mirror of
https://github.com/primedigitaltech/market-assistant.git
synced 2026-07-23 08:31:37 +08:00
640 lines
21 KiB
Python
640 lines
21 KiB
Python
"""
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第八章「文本挖掘探针」独立脚本（**不修改**主报告代码）。
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流程（按细类分组）：清洗（jieba 分词 + 停用词）→ **词云图（可选）** → 词频 / TF-IDF → 共现对 → LDA 主题
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→ 规则化叙事小结 → 文末可选 **细类 LLM 归纳**（与正式报告 ``generate_comment_group_summaries_llm`` / ``llm_comment_group_summaries`` 同源，非 §8.2 情感）。
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依赖（请自行安装）::
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pip install jieba scikit-learn numpy wordcloud matplotlib
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用法（在 ``backend`` 目录下）::
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python -m pipeline.demos.chapter8_text_mining_probe --run-dir \"../data/JD/pipeline_runs/20260413_104252_低GI\"
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python -m pipeline.demos.chapter8_text_mining_probe --run-dir \"...\" --out chapter8_probe.md
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python -m pipeline.demos.chapter8_text_mining_probe --run-dir \"...\" --live-llm
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python -m pipeline.demos.chapter8_text_mining_probe --run-dir \"...\" --live-llm --llm-chunked
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输出：默认写入 ``<run_dir>/chapter8_text_mining_probe.md``。
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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import re
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import sys
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from collections import Counter
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from pathlib import Path
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from typing import Any
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import numpy as np
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BACKEND_ROOT = Path(__file__).resolve().parents[2]
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if str(BACKEND_ROOT) not in sys.path:
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sys.path.insert(0, str(BACKEND_ROOT))
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os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings")
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import django # noqa: E402
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django.setup()
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JCR_ROOT = BACKEND_ROOT / "crawler_copy" / "jd_pc_search"
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if str(JCR_ROOT) not in sys.path:
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sys.path.insert(0, str(JCR_ROOT))
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import jd_competitor_report as jcr # noqa: E402
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import jd_keyword_pipeline as kpl # noqa: E402
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from pipeline.csv_schema import MERGED_FIELD_TO_CSV_HEADER # noqa: E402
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try:
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import jieba # noqa: WPS433
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from sklearn.decomposition import LatentDirichletAllocation # noqa: WPS433
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from sklearn.feature_extraction.text import ( # noqa: WPS433
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CountVectorizer,
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TfidfVectorizer,
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)
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except ImportError as e:
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print(
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"缺少依赖，请先安装：pip install jieba scikit-learn numpy\n"
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f"原始错误: {e}",
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file=sys.stderr,
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)
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sys.exit(1)
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try:
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt # noqa: WPS433
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from wordcloud import WordCloud # noqa: WPS433
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_WORDCLOUD_AVAILABLE = True
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except ImportError:
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plt = None # type: ignore[assignment]
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WordCloud = None # type: ignore[assignment]
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_WORDCLOUD_AVAILABLE = False
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# 精简中文停用词（可换外部文件）；与业务无关，仅用于探针
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_STOP_BASIC: frozenset[str] = frozenset(
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"""
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的 了 和 是 在 也 有 就 都 很 啊 还 吗 吧 呢 呀 哦 噢 哈 呵 与 及 或 等 为 被 让 从 到 把 而 又 对 中 这 那 其 一个 一些 没有 不是 可以 这样 我们 你们 他们 它们 它 会 要 能 去 来 做 用 给 自己 这个 那个 什么 怎么 如果 因为 所以 但是 而且 然后 还是 或者 还有 就是 只是 只是 已经 还是
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非常 真的 比较 特别 感觉 觉得 认为 看到 收到 东西 商品 产品 卖家 买家 店铺 京东 物流 快递 包装 评价 评论 购买 买 卖 收到 天 次 个 款 种 条 块
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""".split()
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)
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def _is_noise_token(w: str) -> bool:
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if len(w) < 2:
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return True
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if w in ("ldquo", "rdquo", "nbsp", "mdash"):
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return True
