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diff --git a/.idea/.gitignore b/.idea/.gitignore
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index 0000000000000000000000000000000000000000..13566b81b018ad684f3a35fee301741b2734c8f4
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+++ b/.idea/.gitignore
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+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
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--- /dev/null
+++ b/CHANGELOG.md
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+# CHANGELOG
+
+## [Unreleased]
+### Requirements
+* numpy now at 1.23.3
+* ....
+
+### Added
+Features
+* PyLinkForwardModel has new link_type 'umbridge' for UM-Bridge type models
+* class `InputSpace` as parent class to `ExpDesigns`
+* `MetaModel` has option to train with OLS+constraints
+
+Examples
+* Example `umbridge_tsunamitutorial` to show two options of using UM-Bridge type models
+* Example `convergence_tests` for the constraints
+
+### Changed
+General
+* Independent functions moved out of classes
+* Constructors of `PostProcessing`, `BayesInference`, `BayesModelComp` are to be given the engine instead of the metamodel
+* Constructor of `Exploration` to be given `ExpDesigns` instead of `MetaModel`
+
+Engine
+* `MetaModelEngine` renamed to `Engine`
+* Split `run()` into `train_normal()` and `train_sequential()`
+* `opt_SeqDesign()` renamed to `choose_next_sample()`
+
+MetaModel
+* `MetaModel` fully independent of model
+* `MetaModel` uses `InputSpace` instead of `ExpDesigns`
+* `create_metamodel()` split into `build_metamodel()` and `fit()`
+* `fit()` renamed to `regression()`
+* `eval_metamodel()` only runs on given samples, full functionality retained in `Engine.eval_metamodel()`
+* Polynomial related functions moved from `MetaModelEngine` to `MetaModel`
+
+PyLinkForwardModel
+* Read-in of MC-references now performed by `PyLinkForwardModel.read_observation()`
+
+ExpDesigns
+* Parameters related to sequential training were moved from `MetaModel` to `ExpDesigns` (`Seq*`, `valid_model_runs`)
+
+### Removed
+* Class `SequentialDesign`
+* `ExpDesigns.method`
+* `MetaModel.create_basis_indices()`
+
+
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index 0000000000000000000000000000000000000000..14663ecb0086f14c8b80083b9ecf9a1df87e7ad5
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diff --git a/examples/.coverage b/examples/.coverage
new file mode 100644
index 0000000000000000000000000000000000000000..254e10e4371d703eefec0f0437f9c0575be3f5ec
Binary files /dev/null and b/examples/.coverage differ
diff --git a/examples/OHagan-function/test_OHagan.py b/examples/OHagan-function/example_OHagan.py
similarity index 78%
rename from examples/OHagan-function/test_OHagan.py
rename to examples/OHagan-function/example_OHagan.py
index 2fc629d3423d7ef3312e0182a8f15d1f0fd18ba0..8c7690767346db5c09e1a7b33595567c26c36a77 100644
--- a/examples/OHagan-function/test_OHagan.py
+++ b/examples/OHagan-function/example_OHagan.py
@@ -16,8 +16,6 @@ of Stuttgart, www.iws.uni-stuttgart.de/lh2/
Pfaffenwaldring 61
70569 Stuttgart
-Created on Wed Jul 10 2019
-
"""
import numpy as np
@@ -28,13 +26,15 @@ import joblib
import sys
sys.path.append("../../src/bayesvalidrox/")
-from pylink.pylink import PyLinkForwardModel
-from surrogate_models.inputs import Input
-from surrogate_models.surrogate_models import MetaModel
-from surrogate_models.meta_model_engine import MetaModelEngine
-from post_processing.post_processing import PostProcessing
-from bayes_inference.bayes_inference import BayesInference
-from bayes_inference.discrepancy import Discrepancy
+from bayesvalidrox.pylink.pylink import PyLinkForwardModel
+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.surrogate_models.meta_model_engine import MetaModelEngine
+from bayesvalidrox.post_processing.post_processing import PostProcessing
+from bayesvalidrox.bayes_inference.bayes_inference import BayesInference
+from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
+from bayesvalidrox.surrogate_models.engine import Engine
import matplotlib
matplotlib.use('agg')
@@ -68,7 +68,7 @@ if __name__ == "__main__":
# =====================================================
# ====== POLYNOMIAL CHAOS EXPANSION METAMODELS ======
# =====================================================
- MetaModelOpts = MetaModel(Inputs, Model)
+ MetaModelOpts = MetaModel(Inputs)
# Select your metamodel method
# 1) PCE (Polynomial Chaos Expansion) 2) aPCE (arbitrary PCE)
@@ -104,45 +104,45 @@ if __name__ == "__main__":
# ------------------------------------------------
# ------ Experimental Design Configuration -------
# ------------------------------------------------
- MetaModelOpts.add_ExpDesign()
+ ExpDesign = ExpDesigns(Inputs)
# One-shot (normal) or Sequential Adaptive (sequential) Design
- MetaModelOpts.ExpDesign.method = 'sequential'
- MetaModelOpts.ExpDesign.n_init_samples = 100
+ ExpDesign.method = 'sequential'
+ ExpDesign.n_init_samples = 100
# Sampling methods
# 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley 6) korobov
# 7) chebyshev(FT) 8) grid(FT) 9) nested_grid(FT) 10)user
- MetaModelOpts.ExpDesign.sampling_method = 'latin_hypercube'
+ ExpDesign.sampling_method = 'latin_hypercube'
# Provide the experimental design object with a hdf5 file
# MetaModelOpts.ExpDesign.hdf5_file = 'ExpDesign_OHagan_orig.hdf5'
# Sequential experimental design (needed only for sequential ExpDesign)
- MetaModelOpts.ExpDesign.n_new_samples = 1
- MetaModelOpts.ExpDesign.n_max_samples = 500 # 150
- MetaModelOpts.ExpDesign.mod_LOO_threshold = 1e-16
+ ExpDesign.n_new_samples = 1
+ ExpDesign.n_max_samples = 500 # 150
+ ExpDesign.mod_LOO_threshold = 1e-16
# ------------------------------------------------
# ------- Sequential Design configuration --------
# ------------------------------------------------
# 1) None 2) 'equal' 3)'epsilon-decreasing' 4) 'adaptive'
- MetaModelOpts.ExpDesign.tradeoff_scheme = None
+ ExpDesign.tradeoff_scheme = None
# MetaModelOpts.ExpDesign.n_replication = 50
# -------- Exploration ------
# 1)'Voronoi' 2)'random' 3)'latin_hypercube' 4)'dual annealing'
- MetaModelOpts.ExpDesign.explore_method = 'latin_hypercube'
+ ExpDesign.explore_method = 'latin_hypercube'
# Use when 'dual annealing' chosen
- MetaModelOpts.ExpDesign.max_func_itr = 200
+ ExpDesign.max_func_itr = 200
# Use when 'Voronoi' or 'random' or 'latin_hypercube' chosen
- MetaModelOpts.ExpDesign.n_canddidate = 10000
- MetaModelOpts.ExpDesign.n_cand_groups = 4
+ ExpDesign.n_canddidate = 10000
+ ExpDesign.n_cand_groups = 4
# -------- Exploitation ------
# 1)'BayesOptDesign' 2)'VarOptDesign' 3)'alphabetic' 4)'Space-filling'
- MetaModelOpts.ExpDesign.exploit_method = 'Space-filling'
+ ExpDesign.exploit_method = 'Space-filling'
# BayesOptDesign -> when data is available
# 1)DKL (Kullback-Leibler Divergence) 2)DPP (D-Posterior-percision)
@@ -151,30 +151,29 @@ if __name__ == "__main__":
# VarBasedOptDesign -> when data is not available
# Only with Vornoi >>> 1)Entropy 2)EIGF, 3)ALM, 4)LOOCV
- MetaModelOpts.ExpDesign.util_func = 'ALM'
+ ExpDesign.util_func = 'ALM'
# alphabetic
# 1)D-Opt (D-Optimality) 2)A-Opt (A-Optimality)
# 3)K-Opt (K-Optimality)
# MetaModelOpts.ExpDesign.util_func = 'D-Opt'
- MetaModelOpts.valid_samples = np.load("data/valid_samples.npy")
- MetaModelOpts.valid_model_runs = {
+ ExpDesign.valid_samples = np.load("data/valid_samples.npy")
+ ExpDesign.valid_model_runs = {
'Z': np.load("data/valid_outputs.npy")
}
# >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
- meta_model_engine = MetaModelEngine(MetaModelOpts)
- meta_model_engine.run()
- PCEModel = meta_model_engine.MetaModel
-
+ engine = Engine(MetaModelOpts, Model, ExpDesign)
+ engine.start_engine()
+ engine.train_sequential()
# Save PCE models
with open(f'PCEModel_{Model.name}.pkl', 'wb') as output:
- joblib.dump(PCEModel, output, 2)
+ joblib.dump(engine.MetaModel, output, 2)
# =====================================================
# ========= POST PROCESSING OF METAMODELS ===========
# =====================================================
- PostPCE = PostProcessing(PCEModel)
+ PostPCE = PostProcessing(engine)
# Plot to check validation visually.
PostPCE.valid_metamodel(n_samples=200)
@@ -184,7 +183,7 @@ if __name__ == "__main__":
PostPCE.check_accuracy(n_samples=3000)
# Plot the evolution of the KLD,BME, and Modified LOOCV error
- if MetaModelOpts.ExpDesign.method == 'sequential':
+ if ExpDesign.method == 'sequential':
PostPCE.plot_seq_design_diagnostics()
# Plot the sobol indices
diff --git a/examples/analytical-function/test_analytical_function.py b/examples/analytical-function/example_analytical_function.py
old mode 100755
new mode 100644
similarity index 71%
rename from examples/analytical-function/test_analytical_function.py
rename to examples/analytical-function/example_analytical_function.py
index fa3daf3ea324416e74c649d521d98f87690e5863..0303411be3ed7e04508ff78f5c162337736ab819
--- a/examples/analytical-function/test_analytical_function.py
+++ b/examples/analytical-function/example_analytical_function.py
@@ -20,20 +20,23 @@ import numpy as np
import pandas as pd
import sys
import joblib
-# import bayesvalidrox
-# Add BayesValidRox path
-sys.path.append("../../src/bayesvalidrox/")
-
-from pylink.pylink import PyLinkForwardModel
-from surrogate_models.inputs import Input
-from surrogate_models.surrogate_models import MetaModel
-from surrogate_models.meta_model_engine import MetaModelEngine
-from post_processing.post_processing import PostProcessing
-from bayes_inference.bayes_inference import BayesInference
-from bayes_inference.discrepancy import Discrepancy
import matplotlib
matplotlib.use('agg')
+# import bayesvalidrox
+# Add BayesValidRox path
+sys.path.append("../../src/")
+
+from bayesvalidrox.pylink.pylink import PyLinkForwardModel
+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.surrogate_models.meta_model_engine import MetaModelEngine
+from bayesvalidrox.post_processing.post_processing import PostProcessing
+from bayesvalidrox.bayes_inference.bayes_inference import BayesInference
+from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
+
+from bayesvalidrox.surrogate_models.engine import Engine
if __name__ == "__main__":
@@ -88,7 +91,7 @@ if __name__ == "__main__":
# =====================================================
# ========== DEFINITION OF THE METAMODEL ============
# =====================================================
- MetaModelOpts = MetaModel(Inputs, Model)
+ MetaModelOpts = MetaModel(Inputs)#, Model)
# Select if you want to preserve the spatial/temporal depencencies
# MetaModelOpts.dim_red_method = 'PCA'
@@ -134,47 +137,46 @@ if __name__ == "__main__":
# ------------------------------------------------
# ------ Experimental Design Configuration -------
# ------------------------------------------------
- MetaModelOpts.add_ExpDesign()
+ ExpDesign = ExpDesigns(Inputs)
# One-shot (normal) or Sequential Adaptive (sequential) Design
- MetaModelOpts.ExpDesign.method = 'sequential'
- MetaModelOpts.ExpDesign.n_init_samples = 3*ndim
+ ExpDesign.method = 'sequential'
+ ExpDesign.n_init_samples = 140#00#3*ndim
# Sampling methods
# 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley
# 6) chebyshev(FT) 7) grid(FT) 8)user
- MetaModelOpts.ExpDesign.sampling_method = 'latin_hypercube'
+ ExpDesign.sampling_method = 'latin_hypercube'
# Provide the experimental design object with a hdf5 file
- # MetaModelOpts.ExpDesign.hdf5_file = 'ExpDesign_AnalyticFunc.hdf5'
-
- # ------------------------------------------------
- # ------- Sequential Design configuration --------
- # ------------------------------------------------
+ #ExpDesign.hdf5_file = 'ExpDesign_AnalyticFunc.hdf5'
+
# Set the sampling parameters
- MetaModelOpts.ExpDesign.n_new_samples = 1
- MetaModelOpts.ExpDesign.n_max_samples = 150
- MetaModelOpts.ExpDesign.mod_LOO_threshold = 1e-16
+ ExpDesign.n_new_samples = 1
+ ExpDesign.n_max_samples = 141#150 # sum of init + sequential
+ ExpDesign.mod_LOO_threshold = 1e-16
- # MetaModelOpts.adapt_verbose = True
+ # ExpDesign.adapt_verbose = True
# 1) None 2) 'equal' 3)'epsilon-decreasing' 4) 'adaptive'
- MetaModelOpts.ExpDesign.tradeoff_scheme = None
+ ExpDesign.tradeoff_scheme = None
# MetaModelOpts.ExpDesign.n_replication = 5
# -------- Exploration ------
# 1)'Voronoi' 2)'random' 3)'latin_hypercube' 4)'LOOCV' 5)'dual annealing'
- MetaModelOpts.ExpDesign.explore_method = 'random'
+ ExpDesign.explore_method = 'random'
# Use when 'dual annealing' chosen
- MetaModelOpts.ExpDesign.max_func_itr = 1000
+ ExpDesign.max_func_itr = 1000
# Use when 'Voronoi' or 'random' or 'latin_hypercube' chosen
- MetaModelOpts.ExpDesign.n_canddidate = 1000
- MetaModelOpts.ExpDesign.n_cand_groups = 4
+ ExpDesign.n_canddidate = 1000
+ ExpDesign.n_cand_groups = 4
# -------- Exploitation ------
# 1)'BayesOptDesign' 2)'BayesActDesign' 3)'VarOptDesign' 4)'alphabetic'
# 5)'Space-filling'
- MetaModelOpts.ExpDesign.exploit_method = 'BayesActDesign'
+ ExpDesign.exploit_method = 'Space-filling'
+ ExpDesign.exploit_method = 'BayesActDesign'
+ ExpDesign.util_func = 'DKL'
# BayesOptDesign/BayesActDesign -> when data is available
# 1) MI (Mutual information) 2) ALC (Active learning McKay)
@@ -185,7 +187,7 @@ if __name__ == "__main__":
# BayesActDesign -> when data is available
# 1) BME (Bayesian model evidence) 2) infEntropy (Information entropy)
# 2)DKL (Kullback-Leibler Divergence)
- MetaModelOpts.ExpDesign.util_func = 'DKL'
+ #MetaModelOpts.ExpDesign.util_func = 'DKL'
# VarBasedOptDesign -> when data is not available
# 1)ALM 2)EIGF, 3)LOOCV
@@ -205,27 +207,28 @@ if __name__ == "__main__":
MetaModelOpts.Discrepancy = DiscrepancyOpts
# Plot the posterior snapshots for SeqDesign
- MetaModelOpts.ExpDesign.post_snapshot = False
- MetaModelOpts.ExpDesign.step_snapshot = 1
- MetaModelOpts.ExpDesign.max_a_post = [0] * ndim
+ ExpDesign.post_snapshot = False
+ ExpDesign.step_snapshot = 1
+ ExpDesign.max_a_post = [0] * ndim
# For calculation of validation error for SeqDesign
prior = np.load(f"data/Prior_{ndim}.npy")
prior_outputs = np.load(f"data/origModelOutput_{ndim}.npy")
likelihood = np.load(f"data/validLikelihoods_{ndim}.npy")
- MetaModelOpts.valid_samples = prior[:500]
- MetaModelOpts.valid_model_runs = {'Z': prior_outputs[:500]}
- # MetaModelOpts.valid_likelihoods = likelihood
-
- # >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
- # Train the meta model
- meta_model_engine = MetaModelEngine(MetaModelOpts)
- meta_model_engine.run()
- PCEModel = meta_model_engine.MetaModel
-
- # Save PCE models
- with open(f'PCEModel_{Model.name}.pkl', 'wb') as output:
- joblib.dump(PCEModel, output, 2)
+ ExpDesign.valid_samples = prior[:500]
+ ExpDesign.valid_model_runs = {'Z': prior_outputs[:500]}
+
+
+ # Run using the new engine
+ #engine = Engine(MetaModelOpts, Model, ExpDesign)
+ #engine.start_engine()
+ #engine.train_normal()
+
+ MetaModelOpts.ExpDesign = ExpDesign
+ engine = Engine(MetaModelOpts, Model, ExpDesign)
+ engine.start_engine()
+ #engine.train_sequential()
+ engine.train_normal()
# Load the objects
# with open(f"PCEModel_{Model.name}.pkl", "rb") as input:
@@ -234,7 +237,7 @@ if __name__ == "__main__":
# =====================================================
# ========= POST PROCESSING OF METAMODELS ===========
# =====================================================
- PostPCE = PostProcessing(PCEModel)
+ PostPCE = PostProcessing(engine)
# Plot to check validation visually.
PostPCE.valid_metamodel(n_samples=1)
@@ -258,25 +261,25 @@ if __name__ == "__main__":
# =====================================================
# ======== Bayesian inference with Emulator ==========
# =====================================================
- BayesOpts = BayesInference(PCEModel)
+ BayesOpts = BayesInference(engine)
BayesOpts.emulator = True
BayesOpts.plot_post_pred = True
# BayesOpts.selected_indices = [0, 3, 5, 7, 9]
# BME Bootstrap
- # BayesOpts.bootstrap = True
- # BayesOpts.n_bootstrap_itrs = 500
- # BayesOpts.bootstrap_noise = 100
+ BayesOpts.bootstrap = True
+ BayesOpts.n_bootstrap_itrs = 500
+ BayesOpts.bootstrap_noise = 100
# Bayesian cross validation
- # BayesOpts.bayes_loocv = True
+ BayesOpts.bayes_loocv = True # TODO: test what this does
# Select the inference method
import emcee
BayesOpts.inference_method = "MCMC"
# Set the MCMC parameters passed to self.mcmc_params
BayesOpts.mcmc_params = {
- 'n_steps': 1e5,
+ 'n_steps': 1e4,#5,
'n_walkers': 30,
'moves': emcee.moves.KDEMove(),
'multiprocessing': False,
@@ -284,6 +287,7 @@ if __name__ == "__main__":
}
# ----- Define the discrepancy model -------
+ obsData = pd.DataFrame(Model.observations, columns=Model.Output.names)
BayesOpts.measurement_error = obsData
# # -- (Option B) --
@@ -293,31 +297,34 @@ if __name__ == "__main__":
BayesOpts.Discrepancy = DiscrepancyOpts
# -- (Option C) --
- # DiscOutputOpts = Input()
- # # # OutputName = 'Z'
- # DiscOutputOpts.add_marginals()
- # DiscOutputOpts.Marginals[0].Nnme = '$\sigma^2_{\epsilon}$'
- # DiscOutputOpts.Marginals[0].dist_type = 'uniform'
- # DiscOutputOpts.Marginals[0].parameters = [0, 10]
- # BayesOpts.Discrepancy = {'known': DiscrepancyOpts,
- # 'infer': Discrepancy(DiscOutputOpts)}
-
- # BayesOpts.bias_inputs = {'Z':np.arange(0, 10, 1.).reshape(-1,1) / 9}
- # DiscOutputOpts = Input()
- # # OutputName = 'lambda'
- # DiscOutputOpts.add_marginals()
- # DiscOutputOpts.Marginals[0].name = '$\lambda$'
- # DiscOutputOpts.Marginals[0].dist_type = 'uniform'
- # DiscOutputOpts.Marginals[0].parameters = [0, 1]
-
- # # OutputName = 'sigma_f'
- # DiscOutputOpts.add_marginals()
- # DiscOutputOpts.Marginals[1].Name = '$\sigma_f$'
- # DiscOutputOpts.Marginals[1].dist_type = 'uniform'
- # DiscOutputOpts.Marginals[1].parameters = [0, 1e-4]
- # BayesOpts.Discrepancy = Discrepancy(DiscOutputOpts)
- # BayesOpts.Discrepancy = {'known': DiscrepancyOpts,
- # 'infer': Discrepancy(DiscOutputOpts)}
+ if 0:
+ DiscOutputOpts = Input()
+ # # # OutputName = 'Z'
+ DiscOutputOpts.add_marginals()
+ DiscOutputOpts.Marginals[0].Nnme = '$\sigma^2_{\epsilon}$'
+ DiscOutputOpts.Marginals[0].dist_type = 'uniform'
+ DiscOutputOpts.Marginals[0].parameters = [0, 10]
+ #BayesOpts.Discrepancy = {'known': DiscrepancyOpts,
+ # 'infer': Discrepancy(DiscOutputOpts)}
+
+ BayesOpts.bias_inputs = {'Z':np.arange(0, 10, 1.).reshape(-1,1) / 9}
+
+ DiscOutputOpts = Input()
+ # OutputName = 'lambda'
+ DiscOutputOpts.add_marginals()
+ DiscOutputOpts.Marginals[0].name = '$\lambda$'
+ DiscOutputOpts.Marginals[0].dist_type = 'uniform'
+ DiscOutputOpts.Marginals[0].parameters = [0, 1]
+
+ # # OutputName = 'sigma_f'
+ DiscOutputOpts.add_marginals()
+ DiscOutputOpts.Marginals[1].Name = '$\sigma_f$'
+ DiscOutputOpts.Marginals[1].dist_type = 'uniform'
+ DiscOutputOpts.Marginals[1].parameters = [0, 1e-4]
+ #BayesOpts.Discrepancy = Discrepancy(DiscOutputOpts)
+ BayesOpts.Discrepancy = {'known': DiscrepancyOpts,
+ 'infer': Discrepancy(DiscOutputOpts)}
+
# Start the calibration/inference
Bayes_PCE = BayesOpts.create_inference()
diff --git a/examples/analytical-function/util/AnalytFuncValid_Test.py b/examples/analytical-function/util/AnalytFuncValid_Test.py
deleted file mode 100644
index b172de0baf73399ad8a1180ab9e8cfe20b4fb7b8..0000000000000000000000000000000000000000
--- a/examples/analytical-function/util/AnalytFuncValid_Test.py
+++ /dev/null
@@ -1,353 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Aug 9 16:25:43 2019
-This example is for the calibration and validation scenario.
-@author: farid
- """
-
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-plt.style.use('seaborn-white')
-plt.rcParams.update({'font.size': 18})
-plt.rc('font', family='sans-serif', serif='Arial')
-plt.rc('figure', figsize = (24, 16))
-plt.rc('text', usetex=True)
-plt.rc('xtick', labelsize=18)
-plt.rc('ytick', labelsize=18)
-plt.rc('axes', labelsize=18)
-
-import seaborn as sns
-import os
-
-try:
- import cPickle as pickle
-except ModuleNotFoundError:
- import pickle
-
-from PyLink.FuncForwardModel import FuncForwardModel
-from surrogate_models.Input import Input
-from surrogate_models.surrogate_models import Metamodel
-from PostProcessing.PostProcessing import PostProcessing
-from BayesInference.BayesInference import BayesInference, Discrepancy
-
-if __name__ == "__main__":
-
- #=====================================================
- #============= COMPUTATIONAL MODEL ================
- #=====================================================
- Model = FuncForwardModel()
-
- Model.Type = 'Function'
- Model.pyFile = 'AnalyticalFunction'
- Model.Name = 'AnalyticFunc'
-
- Model.Output.Names = ['Z']
-
-
- # For Bayesian inversion synthetic data with X=[0,0]
- Model.Observations['Time [s]'] = np.arange(0, 5, 1.) / 5
- Model.Observations['Z'] = np.repeat([2],5)
-
- # For Checking with the MonteCarlo refrence
-# Model.MCReference['Time [s]'] = np.arange(0, 10, 1.) / 9
-# Model.MCReference['mean'] = np.array([127.13880108, 127.12652988, 127.12144698,
-# 127.11754674, 127.11425868, 127.11136184, 127.1087429,
-# 127.10633454, 127.10409289, 127.10198748])
-# Model.MCReference['std'] = np.array([171.2520775, 171.21550753, 171.20352691,
-# 171.19559223, 171.18975202, 171.18525035, 171.18169953,
-# 171.17886925, 171.17660944, 171.17481586])
-
- # For Bayesian inversion synthetic data with X=[1,1]
- Model.ObservationsValid['Time [s]'] = np.arange(0, 5, 1.) / 5
- Model.ObservationsValid['Z']= np.array([2.21773563, 2.11712764,
- 2.02460905, 1.93849485, 1.85761462])
-
- #=====================================================
- #========= PROBABILISTIC INPUT MODEL ==============
- #=====================================================
- # Define the uncertain parameters with their mean and
- # standard deviation
- Inputs = Input()
-
- ndim = 2
- for i in range(ndim):
- Inputs.add_marginals()
- Inputs.Marginals[i].Name = '$X_{%s}$'%(i+1)
- Inputs.Marginals[i].DistType = 'unif'
- Inputs.Marginals[i].Parameters = [-5, 5]
-
- #=====================================================
- #========== DEFINITION OF THE METAMODEL ============
- #=====================================================
- MetaModelOpts = Metamodel(Inputs)
-
- # Select if you want to preserve the spatial/temporal depencencies
- MetaModelOpts.DimRedMethod = 'PCA'
-
- # Select your metamodel method
- # 1) PCE (Polynomial Chaos Expansion) 2) GPE (Gaussian Process Emulator)
- # 3) PCEKriging (PCE + GPE)
- MetaModelOpts.metaModel = 'GPE'
-
- # ------------------------------------------------
- # ------------- PCE Specification ----------------
- # ------------------------------------------------
- # Select the sparse least-square minimization method for
- # the PCE coefficients calculation:
- # 1)OLS: Ordinary Least Square 2)BRR: Bayesian Ridge Regression
- # 3)LARS: Least angle regression 4)ARD: Bayesian ARD Regression
- # 5)FastARD: Fast Bayesian ARD Regression
- # 6)SGDR: Stochastic gradient descent learning
- MetaModelOpts.RegMethod = 'FastARD'
-
- # Specify the max degree to be compared by the adaptive algorithm:
- # The degree with the lowest Leave-One-Out cross-validation (LOO)
- # error (or the highest score=1-LOO)estimator is chosen as the final
- # metamodel.
