def test__calc_flex_warns_if_flex_longer_than_query(
nlp: Language, searcher: FuzzySearcher
) -> None:
"""It provides UserWarning if flex > len(query)."""
query = nlp.make_doc("Test query.")
with pytest.warns(FlexWarning):
searcher._calc_flex(query, 5)
def test__calc_flex_raises_error_if_non_valid_value(
nlp: Language, searcher: FuzzySearcher
) -> None:
"""It raises TypeError if flex is not an int or "default"."""
query = nlp("Test query.")
with pytest.raises(TypeError):
searcher._calc_flex(query, None)
def test_match_raises_error_if_query_not_Doc(searcher: FuzzySearcher,
nlp: Language) -> None:
"""It raises a TypeError if query not a doc."""
doc = nlp("This is a doc")
query = "Not a doc"
with pytest.raises(TypeError):
searcher.match(doc, query)
def test_match_raises_error_when_doc_not_Doc(searcher: FuzzySearcher,
nlp: Language) -> None:
"""It raises a TypeError if doc is not a Doc object."""
doc = "G-rant Anderson lives in TN."
query = nlp("xenomorph")
with pytest.raises(TypeError):
searcher.match(doc, query)
def test_best_match_raises_error_when_query_not_Doc(
searcher: FuzzySearcher, nlp: Language
) -> None:
"""Raises a Type error if query is not a Doc object."""
doc = nlp("G-rant Anderson lives in TN.")
query = "xenomorph"
with pytest.raises(TypeError):
searcher.best_match(doc, query)
def test_multi_match_with_n_less_than_actual_matches(
searcher: FuzzySearcher, nlp: Language
) -> None:
"""It returns the n best fuzzy matches that meet threshold correctly sorted."""
doc = nlp("cow, cow, cow, cow")
query = nlp("cow")
assert searcher.multi_match(doc, query, n=2) == [(0, 1, 100), (2, 3, 100)]
def test_multi_match_return_empty_list_when_no_matches_after_adjust(
searcher: FuzzySearcher, nlp: Language
) -> None:
"""It returns an empty list if no fuzzy matches meet min_r2 threshold."""
doc = nlp("G-rant Anderson lives in TN.")
query = nlp("Garth, Anderdella")
assert searcher.multi_match(doc, query) == []
def test_best_match_return_none_when_no_matches(
searcher: FuzzySearcher, nlp: Language
) -> None:
"""It returns None if no fuzzy match meets threshold."""
doc = nlp("G-rant Anderson lives in TN.")
query = nlp("xenomorph")
assert searcher.best_match(doc, query) is None
def test__scan_doc_with_no_matches(searcher: FuzzySearcher, nlp: Language,
scan_example: Doc) -> None:
"""It returns None if no matches >= min_r1."""
query = nlp.make_doc("xenomorph")
assert (searcher._scan_doc(
scan_example, query, fuzzy_func="simple", min_r1=30, ignore_case=True)
is None)
def test__scan_doc_returns_all_matches_with_no_min_r1(
searcher: FuzzySearcher, nlp: Language, scan_example: Doc
) -> None:
"""It returns all spans of len(query) in doc if min_r1 = 0."""
query = nlp.make_doc("Shirley")
assert searcher._scan_doc(
scan_example, query, fuzzy_func="simple", min_r1=0, ignore_case=True
) == {0: 0, 1: 0, 2: 18, 3: 22, 4: 86}
def test_multi_match_finds_best_matches(searcher: FuzzySearcher, nlp: Language) -> None:
"""It returns all the fuzzy matches that meet threshold correctly sorted."""
doc = nlp("chiken from Popeyes is better than chken from Chick-fil-A")
query = nlp("chicken")
assert searcher.multi_match(doc, query, ignore_case=False) == [
(0, 1, 92),
(6, 7, 83),
]
def test__scan_doc_returns_matches_over_min_r1(
searcher: FuzzySearcher, nlp: Language, scan_example: Doc
) -> None:
"""It returns all spans of len(query) in doc if ratio >= min_r1."""
query = nlp.make_doc("Shirley")
assert searcher._scan_doc(
scan_example, query, fuzzy_func="simple", min_r1=30, ignore_case=True
) == {4: 86}
def test__adjust_left_right_positions_finds_better_match2(
searcher: FuzzySearcher, nlp: Language, adjust_example: Doc) -> None:
"""It optimizes the initial match to find a better match."""
query = nlp.make_doc("Kareem Abdul-Jabbar")
match_values = {0: 33, 1: 39, 2: 41, 3: 33, 5: 37, 6: 59, 7: 84}
assert searcher._adjust_left_right_positions(
adjust_example,
query,
match_values,
pos=7,
fuzzy_func="simple",
min_r2=70,
ignore_case=True,
flex=4,
) == (8, 11, 89)
def test__adjust_left_right_positions_finds_better_match(
searcher: FuzzySearcher, nlp: Language) -> None:
"""It optimizes the initial match to find a better match."""
