mirror of
https://github.com/primedigitaltech/market-assistant.git
synced 2026-07-25 01:34:47 +08:00
9cc8de9006
Remove chart_comment_focus_global_bar / chart_usage_scenarios_global_bar generation and markdown embed; delete leftovers on chart regen. Made-with: Cursor
490 lines
17 KiB
Python
490 lines
17 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 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 _matrix_block_chart_slug(group: str, index: int) -> str:
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"""与 ``jd_competitor_report._scenario_group_asset_slug`` 规则一致（文件名对齐）。"""
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raw = (group or "").strip()
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core = re.sub(r"[^\w\u4e00-\u9fff-]", "", raw)[:20]
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if not core:
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core = "group"
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return f"i{index:02d}_{core}"
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def _parse_price_from_text(s: str) -> float | None:
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t = (s or "").strip().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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return v if 0 < v < 1_000_000 else None
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except ValueError:
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return None
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def _parse_comment_fuzzy_sortable(s: str) -> float | None:
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"""评价量/声量展示文案粗转可排序正数（启发式，非精确条数）。"""
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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+)?)\s*万\+?", t)
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if m:
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return float(m.group(1)) * 10_000
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m = re.search(r"(\d+(?:\.\d+)?)\s*亿\+?", t)
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if m:
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return 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 float(m.group(1)) * 10_000
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m = re.search(r"(\d{2,})\+", t)
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if m:
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return float(m.group(1))
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m = re.search(r"(\d+)", t)
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if m:
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return float(m.group(1))
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return None
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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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_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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def generate_report_charts(run_dir: Path, brief: dict[str, Any]) -> 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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_setup_matplotlib_cjk()
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import matplotlib.pyplot as plt
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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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ax.barh(list(y_pos), values, color="#2563eb", height=0.65)
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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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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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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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bars = ax.barh(list(y_pos), pcts, color="#2563eb", height=0.65)
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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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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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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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for bar, c, p in zip(bars, counts, pcts):
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ax.text(
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min(bar.get_width() + 0.6, ax.get_xlim()[1] * 0.97),
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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=8,
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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_pie(
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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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*,
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max_slices: int = 8,
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) -> None:
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labs, vals = _merge_tail_as_other(labels, values, max_slices=max_slices)
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if not labs or not vals or sum(vals) <= 0:
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return
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fig, ax = plt.subplots(figsize=(7.2, 5.4))
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colors = plt.cm.Set3(range(len(labs)))
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wedges, _t, autotexts = ax.pie(
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vals,
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labels=None,
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autopct=lambda p: f"{p:.1f}%" if p >= 3.5 else "",
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pctdistance=0.72,
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colors=colors,
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startangle=90,
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)
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for t in autotexts:
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t.set_fontsize(8)
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ax.legend(
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wedges,
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labs,
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loc="center left",
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bbox_to_anchor=(1.02, 0.5),
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fontsize=8,
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frameon=False,
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)
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ax.set_title(title, fontsize=12, pad=12)
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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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mix = brief.get("category_mix_top") or []
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labs_m, vals_m = _label_count_pairs(mix)
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save_pie(
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labs_m,
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vals_m,
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"类目/可读名称分布（列表行占比）",
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"chart_category_mix_pie.png",
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)
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save_bar_h(
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labs_m[:15],
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vals_m[:15],
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"类目分布（行数，Top）",
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"chart_category_mix.png",
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"行数",
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)
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brand_mix = brief.get("list_brand_mix_top") or []
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lb, vb = _label_count_pairs(brand_mix, key_label="label")
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save_pie(
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lb,
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vb,
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"品牌列表曝光占比",
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"chart_brand_rows_pie.png",
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)
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shop_mix = brief.get("list_shop_mix_top") or []
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ls, vs = _label_count_pairs(shop_mix, key_label="label")
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save_pie(
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ls,
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vs,
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"店铺列表曝光占比",
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"chart_shop_rows_pie.png",
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)
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sf = brief.get("sales_floor_analysis") or {}
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sf_chart = sf.get("bucket_chart") if isinstance(sf, dict) else None
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labs_sf, vals_sf = _label_count_pairs(sf_chart or [])
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save_bar_h(
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labs_sf,
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vals_sf,
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"销量楼层档位分布（结构行数）",
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"chart_sales_floor_buckets_bar.png",
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"行数",
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)
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def scenario_group_asset_slug(group: str, index: int) -> str:
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"""与 ``jd_competitor_report._scenario_group_asset_slug`` 保持一致。"""
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raw = (group or "").strip()
