mirror of
https://github.com/primedigitaltech/market-assistant.git
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0e224c6a6c
- 删除 backend/pipeline/jd_competitor_report.py；runner/测试与演示改为 from pipeline.competitor_report import jd_report - 爬虫目录 jd_competitor_report.py 仅调用 jd_report.main；文档与 OpenAPI 同步更新 - 命令行推荐：python -m pipeline.competitor_report.jd_report Made-with: Cursor
814 lines
28 KiB
Python
814 lines
28 KiB
Python
"""根据结构化 brief 生成报告用 PNG 统计图（matplotlib），写入 ``run_dir/report_assets/``。"""
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from __future__ import annotations
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import math
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import os
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import re
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from pathlib import Path
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from typing import Any
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def _setup_matplotlib_cjk() -> None:
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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from matplotlib import font_manager
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windir = os.environ.get("WINDIR", r"C:\Windows")
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for name in ("simhei.ttf", "msyh.ttc", "simsun.ttc"):
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fp = Path(windir) / "Fonts" / name
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if fp.is_file():
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try:
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font_manager.fontManager.addfont(str(fp))
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fam = font_manager.FontProperties(fname=str(fp)).get_name()
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plt.rcParams["font.family"] = [fam]
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break
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except Exception:
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continue
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plt.rcParams["axes.unicode_minus"] = False
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def _label_count_pairs(
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items: Any,
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*,
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key_label: str = "label",
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key_count: str = "count",
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cap: int = 40,
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) -> tuple[list[str], list[float]]:
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labs: list[str] = []
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vals: list[float] = []
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if not isinstance(items, list):
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return labs, vals
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for item in items[:cap]:
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if not isinstance(item, dict):
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continue
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lbl = str(item.get(key_label) or "").strip()[:48]
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cnt = item.get(key_count)
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if lbl and isinstance(cnt, (int, float)) and cnt > 0:
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labs.append(lbl)
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vals.append(float(cnt))
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return labs, vals
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def _merge_labeled_counts_tail(
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pairs: list[tuple[str, float]], *, max_items: int
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) -> list[tuple[str, float]]:
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if len(pairs) <= max_items:
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return pairs
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head = pairs[: max_items - 1]
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rest = sum(c for _, c in pairs[max_items - 1 :])
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if rest > 0:
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head.append(("其他", rest))
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return head
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def _float_price_from_cell(s: str) -> float | None:
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t = (s or "").strip().replace(",", "").replace("，", "")
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if not t:
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return None
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m = re.search(r"(\d+(?:\.\d+)?)", t)
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if not m:
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return None
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try:
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v = float(m.group(1))
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except ValueError:
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return None
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if 0 < v < 1_000_000:
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return v
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return None
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def _cn_volume_int(s: str) -> int:
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"""
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从搜索侧文案抽取非负整数（评价量/销量等）：支持「亿」「万」及纯数字；
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如 ``已售50万+ | good:99%好评`` → 500000。
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"""
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t = (s or "").strip().replace(",", "").replace("，", "")
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if not t:
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return 0
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m = re.search(r"(\d+(?:\.\d+)?)\s*亿", t)
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if m:
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return int(round(float(m.group(1)) * 100_000_000))
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m = re.search(r"(\d+(?:\.\d+)?)\s*万", t)
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if m:
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return int(round(float(m.group(1)) * 10_000))
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m2 = re.search(r"(\d+)", t)
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if m2:
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return int(m2.group(1))
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return 0
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def _format_xaxis_int_cn(x: float, _pos: int | None) -> str:
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"""
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横轴大整数刻度：用「万」「亿」表述，避免 matplotlib 默认 ``1e6`` 科学计数法。
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用于销量、评价量、条数等非负计数。
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"""
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if not math.isfinite(x):
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return ""
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if abs(x) < 1e-9:
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return "0"
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ax = abs(x)
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sign = "-" if x < 0 else ""
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if ax < 10_000:
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return sign + str(int(round(ax)))
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if ax < 100_000_000:
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wan = ax / 10_000.0
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if wan >= 1000:
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return sign + f"{wan:.0f}万"
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if wan >= 100:
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return sign + f"{wan:.0f}万"
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if abs(wan - round(wan)) < 1e-6:
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return sign + f"{int(round(wan))}万"
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s = f"{wan:.1f}".rstrip("0").rstrip(".")
