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fix(hashing): ignore casting to float #84

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fix(hashing): ignore casting to float #84

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23 changes: 10 additions & 13 deletions docarray/document/mixins/featurehash.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,19 +67,16 @@ def _any_hash(v):
try:
return int(v) # parse int parameter
except ValueError:
try:
return float(v) # parse float parameter
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@numb3r3 numb3r3 Jan 27, 2022

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We can also check the parsed value, and raise exception then

v = float(v)
if math.isnan(v) or math.isinf(v):
    raise ValueError

except ValueError:
if not v:
# ignore it when the parameter is empty
if not v:
# ignore it when the parameter is empty
return 0
if isinstance(v, str):
v = v.strip()
if v.lower() in {'true', 'yes'}: # parse boolean parameter
return 1
if v.lower() in {'false', 'no'}:
return 0
if isinstance(v, str):
v = v.strip()
if v.lower() in {'true', 'yes'}: # parse boolean parameter
return 1
if v.lower() in {'false', 'no'}:
return 0
if isinstance(v, (tuple, dict, list)):
v = json.dumps(v, sort_keys=True)
if isinstance(v, (tuple, dict, list)):
v = json.dumps(v, sort_keys=True)

return int(hashlib.md5(str(v).encode('utf-8')).hexdigest(), base=16)
13 changes: 10 additions & 3 deletions tests/unit/document/test_feature_hashing.py
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Expand Up @@ -8,10 +8,17 @@
@pytest.mark.parametrize('sparse', [True, False])
@pytest.mark.parametrize('metric', ['jaccard', 'cosine'])
def test_feature_hashing(n_dim, sparse, metric):
da = DocumentArray.empty(3)
da.texts = ['hello world', 'world, bye', 'hello bye']
da = DocumentArray.empty(6)
da.texts = [
'hello world',
'world, bye',
'hello bye',
'infinity test',
'nan test',
'2.3 test',
]
da.apply(lambda d: d.embed_feature_hashing(n_dim=n_dim, sparse=sparse))
assert da.embeddings.shape == (3, n_dim)
assert da.embeddings.shape == (6, n_dim)
da.embeddings = to_numpy_array(da.embeddings)
da.match(da, metric=metric, use_scipy=True)
result = da['@m', ('id', f'scores__{metric}__value')]
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