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Merged
JohannesMessner merged 12 commits into from
Jan 11, 2023
Merged

Debug ci #1002

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d2bfe24
feat: native len for milvus
JohannesMessner Dec 2, 2022
e8422c8
fix: make implementing len non-optional
JohannesMessner Dec 2, 2022
69c471c
Merge remote-tracking branch 'origin/main' into main
JohannesMessner Dec 5, 2022
c1643d0
Merge remote-tracking branch 'origin/main' into main
JohannesMessner Dec 13, 2022
a0c1dd2
Merge remote-tracking branch 'origin/main' into main
JohannesMessner Dec 15, 2022
95c7ff9
Merge remote-tracking branch 'origin/main' into main
JohannesMessner Jan 10, 2023
cbfc6e6
ci: trigger the ci for debugging purposes
JohannesMessner Jan 10, 2023
1e632cb
ci: only run oldproto tests
JohannesMessner Jan 10, 2023
0654713
test: add some context mngrs
JohannesMessner Jan 10, 2023
02326b5
test: remove some tests
JohannesMessner Jan 10, 2023
1dab1c5
test: try to find minimal working changes
JohannesMessner Jan 11, 2023
b3387a4
ci: fix success all tests
JohannesMessner Jan 11, 2023
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144 changes: 80 additions & 64 deletions tests/unit/array/mixins/oldproto/test_eval_class.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,8 @@ def test_eval_mixin_perfect_match(metric_fn, kwargs, storage, config, start_stor
da1 = DocumentArray.empty(10)
da1.embeddings = np.random.random([10, 256])
da1_index = DocumentArray(da1, storage=storage, config=config)
da1.match(da1_index, exclude_self=True)
with da1_index:
da1.match(da1_index, exclude_self=True)
r = da1.evaluate(ground_truth=da1, metrics=[metric_fn], strict=False, **kwargs)[
metric_fn
]
Expand Down Expand Up @@ -80,7 +81,8 @@ def test_eval_mixin_perfect_match_multiple_metrics(storage, config, start_storag
da1 = DocumentArray.empty(10)
da1.embeddings = np.random.random([10, 256])
da1_index = DocumentArray(da1, storage=storage, config=config)
da1.match(da1_index, exclude_self=True)
with da1_index:
da1.match(da1_index, exclude_self=True)
r = da1.evaluate(ground_truth=da1, metrics=metric_fns, strict=False, **kwargs)
for metric_fn in metric_fns:
assert metric_fn in r
Expand Down Expand Up @@ -123,7 +125,8 @@ def test_eval_mixin_perfect_match_labeled(
d.tags = {'label': 'A'}
da1.embeddings = np.random.random([10, 256])
da1_index = DocumentArray(da1, storage=storage, config=config)
da1.match(da1_index, exclude_self=True)
with da1_index:
da1.match(da1_index, exclude_self=True)
r = da1.evaluate(metrics=[metric_fn], **kwargs)[metric_fn]
assert isinstance(r, float)
assert r == 1.0
Expand Down Expand Up @@ -166,7 +169,8 @@ def test_eval_mixin_zero_labeled(storage, config, metric_fn, start_storage, kwar
for d in da2:
d.tags = {'label': 'B'}
da1_index = DocumentArray(da2, storage=storage, config=config)
da1.match(da1_index, exclude_self=True)
with da1_index:
da1.match(da1_index, exclude_self=True)
r = da1.evaluate([metric_fn], **kwargs)[metric_fn]
assert isinstance(r, float)
assert r == 0.0
Expand Down Expand Up @@ -264,9 +268,10 @@ def test_eval_mixin_zero_match(storage, config, metric_fn, start_storage, kwargs
da2 = copy.deepcopy(da1)
da2.embeddings = np.random.random([10, 256])
da2_index = DocumentArray(da2, storage=storage, config=config)
da2.match(da2_index, exclude_self=True)
with da2_index:
da2.match(da2_index, exclude_self=True)