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return sign + s + "万"
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yi = ax / 100_000_000.0
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if abs(yi - round(yi)) < 1e-6:
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return sign + f"{int(round(yi))}亿"
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s = f"{yi:.2f}".rstrip("0").rstrip(".")
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return sign + s + "亿"
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def _merge_tail_as_other(
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labels: list[str], values: list[float], *, max_slices: int
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) -> tuple[list[str], list[float]]:
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pairs = [(l, v) for l, v in zip(labels, values) if v > 0]
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if not pairs:
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return [], []
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if len(pairs) <= max_slices:
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return [p[0] for p in pairs], [p[1] for p in pairs]
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head = pairs[: max_slices - 1]
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rest = sum(v for _, v in pairs[max_slices - 1 :])
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labs = [p[0] for p in head]
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vals = [p[1] for p in head]
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if rest > 0:
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labs.append("其他")
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vals.append(rest)
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return labs, vals
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# 已不再写入报告正文的旧版图，避免 run_dir 里残留误导性 PNG
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# 横向条形图：统一柱厚、柱端数值字号（全文件条形图共用）
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_BARH_HEIGHT = 0.6
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_BAR_VALUE_FONTSIZE = 8
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def _thin_barh_height(n: int) -> float:
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"""
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横向条形图：类目条数 n 较少时降低 barh 的 height（与 y 轴跨度同量纲）。
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n=1 时若仍用 0.6 且 ylim 跨度仅 1，单条会占满大半幅、视觉上极粗。
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"""
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if n <= 0:
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return _BARH_HEIGHT
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if n == 1:
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return 0.30
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if n == 2:
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return 0.44
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if n <= 5:
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return 0.50
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if n <= 10:
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return 0.54
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return _BARH_HEIGHT
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def _set_barh_category_ylim(ax: Any, n: int) -> None:
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"""n 条类目横条时设置纵轴范围；n=1 时略放宽，使柱相对更细。"""
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if n <= 0:
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return
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if n == 1:
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ax.set_ylim(-1.0, 1.0)
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else:
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ax.set_ylim(-0.5, float(n) - 0.5)
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def _fmt_bar_value(v: float, *, as_int: bool = False) -> str:
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if as_int or (math.isfinite(v) and abs(v - round(v)) < 1e-6):
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return str(int(round(v)))
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s = f"{v:.2f}".rstrip("0").rstrip(".")
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return s if s else "0"
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def _annotate_barh_numeric(
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ax: Any,
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bars: Any,
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values: list[float],
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*,
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as_int: bool = False,
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x_pad_ratio: float = 0.02,
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) -> None:
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"""在横向柱末端标注数值；调用前请已设置合适的 xlim。"""
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if not bars or not values:
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return
