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_select_clinical_factors():
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame(
[
[1,1,1,0,0,0,1,0,1],
[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],
[0,0,0,1,0,1,1,1,1],
],
index=[f'sample {i}' for i in range(11)],
columns=[f'LF{i}' for i in range(9)]
) # here the first 3 factors separate the groups and the last 6 do not
durations = [1,2,3,4,5,6, 1000,2000,3000, 4000, 5000] # here the first 3 have short durations, the last 3 longer ones
observed = [True]*11 # all events observed
survival = pd.DataFrame(dict(duration=durations, observed=observed),
index=[f'sample {i}' for i in range(11)])
z_clin = maui_model.select_clinical_factors(survival, cox_penalizer=1)
assert 'LF0' in z_clin.columns
assert 'LF5' not in z_clin.columns
def test_maui_computes_harrells_c():
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame(
[
[1,1,1,0,0,0,1,0,1],
[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],
[0,0,0,1,0,1,1,1,1],
],
index=[f'sample {i}' for i in range(11)],
columns=[f'LF{i}' for i in range(9)]
) # here the first 3 factors separate the groups and the last 6 do not
durations = [1,2,3,4,5,6, 1000,2000,3000, 4000, 5000] # here the first 3 have short durations, the last 3 longer ones
observed = [True]*11 # all events observed
survival = pd.DataFrame(dict(duration=durations, observed=observed),
index=[f'sample {i}' for i in range(11)])
cs = maui_model.c_index(survival, clinical_only=True,
duration_column='duration', observed_column='observed',
cox_penalties=[.1,1,10,100,1000,10000],
cv_folds=2, sel_clin_alpha=.05, sel_clin_penalty=1)
print(cs)
assert np.allclose(cs, [.8,.8])
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_clusters_picks_optimal_k_by_ami():
ami_mock = mock.Mock()
ami_mock.side_effect = [
2,
3,
1,
] # the optimal AMI will be given at the second trial
with mock.patch("sklearn.metrics.adjusted_mutual_info_score", ami_mock):
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame(
np.random.randn(10, 2),
index=[f"sample {i}" for i in range(10)],
columns=["LF1", "LF2"],
)
maui_model.x_ = pd.DataFrame(
np.random.randn(20, 10),
index=[f"feature {i}" for i in range(20)],
columns=[f"sample {i}" for i in range(10)],
)
the_y = pd.Series(np.arange(10), index=maui_model.z_.index)
maui_model.cluster(
ami_y=the_y, optimal_k_range=[1, 2, 3]
) # the second trial is k=2
assert maui_model.optimal_k_ == 2
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_drops_unexplanatody_factors_by_r2():
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,
) # here the first 8 latent factors have R2 above threshold, the last does not
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,
)
z_filt = maui_model.drop_unexplanatory_factors()
assert z_filt.shape[1] == 8
def test_maui_clusters_with_single_k():
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame(np.random.randn(10,2),
index=[f'sample {i}' for i in range(10)],
columns=['LF1', 'LF2'])
maui_model.x_ = pd.DataFrame(np.random.randn(20,10),
index=[f'feature {i}' for i in range(20)],
columns=[f'sample {i}' for i in range(10)])
yhat = maui_model.cluster(5)
assert yhat.shape == (10,)
def test_maui_clusters_picks_optimal_k_by_silhouette():
silhouette_mock = mock.Mock()
silhouette_mock.side_effect = [2,3,1] # the optimal silhouette will be given at the second trial
with mock.patch('sklearn.metrics.silhouette_score', silhouette_mock):
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame(np.random.randn(10,2),
