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"taskId": "yambda-discovery-ranking-two-tower-case-1",
"attemptId": "gemini-3-8-flash-high-case1-fixed-r01-3d6fca60",
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"model": "gemini-3.8-flash",
"sessionId": "317aedbf-e51b-4d7b-9c0e-95e1b8264c02",
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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├───current_model/\n│ ├───train_current_model.py\n│ └───two_tower_model.py\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": "2026-09-29T05:36:38.187Z",
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"timestamp": "2026-09-29T05:36:38.187Z",
"text": "# Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team's next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n ranking script. It can reproduce the public validation ranking and produce a\n hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n `/task/data/test_preference_state.parquet`: active explicit preference state\n at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\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 came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n subsequent `val_*` outcomes.\n2. After selecting an improvement, 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\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the 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 same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user's `test_eligible_candidates.npz`\n candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\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- Do not alter cutoff definitions, target users, candidate eligibility, or\n relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n the deadline.\n"
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"text": "Improving Music Discovery Model\n\nI'm currently focused on enhancing our production music-discovery two-tower model. My immediate priority is to adhere strictly to our Core Mandates and operational guidelines, particularly concerning security, integrity, and ensuring all topic updates are correctly called."
},
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"kind": "reasoning",
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"timestamp": "2026-09-29T05:36:43.180Z",
"text": "Refining Discovery Pipeline\n\nI'm currently examining the specific submission constraints and deliverables for the production music-discovery model. My focus is on ensuring the final output, a ranked list of items per user, precisely matches all required formats and data specifications within the allocated time."
},
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"summary": "Starting the investigation of the production music-discovery two-tower model. In this first phase, I will inspect the codebase in `/task`, examine how training and negative sampling are implemented, run the baseline validation backtest to establish the initial NDCG@10 score, and verify the evaluation mechanics.",
"strategic_intent": "Initialize investigation of the production two-tower model, inspect files, and reproduce baseline validation."
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"resultDisplay": "## 📂 Topic: **Initial Inspection and Baseline Replication**\n\n**Summary:**\nStarting the investigation of the production music-discovery two-tower model. In this first phase, I will inspect the codebase in `/task`, examine how training and negative sampling are implemented, run the baseline validation backtest to establish the initial NDCG@10 score, and verify the evaluation mechanics.\n\n> [!STRATEGY]\n> **Intent:** Initialize investigation of the production two-tower model, inspect files, and reproduce baseline validation.",
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"content": "Current topic: \"Initial Inspection and Baseline Replication\"\nTopic summary: Starting the investigation of the production music-discovery two-tower model. In this first phase, I will inspect the codebase in `/task`, examine how training and negative sampling are implemented, run the baseline validation backtest to establish the initial NDCG@10 score, and verify the evaluation mechanics.\n\nStrategic Intent: Initialize investigation of the production two-tower model, inspect files, and reproduce baseline validation.",
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"timestamp": "2026-09-29T05:36:46.299Z",
"text": "Examining Task Directory\n\nI'm currently reviewing the contents of the `/task` directory. My immediate focus is on understanding the scripts related to evaluation (`evaluate_public.py`, `ranking_evaluation.py`) and the training procedures for the current model (`current_model/train_current_model.py`, `current_model/two_tower`)."
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"description": "solution",
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"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-150 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: 151.\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.\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 )"
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"resultDisplay": "Read lines 1-150 of 158 from ../task/evaluate_public.py",
"description": "../task/evaluate_public.py",
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"timestamp": "2026-09-29T05:36:48.208Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-150 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: 151.\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.\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 )",
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"id": "read_file__call_92119",
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"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-100 of 2165 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\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n @classmethod\n def read(cls, path: Path) -> \"CandidateSet\":\n with np.load(path, allow_pickle=False) as artifact:\n result = cls(\n uids=artifact[\"uids\"].astype(np.int64, copy=True),\n item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n )\n expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n if result.packed.shape != expected:\n raise ValueError(\n f\"invalid candidate artifact shape {result.packed.shape}; \"\n f\"expected {expected}\"\n )\n return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n item_ids: np.ndarray"
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"timestamp": "2026-09-29T05:36:48.207Z",
"resultDisplay": "Read lines 1-100 of 2165 from ../task/current_model/train_current_model.py",
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"timestamp": "2026-09-29T05:36:48.208Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-100 of 2165 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\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n @classmethod\n def read(cls, path: Path) -> \"CandidateSet\":\n with np.load(path, allow_pickle=False) as artifact:\n result = cls(\n uids=artifact[\"uids\"].astype(np.int64, copy=True),\n item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n )\n expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n if result.packed.shape != expected:\n raise ValueError(\n f\"invalid candidate artifact shape {result.packed.shape}; \"\n f\"expected {expected}\"\n )\n return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n item_ids: np.ndarray",
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"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-150 of 434 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: 151.\n\n--- FILE CONTENT (truncated) ---\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n sequence_length: int = 100\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n timestamps: np.ndarray\n items: np.ndarray\n organic: np.ndarray\n completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n timestamps: np.ndarray\n items: np.ndarray\n channel: np.ndarray\n active: np.ndarray\n organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n features = np.column_stack(\n [\n np.log1p(positives),\n np.log1p(organic),\n organic / np.maximum(positives, 1.0),\n 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n ]\n )\n means = features.mean(axis=0, keepdims=True)\n scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n standardized = ((features - means) / scales).astype(np.float32)\n if not np.isfinite(standardized).all():\n raise RuntimeError(\"catalog features contain non-finite values\")\n return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n if len(parts) == 1:\n return parts[0]\n timestamps = np.concatenate([part.timestamps for part in parts])\n items = np.concatenate([part.items for part in parts])\n organic = np.concatenate([part.organic for part in parts])\n completion = np.concatenate([part.completion_pct for part in parts])\n order = np.argsort(timestamps, kind=\"stable\")\n return ListenEvents(\n timestamps=timestamps[order],\n items=items[order],\n organic=organic[order],\n completion_pct=completion[order],\n )\n\n\ndef iter_listen_users(\n path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n parquet = pq.ParquetFile(path)\n pending_uid: int | None = None\n pending: list[ListenEvents] = []\n previous_uid = -1\n for batch in parquet.iter_batches(\n batch_size=750_000,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n ):\n frame = batch.to_pandas()\n mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n if not np.any(keep):\n continue\n uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n items = mapped[keep].astype(np.int32, copy=False)\n organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n if np.any(uids[1:] < uids[:-1]):\n raise RuntimeError(f\"{path.name} is not sorted by uid\")\n boundaries = np.r_[\n 0,\n np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n len(uids),\n ]\n for start, end in zip(boundaries[:-1], boundaries[1:]):\n uid = int(uids[start])\n part = ListenEvents(\n timestamps[start:end].copy(),\n items[start:end].copy(),\n organic[start:end].copy(),\n completion[start:end].copy(),\n )\n if pending_uid is None:\n pending_uid = uid\n elif uid != pending_uid:\n if pending_uid < previous_uid:\n raise RuntimeError(f\"{path.name} user order regressed\")\n yield pending_uid, _combine_listen_chunks(pending)\n previous_uid = pending_uid\n pending_uid = uid"
}
}
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"status": "success",
"timestamp": "2026-09-29T05:36:50.172Z",
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"timestamp": "2026-09-29T05:36:50.173Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-150 of 434 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: 151.\n\n--- FILE CONTENT (truncated) ---\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n sequence_length: int = 100\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n timestamps: np.ndarray\n items: np.ndarray\n organic: np.ndarray\n completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n timestamps: np.ndarray\n items: np.ndarray\n channel: np.ndarray\n active: np.ndarray\n organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n features = np.column_stack(\n [\n np.log1p(positives),\n np.log1p(organic),\n organic / np.maximum(positives, 1.0),\n 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n ]\n )\n means = features.mean(axis=0, keepdims=True)\n scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n standardized = ((features - means) / scales).astype(np.float32)\n if not np.isfinite(standardized).all():\n raise RuntimeError(\"catalog features contain non-finite values\")\n return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n if len(parts) == 1:\n return parts[0]\n timestamps = np.concatenate([part.timestamps for part in parts])\n items = np.concatenate([part.items for part in parts])\n organic = np.concatenate([part.organic for part in parts])\n completion = np.concatenate([part.completion_pct for part in parts])\n order = np.argsort(timestamps, kind=\"stable\")\n return ListenEvents(\n timestamps=timestamps[order],\n items=items[order],\n organic=organic[order],\n completion_pct=completion[order],\n )\n\n\ndef iter_listen_users(\n path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n parquet = pq.ParquetFile(path)\n pending_uid: int | None = None\n pending: list[ListenEvents] = []\n previous_uid = -1\n for batch in parquet.iter_batches(\n batch_size=750_000,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n ):\n frame = batch.to_pandas()\n mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n if not np.any(keep):\n continue\n uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n items = mapped[keep].astype(np.int32, copy=False)\n organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n if np.any(uids[1:] < uids[:-1]):\n raise RuntimeError(f\"{path.name} is not sorted by uid\")\n boundaries = np.r_[\n 0,\n np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n len(uids),\n ]\n for start, end in zip(boundaries[:-1], boundaries[1:]):\n uid = int(uids[start])\n part = ListenEvents(\n timestamps[start:end].copy(),\n items[start:end].copy(),\n organic[start:end].copy(),\n completion[start:end].copy(),\n )\n if pending_uid is None:\n pending_uid = uid\n elif uid != pending_uid:\n if pending_uid < previous_uid:\n raise RuntimeError(f\"{path.name} user order regressed\")\n yield pending_uid, _combine_listen_chunks(pending)\n previous_uid = pending_uid\n pending_uid = uid",
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"name": "read_file",
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"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 151-300 of 434 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: 301.\n\n--- FILE CONTENT (truncated) ---\n pending = []\n pending.append(part)\n if pending_uid is not None:\n yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n heads: list[tuple[int, ListenEvents] | None] = [\n next(stream, None) for stream in streams\n ]\n while any(head is not None for head in heads):\n uid = min(head[0] for head in heads if head is not None)\n parts: list[ListenEvents] = []\n for position, head in enumerate(heads):\n if head is not None and head[0] == uid:\n parts.append(head[1])\n heads[position] = next(streams[position], None)\n yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n data: Path,\n prefixes: tuple[str, ...],\n item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n specifications = (\n (\"likes\", 0, 1, 0),\n (\"unlikes\", 0, 0, 1),\n (\"dislikes\", 1, 1, 0),\n (\"undislikes\", 1, 0, 1),\n )\n parts: list[pd.DataFrame] = []\n for family, channel, active, tie_priority in specifications:\n for prefix in prefixes:\n frame = pd.read_parquet(\n data / f\"{prefix}_{family}.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n mapped = item_index.get_indexer(\n frame.item_id.to_numpy(dtype=np.int64, copy=False)\n )\n keep = mapped >= 0\n if not np.any(keep):\n continue\n retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n retained[\"channel\"] = np.int8(channel)\n retained[\"active\"] = np.int8(active)\n retained[\"tie_priority\"] = np.int8(tie_priority)\n retained[\"organic_like\"] = np.int8(\n family == \"likes\"\n ) * retained.is_organic.astype(np.int8)\n parts.append(retained.drop(columns=\"is_organic\"))\n if not parts:\n return {}\n events = pd.concat(parts, ignore_index=True)\n events = events.sort_values(\n [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n ).reset_index(drop=True)\n result: dict[int, ExplicitEvents] = {}\n for uid, rows in events.groupby(\"uid\", sort=False):\n result[int(uid)] = ExplicitEvents(\n timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n items=rows.item.to_numpy(dtype=np.int32, copy=True),\n channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n active=rows.active.to_numpy(dtype=np.int8, copy=True),\n organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n )\n log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n return ExplicitEvents(\n timestamps=np.empty(0, dtype=np.int64),\n items=np.empty(0, dtype=np.int32),\n channel=np.empty(0, dtype=np.int8),\n active=np.empty(0, dtype=np.int8),\n organic_like=np.empty(0, dtype=np.int8),\n )\n\n\ndef pack_active_items(\n active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n packed = np.full(width, padding_item, dtype=np.uint16)\n if not active:\n return packed\n ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n packed[: len(ordered)] = np.fromiter(\n (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n )\n return packed\n\n\ndef load_inference_state(\n path: Path,\n item_index: pd.Index,\n uids: np.ndarray,\n n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"Pack the task-provided active explicit state.\"\"\"\n state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n state = state.loc[keep, [\"uid\", \"state\"]].copy()\n state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n user_rows = pd.Index(uids)\n liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n like_counts = np.zeros(len(uids), dtype=np.int32)\n dislike_counts = np.zeros(len(uids), dtype=np.int32)\n for value, width, destination, counts in (\n (\"liked\", LIKE_ITEMS, liked, like_counts),\n (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n ):\n selected = state[state.state == value]\n for uid, rows in selected.groupby(\"uid\", sort=False):\n row = int(user_rows.get_indexer([int(uid)])[0])\n if row < 0:\n continue\n items = np.sort(rows.item.unique())\n counts[row] = len(items)\n retained = items[:width]\n destination[row, : len(retained)] = retained.astype(np.uint16)\n return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:36:51.841Z",
"resultDisplay": "Read lines 151-300 of 434 from ../task/current_model/two_tower_model.py",
"description": "../.../two_tower_model.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:36:51.841Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 151-300 of 434 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: 301.\n\n--- FILE CONTENT (truncated) ---\n pending = []\n pending.append(part)\n if pending_uid is not None:\n yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n heads: list[tuple[int, ListenEvents] | None] = [\n next(stream, None) for stream in streams\n ]\n while any(head is not None for head in heads):\n uid = min(head[0] for head in heads if head is not None)\n parts: list[ListenEvents] = []\n for position, head in enumerate(heads):\n if head is not None and head[0] == uid:\n parts.append(head[1])\n heads[position] = next(streams[position], None)\n yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n data: Path,\n prefixes: tuple[str, ...],\n item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n specifications = (\n (\"likes\", 0, 1, 0),\n (\"unlikes\", 0, 0, 1),\n (\"dislikes\", 1, 1, 0),\n (\"undislikes\", 1, 0, 1),\n )\n parts: list[pd.DataFrame] = []\n for family, channel, active, tie_priority in specifications:\n for prefix in prefixes:\n frame = pd.read_parquet(\n data / f\"{prefix}_{family}.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n mapped = item_index.get_indexer(\n frame.item_id.to_numpy(dtype=np.int64, copy=False)\n )\n keep = mapped >= 0\n if not np.any(keep):\n continue\n retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n retained[\"channel\"] = np.int8(channel)\n retained[\"active\"] = np.int8(active)\n retained[\"tie_priority\"] = np.int8(tie_priority)\n retained[\"organic_like\"] = np.int8(\n family == \"likes\"\n ) * retained.is_organic.astype(np.int8)\n parts.append(retained.drop(columns=\"is_organic\"))\n if not parts:\n return {}\n events = pd.concat(parts, ignore_index=True)\n events = events.sort_values(\n [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n ).reset_index(drop=True)\n result: dict[int, ExplicitEvents] = {}\n for uid, rows in events.groupby(\"uid\", sort=False):\n result[int(uid)] = ExplicitEvents(\n timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n items=rows.item.to_numpy(dtype=np.int32, copy=True),\n channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n active=rows.active.to_numpy(dtype=np.int8, copy=True),\n organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n )\n log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n return ExplicitEvents(\n timestamps=np.empty(0, dtype=np.int64),\n items=np.empty(0, dtype=np.int32),\n channel=np.empty(0, dtype=np.int8),\n active=np.empty(0, dtype=np.int8),\n organic_like=np.empty(0, dtype=np.int8),\n )\n\n\ndef pack_active_items(\n active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n packed = np.full(width, padding_item, dtype=np.uint16)\n if not active:\n return packed\n ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n packed[: len(ordered)] = np.fromiter(\n (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n )\n return packed\n\n\ndef load_inference_state(\n path: Path,\n item_index: pd.Index,\n uids: np.ndarray,\n n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"Pack the task-provided active explicit state.\"\"\"\n state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n state = state.loc[keep, [\"uid\", \"state\"]].copy()\n state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n user_rows = pd.Index(uids)\n liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n like_counts = np.zeros(len(uids), dtype=np.int32)\n dislike_counts = np.zeros(len(uids), dtype=np.int32)\n for value, width, destination, counts in (\n (\"liked\", LIKE_ITEMS, liked, like_counts),\n (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n ):\n selected = state[state.state == value]\n for uid, rows in selected.groupby(\"uid\", sort=False):\n row = int(user_rows.get_indexer([int(uid)])[0])\n if row < 0:\n continue\n items = np.sort(rows.item.unique())\n counts[row] = len(items)\n retained = items[:width]\n destination[row, : len(retained)] = retained.astype(np.uint16)\n return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,",
"isError": false
},
"responseMessageId": "e4242a45-1213-427d-b5ce-43a8d716a25c"
}
]
},
{
"id": 8,
"role": "agent",
"timestamp": "2026-09-29T05:36:59.775Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:36:59.775Z",
"callId": "read_file__call_4732919",
"name": "read_file",
"input": {
"file_path": "/task/current_model/two_tower_model.py",
"start_line": 301,
"end_line": 434
},
"recorded": {
"id": "read_file__call_4732919",
"name": "read_file",
"args": {
"file_path": "/task/current_model/two_tower_model.py",
"start_line": 301,
"end_line": 434
},
"result": [
{
"functionResponse": {
"id": "read_file__call_4732919",
"name": "read_file",
"response": {
"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 301-434 of 434 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: 435.\n\n--- FILE CONTENT (truncated) ---\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_1, config.item_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_2, dim),\n )\n nn.init.normal_(self.context_embedding.weight, std=0.02)\n nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n with torch.no_grad():\n self.context_embedding.weight[n_items].zero_()\n\n def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n mask = items.ne(self.n_items)\n vectors = self.context_embedding(items)\n return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n dim=1, keepdim=True\n ).clamp_min(1)\n\n def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n inputs = torch.cat(\n [self.candidate_embedding(items), self.item_features[items]], dim=1\n )\n return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n negatives: torch.Tensor,\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n) -> torch.Tensor:\n \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n invalid = negatives.eq(targets[:, None])\n invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= negatives[:, column, None].eq(\n negatives[:, :column]\n ).any(dim=1)\n return invalid\n\n\ndef draw_filtered_mixed_negatives(\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n n_items: int,\n uniform_count: int,\n inbatch_count: int,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n batch_size = len(targets)\n uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n if corrected_count:\n correction_cpu[\n :, uniform_count : uniform_count + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]]\n negatives = negatives_cpu.to(device)\n correction = correction_cpu.to(device)\n for _ in range(32):\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if not bool(invalid.any()):\n return negatives, correction\n replacements = torch.randint(\n n_items,\n (int(invalid.sum().detach().cpu()),),\n generator=generator,\n device=\"cpu\",\n ).to(device)\n negatives[invalid] = replacements\n correction[invalid] = 1.0\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if bool(invalid.any()):\n raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n return negatives, correction\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:36:59.779Z",
"resultDisplay": "Read lines 301-434 of 434 from ../task/current_model/two_tower_model.py",
"description": "../.../two_tower_model.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:36:59.779Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 301-434 of 434 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: 435.\n\n--- FILE CONTENT (truncated) ---\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_1, config.item_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_2, dim),\n )\n nn.init.normal_(self.context_embedding.weight, std=0.02)\n nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n with torch.no_grad():\n self.context_embedding.weight[n_items].zero_()\n\n def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n mask = items.ne(self.n_items)\n vectors = self.context_embedding(items)\n return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n dim=1, keepdim=True\n ).clamp_min(1)\n\n def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n inputs = torch.cat(\n [self.candidate_embedding(items), self.item_features[items]], dim=1\n )\n return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n negatives: torch.Tensor,\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n) -> torch.Tensor:\n \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n invalid = negatives.eq(targets[:, None])\n invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= negatives[:, column, None].eq(\n negatives[:, :column]\n ).any(dim=1)\n return invalid\n\n\ndef draw_filtered_mixed_negatives(\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n n_items: int,\n uniform_count: int,\n inbatch_count: int,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n batch_size = len(targets)\n uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n if corrected_count:\n correction_cpu[\n :, uniform_count : uniform_count + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]]\n negatives = negatives_cpu.to(device)\n correction = correction_cpu.to(device)\n for _ in range(32):\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if not bool(invalid.any()):\n return negatives, correction\n replacements = torch.randint(\n n_items,\n (int(invalid.sum().detach().cpu()),),\n generator=generator,\n device=\"cpu\",\n ).to(device)\n negatives[invalid] = replacements\n correction[invalid] = 1.0\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if bool(invalid.any()):\n raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n return negatives, correction\n",
"isError": false
},
"responseMessageId": "7181dcd4-93db-477b-bb44-a5d6834ba1e1"
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]
},
{
"id": 9,
"role": "agent",
"timestamp": "2026-09-29T05:37:05.797Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:05.797Z",
"callId": "read_file__call_3322600",
"name": "read_file",
"input": {
"end_line": 300,
"file_path": "/task/current_model/train_current_model.py",
"start_line": 101
},
"recorded": {
"id": "read_file__call_3322600",
"name": "read_file",
"args": {
"end_line": 300,
"file_path": "/task/current_model/train_current_model.py",
"start_line": 101
},
"result": [
{
"functionResponse": {
"id": "read_file__call_3322600",
"name": "read_file",
"response": {
"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 101-300 of 2165 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: 301.\n\n--- FILE CONTENT (truncated) ---\n item_features: np.ndarray\n user_ids: np.ndarray\n offsets: np.ndarray\n sequence_timestamps: np.ndarray\n sequence_items: np.ndarray\n event_features: np.ndarray\n activity_features: np.ndarray\n state_ids: np.ndarray\n liked_states: np.ndarray\n disliked_states: np.ndarray\n state_counts: np.ndarray\n inference_user_rows: np.ndarray\n inference_liked_items: np.ndarray\n inference_disliked_items: np.ndarray\n inference_user_features: np.ndarray\n\n @property\n def lengths(self) -> np.ndarray:\n return np.diff(self.offsets)\n\n @property\n def target_count(self) -> int:\n return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n window_starts: np.ndarray\n training_counts: np.ndarray\n validation_counts: np.ndarray\n history_training_counts: np.ndarray\n\n @property\n def selected_count(self) -> int:\n return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n return base.TowerConfig(\n sequence_length=config.bptt_steps,\n embedding_dim=config.embedding_dim,\n hidden_dim=config.hidden_dim,\n user_hidden_1=config.user_hidden_1,\n user_hidden_2=config.user_hidden_2,\n item_hidden_1=config.item_hidden_1,\n item_hidden_2=config.item_hidden_2,\n dropout=config.dropout,\n epochs=config.epochs,\n learning_rate=config.learning_rate,\n weight_decay=config.weight_decay,\n gradient_clip=config.gradient_clip,\n uniform_negatives=config.uniform_negatives,\n inbatch_negatives=config.inbatch_negatives,\n inbatch_correction_power=config.inbatch_correction_power,\n inbatch_correction_min=config.inbatch_correction_min,\n inbatch_correction_max=config.inbatch_correction_max,\n seed=config.seed,\n )\n\n\ndef append_state_snapshot(\n active_likes: dict[int, int],\n active_dislikes: dict[int, int],\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> int:\n liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n disliked_states.append(\n base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n )\n state_counts.append((len(active_likes), len(active_dislikes)))\n return len(liked_states) - 1\n\n\ndef causal_activity_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n organic: np.ndarray,\n completion: np.ndarray,\n) -> np.ndarray:\n \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n length = len(items)\n result = np.zeros((length, 4), dtype=np.float16)\n if not length:\n return result\n left_rows = np.searchsorted(\n timestamps,\n timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n rows = np.arange(length, dtype=np.int64)\n counts = rows - left_rows + 1\n organic_sum = np.cumsum(organic.astype(np.float64))\n completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n before_organic = np.where(\n left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n before_completion = np.where(\n left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n\n unique_counts = np.empty(length, dtype=np.int32)\n frequencies: dict[int, int] = {}\n left = 0\n for right, raw_item in enumerate(items):\n item = int(raw_item)\n frequencies[item] = frequencies.get(item, 0) + 1\n required_left = int(left_rows[right])\n while left < required_left:\n old_item = int(items[left])\n remaining = frequencies[old_item] - 1\n if remaining:\n frequencies[old_item] = remaining\n else:\n del frequencies[old_item]\n left += 1\n unique_counts[right] = len(frequencies)\n\n result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n result[:, 2] = (\n (organic_sum - before_organic) / np.maximum(counts, 1)\n ).astype(np.float16)\n result[:, 3] = (\n (completion_sum - before_completion)\n / np.maximum(counts, 1)\n / 100.0\n ).astype(np.float16)\n return result\n\n\ndef build_sequence_parts(\n listens: base.ListenEvents,\n explicit: base.ExplicitEvents,\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n items = listens.items[qualifying].astype(np.uint16, copy=False)\n organic = listens.organic[qualifying].astype(np.float32, copy=False)\n completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n if not len(items):\n return None\n\n event_features = np.zeros((len(items), 3), dtype=np.float16)\n event_features[:, 0] = organic.astype(np.float16)\n event_features[:, 1] = (\n np.minimum(completion, 100.0) / 100.0\n ).astype(np.float16)\n if len(items) > 1:\n gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n event_features[1:, 2] = np.exp(\n -gaps.astype(np.float64) / (60.0 * base.DAY)\n ).astype(np.float16)\n\n activity = causal_activity_features(\n timestamps, items, organic, completion\n )\n states = np.zeros(len(items), dtype=np.uint32)\n active_likes: dict[int, int] = {}\n active_dislikes: dict[int, int] = {}\n explicit_position = 0\n current_state = 0\n for row, timestamp in enumerate(timestamps):\n changed = False\n while (\n explicit_position < len(explicit.timestamps)\n and int(explicit.timestamps[explicit_position]) < int(timestamp)\n ):\n item = int(explicit.items[explicit_position])\n event_timestamp = int(explicit.timestamps[explicit_position])\n destination = (\n active_likes\n if int(explicit.channel[explicit_position]) == 0\n else active_dislikes\n )\n if int(explicit.active[explicit_position]) == 1:\n destination[item] = event_timestamp\n else:\n destination.pop(item, None)\n explicit_position += 1\n changed = True"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:05.801Z",
"resultDisplay": "Read lines 101-300 of 2165 from ../task/current_model/train_current_model.py",
"description": "../.../train_current_model.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:37:05.802Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 101-300 of 2165 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: 301.\n\n--- FILE CONTENT (truncated) ---\n item_features: np.ndarray\n user_ids: np.ndarray\n offsets: np.ndarray\n sequence_timestamps: np.ndarray\n sequence_items: np.ndarray\n event_features: np.ndarray\n activity_features: np.ndarray\n state_ids: np.ndarray\n liked_states: np.ndarray\n disliked_states: np.ndarray\n state_counts: np.ndarray\n inference_user_rows: np.ndarray\n inference_liked_items: np.ndarray\n inference_disliked_items: np.ndarray\n inference_user_features: np.ndarray\n\n @property\n def lengths(self) -> np.ndarray:\n return np.diff(self.offsets)\n\n @property\n def target_count(self) -> int:\n return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n window_starts: np.ndarray\n training_counts: np.ndarray\n validation_counts: np.ndarray\n history_training_counts: np.ndarray\n\n @property\n def selected_count(self) -> int:\n return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n return base.TowerConfig(\n sequence_length=config.bptt_steps,\n embedding_dim=config.embedding_dim,\n hidden_dim=config.hidden_dim,\n user_hidden_1=config.user_hidden_1,\n user_hidden_2=config.user_hidden_2,\n item_hidden_1=config.item_hidden_1,\n item_hidden_2=config.item_hidden_2,\n dropout=config.dropout,\n epochs=config.epochs,\n learning_rate=config.learning_rate,\n weight_decay=config.weight_decay,\n gradient_clip=config.gradient_clip,\n uniform_negatives=config.uniform_negatives,\n inbatch_negatives=config.inbatch_negatives,\n inbatch_correction_power=config.inbatch_correction_power,\n inbatch_correction_min=config.inbatch_correction_min,\n inbatch_correction_max=config.inbatch_correction_max,\n seed=config.seed,\n )\n\n\ndef append_state_snapshot(\n active_likes: dict[int, int],\n active_dislikes: dict[int, int],\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> int:\n liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n disliked_states.append(\n base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n )\n state_counts.append((len(active_likes), len(active_dislikes)))\n return len(liked_states) - 1\n\n\ndef causal_activity_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n organic: np.ndarray,\n completion: np.ndarray,\n) -> np.ndarray:\n \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n length = len(items)\n result = np.zeros((length, 4), dtype=np.float16)\n if not length:\n return result\n left_rows = np.searchsorted(\n timestamps,\n timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n rows = np.arange(length, dtype=np.int64)\n counts = rows - left_rows + 1\n organic_sum = np.cumsum(organic.astype(np.float64))\n completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n before_organic = np.where(\n left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n before_completion = np.where(\n left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n\n unique_counts = np.empty(length, dtype=np.int32)\n frequencies: dict[int, int] = {}\n left = 0\n for right, raw_item in enumerate(items):\n item = int(raw_item)\n frequencies[item] = frequencies.get(item, 0) + 1\n required_left = int(left_rows[right])\n while left < required_left:\n old_item = int(items[left])\n remaining = frequencies[old_item] - 1\n if remaining:\n frequencies[old_item] = remaining\n else:\n del frequencies[old_item]\n left += 1\n unique_counts[right] = len(frequencies)\n\n result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n result[:, 2] = (\n (organic_sum - before_organic) / np.maximum(counts, 1)\n ).astype(np.float16)\n result[:, 3] = (\n (completion_sum - before_completion)\n / np.maximum(counts, 1)\n / 100.0\n ).astype(np.float16)\n return result\n\n\ndef build_sequence_parts(\n listens: base.ListenEvents,\n explicit: base.ExplicitEvents,\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n items = listens.items[qualifying].astype(np.uint16, copy=False)\n organic = listens.organic[qualifying].astype(np.float32, copy=False)\n completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n if not len(items):\n return None\n\n event_features = np.zeros((len(items), 3), dtype=np.float16)\n event_features[:, 0] = organic.astype(np.float16)\n event_features[:, 1] = (\n np.minimum(completion, 100.0) / 100.0\n ).astype(np.float16)\n if len(items) > 1:\n gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n event_features[1:, 2] = np.exp(\n -gaps.astype(np.float64) / (60.0 * base.DAY)\n ).astype(np.float16)\n\n activity = causal_activity_features(\n timestamps, items, organic, completion\n )\n states = np.zeros(len(items), dtype=np.uint32)\n active_likes: dict[int, int] = {}\n active_dislikes: dict[int, int] = {}\n explicit_position = 0\n current_state = 0\n for row, timestamp in enumerate(timestamps):\n changed = False\n while (\n explicit_position < len(explicit.timestamps)\n and int(explicit.timestamps[explicit_position]) < int(timestamp)\n ):\n item = int(explicit.items[explicit_position])\n event_timestamp = int(explicit.timestamps[explicit_position])\n destination = (\n active_likes\n if int(explicit.channel[explicit_position]) == 0\n else active_dislikes\n )\n if int(explicit.active[explicit_position]) == 1:\n destination[item] = event_timestamp\n else:\n destination.pop(item, None)\n explicit_position += 1\n changed = True",
"isError": false
},
"responseMessageId": "4caebd4b-d799-4d76-99e1-14cae306d3f5"
}
]
},
{
"id": 10,
"role": "agent",
"timestamp": "2026-09-29T05:37:07.999Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:07.999Z",
