def test_get_output_obj(self, csv_fileobj):
dataset = MatrixDataset.from_csv(csv_fileobj)
alg = Sums(iterator=FixedIterator(5))
# Default should be all fields if none are given, but not accuracy
# unless supervised data given
results = alg.run(dataset)
out1 = BaseClient().get_output_obj(results)
exp_keys = {
f.value
for f in OutputFields if f != OutputFields.ACCURACY
}
assert set(out1.keys()) == exp_keys
sup_data = SupervisedData.from_csv(csv_fileobj)
sup_results = alg.run(sup_data.data)
out2 = BaseClient().get_output_obj(sup_results, sup_data=sup_data)
assert set(out2.keys()) == {f.value for f in OutputFields}
assert out2["trust"] == sup_results.trust
assert out2["belief"] == sup_results.belief
out3 = BaseClient().get_output_obj(results,
output_fields=[OutputFields.TRUST])
assert set(out3.keys()) == {"trust"}
def test_basic(self, data):
"""
Perform Sums on a small graph. The expected results were obtained by
finding eigenvectors of suitable matrices (using numpy "by hand"), as
per Kleinberg paper for Hubs and Authorities
"""
sums = Sums(iterator=ConvergenceIterator(DistanceMeasures.L1, 0.00001))
results = sums.run(data)
assert np.isclose(results.trust["s1"], 1)
assert np.isclose(results.trust["s2"], 0.53208889)
assert np.isclose(results.trust["s3"], 0.34729636)
assert set(results.belief["x"].keys()) == {"one"}
assert np.isclose(results.belief["x"]["one"], 1)
assert set(results.belief["y"].keys()) == {"eight", "nine"}
assert np.isclose(results.belief["y"]["nine"], 0.65270364)
assert np.isclose(results.belief["y"]["eight"], 0.34729636)
assert set(results.belief["z"].keys()) == {"seven"}
assert np.isclose(results.belief["z"]["seven"], 0.87938524)