From 6ded2812b91a058ea861066d29eaa2ec4eb1bdc2 Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Wed, 10 Jul 2024 13:55:54 +0200
Subject: [PATCH] Moved relevant tests to sequential design from engine

---
 Outputs_Bayes_None_Calib/emcee_sampler.h5 | Bin 701520 -> 5973376 bytes
 src/bayesvalidrox.egg-info/SOURCES.txt    |   2 +
 tests/test_SequentialDesign.py            | 704 +++++++++++++++++++++-
 tests/test_engine.py                      | 699 ---------------------
 4 files changed, 704 insertions(+), 701 deletions(-)

diff --git a/Outputs_Bayes_None_Calib/emcee_sampler.h5 b/Outputs_Bayes_None_Calib/emcee_sampler.h5
index 20c29ce06ef054b40b9091828f8b2bffe8d63972..fb19ab42bc20ce5c07901fed541bc29760bfc3d9 100644
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diff --git a/src/bayesvalidrox.egg-info/SOURCES.txt b/src/bayesvalidrox.egg-info/SOURCES.txt
index 1095272ea..5fdca7b71 100644
--- a/src/bayesvalidrox.egg-info/SOURCES.txt
+++ b/src/bayesvalidrox.egg-info/SOURCES.txt
@@ -32,6 +32,7 @@ src/bayesvalidrox/surrogate_models/inputs.py
 src/bayesvalidrox/surrogate_models/orthogonal_matching_pursuit.py
 src/bayesvalidrox/surrogate_models/reg_fast_ard.py
 src/bayesvalidrox/surrogate_models/reg_fast_laplace.py
+src/bayesvalidrox/surrogate_models/sequential_design.py
 src/bayesvalidrox/surrogate_models/surrogate_models.py
 tests/test_BayesInference.py
 tests/test_BayesModelComparison.py
@@ -42,6 +43,7 @@ tests/test_Input.py
 tests/test_InputSpace.py
 tests/test_MCMC.py
 tests/test_MetaModel.py
+tests/test_SequentialDesign.py
 tests/test_engine.py
 tests/test_polyconst.py
 tests/test_pylink.py
\ No newline at end of file
diff --git a/tests/test_SequentialDesign.py b/tests/test_SequentialDesign.py
index 3c9c5fb73..573ac4259 100644
--- a/tests/test_SequentialDesign.py
+++ b/tests/test_SequentialDesign.py
@@ -5,7 +5,7 @@ Test the SequentialDesign class for bayesvalidrox
 Tests are available for the following functions
     logpdf               - x
     subdomain            - x
-Engine:
+SequentialDesign:
     start_seqdesign         
     choose_next_sample
         plotter
@@ -36,11 +36,711 @@ sys.path.append("../src/")
 
 from bayesvalidrox.surrogate_models.inputs import Input
 from bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
+from bayesvalidrox.surrogate_models.sequential_design import SequentialDesign
 from bayesvalidrox.surrogate_models.surrogate_models import MetaModel
 from bayesvalidrox.pylink.pylink import PyLinkForwardModel as PL
 from bayesvalidrox.surrogate_models.engine import Engine
 from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
 
+#%% Test Engine.tradeoff_weights
+
+def test_tradeoff_weights_None() -> None:
+    """
+    Tradeoff weights with no scheme
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    mm = MetaModel(inp)
+    expdes = ExpDesigns(inp)
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    weights = seqDes.tradeoff_weights(None, [[0], [1]], {'Z': [[0.4], [0.5]]})
+    assert weights[0] == 0 and weights[1] == 1
+
+
+def test_tradeoff_weights_equal() -> None:
+    """
+    Tradeoff weights with 'equal' scheme
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    mm = MetaModel(inp)
+    expdes = ExpDesigns(inp)
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    weights = seqDes.tradeoff_weights('equal', [[0], [1]], {'Z': [[0.4], [0.5]]})
+    assert weights[0] == 0.5 and weights[1] == 0.5
+
+
+def test_tradeoff_weights_epsdecr() -> None:
+    """
+    Tradeoff weights with 'epsilon-decreasing' scheme
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    mm = MetaModel(inp)
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 3
+    expdes.X = np.array([[0], [1]])
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    weights = seqDes.tradeoff_weights('epsilon-decreasing', expdes.X, {'Z': [[0.4], [0.5]]})
+    assert weights[0] == 1.0 and weights[1] == 0.0
+
+
+def test_tradeoff_weights_adaptive() -> None:
+    """
+    Tradeoff weights with 'adaptive' scheme
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    mm = MetaModel(inp)
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 3
+    expdes.X = np.array([[0], [1]])
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    weights = seqDes.tradeoff_weights('adaptive', expdes.X, {'Z': [[0.4], [0.5]]})
+    assert weights[0] == 0.5 and weights[1] == 0.5
+
+
+def test_tradeoff_weights_adaptiveit1() -> None:
+    """
+    Tradeoff weights with 'adaptive' scheme for later iteration (not the first)
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    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.fit(expdes.X, expdes.Y)
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes._y_hat_prev, _ = mm.eval_metamodel(samples=np.array([[0.1], [0.2], [0.6]]))
+    seqDes.tradeoff_weights('adaptive', expdes.X, expdes.Y)
+
+
+
+#%% Test Engine.choose_next_sample
+
+def test_choose_next_sample() -> None:
+    """
+    Chooses new sample using all standard settings (exploration, random, space-filling,...)
