def test_groups_correctness(requests):
# We assume that anonlink computes the right results.
with open(DATA_PATH, 'rb') as f:
# Here's some filters I prepared earlier.
filters = pickle.load(f)
candidate_pairs = anonlink.candidate_generation.find_candidate_pairs(
filters, anonlink.similarities.dice_coefficient_accelerated, THRESHOLD)
true_groups = anonlink.solving.greedy_solve(candidate_pairs)
filter_size = len(filters[0][0])
assert all(
len(filter_) == filter_size for dataset in filters
for filter_ in dataset)
packed_filters = [
b''.join(binary_pack_for_upload(f, filter_size)) for f in filters
]
project_data, _ = create_project_upload_data(requests,
packed_filters,
result_type='groups',
binary=True,
hash_size=DATA_HASH_SIZE)
try:
run = post_run(requests, project_data, threshold=THRESHOLD)
result_groups = get_run_result(requests, project_data, run)['groups']
finally:
delete_project(requests, project_data)
# Compare ES result with anonlink.
result_group_set = {frozenset(map(tuple, g)) for g in result_groups}
true_group_set = set(map(frozenset, true_groups))
assert result_group_set == true_group_set
def test_delete_project_after_creating_run_with_clks(
requests, result_type_number_parties):
project, run_id = _create_data_linkage_run(requests,
result_type_number_parties)
delete_project(requests, project)
with pytest.raises(AssertionError):
get_project_description(requests, project)
def project(request, requests, result_type_number_parties):
result_type, number_parties = result_type_number_parties
project = create_project_no_data(
requests,
result_type=result_type,
number_parties=number_parties)
yield project
# Release project resource
delete_project(requests, project)
def a_blocking_project(request, requests):
# a 2 party project with blocking disabled
project = create_project_no_data(
requests,
result_type="groups",
number_parties=2,
uses_blocking=True
)
yield project
# Release project resource
delete_project(requests, project)
def test_similarity_scores(requests, the_truth):
project_data, _ = create_project_upload_data(
requests,
(the_truth['clks_a'], the_truth['clks_b']),
result_type='similarity_scores')
run = post_run(requests, project_data, threshold=the_truth['threshold'])
result = get_run_result(requests, project_data, run, timeout=60)
true_scores = the_truth['similarity_scores']
result_scores = {tuple(index for _, index in sorted([a, b])): score
for a, b, score in result['similarity_scores']}
# Anonlink is more strict on enforcing the k parameter. Hence the
# subset.
assert true_scores.keys() <= result_scores.keys()
for pair in true_scores:
assert true_scores[pair] == result_scores[pair]
delete_project(requests, project_data)
def test_delete_project_types(requests, project):
delete_project(requests, project)
def groups_project(request, requests):
size, overlap, encoding_size, uses_blocking = request.param
prj = create_project_response(requests, size, overlap, 'groups', encoding_size, uses_blocking)
yield prj
delete_project(requests, prj)
def permutations_project(request, requests):
size, overlap, encoding_size = request.param
prj = create_project_response(requests, size, overlap, 'permutations', encoding_size)
yield prj
delete_project(requests, prj)
def similarity_scores_project(request, requests):
size, overlap, encoding_size = request.param
prj = create_project_response(requests, size, overlap, 'similarity_scores', encoding_size)
yield prj
delete_project(requests, prj)