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if re.match(r"^[a-z]{1,8}$", w) and w not in ("gi",):
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return True
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return False
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def _word_freq_from_cut_docs(cut_docs: list[str]) -> dict[str, int]:
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c: Counter[str] = Counter()
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for line in cut_docs:
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for w in line.split():
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if _is_noise_token(w):
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continue
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c[w] += 1
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return dict(c)
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def _font_path_chinese() -> str | None:
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windir = os.environ.get("WINDIR", r"C:\Windows")
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candidates = [
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Path(windir) / "Fonts" / "msyh.ttc",
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Path(windir) / "Fonts" / "msyhbd.ttc",
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Path(windir) / "Fonts" / "simhei.ttf",
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Path(windir) / "Fonts" / "simsun.ttc",
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Path("/usr/share/fonts/truetype/wqy/wqy-microhei.ttc"),
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Path("/usr/share/fonts/truetype/noto/NotoSansCJK-Regular.ttc"),
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]
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for p in candidates:
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try:
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if p.is_file():
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return str(p)
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except OSError:
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continue
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return None
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def _slug_safe(gname: str) -> str:
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s = re.sub(r'[<>:"/\\|?*]', "_", (gname or "").strip())
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return s[:80] if len(s) > 80 else s
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def _save_wordcloud_png(
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freq: dict[str, int],
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out_path: Path,
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*,
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font_path: str | None,
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) -> str:
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"""写入 PNG；成功返回空串，失败返回错误说明。"""
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if not _WORDCLOUD_AVAILABLE or WordCloud is None or plt is None:
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return "wordcloud/matplotlib 未安装"
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if not freq:
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return "词频为空"
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try:
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out_path.parent.mkdir(parents=True, exist_ok=True)
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wc = WordCloud(
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font_path=font_path,
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width=960,
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height=540,
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background_color="white",
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max_words=220,
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relative_scaling=0.35,
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colormap="viridis",
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prefer_horizontal=0.88,
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min_font_size=10,
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).generate_from_frequencies(freq)
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fig, ax = plt.subplots(figsize=(10.5, 6), dpi=120)
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ax.imshow(wc, interpolation="bilinear")
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ax.axis("off")
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fig.tight_layout(pad=0)
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fig.savefig(out_path, bbox_inches="tight", facecolor="white")
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plt.close(fig)
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except Exception as e:
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return str(e)
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return ""
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def _load_run(
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run_dir: Path,
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) -> tuple[str, list[dict[str, str]], list[dict[str, str]]]:
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run_dir = run_dir.resolve()
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merged_path = run_dir / kpl.FILE_MERGED_CSV
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if not merged_path.is_file():
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raise FileNotFoundError(f"缺少合并表: {merged_path}")
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_, merged_rows = jcr._read_csv_rows(merged_path)
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_, comment_rows = jcr._read_csv_rows(run_dir / kpl.FILE_COMMENTS_FLAT_CSV)
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meta_path = run_dir / kpl.FILE_RUN_META_JSON
