def test_sample(self):
## Accurate
n = 2
df_res = gr.eval_sample(self.md, n=n, df_det="nom", seed=101)
np.random.seed(101)
df_truth = pd.DataFrame({"x0": np.random.random(n)})
df_truth["y0"] = df_truth["x0"]
self.assertTrue(gr.df_equal(df_res, df_truth))
## Rounding
df_round = gr.eval_sample(self.md, n=n + 0.1, df_det="nom", seed=101)
self.assertTrue(gr.df_equal(df_round, df_truth))
## Pass-through
df_pass = gr.eval_sample(self.md,
n=n,
skip=True,
df_det="nom",
seed=101)
self.assertTrue(gr.df_equal(df_pass[["x0"]], df_truth[["x0"]]))
## Optional observation index
df_idx = gr.eval_sample(
self.md_mixed,
n=n,
df_det=gr.df_make(x0=[-1, 0, 1]),
seed=101,
index="idx",
)
self.assertTrue(len(set(df_idx.idx)) == n)
def test_monte_carlo(self):
## Accurate
n = 2
df_res = gr.eval_monte_carlo(self.md, n=n, df_det="nom", seed=101)
np.random.seed(101)
df_truth = pd.DataFrame({"x0": np.random.random(n)})
df_truth["y0"] = df_truth["x0"]
self.assertTrue(gr.df_equal(df_res, df_truth))
## Rounding
df_round = gr.eval_monte_carlo(self.md,
n=n + 0.1,
df_det="nom",
seed=101)
self.assertTrue(gr.df_equal(df_round, df_truth))
## Pass-through
df_pass = gr.eval_monte_carlo(self.md,
n=n,
skip=True,
df_det="nom",
seed=101)
self.assertTrue(gr.df_equal(df_pass[["x0"]], df_truth[["x0"]]))
def test_grad_fd(self):
"""Checks the FD code
"""
## Accuracy
df_grad = gr.eval_grad_fd(
self.model_2d, df_base=self.df_2d_nominal, append=False
)
self.assertTrue(np.allclose(df_grad[self.df_2d_grad.columns], self.df_2d_grad))
## Subset
df_grad_sub = gr.eval_grad_fd(
self.model_2d, df_base=self.df_2d_nominal, var=["x"], append=False
)
self.assertTrue(set(df_grad_sub.columns) == set(["Df_Dx", "Dg_Dx"]))
## Flags
md_test = (
gr.Model()
>> gr.cp_function(fun=lambda x: x[0] + x[1] ** 2, var=2, out=1)
>> gr.cp_marginals(x0={"dist": "norm", "loc": 0, "scale": 1})
)
df_base = pd.DataFrame(dict(x0=[0, 1], x1=[0, 1]))
## Multiple base points
df_true = pd.DataFrame(dict(Dy0_Dx0=[1, 1], Dy0_Dx1=[0, 2]))
df_rand = gr.eval_grad_fd(md_test, df_base=df_base, var="rand", append=False)
self.assertTrue(gr.df_equal(df_true[["Dy0_Dx0"]], df_rand, close=True))
df_det = gr.eval_grad_fd(md_test, df_base=df_base, var="det", append=False)
self.assertTrue(gr.df_equal(df_true[["Dy0_Dx1"]], df_det, close=True))
def test_outer(self):
df = pd.DataFrame(dict(x=[1, 2]))
df_outer = pd.DataFrame(dict(y=[3, 4]))
df_true = pd.DataFrame(dict(x=[1, 2, 1, 2], y=[3, 3, 4, 4]))
df_res = gr.tran_outer(df, df_outer)
df_piped = df >> gr.tf_outer(df_outer)
pd.testing.assert_frame_equal(
df_true,
df_res,
check_exact=False,
check_dtype=False,
check_column_type=False,
)
pd.testing.assert_frame_equal(
df_piped,
df_res,
check_exact=False,
check_dtype=False,
check_column_type=False,
)
# Empty cases
gr.df_equal(
df,
gr.tran_outer(pd.DataFrame(), df_outer=df)
)
gr.df_equal(
df,
gr.tran_outer(df, df_outer=pd.DataFrame())
)
def test_pca(self):
df_test = pd.DataFrame(dict(x0=[1, 2, 3], x1=[1, 2, 3]))