"callId": "read_file__call_21890",
"name": "read_file",
"input": {
"file_path": "/task/current_model/train_current_model.py",
"start_line": 301,
"end_line": 600
},
"recorded": {
"id": "read_file__call_21890",
"name": "read_file",
"args": {
"file_path": "/task/current_model/train_current_model.py",
"start_line": 301,
"end_line": 600
},
"result": [
{
"functionResponse": {
"id": "read_file__call_21890",
"name": "read_file",
"response": {
"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 301-600 of 2165 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: 601.\n\n--- FILE CONTENT (truncated) ---\n if changed:\n current_state = append_state_snapshot(\n active_likes,\n active_dislikes,\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n states[row] = np.uint32(current_state)\n return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n event_features: np.ndarray,\n history_end: int,\n like_count: int,\n dislike_count: int,\n) -> np.ndarray:\n start = int(\n np.searchsorted(\n timestamps,\n history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n )\n result = np.zeros(6, dtype=np.float32)\n recent_items = items[start:]\n if len(recent_items):\n result[0] = np.log1p(len(recent_items)) / 8.0\n result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n result[2] = float(np.mean(event_features[start:, 0]))\n result[3] = float(np.mean(event_features[start:, 1]))\n result[4] = np.log1p(like_count) / 6.0\n result[5] = np.log1p(dislike_count) / 4.0\n return result\n\n\ndef prepare_data(\n public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n data = public / \"data\"\n catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n \"popularity_rank\"\n )\n item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n item_index = pd.Index(item_ids)\n n_items = len(item_ids)\n if n_items >= np.iinfo(np.uint16).max:\n raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n if stage == \"validation\":\n prefixes = (\"train\",)\n history_end = base.TRAIN_END\n candidate_name = \"validation_eligible_candidates.npz\"\n state_name = \"validation_preference_state.parquet\"\n else:\n prefixes = (\"train\", \"val\")\n history_end = base.VALIDATION_END\n candidate_name = \"test_eligible_candidates.npz\"\n state_name = \"test_preference_state.parquet\"\n eligibility = CandidateSet.read(data / candidate_name)\n if not np.array_equal(eligibility.item_ids, item_ids):\n raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n inference_likes, inference_dislikes, like_counts, dislike_counts = (\n base.load_inference_state(\n data / state_name,\n item_index,\n eligibility.uids,\n n_items,\n )\n )\n inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n # Snapshot zero is the shared empty explicit state.\n liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n disliked_states = [\n np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n ]\n state_counts: list[tuple[int, int]] = [(0, 0)]\n user_ids: list[int] = []\n offsets = [0]\n timestamp_parts: list[np.ndarray] = []\n item_parts: list[np.ndarray] = []\n event_parts: list[np.ndarray] = []\n activity_parts: list[np.ndarray] = []\n state_parts: list[np.ndarray] = []\n\n listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n started = time.monotonic()\n for seen_users, (uid, listens) in enumerate(\n base.merge_listen_streams(listen_paths, item_index), start=1\n ):\n parts = build_sequence_parts(\n listens,\n explicit_by_user.get(uid, base.empty_explicit_events()),\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n if parts is None:\n continue\n sequence_row = len(user_ids)\n user_ids.append(uid)\n timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n item_parts.append(parts[1])\n event_parts.append(parts[2])\n activity_parts.append(parts[3])\n state_parts.append(parts[4])\n offsets.append(offsets[-1] + len(parts[1]))\n inference_row = inference_rows.get(uid)\n if inference_row is not None:\n inference_sequence_rows[inference_row] = sequence_row\n inference_user_features[inference_row] = final_user_features(\n parts[0],\n parts[1],\n parts[2],\n history_end,\n int(like_counts[inference_row]),\n int(dislike_counts[inference_row]),\n )\n if seen_users % 1_000 == 0:\n log(\n f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n f\"states={len(liked_states):,} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n missing = np.flatnonzero(inference_sequence_rows < 0)\n if len(missing):\n inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n inference_user_features[missing, 5] = (\n np.log1p(dislike_counts[missing]) / 4.0\n )\n log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n prepared = SequenceData(\n item_ids=item_ids,\n item_features=base.build_item_features(catalog),\n user_ids=np.asarray(user_ids, dtype=np.int64),\n offsets=np.asarray(offsets, dtype=np.int64),\n sequence_timestamps=np.concatenate(timestamp_parts),\n sequence_items=np.concatenate(item_parts),\n event_features=np.concatenate(event_parts),\n activity_features=np.concatenate(activity_parts),\n state_ids=np.concatenate(state_parts),\n liked_states=np.stack(liked_states),\n disliked_states=np.stack(disliked_states),\n state_counts=np.asarray(state_counts, dtype=np.int32),\n inference_user_rows=inference_sequence_rows,\n inference_liked_items=inference_likes,\n inference_disliked_items=inference_dislikes,\n inference_user_features=inference_user_features,\n )\n log(\n f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n f\"tokens={len(prepared.sequence_items):,} \"\n f\"next_item_targets={prepared.target_count:,} \"\n f\"states={len(prepared.liked_states):,}\"\n )\n return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n return {\n \"cache_version\": CACHE_VERSION,\n \"stage\": stage,\n \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n }\n\n\ndef save_cache(\n path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n np.savez(\n path,\n metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n **prepared.__dict__,\n )\n log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n with np.load(path, allow_pickle=False) as artifact:\n actual = json.loads(str(artifact[\"metadata\"].item()))\n expected = cache_metadata(stage, config)\n if actual != expected:\n raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n values = {\n field: artifact[field].copy()\n for field in SequenceData.__dataclass_fields__\n }\n prepared = SequenceData(**values)\n log(\n f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n f\"targets={prepared.target_count:,}\"\n )\n return prepared\n\n\ndef load_or_prepare(\n public: Path,\n stage: str,\n config: Config,\n cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:08.002Z",
"resultDisplay": "Read lines 301-600 of 2165 from ../task/current_model/train_current_model.py",
"description": "../.../train_current_model.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:37:08.003Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 301-600 of 2165 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: 601.\n\n--- FILE CONTENT (truncated) ---\n if changed:\n current_state = append_state_snapshot(\n active_likes,\n active_dislikes,\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n states[row] = np.uint32(current_state)\n return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n event_features: np.ndarray,\n history_end: int,\n like_count: int,\n dislike_count: int,\n) -> np.ndarray:\n start = int(\n np.searchsorted(\n timestamps,\n history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n )\n result = np.zeros(6, dtype=np.float32)\n recent_items = items[start:]\n if len(recent_items):\n result[0] = np.log1p(len(recent_items)) / 8.0\n result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n result[2] = float(np.mean(event_features[start:, 0]))\n result[3] = float(np.mean(event_features[start:, 1]))\n result[4] = np.log1p(like_count) / 6.0\n result[5] = np.log1p(dislike_count) / 4.0\n return result\n\n\ndef prepare_data(\n public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n data = public / \"data\"\n catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n \"popularity_rank\"\n )\n item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n item_index = pd.Index(item_ids)\n n_items = len(item_ids)\n if n_items >= np.iinfo(np.uint16).max:\n raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n if stage == \"validation\":\n prefixes = (\"train\",)\n history_end = base.TRAIN_END\n candidate_name = \"validation_eligible_candidates.npz\"\n state_name = \"validation_preference_state.parquet\"\n else:\n prefixes = (\"train\", \"val\")\n history_end = base.VALIDATION_END\n candidate_name = \"test_eligible_candidates.npz\"\n state_name = \"test_preference_state.parquet\"\n eligibility = CandidateSet.read(data / candidate_name)\n if not np.array_equal(eligibility.item_ids, item_ids):\n raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n inference_likes, inference_dislikes, like_counts, dislike_counts = (\n base.load_inference_state(\n data / state_name,\n item_index,\n eligibility.uids,\n n_items,\n )\n )\n inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n # Snapshot zero is the shared empty explicit state.\n liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n disliked_states = [\n np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n ]\n state_counts: list[tuple[int, int]] = [(0, 0)]\n user_ids: list[int] = []\n offsets = [0]\n timestamp_parts: list[np.ndarray] = []\n item_parts: list[np.ndarray] = []\n event_parts: list[np.ndarray] = []\n activity_parts: list[np.ndarray] = []\n state_parts: list[np.ndarray] = []\n\n listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n started = time.monotonic()\n for seen_users, (uid, listens) in enumerate(\n base.merge_listen_streams(listen_paths, item_index), start=1\n ):\n parts = build_sequence_parts(\n listens,\n explicit_by_user.get(uid, base.empty_explicit_events()),\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n if parts is None:\n continue\n sequence_row = len(user_ids)\n user_ids.append(uid)\n timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n item_parts.append(parts[1])\n event_parts.append(parts[2])\n activity_parts.append(parts[3])\n state_parts.append(parts[4])\n offsets.append(offsets[-1] + len(parts[1]))\n inference_row = inference_rows.get(uid)\n if inference_row is not None:\n inference_sequence_rows[inference_row] = sequence_row\n inference_user_features[inference_row] = final_user_features(\n parts[0],\n parts[1],\n parts[2],\n history_end,\n int(like_counts[inference_row]),\n int(dislike_counts[inference_row]),\n )\n if seen_users % 1_000 == 0:\n log(\n f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n f\"states={len(liked_states):,} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n missing = np.flatnonzero(inference_sequence_rows < 0)\n if len(missing):\n inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n inference_user_features[missing, 5] = (\n np.log1p(dislike_counts[missing]) / 4.0\n )\n log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n prepared = SequenceData(\n item_ids=item_ids,\n item_features=base.build_item_features(catalog),\n user_ids=np.asarray(user_ids, dtype=np.int64),\n offsets=np.asarray(offsets, dtype=np.int64),\n sequence_timestamps=np.concatenate(timestamp_parts),\n sequence_items=np.concatenate(item_parts),\n event_features=np.concatenate(event_parts),\n activity_features=np.concatenate(activity_parts),\n state_ids=np.concatenate(state_parts),\n liked_states=np.stack(liked_states),\n disliked_states=np.stack(disliked_states),\n state_counts=np.asarray(state_counts, dtype=np.int32),\n inference_user_rows=inference_sequence_rows,\n inference_liked_items=inference_likes,\n inference_disliked_items=inference_dislikes,\n inference_user_features=inference_user_features,\n )\n log(\n f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n f\"tokens={len(prepared.sequence_items):,} \"\n f\"next_item_targets={prepared.target_count:,} \"\n f\"states={len(prepared.liked_states):,}\"\n )\n return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n return {\n \"cache_version\": CACHE_VERSION,\n \"stage\": stage,\n \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n }\n\n\ndef save_cache(\n path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n np.savez(\n path,\n metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n **prepared.__dict__,\n )\n log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n with np.load(path, allow_pickle=False) as artifact:\n actual = json.loads(str(artifact[\"metadata\"].item()))\n expected = cache_metadata(stage, config)\n if actual != expected:\n raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n values = {\n field: artifact[field].copy()\n for field in SequenceData.__dataclass_fields__\n }\n prepared = SequenceData(**values)\n log(\n f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n f\"targets={prepared.target_count:,}\"\n )\n return prepared\n\n\ndef load_or_prepare(\n public: Path,\n stage: str,\n config: Config,\n cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)",
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},
{
"id": 11,
"role": "agent",
"timestamp": "2026-09-29T05:37:09.850Z",
"blocks": [
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"kind": "tool",
"timestamp": "2026-09-29T05:37:09.850Z",
"callId": "read_file__call_14988",
"name": "read_file",
"input": {
"file_path": "/task/current_model/train_current_model.py",
"start_line": 601,
"end_line": 900
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"recorded": {
"id": "read_file__call_14988",
"name": "read_file",
"args": {
"file_path": "/task/current_model/train_current_model.py",
"start_line": 601,
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{
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"id": "read_file__call_14988",
"name": "read_file",
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"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 601-900 of 2165 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: 901.\n\n--- FILE CONTENT (truncated) ---\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n liked_items = torch.from_numpy(\n prepared.liked_states[unique_states].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.disliked_states[unique_states].astype(np.int64)\n ).to(device)\n liked_vectors = model._mean_context_channel(liked_items)\n disliked_vectors = model._mean_context_channel(disliked_items)\n inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n counts = prepared.state_counts[state_ids]\n return (\n liked_vectors[inverse_rows],\n disliked_vectors[inverse_rows],\n torch.from_numpy(counts.astype(np.float32)).to(device),\n )\n\n\ndef user_queries(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n liked_vectors: torch.Tensor,\n disliked_vectors: torch.Tensor,\n user_features: torch.Tensor,\n) -> torch.Tensor:\n return model.user_tower(\n torch.cat(\n [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n dim=1,\n )\n )\n\n\ndef candidate_vectors(\n model: base.SequentialTwoTower,\n positives: torch.Tensor,\n negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n all_items = torch.cat([positives, negatives.reshape(-1)])\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n positive_vectors = encoded[inverse[: len(positives)]]\n negative_vectors = encoded[inverse[len(positives) :]].reshape(\n len(positives), negatives.shape[1], -1\n )\n return positive_vectors, negative_vectors\n\n\ndef history_windows(\n sequence: np.ndarray,\n start: int,\n count: int,\n width: int,\n padding_item: int,\n) -> np.ndarray:\n base_start = max(0, start - width + 1)\n segment = sequence[base_start : start + count]\n padded = np.concatenate(\n [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n )\n windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n offset = start - base_start\n return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total\n\n\ndef retained_target_window_starts(\n total_targets: np.ndarray,\n validation_counts: np.ndarray,\n training_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n training_region_ends = total_targets - validation_counts\n window_starts = training_region_ends - training_counts\n if (\n np.any(window_starts < 0)\n or np.any(training_counts < 0)\n or np.any(validation_counts < 0)\n ):\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:09.853Z",
"resultDisplay": "Read lines 601-900 of 2165 from ../task/current_model/train_current_model.py",
"description": "../.../train_current_model.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:37:09.854Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 601-900 of 2165 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: 901.\n\n--- FILE CONTENT (truncated) ---\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n liked_items = torch.from_numpy(\n prepared.liked_states[unique_states].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.disliked_states[unique_states].astype(np.int64)\n ).to(device)\n liked_vectors = model._mean_context_channel(liked_items)\n disliked_vectors = model._mean_context_channel(disliked_items)\n inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n counts = prepared.state_counts[state_ids]\n return (\n liked_vectors[inverse_rows],\n disliked_vectors[inverse_rows],\n torch.from_numpy(counts.astype(np.float32)).to(device),\n )\n\n\ndef user_queries(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n liked_vectors: torch.Tensor,\n disliked_vectors: torch.Tensor,\n user_features: torch.Tensor,\n) -> torch.Tensor:\n return model.user_tower(\n torch.cat(\n [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n dim=1,\n )\n )\n\n\ndef candidate_vectors(\n model: base.SequentialTwoTower,\n positives: torch.Tensor,\n negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n all_items = torch.cat([positives, negatives.reshape(-1)])\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n positive_vectors = encoded[inverse[: len(positives)]]\n negative_vectors = encoded[inverse[len(positives) :]].reshape(\n len(positives), negatives.shape[1], -1\n )\n return positive_vectors, negative_vectors\n\n\ndef history_windows(\n sequence: np.ndarray,\n start: int,\n count: int,\n width: int,\n padding_item: int,\n) -> np.ndarray:\n base_start = max(0, start - width + 1)\n segment = sequence[base_start : start + count]\n padded = np.concatenate(\n [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n )\n windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n offset = start - base_start\n return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total\n\n\ndef retained_target_window_starts(\n total_targets: np.ndarray,\n validation_counts: np.ndarray,\n training_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n training_region_ends = total_targets - validation_counts\n window_starts = training_region_ends - training_counts\n if (\n np.any(window_starts < 0)\n or np.any(training_counts < 0)\n or np.any(validation_counts < 0)\n ):\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)\n",
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},
{
"id": 12,
"role": "agent",
"timestamp": "2026-09-29T05:37:11.631Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:11.631Z",
"callId": "read_file__call_7837173",
"name": "read_file",
"input": {
"start_line": 901,
"end_line": 1200,
"file_path": "/task/current_model/train_current_model.py"
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"recorded": {
"id": "read_file__call_7837173",
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"start_line": 901,
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"file_path": "/task/current_model/train_current_model.py"
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"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 901-1200 of 2165 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: 1201.\n\n--- FILE CONTENT (truncated) ---\n history_training = totals - validation\n training = history_training.copy()\n if max_train_targets_per_user:\n training = np.minimum(training, max_train_targets_per_user)\n window_starts = retained_target_window_starts(totals, validation, training)\n return TargetPartition(\n window_starts=window_starts,\n training_counts=training,\n validation_counts=validation,\n history_training_counts=history_training,\n )\n\n\ndef load_organic_like_outcomes(\n public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n frames = [\n pd.read_parquet(\n public / \"data\" / f\"{prefix}_likes.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n for prefix in prefixes\n ]\n likes = pd.concat(frames, ignore_index=True)\n likes = likes[likes.is_organic == 1].copy()\n mapped = pd.Index(item_ids).get_indexer(\n likes.item_id.to_numpy(dtype=np.int64)\n )\n likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n nested: dict[int, dict[int, list[int]]] = {}\n for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n index=False, name=None\n ):\n nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n int(raw_timestamp)\n )\n result = {\n uid: {\n item: np.asarray(timestamps, dtype=np.int64)\n for item, timestamps in items.items()\n }\n for uid, items in nested.items()\n }\n log(\n f\"loaded organic-like outcomes: rows={len(likes):,} \"\n f\"users={len(result):,}\"\n )\n return result\n\n\ndef build_target_weights(\n public: Path,\n stage: str,\n prepared: SequenceData,\n training_counts: np.ndarray,\n config: Config,\n) -> np.ndarray:\n \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n if config.target_weighting == \"uniform\":\n weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n else:\n weights = np.full(\n len(prepared.sequence_items),\n config.ordinary_target_weight,\n dtype=np.float32,\n )\n organic_strong = (\n (prepared.event_features[:, 0] > 0.5)\n & (prepared.event_features[:, 1] >= 0.8)\n )\n weights[organic_strong] = config.organic_strong_target_weight\n if config.target_weighting == \"discovery-graded\":\n like_outcomes = load_organic_like_outcomes(\n public, stage, prepared.item_ids\n )\n history_end = (\n base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n )\n horizon = config.discovery_horizon_days * base.DAY\n grade_three = 0\n for user_row, uid in enumerate(prepared.user_ids):\n start = int(prepared.offsets[user_row])\n end = int(prepared.offsets[user_row + 1])\n if end - start < 2:\n continue\n last_training_target = start + int(training_counts[user_row])\n first_validation_target = last_training_target + 1\n split_timestamp = (\n int(prepared.sequence_timestamps[first_validation_target])\n if first_validation_target < end\n else history_end\n )\n user_likes = like_outcomes.get(int(uid), {})\n seen = {int(prepared.sequence_items[start])}\n for position in range(start + 1, end):\n item = int(prepared.sequence_items[position])\n novel = item not in seen\n seen.add(item)\n if not novel:\n # Repeats remain training examples, but they cannot be\n # eligible discovery outcomes at the corresponding\n # cutoff and therefore keep only the ordinary weight.\n weights[position] = config.ordinary_target_weight\n continue\n timestamps = user_likes.get(item)\n if timestamps is None:\n continue\n timestamp = int(prepared.sequence_timestamps[position])\n upper = min(\n timestamp + horizon,\n split_timestamp\n if position <= last_training_target\n else history_end,\n )\n outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n weights[position] = config.organic_like_target_weight\n grade_three += 1\n log(\n f\"grade-3 first-encounter targets={grade_three:,} \"\n f\"horizon_days={config.discovery_horizon_days}\"\n )\n log(\n f\"target weighting={config.target_weighting} \"\n f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n f\"max={weights.max():.3f}\"\n )\n return weights\n\n\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\n window_starts: np.ndarray,\n process_counts: np.ndarray,\n score_starts: np.ndarray,\n start: int,\n steps: int,\n config: Config,\n) -> tuple[np.ndarray, ...]:\n \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n batch_size = len(users)\n padding_item = len(prepared.item_ids)\n input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n targets = np.zeros((batch_size, steps), dtype=np.uint16)\n weights = np.ones((batch_size, steps), dtype=np.float32)\n scored = np.zeros((batch_size, steps), dtype=bool)\n histories = np.full(\n (batch_size, steps, config.bptt_steps),\n padding_item,\n dtype=np.uint16,\n )\n processed = np.zeros(batch_size, dtype=np.int32)\n\n lengths = prepared.lengths\n for row, user_row in enumerate(users):\n count = min(steps, max(int(process_counts[row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n window_start = int(window_starts[row])\n absolute_start = window_start + start\n positions = slice(\n offset + absolute_start,\n offset + absolute_start + count,\n )\n target_positions = slice(\n offset + absolute_start + 1,\n offset + absolute_start + count + 1,\n )\n input_items[row, :count] = prepared.sequence_items[positions]\n event_features[row, :count] = prepared.event_features[positions]\n activity_features[row, :count] = prepared.activity_features[positions]\n state_ids[row, :count] = prepared.state_ids[positions]\n targets[row, :count] = prepared.sequence_items[target_positions]\n weights[row, :count] = target_weights[target_positions]\n score_from = min(count, max(int(score_starts[row]) - start, 0))\n scored[row, score_from:count] = True\n processed[row] = count\n user_sequence = prepared.sequence_items[\n offset : offset + int(lengths[user_row])\n ]\n histories[row, :count] = history_windows(\n user_sequence,\n absolute_start,\n count,\n config.bptt_steps,\n padding_item,\n )\n return (\n input_items,\n event_features,\n activity_features,\n state_ids,\n targets,\n scored,\n histories,\n processed,\n weights,\n )\n\n\ndef score_target_microbatches(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:11.634Z",
"resultDisplay": "Read lines 901-1200 of 2165 from ../task/current_model/train_current_model.py",
"description": "../.../train_current_model.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:37:11.635Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 901-1200 of 2165 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: 1201.\n\n--- FILE CONTENT (truncated) ---\n history_training = totals - validation\n training = history_training.copy()\n if max_train_targets_per_user:\n training = np.minimum(training, max_train_targets_per_user)\n window_starts = retained_target_window_starts(totals, validation, training)\n return TargetPartition(\n window_starts=window_starts,\n training_counts=training,\n validation_counts=validation,\n history_training_counts=history_training,\n )\n\n\ndef load_organic_like_outcomes(\n public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n frames = [\n pd.read_parquet(\n public / \"data\" / f\"{prefix}_likes.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n for prefix in prefixes\n ]\n likes = pd.concat(frames, ignore_index=True)\n likes = likes[likes.is_organic == 1].copy()\n mapped = pd.Index(item_ids).get_indexer(\n likes.item_id.to_numpy(dtype=np.int64)\n )\n likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n nested: dict[int, dict[int, list[int]]] = {}\n for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n index=False, name=None\n ):\n nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n int(raw_timestamp)\n )\n result = {\n uid: {\n item: np.asarray(timestamps, dtype=np.int64)\n for item, timestamps in items.items()\n }\n for uid, items in nested.items()\n }\n log(\n f\"loaded organic-like outcomes: rows={len(likes):,} \"\n f\"users={len(result):,}\"\n )\n return result\n\n\ndef build_target_weights(\n public: Path,\n stage: str,\n prepared: SequenceData,\n training_counts: np.ndarray,\n config: Config,\n) -> np.ndarray:\n \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n if config.target_weighting == \"uniform\":\n weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n else:\n weights = np.full(\n len(prepared.sequence_items),\n config.ordinary_target_weight,\n dtype=np.float32,\n )\n organic_strong = (\n (prepared.event_features[:, 0] > 0.5)\n & (prepared.event_features[:, 1] >= 0.8)\n )\n weights[organic_strong] = config.organic_strong_target_weight\n if config.target_weighting == \"discovery-graded\":\n like_outcomes = load_organic_like_outcomes(\n public, stage, prepared.item_ids\n )\n history_end = (\n base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n )\n horizon = config.discovery_horizon_days * base.DAY\n grade_three = 0\n for user_row, uid in enumerate(prepared.user_ids):\n start = int(prepared.offsets[user_row])\n end = int(prepared.offsets[user_row + 1])\n if end - start < 2:\n continue\n last_training_target = start + int(training_counts[user_row])\n first_validation_target = last_training_target + 1\n split_timestamp = (\n int(prepared.sequence_timestamps[first_validation_target])\n if first_validation_target < end\n else history_end\n )\n user_likes = like_outcomes.get(int(uid), {})\n seen = {int(prepared.sequence_items[start])}\n for position in range(start + 1, end):\n item = int(prepared.sequence_items[position])\n novel = item not in seen\n seen.add(item)\n if not novel:\n # Repeats remain training examples, but they cannot be\n # eligible discovery outcomes at the corresponding\n # cutoff and therefore keep only the ordinary weight.\n weights[position] = config.ordinary_target_weight\n continue\n timestamps = user_likes.get(item)\n if timestamps is None:\n continue\n timestamp = int(prepared.sequence_timestamps[position])\n upper = min(\n timestamp + horizon,\n split_timestamp\n if position <= last_training_target\n else history_end,\n )\n outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n weights[position] = config.organic_like_target_weight\n grade_three += 1\n log(\n f\"grade-3 first-encounter targets={grade_three:,} \"\n f\"horizon_days={config.discovery_horizon_days}\"\n )\n log(\n f\"target weighting={config.target_weighting} \"\n f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n f\"max={weights.max():.3f}\"\n )\n return weights\n\n\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\n window_starts: np.ndarray,\n process_counts: np.ndarray,\n score_starts: np.ndarray,\n start: int,\n steps: int,\n config: Config,\n) -> tuple[np.ndarray, ...]:\n \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n batch_size = len(users)\n padding_item = len(prepared.item_ids)\n input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n targets = np.zeros((batch_size, steps), dtype=np.uint16)\n weights = np.ones((batch_size, steps), dtype=np.float32)\n scored = np.zeros((batch_size, steps), dtype=bool)\n histories = np.full(\n (batch_size, steps, config.bptt_steps),\n padding_item,\n dtype=np.uint16,\n )\n processed = np.zeros(batch_size, dtype=np.int32)\n\n lengths = prepared.lengths\n for row, user_row in enumerate(users):\n count = min(steps, max(int(process_counts[row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n window_start = int(window_starts[row])\n absolute_start = window_start + start\n positions = slice(\n offset + absolute_start,\n offset + absolute_start + count,\n )\n target_positions = slice(\n offset + absolute_start + 1,\n offset + absolute_start + count + 1,\n )\n input_items[row, :count] = prepared.sequence_items[positions]\n event_features[row, :count] = prepared.event_features[positions]\n activity_features[row, :count] = prepared.activity_features[positions]\n state_ids[row, :count] = prepared.state_ids[positions]\n targets[row, :count] = prepared.sequence_items[target_positions]\n weights[row, :count] = target_weights[target_positions]\n score_from = min(count, max(int(score_starts[row]) - start, 0))\n scored[row, score_from:count] = True\n processed[row] = count\n user_sequence = prepared.sequence_items[\n offset : offset + int(lengths[user_row])\n ]\n histories[row, :count] = history_windows(\n user_sequence,\n absolute_start,\n count,\n config.bptt_steps,\n padding_item,\n )\n return (\n input_items,\n event_features,\n activity_features,\n state_ids,\n targets,\n scored,\n histories,\n processed,\n weights,\n )\n\n\ndef score_target_microbatches(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()",
"isError": false
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},
{
"id": 13,
"role": "agent",
"timestamp": "2026-09-29T05:37:13.675Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:13.675Z",
"callId": "read_file__call_23397",
"name": "read_file",
"input": {
"file_path": "/task/current_model/train_current_model.py",
"end_line": 1500,
"start_line": 1201
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"recorded": {
"id": "read_file__call_23397",
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"file_path": "/task/current_model/train_current_model.py",
"end_line": 1500,
"start_line": 1201
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"id": "read_file__call_23397",
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"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1201-1500 of 2165 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: 1501.\n\n--- FILE CONTENT (truncated) ---\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()\n for name, value in model.state_dict().items()\n }\n\n\ndef save_epoch_checkpoint(\n checkpoint_dir: Path,\n epoch: int,\n model_state: dict[str, torch.Tensor],\n config: Config,\n diagnostics: dict[str, float],"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:13.677Z",
"resultDisplay": "Read lines 1201-1500 of 2165 from ../task/current_model/train_current_model.py",
"description": "../.../train_current_model.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:37:13.678Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1201-1500 of 2165 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: 1501.\n\n--- FILE CONTENT (truncated) ---\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()\n for name, value in model.state_dict().items()\n }\n\n\ndef save_epoch_checkpoint(\n checkpoint_dir: Path,\n epoch: int,\n model_state: dict[str, torch.Tensor],\n config: Config,\n diagnostics: dict[str, float],",
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},
{
"id": 14,
"role": "agent",
"timestamp": "2026-09-29T05:37:15.290Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:15.290Z",
"callId": "read_file__call_163908",
"name": "read_file",
"input": {
"file_path": "/task/current_model/train_current_model.py",
"end_line": 1800,
"start_line": 1501
},
"recorded": {
"id": "read_file__call_163908",
"name": "read_file",
"args": {
"file_path": "/task/current_model/train_current_model.py",
"end_line": 1800,
"start_line": 1501
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"result": [
{
"functionResponse": {
"id": "read_file__call_163908",
"name": "read_file",
"response": {
"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1501-1800 of 2165 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: 1801.\n\n--- FILE CONTENT (truncated) ---\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n objective_changed = (\n stored_filter_scope != config.negative_filter_scope\n or stored_target_weighting != config.target_weighting\n or stored_weight_values != current_weight_values\n or stored_window_values != current_window_values\n )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n lengths = np.where(\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n )\n ordered = np.argsort(lengths, kind=\"stable\")\n result = torch.zeros(\n len(sequence_rows), config.embedding_dim, dtype=torch.float32\n )\n for group_start in range(0, len(ordered), config.user_batch_size):\n output_rows = ordered[group_start : group_start + config.user_batch_size]\n rows = sequence_rows[output_rows]\n group_lengths = lengths[output_rows]\n hidden = torch.zeros("
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:15.293Z",
"resultDisplay": "Read lines 1501-1800 of 2165 from ../task/current_model/train_current_model.py",
"description": "../.../train_current_model.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:37:15.294Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1501-1800 of 2165 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: 1801.\n\n--- FILE CONTENT (truncated) ---\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n objective_changed = (\n stored_filter_scope != config.negative_filter_scope\n or stored_target_weighting != config.target_weighting\n or stored_weight_values != current_weight_values\n or stored_window_values != current_window_values\n )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n lengths = np.where(\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n )\n ordered = np.argsort(lengths, kind=\"stable\")\n result = torch.zeros(\n len(sequence_rows), config.embedding_dim, dtype=torch.float32\n )\n for group_start in range(0, len(ordered), config.user_batch_size):\n output_rows = ordered[group_start : group_start + config.user_batch_size]\n rows = sequence_rows[output_rows]\n group_lengths = lengths[output_rows]\n hidden = torch.zeros(",
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},
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]
},
{
"id": 15,
"role": "agent",
"timestamp": "2026-09-29T05:37:17.262Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:17.262Z",
"callId": "read_file__call_35086",
"name": "read_file",
"input": {
"file_path": "/task/current_model/train_current_model.py",
"end_line": 2165,
"start_line": 1801
},
"recorded": {
"id": "read_file__call_35086",
"name": "read_file",
"args": {
"file_path": "/task/current_model/train_current_model.py",
"end_line": 2165,
"start_line": 1801
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"result": [
{
"functionResponse": {
"id": "read_file__call_35086",
"name": "read_file",
"response": {
"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1801-2165 of 2165 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: 2166.\n\n--- FILE CONTENT (truncated) ---\n 1, len(output_rows), config.embedding_dim, device=device\n )\n for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n steps = min(\n config.bptt_steps,\n int(group_lengths.max(initial=0)) - start,\n )\n items = np.full(\n (len(output_rows), steps),\n len(prepared.item_ids),\n dtype=np.uint16,\n )\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": False,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n \"target_weighting\": config.target_weighting,\n },\n \"config\": asdict(config),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"all_next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n \"training_history\": training_history,\n \"embedding_diagnostics\": embedding_diagnostics,\n \"torch_version\": torch.__version__,\n \"device\": str(device),\n },\n indent=2,\n )\n )\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:17.264Z",
"resultDisplay": "Read lines 1801-2165 of 2165 from ../task/current_model/train_current_model.py",
