273 lines
9.3 KiB
Python
273 lines
9.3 KiB
Python
"""test_graph_build.py — S2 知识图谱的纯单元测试（不联网、不调 gitlink-cli）。
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把「取数」与「算法」分离：build_graph() 只吃已构造好的 mock 数据结构。
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运行：`python test_graph_build.py` 或 `pytest scripts/research/`
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"""
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import os
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import sys
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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import graph_build as G # noqa: E402
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# ---------------------------------------------------------------------------
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# mock 数据构造
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# ---------------------------------------------------------------------------
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def _mock_repos():
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return [
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{
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"fullname": "alice/mindspore-vision",
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"identifier": "mindspore-vision",
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"author": {"login": "alice"},
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"description": "deep learning library for computer vision, "
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"image classification and object detection using CNN",
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"language": {"name": "Python"},
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"praises_count": 120,
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"forked_count": 30,
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},
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{
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"fullname": "bob/mindnlp",
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"identifier": "mindnlp",
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"author": {"login": "bob"},
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"description": "A nlp library: transformer, bert, text classification",
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"language": {"name": "Python"},
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"praises_count": 200,
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"forked_count": 50,
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},
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]
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def _mock_contributors_map():
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# alice 既贡献自己的 repo，也贡献 bob 的 repo（→ 核心学者 + collaborates_with）
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return {
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"alice/mindspore-vision": [
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{"login": "alice", "contribution_perc": "60.0%"},
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{"login": "carol", "contribution_perc": "40.0%"},
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{"login": "i-robot", "contribution_perc": "0.0%"}, # bot，应被过滤
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],
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"bob/mindnlp": [
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{"login": "alice", "contribution_perc": "10.0%"},
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{"login": "bob", "contribution_perc": "90.0%"},
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{"login": "dependabot", "contribution_perc": "1.0%"}, # bot
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],
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}
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def _mock_readmes():
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return {
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"alice/mindspore-vision": "pytorch and resnet for object detection",
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"bob/mindnlp": "pretrain gpt and llm models",
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}
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def _build():
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return G.build_graph(_mock_repos(), _mock_contributors_map(),
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{}, _mock_readmes(), keywords=["vision", "nlp"])
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# ---------------------------------------------------------------------------
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# 工具函数
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# ---------------------------------------------------------------------------
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def test_is_bot():
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assert G.is_bot("i-robot") is True
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assert G.is_bot("dependabot[bot]") is True
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assert G.is_bot("alice") is False
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assert G.is_bot("") is True
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def test_parse_ratio():
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assert G.parse_ratio("60.0%") == 0.6
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assert abs(G.parse_ratio("0.5") - 0.5) < 1e-9
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assert G.parse_ratio(0.3) == 0.3
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assert G.parse_ratio(None) == 0.0
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assert G.parse_ratio("n/a") == 0.0
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# ---------------------------------------------------------------------------
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# 建图：节点计数
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# ---------------------------------------------------------------------------
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def test_node_counts():
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result = _build()
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by_type = {}
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for n in result["nodes"]:
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by_type[n["type"]] = by_type.get(n["type"], 0) + 1
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assert by_type.get("repo", 0) == 2
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# alice / carol / bob = 3 个学者（bot 被过滤）
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assert by_type.get("scholar", 0) == 3
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# 主题：CV + deep_learning + nlp = 至少 3 个
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assert by_type.get("topic", 0) >= 3
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assert result["meta"]["repo_count"] == 2
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assert result["meta"]["scholar_count"] == 3
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def test_repo_node_props():
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result = _build()
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repo_nodes = [n for n in result["nodes"] if n["type"] == "repo"]
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labels = {n["label"] for n in repo_nodes}
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assert "alice/mindspore-vision" in labels
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r = [n for n in repo_nodes if n["label"] == "alice/mindspore-vision"][0]
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assert r["props"]["language"] == "Python"
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assert r["props"]["stars"] == 120
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assert r["props"]["forks"] == 30
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# ---------------------------------------------------------------------------
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# 建图：边
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# ---------------------------------------------------------------------------
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def test_contributes_to_weight():
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result = _build()
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edges = result["edges"]
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contrib = [e for e in edges if e["type"] == "contributes_to"
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and e["source"] == "scholar:alice"
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and e["target"] == "repo:alice/mindspore-vision"]
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assert contrib, "应有 alice→自己repo 的 contributes_to 边"
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# 60% → 0.6
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assert abs(contrib[0]["weight"] - 0.6) < 1e-9
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def test_owns_edge():
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result = _build()
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owns = [e for e in result["edges"] if e["type"] == "owns"
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and e["source"] == "scholar:bob"
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and e["target"] == "repo:bob/mindnlp"]