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.explore_method = 'random'
+    expdes.exploit_method = 'Space-filling'
+    expdes.util_func = 'Space-filling'
+
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    x, nan = seqDes.choose_next_sample()
+    assert x.shape[0] == 1 and x.shape[1] == 1
+
+
+def test_choose_next_sample_da_spaceparallel() -> None:
+    """
+    Chooses new sample using dual-annealing and space-filling, parallel=True
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.explore_method = 'dual-annealing'
+    expdes.exploit_method = 'Space-filling'
+    expdes.util_func = 'Space-filling'
+
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    seqDes.parallel = True
+    x, nan = seqDes.choose_next_sample()
+    assert x.shape[0] == 1 and x.shape[1] == 1
+
+
+def test_choose_next_sample_da_spacenoparallel() -> None:
+    """
+    Chooses new sample using dual-annealing and space-filling, parallel = False
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.explore_method = 'dual-annealing'
+    expdes.exploit_method = 'Space-filling'
+    expdes.util_func = 'Space-filling'
+
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    seqDes.parallel = False
+    x, nan = seqDes.choose_next_sample()
+    assert x.shape[0] == 1 and x.shape[1] == 1
+
+
+def test_choose_next_sample_loo_space() -> None:
+    """
+    Chooses new sample using all LOO-CV and space-filling
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.explore_method = 'LOO-CV'
+    expdes.exploit_method = 'Space-filling'
+    expdes.util_func = 'Space-filling'
+
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    x, nan = seqDes.choose_next_sample()
+    assert x.shape[0] == 1 and x.shape[1] == 1
+
+
+def test_choose_next_sample_vor_space() -> None:
+    """
+    Chooses new sample using voronoi, space-filling
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.explore_method = 'voronoi'
+    expdes.exploit_method = 'Space-filling'
+    expdes.util_func = 'Space-filling'
+
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    x, nan = seqDes.choose_next_sample()
+    assert x.shape[0] == 1 and x.shape[1] == 1
+
+
+def test_choose_next_sample_latin_space() -> None:
+    """
+    Chooses new sample using all latin-hypercube, space-filling
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.explore_method = 'latin-hypercube'
+    expdes.exploit_method = 'Space-filling'
+    expdes.util_func = 'Space-filling'
+
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    x, nan = seqDes.choose_next_sample()
+    assert x.shape[0] == 1 and x.shape[1] == 1
+
+
+def test_choose_next_sample_latin_alphD() -> None:
+    """
+    Chooses new sample using all latin-hypercube, alphabetic (D)
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.explore_method = 'latin-hypercube'
+    expdes.exploit_method = 'alphabetic'
+    expdes.util_func = 'D-Opt'
+
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    x, nan = seqDes.choose_next_sample(var=expdes.util_func)
+    assert x.shape[0] == 1 and x.shape[1] == 1
+
+
+def test_choose_next_sample_latin_alphK() -> None:
+    """
+    Chooses new sample using all latin-hypercube, alphabetic (K)
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.explore_method = 'latin-hypercube'
+    expdes.exploit_method = 'alphabetic'
+    expdes.util_func = 'K-Opt'
+
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    x, nan = seqDes.choose_next_sample(var=expdes.util_func)
+    assert x.shape[0] == 1 and x.shape[1] == 1
+
+
+def test_choose_next_sample_latin_alphA() -> None:
+    """
+    Chooses new sample using all latin-hypercube, alphabetic (A)
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.explore_method = 'latin-hypercube'
+    expdes.exploit_method = 'alphabetic'
+    expdes.util_func = 'A-Opt'
+
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    x, nan = seqDes.choose_next_sample(var=expdes.util_func)
+    assert x.shape[0] == 1 and x.shape[1] == 1
+
+
+def test_choose_next_sample_latin_VarALM() -> None:
+    """
+    Chooses new sample using all latin-hypercube, VarDesign (ALM)
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.tradeoff_scheme = 'equal'
+    expdes.explore_method = 'latin-hypercube'
+    expdes.exploit_method = 'VarOptDesign'
+    expdes.util_func = 'ALM'
+