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x1 = ax.get_xlim()[1]
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if x1 <= 0:
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return
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pad = max(x1 * x_pad_ratio, 0.02 * max(values) if values else 0.1)
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for bar, v in zip(bars, values):
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if v is None or not math.isfinite(float(v)) or float(v) <= 0:
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continue
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w = bar.get_width()
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ax.text(
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w + pad,
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bar.get_y() + bar.get_height() / 2,
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_fmt_bar_value(float(v), as_int=as_int),
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va="center",
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fontsize=_BAR_VALUE_FONTSIZE,
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)
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_OBSOLETE_REPORT_ASSETS: frozenset[str] = frozenset(
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{
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"chart_focus_keywords_bar.png",
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"chart_usage_scenarios.png",
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"chart_usage_scenarios_pie.png",
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"chart_focus_keywords_pie.png",
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"chart_comment_focus_global_bar.png",
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"chart_usage_scenarios_global_bar.png",
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}
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)
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def _cleanup_obsolete_report_assets(out_dir: Path) -> None:
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"""删除历史版本生成的、当前报告不再引用的插图文件。"""
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if not out_dir.is_dir():
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return
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for name in _OBSOLETE_REPORT_ASSETS:
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fp = out_dir / name
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if fp.is_file():
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try:
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fp.unlink()
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except OSError:
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pass
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for fp in out_dir.glob("chart_usage_scenarios_pie__*.png"):
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try:
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fp.unlink()
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except OSError:
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pass
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for pat in (
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"chart_focus_keywords_bar__*.png",
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"chart_usage_scenarios_bar__*.png",
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):
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for fp in out_dir.glob(pat):
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try:
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fp.unlink()
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except OSError:
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pass
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def generate_report_charts(
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run_dir: Path,
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brief: dict[str, Any],
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*,
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report_config: dict[str, Any] | None = None,
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) -> list[str]:
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"""生成扇形/条形 PNG。返回已写入的文件名列表（不含路径）。
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若 ``report_config["chapter8_text_mining_probe"]`` 为真，**不**生成 ``chart_focus_and_scenarios_bar__*.png``
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（与竞品报告 §8.3 文本挖掘探针互斥，避免无效产出）。
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"""
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_setup_matplotlib_cjk()
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import matplotlib.pyplot as plt
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from matplotlib.ticker import FuncFormatter
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out_dir = Path(run_dir).resolve() / "report_assets"
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out_dir.mkdir(parents=True, exist_ok=True)
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_cleanup_obsolete_report_assets(out_dir)