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meta: dict[str, Any] | None = None
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if meta_path.is_file():
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try:
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meta = json.loads(meta_path.read_text(encoding="utf-8"))
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except json.JSONDecodeError:
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meta = None
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kw = ""
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if meta and str(meta.get("keyword") or "").strip():
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kw = str(meta.get("keyword")).strip()
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return kw, merged_rows, comment_rows
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def _cut_one(text: str) -> list[str]:
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s = (text or "").strip()
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if not s:
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return []
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raw = jieba.lcut(s)
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out: list[str] = []
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for w in raw:
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w = w.strip()
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if len(w) < 2:
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continue
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if w in _STOP_BASIC:
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continue
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if re.match(r"^[0-9\s\W_]+$", w):
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continue
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out.append(w)
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return out
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def _docs_cut(texts: list[str]) -> list[str]:
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"""每条评论 → 空格连接的分词串，供 sklearn。"""
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rows: list[str] = []
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for t in texts:
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toks = _cut_one(t)
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if toks:
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rows.append(" ".join(toks))
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return rows
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def _term_freq_top(cut_docs: list[str], top_k: int) -> list[tuple[str, int]]:
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c: Counter[str] = Counter()
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for line in cut_docs:
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for w in line.split():
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c[w] += 1
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return c.most_common(top_k)
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def _tfidf_top(cut_docs: list[str], top_k: int) -> list[tuple[str, float]]:
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if not cut_docs:
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return []
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vec = TfidfVectorizer(max_features=min(2000, max(50, len(cut_docs) * 3)))
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try:
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X = vec.fit_transform(cut_docs)
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except ValueError:
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return []
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feats = np.array(vec.get_feature_names_out())
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scores = np.asarray(X.mean(axis=0)).ravel()
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idx = np.argsort(-scores)[:top_k]
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return [(str(feats[i]), float(scores[i])) for i in idx]
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def _cooc_top_pairs(
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cut_docs: list[str],
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vocab_cap: int,
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pair_top: int,
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) -> list[tuple[str, str, int]]:
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"""同一条评论内无序词对共现（过滤低频词）。"""
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term_freq = Counter()
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for line in cut_docs:
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for w in set(line.split()):
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term_freq[w] += 1
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top_terms = {w for w, _ in term_freq.most_common(vocab_cap)}
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pair_c: Counter[tuple[str, str]] = Counter()
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for line in cut_docs:
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toks = sorted({w for w in line.split() if w in top_terms})
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for i in range(len(toks)):
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for j in range(i + 1, len(toks)):
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a, b = toks[i], toks[j]
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if a > b:
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a, b = b, a
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pair_c[(a, b)] += 1
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return [(a, b, n) for (a, b), n in pair_c.most_common(pair_top)]
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def _lda_topics(
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cut_docs: list[str],
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n_topics: int,
