def test_expectile_sets_correct_distribution_and_link(self, adata_cflare: AnnData):
g = GAM(adata_cflare, expectile=0.2)
g.prepare(adata_cflare.var_names[0], "0")
g.fit()
g.predict()
g.confidence_interval()
assert isinstance(g.model, ExpectileGAM)
assert g.y_test is not None
assert g.conf_int is not None
def test_custom_grid(self, adata_cflare: AnnData):
g = GAM(adata_cflare, grid={"lam": [0.1, 1, 10]})
g.prepare(adata_cflare.var_names[0], "0")
g.fit()
g.predict()
g.confidence_interval()
assert g._grid is not None
assert g._grid == {"lam": [0.1, 1, 10]}
assert g.y_test is not None
assert g.conf_int is not None
def test_default_grid(self, adata_cflare: AnnData):
g = GAM(adata_cflare, grid="default")
g.prepare(adata_cflare.var_names[0], "0")
g.fit()
g.predict()
g.confidence_interval()
assert g._grid is not None
assert not isinstance(g._grid, str)
assert g.y_test is not None
assert g.conf_int is not None
def pygam_model(adata_cflare: AnnData) -> GAM:
m = GAM(adata_cflare)
m.prepare(adata_cflare.var_names[0], "0").fit()
m.predict()
m.confidence_interval()
return m
def test_create_models_lineage_specific_fallback(self, adata: AnnData):
m1 = create_model(adata)
m2 = GAM(adata)
models = _create_models(
{"foo": {"baz": m1, "*": m2}, "bar": {"quux": m2, "*": m1}},
["foo", "bar"],
["baz", "quux", "quas", "wex"],
)
assert set(models.keys()) == {"foo", "bar"}
for k, vs in models.items():
assert set(vs.keys()) == {"baz", "quux", "quas", "wex"}
assert isinstance(models["foo"]["baz"], type(m1))
assert models["foo"]["baz"] is not m1
for l in {"quux", "quas", "wex"}:
assert isinstance(models["foo"][l], type(m2))
assert models["foo"][l] is not m2
assert isinstance(models["bar"]["quux"], type(m2))
assert models["bar"]["quux"] is not m2
for l in {"baz", "quas", "wex"}:
assert isinstance(models["bar"][l], type(m1))
assert models["bar"][l] is not m1
def test_dist_link_combinations(
self, adata_cflare: AnnData, dist: GamDistribution, link: GamLinkFunction
):
g = GAM(adata_cflare, link=link, distribution=dist)
expected_model_type = _gams[dist, link]
assert isinstance(g.model, expected_model_type)
def test_create_models_lineage_specific(self, adata: AnnData):
m1 = create_model(adata)
m2 = GAM(adata)
models = _create_models(
{"foo": {"bar": m1, "baz": m2}}, ["foo"], ["bar", "baz"]
)
assert set(models["foo"].keys()) == {"bar", "baz"}
assert isinstance(models["foo"]["bar"], type(m1))
assert models["foo"]["bar"] is not m1
assert isinstance(models["foo"]["baz"], type(m2))
assert models["foo"]["baz"] is not m2
def test_raises_invalid_kwargs(self, adata_cflare: AnnData):
with pytest.raises(TypeError):
GAM(adata_cflare, n_lineages=12)
def test_expectilegam_invalid_expectile(self, adata: AnnData):
with pytest.raises(ValueError):
GAM(adata, expectile=0)
with pytest.raises(ValueError):
GAM(adata, expectile=1)
def test_invalid_grid_type(self, adata: AnnData):
with pytest.raises(TypeError):
GAM(adata, grid=1311)
def test_invalid_link_function(self, adata: AnnData):
with pytest.raises(ValueError):
GAM(adata, link="foob")
def test_invalid_distribution(self, adata: AnnData):
with pytest.raises(ValueError):
GAM(adata, distribution="foobar")
