From 278d7c2cfae81cfa58f937839da973c04919cc57 Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Thu, 27 Jun 2024 15:49:42 +0200
Subject: [PATCH] Fix for tex issue
---
src/bayesvalidrox/surrogate_models/engine.py | 2 +-
tests/test_BayesInference.py | 2 +
tests/test_Engine.py | 190 ++++++++++---------
3 files changed, 99 insertions(+), 95 deletions(-)
diff --git a/src/bayesvalidrox/surrogate_models/engine.py b/src/bayesvalidrox/surrogate_models/engine.py
index c7a6d00a3..086022ab0 100644
--- a/src/bayesvalidrox/surrogate_models/engine.py
+++ b/src/bayesvalidrox/surrogate_models/engine.py
@@ -1262,7 +1262,7 @@ class Engine:
itrNumber //= self.ExpDesign.n_new_samples
tau2 = -(n_max_samples - initNSamples - 1) / np.log(1e-8)
- exploration_weight = signal.exponential(n_max_samples - initNSamples,
+ exploration_weight = signal.windows.exponential(n_max_samples - initNSamples,
0, tau2, False)[itrNumber]
elif tradeoff_scheme == 'adaptive':
diff --git a/tests/test_BayesInference.py b/tests/test_BayesInference.py
index fa6ab205d..c31a9830e 100644
--- a/tests/test_BayesInference.py
+++ b/tests/test_BayesInference.py
@@ -40,6 +40,8 @@ from bayesvalidrox.bayes_inference.mcmc import MCMC
from bayesvalidrox.bayes_inference.bayes_inference import BayesInference
from bayesvalidrox.bayes_inference.bayes_inference import _logpdf, _kernel_rbf
+import matplotlib as mpl
+mpl.rcParams.update(mpl.rcParamsDefault)
#%% Test _logpdf
diff --git a/tests/test_Engine.py b/tests/test_Engine.py
index 14fda116b..5e9c5efe7 100644
--- a/tests/test_Engine.py
+++ b/tests/test_Engine.py
@@ -33,6 +33,8 @@ import numpy as np
import pandas as pd
import sys
+sys.path.append("src/")
+
from bayesvalidrox.surrogate_models.inputs import Input
from bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
from bayesvalidrox.surrogate_models.surrogate_models import MetaModel
@@ -40,8 +42,8 @@ from bayesvalidrox.pylink.pylink import PyLinkForwardModel as PL
from bayesvalidrox.surrogate_models.engine import Engine
from bayesvalidrox.surrogate_models.engine import hellinger_distance, logpdf, subdomain
-sys.path.append("src/")
-
+import matplotlib as mpl
+mpl.rcParams.update(mpl.rcParamsDefault)
#%% Test Engine constructor
@@ -975,95 +977,95 @@ def test_choose_next_sample_latin_BODMI_() -> None:
sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
engine.choose_next_sample(sigma2=sigma2Dict, var=expdes.util_func)
-
-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)
+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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