dim)\n self.item_mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n # User Tower\n self.user_stats_proj = nn.Linear(stats_dim, embed_dim)\n user_in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.user_mlp = nn.Sequential(\n nn.Linear(user_in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.item_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_all_user_vectors(self, feats):\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user representations for this slice\n all_user_vecs = model.compute_all_user_vectors(feats)\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = all_user_vecs[b_u]\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Evaluate validation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Dot product scores: (8456, 30000)\n # In chunks of 1000 users to keep memory small\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n # (chunk_size, 30000)\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n # Mask out ineligibles\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n # Top 100 per user\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n # Sort within top 100\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n\n print(\n f\"Epoch {epoch}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={val_score['quality_ndcg@10']:.6f} \"\n f\"({time.time() - t_ep:.1f}s)\"\n )\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:16:05.481Z",
"resultDisplay": {
"fileDiff": "Index: prototype_eval.py\n===================================================================\n--- prototype_eval.py\tOriginal\n+++ prototype_eval.py\tWritten\n@@ -0,0 +1,421 @@\n+import os\n+import sys\n+import time\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import pyarrow.parquet as pq\n+import torch\n+import torch.nn as nn\n+import torch.nn.functional as F\n+\n+sys.path.append(\"/task\")\n+from evaluate_public import score, validation_truth\n+from ranking_evaluation import CandidateSet, validate_ranking\n+\n+torch.set_num_threads(8)\n+\n+DATA_DIR = Path(\"/task/data\")\n+OUT_DIR = Path(\"/app/solution\")\n+OUT_DIR.mkdir(parents=True, exist_ok=True)\n+\n+CUTOFF_VAL = 24786800\n+WEEK_SECONDS = 7 * 86400\n+\n+print(\"Loading metadata...\")\n+cat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\n+cat_items = cat_df.item_id.to_numpy()\n+item_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\n+num_items = len(cat_items)\n+\n+target_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\n+target_uids = target_users_df.uid.to_numpy()\n+target_uid_set = set(target_uids)\n+uid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\n+num_targets = len(target_uids)\n+\n+# Item features\n+cat_features = np.zeros((num_items, 4), dtype=np.float32)\n+cat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\n+cat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\n+cat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\n+cat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\n+for c in range(4):\n+ mean = cat_features[:, c].mean()\n+ std = cat_features[:, c].std() + 1e-6\n+ cat_features[:, c] = (cat_features[:, c] - mean) / std\n+\n+item_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n+\n+print(\"Loading raw events...\")\n+t0 = time.time()\n+likes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\n+likes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\n+likes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n+\n+dislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\n+dislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\n+dislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n+\n+min_listen_time = CUTOFF_VAL - 120 * 86400\n+table = pq.read_table(\n+ DATA_DIR / \"train_listens.parquet\",\n+ columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n+ filters=[(\"timestamp\", \">=\", min_listen_time)],\n+)\n+listens_df = table.to_pandas()\n+listens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\n+listens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n+print(f\"Events loaded in {time.time() - t0:.2f}s\")\n+\n+\n+def build_user_features(cutoff_time, user_ids):\n+ u_set = set(user_ids)\n+ t_min = cutoff_time - 90 * 86400\n+ sub_listens = listens_df[\n+ (listens_df.timestamp >= t_min)\n+ & (listens_df.timestamp < cutoff_time)\n+ & (listens_df.uid.isin(u_set))\n+ ].copy()\n+\n+ sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n+\n+ sub_listens[\"w_recent\"] = np.where(\n+ sub_listens.days_ago <= 14.0,\n+ np.exp(-0.1 * sub_listens.days_ago)\n+ * (1.0 + sub_listens.is_organic)\n+ * (sub_listens.played_ratio_pct / 100.0),\n+ 0.0,\n+ )\n+\n+ sub_listens[\"w_history\"] = (\n+ np.exp(-0.02 * sub_listens.days_ago)\n+ * (1.0 + sub_listens.is_organic)\n+ * (sub_listens.played_ratio_pct / 100.0)\n+ )\n+\n+ sub_listens[\"w_strong\"] = np.where(\n+ (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n+ np.exp(-0.05 * sub_listens.days_ago),\n+ 0.0,\n+ )\n+\n+ agg = (\n+ sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n+ .sum()\n+ .reset_index()\n+ )\n+\n+ sub_likes = likes_df[\n+ (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n+ ]\n+ likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n+\n+ sub_dislikes = dislikes_df[\n+ (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n+ ]\n+ dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n+\n+ uid_counts = sub_listens.groupby(\"uid\").agg(\n+ n_listens=(\"item_idx\", \"count\"),\n+ mean_ratio=(\"played_ratio_pct\", \"mean\"),\n+ organic_ratio=(\"is_organic\", \"mean\"),\n+ ).to_dict(orient=\"index\")\n+\n+ user_data = []\n+ user_to_pos = {u: i for i, u in enumerate(user_ids)}\n+ \n+ # Pack for torch\n+ recent_indices, recent_uids, recent_weights = [], [], []\n+ history_indices, history_uids, history_weights = [], [], []\n+ strong_indices, strong_uids, strong_weights = [], [], []\n+ like_indices, like_uids = [], []\n+ dislike_indices, dislike_uids = [], []\n+ stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n+\n+ for i, uid in enumerate(user_ids):\n+ st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n+ n_likes = len(likes_grouped.get(uid, []))\n+ n_dislikes = len(dislikes_grouped.get(uid, []))\n+ stats_arr[i] = [\n+ np.log1p(st[\"n_listens\"]),\n+ st[\"mean_ratio\"] / 100.0,\n+ st[\"organic_ratio\"],\n+ np.log1p(n_likes),\n+ np.log1p(n_dislikes),\n+ ]\n+ l_items = likes_grouped.get(uid, [])\n+ like_indices.extend(l_items)\n+ like_uids.extend([i] * len(l_items))\n+\n+ d_items = dislikes_grouped.get(uid, [])\n+ dislike_indices.extend(d_items)\n+ dislike_uids.extend([i] * len(d_items))\n+\n+ for uid, g in agg.groupby(\"uid\"):\n+ if uid not in user_to_pos:\n+ continue\n+ u_idx = user_to_pos[uid]\n+ # Recent\n+ m_r = g.w_recent > 0\n+ if m_r.any():\n+ recent_indices.extend(g.item_idx[m_r].tolist())\n+ recent_weights.extend(g.w_recent[m_r].tolist())\n+ recent_uids.extend([u_idx] * int(m_r.sum()))\n+ # History\n+ history_indices.extend(g.item_idx.tolist())\n+ history_weights.extend(g.w_history.tolist())\n+ history_uids.extend([u_idx] * len(g))\n+ # Strong\n+ m_s = g.w_strong > 0\n+ if m_s.any():\n+ strong_indices.extend(g.item_idx[m_s].tolist())\n+ strong_weights.extend(g.w_strong[m_s].tolist())\n+ strong_uids.extend([u_idx] * int(m_s.sum()))\n+\n+ N = len(user_ids)\n+ features = {\n+ \"num_users\": N,\n+ \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n+ \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n+ \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n+ \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n+ \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n+ \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n+ \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n+ \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n+ \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n+ \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n+ \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n+ \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n+ \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n+ \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n+ }\n+ return features\n+\n+\n+def extract_slice_data(cutoff_time):\n+ w_end = cutoff_time + WEEK_SECONDS\n+ prior = listens_df[listens_df.timestamp < cutoff_time]\n+ prior_user_items = set(zip(prior.uid, prior.item_idx))\n+\n+ win_listens = listens_df[\n+ (listens_df.timestamp >= cutoff_time)\n+ & (listens_df.timestamp < w_end)\n+ & (listens_df.is_organic == 1)\n+ & (listens_df.played_ratio_pct >= 80)\n+ ]\n+ win_likes = likes_df[\n+ (likes_df.timestamp >= cutoff_time)\n+ & (likes_df.timestamp < w_end)\n+ & (likes_df.is_organic == 1)\n+ ]\n+\n+ targets = {}\n+ for u, i in zip(win_listens.uid, win_listens.item_idx):\n+ if (u, i) not in prior_user_items:\n+ targets[(u, i)] = 1\n+ for u, i in zip(win_likes.uid, win_likes.item_idx):\n+ if (u, i) not in prior_user_items:\n+ targets[(u, i)] = 3\n+\n+ user_positives = {}\n+ for (u, i), grade in targets.items():\n+ user_positives.setdefault(u, []).append((i, grade))\n+\n+ unique_users = sorted(list(user_positives.keys()))\n+ user_feats = build_user_features(cutoff_time, unique_users)\n+ user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n+\n+ pairs = []\n+ for u, items in user_positives.items():\n+ u_idx = user_to_idx[u]\n+ for item_idx, grade in items:\n+ pairs.append((u_idx, item_idx, float(grade)))\n+\n+ return user_feats, pairs\n+\n+\n+print(\"Extracting training slices...\")\n+slice1_feats, slice1_pairs = extract_slice_data(CUTOFF_VAL - WEEK_SECONDS)\n+slice2_feats, slice2_pairs = extract_slice_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\n+slice3_feats, slice3_pairs = extract_slice_data(CUTOFF_VAL - 3 * WEEK_SECONDS)\n+\n+val_user_feats = build_user_features(CUTOFF_VAL, target_uids)\n+\n+\n+class TwoTowerModel(nn.Module):\n+ def __init__(self, num_items, embed_dim, dense_dim, stats_dim, out_dim):\n+ super().__init__()\n+ self.embed_dim = embed_dim\n+ self.out_dim = out_dim\n+ # Shared item embeddings\n+ self.item_emb = nn.Embedding(num_items, embed_dim)\n+ nn.init.normal_(self.item_emb.weight, std=0.05)\n+\n+ # Item Tower\n+ self.item_dense_proj = nn.Linear(dense_dim, embed_dim)\n+ self.item_mlp = nn.Sequential(\n+ nn.Linear(embed_dim * 2, embed_dim),\n+ nn.LayerNorm(embed_dim),\n+ nn.GELU(),\n+ nn.Linear(embed_dim, out_dim),\n+ )\n+\n+ # User Tower\n+ self.user_stats_proj = nn.Linear(stats_dim, embed_dim)\n+ user_in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n+ self.user_mlp = nn.Sequential(\n+ nn.Linear(user_in_dim, embed_dim * 2),\n+ nn.LayerNorm(embed_dim * 2),\n+ nn.GELU(),\n+ nn.Dropout(0.1),\n+ nn.Linear(embed_dim * 2, out_dim),\n+ )\n+\n+ def _pool(self, num_users, indices, uids, weights=None):\n+ out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n+ if len(indices) == 0:\n+ return out\n+ embs = self.item_emb(indices)\n+ if weights is not None:\n+ embs = embs * weights.unsqueeze(-1)\n+ out = torch.index_add(out, 0, uids, embs)\n+ norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n+ return out / norms\n+\n+ def compute_all_user_vectors(self, feats):\n+ N = feats[\"num_users\"]\n+ h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n+ h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n+ h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n+ h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n+ h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n+ s_p = self.user_stats_proj(feats[\"user_stats\"])\n+ x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_p], dim=-1)\n+ u = self.user_mlp(x)\n+ return F.normalize(u, p=2, dim=-1)\n+\n+ def compute_all_item_vectors(self, dense_features):\n+ all_ids = torch.arange(len(dense_features), dtype=torch.long)\n+ embs = self.item_emb(all_ids)\n+ d_p = self.item_dense_proj(dense_features)\n+ x = torch.cat([embs, d_p], dim=-1)\n+ v = self.item_mlp(x)\n+ return F.normalize(v, p=2, dim=-1)\n+\n+ def forward_items(self, item_ids, dense_features):\n+ embs = self.item_emb(item_ids)\n+ d_p = self.item_dense_proj(dense_features[item_ids])\n+ x = torch.cat([embs, d_p], dim=-1)\n+ v = self.item_mlp(x)\n+ return F.normalize(v, p=2, dim=-1)\n+\n+\n+model = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\n+optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\n+temperature = 0.07\n+\n+all_slices = [\n+ (slice1_feats, slice1_pairs),\n+ (slice2_feats, slice2_pairs),\n+ (slice3_feats, slice3_pairs),\n+]\n+\n+# Validation ground truth\n+val_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\n+targets_set = set(target_users_df.uid.astype(int))\n+val_gold = validation_truth(val_eligibility, targets_set)\n+\n+# Validation candidate mask unpacked\n+val_packed = val_eligibility.packed\n+val_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n+\n+print(\"Starting training...\")\n+batch_size = 512\n+num_epochs = 6\n+\n+for epoch in range(1, num_epochs + 1):\n+ t_ep = time.time()\n+ total_loss = 0.0\n+ num_batches = 0\n+\n+ for s_idx, (feats, pairs) in enumerate(all_slices):\n+ # Refresh user representations for this slice\n+ all_user_vecs = model.compute_all_user_vectors(feats)\n+\n+ # Shuffle pairs\n+ np.random.seed(epoch * 100 + s_idx)\n+ perm = np.random.permutation(len(pairs))\n+ pairs_arr = np.array(pairs)[perm]\n+\n+ for b_start in range(0, len(pairs_arr), batch_size):\n+ b_pairs = pairs_arr[b_start : b_start + batch_size]\n+ b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n+ b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n+ b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n+\n+ u_vecs = all_user_vecs[b_u]\n+ i_vecs = model.forward_items(b_i, item_dense_tensor)\n+\n+ # Sample random negatives\n+ neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n+ neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n+\n+ # In-batch candidates: pos + neg\n+ all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n+\n+ # Similarity logits\n+ logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n+\n+ # Mask out false negatives in-batch (same user)\n+ is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n+ is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n+ logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n+\n+ targets = torch.arange(len(b_pairs), dtype=torch.long)\n+ loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n+ loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n+\n+ optimizer.zero_grad()\n+ loss.backward()\n+ optimizer.step()\n+\n+ total_loss += loss.item()\n+ num_batches += 1\n+\n+ # Evaluate validation\n+ with torch.no_grad():\n+ val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n+ all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n+\n+ # Dot product scores: (8456, 30000)\n+ # In chunks of 1000 users to keep memory small\n+ ranked_dict = {}\n+ for start_u in range(0, num_targets, 1000):\n+ end_u = min(start_u + 1000, num_targets)\n+ sub_u = val_u_vecs[start_u:end_u]\n+ # (chunk_size, 30000)\n+ sub_scores = np.matmul(sub_u, all_v_vecs.T)\n+ # Mask out ineligibles\n+ sub_mask = val_unpacked[start_u:end_u]\n+ sub_scores[sub_mask == 0] = -1e9\n+\n+ # Top 100 per user\n+ top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n+ # Sort within top 100\n+ for row in range(len(sub_u)):\n+ u_row = start_u + row\n+ uid = int(target_uids[u_row])\n+ cand_idx = top100_idx[row]\n+ sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n+ ranked_dict[uid] = cat_items[sorted_idx].tolist()\n+\n+ val_score = score(ranked_dict, val_gold)\n+\n+ print(\n+ f\"Epoch {epoch}: loss={total_loss / num_batches:.4f}, \"\n+ f\"val_ndcg@10={val_score['quality_ndcg@10']:.6f} \"\n+ f\"({time.time() - t_ep:.1f}s)\"\n+ )\n",
"fileName": "prototype_eval.py",
"filePath": "/app/solution/prototype_eval.py",
"originalContent": "",
"newContent": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_data = []\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n \n # Pack for torch\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n # Recent\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n # Strong\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n features = {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n return features\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slices...\")\nslice1_feats, slice1_pairs = extract_slice_data(CUTOFF_VAL - WEEK_SECONDS)\nslice2_feats, slice2_pairs = extract_slice_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\nslice3_feats, slice3_pairs = extract_slice_data(CUTOFF_VAL - 3 * WEEK_SECONDS)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\n\n\nclass TwoTowerModel(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, stats_dim, out_dim):\n super().__init__()\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n # Shared item embeddings\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n\n # Item Tower\n self.item_dense_proj = nn.Linear(dense_dim, embed_dim)\n self.item_mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n # User Tower\n self.user_stats_proj = nn.Linear(stats_dim, embed_dim)\n user_in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.user_mlp = nn.Sequential(\n nn.Linear(user_in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.item_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_all_user_vectors(self, feats):\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user representations for this slice\n all_user_vecs = model.compute_all_user_vectors(feats)\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = all_user_vecs[b_u]\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Evaluate validation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Dot product scores: (8456, 30000)\n # In chunks of 1000 users to keep memory small\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n # (chunk_size, 30000)\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n # Mask out ineligibles\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n # Top 100 per user\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n # Sort within top 100\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n\n print(\n f\"Epoch {epoch}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={val_score['quality_ndcg@10']:.6f} \"\n f\"({time.time() - t_ep:.1f}s)\"\n )\n",
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"timestamp": "2026-09-29T06:16:05.482Z",
"content": "Successfully created and wrote to new file: /app/solution/prototype_eval.py. Here is the updated code:\nimport os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_data = []\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n \n # Pack for torch\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n # Recent\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n # Strong\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n features = {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n return features\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slices...\")\nslice1_feats, slice1_pairs = extract_slice_data(CUTOFF_VAL - WEEK_SECONDS)\nslice2_feats, slice2_pairs = extract_slice_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\nslice3_feats, slice3_pairs = extract_slice_data(CUTOFF_VAL - 3 * WEEK_SECONDS)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\n\n\nclass TwoTowerModel(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, stats_dim, out_dim):\n super().__init__()\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n # Shared item embeddings\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n\n # Item Tower\n self.item_dense_proj = nn.Linear(dense_dim, embed_dim)\n self.item_mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n # User Tower\n self.user_stats_proj = nn.Linear(stats_dim, embed_dim)\n user_in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.user_mlp = nn.Sequential(\n nn.Linear(user_in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.item_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_all_user_vectors(self, feats):\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user representations for this slice\n all_user_vecs = model.compute_all_user_vectors(feats)\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = all_user_vecs[b_u]\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Evaluate validation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Dot product scores: (8456, 30000)\n # In chunks of 1000 users to keep memory small\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n # (chunk_size, 30000)\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n # Mask out ineligibles\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n # Top 100 per user\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n # Sort within top 100\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n\n print(\n f\"Epoch {epoch}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={val_score['quality_ndcg@10']:.6f} \"\n f\"({time.time() - t_ep:.1f}s)\"\n )\n",
"isError": false
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"responseMessageId": "ba4002a6-4921-4546-a5f5-e91e84346cfd"
}
]
},
{
"id": 58,
"role": "agent",
"timestamp": "2026-09-29T06:16:09.999Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T06:16:09.999Z",
"callId": "run_shell_command__call_220013",
"name": "run_shell_command",
"input": {
"description": "Run prototype_eval.py",
"command": "python3 /app/solution/prototype_eval.py"
},
"recorded": {
"id": "run_shell_command__call_220013",
"name": "run_shell_command",
"args": {
"description": "Run prototype_eval.py",
"command": "python3 /app/solution/prototype_eval.py"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_220013",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Loading metadata...\nLoading raw events...\nEvents loaded in 2.45s\nExtracting training slices...\nStarting training...\nTraceback (most recent call last):\n File \"/app/solution/prototype_eval.py\", line 382, in \n loss.backward()\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/_tensor.py\", line 647, in backward\n torch.autograd.backward(\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/autograd/__init__.py\", line 354, in backward\n _engine_run_backward(\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/autograd/graph.py\", line 829, in _engine_run_backward\n return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nRuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.\nExit Code: 1\nProcess Group PGID: 2725\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:17:09.118Z",
"resultDisplay": "Loading metadata...\nLoading raw events...\nEvents loaded in 2.45s\nExtracting training slices...\nStarting training...\nTraceback (most recent call last):\n File \"/app/solution/prototype_eval.py\", line 382, in \n loss.backward()\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/_tensor.py\", line 647, in backward\n torch.autograd.backward(\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/autograd/__init__.py\", line 354, in backward\n _engine_run_backward(\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/autograd/graph.py\", line 829, in _engine_run_backward\n return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nRuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.",
"description": "python3 /app/solution/prototype_eval.py",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:17:09.120Z",
"content": "\nOutput: Loading metadata...\nLoading raw events...\nEvents loaded in 2.45s\nExtracting training slices...\nStarting training...\nTraceback (most recent call last):\n File \"/app/solution/prototype_eval.py\", line 382, in \n loss.backward()\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/_tensor.py\", line 647, in backward\n torch.autograd.backward(\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/autograd/__init__.py\", line 354, in backward\n _engine_run_backward(\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/autograd/graph.py\", line 829, in _engine_run_backward\n return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nRuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.\nExit Code: 1\nProcess Group PGID: 2725\n",
"isError": false
},
"responseMessageId": "64b216a4-ed0a-43c6-8229-80a96682a7a1"
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]
},
{
"id": 59,
"role": "agent",
"timestamp": "2026-09-29T06:17:31.953Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:17:31.953Z",
"text": "Analyzing Backpropagation Error\n\nI'm currently dissecting a `RuntimeError` that surfaced. It appears `model.compute_all_user_vectors(feats)` was executed prior to the mini-batch loop. This has resulted in a shared computation graph for `all_user_vecs` that's persisting across batches. When `loss.backward()` was triggered on batch 2, PyTorch encountered an issue trying to backpropagate through this shared structure."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:17:31.953Z",
"text": "Refining User Vector Computation\n\nI'm exploring ways to detach `all_user_vecs` to prevent shared computation graphs, considering options like `torch.no_grad()` or `.detach()`. I need to ensure the user MLP still receives gradients, so I'm examining if pre-computing and detaching pooled views, then recomputing user vectors within each mini-batch, is the most effective approach."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:17:31.953Z",
"callId": "replace__call_963716",
"name": "replace",
"input": {
"allow_multiple": false,
"new_string": " def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user pooled views for this slice using current item embeddings\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n # Fresh forward pass for user batch\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1",
"old_string": " def compute_all_user_vectors(self, feats):\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user representations for this slice\n all_user_vecs = model.compute_all_user_vectors(feats)\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = all_user_vecs[b_u]\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1",
"instruction": "Compute pooled views with no_grad and forward user MLP per batch so graph is fresh per batch",
"file_path": "/app/solution/prototype_eval.py"
},
"recorded": {
"id": "replace__call_963716",
"name": "replace",
"args": {
"allow_multiple": false,
"new_string": " def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user pooled views for this slice using current item embeddings\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n # Fresh forward pass for user batch\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1",
"old_string": " def compute_all_user_vectors(self, feats):\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user representations for this slice\n all_user_vecs = model.compute_all_user_vectors(feats)\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = all_user_vecs[b_u]\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1",
"instruction": "Compute pooled views with no_grad and forward user MLP per batch so graph is fresh per batch",
"file_path": "/app/solution/prototype_eval.py"
},
"result": [
{
"functionResponse": {
"id": "replace__call_963716",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/prototype_eval.py (1 replacements). Here is the updated code:\n...\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n...\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user pooled views for this slice using current item embeddings\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n...\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n # Fresh forward pass for user batch\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:17:31.971Z",
"resultDisplay": {
"fileDiff": "Index: prototype_eval.py\n===================================================================\n--- prototype_eval.py\tCurrent\n+++ prototype_eval.py\tProposed\n@@ -285,25 +285,38 @@\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n \n- def compute_all_user_vectors(self, feats):\n- N = feats[\"num_users\"]\n- h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n- h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n- h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n- h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n- h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n- s_p = self.user_stats_proj(feats[\"user_stats\"])\n- x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_p], dim=-1)\n+ def compute_user_pooled_views(self, feats):\n+ with torch.no_grad():\n+ N = feats[\"num_users\"]\n+ h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n+ h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n+ h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n+ h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n+ h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n+ return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n+\n+ def forward_user(self, pooled_views_batch, stats_batch):\n+ s_p = self.user_stats_proj(stats_batch)\n+ x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n \n+ def compute_all_user_vectors(self, feats):\n+ with torch.no_grad():\n+ pooled = self.compute_user_pooled_views(feats)\n+ s_p = self.user_stats_proj(feats[\"user_stats\"])\n+ x = torch.cat([pooled, s_p], dim=-1)\n+ u = self.user_mlp(x)\n+ return F.normalize(u, p=2, dim=-1)\n+\n def compute_all_item_vectors(self, dense_features):\n- all_ids = torch.arange(len(dense_features), dtype=torch.long)\n- embs = self.item_emb(all_ids)\n- d_p = self.item_dense_proj(dense_features)\n- x = torch.cat([embs, d_p], dim=-1)\n- v = self.item_mlp(x)\n- return F.normalize(v, p=2, dim=-1)\n+ with torch.no_grad():\n+ all_ids = torch.arange(len(dense_features), dtype=torch.long)\n+ embs = self.item_emb(all_ids)\n+ d_p = self.item_dense_proj(dense_features)\n+ x = torch.cat([embs, d_p], dim=-1)\n+ v = self.item_mlp(x)\n+ return F.normalize(v, p=2, dim=-1)\n \n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n@@ -342,8 +355,9 @@\n num_batches = 0\n \n for s_idx, (feats, pairs) in enumerate(all_slices):\n- # Refresh user representations for this slice\n- all_user_vecs = model.compute_all_user_vectors(feats)\n+ # Refresh user pooled views for this slice using current item embeddings\n+ slice_pooled_views = model.compute_user_pooled_views(feats)\n+ slice_stats = feats[\"user_stats\"]\n \n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n@@ -356,7 +370,8 @@\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n \n- u_vecs = all_user_vecs[b_u]\n+ # Fresh forward pass for user batch\n+ u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n \n # Sample random negatives\n",
"fileName": "prototype_eval.py",
"filePath": "/app/solution/prototype_eval.py",