- MetaModelOpts.MinPceDegree = 1 #3
- MetaModelOpts.MaxPceDegree = 8 #15
-
- # q-quasi-norm 0<q<1 (default=1)
- MetaModelOpts.q = 1 #0.75
-
- # Print summary of the regression results
- #MetaModelOpts.DisplayFlag = True
-
- # ------------------------------------------------
- # ------ Experimental Design Configuration -------
- # ------------------------------------------------
-
- # Generate an experimental design of size NrExpDesign based on a latin
- # hypercube sampling of the input model or user-defined values of X and/or Y:
- MetaModelOpts.addExpDesign()
-
- # One-shot (normal) or Sequential Adaptive (sequential) Design
- MetaModelOpts.ExpDesign.Method = 'normal'
- NrofInitSamples = 200 #75
- MetaModelOpts.ExpDesign.NrSamples = NrofInitSamples
-
- # Sampling methods
- # 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley 6) chebyshev(FT)
- # 7) korobov 8) grid(FT) 9) nested_grid(FT) 10)user
- MetaModelOpts.ExpDesign.SamplingMethod = 'random'
-
- # Sequential experimental design (needed only for sequential ExpDesign)
- MetaModelOpts.ExpDesign.NrofNewSample = 1
- MetaModelOpts.ExpDesign.MaxNSamples = 50 #150
- MetaModelOpts.ExpDesign.ModifiedLOOThreshold = 1e-16
-
- # Defining the measurement error, if it's known a priori
- obsData = pd.DataFrame(Model.Observations, columns=Model.Output.Names)
- DiscrepancyOpts = Discrepancy('')
- DiscrepancyOpts.Type = 'Gaussian'
- DiscrepancyOpts.Parameters = obsData ** 2
- MetaModelOpts.Discrepancy = DiscrepancyOpts
-
- # Plot the posterior snapshots for SeqDesign
- MetaModelOpts.ExpDesign.PostSnapshot = False
- MetaModelOpts.ExpDesign.stepSnapshot = 1
- MetaModelOpts.ExpDesign.MAP = (0,0)
- MetaModelOpts.ExpDesign.parNames = ['$X_1$', '$X_2$']
-
- # ------------------------------------------------
- # ------- Sequential Design configuration --------
- # ------------------------------------------------
- # 1) 'None' 2) 'epsilon-decreasing'
- MetaModelOpts.ExpDesign.TradeOffScheme = 'epsilon-decreasing'
- #MetaModelOpts.ExpDesign.nReprications = 2
- # -------- Exploration ------
- #1)'Voronoi' 2)'MC' 3)'LHS' 4)'dual annealing'
- MetaModelOpts.ExpDesign.ExploreMethod = 'Voronoi'
-
- # Use when 'dual annealing' chosen
- MetaModelOpts.ExpDesign.MaxFunItr = 200
-
- # Use when 'MC' or 'LHS' chosen
- MetaModelOpts.ExpDesign.NCandidate = 100
- MetaModelOpts.ExpDesign.NrofCandGroups = 4
-
- # -------- Exploitation ------
- # 1)'BayesOptDesign' 2)'ActiveLearning' 3)'alphabetic' 4)'None' (Space-filling)
- MetaModelOpts.ExpDesign.ExploitMethod = 'None'
-
- # BayesOptDesign -> when data is available
- # 1)DKL (Kullback-Leibler Divergence) 2)DPP (D-Posterior-percision)
- # 3)APP (A-Posterior-percision)
- MetaModelOpts.ExpDesign.UtilityFunction = 'DKL' #['DKL', 'DPP']
-
- # ActiveLearning
- # 1)ALM (Active learning MacKay) 2)ALC (Active learning Cohn)
- # 3)
- #MetaModelOpts.ExpDesign.UtilityFunction = 'ALM' #['ALM', 'ALC']
-
- # alphabetic
- # 1)D-Opt (D-Optimality) 2)A-Opt (A-Optimality)
- # 3)K-Opt (K-Optimality)
- # MetaModelOpts.ExpDesign.UtilityFunction = 'D-Opt' #['D-Opt', 'A-Opt', 'K-Opt']
-
- # >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
- # MetaModelOpts.slicingforValidation = True
- MetaModelOpts.index = 5
- # Adaptive sparse arbitrary polynomial chaos expansion
- # PCEModelCalib, PCEModelValid = MetaModelOpts.create_metamodel(Model)
- PCEModel = MetaModelOpts.create_metamodel(Model)
-
- #=====================================================
- #========= POST PROCESSING OF METAMODELS ===========
- #=====================================================
- #PostPCE = PostProcessing(PCEModelCalib)
- PostPCE = PostProcessing(PCEModel)
-
- PostPCE.Name = 'Calib'
- PostPCE.validMetamodel(nValidSamples= 3)
-
- # Compute the moments and compare with the Monte-Carlo reference
-# PostPCE.plotMoments()
-
- if MetaModelOpts.metaModel != 'GPE':
- # Plot the evolution of the KLD,BME, and Modified LOOCV error
- if MetaModelOpts.ExpDesign.Method == 'sequential':
- PostPCE.seqDesignDiagnosticPlots()
-
- # Plot the sobol indices
- sobol_cell, total_sobol = PostPCE.sobolIndicesPCE()
-
- # Compute and print RMSE error
- PostPCE.accuracyCheckMetaModel(nSamples=3000)
-
-
- #=====================================================
- #======= Bayesian calibration with Emulator =========
- #=====================================================
- # BayesOpts = BayesInference(PCEModelCalib)
- BayesOpts = BayesInference(PCEModel)
- BayesOpts.Name = 'Calib'
- BayesOpts.emulator = True
-
- # BME Bootstrap
- BayesOpts.Bootstrap = True
- BayesOpts.NrofSamples = 100000
- BayesOpts.BootstrapItrNr = 10
- BayesOpts.BootstrapNoise = 0.05
-
- # Select the inference method
- BayesOpts.SamplingMethod = 'MCMC'
- BayesOpts.MCMCnSteps = 1000
- BayesOpts.MCMCnwalkers = 50
- BayesOpts.MultiProcessMCMC = True
-
- BayesOpts.PlotPostDist = True
- BayesOpts.PlotPostPred = True
-
- # ----- Define the discrepancy model -------
- obsData = pd.DataFrame(Model.Observations, columns=Model.Output.Names)
- # (Option B)
- DiscrepancyOpts = Discrepancy('')
- DiscrepancyOpts.Type = 'Gaussian'
- DiscrepancyOpts.Parameters = obsData[:MetaModelOpts.index]**2
-
- BayesOpts.Discrepancy = DiscrepancyOpts
-
- # (Option C)
-# DiscInputOpts = Input()
-#
-# DiscInputOpts.addMarginals()
-# DiscInputOpts.Marginals[0].Name = 'Sigma2'
-# DiscInputOpts.Marginals[0].DistType = 'unif'
-# DiscInputOpts.Marginals[0].Parameters = [0, 5e-1]
-#
-# DiscrepancyOpts = Discrepancy(DiscInputOpts)
-#
-# BayesOpts.Discrepancy = DiscrepancyOpts
-
-
- Bayes_Calib = BayesOpts.create_Inference()
-
- # Make directory for saving the plots/outputs
- newpath = (r'Outputs_'+MetaModelOpts.ExpDesign.Method)
- if not os.path.exists(newpath): os.makedirs(newpath)
-
-
- ## Plot the posterior (Model)
- with sns.axes_style("white"):
- g = sns.jointplot(x=Bayes_Calib.Posterior_df['$X_{1}$'], y=Bayes_Calib.Posterior_df['$X_{2}$'],
- kind="kde", cmap='jet');
- g.ax_joint.collections[0].set_alpha(0)
- g.set_axis_labels("$X_1$", "$X_2$")
-
- g.savefig('./'+newpath+'/Posterior_PCEModel_Calib_'+MetaModelOpts.ExpDesign.Method+'.svg')
- plt.close()
-
-
- #=====================================================
- #======= Bayesian validation with Emulator =========
- #=====================================================
- # BayesOpts_Valid = BayesInference(PCEModelValid)
- BayesOpts_Valid = BayesInference(PCEModel)
-
- BayesOpts_Valid.Name = 'Valid'
- BayesOpts_Valid.emulator = True
-
- # BME Bootstrap
- BayesOpts_Valid.Bootstrap = True
- BayesOpts_Valid.MCMCinitSamples = Bayes_Calib.Posterior_df
- BayesOpts_Valid.BootstrapItrNr = 10
- BayesOpts_Valid.BootstrapNoise = 0.05
-
- # Select the inference method
- BayesOpts_Valid.SamplingMethod = 'MCMC'
- BayesOpts_Valid.MCMCnSteps = 1000
- BayesOpts_Valid.MCMCnwalkers = 50
- BayesOpts.MultiProcessMCMC = True
-
- BayesOpts_Valid.PlotPostDist = True
- BayesOpts_Valid.PlotPostPred = True
-
- # ----- Define the discrepancy model -------
- obsDataValid = pd.DataFrame(Model.ObservationsValid, columns=Model.Output.Names)
- # (Option B)
- DiscrepancyOpts = Discrepancy('')
- DiscrepancyOpts.Type = 'Gaussian'
- DiscrepancyOpts.Parameters = 0.01 * obsDataValid**2
-
- BayesOpts_Valid.Discrepancy = DiscrepancyOpts
-
- Bayes_Valid = BayesOpts_Valid.create_Inference()
-
-
- ## Plot the posterior (PCEModel)
- with sns.axes_style("white"):
- g = sns.jointplot(x=Bayes_Valid.Posterior_df['$X_{1}$'], y=Bayes_Valid.Posterior_df['$X_{2}$'],
- kind="kde", cmap='jet');
- g.ax_joint.collections[0].set_alpha(0)
- g.set_axis_labels("$X_1$", "$X_2$")
- g.savefig('./'+newpath+'/Posterior_PCEModel_Valid_'+MetaModelOpts.ExpDesign.Method+'.svg')
- plt.close()
-
-
- #=====================================================
- #============== Save class objects =================
- #=====================================================
- with open('AnalyticFunc_Results.pkl', 'wb') as output:
- pickle.dump(PCEModel, output, pickle.HIGHEST_PROTOCOL)
-
- # pickle.dump(PCEModelValid, output, pickle.HIGHEST_PROTOCOL)
-
- pickle.dump(PostPCE, output, pickle.HIGHEST_PROTOCOL)
-
- pickle.dump(Bayes_Calib, output, pickle.HIGHEST_PROTOCOL)
-
- pickle.dump(Bayes_Valid, output, pickle.HIGHEST_PROTOCOL)
-
-# del PCEModel
-# del PostPCE
-# del Bayes
-
- # Load the objects
-# with open('AnalyticFunc_Results.pkl', 'rb') as input:
-# PCEModel = pickle.load(input)
-# PostPCE = pickle.load(input)
-# Bayes_PCE = pickle.load(input)
-# Bayes_Model = pickle.load(input)
\ No newline at end of file
diff --git a/examples/analytical-function/util/AnalyticFunc_Demo.py b/examples/analytical-function/util/AnalyticFunc_Demo.py
deleted file mode 100644
index dea66f67dd8907fcc2560bd56a184f9e65f17a38..0000000000000000000000000000000000000000
--- a/examples/analytical-function/util/AnalyticFunc_Demo.py
+++ /dev/null
@@ -1,207 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Aug 9 16:25:43 2019
-
-@author: farid
-"""
-
-import numpy as np
-import os
-import matplotlib.pyplot as plt
-plt.style.use('seaborn-white')
-plt.rcParams.update({'font.size': 16})
-plt.rc('font', family='sans-serif', serif='Arial')
-plt.rc('figure', figsize = (24, 16))
-plt.rc('text', usetex=True)
-plt.rc('xtick', labelsize=16)
-plt.rc('ytick', labelsize=16)
-plt.rc('axes', labelsize=16)
-
-
-from PyLink.FuncForwardModel import FuncForwardModel
-from surrogate_models.Input import Input
-from surrogate_models.surrogate_models import Metamodel
-
-if __name__ == "__main__":
-
- #=====================================================
- #============= COMPUTATIONAL MODEL ================
- #=====================================================
- Model = FuncForwardModel()
-
- Model.Type = 'Function'
- Model.pyFile = 'AnalyticalFunction'
- Model.Name = 'AnalyticFunc'
-
- Model.Output.Names = ['Z']
-
-
- #=====================================================
- #========= PROBABILISTIC INPUT MODEL ==============
- #=====================================================
- # Define the uncertain parameters with their mean and
- # standard deviation
- Inputs = Input()
-
- Inputs.addMarginals()
- Inputs.Marginals[0].Name = 'x1'
- Inputs.Marginals[0].DistType = 'unif'
- Inputs.Marginals[0].Parameters = [-5, 5]
-
- Inputs.addMarginals()
- Inputs.Marginals[1].Name = 'x2'
- Inputs.Marginals[1].DistType = 'unif'
- Inputs.Marginals[1].Parameters = [-5, 5]
-
-
- #=====================================================
- #======== PCE METAMODELS with adaptive aPCE ========
- #=====================================================
- MetaModelOpts = Metamodel(Inputs)
-
- # Specify the max degree to be compared by the adaptive algorithm:
- # The degree with the lowest Leave-One-Out cross-validation (LOO)
- # error (or the highest score=1-LOO)estimator is chosen as the final
- # metamodel.
- MetaModelOpts.MaxPceDegree = 10
-
- # Select the sparse least-square minimization method for
- # the PCE coefficients calculation:
- # 1)OLS: Ordinary Least Square 2)BRR: Bayesian Ridge Regression
- # 3)LARS: Least angle regression 4)ARD: Bayesian ARD Regression
- # 5)SGDR: Stochastic gradient descent learning
- MetaModelOpts.RegMethod = 'OLS'
-
- # Print summary of the regression results
- #MetaModelOpts.DisplayFlag = True
-
- # ------ Experimental Design --------
- # Generate an experimental design of size NrExpDesign based on a latin
- # hypercube sampling of the input model or user-defined values of X and/or Y:
- MetaModelOpts.addExpDesign()
-
- # One-shot (normal) or Sequential Adaptive (sequential) Design
- MetaModelOpts.ExpDesign.Method = 'normal'
- NrofInitSamples = 100
- MetaModelOpts.ExpDesign.NrSamples = NrofInitSamples
-
- # Sampling methods
- # 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley 6) chebyshev(FT)
- # 7) korobov 8) grid(FT) 9) nested_grid(FT) 10)user
- MetaModelOpts.ExpDesign.SamplingMethod = 'latin_hypercube'
-
- # Sequential experimental design (needed only for sequential ExpDesign)
- MetaModelOpts.ExpDesign.MaxNSamples = 100
- MetaModelOpts.ExpDesign.ModifiedLOOThreshold = 1e-4
-
- # ------------------------------------------------
- # ------- Sequential Design configuration --------
- # ------------------------------------------------
- # -------- Exploration ------
- MetaModelOpts.ExpDesign.ExploreMethod = 'MC'
- MetaModelOpts.ExpDesign.NCandidate = 500
- MetaModelOpts.ExpDesign.NrofCandGroups = 4
- # -------- Optimality criteria: alphabetic ------
- MetaModelOpts.ExpDesign.ExploitMethod = 'alphabetic'
-
- # 1)D-Opt (D-Optimality) 2)A-Opt (A-Optimality)
- # 3)K-Opt (K-Optimality)
- MetaModelOpts.ExpDesign.UtilityFunction = 'D-Opt'#['D-Opt', 'A-Opt', 'K-Opt']
-
- # >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
- # Adaptive sparse arbitrary polynomial chaos expansion
- PCEModel_OLS = MetaModelOpts.create_metamodel(Model)
-
- # Extract the basis and coefficients
- Basis_OLS = PCEModel_OLS.BasisDict['Z']['y_2']
- COFFS_OLS = PCEModel_OLS.CoeffsDict['Z']['y_2']
-
- #=====================================================
- #=========== DEMO OF BAYESPCE METAMODEL ===========
- #=====================================================
- fig1=plt.figure()
- barWidth = 0.3
- plt.title("Bar plot of the weights (OLS)")
-
- # Create cyan bars
- r = np.arange(len(Basis_OLS))
- plt.bar(r, COFFS_OLS, width = barWidth, color = 'cyan', bottom=0.001,
- edgecolor = 'black', capsize=7, label='Cofficients')
-
- # general layout
- plt.xticks([r + barWidth for r in range(len(r))], ['term'+str(i+1) for i in range(len(Basis_OLS))])
-
- plt.xlabel("Features")
- plt.ylabel("Values of the weights")
- plt.yscale('symlog', linthreshy=0.1) #('log')
- plt.hlines(0,xmin=r.min() , xmax=r.max() ,color='k')
- plt.xticks(rotation=45)
- plt.legend(loc="upper left")
- plt.show()
-
- # Make directory for saving the plots/outputs
- newpath = (r'Outputs_demo')
- if not os.path.exists(newpath): os.makedirs(newpath)
-
- ## Save the plot the posterior (Model)
- fig1.savefig('./'+newpath+'/Coeffs_AaPCE.svg', bbox_inches='tight')
- plt.close()
-
-
- #=====================================================
- #=========== PCE METAMODELS with BRR aPCE ==========
- #=====================================================
- BRRMetaModelOpts = Metamodel(Inputs)
- BRRMetaModelOpts.MaxPceDegree = 10
- BRRMetaModelOpts.RegMethod = 'BRR'
-
- # ------ Experimental Design --------
- BRRMetaModelOpts.addExpDesign()
- BRRMetaModelOpts.ExpDesign.NrSamples = PCEModel_OLS.ExpDesign.X.shape[0]
- BRRMetaModelOpts.ExpDesign.SamplingMethod = 'user'
- BRRMetaModelOpts.ExpDesign.X = PCEModel_OLS.ExpDesign.X
- BRRMetaModelOpts.ExpDesign.Y = PCEModel_OLS.ExpDesign.Y
- BRRMetaModelOpts.ExpDesign.Method = 'normal'
-
- # >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
- # Adaptive sparse arbitrary polynomial chaos expansion
- PCEModel_BRR = BRRMetaModelOpts.create_metamodel(Model)
-
-
- # Extract the basis and coefficients
- Basis_BRR = PCEModel_BRR.BasisDict['Z']['y_2']
- clf_poly = PCEModel_BRR.clf_poly['Z']['y_2']
-
- COFFS_BRR = clf_poly.coef_
- # Find the error(std) for coeffs
- COFFSError_BRR = clf_poly.sigma_.diagonal()
-
- #=====================================================
- #=========== DEMO OF BAYESPCE METAMODEL ============
- #=====================================================
- fig2=plt.figure()
- barWidth = 0.3
- plt.title("Bar plot of the weights (BayesPCE)")
-
- # Create cyan bars
- r = np.arange(len(Basis_BRR))
- plt.bar(r, COFFS_BRR, width = barWidth, color = 'cyan', bottom=0.001,
- edgecolor = 'black', yerr=COFFSError_BRR, capsize=7, label='Cofficients')
-
- # general layout
- plt.xticks([r + barWidth for r in range(len(r))], ['term'+str(i+1) for i in range(len(Basis_BRR))])
-
- plt.xlabel("Features")
- plt.ylabel("Values of the weights")
- plt.yscale('symlog', linthreshy=0.1) #('log')
- plt.hlines(0,xmin=r.min() , xmax=r.max() ,color='k')
- plt.xticks(rotation=45)
- plt.legend(loc="upper left")
- plt.show()
-
- ## Save the plot the posterior (Model)
- fig2.savefig('./'+newpath+'/Coeffs_BRR.svg', bbox_inches='tight')
- plt.close()
-
-
diff --git a/examples/analytical-function/util/AnalyticalFunction.py b/examples/analytical-function/util/AnalyticalFunction.py
deleted file mode 100644
index c4ed8e05c3ecd841f16d6b2d4757b12d5ecad325..0000000000000000000000000000000000000000
--- a/examples/analytical-function/util/AnalyticalFunction.py
+++ /dev/null
@@ -1,145 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Nov 20 14:48:43 2019
-
-@author: farid
-"""
-import numpy as np
-import scipy.stats as stats
-import matplotlib.pyplot as plt
-import scipy.stats as st
-import seaborn as sns
-
-def AnalyticalFunction(xx, t=None):
- """
- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
- %
- % Analytical Non-Gaussian Function
- %
- % Authors: Farid Mohammadi, University of Stuttgart
- % Sergey Oladyshkin, University of Stuttgart
- % Questions/Comments: Please email Farid Mohammadi at:
- % farid.mohammadi@iws.uni-stuttgart.de
- %
- % Copyright 2019. Farid Mohammadi, University of Stuttgart.
- %
- % THERE IS NO WARRANTY, EXPRESS OR IMPLIED. WE DO NOT ASSUME ANY LIABILITY
- % FOR THE USE OF THIS SOFTWARE. If software is modified to produce
- % derivative works, such modified software should be clearly marked.
- % Additionally, this program is free software; you can redistribute it
- % and/or modify it under the terms of the GNU General Public License as
- % published by the Free Software Foundation; version 2.0 of the License.
- % Accordingly, this program is distributed in the hope that it will be
- % useful, but WITHOUT ANY WARRANTY; without even the implied warranty
- % of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- % General Public License for more details.
- %
- % For function details and reference information, see:
- % https://doi.org/10.3390/e21111081
- %
- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
- %
- % OUTPUT AND INPUTS:
- %
- % Output = row vector of time vectors (s, t)
- % Its structure is:
- % y(t_1), y(t_2), ..., y(t_dt)
- % xx = [x1, x2, ..., xn]
- % xn ~ Uinform(-5, 5)
- % t = vector of times (optional), with default value
- % ( k − 1 ) /9 and k = 1,..., 10
- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
- """
- nParamSets, nParams = xx.shape
-
- if t is None: t = np.arange(0, 10, 1.) / 9
-
- term1 = (xx[:,0]**2 + xx[:,1] - 1)**2
-
- term2 = xx[:,0]**2
-
- term3 = 0.1 * xx[:,0] * np.exp(xx[:,1])
-
- term5 = 0
- if nParams > 2:
- for i in range(2, nParams):
- term5 = term5 + xx[:,i]**3/i
-
- const = term1 + term2 + term3 + 1 + term5
-
- # Compute time dependent term
- term4 = np.zeros((nParamSets,len(t)))
- for idx in range(nParamSets):
- term4[idx] = -2 * xx[idx,0] * np.sqrt(0.5*t)
-
- Output = term4 + np.repeat(const[:,None], len(t), axis=1)
-
- return np.vstack((t, Output))
-
-if __name__ == "__main__":
-
- MCSize = 10000 #1000000
- ndim = 10
- sigma = 2
-
- # -------------------------------------------------------------------------
- # ----------------------- Synthetic data generation -----------------------
- # -------------------------------------------------------------------------
- t = np.arange(0, 10, 1.) / 9
-
- MAP = np.zeros((1, ndim))
- synthethicData = AnalyticalFunction(MAP, t=t)
-
- # -------------------------------------------------------------------------
- # ---------------------- Generate Prior distribution ----------------------
- # -------------------------------------------------------------------------
-
- xx = np.zeros((MCSize, ndim))
-
- params = (-5,5)
-
- for idxDim in range(ndim):
- lower, upper = params
- xx[:,idxDim] = stats.uniform(loc=lower, scale=upper-lower).rvs(size=MCSize)
-
- # -------------------------------------------------------------------------
- # ------------- BME and Kullback-Leibler Divergence -----------------------
- # -------------------------------------------------------------------------
- Outputs = AnalyticalFunction(xx, t=t)
-
- cov_matrix = np.diag(np.repeat(sigma**2, synthethicData.shape[1]))
-
- Likelihoods = st.multivariate_normal.pdf(Outputs[1:], mean=synthethicData[1], cov=cov_matrix)
-
- sns.kdeplot(np.log(Likelihoods[Likelihoods>0]), shade=True, color="g", label='Ref. Likelihood')
-
- normLikelihood = Likelihoods / np.nanmax(Likelihoods)
- # Random numbers between 0 and 1
- unif = np.random.rand(1, MCSize)[0]
-
- # Reject the poorly performed prior
- accepted = normLikelihood >= unif
-
- # Prior-based estimation of BME
- logBME = np.log(np.nanmean(Likelihoods))
- print('\nThe Naive MC-Estimation of BME is %.5f.'%(logBME))
-
- # Posterior-based expectation of likelihoods
- postExpLikelihoods = np.mean(np.log(Likelihoods[accepted]))
-
- # Calculate Kullback-Leibler Divergence
- KLD = postExpLikelihoods - logBME
- print("The Kullback-Leibler divergence estimation is %.5f."%KLD)
-
- # -------------------------------------------------------------------------
- # ----------------- Save the arrays as .npy files -------------------------
- # -------------------------------------------------------------------------
- if MCSize > 500000:
- np.save("data/refBME_KLD_"+str(ndim)+".npy", (logBME, KLD))
- np.save("data/mean_"+str(ndim)+".npy", np.mean(Outputs[1:],axis=0))
- np.save("data/std_"+str(ndim)+".npy", np.std(Outputs[1:],axis=0))
- else:
- np.save("data/Prior_"+str(ndim)+".npy", xx)
- np.save("data/origModelOutput_"+str(ndim)+".npy", Outputs[1:])
- np.save("data/validLikelihoods_"+str(ndim)+".npy", Likelihoods)
diff --git a/examples/analytical-function/util/PCE_vs_Chaospy.py b/examples/analytical-function/util/PCE_vs_Chaospy.py
deleted file mode 100755
index 87a71cea2389e6b639fb5927baf0c6b8cb488d2b..0000000000000000000000000000000000000000
--- a/examples/analytical-function/util/PCE_vs_Chaospy.py
+++ /dev/null
@@ -1,319 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Aug 13 09:53:11 2020
-
-@author: farid
-"""
-import sys, os
-import numpy as np
-import scipy.stats as stats
-from itertools import cycle
-import pandas as pd
-try:
- import cPickle as pickle
-except ModuleNotFoundError:
- import pickle
-from sklearn import linear_model as lm
-from matplotlib.backends.backend_pdf import PdfPages
-import matplotlib.pyplot as plt
-plt.rcParams.update({'font.size': 24})
-plt.rc('figure', figsize = (24, 16))
-plt.rc('font', family='serif', serif='Arial')
-plt.rc('axes', grid = True)
-plt.rc('text', usetex=True)
-plt.rc('xtick', labelsize=24)
-plt.rc('ytick', labelsize=24)
-plt.rc('axes', labelsize=24)
-plt.rc('axes', linewidth=2)
-plt.rc('axes', grid=True)
-plt.rc('grid', linestyle="-")
-plt.rc('savefig', dpi=2000)
-
-import matplotlib
-matplotlib.use('agg')
-# Local
-sys.path.insert(0,'./../bayesian-validation/BayesValidRox/')
-
-# Batch script
-# sys.path.insert(0,'./../../bayesian-validation/BayesValidRox/')
-
-from PyLink.FuncForwardModel import FuncForwardModel
-from surrogate_models.Input import Input
-from surrogate_models.surrogate_models import Metamodel
-from PostProcessing.PostProcessing import PostProcessing
-
-
-# Number of parameters
-ndim = 10 # 2, 10
-
-#=====================================================
-#============= COMPUTATIONAL MODEL ================
-#=====================================================
-Model = FuncForwardModel()
-
-Model.Type = 'Function'
-Model.pyFile = 'AnalyticalFunction'
-Model.Name = 'AnalyticFunc'
-
-Model.Output.Names = ['Z']
-OutputNames = ['Z']
-
-# For Bayesian inversion synthetic data with X=[0,0]
-Model.Observations['Time [s]'] = np.arange(0, 10, 1.) / 9
-Model.Observations['Z'] = np.repeat([2],10)
-
-# For Checking with the MonteCarlo refrence
-Model.MCReference['Time [s]'] = np.arange(0, 10, 1.) / 9
-Model.MCReference['mean'] = np.load("../data/mean_"+str(ndim)+".npy")
-Model.MCReference['std'] = np.load("../data/std_"+str(ndim)+".npy")
-
-#=====================================================
-#========= PROBABILISTIC INPUT MODEL ==============
-#=====================================================
-# Define the uncertain parameters with their mean and
-# standard deviation
-Inputs = Input()
-
-for i in range(ndim):
- Inputs.addMarginals()
- Inputs.Marginals[i].Name = '$X_{%s}$'%(i+1)
- Inputs.Marginals[i].DistType = 'unif'
- Inputs.Marginals[i].Parameters = [-5, 5]
-
-# arbitrary polynomial chaos
-# inputParams = np.load('../data/InputParameters_{}.npy'.format(ndim))
-# for i in range(ndim):
-# Inputs.addMarginals()
-# Inputs.Marginals[i].Name = '$X_{%s}$'%(i+1)
-# Inputs.Marginals[i].InputValues = inputParams[:,i]
-
-#=====================================================
-#========== DEFINITION OF THE METAMODEL ============
-#=====================================================
-MetaModelOpts = Metamodel(Inputs)
-
-# Select if you want to preserve the spatial/temporal depencencies
-MetaModelOpts.DimRedMethod = 'PCA'
-MetaModelOpts.varPCAThreshold = 99.999
-
-# Select your metamodel method
-# 1) PCE (Polynomial Chaos Expansion) 2) GPE (Gaussian Process Emulator)
-# 3) PCEKriging (PCE + GPE)
-MetaModelOpts.metaModel = 'PCEKriging'
-
-# ------------------------------------------------
-# ------------- PCE Specification ----------------
-# ------------------------------------------------
-# Select the sparse least-square minimization method for
-# the PCE coefficients calculation:
-# 1)OLS: Ordinary Least Square 2)BRR: Bayesian Ridge Regression
-# 3)LARS: Least angle regression 4)ARD: Bayesian ARD Regression
-# 5)FastARD: Fast Bayesian ARD Regression
-MetaModelOpts.RegMethod = 'FastARD'
-
-# Specify the max degree to be compared by the adaptive algorithm:
-# The degree with the lowest Leave-One-Out cross-validation (LOO)
-# error (or the highest score=1-LOO)estimator is chosen as the final
-# metamodel.