"description": "../.../train_current_model.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:37:17.265Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1801-2165 of 2165 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: 2166.\n\n--- FILE CONTENT (truncated) ---\n 1, len(output_rows), config.embedding_dim, device=device\n )\n for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n steps = min(\n config.bptt_steps,\n int(group_lengths.max(initial=0)) - start,\n )\n items = np.full(\n (len(output_rows), steps),\n len(prepared.item_ids),\n dtype=np.uint16,\n )\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": False,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n \"target_weighting\": config.target_weighting,\n },\n \"config\": asdict(config),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"all_next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n \"training_history\": training_history,\n \"embedding_diagnostics\": embedding_diagnostics,\n \"torch_version\": torch.__version__,\n \"device\": str(device),\n },\n indent=2,\n )\n )\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n",
"isError": false
},
"responseMessageId": "dfb27eab-1646-4704-9335-4a5447ad14fe"
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]
},
{
"id": 16,
"role": "agent",
"timestamp": "2026-09-29T05:37:19.309Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:19.309Z",
"callId": "read_file__call_84495",
"name": "read_file",
"input": {
"end_line": 150,
"start_line": 1,
"file_path": "/task/ranking_evaluation.py"
},
"recorded": {
"id": "read_file__call_84495",
"name": "read_file",
"args": {
"end_line": 150,
"start_line": 1,
"file_path": "/task/ranking_evaluation.py"
},
"result": [
{
"functionResponse": {
"id": "read_file__call_84495",
"name": "read_file",
"response": {
"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-150 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: 151.\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\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\""
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:19.311Z",
"resultDisplay": "Read lines 1-150 of 232 from ../task/ranking_evaluation.py",
"description": "../task/ranking_evaluation.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:37:19.312Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-150 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: 151.\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\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\"",
"isError": false
},
"responseMessageId": "bf750d38-54d5-48ef-8525-f33082be357d"
}
]
},
{
"id": 17,
"role": "agent",
"timestamp": "2026-09-29T05:37:21.249Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:21.249Z",
"callId": "read_file__call_24702",
"name": "read_file",
"input": {
"start_line": 151,
"end_line": 232,
"file_path": "/task/ranking_evaluation.py"
},
"recorded": {
"id": "read_file__call_24702",
"name": "read_file",
"args": {
"start_line": 151,
"end_line": 232,
"file_path": "/task/ranking_evaluation.py"
},
"result": [
{
"functionResponse": {
"id": "read_file__call_24702",
"name": "read_file",
"response": {
"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 151-232 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) ---\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": "2026-09-29T05:37:21.251Z",
"resultDisplay": "Read lines 151-232 of 232 from ../task/ranking_evaluation.py",
"description": "../task/ranking_evaluation.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:37:21.252Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 151-232 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) ---\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": "2b834425-a789-4a4f-a2be-f51e85816101"
}
]
},
{
"id": 18,
"role": "agent",
"timestamp": "2026-09-29T05:37:23.534Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:23.534Z",
"callId": "run_shell_command__call_3418985",
"name": "run_shell_command",
"input": {
"command": "lscpu | grep \"Model name\\|CPU(s):\" ; free -h",
"description": "Check available CPU cores and memory"
},
"recorded": {
"id": "run_shell_command__call_3418985",
"name": "run_shell_command",
"args": {
"command": "lscpu | grep \"Model name\\|CPU(s):\" ; free -h",
"description": "Check available CPU cores and memory"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3418985",
"name": "run_shell_command",
"response": {
"output": "\nOutput: CPU(s): 8\nModel name: AMD EPYC 7R13 Processor\nNUMA node0 CPU(s): 0-7\n/usr/bin/bash: line 4: free: command not found\nExit Code: 127\nProcess Group PGID: 1618\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:23.565Z",
"resultDisplay": "CPU(s): 8\nModel name: AMD EPYC 7R13 Processor\nNUMA node0 CPU(s): 0-7\n/usr/bin/bash: line 4: free: command not found",
"description": "lscpu | grep \"Model name\\|CPU(s):\" ; free -h",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:37:23.566Z",
"content": "\nOutput: CPU(s): 8\nModel name: AMD EPYC 7R13 Processor\nNUMA node0 CPU(s): 0-7\n/usr/bin/bash: line 4: free: command not found\nExit Code: 127\nProcess Group PGID: 1618\n",
"isError": false
},
"responseMessageId": "04eae389-2a1a-4781-ae84-5a80f8725574"
}
]
},
{
"id": 19,
"role": "agent",
"timestamp": "2026-09-29T05:37:25.289Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:25.289Z",
"callId": "run_shell_command__call_157631",
"name": "run_shell_command",
"input": {
"description": "Check total system memory",
"command": "cat /proc/meminfo | grep MemTotal"
},
"recorded": {
"id": "run_shell_command__call_157631",
"name": "run_shell_command",
"args": {
"description": "Check total system memory",
"command": "cat /proc/meminfo | grep MemTotal"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_157631",
"name": "run_shell_command",
"response": {
"output": "\nOutput: MemTotal: 15988968 kB\nProcess Group PGID: 1623\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:25.303Z",
"resultDisplay": "MemTotal: 15988968 kB",
"description": "cat /proc/meminfo | grep MemTotal",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:37:25.304Z",
"content": "\nOutput: MemTotal: 15988968 kB\nProcess Group PGID: 1623\n",
"isError": false
},
"responseMessageId": "29193bee-2098-441e-9216-da6fb7727a1f"
}
]
},
{
"id": 20,
"role": "agent",
"timestamp": "2026-09-29T05:37:28.423Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:28.423Z",
"callId": "run_shell_command__call_3322848",
"name": "run_shell_command",
"input": {
"description": "Check PyTorch version and default thread count",
"command": "python3 -c \"import torch; print('PyTorch:', torch.__version__, 'threads:', torch.get_num_threads())\""
},
"recorded": {
"id": "run_shell_command__call_3322848",
"name": "run_shell_command",
"args": {
"description": "Check PyTorch version and default thread count",
"command": "python3 -c \"import torch; print('PyTorch:', torch.__version__, 'threads:', torch.get_num_threads())\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3322848",
"name": "run_shell_command",
"response": {
"output": "\nOutput: PyTorch: 2.8.0+cpu threads: 4\nProcess Group PGID: 1627\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:31.333Z",
"resultDisplay": "PyTorch: 2.8.0+cpu threads: 4",
"description": "python3 -c \"import torch; print('PyTorch:', torch.__version__, 'threads:', torch.get_num_threads())\"",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:37:31.334Z",
"content": "\nOutput: PyTorch: 2.8.0+cpu threads: 4\nProcess Group PGID: 1627\n",
"isError": false
},
"responseMessageId": "1a0bc375-0d44-4d82-85b4-457b17f27d14"
}
]
},
{
"id": 21,
"role": "agent",
"timestamp": "2026-09-29T05:37:32.965Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:32.965Z",
"callId": "read_file__call_185357",
"name": "read_file",
"input": {
"end_line": 50,
"start_line": 1,
"file_path": "/task/requirements.txt"
},
"recorded": {
"id": "read_file__call_185357",
"name": "read_file",
"args": {
"end_line": 50,
"start_line": 1,
"file_path": "/task/requirements.txt"
},
"result": [
{
"functionResponse": {
"id": "read_file__call_185357",
"name": "read_file",
"response": {
"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-50 of 62 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: 51.\n\n--- FILE CONTENT (truncated) ---\n# 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"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:32.967Z",
"resultDisplay": "Read lines 1-50 of 62 from ../task/requirements.txt",
"description": "../task/requirements.txt",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:37:32.968Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-50 of 62 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: 51.\n\n--- FILE CONTENT (truncated) ---\n# 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",
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{
"id": 22,
"role": "agent",
"timestamp": "2026-09-29T05:37:34.820Z",
"blocks": [
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"kind": "tool",
"timestamp": "2026-09-29T05:37:34.820Z",
"callId": "run_shell_command__call_139682",
"name": "run_shell_command",
"input": {
"command": "ls -lh /task/data/",
"description": "List files and sizes in /task/data"
},
"recorded": {
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"args": {
"command": "ls -lh /task/data/",
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},
"result": [
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"functionResponse": {
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"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: 1637\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:34.833Z",
"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": "2026-09-29T05:37:34.835Z",
"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: 1637\n",
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]
},
{
"id": 23,
"role": "agent",
"timestamp": "2026-09-29T05:37:41.147Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:37:41.147Z",
"text": "Analyzing Data Sizes\n\nMy focus has shifted to understanding the scale of our datasets. I've noted that `train_listens.parquet` is a substantial 305MB, while `val_listens.parquet` is considerably smaller at 9.5MB. This size disparity is a key observation as I proceed."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:37:41.147Z",
"text": "Optimizing Caching Strategy\n\nI'm now exploring the `--prepared-cache` option in `train_current_model.py`. By specifying `/app/solution/validation_cache.npz`, I believe I can significantly speed up validation runs by avoiding repeated parsing of the large training parquet file. I'm assessing the preparation time to quantify this efficiency gain."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:41.147Z",
"callId": "run_shell_command__call_156415",
"name": "run_shell_command",
"input": {
"description": "Copy initial production model files to /app/solution/",
"command": "cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/"
},
"recorded": {
"id": "run_shell_command__call_156415",
"name": "run_shell_command",
"args": {
"description": "Copy initial production model files to /app/solution/",
"command": "cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_156415",
"name": "run_shell_command",
"response": {
"output": "\nOutput: (empty)\nProcess Group PGID: 1640\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:37:41.162Z",
"resultDisplay": "",
"description": "cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:37:41.164Z",
"content": "\nOutput: (empty)\nProcess Group PGID: 1640\n",
"isError": false
},
"responseMessageId": "7357b7d8-3e8d-4bb5-95b1-08231dbfee80"
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]
},
{
"id": 24,
"role": "agent",
"timestamp": "2026-09-29T05:37:45.290Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:37:45.290Z",
"text": "Running Baseline Validation\n\nI'm currently executing the baseline validation script to replicate the backtest. My focus is on capturing the duration and performance score from this initial run."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:37:45.290Z",
"text": "Confirming Cache Functionality\n\nI'm verifying how the `--prepared-cache` flag interacts with training. My investigation shows it exclusively handles the saving and loading of `SequenceData`, and doesn't influence the training process itself. I've also confirmed its interaction within `load_or_prepare`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:37:45.290Z",
"callId": "run_shell_command__call_183195",
"name": "run_shell_command",
"input": {
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/production_validation.parquet \\\n --prepared-cache /app/solution/validation_cache.npz",
"description": "Run baseline validation training and cache prepared sequence data"
},
"recorded": {
"id": "run_shell_command__call_183195",
"name": "run_shell_command",
"args": {
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/production_validation.parquet \\\n --prepared-cache /app/solution/validation_cache.npz",
"description": "Run baseline validation training and cache prepared sequence data"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_183195",
"name": "run_shell_command",
"response": {
"output": "\nOutput: loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.7s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=14.8s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.1s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=24.9s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.5s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.0s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.2s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=44.8s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_cache.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=196.9s\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.0 validation_seconds=15.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/production_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 356.16,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380482450718,\n \"positive_logit\": 0.7427799576364758,\n \"negative_logit\": -0.6222247063961966,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 209.99864311700003,\n \"validation_loss\": 0.4966362188872719,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 15.44746553799996\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.603016972541809,\n \"query_norm_std\": 0.7697497010231018,\n \"item_norm_mean\": 1.7570279836654663,\n \"item_norm_std\": 0.8279485106468201\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\nProcess Group PGID: 1643\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:43:45.778Z",
"resultDisplay": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.7s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=14.8s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.1s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=24.9s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.5s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.0s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.2s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=44.8s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_cache.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=196.9s\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.0 validation_seconds=15.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/production_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 356.16,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380482450718,\n \"positive_logit\": 0.7427799576364758,\n \"negative_logit\": -0.6222247063961966,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 209.99864311700003,\n \"validation_loss\": 0.4966362188872719,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 15.44746553799996\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.603016972541809,\n \"query_norm_std\": 0.7697497010231018,\n \"item_norm_mean\": 1.7570279836654663,\n \"item_norm_std\": 0.8279485106468201\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}",
"description": "Run baseline validation training and cache prepared sequence data",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:43:45.780Z",
"content": "\nOutput: loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.7s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=14.8s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.1s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=24.9s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.5s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.0s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.2s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=44.8s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_cache.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=196.9s\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.0 validation_seconds=15.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/production_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 356.16,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380482450718,\n \"positive_logit\": 0.7427799576364758,\n \"negative_logit\": -0.6222247063961966,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 209.99864311700003,\n \"validation_loss\": 0.4966362188872719,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 15.44746553799996\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.603016972541809,\n \"query_norm_std\": 0.7697497010231018,\n \"item_norm_mean\": 1.7570279836654663,\n \"item_norm_std\": 0.8279485106468201\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\nProcess Group PGID: 1643\n",
"isError": false
},
"responseMessageId": "9d2ce5c1-93e8-400c-bf09-8e02c9245dbb"
}
]
},
{
"id": 25,
"role": "agent",
"timestamp": "2026-09-29T05:43:53.810Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:43:53.810Z",
"callId": "run_shell_command__call_158768",
"name": "run_shell_command",
"input": {
"description": "Score the baseline validation ranking with evaluate_public.py",
"command": "python /task/evaluate_public.py /app/solution/production_validation.parquet"
},
"recorded": {
"id": "run_shell_command__call_158768",
"name": "run_shell_command",
"args": {
"description": "Score the baseline validation ranking with evaluate_public.py",
"command": "python /task/evaluate_public.py /app/solution/production_validation.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_158768",
"name": "run_shell_command",
"response": {
"output": "\nOutput: {\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1705\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:43:57.179Z",
"resultDisplay": "{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}",
"description": "python /task/evaluate_public.py /app/solution/production_validation.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:43:57.181Z",
"content": "\nOutput: {\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1705\n",
"isError": false
},
"responseMessageId": "7108f314-6211-458f-b40b-8e210fe0d500"
}
]
},
{
"id": 26,
"role": "agent",
"timestamp": "2026-09-29T05:44:03.464Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:03.464Z",
"text": "Saving Baseline Ranking\n\nI'm ensuring our initial performance metric, quality_ndcg@10 of 0.02128 across 4263 users, is securely saved. I'm also confirming the submission format aligns with the deliverable's `/app/solution/submission.parquet` requirement."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:03.464Z",
"text": "Testing Submission Generation\n\nI'm considering the implications of generating a baseline submission now. While the prompt advises keeping the best valid submission, I'm weighing this against the potential benefit of understanding submission generation mechanics and timing before investing precious time."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:03.464Z",
"text": "Investigating Negative Sampling\n\nI'm now digging into how the training objective handles negative examples. My immediate concern is potential CPU thread contention if background training processes run concurrently with my research. I'll be carefully examining the prompt's emphasis on negative sampling limitations and improvements within the fixed CPU budget."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:44:03.464Z",
"callId": "update_topic__call_184388",
"name": "update_topic",
"input": {
"strategic_intent": "Diagnose current negative-sampling strategy and training objective to identify quality bottlenecks.",
"summary": "I have reproduced the baseline validation backtest, achieving quality_ndcg@10 of 0.02128 across 4,263 scored users, and cached the prepared sequence dataset. I am now transitioning to the research and diagnosis phase, focusing specifically on how the training objective selects and uses negative examples, the loss function, the candidate pool, false negative filtering, and in-batch vs uniform sampling.",
"title": "Diagnosing Negative Sampling Strategy"
},
"recorded": {
"id": "update_topic__call_184388",
"name": "update_topic",
"args": {
"strategic_intent": "Diagnose current negative-sampling strategy and training objective to identify quality bottlenecks.",
"summary": "I have reproduced the baseline validation backtest, achieving quality_ndcg@10 of 0.02128 across 4,263 scored users, and cached the prepared sequence dataset. I am now transitioning to the research and diagnosis phase, focusing specifically on how the training objective selects and uses negative examples, the loss function, the candidate pool, false negative filtering, and in-batch vs uniform sampling.",
"title": "Diagnosing Negative Sampling Strategy"
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_184388",
"name": "update_topic",
"response": {
"output": "Current topic: \"Diagnosing Negative Sampling Strategy\"\nTopic summary: I have reproduced the baseline validation backtest, achieving quality_ndcg@10 of 0.02128 across 4,263 scored users, and cached the prepared sequence dataset. I am now transitioning to the research and diagnosis phase, focusing specifically on how the training objective selects and uses negative examples, the loss function, the candidate pool, false negative filtering, and in-batch vs uniform sampling.\n\nStrategic Intent: Diagnose current negative-sampling strategy and training objective to identify quality bottlenecks."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:44:03.466Z",
"resultDisplay": "## 📂 Topic: **Diagnosing Negative Sampling Strategy**\n\n**Summary:**\nI have reproduced the baseline validation backtest, achieving quality_ndcg@10 of 0.02128 across 4,263 scored users, and cached the prepared sequence dataset. I am now transitioning to the research and diagnosis phase, focusing specifically on how the training objective selects and uses negative examples, the loss function, the candidate pool, false negative filtering, and in-batch vs uniform sampling.\n\n> [!STRATEGY]\n> **Intent:** Diagnose current negative-sampling strategy and training objective to identify quality bottlenecks.",
"description": "Update topic to: \"Diagnosing Negative Sampling Strategy\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:44:03.467Z",
"content": "Current topic: \"Diagnosing Negative Sampling Strategy\"\nTopic summary: I have reproduced the baseline validation backtest, achieving quality_ndcg@10 of 0.02128 across 4,263 scored users, and cached the prepared sequence dataset. I am now transitioning to the research and diagnosis phase, focusing specifically on how the training objective selects and uses negative examples, the loss function, the candidate pool, false negative filtering, and in-batch vs uniform sampling.\n\nStrategic Intent: Diagnose current negative-sampling strategy and training objective to identify quality bottlenecks.",
"isError": false
},
"responseMessageId": "a6079471-33cd-478a-9462-49386760f06b"
}
]
},
{
"id": 27,
"role": "agent",
"timestamp": "2026-09-29T05:44:45.534Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Exploring Negative Sampling Strategies\n\nI'm currently dissecting how the model handles negative sampling, specifically examining the configurations for uniform and in-batch negatives, and considering the impact of the `inbatch_correction_power` parameter on the training objective."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Refining Negative Sample Filtering\n\nI'm now focusing on the nuances of `draw_full_history_filtered_negatives`, particularly how the `negative_filter_scope` and `discovery_horizon_days` settings influence the selection and weighting of negative samples, aiming to optimize relevance and avoid known positives effectively."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Generating In-Batch Negatives\n\nI'm working on the logic for in-batch negatives. I'm ensuring that if there aren't enough unique targets within a batch to fulfill the `inbatch_negatives` count, the remaining slots are filled with uniformly sampled items."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Adjusting Negative Sample Generation\n\nI'm refining the generation of in-batch negatives, ensuring that fallback mechanisms fill insufficient unique targets. Additionally, I'm implementing a robust loop to filter out invalid negative samples, iterating until a satisfactory set is achieved, and then applying item-specific corrections."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Evaluating In-Batch Negative Logic\n\nI'm closely examining the in-batch negative sampling mechanism. Specifically, I'm trying to understand the definition and scope of a \"block\" in this context, as it directly impacts how `targets` are used for generating these negatives."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Investigating Dense Block and User Grouping\n\nI'm currently dissecting the `build_dense_block` function and its interaction with user grouping logic. I'm trying to clarify how `users` are consolidated into `groups` and the implications this has on the scale of targets generated within a single block, which appears to be quite substantial."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Clarifying In-Batch Sampling\n\nI'm re-evaluating the in-batch negative sampling strategy. It appears the current implementation samples uniformly from unique batch targets, rather than based on item popularity or batch frequency, which is the standard for two-tower models. This distinction is critical."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Applying In-Batch Popularity Corrections\n\nI'm now focusing on the `build_item_correction` function. I'm analyzing how it calculates item propensities from training targets, applies a power correction based on popularity, and normalizes these corrections to mitigate bias in the negative sampling objective."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Correcting Loss Calculations\n\nI'm realizing a significant flaw in how the `corrected_negative_loss` is calculated. The normalization of `weights` by their sum within the in-batch negative component appears to negate the intended effect of the popularity corrections, which needs immediate revision."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Revising Loss Normalization\n\nI've identified that the normalization of weights within the `corrected_negative_loss` was counterproductive. My current focus is on rectifying this to properly leverage the popularity corrections and ensure the in-batch negative component accurately reflects sampling biases."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Revising Unique Target Sampling\n\nI'm realizing that `torch.unique(targets)` treats all unique items equally, regardless of their actual frequency. This means the current sampling of in-batch negatives from unique targets is uniform, not reflecting actual popularity, which is a critical departure from expected behavior."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Revising In-Batch Sampling Logic\n\nI'm realizing a fundamental issue: in-batch negatives are sampled uniformly from unique targets, ignoring popularity. This, combined with how item corrections were applied, disproportionately penalizes rare items, potentially leading to the model favoring only popular items, which contradicts the discovery objective."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Revising In-Batch Sampling Logic\n\nI'm realizing a fundamental issue: in-batch negatives are sampled uniformly from unique targets, ignoring popularity. This, combined with how item corrections were applied, disproportionately penalizes rare items, potentially leading to the model favoring only popular items, which contradicts the discovery objective."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Revising Uniform Sampling Logic\n\nI'm focusing on how `torch.unique` treats all items equally. This means my in-batch negative sampling, by drawing from unique targets, is currently uniform and doesn't reflect actual item popularity. This needs to be re-evaluated."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Correcting In-Batch Sampling Bias\n\nI've realized that sampling in-batch negatives from unique targets, as my current `torch.unique` approach does, treats all items equally. This is incorrect; in-batch negatives should be sampled proportionally to their frequency within the batch to effectively penalize popular items and align with the standard two-tower model approach."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Revising Logit Calculations\n\nI'm examining the theoretical underpinnings of using `logits - log(p_j)` and its connection to PMI. I'm also scrutinizing the specific implementation of the Binary Cross Entropy loss, particularly how `softplus` is applied to both positive and negative logit calculations, and how the `correction` term is integrated into the negative loss."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Revising Correction Application\n\nI'm analyzing how `correction` is applied to the in-batch negative loss calculation. It appears `weights = correction[:, selected]` is incorrectly using item propensities directly as weights, rather than their normalized, inverse probabilities, which is crucial for accurate loss adjustment."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Applying Inverse Probability Corrections\n\nI'm now focused on calculating the `correction` term by deriving item propensities from target counts. I'm applying a power transformation based on popularity and then normalizing these values to generate inverse probabilities that will be used to adjust the in-batch negative loss, mitigating bias."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Correcting Popularity Bias\n\nI'm identifying that my `raw` calculation for in-batch corrections is inversely related to item probability. This means rare items get large `raw` values, while popular items get small ones. This seems to be counteracting the goal of penalizing popular items."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Analyzing Negative Sampling Sources\n\nI'm currently investigating the source of my uniform negatives, confirming they're sampled from the entire catalog of 30,000 items. I also want to inspect the composition of `unique_targets` from a batch to understand the distribution of items present."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:45.534Z",
"text": "Analyzing Discovery Retrieval\n\nI'm now examining the sources of uniform negatives and the composition of unique targets from batches to understand item distributions. I'm also starting to look into the `candidate_catalog.parquet` for popularity distribution and digging into the definition of \"discovery\" and \"organic\" behavior to clarify retrieval and validation criteria."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:44:45.534Z",
"callId": "grep_search__call_36250",
"name": "grep_search",
"input": {
"dir_path": "/task/current_model",
"pattern": "dislike"
},
"recorded": {
"id": "grep_search__call_36250",
"name": "grep_search",
"args": {
"dir_path": "/task/current_model",
"pattern": "dislike"
},
"result": [
{
"functionResponse": {
"id": "grep_search__call_36250",
"name": "grep_search",
"response": {
"output": "Found 40 matches for pattern \"dislike\" in path \"/task/current_model\":\n---\nFile: train_current_model.py\nL110: disliked_states: np.ndarray\nL114: inference_disliked_items: np.ndarray\nL176: active_dislikes: dict[int, int],\nL179: disliked_states: list[np.ndarray],\nL183: disliked_states.append(\nL184: base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\nL186: state_counts.append((len(active_likes), len(active_dislikes)))\nL252: disliked_states: list[np.ndarray],\nL279: active_dislikes: dict[int, int] = {}\nL293: else active_dislikes\nL304: active_dislikes,\nL307: disliked_states,\nL320: dislike_count: int,\nL337: result[5] = np.log1p(dislike_count) / 4.0\nL369: inference_likes, inference_dislikes, like_counts, dislike_counts = (\nL383: disliked_states = [\nL384: np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\nL405: disliked_states,\nL427: int(dislike_counts[inference_row]),\nL440: np.log1p(dislike_counts[missing]) / 4.0\nL455: disliked_states=np.stack(disliked_states),\nL459: inference_disliked_items=inference_dislikes,\nL784: disliked_items = torch.from_numpy(\nL785: prepared.disliked_states[unique_states].astype(np.int64)\nL788: disliked_vectors = model._mean_context_channel(disliked_items)\nL793: disliked_vectors[inverse_rows],\nL802: disliked_vectors: torch.Tensor,\nL807: [sequence_vectors, liked_vectors, disliked_vectors, user_features],\nL1147: liked_vectors, disliked_vectors, _ = pool_explicit_states(\nL1159: disliked_vectors,\nL1844: disliked_items = torch.from_numpy(\nL1845: prepared.inference_disliked_items[output_rows].astype(np.int64)\nL1851: model._mean_context_channel(disliked_items),\n---\nFile: two_tower_model.py\nL22: DISLIKE_ITEMS = 24\nL184: (\"dislikes\", 1, 1, 0),\nL185: (\"undislikes\", 1, 0, 1),\nL265: disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\nL267: dislike_counts = np.zeros(len(uids), dtype=np.int32)\nL270: (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\nL281: return liked, disliked, like_counts, dislike_counts\n---"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:44:45.554Z",
"resultDisplay": {
"summary": "Found 40 matches",
"matches": [
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 110,
"line": " disliked_states: np.ndarray"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 114,
"line": " inference_disliked_items: np.ndarray"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 176,
"line": " active_dislikes: dict[int, int],"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 179,
"line": " disliked_states: list[np.ndarray],"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 183,
"line": " disliked_states.append("
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 184,
"line": " base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 186,
"line": " state_counts.append((len(active_likes), len(active_dislikes)))"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 252,
"line": " disliked_states: list[np.ndarray],"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 279,
"line": " active_dislikes: dict[int, int] = {}"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 293,
"line": " else active_dislikes"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 304,
"line": " active_dislikes,"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 307,
"line": " disliked_states,"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 320,
"line": " dislike_count: int,"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 337,
"line": " result[5] = np.log1p(dislike_count) / 4.0"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 369,
"line": " inference_likes, inference_dislikes, like_counts, dislike_counts = ("
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 383,
"line": " disliked_states = ["
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 384,
"line": " np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 405,
"line": " disliked_states,"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 427,
"line": " int(dislike_counts[inference_row]),"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 440,
"line": " np.log1p(dislike_counts[missing]) / 4.0"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 455,
"line": " disliked_states=np.stack(disliked_states),"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 459,
"line": " inference_disliked_items=inference_dislikes,"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 784,
"line": " disliked_items = torch.from_numpy("
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 785,
"line": " prepared.disliked_states[unique_states].astype(np.int64)"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 788,
"line": " disliked_vectors = model._mean_context_channel(disliked_items)"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 793,
"line": " disliked_vectors[inverse_rows],"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 802,
"line": " disliked_vectors: torch.Tensor,"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 807,
"line": " [sequence_vectors, liked_vectors, disliked_vectors, user_features],"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 1147,
"line": " liked_vectors, disliked_vectors, _ = pool_explicit_states("
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 1159,
"line": " disliked_vectors,"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 1844,
"line": " disliked_items = torch.from_numpy("
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 1845,
"line": " prepared.inference_disliked_items[output_rows].astype(np.int64)"
},
{
"filePath": "train_current_model.py",
"absolutePath": "/task/current_model/train_current_model.py",
"lineNumber": 1851,
"line": " model._mean_context_channel(disliked_items),"
},
{
"filePath": "two_tower_model.py",
"absolutePath": "/task/current_model/two_tower_model.py",
"lineNumber": 22,
"line": "DISLIKE_ITEMS = 24"
},
{
"filePath": "two_tower_model.py",
"absolutePath": "/task/current_model/two_tower_model.py",
"lineNumber": 184,
"line": " (\"dislikes\", 1, 1, 0),"
},
{
"filePath": "two_tower_model.py",
"absolutePath": "/task/current_model/two_tower_model.py",
"lineNumber": 185,
"line": " (\"undislikes\", 1, 0, 1),"
},
{
"filePath": "two_tower_model.py",
"absolutePath": "/task/current_model/two_tower_model.py",
"lineNumber": 265,
"line": " disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)"
},
{
"filePath": "two_tower_model.py",
"absolutePath": "/task/current_model/two_tower_model.py",
"lineNumber": 267,
"line": " dislike_counts = np.zeros(len(uids), dtype=np.int32)"
},
{
"filePath": "two_tower_model.py",
"absolutePath": "/task/current_model/two_tower_model.py",
"lineNumber": 270,
"line": " (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),"
},
{
"filePath": "two_tower_model.py",
"absolutePath": "/task/current_model/two_tower_model.py",
"lineNumber": 281,
"line": " return liked, disliked, like_counts, dislike_counts"
}
]
},
"description": "'dislike' within ../task/current_model",
"displayName": "SearchText",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:44:45.555Z",
