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Fix plotting tests #284

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Merged
stes merged 1 commit into from
Jan 17, 2026
Merged

Fix plotting tests #284

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96 changes: 46 additions & 50 deletions tests/test_plot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -190,8 +190,8 @@ def test_compare_models_with_different_versions(matplotlib_version):
# minimum version of matplotlib
minimum_version = "3.6"

if packaging.version.parse(
matplotlib_version) < packaging.version.parse(minimum_version):
if packaging.version.parse(matplotlib_version) < packaging.version.parse(
minimum_version):
with pytest.raises(ImportError):
cebra_plot.compare_models(models=fitted_models,
patched_version=matplotlib_version)
Expand Down Expand Up @@ -359,9 +359,6 @@ def test_plot_consistency():

dataset_ids = ["achilles", "buddy", "cicero", "gatsby"]

figure = plt.figure(figsize=(5, 5))
ax = figure.add_subplot()

scores_subs, pairs_subs, datasets_subs = cebra_sklearn_metrics.consistency_score(
embeddings_datasets,
labels=labels_datasets,
Expand All @@ -371,17 +368,21 @@ def test_plot_consistency():
scores_runs, pairs_runs, datasets_runs = cebra_sklearn_metrics.consistency_score(
embeddings_runs, between="runs")

# ------------------------------------------------------------

# between datasets
fig = cebra_plot.plot_consistency(scores_subs,
pairs=pairs_subs,
datasets=datasets_subs)
assert isinstance(fig, matplotlib.axes.Axes)
ax = cebra_plot.plot_consistency(scores_subs,
pairs=pairs_subs,
datasets=datasets_subs)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()

ax = cebra_plot.plot_consistency(scores_subs,
pairs=pairs_subs,
datasets=datasets_subs,
ax=ax)
datasets=datasets_subs)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()

ax = cebra_plot.plot_consistency(
torch.from_numpy(scores_subs),
pairs=pairs_subs,
Expand All @@ -390,119 +391,114 @@ def test_plot_consistency():
title="Test",
text_color=None,
colorbar_label=None,
ax=ax,
)
assert isinstance(fig, matplotlib.axes.Axes)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()

ax = cebra_plot.plot_consistency(torch.from_numpy(scores_subs),
pairs=pairs_subs,
datasets=datasets_subs,
ax=ax)
assert isinstance(fig, matplotlib.axes.Axes)
datasets=datasets_subs)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()

ax = cebra_plot.plot_consistency(
scores_subs.tolist(),
pairs=pairs_subs.tolist(),
datasets=datasets_subs.tolist(),
ax=ax,
)
assert isinstance(fig, matplotlib.axes.Axes)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()

with pytest.raises(ValueError, match="Missing.*datasets.*pairs"):
_ = cebra_plot.plot_consistency(scores_subs, ax=ax)
_ = cebra_plot.plot_consistency(scores_subs)
with pytest.raises(ValueError, match="Missing.*datasets.*pairs"):
_ = cebra_plot.plot_consistency(scores_subs, pairs=pairs_subs, ax=ax)
_ = cebra_plot.plot_consistency(scores_subs, pairs=pairs_subs)
with pytest.raises(ValueError, match="Missing.*datasets.*pairs"):
_ = cebra_plot.plot_consistency(scores_subs,
datasets=datasets_subs,
ax=ax)
_ = cebra_plot.plot_consistency(scores_subs, datasets=datasets_subs)
with pytest.raises(ValueError, match="Shape.*pairs"):
_ = cebra_plot.plot_consistency(
scores_subs,
pairs=np.random.uniform(0, 1, (10, 2)),
datasets=datasets_subs,
ax=ax,
)
with pytest.raises(ValueError, match="Shape.*datasets"):
_ = cebra_plot.plot_consistency(
scores_subs,
pairs=np.random.uniform(0, 1, (10, 2)),
datasets=np.random.uniform(0, 1, (2,)),
ax=ax,
)
with pytest.raises(ValueError, match="Invalid.*scores"):
_ = cebra_plot.plot_consistency(
np.random.uniform(0, 1, (12, 2, 2)),
pairs=pairs_subs,
datasets=datasets_subs,
ax=ax,
)
plt.close("all")

# between runs
fig = cebra_plot.plot_consistency(scores_runs,
pairs=pairs_runs,
datasets=datasets_runs)
assert isinstance(fig, matplotlib.axes.Axes)
plt.close()
ax = cebra_plot.plot_consistency(scores_runs,
pairs=pairs_runs,
datasets=datasets_runs,
ax=ax)
datasets=datasets_runs)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()

ax = cebra_plot.plot_consistency(
scores_runs,
pairs=pairs_runs,
datasets=datasets_runs,
cmap="viridis",
title="Test",
text_color=None,
colorbar_label=None,
ax=ax,
)
assert isinstance(ax, matplotlib.axes.Axes)
ax = cebra_plot.plot_consistency(torch.from_numpy(scores_runs),
plt.close()

ax = cebra_plot.plot_consistency(scores_runs,
pairs=pairs_runs,
datasets=datasets_runs,
ax=ax)
cmap="viridis",
title="Test",
text_color=None,
colorbar_label=None)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()

ax = cebra_plot.plot_consistency(torch.from_numpy(scores_runs),
pairs=pairs_runs,
datasets=datasets_runs)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()

ax = cebra_plot.plot_consistency(
scores_runs.tolist(),
pairs=pairs_runs.tolist(),
datasets=datasets_runs.tolist(),
ax=ax,
)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()

with pytest.raises(ValueError, match="Missing.*datasets.*pairs"):
_ = cebra_plot.plot_consistency(scores_runs, ax=ax)
_ = cebra_plot.plot_consistency(scores_runs)
with pytest.raises(ValueError, match="Missing.*datasets.*pairs"):
_ = cebra_plot.plot_consistency(scores_runs, pairs=pairs_runs, ax=ax)
_ = cebra_plot.plot_consistency(scores_runs, pairs=pairs_runs)
with pytest.raises(ValueError, match="Missing.*datasets.*pairs"):
_ = cebra_plot.plot_consistency(scores_runs,
datasets=datasets_runs,
ax=ax)
_ = cebra_plot.plot_consistency(scores_runs, datasets=datasets_runs)
with pytest.raises(ValueError, match="Shape.*datasets"):
_ = cebra_plot.plot_consistency(
scores_runs,
pairs=np.random.uniform(0, 1, (10, 2)),
datasets=np.random.uniform(0, 1, (4,)),
ax=ax,
)
with pytest.raises(ValueError, match="Shape.*pairs"):
_ = cebra_plot.plot_consistency(
scores_runs,
pairs=np.random.uniform(0, 1, (10, 2)),
datasets=datasets_runs,
ax=ax,
)
with pytest.raises(ValueError, match="Invalid.*dimensions"):
_ = cebra_plot.plot_consistency(
np.random.uniform(0, 1, (12, 2, 2)),
pairs=pairs_runs,
datasets=datasets_runs,
ax=ax,
)
plt.close()
plt.close("all")


@pytest.mark.parametrize("seed", [None, 42, 1024, 454545])
Expand Down