-MetaModelOpts.MinPceDegree = 6
-MetaModelOpts.MaxPceDegree = 6
-# q-quasi-norm 0<q<1 (default=1)
-MetaModelOpts.q = 1.0 if ndim<5 else 0.75
-
-# ------------------------------------------------
-# ------ Experimental Design Configuration -------
-# ------------------------------------------------
-# Generate an experimental design of size NrExpDesign based on a latin
-# hypercube sampling of the input model or user-defined values of X and/or Y:
-MetaModelOpts.addExpDesign()
-
-# One-shot (normal) or Sequential Adaptive (sequential) Design
-MetaModelOpts.ExpDesign.Method = 'normal'
-MetaModelOpts.ExpDesign.NrSamples = 200
-
-# Sampling methods
-# 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley 6) chebyshev(FT)
-# 7) korobov 8) grid(FT) 9) nested_grid(FT) 10)user
-MetaModelOpts.ExpDesign.SamplingMethod = 'random'
-
-# >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
-# Adaptive sparse arbitrary polynomial chaos expansion
-MetaModelOpts.index = 181
-PCEModel = MetaModelOpts.create_metamodel(Model)
-
-# PCEModel = BayesObj.PCEModel
-# Extract slicing index
-# Extract the experimental design
-EDX = PCEModel.ExpDesign.X
-index = PCEModel.index
-EDYDict = PCEModel.ExpDesign.Y
-JDist = PCEModel.ExpDesign.JDist
-X_train = PCEModel.ExpDesign.X
-EDY = PCEModel.ExpDesign.Y
-#=====================================================
-#========= POST PROCESSING OF METAMODELS ===========
-#=====================================================
-PostPCE = PostProcessing(PCEModel)
-
-# Compute the moments and compare with the Monte-Carlo reference
-PostPCE.plotMoments()
-
-# Plot the sobol indices
-sobol_cell, total_sobol = PostPCE.sobolIndicesPCE()
-
-
-#=====================================================
-#============ METAMODELS WITH CHAOSPY =============
-#=====================================================
-import chaospy
-from surrogate_models.RegressionFastARD import RegressionFastARD
-
-def PCATransformation(Output):
-
- # Transform via Principal Component Analysis
- from sklearn.decomposition import PCA as sklearnPCA
-
- n_samples, n_features = Output.shape
- covar_matrix = sklearnPCA(n_components=None, svd_solver='full')
- covar_matrix.fit(Output)
- var = np.cumsum(np.round(covar_matrix.explained_variance_ratio_, decimals=5)*100)
- try:
- selected_n_components = np.where(var>=99.0)[0][0] + 1
- except:
- selected_n_components = min(n_samples, n_features)
-
- nComponents = min(n_samples, n_features, selected_n_components)
-
- pca = sklearnPCA(n_components=nComponents, svd_solver='full', whiten=False)
- OutputMatrix = pca.fit_transform(Output)
-
- return pca, OutputMatrix
-
-def fit_regression(methodConfig, X_train,Y_train,pca=False):
-
- from sklearn.multioutput import MultiOutputRegressor
-
- if pca:
- PCA, Y_train = PCATransformation(Y_train)
- else:
- PCA = None
-
- deg = methodConfig['deg']
- distribution = methodConfig['distribution']
- q = methodConfig['q']
- regmethod = methodConfig['regmethod']
- # Generate Orthogonal Polynomial Expansion:
- orthogonal_expansion = chaospy.orth_ttr(deg, distribution, cross_truncation=q)
-
- PCEModel={}
- PCEModel['config'] = methodConfig
- newPsi = orthogonal_expansion(*X_train.T).T
-
- if regmethod =='FastARD':
- PCEModel = MultiOutputRegressor(RegressionFastARD(fit_intercept=False)).fit(newPsi, Y_train)
- else:
- PCEModel = MultiOutputRegressor(lm.BayesianRidge(fit_intercept=False)).fit(newPsi, Y_train)
-
- # for idx in range(Y_train.shape[1]):
- # clf_poly = RegressionFastARD(fit_intercept=False)
-
- # PCEModel[idx] = clf_poly.fit(newPsi, Y_train[:,idx])
-
- return PCA, PCEModel
-
-def approx_model(model, X,pca=None):
-
- global methodConfig
-
- y_Hat, y_Std = [], []
- deg = methodConfig['deg']
- distribution = methodConfig['distribution']
- q = methodConfig['q']
-
- # Generate Orthogonal Polynomial Expansion:
- orthogonal_expansion = chaospy.orth_ttr(deg, distribution, cross_truncation=q)
-
- psi = orthogonal_expansion(*X.T).T
-
- y_Hat = model.predict(psi).T
-
- # for idx in list(model.keys())[1:]:
- # y_hat, y_std = model[idx].predict(psi, return_std=True)
- # y_Hat.append(y_hat)
- # y_Std.append(y_std)
-
- if pca is None:
- return np.array(y_Hat).T
- else:
- return pca.inverse_transform(np.array(y_Hat).T)# , np.array(y_Std).T
-
-# Select a Distributions:
-distribution = JDist #chaospy.J(*[chaospy.Uniform(-5.0,5.0)]*ndim)
-
-methodConfig = dict()
-y_chaos = dict()
-methodConfig['regmethod'] = 'FastARD' #'FastARD'
-methodConfig['deg'] = PCEModel.MaxPceDegree
-methodConfig['distribution'] = distribution
-methodConfig['q'] = MetaModelOpts.q
-
-
-# Fit regression model
-PCA = True
-for key in OutputNames:
-
- pca, PCEModel_chaospy = fit_regression(methodConfig, X_train,EDY[key],pca=PCA)
- y_chaos[key] = approx_model(PCEModel_chaospy, X_train,pca=pca)
-
-
-#=====================================================
-#================= Visualization ===================
-#=====================================================
-# List of markers and colors
-Color = ['k','b', 'g', 'r']
-Marker = 'x'
-
-Time = EDY['x_values']
-
-nStes = 1
-
-# Run pcemodel
-y_hat, y_std = PCEModel.eval_metamodel(samples=EDX[:nStes]) #(nsamples=100, samples=EDX, name=case)
-
-# create a PdfPages object
-pdf = PdfPages('./PCE.pdf')
-fig = plt.figure()
-
-for key in OutputNames:
-
- fig, ax = plt.subplots()
-
-
- # Plot ExpDesign
- for i, output in enumerate(EDY[key][:nStes]):
- plt.plot(Time,output, alpha=0.35)
-
-
- # Plot PCE with BayesValidRox
- for i, output in enumerate(y_hat[key]):
- plt.plot(Time,output, ls='-', marker='*')
-
-
- # Plot PCE with Chaospy
- for i, output in enumerate(y_chaos[key][:nStes]):
- plt.plot(Time,output, ls='-', marker='*')
-
- plt.legend(loc='best', frameon=True)
- plt.xlabel('Time [s]')
- plt.ylabel(key)
-
- # save the current figure
- pdf.savefig(fig, bbox_inches='tight')
-
- # Destroy the current plot
- plt.clf()
-
-pdf.close()
-
-
diff --git a/examples/analytical-function/util/Psi_BayesValidRox.npy b/examples/analytical-function/util/Psi_BayesValidRox.npy
deleted file mode 100644
index 43373abb38884fbe6f5e8c509443b916a3a0aea7..0000000000000000000000000000000000000000
Binary files a/examples/analytical-function/util/Psi_BayesValidRox.npy and /dev/null differ
diff --git a/examples/analytical-function/util/Psi_Chaospy.npy b/examples/analytical-function/util/Psi_Chaospy.npy
deleted file mode 100644
index dc7974367157dba6f4775091aa09acd314b4c8fe..0000000000000000000000000000000000000000
Binary files a/examples/analytical-function/util/Psi_Chaospy.npy and /dev/null differ
diff --git a/examples/analytical-function/util/__pycache__/AnalyticalFunction.cpython-38.pyc b/examples/analytical-function/util/__pycache__/AnalyticalFunction.cpython-38.pyc
deleted file mode 100644
index e89c6708d7e9e374610f5dec468e2d7c3a94036f..0000000000000000000000000000000000000000
Binary files a/examples/analytical-function/util/__pycache__/AnalyticalFunction.cpython-38.pyc and /dev/null differ
diff --git a/examples/analytical-function/util/dynamic_image.py b/examples/analytical-function/util/dynamic_image.py
deleted file mode 100644
index 8c1478164c37739fc1c7b42d4fe4d108c696deee..0000000000000000000000000000000000000000
--- a/examples/analytical-function/util/dynamic_image.py
+++ /dev/null
@@ -1,142 +0,0 @@
-"""
-=================================================
-Animated image using a precomputed list of images
-=================================================
-
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib.animation as animation
-plt.rcParams.update({'font.size': 16})
-plt.rc('figure', figsize = (12, 8))
-plt.rc('font', family='sans-serif', serif='Arial')
-plt.rc('axes', grid = True)
-plt.rc('text', usetex=True)
-plt.rc('xtick', labelsize=16)
-plt.rc('ytick', labelsize=16)
-plt.rc('axes', labelsize=16)
-
-def posteriorPlot(ax,figPosterior,Posterior, MAP, parNames):
-
- # Initialization
- lw = 3.
- BoundTuples = [(-5,5), (-5,5)]
- NofPa = len(MAP)
-
- # This is the true mean of the second mode that we used above:
- value1 = MAP
-
- # This is the empirical mean of the sample:
- value2 = np.mean(Posterior, axis=0)
-
- if NofPa == 2:
-
- #figPosterior, ax = plt.subplots()
- plt.hist2d(Posterior[:,0], Posterior[:,1], bins=(200, 200),
- range=np.array([BoundTuples[0],BoundTuples[1]]), cmap=plt.cm.jet)
-
- plt.xlabel(parNames[0])
- plt.ylabel(parNames[1])
-
- plt.xlim(BoundTuples[0])
- plt.ylim(BoundTuples[1])
-
- ax.axvline(value1[0], color="g", lw=lw)
- ax.axhline(value1[1], color="g", lw=lw)
- ax.plot(value1[0], value1[1], "sg", lw=lw+1)
-
- #ax.axvline(value2[0], ls='--', color="r", lw=lw)
- #ax.axhline(value2[1], ls='--', color="r", lw=lw)
- #ax.plot(value2[0], value2[1], "sr", lw=lw+1)
-
- else:
- import corner
- figPosterior = corner.corner(Posterior, labels=parNames,
- show_titles=True, title_kwargs={"fontsize": 12})
-
- # Extract the axes
- axes = np.array(figPosterior.axes).reshape((NofPa, NofPa))
-
- # Loop over the diagonal
- for i in range(NofPa):
- ax = axes[i, i]
- ax.axvline(value1[i], color="g")
- ax.axvline(value2[i], ls='--', color="r")
-
- # Loop over the histograms
- for yi in range(NofPa):
- for xi in range(yi):
- ax = axes[yi, xi]
- ax.axvline(value1[xi], color="g")
- ax.axvline(value2[xi], ls='--', color="r")
- ax.axhline(value1[yi], color="g")
- ax.axhline(value2[yi], ls='--', color="r")
- ax.plot(value1[xi], value1[yi], "sg")
- ax.plot(value2[xi], value2[yi], "sr")
-
- plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)
- #figPosterior.set_size_inches((10,10))
- return figPosterior
-
-fig, ax = plt.subplots()
-dirPath = '/home/farid/scientific/bayesian-validation/BayesValidRox/tests/AnalyticalFunction/Outputs_SeqPosteriorComparison/'
-
-nRuns= 35
-Posteriors = {}
-nameList = ['init'] + list(range(1,nRuns+1))
-for i, name in enumerate(nameList):
-# x = np.random.normal(size=50000)
-# y = x * 3 * (i+1) + np.random.normal(size=50000)
-# Posteriors[i] = np.stack((x,y)).T
- Posteriors[i] = np.load(dirPath+'SeqPosterior_%s.npy'%name)
-
-
-# animation function
-def animate(i, Posteriors):
-
- Posterior = Posteriors[i]
-
- cont = posteriorPlot(ax,fig,Posterior, MAP = (0,0), parNames=['$X_1$', '$X_2$'])
-
- plt.title(r'Iteration = %i' % i)
-
- return cont
-
-anim = animation.FuncAnimation(fig, animate, fargs=(Posteriors,))#, frames=nRuns)
-anim.save('animation.mp4')
-
-
-
-#fig = plt.figure()
-#
-#
-#def f(x, y):
-# return np.sin(x) + np.cos(y)
-#
-#x = np.linspace(0, 2 * np.pi, 120)
-#y = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)
-## ims is a list of lists, each row is a list of artists to draw in the
-## current frame; here we are just animating one artist, the image, in
-## each frame
-#ims = []
-#for i in range(60):
-# x += np.pi / 15.
-# y += np.pi / 20.
-# im = plt.imshow(f(x, y), animated=True)
-# ims.append([im])
-#
-#ani = animation.ArtistAnimation(fig, ims, interval=50, blit=True,
-# repeat_delay=1000)
-#
-## To save the animation, use e.g.
-##
-#ani.save("movie.mp4")
-#
-# or
-#
-# from matplotlib.animation import FFMpegWriter
-# writer = FFMpegWriter(fps=15, metadata=dict(artist='Me'), bitrate=1800)
-# ani.save("movie.mp4", writer=writer)
-
-#plt.show()
diff --git a/examples/analytical-function/util/indices_bayesValid.npy b/examples/analytical-function/util/indices_bayesValid.npy
deleted file mode 100644
index 087a2608fb8da023977760ab15582c61c7a8ff6d..0000000000000000000000000000000000000000
Binary files a/examples/analytical-function/util/indices_bayesValid.npy and /dev/null differ
diff --git a/examples/analytical-function/util/indices_chaospy.npy b/examples/analytical-function/util/indices_chaospy.npy
deleted file mode 100644
index 087a2608fb8da023977760ab15582c61c7a8ff6d..0000000000000000000000000000000000000000
Binary files a/examples/analytical-function/util/indices_chaospy.npy and /dev/null differ
diff --git a/examples/analytical-function/util/svg_gif.py b/examples/analytical-function/util/svg_gif.py
deleted file mode 100644
index 4430de69c299a495bba9642ea5d5115e7452bdda..0000000000000000000000000000000000000000
--- a/examples/analytical-function/util/svg_gif.py
+++ /dev/null
@@ -1,47 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Mon May 16 10:51:20 2022
-
-@author: farid
-"""
-
-import imageio
-import os
-import re
-
-
-def tryint(s):
- try:
- return int(s)
- except:
- return s
-
-
-def alphanum_key(s):
- """ Turn a string into a list of string and number chunks.
- "z23a" -> ["z", 23, "a"]
- """
- return [tryint(c) for c in re.split('([0-9]+)', s)]
-
-
-def sort_nicely(input_list):
- """ Sort the given list in the way that humans expect.
- """
- input_list.sort(key=alphanum_key)
-
-
-path = '../adaptivePlots'
-file_ext = 'pdf'
-
-
-filenames = []
-for file in os.listdir(path):
- if file.endswith(f'.{file_ext}'):
- filenames.append(os.path.join(path, file))
-sort_nicely(filenames)
-
-images = []
-for filename in filenames:
- images.append(imageio.imread(filename))
-imageio.mimsave(f'{path}/movie.gif', images)
diff --git a/examples/beam/Beam9points_1/SSBeam_Deflection_1.inp b/examples/beam/Beam9points_1/SSBeam_Deflection_1.inp
new file mode 100644
index 0000000000000000000000000000000000000000..fec3b20b0b63491c95e1deab597784c31448405f
--- /dev/null
+++ b/examples/beam/Beam9points_1/SSBeam_Deflection_1.inp
@@ -0,0 +1,6 @@
+% Input file for the simply supported beam model
+1.5557e-01 % b in m
+2.8052e-01 % h in m
+5 % L in m
+2.2558e+10 % E in Pa
+9.2858e+03 % p in N/m
diff --git a/examples/beam/example_beam.py b/examples/beam/example_beam.py
new file mode 100644
index 0000000000000000000000000000000000000000..84b88255f53ef81ae5eb2861b4550fe0c68052ab
--- /dev/null
+++ b/examples/beam/example_beam.py
@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This test shows a surrogate for a beam deflection model can be created.
+This example also illustrate how a model with an executable and a
+input file can be linked with the bayesvalidrox package.
+
+Author: Farid Mohammadi, M.Sc.
+E-Mail: farid.mohammadi@iws.uni-stuttgart.de
+Department of Hydromechanics and Modelling of Hydrosystems (LH2)
+Institute for Modelling Hydraulic and Environmental Systems (IWS), University
+of Stuttgart www.iws.uni-stuttgart.de/lh2/
+Pfaffenwaldring 61
+70569 Stuttgart
+
+"""
+# Add BayesValidRox path
+import sys
+sys.path.append("../../src/bayesvalidrox/")
+
+from bayesvalidrox.pylink.pylink import PyLinkForwardModel
+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.surrogate_models.meta_model_engine import MetaModelEngine
+from bayesvalidrox.post_processing.post_processing import PostProcessing
+from bayesvalidrox.bayes_inference.bayes_inference import BayesInference
+from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
+
+from bayesvalidrox.surrogate_models.engine import Engine
+
+
+if __name__ == "__main__":
+
+ # =====================================================
+ # ============= COMPUTATIONAL MODEL ================
+ # =====================================================
+ Model = PyLinkForwardModel()
+
+ Model.link_type = 'PyLink'
+ Model.name = 'Beam9points'
+ Model.input_file = "SSBeam_Deflection.inp"
+ Model.input_template = "SSBeam_Deflection.tpl.inp"
+
+ Model.shell_command = "myBeam9points SSBeam_Deflection.inp"
+ Model.Output.parser = 'read_Beam_Deflection'
+ Model.Output.names = ['Deflection [m]']
+ Model.Output.file_names = ["SSBeam_Deflection.out"]
+
+ # For Bayesian inversion
+ Model.meas_file = 'data/MeasuredData.csv'
+ Model.meas_file_valid = 'data/MeasuredData_Valid.csv'
+
+ # For Checking with the MonteCarlo refrence
+ Model.mc_ref_file = 'data/MCrefs_MeanStd.csv'
+
+ # =====================================================
+ # ========= PROBABILISTIC INPUT MODEL ==============
+ # =====================================================
+ # Define the uncertain parameters with their mean and
+ # standard deviation
+ Inputs = Input()
+
+ Inputs.add_marginals()
+ Inputs.Marginals[0].name = 'Beam width'
+ Inputs.Marginals[0].dist_type = 'lognormal'
+ Inputs.Marginals[0].parameters = [0.15, 0.0075]
+
+ Inputs.add_marginals()
+ Inputs.Marginals[1].name = 'Beam height'
+ Inputs.Marginals[1].dist_type = 'lognormal'
+ Inputs.Marginals[1].parameters = [0.3, 0.015]
+
+ Inputs.add_marginals()
+ Inputs.Marginals[2].name = 'Youngs modulus'
+ Inputs.Marginals[2].dist_type = 'lognormal'
+ Inputs.Marginals[2].parameters = [30000e+6, 4500e+6]
+
+ Inputs.add_marginals()
+ Inputs.Marginals[3].name = 'Uniform load'
+ Inputs.Marginals[3].dist_type = 'lognormal'
+ Inputs.Marginals[3].parameters = [1e4, 2e3]
+
+ # =====================================================
+ # ========== DEFINITION OF THE METAMODEL ============
+ # =====================================================
+ MetaModelOpts = MetaModel(Inputs, Model)
+
+ # Select if you want to preserve the spatial/temporal depencencies
+ # MetaModelOpts.dim_red_method = 'PCA'
+ # MetaModelOpts.var_pca_threshold = 99.999
+ # MetaModelOpts.n_pca_components = 12
+
+ # Select your metamodel method
+ # 1) PCE (Polynomial Chaos Expansion) 2) aPCE (arbitrary PCE)
+ # 3) GPE (Gaussian Process Emulator)
+ MetaModelOpts.meta_model_type = 'PCE'
+
+ # ------------------------------------------------
+ # ------------- PCE Specification ----------------
+ # ------------------------------------------------
+ # Select the sparse least-square minimization method for
+ # the PCE coefficients calculation:
+ # 1)OLS: Ordinary Least Square 2)BRR: Bayesian Ridge Regression
+ # 3)LARS: Least angle regression 4)ARD: Bayesian ARD Regression
+ # 5)FastARD: Fast Bayesian ARD Regression
+ # 6)BCS: Bayesian Compressive Sensing
+ # 7)OMP: Orthogonal Matching Pursuit
+ # 8)VBL: Variational Bayesian Learning
+ # 9)EBL: Emperical Bayesian Learning
+ MetaModelOpts.pce_reg_method = 'FastARD'
+
+ # Specify the max degree to be compared by the adaptive algorithm:
+ # The degree with the lowest Leave-One-Out cross-validation (LOO)
+ # error (or the highest score=1-LOO)estimator is chosen as the final
+ # metamodel. pce_deg accepts degree as a scalar or a range.
+ MetaModelOpts.pce_deg = 6
+
+ # q-quasi-norm 0<q<1 (default=1)
+ MetaModelOpts.pce_q_norm = 0.75
+
+ # ------------------------------------------------
+ # ------ Experimental Design Configuration -------
+ # ------------------------------------------------
+ # MetaModelOpts.add_ExpDesign()
+ ExpDesign = ExpDesigns(Inputs)
+
+ # One-shot (normal) or Sequential Adaptive (sequential) Design
+ ExpDesign.n_init_samples = 100
+
+ # Sampling methods
+ # 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley
+ # 6) chebyshev(FT) 7) grid(FT) 8)user
+ ExpDesign.sampling_method = 'latin_hypercube'
+
+ # Provide the experimental design object with a hdf5 file
+ # MetaModelOpts.ExpDesign.hdf5_file = 'ExpDesign_Beam9points.hdf5'
+
+ # ------ Train the surrogate model ------
+ engine = Engine(MetaModelOpts, Model, ExpDesign)
+ engine.start_engine()
+ engine.train_normal()
+
+ # =====================================================
+ # ========= POST PROCESSING OF METAMODELS ===========
+ # =====================================================
+ PostPCE = PostProcessing(engine)
+
+ # Compute the moments and compare with the Monte-Carlo reference
+ PostPCE.plot_moments(xlabel='$\\mathrm{L_{rel}}$ (-)')
+
+ # Plot the sobol indices
+ if MetaModelOpts.meta_model_type != 'GPE':
+ total_sobol = PostPCE.sobol_indices()
+
+ # Plot to check validation visually.