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n_top_words: int,
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) -> tuple[list[list[str]], str]:
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if len(cut_docs) < 4:
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return [], "文本条数过少，跳过 LDA。"
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n_topics = max(2, min(n_topics, len(cut_docs) // 2))
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try:
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vec = CountVectorizer(max_df=0.95, min_df=2, max_features=800)
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X = vec.fit_transform(cut_docs)
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except ValueError as e:
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return [], f"LDA 向量化失败：{e}"
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if X.shape[0] < 3 or X.shape[1] < 3:
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return [], "矩阵过稀疏，跳过 LDA。"
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lda = LatentDirichletAllocation(
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n_components=n_topics,
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max_iter=30,
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learning_method="batch",
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random_state=42,
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n_jobs=1,
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)
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try:
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lda.fit(X)
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except Exception as e:
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return [], f"LDA 拟合失败：{e}"
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names = vec.get_feature_names_out()
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topics: list[list[str]] = []
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for topic_idx, topic in enumerate(lda.components_):
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top_ix = np.argsort(-topic)[:n_top_words]
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topics.append([str(names[i]) for i in top_ix])
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return topics, ""
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def _md_escape(s: str) -> str:
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return (s or "").replace("|", "\\|").replace("\n", " ")
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def _narrative_stub(
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n_raw: int,
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n_cut: int,
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tf_top: list[tuple[str, int]],
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tfidf_top: list[tuple[str, float]],
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cooc: list[tuple[str, str, int]],
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lda_topics: list[list[str]],
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lda_note: str,
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) -> str:
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lines: list[str] = [
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f"本细类有效评论约 **{n_cut}** 条（原始非空 **{n_raw}** 条，经分词去停用后用于建模）。",
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"",
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"**词频 Top**："
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+ (
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"、".join(f"「{w}」({n})" for w, n in tf_top[:12])
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if tf_top
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else "（无）"
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)
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+ "。",
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"",
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"**TF-IDF 加权 Top**（相对区分度）："
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+ (
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"、".join(f"「{w}」({s:.3f})" for w, s in tfidf_top[:12])
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if tfidf_top
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else "（无）"
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)
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+ "。",
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"",
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"**共现较强的词对**（同条评论内，供联想维度）："
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+ (
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"；".join(f"「{a}」-「{b}」({c})" for a, b, c in cooc[:10])
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if cooc
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else "（无）"
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)
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+ "。",
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"",
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]
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if lda_note:
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lines.append(f"*LDA：{lda_note}*")
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lines.append("")
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elif lda_topics:
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lines.append("**LDA 主题（无监督，仅作探索）**：")
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for i, words in enumerate(lda_topics):
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lines.append(f"- 主题 {i + 1}：{'、'.join(words)}")
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lines.append("")
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lines.append(
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"> 以上为主题探索与统计摘要，**不等同**于业务结论；若与星级、规则词表冲突，以人工抽样为准。"
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)
