def setUp(self):
# 2D identity model with permuted df inputs
domain_2d = gr.Domain(bounds={"x": [-1.0, +1], "y": [0.0, 1.0]})
marginals = {}
marginals["x"] = gr.MarginalNamed(
d_name="uniform", d_param={"loc": -1, "scale": 2}
)
marginals["y"] = gr.MarginalNamed(
sign=-1, d_name="uniform", d_param={"loc": 0, "scale": 1}
)
self.model_2d = gr.Model(
functions=[
gr.Function(lambda x: [x[0], x[1]], ["x", "y"], ["f", "g"], "test", 0)
],
domain=domain_2d,
density=gr.Density(
marginals=marginals, copula=gr.CopulaIndependence(var_rand=["x"])
),
)
## Correct results
self.df_2d_nominal = pd.DataFrame(
data={"x": [0.0], "y": [0.5], "f": [0.0], "g": [0.5]}
)
self.df_2d_grad = pd.DataFrame(
data={"Df_Dx": [1.0], "Dg_Dx": [0.0], "Df_Dy": [0.0], "Dg_Dy": [1.0]}
)
self.df_2d_qe = pd.DataFrame(
data={"x": [0.0], "y": [0.1], "f": [0.0], "g": [0.1]}
)
def setUp(self):
self.density = gr.Density(
marginals=dict(
x=gr.MarginalNamed(d_name="uniform",
d_param={
"loc": -1,
"scale": 2
}),
y=gr.MarginalNamed(d_name="uniform",
d_param={
"loc": -1,
"scale": 2
}),
),
copula=gr.CopulaGaussian(["x", "y"],
pd.DataFrame(
dict(var1=["x"],
var2=["y"],
corr=[0.5]))),
)
self.density_ind = gr.Density(
marginals=dict(
x=gr.MarginalNamed(d_name="uniform",
d_param={
"loc": -1,
"scale": 2
}),
y=gr.MarginalNamed(d_name="norm",
d_param={
"loc": 0,
"scale": 1
}),
),
copula=gr.CopulaIndependence(["x", "y"]),
)
self.density_gauss = gr.Density(
marginals=dict(
x=gr.MarginalNamed(d_name="uniform",
d_param={
"loc": -1,
"scale": 2
}),
y=gr.MarginalNamed(d_name="norm",
d_param={
"loc": 0,
"scale": 1
}),
),
copula=gr.CopulaGaussian(["x", "y"],
pd.DataFrame(
dict(var1=["x"],
var2=["y"],
corr=[0.5]))),
)
def test_CopulaIndependence(self):
copula = gr.CopulaIndependence(var_rand=["x", "y"])
df_res = copula.sample(seed=101)
self.assertTrue(set(df_res.columns) == set(["x", "y"]))
## Transforms invariant
z = np.array([0, 0])
u = copula.z2u(z)
zp = copula.u2z(u)
self.assertTrue(np.all(z == zp))
## Jacobian accurate
dudz_fd = np.zeros((2, 2))
dudz_fd[0, :] = (copula.z2u(z + np.array([h, 0])) - copula.z2u(z)) / h
dudz_fd[1, :] = (copula.z2u(z + np.array([0, h])) - copula.z2u(z)) / h
dudz_p = copula.dudz(z)
self.assertTrue(np.allclose(dudz_fd, dudz_p))