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assert owns, "bob 应有 owns 边到自己的 repo"
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def test_covers_topic_weight_range():
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"""covers_topic 权重应在 (0, 1] 且 <= 1。"""
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result = _build()
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covers = [e for e in result["edges"] if e["type"] == "covers_topic"]
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assert covers, "应至少有一条 covers_topic 边"
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for e in covers:
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assert 0.0 <= e["weight"] <= 1.0
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# 命中 computer_vision 主题（description 含 object detection/image classification）
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cv_edges = [e for e in covers if e["target"] == "topic:computer_vision"]
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assert cv_edges, "应识别出 computer_vision 主题"
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def test_collaborates_with():
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"""alice 与 carol 同在 alice/mindspore-vision → 至少一条 collaborates_with。"""
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result = _build()
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collab = [e for e in result["edges"] if e["type"] == "collaborates_with"]
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assert collab, "应有 collaborates_with 边"
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pair = {e["source"] for e in collab}
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assert "scholar:alice" in pair
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def test_related_to_when_topic_cooccur():
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"""computer_vision 与 deep_learning 在同一 repo 共现 → related_to。"""
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result = _build()
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related = [e for e in result["edges"] if e["type"] == "related_to"
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and e["source"] == "topic:computer_vision"]
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# mindspore-vision 的 description 同时命中 CV + deep_learning
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assert related, "共现主题应有 related_to 边"
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assert any(e["target"] == "topic:deep_learning" for e in related)
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# ---------------------------------------------------------------------------
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# 衍生统计
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# ---------------------------------------------------------------------------
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def test_core_scholars():
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result = _build()
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# alice 出现在 2 个 repo → 排首位
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top = result["core_scholars"][0]
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assert top["login"] == "alice"
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assert top["repo_count"] == 2
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def test_topic_heat_top():
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result = _build()
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heat = result["topic_heat"]
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assert heat, "应有主题热度榜"
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# deep_learning 在两个 repo 都命中 → 应在前列
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topics = [h["topic"] for h in heat]
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assert "deep_learning" in topics
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# ---------------------------------------------------------------------------
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# 渲染
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# ---------------------------------------------------------------------------
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def test_render_mermaid_header():
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result = _build()
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mmd = G.render_mermaid(result)
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assert "graph TD" in mmd
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assert "classDef" in mmd # 着色定义
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assert "```" in mmd
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def test_render_mermaid_node_limit():
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"""节点过多时应被截断到 node_limit。"""
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big_repos = []
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big_contribs = {}
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big_readmes = {}
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for i in range(60):
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fn = f"u{i}/repo{i}"
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big_repos.append({
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"fullname": fn, "identifier": f"repo{i}",
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"author": {"login": f"u{i}"}, "description": "deep learning",
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"language": {"name": "Python"}, "praises_count": 0, "forked_count": 0,
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})
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big_contribs[fn] = [{"login": f"u{i}", "contribution_perc": "100%"}]
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big_readmes[fn] = "deep learning"
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result = G.build_graph(big_repos, big_contribs, {}, big_readmes,
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keywords=["dl"])
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mmd = G.render_mermaid(result, node_limit=40)
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# mermaid 里出现的节点声明数应 <= 40
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node_lines = [ln for ln in mmd.splitlines() if '["' in ln and "-->" not in ln]
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assert len(node_lines) <= 40
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def test_render_dot_header():
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result = _build()
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dot = G.render_dot(result)
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assert dot.startswith("digraph G")
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assert "fillcolor" in dot
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def test_render_report_sections():
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result = _build()
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md = G.render_report(result)
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assert "知识图谱报告" in md
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assert "主题热度榜" in md
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assert "核心学者" in md
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assert "deep_learning" in md or "computer_vision" in md
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def test_render_report_empty_safe():
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result = {"scenario": "S2", "keywords": [], "nodes": [], "edges": [],
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"core_scholars": [], "core_teams": [], "topic_heat": [],
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"meta": {"repo_count": 0, "node_count": 0, "edge_count": 0,
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"scholar_count": 0, "topic_count": 0}}
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md = G.render_report(result)
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assert "知识图谱报告" in md
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assert "未识别" in md or "暂无" in md
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def test_empty_inputs():
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"""空输入不应抛异常。"""
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result = G.build_graph([], {}, {}, {})
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assert result["meta"]["node_count"] == 0
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assert result["meta"]["edge_count"] == 0
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assert result["nodes"] == []
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# ---------------------------------------------------------------------------
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def _run_all():
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fns = [v for k, v in sorted(globals().items()) if k.startswith("test_")]
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for fn in fns:
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fn()
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print(f"PASS {fn.__name__}")
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print(f"\nAll {len(fns)} graph_build tests passed.")
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if __name__ == "__main__":
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_run_all()
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