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Fix lf summary no abstain 1446 #1447

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Sep 5, 2019
Merged

Fix lf summary no abstain 1446 #1447

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cfeed25
Add a unit test about lf_summary without ABSTAIN label
Sep 5, 2019
95ca47f
Fix the computation of correct/incorrect values from the confusion ma…
Sep 5, 2019
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7 changes: 6 additions & 1 deletion snorkel/labeling/analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -354,7 +354,12 @@ def lf_summary(
d["Conflicts"] = Series(data=self.lf_conflicts(), index=lf_names)

if Y is not None:
confusions = [confusion_matrix(Y, self.L[:, i])[1:, 1:] for i in range(m)]
labels = np.unique(
np.concatenate((Y.flatten(), self.L.flatten(), np.array([-1])))
)
confusions = [
confusion_matrix(Y, self.L[:, i], labels)[1:, 1:] for i in range(m)
]
corrects = [np.diagonal(conf).sum() for conf in confusions]
incorrects = [
conf.sum() - correct for conf, correct in zip(confusions, corrects)
Expand Down
25 changes: 25 additions & 0 deletions test/labeling/test_analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,15 @@

Y = [0, 1, 2, 0, 1, 2]

L_wo_abstain = [
[3, 3, 3, 5, 4, 3],
[3, 4, 5, 5, 4, 3],
[5, 3, 4, 4, 5, 3],
[4, 4, 5, 4, 3, 3],
[3, 4, 3, 5, 4, 3],
[5, 3, 3, 4, 4, 3],
]


def f(x):
return -1
Expand All @@ -24,6 +33,7 @@ def f(x):
class TestAnalysis(unittest.TestCase):
def setUp(self) -> None:
self.lfa = LFAnalysis(np.array(L))
self.lfa_wo_abstain = LFAnalysis(np.array(L_wo_abstain))
self.Y = np.array(Y)

def test_label_coverage(self) -> None:
Expand Down Expand Up @@ -130,3 +140,18 @@ def test_lf_summary(self) -> None:
def test_wrong_number_of_lfs(self) -> None:
with self.assertRaisesRegex(ValueError, "Number of LFs"):
LFAnalysis(np.array(L), [LabelingFunction(s, f) for s in "ab"])

def test_lf_summary_without_abstain(self) -> None:
df = self.lfa_wo_abstain.lf_summary(self.Y + 4, est_weights=None)
df_expected = pd.DataFrame(
{
"Polarity": [[3, 4, 5], [3, 4], [3, 4, 5], [4, 5], [3, 4, 5], [3]],
"Coverage": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
"Overlaps": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
"Conflicts": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
"Correct": [1, 1, 1, 3, 1, 0],
"Incorrect": [5, 5, 5, 3, 5, 6],
"Emp. Acc.": [1 / 6, 1 / 6, 1 / 6, 3 / 6, 1 / 6, 0],
}
)
pd.testing.assert_frame_equal(df.round(6), df_expected.round(6))