def setUp(self):
self.md = (gr.Model() >> gr.cp_function(
fun=lambda x: x, var=1, out=1, runtime=1) >>
gr.cp_marginals(x0={
"dist": "uniform",
"loc": 0,
"scale": 1
}) >> gr.cp_copula_independence())
self.md_2d = (gr.Model() >> gr.cp_function(
fun=lambda x: x[0], var=2, out=1) >> gr.cp_marginals(
x0={
"dist": "uniform",
"loc": 0,
"scale": 1
},
x1={
"dist": "uniform",
"loc": 0,
"scale": 1
},
) >> gr.cp_copula_independence())
self.md_mixed = (gr.Model() >> gr.cp_function(
fun=lambda x: x[0] + x[1], var=2,
out=1) >> gr.cp_bounds(x0=(-1, +1)) >> gr.cp_marginals(x1={
"dist": "uniform",
"loc": 0,
"scale": 1
}, ) >> gr.cp_copula_independence())
def __init__(self):
"""Setup necessary values"""
self.md = (gr.Model() >> gr.cp_function(
fun=lambda x: x, var=1, out=1, runtime=1))
self.md_var_det = self.md >> gr.cp_bounds(x1=(0, 1))
self.df = pd.DataFrame(data={"x": [0.0], "y": [0.5]})
# declare tests
self.type_tests = [(1, 2), 2, [1, 8]]
def test_nls(self):
## Setup
md_feat = (
gr.Model()
>> gr.cp_function(fun=lambda x: x[0] * x[1] + x[2], var=3, out=1,)
>> gr.cp_bounds(x0=[-1, +1], x2=[0, 0])
>> gr.cp_marginals(x1=dict(dist="norm", loc=0, scale=1))
)
md_const = (
gr.Model()
>> gr.cp_function(fun=lambda x: x[0], var=1, out=1)
>> gr.cp_bounds(x0=(-1, +1))
)
df_response = md_feat >> gr.ev_df(
df=gr.df_make(x0=0.1, x1=[-1, -0.5, +0, +0.5, +1], x2=0)
)
df_data = df_response[["x1", "y0"]]
## Model with features
df_true = gr.df_make(x0=0.1)
df_fit = md_feat >> gr.ev_nls(df_data=df_data, append=False)
pd.testing.assert_frame_equal(
df_fit,
df_true,
check_exact=False,
check_dtype=False,
check_column_type=False,
)
## Fitting synonym
md_feat_fit = df_data >> gr.ft_nls(md=md_feat, verbose=False)
self.assertTrue(set(md_feat_fit.var) == set(["x1", "x2"]))
## Constant model
df_const = gr.df_make(x0=0)
df_fit = md_const >> gr.ev_nls(df_data=gr.df_make(y0=[-1, 0, +1]))
pd.testing.assert_frame_equal(
df_fit,
df_const,
check_exact=False,
check_dtype=False,
check_column_type=False,
)
def test_nominal(self):
"""Checks the implementation of nominal values"""
md = gr.Model() >> gr.cp_bounds(
x0=[-1, +1], x1=[0.1, np.Inf], x2=[-np.Inf, -0.1],
)
df_true = gr.df_make(x0=0.0, x1=+0.1, x2=-0.1)
df_res = gr.eval_nominal(md, df_det="nom", skip=True)
self.assertTrue(gr.df_equal(df_res, df_true))
def setUp(self):
self.md = models.make_test()
self.md_mixed = (
gr.Model()
>> gr.cp_function(fun=lambda x: x[0], var=3, out=1)
>> gr.cp_bounds(x2=(0, 1))
>> gr.cp_marginals(
x0={"dist": "uniform", "loc": 0, "scale": 1},
x1={"dist": "uniform", "loc": 0, "scale": 1},
)
>> gr.cp_copula_independence()
)
def test_opt(self):
# invariant checks
self.inv_test.md_arg(gr.eval_min, df_arg="df_start")
self.inv_test.df_arg(gr.eval_min, df_arg="df_start", acc_none="always")
md_bowl = (gr.Model("Constrained bowl") >> gr.cp_function(
fun=lambda x: x[0]**2 + x[1]**2,
var=["x", "y"],
out=["f"],
) >> gr.cp_function(
fun=lambda x: (x[0] + x[1] + 1),
var=["x", "y"],
out=["g1"],
) >> gr.cp_function(
fun=lambda x: -(-x[0] + x[1] - np.sqrt(2 / 10)),
var=["x", "y"],
out=["g2"],
) >> gr.cp_bounds(
x=(-1, +1),
y=(-1, +1),
))
df_res = md_bowl >> gr.ev_min(
out_min="f",
out_geq=["g1"],
out_leq=["g2"],
)
# Check result
self.assertTrue(abs(df_res.x[0] + np.sqrt(1 / 20)) < 1e-6)
