diff --git a/Outputs_Bayes_None_Calib/emcee_sampler.h5 b/Outputs_Bayes_None_Calib/emcee_sampler.h5
index 20c29ce06ef054b40b9091828f8b2bffe8d63972..fb19ab42bc20ce5c07901fed541bc29760bfc3d9 100644
Binary files a/Outputs_Bayes_None_Calib/emcee_sampler.h5 and b/Outputs_Bayes_None_Calib/emcee_sampler.h5 differ
diff --git a/src/bayesvalidrox.egg-info/SOURCES.txt b/src/bayesvalidrox.egg-info/SOURCES.txt
index 1095272eae0e9115435ccb2a9c66f21241477066..5fdca7b71e2a4f6396d9876cd57f95a7690f566d 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 3c9c5fb731c0e53946d284669a04f7fa7736c455..573ac42596d558fae2c1fad73be75b1aadc8fb5a 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 5e9c5efe768a43246c0618e66ee00e941f57cabc..780d64417f888a8d94e8fcce145f60d55561b437 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)