"content": "Found 40 matches for pattern \"dislike\" in path \"/task/current_model\":\n---\nFile: train_current_model.py\nL110: disliked_states: np.ndarray\nL114: inference_disliked_items: np.ndarray\nL176: active_dislikes: dict[int, int],\nL179: disliked_states: list[np.ndarray],\nL183: disliked_states.append(\nL184: base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\nL186: state_counts.append((len(active_likes), len(active_dislikes)))\nL252: disliked_states: list[np.ndarray],\nL279: active_dislikes: dict[int, int] = {}\nL293: else active_dislikes\nL304: active_dislikes,\nL307: disliked_states,\nL320: dislike_count: int,\nL337: result[5] = np.log1p(dislike_count) / 4.0\nL369: inference_likes, inference_dislikes, like_counts, dislike_counts = (\nL383: disliked_states = [\nL384: np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\nL405: disliked_states,\nL427: int(dislike_counts[inference_row]),\nL440: np.log1p(dislike_counts[missing]) / 4.0\nL455: disliked_states=np.stack(disliked_states),\nL459: inference_disliked_items=inference_dislikes,\nL784: disliked_items = torch.from_numpy(\nL785: prepared.disliked_states[unique_states].astype(np.int64)\nL788: disliked_vectors = model._mean_context_channel(disliked_items)\nL793: disliked_vectors[inverse_rows],\nL802: disliked_vectors: torch.Tensor,\nL807: [sequence_vectors, liked_vectors, disliked_vectors, user_features],\nL1147: liked_vectors, disliked_vectors, _ = pool_explicit_states(\nL1159: disliked_vectors,\nL1844: disliked_items = torch.from_numpy(\nL1845: prepared.inference_disliked_items[output_rows].astype(np.int64)\nL1851: model._mean_context_channel(disliked_items),\n---\nFile: two_tower_model.py\nL22: DISLIKE_ITEMS = 24\nL184: (\"dislikes\", 1, 1, 0),\nL185: (\"undislikes\", 1, 0, 1),\nL265: disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\nL267: dislike_counts = np.zeros(len(uids), dtype=np.int32)\nL270: (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\nL281: return liked, disliked, like_counts, dislike_counts\n---",
"isError": false
},
"responseMessageId": "568279d3-d8ae-4d95-9486-2134e2cc8793"
}
]
},
{
"id": 28,
"role": "agent",
"timestamp": "2026-09-29T05:44:57.879Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:57.879Z",
"text": "Clarifying Negatives\n\nI'm processing the distinction between user context features and negative training examples. My current focus is on understanding the precise role and definition of \"negatives\" during the training phase, particularly in light of `invalid_full_history_negatives`."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:57.879Z",
"text": "Refining Exclusion Logic\n\nI'm digging into the `invalid_full_history_negatives` function. It seems to be excluding known positives from the training prefix and the target item itself, which is a crucial step in defining valid negative samples for training."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:57.879Z",
"text": "Analyzing Target Weighting\n\nI'm now examining `target_weighting` and its impact. My attention is on how `\"organic-strong\"` influences training and what other options exist for this parameter within the configuration, specifically within `train_current_model.py`."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:57.879Z",
"text": "Splitting Data Sequences\n\nI'm currently analyzing how the data sequences are being split. My focus is on the logic that determines the boundary between training and validation sets for each user, particularly the `last_training_target` and `first_validation_target` indices. The handling of sequences shorter than two items and the `split_timestamp` calculation are also key points of investigation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:57.879Z",
"text": "Revising Weighting Strategy\n\nI'm re-evaluating the target weighting. It appears the baseline used \"organic-strong\" rather than the expected \"discovery-graded,\" impacting how I interpret the weight adjustments and the `organic_like_target_weight` logic."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:44:57.879Z",
"text": "Investigating Negative Sampling\n\nI'm focusing on the core of the user's request: how negative examples are selected and used. My analysis is now centered on diagnosing limitations within the current negative-sampling strategy, preparing to identify areas for improvement under the CPU budget constraints."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:44:57.879Z",
"callId": "run_shell_command__call_149716",
"name": "run_shell_command",
"input": {
"description": "Inspect candidate catalog structure and popularity distribution",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nimport torch\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(\"Catalog shape:\", catalog.shape)\nprint(\"Catalog columns:\", catalog.columns.tolist())\nprint(catalog.head(5))\nprint(\"Popularity stats:\")\nprint(catalog[[\"positive_listens\", \"organic_positive_listens\", \"popularity_rank\"]].describe())\n'"
},
"recorded": {
"id": "run_shell_command__call_149716",
"name": "run_shell_command",
"args": {
"description": "Inspect candidate catalog structure and popularity distribution",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nimport torch\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(\"Catalog shape:\", catalog.shape)\nprint(\"Catalog columns:\", catalog.columns.tolist())\nprint(catalog.head(5))\nprint(\"Popularity stats:\")\nprint(catalog[[\"positive_listens\", \"organic_positive_listens\", \"popularity_rank\"]].describe())\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_149716",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog shape: (30000, 4)\nCatalog columns: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\nPopularity stats:\n positive_listens organic_positive_listens popularity_rank\ncount 30000.000000 30000.000000 30000.000000\nmean 668.544300 313.926433 15000.500000\nstd 1053.389553 636.039902 8660.398374\nmin 163.000000 0.000000 1.000000\n25% 228.000000 93.000000 7500.750000\n50% 354.000000 162.000000 15000.500000\n75% 669.250000 309.000000 22500.250000\nmax 26615.000000 20620.000000 30000.000000\nProcess Group PGID: 1733\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:45:01.856Z",
"resultDisplay": "Catalog shape: (30000, 4)\nCatalog columns: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\nPopularity stats:\n positive_listens organic_positive_listens popularity_rank\ncount 30000.000000 30000.000000 30000.000000\nmean 668.544300 313.926433 15000.500000\nstd 1053.389553 636.039902 8660.398374\nmin 163.000000 0.000000 1.000000\n25% 228.000000 93.000000 7500.750000\n50% 354.000000 162.000000 15000.500000\n75% 669.250000 309.000000 22500.250000\nmax 26615.000000 20620.000000 30000.000000",
"description": "Inspect candidate catalog structure and popularity distribution",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:45:01.857Z",
"content": "\nOutput: Catalog shape: (30000, 4)\nCatalog columns: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\nPopularity stats:\n positive_listens organic_positive_listens popularity_rank\ncount 30000.000000 30000.000000 30000.000000\nmean 668.544300 313.926433 15000.500000\nstd 1053.389553 636.039902 8660.398374\nmin 163.000000 0.000000 1.000000\n25% 228.000000 93.000000 7500.750000\n50% 354.000000 162.000000 15000.500000\n75% 669.250000 309.000000 22500.250000\nmax 26615.000000 20620.000000 30000.000000\nProcess Group PGID: 1733\n",
"isError": false
},
"responseMessageId": "6a478d47-5e96-45c5-bb30-4452e5d5c2df"
}
]
},
{
"id": 29,
"role": "agent",
"timestamp": "2026-09-29T05:45:05.047Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:45:05.047Z",
"text": "Inspecting Baseline Predictions\n\nI'm currently focused on validating the baseline's performance. My next step involves a thorough inspection of the predictions made by this baseline model, specifically by examining the `/app/solution/production_validation.parquet` file."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:45:05.047Z",
"callId": "run_shell_command__call_4833814",
"name": "run_shell_command",
"input": {
"description": "Analyze the baseline model's predictions and popularity bias",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\npred = pd.read_parquet(\"/app/solution/production_validation.parquet\")\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\nprint(\"Predictions shape:\", pred.shape)\nprint(\"Unique users:\", pred[\"uid\"].nunique())\nprint(\"Unique items predicted:\", pred[\"item_id\"].nunique())\n\npred_with_pop = pred.join(catalog, on=\"item_id\")\nprint(\"Top 10 predicted items and their frequency in top 10:\")\ntop10_preds = pred[pred[\"rank\"] <= 10]\nprint(top10_preds[\"item_id\"].value_counts().head(20))\nprint(\"Popularity rank of top 10 predicted items:\")\nprint(top10_preds.join(catalog, on=\"item_id\")[\"popularity_rank\"].describe())\nprint(\"Popularity rank of all predicted items:\")\nprint(pred_with_pop[\"popularity_rank\"].describe())\n'"
},
"recorded": {
"id": "run_shell_command__call_4833814",
"name": "run_shell_command",
"args": {
"description": "Analyze the baseline model's predictions and popularity bias",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\npred = pd.read_parquet(\"/app/solution/production_validation.parquet\")\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\nprint(\"Predictions shape:\", pred.shape)\nprint(\"Unique users:\", pred[\"uid\"].nunique())\nprint(\"Unique items predicted:\", pred[\"item_id\"].nunique())\n\npred_with_pop = pred.join(catalog, on=\"item_id\")\nprint(\"Top 10 predicted items and their frequency in top 10:\")\ntop10_preds = pred[pred[\"rank\"] <= 10]\nprint(top10_preds[\"item_id\"].value_counts().head(20))\nprint(\"Popularity rank of top 10 predicted items:\")\nprint(top10_preds.join(catalog, on=\"item_id\")[\"popularity_rank\"].describe())\nprint(\"Popularity rank of all predicted items:\")\nprint(pred_with_pop[\"popularity_rank\"].describe())\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4833814",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Predictions shape: (845600, 3)\nUnique users: 8456\nUnique items predicted: 2575\nTop 10 predicted items and their frequency in top 10:\nitem_id\n6006390 2917\n8213481 2800\n6896222 2712\n8743390 2653\n5436843 2587\n2651314 2580\n8722394 2542\n3542184 2427\n5862961 2292\n906358 2066\n5927996 1872\n9378983 1537\n2185191 1522\n6901374 1463\n3033749 1333\n5252571 1289\n2408923 1215\n4423556 1148\n8351511 1121\n3375935 1107\nName: count, dtype: int64\nPopularity rank of top 10 predicted items:\ncount 84560.000000\nmean 2597.053903\nstd 5715.185504\nmin 1.000000\n25% 14.000000\n50% 111.000000\n75% 2124.000000\nmax 29868.000000\nName: popularity_rank, dtype: float64\nPopularity rank of all predicted items:\ncount 845600.000000\nmean 2991.803713\nstd 5853.839336\nmin 1.000000\n25% 102.000000\n50% 413.000000\n75% 2627.000000\nmax 29992.000000\nName: popularity_rank, dtype: float64\nProcess Group PGID: 1750\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:45:06.706Z",
"resultDisplay": "Predictions shape: (845600, 3)\nUnique users: 8456\nUnique items predicted: 2575\nTop 10 predicted items and their frequency in top 10:\nitem_id\n6006390 2917\n8213481 2800\n6896222 2712\n8743390 2653\n5436843 2587\n2651314 2580\n8722394 2542\n3542184 2427\n5862961 2292\n906358 2066\n5927996 1872\n9378983 1537\n2185191 1522\n6901374 1463\n3033749 1333\n5252571 1289\n2408923 1215\n4423556 1148\n8351511 1121\n3375935 1107\nName: count, dtype: int64\nPopularity rank of top 10 predicted items:\ncount 84560.000000\nmean 2597.053903\nstd 5715.185504\nmin 1.000000\n25% 14.000000\n50% 111.000000\n75% 2124.000000\nmax 29868.000000\nName: popularity_rank, dtype: float64\nPopularity rank of all predicted items:\ncount 845600.000000\nmean 2991.803713\nstd 5853.839336\nmin 1.000000\n25% 102.000000\n50% 413.000000\n75% 2627.000000\nmax 29992.000000\nName: popularity_rank, dtype: float64",
"description": "Analyze the baseline model's predictions and popularity bias",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:45:06.708Z",
"content": "\nOutput: Predictions shape: (845600, 3)\nUnique users: 8456\nUnique items predicted: 2575\nTop 10 predicted items and their frequency in top 10:\nitem_id\n6006390 2917\n8213481 2800\n6896222 2712\n8743390 2653\n5436843 2587\n2651314 2580\n8722394 2542\n3542184 2427\n5862961 2292\n906358 2066\n5927996 1872\n9378983 1537\n2185191 1522\n6901374 1463\n3033749 1333\n5252571 1289\n2408923 1215\n4423556 1148\n8351511 1121\n3375935 1107\nName: count, dtype: int64\nPopularity rank of top 10 predicted items:\ncount 84560.000000\nmean 2597.053903\nstd 5715.185504\nmin 1.000000\n25% 14.000000\n50% 111.000000\n75% 2124.000000\nmax 29868.000000\nName: popularity_rank, dtype: float64\nPopularity rank of all predicted items:\ncount 845600.000000\nmean 2991.803713\nstd 5853.839336\nmin 1.000000\n25% 102.000000\n50% 413.000000\n75% 2627.000000\nmax 29992.000000\nName: popularity_rank, dtype: float64\nProcess Group PGID: 1750\n",
"isError": false
},
"responseMessageId": "0eae0e64-e6ee-433c-af58-fd0e4a6e192e"
}
]
},
{
"id": 30,
"role": "agent",
"timestamp": "2026-09-29T05:45:50.608Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:45:50.608Z",
"text": "Observing Predicted Item Performance\n\nI'm focusing on the performance of top predicted items. Notably, `item_id 6006390` is appearing in the top 10 for a significant portion of users, around 35%. Its median popularity rank among these top predictions is also quite good, sitting at 111 out of 30,000."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:45:50.608Z",
"callId": "run_shell_command__call_27809",
"name": "run_shell_command",
"input": {
"description": "Analyze ground truth validation relevance distribution and popularity",
"command": "python3 -c '\nimport pandas as pd\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\n\ntruth_items = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n truth_items.append({\"uid\": uid, \"item_id\": item, \"grade\": grade})\ndf_truth = pd.DataFrame(truth_items).join(catalog, on=\"item_id\")\nprint(\"Total relevant events in validation:\", len(df_truth))\nprint(\"Unique users with relevant events:\", df_truth[\"uid\"].nunique())\nprint(\"Unique items in relevant events:\", df_truth[\"item_id\"].nunique())\nprint(\"Grade distribution:\", df_truth[\"grade\"].value_counts().to_dict())\nprint(\"Popularity rank of relevant validation items:\")\nprint(df_truth[\"popularity_rank\"].describe())\nprint(\"Positive listens of relevant validation items:\")\nprint(df_truth[\"positive_listens\"].describe())\n'"
},
"recorded": {
"id": "run_shell_command__call_27809",
"name": "run_shell_command",
"args": {
"description": "Analyze ground truth validation relevance distribution and popularity",
"command": "python3 -c '\nimport pandas as pd\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\n\ntruth_items = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n truth_items.append({\"uid\": uid, \"item_id\": item, \"grade\": grade})\ndf_truth = pd.DataFrame(truth_items).join(catalog, on=\"item_id\")\nprint(\"Total relevant events in validation:\", len(df_truth))\nprint(\"Unique users with relevant events:\", df_truth[\"uid\"].nunique())\nprint(\"Unique items in relevant events:\", df_truth[\"item_id\"].nunique())\nprint(\"Grade distribution:\", df_truth[\"grade\"].value_counts().to_dict())\nprint(\"Popularity rank of relevant validation items:\")\nprint(df_truth[\"popularity_rank\"].describe())\nprint(\"Positive listens of relevant validation items:\")\nprint(df_truth[\"positive_listens\"].describe())\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_27809",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 3, in \nModuleNotFoundError: No module named 'evaluate_public'\nExit Code: 1\nProcess Group PGID: 1776\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:45:52.090Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 3, in \nModuleNotFoundError: No module named 'evaluate_public'",
"description": "Analyze ground truth validation relevance distribution and popularity",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:45:52.092Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 3, in \nModuleNotFoundError: No module named 'evaluate_public'\nExit Code: 1\nProcess Group PGID: 1776\n",
"isError": false
},
"responseMessageId": "e6ee6035-ca6a-4790-a612-55546a745377"
}
]
},
{
"id": 31,
"role": "agent",
"timestamp": "2026-09-29T05:46:06.138Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T05:46:06.138Z",
"callId": "run_shell_command__call_121500",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport sys\nsys.path.insert(0, \"/task\")\nimport pandas as pd\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\n\ntruth_items = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n truth_items.append({\"uid\": uid, \"item_id\": item, \"grade\": grade})\ndf_truth = pd.DataFrame(truth_items).join(catalog, on=\"item_id\")\nprint(\"Total relevant events in validation:\", len(df_truth))\nprint(\"Unique users with relevant events:\", df_truth[\"uid\"].nunique())\nprint(\"Unique items in relevant events:\", df_truth[\"item_id\"].nunique())\nprint(\"Grade distribution:\", df_truth[\"grade\"].value_counts().to_dict())\nprint(\"Popularity rank of relevant validation items:\")\nprint(df_truth[\"popularity_rank\"].describe())\nprint(\"Positive listens of relevant validation items:\")\nprint(df_truth[\"positive_listens\"].describe())\n'",
"description": "Analyze ground truth validation relevance distribution and popularity"
},
"recorded": {
"id": "run_shell_command__call_121500",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport sys\nsys.path.insert(0, \"/task\")\nimport pandas as pd\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\n\ntruth_items = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n truth_items.append({\"uid\": uid, \"item_id\": item, \"grade\": grade})\ndf_truth = pd.DataFrame(truth_items).join(catalog, on=\"item_id\")\nprint(\"Total relevant events in validation:\", len(df_truth))\nprint(\"Unique users with relevant events:\", df_truth[\"uid\"].nunique())\nprint(\"Unique items in relevant events:\", df_truth[\"item_id\"].nunique())\nprint(\"Grade distribution:\", df_truth[\"grade\"].value_counts().to_dict())\nprint(\"Popularity rank of relevant validation items:\")\nprint(df_truth[\"popularity_rank\"].describe())\nprint(\"Positive listens of relevant validation items:\")\nprint(df_truth[\"positive_listens\"].describe())\n'",
"description": "Analyze ground truth validation relevance distribution and popularity"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_121500",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total relevant events in validation: 37769\nUnique users with relevant events: 4263\nUnique items in relevant events: 14124\nGrade distribution: {1: 33352, 3: 4417}\nPopularity rank of relevant validation items:\ncount 37769.000000\nmean 8463.087744\nstd 8117.953262\nmin 1.000000\n25% 1733.000000\n50% 5622.000000\n75% 13374.000000\nmax 30000.000000\nName: popularity_rank, dtype: float64\nPositive listens of relevant validation items:\ncount 37769.000000\nmean 1761.124652\nstd 2645.924872\nmin 163.000000\n25% 398.000000\n50% 855.000000\n75% 1963.000000\nmax 26615.000000\nName: positive_listens, dtype: float64\nProcess Group PGID: 1787\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:46:08.231Z",
"resultDisplay": "Total relevant events in validation: 37769\nUnique users with relevant events: 4263\nUnique items in relevant events: 14124\nGrade distribution: {1: 33352, 3: 4417}\nPopularity rank of relevant validation items:\ncount 37769.000000\nmean 8463.087744\nstd 8117.953262\nmin 1.000000\n25% 1733.000000\n50% 5622.000000\n75% 13374.000000\nmax 30000.000000\nName: popularity_rank, dtype: float64\nPositive listens of relevant validation items:\ncount 37769.000000\nmean 1761.124652\nstd 2645.924872\nmin 163.000000\n25% 398.000000\n50% 855.000000\n75% 1963.000000\nmax 26615.000000\nName: positive_listens, dtype: float64",
"description": "Analyze ground truth validation relevance distribution and popularity",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:46:10.180Z",
"content": "\nOutput: Total relevant events in validation: 37769\nUnique users with relevant events: 4263\nUnique items in relevant events: 14124\nGrade distribution: {1: 33352, 3: 4417}\nPopularity rank of relevant validation items:\ncount 37769.000000\nmean 8463.087744\nstd 8117.953262\nmin 1.000000\n25% 1733.000000\n50% 5622.000000\n75% 13374.000000\nmax 30000.000000\nName: popularity_rank, dtype: float64\nPositive listens of relevant validation items:\ncount 37769.000000\nmean 1761.124652\nstd 2645.924872\nmin 163.000000\n25% 398.000000\n50% 855.000000\n75% 1963.000000\nmax 26615.000000\nName: positive_listens, dtype: float64\nProcess Group PGID: 1787\n",
"isError": false
},
"responseMessageId": "eddaca00-0cff-49c3-a1dc-eb07fdc3b3ec"
}
]
},
{
"id": 32,
"role": "agent",
"timestamp": "2026-09-29T05:46:39.567Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Analyzing Validation Data\n\nMy current focus is on dissecting the validation data. I've observed that a substantial 14,124 unique items are marked as relevant. Interestingly, the median popularity rank for these relevant items is 5,622, with the mean rank slightly higher at 8,463. This provides a valuable benchmark for evaluating baseline predictions."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Investigating Bias Causes\n\nI'm digging into why my baseline is so biased. It's predicting only a tiny fraction of items, and those are heavily skewed towards the most popular ones. The negative sampling strategy seems like the prime suspect; popular items get swamped with positive signals, overwhelming the rarer negative ones."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Refining Negative Sampling\n\nI'm investigating how popular items are over-predicted. The issue appears to stem from how negative sampling is handled. I'm examining the `in_batch_negatives` and `uniform_negatives` strategies, specifically the impact of popularity-based sampling and logQ correction on removing popularity bias and retrieving items based on affinity rather than global popularity."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Testing Sampling Strategies\n\nI'm currently testing the effectiveness of `in_batch_negatives` and `uniform_negatives` sampling strategies. My goal is to see how popularity-based sampling and logQ correction impact the removal of popularity bias, ultimately allowing retrieval based on affinity rather than just global popularity."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Adjusting Negative Sampling\n\nI'm now implementing a refined negative sampling approach. The code snippet suggests I'm focusing on how to combine uniform negatives with in-batch negatives, applying a `correction` factor to account for popularity bias during this process."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Analyzing In-Batch Sampling\n\nI'm dissecting how `in_batch_negatives` function. It appears distinct items, regardless of their frequency in the batch, have an equal chance of being selected. This uniformity, contrary to intuition, might be a key factor in the observed biases."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Investigating Source Code\n\nI'm tracing the origin of `item_correction`. My investigation led me to the `build_item_correction` function, which seems to calculate in-batch propensities by counting item occurrences within the target sequences and then deriving probabilities."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Evaluating Correction Impact\n\nI'm analyzing the `raw` calculation and its interaction with clipping. Popular items result in values less than 1, clipped low, while rare items yield high values, clipped high. I need to examine how this `correction` term is ultimately applied within the loss function to understand its bias-mitigation effect."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Adjusting Loss Penalties\n\nI'm seeing how the `correction` term in the loss function modifies penalties. Popular items get their negative penalties reduced, while rare items have theirs amplified, aiming to balance their influence in the training."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Revising Correction Logic\n\nI'm questioning the mathematical basis of the `raw` calculation for importance sampling correction. The current approach seems to invert the intended bias mitigation, potentially reintroducing the very popularity bias it aims to remove by converting in-batch negatives back to a uniform loss."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Questioning Correction Mechanism\n\nI'm re-examining the `raw` calculation's impact on importance sampling correction. My current hypothesis is that it might be counterproductively amplifying popularity bias, not mitigating it, by effectively transforming in-batch negatives into a uniform loss scenario. I need to verify this."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Questioning Correction Mechanism\n\nI'm re-examining the `raw` calculation's impact on importance sampling correction. My current hypothesis is that it might be counterproductively amplifying popularity bias, not mitigating it, by effectively transforming in-batch negatives into a uniform loss scenario. I need to verify this."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Verifying Implementation Details\n\nI'm now examining `train_current_model.py` and `two_tower_model.py` closely. Specifically, I'm checking for the presence of `draw_filtered_mixed_negatives` within `two_tower_model.py` to understand its implementation in drawing negatives."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Tracing Negative Sampling\n\nI'm tracing the code flow for negative sampling. It seems `train_current_model.py` uses `draw_full_history_filtered_negatives` when `negative_filter_scope` is \"full-training-history\", which in turn utilizes `build_known_positive_filter` to prepare the data."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:46:39.567Z",
"text": "Tracing Data Preparation\n\nI am scrutinizing the data preparation steps for negative sampling. The code appears to use `np.unique` on sequence items, but I need to confirm if this effectively captures all items from the user's full training history as a filter."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:46:39.567Z",
"callId": "read_file__call_168261",
"name": "read_file",
"input": {
"start_line": 880,
"file_path": "/task/current_model/train_current_model.py",
"end_line": 925
},
"recorded": {
"id": "read_file__call_168261",
"name": "read_file",
"args": {
"start_line": 880,
"file_path": "/task/current_model/train_current_model.py",
"end_line": 925
},
"result": [
{
"functionResponse": {
"id": "read_file__call_168261",
"name": "read_file",
"response": {
"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 880-925 of 2165 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: 926.\n\n--- FILE CONTENT (truncated) ---\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)\n\n history_training = totals - validation\n training = history_training.copy()\n if max_train_targets_per_user:\n training = np.minimum(training, max_train_targets_per_user)\n window_starts = retained_target_window_starts(totals, validation, training)\n return TargetPartition(\n window_starts=window_starts,\n training_counts=training,\n validation_counts=validation,\n history_training_counts=history_training,\n )\n\n\ndef load_organic_like_outcomes(\n public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n frames = [\n pd.read_parquet(\n public / \"data\" / f\"{prefix}_likes.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n for prefix in prefixes\n ]"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:46:39.572Z",
"resultDisplay": "Read lines 880-925 of 2165 from ../task/current_model/train_current_model.py",
"description": "../.../train_current_model.py",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T05:46:39.574Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 880-925 of 2165 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: 926.\n\n--- FILE CONTENT (truncated) ---\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)\n\n history_training = totals - validation\n training = history_training.copy()\n if max_train_targets_per_user:\n training = np.minimum(training, max_train_targets_per_user)\n window_starts = retained_target_window_starts(totals, validation, training)\n return TargetPartition(\n window_starts=window_starts,\n training_counts=training,\n validation_counts=validation,\n history_training_counts=history_training,\n )\n\n\ndef load_organic_like_outcomes(\n public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n frames = [\n pd.read_parquet(\n public / \"data\" / f\"{prefix}_likes.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n for prefix in prefixes\n ]",
"isError": false
},
"responseMessageId": "12194bfb-efae-42e5-b6a5-3e2b5159b064"
}
]
},
{
"id": 33,
"role": "agent",
"timestamp": "2026-09-29T05:47:19.267Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:19.267Z",
"text": "Analyzing Filter Logic\n\nI'm currently examining the `build_known_positive_filter` function and its conditional application based on `config.negative_filter_scope`. My focus is on understanding how this filter is constructed and when it's invoked, specifically looking at the `full-training-history` scenario."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:19.267Z",
"text": "Investigating User Indices\n\nI'm now questioning the role of `user_rows` within `invalid_full_history_negatives`, as its origin in `run_training_epoch` seems to derive from `users` which are simply indices. This raises concerns about the correctness of how user information is being passed and utilized in the context of negative sampling."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:19.267Z",
"text": "Revising Training Targets\n\nI'm analyzing how `targets` are constructed in `build_dense_block`. The slicing logic, particularly `target_positions`, suggests a focus on predicting the next item in a sequence, which differs from my inference-time understanding where all past encounters are excluded. This discrepancy needs resolution."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:47:19.267Z",
"callId": "run_shell_command__call_146490",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\n\ncache = np.load(\"/app/solution/validation_cache.npz\")\nsequence_items = cache[\"sequence_items\"]\noffsets = cache[\"offsets\"]\nuser_ids = cache[\"user_ids\"]\n\ntotal_targets = 0\nrepeat_targets = 0\n\nfor i in range(min(1000, len(offsets) - 1)):\n start = offsets[i]\n end = offsets[i + 1]\n items = sequence_items[start:end]\n seen = set()\n for item in items:\n if item in seen:\n repeat_targets += 1\n else:\n seen.add(item)\n total_targets += 1\n\nprint(f\"Sample 1000 users: total listens = {total_targets}, repeat listens = {repeat_targets} ({repeat_targets/total_targets*100:.1f}%)\")\n'",
"description": "Check proportion of repeat listens in user sequences"
},
"recorded": {
"id": "run_shell_command__call_146490",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\n\ncache = np.load(\"/app/solution/validation_cache.npz\")\nsequence_items = cache[\"sequence_items\"]\noffsets = cache[\"offsets\"]\nuser_ids = cache[\"user_ids\"]\n\ntotal_targets = 0\nrepeat_targets = 0\n\nfor i in range(min(1000, len(offsets) - 1)):\n start = offsets[i]\n end = offsets[i + 1]\n items = sequence_items[start:end]\n seen = set()\n for item in items:\n if item in seen:\n repeat_targets += 1\n else:\n seen.add(item)\n total_targets += 1\n\nprint(f\"Sample 1000 users: total listens = {total_targets}, repeat listens = {repeat_targets} ({repeat_targets/total_targets*100:.1f}%)\")\n'",
"description": "Check proportion of repeat listens in user sequences"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_146490",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Sample 1000 users: total listens = 2105262, repeat listens = 1510561 (71.8%)\nProcess Group PGID: 1813\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:47:20.083Z",
"resultDisplay": "Sample 1000 users: total listens = 2105262, repeat listens = 1510561 (71.8%)",
"description": "Check proportion of repeat listens in user sequences",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:47:20.085Z",
"content": "\nOutput: Sample 1000 users: total listens = 2105262, repeat listens = 1510561 (71.8%)\nProcess Group PGID: 1813\n",
"isError": false
},
"responseMessageId": "f284b554-f8c0-4640-9880-4372f4df5659"
}
]
},
{
"id": 34,
"role": "agent",
"timestamp": "2026-09-29T05:47:48.819Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:48.819Z",
"text": "Exploring Discovery Grading Logic\n\nI'm currently diving into the logic for \"discovery-graded\" target weighting. Specifically, I'm examining the process of loading organic like outcomes and setting the history end point, with initial focus on the user iteration loop for grading."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:48.819Z",
"text": "Refining Split Timestamp Logic\n\nI'm working through the data segmentation, specifically determining the split timestamp for validation data. My current focus is on handling cases where there are fewer than two training examples, ensuring the `split_timestamp` correctly defaults to `history_end` when necessary, while also accurately identifying unique items within the sequence for novelty checks."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:48.819Z",
"text": "Investigating Negative Example Selection\n\nI'm currently deep-diving into the core of our training objective, specifically how negative examples are selected and then subsequently utilized. My immediate focus is on understanding the `draw_full_history_filtered_negatives` function and the implications of different weighting strategies for repeated items within this selection process."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:48.819Z",
"text": "Implementing In-Batch Negatives\n\nI'm working on the practical implementation of in-batch negative sampling. My current efforts are focused on generating random priorities, masking out true targets, and selecting the top-k unique target indices to form the in-batch negative set, with a fallback for filler generation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:48.819Z",
"text": "Revising In-Batch Sampling\n\nI've identified an issue in the in-batch negative selection process. Taking `torch.unique` on the targets discards crucial frequency information, leading to uniform sampling rather than proportional selection based on item popularity within the batch. This needs correction to reflect true in-batch dynamics."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:48.819Z",
"text": "Correcting Negative Sampling Proportions\n\nI'm revising how uniform negatives are sampled. Currently, all catalog items have equal probability of selection, irrespective of their overall popularity. This creates a severe imbalance where high-listen-count items receive disproportionately more positive than negative training signals, impacting their representation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:48.819Z",
"text": "Addressing Popularity Bias\n\nI'm analyzing how popular items are overwhelmingly favored due to the current training setup. My immediate focus is on understanding how positive and negative logits are calculated and the role of `softplus` and `corrected_negative_loss` in the observed bias, particularly the disproportionate impact on tail tracks."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:48.819Z",
"text": "Analyzing Loss Function Biases\n\nI'm examining the loss calculation, specifically `corrected_negative_loss`. My current focus is on understanding the implications of `F.softplus` on logits and how the combination of uniform and in-batch negatives, along with the `correction` tensor, might be introducing or exacerbating biases towards popular items."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:48.819Z",
"text": "Reversing Correction Logic\n\nI'm analyzing the `correction` term used for in-batch negatives. It appears to be inversely proportional to an item's popularity, meaning popular items get a small correction (low weight) and tail items get a large correction (high weight). This seems to be the exact opposite of the desired outcome."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:48.819Z",
"text": "Correcting Uniform Sampling Approach\n\nI'm realizing the attempt to recover a uniform distribution over catalog items is fundamentally flawed. This uniform approach, by design, amplifies popularity bias due to the significant variance in item popularity. The goal is not a uniform expectation, but rather to balance popularity and affinity."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:48.819Z",
"text": "Analyzing Logit Adjustments\n\nI'm re-evaluating how sampling bias correction impacts logits. By subtracting $\\log P(i)$ during training, the resulting score $s_{ui}$ effectively represents personalized affinity $\\text{PMI}(u, i)$, becoming independent of global popularity at inference time. This is a critical insight for unbiased ranking."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:48.819Z",
"text": "Testing Popularity Sampling\n\nI'm now exploring popularity-based negative sampling, considering probabilities proportional to an item's popularity raised to an exponent. I want to analyze the impact of this versus uniform sampling and investigate combinations of both uniform and popularity-based negatives, as well as in-batch negatives sampled by target frequency."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:48.819Z",
"text": "Revising Invalid Negative Replacement\n\nI'm noticing a critical issue in how invalid negatives are handled. When an invalid negative is identified, it's currently replaced with a random item, and its correction weight is reset to 1.0. This approach effectively discards the information from the invalid draw and treats it as a standard uniform negative, potentially undermining more nuanced sampling strategies."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:47:48.819Z",
"callId": "run_shell_command__call_187975",
"name": "run_shell_command",
"input": {
"description": "Check candidate eligibility stats for validation",
"command": "python3 -c '\nimport numpy as np\n\nnpz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nprint(\"keys:\", npz.files)\nuids = npz[\"uids\"]\nitem_ids = npz[\"item_ids\"]\npacked = npz[\"packed_eligible\"]\nprint(f\"uids: {len(uids)}, item_ids: {len(item_ids)}, packed shape: {packed.shape}\")\n\n# For a few users, check how many items are eligible\nfor r in range(5):\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n print(f\"User {uids[r]}: eligible items = {mask.sum()} / {len(item_ids)}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_187975",
"name": "run_shell_command",
"args": {
"description": "Check candidate eligibility stats for validation",
"command": "python3 -c '\nimport numpy as np\n\nnpz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nprint(\"keys:\", npz.files)\nuids = npz[\"uids\"]\nitem_ids = npz[\"item_ids\"]\npacked = npz[\"packed_eligible\"]\nprint(f\"uids: {len(uids)}, item_ids: {len(item_ids)}, packed shape: {packed.shape}\")\n\n# For a few users, check how many items are eligible\nfor r in range(5):\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n print(f\"User {uids[r]}: eligible items = {mask.sum()} / {len(item_ids)}\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_187975",
"name": "run_shell_command",
"response": {