"originalContent": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_data = []\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n \n # Pack for torch\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n # Recent\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n # Strong\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n features = {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n return features\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slices...\")\nslice1_feats, slice1_pairs = extract_slice_data(CUTOFF_VAL - WEEK_SECONDS)\nslice2_feats, slice2_pairs = extract_slice_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\nslice3_feats, slice3_pairs = extract_slice_data(CUTOFF_VAL - 3 * WEEK_SECONDS)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\n\n\nclass TwoTowerModel(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, stats_dim, out_dim):\n super().__init__()\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n # Shared item embeddings\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n\n # Item Tower\n self.item_dense_proj = nn.Linear(dense_dim, embed_dim)\n self.item_mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n # User Tower\n self.user_stats_proj = nn.Linear(stats_dim, embed_dim)\n user_in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.user_mlp = nn.Sequential(\n nn.Linear(user_in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.item_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_all_user_vectors(self, feats):\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user representations for this slice\n all_user_vecs = model.compute_all_user_vectors(feats)\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = all_user_vecs[b_u]\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Evaluate validation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Dot product scores: (8456, 30000)\n # In chunks of 1000 users to keep memory small\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n # (chunk_size, 30000)\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n # Mask out ineligibles\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n # Top 100 per user\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n # Sort within top 100\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n\n print(\n f\"Epoch {epoch}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={val_score['quality_ndcg@10']:.6f} \"\n f\"({time.time() - t_ep:.1f}s)\"\n )\n",
"newContent": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_data = []\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n \n # Pack for torch\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n # Recent\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n # Strong\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n features = {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n return features\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slices...\")\nslice1_feats, slice1_pairs = extract_slice_data(CUTOFF_VAL - WEEK_SECONDS)\nslice2_feats, slice2_pairs = extract_slice_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\nslice3_feats, slice3_pairs = extract_slice_data(CUTOFF_VAL - 3 * WEEK_SECONDS)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\n\n\nclass TwoTowerModel(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, stats_dim, out_dim):\n super().__init__()\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n # Shared item embeddings\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n\n # Item Tower\n self.item_dense_proj = nn.Linear(dense_dim, embed_dim)\n self.item_mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n # User Tower\n self.user_stats_proj = nn.Linear(stats_dim, embed_dim)\n user_in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.user_mlp = nn.Sequential(\n nn.Linear(user_in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.item_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user pooled views for this slice using current item embeddings\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n # Fresh forward pass for user batch\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Evaluate validation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Dot product scores: (8456, 30000)\n # In chunks of 1000 users to keep memory small\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n # (chunk_size, 30000)\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n # Mask out ineligibles\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n # Top 100 per user\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n # Sort within top 100\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n\n print(\n f\"Epoch {epoch}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={val_score['quality_ndcg@10']:.6f} \"\n f\"({time.time() - t_ep:.1f}s)\"\n )\n",
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"description": "solution/prototype_eval.py: def compute_all_user_vecto... => def compute_user_pooled_vi...",
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"result": {
"timestamp": "2026-09-29T06:17:31.973Z",
"content": "Successfully modified file: /app/solution/prototype_eval.py (1 replacements). Here is the updated code:\n...\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n...\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user pooled views for this slice using current item embeddings\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n...\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n # Fresh forward pass for user batch\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n...",
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},
"responseMessageId": "6b633696-5cc7-4243-9693-ef3228953cad"
}
]
},
{
"id": 60,
"role": "agent",
"timestamp": "2026-09-29T06:17:34.586Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T06:17:34.586Z",
"callId": "run_shell_command__call_309048",
"name": "run_shell_command",
"input": {
"command": "python3 /app/solution/prototype_eval.py",
"description": "Run prototype_eval.py training and validation"
},
"recorded": {
"id": "run_shell_command__call_309048",
"name": "run_shell_command",
"args": {
"command": "python3 /app/solution/prototype_eval.py",
"description": "Run prototype_eval.py training and validation"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_309048",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Loading metadata...\nLoading raw events...\nEvents loaded in 2.46s\nExtracting training slices...\nStarting training...\nEpoch 1: loss=8.9639, val_ndcg@10=0.008040 (7.5s)\nEpoch 2: loss=8.6391, val_ndcg@10=0.010863 (7.7s)\nEpoch 3: loss=8.2330, val_ndcg@10=0.008554 (7.4s)\nEpoch 4: loss=7.9092, val_ndcg@10=0.006901 (7.4s)\nEpoch 5: loss=7.6701, val_ndcg@10=0.004623 (7.4s)\nEpoch 6: loss=7.4679, val_ndcg@10=0.006293 (7.4s)\nProcess Group PGID: 2767\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:19:17.967Z",
"resultDisplay": "Loading metadata...\nLoading raw events...\nEvents loaded in 2.46s\nExtracting training slices...\nStarting training...\nEpoch 1: loss=8.9639, val_ndcg@10=0.008040 (7.5s)\nEpoch 2: loss=8.6391, val_ndcg@10=0.010863 (7.7s)\nEpoch 3: loss=8.2330, val_ndcg@10=0.008554 (7.4s)\nEpoch 4: loss=7.9092, val_ndcg@10=0.006901 (7.4s)\nEpoch 5: loss=7.6701, val_ndcg@10=0.004623 (7.4s)\nEpoch 6: loss=7.4679, val_ndcg@10=0.006293 (7.4s)",
"description": "python3 /app/solution/prototype_eval.py",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:19:17.969Z",
"content": "\nOutput: Loading metadata...\nLoading raw events...\nEvents loaded in 2.46s\nExtracting training slices...\nStarting training...\nEpoch 1: loss=8.9639, val_ndcg@10=0.008040 (7.5s)\nEpoch 2: loss=8.6391, val_ndcg@10=0.010863 (7.7s)\nEpoch 3: loss=8.2330, val_ndcg@10=0.008554 (7.4s)\nEpoch 4: loss=7.9092, val_ndcg@10=0.006901 (7.4s)\nEpoch 5: loss=7.6701, val_ndcg@10=0.004623 (7.4s)\nEpoch 6: loss=7.4679, val_ndcg@10=0.006293 (7.4s)\nProcess Group PGID: 2767\n",
"isError": false
},
"responseMessageId": "f26e7c78-4b01-47c4-9610-d1451c4acc42"
}
]
},
{
"id": 61,
"role": "agent",
"timestamp": "2026-09-29T06:19:49.226Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:19:49.226Z",
"text": "Observing Rapid Progress\n\nI'm seeing impressive speed gains; training three slices took only 7.4 seconds per epoch. Val_ndcg surpassed the baseline early, but then dipped. I'm now focused on diagnosing this decrease, suspecting in-batch negative sampling bias as a primary culprit."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:19:49.226Z",
"text": "Investigating Negative Sampling\n\nMy attention is now fixed on the in-batch negative sampling. I'm examining how frequently occurring positive items are disproportionately affecting negative evaluations and how uniform sampling might be introducing bias without proper popularity debiasing, particularly concerning how other users' positives are treated."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:19:49.226Z",
"text": "Diagnosing Over-Penalization\n\nI've confirmed my suspicion: popular items appearing multiple times within a batch are being incorrectly penalized as negatives for different users. This explains the performance dip as the model was actively driven away from items liked by more than one user."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:19:49.226Z",
"text": "Refining Batch Negatives\n\nI'm now exploring how to correctly handle duplicate positive items within a batch to avoid penalizing them as negatives. My current hypothesis is to mask these out for accurate loss calculation. I'm also considering alternative negative sampling strategies, such as using sampled catalog negatives or a debiased in-batch approach, with a focus on ensuring candidate items in the batch are unique to prevent false negative conflicts."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:19:49.226Z",
"text": "Testing New Strategies\n\nI'm now implementing a refined approach where unique items are paired with random catalog negatives. This setup ensures each item is evaluated only once by the item tower, and critically, eliminates false negative collisions by handling duplicate positives within a batch through masking or multi-label loss functions."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:19:49.226Z",
"text": "Analyzing Feature Influence\n\nI'm now investigating which specific features contribute most to NDCG@10 and delving into the item tower's categorical features, like listen counts and popularity rank, considering how to best incorporate item popularity, perhaps through learned bias or by adjusting the scoring mechanism itself, potentially moving beyond simple dot products of normalized vectors."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:19:49.226Z",
"text": "Incorporating Bias Terms\n\nI'm now exploring how to integrate bias terms directly into the dot product. This allows the model to account for both user affinity and inherent item quality or popularity, ensuring a more nuanced ranking beyond simple embedding similarity."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:19:49.226Z",
"text": "Adding Bias Terms\n\nI'm now formulating a strategy to integrate learned bias terms into the scoring function. The plan is to have the item tower output a vector including a popularity bias, and the user tower output a temperature/bias weight. This will allow scores to balance personalization with item quality, and I'm eager to test this mathematically and then implement it."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:19:49.226Z",
"callId": "run_shell_command__call_421752",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nb_i = torch.tensor([1, 5, 1, 8, 5, 10])\nunique_items, item_inverse = torch.unique(b_i, return_inverse=True)\nprint('unique_items:', unique_items)\nprint('item_inverse:', item_inverse)\nprint('Reconstruction:', unique_items[item_inverse])\n\"",
"description": "Verify unique_items and item_inverse in PyTorch"
},
"recorded": {
"id": "run_shell_command__call_421752",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nb_i = torch.tensor([1, 5, 1, 8, 5, 10])\nunique_items, item_inverse = torch.unique(b_i, return_inverse=True)\nprint('unique_items:', unique_items)\nprint('item_inverse:', item_inverse)\nprint('Reconstruction:', unique_items[item_inverse])\n\"",
"description": "Verify unique_items and item_inverse in PyTorch"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_421752",
"name": "run_shell_command",
"response": {
"output": "\nOutput: unique_items: tensor([ 1, 5, 8, 10])\nitem_inverse: tensor([0, 1, 0, 2, 1, 3])\nReconstruction: tensor([ 1, 5, 1, 8, 5, 10])\nProcess Group PGID: 2820\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:19:52.158Z",
"resultDisplay": "unique_items: tensor([ 1, 5, 8, 10])\nitem_inverse: tensor([0, 1, 0, 2, 1, 3])\nReconstruction: tensor([ 1, 5, 1, 8, 5, 10])",
"description": "Verify unique_items and item_inverse in PyTorch",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:19:53.611Z",
"content": "\nOutput: unique_items: tensor([ 1, 5, 8, 10])\nitem_inverse: tensor([0, 1, 0, 2, 1, 3])\nReconstruction: tensor([ 1, 5, 1, 8, 5, 10])\nProcess Group PGID: 2820\n",
"isError": false
},
"responseMessageId": "40aaa619-8e3a-46fe-bfa5-1782e958d6dd"
}
]
},
{
"id": 62,
"role": "agent",
"timestamp": "2026-09-29T06:20:22.596Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:20:22.596Z",
"text": "Analyzing User Similarities\n\nI'm noticing some interesting patterns. Specifically, users 0 and 2 have identified the exact same target item, which is item 1. This shared preference suggests a potential avenue for collaborative filtering or identifying similar user profiles. It's a good lead to follow."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:20:22.596Z",
"callId": "replace__call_118851",
"name": "replace",
"input": {
"old_string": " def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user pooled views for this slice using current item embeddings\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n # Fresh forward pass for user batch\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1",
"allow_multiple": false,
"file_path": "/app/solution/prototype_eval.py",
"instruction": "Use unique candidate items in batch and incorporate bias dimension in dot product",
"new_string": " def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n # Learnable scaling for item bias\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 8\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # User positive sets for false negative masking\n u_to_pos = {}\n for u_idx, i_idx, _ in pairs:\n u_to_pos.setdefault(u_idx, set()).add(i_idx)\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n\n # Deduplicate target items in batch\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.forward_items(cand_item_ids, item_dense_tensor)\n\n # Dot product logits: (B, num_candidates)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Mask out false negatives: if cand_item_ids[c] in u_to_pos[u], and c != item_inverse[row]\n cand_items_np = cand_item_ids.numpy()\n b_u_np = b_u.numpy()\n inv_np = item_inverse.numpy()\n mask_matrix = torch.zeros_like(logits, dtype=torch.bool)\n for row in range(len(b_u)):\n u_row = b_u_np[row]\n pos_set = u_to_pos.get(u_row, set())\n tgt = inv_np[row]\n for c in range(len(unique_pos_items)):\n if c != tgt and cand_items_np[c] in pos_set:\n mask_matrix[row, c] = True\n\n logits.masked_fill_(mask_matrix, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1"
},
"recorded": {
"id": "replace__call_118851",
"name": "replace",
"args": {
"old_string": " def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user pooled views for this slice using current item embeddings\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n # Fresh forward pass for user batch\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1",
"allow_multiple": false,
"file_path": "/app/solution/prototype_eval.py",
"instruction": "Use unique candidate items in batch and incorporate bias dimension in dot product",
"new_string": " def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n # Learnable scaling for item bias\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 8\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # User positive sets for false negative masking\n u_to_pos = {}\n for u_idx, i_idx, _ in pairs:\n u_to_pos.setdefault(u_idx, set()).add(i_idx)\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n\n # Deduplicate target items in batch\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.forward_items(cand_item_ids, item_dense_tensor)\n\n # Dot product logits: (B, num_candidates)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Mask out false negatives: if cand_item_ids[c] in u_to_pos[u], and c != item_inverse[row]\n cand_items_np = cand_item_ids.numpy()\n b_u_np = b_u.numpy()\n inv_np = item_inverse.numpy()\n mask_matrix = torch.zeros_like(logits, dtype=torch.bool)\n for row in range(len(b_u)):\n u_row = b_u_np[row]\n pos_set = u_to_pos.get(u_row, set())\n tgt = inv_np[row]\n for c in range(len(unique_pos_items)):\n if c != tgt and cand_items_np[c] in pos_set:\n mask_matrix[row, c] = True\n\n logits.masked_fill_(mask_matrix, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1"
},
"result": [
{
"functionResponse": {
"id": "replace__call_118851",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/prototype_eval.py (1 replacements). Here is the updated code:\n...\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n # Learnable scaling for item bias\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n...\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 8\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # User positive sets for false negative masking\n u_to_pos = {}\n for u_idx, i_idx, _ in pairs:\n u_to_pos.setdefault(u_idx, set()).add(i_idx)\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n\n # Deduplicate target items in batch\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.forward_items(cand_item_ids, item_dense_tensor)\n\n # Dot product logits: (B, num_candidates)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Mask out false negatives: if cand_item_ids[c] in u_to_pos[u], and c != item_inverse[row]\n cand_items_np = cand_item_ids.numpy()\n b_u_np = b_u.numpy()\n inv_np = item_inverse.numpy()\n mask_matrix = torch.zeros_like(logits, dtype=torch.bool)\n for row in range(len(b_u)):\n u_row = b_u_np[row]\n pos_set = u_to_pos.get(u_row, set())\n tgt = inv_np[row]\n for c in range(len(unique_pos_items)):\n if c != tgt and cand_items_np[c] in pos_set:\n mask_matrix[row, c] = True\n\n logits.masked_fill_(mask_matrix, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:20:22.611Z",
"resultDisplay": {
"fileDiff": "Index: prototype_eval.py\n===================================================================\n--- prototype_eval.py\tCurrent\n+++ prototype_eval.py\tProposed\n@@ -323,9 +323,41 @@\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n- return F.normalize(v, p=2, dim=-1)\n+ v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n+ b = v[:, -1:]\n+ return torch.cat([v_norm, b], dim=-1)\n \n+ def forward_user(self, pooled_views_batch, stats_batch):\n+ s_p = self.user_stats_proj(stats_batch)\n+ x = torch.cat([pooled_views_batch, s_p], dim=-1)\n+ u = self.user_mlp(x)\n+ u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n+ # Learnable scaling for item bias\n+ alpha = torch.sigmoid(u[:, -1:])\n+ return torch.cat([u_norm, alpha], dim=-1)\n \n+ def compute_all_user_vectors(self, feats):\n+ with torch.no_grad():\n+ pooled = self.compute_user_pooled_views(feats)\n+ s_p = self.user_stats_proj(feats[\"user_stats\"])\n+ x = torch.cat([pooled, s_p], dim=-1)\n+ u = self.user_mlp(x)\n+ u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n+ alpha = torch.sigmoid(u[:, -1:])\n+ return torch.cat([u_norm, alpha], dim=-1)\n+\n+ def compute_all_item_vectors(self, dense_features):\n+ with torch.no_grad():\n+ all_ids = torch.arange(len(dense_features), dtype=torch.long)\n+ embs = self.item_emb(all_ids)\n+ d_p = self.item_dense_proj(dense_features)\n+ x = torch.cat([embs, d_p], dim=-1)\n+ v = self.item_mlp(x)\n+ v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n+ b = v[:, -1:]\n+ return torch.cat([v_norm, b], dim=-1)\n+\n+\n model = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\n optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\n temperature = 0.07\n@@ -347,7 +379,7 @@\n \n print(\"Starting training...\")\n batch_size = 512\n-num_epochs = 6\n+num_epochs = 8\n \n for epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n@@ -355,11 +387,14 @@\n num_batches = 0\n \n for s_idx, (feats, pairs) in enumerate(all_slices):\n- # Refresh user pooled views for this slice using current item embeddings\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n \n- # Shuffle pairs\n+ # User positive sets for false negative masking\n+ u_to_pos = {}\n+ for u_idx, i_idx, _ in pairs:\n+ u_to_pos.setdefault(u_idx, set()).add(i_idx)\n+\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n@@ -370,26 +405,36 @@\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n \n- # Fresh forward pass for user batch\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n- i_vecs = model.forward_items(b_i, item_dense_tensor)\n \n+ # Deduplicate target items in batch\n+ unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n+\n # Sample random negatives\n- neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n- neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n+ neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n+ cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n \n- # In-batch candidates: pos + neg\n- all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n+ cand_vecs = model.forward_items(cand_item_ids, item_dense_tensor)\n \n- # Similarity logits\n- logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n+ # Dot product logits: (B, num_candidates)\n+ logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n \n- # Mask out false negatives in-batch (same user)\n- is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n- is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n- logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n+ # Mask out false negatives: if cand_item_ids[c] in u_to_pos[u], and c != item_inverse[row]\n+ cand_items_np = cand_item_ids.numpy()\n+ b_u_np = b_u.numpy()\n+ inv_np = item_inverse.numpy()\n+ mask_matrix = torch.zeros_like(logits, dtype=torch.bool)\n+ for row in range(len(b_u)):\n+ u_row = b_u_np[row]\n+ pos_set = u_to_pos.get(u_row, set())\n+ tgt = inv_np[row]\n+ for c in range(len(unique_pos_items)):\n+ if c != tgt and cand_items_np[c] in pos_set:\n+ mask_matrix[row, c] = True\n \n- targets = torch.arange(len(b_pairs), dtype=torch.long)\n+ logits.masked_fill_(mask_matrix, -1e9)\n+\n+ targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n \n",
"fileName": "prototype_eval.py",
"filePath": "/app/solution/prototype_eval.py",
"originalContent": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_data = []\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n \n # Pack for torch\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n # Recent\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n # Strong\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n features = {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n return features\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slices...\")\nslice1_feats, slice1_pairs = extract_slice_data(CUTOFF_VAL - WEEK_SECONDS)\nslice2_feats, slice2_pairs = extract_slice_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\nslice3_feats, slice3_pairs = extract_slice_data(CUTOFF_VAL - 3 * WEEK_SECONDS)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\n\n\nclass TwoTowerModel(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, stats_dim, out_dim):\n super().__init__()\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n # Shared item embeddings\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n\n # Item Tower\n self.item_dense_proj = nn.Linear(dense_dim, embed_dim)\n self.item_mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n # User Tower\n self.user_stats_proj = nn.Linear(stats_dim, embed_dim)\n user_in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.user_mlp = nn.Sequential(\n nn.Linear(user_in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.item_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user pooled views for this slice using current item embeddings\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n # Fresh forward pass for user batch\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Evaluate validation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Dot product scores: (8456, 30000)\n # In chunks of 1000 users to keep memory small\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n # (chunk_size, 30000)\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n # Mask out ineligibles\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n # Top 100 per user\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n # Sort within top 100\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n\n print(\n f\"Epoch {epoch}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={val_score['quality_ndcg@10']:.6f} \"\n f\"({time.time() - t_ep:.1f}s)\"\n )\n",
"newContent": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_data = []\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n \n # Pack for torch\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n # Recent\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n # Strong\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n features = {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n return features\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slices...\")\nslice1_feats, slice1_pairs = extract_slice_data(CUTOFF_VAL - WEEK_SECONDS)\nslice2_feats, slice2_pairs = extract_slice_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\nslice3_feats, slice3_pairs = extract_slice_data(CUTOFF_VAL - 3 * WEEK_SECONDS)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\n\n\nclass TwoTowerModel(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, stats_dim, out_dim):\n super().__init__()\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n # Shared item embeddings\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n\n # Item Tower\n self.item_dense_proj = nn.Linear(dense_dim, embed_dim)\n self.item_mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n # User Tower\n self.user_stats_proj = nn.Linear(stats_dim, embed_dim)\n user_in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.user_mlp = nn.Sequential(\n nn.Linear(user_in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.item_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n # Learnable scaling for item bias\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 8\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # User positive sets for false negative masking\n u_to_pos = {}\n for u_idx, i_idx, _ in pairs:\n u_to_pos.setdefault(u_idx, set()).add(i_idx)\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n\n # Deduplicate target items in batch\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.forward_items(cand_item_ids, item_dense_tensor)\n\n # Dot product logits: (B, num_candidates)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Mask out false negatives: if cand_item_ids[c] in u_to_pos[u], and c != item_inverse[row]\n cand_items_np = cand_item_ids.numpy()\n b_u_np = b_u.numpy()\n inv_np = item_inverse.numpy()\n mask_matrix = torch.zeros_like(logits, dtype=torch.bool)\n for row in range(len(b_u)):\n u_row = b_u_np[row]\n pos_set = u_to_pos.get(u_row, set())\n tgt = inv_np[row]\n for c in range(len(unique_pos_items)):\n if c != tgt and cand_items_np[c] in pos_set:\n mask_matrix[row, c] = True\n\n logits.masked_fill_(mask_matrix, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Evaluate validation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Dot product scores: (8456, 30000)\n # In chunks of 1000 users to keep memory small\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n # (chunk_size, 30000)\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n # Mask out ineligibles\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n # Top 100 per user\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n # Sort within top 100\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n\n print(\n f\"Epoch {epoch}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={val_score['quality_ndcg@10']:.6f} \"\n f\"({time.time() - t_ep:.1f}s)\"\n )\n",
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"description": "solution/prototype_eval.py: def forward_items(self, it... => def forward_items(self, it...",
"displayName": "Edit",
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"result": {
"timestamp": "2026-09-29T06:20:22.613Z",
"content": "Successfully modified file: /app/solution/prototype_eval.py (1 replacements). Here is the updated code:\n...\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n # Learnable scaling for item bias\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n...\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 8\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # User positive sets for false negative masking\n u_to_pos = {}\n for u_idx, i_idx, _ in pairs:\n u_to_pos.setdefault(u_idx, set()).add(i_idx)\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n\n # Deduplicate target items in batch\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.forward_items(cand_item_ids, item_dense_tensor)\n\n # Dot product logits: (B, num_candidates)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Mask out false negatives: if cand_item_ids[c] in u_to_pos[u], and c != item_inverse[row]\n cand_items_np = cand_item_ids.numpy()\n b_u_np = b_u.numpy()\n inv_np = item_inverse.numpy()\n mask_matrix = torch.zeros_like(logits, dtype=torch.bool)\n for row in range(len(b_u)):\n u_row = b_u_np[row]\n pos_set = u_to_pos.get(u_row, set())\n tgt = inv_np[row]\n for c in range(len(unique_pos_items)):\n if c != tgt and cand_items_np[c] in pos_set:\n mask_matrix[row, c] = True\n\n logits.masked_fill_(mask_matrix, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n...",
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"role": "agent",
"timestamp": "2026-09-29T06:20:34.311Z",
"blocks": [
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"callId": "run_shell_command__call_123995",
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"description": "Run prototype_eval.py with deduplicated items and bias dimension",
"command": "python3 /app/solution/prototype_eval.py"
},
"recorded": {
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"description": "Run prototype_eval.py with deduplicated items and bias dimension",
"command": "python3 /app/solution/prototype_eval.py"
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"output": "\nOutput: Loading metadata...\nLoading raw events...\nEvents loaded in 2.46s\nExtracting training slices...\nStarting training...\nEpoch 1: loss=8.8755, val_ndcg@10=0.013806 (26.6s)\nEpoch 2: loss=8.5592, val_ndcg@10=0.022638 (26.9s)\nEpoch 3: loss=8.1530, val_ndcg@10=0.020206 (26.4s)\nEpoch 4: loss=7.8067, val_ndcg@10=0.010743 (27.1s)\nEpoch 5: loss=7.5487, val_ndcg@10=0.008173 (27.0s)\nEpoch 6: loss=7.3324, val_ndcg@10=0.007602 (26.8s)\nEpoch 7: loss=7.1409, val_ndcg@10=0.006552 (27.0s)\nEpoch 8: loss=6.9729, val_ndcg@10=0.005241 (26.8s)\nProcess Group PGID: 2830\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:25:06.902Z",
"resultDisplay": "Loading metadata...\nLoading raw events...\nEvents loaded in 2.46s\nExtracting training slices...\nStarting training...\nEpoch 1: loss=8.8755, val_ndcg@10=0.013806 (26.6s)\nEpoch 2: loss=8.5592, val_ndcg@10=0.022638 (26.9s)\nEpoch 3: loss=8.1530, val_ndcg@10=0.020206 (26.4s)\nEpoch 4: loss=7.8067, val_ndcg@10=0.010743 (27.1s)\nEpoch 5: loss=7.5487, val_ndcg@10=0.008173 (27.0s)\nEpoch 6: loss=7.3324, val_ndcg@10=0.007602 (26.8s)\nEpoch 7: loss=7.1409, val_ndcg@10=0.006552 (27.0s)\nEpoch 8: loss=6.9729, val_ndcg@10=0.005241 (26.8s)",
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"result": {
"timestamp": "2026-09-29T06:25:06.905Z",
"content": "\nOutput: Loading metadata...\nLoading raw events...\nEvents loaded in 2.46s\nExtracting training slices...\nStarting training...\nEpoch 1: loss=8.8755, val_ndcg@10=0.013806 (26.6s)\nEpoch 2: loss=8.5592, val_ndcg@10=0.022638 (26.9s)\nEpoch 3: loss=8.1530, val_ndcg@10=0.020206 (26.4s)\nEpoch 4: loss=7.8067, val_ndcg@10=0.010743 (27.1s)\nEpoch 5: loss=7.5487, val_ndcg@10=0.008173 (27.0s)\nEpoch 6: loss=7.3324, val_ndcg@10=0.007602 (26.8s)\nEpoch 7: loss=7.1409, val_ndcg@10=0.006552 (27.0s)\nEpoch 8: loss=6.9729, val_ndcg@10=0.005241 (26.8s)\nProcess Group PGID: 2830\n",
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},
{
"id": 64,
"role": "agent",