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    x, nan = seqDes.choose_next_sample(var=expdes.util_func)
+    assert x.shape[0] == 1 and x.shape[1] == 1
+
+
+def test_choose_next_sample_latin_VarEIGF() -> None:
+    """
+    Chooses new sample using all latin-hypercube, VarDesign (EIGF)
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.tradeoff_scheme = 'equal'
+    expdes.explore_method = 'latin-hypercube'
+    expdes.exploit_method = 'VarOptDesign'
+    expdes.util_func = 'EIGF'
+
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    x, nan = seqDes.choose_next_sample(var=expdes.util_func)
+    assert x.shape[0] == 1 and x.shape[1] == 1
+
+
+# TODO: shape mismatch for the total score
+if 0:
+    def test_choose_next_sample_latin_VarLOO() -> None:
+        """
+        Chooses new sample using all latin-hypercube, VarDesign (LOOCV)
+        """
+        inp = Input()
+        inp.add_marginals()
+        inp.Marginals[0].dist_type = 'normal'
+        inp.Marginals[0].parameters = [0, 1]
+        expdes = ExpDesigns(inp)
+        expdes.n_init_samples = 2
+        expdes.n_max_samples = 4
+        expdes.X = np.array([[0], [1], [0.5]])
+        expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+        expdes.tradeoff_scheme = 'equal'
+        expdes.explore_method = 'latin-hypercube'
+        expdes.exploit_method = 'VarOptDesign'
+        expdes.util_func = 'LOOCV'
+    
+        mm = MetaModel(inp)
+        mm.fit(expdes.X, expdes.Y)
+        expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+        mod = PL()
+        seqDes = SequentialDesign(mm, mod, expdes)
+        seqDes.out_names = ['Z']
+        x, nan = seqDes.choose_next_sample(var=expdes.util_func)
+        assert x.shape[0] == 1 and x.shape[1] == 1
+
+
+def test_choose_next_sample_latin_BODMI() -> None:
+    """
+    Chooses new sample using all latin-hypercube, BayesOptDesign (MI)
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.sampling_method = 'user'
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.tradeoff_scheme = 'equal'
+    expdes.explore_method = 'latin-hypercube'
+    expdes.exploit_method = 'BayesOptDesign'
+    expdes.util_func = 'MI'
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    seqDes.observations = {'Z': np.array([0.45])}
+    # seqDes.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
+    sigma2Dict = {'Z': np.array([0.05])}
+    sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+    seqDes.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
+
+
+def test_choose_next_sample_latin_BODALC() -> None:
+    """
+    Chooses new sample using all latin-hypercube, BayesOptDesign (ALC)
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.tradeoff_scheme = 'equal'
+    expdes.explore_method = 'latin-hypercube'
+    expdes.exploit_method = 'BayesOptDesign'
+    expdes.util_func = 'ALC'
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    seqDes.observations = {'Z': np.array([0.45])}
+    # seqDes.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
+    sigma2Dict = {'Z': np.array([0.05])}
+    sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+    seqDes.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
+
+
+def test_choose_next_sample_latin_BODDKL() -> None:
+    """
+    Chooses new sample using all latin-hypercube, BayesOptDesign (DKL)
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.tradeoff_scheme = 'equal'
+    expdes.explore_method = 'latin-hypercube'
+    expdes.exploit_method = 'BayesOptDesign'
+    expdes.util_func = 'DKL'
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    seqDes.observations = {'Z': np.array([0.45])}
+    # seqDes.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
+    sigma2Dict = {'Z': np.array([0.05])}
+    sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+    seqDes.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
+
+
+def test_choose_next_sample_latin_BODDPP() -> None:
+    """
+    Chooses new sample using all latin-hypercube, BayesOptDesign (DPP)
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.tradeoff_scheme = 'equal'
+    expdes.explore_method = 'latin-hypercube'
+    expdes.exploit_method = 'BayesOptDesign'
+    expdes.util_func = 'DPP'
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    seqDes.observations = {'Z': np.array([0.45])}
+    # seqDes.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
+    sigma2Dict = {'Z': np.array([0.05])}
