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diff --git a/Outputs_Bayes_None_Calib/emcee_sampler.h5 b/Outputs_Bayes_None_Calib/emcee_sampler.h5
index 07e32bcea1754bf24449cccb9a33a415021ec432..20c29ce06ef054b40b9091828f8b2bffe8d63972 100644
Binary files a/Outputs_Bayes_None_Calib/emcee_sampler.h5 and b/Outputs_Bayes_None_Calib/emcee_sampler.h5 differ
diff --git a/examples/analytical-function/example_analytical_function.py b/examples/analytical-function/example_analytical_function.py
index 09261acba65231d3b9782d06fa10ce8c18a95085..52d8bb0042b08680255bb247c36dd9147919437f 100644
--- a/examples/analytical-function/example_analytical_function.py
+++ b/examples/analytical-function/example_analytical_function.py
@@ -25,12 +25,10 @@ matplotlib.use('agg')
# import bayesvalidrox
# Add BayesValidRox path
-sys.path.append("src/")
sys.path.append("../../src/")
-import bayesvalidrox
-from bayesvalidrox import PyLinkForwardModel
-
+from bayesvalidrox.surrogate_models.inputs import Input
+from bayesvalidrox.surrogate_models.input_space import InputSpace
from bayesvalidrox.pylink.pylink import PyLinkForwardModel
from bayesvalidrox.surrogate_models.inputs import Input
from bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
@@ -231,8 +229,9 @@ if __name__ == "__main__":
MetaModelOpts.ExpDesign = ExpDesign
engine = Engine(MetaModelOpts, Model, ExpDesign)
engine.start_engine()
- #engine.train_sequential()
engine.train_normal()
+ stop
+ engine.train_sequential()
# Load the objects
# with open(f"PCEModel_{Model.name}.pkl", "rb") as input:
diff --git a/src/bayesvalidrox/surrogate_models/engine.py b/src/bayesvalidrox/surrogate_models/engine.py
index 24aa0c0d7e71e9201215d4d7292377724e2869c6..022fdb63c04c6782f49148dac9a81ccdc68fcf35 100644
--- a/src/bayesvalidrox/surrogate_models/engine.py
+++ b/src/bayesvalidrox/surrogate_models/engine.py
@@ -200,7 +200,7 @@ class Engine:
# Prepare X samples
# For training the surrogate use ExpDesign.X_tr, ExpDesign.X is for the model to run on
ExpDesign.generate_ED(ExpDesign.n_init_samples,
- transform=True,
+ #transform=True,
max_pce_deg=np.max(MetaModel.pce_deg))
# Run simulations at X
@@ -333,7 +333,7 @@ class Engine:
pce = True
else:
pce = False
- mc_ref = True if bool(self.Model.mc_reference) else False
+ mc_ref = True if self.Model.mc_reference is not None else False
if mc_ref:
self.Model.read_observation('mc_ref')
@@ -347,14 +347,15 @@ class Engine:
output_name = self.out_names
# Setup the Sequential Design object
- self.SeqDes = SequentialDesign(self.MetaModel, self.Model, self.ExpDesign)
+ self.SeqDesign = SequentialDesign(self.MetaModel, self.Model, self.ExpDesign)
# Handle if only one UtilityFunctions is provided
if not isinstance(util_func, list):
util_func = [self.ExpDesign.util_func]
# Read observations or MCReference
- # TODO: recheck the logic in this if statement
+ # TODO: recheck the logic in this if statement
+ # ToDo: this could perhaps be moved into the Sequential Design
if (len(self.Model.observations) != 0 or self.Model.meas_file is not None) and hasattr(self.MetaModel,
'Discrepancy'):
self.observations = self.Model.read_observation()
diff --git a/src/bayesvalidrox/surrogate_models/sequential_design.py b/src/bayesvalidrox/surrogate_models/sequential_design.py
index abe3f1a13aba02d0e75efc5026e7ded13c264821..5f969c28f56cf61701da666495c332424f8b77b0 100644
--- a/src/bayesvalidrox/surrogate_models/sequential_design.py
+++ b/src/bayesvalidrox/surrogate_models/sequential_design.py
@@ -24,6 +24,9 @@ from bayesvalidrox.bayes_inference.bayes_inference import BayesInference
from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
from .exploration import Exploration
+# TODO: These first two functions are direct copies from functions in .engine.
+# Should they be called from there?
+
def logpdf(x, mean, cov):
"""
Computes the likelihood based on a multivariate normal distribution.
