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 setUp(self):
# Default model
self.df_wrong = pd.DataFrame(data={"z": [0.0, 1.0]})
# 2D identity model with permuted df inputs
domain_2d = gr.Domain(bounds={"x0": [-1.0, +1.0], "x1": [0.0, 1.0]})
marginals = {}
marginals["x0"] = gr.MarginalNamed(d_name="uniform",
d_param={
"loc": -1,
"scale": 2
})
marginals["x1"] = 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]], ["x0", "x1"], ["y0", "y1"],
"test", 0),
],
domain=domain_2d,
density=gr.Density(marginals=marginals),
)
self.df_2d = pd.DataFrame(data={"x1": [0.0], "x0": [+1.0]})
self.res_2d = self.model_2d.evaluate_df(self.df_2d)
self.df_median_in = pd.DataFrame({"x0": [0.5], "x1": [0.5]})
self.df_median_out = pd.DataFrame({"x0": [0.0], "x1": [0.5]})
self.model_3d = gr.Model(
functions=[
gr.Function(lambda x: x[0] + x[1] + x[2], ["x", "y", "z"],
["f"], "test", 0)
],
density=gr.Density(marginals=marginals),
)
## Timing check
self.model_slow = gr.Model(functions=[
gr.Function(lambda x: x, ["x0"], ["y0"], "f0", 1),
gr.Function(lambda x: x, ["x0"], ["y1"], "f1", 1),
])
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(
pd.DataFrame(dict(var1=["x"], var2=["y"], corr=[0.5]))
),
)