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created: list[str] = []
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def save_bar_h(
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labels: list[str],
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values: list[float],
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title: str,
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fname: str,
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xlabel: str = "",
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) -> None:
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if not labels or not values or max(values) <= 0:
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return
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n = len(labels)
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fig_h = max(3.2, min(14.0, 0.38 * n + 1.5))
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fig, ax = plt.subplots(figsize=(8.2, fig_h))
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y_pos = range(n)
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bh = _thin_barh_height(n)
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bars = ax.barh(
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list(y_pos), values, color="#2563eb", height=bh
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)
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ax.set_yticks(list(y_pos))
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ax.set_yticklabels(labels, fontsize=9)
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ax.invert_yaxis()
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_set_barh_category_ylim(ax, n)
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ax.set_title(title, fontsize=12, pad=10)
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if xlabel:
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ax.set_xlabel(xlabel, fontsize=9)
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ax.tick_params(axis="y", left=True, right=False, labelleft=True, labelright=False)
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vmax = max(values)
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ax.set_xlim(0, vmax * 1.14 + max(0.08 * vmax, 0.5))
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_annotate_barh_numeric(ax, bars, list(values), as_int=True)
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fig.tight_layout()
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path = out_dir / fname
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fig.savefig(path, dpi=130, bbox_inches="tight")
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plt.close(fig)
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created.append(fname)
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def save_bar_h_share_of_text(
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labels: list[str],
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counts: list[float],
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n_texts: int,
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title: str,
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fname: str,
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) -> None:
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"""
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横轴 = count / n_texts * 100，与报告表格「占有效文本比例」一致（多标签下各柱比例可相加 >100%）。
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"""
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if not labels or not counts or n_texts <= 0 or max(counts) <= 0:
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return
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pcts = [100.0 * c / n_texts for c in counts]
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n_b = len(labels)
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fig_h = max(3.2, min(14.0, 0.38 * n_b + 1.8))
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fig, ax = plt.subplots(figsize=(8.8, fig_h))
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y_pos = range(n_b)
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bh = _thin_barh_height(n_b)
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bars = ax.barh(list(y_pos), pcts, color="#2563eb", height=bh)
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ax.set_yticks(list(y_pos))
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ax.set_yticklabels(labels, fontsize=9)
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ax.invert_yaxis()
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_set_barh_category_ylim(ax, n_b)
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ax.set_title(title, fontsize=12, pad=10)
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ax.set_xlabel("占有效评价文本比例（%）", fontsize=9)
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ax.tick_params(axis="y", left=True, right=False, labelleft=True, labelright=False)
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xmax = max(pcts) * 1.12 + 4.0
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ax.set_xlim(0, max(xmax, max(pcts) + 10.0, 24.0))