+    sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+    seqDes.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
+
+
+def test_choose_next_sample_latin_BODAPP() -> None:
+    """
+    Chooses new sample using all latin-hypercube, BayesOptDesign (APP)
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.tradeoff_scheme = 'equal'
+    expdes.explore_method = 'latin-hypercube'
+    expdes.exploit_method = 'BayesOptDesign'
+    expdes.util_func = 'APP'
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    seqDes.observations = {'Z': np.array([0.45])}
+    # seqDes.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
+    sigma2Dict = {'Z': np.array([0.05])}
+    sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+    seqDes.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
+
+
+def test_choose_next_sample_latin_BODMI_() -> None:
+    """
+    Chooses new sample using all latin-hypercube, BayesOptDesign (MI)
+    """
+    inp = Input()
+    inp.add_marginals()
+    inp.Marginals[0].dist_type = 'normal'
+    inp.Marginals[0].parameters = [0, 1]
+    expdes = ExpDesigns(inp)
+    expdes.n_init_samples = 2
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0], [1], [0.5]])
+    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+    expdes.tradeoff_scheme = 'equal'
+    expdes.explore_method = 'latin-hypercube'
+    expdes.exploit_method = 'BayesOptDesign'
+    expdes.util_func = 'MI'
+    mm = MetaModel(inp)
+    mm.fit(expdes.X, expdes.Y)
+    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+    mod = PL()
+    seqDes = SequentialDesign(mm, mod, expdes)
+    seqDes.out_names = ['Z']
+    seqDes.observations = {'Z': np.array([0.45])}
+    # seqDes.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
+    sigma2Dict = {'Z': np.array([0.05])}
+    sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+    seqDes.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
+
+if 0:
+    def test_choose_next_sample_latin_BADBME() -> None:
+        """
+        Chooses new sample using all latin-hypercube, BayesActDesign (BME)
+        """
+        inp = Input()
+        inp.add_marginals()
+        inp.Marginals[0].dist_type = 'normal'
+        inp.Marginals[0].parameters = [0, 1]
+        expdes = ExpDesigns(inp)
+        expdes.n_init_samples = 2
+        expdes.n_max_samples = 4
+        expdes.X = np.array([[0], [1], [0.5]])
+        expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+        expdes.tradeoff_scheme = 'equal'
+        expdes.explore_method = 'latin-hypercube'
+        expdes.exploit_method = 'BayesActDesign'
+        expdes.util_func = 'BME'
+        mm = MetaModel(inp)
+        mm.fit(expdes.X, expdes.Y)
+        expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+        mod = PL()
+        seqDes = SequentialDesign(mm, mod, expdes)
+        seqDes.out_names = ['Z']
+        seqDes.observations = {'Z': np.array([0.45])}
+        # seqDes.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
+        sigma2Dict = {'Z': np.array([0.05])}
+        sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+        seqDes.n_obs = 1
+        seqDes.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
+    
+    
+    def test_choose_next_sample_latin_BADDKL() -> None:
+        """
+        Chooses new sample using all latin-hypercube, BayesActDesign (DKL)
+        """
+        inp = Input()
+        inp.add_marginals()
+        inp.Marginals[0].dist_type = 'normal'
+        inp.Marginals[0].parameters = [0, 1]
+        expdes = ExpDesigns(inp)
+        expdes.n_init_samples = 2
+        expdes.n_max_samples = 4
+        expdes.X = np.array([[0], [1], [0.5]])
+        expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+        expdes.tradeoff_scheme = 'equal'
+        expdes.explore_method = 'latin-hypercube'
+        expdes.exploit_method = 'BayesActDesign'
+        expdes.util_func = 'DKL'
+        mm = MetaModel(inp)
+        mm.fit(expdes.X, expdes.Y)
+        expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+        mod = PL()
+        seqDes = SequentialDesign(mm, mod, expdes)
+        seqDes.out_names = ['Z']
+        seqDes.observations = {'Z': np.array([0.45])}
+        # seqDes.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
+        sigma2Dict = {'Z': np.array([0.05])}