+ PostPCE.valid_metamodel(n_samples=3)
diff --git a/examples/borehole/borehole.py b/examples/borehole/borehole.py
index b2a0dcd4a08091e05b74cceae3eda3709308ae49..f91cbb8f8a645ce9a48e159b268005213e682619 100644
--- a/examples/borehole/borehole.py
+++ b/examples/borehole/borehole.py
@@ -58,6 +58,7 @@ def borehole(xx, *args):
Hu = xx[:, 5]
Hl = xx[:, 6]
r = xx[:, 7]
+
frac1 = 2 * np.pi * Tu * (Hu-Hl)
@@ -68,6 +69,8 @@ def borehole(xx, *args):
y = frac1 / frac2
# Prepare output dict using standard bayesvalidrox format
- output = {'x_values': np.zeros(1), 'flow rate [m$^3$/yr]': y}
+ #output = {'x_values': np.zeros(1), 'flow rate [m$^3$/yr]': y}
+ output = {'x_values': np.zeros(1), 'flow rate': y}
return output
+
diff --git a/examples/borehole/test_borehole.py b/examples/borehole/example_borehole.py
similarity index 79%
rename from examples/borehole/test_borehole.py
rename to examples/borehole/example_borehole.py
index e28e9a30f76afa9deb158e23532af12623f2d8e2..869a053202cfb7343ee03a9a8f41b8b2fc4c8958 100644
--- a/examples/borehole/test_borehole.py
+++ b/examples/borehole/example_borehole.py
@@ -27,19 +27,26 @@ import joblib
import sys
sys.path.append("../../src/bayesvalidrox/")
-from pylink.pylink import PyLinkForwardModel
-from surrogate_models.inputs import Input
-from surrogate_models.surrogate_models import MetaModel
-from post_processing.post_processing import PostProcessing
-from bayes_inference.bayes_inference import BayesInference
-from bayes_inference.discrepancy import Discrepancy
-from surrogate_models.meta_model_engine import MetaModelEngine
+from bayesvalidrox.pylink.pylink import PyLinkForwardModel
+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.surrogate_models.meta_model_engine import MetaModelEngine
+from bayesvalidrox.post_processing.post_processing import PostProcessing
+from bayesvalidrox.bayes_inference.bayes_inference import BayesInference
+from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
+from bayesvalidrox.surrogate_models.engine import Engine
import matplotlib
matplotlib.use('agg')
+
+# TODO: this is a test in hopes of removing multiprocessing issues
+from multiprocessing import set_start_method
if __name__ == "__main__":
+ #set_start_method('fork', force = True)
+
# =====================================================
# ============= COMPUTATIONAL MODEL ================
# =====================================================
@@ -50,7 +57,7 @@ if __name__ == "__main__":
Model.py_file = 'borehole'
Model.name = 'borehole'
- Model.Output.names = ['flow rate [m$^3$/yr]']
+ Model.Output.names = ['flow rate']#' [m$^3$/yr]']
# =====================================================
# ========= PROBABILISTIC INPUT MODEL ==============
@@ -144,45 +151,45 @@ if __name__ == "__main__":
# ------------------------------------------------
# ------ Experimental Design Configuration -------
# ------------------------------------------------
- MetaModelOpts.add_ExpDesign()
+ ExpDesign = ExpDesigns(Inputs)
# One-shot (normal) or Sequential Adaptive (sequential) Design
- MetaModelOpts.ExpDesign.method = 'sequential'
- MetaModelOpts.ExpDesign.n_init_samples = 50
+ ExpDesign.method = 'sequential'
+ ExpDesign.n_init_samples = 50
# Sampling methods
# 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley 6) korobov
# 7) chebyshev(FT) 8) grid(FT) 9) nested_grid(FT) 10)user
- MetaModelOpts.ExpDesign.sampling_method = 'latin_hypercube'
+ ExpDesign.sampling_method = 'latin_hypercube'
# Provide the experimental design object with a hdf5 file
# MetaModelOpts.ExpDesign.hdf5_file = 'ExpDesign_borehole.hdf5'
# Sequential experimental design (needed only for sequential ExpDesign)
- MetaModelOpts.ExpDesign.n_new_samples = 1
- MetaModelOpts.ExpDesign.n_max_samples = 300
- MetaModelOpts.ExpDesign.mod_LOO_threshold = 1e-16
+ ExpDesign.n_new_samples = 1
+ ExpDesign.n_max_samples = 300
+ ExpDesign.mod_LOO_threshold = 1e-16
# ------------------------------------------------
# ------- Sequential Design configuration --------
# ------------------------------------------------
# 1) None 2) 'equal' 3)'epsilon-decreasing' 4) 'adaptive'
- MetaModelOpts.ExpDesign.tradeoff_scheme = None
+ ExpDesign.tradeoff_scheme = None
# MetaModelOpts.ExpDesign.n_replication = 50
# -------- Exploration ------
# 1)'Voronoi' 2)'random' 3)'latin_hypercube' 4)'dual annealing'
- MetaModelOpts.ExpDesign.explore_method = 'latin_hypercube'
+ ExpDesign.explore_method = 'latin_hypercube'
# Use when 'dual annealing' chosen
- MetaModelOpts.ExpDesign.max_func_itr = 200
+ ExpDesign.max_func_itr = 200
# Use when 'Voronoi' or 'random' or 'latin_hypercube' chosen
- MetaModelOpts.ExpDesign.n_canddidate = 5000
- MetaModelOpts.ExpDesign.n_cand_groups = 4
+ ExpDesign.n_canddidate = 5000
+ ExpDesign.n_cand_groups = 4
# -------- Exploitation ------
# 1)'BayesOptDesign' 2)'VarOptDesign' 3)'alphabetic' 4)'Space-filling'
- MetaModelOpts.ExpDesign.exploit_method = 'Space-filling'
+ ExpDesign.exploit_method = 'Space-filling'
# BayesOptDesign -> when data is available
# 1)DKL (Kullback-Leibler Divergence) 2)DPP (D-Posterior-percision)
@@ -191,31 +198,33 @@ if __name__ == "__main__":
# VarBasedOptDesign -> when data is not available
# Only with Vornoi >>> 1)Entropy 2)EIGF, 3)ALM, 4)LOOCV
- MetaModelOpts.ExpDesign.util_func = 'ALM'
+ ExpDesign.util_func = 'ALM'
# alphabetic
# 1)D-Opt (D-Optimality) 2)A-Opt (A-Optimality)
# 3)K-Opt (K-Optimality)
# MetaModelOpts.ExpDesign.util_func = 'D-Opt'
- MetaModelOpts.valid_samples = np.load("data/valid_samples.npy")
- MetaModelOpts.valid_model_runs = {
- 'flow rate [m$^3$/yr]': np.load("data/valid_outputs.npy")
- }
+ valid_samples = np.load("data/valid_samples.npy")
+ #valid_model_runs = {'flow rate [m$^3$/yr]': np.load("data/valid_outputs.npy")}
+ valid_model_runs = {'flow rate': np.load("data/valid_outputs.npy")}
# >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
# Use MetaModelEngine for sequential experimental design
- meta_model_engine = MetaModelEngine(MetaModelOpts)
- meta_model_engine.run()
- PCEModel = meta_model_engine.MetaModel
+
+ MetaModelOpts.ExpDesign = ExpDesign
+ engine = Engine(MetaModelOpts, Model, ExpDesign)
+ engine.start_engine()
+# engine.train_sequential()
+ engine.train_sequential(parallel = True)
# Save PCE models
with open(f'PCEModel_{Model.name}.pkl', 'wb') as output:
- joblib.dump(PCEModel, output, 2)
+ joblib.dump(engine.MetaModel, output, 2)
# =====================================================
# ========= POST PROCESSING OF METAMODELS ===========
# =====================================================
- PostPCE = PostProcessing(PCEModel)
+ PostPCE = PostProcessing(engine)
# Plot to check validation visually.
PostPCE.valid_metamodel(n_samples=200)
diff --git a/examples/convergence_tests/convergence_test.py b/examples/convergence_tests/convergence_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc56e4cb897cde5cf95c5b832002814da831b663
--- /dev/null
+++ b/examples/convergence_tests/convergence_test.py
@@ -0,0 +1,106 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import matplotlib.pyplot as plt
+import time
+
+def convergence_test(engine, steps, error, train_samples, test_samples,
+ test_data, plot = True, write = True):
+ """
+ Tests the convergence for a metamodel over the number of given training
+ samples in its experimental design.
+ The results are written out and plotted.
+
+ Conditions:
+ If data is not given, then arbitrary samples will be chosen to run the
+ model on
+
+ Parameters
+ ----------
+ engine : bayesvalidrox.Engine
+ Engine with model and metamodel to train. The ExpDesign needs to exist,
+ but sampling method and more will be set in this function
+ steps : list of ints in increasing order
+ The number of samples to train on per iteration
+ error : function(test_mean, test_std, test_data)
+ The function ued to calculate the accuracy of the surrogate,
+ e.g. the L2-norm
+ train_samples : 2d np.array
+ The samples to train on
+ test_samples : 2d np.array
+ The samples to use for the accuracy tests of the trained metamodels
+ test_data : dict of np.arrays
+ Model evaluations matching the test_samples
+ plot : bool, optional
+ Sets if the accuracy should be visualized
+ write : bool, optional
+
+ Returns
+ -------
+ engines : The trained engines
+ accuracy : Results of the accuracy measures on the engines
+
+ """
+ print('')
+ print('STARTING THE CONVERGENCE TEST')
+ # Define output lists
+ engines = []
+ test_runs = {}
+ test_runs['mean'] = []
+ test_runs['std']=[]
+ accuracy = {}
+
+ for n in steps:
+ print('')
+ print(f'---- {n} TRAINING POINTS ----')
+ print('')
+ samples = train_samples[:n]
+
+ # Redo the explerimental design
+ engine.ExpDesign.method = 'normal'
+ engine.ExpDesign.n_init_samples = n
+ engine.ExpDesign.sampling_method = 'user'
+ engine.ExpDesign.X = samples
+ engine.ExpDesign.Y = None
+ engine.start_engine()
+
+ # Train and evaluate
+ start = time.time()
+ engine.train_normal()
+ end = time.time()
+
+ print('')
+ print('Evaluating the metamodel')
+ surrogate = engine.MetaModel
+ test_mean, test_std = surrogate.eval_metamodel(test_samples)
+
+ # Add the results
+ engines.append(engine)
+ test_runs['mean'].append(test_mean)
+ test_runs['std'].append(test_std)
+
+ # Add training time
+ if 'time' not in accuracy.keys():
+ accuracy['time'] = []
+ accuracy['time'].append(end-start)
+
+ # Add error measures
+ er = error(test_mean, test_std, test_data)
+ for key in er:
+ if key not in accuracy.keys():
+ accuracy[key] = []
+ accuracy[key].append(er[key])
+
+ # Save the results?
+ if write:
+ None
+
+ # Plot the results
+ for key in accuracy:
+ if plot:
+ plt.plot(steps, accuracy[key], linestyle = 'dashed')
+ plt.scatter(steps, accuracy[key], label = key)
+ plt.legend()
+ plt.show()
+
+ return engines, accuracy
diff --git a/examples/convergence_tests/example_trainingconvergence.py b/examples/convergence_tests/example_trainingconvergence.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1aea4fe0de0d6467d525564965f8b6e052adda4
--- /dev/null
+++ b/examples/convergence_tests/example_trainingconvergence.py
@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This example shows comparisons of three regression options for the aPC in
+bayesvalidrox: OLS, FastARD and OLS with constraints.
+The trained surrogates are compared with respect to the here defined error
+measure and their computational cost for different numbers of training samples.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import random
+
+from bayesvalidrox.pylink.pylink import PyLinkForwardModel
+from bayesvalidrox.surrogate_models.inputs import Input
+from bayesvalidrox.surrogate_models.engine import Engine
+from bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
+import bayesvalidrox.surrogate_models.surrogate_models as sm
+
+from convergence_test import convergence_test
+from testfunction import testfunction
+
+def error(mean, std, true):
+ """
+ Error function to evaluate the metamodels
+ Returns a dictionary of measures.
+ Here:
+ 'L2': standard L2 error
+
+ Parameters
+ ----------
+ mean : dict
+ Mean-part of surrogate evaluation
+ std : dict
+ Std-part of surrogate evaluation
+ true : dict
+ Mean-part of model evaluation
+
+ Returns
+ -------
+ dict
+ Results of error measures
+
+ """
+ m = mean['Z']
+ t = true['Z']
+ diff = m-t
+ return {'L2': np.linalg.norm(diff)}
+
+
+if __name__=="__main__":
+ random.seed(15)
+
+ # Some model properties and settings
+ t = np.linspace(0,10,10)
+ ndim = 2
+
+ # Plot some evaluations of the model
+ xx, yy = np.meshgrid(np.linspace(0,1,10),np.linspace(0,1,10))
+ x = np.swapaxes(np.vstack([ xx.reshape(-1), yy.reshape(-1) ]),0,1)
+ out_list = []
+ for x_ in x:
+ out_list.append(testfunction([x_],t)['Z'])
+ out = np.array(out_list)
+ mean_out = np.mean(out, axis = 0)
+ std_out = np.std(out, axis = 0)
+
+ for i in out:
+ plt.plot(t,i,alpha = 0.2, color = 'blue')
+ plt.show()
+ plt.plot(t, mean_out)
+ plt.show()
+ plt.plot(t, std_out)
+ plt.show()
+
+ # Define the model
+ Model = PyLinkForwardModel()
+ Model.link_type = 'Function'
+ Model.py_file = 'testfunction'
+ Model.name = 'testfunction'
+ Model.Output.names = ['Z']
+ Model.func_args = {'t':t}
+
+ # Define the uncertain parameters with their mean and
+ # standard deviation
+ Inputs = Input()
+ priors = []
+ for i in range(ndim):
+ Inputs.add_marginals()
+ Inputs.Marginals[i].name = "$\\theta_{"+str(i+1)+"}$"
+ Inputs.Marginals[i].dist_type = 'uniform'
+ Inputs.Marginals[i].parameters = [0, 1]
+ priors.append(np.random.uniform(0,1,1000))
+ priors= np.swapaxes(np.array(priors),0,1)
+
+ # Parameters for the convergence test
+ mini = 2**ndim
+ steps = [10,20,50]
+ steps = [mini, 6, 8, 10, 15,20,25,30,35,40,50]
+
+ # Get the samples and model evaluations
+ train_samples = priors[:steps[-1]]
+ test_samples = priors[steps[-1]:]
+ test_data, test_std = Model.run_model_parallel(test_samples, mp = False)
+
+#%% RUN THE SURROGATES ON THE FIRST BVR CODE
+
+ # Set the surrogate parameters
+ MetaMod = sm.MetaModel(Inputs)
+ MetaMod.meta_model_type = 'aPCE'
+ MetaMod.pce_reg_method = 'FastARD'
+ MetaMod.bootstrap_method = 'fast'
+ MetaMod.n_bootstrap_itrs = 1
+ MetaMod.pce_deg = np.arange(1,2)
+ MetaMod.pce_q_norm = 0.85 if ndim < 5 else 0.5
+
+ # Properties for Physics-informed training
+ #MetaMod.LAR = True
+ MetaMod.apply_constraints = False
+
+ # Init ExpDesign - the rest of the ExpDesign is set in convergence_test
+ ExpDesign = ExpDesigns(Inputs)
+
+ # Start engine
+ engine = Engine(MetaMod, Model, ExpDesign)
+
+ # Run the convergence test
+ results_FastArd = convergence_test(engine, steps, error, train_samples, test_samples, test_data)
+
+ # Change the loss function base-type
+ new_type = 'ols'
+ engine.MetaModel.pce_reg_method = new_type
+ results_OLS = convergence_test(engine, steps, error, train_samples, test_samples, test_data)
+
+ # Do OLS with Constraints
+ # The actual constraints can currently be found and changed in
+ # bayesvalidrox.surrogate_models.MetaModel.regression(). This interface
+ # will be improved in future releases
+ engine.MetaModel.apply_constraints = True
+ engine.MetaModel.init_type = 'zeros'
+ results_PI_0rand = convergence_test(engine, steps, error, train_samples, test_samples, test_data)
+
+ # Plot the results
+ for key in results_FastArd[1]:
+ plt.plot(steps, results_OLS[1][key], linestyle = 'dashdot')
+ plt.scatter(steps, results_OLS[1][key], label = 'OLS')
+ plt.plot(steps, results_FastArd[1][key], linestyle = 'dashed')
+ plt.scatter(steps, results_FastArd[1][key], label = 'FastARD')
+ plt.plot(steps, results_PI_0rand[1][key], linestyle = 'dotted')
+ plt.scatter(steps, results_PI_0rand[1][key], label = 'constrained')
+ plt.legend()
+ plt.title(f'Results for {key}')
+ plt.show()
+
+ # Get test statistics
+ meanOLS, test_std = results_OLS[0][-1].eval_metamodel(test_samples)
+ meanARD, test_std = results_FastArd[0][-1].eval_metamodel(test_samples)
+ meanPI, test_std = results_PI_0rand[0][-1].eval_metamodel(test_samples)
+
+ # Plot test statistics
+ plt.plot(t, np.mean(meanOLS['Z'], axis = 0), label = 'OLS', linestyle = 'dashdot')
+ plt.plot(t, np.mean(meanARD['Z'], axis = 0), label = 'FastARD', linestyle = 'dashed')
+ plt.plot(t, np.mean(meanPI['Z'], axis = 0), label = 'constrained', linestyle = 'dotted')
+ plt.plot(t, mean_out, label = 'data')
+ plt.title('Approximation of mean')
+ plt.legend()
+ plt.show()
+ plt.plot(t, np.std(meanOLS['Z'], axis = 0), label = 'OLS', linestyle = 'dashdot')
+ plt.plot(t, np.std(meanARD['Z'], axis = 0), label = 'FastARD', linestyle = 'dashed')
+ plt.plot(t, np.std(meanPI['Z'], axis = 0), label = 'constrained', linestyle = 'dotted')
+ plt.plot(t, std_out, label = 'data')
+ plt.title('Approximation of std')
+ plt.legend()
+ plt.show()
+
+
\ No newline at end of file
diff --git a/examples/convergence_tests/testfunction.py b/examples/convergence_tests/testfunction.py
new file mode 100644
index 0000000000000000000000000000000000000000..eead90bb1c47e728cc36281c1689776aef21579d
--- /dev/null
+++ b/examples/convergence_tests/testfunction.py
@@ -0,0 +1,15 @@
+# -*- coding: utf-8 -*-
+"""
+Short testfunction for the convergence tests.
+"""
+import numpy as np
+
+def testfunction(x, t):
+ def sig(x):
+ return 1/(1 + np.exp(-x))
+ # X is 2d array
+ x = x[0]
+ out = 30-np.sqrt(t*(x[1]+0.00000001))-x[0]*t-2*np.log(t+1)+x[0]/3-10*x[1]*np.tanh(t-5) -7/(x[0]+0.1)*x[1]*sig(t-9)
+ #out = -10*sig(t-8)
+ out /= 40
+ return {'x_values': t, 'Z': out}
\ No newline at end of file
diff --git a/examples/ishigami/test_ishigami.py b/examples/ishigami/example_ishigami.py
similarity index 80%
rename from examples/ishigami/test_ishigami.py
rename to examples/ishigami/example_ishigami.py
index 313ad38280acfe6bf3a4b8c29b1f94d74da399b7..72c9dc2d31e93601cbbd60bc40b88a31a73bd5b9 100644
--- a/examples/ishigami/test_ishigami.py
+++ b/examples/ishigami/example_ishigami.py
@@ -15,8 +15,6 @@ of Stuttgart, www.iws.uni-stuttgart.de/lh2/
Pfaffenwaldring 61
70569 Stuttgart
-Created on Wed Jul 10 2019
-
"""
import numpy as np
@@ -27,13 +25,16 @@ import joblib
import sys
sys.path.append("../../src/bayesvalidrox/")
-from pylink.pylink import PyLinkForwardModel
-from surrogate_models.inputs import Input
-from surrogate_models.surrogate_models import MetaModel
-from surrogate_models.meta_model_engine import MetaModelEngine
-from post_processing.post_processing import PostProcessing
-from bayes_inference.bayes_inference import BayesInference
-from bayes_inference.discrepancy import Discrepancy
+from bayesvalidrox.pylink.pylink import PyLinkForwardModel
+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.surrogate_models.meta_model_engine import MetaModelEngine
+from bayesvalidrox.post_processing.post_processing import PostProcessing
+from bayesvalidrox.bayes_inference.bayes_inference import BayesInference
+from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
+
+from bayesvalidrox.surrogate_models.engine import Engine
import matplotlib
matplotlib.use('agg')
@@ -110,45 +111,44 @@ if __name__ == "__main__":
# ------------------------------------------------
# ------ Experimental Design Configuration -------
# ------------------------------------------------
- MetaModelOpts.add_ExpDesign()
+ ExpDesign = ExpDesigns(Inputs)
# One-shot (normal) or Sequential Adaptive (sequential) Design
- MetaModelOpts.ExpDesign.method = 'normal'
- MetaModelOpts.ExpDesign.n_init_samples = 50
+ ExpDesign.n_init_samples = 50
# Sampling methods
# 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley 6) korobov
# 7) chebyshev(FT) 8) grid(FT) 9) nested_grid(FT) 10)user
- MetaModelOpts.ExpDesign.sampling_method = 'latin_hypercube'
+ ExpDesign.sampling_method = 'latin_hypercube'
# Provide the experimental design object with a hdf5 file
# MetaModelOpts.ExpDesign.hdf5_file = 'ExpDesign_Ishigami.hdf5'
# Sequential experimental design (needed only for sequential ExpDesign)
- MetaModelOpts.ExpDesign.n_new_samples = 1
- MetaModelOpts.ExpDesign.n_max_samples = 200 # 150
- MetaModelOpts.ExpDesign.mod_LOO_threshold = 1e-16
+ ExpDesign.n_new_samples = 1
+ ExpDesign.n_max_samples = 200 # 150
+ ExpDesign.mod_LOO_threshold = 1e-16
# ------------------------------------------------
# ------- Sequential Design configuration --------
# ------------------------------------------------
# 1) None 2) 'equal' 3)'epsilon-decreasing' 4) 'adaptive'
- MetaModelOpts.ExpDesign.tradeoff_scheme = None
+ ExpDesign.tradeoff_scheme = None
# MetaModelOpts.ExpDesign.n_replication = 50
# -------- Exploration ------
# 1)'Voronoi' 2)'random' 3)'latin_hypercube' 4)'dual annealing'
- MetaModelOpts.ExpDesign.explore_method = 'latin_hypercube'
+ ExpDesign.explore_method = 'latin_hypercube'
# Use when 'dual annealing' chosen
- MetaModelOpts.ExpDesign.max_func_itr = 200
+ ExpDesign.max_func_itr = 200
# Use when 'Voronoi' or 'random' or 'latin_hypercube' chosen
- MetaModelOpts.ExpDesign.n_canddidate = 1000
- MetaModelOpts.ExpDesign.n_cand_groups = 4
+ ExpDesign.n_canddidate = 1000
+ ExpDesign.n_cand_groups = 4
# -------- Exploitation ------
# 1)'BayesOptDesign' 2)'VarOptDesign' 3)'alphabetic' 4)'Space-filling'
- MetaModelOpts.ExpDesign.exploit_method = 'Space-filling'
+ ExpDesign.exploit_method = 'Space-filling'
# BayesOptDesign -> when data is available
# 1)DKL (Kullback-Leibler Divergence) 2)DPP (D-Posterior-percision)
@@ -157,28 +157,29 @@ if __name__ == "__main__":
# VarBasedOptDesign -> when data is not available
# Only with Vornoi >>> 1)Entropy 2)EIGF, 3)ALM, 4)LOOCV
- MetaModelOpts.ExpDesign.util_func = 'ALM'
+ ExpDesign.util_func = 'ALM'
# alphabetic
# 1)D-Opt (D-Optimality) 2)A-Opt (A-Optimality)
# 3)K-Opt (K-Optimality)
# MetaModelOpts.ExpDesign.util_func = 'D-Opt'
- MetaModelOpts.valid_samples = np.load("data/valid_samples.npy")
- MetaModelOpts.valid_model_runs = {'Z': np.load("data/valid_outputs.npy")}
+ ExpDesign.valid_samples = np.load("data/valid_samples.npy")
+ ExpDesign.valid_model_runs = {'Z': np.load("data/valid_outputs.npy")}
# >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
- meta_model_engine = MetaModelEngine(MetaModelOpts)
- meta_model_engine.run()
- PCEModel = meta_model_engine.MetaModel
+ MetaModelOpts.ExpDesign = ExpDesign
+ engine = Engine(MetaModelOpts, Model, ExpDesign)
+ engine.start_engine()
+ engine.train_sequential()
# Save PCE models
with open(f'PCEModel_{Model.name}.pkl', 'wb') as output:
- joblib.dump(PCEModel, output, 2)
+ joblib.dump(engine.MetaModel, output, 2)
# =====================================================
# ========= POST PROCESSING OF METAMODELS ===========
# =====================================================
- PostPCE = PostProcessing(PCEModel)
+ PostPCE = PostProcessing(engine)
# Plot to check validation visually.
PostPCE.valid_metamodel(n_samples=200)
diff --git a/examples/model-comparison/test_model_comparison.py b/examples/model-comparison/example_model_comparison.py
similarity index 75%
rename from examples/model-comparison/test_model_comparison.py
rename to examples/model-comparison/example_model_comparison.py
index d5f523871d99b5115437c2f66e8a1ce2a845f503..d678898c178e9722445f08bd22e3ec93906e2ac7 100644
--- a/examples/model-comparison/test_model_comparison.py
+++ b/examples/model-comparison/example_model_comparison.py
@@ -14,27 +14,31 @@ University of Stuttgart, www.iws.uni-stuttgart.de/lh2/
Pfaffenwaldring 61
70569 Stuttgart
-Created on Wed Oct 10 2021
-
"""
+from copy import deepcopy
import numpy as np
import scipy.io as io
import pandas as pd
import joblib
import sys
sys.path.append("../../src/bayesvalidrox/")
-
-from pylink.pylink import PyLinkForwardModel
-from surrogate_models.inputs import Input
-from surrogate_models.surrogate_models import MetaModel
-from post_processing.post_processing import PostProcessing
-from bayes_inference.bayes_inference import BayesInference
+sys.path.append("../../src/")
+
+from bayesvalidrox.pylink.pylink import PyLinkForwardModel
+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.surrogate_models.meta_model_engine import MetaModelEngine
+from bayesvalidrox.post_processing.post_processing import PostProcessing
+from bayesvalidrox.bayes_inference.bayes_inference import BayesInference
+from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
from bayes_inference.bayes_model_comparison import BayesModelComparison
-from bayes_inference.discrepancy import Discrepancy
-if __name__ == "__main__":
+from bayesvalidrox.surrogate_models.engine import Engine
+
+if __name__ == "__main__":
# Read data
sigma = 0.6
data = {
@@ -168,33 +172,52 @@ if __name__ == "__main__":
# ------------------------------------------------
# ------ Experimental Design Configuration -------
# ------------------------------------------------
- L2_MetaModelOpts.add_ExpDesign()
+ #L2_MetaModelOpts.add_ExpDesign()
+ L2_ExpDesign = ExpDesigns(L2_Inputs)
# One-shot (normal) or Sequential Adaptive (sequential) Design
- L2_MetaModelOpts.ExpDesign.method = 'normal'
- L2_MetaModelOpts.ExpDesign.n_init_samples = 100
+ L2_ExpDesign.n_init_samples = 100
# Sampling methods
# 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley
# 6) chebyshev(FT) 7) grid(FT) 8)user
- L2_MetaModelOpts.ExpDesign.sampling_method = 'latin_hypercube'
+ L2_ExpDesign.sampling_method = 'latin_hypercube'
# ------ Nonlinear cosine model ---------
- NL2_MetaModelOpts = L2_MetaModelOpts.copy_meta_model_opts(NL2_Inputs)
+ NL2_ExpDesign = ExpDesigns(NL2_Inputs)
+ NL2_ExpDesign.method = 'normal'
+ NL2_ExpDesign.n_init_samples = 100
+ NL2_ExpDesign.sampling_method = 'latin_hypercube'
+ NL2_MetaModelOpts = deepcopy(L2_MetaModelOpts)
+ NL2_MetaModelOpts.input_obj = NL2_Inputs
# ------ Nonlinear cosine model ---------
- NL4_MetaModelOpts = L2_MetaModelOpts.copy_meta_model_opts(NL4_Inputs)
+ NL4_MetaModelOpts = deepcopy(L2_MetaModelOpts)
+ NL4_MetaModelOpts.input_obj = NL4_Inputs
+ NL4_ExpDesign = ExpDesigns(NL4_Inputs)
+ NL4_ExpDesign.method = 'normal'
+ NL4_ExpDesign.n_init_samples = 100
+ NL4_ExpDesign.sampling_method = 'latin_hypercube'
# >>>>>> Train the Surrogates <<<<<<<<<<<
- L2_MetaModel = L2_MetaModelOpts.create_metamodel(myL2Model)
- NL2_MetaModel = NL2_MetaModelOpts.create_metamodel(myNL2Model)
- NL4_MetaModel = NL4_MetaModelOpts.create_metamodel(myNL4Model)
+ L2_engine = Engine(L2_MetaModelOpts, myL2Model, L2_ExpDesign)
+ L2_engine.start_engine()
+ L2_engine.train_normal()
+ NL2_engine = Engine(NL2_MetaModelOpts, myNL2Model, NL2_ExpDesign)
+ NL2_engine.start_engine()
+ NL2_engine.train_normal()
+ NL4_engine = Engine(NL4_MetaModelOpts, myNL4Model, NL4_ExpDesign)
+ NL4_engine.start_engine()
+ NL4_engine.train_normal()
+ #L2_MetaModel = L2_MetaModelOpts.create_metamodel(myL2Model)
+ #NL2_MetaModel = NL2_MetaModelOpts.create_metamodel(myNL2Model)
+ #NL4_MetaModel = NL4_MetaModelOpts.create_metamodel(myNL4Model)
# =====================================================
# ========= POST PROCESSING OF METAMODELS ===========
# =====================================================
# ---------- Linear model -------------
- L2_PostPCE = PostProcessing(L2_MetaModel, name=myL2Model.name)
+ L2_PostPCE = PostProcessing(L2_engine, name=myL2Model.name)
