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50 lines
1.7 KiB
Python
50 lines
1.7 KiB
Python
import pytest
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"""Regression test: re-indexing an existing doc_id in InvertedIndex must clear old terms and not inflate total_documents."""
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import os
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import sys
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sys.path.insert(0, os.path.abspath("chapter3/sparse-embedding"))
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from bm25_engine import InvertedIndex, BM25 # noqa: E402
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def test_reindex_maintains_doc_count_and_clears_stale_terms():
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index = InvertedIndex()
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index.add_document(1, "python database sql")
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assert index.total_documents == 1
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assert index.get_posting_list("database") == {1}
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assert index.document_frequency["database"] == 1
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# Re-index doc 1 with completely different terms
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index.add_document(1, "python web fasta")
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assert index.total_documents == 1
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assert index.get_posting_list("database") == set()
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assert "database" not in index.document_frequency
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assert index.get_posting_list("web") == {1}
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assert index.document_frequency["web"] == 1
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def test_reindex_search_engine_bm25_scores():
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index = InvertedIndex()
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index.add_document(10, "machine learning deep learning")
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index.add_document(20, "quantum computing physics")
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assert index.total_documents == 2
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bm25 = BM25(index)
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results_before = bm25.search("machine learning")
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assert len(results_before) == 1
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assert results_before[0][0] == 10
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# Update document 10 to quantum physics
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index.add_document(10, "quantum physics mechanics")
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assert index.total_documents == 2
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# Search for machine learning should yield 0 results
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results_after_old = bm25.search("machine learning")
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assert len(results_after_old) == 0
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# Search for quantum should yield both 10 and 20
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results_after_new = bm25.search("quantum")
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doc_ids = {r[0] for r in results_after_new}
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assert doc_ids == {10, 20}
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