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Simplify and fix InformationWeight Transform with column groups #153

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23bc1d8
Simplify informaton weight with column groups and fix baselines when …
ryandewolfe33 Jul 19, 2026
4aac5d8
Allow semi-supervised targets for info weight
ryandewolfe33 Jul 19, 2026
ea5ae96
Cleanup, bug fix, and consolidate tests
ryandewolfe33 Jul 20, 2026
bdedf50
Remove incorrect doc string
ryandewolfe33 Jul 20, 2026
a20a11e
Fix nans when baseline probability is 0
ryandewolfe33 Jul 20, 2026
0070b19
Fix docstring for baseline probabilities
ryandewolfe33 Jul 20, 2026
323330f
Run black
ryandewolfe33 Jul 20, 2026
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74 changes: 37 additions & 37 deletions vectorizers/tests/test_transformers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,9 @@
np.random.random(size=44),
]

changepoint_position = np.random.randint(11, 100) # changepoint position must be at least window_width in
changepoint_position = np.random.randint(
11, 100
) # changepoint position must be at least window_width in
changepoint_sequence = np.random.poisson(0.75, size=100)
changepoint_sequence[changepoint_position] = 10

Expand Down Expand Up @@ -199,66 +201,54 @@ def test_iw_transformer(prior_strength, approx_prior):

@pytest.mark.parametrize("prior_strength", [0.1, 1.0])
@pytest.mark.parametrize("approx_prior", [True, False])
def test_iw_transformer_supervised(prior_strength, approx_prior):
@pytest.mark.parametrize("target", [None, np.array([0, 1, 1])])
@pytest.mark.parametrize("column_groups", [None, np.array([0, 1, 1])])
def test_iw_transformer_fit_args(prior_strength, approx_prior, target, column_groups):
IWT = InformationWeightTransformer(
prior_strength=prior_strength,
approx_prior=approx_prior,
)
result = IWT.fit_transform(test_matrix, np.array([0, 1, 1]))
result = IWT.fit_transform(test_matrix, target, column_groups=column_groups)
transform = IWT.transform(test_matrix)
print(target, column_groups)
assert np.allclose(result.toarray(), transform.toarray())
assert np.all(IWT.information_weights_ >= 0)


@pytest.mark.parametrize("prior_strength", [0.1, 1.0])
@pytest.mark.parametrize("approx_prior", [True, False])
def test_iw_transformer_column_groups(prior_strength, approx_prior):
IWT = InformationWeightTransformer(
prior_strength=prior_strength,
approx_prior=approx_prior,
)
result = IWT.fit_transform(test_matrix, column_groups=np.array([0, 1, 1]))
transform = IWT.transform(test_matrix)
assert np.allclose(result.toarray(), transform.toarray())


@pytest.mark.parametrize("prior_strength", [0.1, 1.0])
@pytest.mark.parametrize("approx_prior", [True, False])
def test_iw_transformer_column_groups_single_weight(prior_strength, approx_prior):
@pytest.mark.parametrize("target", [None, np.array([0, 1, 1])])
@pytest.mark.parametrize("column_groups", [None, np.array([0, 1, 1])])
def test_iw_transformer_zero_column(
prior_strength, approx_prior, target, column_groups
):
IWT = InformationWeightTransformer(
prior_strength=prior_strength,
approx_prior=approx_prior,
)
result = IWT.fit_transform(
test_matrix,
column_groups=np.array([0, 1, 1]),
single_column_group_weight=np.array([False, True])
test_matrix_zero_column, target, column_groups=column_groups
)
transform = IWT.transform(test_matrix)
assert np.allclose(result.toarray(), transform.toarray())


@pytest.mark.parametrize("prior_strength", [0.1, 1.0])
@pytest.mark.parametrize("approx_prior", [True, False])
def test_iw_transformer_zero_column(prior_strength, approx_prior):
IWT = InformationWeightTransformer(
prior_strength=prior_strength,
approx_prior=approx_prior,
)
result = IWT.fit_transform(test_matrix_zero_column)
transform = IWT.transform(test_matrix_zero_column)
assert np.allclose(result.toarray(), transform.toarray())
assert np.all(IWT.information_weights_ >= 0)


@pytest.mark.parametrize("prior_strength", [0.1, 1.0])
@pytest.mark.parametrize("approx_prior", [True, False])
def test_iw_transformer_zero_row(prior_strength, approx_prior):
@pytest.mark.parametrize("target", [None, np.array([0, 1, 1])])
@pytest.mark.parametrize("column_groups", [None, np.array([0, 1, 1])])
def test_iw_transformer_zero_row(prior_strength, approx_prior, target, column_groups):
IWT = InformationWeightTransformer(
prior_strength=prior_strength,
approx_prior=approx_prior,
)
result = IWT.fit_transform(test_matrix_zero_row)
result = IWT.fit_transform(
test_matrix_zero_row, target, column_groups=column_groups
)
transform = IWT.transform(test_matrix_zero_row)
assert np.allclose(result.toarray(), transform.toarray())
assert np.all(IWT.information_weights_ >= 0)


@pytest.mark.parametrize("algorithm", ["randomized", "arpack"])
Expand All @@ -271,9 +261,13 @@ def test_count_feature_compression_basic(algorithm):

@pytest.mark.parametrize("algorithm", ["randomized", "arpack"])
def test_count_feature_compression_fit_transform_is_fit_and_transform(algorithm):
make_cfc = lambda: CountFeatureCompressionTransformer(n_components=2, algorithm=algorithm)
make_cfc = lambda: CountFeatureCompressionTransformer(
n_components=2, algorithm=algorithm
)
cfc_fit = make_cfc().fit(test_matrix)
assert np.allclose(cfc_fit.transform(test_matrix), make_cfc().fit_transform(test_matrix))
assert np.allclose(
cfc_fit.transform(test_matrix), make_cfc().fit_transform(test_matrix)
)


def test_count_feature_compression_warns():
Expand Down Expand Up @@ -359,14 +353,17 @@ def test_sliding_window_generator_matches_transformer(pad_width, kernel, sample)
for j, point in enumerate(point_cloud):
assert np.allclose(point, generator_result[i][j])


@pytest.mark.parametrize("window_width", [5, 10])
def test_sliding_window_count_changepoint(window_width):
swt = SlidingWindowTransformer(
window_width=window_width, kernels=[("count_changepoint", 1.0, 2.0)],
window_width=window_width,
kernels=[("count_changepoint", 1.0, 2.0)],
)
changepoint_scores = swt.fit_transform([changepoint_sequence])[0].flatten()
assert np.argmax(changepoint_scores) + window_width - 1 == changepoint_position


@pytest.mark.parametrize("pad_width", [0, 1])
@pytest.mark.parametrize(
"kernel",
Expand Down Expand Up @@ -429,10 +426,13 @@ def test_sliding_window_transformer_bad_params():
with pytest.raises(ValueError):
result = swt.fit_transform(test_time_series)


def test_seq_diff_transformer_basic():
sdt = SequentialDifferenceTransformer()
diffs = sdt.fit_transform(test_time_series)
transform_diffs = sdt.transform(test_time_series)
for i, seq_diffs in enumerate(diffs):
assert np.allclose(np.array(seq_diffs), np.array(transform_diffs[i]))
assert np.allclose(test_time_series[i][:-1] + np.ravel(seq_diffs), test_time_series[i][1:])
assert np.allclose(
test_time_series[i][:-1] + np.ravel(seq_diffs), test_time_series[i][1:]
)
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