self.assertTrue(abs(df_res.y[0] - np.sqrt(1 / 20)) < 1e-6)
# Check errors for violated invariants
with self.assertRaises(ValueError):
gr.eval_min(md_bowl, out_min="FALSE")
with self.assertRaises(ValueError):
gr.eval_min(md_bowl, out_min="f", out_geq=["FALSE"])
with self.assertRaises(ValueError):
gr.eval_min(md_bowl, out_min="f", out_eq=["FALSE"])
# Test multiple restarts
df_multi = gr.eval_min(
md_bowl,
out_min="f",
out_geq=["g1"],
out_leq=["g2"],
n_restart=2,
)
self.assertTrue(df_multi.shape[0] == 2)
def test_nls(self):
## Ground-truth model
c_true = 2
a_true = 1
md_true = (gr.Model() >> gr.cp_function(
fun=lambda x: a_true * np.exp(x[0] * c_true) + x[1],
var=["x", "epsilon"],
out=["y"],
) >> gr.cp_marginals(epsilon={
"dist": "norm",
"loc": 0,
"scale": 0.5
}) >> gr.cp_copula_independence())
df_data = md_true >> gr.ev_monte_carlo(
n=5, seed=101, df_det=gr.df_make(x=[0, 1, 2, 3, 4]))
## Model to fit
md_param = (gr.Model() >> gr.cp_function(
fun=lambda x: x[2] * np.exp(x[0] * x[1]),
var=["x", "c", "a"],
out=["y"]) >> gr.cp_bounds(c=[0, 4], a=[0.1, 2.0]))
## Fit the model
md_fit = df_data >> gr.ft_nls(
md=md_param,
verbose=False,
uq_method="linpool",
)
## Unidentifiable model throws warning
# -------------------------
md_unidet = (gr.Model() >> gr.cp_function(
fun=lambda x: x[2] / x[3] * np.exp(x[0] * x[1]),
var=["x", "c", "a", "z"],
out=["y"],
) >> gr.cp_bounds(c=[0, 4], a=[0.1, 2.0], z=[0, 1]))
with self.assertWarns(RuntimeWarning):
gr.fit_nls(
df_data,
md=md_unidet,
uq_method="linpool",
)
## True parameters in wide confidence region
# -------------------------
alpha = 1e-3
self.assertTrue(
(md_fit.density.marginals["c"].q(alpha / 2) <= c_true)
and (c_true <= md_fit.density.marginals["c"].q(1 - alpha / 2)))
self.assertTrue(
(md_fit.density.marginals["a"].q(alpha / 2) <= a_true)
and (a_true <= md_fit.density.marginals["a"].q(1 - alpha / 2)))
## Model with fixed parameter
# -------------------------
md_fixed = (gr.Model() >> gr.cp_function(
fun=lambda x: x[2] * np.exp(x[0] * x[1]),
var=["x", "c", "a"],
out=["y"]) >> gr.cp_bounds(c=[0, 4], a=[1, 1]))
md_fit_fixed = df_data >> gr.ft_nls(
md=md_fixed, verbose=False, uq_method="linpool")
# Test that fixed model can evaluate successfully
gr.eval_monte_carlo(md_fit_fixed, n=1, df_det="nom")
## Trajectory model
# -------------------------
md_base = models.make_trajectory_linear()
md_fit = data.df_trajectory_windowed >> gr.ft_nls(
md=md_base, method="SLSQP", tol=1e-3)
df_tmp = md_fit >> gr.ev_nominal(df_det="nom")
def test_empty_functions(self):
md = gr.Model() >> gr.cp_bounds(x=[-1, +1])
with self.assertRaises(ValueError):
gr.eval_nominal(md)
def test_nls(self):
## Setup
md_feat = (gr.Model() >> gr.cp_function(
fun=lambda x: x[0] * x[1] + x[2],
var=3,
out=1,
) >> gr.cp_bounds(x0=[-1, +1], x2=[0, 0]) >>
gr.cp_marginals(x1=dict(dist="norm", loc=0, scale=1)))
md_const = (gr.Model() >> gr.cp_function(
fun=lambda x: x[0], var=1, out=1) >> gr.cp_bounds(x0=(-1, +1)))
df_response = md_feat >> gr.ev_df(
df=gr.df_make(x0=0.1, x1=[-1, -0.5, +0, +0.5, +1], x2=0))
df_data = df_response[["x1", "y0"]]
## Model with features
df_true = gr.df_make(x0=0.1)
df_fit = md_feat >> gr.ev_nls(df_data=df_data, append=False)
pd.testing.assert_frame_equal(
df_fit,
df_true,
check_exact=False,