"output": "\nOutput: keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456, item_ids: 30000, packed shape: (8456, 3750)\nUser 100: eligible items = 29318 / 30000\nUser 200: eligible items = 29832 / 30000\nUser 300: eligible items = 29931 / 30000\nUser 600: eligible items = 29320 / 30000\nUser 700: eligible items = 28347 / 30000\nProcess Group PGID: 1829\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:47:49.327Z",
"resultDisplay": "keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456, item_ids: 30000, packed shape: (8456, 3750)\nUser 100: eligible items = 29318 / 30000\nUser 200: eligible items = 29832 / 30000\nUser 300: eligible items = 29931 / 30000\nUser 600: eligible items = 29320 / 30000\nUser 700: eligible items = 28347 / 30000",
"description": "Check candidate eligibility stats for validation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:47:49.330Z",
"content": "\nOutput: keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456, item_ids: 30000, packed shape: (8456, 3750)\nUser 100: eligible items = 29318 / 30000\nUser 200: eligible items = 29832 / 30000\nUser 300: eligible items = 29931 / 30000\nUser 600: eligible items = 29320 / 30000\nUser 700: eligible items = 28347 / 30000\nProcess Group PGID: 1829\n",
"isError": false
},
"responseMessageId": "31395bc3-7cd4-4bd1-bdbc-ea88092e759f"
}
]
},
{
"id": 35,
"role": "agent",
"timestamp": "2026-09-29T05:47:59.470Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:59.470Z",
"text": "Analyzing Candidate Eligibility\n\nMy focus has shifted to understanding how baseline rankings are currently computed. I'm reviewing the `rank_candidates` function, particularly how it handles the vast majority of items (over 29,000) that are eligible for users, excluding only those already seen in training history."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:59.470Z",
"text": "Refining Ranking Logic\n\nI'm currently examining the `rank_candidates` function, specifically how it processes eligible items. My attention is on the batch processing of item encodings and the subsequent scoring mechanism against user queries."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:59.470Z",
"text": "Calculating Top Scores\n\nI'm digging into the score calculation, noting it's a direct dot product between user and item vectors. This ensures fast, precomputed serving. I'm confirming the masking logic correctly excludes ineligible items before selecting the top N."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:47:59.470Z",
"text": "Investigating Negative Sampling\n\nMy current thoughts are centered on the prompt's explicit instruction to focus on negative sampling strategies within the training objective. I need to carefully investigate how negative examples are selected and used, diagnose any limitations, and then strategize improvements under the given CPU constraints."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:47:59.470Z",
"callId": "run_shell_command__call_22951",
"name": "run_shell_command",
"input": {
"command": "ls -la /app/solution/",
"description": "List contents of /app/solution/"
},
"recorded": {
"id": "run_shell_command__call_22951",
"name": "run_shell_command",
"args": {
"command": "ls -la /app/solution/",
"description": "List contents of /app/solution/"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_22951",
"name": "run_shell_command",
"response": {
"output": "\nOutput: total 524012\ndrwxr-xr-x 1 agent agent 4096 Sep 29 05:43 .\ndrwxr-xr-x 1 agent agent 4096 Sep 29 05:33 ..\n-rw-r--r-- 1 agent agent 1244228 Sep 29 05:43 production_validation.parquet\n-r--r--r-- 1 agent agent 80058 Sep 29 05:37 train_current_model.py\n-r--r--r-- 1 agent agent 15985 Sep 29 05:37 two_tower_model.py\n-rw-r--r-- 1 agent agent 535225808 Sep 29 05:38 validation_cache.npz\nProcess Group PGID: 1839\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:47:59.486Z",
"resultDisplay": "total 524012\ndrwxr-xr-x 1 agent agent 4096 Sep 29 05:43 .\ndrwxr-xr-x 1 agent agent 4096 Sep 29 05:33 ..\n-rw-r--r-- 1 agent agent 1244228 Sep 29 05:43 production_validation.parquet\n-r--r--r-- 1 agent agent 80058 Sep 29 05:37 train_current_model.py\n-r--r--r-- 1 agent agent 15985 Sep 29 05:37 two_tower_model.py\n-rw-r--r-- 1 agent agent 535225808 Sep 29 05:38 validation_cache.npz",
"description": "ls -la /app/solution/",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:47:59.487Z",
"content": "\nOutput: total 524012\ndrwxr-xr-x 1 agent agent 4096 Sep 29 05:43 .\ndrwxr-xr-x 1 agent agent 4096 Sep 29 05:33 ..\n-rw-r--r-- 1 agent agent 1244228 Sep 29 05:43 production_validation.parquet\n-r--r--r-- 1 agent agent 80058 Sep 29 05:37 train_current_model.py\n-r--r--r-- 1 agent agent 15985 Sep 29 05:37 two_tower_model.py\n-rw-r--r-- 1 agent agent 535225808 Sep 29 05:38 validation_cache.npz\nProcess Group PGID: 1839\n",
"isError": false
},
"responseMessageId": "55521040-7e2a-473e-8ee6-16a5963654b7"
}
]
},
{
"id": 36,
"role": "agent",
"timestamp": "2026-09-29T05:48:03.450Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:48:03.450Z",
"text": "Confirming Data Availability\n\nThe `production_validation.parquet` file has been located. However, I've realized it only contains predicted ranks, not the raw scores needed for further analysis. I'm now investigating the training process and the `validation_cache.npz` file."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:48:03.450Z",
"callId": "run_shell_command__call_5149439",
"name": "run_shell_command",
"input": {
"description": "Check cache load time",
"command": "python3 -c '\nimport time\nt0 = time.time()\nimport sys\nsys.path.insert(0, \"/app/solution\")\nfrom train_current_model import load_cache, Config\nfrom pathlib import Path\ncfg = Config()\nprepared = load_cache(Path(\"/app/solution/validation_cache.npz\"), \"validation\", cfg)\nprint(\"Loaded cache in\", round(time.time() - t0, 2), \"s\")\n'"
},
"recorded": {
"id": "run_shell_command__call_5149439",
"name": "run_shell_command",
"args": {
"description": "Check cache load time",
"command": "python3 -c '\nimport time\nt0 = time.time()\nimport sys\nsys.path.insert(0, \"/app/solution\")\nfrom train_current_model import load_cache, Config\nfrom pathlib import Path\ncfg = Config()\nprepared = load_cache(Path(\"/app/solution/validation_cache.npz\"), \"validation\", cfg)\nprint(\"Loaded cache in\", round(time.time() - t0, 2), \"s\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5149439",
"name": "run_shell_command",
"response": {
"output": "\nOutput: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nLoaded cache in 3.94 s\nProcess Group PGID: 1842\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:48:07.687Z",
"resultDisplay": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nLoaded cache in 3.94 s",
"description": "Check cache load time",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:48:07.689Z",
"content": "\nOutput: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nLoaded cache in 3.94 s\nProcess Group PGID: 1842\n",
"isError": false
},
"responseMessageId": "622946a3-2426-4a4b-967f-c1a228e561d1"
}
]
},
{
"id": 37,
"role": "agent",
"timestamp": "2026-09-29T05:49:49.641Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Analyzing Training Time\n\nI've confirmed the cache loads in under four seconds. My current focus is on understanding the training duration. The baseline run shows an initial train loss of 0.53984 with a validation loss of 0.49664, and a pair accuracy of 0.755 across over a million training targets and 130,000 validation targets for the first epoch."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Optimizing Training Duration\n\nI've pinpointed that a full training epoch, including evaluation and ranking, takes under five minutes. My current efforts are concentrated on dissecting the 210-second training component within `run_training_epoch`, specifically by profiling the `build_dense_block` and `draw_training_negatives` functions to identify potential bottlenecks."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Investigating Negative Sampling\n\nI'm diving into the negative sampling logic. Specifically, I've observed a nested loop structure within the `draw_training_negatives` function that's computationally intensive. My aim is to optimize this section, which involves repeated checks and potential regeneration of invalid negatives."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Refining Negative Sampling\n\nI'm now meticulously dissecting the trainer's objective function and its approach to negative example selection. My primary focus is on diagnosing and enhancing the current negative sampling strategy, ensuring it fits within the fixed CPU budget and aligns with the public backtest validation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Refining Negative Sampling Strategy\n\nI'm now meticulously dissecting the trainer's objective function and its approach to negative example selection. My primary focus is on diagnosing and enhancing the current negative sampling strategy, ensuring it fits within the fixed CPU budget and aligns with the public backtest validation. I've observed that in-batch negatives aren't sampled proportionally to frequency and want to explore broader popularity-based sampling."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Exploring Explicit Negatives\n\nI'm now considering explicit negative signals, specifically user dislikes. I need to determine if and how these explicit negatives can be incorporated into the sampling strategy to improve training and prevent recommending disliked content during candidate generation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Incorporating Dislikes\n\nI'm now analyzing how user dislikes, encompassing all train encounters, are excluded from candidate eligibility. My core question is whether the model learns from these dislikes during training. I'm investigating explicit dislikes as potential negative examples versus unencountered items as candidate negatives."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Investigating Target Counts\n\nI'm examining the `target_count` variable in the `pack_positive_items` function. My concern is that it might be incorrectly including an extra item in the positive set, potentially skewing the training data and impacting downstream accuracy."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Examining Offsets\n\nI'm scrutinizing how user data offsets are handled, particularly within `pack_positive_items`. My current hypothesis is that the `target_count` might be incorrectly incrementing beyond the actual user sequence length, leading to an overestimation of positive examples in training."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Analyzing Loss Function\n\nI'm scrutinizing the `corrected_negative_loss` function. My focus is on how it utilizes both uniform and in-batch negatives, particularly the weighting applied to in-batch negatives via the `correction` tensor, and how this integrates with the overall loss calculation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Adjusting Loss Weights\n\nI'm re-evaluating the `corrected_negative_loss` by analyzing how weights are applied to in-batch negatives. It appears tail negatives are disproportionately penalized compared to popular ones, and I'm investigating why this `correction` mechanism is implemented this way."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Revising Correction Logic\n\nI've identified that the `correction` term, intended to account for sampling bias, is inversely proportional to the square root of item popularity. This disproportionately penalizes rare items, causing the model to favor popular items during prediction, which I believe is the source of the observed recommendation bias."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Correcting Popularity Bias\n\nI've determined the correction term subtracts a value inversely proportional to the square root of item popularity. This over-penalizes rare items, causing my model to favor popular ones. I'm now investigating how this aligns with standard approaches like subtracting log probabilities."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Analyzing BCE Loss\n\nI've realized that subtracting $\\log(p_j)$ in sampled softmax creates an unbiased estimator of the full catalog softmax. My current focus is on how this concept translates to Binary Cross-Entropy (BCE) loss, specifically analyzing the gradient for $s(u, j)$ when sampling from $Q(j)$ and how it differs from the sampled softmax approach."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Decomposing Score Components\n\nI've realized that my current BCE loss setup, with uniform negative sampling, results in the model's score implicitly learning both user-item affinity and item popularity. Given the vast variance in item popularity, this popularity term currently dominates, explaining why recommendations are overly skewed towards universally popular items. I'm now exploring how to adjust the negative sampling strategy to mitigate this."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Learning Personalized Affinity\n\nI've discovered that by sampling negatives proportional to popularity and setting the derivative of the loss to zero, the global item popularity term cancels out entirely. This means the learned score $s(u, j)$ effectively represents the Pointwise Mutual Information (PMI), capturing how much more a user likes an item than the average user, leading to personalized rankings rather than popularity-based ones."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Confirming Popularity Independence\n\nI've confirmed that sampling negatives proportionally to their popularity effectively cancels out the global item popularity term in the loss function. This means the learned score now truly reflects personalized user-item affinity rather than just broad popularity, allowing for better discovery of niche items."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:49.641Z",
"text": "Testing Popularity Sampling\n\nI'm benchmarking popularity-based negative sampling using `torch.multinomial` on CPU. My experiments aim to assess its speed and feasibility for 30,000 items, exploring catalog popularity distributions with varying alpha values and direct in-batch negative sampling."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:49:49.641Z",
"callId": "run_shell_command__call_52313",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport torch\nimport time\n\nn_items = 30000\nweights = torch.ones(n_items)\nbatch_size = 6400\nnum_neg = 32\n\nt0 = time.time()\nfor _ in range(10):\n samples = torch.multinomial(weights, num_neg, replacement=True)\nprint(\"Multinomial (1D, batch=32):\", round(time.time() - t0, 4), \"s\")\n\n# Or sampling (batch_size, num_neg) from a 1D distribution\nt0 = time.time()\nfor _ in range(10):\n # Flattened draw\n draws = torch.multinomial(weights, batch_size * num_neg, replacement=True).reshape(batch_size, num_neg)\nprint(\"Multinomial (flattened 6400*32):\", round(time.time() - t0, 4), \"s\")\n'",
"description": "Benchmark torch.multinomial for popularity negative sampling"
},
"recorded": {
"id": "run_shell_command__call_52313",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport torch\nimport time\n\nn_items = 30000\nweights = torch.ones(n_items)\nbatch_size = 6400\nnum_neg = 32\n\nt0 = time.time()\nfor _ in range(10):\n samples = torch.multinomial(weights, num_neg, replacement=True)\nprint(\"Multinomial (1D, batch=32):\", round(time.time() - t0, 4), \"s\")\n\n# Or sampling (batch_size, num_neg) from a 1D distribution\nt0 = time.time()\nfor _ in range(10):\n # Flattened draw\n draws = torch.multinomial(weights, batch_size * num_neg, replacement=True).reshape(batch_size, num_neg)\nprint(\"Multinomial (flattened 6400*32):\", round(time.time() - t0, 4), \"s\")\n'",
"description": "Benchmark torch.multinomial for popularity negative sampling"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_52313",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Multinomial (1D, batch=32): 0.0005 s\nMultinomial (flattened 6400*32): 0.2311 s\nProcess Group PGID: 1859\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:49:52.781Z",
"resultDisplay": "Multinomial (1D, batch=32): 0.0005 s\nMultinomial (flattened 6400*32): 0.2311 s",
"description": "Benchmark torch.multinomial for popularity negative sampling",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:49:52.783Z",
"content": "\nOutput: Multinomial (1D, batch=32): 0.0005 s\nMultinomial (flattened 6400*32): 0.2311 s\nProcess Group PGID: 1859\n",
"isError": false
},
"responseMessageId": "ae69aca8-0c08-42ba-b07f-b443074a6c52"
}
]
},
{
"id": 38,
"role": "agent",
"timestamp": "2026-09-29T05:49:57.897Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:49:57.897Z",
"text": "Optimizing Drawing Performance\n\nMy latest findings indicate a remarkable 0.23-second execution for 10 batches of 200,000 draws. I'm now exploring alternative sampling methods, including NumPy's `np.random.choice`, alias methods, and rejection sampling, to further enhance this efficiency. My current focus is on leveraging pre-populated item ID arrays for potential speed-ups."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:49:57.897Z",
"callId": "run_shell_command__call_160511",
"name": "run_shell_command",
"input": {
"description": "Benchmark table lookup sampling",
"command": "python3 -c '\nimport torch\nimport time\n\ntable = torch.randint(30000, (1_000_000,))\nbatch_size = 6400\nnum_neg = 32\n\nt0 = time.time()\nfor _ in range(10):\n idx = torch.randint(len(table), (batch_size, num_neg))\n samples = table[idx]\nprint(\"Table lookup sampling (10 iterations):\", round(time.time() - t0, 4), \"s\")\n'"
},
"recorded": {
"id": "run_shell_command__call_160511",
"name": "run_shell_command",
"args": {
"description": "Benchmark table lookup sampling",
"command": "python3 -c '\nimport torch\nimport time\n\ntable = torch.randint(30000, (1_000_000,))\nbatch_size = 6400\nnum_neg = 32\n\nt0 = time.time()\nfor _ in range(10):\n idx = torch.randint(len(table), (batch_size, num_neg))\n samples = table[idx]\nprint(\"Table lookup sampling (10 iterations):\", round(time.time() - t0, 4), \"s\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_160511",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Table lookup sampling (10 iterations): 0.0196 s\nProcess Group PGID: 1875\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:50:00.819Z",
"resultDisplay": "Table lookup sampling (10 iterations): 0.0196 s",
"description": "Benchmark table lookup sampling",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:50:00.821Z",
"content": "\nOutput: Table lookup sampling (10 iterations): 0.0196 s\nProcess Group PGID: 1875\n",
"isError": false
},
"responseMessageId": "519f2aa2-0d26-4e46-b579-b42924551e72"
}
]
},
{
"id": 39,
"role": "agent",
"timestamp": "2026-09-29T05:50:36.514Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:36.514Z",
"text": "Analyzing Negative Sampling Factors\n\nThe table lookup overhead is remarkably low at 1.9 ms per batch, essentially negligible. My focus now shifts to other elements influencing negative sampling and the training objective, specifically examining target weighting and its configuration."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:36.514Z",
"text": "Examining Target Weighting Strategies\n\nMy attention has moved to understanding \"discovery-graded\" target weighting. I'm digging into the `train_current_model.py` script, specifically lines 981-1025, to analyze how user interactions and timestamps influence this weighting method during training and validation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:36.514Z",
"text": "Investigating Target Weighting\n\nI'm currently scrutinizing the \"discovery-graded\" target weighting. My analysis focuses on how user interactions and timestamps, as seen in `train_current_model.py` lines 981-1025, dynamically adjust weights for novel items during training and validation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:36.514Z",
"text": "Refining Target Weighting Logic\n\nI'm dissecting how \"discovery-graded\" weighting operates. It assigns 0.25 for repeats, 1.0 for novel organic listens, and a significant 3.0 for novel listens followed by an organic like within the defined horizon. I'm now clarifying the \"organic-strong\" effect."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:36.514Z",
"text": "Clarifying Organic-Strong Weights\n\nI've identified that repeat listens weighted 1.0 via \"organic-strong\" conditions, despite 71.8% of listens being repeats and repeats being unrecommendable at test time. This seems counterproductive to the objective of predicting novel tracks, and grade 3 listens weren't weighted 3x as expected."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:36.514Z",
"text": "Exploring Negative Sampling Methods\n\nI'm now systematically investigating alternative negative sampling strategies. My current focus is on exploring popularity-biased sampling, considering different $\\alpha$ values, and evaluating combinations of uniform and popularity-weighted negatives. I'm also looking into implementing true in-batch negative sampling."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:36.514Z",
"text": "Refining Negative Sampling\n\nI'm realizing that uniform negatives might not be sufficient. I need to consider sampling from items the user *could* encounter at test time, especially popular ones they haven't seen, to train the model to avoid them effectively."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:36.514Z",
"text": "Testing Loss Functions\n\nI am now exploring different loss functions. I'm considering removing inverse-popularity weighting from BCE, applying logQ correction for sampled softmax, or investigating BPR. The core question is how these changes affect the model's ability to predict novel tracks effectively."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:36.514Z",
"text": "Investigating BCE Implementation\n\nI'm examining the baseline's use of binary cross-entropy, particularly the `F.softplus` application. I need to verify this implementation against the script's actual logic for `positive_loss` and `negative_loss`, including the `corrected_negative_loss` function.\n\n**Developing Experimentation Framework**\n\nI'm working on creating a streamlined experiment script to facilitate validation and NDCG@10 evaluation. The goal is to significantly reduce iteration time, aiming for quick turnarounds to test hypotheses efficiently within a limited timeframe."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:36.514Z",
"text": "Profiling Training Bottlenecks\n\nI'm digging into the 210-second training time. I've identified that `build_dense_block` and `draw_full_history_filtered_negatives` within each batch, utilizing Python loops and NumPy, might be significant bottlenecks. I'm also questioning the `threads` default and its impact."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:50:36.514Z",
"text": "Optimizing Negative Filtering\n\nI'm seeing significant computational cost in how negatives are filtered. The nested loops and large array comparisons, especially involving `liked_items`, are creating an $O(B \\times K^2)$ or $O(B \\times K \\times L)$ complexity where $K$ is the number of negatives and $L$ is the number of liked items. This needs a much more efficient approach."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:50:36.514Z",
"callId": "run_shell_command__call_141642",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport sys\nsys.path.insert(0, \"/app/solution\")\nimport time\nimport torch\nimport numpy as np\nfrom pathlib import Path\nfrom train_current_model import Config, load_cache, chronological_target_partition, build_target_weights, build_item_correction, build_known_positive_filter, model_config, build_dense_block, event_sequence, draw_training_negatives, score_target_microbatches\nimport two_tower_model as base\n\ntorch.set_num_threads(8)\nconfig = Config()\nprepared = load_cache(Path(\"/app/solution/validation_cache.npz\"), \"validation\", config)\npartition = chronological_target_partition(prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user)\nmodel = base.SequentialTwoTower(prepared.item_features, model_config(config))\ndevice = torch.device(\"cpu\")\n\ntrain_users = np.flatnonzero(partition.training_counts > 0)\nsorted_users = train_users[np.argsort(partition.training_counts[train_users], kind=\"stable\")]\ngroups = [sorted_users[start : start + config.user_batch_size] for start in range(0, len(sorted_users), config.user_batch_size)]\n\nknown_positive_packed = build_known_positive_filter(prepared, partition.history_training_counts)\ntarget_weights = build_target_weights(Path(\"/task\"), \"validation\", prepared, partition.history_training_counts, config)\nitem_correction = build_item_correction(prepared, config, partition.window_starts, partition.training_counts)\ngenerator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10000)\n\nusers = groups[0]\ngroup_counts = partition.training_counts[users]\nhidden = torch.zeros(1, len(users), config.embedding_dim)\n\nt0 = time.time()\nblock = build_dense_block(prepared, target_weights, users, partition.window_starts[users], group_counts, np.zeros(len(users), dtype=np.int64), 0, min(100, int(group_counts.max())), config)\nt_block = time.time() - t0\n\nt0 = time.time()\nsequence_outputs, _ = event_sequence(model, torch.from_numpy(block[0].astype(np.int64)), torch.from_numpy(block[1].astype(np.float32)), hidden)\nt_gru = time.time() - t0\n\nflat_scored = block[5].reshape(-1)\nmask = torch.from_numpy(flat_scored)\nsequence_vectors = sequence_outputs.reshape(-1, config.embedding_dim)[mask]\nflat_states = block[3].reshape(-1)[flat_scored]\npositive_items = torch.from_numpy(block[4].reshape(-1)[flat_scored].astype(np.int64))\nknown_history = torch.from_numpy(block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(np.int64))\nliked_items = torch.from_numpy(prepared.liked_states[flat_states].astype(np.int64))\nflat_user_rows = np.broadcast_to(users[:, None], block[5].shape).reshape(-1)[flat_scored]\n\nt0 = time.time()\nnegatives, negative_weights = draw_training_negatives(positive_items, known_history, liked_items, flat_user_rows, known_positive_packed, prepared, config, item_correction, generator, device)\nt_neg = time.time() - t0\n\nt0 = time.time()\nstats, seq_grad = score_target_microbatches(model, prepared, config, sequence_vectors, flat_states, block[2].reshape(-1, 4)[flat_scored].astype(np.float32), positive_items, negatives, negative_weights, block[8].reshape(-1)[flat_scored], device, backward=True)\nt_score = time.time() - t0\n\nprint(f\"Time breakdown: block={t_block:.3f}s, gru={t_gru:.3f}s, neg={t_neg:.3f}s, score+bwd={t_score:.3f}s\")\n'",
"description": "Profile step-by-step time breakdown for one training step"
},
"recorded": {
"id": "run_shell_command__call_141642",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport sys\nsys.path.insert(0, \"/app/solution\")\nimport time\nimport torch\nimport numpy as np\nfrom pathlib import Path\nfrom train_current_model import Config, load_cache, chronological_target_partition, build_target_weights, build_item_correction, build_known_positive_filter, model_config, build_dense_block, event_sequence, draw_training_negatives, score_target_microbatches\nimport two_tower_model as base\n\ntorch.set_num_threads(8)\nconfig = Config()\nprepared = load_cache(Path(\"/app/solution/validation_cache.npz\"), \"validation\", config)\npartition = chronological_target_partition(prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user)\nmodel = base.SequentialTwoTower(prepared.item_features, model_config(config))\ndevice = torch.device(\"cpu\")\n\ntrain_users = np.flatnonzero(partition.training_counts > 0)\nsorted_users = train_users[np.argsort(partition.training_counts[train_users], kind=\"stable\")]\ngroups = [sorted_users[start : start + config.user_batch_size] for start in range(0, len(sorted_users), config.user_batch_size)]\n\nknown_positive_packed = build_known_positive_filter(prepared, partition.history_training_counts)\ntarget_weights = build_target_weights(Path(\"/task\"), \"validation\", prepared, partition.history_training_counts, config)\nitem_correction = build_item_correction(prepared, config, partition.window_starts, partition.training_counts)\ngenerator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10000)\n\nusers = groups[0]\ngroup_counts = partition.training_counts[users]\nhidden = torch.zeros(1, len(users), config.embedding_dim)\n\nt0 = time.time()\nblock = build_dense_block(prepared, target_weights, users, partition.window_starts[users], group_counts, np.zeros(len(users), dtype=np.int64), 0, min(100, int(group_counts.max())), config)\nt_block = time.time() - t0\n\nt0 = time.time()\nsequence_outputs, _ = event_sequence(model, torch.from_numpy(block[0].astype(np.int64)), torch.from_numpy(block[1].astype(np.float32)), hidden)\nt_gru = time.time() - t0\n\nflat_scored = block[5].reshape(-1)\nmask = torch.from_numpy(flat_scored)\nsequence_vectors = sequence_outputs.reshape(-1, config.embedding_dim)[mask]\nflat_states = block[3].reshape(-1)[flat_scored]\npositive_items = torch.from_numpy(block[4].reshape(-1)[flat_scored].astype(np.int64))\nknown_history = torch.from_numpy(block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(np.int64))\nliked_items = torch.from_numpy(prepared.liked_states[flat_states].astype(np.int64))\nflat_user_rows = np.broadcast_to(users[:, None], block[5].shape).reshape(-1)[flat_scored]\n\nt0 = time.time()\nnegatives, negative_weights = draw_training_negatives(positive_items, known_history, liked_items, flat_user_rows, known_positive_packed, prepared, config, item_correction, generator, device)\nt_neg = time.time() - t0\n\nt0 = time.time()\nstats, seq_grad = score_target_microbatches(model, prepared, config, sequence_vectors, flat_states, block[2].reshape(-1, 4)[flat_scored].astype(np.float32), positive_items, negatives, negative_weights, block[8].reshape(-1)[flat_scored], device, backward=True)\nt_score = time.time() - t0\n\nprint(f\"Time breakdown: block={t_block:.3f}s, gru={t_gru:.3f}s, neg={t_neg:.3f}s, score+bwd={t_score:.3f}s\")\n'",
"description": "Profile step-by-step time breakdown for one training step"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_141642",
"name": "run_shell_command",
"response": {
"output": "\nOutput: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\nTime breakdown: block=0.002s, gru=0.003s, neg=0.002s, score+bwd=0.037s\nProcess Group PGID: 1888\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:50:41.756Z",
"resultDisplay": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\nTime breakdown: block=0.002s, gru=0.003s, neg=0.002s, score+bwd=0.037s",
"description": "Profile step-by-step time breakdown for one training step",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:50:41.758Z",
"content": "\nOutput: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\nTime breakdown: block=0.002s, gru=0.003s, neg=0.002s, score+bwd=0.037s\nProcess Group PGID: 1888\n",
"isError": false
},
"responseMessageId": "73d9f637-a1db-413c-9f1f-0cccd3b00e25"
}
]
},
{
"id": 40,
"role": "agent",
"timestamp": "2026-09-29T05:51:05.828Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:05.828Z",
"text": "Analyzing Performance Bottlenecks\n\nI'm currently dissecting the timing breakdown. The neural network computations, encompassing candidate encoding, user MLP, loss calculation, and backpropagation, are consuming roughly 80% of the total time, which aligns with expected PyTorch behavior. This indicates that the core model processing is indeed the primary area of focus for optimization."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:05.828Z",
"text": "Exploring Negative Sampling\n\nI'm currently investigating the impact of negative sampling strategies. My focus is on adjusting `inbatch_correction_power` and understanding the `corrected_negative_loss` function's mechanics, particularly how it handles the interaction between uniform and in-batch negatives, to see how these changes affect NDCG@10 performance."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:05.828Z",
"text": "Refining Negative Sampling Logic\n\nI'm digging into the in-batch negative sampling. It seems my understanding of the `correction` term was incomplete. If `inbatch_correction_power` is zero, the `correction` and `raw` weights become uniformly one. I'm now scrutinizing the sampling process within `draw_full_history_filtered_negatives` to ensure it’s correctly handling unique targets and priorities, as this could be another source of error."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:05.828Z",
"text": "Understanding In-Batch Negatives\n\nI'm clarifying why `unique_targets` are essential for in-batch negative sampling. My current focus is on the standard two-tower approach where positive items within a batch implicitly serve as negatives for other queries, an elegant design I'm exploring further."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:05.828Z",
"text": "Investigating Sampling Strategy\n\nI'm re-examining how negatives are sampled. The current method selects 8 in-batch negatives randomly from unique targets and adds 24 uniform negatives per target, resulting in 32 encoded negatives. I'm now considering the impact of sampling negatives based on catalog popularity."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:05.828Z",
"text": "Adjusting Loss Calculation\n\nI'm revisiting the LogQ correction mechanism for sampled softmax loss. My current focus is on how subtracting $\\log q_j$ from logits, where $q_j$ is the candidate proposal distribution, effectively re-weights negative samples in relation to their popularity, potentially mitigating the issue of highly popular items becoming overly frequent negatives."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:05.828Z",
"text": "Deriving Logit Behavior\n\nI'm realizing that the LogQ correction, when subtracted from logits, is effectively performing importance weighting for the denominator. This transforms the model's objective towards learning a score proportional to $\\text{PMI}(u, i)$, suggesting a deeper connection between sampled softmax and intrinsic user-item association."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:05.828Z",
"text": "Testing Loss Functions\n\nI am exploring alternative loss functions like BCE or margin loss, alongside the current softmax approach, to understand their impact on ranking performance. My immediate next step is to set up a quick, standalone experiment to test these on validation data without external processes, evaluating their speed and efficacy."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:51:05.828Z",
"callId": "run_shell_command__call_161411",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport sys\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\nimport evaluate_public\nfrom pathlib import Path\nimport pandas as pd\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = evaluate_public.CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# Test scoring baseline\ndf = pd.read_parquet(\"/app/solution/production_validation.parquet\")\nranked = evaluate_public.ranked_items(df)\nres = evaluate_public.score(ranked, graded)\nprint(\"Scored baseline:\", res)\n'",
"description": "Test evaluation function directly in Python"
},
"recorded": {
"id": "run_shell_command__call_161411",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport sys\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\nimport evaluate_public\nfrom pathlib import Path\nimport pandas as pd\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = evaluate_public.CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# Test scoring baseline\ndf = pd.read_parquet(\"/app/solution/production_validation.parquet\")\nranked = evaluate_public.ranked_items(df)\nres = evaluate_public.score(ranked, graded)\nprint(\"Scored baseline:\", res)\n'",
"description": "Test evaluation function directly in Python"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_161411",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Scored baseline: {'quality_ndcg@10': 0.02128, 'quality_users': 4263}\nProcess Group PGID: 1919\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:51:08.255Z",
"resultDisplay": "Scored baseline: {'quality_ndcg@10': 0.02128, 'quality_users': 4263}",
"description": "Test evaluation function directly in Python",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:51:08.256Z",
"content": "\nOutput: Scored baseline: {'quality_ndcg@10': 0.02128, 'quality_users': 4263}\nProcess Group PGID: 1919\n",
"isError": false
},
"responseMessageId": "b7fa0124-1da5-47b2-82c9-fa6972444f5d"
}
]
},
{
"id": 41,
"role": "agent",
"timestamp": "2026-09-29T05:51:51.359Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:51.359Z",
"text": "Testing Scoring Speed\n\nThe direct Python scoring function is impressively fast, completing in just 0.5 seconds. My next steps involve a deep dive into how modifying the negative sampling strategy and the choice of loss function impact this performance. I'm particularly keen to test specific hypotheses regarding negative sampling."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:51.359Z",
"text": "Exploring Negative Sampling Power\n\nI'm investigating the impact of `inbatch_correction_power` within `build_item_correction`. Specifically, I'm considering how a negative exponent might alter the correction weights. My current focus is on understanding the mathematical implications and potential performance shifts this could introduce."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:51.359Z",
"text": "Developing Experiment Framework\n\nI am now focused on building a modular script to rigorously test various negative sampling strategies and loss functions. This will allow me to systematically evaluate their impact on key metrics like NDCG@10, moving beyond theoretical considerations to empirical validation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:51:51.359Z",
"text": "Evaluating Sampling and Loss\n\nI'm now setting up experiments to compare different negative sampling methods, including uniform, popularity-based, and in-batch approaches. Alongside this, I'm analyzing the effects of various loss functions, such as BCE with different weighting schemes and sampled softmax variations. My goal is to empirically validate which combinations yield the best performance."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:51:51.359Z",
"callId": "update_topic__call_58721",
"name": "update_topic",
"input": {
"title": "Executing Validation Experiments",
"summary": "I have diagnosed key limitations in the production model: severe popularity bias driven by uniform/inverted negative weighting, lack of proper in-batch/popularity negative sampling, and uncalibrated BCE loss. I am now creating a modular validation experimentation script to systematically evaluate negative sampling distributions (popularity-biased, true in-batch), loss functions (unweighted BCE, logQ-corrected BCE, and sampled softmax cross-entropy), and discovery-graded target weighting.",
"strategic_intent": "Develop and execute controlled negative-sampling and loss experiments on the public validation split."