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core = re.sub(r"[^\w\u4e00-\u9fff-]", "", raw)[:20]
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if not core:
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core = "group"
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return f"i{index:02d}_{core}"
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by_grp = brief.get("usage_scenarios_by_matrix_group") or []
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if isinstance(by_grp, list):
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for item in by_grp:
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if not isinstance(item, dict):
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continue
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slug = (item.get("chart_slug") or "").strip()
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gname = str(item.get("group") or "").strip()[:24]
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idx = item.get("matrix_group_index")
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if not slug and gname != "" and isinstance(idx, int):
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slug = scenario_group_asset_slug(gname, idx)
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if not slug:
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continue
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scen_rows = item.get("scenarios") or []
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n_unit = int(item.get("effective_text_units") or 0)
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gpairs: list[tuple[str, float]] = []
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if isinstance(scen_rows, list):
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for r in scen_rows:
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if not isinstance(r, dict):
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continue
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lb = str(r.get("scenario") or "").strip()[:48]
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c = r.get("count")
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if lb and isinstance(c, (int, float)) and c > 0:
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gpairs.append((lb, float(c)))
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gpairs = _merge_labeled_counts_tail(gpairs, max_items=14)
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if gpairs and n_unit > 0:
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gl = [p[0] for p in gpairs]
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gv = [p[1] for p in gpairs]
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title_base = f"「{gname}」· 场景/用途" if gname else "细类 · 场景/用途"
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save_bar_h_share_of_text(
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gl,
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gv,
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n_unit,
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f"{title_base}（占有效评价文本比例）",
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f"chart_usage_scenarios_bar__{slug}.png",
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)
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fb = brief.get("consumer_feedback_by_matrix_group") or []
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if isinstance(fb, list):
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for item in fb:
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if not isinstance(item, dict):
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continue
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slug = (item.get("chart_slug") or "").strip()
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gname = str(item.get("group") or "").strip()[:24]
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idx = item.get("matrix_group_index")
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if not slug and gname != "" and isinstance(idx, int):
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slug = scenario_group_asset_slug(gname, idx)
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if not slug:
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continue
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hk = item.get("focus_keyword_hits") or []
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wl: list[str] = []
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vl: list[float] = []
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if isinstance(hk, list):
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for row in hk[:20]:
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if not isinstance(row, dict):
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continue
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w = str(row.get("word") or "").strip()[:32]
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c = row.get("count")
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if w and isinstance(c, (int, float)) and c > 0:
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wl.append(w)
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vl.append(float(c))
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wl = wl[:18]
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vl = vl[:18]
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if not wl:
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continue
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tkw = f"「{gname}」· 关注词命中次数" if gname else "细类 · 关注词命中次数"
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save_bar_h(wl, vl, tkw, f"chart_focus_keywords_bar__{slug}.png", "命中次数")
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matrix_compact = bool(brief.get("matrix_compact_section", True))
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mg = brief.get("matrix_by_group") or []
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if matrix_compact and isinstance(mg, list):
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for gi, block in enumerate(mg):
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if not isinstance(block, dict):
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continue
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gname = str(block.get("group") or "").strip()
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skus = block.get("skus")
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if not isinstance(skus, list) or not skus:
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continue
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slug = _matrix_block_chart_slug(gname, gi)
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triples: list[tuple[str, float, float]] = []
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for s in skus:
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if not isinstance(s, dict):
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continue
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title = (s.get("title") or "").strip()[:30] or str(
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s.get("sku_id") or ""
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).strip()[:14] or "—"
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p = _parse_price_from_text(str(s.get("detail_price_final") or ""))
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if p is None:
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p = _parse_price_from_text(
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str(s.get("coupon_or_detail_price") or "")
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)
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if p is None:
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p = _parse_price_from_text(str(s.get("list_price_show") or ""))
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v = _parse_comment_fuzzy_sortable(str(s.get("comment_fuzzy") or ""))
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triples.append((title, p or 0.0, v or 0.0))
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if not triples:
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continue
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triples.sort(key=lambda x: x[1], reverse=True)
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triples = triples[:36]
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labs = [t[0] for t in triples]
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prs = [t[1] for t in triples]
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cms = [t[2] for t in triples]
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tbase = f"「{gname}」" if gname else "细类"
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if max(prs) > 0:
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save_bar_h(
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labs,
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prs,
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f"{tbase}· 详情/券后价（元）",
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f"chart_matrix_price__{slug}.png",
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"元",
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)
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if max(cms) > 0:
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save_bar_h(
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labs,
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cms,
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f"{tbase}· 评价量展示（粗算排序，非精确条数）",
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f"chart_matrix_comments__{slug}.png",
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"粗算值",
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)
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sent = brief.get("comment_sentiment_lexicon") or {}
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if isinstance(sent, dict):
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pie_labs = ["偏正向", "偏负向", "正负混合", "中性/空"]
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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,
|
||
"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,
|
||
"负向/混合语境 · 负向口语短语命中条数",
|
||
"chart_negative_lexemes_bar.png",
|
||
"条数",
|
||
)
|
||
|
||
return created
|