r = da1.evaluate(ground_truth=da2, metrics=[metric_fn], **kwargs)[metric_fn]
r = da1.evaluate(ground_truth=da2, metrics=[metric_fn], **kwargs)[metric_fn]
assert isinstance(r, float)
assert r == 1.0
for d in da1:
Expand Down Expand Up @@ -337,17 +342,20 @@ def test_same_hash_same_len_fun_should_work(storage, config, start_storage):
da1 = DocumentArray.empty(10)
da1.embeddings = np.random.random([10, 3])
da1_index = DocumentArray(da1, storage=storage, config=config)
da1.match(da1_index)
with da1_index:
da1.match(da1_index)
da2 = DocumentArray.empty(10)
da2.embeddings = np.random.random([10, 3])
da2_index = DocumentArray(da1, storage=storage, config=config)
da2.match(da2_index)
with pytest.raises(ValueError):
da1.evaluate(ground_truth=da2, metrics=['precision_at_k'])
for d1, d2 in zip(da1, da2):
d1.id = d2.id
with da2_index:
da2.match(da2_index)
with da1_index, da2_index:
with pytest.raises(ValueError):
da1.evaluate(ground_truth=da2, metrics=['precision_at_k'])
for d1, d2 in zip(da1, da2):
d1.id = d2.id

da1.evaluate(ground_truth=da2, metrics=['precision_at_k'])
da1.evaluate(ground_truth=da2, metrics=['precision_at_k'])


@pytest.mark.parametrize(
Expand All @@ -368,7 +376,8 @@ def test_adding_noise(storage, config, start_storage):

da.embeddings = np.random.random([10, 3])
da_index = DocumentArray(da, storage=storage, config=config)
da.match(da_index, exclude_self=True)
with da_index:
da.match(da_index, exclude_self=True)

da2 = copy.deepcopy(da)

Expand Down Expand Up @@ -410,17 +419,18 @@ def test_adding_noise(storage, config, start_storage):
def test_diff_match_len_in_gd(storage, config, metric_fn, start_storage, kwargs):
da1 = DocumentArray.empty(10)
da1.embeddings = np.random.random([10, 128])
da1_index = DocumentArray(da1, storage=storage, config=config)
# da1_index = DocumentArray(da1, storage=storage, config=config)
da1.match(da1, exclude_self=True)

da2 = copy.deepcopy(da1)
da2.embeddings = np.random.random([10, 128])
da2_index = DocumentArray(da2, storage=storage, config=config)
da2.match(da2_index, exclude_self=True)
# pop some matches from first document
da2[0].matches.pop(8)
with da2_index:
da2.match(da2_index, exclude_self=True)
# pop some matches from first document
da2[0].matches.pop(8)

r = da1.evaluate(ground_truth=da2, metrics=[metric_fn], **kwargs)[metric_fn]
r = da1.evaluate(ground_truth=da2, metrics=[metric_fn], **kwargs)[metric_fn]
assert isinstance(r, float)
np.testing.assert_allclose(r, 1.0, rtol=1e-2) #
for d in da1:
Expand Down Expand Up @@ -486,7 +496,8 @@ def test_useless_groundtruth_warning_should_raise(storage, config, start_storage
d.tags = {'label': 'A'}
da1.embeddings = np.random.random([10, 256])
da1_index = DocumentArray(da1, storage=storage, config=config)
da1.match(da1_index, exclude_self=True)
with da1_index:
da1.match(da1_index, exclude_self=True)
da2 = DocumentArray.empty(10)
with pytest.warns(UserWarning):
da1.evaluate(ground_truth=da2, metrics=['precision_at_k'])
Expand Down Expand Up @@ -518,13 +529,14 @@ def test_embed_and_evaluate_single_da(storage, config, start_storage):
dummy_embed_function(gt)
gt.match(gt, limit=3)

res = queries_da.embed_and_evaluate(
ground_truth=gt,
metrics=['precision_at_k', 'reciprocal_rank'],
embed_funcs=dummy_embed_function,
match_batch_size=1,
limit=3,
)
with queries_da:
res = queries_da.embed_and_evaluate(
ground_truth=gt,
metrics=['precision_at_k', 'reciprocal_rank'],
embed_funcs=dummy_embed_function,
match_batch_size=1,
limit=3,
)
assert all([v == 1.0 for v in res.values()])


