Agent/examples/demand/piaoquan_prepare.py

import json
from collections import defaultdict
from pathlib import Path

from examples.demand.data_query_tools import get_rov_by_merge_leve2_and_video_ids
from examples.demand.db_manager import DatabaseManager
from examples.demand.models import TopicPatternElement, TopicPatternExecution
from examples.demand.pattern_builds.pattern_service import run_mining


db = DatabaseManager()


def _safe_float(value):
    if value is None:
        return 0.0
    try:
        return float(value)
    except (TypeError, ValueError):
        return 0.0


def _build_category_scores(name_scores, name_paths, name_post_ids):
    """
    计算分类路径节点权重：
    - 一个 name 的 score 贡献给其路径上的每个节点
    - 同一个 name 多条路径时，每条路径都累加
    """
    node_scores = defaultdict(float)
    node_post_ids = defaultdict(set)
    for name, score in name_scores.items():
        paths = name_paths.get(name, set())
        post_ids = name_post_ids.get(name, set())
        for category_path in paths:
            if not category_path:
                continue
            nodes = [segment.strip() for segment in category_path.split(">") if segment.strip()]
            for idx in range(len(nodes)):
                prefix = ">".join(nodes[: idx + 1])
                node_scores[prefix] += score
                if post_ids:
                    node_post_ids[prefix].update(post_ids)
    return node_scores, node_post_ids


def _write_json(path, payload):
    with open(path, "w", encoding="utf-8") as f:
        json.dump(payload, f, ensure_ascii=False, indent=2)


def prepare(execution_id):
    session = db.get_session()
    try:
        execution = session.query(TopicPatternExecution).filter(
            TopicPatternExecution.id == execution_id
        ).first()
        if not execution:
            raise ValueError(f"execution_id 不存在: {execution_id}")

        merge_leve2 = execution.merge_leve2

        rows = session.query(TopicPatternElement).filter(
            TopicPatternElement.execution_id == execution_id
        ).all()
        if not rows:
            return {"message": "没有可处理的数据", "execution_id": execution_id}

        # 1) 去重 post_id 拉取 ROV
        all_post_ids = sorted({r.post_id for r in rows if r.post_id})
        rov_by_post_id = get_rov_by_merge_leve2_and_video_ids(merge_leve2, all_post_ids) if all_post_ids else {}

        # 2) 按 element_type 分组，计算 name 的平均 ROV 分
        grouped = {
            "实质": {
                "name_post_ids": defaultdict(set),
                "name_paths": defaultdict(set),
            },
            "形式": {
                "name_post_ids": defaultdict(set),
                "name_paths": defaultdict(set),
            },
            "意图": {
                "name_post_ids": defaultdict(set),
                "name_paths": defaultdict(set),
            },
        }

        for r in rows:
            element_type = (r.element_type or "").strip()
            if element_type not in grouped:
                continue
            name = (r.name or "").strip()
            if not name:
                continue

            if r.post_id:
                grouped[element_type]["name_post_ids"][name].add(r.post_id)
            if r.category_path:
                grouped[element_type]["name_paths"][name].add(r.category_path.strip())

        output_dir = Path(__file__).parent / "data" / str(execution_id)
        output_dir.mkdir(parents=True, exist_ok=True)

        summary = {"execution_id": execution_id, "merge_leve2": merge_leve2, "files": {}}

        for element_type, data in grouped.items():
            name_post_ids = data["name_post_ids"]
            name_paths = data["name_paths"]

            name_scores = {}
            for name, post_ids in name_post_ids.items():
                rovs = [_safe_float(rov_by_post_id.get(pid, 0.0)) for pid in post_ids]
                score = sum(rovs) / len(rovs) if rovs else 0.0
                name_scores[name] = score

            raw_elements = []
            for name, score in name_scores.items():
                post_ids_set = name_post_ids.get(name, set())
                raw_elements.append(
                    {
                        "name": name,
                        "score": round(score, 6),
                        # 不在结果文件里输出帖子 ID 明细，避免体积过大/泄露。
                        "post_ids_count": len(post_ids_set),
                        "category_paths": sorted(list(name_paths.get(name, set()))),
                    }
                )

            # 通过（score, name）确保排序稳定，进而生成可重复的 id。
            element_payload = sorted(
                raw_elements,
                key=lambda x: (-x["score"], x["name"]),
            )

            # 3) 计算分类路径节点权重（节点分 = 覆盖的 name score 求和）
            category_scores, category_post_ids = _build_category_scores(
                name_scores, name_paths, name_post_ids
            )
            category_payload = sorted(
                [
                    {
                        "category_path": path,
                        "category": path.split(">")[-1].strip() if path else "",
                        "score": round(score, 6),
                        "post_ids_count": len(category_post_ids.get(path, set())),
                    }
                    for path, score in category_scores.items()
                ],
                key=lambda x: x["score"],
                reverse=True,
            )

            element_file = output_dir / f"{element_type}_元素.json"
            category_file = output_dir / f"{element_type}_分类.json"
            _write_json(element_file, element_payload)
            _write_json(category_file, category_payload)

            summary["files"][f"{element_type}_元素"] = str(element_file)
            summary["files"][f"{element_type}_分类"] = str(category_file)

        return summary
    finally:
        session.close()

def piaoquan_prepare(cluster_name):
    execution_id = run_mining(cluster_name=cluster_name, merge_leve2=cluster_name, platform='piaoquan')
    if execution_id:
        prepare(execution_id)
        return execution_id

if __name__ == '__main__':
    cluster_name = '历史名人'

    execution_id = run_mining(cluster_name=cluster_name, merge_leve2=cluster_name)

    # execution_id = piaoquan_prepare(cluster_name=cluster_name)
    # print(execution_id)