@@ -86,6 +89,7 @@ def subdomain(Bounds, n_new_samples):
return Subdomains
+
class SequentialDesign:
"""
Contains options for choosing the next training sample iteratively.
@@ -93,12 +97,13 @@ class SequentialDesign:
"""
def __init__(self, MetaMod, Model, ExpDes, parallel=False):
- self.MetaModel = MetaMod # Surrogate should be trained
+ self.MetaModel = MetaMod # Surrogate should be trained
self.Model = Model
self.ExpDesign = ExpDes
self.parallel = parallel
# Init other parameters
+ self.out_names = self.Model.Output.names
def start_seqdesign(self) -> None:
"""
@@ -109,8 +114,19 @@ class SequentialDesign:
None
"""
- None
+ # Read observations or MCReference
+ # TODO: recheck the logic in this if statement
+ # ToDo: this could perhaps be moved into the Sequential Design
+ if (len(self.Model.observations) != 0 or self.Model.meas_file is not None) and hasattr(self.MetaModel,
+ 'Discrepancy'):
+ self.observations = self.Model.read_observation()
+ obs_data = self.observations
+ else:
+ obs_data = []
+ # TODO: TotalSigma2 not defined if not in this else???
+ # TODO: no self.observations if in here
+ TotalSigma2 = {}
# -------------------------------------------------------------------------
def choose_next_sample(self, sigma2=None, n_candidates=5, var='DKL'):
@@ -137,8 +153,10 @@ class SequentialDesign:
Xnew : array (n_samples, n_params)
Selected new training point(s).
"""
+ self.start_seqdesign()
# Initialization
+ # TODO: check if all of these variables are necessary, or if some should be class attributes
Bounds = self.ExpDesign.bound_tuples
n_new_samples = self.ExpDesign.n_new_samples
explore_method = self.ExpDesign.explore_method
@@ -325,7 +343,6 @@ class SequentialDesign:
if not hasattr(self.ExpDesign, 'max_func_itr'):
raise AttributeError('max_func_itr not given to the experimental design')
-
# Create a sample pool for rejection sampling
# ToDo: remove from here, add only to BayesOptDesign option
MCsize = 15000
@@ -379,7 +396,7 @@ class SequentialDesign:
# Accumulate the samples
# ToDo: Stop assuming 2 sets of samples (should only be 1)
finalCandidates = np.concatenate((allCandidates, candidates), axis=0) # ToDo: Remove
- finalCandidates = np.unique(finalCandidates, axis=0) # ToDo: Remove
+ finalCandidates = np.unique(finalCandidates, axis=0) # ToDo: Remove
# Calculations take into account both exploration and exploitation
# samples without duplicates
@@ -416,7 +433,7 @@ class SequentialDesign:
# find an optimal point subset to add to the initial design by
# maximization of the utility score and taking care of NaN values
temp = totalScore.copy()
- temp[np.isnan(totalScore)] = -np.inf # Since we are maximizing
+ temp[np.isnan(totalScore)] = -np.inf # Since we are maximizing
sorted_idxtotalScore = np.argsort(temp)[::-1]
bestIdx = sorted_idxtotalScore[:n_new_samples]
@@ -728,7 +745,6 @@ class SequentialDesign:
var)
return index, -1 * U_J_d
-
# -------------------------------------------------------------------------
def util_VarBasedDesign(self, X_can, index, util_func='Entropy'):
"""
@@ -1757,4 +1773,4 @@ class SequentialDesign:
al_unique_index = np.arange(prior_samples[:self.mc_samples + n_tp, :].shape[0])[aux2_]
al_unique_index = al_unique_index[:self.mc_samples]
- return al_unique_index
\ No newline at end of file
+ return al_unique_index
diff --git a/tests/test_BayesInference.py b/tests/test_BayesInference.py
index c31a9830e7d7e4d186936d955920885adfd39300..25c0f7ddbe71575f509fd3fec81ef7f623712bc9 100644
--- a/tests/test_BayesInference.py
+++ b/tests/test_BayesInference.py
@@ -22,6 +22,7 @@ class BayesInference:
_plot_max_a_posteriori Need working model to test this
_plot_post_predictive - x
"""
+
import sys
import pytest
import numpy as np
@@ -40,8 +41,6 @@ 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
@@ -913,8 +912,7 @@ def test_plot_log_BME() -> None:
engine = Engine(mm, mod, expdes)
bi = BayesInference(engine)
- #bi.log_BME = np.array([[0, 0.2], [0, 0.2]])