"timestamp": "2026-09-29T06:25:39.837Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:25:39.837Z",
"text": "Observing the Trend\n\nMy recent analysis shows a concerning overfitting trend. The model's performance spiked initially, exceeding the baseline, but then deteriorated. I've identified the likely culprit: a fixed learning rate of 1e-3 without scheduling or regularization, which appears to be causing it to over-specialize on the training data."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:25:39.837Z",
"text": "Implementing Regularization\n\nI'm now exploring stronger regularization techniques. This involves increasing `weight_decay` and experimenting with a cosine learning rate schedule. I'm also investigating ways to optimize the masking operation, which is currently a bottleneck. Additionally, I plan to increase the number of training slices to provide more data and combat overfitting."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:25:39.837Z",
"text": "Investigating Likes\n\nMy current focus is on boosting NDCG@10 by analyzing top-ranked recommendations. I've discovered a critical oversight: the validation user features did not include likes from `validation_preference_state.parquet`, only `train_likes.parquet`. This omission of over 400,000 active liked tracks is likely impacting performance."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:25:39.837Z",
"text": "Validating Preferences\n\nI'm now verifying the impact of incorporating user active likes and dislikes from `validation_preference_state.parquet`. My investigation into item features is also underway, aiming to enrich recommendations with metrics like total likes, dislike ratios, and listen completion rates, which I believe will significantly improve NDCG@10."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:25:39.837Z",
"text": "Tuning the Model\n\nI'm now exploring the impact of weighting Grade 3 likes more heavily (5.0 or 7.0) to prioritize highly appreciated items. Additionally, I'm experimenting with different temperature values ($\\tau$) and increasing training slices to six for greater temporal diversity and to combat overfitting. I'm also investigating item embedding initialization strategies."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:25:39.837Z",
"callId": "update_topic__call_53889",
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"input": {
"summary": "In Phase 2, built and validated the first PyTorch Two-Tower ranker. Discovered that deduplicating batch items and adding an item bias dimension achieved 0.022638 NDCG@10 at epoch 2 (more than 2x baseline 0.01071). Moving to Phase 3: incorporating preference state, expanding training slices to 6 weeks, vectorizing batch negative masking, tuning learning rate and regularization, and engineering richer item and user features.",
"strategic_intent": "Systematically improve the two-tower model architecture, features, and training recipe.",
"title": "Phase 3: Architecture & Feature Engineering Optimization"
},
"recorded": {
"id": "update_topic__call_53889",
"name": "update_topic",
"args": {
"summary": "In Phase 2, built and validated the first PyTorch Two-Tower ranker. Discovered that deduplicating batch items and adding an item bias dimension achieved 0.022638 NDCG@10 at epoch 2 (more than 2x baseline 0.01071). Moving to Phase 3: incorporating preference state, expanding training slices to 6 weeks, vectorizing batch negative masking, tuning learning rate and regularization, and engineering richer item and user features.",
"strategic_intent": "Systematically improve the two-tower model architecture, features, and training recipe.",
"title": "Phase 3: Architecture & Feature Engineering Optimization"
},
"result": [
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"output": "Current topic: \"Phase 3: Architecture & Feature Engineering Optimization\"\nTopic summary: In Phase 2, built and validated the first PyTorch Two-Tower ranker. Discovered that deduplicating batch items and adding an item bias dimension achieved 0.022638 NDCG@10 at epoch 2 (more than 2x baseline 0.01071). Moving to Phase 3: incorporating preference state, expanding training slices to 6 weeks, vectorizing batch negative masking, tuning learning rate and regularization, and engineering richer item and user features.\n\nStrategic Intent: Systematically improve the two-tower model architecture, features, and training recipe."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:25:39.840Z",
"resultDisplay": "## 📂 Topic: **Phase 3: Architecture & Feature Engineering Optimization**\n\n**Summary:**\nIn Phase 2, built and validated the first PyTorch Two-Tower ranker. Discovered that deduplicating batch items and adding an item bias dimension achieved 0.022638 NDCG@10 at epoch 2 (more than 2x baseline 0.01071). Moving to Phase 3: incorporating preference state, expanding training slices to 6 weeks, vectorizing batch negative masking, tuning learning rate and regularization, and engineering richer item and user features.\n\n> [!STRATEGY]\n> **Intent:** Systematically improve the two-tower model architecture, features, and training recipe.",
"description": "Update topic to: \"Phase 3: Architecture & Feature Engineering Optimization\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T06:25:39.842Z",
"content": "Current topic: \"Phase 3: Architecture & Feature Engineering Optimization\"\nTopic summary: In Phase 2, built and validated the first PyTorch Two-Tower ranker. Discovered that deduplicating batch items and adding an item bias dimension achieved 0.022638 NDCG@10 at epoch 2 (more than 2x baseline 0.01071). Moving to Phase 3: incorporating preference state, expanding training slices to 6 weeks, vectorizing batch negative masking, tuning learning rate and regularization, and engineering richer item and user features.\n\nStrategic Intent: Systematically improve the two-tower model architecture, features, and training recipe.",
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{
"id": 65,
"role": "agent",
"timestamp": "2026-09-29T06:25:53.437Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:25:53.437Z",
"text": "Investigating Batch Negative Masking Speed\n\nI'm currently dissecting the performance of batch negative masking within `prototype_eval.py`. My focus is on understanding why each epoch is averaging 27 seconds, by examining the operations within each batch, specifically the conversion to numpy arrays and the subsequent row iteration."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:25:53.437Z",
"text": "Optimizing Masking Logic\n\nI've identified a significant performance bottleneck: a Python set lookup occurring within a nested loop structure. This is leading to millions of unnecessary iterations per epoch. My current focus is on exploring vectorized alternatives to eliminate these Python loops and drastically improve processing speed."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:25:53.437Z",
"callId": "run_shell_command__call_39651",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport torch\n\nb_u = torch.tensor([10, 20, 10, 30, 20])\nb_i = torch.tensor([100, 200, 105, 300, 200])\nunique_items, item_inverse = torch.unique(b_i, return_inverse=True)\nprint('unique_items:', unique_items)\nprint('item_inverse:', item_inverse)\n\n# For row r, the target column is item_inverse[r].\n# If another row r2 has the same user b_u[r] == b_u[r2], then column item_inverse[r2] is also a positive for that user!\n# We want mask[r, item_inverse[r2]] = True whenever b_u[r] == b_u[r2] and item_inverse[r] != item_inverse[r2].\n\nnum_cand = len(unique_items) + 2 # plus 2 random negs\nB = len(b_u)\nmask = torch.zeros((B, num_cand), dtype=torch.bool)\n\n# Vectorized:\nsame_u = (b_u.unsqueeze(1) == b_u.unsqueeze(0)) # (B, B)\ndiff_cand = (item_inverse.unsqueeze(1) != item_inverse.unsqueeze(0)) # (B, B)\nfalse_neg_pairs = same_u & diff_cand # (B, B)\n\n# Scatter into mask: for each row r, set column item_inverse[c] to True if false_neg_pairs[r, c]\nrow_idx, col_idx = torch.nonzero(false_neg_pairs, as_tuple=True)\nmask[row_idx, item_inverse[col_idx]] = True\n\nprint('Mask:\\n', mask[:, :len(unique_items)])\nfor r in range(B):\n print(f'Row {r} (user {b_u[r].item()}, target {item_inverse[r].item()}): false neg columns: {torch.nonzero(mask[r]).flatten().tolist()}')\n\"",
"description": "Test vectorized false negative masking"
},
"recorded": {
"id": "run_shell_command__call_39651",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport torch\n\nb_u = torch.tensor([10, 20, 10, 30, 20])\nb_i = torch.tensor([100, 200, 105, 300, 200])\nunique_items, item_inverse = torch.unique(b_i, return_inverse=True)\nprint('unique_items:', unique_items)\nprint('item_inverse:', item_inverse)\n\n# For row r, the target column is item_inverse[r].\n# If another row r2 has the same user b_u[r] == b_u[r2], then column item_inverse[r2] is also a positive for that user!\n# We want mask[r, item_inverse[r2]] = True whenever b_u[r] == b_u[r2] and item_inverse[r] != item_inverse[r2].\n\nnum_cand = len(unique_items) + 2 # plus 2 random negs\nB = len(b_u)\nmask = torch.zeros((B, num_cand), dtype=torch.bool)\n\n# Vectorized:\nsame_u = (b_u.unsqueeze(1) == b_u.unsqueeze(0)) # (B, B)\ndiff_cand = (item_inverse.unsqueeze(1) != item_inverse.unsqueeze(0)) # (B, B)\nfalse_neg_pairs = same_u & diff_cand # (B, B)\n\n# Scatter into mask: for each row r, set column item_inverse[c] to True if false_neg_pairs[r, c]\nrow_idx, col_idx = torch.nonzero(false_neg_pairs, as_tuple=True)\nmask[row_idx, item_inverse[col_idx]] = True\n\nprint('Mask:\\n', mask[:, :len(unique_items)])\nfor r in range(B):\n print(f'Row {r} (user {b_u[r].item()}, target {item_inverse[r].item()}): false neg columns: {torch.nonzero(mask[r]).flatten().tolist()}')\n\"",
"description": "Test vectorized false negative masking"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_39651",
"name": "run_shell_command",
"response": {
"output": "\nOutput: unique_items: tensor([100, 105, 200, 300])\nitem_inverse: tensor([0, 2, 1, 3, 2])\nMask:\n tensor([[False, True, False, False],\n [False, False, False, False],\n [ True, False, False, False],\n [False, False, False, False],\n [False, False, False, False]])\nRow 0 (user 10, target 0): false neg columns: [1]\nRow 1 (user 20, target 2): false neg columns: []\nRow 2 (user 10, target 1): false neg columns: [0]\nRow 3 (user 30, target 3): false neg columns: []\nRow 4 (user 20, target 2): false neg columns: []\nProcess Group PGID: 2896\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:25:56.371Z",
"resultDisplay": "unique_items: tensor([100, 105, 200, 300])\nitem_inverse: tensor([0, 2, 1, 3, 2])\nMask:\n tensor([[False, True, False, False],\n [False, False, False, False],\n [ True, False, False, False],\n [False, False, False, False],\n [False, False, False, False]])\nRow 0 (user 10, target 0): false neg columns: [1]\nRow 1 (user 20, target 2): false neg columns: []\nRow 2 (user 10, target 1): false neg columns: [0]\nRow 3 (user 30, target 3): false neg columns: []\nRow 4 (user 20, target 2): false neg columns: []",
"description": "Test vectorized false negative masking",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:25:56.373Z",
"content": "\nOutput: unique_items: tensor([100, 105, 200, 300])\nitem_inverse: tensor([0, 2, 1, 3, 2])\nMask:\n tensor([[False, True, False, False],\n [False, False, False, False],\n [ True, False, False, False],\n [False, False, False, False],\n [False, False, False, False]])\nRow 0 (user 10, target 0): false neg columns: [1]\nRow 1 (user 20, target 2): false neg columns: []\nRow 2 (user 10, target 1): false neg columns: [0]\nRow 3 (user 30, target 3): false neg columns: []\nRow 4 (user 20, target 2): false neg columns: []\nProcess Group PGID: 2896\n",
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},
{
"id": 66,
"role": "agent",
"timestamp": "2026-09-29T06:26:06.144Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:26:06.144Z",
"text": "Optimizing for speed\n\nI've successfully vectorized a crucial segment of the processing, replacing Python loops with efficient PyTorch operations. This optimization is showing significant speedups, reducing an epoch's runtime from 27 seconds."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:26:06.144Z",
"text": "Integrating preference data\n\nI'm now focusing on incorporating the explicit preference states from the validation and test datasets. I've observed the substantial volume of liked and disliked items and am developing a method to reconstruct the active preference state at various timestamps to ensure accurate feature generation for training slices."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:26:06.144Z",
"callId": "run_shell_command__call_622794",
"name": "run_shell_command",
"input": {
"description": "Verify reconstruction of active preference state",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\n\n# Check user 100\nuid = 100\nu_l = likes[likes.uid == uid]\nu_ul = unlikes[unlikes.uid == uid]\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nv_pref_100_liked = set(v_pref[(v_pref.uid == uid) & (v_pref.state == 'liked')].item_id)\nprint('User 100 pref state liked count:', len(v_pref_100_liked))\n\n# Last action per (uid, item_id)\nl_action = pd.concat([\n u_l[['item_id', 'timestamp']].assign(action=1),\n u_ul[['item_id', 'timestamp']].assign(action=0)\n]).sort_values('timestamp').groupby('item_id').last()\n\nrec_liked = set(l_action[l_action.action == 1].index)\nprint('Reconstructed liked count:', len(rec_liked))\nprint('Match exactly?', rec_liked == v_pref_100_liked)\n\""
},
"recorded": {
"id": "run_shell_command__call_622794",
"name": "run_shell_command",
"args": {
"description": "Verify reconstruction of active preference state",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\n\n# Check user 100\nuid = 100\nu_l = likes[likes.uid == uid]\nu_ul = unlikes[unlikes.uid == uid]\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nv_pref_100_liked = set(v_pref[(v_pref.uid == uid) & (v_pref.state == 'liked')].item_id)\nprint('User 100 pref state liked count:', len(v_pref_100_liked))\n\n# Last action per (uid, item_id)\nl_action = pd.concat([\n u_l[['item_id', 'timestamp']].assign(action=1),\n u_ul[['item_id', 'timestamp']].assign(action=0)\n]).sort_values('timestamp').groupby('item_id').last()\n\nrec_liked = set(l_action[l_action.action == 1].index)\nprint('Reconstructed liked count:', len(rec_liked))\nprint('Match exactly?', rec_liked == v_pref_100_liked)\n\""
},
"result": [
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"functionResponse": {
"id": "run_shell_command__call_622794",
"name": "run_shell_command",
"response": {
"output": "\nOutput: User 100 pref state liked count: 13\nReconstructed liked count: 19\nMatch exactly? False\nProcess Group PGID: 2906\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:26:07.770Z",
"resultDisplay": "User 100 pref state liked count: 13\nReconstructed liked count: 19\nMatch exactly? False",
"description": "Verify reconstruction of active preference state",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:26:07.771Z",
"content": "\nOutput: User 100 pref state liked count: 13\nReconstructed liked count: 19\nMatch exactly? False\nProcess Group PGID: 2906\n",
"isError": false
},
"responseMessageId": "1a1490c7-2f6d-45cf-9119-030188cfcaba"
}
]
},
{
"id": 67,
"role": "agent",
"timestamp": "2026-09-29T06:26:11.506Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T06:26:11.506Z",
"callId": "run_shell_command__call_171168",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nuid = 100\nu_l = likes[(likes.uid == uid) & (likes.item_id.isin(cat_set))]\nu_ul = unlikes[(unlikes.uid == uid) & (unlikes.item_id.isin(cat_set))]\nl_action = pd.concat([\n u_l[['item_id', 'timestamp']].assign(action=1),\n u_ul[['item_id', 'timestamp']].assign(action=0)\n]).sort_values('timestamp').groupby('item_id').last()\n\nrec_liked = set(l_action[l_action.action == 1].index)\nv_pref_100_liked = set(v_pref[(v_pref.uid == uid) & (v_pref.state == 'liked')].item_id)\nprint('Catalog filtered reconstructed:', len(rec_liked))\nprint('Match exactly?', rec_liked == v_pref_100_liked)\n\"",
"description": "Check catalog filtering on preference state"
},
"recorded": {
"id": "run_shell_command__call_171168",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nuid = 100\nu_l = likes[(likes.uid == uid) & (likes.item_id.isin(cat_set))]\nu_ul = unlikes[(unlikes.uid == uid) & (unlikes.item_id.isin(cat_set))]\nl_action = pd.concat([\n u_l[['item_id', 'timestamp']].assign(action=1),\n u_ul[['item_id', 'timestamp']].assign(action=0)\n]).sort_values('timestamp').groupby('item_id').last()\n\nrec_liked = set(l_action[l_action.action == 1].index)\nv_pref_100_liked = set(v_pref[(v_pref.uid == uid) & (v_pref.state == 'liked')].item_id)\nprint('Catalog filtered reconstructed:', len(rec_liked))\nprint('Match exactly?', rec_liked == v_pref_100_liked)\n\"",
"description": "Check catalog filtering on preference state"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_171168",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog filtered reconstructed: 13\nMatch exactly? True\nProcess Group PGID: 2923\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:26:13.239Z",
"resultDisplay": "Catalog filtered reconstructed: 13\nMatch exactly? True",
"description": "Check catalog filtering on preference state",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:26:13.241Z",
"content": "\nOutput: Catalog filtered reconstructed: 13\nMatch exactly? True\nProcess Group PGID: 2923\n",
"isError": false
},
"responseMessageId": "02a9bd54-df65-4a8f-aa85-21ebcea7e661"
}
]
},
{
"id": 68,
"role": "agent",
"timestamp": "2026-09-29T06:26:19.275Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:26:19.275Z",
"text": "Confirming Exact Matches\n\nI've confirmed that `validation_preference_state.parquet` precisely represents the active likes and dislikes. This means for any training cutoff $T_w$, I can accurately determine the active likes and dislikes by examining the most recent action prior to $T_w$."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:26:19.275Z",
"callId": "run_shell_command__call_770106",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport time, pandas as pd, numpy as np, pyarrow.parquet as pq\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Listens stats\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id', 'played_ratio_pct', 'is_organic', 'track_length_seconds'])\ndf_l = table.to_pandas()\ndf_l = df_l[df_l.item_id.isin(item_to_idx)]\n\nl_stats = df_l.groupby('item_id').agg(\n total_plays=('played_ratio_pct', 'count'),\n mean_ratio=('played_ratio_pct', 'mean'),\n strong_ratio=('played_ratio_pct', lambda x: (x >= 80).mean()),\n organic_ratio=('is_organic', 'mean'),\n track_len=('track_length_seconds', 'median'),\n).reset_index()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_to_idx)]\nlike_counts = likes.groupby('item_id').size().to_dict()\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ndislikes = dislikes[dislikes.item_id.isin(item_to_idx)]\ndislike_counts = dislikes.groupby('item_id').size().to_dict()\n\nprint(f'Item features computed in {time.time() - t0:.2f}s')\n\"",
"description": "Test computing rich item features"
},
"recorded": {
"id": "run_shell_command__call_770106",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport time, pandas as pd, numpy as np, pyarrow.parquet as pq\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Listens stats\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id', 'played_ratio_pct', 'is_organic', 'track_length_seconds'])\ndf_l = table.to_pandas()\ndf_l = df_l[df_l.item_id.isin(item_to_idx)]\n\nl_stats = df_l.groupby('item_id').agg(\n total_plays=('played_ratio_pct', 'count'),\n mean_ratio=('played_ratio_pct', 'mean'),\n strong_ratio=('played_ratio_pct', lambda x: (x >= 80).mean()),\n organic_ratio=('is_organic', 'mean'),\n track_len=('track_length_seconds', 'median'),\n).reset_index()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_to_idx)]\nlike_counts = likes.groupby('item_id').size().to_dict()\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ndislikes = dislikes[dislikes.item_id.isin(item_to_idx)]\ndislike_counts = dislikes.groupby('item_id').size().to_dict()\n\nprint(f'Item features computed in {time.time() - t0:.2f}s')\n\"",
"description": "Test computing rich item features"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_770106",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Item features computed in 8.26s\nProcess Group PGID: 2940\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:26:29.117Z",
"resultDisplay": "Item features computed in 8.26s",
"description": "Test computing rich item features",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:26:29.119Z",
"content": "\nOutput: Item features computed in 8.26s\nProcess Group PGID: 2940\n",
"isError": false
},
"responseMessageId": "cbb02329-c9d6-451f-b32c-c53ef3af5d47"
}
]
},
{
"id": 69,
"role": "agent",
"timestamp": "2026-09-29T06:27:13.593Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:27:13.593Z",
"text": "Achieving Rich Features\n\nI've successfully generated rich features for all 30,000 items within 8.26 seconds. This now provides each candidate track with the `log1p(positive_listens)`, `log1p(organic_positive_listens)`, and `popularity` features."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:27:13.593Z",
"text": "Evaluating Feature Contributions\n\nI'm now assessing the predictive power of the newly generated features. My focus is on understanding how metrics like `strong_ratio`, `likes_per_play`, and `like_ratio` correlate with listener engagement, aiming to quantify intrinsic track quality alongside semantic content from embeddings."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:27:13.593Z",
"text": "Refining Training Strategy\n\nI'm now exploring an improved training setup, investigating a lower learning rate of 3e-4 with CosineAnnealingLR and weight decay of 1e-4. I also plan to utilize six training slices to significantly increase the training data and optimize negative masking for faster epoch completion."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:27:13.593Z",
"callId": "write_file__call_194153",
"name": "write_file",
"input": {
"file_path": "/app/solution/train_improved.py",
"instruction": "Write train_improved.py with 6 slices, vectorized mask, and validation tracking",
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\nt0 = time.time()\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\nunlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\nunlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\nunlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nundislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\nundislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\nundislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n\n# Load listens for last 150 days\nmin_listen_time = CUTOFF_VAL - 150 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Loaded logs in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n\n# 3. Compute rich item features\nprint(\"3. Computing item features...\")\nt1 = time.time()\nl_grouped = listens_df.groupby(\"item_idx\").agg(\n total_plays=(\"played_ratio_pct\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n organic_ratio=(\"is_organic\", \"mean\"),\n med_length=(\"track_length_seconds\", \"median\"),\n).reset_index()\n\nitem_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\nlike_counts = likes_df.groupby(\"item_idx\").size().to_dict()\ndislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n\nNUM_ITEM_FEATS = 10\nitem_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\nfor idx in range(num_items):\n pos_l = cat_df.positive_listens.iloc[idx]\n org_l = cat_df.organic_positive_listens.iloc[idx]\n pop_r = cat_df.popularity_rank.iloc[idx]\n st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n n_lik = like_counts.get(idx, 0)\n n_dis = dislike_counts.get(idx, 0)\n\n item_dense[idx, 0] = np.log1p(pos_l)\n item_dense[idx, 1] = np.log1p(org_l)\n item_dense[idx, 2] = pop_r / float(num_items)\n item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n item_dense[idx, 5] = st[\"strong_ratio\"]\n item_dense[idx, 6] = st[\"organic_ratio\"]\n item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n item_dense[idx, 8] = np.log1p(n_lik)\n item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n\n# Normalize dense features\nfor c in range(NUM_ITEM_FEATS):\n m = item_dense[:, c].mean()\n s = item_dense[:, c].std() + 1e-6\n item_dense[:, c] = (item_dense[:, c] - m) / s\n\nitem_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\nprint(f\"Item features ready in {time.time() - t1:.2f}s\")\n\n\n# 4. Preference state helper\ndef get_active_preference_state(cutoff_time, user_set):\n \"\"\"Reconstruct active catalog likes and dislikes before cutoff_time.\"\"\"\n l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n\n l_act = pd.concat([\n l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n\n active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n d_act = pd.concat([\n d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n\n active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n return active_likes, active_dislikes\n\n\ndef build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n if pref_likes is None or pref_dislikes is None:\n pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_lik = len(pref_likes.get(uid, []))\n n_dis = len(pref_dislikes.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_lik),\n np.log1p(n_dis),\n (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n ]\n l_items = pref_likes.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = pref_dislikes.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\n# 5. Extract training slices\ndef extract_slice(cutoff_time):\n t_start = time.time()\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n print(f\"Slice (cutoff={cutoff_time}): {len(unique_users)} users, {len(pairs)} pairs in {time.time() - t_start:.2f}s\")\n return user_feats, pairs\n\n\nprint(\"4. Extracting training slices (6 weeks)...\")\ntrain_slices = []\nfor w in range(1, 7):\n s_feats, s_pairs = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs))\n\n# 6. Extract validation features\nprint(\"5. Extracting validation features...\")\nv_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_likes_dict = {u: [] for u in target_uids}\nval_dislikes_dict = {u: [] for u in target_uids}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == \"liked\":\n val_likes_dict[int(uid)].append(idx)\n elif state == \"disliked\":\n val_dislikes_dict[int(uid)].append(idx)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\n\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\n# 7. Model definition\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.03)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=64, dense_dim=10, stats_dim=6, out_dim=64):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\nmodel = TwoTowerRanker(num_items, embed_dim=64, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)\ntemperature = 0.07\nbatch_size = 512\nnum_epochs = 10\n\nprint(\"\\n6. Training Two-Tower Model...\")\nbest_ndcg = 0.0\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n B = len(b_pairs)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n\n # Deduplicate target items in batch\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n\n # Logits: (B, num_cand)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Vectorized false negative mask:\n # Mask out other columns where user is the same but target column differs\n same_u = (b_u.unsqueeze(1) == b_u.unsqueeze(0))\n diff_cand = (item_inverse.unsqueeze(1) != item_inverse.unsqueeze(0))\n false_neg_pairs = same_u & diff_cand\n row_idx, col_idx = torch.nonzero(false_neg_pairs, as_tuple=True)\n if len(row_idx) > 0:\n mask = torch.zeros_like(logits, dtype=torch.bool)\n mask[row_idx, item_inverse[col_idx]] = True\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 5.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Validation evaluation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n curr_ndcg = val_score[\"quality_ndcg@10\"]\n if curr_ndcg > best_ndcg:\n best_ndcg = curr_ndcg\n # Save checkpoint\n torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n"
},
"recorded": {
"id": "write_file__call_194153",
"name": "write_file",
"args": {
"file_path": "/app/solution/train_improved.py",
"instruction": "Write train_improved.py with 6 slices, vectorized mask, and validation tracking",
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\nt0 = time.time()\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\nunlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\nunlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\nunlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nundislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\nundislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\nundislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n\n# Load listens for last 150 days\nmin_listen_time = CUTOFF_VAL - 150 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Loaded logs in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n\n# 3. Compute rich item features\nprint(\"3. Computing item features...\")\nt1 = time.time()\nl_grouped = listens_df.groupby(\"item_idx\").agg(\n total_plays=(\"played_ratio_pct\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n organic_ratio=(\"is_organic\", \"mean\"),\n med_length=(\"track_length_seconds\", \"median\"),\n).reset_index()\n\nitem_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\nlike_counts = likes_df.groupby(\"item_idx\").size().to_dict()\ndislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n\nNUM_ITEM_FEATS = 10\nitem_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\nfor idx in range(num_items):\n pos_l = cat_df.positive_listens.iloc[idx]\n org_l = cat_df.organic_positive_listens.iloc[idx]\n pop_r = cat_df.popularity_rank.iloc[idx]\n st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n n_lik = like_counts.get(idx, 0)\n n_dis = dislike_counts.get(idx, 0)\n\n item_dense[idx, 0] = np.log1p(pos_l)\n item_dense[idx, 1] = np.log1p(org_l)\n item_dense[idx, 2] = pop_r / float(num_items)\n item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n item_dense[idx, 5] = st[\"strong_ratio\"]\n item_dense[idx, 6] = st[\"organic_ratio\"]\n item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n item_dense[idx, 8] = np.log1p(n_lik)\n item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n\n# Normalize dense features\nfor c in range(NUM_ITEM_FEATS):\n m = item_dense[:, c].mean()\n s = item_dense[:, c].std() + 1e-6\n item_dense[:, c] = (item_dense[:, c] - m) / s\n\nitem_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\nprint(f\"Item features ready in {time.time() - t1:.2f}s\")\n\n\n# 4. Preference state helper\ndef get_active_preference_state(cutoff_time, user_set):\n \"\"\"Reconstruct active catalog likes and dislikes before cutoff_time.\"\"\"\n l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n\n l_act = pd.concat([\n l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n\n active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n d_act = pd.concat([\n d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n\n active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n return active_likes, active_dislikes\n\n\ndef build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n if pref_likes is None or pref_dislikes is None:\n pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_lik = len(pref_likes.get(uid, []))\n n_dis = len(pref_dislikes.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_lik),\n np.log1p(n_dis),\n (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n ]\n l_items = pref_likes.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = pref_dislikes.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\n# 5. Extract training slices\ndef extract_slice(cutoff_time):\n t_start = time.time()\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n print(f\"Slice (cutoff={cutoff_time}): {len(unique_users)} users, {len(pairs)} pairs in {time.time() - t_start:.2f}s\")\n return user_feats, pairs\n\n\nprint(\"4. Extracting training slices (6 weeks)...\")\ntrain_slices = []\nfor w in range(1, 7):\n s_feats, s_pairs = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs))\n\n# 6. Extract validation features\nprint(\"5. Extracting validation features...\")\nv_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_likes_dict = {u: [] for u in target_uids}\nval_dislikes_dict = {u: [] for u in target_uids}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == \"liked\":\n val_likes_dict[int(uid)].append(idx)\n elif state == \"disliked\":\n val_dislikes_dict[int(uid)].append(idx)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\n\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\n# 7. Model definition\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.03)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=64, dense_dim=10, stats_dim=6, out_dim=64):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\nmodel = TwoTowerRanker(num_items, embed_dim=64, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)\ntemperature = 0.07\nbatch_size = 512\nnum_epochs = 10\n\nprint(\"\\n6. Training Two-Tower Model...\")\nbest_ndcg = 0.0\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n B = len(b_pairs)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n\n # Deduplicate target items in batch\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n\n # Logits: (B, num_cand)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Vectorized false negative mask:\n # Mask out other columns where user is the same but target column differs\n same_u = (b_u.unsqueeze(1) == b_u.unsqueeze(0))\n diff_cand = (item_inverse.unsqueeze(1) != item_inverse.unsqueeze(0))\n false_neg_pairs = same_u & diff_cand\n row_idx, col_idx = torch.nonzero(false_neg_pairs, as_tuple=True)\n if len(row_idx) > 0:\n mask = torch.zeros_like(logits, dtype=torch.bool)\n mask[row_idx, item_inverse[col_idx]] = True\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 5.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Validation evaluation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n curr_ndcg = val_score[\"quality_ndcg@10\"]\n if curr_ndcg > best_ndcg:\n best_ndcg = curr_ndcg\n # Save checkpoint\n torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_194153",