+        sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+        seqDes.n_obs = 1
+        seqDes.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
+    
+    
+    def test_choose_next_sample_latin_BADinfEntropy() -> None:
+        """
+        Chooses new sample using all latin-hypercube, BayesActDesign (infEntropy)
+        """
+        inp = Input()
+        inp.add_marginals()
+        inp.Marginals[0].dist_type = 'normal'
+        inp.Marginals[0].parameters = [0, 1]
+        expdes = ExpDesigns(inp)
+        expdes.n_init_samples = 2
+        expdes.n_max_samples = 4
+        expdes.X = np.array([[0], [1], [0.5]])
+        expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
+        expdes.tradeoff_scheme = 'equal'
+        expdes.explore_method = 'latin-hypercube'
+        expdes.exploit_method = 'BayesActDesign'
+        expdes.util_func = 'infEntropy'
+        mm = MetaModel(inp)
+        mm.fit(expdes.X, expdes.Y)
+        expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+        mod = PL()
+        seqDes = SequentialDesign(mm, mod, expdes)
+        seqDes.out_names = ['Z']
+        seqDes.observations = {'Z': np.array([0.45])}
+        # seqDes.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
+        sigma2Dict = {'Z': np.array([0.05])}
+        sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+        seqDes.n_obs = 1
+        seqDes.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
 
 
 
@@ -72,7 +772,7 @@ if __name__ == '__main__':
     mod.Output.names = ['Z']
     mod.n_obs = 1
 
-    engine = Engine(mm, mod, expdes)
+    seqDes = SequentialDesign(mm, mod, expdes)
 
     sigma2Dict = {'Z': np.array([0.05])}
     sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
diff --git a/tests/test_engine.py b/tests/test_engine.py
index 5e9c5efe7..780d64417 100644
--- a/tests/test_engine.py
+++ b/tests/test_engine.py
@@ -370,702 +370,3 @@ def test_subdomain() -> None:
     """
     subdomain([(0, 1), (0, 1)], 2)
 
-
-#%% Test Engine.tradeoff_weights
-
-def test_tradeoff_weights_None() -> None:
-    """
-    Tradeoff weights with no scheme
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    mm = MetaModel(inp)
-    expdes = ExpDesigns(inp)
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    weights = engine.tradeoff_weights(None, [[0], [1]], {'Z': [[0.4], [0.5]]})
-    assert weights[0] == 0 and weights[1] == 1
-
-
-def test_tradeoff_weights_equal() -> None:
-    """
-    Tradeoff weights with 'equal' scheme
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    mm = MetaModel(inp)
-    expdes = ExpDesigns(inp)
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    weights = engine.tradeoff_weights('equal', [[0], [1]], {'Z': [[0.4], [0.5]]})
-    assert weights[0] == 0.5 and weights[1] == 0.5
-
-
-def test_tradeoff_weights_epsdecr() -> None:
-    """
-    Tradeoff weights with 'epsilon-decreasing' scheme
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    mm = MetaModel(inp)
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 3
-    expdes.X = np.array([[0], [1]])
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    weights = engine.tradeoff_weights('epsilon-decreasing', expdes.X, {'Z': [[0.4], [0.5]]})
-    assert weights[0] == 1.0 and weights[1] == 0.0
-
-
-def test_tradeoff_weights_adaptive() -> None:
-    """
-    Tradeoff weights with 'adaptive' scheme
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    mm = MetaModel(inp)
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 3
-    expdes.X = np.array([[0], [1]])
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    weights = engine.tradeoff_weights('adaptive', expdes.X, {'Z': [[0.4], [0.5]]})
-    assert weights[0] == 0.5 and weights[1] == 0.5
-
-
-def test_tradeoff_weights_adaptiveit1() -> None:
-    """
-    Tradeoff weights with 'adaptive' scheme for later iteration (not the first)
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    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.fit(expdes.X, expdes.Y)
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine._y_hat_prev, _ = mm.eval_metamodel(samples=np.array([[0.1], [0.2], [0.6]]))
-    engine.tradeoff_weights('adaptive', expdes.X, expdes.Y)
-
-
-#%% Test Engine.choose_next_sample
-
-def test_choose_next_sample() -> None:
-    """
-    Chooses new sample using all standard settings (exploration, random, space-filling,...)
