| import numpy as np |
| from backend.app.engine.bm25 import SimpleBM25 |
|
|
|
|
| class TestSimpleBM25: |
| def test_fit_and_score(self): |
| bm25 = SimpleBM25() |
| docs = [ |
| "black leather jacket mens", |
| "red floral dress womens", |
| "blue denim jeans casual", |
| ] |
| bm25.fit(docs) |
| assert bm25.n_docs == 3 |
|
|
| scores = bm25.score_candidates("black leather", [0, 1, 2]) |
| assert scores[0] > scores[1] |
| assert scores[0] > scores[2] |
|
|
| def test_empty_query(self): |
| bm25 = SimpleBM25() |
| bm25.fit(["black dress", "red shoes"]) |
| scores = bm25.score_candidates("", [0, 1]) |
| assert np.all(scores == 0.0) |
|
|
| def test_unknown_terms(self): |
| bm25 = SimpleBM25() |
| bm25.fit(["black dress"]) |
| scores = bm25.score_candidates("xyznotaword", [0]) |
| assert scores[0] == 0.0 |
|
|
| def test_out_of_range_index(self): |
| bm25 = SimpleBM25() |
| bm25.fit(["black dress"]) |
| scores = bm25.score_candidates("black", [0, 999]) |
| assert scores[0] > 0 |
| assert scores[1] == 0.0 |
|
|
| def test_exact_match_scores_higher(self): |
| bm25 = SimpleBM25() |
| bm25.fit(["black leather jacket", "red silk dress", "blue cotton shirt"]) |
| scores = bm25.score_candidates("black leather jacket", [0, 1, 2]) |
| assert scores[0] > scores[1] |
| assert scores[0] > scores[2] |
|
|
| def test_doc_frequency_computed(self): |
| bm25 = SimpleBM25() |
| bm25.fit(["black dress", "black shoes", "red dress"]) |
| assert bm25.df["black"] == 2 |
| assert bm25.df["dress"] == 2 |
| assert bm25.df["red"] == 1 |
|
|