check_dtype=False,
check_column_type=False,
)
## Fitting synonym
md_feat_fit = df_data >> gr.ft_nls(md=md_feat, verbose=False)
self.assertTrue(set(md_feat_fit.var) == set(["x1", "x2"]))
## Constant model
df_const = gr.df_make(x0=0)
df_fit = md_const >> gr.ev_nls(df_data=gr.df_make(y0=[-1, 0, +1]))
pd.testing.assert_frame_equal(
df_fit,
df_const,
check_exact=False,
check_dtype=False,
check_column_type=False,
)
## Multiple restarts works
df_multi = gr.eval_nls(md_feat, df_data=df_data, n_restart=2)
self.assertTrue(df_multi.shape[0] == 2)
## Specified initial guess
df_spec = gr.eval_nls(md_feat,
df_data=df_data,
df_init=gr.df_make(x0=0.5),
append=False)
pd.testing.assert_frame_equal(
df_spec,
df_true,
check_exact=False,
check_dtype=False,
check_column_type=False,
)
# Raises if incorrect guess data
with self.assertRaises(ValueError):
gr.eval_nls(md_feat, df_data=df_data, df_init=gr.df_make(foo=0.5))
def test_contour(self):
md1 = (gr.Model() >> gr.cp_vec_function(
fun=lambda df: gr.df_make(
f=df.x**2 + df.y**2,
g=df.x + df.y,
),
var=["x", "y"],
out=["f", "g"],
) >> gr.cp_bounds(
x=(-1, +1),
y=(-1, +1),
))
md2 = (gr.Model() >> gr.cp_vec_function(
fun=lambda df: gr.df_make(f=(df.c) * df.x + (1 - df.c) * df.y, ),
var=["x", "y", "c"],
out=["f"],
) >> gr.cp_bounds(
x=(-1, +1),
y=(-1, +1),
c=(+0, +1),
))
## Basic functionality
df_res1 = (
md1 >> gr.ev_contour(
var=["x", "y"],
out=["f", "g"],
n_side=10, # Coarse, for speed
))
# Contains correct columns
self.assertTrue("x" in df_res1.columns)
self.assertTrue("y" in df_res1.columns)
df_res2 = (
md2 >> gr.ev_contour(
var=["x", "y"],
out=["f"],
df=gr.df_make(c=[0, 1]),
n_side=10, # Coarse, for speed
))
# Contains auxiliary variable
self.assertTrue("c" in df_res2.columns)
df_res3 = (
md1 >> gr.ev_contour(
var=["x", "y"],
out=["g"],
levels=dict(g=[-1, 0, +1]),
n_side=10, # Coarse, for speed
))
# Correct manual levels
self.assertTrue(set(df_res3.level) == {-1, 0, +1})
# Correct manual levels
with self.assertWarns(Warning):
df_res4 = (
md1 >> gr.ev_contour(
var=["x", "y"],
out=["g"],
levels=dict(g=[-1, 0, +1, +1e3]),
n_side=10, # Coarse, for speed
))
# Correct manual levels
self.assertTrue(set(df_res4.level) == {-1, 0, +1})
## Check assertions
# No var
with self.assertRaises(ValueError):
res = (md1 >> gr.ev_contour(out=["f"]))
# Incorrect number of inputs
with self.assertRaises(ValueError):
res = (md1 >> gr.ev_contour(var=["x", "y", "z"]))
# Unavailable inputs
with self.assertRaises(ValueError):
res = (md1 >> gr.ev_contour(var=["foo", "bar"]))
# Unsupported input
with self.assertRaises(ValueError):
res = (md2 >> gr.ev_contour(
var=["x", "y"],
out=["f"],
))
with self.assertRaises(ValueError):
res = (md2 >> gr.ev_contour(
var=["x", "y"], out=["f"], df=gr.df_make(foo=1)))
# Zero bound width
with self.assertRaises(ValueError):
res = (
gr.Model() >> gr.cp_vec_function(
fun=lambda df: gr.df_make(
f=df.x**2 + df.y**2,
g=df.x + df.y,
),
var=["x", "y"],
out=["f", "g"],
) >> gr.cp_bounds(
x=(0, 0),
y=(-1, +1),
) >> gr.ev_contour(
var=["x", "y"],
out=["f", "g"],
n_side=10, # Coarse, for speed
))
# No out
with self.assertRaises(ValueError):
res = (md1 >> gr.ev_contour(var=["x", "y"]))
# Output unavailable
with self.assertRaises(ValueError):
res = (md1 >> gr.ev_contour(var=["x", "y"], out=["foo"]))
