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_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