df_offset = pd.DataFrame(dict(x0=[1, 2, 3], x1=[3, 4, 5]))
df_scaled = pd.DataFrame(dict(x0=[1, 2, 3], x1=[1, 3, 5]))
df_true = pd.DataFrame(
dict(
lam=[2, 0],
x0=[1 / np.sqrt(2), 1 / np.sqrt(2)],
x1=[1 / np.sqrt(2), -1 / np.sqrt(2)],
)
)
## Check correctness
df_pca = df_test >> gr.tf_pca()
self.assertTrue(np.allclose(df_true.lam, df_pca.lam))
## Offset data should not affect results
df_pca_off = df_offset >> gr.tf_pca()
self.assertTrue(gr.df_equal(df_pca, df_pca_off))
## Check standardized form
df_pca_scaled = df_test >> gr.tf_pca(standardize=True)
self.assertTrue(gr.df_equal(df_pca, df_pca_scaled))
def test_conservative(self):
## Accuracy
df_res = gr.eval_conservative(self.model_2d, quantiles=[0.1, 0.1])
self.assertTrue(gr.df_equal(self.df_2d_qe, df_res, close=True))
## Invariant checks
self.inv_test.md_arg(gr.eval_conservative, df_arg="df_det")
self.inv_test.df_arg(gr.eval_conservative,
df_arg="df_det",
shortcut=True,
acc_none="var_det")
## Repeat scalar value
self.assertTrue(
gr.df_equal(
self.df_2d_qe,
gr.eval_conservative(self.model_2d, quantiles=0.1),
close=True,
))
## Pass-through
self.assertTrue(
gr.df_equal(
self.df_2d_qe.drop(["f", "g"], axis=1),
gr.eval_conservative(self.model_2d, quantiles=0.1, skip=True),
close=True,
))
def test_conversion(self):
df_pr_true = pd.DataFrame(dict(x=[0.5], y=[0.5]))
df_sp_true = pd.DataFrame(dict(x=[0.0], y=[0.0]))
df_pr_res = self.density.sample2pr(df_sp_true)
df_sp_res = self.density.pr2sample(df_pr_true)
self.assertTrue(gr.df_equal(df_pr_true, df_pr_res))
self.assertTrue(gr.df_equal(df_sp_true, df_sp_res))
def test_explode(self):
df_base = gr.df_make(x=[1, 2], y=[[3, 4], [5, 6]])
df_str = gr.df_make(x=[1, 2], y=[["3", "4"], ["5", "6"]])
df_true = gr.df_make(x=[1, 1, 2, 2], y=[3, 4, 5, 6])
df_res = df_base >> gr.tf_explode(X.y)
df_res_s = df_base >> gr.tf_explode(X.y, convert=True)
self.assertTrue(gr.df_equal(df_true, df_res, close=True))
self.assertTrue(gr.df_equal(df_true, df_res_s, close=True))
def test_dropna(self):
df = gr.df_make(x=[1.0, 2.0, 3.0],
y=[1.0, np.nan, 3.0],
z=[1.0, 2.0, np.nan])
df_true_default = gr.df_make(x=[1.0], y=[1.0], z=[1.0])
df_true_y = gr.df_make(x=[1.0, 3.0], y=[1.0, 3.0], z=[1.0, np.nan])
df_res_default = df >> gr.tf_dropna()
df_res_y = df >> gr.tf_dropna(subset=["y"])
self.assertTrue(gr.df_equal(df_true_default, df_res_default))
self.assertTrue(gr.df_equal(df_true_y, df_res_y))
def test_nominal(self):
"""Checks the nominal evaluation is accurate
"""
df_res = gr.eval_nominal(self.model_2d)
## Accurate
self.assertTrue(gr.df_equal(self.df_2d_nominal, df_res))
## Pass-through
self.assertTrue(
gr.df_equal(
self.df_2d_nominal.drop(["f", "g"], axis=1),
gr.eval_nominal(self.model_2d, skip=True),
))
def test_transforms(self):
## Setup
df_corr = pd.DataFrame(dict(var1=["x"], var2=["y"], corr=[0.5]))
Sigma_h = np.linalg.cholesky(np.array([[1.0, 0.5], [0.5, 1.0]]))
md = (
gr.Model() >> gr.cp_marginals(x=dict(dist="norm", loc=0, scale=1),