},
"recorded": {
"id": "update_topic__call_58721",
"name": "update_topic",
"args": {
"title": "Executing Validation Experiments",
"summary": "I have diagnosed key limitations in the production model: severe popularity bias driven by uniform/inverted negative weighting, lack of proper in-batch/popularity negative sampling, and uncalibrated BCE loss. I am now creating a modular validation experimentation script to systematically evaluate negative sampling distributions (popularity-biased, true in-batch), loss functions (unweighted BCE, logQ-corrected BCE, and sampled softmax cross-entropy), and discovery-graded target weighting.",
"strategic_intent": "Develop and execute controlled negative-sampling and loss experiments on the public validation split."
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_58721",
"name": "update_topic",
"response": {
"output": "Current topic: \"Executing Validation Experiments\"\nTopic summary: I have diagnosed key limitations in the production model: severe popularity bias driven by uniform/inverted negative weighting, lack of proper in-batch/popularity negative sampling, and uncalibrated BCE loss. I am now creating a modular validation experimentation script to systematically evaluate negative sampling distributions (popularity-biased, true in-batch), loss functions (unweighted BCE, logQ-corrected BCE, and sampled softmax cross-entropy), and discovery-graded target weighting.\n\nStrategic Intent: Develop and execute controlled negative-sampling and loss experiments on the public validation split."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:51:51.362Z",
"resultDisplay": "## 📂 Topic: **Executing Validation Experiments**\n\n**Summary:**\nI have diagnosed key limitations in the production model: severe popularity bias driven by uniform/inverted negative weighting, lack of proper in-batch/popularity negative sampling, and uncalibrated BCE loss. I am now creating a modular validation experimentation script to systematically evaluate negative sampling distributions (popularity-biased, true in-batch), loss functions (unweighted BCE, logQ-corrected BCE, and sampled softmax cross-entropy), and discovery-graded target weighting.\n\n> [!STRATEGY]\n> **Intent:** Develop and execute controlled negative-sampling and loss experiments on the public validation split.",
"description": "Update topic to: \"Executing Validation Experiments\"",
"displayName": "Update Topic Context",
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"result": {
"timestamp": "2026-09-29T05:51:51.363Z",
"content": "Current topic: \"Executing Validation Experiments\"\nTopic summary: I have diagnosed key limitations in the production model: severe popularity bias driven by uniform/inverted negative weighting, lack of proper in-batch/popularity negative sampling, and uncalibrated BCE loss. I am now creating a modular validation experimentation script to systematically evaluate negative sampling distributions (popularity-biased, true in-batch), loss functions (unweighted BCE, logQ-corrected BCE, and sampled softmax cross-entropy), and discovery-graded target weighting.\n\nStrategic Intent: Develop and execute controlled negative-sampling and loss experiments on the public validation split.",
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{
"id": 42,
"role": "agent",
"timestamp": "2026-09-29T05:52:19.710Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:19.710Z",
"text": "Exploring experiment execution script\n\nI'm currently mapping out `run_experiment.py`. My thinking is leaning towards importing from `train_current_model.py` and then selectively overriding components, or perhaps creating a parameterized version to manage variations. I need to analyze `train_current_model.py` further to understand which specific functions will be impacted by these changes."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:19.710Z",
"text": "Refining scoring logic\n\nI'm focusing on `score_target_microbatches` now, as its parameters `sequence_vectors`, `flat_states`, and `flat_activity` suggest it's central to processing sequence data. Understanding how these are fed and used will inform how I integrate it."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:19.710Z",
"text": "Analyzing scoring implementation details\n\nMy current focus is on the `score_target_microbatches` function. I'm dissecting the microbatch loop, particularly how `sequence_vectors` are handled with `requires_grad_(True)` when `backward` is set. This seems critical for gradient calculation during backpropagation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:19.710Z",
"text": "Calculating loss summation\n\nI'm examining the loss summation within the current microbatch. My focus is on ensuring that the `loss_sum` remains finite, as an `RuntimeError` is raised if it becomes non-finite. This check is crucial for stable training."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:19.710Z",
"text": "Investigating backward propagation logic\n\nI'm digging into how the backward pass functions within the microbatch loop. Specifically, I'm tracing how `loss_sum` is calculated and if `sequence_input.grad` is indeed being populated correctly. Ensuring gradient flow here is key to my current adjustments."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:19.710Z",
"text": "Analyzing loss calculation for negatives\n\nI'm now examining how negative items are sampled and incorporated into the loss. Specifically, I'm looking into the impact of using catalog popularity weights for negative sampling and how this might interact with the `cross_entropy` calculation and the `temperature` parameter."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:19.710Z",
"text": "Sampling negative items strategically\n\nI'm now focused on the distribution for negative item sampling. My thinking revolves around using catalog popularity, weighted by $\\alpha$ values from 0.5 to 1.0, to create a mix of uniform and popularity-based negatives. This aims to both cover the entire catalog and specifically penalize popular over-recommendations, potentially alongside true in-batch negatives."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:52:19.710Z",
"callId": "write_file__call_4493679",
"name": "write_file",
"input": {
"instruction": "Create run_experiment.py for testing negative sampling strategies",
"file_path": "/app/solution/run_experiment.py",
"content": "#!/usr/bin/env python3\n\"\"\"Run controlled experiments on the public validation split.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\nfrom dataclasses import replace\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn.functional as F\n\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\n\nimport train_current_model as trainer\nimport two_tower_model as base\nimport evaluate_public\n\n\ndef build_popularity_table(prepared: trainer.SequenceData, power: float = 0.75, table_size: int = 500_000) -> torch.Tensor:\n \"\"\"Precompute a table for ultra-fast popularity-weighted negative sampling.\"\"\"\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n item_counts = np.random.multinomial(table_size, probs)\n table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n np.random.shuffle(table)\n return torch.from_numpy(table.astype(np.int64))\n\n\ndef custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n) -> torch.Tensor:\n batch_size = len(positive_items)\n targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n\n total_neg = uniform_count + pop_count\n negatives = np.empty((batch_size, total_neg), dtype=np.int64)\n\n if uniform_count > 0:\n negatives[:, :uniform_count] = torch.randint(\n n_items, (batch_size, uniform_count), generator=generator, device=\"cpu\"\n ).numpy()\n\n if pop_count > 0 and pop_table is not None:\n idx = torch.randint(len(pop_table), (batch_size, pop_count), generator=generator, device=\"cpu\")\n negatives[:, uniform_count:] = pop_table[idx].numpy()\n\n # Reject invalid negatives\n for _ in range(32):\n invalid = trainer.invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n break\n num_invalid = int(invalid.sum())\n if pop_table is not None:\n repl_idx = torch.randint(len(pop_table), (num_invalid,), generator=generator, device=\"cpu\")\n negatives[invalid] = pop_table[repl_idx].numpy()\n else:\n negatives[invalid] = torch.randint(n_items, (num_invalid,), generator=generator, device=\"cpu\").numpy()\n\n return torch.from_numpy(negatives).to(device)\n\n\ndef score_target_microbatches_custom(\n model: base.SequentialTwoTower,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n loss_type: str = \"bce_unweighted\",\n temperature: float = 1.0,\n item_logq: torch.Tensor | None = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = sequence_vectors[begin:end].detach().requires_grad_(True)\n else:\n sequence_input = sequence_vectors[begin:end]\n\n liked_vectors, disliked_vectors, _ = trainer.pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n\n query_vectors = trainer.user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = trainer.candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n\n if loss_type == \"bce_unweighted\":\n # BCE where all negatives have equal weight (no inverse propensity downweighting)\n pos_loss = F.softplus(-positive_logits)\n neg_loss = F.softplus(negative_logits).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type == \"bce_logq\":\n # BCE with logQ correction: s - log(q)\n pos_lq = item_logq[positive_items[begin:end]]\n neg_lq = item_logq[negatives[begin:end]]\n pos_loss = F.softplus(-(positive_logits - pos_lq))\n neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type.startswith(\"softmax\"):\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n if \"logq\" in loss_type and item_logq is not None:\n pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n neg_lq = item_logq[negatives[begin:end]]\n all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n all_logits = all_logits - all_logq\n scaled_logits = all_logits / temperature\n labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * loss).sum()\n else:\n raise ValueError(f\"Unknown loss_type: {loss_type}\")\n\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n"
},
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"id": "write_file__call_4493679",
"name": "write_file",
"args": {
"instruction": "Create run_experiment.py for testing negative sampling strategies",
"file_path": "/app/solution/run_experiment.py",
"content": "#!/usr/bin/env python3\n\"\"\"Run controlled experiments on the public validation split.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\nfrom dataclasses import replace\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn.functional as F\n\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\n\nimport train_current_model as trainer\nimport two_tower_model as base\nimport evaluate_public\n\n\ndef build_popularity_table(prepared: trainer.SequenceData, power: float = 0.75, table_size: int = 500_000) -> torch.Tensor:\n \"\"\"Precompute a table for ultra-fast popularity-weighted negative sampling.\"\"\"\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n item_counts = np.random.multinomial(table_size, probs)\n table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n np.random.shuffle(table)\n return torch.from_numpy(table.astype(np.int64))\n\n\ndef custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n) -> torch.Tensor:\n batch_size = len(positive_items)\n targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n\n total_neg = uniform_count + pop_count\n negatives = np.empty((batch_size, total_neg), dtype=np.int64)\n\n if uniform_count > 0:\n negatives[:, :uniform_count] = torch.randint(\n n_items, (batch_size, uniform_count), generator=generator, device=\"cpu\"\n ).numpy()\n\n if pop_count > 0 and pop_table is not None:\n idx = torch.randint(len(pop_table), (batch_size, pop_count), generator=generator, device=\"cpu\")\n negatives[:, uniform_count:] = pop_table[idx].numpy()\n\n # Reject invalid negatives\n for _ in range(32):\n invalid = trainer.invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n break\n num_invalid = int(invalid.sum())\n if pop_table is not None:\n repl_idx = torch.randint(len(pop_table), (num_invalid,), generator=generator, device=\"cpu\")\n negatives[invalid] = pop_table[repl_idx].numpy()\n else:\n negatives[invalid] = torch.randint(n_items, (num_invalid,), generator=generator, device=\"cpu\").numpy()\n\n return torch.from_numpy(negatives).to(device)\n\n\ndef score_target_microbatches_custom(\n model: base.SequentialTwoTower,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n loss_type: str = \"bce_unweighted\",\n temperature: float = 1.0,\n item_logq: torch.Tensor | None = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = sequence_vectors[begin:end].detach().requires_grad_(True)\n else:\n sequence_input = sequence_vectors[begin:end]\n\n liked_vectors, disliked_vectors, _ = trainer.pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n\n query_vectors = trainer.user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = trainer.candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n\n if loss_type == \"bce_unweighted\":\n # BCE where all negatives have equal weight (no inverse propensity downweighting)\n pos_loss = F.softplus(-positive_logits)\n neg_loss = F.softplus(negative_logits).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type == \"bce_logq\":\n # BCE with logQ correction: s - log(q)\n pos_lq = item_logq[positive_items[begin:end]]\n neg_lq = item_logq[negatives[begin:end]]\n pos_loss = F.softplus(-(positive_logits - pos_lq))\n neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type.startswith(\"softmax\"):\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n if \"logq\" in loss_type and item_logq is not None:\n pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n neg_lq = item_logq[negatives[begin:end]]\n all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n all_logits = all_logits - all_logq\n scaled_logits = all_logits / temperature\n labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * loss).sum()\n else:\n raise ValueError(f\"Unknown loss_type: {loss_type}\")\n\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_4493679",
"name": "write_file",
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"output": "Successfully created and wrote to new file: /app/solution/run_experiment.py. Here is the updated code:\n#!/usr/bin/env python3\n\"\"\"Run controlled experiments on the public validation split.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\nfrom dataclasses import replace\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn.functional as F\n\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\n\nimport train_current_model as trainer\nimport two_tower_model as base\nimport evaluate_public\n\n\ndef build_popularity_table(prepared: trainer.SequenceData, power: float = 0.75, table_size: int = 500_000) -> torch.Tensor:\n \"\"\"Precompute a table for ultra-fast popularity-weighted negative sampling.\"\"\"\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n item_counts = np.random.multinomial(table_size, probs)\n table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n np.random.shuffle(table)\n return torch.from_numpy(table.astype(np.int64))\n\n\ndef custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n) -> torch.Tensor:\n batch_size = len(positive_items)\n targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n\n total_neg = uniform_count + pop_count\n negatives = np.empty((batch_size, total_neg), dtype=np.int64)\n\n if uniform_count > 0:\n negatives[:, :uniform_count] = torch.randint(\n n_items, (batch_size, uniform_count), generator=generator, device=\"cpu\"\n ).numpy()\n\n if pop_count > 0 and pop_table is not None:\n idx = torch.randint(len(pop_table), (batch_size, pop_count), generator=generator, device=\"cpu\")\n negatives[:, uniform_count:] = pop_table[idx].numpy()\n\n # Reject invalid negatives\n for _ in range(32):\n invalid = trainer.invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n break\n num_invalid = int(invalid.sum())\n if pop_table is not None:\n repl_idx = torch.randint(len(pop_table), (num_invalid,), generator=generator, device=\"cpu\")\n negatives[invalid] = pop_table[repl_idx].numpy()\n else:\n negatives[invalid] = torch.randint(n_items, (num_invalid,), generator=generator, device=\"cpu\").numpy()\n\n return torch.from_numpy(negatives).to(device)\n\n\ndef score_target_microbatches_custom(\n model: base.SequentialTwoTower,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n loss_type: str = \"bce_unweighted\",\n temperature: float = 1.0,\n item_logq: torch.Tensor | None = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = sequence_vectors[begin:end].detach().requires_grad_(True)\n else:\n sequence_input = sequence_vectors[begin:end]\n\n liked_vectors, disliked_vectors, _ = trainer.pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n\n query_vectors = trainer.user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = trainer.candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n\n if loss_type == \"bce_unweighted\":\n # BCE where all negatives have equal weight (no inverse propensity downweighting)\n pos_loss = F.softplus(-positive_logits)\n neg_loss = F.softplus(negative_logits).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type == \"bce_logq\":\n # BCE with logQ correction: s - log(q)\n pos_lq = item_logq[positive_items[begin:end]]\n neg_lq = item_logq[negatives[begin:end]]\n pos_loss = F.softplus(-(positive_logits - pos_lq))\n neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type.startswith(\"softmax\"):\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n if \"logq\" in loss_type and item_logq is not None:\n pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n neg_lq = item_logq[negatives[begin:end]]\n all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n all_logits = all_logits - all_logq\n scaled_logits = all_logits / temperature\n labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * loss).sum()\n else:\n raise ValueError(f\"Unknown loss_type: {loss_type}\")\n\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:52:19.721Z",
"resultDisplay": {
"fileDiff": "Index: run_experiment.py\n===================================================================\n--- run_experiment.py\tOriginal\n+++ run_experiment.py\tWritten\n@@ -0,0 +1,193 @@\n+#!/usr/bin/env python3\n+\"\"\"Run controlled experiments on the public validation split.\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+import sys\n+import time\n+from pathlib import Path\n+from dataclasses import replace\n+\n+import numpy as np\n+import pandas as pd\n+import torch\n+import torch.nn.functional as F\n+\n+sys.path.insert(0, \"/app/solution\")\n+sys.path.insert(0, \"/task\")\n+\n+import train_current_model as trainer\n+import two_tower_model as base\n+import evaluate_public\n+\n+\n+def build_popularity_table(prepared: trainer.SequenceData, power: float = 0.75, table_size: int = 500_000) -> torch.Tensor:\n+ \"\"\"Precompute a table for ultra-fast popularity-weighted negative sampling.\"\"\"\n+ counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n+ probs = np.power(counts + 1.0, power)\n+ probs /= probs.sum()\n+ item_counts = np.random.multinomial(table_size, probs)\n+ table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n+ np.random.shuffle(table)\n+ return torch.from_numpy(table.astype(np.int64))\n+\n+\n+def custom_draw_negatives(\n+ positive_items: torch.Tensor,\n+ user_rows: np.ndarray,\n+ liked_items: torch.Tensor,\n+ known_positive_packed: np.ndarray,\n+ n_items: int,\n+ uniform_count: int,\n+ pop_count: int,\n+ pop_table: torch.Tensor | None,\n+ generator: torch.Generator,\n+ device: torch.device,\n+) -> torch.Tensor:\n+ batch_size = len(positive_items)\n+ targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n+ liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n+\n+ total_neg = uniform_count + pop_count\n+ negatives = np.empty((batch_size, total_neg), dtype=np.int64)\n+\n+ if uniform_count > 0:\n+ negatives[:, :uniform_count] = torch.randint(\n+ n_items, (batch_size, uniform_count), generator=generator, device=\"cpu\"\n+ ).numpy()\n+\n+ if pop_count > 0 and pop_table is not None:\n+ idx = torch.randint(len(pop_table), (batch_size, pop_count), generator=generator, device=\"cpu\")\n+ negatives[:, uniform_count:] = pop_table[idx].numpy()\n+\n+ # Reject invalid negatives\n+ for _ in range(32):\n+ invalid = trainer.invalid_full_history_negatives(\n+ negatives,\n+ targets_cpu,\n+ user_rows,\n+ known_positive_packed,\n+ liked_cpu,\n+ )\n+ if not np.any(invalid):\n+ break\n+ num_invalid = int(invalid.sum())\n+ if pop_table is not None:\n+ repl_idx = torch.randint(len(pop_table), (num_invalid,), generator=generator, device=\"cpu\")\n+ negatives[invalid] = pop_table[repl_idx].numpy()\n+ else:\n+ negatives[invalid] = torch.randint(n_items, (num_invalid,), generator=generator, device=\"cpu\").numpy()\n+\n+ return torch.from_numpy(negatives).to(device)\n+\n+\n+def score_target_microbatches_custom(\n+ model: base.SequentialTwoTower,\n+ prepared: trainer.SequenceData,\n+ config: trainer.Config,\n+ sequence_vectors: torch.Tensor,\n+ flat_states: np.ndarray,\n+ flat_activity: np.ndarray,\n+ positive_items: torch.Tensor,\n+ negatives: torch.Tensor,\n+ sample_weights: np.ndarray,\n+ device: torch.device,\n+ backward: bool,\n+ loss_type: str = \"bce_unweighted\",\n+ temperature: float = 1.0,\n+ item_logq: torch.Tensor | None = None,\n+) -> tuple[dict[str, float], torch.Tensor | None]:\n+ target_count = len(positive_items)\n+ total_weight = float(sample_weights.sum())\n+ if total_weight <= 0:\n+ raise RuntimeError(\"target block has no positive sample weight\")\n+ sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n+ statistics = {\n+ \"loss_sum\": 0.0,\n+ \"positive_sum\": 0.0,\n+ \"negative_sum\": 0.0,\n+ \"pair_correct\": 0.0,\n+ \"weight_sum\": 0.0,\n+ }\n+\n+ for begin in range(0, target_count, config.loss_microbatch_size):\n+ end = min(begin + config.loss_microbatch_size, target_count)\n+ selected_states = flat_states[begin:end]\n+ if backward:\n+ sequence_input = sequence_vectors[begin:end].detach().requires_grad_(True)\n+ else:\n+ sequence_input = sequence_vectors[begin:end]\n+\n+ liked_vectors, disliked_vectors, _ = trainer.pool_explicit_states(\n+ model, prepared, selected_states, device\n+ )\n+ counts = prepared.state_counts[selected_states]\n+ numeric = np.zeros((end - begin, 6), dtype=np.float32)\n+ numeric[:, :4] = flat_activity[begin:end]\n+ numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n+ numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n+\n+ query_vectors = trainer.user_queries(\n+ model,\n+ sequence_input,\n+ liked_vectors,\n+ disliked_vectors,\n+ torch.from_numpy(numeric).to(device),\n+ )\n+ positive_vectors, negative_vectors = trainer.candidate_vectors(\n+ model,\n+ positive_items[begin:end],\n+ negatives[begin:end],\n+ )\n+ positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n+ negative_logits = torch.einsum(\n+ \"bd,bkd->bk\", query_vectors, negative_vectors\n+ )\n+\n+ selected_weights = torch.from_numpy(\n+ sample_weights[begin:end].astype(np.float32, copy=False)\n+ ).to(device)\n+\n+ if loss_type == \"bce_unweighted\":\n+ # BCE where all negatives have equal weight (no inverse propensity downweighting)\n+ pos_loss = F.softplus(-positive_logits)\n+ neg_loss = F.softplus(negative_logits).mean(dim=1)\n+ loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n+ elif loss_type == \"bce_logq\":\n+ # BCE with logQ correction: s - log(q)\n+ pos_lq = item_logq[positive_items[begin:end]]\n+ neg_lq = item_logq[negatives[begin:end]]\n+ pos_loss = F.softplus(-(positive_logits - pos_lq))\n+ neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n+ loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n+ elif loss_type.startswith(\"softmax\"):\n+ all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n+ if \"logq\" in loss_type and item_logq is not None:\n+ pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n+ neg_lq = item_logq[negatives[begin:end]]\n+ all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n+ all_logits = all_logits - all_logq\n+ scaled_logits = all_logits / temperature\n+ labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n+ loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n+ loss_sum = (selected_weights * loss).sum()\n+ else:\n+ raise ValueError(f\"Unknown loss_type: {loss_type}\")\n+\n+ if not bool(torch.isfinite(loss_sum)):\n+ raise RuntimeError(\"training produced non-finite loss\")\n+ if backward:\n+ (loss_sum / total_weight).backward()\n+ if sequence_input.grad is None:\n+ raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n+ sequence_gradient[begin:end] = sequence_input.grad\n+\n+ statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n+ statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n+ statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n+ statistics[\"pair_correct\"] += float(\n+ (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n+ )\n+ statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n+\n+ return statistics, sequence_gradient\n",
"fileName": "run_experiment.py",
"filePath": "/app/solution/run_experiment.py",
"originalContent": "",
"newContent": "#!/usr/bin/env python3\n\"\"\"Run controlled experiments on the public validation split.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\nfrom dataclasses import replace\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn.functional as F\n\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\n\nimport train_current_model as trainer\nimport two_tower_model as base\nimport evaluate_public\n\n\ndef build_popularity_table(prepared: trainer.SequenceData, power: float = 0.75, table_size: int = 500_000) -> torch.Tensor:\n \"\"\"Precompute a table for ultra-fast popularity-weighted negative sampling.\"\"\"\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n item_counts = np.random.multinomial(table_size, probs)\n table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n np.random.shuffle(table)\n return torch.from_numpy(table.astype(np.int64))\n\n\ndef custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n) -> torch.Tensor:\n batch_size = len(positive_items)\n targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n\n total_neg = uniform_count + pop_count\n negatives = np.empty((batch_size, total_neg), dtype=np.int64)\n\n if uniform_count > 0:\n negatives[:, :uniform_count] = torch.randint(\n n_items, (batch_size, uniform_count), generator=generator, device=\"cpu\"\n ).numpy()\n\n if pop_count > 0 and pop_table is not None:\n idx = torch.randint(len(pop_table), (batch_size, pop_count), generator=generator, device=\"cpu\")\n negatives[:, uniform_count:] = pop_table[idx].numpy()\n\n # Reject invalid negatives\n for _ in range(32):\n invalid = trainer.invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n break\n num_invalid = int(invalid.sum())\n if pop_table is not None:\n repl_idx = torch.randint(len(pop_table), (num_invalid,), generator=generator, device=\"cpu\")\n negatives[invalid] = pop_table[repl_idx].numpy()\n else:\n negatives[invalid] = torch.randint(n_items, (num_invalid,), generator=generator, device=\"cpu\").numpy()\n\n return torch.from_numpy(negatives).to(device)\n\n\ndef score_target_microbatches_custom(\n model: base.SequentialTwoTower,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n loss_type: str = \"bce_unweighted\",\n temperature: float = 1.0,\n item_logq: torch.Tensor | None = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = sequence_vectors[begin:end].detach().requires_grad_(True)\n else:\n sequence_input = sequence_vectors[begin:end]\n\n liked_vectors, disliked_vectors, _ = trainer.pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n\n query_vectors = trainer.user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = trainer.candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n\n if loss_type == \"bce_unweighted\":\n # BCE where all negatives have equal weight (no inverse propensity downweighting)\n pos_loss = F.softplus(-positive_logits)\n neg_loss = F.softplus(negative_logits).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type == \"bce_logq\":\n # BCE with logQ correction: s - log(q)\n pos_lq = item_logq[positive_items[begin:end]]\n neg_lq = item_logq[negatives[begin:end]]\n pos_loss = F.softplus(-(positive_logits - pos_lq))\n neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type.startswith(\"softmax\"):\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n if \"logq\" in loss_type and item_logq is not None:\n pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n neg_lq = item_logq[negatives[begin:end]]\n all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n all_logits = all_logits - all_logq\n scaled_logits = all_logits / temperature\n labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * loss).sum()\n else:\n raise ValueError(f\"Unknown loss_type: {loss_type}\")\n\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n",
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"result": {
"timestamp": "2026-09-29T05:52:19.723Z",
"content": "Successfully created and wrote to new file: /app/solution/run_experiment.py. Here is the updated code:\n#!/usr/bin/env python3\n\"\"\"Run controlled experiments on the public validation split.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\nfrom dataclasses import replace\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn.functional as F\n\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\n\nimport train_current_model as trainer\nimport two_tower_model as base\nimport evaluate_public\n\n\ndef build_popularity_table(prepared: trainer.SequenceData, power: float = 0.75, table_size: int = 500_000) -> torch.Tensor:\n \"\"\"Precompute a table for ultra-fast popularity-weighted negative sampling.\"\"\"\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n item_counts = np.random.multinomial(table_size, probs)\n table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n np.random.shuffle(table)\n return torch.from_numpy(table.astype(np.int64))\n\n\ndef custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n) -> torch.Tensor:\n batch_size = len(positive_items)\n targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n\n total_neg = uniform_count + pop_count\n negatives = np.empty((batch_size, total_neg), dtype=np.int64)\n\n if uniform_count > 0:\n negatives[:, :uniform_count] = torch.randint(\n n_items, (batch_size, uniform_count), generator=generator, device=\"cpu\"\n ).numpy()\n\n if pop_count > 0 and pop_table is not None:\n idx = torch.randint(len(pop_table), (batch_size, pop_count), generator=generator, device=\"cpu\")\n negatives[:, uniform_count:] = pop_table[idx].numpy()\n\n # Reject invalid negatives\n for _ in range(32):\n invalid = trainer.invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n break\n num_invalid = int(invalid.sum())\n if pop_table is not None:\n repl_idx = torch.randint(len(pop_table), (num_invalid,), generator=generator, device=\"cpu\")\n negatives[invalid] = pop_table[repl_idx].numpy()\n else:\n negatives[invalid] = torch.randint(n_items, (num_invalid,), generator=generator, device=\"cpu\").numpy()\n\n return torch.from_numpy(negatives).to(device)\n\n\ndef score_target_microbatches_custom(\n model: base.SequentialTwoTower,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n loss_type: str = \"bce_unweighted\",\n temperature: float = 1.0,\n item_logq: torch.Tensor | None = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = sequence_vectors[begin:end].detach().requires_grad_(True)\n else:\n sequence_input = sequence_vectors[begin:end]\n\n liked_vectors, disliked_vectors, _ = trainer.pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n\n query_vectors = trainer.user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = trainer.candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n\n if loss_type == \"bce_unweighted\":\n # BCE where all negatives have equal weight (no inverse propensity downweighting)\n pos_loss = F.softplus(-positive_logits)\n neg_loss = F.softplus(negative_logits).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type == \"bce_logq\":\n # BCE with logQ correction: s - log(q)\n pos_lq = item_logq[positive_items[begin:end]]\n neg_lq = item_logq[negatives[begin:end]]\n pos_loss = F.softplus(-(positive_logits - pos_lq))\n neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type.startswith(\"softmax\"):\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n if \"logq\" in loss_type and item_logq is not None:\n pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n neg_lq = item_logq[negatives[begin:end]]\n all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n all_logits = all_logits - all_logq\n scaled_logits = all_logits / temperature\n labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * loss).sum()\n else:\n raise ValueError(f\"Unknown loss_type: {loss_type}\")\n\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n",
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"timestamp": "2026-09-29T05:52:23.741Z",
"blocks": [
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"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:23.741Z",
"text": "Developing `run_experiment.py`\n\nI'm now focused on completing `run_experiment.py` by integrating the training loop, ranking, and evaluation mechanisms. This involves implementing `run_training_epoch_custom`, the `train_and_evaluate` function, and defining necessary CLI arguments for execution."