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return "\n".join(lines)
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def _effective_focus_words(run_dir: Path) -> tuple[str, ...]:
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"""与 ``runner.write_competitor_analysis_for_run_dir`` 一致：优先 ``effective_report_config.json``。"""
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p = run_dir / "effective_report_config.json"
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if p.is_file():
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try:
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eff = json.loads(p.read_text(encoding="utf-8"))
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except json.JSONDecodeError:
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eff = None
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if isinstance(eff, dict):
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fw_src = eff.get("comment_focus_words") or list(jcr.COMMENT_FOCUS_WORDS)
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fw_tuple = tuple(
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str(x).strip() for x in fw_src if str(x).strip()
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) or jcr.COMMENT_FOCUS_WORDS
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return fw_tuple
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return jcr.COMMENT_FOCUS_WORDS
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def _run_comment_groups_llm_section(
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*,
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run_dir: Path,
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keyword: str,
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merged_rows: list[dict[str, str]],
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comment_rows: list[dict[str, str]],
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llm_chunked: bool,
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) -> str:
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"""
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与正式报告 **§8 末** 一致：``build_comment_groups_llm_payload`` +
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``generate_comment_group_summaries_llm``（或 chunked 变体）。
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"""
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sku_h = MERGED_FIELD_TO_CSV_HEADER["sku_id"]
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title_h = MERGED_FIELD_TO_CSV_HEADER["title"]
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fb = jcr._consumer_feedback_by_matrix_group(
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merged_rows=merged_rows,
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comment_rows=comment_rows,
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sku_header=sku_h,
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)
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fw = _effective_focus_words(run_dir)
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pl = jcr.build_comment_groups_llm_payload(
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feedback_groups=fb,
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focus_words=fw,
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merged_rows=merged_rows,
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sku_header=sku_h,
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title_h=title_h,
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)
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if not pl:
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return (
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"> **细类 LLM 归纳**：payload 为空（无可用评价分组或全部被过滤），跳过。"
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)
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try:
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if llm_chunked:
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from pipeline.llm.generate import ( # noqa: WPS433
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generate_comment_group_summaries_llm_chunked,
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)
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body = generate_comment_group_summaries_llm_chunked(pl, keyword=keyword)
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else:
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from pipeline.llm.generate import ( # noqa: WPS433
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generate_comment_group_summaries_llm,
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)
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body = generate_comment_group_summaries_llm(pl, keyword=keyword)
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except Exception as e:
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return f"> **细类 LLM 归纳**调用失败：{e}"
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return body.strip()
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def build_markdown(
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run_dir: Path,
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*,
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min_texts: int,
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lda_topics_n: int,
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top_k_words: int,
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cooc_vocab: int,
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cooc_pairs: int,
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live_llm: bool,
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llm_chunked: bool,
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wordcloud_enabled: bool,
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wordcloud_max: int,
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) -> str:
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kw, merged, comments = _load_run(run_dir)