index=[f'sample {i}' for i in range(10)],
columns=['LF1', 'LF2'])
maui_model.x_ = pd.DataFrame(np.random.randn(20,10),
index=[f'feature {i}' for i in range(20)],
columns=[f'sample {i}' for i in range(10)])
maui_model.cluster(optimal_k_method='silhouette', optimal_k_range=[1,2,3]) # the second trial is k=2
assert maui_model.optimal_k_ == 2
def test_maui_clusters_only_samples_in_y_index_when_optimizing():
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame(np.random.randn(10,2),
index=[f'sample {i}' for i in range(10)],
columns=['LF1', 'LF2'])
maui_model.x_ = pd.DataFrame(np.random.randn(20,10),
index=[f'feature {i}' for i in range(20)],
columns=[f'sample {i}' for i in range(10)])
y = pd.Series(['a','a','a','b','b','b'],
index=[f'sample {i}' for i in range(6)])
yhat = maui_model.cluster(ami_y=y, optimal_k_range=[1,2,3])
assert set(yhat.index) == set(y.index)
def test_maui_clusters_picks_optimal_k_with_custom_scoring():
scorer = mock.Mock()
scorer.side_effect = [2,3,1] # the optimal AMI will be given at the second trial
scorer.__name__ = 'mock_scorer'
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame(np.random.randn(10,2),
index=[f'sample {i}' for i in range(10)],
columns=['LF1', 'LF2'])
maui_model.x_ = pd.DataFrame(np.random.randn(20,10),
index=[f'feature {i}' for i in range(20)],
columns=[f'sample {i}' for i in range(10)])
maui_model.cluster(optimal_k_method=scorer, optimal_k_range=[1,2,3]) # the second trial is k=2
assert maui_model.optimal_k_ == 2
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_clusters_only_samples_in_y_index_when_optimizing():
np.random.seed(0)
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame(
np.random.randn(10, 2),
index=[f"sample {i}" for i in range(10)],
columns=["LF1", "LF2"],
)
maui_model.x_ = pd.DataFrame(
np.random.randn(20, 10),
index=[f"feature {i}" for i in range(20)],
columns=[f"sample {i}" for i in range(10)],
)
y = pd.Series(["a", "a", "a", "b", "b", "b"],
index=[f"sample {i}" for i in range(6)])
yhat = maui_model.cluster(ami_y=y, optimal_k_range=[1, 2, 3])
assert set(yhat.index) == set(y.index)
def test_maui_computes_roc_and_auc():
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame([
[0, 1, 1, 1, 0, 1, 1, 0, 0],
[1, 0, 0, 0, 0, 0, 1, 1, 0],
[1, 0, 1, 0, 0, 0, 1, 1, 0],
[1, 0, 0, 1, 0, 0, 1, 1, 0],
[1, 0, 0, 0, 1, 1, 1, 1, 0],
[1, 1, 1, 0, 0, 0, 1, 1, 1],
],
index=[f'sample {i}' for i in range(6)],
columns=[f'LF{i}' for i in range(9)])
y = pd.Series(['a', 'b', 'a', 'c', 'b', 'c'], index=maui_model.z_.index)
rocs = maui_model.compute_roc(y, cv_folds=2)
assert rocs == maui_model.roc_curves_
assert 'a' in rocs
assert 'b' in rocs
assert 'c' in rocs
assert "mean" in rocs
aucs = maui_model.compute_auc(y, cv_folds=2)
assert aucs == maui_model.aucs_
def test_maui_computes_roc_and_auc():
maui_model = Maui(n_hidden=[10], n_latent=2, epochs=1)
maui_model.z_ = pd.DataFrame(
[
[0, 1, 1, 1, 0, 1, 1, 0, 0],
[1, 0, 0, 0, 0, 0, 1, 1, 0],
[1, 0, 1, 0, 0, 0, 1, 1, 0],
[1, 0, 0, 1, 0, 0, 1, 1, 0],
[1, 0, 0, 0, 1, 1, 1, 1, 0],
[1, 1, 1, 0, 0, 0, 1, 1, 1],
],
index=[f"sample {i}" for i in range(6)],
columns=[f"LF{i}" for i in range(9)],
)
y = pd.Series(["a", "b", "a", "c", "b", "c"], index=maui_model.z_.index)
rocs = maui_model.compute_roc(y, cv_folds=2)
assert rocs == maui_model.roc_curves_
assert "a" in rocs
assert "b" in rocs
assert "c" in rocs
assert "mean" in rocs
aucs = maui_model.compute_auc(y, cv_folds=2)
assert aucs == maui_model.aucs_