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tests/test_rag_utils.py

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+import numpy as np
+from geomretrieval.rag_top10 import _minmax_hi, _zscore
+
+def test_minmax_hi_is_bounded_and_monotone():
+    x=np.array([2.0,5.0,11.0],dtype=np.float32)
+    y=_minmax_hi(x)
+    assert np.allclose(y,[0.0,1/3,1.0])
+
+def test_zscore_constant_is_zero():
+    assert np.allclose(_zscore(np.ones(4,dtype=np.float32)),0)
+
+from geomretrieval import FrozenConfig, GeometricIndex, RAGTop10Config, RAGTop10Ranker
+
+def test_rag_top10_ranker_runs_on_toy_index():
+    docs=[
+        'car automobile engine road vehicle',
+        'automobile vehicle insurance motor road',
+        'river bank flood erosion water',
+        'bank account credit loan interest',
+        'vitamin respiratory infection clinical study',
+    ]
+    ids=[f'd{i}' for i in range(len(docs))]
+    idx=GeometricIndex.build(docs,ids,FrozenConfig(max_features=100,F=2,B=8,S=4,L=4,assoc_k=6,route_k=4,route_budget=6,rerank_pool=5,semantic_k=3,output_k=5),verbose=False)
+    ranker=RAGTop10Ranker(idx,RAGTop10Config(pool_size=5,semantic_k=3,hq_top_branches=3,branch_quality_top_docs=2))
+    out=ranker.search('automobile road insurance',k=3)
+    assert len(out)>=1
+    assert out[0] in {'d0','d1'}

tests/test_toy.py

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+from geomretrieval import FrozenConfig, GeometricIndex, evaluate_run
+
+
+def test_toy_build_and_search():
+    docs = [
+        "car automobile engine road vehicle",
+        "automobile vehicle insurance motor road",
+        "river bank flood erosion water",
+        "bank account credit loan interest",
+        "vitamin d respiratory infection clinical study",
+        "random unrelated astronomy galaxy star",
+    ]
+    ids = [f"d{i}" for i in range(len(docs))]
+    # Small toy corpus cannot support the production widths; keep the same
+    # architecture while mechanically reducing vocabulary-dependent dimensions.
+    cfg = FrozenConfig(
+        max_features=100,
+        F=2,
+        B=8,
+        S=4,
+        L=4,
+        assoc_k=6,
+        route_k=4,
+        route_budget=6,
+        rerank_pool=5,
+        semantic_k=3,
+        output_k=5,
+    )
+    idx = GeometricIndex.build(docs, ids, cfg, verbose=False)
+    out = idx.search("automobile road insurance", k=3)
+    assert 1 <= len(out) <= 3
+    assert out[0] in {"d0", "d1"}
+
+    run = {"q1": out}
+    qrels = {"q1": {"d0": 1.0, "d1": 1.0}}
+    m = evaluate_run(run, qrels, ks=(1, 3), ndcg_k=3, mrr_k=3)
+    assert m["Hit@1"] == 1.0
+    assert m["MRR@3"] == 1.0