},
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"timestamp": "2026-09-29T05:52:23.741Z",
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"id": "read_file__call_190417",
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"output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 150-194 of 194 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: 195.\n\n--- FILE CONTENT (truncated) ---\n\n if loss_type == \"bce_unweighted\":\n # BCE where all negatives have equal weight (no inverse propensity downweighting)\n pos_loss = F.softplus(-positive_logits)\n neg_loss = F.softplus(negative_logits).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type == \"bce_logq\":\n # BCE with logQ correction: s - log(q)\n pos_lq = item_logq[positive_items[begin:end]]\n neg_lq = item_logq[negatives[begin:end]]\n pos_loss = F.softplus(-(positive_logits - pos_lq))\n neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type.startswith(\"softmax\"):\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n if \"logq\" in loss_type and item_logq is not None:\n pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n neg_lq = item_logq[negatives[begin:end]]\n all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n all_logits = all_logits - all_logq\n scaled_logits = all_logits / temperature\n labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * loss).sum()\n else:\n raise ValueError(f\"Unknown loss_type: {loss_type}\")\n\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n"
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"status": "success",
"timestamp": "2026-09-29T05:52:23.747Z",
"resultDisplay": "Read lines 150-194 of 194 from solution/run_experiment.py",
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"timestamp": "2026-09-29T05:52:23.748Z",
"content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 150-194 of 194 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: 195.\n\n--- FILE CONTENT (truncated) ---\n\n if loss_type == \"bce_unweighted\":\n # BCE where all negatives have equal weight (no inverse propensity downweighting)\n pos_loss = F.softplus(-positive_logits)\n neg_loss = F.softplus(negative_logits).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type == \"bce_logq\":\n # BCE with logQ correction: s - log(q)\n pos_lq = item_logq[positive_items[begin:end]]\n neg_lq = item_logq[negatives[begin:end]]\n pos_loss = F.softplus(-(positive_logits - pos_lq))\n neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type.startswith(\"softmax\"):\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n if \"logq\" in loss_type and item_logq is not None:\n pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n neg_lq = item_logq[negatives[begin:end]]\n all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n all_logits = all_logits - all_logq\n scaled_logits = all_logits / temperature\n labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * loss).sum()\n else:\n raise ValueError(f\"Unknown loss_type: {loss_type}\")\n\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n",
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{
"id": 44,
"role": "agent",
"timestamp": "2026-09-29T05:52:53.512Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:53.512Z",
"text": "Implementing Core Functions\n\nI'm currently focused on integrating the remaining training and evaluation functions into `run_experiment.py`. This includes adding `run_training_epoch_custom` and `train_custom`. My next step will be to flesh out the `main()` function, ensuring it correctly handles the `--uniform-negatives` argument with its default value."
},
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"kind": "tool",
"timestamp": "2026-09-29T05:52:53.512Z",
"callId": "write_file__call_37765",
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"content": "#!/usr/bin/env python3\n\"\"\"Run controlled experiments on the public validation split.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\nfrom dataclasses import replace\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn.functional as F\n\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\n\nimport train_current_model as trainer\nimport two_tower_model as base\nimport evaluate_public\n\n\ndef build_popularity_table(prepared: trainer.SequenceData, power: float = 0.75, table_size: int = 500_000) -> torch.Tensor:\n \"\"\"Precompute a table for ultra-fast popularity-weighted negative sampling.\"\"\"\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n item_counts = np.random.multinomial(table_size, probs)\n table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n np.random.shuffle(table)\n return torch.from_numpy(table.astype(np.int64))\n\n\ndef compute_item_logq(prepared: trainer.SequenceData, power: float = 0.75) -> torch.Tensor:\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n logq = np.log(probs).astype(np.float32)\n return torch.from_numpy(logq)\n\n\ndef custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n) -> torch.Tensor:\n batch_size = len(positive_items)\n targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n\n total_neg = uniform_count + pop_count\n negatives = np.empty((batch_size, total_neg), dtype=np.int64)\n\n if uniform_count > 0:\n negatives[:, :uniform_count] = torch.randint(\n n_items, (batch_size, uniform_count), generator=generator, device=\"cpu\"\n ).numpy()\n\n if pop_count > 0 and pop_table is not None:\n idx = torch.randint(len(pop_table), (batch_size, pop_count), generator=generator, device=\"cpu\")\n negatives[:, uniform_count:] = pop_table[idx].numpy()\n\n # Reject invalid negatives\n for _ in range(32):\n invalid = trainer.invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n break\n num_invalid = int(invalid.sum())\n if pop_table is not None:\n repl_idx = torch.randint(len(pop_table), (num_invalid,), generator=generator, device=\"cpu\")\n negatives[invalid] = pop_table[repl_idx].numpy()\n else:\n negatives[invalid] = torch.randint(n_items, (num_invalid,), generator=generator, device=\"cpu\").numpy()\n\n return torch.from_numpy(negatives).to(device)\n\n\ndef score_target_microbatches_custom(\n model: base.SequentialTwoTower,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n loss_type: str = \"bce_unweighted\",\n temperature: float = 1.0,\n item_logq: torch.Tensor | None = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = sequence_vectors[begin:end].detach().requires_grad_(True)\n else:\n sequence_input = sequence_vectors[begin:end]\n\n liked_vectors, disliked_vectors, _ = trainer.pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n\n query_vectors = trainer.user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = trainer.candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n\n if loss_type == \"bce_unweighted\":\n # BCE where all negatives have equal weight (no inverse propensity downweighting)\n pos_loss = F.softplus(-positive_logits)\n neg_loss = F.softplus(negative_logits).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type == \"bce_logq\":\n # BCE with logQ correction: s - log(q)\n pos_lq = item_logq[positive_items[begin:end]]\n neg_lq = item_logq[negatives[begin:end]]\n pos_loss = F.softplus(-(positive_logits - pos_lq))\n neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type.startswith(\"softmax\"):\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n if \"logq\" in loss_type and item_logq is not None:\n pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n neg_lq = item_logq[negatives[begin:end]]\n all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n all_logits = all_logits - all_logq\n scaled_logits = all_logits / temperature\n labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * loss).sum()\n else:\n raise ValueError(f\"Unknown loss_type: {loss_type}\")\n\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n\n\ndef run_training_epoch_custom(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n known_positive_packed: np.ndarray | None,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n loss_type: str,\n temperature: float,\n item_logq: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = uniform_count + pop_count\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = trainer.build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = trainer.event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n\n negatives = custom_draw_negatives(\n positive_items,\n flat_user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n uniform_count,\n pop_count,\n pop_table,\n generator,\n device,\n )\n\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches_custom(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n loss_type=loss_type,\n temperature=temperature,\n item_logq=item_logq,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n torch.nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 250 == 0:\n running_loss = total_loss / max(total_weight, 1.0e-12)\n trainer.log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"running_loss={running_loss:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--uniform-negatives\", type=int, default=16)\n parser.add_argument(\"--pop-negatives\", type=int, default=16)\n parser.add_argument(\"--pop-power\", type=float, default=0.75)\n parser.add_argument(\n \"--loss-type\",\n choices=(\"bce_unweighted\", \"bce_logq\", \"softmax\", \"softmax_logq\"),\n default=\"bce_unweighted\",\n )\n parser.add_argument(\"--temperature\", type=float, default=0.2)\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"organic-strong\", \"discovery-graded\"),\n default=\"organic-strong\",\n )\n parser.add_argument(\"--epochs\", type=int, default=1)\n parser.add_argument(\"--learning-rate\", type=float, default=1e-3)\n parser.add_argument(\"--output\", type=Path, default=Path(\"/app/solution/exp_output.parquet\"))\n args = parser.parse_args()\n\n torch.set_num_threads(8)\n config = trainer.Config(\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n target_weighting=args.target_weighting,\n )\n trainer.seed_everything(config.seed)\n device = torch.device(\"cpu\")\n\n public = Path(\"/task\")\n cache_path = Path(\"/app/solution/validation_cache.npz\")\n prepared = trainer.load_cache(cache_path, \"validation\", config)\n eligibility = trainer.CandidateSet.read(public / \"data\" / \"validation_eligible_candidates.npz\")\n\n partition = trainer.chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n\n model = base.SequentialTwoTower(prepared.item_features, trainer.model_config(config)).to(device)\n optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay)\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n\n target_weights = trainer.build_target_weights(\n public,\n \"validation\",\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = trainer.build_known_positive_filter(prepared, partition.history_training_counts)\n\n pop_table = None\n if args.pop_negatives > 0:\n pop_table = build_popularity_table(prepared, power=args.pop_power)\n trainer.log(f\"built popularity table: power={args.pop_power} size={len(pop_table):,}\")\n\n item_logq = None\n if \"logq\" in args.loss_type:\n item_logq = compute_item_logq(prepared, power=args.pop_power).to(device)\n\n for epoch in range(1, config.epochs + 1):\n diag = run_training_epoch_custom(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n partition.training_counts,\n known_positive_packed,\n args.uniform_negatives,\n args.pop_negatives,\n pop_table,\n args.loss_type,\n args.temperature,\n item_logq,\n generator,\n device,\n epoch,\n )\n trainer.log(f\"Epoch {epoch} finished: loss={diag['loss']:.5f} pair_acc={diag['pair_accuracy']:.4f} in {diag['seconds']:.1f}s\")\n\n ranking, _ = trainer.rank_candidates(model, prepared, eligibility, config, device, ranking_batch_size=64)\n trainer.write_ranking(ranking, args.output)\n\n # Evaluate NDCG@10\n targets_frame = pd.read_parquet(public / \"data\" / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n cand_set = evaluate_public.CandidateSet(public / \"data\" / \"validation_eligible_candidates.npz\")\n graded = evaluate_public.validation_truth(cand_set, targets)\n ranked = evaluate_public.ranked_items(ranking)\n score_report = evaluate_public.score(ranked, graded)\n trainer.log(f\"VALIDATION SCORE: {score_report}\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"file_path": "/app/solution/run_experiment.py",
"instruction": "Add complete training, ranking and evaluation logic to run_experiment.py"
},
"recorded": {
"id": "write_file__call_37765",
"name": "write_file",
"args": {
"content": "#!/usr/bin/env python3\n\"\"\"Run controlled experiments on the public validation split.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\nfrom dataclasses import replace\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn.functional as F\n\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\n\nimport train_current_model as trainer\nimport two_tower_model as base\nimport evaluate_public\n\n\ndef build_popularity_table(prepared: trainer.SequenceData, power: float = 0.75, table_size: int = 500_000) -> torch.Tensor:\n \"\"\"Precompute a table for ultra-fast popularity-weighted negative sampling.\"\"\"\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n item_counts = np.random.multinomial(table_size, probs)\n table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n np.random.shuffle(table)\n return torch.from_numpy(table.astype(np.int64))\n\n\ndef compute_item_logq(prepared: trainer.SequenceData, power: float = 0.75) -> torch.Tensor:\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n logq = np.log(probs).astype(np.float32)\n return torch.from_numpy(logq)\n\n\ndef custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n) -> torch.Tensor:\n batch_size = len(positive_items)\n targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n\n total_neg = uniform_count + pop_count\n negatives = np.empty((batch_size, total_neg), dtype=np.int64)\n\n if uniform_count > 0:\n negatives[:, :uniform_count] = torch.randint(\n n_items, (batch_size, uniform_count), generator=generator, device=\"cpu\"\n ).numpy()\n\n if pop_count > 0 and pop_table is not None:\n idx = torch.randint(len(pop_table), (batch_size, pop_count), generator=generator, device=\"cpu\")\n negatives[:, uniform_count:] = pop_table[idx].numpy()\n\n # Reject invalid negatives\n for _ in range(32):\n invalid = trainer.invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n break\n num_invalid = int(invalid.sum())\n if pop_table is not None:\n repl_idx = torch.randint(len(pop_table), (num_invalid,), generator=generator, device=\"cpu\")\n negatives[invalid] = pop_table[repl_idx].numpy()\n else:\n negatives[invalid] = torch.randint(n_items, (num_invalid,), generator=generator, device=\"cpu\").numpy()\n\n return torch.from_numpy(negatives).to(device)\n\n\ndef score_target_microbatches_custom(\n model: base.SequentialTwoTower,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n loss_type: str = \"bce_unweighted\",\n temperature: float = 1.0,\n item_logq: torch.Tensor | None = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = sequence_vectors[begin:end].detach().requires_grad_(True)\n else:\n sequence_input = sequence_vectors[begin:end]\n\n liked_vectors, disliked_vectors, _ = trainer.pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n\n query_vectors = trainer.user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = trainer.candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n\n if loss_type == \"bce_unweighted\":\n # BCE where all negatives have equal weight (no inverse propensity downweighting)\n pos_loss = F.softplus(-positive_logits)\n neg_loss = F.softplus(negative_logits).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type == \"bce_logq\":\n # BCE with logQ correction: s - log(q)\n pos_lq = item_logq[positive_items[begin:end]]\n neg_lq = item_logq[negatives[begin:end]]\n pos_loss = F.softplus(-(positive_logits - pos_lq))\n neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type.startswith(\"softmax\"):\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n if \"logq\" in loss_type and item_logq is not None:\n pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n neg_lq = item_logq[negatives[begin:end]]\n all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n all_logits = all_logits - all_logq\n scaled_logits = all_logits / temperature\n labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * loss).sum()\n else:\n raise ValueError(f\"Unknown loss_type: {loss_type}\")\n\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n\n\ndef run_training_epoch_custom(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n known_positive_packed: np.ndarray | None,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n loss_type: str,\n temperature: float,\n item_logq: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = uniform_count + pop_count\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = trainer.build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = trainer.event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n\n negatives = custom_draw_negatives(\n positive_items,\n flat_user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n uniform_count,\n pop_count,\n pop_table,\n generator,\n device,\n )\n\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches_custom(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n loss_type=loss_type,\n temperature=temperature,\n item_logq=item_logq,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n torch.nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 250 == 0:\n running_loss = total_loss / max(total_weight, 1.0e-12)\n trainer.log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"running_loss={running_loss:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--uniform-negatives\", type=int, default=16)\n parser.add_argument(\"--pop-negatives\", type=int, default=16)\n parser.add_argument(\"--pop-power\", type=float, default=0.75)\n parser.add_argument(\n \"--loss-type\",\n choices=(\"bce_unweighted\", \"bce_logq\", \"softmax\", \"softmax_logq\"),\n default=\"bce_unweighted\",\n )\n parser.add_argument(\"--temperature\", type=float, default=0.2)\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"organic-strong\", \"discovery-graded\"),\n default=\"organic-strong\",\n )\n parser.add_argument(\"--epochs\", type=int, default=1)\n parser.add_argument(\"--learning-rate\", type=float, default=1e-3)\n parser.add_argument(\"--output\", type=Path, default=Path(\"/app/solution/exp_output.parquet\"))\n args = parser.parse_args()\n\n torch.set_num_threads(8)\n config = trainer.Config(\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n target_weighting=args.target_weighting,\n )\n trainer.seed_everything(config.seed)\n device = torch.device(\"cpu\")\n\n public = Path(\"/task\")\n cache_path = Path(\"/app/solution/validation_cache.npz\")\n prepared = trainer.load_cache(cache_path, \"validation\", config)\n eligibility = trainer.CandidateSet.read(public / \"data\" / \"validation_eligible_candidates.npz\")\n\n partition = trainer.chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n\n model = base.SequentialTwoTower(prepared.item_features, trainer.model_config(config)).to(device)\n optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay)\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n\n target_weights = trainer.build_target_weights(\n public,\n \"validation\",\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = trainer.build_known_positive_filter(prepared, partition.history_training_counts)\n\n pop_table = None\n if args.pop_negatives > 0:\n pop_table = build_popularity_table(prepared, power=args.pop_power)\n trainer.log(f\"built popularity table: power={args.pop_power} size={len(pop_table):,}\")\n\n item_logq = None\n if \"logq\" in args.loss_type:\n item_logq = compute_item_logq(prepared, power=args.pop_power).to(device)\n\n for epoch in range(1, config.epochs + 1):\n diag = run_training_epoch_custom(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n partition.training_counts,\n known_positive_packed,\n args.uniform_negatives,\n args.pop_negatives,\n pop_table,\n args.loss_type,\n args.temperature,\n item_logq,\n generator,\n device,\n epoch,\n )\n trainer.log(f\"Epoch {epoch} finished: loss={diag['loss']:.5f} pair_acc={diag['pair_accuracy']:.4f} in {diag['seconds']:.1f}s\")\n\n ranking, _ = trainer.rank_candidates(model, prepared, eligibility, config, device, ranking_batch_size=64)\n trainer.write_ranking(ranking, args.output)\n\n # Evaluate NDCG@10\n targets_frame = pd.read_parquet(public / \"data\" / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n cand_set = evaluate_public.CandidateSet(public / \"data\" / \"validation_eligible_candidates.npz\")\n graded = evaluate_public.validation_truth(cand_set, targets)\n ranked = evaluate_public.ranked_items(ranking)\n score_report = evaluate_public.score(ranked, graded)\n trainer.log(f\"VALIDATION SCORE: {score_report}\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"file_path": "/app/solution/run_experiment.py",
"instruction": "Add complete training, ranking and evaluation logic to run_experiment.py"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_37765",
"name": "write_file",
"response": {
"output": "Successfully overwrote file: /app/solution/run_experiment.py. Here is the updated code:\n...\n table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n np.random.shuffle(table)\n return torch.from_numpy(table.astype(np.int64))\n\n\ndef compute_item_logq(prepared: trainer.SequenceData, power: float = 0.75) -> torch.Tensor:\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n logq = np.log(probs).astype(np.float32)\n return torch.from_numpy(logq)\n\n\ndef custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n...\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n\n\ndef run_training_epoch_custom(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n known_positive_packed: np.ndarray | None,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n loss_type: str,\n temperature: float,\n item_logq: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = uniform_count + pop_count\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = trainer.build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = trainer.event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n\n negatives = custom_draw_negatives(\n positive_items,\n flat_user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n uniform_count,\n pop_count,\n pop_table,\n generator,\n device,\n )\n\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches_custom(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n loss_type=loss_type,\n temperature=temperature,\n item_logq=item_logq,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n torch.nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 250 == 0:\n running_loss = total_loss / max(total_weight, 1.0e-12)\n trainer.log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"running_loss={running_loss:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--uniform-negatives\", type=int, default=16)\n parser.add_argument(\"--pop-negatives\", type=int, default=16)\n parser.add_argument(\"--pop-power\", type=float, default=0.75)\n parser.add_argument(\n \"--loss-type\",\n choices=(\"bce_unweighted\", \"bce_logq\", \"softmax\", \"softmax_logq\"),\n default=\"bce_unweighted\",\n )\n parser.add_argument(\"--temperature\", type=float, default=0.2)\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"organic-strong\", \"discovery-graded\"),\n default=\"organic-strong\",\n )\n parser.add_argument(\"--epochs\", type=int, default=1)\n parser.add_argument(\"--learning-rate\", type=float, default=1e-3)\n parser.add_argument(\"--output\", type=Path, default=Path(\"/app/solution/exp_output.parquet\"))\n args = parser.parse_args()\n\n torch.set_num_threads(8)\n config = trainer.Config(\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n target_weighting=args.target_weighting,\n )\n trainer.seed_everything(config.seed)\n device = torch.device(\"cpu\")\n\n public = Path(\"/task\")\n cache_path = Path(\"/app/solution/validation_cache.npz\")\n prepared = trainer.load_cache(cache_path, \"validation\", config)\n eligibility = trainer.CandidateSet.read(public / \"data\" / \"validation_eligible_candidates.npz\")\n\n partition = trainer.chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n\n model = base.SequentialTwoTower(prepared.item_features, trainer.model_config(config)).to(device)\n optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay)\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n\n target_weights = trainer.build_target_weights(\n public,\n \"validation\",\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = trainer.build_known_positive_filter(prepared, partition.history_training_counts)\n\n pop_table = None\n if args.pop_negatives > 0:\n pop_table = build_popularity_table(prepared, power=args.pop_power)\n trainer.log(f\"built popularity table: power={args.pop_power} size={len(pop_table):,}\")\n\n item_logq = None\n if \"logq\" in args.loss_type:\n item_logq = compute_item_logq(prepared, power=args.pop_power).to(device)\n\n for epoch in range(1, config.epochs + 1):\n diag = run_training_epoch_custom(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n partition.training_counts,\n known_positive_packed,\n args.uniform_negatives,\n args.pop_negatives,\n pop_table,\n args.loss_type,\n args.temperature,\n item_logq,\n generator,\n device,\n epoch,\n )\n trainer.log(f\"Epoch {epoch} finished: loss={diag['loss']:.5f} pair_acc={diag['pair_accuracy']:.4f} in {diag['seconds']:.1f}s\")\n\n ranking, _ = trainer.rank_candidates(model, prepared, eligibility, config, device, ranking_batch_size=64)\n trainer.write_ranking(ranking, args.output)\n\n # Evaluate NDCG@10\n targets_frame = pd.read_parquet(public / \"data\" / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n cand_set = evaluate_public.CandidateSet(public / \"data\" / \"validation_eligible_candidates.npz\")\n graded = evaluate_public.validation_truth(cand_set, targets)\n ranked = evaluate_public.ranked_items(ranking)\n score_report = evaluate_public.score(ranked, graded)\n trainer.log(f\"VALIDATION SCORE: {score_report}\")\n\n\nif __name__ == \"__main__\":\n main()\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:52:53.521Z",
"resultDisplay": {
"fileDiff": "Index: run_experiment.py\n===================================================================\n--- run_experiment.py\tOriginal\n+++ run_experiment.py\tWritten\n@@ -32,6 +32,14 @@\n return torch.from_numpy(table.astype(np.int64))\n \n \n+def compute_item_logq(prepared: trainer.SequenceData, power: float = 0.75) -> torch.Tensor:\n+ counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n+ probs = np.power(counts + 1.0, power)\n+ probs /= probs.sum()\n+ logq = np.log(probs).astype(np.float32)\n+ return torch.from_numpy(logq)\n+\n+\n def custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n@@ -191,3 +199,263 @@\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n \n return statistics, sequence_gradient\n+\n+\n+def run_training_epoch_custom(\n+ model: base.SequentialTwoTower,\n+ optimizer: torch.optim.Optimizer,\n+ prepared: trainer.SequenceData,\n+ config: trainer.Config,\n+ target_weights: np.ndarray,\n+ window_starts: np.ndarray,\n+ target_counts: np.ndarray,\n+ known_positive_packed: np.ndarray | None,\n+ uniform_count: int,\n+ pop_count: int,\n+ pop_table: torch.Tensor | None,\n+ loss_type: str,\n+ temperature: float,\n+ item_logq: torch.Tensor | None,\n+ generator: torch.Generator,\n+ device: torch.device,\n+ epoch: int,\n+) -> dict[str, float]:\n+ model.train()\n+ train_users = np.flatnonzero(target_counts > 0)\n+ sorted_users = train_users[\n+ np.argsort(target_counts[train_users], kind=\"stable\")\n+ ]\n+ groups = [\n+ sorted_users[start : start + config.user_batch_size]\n+ for start in range(0, len(sorted_users), config.user_batch_size)\n+ ]\n+ group_order = torch.randperm(len(groups), generator=generator).numpy()\n+ negative_count = uniform_count + pop_count\n+ total_loss = 0.0\n+ total_targets = 0\n+ total_weight = 0.0\n+ total_positive = 0.0\n+ total_negative = 0.0\n+ total_pair_correct = 0.0\n+ batches = 0\n+ started = time.monotonic()\n+\n+ for group_number in group_order:\n+ users = groups[int(group_number)]\n+ group_counts = target_counts[users]\n+ hidden = torch.zeros(\n+ 1, len(users), config.embedding_dim, device=device\n+ )\n+ max_targets = int(group_counts.max())\n+ for start in range(0, max_targets, config.bptt_steps):\n+ steps = min(config.bptt_steps, max_targets - start)\n+ block = trainer.build_dense_block(\n+ prepared,\n+ target_weights,\n+ users,\n+ window_starts[users],\n+ group_counts,\n+ np.zeros(len(users), dtype=np.int64),\n+ start,\n+ steps,\n+ config,\n+ )\n+ sequence_outputs, _ = trainer.event_sequence(\n+ model,\n+ torch.from_numpy(block[0].astype(np.int64)).to(device),\n+ torch.from_numpy(block[1].astype(np.float32)).to(device),\n+ hidden,\n+ )\n+ hidden = torch.stack(\n+ [\n+ sequence_outputs[row, count - 1]\n+ if count\n+ else hidden[0, row]\n+ for row, count in enumerate(block[7])\n+ ]\n+ ).unsqueeze(0).detach()\n+ flat_scored = block[5].reshape(-1)\n+ if not np.any(flat_scored):\n+ continue\n+ mask = torch.from_numpy(flat_scored).to(device)\n+ sequence_vectors = sequence_outputs.reshape(\n+ -1, config.embedding_dim\n+ )[mask]\n+ flat_states = block[3].reshape(-1)[flat_scored]\n+ positive_items = torch.from_numpy(\n+ block[4].reshape(-1)[flat_scored].astype(np.int64)\n+ ).to(device)\n+ liked_items = torch.from_numpy(\n+ prepared.liked_states[flat_states].astype(np.int64)\n+ ).to(device)\n+ flat_user_rows = np.broadcast_to(\n+ users[:, None], block[5].shape\n+ ).reshape(-1)[flat_scored]\n+\n+ negatives = custom_draw_negatives(\n+ positive_items,\n+ flat_user_rows,\n+ liked_items,\n+ known_positive_packed,\n+ len(prepared.item_ids),\n+ uniform_count,\n+ pop_count,\n+ pop_table,\n+ generator,\n+ device,\n+ )\n+\n+ optimizer.zero_grad(set_to_none=True)\n+ statistics, sequence_gradient = score_target_microbatches_custom(\n+ model,\n+ prepared,\n+ config,\n+ sequence_vectors,\n+ flat_states,\n+ block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n+ positive_items,\n+ negatives,\n+ block[8].reshape(-1)[flat_scored],\n+ device,\n+ backward=True,\n+ loss_type=loss_type,\n+ temperature=temperature,\n+ item_logq=item_logq,\n+ )\n+ if sequence_gradient is None:\n+ raise RuntimeError(\"training block omitted its sequence gradient\")\n+ sequence_vectors.backward(sequence_gradient)\n+ torch.nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n+ optimizer.step()\n+\n+ count = len(positive_items)\n+ total_loss += statistics[\"loss_sum\"]\n+ total_targets += count\n+ total_weight += statistics[\"weight_sum\"]\n+ total_positive += statistics[\"positive_sum\"]\n+ total_negative += statistics[\"negative_sum\"]\n+ total_pair_correct += statistics[\"pair_correct\"]\n+ batches += 1\n+ if batches % 250 == 0:\n+ running_loss = total_loss / max(total_weight, 1.0e-12)\n+ trainer.log(\n+ f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n+ f\"targets={total_targets:,} \"\n+ f\"running_loss={running_loss:.6f} \"\n+ f\"elapsed={time.monotonic() - started:.1f}s\"\n+ )\n+\n+ return {\n+ \"epoch\": float(epoch),\n+ \"loss\": total_loss / max(total_weight, 1.0e-12),\n+ \"positive_logit\": total_positive / max(total_targets, 1),\n+ \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n+ \"pair_accuracy\": total_pair_correct / max(total_targets * negative_count, 1),\n+ \"targets\": float(total_targets),\n+ \"target_weight_sum\": total_weight,\n+ \"batches\": float(batches),\n+ \"seconds\": time.monotonic() - started,\n+ }\n+\n+\n+def main() -> None:\n+ parser = argparse.ArgumentParser()\n+ parser.add_argument(\"--uniform-negatives\", type=int, default=16)\n+ parser.add_argument(\"--pop-negatives\", type=int, default=16)\n+ parser.add_argument(\"--pop-power\", type=float, default=0.75)\n+ parser.add_argument(\n+ \"--loss-type\",\n+ choices=(\"bce_unweighted\", \"bce_logq\", \"softmax\", \"softmax_logq\"),\n+ default=\"bce_unweighted\",\n+ )\n+ parser.add_argument(\"--temperature\", type=float, default=0.2)\n+ parser.add_argument(\n+ \"--target-weighting\",\n+ choices=(\"organic-strong\", \"discovery-graded\"),\n+ default=\"organic-strong\",\n+ )\n+ parser.add_argument(\"--epochs\", type=int, default=1)\n+ parser.add_argument(\"--learning-rate\", type=float, default=1e-3)\n+ parser.add_argument(\"--output\", type=Path, default=Path(\"/app/solution/exp_output.parquet\"))\n+ args = parser.parse_args()\n+\n+ torch.set_num_threads(8)\n+ config = trainer.Config(\n+ epochs=args.epochs,\n+ learning_rate=args.learning_rate,\n+ target_weighting=args.target_weighting,\n+ )\n+ trainer.seed_everything(config.seed)\n+ device = torch.device(\"cpu\")\n+\n+ public = Path(\"/task\")\n+ cache_path = Path(\"/app/solution/validation_cache.npz\")\n+ prepared = trainer.load_cache(cache_path, \"validation\", config)\n+ eligibility = trainer.CandidateSet.read(public / \"data\" / \"validation_eligible_candidates.npz\")\n+\n+ partition = trainer.chronological_target_partition(\n+ prepared,\n+ config.validation_fraction,\n+ config.max_train_targets_per_user,\n+ config.max_validation_targets_per_user,\n+ )\n+\n+ model = base.SequentialTwoTower(prepared.item_features, trainer.model_config(config)).to(device)\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay)\n+ generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n+\n+ target_weights = trainer.build_target_weights(\n+ public,\n+ \"validation\",\n+ prepared,\n+ partition.history_training_counts,\n+ config,\n+ )\n+ known_positive_packed = trainer.build_known_positive_filter(prepared, partition.history_training_counts)\n+\n+ pop_table = None\n+ if args.pop_negatives > 0:\n+ pop_table = build_popularity_table(prepared, power=args.pop_power)\n+ trainer.log(f\"built popularity table: power={args.pop_power} size={len(pop_table):,}\")\n+\n+ item_logq = None\n+ if \"logq\" in args.loss_type:\n+ item_logq = compute_item_logq(prepared, power=args.pop_power).to(device)\n+\n+ for epoch in range(1, config.epochs + 1):\n+ diag = run_training_epoch_custom(\n+ model,\n+ optimizer,\n+ prepared,\n+ config,\n+ target_weights,\n+ partition.window_starts,\n+ partition.training_counts,\n+ known_positive_packed,\n+ args.uniform_negatives,\n+ args.pop_negatives,\n+ pop_table,\n+ args.loss_type,\n+ args.temperature,\n+ item_logq,\n+ generator,\n+ device,\n+ epoch,\n+ )\n+ trainer.log(f\"Epoch {epoch} finished: loss={diag['loss']:.5f} pair_acc={diag['pair_accuracy']:.4f} in {diag['seconds']:.1f}s\")\n+\n+ ranking, _ = trainer.rank_candidates(model, prepared, eligibility, config, device, ranking_batch_size=64)\n+ trainer.write_ranking(ranking, args.output)\n+\n+ # Evaluate NDCG@10\n+ targets_frame = pd.read_parquet(public / \"data\" / \"target_users.parquet\")\n+ targets = set(targets_frame.uid.astype(int))\n+ cand_set = evaluate_public.CandidateSet(public / \"data\" / \"validation_eligible_candidates.npz\")\n+ graded = evaluate_public.validation_truth(cand_set, targets)\n+ ranked = evaluate_public.ranked_items(ranking)\n+ score_report = evaluate_public.score(ranked, graded)\n+ trainer.log(f\"VALIDATION SCORE: {score_report}\")\n+\n+\n+if __name__ == \"__main__\":\n+ main()\n",
