From d5b857868b76aaa6d990d79769a0f15a379cb950 Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Mon, 26 Aug 2024 11:06:37 +0200
Subject: [PATCH] [tests] More updates for the tests
---
src/bayesvalidrox/surrogate_models/engine.py | 2 +-
tests/test_SequentialDesign.py | 96 ++++++++++----------
2 files changed, 49 insertions(+), 49 deletions(-)
diff --git a/src/bayesvalidrox/surrogate_models/engine.py b/src/bayesvalidrox/surrogate_models/engine.py
index a6d2447c6..c9a19b8aa 100644
--- a/src/bayesvalidrox/surrogate_models/engine.py
+++ b/src/bayesvalidrox/surrogate_models/engine.py
@@ -947,7 +947,7 @@ class Engine:
distHellinger = 0.0
# Bayesian inference with Emulator only for 2D problem
- if post_snapshot and self.MetaModel.n_params == 2 and not idx % 5:
+ if post_snapshot and self.MetaModel.ndim == 2 and not idx % 5:
bayes = BayesInference(self)
bayes.emulator = True
diff --git a/tests/test_SequentialDesign.py b/tests/test_SequentialDesign.py
index bcffc5d05..0f90425ef 100644
--- a/tests/test_SequentialDesign.py
+++ b/tests/test_SequentialDesign.py
@@ -134,10 +134,10 @@ def test_tradeoff_weights_adaptiveit1() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
mm = PCE(inp)
mm.fit(expdes.X, expdes.Y)
mod = PL()
@@ -161,10 +161,10 @@ def test_choose_next_sample() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.explore_method = 'random'
expdes.exploit_method = 'Space-filling'
expdes.util_func = 'Space-filling'
@@ -191,10 +191,10 @@ def test_choose_next_sample_da_spaceparallel() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.explore_method = 'dual-annealing'
expdes.exploit_method = 'Space-filling'
expdes.util_func = 'Space-filling'
@@ -222,10 +222,10 @@ def test_choose_next_sample_da_spacenoparallel() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.explore_method = 'dual-annealing'
expdes.exploit_method = 'Space-filling'
expdes.util_func = 'Space-filling'
@@ -253,10 +253,10 @@ def test_choose_next_sample_loo_space() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.explore_method = 'LOO-CV'
expdes.exploit_method = 'Space-filling'
expdes.util_func = 'Space-filling'
@@ -283,10 +283,10 @@ def test_choose_next_sample_vor_space() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.explore_method = 'voronoi'
expdes.exploit_method = 'Space-filling'
expdes.util_func = 'Space-filling'
@@ -318,10 +318,10 @@ def test_choose_next_sample_latin_space() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'Space-filling'
expdes.util_func = 'Space-filling'
@@ -348,10 +348,10 @@ def test_choose_next_sample_latin_alphD() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'alphabetic'
expdes.util_func = 'D-Opt'
@@ -378,10 +378,10 @@ def test_choose_next_sample_latin_alphK() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'alphabetic'
expdes.util_func = 'K-Opt'
@@ -408,10 +408,10 @@ def test_choose_next_sample_latin_alphA() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'alphabetic'
expdes.util_func = 'A-Opt'
@@ -438,10 +438,10 @@ def test_choose_next_sample_latin_VarALM() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.tradeoff_scheme = 'equal'
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'VarOptDesign'
@@ -469,10 +469,10 @@ def test_choose_next_sample_latin_VarEIGF() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.tradeoff_scheme = 'equal'
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'VarOptDesign'
@@ -502,10 +502,10 @@ if 0:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.tradeoff_scheme = 'equal'
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'VarOptDesign'
@@ -534,10 +534,10 @@ def test_choose_next_sample_latin_BODMI() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.tradeoff_scheme = 'equal'
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesOptDesign'
@@ -567,10 +567,10 @@ def test_choose_next_sample_vor_BODMI() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.tradeoff_scheme = 'equal'
expdes.explore_method = 'voronoi'
expdes.exploit_method = 'BayesOptDesign'
@@ -605,10 +605,10 @@ def test_choose_next_sample_latin_BODALC() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.tradeoff_scheme = 'equal'
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesOptDesign'
@@ -638,10 +638,10 @@ def test_choose_next_sample_latin_BODDKL() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.tradeoff_scheme = 'equal'
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesOptDesign'
@@ -671,10 +671,10 @@ def test_choose_next_sample_latin_BODDPP() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.tradeoff_scheme = 'equal'
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesOptDesign'
@@ -704,10 +704,10 @@ def test_choose_next_sample_latin_BODAPP() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.tradeoff_scheme = 'equal'
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesOptDesign'
@@ -737,10 +737,10 @@ def test_choose_next_sample_latin_BODMI_() -> None:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.tradeoff_scheme = 'equal'
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesOptDesign'
@@ -770,10 +770,10 @@ if 0:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.tradeoff_scheme = 'equal'
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesActDesign'
@@ -804,10 +804,10 @@ if 0:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.tradeoff_scheme = 'equal'
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesActDesign'
@@ -838,10 +838,10 @@ if 0:
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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.tradeoff_scheme = 'equal'
expdes.explore_method = 'latin-hypercube'
expdes.exploit_method = 'BayesActDesign'
@@ -921,10 +921,10 @@ if __name__ == '__main__':
expdes = ExpDesigns(inp)
expdes.init_param_space(max_deg=1)
- 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.n_init_samples = expdes.X.shape[0]
+ expdes.n_max_samples = 4
expdes.x_values = np.array([0]) # Error in plots if this is not
mm = PCE(inp)
--
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