# Plot to check validation visually.
L2_PostPCE.valid_metamodel(n_samples=3)
@@ -206,10 +229,10 @@ if __name__ == "__main__":
L2_PostPCE.check_accuracy(n_samples=3000)
# Plot the sobol indices
- sobol_cell, total_sobol = L2_PostPCE.sobol_indices()
+ total_sobol = L2_PostPCE.sobol_indices()
# ---------- Linear model -------------
- NL2_PostPCE = PostProcessing(NL2_MetaModel, name=myNL2Model.name)
+ NL2_PostPCE = PostProcessing(NL2_engine, name=myNL2Model.name)
# Plot to check validation visually.
NL2_PostPCE.valid_metamodel(n_samples=3)
@@ -221,10 +244,10 @@ if __name__ == "__main__":
NL2_PostPCE.check_accuracy(n_samples=3000)
# Plot the sobol indices
- sobol_cell, total_sobol = NL2_PostPCE.sobol_indices()
+ total_sobol = NL2_PostPCE.sobol_indices()
# ------ Nonlinear cosine model ---------
- NL4_PostPCE = PostProcessing(NL4_MetaModel, name=myNL4Model.name)
+ NL4_PostPCE = PostProcessing(NL4_engine, name=myNL4Model.name)
# Plot to check validation visually.
NL4_PostPCE.valid_metamodel(n_samples=3)
@@ -236,7 +259,7 @@ if __name__ == "__main__":
NL4_PostPCE.check_accuracy(n_samples=3000)
# Plot the sobol indices
- sobol_cell, total_sobol = NL4_PostPCE.sobol_indices()
+ total_sobol = NL4_PostPCE.sobol_indices()
# =====================================================
# ========= BAYESIAN MULTIMODEL COMPARISON ===========
@@ -249,15 +272,15 @@ if __name__ == "__main__":
# ----- Define the options model -------
meta_models = {
- "linear": L2_MetaModel,
- "exponential": NL4_MetaModel,
- "cosine": NL4_MetaModel
+ "linear": L2_engine,
+ "exponential": NL2_engine,
+ "cosine": NL4_engine
}
- # BME Bootstrap optuions
+ # BME Bootstrap options
opts_bootstrap = {
"bootstrap": True,
- "n_samples": 10000,
+ "n_samples": 100,#0,#0, # TODO: difference between this and the n_bootstrap set below?
"Discrepancy": DiscrepancyOpts,
"emulator": True,
"plot_post_pred": False
@@ -266,10 +289,10 @@ if __name__ == "__main__":
# Run model comparison
BayesOpts = BayesModelComparison(
justifiability=True,
- n_bootstarp=1000,
- just_n_meas=2
+ n_bootstrap=100,#0,#00,
+ #just_n_meas=2
)
- output_dict = BayesOpts.create_model_comparison(
+ output_dict = BayesOpts.model_comparison_all(
meta_models,
opts_bootstrap
)
diff --git a/examples/pollution/test_pollution.py b/examples/pollution/example_pollution.py
similarity index 100%
rename from examples/pollution/test_pollution.py
rename to examples/pollution/example_pollution.py
diff --git a/examples/pollution/test_valid_pollution.py b/examples/pollution/example_valid_pollution.py
similarity index 87%
rename from examples/pollution/test_valid_pollution.py
rename to examples/pollution/example_valid_pollution.py
index e2aacbe5d2186c38287e552ae588332150dd519b..74b87787b741a0c990c6d1ccbf06596b43256a57 100644
--- a/examples/pollution/test_valid_pollution.py
+++ b/examples/pollution/example_valid_pollution.py
@@ -13,8 +13,6 @@ of Stuttgart, www.iws.uni-stuttgart.de/lh2/
Pfaffenwaldring 61
70569 Stuttgart
-Created on Fri Aug 9 2019
-
"""
import numpy as np
@@ -25,12 +23,15 @@ import joblib
import sys
sys.path.append("../../src/bayesvalidrox/")
-from pylink.pylink import PyLinkForwardModel
-from surrogate_models.inputs import Input
-from surrogate_models.surrogate_models import MetaModel
-from post_processing.post_processing import PostProcessing
-from bayes_inference.bayes_inference import BayesInference
-from bayes_inference.discrepancy import Discrepancy
+from bayesvalidrox.pylink.pylink import PyLinkForwardModel
+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.surrogate_models.meta_model_engine import MetaModelEngine
+from bayesvalidrox.post_processing.post_processing import PostProcessing
+from bayesvalidrox.bayes_inference.bayes_inference import BayesInference
+from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
+from bayesvalidrox.surrogate_models.engine import Engine
if __name__ == "__main__":
@@ -92,7 +93,7 @@ if __name__ == "__main__":
# =====================================================
# ====== POLYNOMIAL CHAOS EXPANSION METAMODELS ======
# =====================================================
- MetaModelOpts = MetaModel(Inputs, Model)
+ MetaModelOpts = MetaModel(Inputs)
# Select if you want to preserve the spatial/temporal depencencies
MetaModelOpts.dim_red_method = 'PCA'
@@ -136,27 +137,29 @@ if __name__ == "__main__":
# MetaModelOpts.verbose = True
# ------ Experimental Design --------
- MetaModelOpts.add_ExpDesign()
+ ExpDesign = ExpDesigns(Inputs)
# One-shot (normal) or Sequential Adaptive (sequential) Design
- MetaModelOpts.ExpDesign.method = 'normal'
- MetaModelOpts.ExpDesign.n_init_samples = 150
+ ExpDesign.method = 'normal'
+ ExpDesign.n_init_samples = 150
# Sampling methods
# 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley 6) korobov
# 7) chebyshev(FT) 8) grid(FT) 9) nested_grid(FT) 10)user
- MetaModelOpts.ExpDesign.sampling_method = 'latin_hypercube'
+ ExpDesign.sampling_method = 'latin_hypercube'
# Provide the experimental design object with a hdf5 file
# MetaModelOpts.ExpDesign.hdf5_file = 'ExpDesign_AnalyticFunc.hdf5'
# >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
# Adaptive sparse arbitrary polynomial chaos expansion
- PCEModel = MetaModelOpts.create_metamodel()
-
+ engine = Engine(MetaModelOpts, Model, ExpDesign)
+ engine.start_engine()
+ engine.train_normal()
+
# Save PCE models
with open(f"PCEModel_{Model.name}.pkl", "wb") as output:
- joblib.dump(PCEModel, output, 2)
+ joblib.dump(engine.MetaModel, output, 2)
# Load the objects
# with open(f"PCEModel_{Model.name}.pkl", "rb") as input:
@@ -165,7 +168,7 @@ if __name__ == "__main__":
# =====================================================
# ========= POST PROCESSING OF METAMODELS ===========
# =====================================================
- PostPCE = PostProcessing(PCEModel)
+ PostPCE = PostProcessing(engine)
# Plot to check validation visually.
PostPCE.valid_metamodel(n_samples=3)
@@ -188,14 +191,14 @@ if __name__ == "__main__":
# =====================================================
# ======== BAYESIAN INFERENCE (CALIBRATION) ==========
# =====================================================
- BayesOpts = BayesInference(PCEModel)
+ BayesOpts = BayesInference(engine)
BayesOpts.emulator = True
# Select the inference method
BayesOpts.inference_method = "MCMC"
# Set the MCMC parameters
BayesOpts.mcmc_params = {
- 'n_steps': 1e5,
+ 'n_steps': 1e3,#5,
'n_walkers': 30,
'multiprocessing': False,
'verbose': False
@@ -263,20 +266,24 @@ if __name__ == "__main__":
ValidInputs.Marginals[3].name = "$\\tau$"
ValidInputs.Marginals[3].input_data = BayesCalib.posterior_df['$\\tau$']
- MetaModelOpts_valid = MetaModelOpts.copy_meta_model_opts(
- ValidInputs, ValidModel)
- ValidMetaModel = MetaModelOpts_valid.create_metamodel()
+
+ import copy
+ MetaModelOpts_valid = copy.deepcopy(MetaModelOpts)
+ MetaModelOpts_valid.input_obj = ValidInputs
+ valid_engine = Engine(MetaModelOpts_valid, ValidModel, copy.deepcopy(ExpDesign))
+ valid_engine.start_engine()
+ valid_engine.train_normal()
# Save PCE models
with open(f"PCEModel_{ValidModel.name}.pkl", "wb") as output:
- joblib.dump(ValidMetaModel, output, 2)
+ joblib.dump(valid_engine.MetaModel, output, 2)
# Load the objects
# with open(f"PCEModel_{ValidModel.name}.pkl", "rb") as input:
# ValidPCEModel = joblib.load(input)
# ------- Baysian inference definition --------
- BayesOptsValid = BayesInference(ValidMetaModel)
+ BayesOptsValid = BayesInference(valid_engine)
BayesOptsValid.name = "Valid"
BayesOptsValid.emulator = True
diff --git a/examples/umbridge_tsunamitutorial/example_umbridge_testmodel.py b/examples/umbridge_tsunamitutorial/example_umbridge_testmodel.py
new file mode 100644
index 0000000000000000000000000000000000000000..22a0f750e96c4b7bf14913eb97f0040fb360a257
--- /dev/null
+++ b/examples/umbridge_tsunamitutorial/example_umbridge_testmodel.py
@@ -0,0 +1,77 @@
+# -*- coding: utf-8 -*-
+"""
+Example and test for using UM-Bridge with bayesvalidrox.
+This example uses the `testmodel` and sets the model type explicitly as
+UM-Bridge for the pylink model.
+
+An example for using UM-Bridge with an explicitly defined model can be found in
+`example_umbridge_tsunamimodel.py`
+
+"""
+
+import joblib
+import numpy as np
+import umbridge
+import bayesvalidrox as bv
+
+from bayesvalidrox.pylink.pylink import PyLinkForwardModel
+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.post_processing.post_processing import PostProcessing
+from bayesvalidrox.surrogate_models.engine import Engine
+
+
+if __name__ == '__main__':
+
+ # This model has 2 inputs and four outputs
+ n_prior_sample = 1000
+ priors = bv.Input()
+ priors.add_marginals()
+ priors.Marginals[0].name = 'x'
+ priors.Marginals[0].input_data = np.random.uniform(50,150,n_prior_sample)
+
+ # Define the model directly via Pylink
+ model = PyLinkForwardModel()
+ model.link_type = 'umbridge'
+ model.host = 'http://testmodel.linusseelinger.de'
+ model.modelparams = {}
+ model.name = 'testmodel'
+ model.Output.names = ['y']
+ model.x_values = np.array([0])
+
+ # Create the surrogate
+ surrogate_opts = MetaModel(priors)
+
+ # Select the surrogate type and properties
+ surrogate_opts.meta_model_type = 'aPCE'
+ surrogate_opts.pce_reg_method = 'FastARD'
+ surrogate_opts.pce_deg = np.arange(1, 5)
+ surrogate_opts.pce_q_norm = 0.4
+
+ # Define the experimental design and sampling methods
+ ExpDesign = ExpDesigns(priors)
+ ExpDesign.n_init_samples = 50
+ ExpDesign.sampling_method = 'latin-hypercube'
+
+ # Start and run the engine
+ engine = Engine(surrogate_opts, model, ExpDesign)
+ engine.start_engine()
+ engine.train_normal(parallel = False)
+ print('Surrogate completed')
+ print('')
+
+ # Save surrogate
+ with open('testmodel.pk1', 'wb') as output:
+ joblib.dump(engine.MetaModel, output, 2)
+
+ # Post processing
+ L2_PostPCE = PostProcessing(engine)
+ L2_PostPCE.plot_moments(plot_type='line')
+
+ # Plot to check validation visually.
+ L2_PostPCE.valid_metamodel(n_samples=1)
+
+ # Compute and print RMSE error
+ L2_PostPCE.check_accuracy(n_samples=300)
+ total_sobol = L2_PostPCE.sobol_indices()
diff --git a/examples/umbridge_tsunamitutorial/example_umbridge_tsunamitutorial.py b/examples/umbridge_tsunamitutorial/example_umbridge_tsunamitutorial.py
new file mode 100644
index 0000000000000000000000000000000000000000..c456787d0c5d9d9714083b406895b8096a4d16ce
--- /dev/null
+++ b/examples/umbridge_tsunamitutorial/example_umbridge_tsunamitutorial.py
@@ -0,0 +1,183 @@
+# -*- coding: utf-8 -*-
+"""
+Example and test for using UM-Bridge with bayesvalidrox.
+Example model is the tsunami model described here:
+ https://um-bridge-benchmarks.readthedocs.io/en/docs/inverse-benchmarks/exahype-tsunami.html
+It needs to run separately while starting this example.
+ docker run -it -p 4242:4242 -v ~/tsunami_output:/output linusseelinger/model-exahype-tsunami
+
+This example uses models that are exmplicitly defined as functions outside of
+PyLink. An example for the link_type 'umbdridge' can be found in
+`example_umbridge_testmodel.py`.
+
+"""
+
+import copy
+import joblib
+import numpy as np
+import pandas as pd
+import umbridge
+
+import bayesvalidrox as bv
+from tsunami_model import tsunami_model
+
+if __name__ == '__main__':
+ # This model has 2 inputs and four outputs
+ n_prior_sample = 1000
+ priors = bv.Input()
+ priors.add_marginals()
+ priors.Marginals[0].name = 'x'
+ priors.Marginals[0].input_data = np.random.uniform(50,150,n_prior_sample)
+ priors.add_marginals()
+ priors.Marginals[1].name = 'y'
+ priors.Marginals[1].input_data = np.random.uniform(50,150,n_prior_sample)
+
+ # Define the model - level 0
+ model0 = bv.PyLinkForwardModel()
+ model0.link_type = 'function'
+ model0.py_file = 'tsunami_model'
+ model0.name = 'tsunami_model'
+ model0.Output.names = ['T1', 'T2', 'H1', 'H2']
+ #model.observations = data_dict1_times
+
+ # Define the model - level 1
+ model1 = bv.PyLinkForwardModel()
+ model1.link_type = 'function'
+ model1.py_file = 'tsunami_model1'
+ model1.name = 'tsunami_model1'
+ model1.Output.names = ['T1', 'T2', 'H1', 'H2']
+ #model.observations = data_dict1_times
+
+ # Create the surrogate
+ surrogate_opts0 = bv.MetaModel(priors, model0)
+
+ # Select your metamodel method
+ surrogate_opts0.meta_model_type = 'aPCE'
+ surrogate_opts0.pce_reg_method = 'FastARD'
+ surrogate_opts0.pce_deg = np.arange(1, 5)
+ surrogate_opts0.pce_q_norm = 0.4#1.0
+
+ # Define ExpDesign - this is the same for both models
+ ExpDesign0 = bv.ExpDesigns(priors)
+ ExpDesign0.method = 'normal'
+ ExpDesign0.n_init_samples = 50
+ ExpDesign0.sampling_method = 'latin-hypercube'
+ ExpDesign1 = copy.deepcopy(ExpDesign0)
+
+ # Start and run the engine
+ engine0 = bv.Engine(surrogate_opts0, model0, ExpDesign0)
+ engine0.start_engine()
+ engine0.train_normal(parallel = False)
+ print('Surrogate 0 completed')
+ print('')
+
+ # Create the surrogate
+ surrogate_opts1 = bv.MetaModel(priors, model1)
+
+ # Select your metamodel method
+ surrogate_opts1.meta_model_type = 'aPCE'
+ surrogate_opts1.pce_reg_method = 'FastARD'
+ surrogate_opts1.pce_deg = np.arange(1, 5)
+ surrogate_opts1.pce_q_norm = 0.4#1.0
+
+ # Start and run the engine
+ engine1 = bv.Engine(surrogate_opts1, model1, ExpDesign1)
+ engine1.start_engine()
+ engine1.train_normal(parallel = False)
+ print('Surrogate 1 completed')
+ print('')
+
+ # Save surrogate
+ with open('tsunami0.pk1', 'wb') as output:
+ joblib.dump(engine0.MetaModel, output, 2)
+
+ # Save surrogate
+ with open('tsunami1.pk1', 'wb') as output:
+ joblib.dump(engine1.MetaModel, output, 2)
+
+ # Post processing on model 1
+ L2_PostPCE = bv.PostProcessing(engine1)
+ L2_PostPCE.plot_moments(plot_type='line')
+ # Plot to check validation visually.
+ L2_PostPCE.valid_metamodel(n_samples=1)
+
+ # Compute and print RMSE error
+ L2_PostPCE.check_accuracy(n_samples=300)
+ total_sobol = L2_PostPCE.sobol_indices()
+
+ # Get reference evaluation as 'true data'
+ true_data = tsunami_model(np.array([[90.0,60.0]]))
+ model0.observations = true_data
+ model1.observations = true_data
+ true_data_nox = copy.deepcopy(true_data)
+ true_data_nox.pop('x_values')
+
+ # Direct surrogate evaluation
+ mean, std = engine1.MetaModel.eval_metamodel(np.array([[90.0,60.0]]))
+
+ # Bayesian Inference
+ # Set uncertainty
+ obsData = pd.DataFrame(true_data_nox, columns=model0.Output.names)
+ DiscrepancyOpts = bv.Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData*0.15)**2
+
+ # Parameter estimation / single model validation via TOM for model 1
+ BayesOpts = bv.BayesInference(engine1)
+ BayesOpts.emulator= True
+ BayesOpts.plot_post_pred = True
+ #BayesOpts.inference_method = 'rejection'
+ BayesOpts.Discrepancy = DiscrepancyOpts
+ BayesOpts.bootstrap = True
+ BayesOpts.n_bootstrap_itrs = 10
+ BayesOpts.bootstrap_noise = 0.05
+ BayesOpts.out_dir = ''
+
+ # Set the MCMC parameters
+ import emcee
+ BayesOpts.inference_method = "MCMC"
+ BayesOpts.mcmc_params = {
+ 'n_steps': 1e3,
+ 'n_walkers': 30,
+ 'moves': emcee.moves.KDEMove(),
+ 'multiprocessing': False,
+ 'verbose': False
+ }
+
+ Bayes = BayesOpts.create_inference()
+
+ # Pkl the inference results
+ with open(f'Bayes_{model0.name}.pkl', 'wb') as output:
+ joblib.dump(Bayes, output, 2)
+
+ # Model Comparison
+ # Set the models
+ meta_models = {
+ "M0": engine0,
+ "M1": engine1,
+ }
+
+ # BME Bootstrap options
+ opts_bootstrap = {
+ "bootstrap": True,
+ "n_samples": 1000,
+ "Discrepancy": DiscrepancyOpts,
+ "emulator": True,
+ "plot_post_pred": False
+ }
+
+ # Run model comparison
+ BayesOpts = bv.BayesModelComparison(
+ justifiability=True,
+ n_bootstarp=10,
+ just_n_meas=2
+ )
+ output_dict = BayesOpts.create_model_comparison(
+ meta_models,
+ opts_bootstrap
+ )
+
+ # save model comparison results
+ with open('ModelComparison.pkl', 'wb') as f:
+ joblib.dump(output_dict, f)
+
\ No newline at end of file
diff --git a/examples/umbridge_tsunamitutorial/tsunami_model.py b/examples/umbridge_tsunamitutorial/tsunami_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d837a9ae4bb27e8ef2ca622011e3e67012f2e94
--- /dev/null
+++ b/examples/umbridge_tsunamitutorial/tsunami_model.py
@@ -0,0 +1,16 @@
+# -*- coding: utf-8 -*-
+"""
+Runs the umbridge command for the tsunami model.
+"""
+import umbridge
+import numpy as np
+
+def tsunami_model(params):
+ model = umbridge.HTTPModel('http://localhost:4242', 'forward')
+ out = np.array(model(np.ndarray.tolist(params), {'level':0}))
+
+ return {'T1':out[:,0], 'T2':out[:,1], 'H1':out[:,2], 'H2':out[:,3],
+ 'x_values':[0]}
+
+
+
\ No newline at end of file
diff --git a/examples/umbridge_tsunamitutorial/tsunami_model1.py b/examples/umbridge_tsunamitutorial/tsunami_model1.py
new file mode 100644
index 0000000000000000000000000000000000000000..239670773cf71e84b4ef0caec35c4e024ca1bd66
--- /dev/null
+++ b/examples/umbridge_tsunamitutorial/tsunami_model1.py
@@ -0,0 +1,14 @@
+# -*- coding: utf-8 -*-
+"""
+Runs the umbridge command for the tsunami model on level 1.
+"""
+import umbridge
+import numpy as np
+
+def tsunami_model1(params):
+ model = umbridge.HTTPModel('http://localhost:4242', 'forward')
+ out = np.array(model(np.ndarray.tolist(params), {'level':1}))
+
+ return {'T1': out[:,0], 'T2': out[:,1], 'H1': out[:,2], 'H2': out[:,3], 'x_values': [0]}
+
+
\ No newline at end of file
diff --git a/setup.cfg b/setup.cfg
index 78940cfb54d78b867d8d4447ee25162b9adfed91..b09a32ac89dc7ebcb5d503234d6b656eeb82fc8e 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,8 +1,8 @@
[metadata]
name = bayesvalidrox
-version = 0.0.5
-author = Farid Mohammadi
-author_email = farid.mohammadi@iws.uni-stuttgart.de
+version = 1.0.0
+author = Farid Mohammadi, Rebecca Kohlhaas
+author_email = farid.mohammadi@iws.uni-stuttgart.de, rebecca.kohlhaas@iws.uni-stuttgart.de
description = An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models.
long_description = file: README.md
long_description_content_type = text/markdown
@@ -20,19 +20,20 @@ package_dir =
packages = find:
python_requires = >=3.8
install_requires =
- numpy==1.22.1
- pandas==1.2.4
- joblib==1.0.1
- matplotlib==3.4.2
+ pandas==1.4.4
+ joblib==1.1.1
+ matplotlib==3.8.0
seaborn==0.11.1
scipy==1.7.3
- scikit-learn==0.24.2
+ scikit-learn==1.3.1
tqdm==4.61.1
chaospy==4.3.3
emcee==3.0.2
corner==2.2.1
- h5py==3.2.1
+ h5py==3.9.0
statsmodels==0.13.2
+ multiprocess==0.70.16
+ umbridge==1.2.4
[options.packages.find]
where = src
diff --git a/src/bayesvalidrox.egg-info/PKG-INFO b/src/bayesvalidrox.egg-info/PKG-INFO
new file mode 100644
index 0000000000000000000000000000000000000000..279feebe3b7b4b5a53bdc43208dd65c926f06f2f
--- /dev/null
+++ b/src/bayesvalidrox.egg-info/PKG-INFO
@@ -0,0 +1,87 @@
+Metadata-Version: 2.1
+Name: bayesvalidrox
+Version: 1.0.0
+Summary: An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models.
+Home-page: https://git.iws.uni-stuttgart.de/inversemodeling/bayesian-validation
+Author: Farid Mohammadi, Rebecca Kohlhaas
+Author-email: farid.mohammadi@iws.uni-stuttgart.de, rebecca.kohlhaas@iws.uni-stuttgart.de
+Classifier: Programming Language :: Python :: 3
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown
+License-File: LICENCE.md
+Requires-Dist: pandas==1.4.4
+Requires-Dist: joblib==1.1.1
+Requires-Dist: matplotlib==3.8.0
+Requires-Dist: seaborn==0.11.1
+Requires-Dist: scipy==1.7.3
+Requires-Dist: scikit-learn==1.3.1
+Requires-Dist: tqdm==4.61.1
+Requires-Dist: chaospy==4.3.3
+Requires-Dist: emcee==3.0.2
+Requires-Dist: corner==2.2.1
+Requires-Dist: h5py==3.9.0
+Requires-Dist: statsmodels==0.13.2
+Requires-Dist: multiprocess==0.70.16
+Requires-Dist: umbridge==1.2.4
+
+# BayesValidRox
+
+<div align="center">
+ <img src="https://git.iws.uni-stuttgart.de/inversemodeling/bayesian-validation/-/raw/master/docs/logo/bayesvalidrox-logo.png" alt="bayesvalidrox logo"/>
+</div>
+
+An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models.
+This framework provides an automated workflow for surrogate-based sensitivity analysis, Bayesian calibration, and validation of computational models with a modular structure.