"name": "write_file",
"response": {
"output": "Successfully created and wrote to new file: /app/solution/train_improved.py. Here is the updated code:\nimport os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\nt0 = time.time()\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\nunlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\nunlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\nunlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nundislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\nundislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\nundislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n\n# Load listens for last 150 days\nmin_listen_time = CUTOFF_VAL - 150 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Loaded logs in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n\n# 3. Compute rich item features\nprint(\"3. Computing item features...\")\nt1 = time.time()\nl_grouped = listens_df.groupby(\"item_idx\").agg(\n total_plays=(\"played_ratio_pct\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n organic_ratio=(\"is_organic\", \"mean\"),\n med_length=(\"track_length_seconds\", \"median\"),\n).reset_index()\n\nitem_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\nlike_counts = likes_df.groupby(\"item_idx\").size().to_dict()\ndislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n\nNUM_ITEM_FEATS = 10\nitem_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\nfor idx in range(num_items):\n pos_l = cat_df.positive_listens.iloc[idx]\n org_l = cat_df.organic_positive_listens.iloc[idx]\n pop_r = cat_df.popularity_rank.iloc[idx]\n st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n n_lik = like_counts.get(idx, 0)\n n_dis = dislike_counts.get(idx, 0)\n\n item_dense[idx, 0] = np.log1p(pos_l)\n item_dense[idx, 1] = np.log1p(org_l)\n item_dense[idx, 2] = pop_r / float(num_items)\n item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n item_dense[idx, 5] = st[\"strong_ratio\"]\n item_dense[idx, 6] = st[\"organic_ratio\"]\n item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n item_dense[idx, 8] = np.log1p(n_lik)\n item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n\n# Normalize dense features\nfor c in range(NUM_ITEM_FEATS):\n m = item_dense[:, c].mean()\n s = item_dense[:, c].std() + 1e-6\n item_dense[:, c] = (item_dense[:, c] - m) / s\n\nitem_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\nprint(f\"Item features ready in {time.time() - t1:.2f}s\")\n\n\n# 4. Preference state helper\ndef get_active_preference_state(cutoff_time, user_set):\n \"\"\"Reconstruct active catalog likes and dislikes before cutoff_time.\"\"\"\n l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n\n l_act = pd.concat([\n l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n\n active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n d_act = pd.concat([\n d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n\n active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n return active_likes, active_dislikes\n\n\ndef build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n if pref_likes is None or pref_dislikes is None:\n pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_lik = len(pref_likes.get(uid, []))\n n_dis = len(pref_dislikes.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_lik),\n np.log1p(n_dis),\n (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n ]\n l_items = pref_likes.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = pref_dislikes.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\n# 5. Extract training slices\ndef extract_slice(cutoff_time):\n t_start = time.time()\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n print(f\"Slice (cutoff={cutoff_time}): {len(unique_users)} users, {len(pairs)} pairs in {time.time() - t_start:.2f}s\")\n return user_feats, pairs\n\n\nprint(\"4. Extracting training slices (6 weeks)...\")\ntrain_slices = []\nfor w in range(1, 7):\n s_feats, s_pairs = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs))\n\n# 6. Extract validation features\nprint(\"5. Extracting validation features...\")\nv_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_likes_dict = {u: [] for u in target_uids}\nval_dislikes_dict = {u: [] for u in target_uids}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == \"liked\":\n val_likes_dict[int(uid)].append(idx)\n elif state == \"disliked\":\n val_dislikes_dict[int(uid)].append(idx)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\n\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\n# 7. Model definition\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.03)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=64, dense_dim=10, stats_dim=6, out_dim=64):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\nmodel = TwoTowerRanker(num_items, embed_dim=64, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)\ntemperature = 0.07\nbatch_size = 512\nnum_epochs = 10\n\nprint(\"\\n6. Training Two-Tower Model...\")\nbest_ndcg = 0.0\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n B = len(b_pairs)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n\n # Deduplicate target items in batch\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n\n # Logits: (B, num_cand)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Vectorized false negative mask:\n # Mask out other columns where user is the same but target column differs\n same_u = (b_u.unsqueeze(1) == b_u.unsqueeze(0))\n diff_cand = (item_inverse.unsqueeze(1) != item_inverse.unsqueeze(0))\n false_neg_pairs = same_u & diff_cand\n row_idx, col_idx = torch.nonzero(false_neg_pairs, as_tuple=True)\n if len(row_idx) > 0:\n mask = torch.zeros_like(logits, dtype=torch.bool)\n mask[row_idx, item_inverse[col_idx]] = True\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 5.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Validation evaluation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n curr_ndcg = val_score[\"quality_ndcg@10\"]\n if curr_ndcg > best_ndcg:\n best_ndcg = curr_ndcg\n # Save checkpoint\n torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:27:13.604Z",
"resultDisplay": {
"fileDiff": "Index: train_improved.py\n===================================================================\n--- train_improved.py\tOriginal\n+++ train_improved.py\tWritten\n@@ -0,0 +1,508 @@\n+import os\n+import sys\n+import time\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import pyarrow.parquet as pq\n+import torch\n+import torch.nn as nn\n+import torch.nn.functional as F\n+\n+sys.path.append(\"/task\")\n+from evaluate_public import score, validation_truth\n+from ranking_evaluation import CandidateSet\n+\n+torch.set_num_threads(8)\n+\n+DATA_DIR = Path(\"/task/data\")\n+OUT_DIR = Path(\"/app/solution\")\n+OUT_DIR.mkdir(parents=True, exist_ok=True)\n+\n+CUTOFF_VAL = 24786800\n+WEEK_SECONDS = 7 * 86400\n+\n+print(\"1. Loading metadata...\")\n+t0 = time.time()\n+cat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\n+cat_items = cat_df.item_id.to_numpy()\n+item_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\n+num_items = len(cat_items)\n+\n+target_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\n+target_uids = target_users_df.uid.to_numpy()\n+target_uid_set = set(target_uids)\n+num_targets = len(target_uids)\n+\n+print(\"2. Loading interaction logs...\")\n+likes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\n+likes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\n+likes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n+\n+unlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\n+unlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\n+unlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n+\n+dislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\n+dislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\n+dislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n+\n+undislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\n+undislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\n+undislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n+\n+# Load listens for last 150 days\n+min_listen_time = CUTOFF_VAL - 150 * 86400\n+table = pq.read_table(\n+ DATA_DIR / \"train_listens.parquet\",\n+ columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n+ filters=[(\"timestamp\", \">=\", min_listen_time)],\n+)\n+listens_df = table.to_pandas()\n+listens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\n+listens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n+print(f\"Loaded logs in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n+\n+# 3. Compute rich item features\n+print(\"3. Computing item features...\")\n+t1 = time.time()\n+l_grouped = listens_df.groupby(\"item_idx\").agg(\n+ total_plays=(\"played_ratio_pct\", \"count\"),\n+ mean_ratio=(\"played_ratio_pct\", \"mean\"),\n+ strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n+ organic_ratio=(\"is_organic\", \"mean\"),\n+ med_length=(\"track_length_seconds\", \"median\"),\n+).reset_index()\n+\n+item_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\n+like_counts = likes_df.groupby(\"item_idx\").size().to_dict()\n+dislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n+\n+NUM_ITEM_FEATS = 10\n+item_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\n+for idx in range(num_items):\n+ pos_l = cat_df.positive_listens.iloc[idx]\n+ org_l = cat_df.organic_positive_listens.iloc[idx]\n+ pop_r = cat_df.popularity_rank.iloc[idx]\n+ st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n+ n_lik = like_counts.get(idx, 0)\n+ n_dis = dislike_counts.get(idx, 0)\n+\n+ item_dense[idx, 0] = np.log1p(pos_l)\n+ item_dense[idx, 1] = np.log1p(org_l)\n+ item_dense[idx, 2] = pop_r / float(num_items)\n+ item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n+ item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n+ item_dense[idx, 5] = st[\"strong_ratio\"]\n+ item_dense[idx, 6] = st[\"organic_ratio\"]\n+ item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n+ item_dense[idx, 8] = np.log1p(n_lik)\n+ item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n+\n+# Normalize dense features\n+for c in range(NUM_ITEM_FEATS):\n+ m = item_dense[:, c].mean()\n+ s = item_dense[:, c].std() + 1e-6\n+ item_dense[:, c] = (item_dense[:, c] - m) / s\n+\n+item_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\n+print(f\"Item features ready in {time.time() - t1:.2f}s\")\n+\n+\n+# 4. Preference state helper\n+def get_active_preference_state(cutoff_time, user_set):\n+ \"\"\"Reconstruct active catalog likes and dislikes before cutoff_time.\"\"\"\n+ l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n+ ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n+ d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n+ ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n+\n+ l_act = pd.concat([\n+ l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n+ ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n+ ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n+\n+ active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n+\n+ d_act = pd.concat([\n+ d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n+ ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n+ ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n+\n+ active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n+\n+ return active_likes, active_dislikes\n+\n+\n+def build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n+ u_set = set(user_ids)\n+ t_min = cutoff_time - 90 * 86400\n+\n+ sub_listens = listens_df[\n+ (listens_df.timestamp >= t_min)\n+ & (listens_df.timestamp < cutoff_time)\n+ & (listens_df.uid.isin(u_set))\n+ ].copy()\n+\n+ sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n+\n+ sub_listens[\"w_recent\"] = np.where(\n+ sub_listens.days_ago <= 14.0,\n+ np.exp(-0.1 * sub_listens.days_ago)\n+ * (1.0 + sub_listens.is_organic)\n+ * (sub_listens.played_ratio_pct / 100.0),\n+ 0.0,\n+ )\n+\n+ sub_listens[\"w_history\"] = (\n+ np.exp(-0.02 * sub_listens.days_ago)\n+ * (1.0 + sub_listens.is_organic)\n+ * (sub_listens.played_ratio_pct / 100.0)\n+ )\n+\n+ sub_listens[\"w_strong\"] = np.where(\n+ (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n+ np.exp(-0.05 * sub_listens.days_ago),\n+ 0.0,\n+ )\n+\n+ agg = (\n+ sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n+ .sum()\n+ .reset_index()\n+ )\n+\n+ if pref_likes is None or pref_dislikes is None:\n+ pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n+\n+ uid_counts = sub_listens.groupby(\"uid\").agg(\n+ n_listens=(\"item_idx\", \"count\"),\n+ mean_ratio=(\"played_ratio_pct\", \"mean\"),\n+ organic_ratio=(\"is_organic\", \"mean\"),\n+ ).to_dict(orient=\"index\")\n+\n+ user_to_pos = {u: i for i, u in enumerate(user_ids)}\n+ recent_indices, recent_uids, recent_weights = [], [], []\n+ history_indices, history_uids, history_weights = [], [], []\n+ strong_indices, strong_uids, strong_weights = [], [], []\n+ like_indices, like_uids = [], []\n+ dislike_indices, dislike_uids = [], []\n+ stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n+\n+ for i, uid in enumerate(user_ids):\n+ st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n+ n_lik = len(pref_likes.get(uid, []))\n+ n_dis = len(pref_dislikes.get(uid, []))\n+ stats_arr[i] = [\n+ np.log1p(st[\"n_listens\"]),\n+ st[\"mean_ratio\"] / 100.0,\n+ st[\"organic_ratio\"],\n+ np.log1p(n_lik),\n+ np.log1p(n_dis),\n+ (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n+ ]\n+ l_items = pref_likes.get(uid, [])\n+ like_indices.extend(l_items)\n+ like_uids.extend([i] * len(l_items))\n+\n+ d_items = pref_dislikes.get(uid, [])\n+ dislike_indices.extend(d_items)\n+ dislike_uids.extend([i] * len(d_items))\n+\n+ for uid, g in agg.groupby(\"uid\"):\n+ if uid not in user_to_pos:\n+ continue\n+ u_idx = user_to_pos[uid]\n+ m_r = g.w_recent > 0\n+ if m_r.any():\n+ recent_indices.extend(g.item_idx[m_r].tolist())\n+ recent_weights.extend(g.w_recent[m_r].tolist())\n+ recent_uids.extend([u_idx] * int(m_r.sum()))\n+\n+ history_indices.extend(g.item_idx.tolist())\n+ history_weights.extend(g.w_history.tolist())\n+ history_uids.extend([u_idx] * len(g))\n+\n+ m_s = g.w_strong > 0\n+ if m_s.any():\n+ strong_indices.extend(g.item_idx[m_s].tolist())\n+ strong_weights.extend(g.w_strong[m_s].tolist())\n+ strong_uids.extend([u_idx] * int(m_s.sum()))\n+\n+ N = len(user_ids)\n+ return {\n+ \"num_users\": N,\n+ \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n+ \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n+ \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n+ \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n+ \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n+ \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n+ \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n+ \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n+ \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n+ \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n+ \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n+ \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n+ \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n+ \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n+ }\n+\n+\n+# 5. Extract training slices\n+def extract_slice(cutoff_time):\n+ t_start = time.time()\n+ w_end = cutoff_time + WEEK_SECONDS\n+ prior = listens_df[listens_df.timestamp < cutoff_time]\n+ prior_user_items = set(zip(prior.uid, prior.item_idx))\n+\n+ win_listens = listens_df[\n+ (listens_df.timestamp >= cutoff_time)\n+ & (listens_df.timestamp < w_end)\n+ & (listens_df.is_organic == 1)\n+ & (listens_df.played_ratio_pct >= 80)\n+ ]\n+ win_likes = likes_df[\n+ (likes_df.timestamp >= cutoff_time)\n+ & (likes_df.timestamp < w_end)\n+ & (likes_df.is_organic == 1)\n+ ]\n+\n+ targets = {}\n+ for u, i in zip(win_listens.uid, win_listens.item_idx):\n+ if (u, i) not in prior_user_items:\n+ targets[(u, i)] = 1\n+ for u, i in zip(win_likes.uid, win_likes.item_idx):\n+ if (u, i) not in prior_user_items:\n+ targets[(u, i)] = 3\n+\n+ user_positives = {}\n+ for (u, i), grade in targets.items():\n+ user_positives.setdefault(u, []).append((i, grade))\n+\n+ unique_users = sorted(list(user_positives.keys()))\n+ user_feats = build_user_features(cutoff_time, unique_users)\n+ user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n+\n+ pairs = []\n+ for u, items in user_positives.items():\n+ u_idx = user_to_idx[u]\n+ for item_idx, grade in items:\n+ pairs.append((u_idx, item_idx, float(grade)))\n+\n+ print(f\"Slice (cutoff={cutoff_time}): {len(unique_users)} users, {len(pairs)} pairs in {time.time() - t_start:.2f}s\")\n+ return user_feats, pairs\n+\n+\n+print(\"4. Extracting training slices (6 weeks)...\")\n+train_slices = []\n+for w in range(1, 7):\n+ s_feats, s_pairs = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n+ train_slices.append((s_feats, s_pairs))\n+\n+# 6. Extract validation features\n+print(\"5. Extracting validation features...\")\n+v_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\n+val_likes_dict = {u: [] for u in target_uids}\n+val_dislikes_dict = {u: [] for u in target_uids}\n+for uid, item, state in v_pref.itertuples(index=False):\n+ idx = item_to_idx.get(int(item))\n+ if idx is not None:\n+ if state == \"liked\":\n+ val_likes_dict[int(uid)].append(idx)\n+ elif state == \"disliked\":\n+ val_dislikes_dict[int(uid)].append(idx)\n+\n+val_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\n+\n+val_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\n+targets_set = set(target_users_df.uid.astype(int))\n+val_gold = validation_truth(val_eligibility, targets_set)\n+val_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n+\n+\n+# 7. Model definition\n+class ItemTower(nn.Module):\n+ def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n+ super().__init__()\n+ self.item_emb = nn.Embedding(num_items, embed_dim)\n+ nn.init.normal_(self.item_emb.weight, std=0.03)\n+ self.dense_proj = nn.Linear(dense_dim, embed_dim)\n+ self.mlp = nn.Sequential(\n+ nn.Linear(embed_dim * 2, embed_dim),\n+ nn.LayerNorm(embed_dim),\n+ nn.GELU(),\n+ nn.Linear(embed_dim, out_dim),\n+ )\n+\n+ def forward(self, item_ids, dense_features):\n+ embs = self.item_emb(item_ids)\n+ d_p = self.dense_proj(dense_features[item_ids])\n+ x = torch.cat([embs, d_p], dim=-1)\n+ v = self.mlp(x)\n+ v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n+ b = v[:, -1:]\n+ return torch.cat([v_norm, b], dim=-1)\n+\n+\n+class UserTower(nn.Module):\n+ def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n+ super().__init__()\n+ self.shared_emb = shared_item_emb\n+ self.embed_dim = embed_dim\n+ self.stats_proj = nn.Linear(stats_dim, embed_dim)\n+ in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n+ self.mlp = nn.Sequential(\n+ nn.Linear(in_dim, embed_dim * 2),\n+ nn.LayerNorm(embed_dim * 2),\n+ nn.GELU(),\n+ nn.Dropout(0.1),\n+ nn.Linear(embed_dim * 2, out_dim),\n+ )\n+\n+ def _pool(self, num_users, indices, uids, weights=None):\n+ out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n+ if len(indices) == 0:\n+ return out\n+ embs = self.shared_emb(indices)\n+ if weights is not None:\n+ embs = embs * weights.unsqueeze(-1)\n+ out = torch.index_add(out, 0, uids, embs)\n+ norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n+ return out / norms\n+\n+ def compute_user_pooled_views(self, feats):\n+ with torch.no_grad():\n+ N = feats[\"num_users\"]\n+ h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n+ h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n+ h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n+ h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n+ h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n+ return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n+\n+ def forward_user(self, pooled_views, stats):\n+ s_p = self.stats_proj(stats)\n+ x = torch.cat([pooled_views, s_p], dim=-1)\n+ u = self.mlp(x)\n+ u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n+ alpha = torch.sigmoid(u[:, -1:])\n+ return torch.cat([u_norm, alpha], dim=-1)\n+\n+\n+class TwoTowerRanker(nn.Module):\n+ def __init__(self, num_items, embed_dim=64, dense_dim=10, stats_dim=6, out_dim=64):\n+ super().__init__()\n+ self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n+ self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n+\n+ def compute_all_user_vectors(self, feats):\n+ with torch.no_grad():\n+ pooled = self.user_tower.compute_user_pooled_views(feats)\n+ return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n+\n+ def compute_all_item_vectors(self, dense_features):\n+ with torch.no_grad():\n+ all_ids = torch.arange(len(dense_features), dtype=torch.long)\n+ return self.item_tower(all_ids, dense_features)\n+\n+\n+model = TwoTowerRanker(num_items, embed_dim=64, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=64)\n+optimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)\n+temperature = 0.07\n+batch_size = 512\n+num_epochs = 10\n+\n+print(\"\\n6. Training Two-Tower Model...\")\n+best_ndcg = 0.0\n+\n+for epoch in range(1, num_epochs + 1):\n+ t_ep = time.time()\n+ total_loss = 0.0\n+ num_batches = 0\n+\n+ for s_idx, (feats, pairs) in enumerate(train_slices):\n+ slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n+ slice_stats = feats[\"user_stats\"]\n+\n+ np.random.seed(epoch * 100 + s_idx)\n+ perm = np.random.permutation(len(pairs))\n+ pairs_arr = np.array(pairs)[perm]\n+\n+ for b_start in range(0, len(pairs_arr), batch_size):\n+ b_pairs = pairs_arr[b_start : b_start + batch_size]\n+ b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n+ b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n+ b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n+ B = len(b_pairs)\n+\n+ u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n+\n+ # Deduplicate target items in batch\n+ unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n+\n+ # Sample random negatives\n+ neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n+ cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n+\n+ cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n+\n+ # Logits: (B, num_cand)\n+ logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n+\n+ # Vectorized false negative mask:\n+ # Mask out other columns where user is the same but target column differs\n+ same_u = (b_u.unsqueeze(1) == b_u.unsqueeze(0))\n+ diff_cand = (item_inverse.unsqueeze(1) != item_inverse.unsqueeze(0))\n+ false_neg_pairs = same_u & diff_cand\n+ row_idx, col_idx = torch.nonzero(false_neg_pairs, as_tuple=True)\n+ if len(row_idx) > 0:\n+ mask = torch.zeros_like(logits, dtype=torch.bool)\n+ mask[row_idx, item_inverse[col_idx]] = True\n+ logits.masked_fill_(mask, -1e9)\n+\n+ targets = item_inverse\n+ loss_weights = torch.where(b_grades >= 3.0, 5.0, 1.0)\n+ loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n+\n+ optimizer.zero_grad()\n+ loss.backward()\n+ optimizer.step()\n+\n+ total_loss += loss.item()\n+ num_batches += 1\n+\n+ # Validation evaluation\n+ with torch.no_grad():\n+ val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n+ all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n+\n+ ranked_dict = {}\n+ for start_u in range(0, num_targets, 1000):\n+ end_u = min(start_u + 1000, num_targets)\n+ sub_u = val_u_vecs[start_u:end_u]\n+ sub_scores = np.matmul(sub_u, all_v_vecs.T)\n+ sub_mask = val_unpacked[start_u:end_u]\n+ sub_scores[sub_mask == 0] = -1e9\n+\n+ top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n+ for row in range(len(sub_u)):\n+ u_row = start_u + row\n+ uid = int(target_uids[u_row])\n+ cand_idx = top100_idx[row]\n+ sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n+ ranked_dict[uid] = cat_items[sorted_idx].tolist()\n+\n+ val_score = score(ranked_dict, val_gold)\n+ curr_ndcg = val_score[\"quality_ndcg@10\"]\n+ if curr_ndcg > best_ndcg:\n+ best_ndcg = curr_ndcg\n+ # Save checkpoint\n+ torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n+\n+ print(\n+ f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n+ f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n+ f\"[{time.time() - t_ep:.1f}s]\"\n+ )\n",
"fileName": "train_improved.py",
"filePath": "/app/solution/train_improved.py",
"originalContent": "",
"newContent": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\nt0 = time.time()\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\nunlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\nunlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\nunlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nundislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\nundislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\nundislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n\n# Load listens for last 150 days\nmin_listen_time = CUTOFF_VAL - 150 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Loaded logs in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n\n# 3. Compute rich item features\nprint(\"3. Computing item features...\")\nt1 = time.time()\nl_grouped = listens_df.groupby(\"item_idx\").agg(\n total_plays=(\"played_ratio_pct\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n organic_ratio=(\"is_organic\", \"mean\"),\n med_length=(\"track_length_seconds\", \"median\"),\n).reset_index()\n\nitem_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\nlike_counts = likes_df.groupby(\"item_idx\").size().to_dict()\ndislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n\nNUM_ITEM_FEATS = 10\nitem_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\nfor idx in range(num_items):\n pos_l = cat_df.positive_listens.iloc[idx]\n org_l = cat_df.organic_positive_listens.iloc[idx]\n pop_r = cat_df.popularity_rank.iloc[idx]\n st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n n_lik = like_counts.get(idx, 0)\n n_dis = dislike_counts.get(idx, 0)\n\n item_dense[idx, 0] = np.log1p(pos_l)\n item_dense[idx, 1] = np.log1p(org_l)\n item_dense[idx, 2] = pop_r / float(num_items)\n item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n item_dense[idx, 5] = st[\"strong_ratio\"]\n item_dense[idx, 6] = st[\"organic_ratio\"]\n item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n item_dense[idx, 8] = np.log1p(n_lik)\n item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n\n# Normalize dense features\nfor c in range(NUM_ITEM_FEATS):\n m = item_dense[:, c].mean()\n s = item_dense[:, c].std() + 1e-6\n item_dense[:, c] = (item_dense[:, c] - m) / s\n\nitem_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\nprint(f\"Item features ready in {time.time() - t1:.2f}s\")\n\n\n# 4. Preference state helper\ndef get_active_preference_state(cutoff_time, user_set):\n \"\"\"Reconstruct active catalog likes and dislikes before cutoff_time.\"\"\"\n l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n\n l_act = pd.concat([\n l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n\n active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n d_act = pd.concat([\n d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n\n active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n return active_likes, active_dislikes\n\n\ndef build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n if pref_likes is None or pref_dislikes is None:\n pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_lik = len(pref_likes.get(uid, []))\n n_dis = len(pref_dislikes.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_lik),\n np.log1p(n_dis),\n (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n ]\n l_items = pref_likes.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = pref_dislikes.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\n# 5. Extract training slices\ndef extract_slice(cutoff_time):\n t_start = time.time()\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n print(f\"Slice (cutoff={cutoff_time}): {len(unique_users)} users, {len(pairs)} pairs in {time.time() - t_start:.2f}s\")\n return user_feats, pairs\n\n\nprint(\"4. Extracting training slices (6 weeks)...\")\ntrain_slices = []\nfor w in range(1, 7):\n s_feats, s_pairs = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs))\n\n# 6. Extract validation features\nprint(\"5. Extracting validation features...\")\nv_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_likes_dict = {u: [] for u in target_uids}\nval_dislikes_dict = {u: [] for u in target_uids}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == \"liked\":\n val_likes_dict[int(uid)].append(idx)\n elif state == \"disliked\":\n val_dislikes_dict[int(uid)].append(idx)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\n\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\n# 7. Model definition\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.03)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=64, dense_dim=10, stats_dim=6, out_dim=64):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\nmodel = TwoTowerRanker(num_items, embed_dim=64, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)\ntemperature = 0.07\nbatch_size = 512\nnum_epochs = 10\n\nprint(\"\\n6. Training Two-Tower Model...\")\nbest_ndcg = 0.0\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n B = len(b_pairs)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n\n # Deduplicate target items in batch\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n\n # Logits: (B, num_cand)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Vectorized false negative mask:\n # Mask out other columns where user is the same but target column differs\n same_u = (b_u.unsqueeze(1) == b_u.unsqueeze(0))\n diff_cand = (item_inverse.unsqueeze(1) != item_inverse.unsqueeze(0))\n false_neg_pairs = same_u & diff_cand\n row_idx, col_idx = torch.nonzero(false_neg_pairs, as_tuple=True)\n if len(row_idx) > 0:\n mask = torch.zeros_like(logits, dtype=torch.bool)\n mask[row_idx, item_inverse[col_idx]] = True\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 5.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Validation evaluation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n curr_ndcg = val_score[\"quality_ndcg@10\"]\n if curr_ndcg > best_ndcg:\n best_ndcg = curr_ndcg\n # Save checkpoint\n torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n",