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.explore_method = 'random'
-    expdes.exploit_method = 'Space-filling'
-    expdes.util_func = 'Space-filling'
-
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    x, nan = engine.choose_next_sample()
-    assert x.shape[0] == 1 and x.shape[1] == 1
-
-
-def test_choose_next_sample_da_spaceparallel() -> None:
-    """
-    Chooses new sample using dual-annealing and space-filling, parallel=True
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.explore_method = 'dual-annealing'
-    expdes.exploit_method = 'Space-filling'
-    expdes.util_func = 'Space-filling'
-
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    engine.parallel = True
-    x, nan = engine.choose_next_sample()
-    assert x.shape[0] == 1 and x.shape[1] == 1
-
-
-def test_choose_next_sample_da_spacenoparallel() -> None:
-    """
-    Chooses new sample using dual-annealing and space-filling, parallel = False
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.explore_method = 'dual-annealing'
-    expdes.exploit_method = 'Space-filling'
-    expdes.util_func = 'Space-filling'
-
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    engine.parallel = False
-    x, nan = engine.choose_next_sample()
-    assert x.shape[0] == 1 and x.shape[1] == 1
-
-
-def test_choose_next_sample_loo_space() -> None:
-    """
-    Chooses new sample using all LOO-CV and space-filling
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.explore_method = 'LOO-CV'
-    expdes.exploit_method = 'Space-filling'
-    expdes.util_func = 'Space-filling'
-
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    x, nan = engine.choose_next_sample()
-    assert x.shape[0] == 1 and x.shape[1] == 1
-
-
-def test_choose_next_sample_vor_space() -> None:
-    """
-    Chooses new sample using voronoi, space-filling
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.explore_method = 'voronoi'
-    expdes.exploit_method = 'Space-filling'
-    expdes.util_func = 'Space-filling'
-
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    x, nan = engine.choose_next_sample()
-    assert x.shape[0] == 1 and x.shape[1] == 1
-
-
-def test_choose_next_sample_latin_space() -> None:
-    """
-    Chooses new sample using all latin-hypercube, space-filling
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.explore_method = 'latin-hypercube'
-    expdes.exploit_method = 'Space-filling'
-    expdes.util_func = 'Space-filling'
-
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    x, nan = engine.choose_next_sample()
-    assert x.shape[0] == 1 and x.shape[1] == 1
-
-
-def test_choose_next_sample_latin_alphD() -> None:
-    """
-    Chooses new sample using all latin-hypercube, alphabetic (D)
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.explore_method = 'latin-hypercube'
-    expdes.exploit_method = 'alphabetic'
-    expdes.util_func = 'D-Opt'
-
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    x, nan = engine.choose_next_sample(var=expdes.util_func)
-    assert x.shape[0] == 1 and x.shape[1] == 1
-
-
-def test_choose_next_sample_latin_alphK() -> None:
-    """
-    Chooses new sample using all latin-hypercube, alphabetic (K)
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.explore_method = 'latin-hypercube'
-    expdes.exploit_method = 'alphabetic'
-    expdes.util_func = 'K-Opt'
-
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    x, nan = engine.choose_next_sample(var=expdes.util_func)
-    assert x.shape[0] == 1 and x.shape[1] == 1
-
-
-def test_choose_next_sample_latin_alphA() -> None:
-    """
-    Chooses new sample using all latin-hypercube, alphabetic (A)
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.explore_method = 'latin-hypercube'
-    expdes.exploit_method = 'alphabetic'
-    expdes.util_func = 'A-Opt'
-
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    x, nan = engine.choose_next_sample(var=expdes.util_func)
-    assert x.shape[0] == 1 and x.shape[1] == 1
-
-
-def test_choose_next_sample_latin_VarALM() -> None:
-    """
-    Chooses new sample using all latin-hypercube, VarDesign (ALM)
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.tradeoff_scheme = 'equal'
-    expdes.explore_method = 'latin-hypercube'
-    expdes.exploit_method = 'VarOptDesign'
-    expdes.util_func = 'ALM'
-
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    x, nan = engine.choose_next_sample(var=expdes.util_func)
-    assert x.shape[0] == 1 and x.shape[1] == 1
-
-
-def test_choose_next_sample_latin_VarEIGF() -> None:
-    """
-    Chooses new sample using all latin-hypercube, VarDesign (EIGF)
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.tradeoff_scheme = 'equal'
-    expdes.explore_method = 'latin-hypercube'
-    expdes.exploit_method = 'VarOptDesign'
-    expdes.util_func = 'EIGF'
-