+
+## Authors
+- [@farid](https://git.iws.uni-stuttgart.de/farid)
+
+## Installation
+The best practive is to create a virtual environment and install the package inside it.
+
+To create and activate the virtual environment run the following command in the terminal:
+```bash
+ python3 -m venv bayes_env
+ cd bayes_env
+ source bin/activate
+```
+You can replace `bayes_env` with your preferred name. For more information on virtual environments see [this link](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/).
+
+Now, you can install the latest release of the package on PyPI inside the venv with:
+```bash
+ pip install bayesvalidrox
+```
+and installing the version on the master branch can be done by cloning this repo and installing:
+```bash
+ git clone https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox.git
+ cd bayesvalidrox
+ pip install .
+```
+
+## Features
+* Surrogate modeling with Polynomial Chaos Expansion
+* Global sensitivity analysis using Sobol Indices
+* Bayesian calibration with MCMC using `emcee` package
+* Bayesian validation with model weights for multi-model setting
+
+## Requirements
+* numpy==1.22.1
+* pandas==1.2.4
+* joblib==1.0.1
+* matplotlib==3.4.2
+* seaborn==0.11.1
+* scikit-learn==0.24.2
+* tqdm==4.61.1
+* chaospy==4.3.3
+* emcee==3.0.2
+* corner==2.2.1
+* h5py==3.2.1
+* statsmodels==0.13.2
+
+## TexLive for Plotting with matplotlib
+Here you need super user rights
+```bash
+sudo apt-get install dvipng texlive-latex-extra texlive-fonts-recommended cm-super
+```
diff --git a/src/bayesvalidrox.egg-info/SOURCES.txt b/src/bayesvalidrox.egg-info/SOURCES.txt
new file mode 100644
index 0000000000000000000000000000000000000000..344e9840627bb3e5a89593dbd9256472a8ef41d9
--- /dev/null
+++ b/src/bayesvalidrox.egg-info/SOURCES.txt
@@ -0,0 +1,46 @@
+LICENCE.md
+README.md
+pyproject.toml
+setup.cfg
+src/bayesvalidrox/__init__.py
+src/bayesvalidrox/bayesvalidrox.mplstyle
+src/bayesvalidrox.egg-info/PKG-INFO
+src/bayesvalidrox.egg-info/SOURCES.txt
+src/bayesvalidrox.egg-info/dependency_links.txt
+src/bayesvalidrox.egg-info/requires.txt
+src/bayesvalidrox.egg-info/top_level.txt
+src/bayesvalidrox/bayes_inference/__init__.py
+src/bayesvalidrox/bayes_inference/bayes_inference.py
+src/bayesvalidrox/bayes_inference/bayes_model_comparison.py
+src/bayesvalidrox/bayes_inference/discrepancy.py
+src/bayesvalidrox/bayes_inference/mcmc.py
+src/bayesvalidrox/post_processing/__init__.py
+src/bayesvalidrox/post_processing/post_processing.py
+src/bayesvalidrox/pylink/__init__.py
+src/bayesvalidrox/pylink/pylink.py
+src/bayesvalidrox/surrogate_models/__init__.py
+src/bayesvalidrox/surrogate_models/adaptPlot.py
+src/bayesvalidrox/surrogate_models/apoly_construction.py
+src/bayesvalidrox/surrogate_models/bayes_linear.py
+src/bayesvalidrox/surrogate_models/engine.py
+src/bayesvalidrox/surrogate_models/eval_rec_rule.py
+src/bayesvalidrox/surrogate_models/exp_designs.py
+src/bayesvalidrox/surrogate_models/exploration.py
+src/bayesvalidrox/surrogate_models/glexindex.py
+src/bayesvalidrox/surrogate_models/input_space.py
+src/bayesvalidrox/surrogate_models/inputs.py
+src/bayesvalidrox/surrogate_models/meta_model_engine.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_BayesModelComparison.py
+tests/test_Discrepancy.py
+tests/test_ExpDesign.py
+tests/test_Input.py
+tests/test_InputSpace.py
+tests/test_MetaModel.py
+tests/test_engine.py
+tests/test_polyconst.py
+tests/test_pylink.py
\ No newline at end of file
diff --git a/src/bayesvalidrox.egg-info/dependency_links.txt b/src/bayesvalidrox.egg-info/dependency_links.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/src/bayesvalidrox.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/src/bayesvalidrox.egg-info/requires.txt b/src/bayesvalidrox.egg-info/requires.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cc8edb95d2042c4d448c4885919eea0e9406e805
--- /dev/null
+++ b/src/bayesvalidrox.egg-info/requires.txt
@@ -0,0 +1,14 @@
+pandas==1.4.4
+joblib==1.1.1
+matplotlib==3.8.0
+seaborn==0.11.1
+scipy==1.7.3
+scikit-learn==1.3.1
+tqdm==4.61.1
+chaospy==4.3.3
+emcee==3.0.2
+corner==2.2.1
+h5py==3.9.0
+statsmodels==0.13.2
+multiprocess==0.70.16
+umbridge==1.2.4
diff --git a/src/bayesvalidrox.egg-info/top_level.txt b/src/bayesvalidrox.egg-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2753d5241cbf16d781d567f4c1b679ee870989a4
--- /dev/null
+++ b/src/bayesvalidrox.egg-info/top_level.txt
@@ -0,0 +1 @@
+bayesvalidrox
diff --git a/src/bayesvalidrox/__init__.py b/src/bayesvalidrox/__init__.py
index 55c14687472fe6ff00a2438f31b9ba9ecd2992cd..6c11bd3730d31b602b1b4538de1856de04ffc909 100644
--- a/src/bayesvalidrox/__init__.py
+++ b/src/bayesvalidrox/__init__.py
@@ -1,10 +1,12 @@
# -*- coding: utf-8 -*-
-__version__ = "0.0.5"
+__version__ = "1.0.0"
from .pylink.pylink import PyLinkForwardModel
from .surrogate_models.surrogate_models import MetaModel
-from .surrogate_models.meta_model_engine import MetaModelEngine
+#from .surrogate_models.meta_model_engine import MetaModelEngine
+from .surrogate_models.engine import Engine
from .surrogate_models.inputs import Input
+from .surrogate_models.exp_designs import ExpDesigns
from .post_processing.post_processing import PostProcessing
from .bayes_inference.bayes_inference import BayesInference
from .bayes_inference.bayes_model_comparison import BayesModelComparison
@@ -16,7 +18,9 @@ __all__ = [
"Input",
"Discrepancy",
"MetaModel",
- "MetaModelEngine",
+ #"MetaModelEngine",
+ "Engine",
+ "ExpDesigns",
"PostProcessing",
"BayesInference",
"BayesModelComparison"
diff --git a/src/bayesvalidrox/bayes_inference/bayes_inference.py b/src/bayesvalidrox/bayes_inference/bayes_inference.py
index c8073ba76ebeb3249646e17b7f395f8a2472e2f6..01815f59a07f87426b4903ba21a79b95cc7c16d8 100644
--- a/src/bayesvalidrox/bayes_inference/bayes_inference.py
+++ b/src/bayesvalidrox/bayes_inference/bayes_inference.py
@@ -1,25 +1,25 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
-import numpy as np
-import os
import copy
-import pandas as pd
-from tqdm import tqdm
-from scipy import stats
-import scipy.linalg as spla
-import joblib
-import seaborn as sns
+import gc
+import multiprocessing
+import os
+
import corner
import h5py
-import multiprocessing
-import gc
-from sklearn.metrics import mean_squared_error, r2_score
-from sklearn import preprocessing
-from matplotlib.patches import Patch
import matplotlib.lines as mlines
-from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pylab as plt
+import numpy as np
+import pandas as pd
+import scipy.linalg as spla
+import seaborn as sns
+from matplotlib.backends.backend_pdf import PdfPages
+from matplotlib.patches import Patch
+from scipy import stats
+from sklearn import preprocessing
+from sklearn.metrics import mean_squared_error, r2_score
+from tqdm import tqdm
from .mcmc import MCMC
@@ -28,6 +28,92 @@ plt.style.use(os.path.join(os.path.split(__file__)[0],
'../', 'bayesvalidrox.mplstyle'))
+# -------------------------------------------------------------------------
+def _kernel_rbf(X, hyperparameters):
+ """
+ Isotropic squared exponential kernel.
+
+ Higher l values lead to smoother functions and therefore to coarser
+ approximations of the training data. Lower l values make functions
+ more wiggly with wide uncertainty regions between training data points.
+
+ sigma_f controls the marginal variance of b(x)
+
+ Parameters
+ ----------
+ X : ndarray of shape (n_samples_X, n_features)
+
+ hyperparameters : Dict
+ Lambda characteristic length
+ sigma_f controls the marginal variance of b(x)
+ sigma_0 unresolvable error nugget term, interpreted as random
+ error that cannot be attributed to measurement error.
+ Returns
+ -------
+ var_cov_matrix : ndarray of shape (n_samples_X,n_samples_X)
+ Kernel k(X, X).
+
+ """
+ from sklearn.gaussian_process.kernels import RBF
+ min_max_scaler = preprocessing.MinMaxScaler()
+ X_minmax = min_max_scaler.fit_transform(X)
+
+ nparams = len(hyperparameters)
+ if nparams < 3:
+ raise AttributeError('Provide 3 parameters for the RBF kernel!')
+
+ # characteristic length (0,1]
+ Lambda = hyperparameters[0]
+ # sigma_f controls the marginal variance of b(x)
+ sigma2_f = hyperparameters[1]
+
+ rbf = RBF(length_scale=Lambda)
+ cov_matrix = sigma2_f * rbf(X_minmax)
+
+ # (unresolvable error) nugget term that is interpreted as random
+ # error that cannot be attributed to measurement error.
+ sigma2_0 = hyperparameters[2:]
+ for i, j in np.ndindex(cov_matrix.shape):
+ cov_matrix[i, j] += np.sum(sigma2_0) if i == j else 0
+
+ return cov_matrix
+
+
+# -------------------------------------------------------------------------
+def _logpdf(x, mean, cov):
+ """
+ Computes the likelihood based on a multivariate normal distribution.
+
+ Parameters
+ ----------
+ x : TYPE
+ DESCRIPTION.
+ mean : array_like
+ Observation data.
+ cov : 2d array
+ Covariance matrix of the distribution.
+
+ Returns
+ -------
+ log_lik : float
+ Log likelihood.
+
+ """
+
+ # Tranform into np arrays
+ x = np.array(x)
+ mean = np.array(mean)
+ cov = np.array(cov)
+
+ n = len(mean)
+ L = spla.cholesky(cov, lower=True)
+ beta = np.sum(np.log(np.diag(L)))
+ dev = x - mean
+ alpha = dev.dot(spla.cho_solve((L, True), dev))
+ log_lik = -0.5 * alpha - beta - n / 2. * np.log(2 * np.pi)
+ return log_lik
+
+
class BayesInference:
"""
A class to perform Bayesian Analysis.
@@ -42,7 +128,7 @@ class BayesInference:
of the variance matrix for a multivariate normal likelihood.
name : str, optional
The type of analysis, either calibration (`Calib`) or validation
- (`Valid`). The default is `'Calib'`.
+ (`Valid`). The default is `'Calib'`. # TODO: what is going on here for validation?
emulator : bool, optional
Analysis with emulator (MetaModel). The default is `True`.
bootstrap : bool, optional
@@ -55,11 +141,11 @@ class BayesInference:
A dictionary with the selected indices of each model output. The
default is `None`. If `None`, all measurement points are used in the
analysis.
- samples : array of shape (n_samples, n_params), optional
+ prior_samples : array of shape (n_samples, n_params), optional
The samples to be used in the analysis. The default is `None`. If
None the samples are drawn from the probablistic input parameter
object of the MetaModel object.
- n_samples : int, optional
+ n_prior_samples : int, optional
Number of samples to be used in the analysis. The default is `500000`.
If samples is not `None`, this argument will be assigned based on the
number of samples given.
@@ -125,25 +211,32 @@ class BayesInference:
"""
- def __init__(self, MetaModel, discrepancy=None, emulator=True,
+ def __init__(self, engine, discrepancy=None, emulator=True,
name='Calib', bootstrap=False, req_outputs=None,
- selected_indices=None, samples=None, n_samples=100000,
+ selected_indices=None, prior_samples=None, n_prior_samples=100000,
measured_data=None, inference_method='rejection',
mcmc_params=None, bayes_loocv=False, n_bootstrap_itrs=1,
perturbed_data=[], bootstrap_noise=0.05, just_analysis=False,
valid_metrics=['BME'], plot_post_pred=True,
plot_map_pred=False, max_a_posteriori='mean',
- corner_title_fmt='.2e'):
-
- self.MetaModel = MetaModel
+ corner_title_fmt='.2e', out_dir='', bmc=False):
+
+ self.log_BME_tom = None
+ self.inf_entropy = None
+ self.log_BME = None
+ self.KLD = None
+ self.__mean_pce_prior_pred = None
+ if perturbed_data is None:
+ perturbed_data = []
+ self.engine = engine
self.Discrepancy = discrepancy
self.emulator = emulator
self.name = name
self.bootstrap = bootstrap
self.req_outputs = req_outputs
self.selected_indices = selected_indices
- self.samples = samples
- self.n_samples = n_samples
+ self.prior_samples = prior_samples
+ self.n_prior_samples = n_prior_samples
self.measured_data = measured_data
self.inference_method = inference_method
self.mcmc_params = mcmc_params
@@ -157,45 +250,67 @@ class BayesInference:
self.plot_map_pred = plot_map_pred
self.max_a_posteriori = max_a_posteriori
self.corner_title_fmt = corner_title_fmt
+ self.out_dir = out_dir
+ self.bmc = bmc # Set to True, if you want to cut short to only Model Comparison
+
+ # Other properties and parameters (found in code, but never set)
+ self.error_model = False # TODO: no example or use case for this!
+ self.bias_inputs = None
+ self.measurement_error = None # TODO: what is this?
+ self.sigma2s = None
+ self.log_likes = None
+ self.n_tot_measurement = None
+ self.Discrepancy = None
+ self.posterior_df = None
+ self.error_MetaModel = None
+ self._mean_pce_prior_pred = None
+ self._std_pce_prior_pred = None
+ self.__model_prior_pred = None
+ self.MCMC_Obj = None
+
+ # System settings
+ if os.name == 'nt':
+ print('')
+ print('WARNING: Performing the inference on windows can lead to reduced accuracy!')
+ print('')
+ self.dtype = np.longdouble
+ else:
+ self.dtype = np.float128
- # -------------------------------------------------------------------------
- def create_inference(self):
+ def setup_inference(self):
"""
- Starts the inference.
-
- Returns
- -------
- BayesInference : obj
- The Bayes inference object.
-
+ This function sets up the inference by checking the inputs and getting
+ needed data.
"""
-
- # Set some variables
- MetaModel = self.MetaModel
- Model = MetaModel.ModelObj
- n_params = MetaModel.n_params
- output_names = Model.Output.names
- par_names = MetaModel.ExpDesign.par_names
-
- # If the prior is set by the user, take it.
- if self.samples is None:
- self.samples = MetaModel.ExpDesign.generate_samples(
- self.n_samples, 'random')
+ Model = self.engine.Model
+
+ # Create output directory
+ if self.out_dir == '':
+ self.out_dir = f'Outputs_Bayes_{self.engine.Model.name}_{self.name}'
+ os.makedirs(self.out_dir, exist_ok=True)
+
+ # If the prior is set by the user, take it, else generate from ExpDes
+ if self.prior_samples is None:
+ print('Generating prior samples from the Experimental Design.')
+ self.prior_samples = self.engine.ExpDesign.generate_samples(
+ self.n_prior_samples, 'random')
else:
try:
- samples = self.samples.values
+ samples = self.prior_samples.values
except AttributeError:
- samples = self.samples
+ samples = self.prior_samples
# Take care of an additional Sigma2s
- self.samples = samples[:, :n_params]
+ self.prior_samples = samples[:, :self.engine.MetaModel.n_params]
# Update number of samples
- self.n_samples = self.samples.shape[0]
+ self.n_prior_samples = self.prior_samples.shape[0]
# ---------- Preparation of observation data ----------
- # Read observation data and perturb it if requested.
+ # Read observation data
+ # TODO: later use valid #of measurements. but here only get the model observations?
if self.measured_data is None:
+ print('Reading the observation data.')
self.measured_data = Model.read_observation(case=self.name)
# Convert measured_data to a data frame
if not isinstance(self.measured_data, pd.DataFrame):
@@ -204,11 +319,13 @@ class BayesInference:
# Extract the total number of measurement points
if self.name.lower() == 'calib':
self.n_tot_measurement = Model.n_obs
- else:
+ elif self.name.lower() == 'valid':
self.n_tot_measurement = Model.n_obs_valid
+ else:
+ raise AttributeError('The set inference type is not known! Use either `calib` or `valid`')
# Find measurement error (if not given) for post predictive plot
- if not hasattr(self, 'measurement_error'):
+ if self.measurement_error is None:
if isinstance(self.Discrepancy, dict):
Disc = self.Discrepancy['known']
else:
@@ -221,373 +338,380 @@ class BayesInference:
self.measurement_error = np.sqrt(Disc.parameters)
except TypeError:
pass
+ # TODO: need a transformation for given measurement error?
- # ---------- Preparation of variance for covariance matrix ----------
- # Independent and identically distributed
- total_sigma2 = dict()
+ # Get Discrepancy type
opt_sigma_flag = isinstance(self.Discrepancy, dict)
opt_sigma = None
- for key_idx, key in enumerate(output_names):
+ # Option A: known error with unknown bias term
+ if opt_sigma_flag and opt_sigma is None:
+ opt_sigma = 'A'
+ # Option B: The sigma2 is known (no bias term)
+ elif self.Discrepancy.parameters is not None:
+ opt_sigma = 'B'
+ # Option C: The sigma2 is unknown (bias term including error)
+ elif not isinstance(self.Discrepancy.InputDisc, str):
+ opt_sigma = 'C'
+ self.Discrepancy.opt_sigma = opt_sigma
+
+ # Set MCMC params if used
+ if self.inference_method.lower() == 'mcmc':
+ if self.mcmc_params is None:
+ self.mcmc_params = {}
+ par_list = ['init_samples', 'n_walkers', 'n_burn', 'n_steps',
+ 'moves', 'multiprocessing', 'verbose']
+ init_val = [None, 100, 200, 100000, None, False, False]
+ for i in range(len(par_list)):
+ if par_list[i] not in list(self.mcmc_params.keys()):
+ self.mcmc_params[par_list[i]] = init_val[i]
+ # -------------------------------------------------------------------------
+ def create_inference(self):
+ """
+ Starts the inference.
+
+ Returns
+ -------
+ BayesInference : obj
+ The Bayes inference object.
+
+ # TODO: should this function really return the class?
+
+ """
+ # Do general set up and check some parameters
+ self.setup_inference()
+
+ # ---------- Preparation of variance for covariance matrix ----------
+ # Independent and identically distributed # TODO: ??
+ total_sigma2 = dict()
+ opt_sigma = self.Discrepancy.opt_sigma
+ for key_idx, key in enumerate(self.engine.Model.Output.names):
# Find opt_sigma
- if opt_sigma_flag and opt_sigma is None:
- # Option A: known error with unknown bias term
- opt_sigma = 'A'
+ sigma2 = None
+ if opt_sigma == 'A':
known_discrepancy = self.Discrepancy['known']
self.Discrepancy = self.Discrepancy['infer']
sigma2 = np.array(known_discrepancy.parameters[key])
- elif opt_sigma == 'A' or self.Discrepancy.parameters is not None:
- # Option B: The sigma2 is known (no bias term)
- if opt_sigma == 'A':
- sigma2 = np.array(known_discrepancy.parameters[key])
- else:
- opt_sigma = 'B'
- sigma2 = np.array(self.Discrepancy.parameters[key])
+ elif opt_sigma == 'B':
+ sigma2 = np.array(self.Discrepancy.parameters[key])
- elif not isinstance(self.Discrepancy.InputDisc, str):
- # Option C: The sigma2 is unknown (bias term including error)
- opt_sigma = 'C'
- self.Discrepancy.opt_sigma = opt_sigma
+ elif opt_sigma == 'C':
n_measurement = self.measured_data[key].values.shape
sigma2 = np.zeros((n_measurement[0]))
-
total_sigma2[key] = sigma2
- self.Discrepancy.opt_sigma = opt_sigma
- self.Discrepancy.total_sigma2 = total_sigma2
+ # TODO: can use this to debug!
+ self.Discrepancy.total_sigma2 = total_sigma2
# If inferred sigma2s obtained from e.g. calibration are given
try:
- self.sigma2s = self.Discrepancy.get_sample(self.n_samples)
+ self.sigma2s = self.Discrepancy.get_sample(self.n_prior_samples)
except:
- pass
+ pass # TODO: should an error be raised in this case? Should this at least be checked against opt_sigma?
# ---------------- Bootstrap & TOM --------------------
if self.bootstrap or self.bayes_loocv or self.just_analysis:
- if len(self.perturbed_data) == 0:
- # zero mean noise Adding some noise to the observation function
- self.perturbed_data = self._perturb_data(
- self.measured_data, output_names
- )
- else:
- self.n_bootstrap_itrs = len(self.perturbed_data)
-
- # -------- Model Discrepancy -----------
- if hasattr(self, 'error_model') and self.error_model \
- and self.name.lower() != 'calib':
- # Select posterior mean as MAP
- MAP_theta = self.samples.mean(axis=0).reshape((1, n_params))
- # MAP_theta = stats.mode(self.samples,axis=0)[0]
-
- # Evaluate the (meta-)model at the MAP
- y_MAP, y_std_MAP = MetaModel.eval_metamodel(samples=MAP_theta)
-
- # Train a GPR meta-model using MAP
- self.error_MetaModel = MetaModel.create_model_error(
- self.bias_inputs, y_MAP, Name=self.name
- )
+ self.perform_bootstrap(total_sigma2)
+ if self.bmc:
+ return self
+ else:
+ print('No bootstrap for TOM performed!') # TODO: stop the code? Use n_bootstrap = 1?
- # -----------------------------------------------------
- # ----- Loop over the perturbed observation data ------
- # -----------------------------------------------------
- # Initilize arrays
- logLikelihoods = np.zeros((self.n_samples, self.n_bootstrap_itrs),
- dtype=np.float16)
- BME_Corr = np.zeros((self.n_bootstrap_itrs))
- log_BME = np.zeros((self.n_bootstrap_itrs))
- KLD = np.zeros((self.n_bootstrap_itrs))
- inf_entropy = np.zeros((self.n_bootstrap_itrs))
-
- # Compute the prior predtions
- # Evaluate the MetaModel
- if self.emulator:
- y_hat, y_std = MetaModel.eval_metamodel(samples=self.samples)
- self.__mean_pce_prior_pred = y_hat
- self._std_pce_prior_pred = y_std
+ # ---------------- Parameter Bayesian inference ----------------
+ # Convert to a dataframe if samples are provided after calibration.
+ if self.name.lower() == 'valid':
+ self.posterior_df = pd.DataFrame(self.prior_samples, columns=self.engine.ExpDesign.par_names)
+ # Instantiate the MCMC object
+ elif self.inference_method.lower() == 'mcmc':
+ #MCMC_Obj = MCMC(self)
+ # Decoupled MCMC from BayesInf
+ self.MCMC_Obj = MCMC(self.engine, self.mcmc_params,
+ self.Discrepancy,
+ # TODO: add this in when the error_MetaModel/error_model works
+ # self.BiasInputs,
+ self.bias_inputs,
+ self.error_model, self.req_outputs,
+ self.selected_indices, self.emulator,
+ self.out_dir, self.name)
+ self.posterior_df = self.MCMC_Obj.run_sampler(
+ self.measured_data, total_sigma2
+ )
+ # Rejection sampling
+ elif self.inference_method.lower() == 'rejection':
+ self.posterior_df = self._rejection_sampling()
+ else:
+ raise AttributeError('The chosen inference method is not available!')
- # Correct the predictions with Model discrepancy
- if hasattr(self, 'error_model') and self.error_model:
- y_hat_corr, y_std = self.error_MetaModel.eval_model_error(
- self.bias_inputs, self.__mean_pce_prior_pred
- )
- self.__mean_pce_prior_pred = y_hat_corr
- self._std_pce_prior_pred = y_std
+ # Provide posterior's summary
+ print('\n')
+ print('-' * 15 + 'Posterior summary' + '-' * 15)
+ pd.options.display.max_columns = None
+ pd.options.display.max_rows = None
+ print(self.posterior_df.describe())
+ print('-' * 50)
- # Surrogate model's error using RMSE of test data
- if hasattr(MetaModel, 'rmse'):
- surrError = MetaModel.rmse
- else:
- surrError = None
+ # -------- Model Discrepancy -----------
+ if self.error_model and self.name.lower() == 'calib' and self.MCMC_Obj is not None: # TODO: where is this used
+ # and what does it actually do there?
