def test_holdout_local_score_null_spbn():
spbn = pbn.SemiparametricBN([('a', 'b'), ('a', 'c'), ('a', 'd'),
('b', 'c'), ('b', 'd'), ('c', 'd')],
[('a', pbn.CKDEType()), ('c', pbn.CKDEType())])
np.random.seed(0)
a_null = np.random.randint(0, SIZE, size=100)
b_null = np.random.randint(0, SIZE, size=100)
c_null = np.random.randint(0, SIZE, size=100)
d_null = np.random.randint(0, SIZE, size=100)
df_null = df.copy()
df_null.loc[df_null.index[a_null], 'a'] = np.nan
df_null.loc[df_null.index[b_null], 'b'] = np.nan
df_null.loc[df_null.index[c_null], 'c'] = np.nan
df_null.loc[df_null.index[d_null], 'd'] = np.nan
hl = pbn.HoldoutLikelihood(df_null, 0.2, seed)
assert np.isclose(
hl.local_score(spbn, 'a', []),
numpy_local_score(pbn.CKDEType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'a', []))
assert np.isclose(
hl.local_score(spbn, 'b', ['a']),
numpy_local_score(pbn.LinearGaussianCPDType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'b', ['a']))
assert np.isclose(
hl.local_score(spbn, 'c', ['a', 'b']),
numpy_local_score(pbn.CKDEType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'c', ['a', 'b']))
assert np.isclose(
hl.local_score(spbn, 'd', ['a', 'b', 'c']),
numpy_local_score(pbn.LinearGaussianCPDType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'd', ['a', 'b', 'c']))
assert np.isclose(
hl.local_score(spbn, 'd', ['a', 'b', 'c']),
numpy_local_score(pbn.LinearGaussianCPDType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'd', ['b', 'c', 'a']))
assert hl.local_score(spbn, 'a') == hl.local_score(spbn, 'a',
spbn.parents('a'))
assert hl.local_score(spbn, 'b') == hl.local_score(spbn, 'b',
spbn.parents('b'))
assert hl.local_score(spbn, 'c') == hl.local_score(spbn, 'c',
spbn.parents('c'))
assert hl.local_score(spbn, 'd') == hl.local_score(spbn, 'd',
spbn.parents('d'))
def test_holdout_score():
gbn = pbn.GaussianNetwork([('a', 'b'), ('a', 'c'), ('a', 'd'), ('b', 'c'),
('b', 'd'), ('c', 'd')])
hl = pbn.HoldoutLikelihood(df, 0.2, 0)
assert np.isclose(
hl.score(gbn),
(hl.local_score(gbn, 'a', []) + hl.local_score(gbn, 'b', ['a']) +
hl.local_score(gbn, 'c', ['a', 'b']) +
hl.local_score(gbn, 'd', ['a', 'b', 'c'])))
spbn = pbn.SemiparametricBN([('a', 'b'), ('a', 'c'), ('a', 'd'),
('b', 'c'), ('b', 'd'), ('c', 'd')],
[('a', pbn.CKDEType()), ('c', pbn.CKDEType())])
assert np.isclose(hl.score(spbn),
(hl.local_score(spbn, 'a') + hl.local_score(spbn, 'b') +
hl.local_score(spbn, 'c') + hl.local_score(spbn, 'd')))
def test_holdout_local_score_spbn():
spbn = pbn.SemiparametricBN([('a', 'b'), ('a', 'c'), ('a', 'd'),
('b', 'c'), ('b', 'd'), ('c', 'd')],
[('a', pbn.CKDEType()), ('c', pbn.CKDEType())])
hl = pbn.HoldoutLikelihood(df, 0.2, seed)
assert np.isclose(
hl.local_score(spbn, 'a', []),
numpy_local_score(pbn.CKDEType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'a', []))
assert np.isclose(
hl.local_score(spbn, 'b', ['a']),
numpy_local_score(pbn.LinearGaussianCPDType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'b', ['a']))
assert np.isclose(
hl.local_score(spbn, 'c', ['a', 'b']),
numpy_local_score(pbn.CKDEType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'c', ['a', 'b']))
assert np.isclose(
hl.local_score(spbn, 'd', ['a', 'b', 'c']),
numpy_local_score(pbn.LinearGaussianCPDType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'd', ['a', 'b', 'c']))
assert np.isclose(
hl.local_score(spbn, 'd', ['a', 'b', 'c']),
numpy_local_score(pbn.LinearGaussianCPDType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'd', ['b', 'c', 'a']))
assert hl.local_score(spbn, 'a') == hl.local_score(spbn, 'a',
spbn.parents('a'))
assert hl.local_score(spbn, 'b') == hl.local_score(spbn, 'b',
spbn.parents('b'))
assert hl.local_score(spbn, 'c') == hl.local_score(spbn, 'c',
spbn.parents('c'))
assert hl.local_score(spbn, 'd') == hl.local_score(spbn, 'd',
spbn.parents('d'))
def test_holdout_local_score_gbn():
gbn = pbn.GaussianNetwork([('a', 'b'), ('a', 'c'), ('a', 'd'), ('b', 'c'),
('b', 'd'), ('c', 'd')])
hl = pbn.HoldoutLikelihood(df, 0.2, seed)
assert np.isclose(
hl.local_score(gbn, 'a', []),
numpy_local_score(pbn.LinearGaussianCPDType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'a', []))
assert np.isclose(
hl.local_score(gbn, 'b', ['a']),
numpy_local_score(pbn.LinearGaussianCPDType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'b', ['a']))
assert np.isclose(
hl.local_score(gbn, 'c', ['a', 'b']),
numpy_local_score(pbn.LinearGaussianCPDType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'c', ['a', 'b']))
assert np.isclose(
hl.local_score(gbn, 'd', ['a', 'b', 'c']),
numpy_local_score(pbn.LinearGaussianCPDType(),
hl.training_data().to_pandas(),
hl.test_data().to_pandas(), 'd', ['a', 'b', 'c']))
assert np.isclose(hl.local_score(gbn, 'd', ['a', 'b', 'c']),
hl.local_score(gbn, 'd', ['b', 'c', 'a']))
assert hl.local_score(gbn, 'a') == hl.local_score(gbn, 'a',
gbn.parents('a'))
assert hl.local_score(gbn, 'b') == hl.local_score(gbn, 'b',
gbn.parents('b'))
assert hl.local_score(gbn, 'c') == hl.local_score(gbn, 'c',
gbn.parents('c'))
assert hl.local_score(gbn, 'd') == hl.local_score(gbn, 'd',
gbn.parents('d'))
def test_holdout_create():
s = pbn.HoldoutLikelihood(df)
assert s.training_data().num_rows == 0.8 * SIZE
assert s.test_data().num_rows == 0.2 * SIZE
s = pbn.HoldoutLikelihood(df, 0.5)
assert s.training_data().num_rows == 0.5 * SIZE
assert s.test_data().num_rows == 0.5 * SIZE
s = pbn.HoldoutLikelihood(df, 0.2, 0)
s2 = pbn.HoldoutLikelihood(df, 0.2, 0)
assert s.training_data().equals(s2.training_data())
assert s.test_data().equals(s2.test_data())
with pytest.raises(ValueError) as ex:
s = pbn.HoldoutLikelihood(df, 10, 0)
assert "test_ratio must be a number" in str(ex.value)
with pytest.raises(ValueError) as ex:
s = pbn.HoldoutLikelihood(df, 0, 0)
assert "test_ratio must be a number" in str(ex.value)