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x1 = ax.get_xlim()[1]
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pad = max(x1 * 0.015, 0.35)
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for bar, c, p in zip(bars, counts, pcts):
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ax.text(
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min(bar.get_width() + pad, x1 * 0.985),
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bar.get_y() + bar.get_height() / 2,
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f"{int(c)}条 · {p:.1f}%",
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va="center",
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fontsize=_BAR_VALUE_FONTSIZE,
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)
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fig.tight_layout()
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path = out_dir / fname
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fig.savefig(path, dpi=130, bbox_inches="tight")
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plt.close(fig)
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created.append(fname)
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def save_combo_focus_scenario_bar(
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*,
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gname: str,
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slug: str,
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wl: list[str],
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vl: list[float],
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gl: list[str],
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gv: list[float],
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n_texts: int,
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) -> None:
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"""左：关注词命中次数；右：场景占有效文本 %。两侧 **各自独立 Y 轴**（类目互不混用）。"""
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has_l = bool(wl and vl and max(vl) > 0)
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has_r = bool(gl and gv and n_texts > 0 and max(gv) > 0)
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if not has_l and not has_r:
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return
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n_l = len(wl) if has_l else 0
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n_r = len(gl) if has_r else 0
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n_ref = max(n_l, n_r, 1)
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fig_h = max(3.4, min(14.0, 0.38 * n_ref + 2.8))
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fig = plt.figure(figsize=(10.8, fig_h))
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ttl = (gname or "").strip()[:22] or "细类"
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fig.suptitle(
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f"「{ttl}」· 关注词与使用场景",
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fontsize=11,
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y=0.98,
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)
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# 底对齐、高度按各自类目数比例分配，避免共用一个「拉伸后的」纵轴比例尺
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base_bottom = 0.10
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ax_w = 0.36
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x_gap = 0.06
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x_l = 0.07
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x_r = x_l + ax_w + x_gap
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max_h = 0.72
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n_den = max(n_ref, 1)
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h_l = max(0.26, max_h * (max(n_l, 1) / n_den)) if has_l else max(0.26, max_h * 0.35)
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h_r = max(0.26, max_h * (max(n_r, 1) / n_den)) if has_r else max(0.26, max_h * 0.35)
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ax_l = fig.add_axes([x_l, base_bottom, ax_w, h_l])
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ax_r = fig.add_axes([x_r, base_bottom, ax_w, h_r])
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if has_l:
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y_pos = list(range(n_l))
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bh_l = _thin_barh_height(n_l)
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bars_l = ax_l.barh(
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y_pos, vl[:n_l], color="#2563eb", height=bh_l
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)
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ax_l.set_yticks(y_pos)
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ax_l.set_yticklabels(wl[:n_l], fontsize=8)
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ax_l.invert_yaxis()
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_set_barh_category_ylim(ax_l, n_l)
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ax_l.set_xlabel("关注词子串命中次数", fontsize=9)
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ax_l.set_title("关注词", fontsize=10, pad=6)