+ self.create_error_model(opt_sigma=opt_sigma,
+ type_='posterior', sampler=self.MCMC_Obj)
- else:
- # Evaluate the original model
- self.__model_prior_pred = self._eval_model(
- samples=self.samples, key='PriorPred'
- )
- surrError = None
+ # -------- Posterior predictive -----------
+ self._posterior_predictive()
- # Start the likelihood-BME computations for the perturbed data
- for itr_idx, data in tqdm(
- enumerate(self.perturbed_data),
- total=self.n_bootstrap_itrs,
- desc="Boostraping the BME calculations", ascii=True
- ):
+ # ------------------ Visualization --------------------
+ # Posterior parameters
+ self.plot_post_params(opt_sigma)
- # ---------------- Likelihood calculation ----------------
- if self.emulator:
- model_evals = self.__mean_pce_prior_pred
- else:
- model_evals = self.__model_prior_pred
-
- # Leave one out
- if self.bayes_loocv or self.just_analysis:
- self.selected_indices = np.nonzero(data)[0]
-
- # Prepare data dataframe
- nobs = list(self.measured_data.count().values[1:])
- numbers = list(np.cumsum(nobs))
- indices = list(zip([0] + numbers, numbers))
- data_dict = {
- output_names[i]: data[j:k] for i, (j, k) in
- enumerate(indices)
- }
-
- # Unknown sigma2
- if opt_sigma == 'C' or hasattr(self, 'sigma2s'):
- logLikelihoods[:, itr_idx] = self.normpdf(
- model_evals, data_dict, total_sigma2,
- sigma2=self.sigma2s, std=surrError
- )
- else:
- # known sigma2
- logLikelihoods[:, itr_idx] = self.normpdf(
- model_evals, data_dict, total_sigma2,
- std=surrError
- )
+ # Plot MAP
+ if self.plot_map_pred:
+ self._plot_max_a_posteriori()
- # ---------------- BME Calculations ----------------
- # BME (log)
- log_BME[itr_idx] = np.log(
- np.nanmean(np.exp(logLikelihoods[:, itr_idx],
- dtype=np.float128))
- )
+ # Plot log_BME dist
+ if self.bootstrap:
+ self.plot_log_BME()
- # BME correction when using Emulator
- if self.emulator:
- BME_Corr[itr_idx] = self.__corr_factor_BME(
- data_dict, total_sigma2, log_BME[itr_idx]
- )
+ # Plot posterior predictive
+ if self.plot_post_pred:
+ self._plot_post_predictive()
- # Rejection Step
- if 'kld' in list(map(str.lower, self.valid_metrics)) and\
- 'inf_entropy' in list(map(str.lower, self.valid_metrics)):
- # Random numbers between 0 and 1
- unif = np.random.rand(1, self.n_samples)[0]
-
- # Reject the poorly performed prior
- Likelihoods = np.exp(logLikelihoods[:, itr_idx],
- dtype=np.float64)
- accepted = (Likelihoods/np.max(Likelihoods)) >= unif
- posterior = self.samples[accepted]
-
- # Posterior-based expectation of likelihoods
- postExpLikelihoods = np.mean(
- logLikelihoods[:, itr_idx][accepted]
- )
+ return self
- # Calculate Kullback-Leibler Divergence
- KLD[itr_idx] = postExpLikelihoods - log_BME[itr_idx]
-
- # Posterior-based expectation of prior densities
- if 'inf_entropy' in list(map(str.lower, self.valid_metrics)):
- n_thread = int(0.875 * multiprocessing.cpu_count())
- with multiprocessing.Pool(n_thread) as p:
- postExpPrior = np.mean(np.concatenate(
- p.map(
- MetaModel.ExpDesign.JDist.pdf,
- np.array_split(posterior.T, n_thread, axis=1))
- )
- )
- # Information Entropy based on Entropy paper Eq. 38
- inf_entropy[itr_idx] = log_BME[itr_idx] - postExpPrior - \
- postExpLikelihoods
-
- # Clear memory
- gc.collect(generation=2)
-
- # ---------- Store metrics for perturbed data set ----------------
- # Likelihoods (Size: n_samples, n_bootstrap_itr)
- self.log_likes = logLikelihoods
-
- # BME (log), KLD, infEntropy (Size: 1,n_bootstrap_itr)
- self.log_BME = log_BME
-
- # BMECorrFactor (log) (Size: 1,n_bootstrap_itr)
- if self.emulator:
- self.log_BME_corr_factor = BME_Corr
+ def create_error_model(self, type_='posterior', opt_sigma='B', sampler=None):
+ """
+ Creates an error model in the engine.MetaModel based on input dist
+ samples of the chosen type
- if 'kld' in list(map(str.lower, self.valid_metrics)):
- self.KLD = KLD
- if 'inf_entropy' in list(map(str.lower, self.valid_metrics)):
- self.inf_entropy = inf_entropy
+ Parameters
+ ----------
+ opt_sigma : string, optional
+ Type of uncertainty description, only used if type_=='posterior'.
+ The default is 'B'
+ type_ : string
+ Type of parameter samples to use, either 'prior' or 'posterior'.
+ The default is 'posterior'.
+ sampler : MCMC, optional
+ Should be an MCMC object if type=='posterior' and MCMC is used in
+ the inference.In al other cases this parameter is not needed.
- # BME = BME + BMECorrFactor
- if self.emulator:
- self.log_BME += self.log_BME_corr_factor
+ Returns
+ -------
+ None.
- # ---------------- Parameter Bayesian inference ----------------
- if self.inference_method.lower() == 'mcmc':
- # Instantiate the MCMC object
- MCMC_Obj = MCMC(self)
- self.posterior_df = MCMC_Obj.run_sampler(
- self.measured_data, total_sigma2
- )
+ """
+ n_params = self.engine.MetaModel.n_params
- elif self.name.lower() == 'valid':
- # Convert to a dataframe if samples are provided after calibration.
- self.posterior_df = pd.DataFrame(self.samples, columns=par_names)
+ # Get MAP estimate from prior samples
+ if type_ == 'prior':
+ # Select prior ? mean as MAP
+ MAP_theta = self.prior_samples.mean(axis=0).reshape((1, n_params))
- else:
- # Rejection sampling
- self.posterior_df = self._rejection_sampling()
+ # Evaluate the (meta-)model at the MAP
+ y_MAP, y_std_MAP = self.engine.MetaModel.eval_metamodel(samples=MAP_theta)
- # Provide posterior's summary
- print('\n')
- print('-'*15 + 'Posterior summary' + '-'*15)
- pd.options.display.max_columns = None
- pd.options.display.max_rows = None
- print(self.posterior_df.describe())
- print('-'*50)
+ # Train a GPR meta-model using MAP
+ self.error_MetaModel = self.engine.MetaModel.create_model_error(
+ self.bias_inputs, y_MAP, self.measured_data, name=self.name
+ )
- # -------- Model Discrepancy -----------
- if hasattr(self, 'error_model') and self.error_model \
- and self.name.lower() == 'calib':
+ # Get MAP estimate from posterior samples
+ if type_ == 'posterior':
if self.inference_method.lower() == 'mcmc':
- self.error_MetaModel = MCMC_Obj.error_MetaModel
+ self.error_MetaModel = sampler.error_MetaModel
else:
# Select posterior mean as MAP
if opt_sigma == "B":
posterior_df = self.posterior_df.values
else:
- posterior_df = self.posterior_df.values[:, :-Model.n_outputs]
+ posterior_df = self.posterior_df.values[:, :-self.engine.Model.n_outputs]
# Select posterior mean as Maximum a posteriori
map_theta = posterior_df.mean(axis=0).reshape((1, n_params))
# map_theta = stats.mode(Posterior_df,axis=0)[0]
# Evaluate the (meta-)model at the MAP
- y_MAP, y_std_MAP = MetaModel.eval_metamodel(samples=map_theta)
+ y_MAP, y_std_MAP = self.engine.MetaModel.eval_metamodel(samples=map_theta)
# Train a GPR meta-model using MAP
- self.error_MetaModel = MetaModel.create_model_error(
- self.bias_inputs, y_MAP, Name=self.name
- )
-
- # -------- Posterior perdictive -----------
- self._posterior_predictive()
-
- # -----------------------------------------------------
- # ------------------ Visualization --------------------
- # -----------------------------------------------------
- # Create Output directory, if it doesn't exist already.
- out_dir = f'Outputs_Bayes_{Model.name}_{self.name}'
- os.makedirs(out_dir, exist_ok=True)
-
- # -------- Posteior parameters --------
- if opt_sigma != "B":
- par_names.extend(
- [self.Discrepancy.InputDisc.Marginals[i].name for i
- in range(len(self.Discrepancy.InputDisc.Marginals))]
+ self.error_MetaModel = self.engine.MetaModel.create_model_error(
+ self.bias_inputs, y_MAP, self.measured_data, name=self.name
)
- # Pot with corner
- figPosterior = corner.corner(self.posterior_df.to_numpy(),
- labels=par_names,
- quantiles=[0.15, 0.5, 0.85],
- show_titles=True,
- title_fmt=self.corner_title_fmt,
- labelpad=0.2,
- use_math_text=True,
- title_kwargs={"fontsize": 28},
- plot_datapoints=False,
- plot_density=False,
- fill_contours=True,
- smooth=0.5,
- smooth1d=0.5)
- # Loop over axes and set x limits
- if opt_sigma == "B":
- axes = np.array(figPosterior.axes).reshape(
- (len(par_names), len(par_names))
- )
- for yi in range(len(par_names)):
- ax = axes[yi, yi]
- ax.set_xlim(MetaModel.bound_tuples[yi])
- for xi in range(yi):
- ax = axes[yi, xi]
- ax.set_xlim(MetaModel.bound_tuples[xi])
- plt.close()
+ def perform_bootstrap(self, total_sigma2):
+ """
+ Perform bootstrap to get TOM (??)
+
+ Parameters
+ ----------
+ total_sigma2 : dict
+ Dictionary containing the sigma2 for the training(?) data
+ Returns
+ -------
+ None.
- # Turn off gridlines
- for ax in figPosterior.axes:
- ax.grid(False)
+ """
+ MetaModel = self.engine.MetaModel
+ output_names = self.engine.Model.Output.names
+ opt_sigma = self.Discrepancy.opt_sigma
- if self.emulator:
- plotname = f'/Posterior_Dist_{Model.name}_emulator'
+ # Adding some zero mean noise to the observation function
+ if len(self.perturbed_data) == 0:
+ self.perturbed_data = self._perturb_data(
+ self.measured_data, output_names
+ )
else:
- plotname = f'/Posterior_Dist_{Model.name}'
+ self.n_bootstrap_itrs = len(self.perturbed_data)
- figPosterior.set_size_inches((24, 16))
- figPosterior.savefig(f'./{out_dir}{plotname}.pdf',
- bbox_inches='tight')
+ # -------- Model Discrepancy -----------
+ if self.error_model and self.name.lower() == 'valid': # TODO: what should be set so that this is tested?
+ print('Creating the error model.')
+ self.create_error_model(type_='prior')
+ # -----------------------------------------------------
+ # ----- Loop over the perturbed observation data ------
+ # -----------------------------------------------------
+ # Initilize arrays
+ logLikelihoods = np.zeros((self.n_prior_samples, self.n_bootstrap_itrs),
+ dtype=np.float16)
+ BME_Corr = np.zeros(self.n_bootstrap_itrs)
+ log_BME = np.zeros(self.n_bootstrap_itrs)
+ KLD = np.zeros(self.n_bootstrap_itrs)
+ inf_entropy = np.zeros(self.n_bootstrap_itrs)
+
+ # Compute the prior predictions
+ # Evaluate the MetaModel
+ if self.emulator:
+ print('Evaluating the metamodel on the prior samples.')
+ y_hat, y_std = MetaModel.eval_metamodel(samples=self.prior_samples)
+ self.__mean_pce_prior_pred = y_hat
+ self._std_pce_prior_pred = y_std
+
+ # Correct the predictions with Model discrepancy
+ if self.error_model: # TODO this does not check for calib?
+ y_hat_corr, y_std = self.error_MetaModel.eval_model_error(
+ self.bias_inputs, self.__mean_pce_prior_pred)
+ self.__mean_pce_prior_pred = y_hat_corr
+ self._std_pce_prior_pred = y_std
- # -------- Plot MAP --------
- if self.plot_map_pred:
- self._plot_max_a_posteriori()
+ # Surrogate model's error using RMSE of test data
+ if MetaModel.rmse is not None:
+ surrError = MetaModel.rmse
+ else:
+ surrError = None
+ model_evals = self.__mean_pce_prior_pred
- # -------- Plot log_BME dist --------
- if self.bootstrap:
+ # Evaluate the model
+ else:
+ print('Evaluating the model on the prior samples.')
+ self.__model_prior_pred = self._eval_model(
+ samples=self.prior_samples, key='PriorPred')
+ model_evals = self.__model_prior_pred
+ surrError = None
+
+ # Start the likelihood-BME computations for the perturbed data
+ for itr_idx, data in tqdm(
+ enumerate(self.perturbed_data),
+ total=self.n_bootstrap_itrs,
+ desc="Bootstrapping the BME calculations", ascii=True
+ ):
+
+ # ---------------- Likelihood calculation ----------------
+ # Leave one out (see form of perturbed data)
+ if self.bayes_loocv or self.just_analysis:
+ # Consider only non-zero entries
+ self.selected_indices = np.nonzero(data)[0]
+
+ # Prepare data dataframe # TODO: what's with this transformation?
+ nobs = list(self.measured_data.count().values[1:])
+ numbers = list(np.cumsum(nobs))
+ indices = list(zip([0] + numbers, numbers))
+ data_dict = {
+ output_names[i]: data[j:k] for i, (j, k) in
+ enumerate(indices)
+ }
+
+ # Unknown sigma2
+ if opt_sigma == 'C' or self.sigma2s is not None:
+ logLikelihoods[:, itr_idx] = self.normpdf(
+ model_evals, data_dict, total_sigma2,
+ sigma2=self.sigma2s, std=surrError
+ )
+ else:
+ # known sigma2
+ logLikelihoods[:, itr_idx] = self.normpdf(
+ model_evals, data_dict, total_sigma2,
+ std=surrError
+ )
+ # ---------------- BME Calculations ----------------
+ # BME (log)
+ log_BME[itr_idx] = np.log(
+ np.nanmean(np.exp(logLikelihoods[:, itr_idx],
+ dtype=self.dtype))
+ )
+
+ # BME correction when using Emulator
+ if self.emulator:
+ BME_Corr[itr_idx] = self._corr_factor_BME(
+ data_dict, total_sigma2, log_BME[itr_idx]
+ )
- # Computing the TOM performance
- self.log_BME_tom = stats.chi2.rvs(
- self.n_tot_measurement, size=self.log_BME.shape[0]
+ # Rejection Step
+ if 'kld' in list(map(str.lower, self.valid_metrics)) and \
+ 'inf_entropy' in list(map(str.lower, self.valid_metrics)): # TODO: why and and not or?
+ # Random numbers between 0 and 1
+ unif = np.random.rand(1, self.n_prior_samples)[0]
+
+ # Reject the poorly performed prior
+ Likelihoods = np.exp(logLikelihoods[:, itr_idx],
+ dtype=np.float64)
+ accepted = (Likelihoods / np.max(Likelihoods)) >= unif
+ posterior = self.prior_samples[accepted]
+
+ # Posterior-based expectation of likelihoods
+ postExpLikelihoods = np.mean(
+ logLikelihoods[:, itr_idx][accepted]
)
- fig, ax = plt.subplots()
- sns.kdeplot(self.log_BME_tom, ax=ax, color="green", shade=True)
- sns.kdeplot(
- self.log_BME, ax=ax, color="blue", shade=True,
- label='Model BME')
+ # Calculate Kullback-Leibler Divergence
+ KLD[itr_idx] = postExpLikelihoods - log_BME[itr_idx]
- ax.set_xlabel('log$_{10}$(BME)')
- ax.set_ylabel('Probability density')
+ # Posterior-based expectation of prior densities
+ if 'inf_entropy' in list(map(str.lower, self.valid_metrics)):
+ n_thread = int(0.875 * multiprocessing.cpu_count())
+ with multiprocessing.Pool(n_thread) as p:
+ postExpPrior = np.mean(np.concatenate(
+ p.map(
+ self.engine.ExpDesign.JDist.pdf,
+ np.array_split(posterior.T, n_thread, axis=1))
+ )
+ )
+ # Information Entropy based on Entropy paper Eq. 38
+ inf_entropy[itr_idx] = log_BME[itr_idx] - postExpPrior - postExpLikelihoods
- legend_elements = [
- Patch(facecolor='green', edgecolor='green', label='TOM BME'),
- Patch(facecolor='blue', edgecolor='blue', label='Model BME')
- ]
- ax.legend(handles=legend_elements)
+ # Clear memory
+ gc.collect(generation=2)
- if self.emulator:
- plotname = f'/BME_hist_{Model.name}_emulator'
- else:
- plotname = f'/BME_hist_{Model.name}'
+ # ---------- Store metrics for perturbed data set ----------------
+ # Likelihoods (Size: n_samples, n_bootstrap_itr)
+ self.log_likes = logLikelihoods
- plt.savefig(f'./{out_dir}{plotname}.pdf', bbox_inches='tight')
- plt.show()
- plt.close()
+ # BME (log), KLD, infEntropy (Size: 1,n_bootstrap_itr)
+ self.log_BME = log_BME
- # -------- Posteior perdictives --------
- if self.plot_post_pred:
- # Plot the posterior predictive
- self._plot_post_predictive()
+ # BMECorrFactor (log) (Size: 1,n_bootstrap_itr)
+ # BME = BME + BMECorrFactor
+ if self.emulator:
+ self.log_BME += BME_Corr
- return self
+ if 'kld' in list(map(str.lower, self.valid_metrics)):
+ self.KLD = KLD
+ if 'inf_entropy' in list(map(str.lower, self.valid_metrics)):
+ self.inf_entropy = inf_entropy
# -------------------------------------------------------------------------
def _perturb_data(self, data, output_names):
"""
- Returns an array with n_bootstrap_itrs rowsof perturbed data.
+ Returns an array with n_bootstrap_itrs rows of perturbed data.
The first row includes the original observation data.
If `self.bayes_loocv` is True, a 2d-array will be returned with
repeated rows and zero diagonal entries.
@@ -597,7 +721,7 @@ class BayesInference:
data : pandas DataFrame
Observation data.
output_names : list
- List of the output names.
+ The output names.
Returns
-------
@@ -609,12 +733,11 @@ class BayesInference:
obs_data = data[output_names].values
n_measurement, n_outs = obs_data.shape
self.n_tot_measurement = obs_data[~np.isnan(obs_data)].shape[0]
- # Number of bootstrap iterations
- if self.bayes_loocv:
- self.n_bootstrap_itrs = self.n_tot_measurement
# Pass loocv dataset
if self.bayes_loocv:
+ # Number of bootstrap iterations
+ self.n_bootstrap_itrs = self.n_tot_measurement
obs = obs_data.T[~np.isnan(obs_data.T)]
final_data = np.repeat(np.atleast_2d(obs), self.n_bootstrap_itrs,
axis=0)
@@ -622,15 +745,18 @@ class BayesInference:
return final_data
else:
+ # Init return data with original data
final_data = np.zeros(
(self.n_bootstrap_itrs, self.n_tot_measurement)
- )
+ )
final_data[0] = obs_data.T[~np.isnan(obs_data.T)]
for itrIdx in range(1, self.n_bootstrap_itrs):
data = np.zeros((n_measurement, n_outs))
for idx in range(len(output_names)):
+ # Perturb the data
std = np.nanstd(obs_data[:, idx])
if std == 0:
+ print('Note: Use std=0.01 for perturbation')
std = 0.001
noise = std * noise_level
data[:, idx] = np.add(
@@ -642,45 +768,17 @@ class BayesInference:
return final_data
- # -------------------------------------------------------------------------
- def _logpdf(self, x, mean, cov):
- """
- computes the likelihood based on a multivariate normal distribution.
-
- Parameters
- ----------
- x : TYPE
- DESCRIPTION.
- mean : array_like
- Observation data.
- cov : 2d array
- Covariance matrix of the distribution.
-
- Returns
- -------
- log_lik : float
- Log likelihood.
-
- """
- n = len(mean)
- L = spla.cholesky(cov, lower=True)
- beta = np.sum(np.log(np.diag(L)))
- dev = x - mean
- alpha = dev.dot(spla.cho_solve((L, True), dev))
- log_lik = -0.5 * alpha - beta - n / 2. * np.log(2 * np.pi)
- return log_lik
-
# -------------------------------------------------------------------------
def _eval_model(self, samples=None, key='MAP'):
"""
- Evaluates Forward Model.
+ Evaluates Forward Model and zips the results
Parameters
----------
samples : array of shape (n_samples, n_params), optional
Parameter sets. The default is None.
key : str, optional
- Key string to be passed to the run_model_parallel method.
+ Descriptive key string for the run_model_parallel method.
The default is 'MAP'.
Returns
@@ -689,18 +787,17 @@ class BayesInference:
Model outputs.
"""
- MetaModel = self.MetaModel
- Model = MetaModel.ModelObj
+ Model = self.engine.Model
if samples is None:
- self.samples = MetaModel.ExpDesign.generate_samples(
- self.n_samples, 'random')
+ self.prior_samples = self.engine.ExpDesign.generate_samples(
+ self.n_prior_samples, 'random')
else:
- self.samples = samples
- self.n_samples = len(samples)
+ self.prior_samples = samples
+ self.n_prior_samples = len(samples)
model_outputs, _ = Model.run_model_parallel(
- self.samples, key_str=key+self.name)
+ self.prior_samples, key_str=key + self.name)
# Clean up
# Zip the subdirectories
@@ -713,55 +810,6 @@ class BayesInference:
return model_outputs
- # -------------------------------------------------------------------------
- def _kernel_rbf(self, X, hyperparameters):
- """
- Isotropic squared exponential kernel.
-
- Higher l values lead to smoother functions and therefore to coarser
- approximations of the training data. Lower l values make functions
- more wiggly with wide uncertainty regions between training data points.
-
- sigma_f controls the marginal variance of b(x)
-
- Parameters
- ----------
- X : ndarray of shape (n_samples_X, n_features)
-
- hyperparameters : Dict
- Lambda characteristic length
- sigma_f controls the marginal variance of b(x)
- sigma_0 unresolvable error nugget term, interpreted as random
- error that cannot be attributed to measurement error.
- Returns
- -------
- var_cov_matrix : ndarray of shape (n_samples_X,n_samples_X)
- Kernel k(X, X).
-
- """
- from sklearn.gaussian_process.kernels import RBF
- min_max_scaler = preprocessing.MinMaxScaler()
- X_minmax = min_max_scaler.fit_transform(X)
-
- nparams = len(hyperparameters)
- # characteristic length (0,1]
- Lambda = hyperparameters[0]
- # sigma_f controls the marginal variance of b(x)
- sigma2_f = hyperparameters[1]
-
- # cov_matrix = sigma2_f*rbf_kernel(X_minmax, gamma = 1/Lambda**2)
-
- rbf = RBF(length_scale=Lambda)
- cov_matrix = sigma2_f * rbf(X_minmax)
- if nparams > 2:
- # (unresolvable error) nugget term that is interpreted as random
- # error that cannot be attributed to measurement error.
- sigma2_0 = hyperparameters[2:]
- for i, j in np.ndindex(cov_matrix.shape):
- cov_matrix[i, j] += np.sum(sigma2_0) if i == j else 0
-
- return cov_matrix
-
# -------------------------------------------------------------------------
def normpdf(self, outputs, obs_data, total_sigma2s, sigma2=None, std=None):
"""
@@ -777,7 +825,7 @@ class BayesInference:
A dictionary/dataframe containing the observation data.
total_sigma2s : dict
A dictionary with known values of the covariance diagonal entries,
- a.k.a sigma^2.
+ a.k.a. sigma^2.
sigma2 : array, optional
An array of the sigma^2 samples, when the covariance diagonal
entries are unknown and are being jointly inferred. The default is
@@ -793,16 +841,16 @@ class BayesInference:
Likelihoods.
"""
- Model = self.MetaModel.ModelObj
+ Model = self.engine.Model
logLik = 0.0
# Extract the requested model outputs for likelihood calulation
if self.req_outputs is None:
- req_outputs = Model.Output.names
+ req_outputs = Model.Output.names # TODO: should this then be saved as self.req_outputs?
else:
req_outputs = list(self.req_outputs)
- # Loop over the outputs
+ # Loop over the output keys
for idx, out in enumerate(req_outputs):
# (Meta)Model Output
@@ -814,26 +862,25 @@ class BayesInference:
except AttributeError:
data = obs_data[out][~np.isnan(obs_data[out])]
- # Prepare sigma2s
+ # Prepare data uncertainty / error estimation (sigma2s)
non_nan_indices = ~np.isnan(total_sigma2s[out])
tot_sigma2s = total_sigma2s[out][non_nan_indices][:nout]
- # Add the std of the PCE is chosen as emulator.
+ # Add the std of the PCE if an emulator is used
if self.emulator:
if std is not None:
- tot_sigma2s += std[out]**2
-
- # Covariance Matrix
- covMatrix = np.diag(tot_sigma2s)
+ tot_sigma2s += std[out] ** 2
# Select the data points to compare
try:
indices = self.selected_indices[out]
except:
indices = list(range(nout))
- covMatrix = np.diag(covMatrix[indices, indices])
- # If sigma2 is not given, use given total_sigma2s
+ # Set up Covariance Matrix
+ covMatrix = np.diag(np.diag(tot_sigma2s)[indices, indices])
+
+ # If sigma2 is not given, use given total_sigma2s and move to next itr
if sigma2 is None:
logLik += stats.multivariate_normal.logpdf(
outputs[out][:, indices], data[indices], covMatrix)
@@ -849,26 +896,24 @@ class BayesInference:
# Covariance Matrix
covMatrix = np.diag(tot_sigma2s)
- if sigma2 is not None:
- # Check the type error term
- if hasattr(self, 'bias_inputs') and \
- not hasattr(self, 'error_model'):
- # Infer a Bias model usig Gaussian Process Regression
- bias_inputs = np.hstack(
- (self.bias_inputs[out],
- tot_outputs[s_idx].reshape(-1, 1)))
-
- params = sigma2[s_idx, idx*3:(idx+1)*3]
- covMatrix = self._kernel_rbf(bias_inputs, params)
- else:
- # Infer equal sigma2s
- try:
- sigma_2 = sigma2[s_idx, idx]
- except TypeError:
- sigma_2 = 0.0
+ # Check the type error term
+ if self.bias_inputs is not None and self.error_model is None:
+ # Infer a Bias model usig Gaussian Process Regression
+ bias_inputs = np.hstack(
+ (self.bias_inputs[out],
+ tot_outputs[s_idx].reshape(-1, 1)))
+
+ params = sigma2[s_idx, idx * 3:(idx + 1) * 3]
+ covMatrix = _kernel_rbf(bias_inputs, params)
+ else:
+ # Infer equal sigma2s
+ try:
+ sigma_2 = sigma2[s_idx, idx]
+ except TypeError:
+ sigma_2 = 0.0
- covMatrix += sigma_2 * np.eye(nout)
- # covMatrix = np.diag(sigma2 * total_sigma2s)
+ covMatrix += sigma_2 * np.eye(nout)
+ # covMatrix = np.diag(sigma2 * total_sigma2s)
# Select the data points to compare
try:
@@ -878,86 +923,45 @@ class BayesInference:
covMatrix = np.diag(covMatrix[indices, indices])
# Compute loglikelihood
- logliks[s_idx] = self._logpdf(
+ logliks[s_idx] = _logpdf(
tot_outputs[s_idx, indices], data[indices], covMatrix
- )
-
+ )
logLik += logliks
return logLik
# -------------------------------------------------------------------------
- def _corr_factor_BME_old(self, Data, total_sigma2s, posterior):
+ def _corr_factor_BME(self, obs_data, total_sigma2s, logBME):
"""
Calculates the correction factor for BMEs.