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"description": "Writing to solution/train_improved.py",
"displayName": "WriteFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T06:27:13.606Z",
"content": "Successfully created and wrote to new file: /app/solution/train_improved.py. Here is the updated code:\nimport os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\nt0 = time.time()\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\nunlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\nunlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\nunlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nundislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\nundislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\nundislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n\n# Load listens for last 150 days\nmin_listen_time = CUTOFF_VAL - 150 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Loaded logs in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n\n# 3. Compute rich item features\nprint(\"3. Computing item features...\")\nt1 = time.time()\nl_grouped = listens_df.groupby(\"item_idx\").agg(\n total_plays=(\"played_ratio_pct\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n organic_ratio=(\"is_organic\", \"mean\"),\n med_length=(\"track_length_seconds\", \"median\"),\n).reset_index()\n\nitem_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\nlike_counts = likes_df.groupby(\"item_idx\").size().to_dict()\ndislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n\nNUM_ITEM_FEATS = 10\nitem_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\nfor idx in range(num_items):\n pos_l = cat_df.positive_listens.iloc[idx]\n org_l = cat_df.organic_positive_listens.iloc[idx]\n pop_r = cat_df.popularity_rank.iloc[idx]\n st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n n_lik = like_counts.get(idx, 0)\n n_dis = dislike_counts.get(idx, 0)\n\n item_dense[idx, 0] = np.log1p(pos_l)\n item_dense[idx, 1] = np.log1p(org_l)\n item_dense[idx, 2] = pop_r / float(num_items)\n item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n item_dense[idx, 5] = st[\"strong_ratio\"]\n item_dense[idx, 6] = st[\"organic_ratio\"]\n item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n item_dense[idx, 8] = np.log1p(n_lik)\n item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n\n# Normalize dense features\nfor c in range(NUM_ITEM_FEATS):\n m = item_dense[:, c].mean()\n s = item_dense[:, c].std() + 1e-6\n item_dense[:, c] = (item_dense[:, c] - m) / s\n\nitem_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\nprint(f\"Item features ready in {time.time() - t1:.2f}s\")\n\n\n# 4. Preference state helper\ndef get_active_preference_state(cutoff_time, user_set):\n \"\"\"Reconstruct active catalog likes and dislikes before cutoff_time.\"\"\"\n l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n\n l_act = pd.concat([\n l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n\n active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n d_act = pd.concat([\n d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n\n active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n return active_likes, active_dislikes\n\n\ndef build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n if pref_likes is None or pref_dislikes is None:\n pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_lik = len(pref_likes.get(uid, []))\n n_dis = len(pref_dislikes.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_lik),\n np.log1p(n_dis),\n (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n ]\n l_items = pref_likes.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = pref_dislikes.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\n# 5. Extract training slices\ndef extract_slice(cutoff_time):\n t_start = time.time()\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n print(f\"Slice (cutoff={cutoff_time}): {len(unique_users)} users, {len(pairs)} pairs in {time.time() - t_start:.2f}s\")\n return user_feats, pairs\n\n\nprint(\"4. Extracting training slices (6 weeks)...\")\ntrain_slices = []\nfor w in range(1, 7):\n s_feats, s_pairs = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs))\n\n# 6. Extract validation features\nprint(\"5. Extracting validation features...\")\nv_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_likes_dict = {u: [] for u in target_uids}\nval_dislikes_dict = {u: [] for u in target_uids}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == \"liked\":\n val_likes_dict[int(uid)].append(idx)\n elif state == \"disliked\":\n val_dislikes_dict[int(uid)].append(idx)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\n\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\n# 7. Model definition\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.03)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=64, dense_dim=10, stats_dim=6, out_dim=64):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\nmodel = TwoTowerRanker(num_items, embed_dim=64, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)\ntemperature = 0.07\nbatch_size = 512\nnum_epochs = 10\n\nprint(\"\\n6. Training Two-Tower Model...\")\nbest_ndcg = 0.0\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n B = len(b_pairs)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n\n # Deduplicate target items in batch\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n\n # Logits: (B, num_cand)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Vectorized false negative mask:\n # Mask out other columns where user is the same but target column differs\n same_u = (b_u.unsqueeze(1) == b_u.unsqueeze(0))\n diff_cand = (item_inverse.unsqueeze(1) != item_inverse.unsqueeze(0))\n false_neg_pairs = same_u & diff_cand\n row_idx, col_idx = torch.nonzero(false_neg_pairs, as_tuple=True)\n if len(row_idx) > 0:\n mask = torch.zeros_like(logits, dtype=torch.bool)\n mask[row_idx, item_inverse[col_idx]] = True\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 5.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Validation evaluation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n curr_ndcg = val_score[\"quality_ndcg@10\"]\n if curr_ndcg > best_ndcg:\n best_ndcg = curr_ndcg\n # Save checkpoint\n torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n",
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{
"id": 70,
"role": "agent",
"timestamp": "2026-09-29T06:27:35.918Z",
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"name": "run_shell_command",
"response": {
"output": "\nOutput: 1. Loading metadata...\n2. Loading interaction logs...\nLoaded logs in 3.12s (listens: 18800747)\n3. Computing item features...\nItem features ready in 4.55s\n4. Extracting training slices (6 weeks)...\nSlice (cutoff=24182000): 4968 users, 49683 pairs in 12.80s\nSlice (cutoff=23577200): 4927 users, 47773 pairs in 12.52s\nSlice (cutoff=22972400): 4774 users, 47336 pairs in 12.03s\nSlice (cutoff=22367600): 4668 users, 46200 pairs in 11.37s\nSlice (cutoff=21762800): 4643 users, 45826 pairs in 10.97s\nSlice (cutoff=21158000): 4513 users, 42793 pairs in 10.71s\n5. Extracting validation features...\n\n6. Training Two-Tower Model...\nEpoch 1: loss=11.0525, val_ndcg@10=0.014643 (best=0.014643) [12.1s]\nEpoch 2: loss=10.6424, val_ndcg@10=0.009121 (best=0.014643) [11.5s]\nEpoch 3: loss=10.1821, val_ndcg@10=0.015472 (best=0.015472) [11.5s]\nEpoch 4: loss=9.8330, val_ndcg@10=0.009633 (best=0.015472) [11.4s]\nEpoch 5: loss=9.5499, val_ndcg@10=0.006211 (best=0.015472) [11.5s]\nEpoch 6: loss=9.3125, val_ndcg@10=0.005576 (best=0.015472) [11.7s]\nEpoch 7: loss=9.1088, val_ndcg@10=0.005108 (best=0.015472) [11.4s]\nEpoch 8: loss=8.9413, val_ndcg@10=0.004401 (best=0.015472) [11.4s]\nEpoch 9: loss=8.7853, val_ndcg@10=0.003654 (best=0.015472) [11.4s]\nEpoch 10: loss=8.6605, val_ndcg@10=0.003811 (best=0.015472) [11.2s]\nProcess Group PGID: 2974\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:31:16.826Z",
"resultDisplay": "1. Loading metadata...\n2. Loading interaction logs...\nLoaded logs in 3.12s (listens: 18800747)\n3. Computing item features...\nItem features ready in 4.55s\n4. Extracting training slices (6 weeks)...\nSlice (cutoff=24182000): 4968 users, 49683 pairs in 12.80s\nSlice (cutoff=23577200): 4927 users, 47773 pairs in 12.52s\nSlice (cutoff=22972400): 4774 users, 47336 pairs in 12.03s\nSlice (cutoff=22367600): 4668 users, 46200 pairs in 11.37s\nSlice (cutoff=21762800): 4643 users, 45826 pairs in 10.97s\nSlice (cutoff=21158000): 4513 users, 42793 pairs in 10.71s\n5. Extracting validation features...\n\n6. Training Two-Tower Model...\nEpoch 1: loss=11.0525, val_ndcg@10=0.014643 (best=0.014643) [12.1s]\nEpoch 2: loss=10.6424, val_ndcg@10=0.009121 (best=0.014643) [11.5s]\nEpoch 3: loss=10.1821, val_ndcg@10=0.015472 (best=0.015472) [11.5s]\nEpoch 4: loss=9.8330, val_ndcg@10=0.009633 (best=0.015472) [11.4s]\nEpoch 5: loss=9.5499, val_ndcg@10=0.006211 (best=0.015472) [11.5s]\nEpoch 6: loss=9.3125, val_ndcg@10=0.005576 (best=0.015472) [11.7s]\nEpoch 7: loss=9.1088, val_ndcg@10=0.005108 (best=0.015472) [11.4s]\nEpoch 8: loss=8.9413, val_ndcg@10=0.004401 (best=0.015472) [11.4s]\nEpoch 9: loss=8.7853, val_ndcg@10=0.003654 (best=0.015472) [11.4s]\nEpoch 10: loss=8.6605, val_ndcg@10=0.003811 (best=0.015472) [11.2s]",
"description": "python3 /app/solution/train_improved.py",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:31:16.828Z",
"content": "\nOutput: 1. Loading metadata...\n2. Loading interaction logs...\nLoaded logs in 3.12s (listens: 18800747)\n3. Computing item features...\nItem features ready in 4.55s\n4. Extracting training slices (6 weeks)...\nSlice (cutoff=24182000): 4968 users, 49683 pairs in 12.80s\nSlice (cutoff=23577200): 4927 users, 47773 pairs in 12.52s\nSlice (cutoff=22972400): 4774 users, 47336 pairs in 12.03s\nSlice (cutoff=22367600): 4668 users, 46200 pairs in 11.37s\nSlice (cutoff=21762800): 4643 users, 45826 pairs in 10.97s\nSlice (cutoff=21158000): 4513 users, 42793 pairs in 10.71s\n5. Extracting validation features...\n\n6. Training Two-Tower Model...\nEpoch 1: loss=11.0525, val_ndcg@10=0.014643 (best=0.014643) [12.1s]\nEpoch 2: loss=10.6424, val_ndcg@10=0.009121 (best=0.014643) [11.5s]\nEpoch 3: loss=10.1821, val_ndcg@10=0.015472 (best=0.015472) [11.5s]\nEpoch 4: loss=9.8330, val_ndcg@10=0.009633 (best=0.015472) [11.4s]\nEpoch 5: loss=9.5499, val_ndcg@10=0.006211 (best=0.015472) [11.5s]\nEpoch 6: loss=9.3125, val_ndcg@10=0.005576 (best=0.015472) [11.7s]\nEpoch 7: loss=9.1088, val_ndcg@10=0.005108 (best=0.015472) [11.4s]\nEpoch 8: loss=8.9413, val_ndcg@10=0.004401 (best=0.015472) [11.4s]\nEpoch 9: loss=8.7853, val_ndcg@10=0.003654 (best=0.015472) [11.4s]\nEpoch 10: loss=8.6605, val_ndcg@10=0.003811 (best=0.015472) [11.2s]\nProcess Group PGID: 2974\n",
"isError": false
},
"responseMessageId": "1bcfdc87-02b6-4f54-bd11-3bb9f40f73cc"
}
]
},
{
"id": 71,
"role": "agent",
"timestamp": "2026-09-29T06:35:45.674Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:35:45.674Z",
"text": "Investigating Evaluation Discrepancy\n\nI'm scrutinizing the fine-grained code differences between `prototype_eval.py` and `train_improved.py` to pinpoint why the former achieved a significantly higher `val_ndcg@10` of 0.022638 compared to the latter's 0.015472. My focus is on identifying the precise algorithmic or implementation variations that led to this outcome."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:35:45.674Z",
"text": "Analyzing Feature Differences\n\nI've identified key discrepancies in feature processing. `prototype_eval.py` uses fewer, unstandardized dense features, including raw `log1p(positive_listens)` values around 10.0. In contrast, `train_improved.py` employs more features, standardized with mean/std. I'm investigating why the simpler, unstandardized approach yielded superior results."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:35:45.674Z",
"text": "Discovering Divergences\n\nMy analysis revealed that categorical features *were* indeed standardized in `prototype_eval.py`. Further investigation shows differing `loss_weights` for grades >= 3.0 (3.0 vs. 5.0) and varying learning rates and weight decays in the AdamW optimizer. I'm now exploring the impact of these adjustments and the false negative mask implementation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:35:45.674Z",
"text": "Identifying False Negative Logic\n\nI've discovered a critical divergence in how false negatives are handled. My earlier investigation of `prototype_eval.py` missed that its `u_to_pos` included all positive items for a user for the entire week, not just those within the current mini-batch. This broader context is key.\n\n**Revisiting False Negative Handling**\n\nI'm now re-evaluating the false negative masking implementation. The `prototype_eval.py` approach of considering all positive items from the entire week for a user, rather than just those in the current batch as done in `train_improved.py`, appears to be a significant factor in the observed performance difference.\n\n**Clarifying Positive Item Scope**\n\nMy focus has shifted to the scope of positive items used in false negative generation. `prototype_eval.py` incorporates a user's entire week of positive items, a broader definition than the mini-batch specific approach in `train_improved.py`, which likely impacts the model's learning and evaluation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:35:45.674Z",
"text": "Investigating Overfitting Trends\n\nI'm now focused on the decay in validation NDCG observed after epoch 2, despite decreasing training loss. This pattern strongly suggests overfitting or distribution drift, and I'm analyzing item embedding updates and MLP behavior within the training loop to understand the root cause."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:35:45.674Z",
"callId": "run_shell_command__call_636616",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport torch\nimport numpy as np\n\n# Let's inspect best_model.pt saved from train_improved\nmodel_path = '/app/solution/best_model.pt'\nif os.path.exists(model_path):\n sd = torch.load(model_path, weights_only=True)\n print('Keys:', sd.keys())\n print('item_emb weight norm:', torch.norm(sd['item_tower.item_emb.weight']).item())\n\"",
"description": "Check saved best model"
},
"recorded": {
"id": "run_shell_command__call_636616",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport torch\nimport numpy as np\n\n# Let's inspect best_model.pt saved from train_improved\nmodel_path = '/app/solution/best_model.pt'\nif os.path.exists(model_path):\n sd = torch.load(model_path, weights_only=True)\n print('Keys:', sd.keys())\n print('item_emb weight norm:', torch.norm(sd['item_tower.item_emb.weight']).item())\n\"",
"description": "Check saved best model"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_636616",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 7, in \nNameError: name 'os' is not defined. Did you forget to import 'os'?\nExit Code: 1\nProcess Group PGID: 3057\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:35:48.609Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 7, in \nNameError: name 'os' is not defined. Did you forget to import 'os'?",
"description": "Check saved best model",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:35:48.611Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 7, in \nNameError: name 'os' is not defined. Did you forget to import 'os'?\nExit Code: 1\nProcess Group PGID: 3057\n",
"isError": false
},
"responseMessageId": "2367d11c-2420-4307-8d21-a94e43dc4ebd"
}
]
},
{
"id": 72,
"role": "agent",
"timestamp": "2026-09-29T06:35:54.101Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T06:35:54.101Z",
"callId": "run_shell_command__call_132519",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport os, torch\nmodel_path = '/app/solution/best_model.pt'\nif os.path.exists(model_path):\n sd = torch.load(model_path, weights_only=True)\n print('Keys:', list(sd.keys()))\n print('item_emb norm:', torch.norm(sd['item_tower.item_emb.weight']).item())\n print('item_emb mean, std:', sd['item_tower.item_emb.weight'].mean().item(), sd['item_tower.item_emb.weight'].std().item())\n\"",
"description": "Inspect best_model.pt parameters"
},
"recorded": {
"id": "run_shell_command__call_132519",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport os, torch\nmodel_path = '/app/solution/best_model.pt'\nif os.path.exists(model_path):\n sd = torch.load(model_path, weights_only=True)\n print('Keys:', list(sd.keys()))\n print('item_emb norm:', torch.norm(sd['item_tower.item_emb.weight']).item())\n print('item_emb mean, std:', sd['item_tower.item_emb.weight'].mean().item(), sd['item_tower.item_emb.weight'].std().item())\n\"",
"description": "Inspect best_model.pt parameters"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_132519",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Keys: ['item_tower.item_emb.weight', 'item_tower.dense_proj.weight', 'item_tower.dense_proj.bias', 'item_tower.mlp.0.weight', 'item_tower.mlp.0.bias', 'item_tower.mlp.1.weight', 'item_tower.mlp.1.bias', 'item_tower.mlp.3.weight', 'item_tower.mlp.3.bias', 'user_tower.shared_emb.weight', 'user_tower.stats_proj.weight', 'user_tower.stats_proj.bias', 'user_tower.mlp.0.weight', 'user_tower.mlp.0.bias', 'user_tower.mlp.1.weight', 'user_tower.mlp.1.bias', 'user_tower.mlp.4.weight', 'user_tower.mlp.4.bias']\nitem_emb norm: 67.66739654541016\nitem_emb mean, std: 0.0011243692133575678 0.048823002725839615\nProcess Group PGID: 3067\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:35:57.040Z",
"resultDisplay": "Keys: ['item_tower.item_emb.weight', 'item_tower.dense_proj.weight', 'item_tower.dense_proj.bias', 'item_tower.mlp.0.weight', 'item_tower.mlp.0.bias', 'item_tower.mlp.1.weight', 'item_tower.mlp.1.bias', 'item_tower.mlp.3.weight', 'item_tower.mlp.3.bias', 'user_tower.shared_emb.weight', 'user_tower.stats_proj.weight', 'user_tower.stats_proj.bias', 'user_tower.mlp.0.weight', 'user_tower.mlp.0.bias', 'user_tower.mlp.1.weight', 'user_tower.mlp.1.bias', 'user_tower.mlp.4.weight', 'user_tower.mlp.4.bias']\nitem_emb norm: 67.66739654541016\nitem_emb mean, std: 0.0011243692133575678 0.048823002725839615",
"description": "Inspect best_model.pt parameters",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:35:57.042Z",
"content": "\nOutput: Keys: ['item_tower.item_emb.weight', 'item_tower.dense_proj.weight', 'item_tower.dense_proj.bias', 'item_tower.mlp.0.weight', 'item_tower.mlp.0.bias', 'item_tower.mlp.1.weight', 'item_tower.mlp.1.bias', 'item_tower.mlp.3.weight', 'item_tower.mlp.3.bias', 'user_tower.shared_emb.weight', 'user_tower.stats_proj.weight', 'user_tower.stats_proj.bias', 'user_tower.mlp.0.weight', 'user_tower.mlp.0.bias', 'user_tower.mlp.1.weight', 'user_tower.mlp.1.bias', 'user_tower.mlp.4.weight', 'user_tower.mlp.4.bias']\nitem_emb norm: 67.66739654541016\nitem_emb mean, std: 0.0011243692133575678 0.048823002725839615\nProcess Group PGID: 3067\n",
"isError": false
},
"responseMessageId": "8c4dbd5a-39ff-4696-93bf-9840b622d2e1"
}
]
},
{
"id": 73,
"role": "agent",
"timestamp": "2026-09-29T06:36:36.172Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:36:36.172Z",
"text": "Evaluating Prototype Performance\n\nMy recent focus has been on the `prototype_eval.py` script. I've observed that Epoch 2 yielded a score of `0.022638`. I am now proceeding to save the model checkpoint from this epoch and will evaluate its performance using `python /task/evaluate_public.py`. This step is crucial for understanding the current state of the prototype."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:36:36.172Z",
"text": "Investigating Score Discrepancies\n\nI'm currently examining why `prototype_eval.py` scored 0.022638 while `train_improved.py` achieved 0.015472. My initial thoughts point to the reduced number of slices (3 vs. 6) in `prototype_eval.py` focusing on more recent data, a different learning rate of `1e-3`, and a crucial change in how the `mask_matrix` operates, only masking items present in the user's weekly positive set within the batch."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:36:36.172Z",
"callId": "run_shell_command__call_815658",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport time\n\ncand_set = set(range(400))\nu_pos = {i: set(range(i, i+10)) for i in range(512)}\n\nt0 = time.time()\nfor _ in range(100):\n for r in range(512):\n overlap = u_pos[r] & cand_set\nprint('512 set intersections x 100 took:', round(time.time() - t0, 3), 's')\n\"",
"description": "Benchmark set intersection for false negative masking"
},
"recorded": {
"id": "run_shell_command__call_815658",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport time\n\ncand_set = set(range(400))\nu_pos = {i: set(range(i, i+10)) for i in range(512)}\n\nt0 = time.time()\nfor _ in range(100):\n for r in range(512):\n overlap = u_pos[r] & cand_set\nprint('512 set intersections x 100 took:', round(time.time() - t0, 3), 's')\n\"",
"description": "Benchmark set intersection for false negative masking"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_815658",
"name": "run_shell_command",
"response": {
"output": "\nOutput: 512 set intersections x 100 took: 0.018 s\nProcess Group PGID: 3080\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:36:36.223Z",
"resultDisplay": "512 set intersections x 100 took: 0.018 s",
"description": "Benchmark set intersection for false negative masking",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:36:36.224Z",
"content": "\nOutput: 512 set intersections x 100 took: 0.018 s\nProcess Group PGID: 3080\n",
"isError": false
},
"responseMessageId": "66f08dfb-d456-4861-8764-6794782b47bf"
}
]
},
{
"id": 74,
"role": "agent",
"timestamp": "2026-09-29T06:36:53.433Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:36:53.433Z",
"text": "Optimizing Inference Speed\n\nMy recent analysis revealed that set intersection isn't the bottleneck. The slowdown actually stems from the `cand_items_np[c] in pos_set` operation within `prototype_eval.py`. I'm now focusing on optimizing that specific line of code."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:36:53.433Z",
"text": "Refining User Representations\n\nI've confirmed the set intersection approach drastically improved performance, now taking milliseconds. My focus has shifted to further enhancing the Two-Tower ranker's effectiveness, specifically examining the components within the User Tower, such as the weighting of recent versus historical listens."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:36:53.433Z",
"callId": "run_shell_command__call_583571",
"name": "run_shell_command",
"input": {
"description": "Check listens in last 3 days",
"command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ncutoff = 24786800\nt_start = cutoff - 3 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid'], filters=[('timestamp', '>=', t_start)])\nuids = table['uid'].to_numpy()\nprint('Users with listens in last 3 days:', len(set(uids)))\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('Target users with listens in last 3 days:', len(set(uids) & set(targets.uid)), '/', len(targets))\n\""
},
"recorded": {
"id": "run_shell_command__call_583571",
"name": "run_shell_command",
"args": {
"description": "Check listens in last 3 days",
"command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ncutoff = 24786800\nt_start = cutoff - 3 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid'], filters=[('timestamp', '>=', t_start)])\nuids = table['uid'].to_numpy()\nprint('Users with listens in last 3 days:', len(set(uids)))\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('Target users with listens in last 3 days:', len(set(uids) & set(targets.uid)), '/', len(targets))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_583571",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Users with listens in last 3 days: 6511\nTarget users with listens in last 3 days: 6400 / 8456\nProcess Group PGID: 3089\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:36:55.303Z",
"resultDisplay": "Users with listens in last 3 days: 6511\nTarget users with listens in last 3 days: 6400 / 8456",
"description": "Check listens in last 3 days",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:36:55.305Z",
"content": "\nOutput: Users with listens in last 3 days: 6511\nTarget users with listens in last 3 days: 6400 / 8456\nProcess Group PGID: 3089\n",
"isError": false
},
"responseMessageId": "3d789d71-dff9-4663-9e34-6a2ed6943a45"
}
]
},
{
"id": 75,
"role": "agent",
"timestamp": "2026-09-29T06:37:11.610Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:37:11.610Z",
"text": "Analyzing User Activity\n\nMy focus has shifted to understanding item co-occurrence. It's interesting to note that a significant portion of our target users, 76%, have shown ultra-recent activity within the last 72 hours. This high engagement is encouraging as I delve into this next analytical step."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:37:11.610Z",
"text": "Exploring Embedding Initialization\n\nI'm investigating how Two-Tower models link past and future music. The shared item embedding acts as the bridge. I'm considering pre-initializing these embeddings with Word2Vec or SVD on session co-occurrence to potentially improve learning speed and accuracy, as the model needs to learn all item similarities from scratch otherwise."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:37:11.610Z",
"callId": "run_shell_command__call_366270",
"name": "run_shell_command",
"input": {
"description": "Benchmark item-item co-occurrence computation",
"command": "python3 -c \"\nimport time, scipy.sparse as sp, numpy as np, pandas as pd, pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\ncutoff = 24786800\n\n# Take listens in last 30 days\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id'],\n filters=[('timestamp', '>=', cutoff - 30 * 86400)])\ndf = table.to_pandas()\ndf = df[df.item_id.isin(item_to_idx)]\ndf['item_idx'] = df.item_id.map(item_to_idx)\ndf = df.drop_duplicates(['uid', 'item_idx'])\n\n# User-item sparse matrix (users in last 30 days x 30000 items)\nuid_map = {u: i for i, u in enumerate(df.uid.unique())}\nuser_rows = df.uid.map(uid_map).to_numpy()\nitem_cols = df.item_idx.to_numpy()\ndata = np.ones(len(df), dtype=np.float32)\n\nU = sp.csr_matrix((data, (user_rows, item_cols)), shape=(len(uid_map), 30000))\nprint(f'User-Item matrix created in {time.time() - t0:.2f}s, shape: {U.shape}, nnz: {U.nnz}')\n\n# Item-item co-occurrence: C = U.T @ U\nt1 = time.time()\nC = U.T @ U # shape (30000, 30000)\nprint(f'Item-Item co-occurrence computed in {time.time() - t1:.2f}s, nnz: {C.nnz}')\n\""
},
"recorded": {
"id": "run_shell_command__call_366270",
"name": "run_shell_command",
"args": {
"description": "Benchmark item-item co-occurrence computation",
"command": "python3 -c \"\nimport time, scipy.sparse as sp, numpy as np, pandas as pd, pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\ncutoff = 24786800\n\n# Take listens in last 30 days\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id'],\n filters=[('timestamp', '>=', cutoff - 30 * 86400)])\ndf = table.to_pandas()\ndf = df[df.item_id.isin(item_to_idx)]\ndf['item_idx'] = df.item_id.map(item_to_idx)\ndf = df.drop_duplicates(['uid', 'item_idx'])\n\n# User-item sparse matrix (users in last 30 days x 30000 items)\nuid_map = {u: i for i, u in enumerate(df.uid.unique())}\nuser_rows = df.uid.map(uid_map).to_numpy()\nitem_cols = df.item_idx.to_numpy()\ndata = np.ones(len(df), dtype=np.float32)\n\nU = sp.csr_matrix((data, (user_rows, item_cols)), shape=(len(uid_map), 30000))\nprint(f'User-Item matrix created in {time.time() - t0:.2f}s, shape: {U.shape}, nnz: {U.nnz}')\n\n# Item-item co-occurrence: C = U.T @ U\nt1 = time.time()\nC = U.T @ U # shape (30000, 30000)\nprint(f'Item-Item co-occurrence computed in {time.time() - t1:.2f}s, nnz: {C.nnz}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_366270",
"name": "run_shell_command",
"response": {
"output": "\nOutput: User-Item matrix created in 1.33s, shape: (8512, 30000), nnz: 2157185\nItem-Item co-occurrence computed in 7.33s, nnz: 287150348\nProcess Group PGID: 3111\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:37:23.616Z",
"resultDisplay": "User-Item matrix created in 1.33s, shape: (8512, 30000), nnz: 2157185\nItem-Item co-occurrence computed in 7.33s, nnz: 287150348",
"description": "Benchmark item-item co-occurrence computation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:37:23.617Z",
"content": "\nOutput: User-Item matrix created in 1.33s, shape: (8512, 30000), nnz: 2157185\nItem-Item co-occurrence computed in 7.33s, nnz: 287150348\nProcess Group PGID: 3111\n",
"isError": false
},
"responseMessageId": "d7203883-e08b-470f-8baf-b3767919de5d"
}
]
},
{
"id": 76,
"role": "agent",
"timestamp": "2026-09-29T06:38:11.527Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:38:11.527Z",
"text": "Analyzing Matrix Size\n\nI'm processing the item-item co-occurrence matrix, which currently stands at an impressive 287 million entries. This dense-ish 30,000x30,000 matrix is being handled in roughly 7.3 seconds. I'm keeping in mind the instruction that this benchmark scales to millions of users and items in production."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:38:11.527Z",
"text": "Refining Training Strategy\n\nMy focus has shifted to ensuring the training methodology adheres to the constraint of avoiding global user-item matrix construction or decomposition, which is critical for scalability. I am now examining the Two-Tower model's performance and exploring optimizations for its training recipe to further enhance metrics like NDCG@10."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:38:11.527Z",
"text": "Exploring Embedding Dimensions\n\nI'm currently considering doubling the embedding dimension to 128. This enhancement should significantly boost the model's capacity to capture nuanced music styles and artist relationships without a substantial memory overhead. The focus is on evaluating its impact on representing richer user and item features within the Two-Tower architecture."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:38:11.527Z",
"text": "Adapting Interaction Score\n\nI'm refining the score calculation to integrate item popularity and bias directly into the user and item embeddings for a more unified dot product. This change aims to streamline the relevance scoring mechanism.\n\n**Optimizing Training Objectives**\n\nMy current task involves experimenting with the InfoNCE loss and sampled softmax, particularly concerning batch negative sampling strategies and graded loss weighting. The goal is to improve the model's ability to distinguish relevant items."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:38:11.527Z",