y=dict(dist="norm", loc=0, scale=1))
>> gr.cp_copula_gaussian(df_corr=df_corr))
## Copula and marginals have same var_rand order
self.assertTrue(
list(md.density.marginals) == md.density.copula.var_rand)
## Transforms invariant
z = np.array([0, 0])
x = md.z2x(z)
zp = md.x2z(x)
self.assertTrue(np.all(z == zp))
df_z = gr.df_make(x=0.0, y=0.0)
df_x = md.norm2rand(df_z)
df_zp = md.rand2norm(df_x)
self.assertTrue(gr.df_equal(df_z, df_zp))
## Jacobian accurate
dxdz_fd = np.zeros((2, 2))
dxdz_fd[0, :] = (md.z2x(z + np.array([h, 0])) - md.z2x(z)) / h
dxdz_fd[1, :] = (md.z2x(z + np.array([0, h])) - md.z2x(z)) / h
dxdz_p = md.dxdz(z)
self.assertTrue(np.allclose(dxdz_fd, dxdz_p))
def test_gp(self):
## Fit routine creates usable model
md_fit = fit.fit_gp(self.df_smooth, md=self.md_smooth)
df_res = gr.eval_df(md_fit, self.df_smooth[self.md_smooth.var])
## GP provides std estimates
self.assertTrue("y_sd" in df_res.columns)
## GP is an interpolation
self.assertTrue(
gr.df_equal(
df_res[["x", "y_mean",
"z_mean"]].rename({
"y_mean": "y",
"z_mean": "z"
},
axis=1),
self.df_smooth,
close=True,
))
## Fit copies model data
self.assertTrue(set(md_fit.var) == set(self.md_smooth.var))
self.assertTrue(
set(md_fit.out) == set(
map(lambda s: s + "_mean", self.md_smooth.out)).union(
set(map(lambda s: s + "_sd", self.md_smooth.out))))
def test_lolo(self):
## Fit routine creates usable model
md_fit = fit.fit_lolo(
self.df_tree,
md=self.md_tree,
max_depth=1, # True tree is a stump
seed=102,
)
df_res = gr.eval_df(md_fit, self.df_tree[self.md_tree.var])
## lolo seems to interpolate middle values; check ends only
self.assertTrue(
gr.df_equal(
df_res[["y", "z"]].iloc[[0, 1, -2, -1]],
self.df_tree[["y", "z"]].iloc[[0, 1, -2, -1]],
close=True,
precision=1,
)
)
## Fit copies model data, plus predictive std
self.assertTrue(set(md_fit.var) == set(self.md_tree.var))
self.assertTrue(
set(md_fit.out)
== set(self.md_tree.out + list(map(lambda s: s + "_std", self.md_tree.out)))
)
def test_rf(self):
## Fit routine creates usable model
md_fit = fit.fit_rf(
self.df_tree,
md=self.md_tree,
max_depth=1, # True tree is a stump
seed=101,
)
df_res = gr.eval_df(md_fit, self.df_tree[self.md_tree.var])
## RF can approximately recover a tree; check ends only
self.assertTrue(
gr.df_equal(
df_res[["y_mean", "z_mean"]].iloc[[0, 1, -2, -1]],
self.df_tree[["y", "z"]].iloc[[0, 1, -2, -1]] >> gr.tf_rename(
y_mean="y", z_mean="z"),
close=True,
precision=1,
))
## Fit copies model data
self.assertTrue(set(md_fit.var) == set(self.md_tree.var))
self.assertTrue(
set(md_fit.out) == set(map(lambda s: s + "_mean",
self.md_tree.out)))
def test_ev_nominal(self):
"""Check ev_nominal()
"""
df_res = gr.eval_nominal(self.model_default, df_det="nom")
self.assertTrue(
gr.df_equal(df_res,
self.model_default >> gr.ev_nominal(df_det="nom")))
def test_ev_df(self):
"""Check ev_df()
"""
df_res = gr.eval_df(self.model_default, df=self.df_test)
self.assertTrue(
