From 9574ae1f5f0d1c990d838e31e044950cff243c12 Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Mon, 26 Aug 2024 10:39:31 +0200
Subject: [PATCH] [tests] Use PCE instead of MetaModel in tests that need a
fitted surrogate
---
.coverage.DESKTOP-ATMEKSV.10624.XjrvRHAx | 0
.coverage.DESKTOP-ATMEKSV.22780.XWOfrNvx | 0
.coverage.DESKTOP-ATMEKSV.25100.XbtpWDFx | 0
.coverage.DESKTOP-ATMEKSV.34604.XhfDgthx | 0
.coverage.DESKTOP-ATMEKSV.37188.XIOzbGrx | 0
src/bayesvalidrox/surrogate_models/engine.py | 2 +
tests/test_BayesInference.py | 19 ++++----
tests/test_SequentialDesign.py | 49 ++++++++++----------
tests/test_engine.py | 13 +++---
9 files changed, 44 insertions(+), 39 deletions(-)
delete mode 100644 .coverage.DESKTOP-ATMEKSV.10624.XjrvRHAx
delete mode 100644 .coverage.DESKTOP-ATMEKSV.22780.XWOfrNvx
delete mode 100644 .coverage.DESKTOP-ATMEKSV.25100.XbtpWDFx
delete mode 100644 .coverage.DESKTOP-ATMEKSV.34604.XhfDgthx
delete mode 100644 .coverage.DESKTOP-ATMEKSV.37188.XIOzbGrx
diff --git a/.coverage.DESKTOP-ATMEKSV.10624.XjrvRHAx b/.coverage.DESKTOP-ATMEKSV.10624.XjrvRHAx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/.coverage.DESKTOP-ATMEKSV.22780.XWOfrNvx b/.coverage.DESKTOP-ATMEKSV.22780.XWOfrNvx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/.coverage.DESKTOP-ATMEKSV.25100.XbtpWDFx b/.coverage.DESKTOP-ATMEKSV.25100.XbtpWDFx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/.coverage.DESKTOP-ATMEKSV.34604.XhfDgthx b/.coverage.DESKTOP-ATMEKSV.34604.XhfDgthx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/.coverage.DESKTOP-ATMEKSV.37188.XIOzbGrx b/.coverage.DESKTOP-ATMEKSV.37188.XIOzbGrx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/src/bayesvalidrox/surrogate_models/engine.py b/src/bayesvalidrox/surrogate_models/engine.py
index 8720ed0f8..0c4f15263 100644
--- a/src/bayesvalidrox/surrogate_models/engine.py
+++ b/src/bayesvalidrox/surrogate_models/engine.py
@@ -655,6 +655,8 @@ class Engine:
likelihoods = 1.0
# Loop over the outputs
+ print(self.out_names)
+ print(y_hat_pce)
for idx, out in enumerate(self.out_names):
# (Meta)Model Output
diff --git a/tests/test_BayesInference.py b/tests/test_BayesInference.py
index 9048776c8..65a5e9cca 100644
--- a/tests/test_BayesInference.py
+++ b/tests/test_BayesInference.py
@@ -34,6 +34,7 @@ sys.path.append("../src/")
from bayesvalidrox.surrogate_models.inputs import Input
from bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
from bayesvalidrox.surrogate_models.surrogate_models import MetaModel
+from bayesvalidrox.surrogate_models.polynomial_chaos import PCE
from bayesvalidrox.pylink.pylink import PyLinkForwardModel as PL
from bayesvalidrox.surrogate_models.engine import Engine
from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
@@ -110,7 +111,7 @@ def test_create_inference() -> None:
expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
expdes.x_values = np.array([0]) # Error in plots if this is not available
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
@@ -258,7 +259,7 @@ def test_corr_factor_BME() -> None:
expdes.X = np.array([[0], [1], [0.5]])
expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -285,7 +286,7 @@ def test_corr_factor_BME_selectedindices() -> None:
expdes.X = np.array([[0], [1], [0.5]])
expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -570,7 +571,7 @@ def test_perform_bootstrap() -> None:
expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
expdes.x_values = np.array([0]) # Error in plots if this is not available
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.n_params = 1
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
@@ -613,7 +614,7 @@ def test_perform_bootstrap_bayesloocv() -> None:
expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
expdes.x_values = np.array([0]) # Error in plots if this is not available
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.n_params = 1
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
@@ -659,7 +660,7 @@ def create_error_model_prior() -> None:
expdes.X = np.array([[0], [1], [0.5]])
expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.n_params = 1
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
@@ -701,7 +702,7 @@ def create_error_model_posterior() -> None:
expdes.X = np.array([[0], [1], [0.5]])
expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.n_params = 1
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
@@ -750,7 +751,7 @@ def test_posterior_predictive() -> None:
expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
expdes.x_values = np.array([0]) # Error in plots if this is not available
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.n_params = 1
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
@@ -1053,7 +1054,7 @@ if __name__ == '__main__':
expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
expdes.x_values = np.array([0]) # Error in plots if this is not
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.n_params = 1
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
diff --git a/tests/test_SequentialDesign.py b/tests/test_SequentialDesign.py
index f922a77d7..bcffc5d05 100644
--- a/tests/test_SequentialDesign.py
+++ b/tests/test_SequentialDesign.py
@@ -39,6 +39,7 @@ from bayesvalidrox.surrogate_models.inputs import Input
from bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
from bayesvalidrox.surrogate_models.sequential_design import SequentialDesign, hellinger_distance, subdomain, logpdf
from bayesvalidrox.surrogate_models.surrogate_models import MetaModel
+from bayesvalidrox.surrogate_models.polynomial_chaos import PCE
from bayesvalidrox.pylink.pylink import PyLinkForwardModel as PL
from bayesvalidrox.surrogate_models.engine import Engine
from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
@@ -137,7 +138,7 @@ def test_tradeoff_weights_adaptiveit1() -> None:
expdes.n_max_samples = 4
expdes.X = np.array([[0], [1], [0.5]])
expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
mod = PL()
@@ -168,7 +169,7 @@ def test_choose_next_sample() -> None:
expdes.exploit_method = 'Space-filling'
expdes.util_func = 'Space-filling'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -198,7 +199,7 @@ def test_choose_next_sample_da_spaceparallel() -> None:
expdes.exploit_method = 'Space-filling'
expdes.util_func = 'Space-filling'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -229,7 +230,7 @@ def test_choose_next_sample_da_spacenoparallel() -> None:
expdes.exploit_method = 'Space-filling'
expdes.util_func = 'Space-filling'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -260,7 +261,7 @@ def test_choose_next_sample_loo_space() -> None:
expdes.exploit_method = 'Space-filling'
expdes.util_func = 'Space-filling'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -290,7 +291,7 @@ def test_choose_next_sample_vor_space() -> None:
expdes.exploit_method = 'Space-filling'
expdes.util_func = 'Space-filling'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -325,7 +326,7 @@ def test_choose_next_sample_latin_space() -> None:
expdes.exploit_method = 'Space-filling'
expdes.util_func = 'Space-filling'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -355,7 +356,7 @@ def test_choose_next_sample_latin_alphD() -> None:
expdes.exploit_method = 'alphabetic'
expdes.util_func = 'D-Opt'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -385,7 +386,7 @@ def test_choose_next_sample_latin_alphK() -> None:
expdes.exploit_method = 'alphabetic'
expdes.util_func = 'K-Opt'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -415,7 +416,7 @@ def test_choose_next_sample_latin_alphA() -> None:
expdes.exploit_method = 'alphabetic'
expdes.util_func = 'A-Opt'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -446,7 +447,7 @@ def test_choose_next_sample_latin_VarALM() -> None:
expdes.exploit_method = 'VarOptDesign'
expdes.util_func = 'ALM'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -477,7 +478,7 @@ def test_choose_next_sample_latin_VarEIGF() -> None:
expdes.exploit_method = 'VarOptDesign'
expdes.util_func = 'EIGF'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -510,7 +511,7 @@ if 0:
expdes.exploit_method = 'VarOptDesign'
expdes.util_func = 'LOOCV'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -541,7 +542,7 @@ def test_choose_next_sample_latin_BODMI() -> None:
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesOptDesign'
expdes.util_func = 'MI'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -574,7 +575,7 @@ def test_choose_next_sample_vor_BODMI() -> None:
expdes.explore_method = 'voronoi'
expdes.exploit_method = 'BayesOptDesign'