"text": "Tuning Two-Tower Parameters\n\nI'm now systematically testing configurations for embedding dimension, temperature, learning rate, and weight decay. My goal is to optimize NDCG@10 through early stopping based on validation performance, and I'm excited to see how these variations impact the results, especially with efficient top-100 generation."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:38:11.527Z",
"callId": "write_file__call_191882",
"name": "write_file",
"input": {
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\nunlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\nunlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\nunlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nundislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\nundislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\nundislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n\n# Item features\nprint(\"3. Computing item features...\")\nl_grouped = listens_df.groupby(\"item_idx\").agg(\n total_plays=(\"played_ratio_pct\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n organic_ratio=(\"is_organic\", \"mean\"),\n med_length=(\"track_length_seconds\", \"median\"),\n).reset_index()\n\nitem_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\nlike_counts = likes_df.groupby(\"item_idx\").size().to_dict()\ndislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n\nNUM_ITEM_FEATS = 10\nitem_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\nfor idx in range(num_items):\n pos_l = cat_df.positive_listens.iloc[idx]\n org_l = cat_df.organic_positive_listens.iloc[idx]\n pop_r = cat_df.popularity_rank.iloc[idx]\n st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n n_lik = like_counts.get(idx, 0)\n n_dis = dislike_counts.get(idx, 0)\n\n item_dense[idx, 0] = np.log1p(pos_l)\n item_dense[idx, 1] = np.log1p(org_l)\n item_dense[idx, 2] = pop_r / float(num_items)\n item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n item_dense[idx, 5] = st[\"strong_ratio\"]\n item_dense[idx, 6] = st[\"organic_ratio\"]\n item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n item_dense[idx, 8] = np.log1p(n_lik)\n item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n\nfor c in range(NUM_ITEM_FEATS):\n m = item_dense[:, c].mean()\n s = item_dense[:, c].std() + 1e-6\n item_dense[:, c] = (item_dense[:, c] - m) / s\n\nitem_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\n\n\ndef get_active_preference_state(cutoff_time, user_set):\n l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n\n l_act = pd.concat([\n l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n d_act = pd.concat([\n d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n return active_likes, active_dislikes\n\n\ndef build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n if pref_likes is None or pref_dislikes is None:\n pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_lik = len(pref_likes.get(uid, []))\n n_dis = len(pref_dislikes.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_lik),\n np.log1p(n_dis),\n (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n ]\n l_items = pref_likes.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = pref_dislikes.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\ndef extract_slice(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n user_all_pos = {idx: set() for idx in range(len(unique_users))}\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n user_all_pos[u_idx].add(item_idx)\n\n return user_feats, pairs, user_all_pos\n\n\nprint(\"4. Extracting recent slices (3 weeks)...\")\ntrain_slices = []\nfor w in range(1, 4):\n s_feats, s_pairs, s_pos = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs, s_pos))\n\nprint(\"5. Extracting validation features...\")\nv_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_likes_dict = {u: [] for u in target_uids}\nval_dislikes_dict = {u: [] for u in target_uids}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == \"liked\":\n val_likes_dict[int(uid)].append(idx)\n elif state == \"disliked\":\n val_dislikes_dict[int(uid)].append(idx)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=128, dense_dim=10, stats_dim=6, out_dim=128):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\ndef train_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=6):\n print(f\"\\n--- Testing config: dim={embed_dim}, lr={lr}, temp={temperature}, wd={weight_decay} ---\")\n model = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=embed_dim)\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=lr * 0.1)\n batch_size = 512\n best_ndcg = 0.0\n\n for epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Fast false negative masking via set intersection\n cand_set = set(unique_pos_items.tolist())\n cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n b_u_list = b_u.tolist()\n inv_list = item_inverse.tolist()\n\n mask = torch.zeros_like(logits, dtype=torch.bool)\n for r, u in enumerate(b_u_list):\n overlap = u_all_pos[u] & cand_set\n tgt_col = inv_list[r]\n for item in overlap:\n c = cand_pos_map[item]\n if c != tgt_col:\n mask[r, c] = True\n\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n scheduler.step()\n\n # Validation eval\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n curr_ndcg = val_score[\"quality_ndcg@10\"]\n if curr_ndcg > best_ndcg:\n best_ndcg = curr_ndcg\n torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n\n return best_ndcg\n\n\ntrain_and_eval(embed_dim=64, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\ntrain_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\n",
"instruction": "Write tune_two_tower.py to benchmark embed_dim=64 vs 128 with cosine decay and fast set masking",
"file_path": "/app/solution/tune_two_tower.py"
},
"recorded": {
"id": "write_file__call_191882",
"name": "write_file",
"args": {
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\nunlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\nunlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\nunlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nundislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\nundislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\nundislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n\n# Item features\nprint(\"3. Computing item features...\")\nl_grouped = listens_df.groupby(\"item_idx\").agg(\n total_plays=(\"played_ratio_pct\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n organic_ratio=(\"is_organic\", \"mean\"),\n med_length=(\"track_length_seconds\", \"median\"),\n).reset_index()\n\nitem_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\nlike_counts = likes_df.groupby(\"item_idx\").size().to_dict()\ndislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n\nNUM_ITEM_FEATS = 10\nitem_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\nfor idx in range(num_items):\n pos_l = cat_df.positive_listens.iloc[idx]\n org_l = cat_df.organic_positive_listens.iloc[idx]\n pop_r = cat_df.popularity_rank.iloc[idx]\n st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n n_lik = like_counts.get(idx, 0)\n n_dis = dislike_counts.get(idx, 0)\n\n item_dense[idx, 0] = np.log1p(pos_l)\n item_dense[idx, 1] = np.log1p(org_l)\n item_dense[idx, 2] = pop_r / float(num_items)\n item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n item_dense[idx, 5] = st[\"strong_ratio\"]\n item_dense[idx, 6] = st[\"organic_ratio\"]\n item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n item_dense[idx, 8] = np.log1p(n_lik)\n item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n\nfor c in range(NUM_ITEM_FEATS):\n m = item_dense[:, c].mean()\n s = item_dense[:, c].std() + 1e-6\n item_dense[:, c] = (item_dense[:, c] - m) / s\n\nitem_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\n\n\ndef get_active_preference_state(cutoff_time, user_set):\n l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n\n l_act = pd.concat([\n l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n d_act = pd.concat([\n d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n return active_likes, active_dislikes\n\n\ndef build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n if pref_likes is None or pref_dislikes is None:\n pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_lik = len(pref_likes.get(uid, []))\n n_dis = len(pref_dislikes.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_lik),\n np.log1p(n_dis),\n (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n ]\n l_items = pref_likes.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = pref_dislikes.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\ndef extract_slice(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n user_all_pos = {idx: set() for idx in range(len(unique_users))}\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n user_all_pos[u_idx].add(item_idx)\n\n return user_feats, pairs, user_all_pos\n\n\nprint(\"4. Extracting recent slices (3 weeks)...\")\ntrain_slices = []\nfor w in range(1, 4):\n s_feats, s_pairs, s_pos = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs, s_pos))\n\nprint(\"5. Extracting validation features...\")\nv_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_likes_dict = {u: [] for u in target_uids}\nval_dislikes_dict = {u: [] for u in target_uids}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == \"liked\":\n val_likes_dict[int(uid)].append(idx)\n elif state == \"disliked\":\n val_dislikes_dict[int(uid)].append(idx)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=128, dense_dim=10, stats_dim=6, out_dim=128):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\ndef train_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=6):\n print(f\"\\n--- Testing config: dim={embed_dim}, lr={lr}, temp={temperature}, wd={weight_decay} ---\")\n model = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=embed_dim)\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=lr * 0.1)\n batch_size = 512\n best_ndcg = 0.0\n\n for epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Fast false negative masking via set intersection\n cand_set = set(unique_pos_items.tolist())\n cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n b_u_list = b_u.tolist()\n inv_list = item_inverse.tolist()\n\n mask = torch.zeros_like(logits, dtype=torch.bool)\n for r, u in enumerate(b_u_list):\n overlap = u_all_pos[u] & cand_set\n tgt_col = inv_list[r]\n for item in overlap:\n c = cand_pos_map[item]\n if c != tgt_col:\n mask[r, c] = True\n\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n scheduler.step()\n\n # Validation eval\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n curr_ndcg = val_score[\"quality_ndcg@10\"]\n if curr_ndcg > best_ndcg:\n best_ndcg = curr_ndcg\n torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n\n return best_ndcg\n\n\ntrain_and_eval(embed_dim=64, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\ntrain_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\n",
"instruction": "Write tune_two_tower.py to benchmark embed_dim=64 vs 128 with cosine decay and fast set masking",
"file_path": "/app/solution/tune_two_tower.py"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_191882",
"name": "write_file",
"response": {
"output": "Successfully created and wrote to new file: /app/solution/tune_two_tower.py. Here is the updated code:\nimport os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\nunlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\nunlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\nunlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nundislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\nundislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\nundislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n\n# Item features\nprint(\"3. Computing item features...\")\nl_grouped = listens_df.groupby(\"item_idx\").agg(\n total_plays=(\"played_ratio_pct\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n organic_ratio=(\"is_organic\", \"mean\"),\n med_length=(\"track_length_seconds\", \"median\"),\n).reset_index()\n\nitem_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\nlike_counts = likes_df.groupby(\"item_idx\").size().to_dict()\ndislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n\nNUM_ITEM_FEATS = 10\nitem_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\nfor idx in range(num_items):\n pos_l = cat_df.positive_listens.iloc[idx]\n org_l = cat_df.organic_positive_listens.iloc[idx]\n pop_r = cat_df.popularity_rank.iloc[idx]\n st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n n_lik = like_counts.get(idx, 0)\n n_dis = dislike_counts.get(idx, 0)\n\n item_dense[idx, 0] = np.log1p(pos_l)\n item_dense[idx, 1] = np.log1p(org_l)\n item_dense[idx, 2] = pop_r / float(num_items)\n item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n item_dense[idx, 5] = st[\"strong_ratio\"]\n item_dense[idx, 6] = st[\"organic_ratio\"]\n item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n item_dense[idx, 8] = np.log1p(n_lik)\n item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n\nfor c in range(NUM_ITEM_FEATS):\n m = item_dense[:, c].mean()\n s = item_dense[:, c].std() + 1e-6\n item_dense[:, c] = (item_dense[:, c] - m) / s\n\nitem_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\n\n\ndef get_active_preference_state(cutoff_time, user_set):\n l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n\n l_act = pd.concat([\n l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n d_act = pd.concat([\n d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n return active_likes, active_dislikes\n\n\ndef build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n if pref_likes is None or pref_dislikes is None:\n pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_lik = len(pref_likes.get(uid, []))\n n_dis = len(pref_dislikes.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_lik),\n np.log1p(n_dis),\n (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n ]\n l_items = pref_likes.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = pref_dislikes.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\ndef extract_slice(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n user_all_pos = {idx: set() for idx in range(len(unique_users))}\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n user_all_pos[u_idx].add(item_idx)\n\n return user_feats, pairs, user_all_pos\n\n\nprint(\"4. Extracting recent slices (3 weeks)...\")\ntrain_slices = []\nfor w in range(1, 4):\n s_feats, s_pairs, s_pos = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs, s_pos))\n\nprint(\"5. Extracting validation features...\")\nv_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_likes_dict = {u: [] for u in target_uids}\nval_dislikes_dict = {u: [] for u in target_uids}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == \"liked\":\n val_likes_dict[int(uid)].append(idx)\n elif state == \"disliked\":\n val_dislikes_dict[int(uid)].append(idx)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=128, dense_dim=10, stats_dim=6, out_dim=128):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\ndef train_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=6):\n print(f\"\\n--- Testing config: dim={embed_dim}, lr={lr}, temp={temperature}, wd={weight_decay} ---\")\n model = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=embed_dim)\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=lr * 0.1)\n batch_size = 512\n best_ndcg = 0.0\n\n for epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Fast false negative masking via set intersection\n cand_set = set(unique_pos_items.tolist())\n cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n b_u_list = b_u.tolist()\n inv_list = item_inverse.tolist()\n\n mask = torch.zeros_like(logits, dtype=torch.bool)\n for r, u in enumerate(b_u_list):\n overlap = u_all_pos[u] & cand_set\n tgt_col = inv_list[r]\n for item in overlap:\n c = cand_pos_map[item]\n if c != tgt_col:\n mask[r, c] = True\n\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n scheduler.step()\n\n # Validation eval\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n curr_ndcg = val_score[\"quality_ndcg@10\"]\n if curr_ndcg > best_ndcg:\n best_ndcg = curr_ndcg\n torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n\n return best_ndcg\n\n\ntrain_and_eval(embed_dim=64, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\ntrain_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:38:11.537Z",
"resultDisplay": {
"fileDiff": "Index: tune_two_tower.py\n===================================================================\n--- tune_two_tower.py\tOriginal\n+++ tune_two_tower.py\tWritten\n@@ -0,0 +1,500 @@\n+import os\n+import sys\n+import time\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import pyarrow.parquet as pq\n+import torch\n+import torch.nn as nn\n+import torch.nn.functional as F\n+\n+sys.path.append(\"/task\")\n+from evaluate_public import score, validation_truth\n+from ranking_evaluation import CandidateSet\n+\n+torch.set_num_threads(8)\n+\n+DATA_DIR = Path(\"/task/data\")\n+OUT_DIR = Path(\"/app/solution\")\n+OUT_DIR.mkdir(parents=True, exist_ok=True)\n+\n+CUTOFF_VAL = 24786800\n+WEEK_SECONDS = 7 * 86400\n+\n+print(\"1. Loading metadata...\")\n+cat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\n+cat_items = cat_df.item_id.to_numpy()\n+item_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\n+num_items = len(cat_items)\n+\n+target_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\n+target_uids = target_users_df.uid.to_numpy()\n+target_uid_set = set(target_uids)\n+num_targets = len(target_uids)\n+\n+print(\"2. Loading interaction logs...\")\n+likes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\n+likes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\n+likes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n+\n+unlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\n+unlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\n+unlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n+\n+dislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\n+dislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\n+dislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n+\n+undislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\n+undislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\n+undislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n+\n+min_listen_time = CUTOFF_VAL - 120 * 86400\n+table = pq.read_table(\n+ DATA_DIR / \"train_listens.parquet\",\n+ columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n+ filters=[(\"timestamp\", \">=\", min_listen_time)],\n+)\n+listens_df = table.to_pandas()\n+listens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\n+listens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n+\n+# Item features\n+print(\"3. Computing item features...\")\n+l_grouped = listens_df.groupby(\"item_idx\").agg(\n+ total_plays=(\"played_ratio_pct\", \"count\"),\n+ mean_ratio=(\"played_ratio_pct\", \"mean\"),\n+ strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n+ organic_ratio=(\"is_organic\", \"mean\"),\n+ med_length=(\"track_length_seconds\", \"median\"),\n+).reset_index()\n+\n+item_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\n+like_counts = likes_df.groupby(\"item_idx\").size().to_dict()\n+dislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n+\n+NUM_ITEM_FEATS = 10\n+item_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\n+for idx in range(num_items):\n+ pos_l = cat_df.positive_listens.iloc[idx]\n+ org_l = cat_df.organic_positive_listens.iloc[idx]\n+ pop_r = cat_df.popularity_rank.iloc[idx]\n+ st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n+ n_lik = like_counts.get(idx, 0)\n+ n_dis = dislike_counts.get(idx, 0)\n+\n+ item_dense[idx, 0] = np.log1p(pos_l)\n+ item_dense[idx, 1] = np.log1p(org_l)\n+ item_dense[idx, 2] = pop_r / float(num_items)\n+ item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n+ item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n+ item_dense[idx, 5] = st[\"strong_ratio\"]\n+ item_dense[idx, 6] = st[\"organic_ratio\"]\n+ item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n+ item_dense[idx, 8] = np.log1p(n_lik)\n+ item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n+\n+for c in range(NUM_ITEM_FEATS):\n+ m = item_dense[:, c].mean()\n+ s = item_dense[:, c].std() + 1e-6\n+ item_dense[:, c] = (item_dense[:, c] - m) / s\n+\n+item_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\n+\n+\n+def get_active_preference_state(cutoff_time, user_set):\n+ l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n+ ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n+ d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n+ ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n+\n+ l_act = pd.concat([\n+ l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n+ ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n+ ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n+ active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n+\n+ d_act = pd.concat([\n+ d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n+ ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n+ ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n+ active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n+\n+ return active_likes, active_dislikes\n+\n+\n+def build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n+ u_set = set(user_ids)\n+ t_min = cutoff_time - 90 * 86400\n+\n+ sub_listens = listens_df[\n+ (listens_df.timestamp >= t_min)\n+ & (listens_df.timestamp < cutoff_time)\n+ & (listens_df.uid.isin(u_set))\n+ ].copy()\n+\n+ sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n+\n+ sub_listens[\"w_recent\"] = np.where(\n+ sub_listens.days_ago <= 14.0,\n+ np.exp(-0.1 * sub_listens.days_ago)\n+ * (1.0 + sub_listens.is_organic)\n+ * (sub_listens.played_ratio_pct / 100.0),\n+ 0.0,\n+ )\n+\n+ sub_listens[\"w_history\"] = (\n+ np.exp(-0.02 * sub_listens.days_ago)\n+ * (1.0 + sub_listens.is_organic)\n+ * (sub_listens.played_ratio_pct / 100.0)\n+ )\n+\n+ sub_listens[\"w_strong\"] = np.where(\n+ (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n+ np.exp(-0.05 * sub_listens.days_ago),\n+ 0.0,\n+ )\n+\n+ agg = (\n+ sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n+ .sum()\n+ .reset_index()\n+ )\n+\n+ if pref_likes is None or pref_dislikes is None:\n+ pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n+\n+ uid_counts = sub_listens.groupby(\"uid\").agg(\n+ n_listens=(\"item_idx\", \"count\"),\n+ mean_ratio=(\"played_ratio_pct\", \"mean\"),\n+ organic_ratio=(\"is_organic\", \"mean\"),\n+ ).to_dict(orient=\"index\")\n+\n+ user_to_pos = {u: i for i, u in enumerate(user_ids)}\n+ recent_indices, recent_uids, recent_weights = [], [], []\n+ history_indices, history_uids, history_weights = [], [], []\n+ strong_indices, strong_uids, strong_weights = [], [], []\n+ like_indices, like_uids = [], []\n+ dislike_indices, dislike_uids = [], []\n+ stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n+\n+ for i, uid in enumerate(user_ids):\n+ st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n+ n_lik = len(pref_likes.get(uid, []))\n+ n_dis = len(pref_dislikes.get(uid, []))\n+ stats_arr[i] = [\n+ np.log1p(st[\"n_listens\"]),\n+ st[\"mean_ratio\"] / 100.0,\n+ st[\"organic_ratio\"],\n+ np.log1p(n_lik),\n+ np.log1p(n_dis),\n+ (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n+ ]\n+ l_items = pref_likes.get(uid, [])\n+ like_indices.extend(l_items)\n+ like_uids.extend([i] * len(l_items))\n+\n+ d_items = pref_dislikes.get(uid, [])\n+ dislike_indices.extend(d_items)\n+ dislike_uids.extend([i] * len(d_items))\n+\n+ for uid, g in agg.groupby(\"uid\"):\n+ if uid not in user_to_pos:\n+ continue\n+ u_idx = user_to_pos[uid]\n+ m_r = g.w_recent > 0\n+ if m_r.any():\n+ recent_indices.extend(g.item_idx[m_r].tolist())\n+ recent_weights.extend(g.w_recent[m_r].tolist())\n+ recent_uids.extend([u_idx] * int(m_r.sum()))\n+\n+ history_indices.extend(g.item_idx.tolist())\n+ history_weights.extend(g.w_history.tolist())\n+ history_uids.extend([u_idx] * len(g))\n+\n+ m_s = g.w_strong > 0\n+ if m_s.any():\n+ strong_indices.extend(g.item_idx[m_s].tolist())\n+ strong_weights.extend(g.w_strong[m_s].tolist())\n+ strong_uids.extend([u_idx] * int(m_s.sum()))\n+\n+ N = len(user_ids)\n+ return {\n+ \"num_users\": N,\n+ \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n+ \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n+ \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n+ \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n+ \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n+ \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n+ \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n+ \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n+ \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n+ \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n+ \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n+ \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n+ \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n+ \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n+ }\n+\n+\n+def extract_slice(cutoff_time):\n+ w_end = cutoff_time + WEEK_SECONDS\n+ prior = listens_df[listens_df.timestamp < cutoff_time]\n+ prior_user_items = set(zip(prior.uid, prior.item_idx))\n+\n+ win_listens = listens_df[\n+ (listens_df.timestamp >= cutoff_time)\n+ & (listens_df.timestamp < w_end)\n+ & (listens_df.is_organic == 1)\n+ & (listens_df.played_ratio_pct >= 80)\n+ ]\n+ win_likes = likes_df[\n+ (likes_df.timestamp >= cutoff_time)\n+ & (likes_df.timestamp < w_end)\n+ & (likes_df.is_organic == 1)\n+ ]\n+\n+ targets = {}\n+ for u, i in zip(win_listens.uid, win_listens.item_idx):\n+ if (u, i) not in prior_user_items:\n+ targets[(u, i)] = 1\n+ for u, i in zip(win_likes.uid, win_likes.item_idx):\n+ if (u, i) not in prior_user_items:\n+ targets[(u, i)] = 3\n+\n+ user_positives = {}\n+ for (u, i), grade in targets.items():\n+ user_positives.setdefault(u, []).append((i, grade))\n+\n+ unique_users = sorted(list(user_positives.keys()))\n+ user_feats = build_user_features(cutoff_time, unique_users)\n+ user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n+\n+ pairs = []\n+ user_all_pos = {idx: set() for idx in range(len(unique_users))}\n+ for u, items in user_positives.items():\n+ u_idx = user_to_idx[u]\n+ for item_idx, grade in items:\n+ pairs.append((u_idx, item_idx, float(grade)))\n+ user_all_pos[u_idx].add(item_idx)\n+\n+ return user_feats, pairs, user_all_pos\n+\n+\n+print(\"4. Extracting recent slices (3 weeks)...\")\n+train_slices = []\n+for w in range(1, 4):\n+ s_feats, s_pairs, s_pos = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n+ train_slices.append((s_feats, s_pairs, s_pos))\n+\n+print(\"5. Extracting validation features...\")\n+v_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\n+val_likes_dict = {u: [] for u in target_uids}\n+val_dislikes_dict = {u: [] for u in target_uids}\n+for uid, item, state in v_pref.itertuples(index=False):\n+ idx = item_to_idx.get(int(item))\n+ if idx is not None:\n+ if state == \"liked\":\n+ val_likes_dict[int(uid)].append(idx)\n+ elif state == \"disliked\":\n+ val_dislikes_dict[int(uid)].append(idx)\n+\n+val_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\n+val_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\n+targets_set = set(target_users_df.uid.astype(int))\n+val_gold = validation_truth(val_eligibility, targets_set)\n+val_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n+\n+\n+class ItemTower(nn.Module):\n+ def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n+ super().__init__()\n+ self.item_emb = nn.Embedding(num_items, embed_dim)\n+ nn.init.normal_(self.item_emb.weight, std=0.05)\n+ self.dense_proj = nn.Linear(dense_dim, embed_dim)\n+ self.mlp = nn.Sequential(\n+ nn.Linear(embed_dim * 2, embed_dim),\n+ nn.LayerNorm(embed_dim),\n+ nn.GELU(),\n+ nn.Linear(embed_dim, out_dim),\n+ )\n+\n+ def forward(self, item_ids, dense_features):\n+ embs = self.item_emb(item_ids)\n+ d_p = self.dense_proj(dense_features[item_ids])\n+ x = torch.cat([embs, d_p], dim=-1)\n+ v = self.mlp(x)\n+ v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n+ b = v[:, -1:]\n+ return torch.cat([v_norm, b], dim=-1)\n+\n+\n+class UserTower(nn.Module):\n+ def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n+ super().__init__()\n+ self.shared_emb = shared_item_emb\n+ self.embed_dim = embed_dim\n+ self.stats_proj = nn.Linear(stats_dim, embed_dim)\n+ in_dim = embed_dim * 5 + embed_dim\n+ self.mlp = nn.Sequential(\n+ nn.Linear(in_dim, embed_dim * 2),\n+ nn.LayerNorm(embed_dim * 2),\n+ nn.GELU(),\n+ nn.Dropout(0.1),\n+ nn.Linear(embed_dim * 2, out_dim),\n+ )\n+\n+ def _pool(self, num_users, indices, uids, weights=None):\n+ out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n+ if len(indices) == 0:\n+ return out\n+ embs = self.shared_emb(indices)\n+ if weights is not None:\n+ embs = embs * weights.unsqueeze(-1)\n+ out = torch.index_add(out, 0, uids, embs)\n+ norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n+ return out / norms\n+\n+ def compute_user_pooled_views(self, feats):\n+ with torch.no_grad():\n+ N = feats[\"num_users\"]\n+ h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n+ h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n+ h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n+ h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n+ h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n+ return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n+\n+ def forward_user(self, pooled_views, stats):\n+ s_p = self.stats_proj(stats)\n+ x = torch.cat([pooled_views, s_p], dim=-1)\n+ u = self.mlp(x)\n+ u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n+ alpha = torch.sigmoid(u[:, -1:])\n+ return torch.cat([u_norm, alpha], dim=-1)\n+\n+\n+class TwoTowerRanker(nn.Module):\n+ def __init__(self, num_items, embed_dim=128, dense_dim=10, stats_dim=6, out_dim=128):\n+ super().__init__()\n+ self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n+ self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n+\n+ def compute_all_user_vectors(self, feats):\n+ with torch.no_grad():\n+ pooled = self.user_tower.compute_user_pooled_views(feats)\n+ return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n+\n+ def compute_all_item_vectors(self, dense_features):\n+ with torch.no_grad():\n+ all_ids = torch.arange(len(dense_features), dtype=torch.long)\n+ return self.item_tower(all_ids, dense_features)\n+\n+\n+def train_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=6):\n+ print(f\"\\n--- Testing config: dim={embed_dim}, lr={lr}, temp={temperature}, wd={weight_decay} ---\")\n+ model = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=embed_dim)\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n+ scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=lr * 0.1)\n+ batch_size = 512\n+ best_ndcg = 0.0\n+\n+ for epoch in range(1, num_epochs + 1):\n+ t_ep = time.time()\n+ total_loss = 0.0\n+ num_batches = 0\n+\n+ for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n+ slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n+ slice_stats = feats[\"user_stats\"]\n+\n+ np.random.seed(epoch * 100 + s_idx)\n+ perm = np.random.permutation(len(pairs))\n+ pairs_arr = np.array(pairs)[perm]\n+\n+ for b_start in range(0, len(pairs_arr), batch_size):\n+ b_pairs = pairs_arr[b_start : b_start + batch_size]\n+ b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n+ b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n+ b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n+\n+ u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n+ unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n+\n+ neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n+ cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n+\n+ cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n+ logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n+\n+ # Fast false negative masking via set intersection\n+ cand_set = set(unique_pos_items.tolist())\n+ cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n+ b_u_list = b_u.tolist()\n+ inv_list = item_inverse.tolist()\n+\n+ mask = torch.zeros_like(logits, dtype=torch.bool)\n+ for r, u in enumerate(b_u_list):\n+ overlap = u_all_pos[u] & cand_set\n+ tgt_col = inv_list[r]\n+ for item in overlap:\n+ c = cand_pos_map[item]\n+ if c != tgt_col:\n+ mask[r, c] = True\n+\n+ logits.masked_fill_(mask, -1e9)\n+\n+ targets = item_inverse\n+ loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n+ loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n+\n+ optimizer.zero_grad()\n+ loss.backward()\n+ optimizer.step()\n+\n+ total_loss += loss.item()\n+ num_batches += 1\n+\n+ scheduler.step()\n+\n+ # Validation eval\n+ with torch.no_grad():\n+ val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n+ all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n+\n+ ranked_dict = {}\n+ for start_u in range(0, num_targets, 1000):\n+ end_u = min(start_u + 1000, num_targets)\n+ sub_u = val_u_vecs[start_u:end_u]\n+ sub_scores = np.matmul(sub_u, all_v_vecs.T)\n+ sub_mask = val_unpacked[start_u:end_u]\n+ sub_scores[sub_mask == 0] = -1e9\n+\n+ top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n+ for row in range(len(sub_u)):\n+ u_row = start_u + row\n+ uid = int(target_uids[u_row])\n+ cand_idx = top100_idx[row]\n+ sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n+ ranked_dict[uid] = cat_items[sorted_idx].tolist()\n+\n+ val_score = score(ranked_dict, val_gold)\n+ curr_ndcg = val_score[\"quality_ndcg@10\"]\n+ if curr_ndcg > best_ndcg:\n+ best_ndcg = curr_ndcg\n+ torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n+\n+ print(\n+ f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n+ f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n+ f\"[{time.time() - t_ep:.1f}s]\"\n+ )\n+\n+ return best_ndcg\n+\n+\n+train_and_eval(embed_dim=64, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\n+train_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\n",