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    x, nan = engine.choose_next_sample(var=expdes.util_func)
-    assert x.shape[0] == 1 and x.shape[1] == 1
-
-
-# TODO: shape mismatch for the total score
-if 0:
-    def test_choose_next_sample_latin_VarLOO() -> None:
-        """
-        Chooses new sample using all latin-hypercube, VarDesign (LOOCV)
-        """
-        inp = Input()
-        inp.add_marginals()
-        inp.Marginals[0].dist_type = 'normal'
-        inp.Marginals[0].parameters = [0, 1]
-        expdes = ExpDesigns(inp)
-        expdes.n_init_samples = 2
-        expdes.n_max_samples = 4
-        expdes.X = np.array([[0], [1], [0.5]])
-        expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-        expdes.tradeoff_scheme = 'equal'
-        expdes.explore_method = 'latin-hypercube'
-        expdes.exploit_method = 'VarOptDesign'
-        expdes.util_func = 'LOOCV'
-    
-        mm = MetaModel(inp)
-        mm.fit(expdes.X, expdes.Y)
-        expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-        mod = PL()
-        engine = Engine(mm, mod, expdes)
-        engine.out_names = ['Z']
-        x, nan = engine.choose_next_sample(var=expdes.util_func)
-        assert x.shape[0] == 1 and x.shape[1] == 1
-
-
-def test_choose_next_sample_latin_BODMI() -> None:
-    """
-    Chooses new sample using all latin-hypercube, BayesOptDesign (MI)
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.sampling_method = 'user'
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.tradeoff_scheme = 'equal'
-    expdes.explore_method = 'latin-hypercube'
-    expdes.exploit_method = 'BayesOptDesign'
-    expdes.util_func = 'MI'
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    engine.observations = {'Z': np.array([0.45])}
-    # engine.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
-    sigma2Dict = {'Z': np.array([0.05])}
-    sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
-    engine.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
-
-
-def test_choose_next_sample_latin_BODALC() -> None:
-    """
-    Chooses new sample using all latin-hypercube, BayesOptDesign (ALC)
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.tradeoff_scheme = 'equal'
-    expdes.explore_method = 'latin-hypercube'
-    expdes.exploit_method = 'BayesOptDesign'
-    expdes.util_func = 'ALC'
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    engine.observations = {'Z': np.array([0.45])}
-    # engine.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
-    sigma2Dict = {'Z': np.array([0.05])}
-    sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
-    engine.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
-
-
-def test_choose_next_sample_latin_BODDKL() -> None:
-    """
-    Chooses new sample using all latin-hypercube, BayesOptDesign (DKL)
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.tradeoff_scheme = 'equal'
-    expdes.explore_method = 'latin-hypercube'
-    expdes.exploit_method = 'BayesOptDesign'
-    expdes.util_func = 'DKL'
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    engine.observations = {'Z': np.array([0.45])}
-    # engine.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
-    sigma2Dict = {'Z': np.array([0.05])}
-    sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
-    engine.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
-
-
-def test_choose_next_sample_latin_BODDPP() -> None:
-    """
-    Chooses new sample using all latin-hypercube, BayesOptDesign (DPP)
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.tradeoff_scheme = 'equal'
-    expdes.explore_method = 'latin-hypercube'
-    expdes.exploit_method = 'BayesOptDesign'
-    expdes.util_func = 'DPP'
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    engine.observations = {'Z': np.array([0.45])}
-    # engine.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
-    sigma2Dict = {'Z': np.array([0.05])}
-    sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
-    engine.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
-
-
-def test_choose_next_sample_latin_BODAPP() -> None:
-    """
-    Chooses new sample using all latin-hypercube, BayesOptDesign (APP)
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.tradeoff_scheme = 'equal'
-    expdes.explore_method = 'latin-hypercube'
-    expdes.exploit_method = 'BayesOptDesign'
-    expdes.util_func = 'APP'
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    engine.observations = {'Z': np.array([0.45])}
-    # engine.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
-    sigma2Dict = {'Z': np.array([0.05])}
-    sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
-    engine.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
-
-
-def test_choose_next_sample_latin_BODMI_() -> None:
-    """
-    Chooses new sample using all latin-hypercube, BayesOptDesign (MI)
-    """