Expand Down Expand Up @@ -601,15 +613,16 @@ def test_embed_and_evaluate_two_das(storage, config, sample_size, start_storage)
dummy_embed_function(gt_index)
gt_queries.match(gt_index, limit=3)

res = queries_da.embed_and_evaluate(
ground_truth=gt_queries,
index_data=index_da,
metrics=['precision_at_k', 'reciprocal_rank'],
embed_funcs=dummy_embed_function,
match_batch_size=1,
limit=3,
query_sample_size=sample_size,
)
with index_da:
res = queries_da.embed_and_evaluate(
ground_truth=gt_queries,
index_data=index_da,
metrics=['precision_at_k', 'reciprocal_rank'],
embed_funcs=dummy_embed_function,
match_batch_size=1,
limit=3,
query_sample_size=sample_size,
)
assert all([v == 1.0 for v in res.values()])


Expand Down Expand Up @@ -681,25 +694,26 @@ def emb_func(da):
da1 = DocumentArray([Document(text=str(i), tags={label_tag: i}) for i in range(3)])
da2 = DocumentArray(da1, storage=storage, config=config, copy=True)

if (
use_index
): # query and index da are distinct # (different embeddings are generated)
res = da1.embed_and_evaluate(
index_data=da2,
metrics=metric_fns,
embed_funcs=emb_func,
match_batch_size=1,
limit=3,
label_tag=label_tag,
)
else: # query and index are the same (embeddings of both das are equal)
res = da2.embed_and_evaluate(
metrics=metric_fns,
embed_funcs=emb_func,
match_batch_size=1,
limit=3,
label_tag=label_tag,
)
with da2:
if (
use_index
): # query and index da are distinct # (different embeddings are generated)
res = da1.embed_and_evaluate(
index_data=da2,
metrics=metric_fns,
embed_funcs=emb_func,
match_batch_size=1,
limit=3,
label_tag=label_tag,
)
else: # query and index are the same (embeddings of both das are equal)
res = da2.embed_and_evaluate(
metrics=metric_fns,
embed_funcs=emb_func,
match_batch_size=1,
limit=3,
label_tag=label_tag,
)
for key in metric_fns:
assert key in res
assert abs(res[key] - expected[key]) < 1e-4
Expand Down Expand Up @@ -799,9 +813,10 @@ def test_embed_and_evaluate_with_embed_model(
[Document(text=f'some text {i}', tags={'label': str(i)}) for i in range(5)]
)
da = DocumentArray(da, storage=storage, config=config)
res = da.embed_and_evaluate(
metrics=['precision_at_k'], embed_models=model, collate_fns=collate_fn
)
with da:
res = da.embed_and_evaluate(
metrics=['precision_at_k'], embed_models=model, collate_fns=collate_fn
)
assert res
assert res['precision_at_k'] == 0.2

Expand Down Expand Up @@ -873,12 +888,13 @@ def emb_func(da):
)
da2 = DocumentArray(da1, storage=storage, config=config, copy=True)

res = da1.embed_and_evaluate(
index_data=da2,
metrics=metric_fns,
embed_funcs=emb_func,
query_sample_size=sample_size,
)
with da2:
res = da1.embed_and_evaluate(
index_data=da2,
metrics=metric_fns,
embed_funcs=emb_func,
query_sample_size=sample_size,
)
expected_size = (
sample_size if sample_size and (sample_size < len(da1)) else len(da1)
)
Expand Down