mirror of
https://github.com/primedigitaltech/market-assistant.git
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23ddf115ee
删除评价正负面粗判（扇形图、口语短语条形图、摘要及同口径 LLM 块）；文本挖掘/关注词路径统一为新的 8.2；brief 不再含 comment_sentiment_lexicon；默认关闭 llm_comment_sentiment；清理旧插图文件名；同步策略与全稿提示。 Made-with: Cursor
206 lines
8.2 KiB
Python
206 lines
8.2 KiB
Python
"""结构化竞品摘要：空样本烟测（不依赖真实 run_dir CSV）。"""
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from __future__ import annotations
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import tempfile
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from pathlib import Path
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from django.test import SimpleTestCase
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from pipeline.competitor_report import jd_report as jcr
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from pipeline.competitor_report.comment_sentiment import (
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_comment_sentiment_lexicon,
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build_comment_sentiment_llm_payload,
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)
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from pipeline.csv.schema import infer_total_sales_from_sales_floor
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from pipeline.reporting.charts import _cn_volume_int
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class BuildCompetitorBriefTests(SimpleTestCase):
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def test_empty_merged_json_safe(self) -> None:
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with tempfile.TemporaryDirectory() as td:
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run_dir = Path(td)
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(run_dir / "pc_search_raw").mkdir(parents=True)
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out = jcr.build_competitor_brief(
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run_dir=run_dir,
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keyword="测试",
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merged_rows=[],
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search_export_rows=[],
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comment_rows=[],
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meta=None,
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)
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self.assertEqual(out["schema_version"], 1)
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self.assertEqual(out["scope"]["merged_sku_count"], 0)
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self.assertIsInstance(out["strategy_hints"], list)
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self.assertEqual(out["matrix_by_group"], [])
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self.assertNotIn("comment_sentiment_lexicon", out)
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import json
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json.dumps(out)
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def test_comment_sentiment_llm_payload_has_semantic_pool(self) -> None:
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texts = ["口感软硬适中很好吃", "太差了不建议"]
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attr = [f"【细类：A｜SKU：1｜品名：x｜店铺：y】{t}" for t in texts]
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pl = build_comment_sentiment_llm_payload(
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texts,
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attributed_texts=attr,
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shuffle_seed="unit-test-seed",
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semantic_pool_max=10,
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)
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self.assertIn("sample_reviews_semantic_pool", pl)
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self.assertEqual(pl.get("sentiment_bucket_method"), "keyword_substring_heuristic")
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self.assertGreaterEqual(len(pl["sample_reviews_semantic_pool"]), 1)
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def test_comment_sentiment_score_then_lexeme(self) -> None:
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texts = ["很好吃", "太差了", "一般般"]
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scores = [5, 1, 3]
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lex = _comment_sentiment_lexicon(texts, scores)
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self.assertEqual(lex.get("method"), "score_then_lexeme")
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self.assertEqual(lex.get("positive_only"), 1)
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self.assertEqual(lex.get("negative_only"), 1)
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self.assertEqual(lex.get("neutral_or_empty"), 1)
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pl = build_comment_sentiment_llm_payload(texts, scores=scores)
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self.assertEqual(pl.get("sentiment_bucket_method"), "score_then_lexeme")
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def test_comment_sentiment_all_scores_missing_falls_back_keyword(self) -> None:
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texts = ["好吃推荐", "差评"]
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scores = [None, None]
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lex = _comment_sentiment_lexicon(texts, scores)
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self.assertEqual(lex.get("method"), "keyword_lexicon")
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def test_custom_focus_words_in_report_config(self) -> None:
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with tempfile.TemporaryDirectory() as td:
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run_dir = Path(td)
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(run_dir / "pc_search_raw").mkdir(parents=True)
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out = jcr.build_competitor_brief(
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run_dir=run_dir,
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keyword="测试",
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merged_rows=[],
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search_export_rows=[],
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comment_rows=[
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{
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"tagCommentContent": "自定义词阿尔法出现两次 自定义词阿尔法",
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}
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],
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meta=None,
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report_config={"comment_focus_words": ["自定义词阿尔法"]},
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)
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words = {x["word"] for x in out["comment_focus_keywords"]}
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self.assertIn("自定义词阿尔法", words)
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def test_matrix_groups_require_detail_category_path(self) -> None:
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sku_h = "SKU(skuId)"
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merged = [
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{
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sku_h: "111",
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"detail_category_path": "食品饮料 > 休闲食品 > 饼干 > 粗粮饼干",
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"标题(wareName)": "A",
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},
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{sku_h: "222", "标题(wareName)": "B"},
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]
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groups = jcr._merged_rows_grouped_for_matrix(merged)
