def test_maui_updates_neural_weight_product_when_training():
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
z_before = maui_model.fit_transform({"d1": df1, "d2": df2})
nwp_before_fine_tuning = maui_model.get_neural_weight_product()
maui_model.fine_tune({"d1": df1, "d2": df2})
z_after = maui_model.transform({"d1": df1, "d2": df2})
nwp_after_fine_tuning = maui_model.get_neural_weight_product()
assert not np.allclose(z_before, z_after)
assert not np.allclose(nwp_before_fine_tuning, nwp_after_fine_tuning)
def test_maui_complains_if_fine_tune_with_wrong_features():
maui_model = Maui(n_hidden=[], n_latent=2, epochs=1)
maui_model.fit({"d1": df1, "d2": df2})
df1_wrong_features = df1.reindex(df1.index[:len(df1.index) - 1])
with pytest.raises(ValueError):
z = maui_model.fine_tune({"df1": df1_wrong_features, "df2": df2})
def test_maui_can_fine_tune():
maui_model = Maui(n_hidden=[], n_latent=2, epochs=1)
maui_model = maui_model.fit({"d1": df1, "d2": df2})
maui_model.fine_tune({"d1": df1, "d2": df2}, epochs=1)