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ax_l.tick_params(axis="y", left=True, right=False, labelleft=True, labelright=False)
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vmax_l = max(vl[:n_l])
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ax_l.set_xlim(0, vmax_l * 1.14 + max(0.5, 0.08 * vmax_l))
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_annotate_barh_numeric(
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ax_l, bars_l, list(vl[:n_l]), as_int=True
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)
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else:
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ax_l.text(
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0.5,
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0.5,
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"本细类无关注词命中\n或无数文本",
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ha="center",
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va="center",
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transform=ax_l.transAxes,
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fontsize=10,
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color="#64748b",
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)
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ax_l.set_axis_off()
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if has_r:
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pcts = [100.0 * c / n_texts for c in gv[: len(gl)]]
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n_b = len(gl)
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y_pos = list(range(n_b))
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bh_r = _thin_barh_height(n_b)
|
||
bars = ax_r.barh(y_pos, pcts, color="#059669", height=bh_r)
|
||
ax_r.set_yticks(y_pos)
|
||
ax_r.set_yticklabels(gl[:n_b], fontsize=8)
|
||
ax_r.invert_yaxis()
|
||
_set_barh_category_ylim(ax_r, n_b)
|
||
ax_r.set_xlabel("占有效评价文本比例（%）", fontsize=9)
|
||
if pcts:
|
||
xmax = max(pcts) * 1.12 + 4.0
|
||
ax_r.set_xlim(0, max(xmax, max(pcts) + 10.0, 24.0))
|
||
else:
|
||
ax_r.set_xlim(0, 24.0)
|
||
x1r = ax_r.get_xlim()[1]
|
||
pad_r = max(x1r * 0.015, 0.35)
|
||
for bar, c, p in zip(bars, gv[:n_b], pcts):
|
||
ax_r.text(
|
||
min(bar.get_width() + pad_r, x1r * 0.985),
|
||
bar.get_y() + bar.get_height() / 2,
|
||
f"{int(c)}条 · {p:.1f}%",
|
||
va="center",
|
||
fontsize=_BAR_VALUE_FONTSIZE,
|
||
)
|
||
ax_r.set_title("使用场景", fontsize=10, pad=6)
|
||
ax_r.yaxis.tick_left()
|
||
ax_r.yaxis.set_label_position("left")
|
||
ax_r.tick_params(axis="y", left=True, right=False, labelleft=True, labelright=False)
|
||
else:
|
||
ax_r.text(
|
||
0.5,
|
||
0.5,
|
||
"本细类无场景词命中\n或无数文本",
|
||
ha="center",
|
||
va="center",
|
||
transform=ax_r.transAxes,
|
||
fontsize=10,
|
||
color="#64748b",
|
||
)
|
||
ax_r.set_axis_off()
|
||
path = out_dir / f"chart_focus_and_scenarios_bar__{slug}.png"
|
||
fig.savefig(path, dpi=130, bbox_inches="tight")
|
||
plt.close(fig)
|
||
created.append(path.name)
|
||
|
||
def save_pie(
|
||
labels: list[str],
|
||
values: list[float],
|
||
title: str,
|
||
fname: str,
|
||
*,
|
||
max_slices: int = 8,
|
||
figsize: tuple[float, float] | None = None,
|
||
dpi: int = 120,
|
||
) -> None:
|
||
labs, vals = _merge_tail_as_other(labels, values, max_slices=max_slices)
|
||
if not labs or not vals or sum(vals) <= 0:
|
||
return
|
||
fs = figsize if figsize is not None else (4.9, 3.65)
|
||
fig, ax = plt.subplots(figsize=fs)
|
||
colors = plt.cm.Set3(range(len(labs)))
|
||
wedges, _t, autotexts = ax.pie(
|
||
vals,
|
||
labels=None,
|
||
autopct=lambda p: f"{p:.1f}%" if p >= 3.5 else "",
|
||
pctdistance=0.72,
|
||
colors=colors,
|
||
startangle=90,
|
||
)
|
||
for t in autotexts:
|
||
t.set_fontsize(8)
|
||
ax.legend(
|
||
wedges,
|
||
labs,
|
||
loc="center left",
|
||
bbox_to_anchor=(1.02, 0.5),
|
||
fontsize=8,
|
||
frameon=False,
|
||
)
|
||
ax.set_title(title, fontsize=12, pad=12)
|
||
fig.tight_layout()
|
||
path = out_dir / fname
|
||
fig.savefig(path, dpi=dpi, bbox_inches="tight")
|
||
plt.close(fig)
|
||
created.append(fname)
|
||
|
||
mix = brief.get("category_mix_top") or []
|
||
labs_m, vals_m = _label_count_pairs(mix)
|
||
save_pie(
|
||
labs_m,
|
||
vals_m,
|
||
"细类分布（合并表 SKU）",
|
||
"chart_category_mix_pie.png",
|
||
)
|
||
save_bar_h(
|
||
labs_m[:15],
|
||
vals_m[:15],
|
||
"细类分布（合并表 SKU 数，Top）",
|
||
"chart_category_mix.png",
|
||
"SKU 数",
|
||
)
|
||
|
||
brand_mix = brief.get("list_brand_mix_top") or []
|
||
lb, vb = _label_count_pairs(brand_mix, key_label="label")
|
||
save_pie(
|
||
lb,
|
||
vb,
|
||
"品牌列表曝光占比",
|
||
"chart_brand_rows_pie.png",
|
||
)
|
||
|
||
shop_mix = brief.get("list_shop_mix_top") or []
|
||
ls, vs = _label_count_pairs(shop_mix, key_label="label")
|
||
save_pie(
|
||
ls,
|
||
vs,
|
||
"店铺列表曝光占比",
|
||
"chart_shop_rows_pie.png",
|
||
)
|
||
|
||
def scenario_group_asset_slug(group: str, index: int) -> str:
|
||
"""与 ``pipeline.competitor_report.jd_report`` / ``report_md_helpers._scenario_group_asset_slug`` 保持一致。"""
|
||
raw = (group or "").strip()
|
||