gr.df_equal(df_res,
self.model_default >> gr.ev_df(df=self.df_test)))
def test_ev_sinews(self):
"""Check ev_sinews()
"""
df_res = gr.eval_sinews(self.model_default, seed=101, df_det="nom")
self.assertTrue(
gr.df_equal(
df_res,
self.model_default >> gr.ev_sinews(seed=101, df_det="nom")))
def test_ev_grad_fd(self):
"""Check ev_grad_fd()
"""
df_res = gr.eval_grad_fd(self.model_default, df_base=self.df_test)
self.assertTrue(
gr.df_equal(
df_res,
self.model_default >> gr.ev_grad_fd(df_base=self.df_test)))
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 test_drop_out(self):
"""Checks that output column names are properly dropped"""
md = gr.Model() >> gr.cp_function(lambda x: x[0] + 1, var=1, out=1)
df_in = gr.df_make(x0=[0, 1, 2], y0=[0, 1, 2])
df_true = gr.df_make(x0=[0, 1, 2], y0=[1, 2, 3])
df_res = md >> gr.ev_df(df=df_in)
self.assertTrue(gr.df_equal(df_res, df_true, close=True))
def test_tran_md(self):
md = models.make_test()
## Check for identical responses
df = gr.df_make(x0=1, x1=1, x2=1)
df_ev = gr.eval_df(md, df=df)
df_tf = gr.tran_md(df, md=md)
self.assertTrue(gr.df_equal(df_ev, df_tf))
def test_tran_poly(self):
df = gr.df_make(x=[0.0, 1.0, 0.0], y=[0.0, 0.0, 1.0], z=[1.0, 2.0, 3.0],)
df_true = df.copy()
df_true["1"] = [1.0, 1.0, 1.0]
df_true["x^2"] = [0.0, 1.0, 0.0]
df_true["x y"] = [0.0, 0.0, 0.0]
df_true["y^2"] = [0.0, 0.0, 1.0]
df_res = gr.tran_poly(df, var=["x", "y"], degree=2, keep=True)
self.assertTrue(gr.df_equal(df_true, df_res[df_true.columns]))
def test_lhs(self):
## Accurate
n = 2
df_res = ev.eval_lhs(self.md_2d, n=n, df_det="nom", seed=101)
np.random.seed(101)
df_truth = pd.DataFrame(data=lhs(2, samples=n), columns=["x0", "x1"])
df_truth["y0"] = df_truth["x0"]
self.assertTrue(gr.df_equal(df_res, df_truth))
## Rounding
df_round = ev.eval_lhs(self.md_2d, n=n + 0.1, df_det="nom", seed=101)
self.assertTrue(gr.df_equal(df_round, df_truth))
## Pass-through
df_pass = ev.eval_lhs(self.md_2d, n=n, skip=True, df_det="nom", seed=101)
self.assertTrue(gr.df_equal(df_pass, df_truth[["x0", "x1"]]))
def test_nominal(self):
"""Checks the nominal evaluation is accurate
"""
df_res = gr.eval_nominal(self.model_2d)
## Accurate
self.assertTrue(gr.df_equal(self.df_2d_nominal, df_res))
## Invariant checks
self.inv_test.md_arg(gr.eval_nominal, df_arg="df_det")
self.inv_test.df_arg(gr.eval_nominal,
df_arg="df_det",
shortcut=True,
acc_none="var_det")
## Pass-through
self.assertTrue(
gr.df_equal(
self.df_2d_nominal.drop(["f", "g"], axis=1),
gr.eval_nominal(self.model_2d, skip=True),
))
def test_conservative(self):
## Accuracy
df_res = gr.eval_conservative(self.model_2d, quantiles=[0.1, 0.1])
self.assertTrue(gr.df_equal(self.df_2d_qe, df_res, close=True))
## Repeat scalar value
self.assertTrue(
gr.df_equal(
self.df_2d_qe,
gr.eval_conservative(self.model_2d, quantiles=0.1),
close=True,
))
## Pass-through
self.assertTrue(
gr.df_equal(
self.df_2d_qe.drop(["f", "g"], axis=1),
gr.eval_conservative(self.model_2d, quantiles=0.1, skip=True),