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self.assertEqual(len(groups), 1)
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self.assertEqual(len(groups[0][1]), 1)
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self.assertEqual(groups[0][1][0][sku_h], "111")
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smap = jcr._sku_to_matrix_group_map(merged, sku_h)
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self.assertEqual(smap.get("111"), "饼干")
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self.assertNotIn("222", smap)
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fb = jcr._consumer_feedback_by_matrix_group(
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merged_rows=merged,
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comment_rows=[
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{"sku": "222", "tagCommentContent": "缺路径仍不应计入按细类统计"},
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{"sku": "111", "tagCommentContent": "有路径进细类"},
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],
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sku_header=sku_h,
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)
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counts = {g: len(cr) for g, cr, _ in fb}
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self.assertEqual(counts.get("饼干"), 1)
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def test_comment_lines_with_product_context_prefix(self) -> None:
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"""评价抽样须带细类/SKU/品名前缀，便于归因。"""
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sku_h = "SKU(skuId)"
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title_h = "标题(wareName)"
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merged = [
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{
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sku_h: "100",
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title_h: "低GI全麦饼干1kg",
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"detail_brand": "B",
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"detail_price_final": "29",
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"detail_shop_name": "店",
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"detail_category_path": "休闲食品 > 饼干 > 粗粮饼干",
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"detail_product_attributes": "x",
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},
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]
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comments = [{"sku": "100", "tagCommentContent": "整体口感还差点意思"}]
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lines = jcr._comment_lines_with_product_context(
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comments, merged, sku_header=sku_h, title_h=title_h
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)
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self.assertEqual(len(lines), 1)
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self.assertIn("【细类：", lines[0])
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self.assertIn("SKU：100", lines[0])
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self.assertIn("品名：", lines[0])
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self.assertIn("店铺：", lines[0])
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self.assertIn("整体口感还差点意思", lines[0])
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def test_scenario_groups_llm_payload_matches_section_8_4_counts(self) -> None:
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sku_h = "SKU(skuId)"
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merged = [
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{
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sku_h: "111",
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"detail_category_path": "食品饮料 > 休闲食品 > 饼干 > 粗粮饼干",
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"标题(wareName)": "A饼",
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"detail_shop_name": "店甲",
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},
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]
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scen = (("早餐/代餐", ("早餐",)),)
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fb = jcr._consumer_feedback_by_matrix_group(
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merged_rows=merged,
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comment_rows=[
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{"sku": "111", "tagCommentContent": "早上当早餐吃还不错"},
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],
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sku_header=sku_h,
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)
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pl = jcr.build_scenario_groups_llm_payload(
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feedback_groups=fb,
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scenario_groups=scen,
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merged_rows=merged,
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sku_header=sku_h,
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title_h="标题(wareName)",
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)
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self.assertIn("groups", pl)
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self.assertIn("scenario_lexicon", pl)
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g0 = pl["groups"][0]
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self.assertEqual(g0["group"], "饼干")
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self.assertEqual(g0["effective_text_count"], 1)
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self.assertEqual(g0["scenario_distribution"][0]["mention_rows"], 1)
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self.assertEqual(
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g0["scenario_distribution"][0]["scenario"], "早餐/代餐"
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)
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def test_cn_volume_int_parses_total_sales_trailer(self) -> None:
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self.assertEqual(
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_cn_volume_int("已售50万+ | good:99%好评"), 500_000
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)
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self.assertEqual(_cn_volume_int("2.5亿件"), 250_000_000)
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def test_mix_top_remainder_sums_to_all_rows(self) -> None:
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"""mix_top 各 count 之和须等于 strip 后可统计行数（与扇图同源）。"""
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names = [f"店{i}" for i in range(30)]
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mix = jcr._counter_mix_top_rows_with_remainder(
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names, top_n=24, remainder_label="（其余店铺）"
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)
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self.assertEqual(sum(v for _, v in mix), 30)
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self.assertEqual(mix[-1][0], "（其余店铺）")
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self.assertEqual(mix[-1][1], 6)
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self.assertEqual(len(jcr._structure_names_for_pie_counter(names)), 30)
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def test_infer_total_sales_from_sales_floor(self) -> None:
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self.assertEqual(
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infer_total_sales_from_sales_floor("good:99%好评 | 已售50万+"),
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"已售50万+",
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)
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self.assertEqual(infer_total_sales_from_sales_floor(""), "")
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