doc = nlp.make_doc("Patient was prescribed Zithromax tablets.")
query = nlp.make_doc("zithromax tablet")
match_values = {0: 30, 2: 50, 3: 97, 4: 50}
assert searcher._adjust_left_right_positions(
doc,
query,
match_values,
pos=3,
fuzzy_func="simple",
min_r2=70,
ignore_case=True,
flex=2,
) == (3, 5, 97)
def test__adjust_left_right_positions_with_no_flex(searcher: FuzzySearcher,
nlp: Language) -> None:
"""It returns the intial match when flex value = 0."""
doc = nlp.make_doc("Patient was prescribed Zithroma tablets.")
query = nlp.make_doc("zithromax")
match_values = {3: 94}
assert searcher._adjust_left_right_positions(
doc,
query,
match_values,
pos=3,
fuzzy_func="simple",
min_r2=70,
ignore_case=True,
flex=0,
) == (3, 4, 94)
def test__indice_maxes_returns_all_keys_if_n_is_0(
searcher: FuzzySearcher, initial_matches: Dict[int, int]) -> None:
"""It returns input unchanged if n is 0."""
assert searcher._indice_maxes(initial_matches, 0) == [1, 4, 5, 8, 9]
def test__indice_maxes_returns_n_keys_with_max_values(
searcher: FuzzySearcher, initial_matches: Dict[int, int]) -> None:
"""It returns the n keys correctly sorted."""
assert searcher._indice_maxes(initial_matches, 3) == [8, 9, 4]
def test__calc_flex_passes_through_valid_value(
nlp: Language, searcher: FuzzySearcher) -> None:
"""It passes through a valid flex value (<= len(query))."""
query = nlp.make_doc("Test query.")
assert searcher._calc_flex(query, 1) == 1
def test__calc_flex_with_default(nlp: Language,
searcher: FuzzySearcher) -> None:
"""It returns len(query) if set with "default"."""
query = nlp.make_doc("Test query.")
assert searcher._calc_flex(query, "default") == 3
def test_compare_without_ignore_case(searcher: FuzzySearcher) -> None:
"""Checks ignore_case is working."""
assert searcher.compare("SPACZZ", "spaczz", ignore_case=False) == 0
def test_compare_works_with_defaults(searcher: FuzzySearcher) -> None:
"""Checks compare is working as intended."""
assert searcher.compare("spaczz", "spacy") == 73
def test_get_fuzzy_alg_raises_error_with_unknown_name(
searcher: FuzzySearcher) -> None:
"""It raises a ValueError if fuzzy_func does not match a predefined key name."""
with pytest.raises(ValueError):
searcher.get_fuzzy_func("unkown")
def test_get_fuzzy_alg_returns_alg(searcher: FuzzySearcher) -> None:
"""It returns the expected fuzzy matching function."""
func = searcher.get_fuzzy_func("simple")
assert func == fuzz.ratio
def test_fuzzysearcherer_uses_passed_config() -> None:
"""It uses the config passed to it."""
config = FuzzyConfig()
searcher = FuzzySearcher(config=config)
assert searcher._config._fuzzy_funcs
def test_fuzzysearcher_raises_error_if_config_is_not_fuzzyconfig() -> None:
"""It raises a TypeError if config is not recognized string or FuzzyConfig."""
with pytest.raises(TypeError):
FuzzySearcher(config="Will cause error")
def test_match_return_empty_list_when_no_matches_after_scan(
searcher: FuzzySearcher, nlp: Language) -> None:
"""It returns an empty list if no fuzzy matches meet min_r1 threshold."""
doc = nlp("G-rant Anderson lives in TN.")
query = nlp("xenomorph")
assert searcher.match(doc, query) == []
def test__filter_overlapping_matches_filters_correctly(
searcher: FuzzySearcher, ) -> None:
"""It only returns the first match if more than one encompass the same tokens."""
matches = [(1, 2, 80), (1, 3, 70)]
assert searcher._filter_overlapping_matches(matches) == [(1, 2, 80)]
def test_fuzzysearcher_has_empty_config_if_empty_passed() -> None:
"""Its config is empty."""
searcher = FuzzySearcher(config="empty")
assert not searcher._config._fuzzy_funcs
def test_match_works_with_defaults(searcher: FuzzySearcher) -> None:
"""Checks match is working as intended."""
assert searcher.match("spaczz", "spacy") == 73
def searcher() -> FuzzySearcher:
"""It returns a default fuzzy searcher."""
return FuzzySearcher()