"fileName": "run_experiment.py",
"filePath": "/app/solution/run_experiment.py",
"originalContent": "#!/usr/bin/env python3\n\"\"\"Run controlled experiments on the public validation split.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\nfrom dataclasses import replace\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn.functional as F\n\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\n\nimport train_current_model as trainer\nimport two_tower_model as base\nimport evaluate_public\n\n\ndef build_popularity_table(prepared: trainer.SequenceData, power: float = 0.75, table_size: int = 500_000) -> torch.Tensor:\n \"\"\"Precompute a table for ultra-fast popularity-weighted negative sampling.\"\"\"\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n item_counts = np.random.multinomial(table_size, probs)\n table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n np.random.shuffle(table)\n return torch.from_numpy(table.astype(np.int64))\n\n\ndef custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n) -> torch.Tensor:\n batch_size = len(positive_items)\n targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n\n total_neg = uniform_count + pop_count\n negatives = np.empty((batch_size, total_neg), dtype=np.int64)\n\n if uniform_count > 0:\n negatives[:, :uniform_count] = torch.randint(\n n_items, (batch_size, uniform_count), generator=generator, device=\"cpu\"\n ).numpy()\n\n if pop_count > 0 and pop_table is not None:\n idx = torch.randint(len(pop_table), (batch_size, pop_count), generator=generator, device=\"cpu\")\n negatives[:, uniform_count:] = pop_table[idx].numpy()\n\n # Reject invalid negatives\n for _ in range(32):\n invalid = trainer.invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n break\n num_invalid = int(invalid.sum())\n if pop_table is not None:\n repl_idx = torch.randint(len(pop_table), (num_invalid,), generator=generator, device=\"cpu\")\n negatives[invalid] = pop_table[repl_idx].numpy()\n else:\n negatives[invalid] = torch.randint(n_items, (num_invalid,), generator=generator, device=\"cpu\").numpy()\n\n return torch.from_numpy(negatives).to(device)\n\n\ndef score_target_microbatches_custom(\n model: base.SequentialTwoTower,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n loss_type: str = \"bce_unweighted\",\n temperature: float = 1.0,\n item_logq: torch.Tensor | None = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = sequence_vectors[begin:end].detach().requires_grad_(True)\n else:\n sequence_input = sequence_vectors[begin:end]\n\n liked_vectors, disliked_vectors, _ = trainer.pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n\n query_vectors = trainer.user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = trainer.candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n\n if loss_type == \"bce_unweighted\":\n # BCE where all negatives have equal weight (no inverse propensity downweighting)\n pos_loss = F.softplus(-positive_logits)\n neg_loss = F.softplus(negative_logits).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type == \"bce_logq\":\n # BCE with logQ correction: s - log(q)\n pos_lq = item_logq[positive_items[begin:end]]\n neg_lq = item_logq[negatives[begin:end]]\n pos_loss = F.softplus(-(positive_logits - pos_lq))\n neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type.startswith(\"softmax\"):\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n if \"logq\" in loss_type and item_logq is not None:\n pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n neg_lq = item_logq[negatives[begin:end]]\n all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n all_logits = all_logits - all_logq\n scaled_logits = all_logits / temperature\n labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * loss).sum()\n else:\n raise ValueError(f\"Unknown loss_type: {loss_type}\")\n\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n",
"newContent": "#!/usr/bin/env python3\n\"\"\"Run controlled experiments on the public validation split.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\nfrom dataclasses import replace\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn.functional as F\n\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\n\nimport train_current_model as trainer\nimport two_tower_model as base\nimport evaluate_public\n\n\ndef build_popularity_table(prepared: trainer.SequenceData, power: float = 0.75, table_size: int = 500_000) -> torch.Tensor:\n \"\"\"Precompute a table for ultra-fast popularity-weighted negative sampling.\"\"\"\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n item_counts = np.random.multinomial(table_size, probs)\n table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n np.random.shuffle(table)\n return torch.from_numpy(table.astype(np.int64))\n\n\ndef compute_item_logq(prepared: trainer.SequenceData, power: float = 0.75) -> torch.Tensor:\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n logq = np.log(probs).astype(np.float32)\n return torch.from_numpy(logq)\n\n\ndef custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n) -> torch.Tensor:\n batch_size = len(positive_items)\n targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n\n total_neg = uniform_count + pop_count\n negatives = np.empty((batch_size, total_neg), dtype=np.int64)\n\n if uniform_count > 0:\n negatives[:, :uniform_count] = torch.randint(\n n_items, (batch_size, uniform_count), generator=generator, device=\"cpu\"\n ).numpy()\n\n if pop_count > 0 and pop_table is not None:\n idx = torch.randint(len(pop_table), (batch_size, pop_count), generator=generator, device=\"cpu\")\n negatives[:, uniform_count:] = pop_table[idx].numpy()\n\n # Reject invalid negatives\n for _ in range(32):\n invalid = trainer.invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n break\n num_invalid = int(invalid.sum())\n if pop_table is not None:\n repl_idx = torch.randint(len(pop_table), (num_invalid,), generator=generator, device=\"cpu\")\n negatives[invalid] = pop_table[repl_idx].numpy()\n else:\n negatives[invalid] = torch.randint(n_items, (num_invalid,), generator=generator, device=\"cpu\").numpy()\n\n return torch.from_numpy(negatives).to(device)\n\n\ndef score_target_microbatches_custom(\n model: base.SequentialTwoTower,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n loss_type: str = \"bce_unweighted\",\n temperature: float = 1.0,\n item_logq: torch.Tensor | None = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = sequence_vectors[begin:end].detach().requires_grad_(True)\n else:\n sequence_input = sequence_vectors[begin:end]\n\n liked_vectors, disliked_vectors, _ = trainer.pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n\n query_vectors = trainer.user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = trainer.candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n\n if loss_type == \"bce_unweighted\":\n # BCE where all negatives have equal weight (no inverse propensity downweighting)\n pos_loss = F.softplus(-positive_logits)\n neg_loss = F.softplus(negative_logits).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type == \"bce_logq\":\n # BCE with logQ correction: s - log(q)\n pos_lq = item_logq[positive_items[begin:end]]\n neg_lq = item_logq[negatives[begin:end]]\n pos_loss = F.softplus(-(positive_logits - pos_lq))\n neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type.startswith(\"softmax\"):\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n if \"logq\" in loss_type and item_logq is not None:\n pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n neg_lq = item_logq[negatives[begin:end]]\n all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n all_logits = all_logits - all_logq\n scaled_logits = all_logits / temperature\n labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * loss).sum()\n else:\n raise ValueError(f\"Unknown loss_type: {loss_type}\")\n\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n\n\ndef run_training_epoch_custom(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n known_positive_packed: np.ndarray | None,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n loss_type: str,\n temperature: float,\n item_logq: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = uniform_count + pop_count\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = trainer.build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = trainer.event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n\n negatives = custom_draw_negatives(\n positive_items,\n flat_user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n uniform_count,\n pop_count,\n pop_table,\n generator,\n device,\n )\n\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches_custom(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n loss_type=loss_type,\n temperature=temperature,\n item_logq=item_logq,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n torch.nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 250 == 0:\n running_loss = total_loss / max(total_weight, 1.0e-12)\n trainer.log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"running_loss={running_loss:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--uniform-negatives\", type=int, default=16)\n parser.add_argument(\"--pop-negatives\", type=int, default=16)\n parser.add_argument(\"--pop-power\", type=float, default=0.75)\n parser.add_argument(\n \"--loss-type\",\n choices=(\"bce_unweighted\", \"bce_logq\", \"softmax\", \"softmax_logq\"),\n default=\"bce_unweighted\",\n )\n parser.add_argument(\"--temperature\", type=float, default=0.2)\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"organic-strong\", \"discovery-graded\"),\n default=\"organic-strong\",\n )\n parser.add_argument(\"--epochs\", type=int, default=1)\n parser.add_argument(\"--learning-rate\", type=float, default=1e-3)\n parser.add_argument(\"--output\", type=Path, default=Path(\"/app/solution/exp_output.parquet\"))\n args = parser.parse_args()\n\n torch.set_num_threads(8)\n config = trainer.Config(\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n target_weighting=args.target_weighting,\n )\n trainer.seed_everything(config.seed)\n device = torch.device(\"cpu\")\n\n public = Path(\"/task\")\n cache_path = Path(\"/app/solution/validation_cache.npz\")\n prepared = trainer.load_cache(cache_path, \"validation\", config)\n eligibility = trainer.CandidateSet.read(public / \"data\" / \"validation_eligible_candidates.npz\")\n\n partition = trainer.chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n\n model = base.SequentialTwoTower(prepared.item_features, trainer.model_config(config)).to(device)\n optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay)\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n\n target_weights = trainer.build_target_weights(\n public,\n \"validation\",\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = trainer.build_known_positive_filter(prepared, partition.history_training_counts)\n\n pop_table = None\n if args.pop_negatives > 0:\n pop_table = build_popularity_table(prepared, power=args.pop_power)\n trainer.log(f\"built popularity table: power={args.pop_power} size={len(pop_table):,}\")\n\n item_logq = None\n if \"logq\" in args.loss_type:\n item_logq = compute_item_logq(prepared, power=args.pop_power).to(device)\n\n for epoch in range(1, config.epochs + 1):\n diag = run_training_epoch_custom(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n partition.training_counts,\n known_positive_packed,\n args.uniform_negatives,\n args.pop_negatives,\n pop_table,\n args.loss_type,\n args.temperature,\n item_logq,\n generator,\n device,\n epoch,\n )\n trainer.log(f\"Epoch {epoch} finished: loss={diag['loss']:.5f} pair_acc={diag['pair_accuracy']:.4f} in {diag['seconds']:.1f}s\")\n\n ranking, _ = trainer.rank_candidates(model, prepared, eligibility, config, device, ranking_batch_size=64)\n trainer.write_ranking(ranking, args.output)\n\n # Evaluate NDCG@10\n targets_frame = pd.read_parquet(public / \"data\" / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n cand_set = evaluate_public.CandidateSet(public / \"data\" / \"validation_eligible_candidates.npz\")\n graded = evaluate_public.validation_truth(cand_set, targets)\n ranked = evaluate_public.ranked_items(ranking)\n score_report = evaluate_public.score(ranked, graded)\n trainer.log(f\"VALIDATION SCORE: {score_report}\")\n\n\nif __name__ == \"__main__\":\n main()\n",
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"timestamp": "2026-09-29T05:52:53.523Z",
"content": "Successfully overwrote file: /app/solution/run_experiment.py. Here is the updated code:\n...\n table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n np.random.shuffle(table)\n return torch.from_numpy(table.astype(np.int64))\n\n\ndef compute_item_logq(prepared: trainer.SequenceData, power: float = 0.75) -> torch.Tensor:\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n logq = np.log(probs).astype(np.float32)\n return torch.from_numpy(logq)\n\n\ndef custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n...\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n\n\ndef run_training_epoch_custom(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n known_positive_packed: np.ndarray | None,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n loss_type: str,\n temperature: float,\n item_logq: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = uniform_count + pop_count\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = trainer.build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = trainer.event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n\n negatives = custom_draw_negatives(\n positive_items,\n flat_user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n uniform_count,\n pop_count,\n pop_table,\n generator,\n device,\n )\n\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches_custom(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n loss_type=loss_type,\n temperature=temperature,\n item_logq=item_logq,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n torch.nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 250 == 0:\n running_loss = total_loss / max(total_weight, 1.0e-12)\n trainer.log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"running_loss={running_loss:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--uniform-negatives\", type=int, default=16)\n parser.add_argument(\"--pop-negatives\", type=int, default=16)\n parser.add_argument(\"--pop-power\", type=float, default=0.75)\n parser.add_argument(\n \"--loss-type\",\n choices=(\"bce_unweighted\", \"bce_logq\", \"softmax\", \"softmax_logq\"),\n default=\"bce_unweighted\",\n )\n parser.add_argument(\"--temperature\", type=float, default=0.2)\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"organic-strong\", \"discovery-graded\"),\n default=\"organic-strong\",\n )\n parser.add_argument(\"--epochs\", type=int, default=1)\n parser.add_argument(\"--learning-rate\", type=float, default=1e-3)\n parser.add_argument(\"--output\", type=Path, default=Path(\"/app/solution/exp_output.parquet\"))\n args = parser.parse_args()\n\n torch.set_num_threads(8)\n config = trainer.Config(\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n target_weighting=args.target_weighting,\n )\n trainer.seed_everything(config.seed)\n device = torch.device(\"cpu\")\n\n public = Path(\"/task\")\n cache_path = Path(\"/app/solution/validation_cache.npz\")\n prepared = trainer.load_cache(cache_path, \"validation\", config)\n eligibility = trainer.CandidateSet.read(public / \"data\" / \"validation_eligible_candidates.npz\")\n\n partition = trainer.chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n\n model = base.SequentialTwoTower(prepared.item_features, trainer.model_config(config)).to(device)\n optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay)\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n\n target_weights = trainer.build_target_weights(\n public,\n \"validation\",\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = trainer.build_known_positive_filter(prepared, partition.history_training_counts)\n\n pop_table = None\n if args.pop_negatives > 0:\n pop_table = build_popularity_table(prepared, power=args.pop_power)\n trainer.log(f\"built popularity table: power={args.pop_power} size={len(pop_table):,}\")\n\n item_logq = None\n if \"logq\" in args.loss_type:\n item_logq = compute_item_logq(prepared, power=args.pop_power).to(device)\n\n for epoch in range(1, config.epochs + 1):\n diag = run_training_epoch_custom(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n partition.training_counts,\n known_positive_packed,\n args.uniform_negatives,\n args.pop_negatives,\n pop_table,\n args.loss_type,\n args.temperature,\n item_logq,\n generator,\n device,\n epoch,\n )\n trainer.log(f\"Epoch {epoch} finished: loss={diag['loss']:.5f} pair_acc={diag['pair_accuracy']:.4f} in {diag['seconds']:.1f}s\")\n\n ranking, _ = trainer.rank_candidates(model, prepared, eligibility, config, device, ranking_batch_size=64)\n trainer.write_ranking(ranking, args.output)\n\n # Evaluate NDCG@10\n targets_frame = pd.read_parquet(public / \"data\" / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n cand_set = evaluate_public.CandidateSet(public / \"data\" / \"validation_eligible_candidates.npz\")\n graded = evaluate_public.validation_truth(cand_set, targets)\n ranked = evaluate_public.ranked_items(ranking)\n score_report = evaluate_public.score(ranked, graded)\n trainer.log(f\"VALIDATION SCORE: {score_report}\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"isError": false
},
"responseMessageId": "a991d802-836d-4349-8acd-5a0780da3cb2"
}
]
},
{
"id": 45,
"role": "agent",
"timestamp": "2026-09-29T05:52:57.749Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T05:52:57.749Z",
"text": "Starting First Experiment\n\nI'm initiating the first experimental run, focusing on the `--uniform-negatives 16 --pop-negatives 16 --pop-power 0.75 --loss-type bce_unweighted` configuration. My immediate goal is to observe the impact of these parameters on the validation score, keeping the baseline performance in mind."
},
{
"kind": "tool",
"timestamp": "2026-09-29T05:52:57.749Z",
"callId": "run_shell_command__call_4837884",
"name": "run_shell_command",
"input": {
"command": "python3 /app/solution/run_experiment.py \\\n --uniform-negatives 16 \\\n --pop-negatives 16 \\\n --pop-power 0.75 \\\n --loss-type bce_unweighted \\\n --target-weighting organic-strong \\\n --output /app/solution/exp1.parquet",
"description": "Run Experiment 1: 16 uniform + 16 popularity negatives with unweighted BCE"
},
"recorded": {
"id": "run_shell_command__call_4837884",
"name": "run_shell_command",
"args": {
"command": "python3 /app/solution/run_experiment.py \\\n --uniform-negatives 16 \\\n --pop-negatives 16 \\\n --pop-power 0.75 \\\n --loss-type bce_unweighted \\\n --target-weighting organic-strong \\\n --output /app/solution/exp1.parquet",
"description": "Run Experiment 1: 16 uniform + 16 popularity negatives with unweighted BCE"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4837884",
"name": "run_shell_command",
"response": {
"output": "\nOutput: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nbuilt popularity table: power=0.75 size=500,000\nepoch=1/1 batches=250 targets=987,417 running_loss=0.578803 elapsed=182.4s\nEpoch 1 finished: loss=0.57662 pair_acc=0.7400 in 195.1s\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\nVALIDATION SCORE: {'quality_ndcg@10': 0.021723, 'quality_users': 4263}\nProcess Group PGID: 1945\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T05:57:40.576Z",
"resultDisplay": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nbuilt popularity table: power=0.75 size=500,000\nepoch=1/1 batches=250 targets=987,417 running_loss=0.578803 elapsed=182.4s\nEpoch 1 finished: loss=0.57662 pair_acc=0.7400 in 195.1s\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\nVALIDATION SCORE: {'quality_ndcg@10': 0.021723, 'quality_users': 4263}",
"description": "Run Experiment 1: 16 uniform + 16 popularity negatives with unweighted BCE",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T05:57:40.578Z",
"content": "\nOutput: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nbuilt popularity table: power=0.75 size=500,000\nepoch=1/1 batches=250 targets=987,417 running_loss=0.578803 elapsed=182.4s\nEpoch 1 finished: loss=0.57662 pair_acc=0.7400 in 195.1s\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\nVALIDATION SCORE: {'quality_ndcg@10': 0.021723, 'quality_users': 4263}\nProcess Group PGID: 1945\n",
"isError": false
},
"responseMessageId": "b2ed8a39-f5b3-449e-97e8-1b6fce4eacdd"
}
]
},
{
"id": 46,
"role": "agent",
"timestamp": "2026-09-29T06:01:21.958Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:01:21.958Z",
"text": "Improving NDCG Performance\n\nI've observed a positive uptick in NDCG@10, now standing at 0.021723. My current focus is on further enhancing this by exploring the impact of increased popularity negatives or a higher popularity power weighting. I'll also be investigating the effects of softmax and sampling strategies next."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:01:21.958Z",
"text": "Evaluating Loss Functions\n\nI'm currently dissecting the implications of cross-entropy versus BCE loss, and more critically, the impact of `discovery-graded` target weighting. The significant weighting given to repeat listens within `discovery-graded` seems problematic given their ineligibility at test time. I'm also examining the role of softmax and appropriate temperature scaling for logits."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:01:21.958Z",
"callId": "run_shell_command__call_49930",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport pandas as pd\n\npred1 = pd.read_parquet(\"/app/solution/exp1.parquet\")\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\nprint(\"Exp1 unique items predicted:\", pred1[\"item_id\"].nunique())\ntop10_1 = pred1[pred1[\"rank\"] <= 10].join(catalog, on=\"item_id\")\nprint(\"Exp1 top 10 popularity rank:\")\nprint(top10_1[\"popularity_rank\"].describe())\n'",
"description": "Inspect Exp 1 predictions and popularity rank distribution"
},
"recorded": {
"id": "run_shell_command__call_49930",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport pandas as pd\n\npred1 = pd.read_parquet(\"/app/solution/exp1.parquet\")\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\nprint(\"Exp1 unique items predicted:\", pred1[\"item_id\"].nunique())\ntop10_1 = pred1[pred1[\"rank\"] <= 10].join(catalog, on=\"item_id\")\nprint(\"Exp1 top 10 popularity rank:\")\nprint(top10_1[\"popularity_rank\"].describe())\n'",
"description": "Inspect Exp 1 predictions and popularity rank distribution"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_49930",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Exp1 unique items predicted: 2284\nExp1 top 10 popularity rank:\ncount 84560.000000\nmean 7597.098735\nstd 6991.639796\nmin 2.000000\n25% 2035.000000\n50% 6491.000000\n75% 11957.000000\nmax 29915.000000\nName: popularity_rank, dtype: float64\nProcess Group PGID: 2031\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:01:23.533Z",
"resultDisplay": "Exp1 unique items predicted: 2284\nExp1 top 10 popularity rank:\ncount 84560.000000\nmean 7597.098735\nstd 6991.639796\nmin 2.000000\n25% 2035.000000\n50% 6491.000000\n75% 11957.000000\nmax 29915.000000\nName: popularity_rank, dtype: float64",
"description": "Inspect Exp 1 predictions and popularity rank distribution",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:01:24.736Z",
"content": "\nOutput: Exp1 unique items predicted: 2284\nExp1 top 10 popularity rank:\ncount 84560.000000\nmean 7597.098735\nstd 6991.639796\nmin 2.000000\n25% 2035.000000\n50% 6491.000000\n75% 11957.000000\nmax 29915.000000\nName: popularity_rank, dtype: float64\nProcess Group PGID: 2031\n",
"isError": false
},
"responseMessageId": "0fb9099f-7441-47e7-b62e-9c951efb3bd9"
}
]
},
{
"id": 47,
"role": "agent",
"timestamp": "2026-09-29T06:05:14.018Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:05:14.018Z",
"text": "Analyzing Popularity Trends\n\nMy analysis of the top 10 predicted items reveals a stark difference between the baseline and Experiment 1. The baseline shows a mean popularity rank of 2,597 and a median of 111, indicating reasonable predictions. However, Experiment 1's results are concerning, with a drastically increased mean rank of 7,597 and a median of 6,491, suggesting a significant degradation in prediction quality."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:05:14.018Z",
"text": "Investigating Logits Scaling\n\nI've realized the baseline was skewed; Experiment 1 actually aligns better with the ground truth distribution for discovery relevance. Now I'm exploring Softmax loss and different temperature settings, particularly `softmax_logq`, to understand how scaling logits by `logq` affects predictions for popular versus rare items."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:05:14.018Z",
"text": "Adjusting Logits for Discovery\n\nI'm revisiting the logits scaling, realizing that subtracting `logq` might be counterproductive for discovery-focused ranking at inference. My current focus is on how omitting this subtraction during training influences the learned dot products, potentially capturing PMI directly and favoring discovery over sheer popularity."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:05:14.018Z",
"text": "Testing Discovery Objective\n\nI'm shifting focus to the music discovery goal: prioritizing personalized affinity (PMI) over global popularity. I'll test `target_weighting = \"discovery-graded\"` with `loss_type = \"bce_unweighted\"`, and also a `softmax` loss configuration to see how these impact predictions."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:05:14.018Z",
"callId": "run_shell_command__call_5545169",
"name": "run_shell_command",
"input": {
"description": "Check runtime and stats of discovery-graded target weighting",
"command": "python3 -c '\nimport sys\nsys.path.insert(0, \"/app/solution\")\nfrom train_current_model import build_target_weights, Config, load_cache, chronological_target_partition\nfrom pathlib import Path\nimport time\n\ncfg = Config(target_weighting=\"discovery-graded\")\nprepared = load_cache(Path(\"/app/solution/validation_cache.npz\"), \"validation\", cfg)\npartition = chronological_target_partition(prepared, cfg.validation_fraction, cfg.max_train_targets_per_user, cfg.max_validation_targets_per_user)\nt0 = time.time()\nweights = build_target_weights(Path(\"/task\"), \"validation\", prepared, partition.history_training_counts, cfg)\nprint(\"discovery-graded weights computed in\", round(time.time() - t0, 2), \"s\")\nprint(\"Weights mean:\", weights.mean(), \"min:\", weights.min(), \"max:\", weights.max())\n'"
},
"recorded": {
"id": "run_shell_command__call_5545169",
"name": "run_shell_command",
"args": {
"description": "Check runtime and stats of discovery-graded target weighting",
"command": "python3 -c '\nimport sys\nsys.path.insert(0, \"/app/solution\")\nfrom train_current_model import build_target_weights, Config, load_cache, chronological_target_partition\nfrom pathlib import Path\nimport time\n\ncfg = Config(target_weighting=\"discovery-graded\")\nprepared = load_cache(Path(\"/app/solution/validation_cache.npz\"), \"validation\", cfg)\npartition = chronological_target_partition(prepared, cfg.validation_fraction, cfg.max_train_targets_per_user, cfg.max_validation_targets_per_user)\nt0 = time.time()\nweights = build_target_weights(Path(\"/task\"), \"validation\", prepared, partition.history_training_counts, cfg)\nprint(\"discovery-graded weights computed in\", round(time.time() - t0, 2), \"s\")\nprint(\"Weights mean:\", weights.mean(), \"min:\", weights.min(), \"max:\", weights.max())\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5545169",
"name": "run_shell_command",
"response": {
"output": "\nOutput: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nloaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.250 mean=0.310 max=3.000\ndiscovery-graded weights computed in 7.46 s\nWeights mean: 0.30997276 min: 0.25 max: 3.0\nProcess Group PGID: 2069\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T06:05:25.726Z",
"resultDisplay": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nloaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.250 mean=0.310 max=3.000\ndiscovery-graded weights computed in 7.46 s\nWeights mean: 0.30997276 min: 0.25 max: 3.0",
"description": "Check runtime and stats of discovery-graded target weighting",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T06:05:25.728Z",
"content": "\nOutput: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nloaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.250 mean=0.310 max=3.000\ndiscovery-graded weights computed in 7.46 s\nWeights mean: 0.30997276 min: 0.25 max: 3.0\nProcess Group PGID: 2069\n",
"isError": false
},
"responseMessageId": "afc41ecb-a4d2-4d9e-8033-5ae868fbe534"
}
]
},
{
"id": 48,
"role": "agent",
"timestamp": "2026-09-29T06:05:31.055Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:05:31.055Z",
"text": "Discovering Initial Targets\n\nMy recent focus has been on the initial discovery phase. I've successfully run the `discovery-graded` weights, which completed remarkably fast at just 7.4 seconds. This process identified a substantial 46,417 grade-3 targets, assigning them a weight of 3.0. Additionally, repeat listens have been assigned a weight of 0.25. I'm now preparing to initiate Experiment 2, which will involve the `--uniform-negatives` parameter."
},
{
"kind": "tool",
"timestamp": "2026-09-29T06:05:31.055Z",
"callId": "run_shell_command__call_144382",
"name": "run_shell_command",
"input": {
"description": "Run Experiment 2: 16 uniform + 16 pop negatives with discovery-graded weighting",
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"blocks": [
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"timestamp": "2026-09-29T06:22:24.159Z",
"text": "Observing Validation Improvements\n\nMy validation NDCG@10 metric has seen a significant boost, climbing to 0.024020 from 0.021280, a nearly 13% relative gain. Concurrently, the pair accuracy reached 0.7478. This positive shift seems linked to the implementation of `discovery-graded` target weighting."
},
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"representation": "summary",
"timestamp": "2026-09-29T06:22:24.159Z",
"text": "Testing Negative Sampling\n\nI'm now focusing on refining negative sampling strategies. My current plan is to investigate different popularity weighting exponents ($\\alpha$) and alter the mix of uniform versus popularity-biased negatives. I'll also analyze the computational cost of increasing the total number of negatives."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T06:22:24.159Z",
"text": "Evaluating Softmax Temperatures\n\nI'm exploring the impact of `softmax` loss with varying temperature parameters. Initial logit scale analysis suggests temperatures around 0.15-0.25 might be optimal for achieving good separation between positive and negative examples within the sampled batch. I will proceed with testing $\\tau = 0.2$ and $\\tau = 0.1$."
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"content": "\nOutput: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nloaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.250 mean=0.310 max=3.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nbuilt popularity table: power=0.75 size=500,000\nepoch=1/1 batches=250 targets=987,417 running_loss=2.947044 elapsed=182.5s\nEpoch 1 finished: loss=2.93405 pair_acc=0.7597 in 194.8s\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\nVALIDATION SCORE: {'quality_ndcg@10': 0.026195, 'quality_users': 4263}\nProcess Group PGID: 2225\n",
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"timestamp": "2026-09-29T06:27:43.871Z",
"text": "Improving NDCG@10 Score\n\nMy recent efforts have significantly boosted NDCG@10 to 0.026195, a substantial 23.1% relative increase from 0.021280. This advancement was achieved with a training and ranking time of approximately 255 seconds, while also maintaining a pair accuracy of 0.7597."
},
{
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"timestamp": "2026-09-29T06:27:43.871Z",
"text": "Tuning for Higher Scores\n\nI'm analyzing various parameters to further improve the NDCG@10 score. Specifically, I'm exploring optimal softmax temperature values, different negative sampling configurations (varying `pop_power` and the number/mix of uniform and popularity negatives), and the impact of learning rate and epoch count on performance, keeping the 60-minute time limit in mind."
},
{
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"timestamp": "2026-09-29T06:27:43.871Z",
"text": "Refining Hyperparameters Rapidly\n\nWith only 25 minutes remaining, I'm prioritizing 2-3 final validation experiments. My current focus is on optimizing softmax temperature and negative sampling strategies, specifically exploring a lower temperature of 0.15 and adjusting the mix of uniform and popularity-based negatives, considering the skewed nature of catalog popularity."
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"status": "success",
"timestamp": "2026-09-29T06:32:34.060Z",
"resultDisplay": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nloaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.250 mean=0.310 max=3.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nbuilt popularity table: power=0.75 size=500,000\nepoch=1/1 batches=250 targets=987,417 running_loss=2.950549 elapsed=182.0s\nEpoch 1 finished: loss=2.93730 pair_acc=0.7597 in 194.1s\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\nVALIDATION SCORE: {'quality_ndcg@10': 0.023909, 'quality_users': 4263}",
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"content": "\nOutput: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nloaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.250 mean=0.310 max=3.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nbuilt popularity table: power=0.75 size=500,000\nepoch=1/1 batches=250 targets=987,417 running_loss=2.950549 elapsed=182.0s\nEpoch 1 finished: loss=2.93730 pair_acc=0.7597 in 194.1s\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\nVALIDATION SCORE: {'quality_ndcg@10': 0.023909, 'quality_users': 4263}\nProcess Group PGID: 2289\n",
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"text": "Adjusting Hyperparameters\n\nMy exploration into temperature values has yielded an interesting outcome: a slightly higher temperature of 0.20 resulted in a marginal improvement, achieving 0.026195 compared to 0.15's 0.023909. This suggests that the lower temperature may have over-emphasized hard negatives, negatively impacting overall quality. I'm now considering further adjustments."
}
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