core = re.sub(r"[^\w\u4e00-\u9fff-]", "", raw)[:20]
|
||
if not core:
|
||
core = "group"
|
||
return f"i{index:02d}_{core}"
|
||
|
||
_skip_focus_scenario_combo = bool(
|
||
isinstance(report_config, dict)
|
||
and report_config.get("chapter8_text_mining_probe")
|
||
)
|
||
if _skip_focus_scenario_combo:
|
||
for fp in out_dir.glob("chart_focus_and_scenarios_bar__*.png"):
|
||
try:
|
||
fp.unlink()
|
||
except OSError:
|
||
pass
|
||
if not _skip_focus_scenario_combo:
|
||
scen_by_slug: dict[str, tuple[list[str], list[float], int]] = {}
|
||
by_grp = brief.get("usage_scenarios_by_matrix_group") or []
|
||
if isinstance(by_grp, list):
|
||
for item in by_grp:
|
||
if not isinstance(item, dict):
|
||
continue
|
||
slug = (item.get("chart_slug") or "").strip()
|
||
gname = str(item.get("group") or "").strip()[:24]
|
||
idx = item.get("matrix_group_index")
|
||
if not slug and gname != "" and isinstance(idx, int):
|
||
slug = scenario_group_asset_slug(gname, idx)
|
||
if not slug:
|
||
continue
|
||
scen_rows = item.get("scenarios") or []
|
||
n_unit = int(item.get("effective_text_units") or 0)
|
||
gpairs: list[tuple[str, float]] = []
|
||
if isinstance(scen_rows, list):
|
||
for r in scen_rows:
|
||
if not isinstance(r, dict):
|
||
continue
|
||
lb = str(r.get("scenario") or "").strip()[:48]
|
||
c = r.get("count")
|
||
if lb and isinstance(c, (int, float)) and c > 0:
|
||
gpairs.append((lb, float(c)))
|
||
gpairs = _merge_labeled_counts_tail(gpairs, max_items=14)
|
||
if gpairs and n_unit > 0:
|
||
scen_by_slug[slug] = (
|
||
[p[0] for p in gpairs],
|
||
[p[1] for p in gpairs],
|
||
n_unit,
|
||
)
|
||
|
||
fb = brief.get("consumer_feedback_by_matrix_group") or []
|
||
if isinstance(fb, list):
|
||
for item in fb:
|
||
if not isinstance(item, dict):
|
||
continue
|
||
slug = (item.get("chart_slug") or "").strip()
|
||
gname = str(item.get("group") or "").strip()[:24]
|
||
idx = item.get("matrix_group_index")
|
||
if not slug and gname != "" and isinstance(idx, int):
|
||
slug = scenario_group_asset_slug(gname, idx)
|
||
if not slug:
|
||
continue
|
||
hk = item.get("focus_keyword_hits") or []
|
||
wl: list[str] = []
|
||
vl: list[float] = []
|
||
if isinstance(hk, list):
|
||
for row in hk[:20]:
|
||
if not isinstance(row, dict):
|
||
continue
|
||
w = str(row.get("word") or "").strip()[:32]
|
||
c = row.get("count")
|
||
if w and isinstance(c, (int, float)) and c > 0:
|
||
wl.append(w)
|
||
vl.append(float(c))
|
||
wl = wl[:18]
|
||
vl = vl[:18]
|
||
gl, gv, n_scen = scen_by_slug.get(slug, ([], [], 0))
|
||
n_unit_fb = int(item.get("effective_comment_text_units") or 0)
|
||
n_texts = n_scen if n_scen > 0 else n_unit_fb
|
||
save_combo_focus_scenario_bar(
|
||
gname=gname,
|
||
slug=slug,
|
||
wl=wl,
|
||
vl=vl,
|
||
gl=gl,
|
||
gv=gv,
|
||
n_texts=n_texts,
|
||
)
|
||
|
||
sent = brief.get("comment_sentiment_lexicon") or {}
|
||
if isinstance(sent, dict):
|
||
_score_mode = (sent.get("method") or "") == "score_then_lexeme"
|
||
pie_labs = (
|
||
["偏正向(4～5星)", "偏负向(1～2星)", "关键词混合", "中性/空"]
|
||
if _score_mode
|
||
else ["偏正向", "偏负向", "正负混合", "中性/空"]
|
||
)
|
||
pie_vals = [
|
||
float(sent.get("positive_only") or 0),
|
||
float(sent.get("negative_only") or 0),
|
||
float(sent.get("mixed_positive_and_negative") or 0),
|
||
float(sent.get("neutral_or_empty") or 0),
|
||
]
|
||
pl = [a for a, b in zip(pie_labs, pie_vals) if b > 0]
|
||
pv = [b for b in pie_vals if b > 0]
|
||
save_pie(pl, pv, "评价语气四象限占比", "chart_sentiment_overview_pie.png")
|
||
save_bar_h(
|
||
pl,
|
||
pv,
|
||
"评价正负面粗判（条数）",
|
||
"chart_sentiment.png",
|
||
"条数",
|
||
)
|
||
|
||
pos_h = sent.get("positive_tone_lexeme_hits") or []
|
||
neg_h = sent.get("negative_tone_lexeme_hits") or []
|
||
plx, pvx = _label_count_pairs(
|
||
pos_h, key_label="word", key_count="texts_matched", cap=16
|
||
)
|
||
save_bar_h(
|
||
plx,
|
||
pvx,
|
||
(
|
||
"4～5星语境 · 正向口语短语命中条数"
|
||
if _score_mode
|
||
else "正向/混合语境 · 正向口语短语命中条数"
|
||
),
|
||
"chart_positive_lexemes_bar.png",
|
||
"条数",
|
||
)
|
||
nlx, nvx = _label_count_pairs(
|
||
neg_h, key_label="word", key_count="texts_matched", cap=16
|
||
)
|
||
save_bar_h(
|
||
nlx,
|
||
nvx,
|
||
(
|
||
"1～2星语境 · 负向口语短语命中条数"
|
||
if _score_mode
|
||
else "负向/混合语境 · 负向口语短语命中条数"
|
||
),
|
||
"chart_negative_lexemes_bar.png",
|
||
"条数",
|
||
)
|
||
|
||
matrix_groups = brief.get("matrix_by_group") or []
|
||
if isinstance(matrix_groups, list):
|
||
for gi, block in enumerate(matrix_groups):
|
||
if not isinstance(block, dict):
|
||
continue
|
||
gname = str(block.get("group") or "").strip()
|
||
skus = block.get("skus") or []
|
||
if not isinstance(skus, list) or not skus:
|
||
continue
|
||
slug = scenario_group_asset_slug(gname, gi)
|
||
rows_data: list[tuple[str, float | None, int]] = []
|
||
for s in skus:
|
||
if not isinstance(s, dict):
|
||
continue
|
||
title = str(s.get("title") or "").strip()
|
||
sku = str(s.get("sku_id") or "").strip()
|
||
# 与 §5 矩阵「产品」列一致：纵轴优先品名，无标题时再退化为 SKU
|
||
if title:
|
||
label = title if len(title) <= 48 else title[:46] + "…"
|
||
elif sku:
|
||
label = sku if len(sku) <= 22 else sku[:20] + "…"
|
||
else:
|
||
label = "?"