expdes.util_func = 'MI'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -612,7 +613,7 @@ def test_choose_next_sample_latin_BODALC() -> None:
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesOptDesign'
expdes.util_func = 'ALC'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -645,7 +646,7 @@ def test_choose_next_sample_latin_BODDKL() -> None:
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesOptDesign'
expdes.util_func = 'DKL'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -678,7 +679,7 @@ def test_choose_next_sample_latin_BODDPP() -> None:
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesOptDesign'
expdes.util_func = 'DPP'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -711,7 +712,7 @@ def test_choose_next_sample_latin_BODAPP() -> None:
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesOptDesign'
expdes.util_func = 'APP'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -744,7 +745,7 @@ def test_choose_next_sample_latin_BODMI_() -> None:
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesOptDesign'
expdes.util_func = 'MI'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -777,7 +778,7 @@ if 0:
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesActDesign'
expdes.util_func = 'BME'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -811,7 +812,7 @@ if 0:
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesActDesign'
expdes.util_func = 'DKL'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -845,7 +846,7 @@ if 0:
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesActDesign'
expdes.util_func = 'infEntropy'
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
@@ -926,7 +927,7 @@ if __name__ == '__main__':
expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
expdes.x_values = np.array([0]) # Error in plots if this is not
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.n_params = 1
mm.fit(expdes.X, expdes.Y)
expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
diff --git a/tests/test_engine.py b/tests/test_engine.py
index 02014ca92..cbb0e8a3f 100644
--- a/tests/test_engine.py
+++ b/tests/test_engine.py
@@ -39,6 +39,7 @@ sys.path.append("../src/")
from bayesvalidrox.surrogate_models.inputs import Input
from bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
from bayesvalidrox.surrogate_models.surrogate_models import MetaModel
+from bayesvalidrox.surrogate_models.polynomial_chaos import PCE
from bayesvalidrox.pylink.pylink import PyLinkForwardModel as PL
from bayesvalidrox.surrogate_models.engine import Engine
@@ -92,7 +93,7 @@ def test__error_Mean_Std_nomc() -> None:
inp.add_marginals()
inp.Marginals[0].dist_type = 'normal'
inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit([[0.0], [1.0]], {'Z': [[0.5], [0.5]]})
expdes = ExpDesigns(inp)
mod = PL()
@@ -125,7 +126,7 @@ def test__validError() -> None:
inp.add_marginals()
inp.Marginals[0].dist_type = 'normal'
inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit([[0.0], [1.0]], {'Z': [[0.5], [0.5]]})
expdes = ExpDesigns(inp)
mod = PL()
@@ -148,7 +149,7 @@ def test__BME_Calculator() -> None:
inp.add_marginals()
inp.Marginals[0].dist_type = 'normal'
inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit([[0.0], [0.5], [1.0]], {'Z': [[0.5], [0.4], [0.5]]})
expdes = ExpDesigns(inp)
expdes.generate_ED(2, max_pce_deg=1)
@@ -171,7 +172,7 @@ def test__BME_Calculator_rmse() -> None:
inp.add_marginals()
inp.Marginals[0].dist_type = 'normal'
inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit([[0.0], [0.5], [1.0]], {'Z': [[0.5], [0.4], [0.5]]})
expdes = ExpDesigns(inp)
expdes.generate_ED(2, max_pce_deg=1)
@@ -194,7 +195,7 @@ def test__BME_Calculator_lik() -> None:
inp.add_marginals()
inp.Marginals[0].dist_type = 'normal'
inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit([[0.0], [0.5], [1.0]], {'Z': [[0.5], [0.4], [0.5]]})
expdes = ExpDesigns(inp)
expdes.generate_ED(2, max_pce_deg=1)
@@ -222,7 +223,7 @@ def test__BME_Calculator_2d() -> None:
inp.add_marginals()
inp.Marginals[1].dist_type = 'normal'
inp.Marginals[1].parameters = [0, 1]
- mm = MetaModel(inp)
+ mm = PCE(inp)
mm.fit([[0.0, 0.0], [0.5, 0.1], [1.0, 0.9]], {'Z': [[0.5], [0.4], [0.5]]})
expdes = ExpDesigns(inp)
expdes.generate_ED(2, max_pce_deg=1)
--
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