close=True,
))
def test_df_make(self):
# Check correctness
df_true = pd.DataFrame(dict(x=[0, 1], y=[0, 0], z=[1, 1]))
df_res = gr.df_make(x=[0, 1], y=[0], z=1)
self.assertTrue(gr.df_equal(df_true, df_res))
# Check for mismatch
with self.assertRaises(ValueError):
gr.df_make(x=[1, 2, 3], y=[1, 2])
# Catch an intention operator
with self.assertRaises(ValueError):
gr.df_make(y=DF.x)
def test_gp(self):
## Fit routine creates usable model
md_fit = fit.fit_gp(self.df_smooth, md=self.md_smooth)
df_res = gr.eval_df(md_fit, self.df_smooth[self.md_smooth.var])
## GP provides std estimates
# self.assertTrue("y_std" in df_res.columns)
## GP is an interpolation
self.assertTrue(gr.df_equal(df_res, self.df_smooth, close=True))
## Fit copies model data
self.assertTrue(set(md_fit.var) == set(self.md_smooth.var))
self.assertTrue(set(md_fit.out) == set(self.md_smooth.out))
def test_bootstrap(self):
df_stang = data.df_stang
df_stang._meta = "foo"
def tran_stats(df):
val = df.select_dtypes(include="number").values
means = np.mean(val, axis=0)
stds = np.std(val, axis=0)
# Check metadata propagation
self.assertTrue(df._meta == "foo")
return pd.DataFrame(
data={
"var": df.select_dtypes(include="number").columns,
"mean": means,
"std": stds,
})
df_res = gr.tran_bootstrap(df_stang,
tran=tran_stats,
n_boot=3e0,
n_sub=3e0,
seed=101)
df_sel = gr.tran_bootstrap(df_stang,
tran=tran_stats,
n_boot=3e0,
n_sub=3e0,
seed=101,
col_sel=["mean"])
df_piped = df_stang >> gr.tf_bootstrap(
tran=tran_stats, n_boot=3e0, n_sub=3e0, seed=101)
## Test output shape
self.assertTrue(
set(df_res.columns) == set([
"var", "mean", "mean_lo", "mean_up", "std", "std_lo", "std_up"
]))
self.assertTrue(df_res.shape[0] == 4)
self.assertTrue(
set(df_sel.columns) == set(
["var", "mean", "mean_lo", "mean_up", "std"]))
self.assertTrue(df_sel.shape[0] == 4)
## Test pipe
self.assertTrue(gr.df_equal(df_res, df_piped))
def test_ria(self):
## Test accuracy
df_res = self.md >> gr.ev_form_ria(df_det="nom", limits=["g"])
self.assertTrue(np.allclose(df_res["beta_g"], [self.beta_true], atol=1e-3))
## Test MPP mapped correctly
df_mpp = self.md_log >> gr.ev_form_ria(df_det="nom", limits=["g"])
self.assertTrue(
gr.df_equal(df_mpp, self.df_mpp[["x", "y", "beta_g"]], close=True)
)
## Test flatten
df_beam = self.md_beam >> gr.ev_form_ria(
df_det="nom", limits=["g_stress", "g_disp"], append=False
)
self.assertTrue(df_beam.shape[0] == 1)
def test_pma(self):
## Test accuracy
df_res = self.md >> gr.ev_form_pma(df_det="nom", betas=dict(g=self.beta_true))
self.assertTrue(np.allclose(df_res["g"], [0], atol=1e-3))
## Test MPP mapped correctly
df_mpp = self.md_log >> gr.ev_form_pma(df_det="nom", betas=dict(g=1.0))
self.assertTrue(
gr.df_equal(df_mpp, self.df_mpp[["x", "y", "g"]], close=True)
)
## Test flatten
df_beam = self.md_beam >> gr.ev_form_pma(
df_det="nom", betas={"g_stress": 3, "g_disp": 3}, append=False,
)
self.assertTrue(df_beam.shape[0] == 1)