"fileName": "tune_two_tower.py",
"filePath": "/app/solution/tune_two_tower.py",
"originalContent": "",
"newContent": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\nunlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\nunlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\nunlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nundislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\nundislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\nundislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n\n# Item features\nprint(\"3. Computing item features...\")\nl_grouped = listens_df.groupby(\"item_idx\").agg(\n total_plays=(\"played_ratio_pct\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n organic_ratio=(\"is_organic\", \"mean\"),\n med_length=(\"track_length_seconds\", \"median\"),\n).reset_index()\n\nitem_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\nlike_counts = likes_df.groupby(\"item_idx\").size().to_dict()\ndislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n\nNUM_ITEM_FEATS = 10\nitem_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\nfor idx in range(num_items):\n pos_l = cat_df.positive_listens.iloc[idx]\n org_l = cat_df.organic_positive_listens.iloc[idx]\n pop_r = cat_df.popularity_rank.iloc[idx]\n st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n n_lik = like_counts.get(idx, 0)\n n_dis = dislike_counts.get(idx, 0)\n\n item_dense[idx, 0] = np.log1p(pos_l)\n item_dense[idx, 1] = np.log1p(org_l)\n item_dense[idx, 2] = pop_r / float(num_items)\n item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n item_dense[idx, 5] = st[\"strong_ratio\"]\n item_dense[idx, 6] = st[\"organic_ratio\"]\n item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n item_dense[idx, 8] = np.log1p(n_lik)\n item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n\nfor c in range(NUM_ITEM_FEATS):\n m = item_dense[:, c].mean()\n s = item_dense[:, c].std() + 1e-6\n item_dense[:, c] = (item_dense[:, c] - m) / s\n\nitem_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\n\n\ndef get_active_preference_state(cutoff_time, user_set):\n l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n\n l_act = pd.concat([\n l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n d_act = pd.concat([\n d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n return active_likes, active_dislikes\n\n\ndef build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n if pref_likes is None or pref_dislikes is None:\n pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_lik = len(pref_likes.get(uid, []))\n n_dis = len(pref_dislikes.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_lik),\n np.log1p(n_dis),\n (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n ]\n l_items = pref_likes.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = pref_dislikes.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\ndef extract_slice(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n user_all_pos = {idx: set() for idx in range(len(unique_users))}\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n user_all_pos[u_idx].add(item_idx)\n\n return user_feats, pairs, user_all_pos\n\n\nprint(\"4. Extracting recent slices (3 weeks)...\")\ntrain_slices = []\nfor w in range(1, 4):\n s_feats, s_pairs, s_pos = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs, s_pos))\n\nprint(\"5. Extracting validation features...\")\nv_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_likes_dict = {u: [] for u in target_uids}\nval_dislikes_dict = {u: [] for u in target_uids}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == \"liked\":\n val_likes_dict[int(uid)].append(idx)\n elif state == \"disliked\":\n val_dislikes_dict[int(uid)].append(idx)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=128, dense_dim=10, stats_dim=6, out_dim=128):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\ndef train_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=6):\n print(f\"\\n--- Testing config: dim={embed_dim}, lr={lr}, temp={temperature}, wd={weight_decay} ---\")\n model = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=embed_dim)\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=lr * 0.1)\n batch_size = 512\n best_ndcg = 0.0\n\n for epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Fast false negative masking via set intersection\n cand_set = set(unique_pos_items.tolist())\n cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n b_u_list = b_u.tolist()\n inv_list = item_inverse.tolist()\n\n mask = torch.zeros_like(logits, dtype=torch.bool)\n for r, u in enumerate(b_u_list):\n overlap = u_all_pos[u] & cand_set\n tgt_col = inv_list[r]\n for item in overlap:\n c = cand_pos_map[item]\n if c != tgt_col:\n mask[r, c] = True\n\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n scheduler.step()\n\n # Validation eval\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n curr_ndcg = val_score[\"quality_ndcg@10\"]\n if curr_ndcg > best_ndcg:\n best_ndcg = curr_ndcg\n torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n\n return best_ndcg\n\n\ntrain_and_eval(embed_dim=64, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\ntrain_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\n",
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"timestamp": "2026-09-29T06:38:13.953Z",
"content": "Successfully created and wrote to new file: /app/solution/tune_two_tower.py. Here is the updated code:\nimport os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\nunlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\nunlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\nunlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nundislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\nundislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\nundislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n\n# Item features\nprint(\"3. Computing item features...\")\nl_grouped = listens_df.groupby(\"item_idx\").agg(\n total_plays=(\"played_ratio_pct\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n organic_ratio=(\"is_organic\", \"mean\"),\n med_length=(\"track_length_seconds\", \"median\"),\n).reset_index()\n\nitem_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\nlike_counts = likes_df.groupby(\"item_idx\").size().to_dict()\ndislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n\nNUM_ITEM_FEATS = 10\nitem_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\nfor idx in range(num_items):\n pos_l = cat_df.positive_listens.iloc[idx]\n org_l = cat_df.organic_positive_listens.iloc[idx]\n pop_r = cat_df.popularity_rank.iloc[idx]\n st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n n_lik = like_counts.get(idx, 0)\n n_dis = dislike_counts.get(idx, 0)\n\n item_dense[idx, 0] = np.log1p(pos_l)\n item_dense[idx, 1] = np.log1p(org_l)\n item_dense[idx, 2] = pop_r / float(num_items)\n item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n item_dense[idx, 5] = st[\"strong_ratio\"]\n item_dense[idx, 6] = st[\"organic_ratio\"]\n item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n item_dense[idx, 8] = np.log1p(n_lik)\n item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n\nfor c in range(NUM_ITEM_FEATS):\n m = item_dense[:, c].mean()\n s = item_dense[:, c].std() + 1e-6\n item_dense[:, c] = (item_dense[:, c] - m) / s\n\nitem_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\n\n\ndef get_active_preference_state(cutoff_time, user_set):\n l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n\n l_act = pd.concat([\n l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n d_act = pd.concat([\n d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n return active_likes, active_dislikes\n\n\ndef build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n if pref_likes is None or pref_dislikes is None:\n pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_lik = len(pref_likes.get(uid, []))\n n_dis = len(pref_dislikes.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_lik),\n np.log1p(n_dis),\n (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n ]\n l_items = pref_likes.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = pref_dislikes.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\ndef extract_slice(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n user_all_pos = {idx: set() for idx in range(len(unique_users))}\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n user_all_pos[u_idx].add(item_idx)\n\n return user_feats, pairs, user_all_pos\n\n\nprint(\"4. Extracting recent slices (3 weeks)...\")\ntrain_slices = []\nfor w in range(1, 4):\n s_feats, s_pairs, s_pos = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs, s_pos))\n\nprint(\"5. Extracting validation features...\")\nv_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_likes_dict = {u: [] for u in target_uids}\nval_dislikes_dict = {u: [] for u in target_uids}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == \"liked\":\n val_likes_dict[int(uid)].append(idx)\n elif state == \"disliked\":\n val_dislikes_dict[int(uid)].append(idx)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=128, dense_dim=10, stats_dim=6, out_dim=128):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\ndef train_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=6):\n print(f\"\\n--- Testing config: dim={embed_dim}, lr={lr}, temp={temperature}, wd={weight_decay} ---\")\n model = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=embed_dim)\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=lr * 0.1)\n batch_size = 512\n best_ndcg = 0.0\n\n for epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Fast false negative masking via set intersection\n cand_set = set(unique_pos_items.tolist())\n cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n b_u_list = b_u.tolist()\n inv_list = item_inverse.tolist()\n\n mask = torch.zeros_like(logits, dtype=torch.bool)\n for r, u in enumerate(b_u_list):\n overlap = u_all_pos[u] & cand_set\n tgt_col = inv_list[r]\n for item in overlap:\n c = cand_pos_map[item]\n if c != tgt_col:\n mask[r, c] = True\n\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n scheduler.step()\n\n # Validation eval\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n curr_ndcg = val_score[\"quality_ndcg@10\"]\n if curr_ndcg > best_ndcg:\n best_ndcg = curr_ndcg\n torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n\n return best_ndcg\n\n\ntrain_and_eval(embed_dim=64, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\ntrain_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\n",
"isError": false
},
"responseMessageId": "5195e953-49cf-4da8-af34-0e059fc3a294"
}
]
},
{
"id": 77,
"role": "agent",
"timestamp": "2026-09-29T06:38:16.839Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T06:38:16.839Z",
"callId": "run_shell_command__call_49039",
"name": "run_shell_command",
"input": {
"command": "python3 /app/solution/tune_two_tower.py",
"description": "Run tune_two_tower.py"
},
"recorded": {
"id": "run_shell_command__call_49039",
"name": "run_shell_command",
"args": {
"command": "python3 /app/solution/tune_two_tower.py",
"description": "Run tune_two_tower.py"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_49039",
"name": "run_shell_command",
"response": {
"output": "\nOutput: 1. Loading metadata...\n2. Loading interaction logs...\n3. Computing item features...\n4. Extracting recent slices (3 weeks)...\n5. Extracting validation features...\n\n--- Testing config: dim=64, lr=0.001, temp=0.07, wd=1e-05 ---\nEpoch 1: loss=8.7934, val_ndcg@10=0.012061 (best=0.012061) [10.4s]\nEpoch 2: loss=8.4039, val_ndcg@10=0.015618 (best=0.015618) [10.3s]\nEpoch 3: loss=7.9697, val_ndcg@10=0.010337 (best=0.015618) [10.3s]\nEpoch 4: loss=7.6199, val_ndcg@10=0.012190 (best=0.015618) [10.9s]\nEpoch 5: loss=7.3775, val_ndcg@10=0.011007 (best=0.015618) [10.3s]\n\n--- Testing config: dim=128, lr=0.001, temp=0.07, wd=1e-05 ---\nEpoch 1: loss=8.7660, val_ndcg@10=0.014446 (best=0.014446) [15.1s]\nEpoch 2: loss=8.3346, val_ndcg@10=0.017088 (best=0.017088) [14.7s]\nEpoch 3: loss=7.8490, val_ndcg@10=0.012533 (best=0.017088) [14.6s]\nEpoch 4: loss=7.4356, val_ndcg@10=0.010879 (best=0.017088) [15.2s]\nEpoch 5: loss=7.1400, val_ndcg@10=0.008310 (best=0.017088) [14.6s]\nProcess Group PGID: 3146\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:41:26.699Z",
"resultDisplay": "1. Loading metadata...\n2. Loading interaction logs...\n3. Computing item features...\n4. Extracting recent slices (3 weeks)...\n5. Extracting validation features...\n\n--- Testing config: dim=64, lr=0.001, temp=0.07, wd=1e-05 ---\nEpoch 1: loss=8.7934, val_ndcg@10=0.012061 (best=0.012061) [10.4s]\nEpoch 2: loss=8.4039, val_ndcg@10=0.015618 (best=0.015618) [10.3s]\nEpoch 3: loss=7.9697, val_ndcg@10=0.010337 (best=0.015618) [10.3s]\nEpoch 4: loss=7.6199, val_ndcg@10=0.012190 (best=0.015618) [10.9s]\nEpoch 5: loss=7.3775, val_ndcg@10=0.011007 (best=0.015618) [10.3s]\n\n--- Testing config: dim=128, lr=0.001, temp=0.07, wd=1e-05 ---\nEpoch 1: loss=8.7660, val_ndcg@10=0.014446 (best=0.014446) [15.1s]\nEpoch 2: loss=8.3346, val_ndcg@10=0.017088 (best=0.017088) [14.7s]\nEpoch 3: loss=7.8490, val_ndcg@10=0.012533 (best=0.017088) [14.6s]\nEpoch 4: loss=7.4356, val_ndcg@10=0.010879 (best=0.017088) [15.2s]\nEpoch 5: loss=7.1400, val_ndcg@10=0.008310 (best=0.017088) [14.6s]",
"description": "python3 /app/solution/tune_two_tower.py",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:41:26.701Z",
"content": "\nOutput: 1. Loading metadata...\n2. Loading interaction logs...\n3. Computing item features...\n4. Extracting recent slices (3 weeks)...\n5. Extracting validation features...\n\n--- Testing config: dim=64, lr=0.001, temp=0.07, wd=1e-05 ---\nEpoch 1: loss=8.7934, val_ndcg@10=0.012061 (best=0.012061) [10.4s]\nEpoch 2: loss=8.4039, val_ndcg@10=0.015618 (best=0.015618) [10.3s]\nEpoch 3: loss=7.9697, val_ndcg@10=0.010337 (best=0.015618) [10.3s]\nEpoch 4: loss=7.6199, val_ndcg@10=0.012190 (best=0.015618) [10.9s]\nEpoch 5: loss=7.3775, val_ndcg@10=0.011007 (best=0.015618) [10.3s]\n\n--- Testing config: dim=128, lr=0.001, temp=0.07, wd=1e-05 ---\nEpoch 1: loss=8.7660, val_ndcg@10=0.014446 (best=0.014446) [15.1s]\nEpoch 2: loss=8.3346, val_ndcg@10=0.017088 (best=0.017088) [14.7s]\nEpoch 3: loss=7.8490, val_ndcg@10=0.012533 (best=0.017088) [14.6s]\nEpoch 4: loss=7.4356, val_ndcg@10=0.010879 (best=0.017088) [15.2s]\nEpoch 5: loss=7.1400, val_ndcg@10=0.008310 (best=0.017088) [14.6s]\nProcess Group PGID: 3146\n",
"isError": false
},
"responseMessageId": "26d7ba7f-fe26-4155-ac6c-bca4c3c42510"
}
]
},
{
"id": 78,
"role": "agent",
"timestamp": "2026-09-29T06:41:41.004Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:41:41.004Z",
"text": "Analyzing Dimension Impact\n\nI've observed that a dimension of 128 yields better NDCG results compared to the previous setting. My current focus is on dissecting the discrepancies between my current evaluation and the `prototype_eval.py` script's output to pinpoint the root causes of the performance gap."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:41:41.004Z",
"text": "Investigating Feature Divergence\n\nI'm finding that the feature sets used in `tune_two_tower.py` and `prototype_eval.py` differ significantly, particularly in categorical and user statistics. A critical divergence appears in how user history, specifically likes and unlikes, is handled, impacting validation data representation."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:41:41.004Z",
"callId": "run_shell_command__call_138023",
"name": "run_shell_command",
"input": {
"description": "Compare train_likes and v_pref liked counts",
"command": "python3 -c \"\nimport pandas as pd\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\nlikes_cat = likes[likes.item_id.isin(cat_set)]\nv_liked = v_pref[v_pref.state == 'liked']\n\nprint('train_likes in catalog per user:', likes_cat.groupby('uid').size().mean())\nprint('v_pref liked per user:', v_liked.groupby('uid').size().mean())\n\""
},
"recorded": {
"id": "run_shell_command__call_138023",
"name": "run_shell_command",
"args": {
"description": "Compare train_likes and v_pref liked counts",
"command": "python3 -c \"\nimport pandas as pd\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\nlikes_cat = likes[likes.item_id.isin(cat_set)]\nv_liked = v_pref[v_pref.state == 'liked']\n\nprint('train_likes in catalog per user:', likes_cat.groupby('uid').size().mean())\nprint('v_pref liked per user:', v_liked.groupby('uid').size().mean())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_138023",
"name": "run_shell_command",
"response": {
"output": "\nOutput: train_likes in catalog per user: 70.22199721554233\nv_pref liked per user: 61.816368739843405\nProcess Group PGID: 3199\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:41:42.735Z",
"resultDisplay": "train_likes in catalog per user: 70.22199721554233\nv_pref liked per user: 61.816368739843405",
"description": "Compare train_likes and v_pref liked counts",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:41:42.736Z",
"content": "\nOutput: train_likes in catalog per user: 70.22199721554233\nv_pref liked per user: 61.816368739843405\nProcess Group PGID: 3199\n",
"isError": false
},
"responseMessageId": "03dd65c1-0049-4c08-ae83-eb6a25d1ff3f"
}
]
},
{
"id": 79,
"role": "agent",
"timestamp": "2026-09-29T06:41:46.854Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T06:41:46.854Z",
"callId": "read_file__call_46040",
"name": "read_file",
"input": {
"start_line": 270,
"file_path": "/app/solution/prototype_eval.py",
"end_line": 330
},
"recorded": {
"id": "read_file__call_46040",
"name": "read_file",
"args": {
"start_line": 270,
"file_path": "/app/solution/prototype_eval.py",
"end_line": 330
},
"result": [
{
"functionResponse": {
"id": "read_file__call_46040",
"name": "read_file",
"response": {
"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 270-330 of 482 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 331.\n\n--- FILE CONTENT (truncated) ---\n nn.Linear(user_in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.item_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:41:46.863Z",
"resultDisplay": "Read lines 270-330 of 482 from solution/prototype_eval.py",
"description": "solution/prototype_eval.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T06:41:46.864Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 270-330 of 482 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 331.\n\n--- FILE CONTENT (truncated) ---\n nn.Linear(user_in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.item_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):",
"isError": false
},
"responseMessageId": "50689961-0755-430f-9df0-1d94faf753aa"
}
]
},
{
"id": 80,
"role": "agent",
"timestamp": "2026-09-29T06:41:55.913Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:41:55.913Z",
"text": "Detecting Code Redundancy\n\nI've identified a significant issue where the `forward_user` function was inadvertently defined twice within `prototype_eval.py`. This duplication, occurring at lines 302-306 and again at lines 327-330, necessitates immediate correction to ensure the script functions as intended."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:41:55.913Z",
"text": "Resolving Function Conflicts\n\nI've discovered that a duplicate `forward_user` definition in `prototype_eval.py` led to the later definition overwriting the earlier one. My immediate focus is on rectifying this, ensuring the correct implementation is utilized while also examining how `compute_all_user_vectors` and `compute_all_item_vectors` are affected."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:41:55.913Z",
"text": "Analyzing Evaluation Scores\n\nMy current focus is on understanding the surprisingly low evaluation score. It appears that during evaluation, both user and item vectors were purely normalized unit vectors, meaning only taste alignment was considered, without any bias component. This lack of bias likely contributed to the suboptimal performance observed."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:41:55.913Z",
"text": "Investigating Logit Adjustment\n\nI've uncovered that during training, a logit adjustment mechanism involving bias terms was applied to user and item vectors in `prototype_eval.py`. My current focus is on verifying how this impacts evaluation when only normalized vectors are used, potentially explaining the observed score discrepancies."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:41:55.913Z",
"callId": "run_shell_command__call_51123",
"name": "run_shell_command",
"input": {
"description": "Run prototype_eval to see epoch 2 output",
"command": "python3 -c \"\nimport sys\nsys.path.append('/app/solution')\n# Let's run prototype_eval for 2 epochs and save validation.parquet\nfrom prototype_eval import *\n\""
},
"recorded": {
"id": "run_shell_command__call_51123",
"name": "run_shell_command",
"args": {
"description": "Run prototype_eval to see epoch 2 output",
"command": "python3 -c \"\nimport sys\nsys.path.append('/app/solution')\n# Let's run prototype_eval for 2 epochs and save validation.parquet\nfrom prototype_eval import *\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_51123",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Loading metadata...\nLoading raw events...\nEvents loaded in 2.47s\nExtracting training slices...\nStarting training...\nEpoch 1: loss=8.8844, val_ndcg@10=0.011395 (26.8s)\nEpoch 2: loss=8.5874, val_ndcg@10=0.017676 (26.7s)\nEpoch 3: loss=8.1819, val_ndcg@10=0.019507 (26.6s)\nEpoch 4: loss=7.8262, val_ndcg@10=0.013666 (27.1s)\nEpoch 5: loss=7.5620, val_ndcg@10=0.014345 (27.0s)\nEpoch 6: loss=7.3452, val_ndcg@10=0.013599 (27.1s)\nEpoch 7: loss=7.1613, val_ndcg@10=0.008978 (26.5s)\nEpoch 8: loss=7.0053, val_ndcg@10=0.007581 (26.6s)\nProcess Group PGID: 3218\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:46:28.885Z",
"resultDisplay": "Loading metadata...\nLoading raw events...\nEvents loaded in 2.47s\nExtracting training slices...\nStarting training...\nEpoch 1: loss=8.8844, val_ndcg@10=0.011395 (26.8s)\nEpoch 2: loss=8.5874, val_ndcg@10=0.017676 (26.7s)\nEpoch 3: loss=8.1819, val_ndcg@10=0.019507 (26.6s)\nEpoch 4: loss=7.8262, val_ndcg@10=0.013666 (27.1s)\nEpoch 5: loss=7.5620, val_ndcg@10=0.014345 (27.0s)\nEpoch 6: loss=7.3452, val_ndcg@10=0.013599 (27.1s)\nEpoch 7: loss=7.1613, val_ndcg@10=0.008978 (26.5s)\nEpoch 8: loss=7.0053, val_ndcg@10=0.007581 (26.6s)",
"description": "Run prototype_eval to see epoch 2 output",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:46:28.887Z",
"content": "\nOutput: Loading metadata...\nLoading raw events...\nEvents loaded in 2.47s\nExtracting training slices...\nStarting training...\nEpoch 1: loss=8.8844, val_ndcg@10=0.011395 (26.8s)\nEpoch 2: loss=8.5874, val_ndcg@10=0.017676 (26.7s)\nEpoch 3: loss=8.1819, val_ndcg@10=0.019507 (26.6s)\nEpoch 4: loss=7.8262, val_ndcg@10=0.013666 (27.1s)\nEpoch 5: loss=7.5620, val_ndcg@10=0.014345 (27.0s)\nEpoch 6: loss=7.3452, val_ndcg@10=0.013599 (27.1s)\nEpoch 7: loss=7.1613, val_ndcg@10=0.008978 (26.5s)\nEpoch 8: loss=7.0053, val_ndcg@10=0.007581 (26.6s)\nProcess Group PGID: 3218\n",
"isError": false
},
"responseMessageId": "b0d510ed-e27f-4ae3-9019-69bb3de47cc8"
}
]
},
{
"id": 81,
"role": "agent",
"timestamp": "2026-09-29T06:46:41.595Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:46:41.595Z",
"text": "Investigating Performance Fluctuations\n\nI'm currently analyzing the training progression, specifically focusing on the fluctuating validation scores. Epoch 4 showed a notable improvement, dropping to 0.013666, though the overall range between epochs 2 and 3 remained between 0.0195 and 0.0226. I'm now digging into the loss function at epoch 1 (value 8) to understand why overfitting might be occurring after epoch 3."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:46:41.595Z",
"text": "Unraveling Representation Collapse\n\nI'm now understanding the issue: my contrastive loss is overfitting the item embeddings in recommendation retrieval. This is causing representation collapse, where positive items are pulled too close to specific training users, distorting their embeddings. The existing low weight decay likely exacerbated this, and I'm exploring how dropout and LayerNorm can mitigate this drift."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:46:41.595Z",
"text": "Adjusting Training Dynamics\n\nI'm now exploring how to prevent the MLP from memorizing specific item combinations. My focus is on reducing training steps to 1-3 epochs and utilizing a smaller learning rate with cosine decay to avoid large parameter jumps. I'm also investigating item popularity regularization by subtracting $\\log P(i)$ from logits, aiming to learn PMI instead of just popularity."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:46:41.595Z",
"callId": "run_shell_command__call_204535",
"name": "run_shell_command",
"input": {
"description": "Inspect log_q distribution",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\npos_l = cat.positive_listens.to_numpy().astype(np.float64)\n# Probability of each item\nprob = pos_l / pos_l.sum()\nlog_q = np.log(prob + 1e-10)\nprint('log_q min, median, max:', log_q.min(), np.median(log_q), log_q.max())\n\""
},
"recorded": {
"id": "run_shell_command__call_204535",
"name": "run_shell_command",
"args": {
"description": "Inspect log_q distribution",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\npos_l = cat.positive_listens.to_numpy().astype(np.float64)\n# Probability of each item\nprob = pos_l / pos_l.sum()\nlog_q = np.log(prob + 1e-10)\nprint('log_q min, median, max:', log_q.min(), np.median(log_q), log_q.max())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_204535",
"name": "run_shell_command",
"response": {
"output": "\nOutput: log_q min, median, max: -11.720292817526948 -10.944752744006943 -6.624825001790236\nProcess Group PGID: 3284\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:46:43.130Z",
"resultDisplay": "log_q min, median, max: -11.720292817526948 -10.944752744006943 -6.624825001790236",
"description": "Inspect log_q distribution",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:46:43.132Z",
"content": "\nOutput: log_q min, median, max: -11.720292817526948 -10.944752744006943 -6.624825001790236\nProcess Group PGID: 3284\n",
"isError": false
},
"responseMessageId": "86c65060-6766-40cf-bae9-5f00702eef55"
}
]
},
{
"id": 82,
"role": "agent",
"timestamp": "2026-09-29T06:48:01.818Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:48:01.818Z",
"text": "Developing Training Logic\n\nI'm now focusing on implementing the `train_logq.py` script. The core of this involves integrating the `log_q` correction directly into the training loop. This means carefully handling the logits calculation for both in-batch positive items and sampled negatives, specifically using the formula $S_{b, c} = \\frac{\\mathbf{u}_b \\cdot \\mathbf{v}_c}{\\tau}$."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:48:01.818Z",
"text": "Implementing Correction Factors\n\nI'm currently integrating the log probability correction into the training process, specifically by adding $\\lambda \\log P(c)$. This addresses the higher probability of popular items appearing in candidate sets. At inference, I'm exploring how to combine the dot product with a pre-computed item score, controlled by a learned scalar from the user tower."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:48:01.818Z",
"callId": "write_file__call_572223",
"name": "write_file",
"input": {
"file_path": "/app/solution/train_logq.py",
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Item popularity prob\npos_listens = cat_df.positive_listens.to_numpy().astype(np.float32)\nitem_probs = pos_listens / pos_listens.sum()\nlog_q_tensor = torch.tensor(np.log(item_probs + 1e-10), dtype=torch.float32)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n user_all_pos = {idx: set() for idx in range(len(unique_users))}\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n user_all_pos[u_idx].add(item_idx)\n\n return user_feats, pairs, user_all_pos\n\n\nprint(\"Extracting training slices...\")\ntrain_slices = []\nfor w in range(1, 4):\n s_feats, s_pairs, s_pos = extract_slice_data(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs, s_pos))\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n # Normalize first out_dim - 1 dimensions\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n # Last dimension is learned item bias\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.15),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n # Learnable scaling for item bias\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\nembed_dim = 64\nout_dim = 64\nmodel = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=4, stats_dim=5, out_dim=out_dim)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\nbatch_size = 512\nnum_epochs = 6\n\nprint(\"\\nStarting training with fast set masking...\")\nbest_ndcg = 0.0\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n\n # Similarity logits: dot product\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Mask out other weekly positives for this user\n cand_set = set(unique_pos_items.tolist())\n cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n b_u_list = b_u.tolist()\n inv_list = item_inverse.tolist()\n\n mask = torch.zeros_like(logits, dtype=torch.bool)\n for r, u in enumerate(b_u_list):\n overlap = u_all_pos[u] & cand_set\n tgt_col = inv_list[r]\n for item in overlap:\n c = cand_pos_map[item]\n if c != tgt_col:\n mask[r, c] = True\n\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Validation evaluation: test BOTH pure cosine AND dot product\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Mode A: full dot product (cosine + alpha * b)\n ranked_dict_dot = {}\n # Mode B: pure cosine (first 63 dimensions)\n ranked_dict_cos = {}\n\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_mask = val_unpacked[start_u:end_u]\n\n # Full dot product\n sub_scores_dot = np.matmul(sub_u, all_v_vecs.T)\n sub_scores_dot[sub_mask == 0] = -1e9\n top100_idx_dot = np.argpartition(-sub_scores_dot, 100, axis=1)[:, :100]\n\n # Pure cosine\n sub_scores_cos = np.matmul(sub_u[:, :-1], all_v_vecs[:, :-1].T)\n sub_scores_cos[sub_mask == 0] = -1e9\n top100_idx_cos = np.argpartition(-sub_scores_cos, 100, axis=1)[:, :100]\n\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n\n cand_dot = top100_idx_dot[row]\n sorted_dot = cand_dot[np.argsort(-sub_scores_dot[row, cand_dot])]\n ranked_dict_dot[uid] = cat_items[sorted_dot].tolist()\n\n cand_cos = top100_idx_cos[row]\n sorted_cos = cand_cos[np.argsort(-sub_scores_cos[row, cand_cos])]\n ranked_dict_cos[uid] = cat_items[sorted_cos].tolist()\n\n score_dot = score(ranked_dict_dot, val_gold)[\"quality_ndcg@10\"]\n score_cos = score(ranked_dict_cos, val_gold)[\"quality_ndcg@10\"]\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg (dot)={score_dot:.6f}, val_ndcg (cos)={score_cos:.6f} \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n",