|
||
p: float | None = None
|
||
for k in (
|
||
"detail_price_final",
|
||
"list_price_show",
|
||
"coupon_or_detail_price",
|
||
):
|
||
p = _float_price_from_cell(str(s.get(k) or ""))
|
||
if p is not None:
|
||
break
|
||
sales = _cn_volume_int(str(s.get("total_sales") or ""))
|
||
rows_data.append((label, p, sales))
|
||
rows_data.sort(key=lambda x: x[0])
|
||
if not rows_data:
|
||
continue
|
||
if not any(
|
||
(pr is not None and pr > 0) or sv > 0
|
||
for _, pr, sv in rows_data
|
||
):
|
||
continue
|
||
n = len(rows_data)
|
||
labels_mx = [x[0] for x in rows_data]
|
||
prices_mx = [x[1] for x in rows_data]
|
||
sales_mx = [x[2] for x in rows_data]
|
||
y_pos = list(range(n))
|
||
fig_h = max(3.4, min(14.0, 0.38 * n + 2.4))
|
||
fig, (ax_l, ax_r) = plt.subplots(
|
||
1, 2, figsize=(10.6, fig_h), sharey=True
|
||
)
|
||
bh_mx = _thin_barh_height(n)
|
||
price_w = [
|
||
float(pr)
|
||
if pr is not None and pr > 0 and math.isfinite(pr)
|
||
else 0.0
|
||
for pr in prices_mx
|
||
]
|
||
bars_pl = ax_l.barh(
|
||
y_pos, price_w, height=bh_mx, color="#2563eb"
|
||
)
|
||
ax_l.set_yticks(y_pos)
|
||
ax_l.set_yticklabels(labels_mx, fontsize=8)
|
||
ax_l.invert_yaxis()
|
||
_set_barh_category_ylim(ax_l, n)
|
||
ax_l.set_xlabel("展示价（元）", fontsize=9)
|
||
ax_l.set_title("展示价", fontsize=10, pad=8)
|
||
ax_l.tick_params(axis="y", left=True, right=False, labelleft=True, labelright=False)
|
||
pmax = max(price_w) if price_w else 0.0
|
||
if pmax > 0:
|
||
ax_l.set_xlim(0, pmax * 1.12 + max(0.08 * pmax, 0.5))
|
||
else:
|
||
ax_l.set_xlim(0, 1)
|
||
pad_p = max(ax_l.get_xlim()[1] * 0.012, 0.08)
|
||
for bar, pr in zip(bars_pl, prices_mx):
|
||
if pr is not None and pr > 0 and math.isfinite(pr):
|
||
ax_l.text(
|
||
bar.get_width() + pad_p,
|
||
bar.get_y() + bar.get_height() / 2,
|
||
_fmt_bar_value(float(pr), as_int=False),
|
||
va="center",
|
||
fontsize=_BAR_VALUE_FONTSIZE,
|
||
)
|
||
sales_f = [float(s) for s in sales_mx]
|
||
bars_sr = ax_r.barh(
|
||
y_pos, sales_f, height=bh_mx, color="#059669"
|
||
)
|
||
ax_r.set_xlabel("销量", fontsize=9)
|
||
ax_r.set_title("销量", fontsize=10, pad=8)
|
||
ax_r.xaxis.set_major_formatter(
|
||
FuncFormatter(_format_xaxis_int_cn)
|
||
)
|
||
ax_r.tick_params(axis="y", left=False, labelleft=False)
|
||
smax = max(sales_f) if sales_f else 0.0
|
||
if smax > 0:
|
||
ax_r.set_xlim(0, smax * 1.1 + max(0.04 * smax, smax * 0.02))
|
||
else:
|
||
ax_r.set_xlim(0, 1)
|
||
pad_s = max(ax_r.get_xlim()[1] * 0.008, smax * 0.01 if smax else 0.1)
|
||
for bar, sv in zip(bars_sr, sales_mx):
|
||
if sv > 0:
|
||
ax_r.text(
|
||
bar.get_width() + pad_s,
|
||
bar.get_y() + bar.get_height() / 2,
|
||
_format_xaxis_int_cn(float(sv), None),
|
||
va="center",
|
||
fontsize=_BAR_VALUE_FONTSIZE,
|
||
)
|
||
ttl = gname[:22] if gname else "细类"
|
||
fig.suptitle(
|
||
f"「{ttl}」· 竞品矩阵：价格与销量",
|
||
fontsize=11,
|
||
y=1.01,
|
||
)
|
||
fig.tight_layout()
|
||
out_mx = out_dir / f"chart_matrix_prices_sales__{slug}.png"
|
||
fig.savefig(out_mx, dpi=130, bbox_inches="tight")
|
||
plt.close(fig)
|
||
created.append(out_mx.name)
|
||
|
||
return created
|