def test_maui_merge_latent_factors_complains_if_unknown_merge_by():
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame(
[
[1, 1, 1, 0, 0, 0, 1, 0, 0],
[1, 1, 1, 1, 0, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0, 0, 1, 0],
[0, 0, 0, 1, 0, 0, 1, 1, 0],
[0, 0, 0, 1, 0, 0, 1, 1, 0],
[0, 0, 0, 1, 0, 0, 1, 1, 0],
[0, 0, 0, 1, 0, 0, 1, 1, 0],
[0, 0, 0, 1, 0, 1, 1, 1, 0],
],
index=[f"sample {i}" for i in range(11)],
columns=[f"LF{i}" for i in range(9)],
dtype=float,
) # expect 0,1,2 to be merged, and 3,7 to be merged
with pytest.raises(Exception):
z_merged = maui_model.merge_similar_latent_factors(
distance_in="xxx", distance_metric="euclidean"
)
def test_maui_merges_latent_factors():
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame(
[
[1,1,1,0,0,0,1,0,0],
[1,1,1,1,0,1,1,1,0],
[1,1,1,1,0,1,1,1,0],
[1,1,1,1,0,1,1,1,0],
[1,1,1,1,0,1,1,1,0],
[1,1,1,1,1,0,0,1,0],
[0,0,0,1,0,0,1,1,0],
[0,0,0,1,0,0,1,1,0],
[0,0,0,1,0,0,1,1,0],
[0,0,0,1,0,0,1,1,0],
[0,0,0,1,0,1,1,1,0],
],
index=[f'sample {i}' for i in range(11)],
columns=[f'LF{i}' for i in range(9)],
dtype=float
) # expect 0,1,2 to be merged, and 3,7 to be merged
z_merged = maui_model.merge_similar_latent_factors(distance_metric='euclidean')
assert z_merged.shape[1] == 6
assert '0_1_2' in z_merged.columns
assert '3_7' in z_merged.columns
def test_maui_merges_latent_factors_by_w():
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame(
[
[1, 1, 1, 0, 0, 0, 1, 0, 0],
[1, 1, 1, 1, 0, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0, 0, 1, 0],
[0, 0, 0, 1, 0, 0, 1, 1, 0],
[0, 0, 0, 1, 0, 0, 1, 1, 0],
[0, 0, 0, 1, 0, 0, 1, 1, 0],
[0, 0, 0, 1, 0, 0, 1, 1, 0],
[0, 0, 0, 1, 0, 1, 1, 1, 0],
],
index=[f"sample {i}" for i in range(11)],
columns=[f"LF{i}" for i in range(9)],
dtype=float,
)
maui_model.x_ = pd.DataFrame(
[[1], [1], [1], [1], [1], [1], [0], [0], [0], [0], [0]],
index=[f"sample {i}" for i in range(11)],
columns=["Feature 1"],
dtype=float,
)
# with these z and x, expect 0,1,2 and 4,5 and 3,6,7
z_merged = maui_model.merge_similar_latent_factors(
distance_in="w", distance_metric="euclidean"
)
assert z_merged.shape[1] == 4
assert "0_1_2" in z_merged.columns
assert "3_6_7" in z_merged.columns
assert "4_5" in z_merged.columns
def test_maui_merges_latent_factors_by_w():
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame(
[
[1,1,1,0,0,0,1,0,0],
[1,1,1,1,0,1,1,1,0],
[1,1,1,1,0,1,1,1,0],
[1,1,1,1,0,1,1,1,0],
[1,1,1,1,0,1,1,1,0],
[1,1,1,1,1,0,0,1,0],
[0,0,0,1,0,0,1,1,0],
[0,0,0,1,0,0,1,1,0],
[0,0,0,1,0,0,1,1,0],
[0,0,0,1,0,0,1,1,0],
[0,0,0,1,0,1,1,1,0],
],
index=[f'sample {i}' for i in range(11)],
columns=[f'LF{i}' for i in range(9)],
dtype=float
)
maui_model.x_ = pd.DataFrame(
[
[1],
[1],
[1],
[1],
[1],
[1],
[0],
[0],
[0],
[0],
[0],
],
index=[f'sample {i}' for i in range(11)],
columns=['Feature 1'],
dtype=float)
# with these z and x, expect 0,1,2 and 4,5 and 3,6,7
z_merged = maui_model.merge_similar_latent_factors(distance_in='w',
distance_metric='euclidean')
assert z_merged.shape[1] == 4
assert '0_1_2' in z_merged.columns
assert '3_6_7' in z_merged.columns
assert '4_5' in z_merged.columns