"instruction": "Write train_logq.py comparing dot product vs pure cosine at validation"
},
"recorded": {
"id": "write_file__call_572223",
"name": "write_file",
"args": {
"file_path": "/app/solution/train_logq.py",
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Item popularity prob\npos_listens = cat_df.positive_listens.to_numpy().astype(np.float32)\nitem_probs = pos_listens / pos_listens.sum()\nlog_q_tensor = torch.tensor(np.log(item_probs + 1e-10), dtype=torch.float32)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n user_all_pos = {idx: set() for idx in range(len(unique_users))}\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n user_all_pos[u_idx].add(item_idx)\n\n return user_feats, pairs, user_all_pos\n\n\nprint(\"Extracting training slices...\")\ntrain_slices = []\nfor w in range(1, 4):\n s_feats, s_pairs, s_pos = extract_slice_data(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs, s_pos))\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n # Normalize first out_dim - 1 dimensions\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n # Last dimension is learned item bias\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.15),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n # Learnable scaling for item bias\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\nembed_dim = 64\nout_dim = 64\nmodel = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=4, stats_dim=5, out_dim=out_dim)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\nbatch_size = 512\nnum_epochs = 6\n\nprint(\"\\nStarting training with fast set masking...\")\nbest_ndcg = 0.0\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n\n # Similarity logits: dot product\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Mask out other weekly positives for this user\n cand_set = set(unique_pos_items.tolist())\n cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n b_u_list = b_u.tolist()\n inv_list = item_inverse.tolist()\n\n mask = torch.zeros_like(logits, dtype=torch.bool)\n for r, u in enumerate(b_u_list):\n overlap = u_all_pos[u] & cand_set\n tgt_col = inv_list[r]\n for item in overlap:\n c = cand_pos_map[item]\n if c != tgt_col:\n mask[r, c] = True\n\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Validation evaluation: test BOTH pure cosine AND dot product\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Mode A: full dot product (cosine + alpha * b)\n ranked_dict_dot = {}\n # Mode B: pure cosine (first 63 dimensions)\n ranked_dict_cos = {}\n\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_mask = val_unpacked[start_u:end_u]\n\n # Full dot product\n sub_scores_dot = np.matmul(sub_u, all_v_vecs.T)\n sub_scores_dot[sub_mask == 0] = -1e9\n top100_idx_dot = np.argpartition(-sub_scores_dot, 100, axis=1)[:, :100]\n\n # Pure cosine\n sub_scores_cos = np.matmul(sub_u[:, :-1], all_v_vecs[:, :-1].T)\n sub_scores_cos[sub_mask == 0] = -1e9\n top100_idx_cos = np.argpartition(-sub_scores_cos, 100, axis=1)[:, :100]\n\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n\n cand_dot = top100_idx_dot[row]\n sorted_dot = cand_dot[np.argsort(-sub_scores_dot[row, cand_dot])]\n ranked_dict_dot[uid] = cat_items[sorted_dot].tolist()\n\n cand_cos = top100_idx_cos[row]\n sorted_cos = cand_cos[np.argsort(-sub_scores_cos[row, cand_cos])]\n ranked_dict_cos[uid] = cat_items[sorted_cos].tolist()\n\n score_dot = score(ranked_dict_dot, val_gold)[\"quality_ndcg@10\"]\n score_cos = score(ranked_dict_cos, val_gold)[\"quality_ndcg@10\"]\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg (dot)={score_dot:.6f}, val_ndcg (cos)={score_cos:.6f} \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n",
"instruction": "Write train_logq.py comparing dot product vs pure cosine at validation"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_572223",
"name": "write_file",
"response": {
"output": "Successfully created and wrote to new file: /app/solution/train_logq.py. Here is the updated code:\nimport os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Item popularity prob\npos_listens = cat_df.positive_listens.to_numpy().astype(np.float32)\nitem_probs = pos_listens / pos_listens.sum()\nlog_q_tensor = torch.tensor(np.log(item_probs + 1e-10), dtype=torch.float32)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n user_all_pos = {idx: set() for idx in range(len(unique_users))}\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n user_all_pos[u_idx].add(item_idx)\n\n return user_feats, pairs, user_all_pos\n\n\nprint(\"Extracting training slices...\")\ntrain_slices = []\nfor w in range(1, 4):\n s_feats, s_pairs, s_pos = extract_slice_data(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs, s_pos))\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n # Normalize first out_dim - 1 dimensions\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n # Last dimension is learned item bias\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.15),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n # Learnable scaling for item bias\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\nembed_dim = 64\nout_dim = 64\nmodel = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=4, stats_dim=5, out_dim=out_dim)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\nbatch_size = 512\nnum_epochs = 6\n\nprint(\"\\nStarting training with fast set masking...\")\nbest_ndcg = 0.0\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n\n # Similarity logits: dot product\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Mask out other weekly positives for this user\n cand_set = set(unique_pos_items.tolist())\n cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n b_u_list = b_u.tolist()\n inv_list = item_inverse.tolist()\n\n mask = torch.zeros_like(logits, dtype=torch.bool)\n for r, u in enumerate(b_u_list):\n overlap = u_all_pos[u] & cand_set\n tgt_col = inv_list[r]\n for item in overlap:\n c = cand_pos_map[item]\n if c != tgt_col:\n mask[r, c] = True\n\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Validation evaluation: test BOTH pure cosine AND dot product\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Mode A: full dot product (cosine + alpha * b)\n ranked_dict_dot = {}\n # Mode B: pure cosine (first 63 dimensions)\n ranked_dict_cos = {}\n\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_mask = val_unpacked[start_u:end_u]\n\n # Full dot product\n sub_scores_dot = np.matmul(sub_u, all_v_vecs.T)\n sub_scores_dot[sub_mask == 0] = -1e9\n top100_idx_dot = np.argpartition(-sub_scores_dot, 100, axis=1)[:, :100]\n\n # Pure cosine\n sub_scores_cos = np.matmul(sub_u[:, :-1], all_v_vecs[:, :-1].T)\n sub_scores_cos[sub_mask == 0] = -1e9\n top100_idx_cos = np.argpartition(-sub_scores_cos, 100, axis=1)[:, :100]\n\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n\n cand_dot = top100_idx_dot[row]\n sorted_dot = cand_dot[np.argsort(-sub_scores_dot[row, cand_dot])]\n ranked_dict_dot[uid] = cat_items[sorted_dot].tolist()\n\n cand_cos = top100_idx_cos[row]\n sorted_cos = cand_cos[np.argsort(-sub_scores_cos[row, cand_cos])]\n ranked_dict_cos[uid] = cat_items[sorted_cos].tolist()\n\n score_dot = score(ranked_dict_dot, val_gold)[\"quality_ndcg@10\"]\n score_cos = score(ranked_dict_cos, val_gold)[\"quality_ndcg@10\"]\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg (dot)={score_dot:.6f}, val_ndcg (cos)={score_cos:.6f} \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:48:01.827Z",
"resultDisplay": {
"fileDiff": "Index: train_logq.py\n===================================================================\n--- train_logq.py\tOriginal\n+++ train_logq.py\tWritten\n@@ -0,0 +1,452 @@\n+import os\n+import sys\n+import time\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import pyarrow.parquet as pq\n+import torch\n+import torch.nn as nn\n+import torch.nn.functional as F\n+\n+sys.path.append(\"/task\")\n+from evaluate_public import score, validation_truth\n+from ranking_evaluation import CandidateSet\n+\n+torch.set_num_threads(8)\n+\n+DATA_DIR = Path(\"/task/data\")\n+OUT_DIR = Path(\"/app/solution\")\n+OUT_DIR.mkdir(parents=True, exist_ok=True)\n+\n+CUTOFF_VAL = 24786800\n+WEEK_SECONDS = 7 * 86400\n+\n+print(\"1. Loading metadata...\")\n+cat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\n+cat_items = cat_df.item_id.to_numpy()\n+item_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\n+num_items = len(cat_items)\n+\n+# Item popularity prob\n+pos_listens = cat_df.positive_listens.to_numpy().astype(np.float32)\n+item_probs = pos_listens / pos_listens.sum()\n+log_q_tensor = torch.tensor(np.log(item_probs + 1e-10), dtype=torch.float32)\n+\n+target_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\n+target_uids = target_users_df.uid.to_numpy()\n+num_targets = len(target_uids)\n+\n+print(\"2. Loading interaction logs...\")\n+likes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\n+likes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\n+likes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n+\n+dislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\n+dislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\n+dislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n+\n+min_listen_time = CUTOFF_VAL - 120 * 86400\n+table = pq.read_table(\n+ DATA_DIR / \"train_listens.parquet\",\n+ columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n+ filters=[(\"timestamp\", \">=\", min_listen_time)],\n+)\n+listens_df = table.to_pandas()\n+listens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\n+listens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n+\n+# Item features\n+cat_features = np.zeros((num_items, 4), dtype=np.float32)\n+cat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\n+cat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\n+cat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\n+cat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\n+for c in range(4):\n+ mean = cat_features[:, c].mean()\n+ std = cat_features[:, c].std() + 1e-6\n+ cat_features[:, c] = (cat_features[:, c] - mean) / std\n+\n+item_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n+\n+\n+def build_user_features(cutoff_time, user_ids):\n+ u_set = set(user_ids)\n+ t_min = cutoff_time - 90 * 86400\n+ sub_listens = listens_df[\n+ (listens_df.timestamp >= t_min)\n+ & (listens_df.timestamp < cutoff_time)\n+ & (listens_df.uid.isin(u_set))\n+ ].copy()\n+\n+ sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n+\n+ sub_listens[\"w_recent\"] = np.where(\n+ sub_listens.days_ago <= 14.0,\n+ np.exp(-0.1 * sub_listens.days_ago)\n+ * (1.0 + sub_listens.is_organic)\n+ * (sub_listens.played_ratio_pct / 100.0),\n+ 0.0,\n+ )\n+\n+ sub_listens[\"w_history\"] = (\n+ np.exp(-0.02 * sub_listens.days_ago)\n+ * (1.0 + sub_listens.is_organic)\n+ * (sub_listens.played_ratio_pct / 100.0)\n+ )\n+\n+ sub_listens[\"w_strong\"] = np.where(\n+ (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n+ np.exp(-0.05 * sub_listens.days_ago),\n+ 0.0,\n+ )\n+\n+ agg = (\n+ sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n+ .sum()\n+ .reset_index()\n+ )\n+\n+ sub_likes = likes_df[\n+ (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n+ ]\n+ likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n+\n+ sub_dislikes = dislikes_df[\n+ (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n+ ]\n+ dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n+\n+ uid_counts = sub_listens.groupby(\"uid\").agg(\n+ n_listens=(\"item_idx\", \"count\"),\n+ mean_ratio=(\"played_ratio_pct\", \"mean\"),\n+ organic_ratio=(\"is_organic\", \"mean\"),\n+ ).to_dict(orient=\"index\")\n+\n+ user_to_pos = {u: i for i, u in enumerate(user_ids)}\n+ recent_indices, recent_uids, recent_weights = [], [], []\n+ history_indices, history_uids, history_weights = [], [], []\n+ strong_indices, strong_uids, strong_weights = [], [], []\n+ like_indices, like_uids = [], []\n+ dislike_indices, dislike_uids = [], []\n+ stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n+\n+ for i, uid in enumerate(user_ids):\n+ st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n+ n_likes = len(likes_grouped.get(uid, []))\n+ n_dislikes = len(dislikes_grouped.get(uid, []))\n+ stats_arr[i] = [\n+ np.log1p(st[\"n_listens\"]),\n+ st[\"mean_ratio\"] / 100.0,\n+ st[\"organic_ratio\"],\n+ np.log1p(n_likes),\n+ np.log1p(n_dislikes),\n+ ]\n+ l_items = likes_grouped.get(uid, [])\n+ like_indices.extend(l_items)\n+ like_uids.extend([i] * len(l_items))\n+\n+ d_items = dislikes_grouped.get(uid, [])\n+ dislike_indices.extend(d_items)\n+ dislike_uids.extend([i] * len(d_items))\n+\n+ for uid, g in agg.groupby(\"uid\"):\n+ if uid not in user_to_pos:\n+ continue\n+ u_idx = user_to_pos[uid]\n+ m_r = g.w_recent > 0\n+ if m_r.any():\n+ recent_indices.extend(g.item_idx[m_r].tolist())\n+ recent_weights.extend(g.w_recent[m_r].tolist())\n+ recent_uids.extend([u_idx] * int(m_r.sum()))\n+\n+ history_indices.extend(g.item_idx.tolist())\n+ history_weights.extend(g.w_history.tolist())\n+ history_uids.extend([u_idx] * len(g))\n+\n+ m_s = g.w_strong > 0\n+ if m_s.any():\n+ strong_indices.extend(g.item_idx[m_s].tolist())\n+ strong_weights.extend(g.w_strong[m_s].tolist())\n+ strong_uids.extend([u_idx] * int(m_s.sum()))\n+\n+ N = len(user_ids)\n+ return {\n+ \"num_users\": N,\n+ \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n+ \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n+ \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n+ \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n+ \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n+ \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n+ \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n+ \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n+ \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n+ \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n+ \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n+ \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n+ \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n+ \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n+ }\n+\n+\n+def extract_slice_data(cutoff_time):\n+ w_end = cutoff_time + WEEK_SECONDS\n+ prior = listens_df[listens_df.timestamp < cutoff_time]\n+ prior_user_items = set(zip(prior.uid, prior.item_idx))\n+\n+ win_listens = listens_df[\n+ (listens_df.timestamp >= cutoff_time)\n+ & (listens_df.timestamp < w_end)\n+ & (listens_df.is_organic == 1)\n+ & (listens_df.played_ratio_pct >= 80)\n+ ]\n+ win_likes = likes_df[\n+ (likes_df.timestamp >= cutoff_time)\n+ & (likes_df.timestamp < w_end)\n+ & (likes_df.is_organic == 1)\n+ ]\n+\n+ targets = {}\n+ for u, i in zip(win_listens.uid, win_listens.item_idx):\n+ if (u, i) not in prior_user_items:\n+ targets[(u, i)] = 1\n+ for u, i in zip(win_likes.uid, win_likes.item_idx):\n+ if (u, i) not in prior_user_items:\n+ targets[(u, i)] = 3\n+\n+ user_positives = {}\n+ for (u, i), grade in targets.items():\n+ user_positives.setdefault(u, []).append((i, grade))\n+\n+ unique_users = sorted(list(user_positives.keys()))\n+ user_feats = build_user_features(cutoff_time, unique_users)\n+ user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n+\n+ pairs = []\n+ user_all_pos = {idx: set() for idx in range(len(unique_users))}\n+ for u, items in user_positives.items():\n+ u_idx = user_to_idx[u]\n+ for item_idx, grade in items:\n+ pairs.append((u_idx, item_idx, float(grade)))\n+ user_all_pos[u_idx].add(item_idx)\n+\n+ return user_feats, pairs, user_all_pos\n+\n+\n+print(\"Extracting training slices...\")\n+train_slices = []\n+for w in range(1, 4):\n+ s_feats, s_pairs, s_pos = extract_slice_data(CUTOFF_VAL - w * WEEK_SECONDS)\n+ train_slices.append((s_feats, s_pairs, s_pos))\n+\n+val_user_feats = build_user_features(CUTOFF_VAL, target_uids)\n+val_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\n+targets_set = set(target_users_df.uid.astype(int))\n+val_gold = validation_truth(val_eligibility, targets_set)\n+val_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n+\n+\n+class ItemTower(nn.Module):\n+ def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n+ super().__init__()\n+ self.item_emb = nn.Embedding(num_items, embed_dim)\n+ nn.init.normal_(self.item_emb.weight, std=0.05)\n+ self.dense_proj = nn.Linear(dense_dim, embed_dim)\n+ self.mlp = nn.Sequential(\n+ nn.Linear(embed_dim * 2, embed_dim),\n+ nn.LayerNorm(embed_dim),\n+ nn.GELU(),\n+ nn.Linear(embed_dim, out_dim),\n+ )\n+\n+ def forward(self, item_ids, dense_features):\n+ embs = self.item_emb(item_ids)\n+ d_p = self.dense_proj(dense_features[item_ids])\n+ x = torch.cat([embs, d_p], dim=-1)\n+ v = self.mlp(x)\n+ # Normalize first out_dim - 1 dimensions\n+ v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n+ # Last dimension is learned item bias\n+ b = v[:, -1:]\n+ return torch.cat([v_norm, b], dim=-1)\n+\n+\n+class UserTower(nn.Module):\n+ def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n+ super().__init__()\n+ self.shared_emb = shared_item_emb\n+ self.embed_dim = embed_dim\n+ self.stats_proj = nn.Linear(stats_dim, embed_dim)\n+ in_dim = embed_dim * 5 + embed_dim\n+ self.mlp = nn.Sequential(\n+ nn.Linear(in_dim, embed_dim * 2),\n+ nn.LayerNorm(embed_dim * 2),\n+ nn.GELU(),\n+ nn.Dropout(0.15),\n+ nn.Linear(embed_dim * 2, out_dim),\n+ )\n+\n+ def _pool(self, num_users, indices, uids, weights=None):\n+ out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n+ if len(indices) == 0:\n+ return out\n+ embs = self.shared_emb(indices)\n+ if weights is not None:\n+ embs = embs * weights.unsqueeze(-1)\n+ out = torch.index_add(out, 0, uids, embs)\n+ norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n+ return out / norms\n+\n+ def compute_user_pooled_views(self, feats):\n+ with torch.no_grad():\n+ N = feats[\"num_users\"]\n+ h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n+ h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n+ h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n+ h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n+ h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n+ return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n+\n+ def forward_user(self, pooled_views, stats):\n+ s_p = self.stats_proj(stats)\n+ x = torch.cat([pooled_views, s_p], dim=-1)\n+ u = self.mlp(x)\n+ u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n+ # Learnable scaling for item bias\n+ alpha = torch.sigmoid(u[:, -1:])\n+ return torch.cat([u_norm, alpha], dim=-1)\n+\n+\n+class TwoTowerRanker(nn.Module):\n+ def __init__(self, num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64):\n+ super().__init__()\n+ self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n+ self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n+\n+ def compute_all_user_vectors(self, feats):\n+ with torch.no_grad():\n+ pooled = self.user_tower.compute_user_pooled_views(feats)\n+ return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n+\n+ def compute_all_item_vectors(self, dense_features):\n+ with torch.no_grad():\n+ all_ids = torch.arange(len(dense_features), dtype=torch.long)\n+ return self.item_tower(all_ids, dense_features)\n+\n+\n+embed_dim = 64\n+out_dim = 64\n+model = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=4, stats_dim=5, out_dim=out_dim)\n+optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\n+temperature = 0.07\n+batch_size = 512\n+num_epochs = 6\n+\n+print(\"\\nStarting training with fast set masking...\")\n+best_ndcg = 0.0\n+\n+for epoch in range(1, num_epochs + 1):\n+ t_ep = time.time()\n+ total_loss = 0.0\n+ num_batches = 0\n+\n+ for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n+ slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n+ slice_stats = feats[\"user_stats\"]\n+\n+ np.random.seed(epoch * 100 + s_idx)\n+ perm = np.random.permutation(len(pairs))\n+ pairs_arr = np.array(pairs)[perm]\n+\n+ for b_start in range(0, len(pairs_arr), batch_size):\n+ b_pairs = pairs_arr[b_start : b_start + batch_size]\n+ b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n+ b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n+ b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n+\n+ u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n+ unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n+\n+ neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n+ cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n+\n+ cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n+\n+ # Similarity logits: dot product\n+ logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n+\n+ # Mask out other weekly positives for this user\n+ cand_set = set(unique_pos_items.tolist())\n+ cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n+ b_u_list = b_u.tolist()\n+ inv_list = item_inverse.tolist()\n+\n+ mask = torch.zeros_like(logits, dtype=torch.bool)\n+ for r, u in enumerate(b_u_list):\n+ overlap = u_all_pos[u] & cand_set\n+ tgt_col = inv_list[r]\n+ for item in overlap:\n+ c = cand_pos_map[item]\n+ if c != tgt_col:\n+ mask[r, c] = True\n+\n+ logits.masked_fill_(mask, -1e9)\n+\n+ targets = item_inverse\n+ loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n+ loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n+\n+ optimizer.zero_grad()\n+ loss.backward()\n+ optimizer.step()\n+\n+ total_loss += loss.item()\n+ num_batches += 1\n+\n+ # Validation evaluation: test BOTH pure cosine AND dot product\n+ with torch.no_grad():\n+ val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n+ all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n+\n+ # Mode A: full dot product (cosine + alpha * b)\n+ ranked_dict_dot = {}\n+ # Mode B: pure cosine (first 63 dimensions)\n+ ranked_dict_cos = {}\n+\n+ for start_u in range(0, num_targets, 1000):\n+ end_u = min(start_u + 1000, num_targets)\n+ sub_u = val_u_vecs[start_u:end_u]\n+ sub_mask = val_unpacked[start_u:end_u]\n+\n+ # Full dot product\n+ sub_scores_dot = np.matmul(sub_u, all_v_vecs.T)\n+ sub_scores_dot[sub_mask == 0] = -1e9\n+ top100_idx_dot = np.argpartition(-sub_scores_dot, 100, axis=1)[:, :100]\n+\n+ # Pure cosine\n+ sub_scores_cos = np.matmul(sub_u[:, :-1], all_v_vecs[:, :-1].T)\n+ sub_scores_cos[sub_mask == 0] = -1e9\n+ top100_idx_cos = np.argpartition(-sub_scores_cos, 100, axis=1)[:, :100]\n+\n+ for row in range(len(sub_u)):\n+ u_row = start_u + row\n+ uid = int(target_uids[u_row])\n+\n+ cand_dot = top100_idx_dot[row]\n+ sorted_dot = cand_dot[np.argsort(-sub_scores_dot[row, cand_dot])]\n+ ranked_dict_dot[uid] = cat_items[sorted_dot].tolist()\n+\n+ cand_cos = top100_idx_cos[row]\n+ sorted_cos = cand_cos[np.argsort(-sub_scores_cos[row, cand_cos])]\n+ ranked_dict_cos[uid] = cat_items[sorted_cos].tolist()\n+\n+ score_dot = score(ranked_dict_dot, val_gold)[\"quality_ndcg@10\"]\n+ score_cos = score(ranked_dict_cos, val_gold)[\"quality_ndcg@10\"]\n+\n+ print(\n+ f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n+ f\"val_ndcg (dot)={score_dot:.6f}, val_ndcg (cos)={score_cos:.6f} \"\n+ f\"[{time.time() - t_ep:.1f}s]\"\n+ )\n",
"fileName": "train_logq.py",
"filePath": "/app/solution/train_logq.py",
"originalContent": "",
"newContent": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Item popularity prob\npos_listens = cat_df.positive_listens.to_numpy().astype(np.float32)\nitem_probs = pos_listens / pos_listens.sum()\nlog_q_tensor = torch.tensor(np.log(item_probs + 1e-10), dtype=torch.float32)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n user_all_pos = {idx: set() for idx in range(len(unique_users))}\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n user_all_pos[u_idx].add(item_idx)\n\n return user_feats, pairs, user_all_pos\n\n\nprint(\"Extracting training slices...\")\ntrain_slices = []\nfor w in range(1, 4):\n s_feats, s_pairs, s_pos = extract_slice_data(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs, s_pos))\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n # Normalize first out_dim - 1 dimensions\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n # Last dimension is learned item bias\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.15),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n # Learnable scaling for item bias\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\nembed_dim = 64\nout_dim = 64\nmodel = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=4, stats_dim=5, out_dim=out_dim)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\nbatch_size = 512\nnum_epochs = 6\n\nprint(\"\\nStarting training with fast set masking...\")\nbest_ndcg = 0.0\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n\n # Similarity logits: dot product\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Mask out other weekly positives for this user\n cand_set = set(unique_pos_items.tolist())\n cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n b_u_list = b_u.tolist()\n inv_list = item_inverse.tolist()\n\n mask = torch.zeros_like(logits, dtype=torch.bool)\n for r, u in enumerate(b_u_list):\n overlap = u_all_pos[u] & cand_set\n tgt_col = inv_list[r]\n for item in overlap:\n c = cand_pos_map[item]\n if c != tgt_col:\n mask[r, c] = True\n\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Validation evaluation: test BOTH pure cosine AND dot product\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Mode A: full dot product (cosine + alpha * b)\n ranked_dict_dot = {}\n # Mode B: pure cosine (first 63 dimensions)\n ranked_dict_cos = {}\n\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_mask = val_unpacked[start_u:end_u]\n\n # Full dot product\n sub_scores_dot = np.matmul(sub_u, all_v_vecs.T)\n sub_scores_dot[sub_mask == 0] = -1e9\n top100_idx_dot = np.argpartition(-sub_scores_dot, 100, axis=1)[:, :100]\n\n # Pure cosine\n sub_scores_cos = np.matmul(sub_u[:, :-1], all_v_vecs[:, :-1].T)\n sub_scores_cos[sub_mask == 0] = -1e9\n top100_idx_cos = np.argpartition(-sub_scores_cos, 100, axis=1)[:, :100]\n\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n\n cand_dot = top100_idx_dot[row]\n sorted_dot = cand_dot[np.argsort(-sub_scores_dot[row, cand_dot])]\n ranked_dict_dot[uid] = cat_items[sorted_dot].tolist()\n\n cand_cos = top100_idx_cos[row]\n sorted_cos = cand_cos[np.argsort(-sub_scores_cos[row, cand_cos])]\n ranked_dict_cos[uid] = cat_items[sorted_cos].tolist()\n\n score_dot = score(ranked_dict_dot, val_gold)[\"quality_ndcg@10\"]\n score_cos = score(ranked_dict_cos, val_gold)[\"quality_ndcg@10\"]\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg (dot)={score_dot:.6f}, val_ndcg (cos)={score_cos:.6f} \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n",
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"timestamp": "2026-09-29T06:48:01.829Z",
"content": "Successfully created and wrote to new file: /app/solution/train_logq.py. Here is the updated code:\nimport os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Item popularity prob\npos_listens = cat_df.positive_listens.to_numpy().astype(np.float32)\nitem_probs = pos_listens / pos_listens.sum()\nlog_q_tensor = torch.tensor(np.log(item_probs + 1e-10), dtype=torch.float32)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n user_all_pos = {idx: set() for idx in range(len(unique_users))}\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n user_all_pos[u_idx].add(item_idx)\n\n return user_feats, pairs, user_all_pos\n\n\nprint(\"Extracting training slices...\")\ntrain_slices = []\nfor w in range(1, 4):\n s_feats, s_pairs, s_pos = extract_slice_data(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs, s_pos))\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n # Normalize first out_dim - 1 dimensions\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n # Last dimension is learned item bias\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.15),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n # Learnable scaling for item bias\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\nembed_dim = 64\nout_dim = 64\nmodel = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=4, stats_dim=5, out_dim=out_dim)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\nbatch_size = 512\nnum_epochs = 6\n\nprint(\"\\nStarting training with fast set masking...\")\nbest_ndcg = 0.0\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n\n # Similarity logits: dot product\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Mask out other weekly positives for this user\n cand_set = set(unique_pos_items.tolist())\n cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n b_u_list = b_u.tolist()\n inv_list = item_inverse.tolist()\n\n mask = torch.zeros_like(logits, dtype=torch.bool)\n for r, u in enumerate(b_u_list):\n overlap = u_all_pos[u] & cand_set\n tgt_col = inv_list[r]\n for item in overlap:\n c = cand_pos_map[item]\n if c != tgt_col:\n mask[r, c] = True\n\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Validation evaluation: test BOTH pure cosine AND dot product\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Mode A: full dot product (cosine + alpha * b)\n ranked_dict_dot = {}\n # Mode B: pure cosine (first 63 dimensions)\n ranked_dict_cos = {}\n\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_mask = val_unpacked[start_u:end_u]\n\n # Full dot product\n sub_scores_dot = np.matmul(sub_u, all_v_vecs.T)\n sub_scores_dot[sub_mask == 0] = -1e9\n top100_idx_dot = np.argpartition(-sub_scores_dot, 100, axis=1)[:, :100]\n\n # Pure cosine\n sub_scores_cos = np.matmul(sub_u[:, :-1], all_v_vecs[:, :-1].T)\n sub_scores_cos[sub_mask == 0] = -1e9\n top100_idx_cos = np.argpartition(-sub_scores_cos, 100, axis=1)[:, :100]\n\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n\n cand_dot = top100_idx_dot[row]\n sorted_dot = cand_dot[np.argsort(-sub_scores_dot[row, cand_dot])]\n ranked_dict_dot[uid] = cat_items[sorted_dot].tolist()\n\n cand_cos = top100_idx_cos[row]\n sorted_cos = cand_cos[np.argsort(-sub_scores_cos[row, cand_cos])]\n ranked_dict_cos[uid] = cat_items[sorted_cos].tolist()\n\n score_dot = score(ranked_dict_dot, val_gold)[\"quality_ndcg@10\"]\n score_cos = score(ranked_dict_cos, val_gold)[\"quality_ndcg@10\"]\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg (dot)={score_dot:.6f}, val_ndcg (cos)={score_cos:.6f} \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n",
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