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"""Build the unified 20-task public-sample task-suite index."""
from __future__ import annotations
import json
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
ROOT = Path(__file__).resolve().parents[1]
SUMMARY_PATH = ROOT / "docs/data/summary_metrics.json"
WALKTHROUGHS_PATH = ROOT / "docs/data/task_walkthroughs.json"
ADDITIONAL_TASKS_PATH = ROOT / "docs/data/tier2_task_suite.json"
OUTPUT_JSON = ROOT / "docs/data/task_suite_20.json"
OUTPUT_MD = ROOT / "TASK_SUITE_20.md"
def read_json(path: Path) -> dict[str, Any]:
return json.loads(path.read_text(encoding="utf-8"))
def metric_value(metrics: dict[str, Any], metric_key: str | None) -> float | None:
if not metrics or not metric_key:
return None
if "primary_score" in metrics:
return metrics.get("primary_score")
return metrics.get(metric_key)
def count_fields(metrics: dict[str, Any]) -> dict[str, Any]:
keys = [
"num_windows",
"num_samples",
"num_queries",
"num_eval_windows",
"num_train_windows",
"num_test_windows",
"num_train_samples",
"num_test_samples",
"num_classes",
"num_labels",
]
return {key: metrics[key] for key in keys if key in metrics}
def source_for(task_id: str, origin: str, neural: bool = False) -> str:
if origin == "original_public_sample_tasks":
prefix = "results/episode_task_suite/neural_mlp" if neural else "results/episode_task_suite"
return f"{prefix}/{task_id}/metrics.json"
prefix = "results/episode_task_suite/tier2_task_suite/neural_mlp" if neural else "results/episode_task_suite/tier2_task_suite"
return f"{prefix}/{task_id}/metrics.json"
def build_core_tasks(summary: dict[str, Any], walkthroughs: dict[str, Any]) -> list[dict[str, Any]]:
suite = summary["suite"]
minimal_tasks = suite.get("tasks", {})
neural_tasks = suite.get("neural_tasks", {})
rows: list[dict[str, Any]] = []
for task_id, walkthrough in walkthroughs["tasks"].items():
metric = walkthrough.get("metric", {})
metric_key = metric.get("key")
minimal = minimal_tasks.get(task_id, {})
neural = neural_tasks.get(task_id, {})
rows.append(
{
"task_id": task_id,
"task_display_name": walkthrough.get("display_name") or walkthrough.get("research_name") or task_id,
"research_name": walkthrough.get("research_name"),
"origin": "original_public_sample_tasks",
"origin_count_label": "original task",
"family": walkthrough.get("task_family"),
"architecture_family": walkthrough.get("architecture_family"),
"primary_direction": walkthrough.get("primary_direction"),
"input": walkthrough.get("input"),
"input_short": walkthrough.get("input_short"),
"process": walkthrough.get("process_short"),
"output": walkthrough.get("output"),
"output_short": walkthrough.get("output_short"),
"metric_key": metric_key,
"metric_name": metric.get("name"),
"metric_direction": metric.get("direction"),
"minimal_primary_metric": metric_value(minimal, metric_key),
"neural_primary_metric": metric_value(neural, metric_key),
"counts": count_fields(minimal),
"meaning": walkthrough.get("card_blurb") or walkthrough.get("plain_goal"),
"artifact_sources": {
"walkthrough": f"results/episode_task_suite/task_walkthroughs/{task_id}.md",
"minimal_metrics": source_for(task_id, "original_public_sample_tasks", neural=False),
"neural_metrics": source_for(task_id, "original_public_sample_tasks", neural=True),
},
}
)
return rows
def build_additional_tasks(additional: dict[str, Any]) -> list[dict[str, Any]]:
rows: list[dict[str, Any]] = []
for task_id, spec in additional.get("task_specs", {}).items():
result = additional.get("tasks", {}).get(task_id, {})
minimal = result.get("minimal") or {}
neural = result.get("neural_mlp") or {}
metric_key = spec.get("metric_key")
rows.append(
{
"task_id": task_id,
"task_display_name": spec.get("name", task_id.replace("_", " ").title()),
"research_name": spec.get("name", task_id.replace("_", " ").title()),
"origin": "additional_public_sample_tasks",
"origin_count_label": "additional task",
"family": spec.get("family"),
"architecture_family": minimal.get("model_family"),
"primary_direction": spec.get("research_direction", "sample-supported extension"),
"input": spec.get("input"),
"input_short": spec.get("input"),
"process": "shared window features -> task-specific target builder -> minimal/neural head",
"output": spec.get("target"),
"output_short": spec.get("target"),
"metric_key": metric_key,
"metric_name": spec.get("metric_name"),
"metric_direction": spec.get("metric_direction"),
"minimal_primary_metric": metric_value(minimal, metric_key),
"neural_primary_metric": metric_value(neural, metric_key),
"counts": count_fields(minimal),
"meaning": spec.get("meaning"),
"artifact_sources": {
"legacy_result_directory": "results/episode_task_suite/tier2_task_suite/",
"minimal_metrics": source_for(task_id, "additional_public_sample_tasks", neural=False),
"neural_metrics": source_for(task_id, "additional_public_sample_tasks", neural=True),
},
}
)
return rows
def build_payload() -> dict[str, Any]:
summary = read_json(SUMMARY_PATH)
walkthroughs = read_json(WALKTHROUGHS_PATH)
additional = read_json(ADDITIONAL_TASKS_PATH)