def test_nls(self):
## Ground-truth model
c_true = 2
a_true = 1
md_true = (gr.Model() >> gr.cp_function(
fun=lambda x: a_true * np.exp(x[0] * c_true) + x[1],
var=["x", "epsilon"],
out=["y"],
) >> gr.cp_marginals(epsilon={
"dist": "norm",
"loc": 0,
"scale": 0.5
}) >> gr.cp_copula_independence())
df_data = md_true >> gr.ev_sample(
n=5, seed=101, df_det=gr.df_make(x=[0, 1, 2, 3, 4]))
## Model to fit
md_param = (gr.Model() >> gr.cp_function(
fun=lambda x: x[2] * np.exp(x[0] * x[1]),
var=["x", "c", "a"],
out=["y"]) >> gr.cp_bounds(c=[0, 4], a=[0.1, 2.0]))
## Fit the model
md_fit = df_data >> gr.ft_nls(
md=md_param,
verbose=False,
uq_method="linpool",
)
## Unidentifiable model throws warning
# -------------------------
md_unidet = (gr.Model() >> gr.cp_function(
fun=lambda x: x[2] / x[3] * np.exp(x[0] * x[1]),
var=["x", "c", "a", "z"],
out=["y"],
) >> gr.cp_bounds(c=[0, 4], a=[0.1, 2.0], z=[0, 1]))
with self.assertWarns(RuntimeWarning):
gr.fit_nls(
df_data,
md=md_unidet,
uq_method="linpool",
)
## True parameters in wide confidence region
# -------------------------
alpha = 1e-3
self.assertTrue(
(md_fit.density.marginals["c"].q(alpha / 2) <= c_true)
and (c_true <= md_fit.density.marginals["c"].q(1 - alpha / 2)))
self.assertTrue(
(md_fit.density.marginals["a"].q(alpha / 2) <= a_true)
and (a_true <= md_fit.density.marginals["a"].q(1 - alpha / 2)))
## Model with fixed parameter
# -------------------------
md_fixed = (gr.Model() >> gr.cp_function(
fun=lambda x: x[2] * np.exp(x[0] * x[1]),
var=["x", "c", "a"],
out=["y"]) >> gr.cp_bounds(c=[0, 4], a=[1, 1]))
md_fit_fixed = df_data >> gr.ft_nls(
md=md_fixed, verbose=False, uq_method="linpool")
# Test that fixed model can evaluate successfully
gr.eval_sample(md_fit_fixed, n=1, df_det="nom")
## Trajectory model
# -------------------------
md_base = models.make_trajectory_linear()
md_fit = data.df_trajectory_windowed >> gr.ft_nls(
md=md_base, method="SLSQP", tol=1e-3)
df_tmp = md_fit >> gr.ev_nominal(df_det="nom")
## Select output for fitting
# -------------------------
# Split model has inconsistent "true" parameter value
md_split = (gr.Model("Split") >> gr.cp_vec_function(
fun=lambda df: gr.df_make(
f=1 * df.c * df.x,
g=2 * df.c * df.x,
),
var=["c", "x"],
out=["f", "g"],
) >> gr.cp_bounds(
x=(-1, +1),
c=(-1, +1),
))
df_split = (gr.df_make(x=gr.linspace(-1, +1, 100)) >> gr.tf_mutate(
f=X.x, g=X.x))
# Fitting both outputs: cannot achieve mse ~= 0
df_both = (df_split >> gr.ft_nls(md_split, out=["f", "g"]) >>
gr.ev_df(df_split >> gr.tf_rename(f_t=X.f, g_t=X.g)) >>
gr.tf_summarize(
mse_f=gr.mse(X.f, X.f_t),
mse_g=gr.mse(X.g, X.g_t),
))
self.assertTrue(df_both.mse_f[0] > 0)
self.assertTrue(df_both.mse_g[0] > 0)
# Fitting "f" only
df_f = (df_split >> gr.ft_nls(md_split, out=["f"]) >>
gr.ev_df(df_split >> gr.tf_rename(f_t=X.f, g_t=X.g)) >>
gr.tf_summarize(
mse_f=gr.mse(X.f, X.f_t),
mse_g=gr.mse(X.g, X.g_t),
))
self.assertTrue(df_f.mse_f[0] < 1e-16)
self.assertTrue(df_f.mse_g[0] > 0)
# Fitting "g" only
df_g = (df_split >> gr.ft_nls(md_split, out=["g"]) >>
gr.ev_df(df_split >> gr.tf_rename(f_t=X.f, g_t=X.g)) >>
gr.tf_summarize(
mse_f=gr.mse(X.f, X.f_t),
mse_g=gr.mse(X.g, X.g_t),
))
self.assertTrue(df_g.mse_f[0] > 0)
self.assertTrue(df_g.mse_g[0] < 1e-16)