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sku_h = MERGED_FIELD_TO_CSV_HEADER["sku_id"]
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groups = jcr._consumer_feedback_by_matrix_group(
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merged_rows=merged,
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comment_rows=comments,
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sku_header=sku_h,
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)
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lines: list[str] = [
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"# 八、消费者反馈与用户画像（文本挖掘探针 · 实验稿）",
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"",
|
||
f"- **运行目录**：`{run_dir}`",
|
||
f"- **监测词（run_meta）**：{kw or '—'}",
|
||
f"- **生成脚本**：`pipeline.demos.chapter8_text_mining_probe`",
|
||
"",
|
||
"## 8.0 说明",
|
||
"",
|
||
"本稿为**独立探针**，流程参考「清洗 → 词云（可选）→ 词频/TF-IDF → 共现 → LDA → 叙事小结」；"
|
||
"文末可选 **细类 LLM 归纳**（与正式 ``llm_comment_group_summaries`` / ``generate_comment_group_summaries_llm`` 同源）。"
|
||
"与线上一致的部分：**细类划分与 SKU 归因**复用 ``jd_competitor_report._consumer_feedback_by_matrix_group``；"
|
||
"其余为 **jieba + sklearn** 的开放词表分析，**不替代**正式报告中的规则统计。",
|
||
"",
|
||
"---",
|
||
"",
|
||
]
|
||
if wordcloud_enabled and not _WORDCLOUD_AVAILABLE:
|
||
lines.extend(
|
||
[
|
||
"> **词云**：当前环境未安装 ``wordcloud`` / ``matplotlib``，已跳过出图。"
|
||
"请执行：``pip install wordcloud matplotlib``。",
|
||
"",
|
||
]
|
||
)
|
||
elif wordcloud_enabled and not _font_path_chinese():
|
||
lines.extend(
|
||
[
|
||
"> **词云字体**：未检测到常见中文字体路径，词云可能出现方框；"
|
||
"Windows 可确认 ``C:\\Windows\\Fonts\\msyh.ttc`` 是否存在。",
|
||
"",
|
||
]
|
||
)
|
||
|
||
wc_n = 0
|
||
for gname, _cr_rows, texts in groups:
|
||
n_raw = len([t for t in texts if (t or "").strip()])
|
||
if n_raw < min_texts:
|
||
lines.extend(
|
||
[
|
||
f"## {gname}",
|
||
"",
|
||
f"*本细类有效文本 {n_raw} 条，低于 ``--min-texts``={min_texts}，跳过。*",
|
||
"",
|
||
"---",
|
||
"",
|
||
]
|
||
)
|
||
continue
|
||
|
||
cut_docs = _docs_cut(texts)
|
||
if len(cut_docs) < 2:
|
||
lines.extend(
|
||
[
|
||
f"## {gname}",
|
||
"",
|
||
"*分词后不足 2 条，跳过。*",
|
||
"",
|
||
"---",
|
||
"",
|
||
]
|
||
)
|
||
continue
|
||
|
||
tf_top = _term_freq_top(cut_docs, top_k_words)
|
||
tfidf_top = _tfidf_top(cut_docs, top_k_words)
|
||
cooc = _cooc_top_pairs(cut_docs, cooc_vocab, cooc_pairs)
|
||
lda_t, lda_err = _lda_topics(cut_docs, lda_topics_n, 12)
|
||
|
||
lines.extend([f"## {gname}", ""])
|
||
if (
|
||
wordcloud_enabled
|
||
and _WORDCLOUD_AVAILABLE
|
||
and wc_n < wordcloud_max
|
||
):
|
||
freq_wc = _word_freq_from_cut_docs(cut_docs)
|
||
fn = f"wordcloud_probe__{wc_n:02d}_{_slug_safe(gname)}.png"
|
||
img_path = run_dir.resolve() / "report_assets" / fn
|
||
err_wc = _save_wordcloud_png(
|
||
freq_wc,
|
||
img_path,
|
||
font_path=_font_path_chinese(),
|
||
)
|
||
if not err_wc:
|
||
lines.append(
|
||
f""
|
||
)
|
||
lines.append("")
|
||
wc_n += 1
|
||
else:
|
||
lines.append(f"> 词云未生成：{err_wc}")
|
||
lines.append("")
|
||
lines.append(_narrative_stub(
|
||
n_raw,
|
||
len(cut_docs),
|
||
tf_top,
|
||
tfidf_top,
|
||
cooc,
|
||
lda_t,
|
||
lda_err,
|
||
))
|
||
lines.extend(["", "---", ""])
|
||
|
||
lines.extend(
|
||
[
|
||
"",
|
||
"---",
|
||
"",
|
||
"## 细类评论要点归纳（大模型 · 与正式报告 §8 末同源）",
|
||
"",
|
||
"> 输入为 ``build_comment_groups_llm_payload``：关注词子串命中、有效文本行、带 SKU/品名/店铺前缀的评价摘录；"
|
||
"模型为 ``COMMENT_GROUPS_SYSTEM``（与 ``generate_comment_group_summaries_llm`` 一致）。"
|
||
"上方 jieba / TF-IDF / LDA / 词云为**独立探针**，**未**注入本段 payload。",
|
||
"",
|
||
]
|
||
)
|
||
if live_llm:
|
||
lines.append(
|
||
_run_comment_groups_llm_section(
|
||
run_dir=run_dir,
|
||
keyword=kw,
|
||
merged_rows=merged,
|
||
comment_rows=comments,
|
||
llm_chunked=llm_chunked,
|
||
)
|
||
)
|
||
else:
|
||
lines.append(
|
||
"> **细类 LLM 归纳**：未启用。请使用 ``--live-llm``（需 ``AI_crawler`` 等可用）；"
|
||
"细类很多、单次 JSON 易超上下文时可加 ``--llm-chunked``（逐细类调用后拼接）。"
|
||
)
|
||
|
||
lines.append("")
|
||
lines.append("*（完）*")
|
||
return "\n".join(lines)
|
||
|
||
|
||
def main() -> None:
|
||
ap = argparse.ArgumentParser(description="第八章文本挖掘探针（独立脚本）")
|
||
ap.add_argument(
|
||
"--run-dir",
|
||
type=Path,
|
||
required=True,
|
||
help="pipeline_runs 下某批次目录（含 keyword_pipeline_merged.csv、comments_flat.csv）",
|
||
)
|
||
ap.add_argument(
|
||
"--out",
|
||
type=Path,
|
||
default=None,
|
||
help="输出 Markdown 路径（默认 <run_dir>/chapter8_text_mining_probe.md）",
|
||
)
|
||
ap.add_argument("--min-texts", type=int, default=8, help="细类最少评论条数才分析")
|
||
ap.add_argument("--lda-topics", type=int, default=4, help="LDA 主题数上限（会按样本量裁剪）")
|
||
ap.add_argument("--top-k-words", type=int, default=30, help="词频/TF-IDF 展示长度")
|
||
ap.add_argument("--cooc-vocab", type=int, default=80, help="共现矩阵保留的高频词数")
|
||
ap.add_argument("--cooc-pairs", type=int, default=25, help="输出词对数量")
|
||
ap.add_argument(
|
||
"--live-llm",
|
||
action="store_true",
|
||
help=(
|
||
"文末调用与正式管线一致的细类归纳："
|
||
"``generate_comment_group_summaries_llm``（需 AI_crawler 等可用）"
|
||
),
|
||
)
|
||
ap.add_argument(
|
||
"--llm-chunked",
|
||
action="store_true",
|
||
help="细类逐条调用 ``generate_comment_group_summaries_llm_chunked``，防上下文过长",
|
||
)
|
||
ap.add_argument(
|
||
"--no-wordcloud",
|
||
action="store_true",
|
||
help="不生成词云 PNG（默认生成，需 wordcloud+matplotlib）",
|
||
)
|
||
ap.add_argument(
|
||
"--wordcloud-max",
|
||
type=int,
|
||
default=40,
|
||
help="最多为多少个细类各出一张词云（防文件过多）",
|
||
)
|
||
args = ap.parse_args()
|
||
|
||
md = build_markdown(
|
||
args.run_dir,
|
||
min_texts=args.min_texts,
|
||
lda_topics_n=args.lda_topics,
|
||
top_k_words=args.top_k_words,
|
||
cooc_vocab=args.cooc_vocab,
|
||
cooc_pairs=args.cooc_pairs,
|
||
live_llm=args.live_llm,
|
||
llm_chunked=args.llm_chunked,
|
||
wordcloud_enabled=not args.no_wordcloud,
|
||
wordcloud_max=max(0, args.wordcloud_max),
|
||
)
|
||
out = args.out or (args.run_dir.resolve() / "chapter8_text_mining_probe.md")
|
||
out.write_text(md, encoding="utf-8")
|
||
print(f"已写入: {out}", flush=True)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|