suite = summary["suite"]
tasks = build_core_tasks(summary, walkthroughs) + build_additional_tasks(additional)
for idx, row in enumerate(tasks, start=1):
row["task_number"] = idx
row["suite_label"] = f"Task {idx:02d}"
return {
"title": "Ropedia Xperience-10M Unified 20-Task Suite",
"status": "pass",
"generated_at_utc": datetime.now(timezone.utc).isoformat(timespec="seconds"),
"task_count": len(tasks),
"task_count_breakdown": {
"original_public_sample_tasks": 12,
"additional_public_sample_tasks": len(tasks) - 12,
"total_unified_tasks": len(tasks),
},
"unification_policy": {
"public_framing": "The suite is presented as one 20-task benchmark surface. Tasks 1-12 are the original public-sample tasks; tasks 13-20 are additional sample-supported tasks that use the same window/split/baseline contract.",
"legacy_path_note": "The directory and file name tier2_task_suite are retained only for backward-compatible artifact links; they are not a separate public benchmark tier.",
},
"dataset_scope": {
"sample_episode_count": 1,
"annotation": suite.get("annotation"),
"num_frames": suite.get("num_frames"),
"num_windows": suite.get("num_windows"),
"feature_dim": suite.get("feature_dim"),
"window_frames": suite.get("window_frames"),
"stride_frames": suite.get("stride_frames"),
"split_policy": "single_episode_chronological_70_30",
"raw_hdf5_required_for_tasks_13_20_regeneration": True,
"raw_data_redistributed": False,
},
"setup_alignment": {
"same_window_unit": "20-frame aligned windows",
"same_stride": "5 frames",
"same_feature_manifest": "results/episode_task_suite/feature_manifest.json",
"same_shared_tensor": "results/episode_task_suite/shared_windows.npz",
"same_split": "chronological 70/30 train/test split within the public sample episode",
"same_baseline_pattern": "minimal interpretable heads plus compact neural MLP heads",
"same_leakage_policy": "Target-side future, contact, object, caption, relation, and interaction signals are excluded from inputs unless language is explicitly the query.",
},
"source_files": [
"docs/data/summary_metrics.json",
"docs/data/task_walkthroughs.json",
"docs/data/tier2_task_suite.json",
"results/episode_task_suite/summary_report.json",
"results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
"results/episode_task_suite/windows.csv",
"results/episode_task_suite/feature_manifest.json",
],
"tasks": tasks,
}
def fmt(value: float | None) -> str:
return "n/a" if value is None else f"{value:.4f}"
def render_markdown(payload: dict[str, Any]) -> str:
scope = payload["dataset_scope"]
lines = [
"# Unified 20-Task Suite",
"",
"The public Xperience-10M sample task surface is one unified set of 20 tasks.",
"Tasks 1-12 are the original public-sample tasks. Tasks 13-20 are additional",
"sample-supported tasks attached to the same window, split, feature, baseline,",
"and leakage-control contract.",
"",
"Historical artifact paths containing `tier2_task_suite` are kept for stable",
"links, but they should be read as the result directory for tasks 13-20, not",
"as a separate benchmark tier.",
"",
"## Shared Setup",
"",
f"- Episode scope: `{scope['sample_episode_count']}` public sample episode.",
f"- Frames/windows: `{scope['num_frames']:,}` frames and `{scope['num_windows']:,}` aligned windows.",
f"- Windowing: `{scope['window_frames']}` frames per window, stride `{scope['stride_frames']}` frames.",
f"- Feature vector: `{scope['feature_dim']:,}` dimensions from the shared feature manifest.",
"- Split: chronological 70/30 train/test by time within the sample episode.",
"- Baselines: minimal interpretable heads and compact neural MLP heads.",
"- Raw data: MP4/HDF5/RRD files are not redistributed.",
"",
"## Task Table",
"",
"| # | Task | Artifact id | Origin | Input -> output | Primary metric | Minimal | Neural |",
"| ---: | --- | --- | --- | --- | --- | ---: | ---: |",
]
for row in payload["tasks"]:
metric_direction = "higher better" if row.get("metric_direction") == "higher" else "lower better"
lines.append(
"| {num} | {name} | `{task_id}` | {origin} | {inp} -> {out} | {metric} ({direction}) | {minimal} | {neural} |".format(
num=row["task_number"],
name=row["task_display_name"],
task_id=row["task_id"],
origin=row["origin_count_label"],
inp=row.get("input_short") or row.get("input"),
out=row.get("output_short") or row.get("output"),
metric=row.get("metric_name") or row.get("metric_key"),
direction=metric_direction,
minimal=fmt(row.get("minimal_primary_metric")),
neural=fmt(row.get("neural_primary_metric")),
)
)
lines.extend(
[
"",
"## Machine-Readable Copy",
"",
"The JSON mirror is `docs/data/task_suite_20.json`.",
"",
]
)
return "\n".join(lines)
def main() -> int:
payload = build_payload()
OUTPUT_JSON.parent.mkdir(parents=True, exist_ok=True)
OUTPUT_JSON.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
OUTPUT_MD.write_text(render_markdown(payload), encoding="utf-8")
print(f"PASS: wrote {OUTPUT_JSON}")
print(f"PASS: wrote {OUTPUT_MD}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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