| 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'} |
|
|