-    inp = Input()
-    inp.add_marginals()
-    inp.Marginals[0].dist_type = 'normal'
-    inp.Marginals[0].parameters = [0, 1]
-    expdes = ExpDesigns(inp)
-    expdes.n_init_samples = 2
-    expdes.n_max_samples = 4
-    expdes.X = np.array([[0], [1], [0.5]])
-    expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-    expdes.tradeoff_scheme = 'equal'
-    expdes.explore_method = 'latin-hypercube'
-    expdes.exploit_method = 'BayesOptDesign'
-    expdes.util_func = 'MI'
-    mm = MetaModel(inp)
-    mm.fit(expdes.X, expdes.Y)
-    expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-    mod = PL()
-    engine = Engine(mm, mod, expdes)
-    engine.out_names = ['Z']
-    engine.observations = {'Z': np.array([0.45])}
-    # engine.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
-    sigma2Dict = {'Z': np.array([0.05])}
-    sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
-    engine.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
-
-if 0:
-    def test_choose_next_sample_latin_BADBME() -> None:
-        """
-        Chooses new sample using all latin-hypercube, BayesActDesign (BME)
-        """
-        inp = Input()
-        inp.add_marginals()
-        inp.Marginals[0].dist_type = 'normal'
-        inp.Marginals[0].parameters = [0, 1]
-        expdes = ExpDesigns(inp)
-        expdes.n_init_samples = 2
-        expdes.n_max_samples = 4
-        expdes.X = np.array([[0], [1], [0.5]])
-        expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-        expdes.tradeoff_scheme = 'equal'
-        expdes.explore_method = 'latin-hypercube'
-        expdes.exploit_method = 'BayesActDesign'
-        expdes.util_func = 'BME'
-        mm = MetaModel(inp)
-        mm.fit(expdes.X, expdes.Y)
-        expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-        mod = PL()
-        engine = Engine(mm, mod, expdes)
-        engine.out_names = ['Z']
-        engine.observations = {'Z': np.array([0.45])}
-        # engine.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
-        sigma2Dict = {'Z': np.array([0.05])}
-        sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
-        engine.n_obs = 1
-        engine.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
-    
-    
-    def test_choose_next_sample_latin_BADDKL() -> None:
-        """
-        Chooses new sample using all latin-hypercube, BayesActDesign (DKL)
-        """
-        inp = Input()
-        inp.add_marginals()
-        inp.Marginals[0].dist_type = 'normal'
-        inp.Marginals[0].parameters = [0, 1]
-        expdes = ExpDesigns(inp)
-        expdes.n_init_samples = 2
-        expdes.n_max_samples = 4
-        expdes.X = np.array([[0], [1], [0.5]])
-        expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-        expdes.tradeoff_scheme = 'equal'
-        expdes.explore_method = 'latin-hypercube'
-        expdes.exploit_method = 'BayesActDesign'
-        expdes.util_func = 'DKL'
-        mm = MetaModel(inp)
-        mm.fit(expdes.X, expdes.Y)
-        expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-        mod = PL()
-        engine = Engine(mm, mod, expdes)
-        engine.out_names = ['Z']
-        engine.observations = {'Z': np.array([0.45])}
-        # engine.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
-        sigma2Dict = {'Z': np.array([0.05])}
-        sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
-        engine.n_obs = 1
-        engine.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
-    
-    
-    def test_choose_next_sample_latin_BADinfEntropy() -> None:
-        """
-        Chooses new sample using all latin-hypercube, BayesActDesign (infEntropy)
-        """
-        inp = Input()
-        inp.add_marginals()
-        inp.Marginals[0].dist_type = 'normal'
-        inp.Marginals[0].parameters = [0, 1]
-        expdes = ExpDesigns(inp)
-        expdes.n_init_samples = 2
-        expdes.n_max_samples = 4
-        expdes.X = np.array([[0], [1], [0.5]])
-        expdes.Y = {'Z': [[0.4], [0.5], [0.45]]}
-        expdes.tradeoff_scheme = 'equal'
-        expdes.explore_method = 'latin-hypercube'
-        expdes.exploit_method = 'BayesActDesign'
-        expdes.util_func = 'infEntropy'
-        mm = MetaModel(inp)
-        mm.fit(expdes.X, expdes.Y)
-        expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
-        mod = PL()
-        engine = Engine(mm, mod, expdes)
-        engine.out_names = ['Z']
-        engine.observations = {'Z': np.array([0.45])}
-        # engine.choose_next_sample(sigma2=None, n_candidates=5, var='DKL')
-        sigma2Dict = {'Z': np.array([0.05])}
-        sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
-        engine.n_obs = 1
-        engine.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
-- 
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