- bi.log_BME = np.array([0, 0.2, 0, 0.2])
+ bi.log_BME = np.array([[0, 0.2], [0, 0.2]])
bi.n_tot_measurement = 1
bi.plot_log_BME()
@@ -938,7 +936,7 @@ def test_plot_log_BME_noemulator() -> None:
engine = Engine(mm, mod, expdes)
bi = BayesInference(engine)
- bi.log_BME = np.array([0, 0.2, 0, 0.2])
+ bi.log_BME = np.array([[0, 0.2], [0, 0.2]])
bi.n_tot_measurement = 1
bi.emulator = False
bi.plot_log_BME()
diff --git a/tests/test_InputSpace.py b/tests/test_InputSpace.py
index 7703f1141c70fe028444debd9b17d4ecfed12adf..40f26956376d07e59161cff6418aab1cab2c898f 100644
--- a/tests/test_InputSpace.py
+++ b/tests/test_InputSpace.py
@@ -13,11 +13,12 @@ import sys
import pytest
import numpy as np
+sys.path.append("src/")
+sys.path.append("../src/")
+
from bayesvalidrox.surrogate_models.inputs import Input
from bayesvalidrox.surrogate_models.input_space import InputSpace
-sys.path.append("src/")
-sys.path.append("../src/")
#%% Test ExpDesign.check_valid_input
diff --git a/tests/test_MetaModel.py b/tests/test_MetaModel.py
index 3b67f6699e9867ef61264d2d45f4b61d24a660d8..bf6c3c39ccd8a7251cdb04e1437c583194c7c2dc 100644
--- a/tests/test_MetaModel.py
+++ b/tests/test_MetaModel.py
@@ -29,12 +29,13 @@ import numpy as np
import pytest
import sys
+sys.path.append("src/")
+
from bayesvalidrox.surrogate_models.inputs import Input
from bayesvalidrox.surrogate_models.input_space import InputSpace
from bayesvalidrox.surrogate_models.surrogate_models import MetaModel, corr_loocv_error, create_psi
from bayesvalidrox.surrogate_models.surrogate_models import gaussian_process_emulator
-sys.path.append("src/")
#%% Test MetaMod constructor on its own
diff --git a/tests/test_SequentialDesign.py b/tests/test_SequentialDesign.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c9c5fb731c0e53946d284669a04f7fa7736c455
--- /dev/null
+++ b/tests/test_SequentialDesign.py
@@ -0,0 +1,83 @@
+# -*- coding: utf-8 -*-
+"""
+Test the SequentialDesign class for bayesvalidrox
+
+Tests are available for the following functions
+ logpdf - x
+ subdomain - x
+Engine:
+ start_seqdesign
+ choose_next_sample
+ plotter
+ tradoff_weights - x
+ run_util_func
+ util_VarBasedDesign
+ util_BayesianActiveDesign
+ util_BayesianDesign
+ dual_annealing
+ util_AlphOptDesign
+ _normpdf - x Also move outside the class?
+ _corr_factor_BME Not used again in this class
+ _posteriorPlot - x
+ _BME_Calculator - x
+ _validError - x
+ _error_Mean_Std - x
+ _select_indices
+
+
+"""
+
+import sys
+import pytest
+import numpy as np
+import pandas as pd
+
+sys.path.append("../src/")
+
+from bayesvalidrox.surrogate_models.inputs import Input
+from bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
+from bayesvalidrox.surrogate_models.surrogate_models import MetaModel
+from bayesvalidrox.pylink.pylink import PyLinkForwardModel as PL
+from bayesvalidrox.surrogate_models.engine import Engine
+from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
+
+
+
+
+#%% Main runs
+if __name__ == '__main__':
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ # prior_samples = np.swapaxes(np.array([np.random.normal(0,1,10)]),0,1)
+
+ 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.x_values = np.array([0]) # Error in plots if this is not
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ mm.fit(expdes.X, expdes.Y)
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
+ # y_hat, y_std = mm.eval_metamodel(prior_samples)
+
+ mod = PL()
+ mod.observations = {'Z': np.array([0.45])}
+ mod.observations = {'Z': np.array([0.45]), 'x_values': np.array([0])} # Error if x_values not given
+ mod.Output.names = ['Z']
+ mod.n_obs = 1
+
+ engine = Engine(mm, mod, expdes)
+
+ sigma2Dict = {'Z': np.array([0.05])}
+ sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+ obsData = pd.DataFrame({'Z': np.array([0.45]), 'x_values': np.array([0])}, columns=mod.Output.names)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData * 0.15) ** 2
+ DiscrepancyOpts.opt_sigma = 'B'