- """
- MetaModel = self.MetaModel
- OrigModelOutput = MetaModel.ExpDesign.Y
- Model = MetaModel.ModelObj
-
- # Posterior with guassian-likelihood
- postDist = stats.gaussian_kde(posterior.T)
-
- # Remove NaN
- Data = Data[~np.isnan(Data)]
- total_sigma2s = total_sigma2s[~np.isnan(total_sigma2s)]
-
- # Covariance Matrix
- covMatrix = np.diag(total_sigma2s[:self.n_tot_measurement])
-
- # Extract the requested model outputs for likelihood calulation
- if self.req_outputs is None:
- OutputType = Model.Output.names
- else:
- OutputType = list(self.req_outputs)
-
- # SampleSize = OrigModelOutput[OutputType[0]].shape[0]
-
-
- # Flatten the OutputType for OrigModel
- TotalOutputs = np.concatenate([OrigModelOutput[x] for x in OutputType], 1)
-
- NrofBayesSamples = self.n_samples
- # Evaluate MetaModel on the experimental design
- Samples = MetaModel.ExpDesign.X
- OutputRS, stdOutputRS = MetaModel.eval_metamodel(samples=Samples)
-
- # Reset the NrofSamples to NrofBayesSamples
- self.n_samples = NrofBayesSamples
-
- # Flatten the OutputType for MetaModel
- TotalPCEOutputs = np.concatenate([OutputRS[x] for x in OutputRS], 1)
- TotalPCEstdOutputRS= np.concatenate([stdOutputRS[x] for x in stdOutputRS], 1)
+
+ Parameters
+ ----------
+ obs_data : dict
+ A dictionary/dataframe containing the observation data.
+ total_sigma2s : dict
+ A dictionary with known values of the covariance diagonal entries,
+ a.k.a sigma^2.
+ logBME : ??
+ The log_BME obtained from the estimated likelihoods
- logweight = 0
- for i, sample in enumerate(Samples):
- # Compute likelilhood output vs RS
- covMatrix = np.diag(TotalPCEstdOutputRS[i]**2)
- logLik = self._logpdf(TotalOutputs[i], TotalPCEOutputs[i], covMatrix)
- # Compute posterior likelihood of the collocation points
- logpostLik = np.log(postDist.pdf(sample[:, None]))[0]
- if logpostLik != -np.inf:
- logweight += logLik + logpostLik
- return logweight
+ Returns
+ -------
+ np.log(weights) : ??
+ Correction factors # TODO: factors or log of factors?
- # -------------------------------------------------------------------------
- def __corr_factor_BME(self, obs_data, total_sigma2s, logBME):
- """
- Calculates the correction factor for BMEs.
"""
- MetaModel = self.MetaModel
- samples = MetaModel.ExpDesign.X
- model_outputs = MetaModel.ExpDesign.Y
- Model = MetaModel.ModelObj
- n_samples = samples.shape[0]
-
# Extract the requested model outputs for likelihood calulation
- output_names = Model.Output.names
+ MetaModel = self.engine.MetaModel
+ samples = self.engine.ExpDesign.X
+ model_outputs = self.engine.ExpDesign.Y
+ n_samples = samples.shape[0]
+ output_names = self.engine.Model.Output.names
# Evaluate MetaModel on the experimental design and ValidSet
OutputRS, stdOutputRS = MetaModel.eval_metamodel(samples=samples)
- logLik_data = np.zeros((n_samples))
- logLik_model = np.zeros((n_samples))
+ logLik_data = np.zeros(n_samples)
+ logLik_model = np.zeros(n_samples)
# Loop over the outputs
for idx, out in enumerate(output_names):
@@ -986,7 +990,7 @@ class BayesInference:
y_m_hat = OutputRS[out][i]
# CovMatrix with the surrogate error
- covMatrix = np.eye(len(y_m)) * 1/(2*np.pi)
+ covMatrix = np.eye(len(y_m)) * 1 / (2 * np.pi)
# Select the data points to compare
try:
@@ -997,20 +1001,20 @@ class BayesInference:
covMatrix_data = np.diag(covMatrix_data[indices, indices])
# Compute likelilhood output vs data
- logLik_data[i] += self._logpdf(
+ logLik_data[i] += _logpdf(
y_m_hat[indices], data[indices],
covMatrix_data
- )
+ )
# Compute likelilhood output vs surrogate
- logLik_model[i] += self._logpdf(
+ logLik_model[i] += _logpdf(
y_m_hat[indices], y_m[indices],
covMatrix
- )
+ )
# Weight
logLik_data -= logBME
- weights = np.mean(np.exp(logLik_model+logLik_data))
+ weights = np.mean(np.exp(logLik_model + logLik_data))
return np.log(weights)
@@ -1026,45 +1030,43 @@ class BayesInference:
Posterior samples of the input parameters.
"""
+ if self.prior_samples is None:
+ raise AttributeError('No prior samples available!')
- MetaModel = self.MetaModel
+ if self.log_likes is None:
+ raise AttributeError('No log-likelihoods available!')
+
+ # Get sigmas # TODO: is this data uncertainty?
try:
sigma2_prior = self.Discrepancy.sigma2_prior
except:
sigma2_prior = None
- # Check if the discrepancy is defined as a distribution:
- samples = self.samples
-
+ # Combine samples and sigma2 for the return
+ samples = self.prior_samples
if sigma2_prior is not None:
samples = np.hstack((samples, sigma2_prior))
# Take the first column of Likelihoods (Observation data without noise)
if self.just_analysis or self.bayes_loocv:
- index = self.n_tot_measurement-1
- likelihoods = np.exp(self.log_likes[:, index], dtype=np.float128)
+ index = self.n_tot_measurement - 1
else:
- likelihoods = np.exp(self.log_likes[:, 0], dtype=np.float128)
+ index = 0
+
+ # Use longdouble on windows, float128 on linux
+ likelihoods = np.exp(self.log_likes[:, index], dtype=self.dtype)
n_samples = len(likelihoods)
- norm_ikelihoods = likelihoods / np.max(likelihoods)
+ norm_likelihoods = likelihoods / np.max(likelihoods)
# Normalize based on min if all Likelihoods are zero
if all(likelihoods == 0.0):
likelihoods = self.log_likes[:, 0]
- norm_ikelihoods = likelihoods / np.min(likelihoods)
+ norm_likelihoods = likelihoods / np.min(likelihoods)
- # Random numbers between 0 and 1
+ # Reject the poorly performed prior compared to a uniform distribution
unif = np.random.rand(1, n_samples)[0]
-
- # Reject the poorly performed prior
- accepted_samples = samples[norm_ikelihoods >= unif]
-
- # Output the Posterior
- par_names = MetaModel.ExpDesign.par_names
- if sigma2_prior is not None:
- for name in self.Discrepancy.name:
- par_names.append(name)
+ accepted_samples = samples[norm_likelihoods >= unif]
return pd.DataFrame(accepted_samples, columns=sigma2_prior)
@@ -1086,25 +1088,21 @@ class BayesInference:
"""
- MetaModel = self.MetaModel
- Model = MetaModel.ModelObj
+ MetaModel = self.engine.MetaModel
+ Model = self.engine.Model
- # Make a directory to save the prior/posterior predictive
- out_dir = f'Outputs_Bayes_{Model.name}_{self.name}'
- os.makedirs(out_dir, exist_ok=True)
-
- # Read observation data and perturb it if requested
+ # Read observation data and perturb it if requested # TODO: where is the perturbation?
if self.measured_data is None:
self.measured_data = Model.read_observation(case=self.name)
if not isinstance(self.measured_data, pd.DataFrame):
self.measured_data = pd.DataFrame(self.measured_data)
- # X_values
- x_values = MetaModel.ExpDesign.x_values
-
+ # X_values and prior sigma2
+ x_values = self.engine.ExpDesign.x_values
try:
- sigma2_prior = self.Discrepancy.sigma2_prior
+ sigma2_prior = self.Discrepancy.sigma2_prior # TODO: what is this? Looks to be built for a different
+ # Discrepancy structure
except:
sigma2_prior = None
@@ -1112,45 +1110,47 @@ class BayesInference:
posterior_df = self.posterior_df
# Take care of the sigma2
- if sigma2_prior is not None:
+ sigma2s = None
+ if sigma2_prior is not None: # TODO: why is this the if for this code?
try:
- sigma2s = posterior_df[self.Discrepancy.name].values
+ sigma2s = posterior_df[self.Discrepancy.name].values # TODO: what is Discrepancy.name?
posterior_df = posterior_df.drop(
labels=self.Discrepancy.name, axis=1
- )
+ )
except:
sigma2s = self.sigma2s
# Posterior predictive
if self.emulator:
- if self.inference_method == 'rejection':
- prior_pred = self.__mean_pce_prior_pred
- if self.name.lower() != 'calib':
- post_pred = self.__mean_pce_prior_pred
+ if self.inference_method.lower() == 'rejection': # TODO: combine these two? Why is there no
+ # post_pred_std for rejection sampling?
+ prior_pred = self._mean_pce_prior_pred
+ if self.name.lower() == 'valid':
+ post_pred = self._mean_pce_prior_pred
post_pred_std = self._std_pce_prior_pred
else:
- post_pred, post_pred_std = MetaModel.eval_metamodel(
+ post_pred, post_pred_std = MetaModel.eval_metamodel( # TODO: recheck if this is needed
samples=posterior_df.values
- )
+ )
- else:
- if self.inference_method == 'rejection':
+ else: # TODO: see emulator version
+ if self.inference_method.lower() == 'rejection':
prior_pred = self.__model_prior_pred
- if self.name.lower() != 'calib':
+ if self.name.lower() == 'valid':
post_pred = self.__mean_pce_prior_pred,
post_pred_std = self._std_pce_prior_pred
else:
post_pred = self._eval_model(
samples=posterior_df.values, key='PostPred'
- )
+ )
# Correct the predictions with Model discrepancy
- if hasattr(self, 'error_model') and self.error_model:
+ if self.error_model:
y_hat, y_std = self.error_MetaModel.eval_model_error(
self.bias_inputs, post_pred
- )
+ )
post_pred, post_pred_std = y_hat, y_std
- # Add discrepancy from likelihood Sample to the current posterior runs
+ # Add discrepancy from likelihood samples to the current posterior runs
total_sigma2 = self.Discrepancy.total_sigma2
post_pred_withnoise = copy.deepcopy(post_pred)
for varIdx, var in enumerate(Model.Output.names):
@@ -1162,16 +1162,15 @@ class BayesInference:
tot_sigma2 = clean_sigma2[:len(pred)]
cov = np.diag(tot_sigma2)
- # Check the type error term
+ # Account for additional error terms
if sigma2_prior is not None:
# Inferred sigma2s
- if hasattr(self, 'bias_inputs') and \
- not hasattr(self, 'error_model'):
+ if self.bias_inputs is not None and self.error_model is None:
# TODO: Infer a Bias model usig GPR
bias_inputs = np.hstack((
self.bias_inputs[var], pred.reshape(-1, 1)))
- params = sigma2s[i, varIdx*3:(varIdx+1)*3]
- cov = self._kernel_rbf(bias_inputs, params)
+ params = sigma2s[i, varIdx * 3:(varIdx + 1) * 3]
+ cov = _kernel_rbf(bias_inputs, params)
else:
# Infer equal sigma2s
try:
@@ -1182,25 +1181,25 @@ class BayesInference:
# Convert biasSigma2s to a covMatrix
cov += sigma2 * np.eye(len(pred))
+ # Add predictive metamodel error/uncertainty
if self.emulator:
- if hasattr(MetaModel, 'rmse') and \
- MetaModel.rmse is not None:
+ if MetaModel.rmse is not None:
stdPCE = MetaModel.rmse[var]
else:
stdPCE = post_pred_std[var][i]
# Expected value of variance (Assump: i.i.d stds)
- cov += np.diag(stdPCE**2)
+ cov += np.diag(stdPCE ** 2)
# Sample a multivariate normal distribution with mean of
- # prediction and variance of cov
+ # posterior prediction and variance of cov
post_pred_withnoise[var][i] = np.random.multivariate_normal(
pred, cov, 1
- )
+ )
# ----- Prior Predictive -----
if self.inference_method.lower() == 'rejection':
# Create hdf5 metadata
- hdf5file = f'{out_dir}/priorPredictive.hdf5'
+ hdf5file = f'{self.out_dir}/priorPredictive.hdf5'
hdf5_exist = os.path.exists(hdf5file)
if hdf5_exist:
os.remove(hdf5file)
@@ -1221,7 +1220,7 @@ class BayesInference:
# ----- Posterior Predictive only model evaluations -----
# Create hdf5 metadata
- hdf5file = out_dir+'/postPredictive_wo_noise.hdf5'
+ hdf5file = self.out_dir + '/postPredictive_wo_noise.hdf5'
hdf5_exist = os.path.exists(hdf5file)
if hdf5_exist:
os.remove(hdf5file)
@@ -1242,7 +1241,7 @@ class BayesInference:
# ----- Posterior Predictive with noise -----
# Create hdf5 metadata
- hdf5file = out_dir+'/postPredictive.hdf5'
+ hdf5file = self.out_dir + '/postPredictive.hdf5'
hdf5_exist = os.path.exists(hdf5file)
if hdf5_exist:
os.remove(hdf5file)
@@ -1275,11 +1274,9 @@ class BayesInference:
"""
- MetaModel = self.MetaModel
- Model = MetaModel.ModelObj
- out_dir = f'Outputs_Bayes_{Model.name}_{self.name}'
+ MetaModel = self.engine.MetaModel
+ Model = self.engine.Model
opt_sigma = self.Discrepancy.opt_sigma
-
# -------- Find MAP and run MetaModel and origModel --------
# Compute the MAP
if self.max_a_posteriori.lower() == 'mean':
@@ -1290,6 +1287,8 @@ class BayesInference:
map_theta = Posterior_df.mean(axis=0).reshape(
(1, MetaModel.n_params))
else:
+ # TODO: here just a fix, no previous mention of Posterior_df!
+ Posterior_df = None
map_theta = stats.mode(Posterior_df.values, axis=0)[0]
# Prin report
print("\nPoint estimator:\n", map_theta[0])
@@ -1313,10 +1312,9 @@ class BayesInference:
Marker = 'x'
# Create a PdfPages object
- pdf = PdfPages(f'./{out_dir}MAP_PCE_vs_Model_{self.name}.pdf')
+ pdf = PdfPages(f'./{self.out_dir}MAP_PCE_vs_Model_{self.name}.pdf')
fig = plt.figure()
for i, key in enumerate(Model.Output.names):
-
y_val = map_orig_model[key]
y_pce_val = map_metamodel_mean[key]
y_pce_val_std = map_metamodel_std[key]
@@ -1327,13 +1325,13 @@ class BayesInference:
plt.plot(
x_values, y_pce_val[i], color=Color[i], lw=2.0,
marker=Marker, linestyle='--', label='$Y_{MAP}^{PCE}$'
- )
+ )
# plot the confidence interval
plt.fill_between(
- x_values, y_pce_val[i] - 1.96*y_pce_val_std[i],
- y_pce_val[i] + 1.96*y_pce_val_std[i],
+ x_values, y_pce_val[i] - 1.96 * y_pce_val_std[i],
+ y_pce_val[i] + 1.96 * y_pce_val_std[i],
color=Color[i], alpha=0.15
- )
+ )
# Calculate the adjusted R_squared and RMSE
R2 = r2_score(y_pce_val.reshape(-1, 1), y_val.reshape(-1, 1))
@@ -1348,7 +1346,7 @@ class BayesInference:
fig.canvas.draw()
p = leg.get_window_extent().inverse_transformed(ax.transAxes)
ax.text(
- p.p0[1]-0.05, p.p1[1]-0.25,
+ p.p0[1] - 0.05, p.p1[1] - 0.25,
f'RMSE = {rmse:.3f}\n$R^2$ = {R2:.3f}',
transform=ax.transAxes, color='black',
bbox=dict(facecolor='none', edgecolor='black',
@@ -1364,6 +1362,110 @@ class BayesInference:
pdf.close()
+ def plot_post_params(self, opt_sigma):
+ """
+ Plots the multivar. posterior parameter distribution.
+
+
+ Parameters
+ ----------
+ opt_sigma : string
+ Type of uncertainty description available.
+
+ Returns
+ -------
+ None.
+
+ """
+ par_names = self.engine.ExpDesign.par_names
+ if opt_sigma != "B":
+ par_names.extend(
+ [self.Discrepancy.InputDisc.Marginals[i].name for i
+ in range(len(self.Discrepancy.InputDisc.Marginals))]
+ )
+ # Pot with corner
+ figPosterior = corner.corner(self.posterior_df.to_numpy(),
+ labels=par_names,
+ quantiles=[0.15, 0.5, 0.85],
+ show_titles=True,
+ title_fmt=self.corner_title_fmt,
+ labelpad=0.2,
+ use_math_text=True,
+ title_kwargs={"fontsize": 28},
+ plot_datapoints=False,
+ plot_density=False,
+ fill_contours=True,
+ smooth=0.5,
+ smooth1d=0.5)
+
+ # Loop over axes and set x limits
+ if opt_sigma == "B":
+ axes = np.array(figPosterior.axes).reshape(
+ (len(par_names), len(par_names))
+ )
+ for yi in range(len(par_names)):
+ ax = axes[yi, yi]
+ ax.set_xlim(self.engine.ExpDesign.bound_tuples[yi])
+ for xi in range(yi):
+ ax = axes[yi, xi]
+ ax.set_xlim(self.engine.ExpDesign.bound_tuples[xi])
+ plt.close()
+
+ # Turn off gridlines
+ for ax in figPosterior.axes:
+ ax.grid(False)
+
+ if self.emulator:
+ plotname = f'/Posterior_Dist_{self.engine.Model.name}_emulator'
+ else:
+ plotname = f'/Posterior_Dist_{self.engine.Model.name}'
+
+ figPosterior.set_size_inches((24, 16))
+ figPosterior.savefig(f'./{self.out_dir}{plotname}.pdf',
+ bbox_inches='tight')
+
+ plt.clf()
+
+ def plot_log_BME(self):
+ """
+ Plots the log_BME if bootstrap is active.
+
+ Returns
+ -------
+ None.
+
+ """
+
+ # Computing the TOM performance
+ self.log_BME_tom = stats.chi2.rvs(
+ self.n_tot_measurement, size=self.log_BME.shape[0]
+ )
+
+ fig, ax = plt.subplots()
+ sns.kdeplot(self.log_BME_tom, ax=ax, color="green", shade=True)
+ sns.kdeplot(
+ self.log_BME, ax=ax, color="blue", shade=True,
+ label='Model BME')
+
+ ax.set_xlabel('log$_{10}$(BME)')
+ ax.set_ylabel('Probability density')
+
+ legend_elements = [
+ Patch(facecolor='green', edgecolor='green', label='TOM BME'),
+ Patch(facecolor='blue', edgecolor='blue', label='Model BME')
+ ]
+ ax.legend(handles=legend_elements)
+
+ if self.emulator:
+ plotname = f'/BME_hist_{self.engine.Model.name}_emulator'
+ else:
+ plotname = f'/BME_hist_{self.engine.Model.name}'
+
+ plt.savefig(f'./{self.out_dir}{plotname}.pdf', bbox_inches='tight')
+
+ plt.show()
+ plt.clf()
+
# -------------------------------------------------------------------------
def _plot_post_predictive(self):
"""
@@ -1375,8 +1477,7 @@ class BayesInference:
"""
- Model = self.MetaModel.ModelObj
- out_dir = f'Outputs_Bayes_{Model.name}_{self.name}'
+ Model = self.engine.Model
# Plot the posterior predictive
for out_idx, out_name in enumerate(Model.Output.names):
fig, ax = plt.subplots()
@@ -1385,11 +1486,10 @@ class BayesInference:
# --- Read prior and posterior predictive ---
if self.inference_method == 'rejection' and \
- self.name.lower() != 'valid':
+ self.name.lower() == 'calib':
# --- Prior ---
# Load posterior predictive
- f = h5py.File(
- f'{out_dir}/priorPredictive.hdf5', 'r+')
+ f = h5py.File(f'{self.out_dir}/priorPredictive.hdf5', 'r+')
try:
x_coords = np.array(f[f"x_values/{out_name}"])
@@ -1398,10 +1498,8 @@ class BayesInference:
X_values = np.repeat(x_coords, 10000)
- prior_pred_df = {}
- prior_pred_df[x_key] = X_values
- prior_pred_df[out_name] = np.array(
- f[f"EDY/{out_name}"])[:10000].flatten('F')
+ prior_pred_df = {x_key: X_values, out_name: np.array(
+ f[f"EDY/{out_name}"])[:10000].flatten('F')}
prior_pred_df = pd.DataFrame(prior_pred_df)
tags_post = ['prior'] * len(prior_pred_df)
@@ -1411,16 +1509,13 @@ class BayesInference:
f.close()
# --- Posterior ---
- f = h5py.File(f"{out_dir}/postPredictive.hdf5", 'r+')
+ f = h5py.File(f"{self.out_dir}/postPredictive.hdf5", 'r+')
X_values = np.repeat(
x_coords, np.array(f[f"EDY/{out_name}"]).shape[0])
- post_pred_df = {}
- post_pred_df[x_key] = X_values
- post_pred_df[out_name] = np.array(
- f[f"EDY/{out_name}"]).flatten('F')
-
+ post_pred_df = {x_key: X_values, out_name: np.array(
+ f[f"EDY/{out_name}"]).flatten('F')}
post_pred_df = pd.DataFrame(post_pred_df)
tags_post = ['posterior'] * len(post_pred_df)
@@ -1448,7 +1543,7 @@ class BayesInference:
ax.errorbar(
x_coords, obs_data[out_name].values,
- yerr=1.96*self.measurement_error[out_name],
+ yerr=1.96 * self.measurement_error[out_name],
ecolor='g', fmt=' ', zorder=-1)
# Add labels to the legend
@@ -1466,7 +1561,7 @@ class BayesInference:
else:
# Load posterior predictive
- f = h5py.File(f"{out_dir}/postPredictive.hdf5", 'r+')
+ f = h5py.File(f"{self.out_dir}/postPredictive.hdf5", 'r+')
try:
x_coords = np.array(f[f"x_values/{out_name}"])
@@ -1481,7 +1576,7 @@ class BayesInference:
x_coords, mu, marker='o', color='b',
label='Mean Post. Predictive')
plt.fill_between(
- x_coords, mu-1.96*std, mu+1.96*std, color='b',
+ x_coords, mu - 1.96 * std, mu + 1.96 * std, color='b',
alpha=0.15)
# --- Plot Data ---
@@ -1490,11 +1585,11 @@ class BayesInference:
'ko', label='data', markeredgecolor='w')
# --- Plot ExpDesign ---
- orig_ED_Y = self.MetaModel.ExpDesign.Y[out_name]
+ orig_ED_Y = self.engine.ExpDesign.Y[out_name]
for output in orig_ED_Y:
plt.plot(
x_coords, output, color='grey', alpha=0.15
- )
+ )
# Add labels for axes
plt.xlabel('Time [s]')
@@ -1517,5 +1612,7 @@ class BayesInference:
else:
plotname = f'/Post_Prior_Perd_{Model.name}'
- fig.savefig(f'./{out_dir}{plotname}_{out_name}.pdf',
+ fig.savefig(f'./{self.out_dir}{plotname}_{out_name}.pdf',
bbox_inches='tight')
+
+ plt.clf()
diff --git a/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py b/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py
index 4d829f61a118b7544298e469272fc05f5d0f0b7f..a26eaa886d6bfda11c23e0e2a490f9f93fee1c45 100644
--- a/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py
+++ b/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py
@@ -1,10 +1,7 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
-"""
-Created on Sat Aug 24 16:04:06 2019
-@author: farid
-"""
+import emcee
import numpy as np
import os
from scipy import stats
@@ -12,6 +9,7 @@ import seaborn as sns
import matplotlib.patches as patches
import matplotlib.colors as mcolors
import matplotlib.pylab as plt
+import pandas as pd
from .bayes_inference import BayesInference
# Load the mplstyle
@@ -31,86 +29,89 @@ class BayesModelComparison:
`True`.
perturbed_data : array of shape (n_bootstrap_itrs, n_obs), optional
User defined perturbed data. The default is `None`.
- n_bootstarp : int
+ n_bootstrap : int
Number of bootstrap iteration. The default is `1000`.
data_noise_level : float
A noise level to perturb the data set. The default is `0.01`.
- just_n_meas : int
- Number of measurements considered for visualization of the
- justifiability results.
"""
def __init__(self, justifiability=True, perturbed_data=None,
- n_bootstarp=1000, data_noise_level=0.01, just_n_meas=2):
+ n_bootstrap=1000, data_noise_level=0.01,
+ use_Bayes_settings = True, emulator = True, out_dir = 'Outputs_Comparison/'):
+ # TODO: check valid ranges of the parameters
+
self.justifiability = justifiability
self.perturbed_data = perturbed_data
- self.n_bootstarp = n_bootstarp
+ self.n_bootstrap = n_bootstrap
self.data_noise_level = data_noise_level
- self.just_n_meas = just_n_meas
-
+ self.use_Bayes_settings = use_Bayes_settings
+ self.emulator = emulator
+ self.out_dir = out_dir
+
+ # Other parameters
+ self.n_meas = None
+ self.BF_data = None
+ self.just_data = None
+ self.BME_dict = None
+ self.set_up = False
+ self.dtype = None
+ self.bayes_dict = None
+ self.just_bayes_dict = None
+ self.model_weights = None
+ self.model_weights_dict = None
+ self.just_model_weights_dict = None
+
+
# --------------------------------------------------------------------------
- def create_model_comparison(self, model_dict, opts_dict):
+ def setup(self, model_dict):
"""
- Starts the two-stage model comparison.
- Stage I: Compare models using Bayes factors.
- Stage II: Compare models via justifiability analysis.
-
- Parameters
- ----------
- model_dict : dict
- A dictionary including the metamodels.
- opts_dict : dict
- A dictionary given the `BayesInference` options.
-
- Example:
-
- >>> opts_bootstrap = {
- "bootstrap": True,
- "n_samples": 10000,
- "Discrepancy": DiscrepancyOpts,
- "emulator": True,
- "plot_post_pred": True
- }
+ Initialize parameters that are needed for all types of model comparison
Returns
-------
- output : dict
- A dictionary containing the objects and the model weights for the
- comparison using Bayes factors and justifiability analysis.
+ None.
"""
+
+ if not isinstance(model_dict, dict):
+ raise Exception("To run model comparsion, you need to pass a "
+ "dictionary of models.")
- # Bayes factor
- bayes_dict_bf, model_weights_dict_bf = self.compare_models(
- model_dict, opts_dict
- )
-
- output = {
- 'Bayes objects BF': bayes_dict_bf,
- 'Model weights BF': model_weights_dict_bf
- }
+ # Extract model names
+ self.model_names = [*model_dict]
- # Justifiability analysis
- if self.justifiability:
- bayes_dict_ja, model_weights_dict_ja = self.compare_models(
- model_dict, opts_dict, justifiability=True
- )
+ # Compute total number of the measurement points
+ # TODO: there could be a different option for this here
+ Engine = list(model_dict.items())[0][1]
+ Engine.Model.read_observation()
+ self.n_meas = Engine.Model.n_obs
- output['Bayes objects JA'] = bayes_dict_ja
- output['Model weights JA'] = model_weights_dict_ja
+ # Find n_bootstrap
+ if self.perturbed_data is not None:
+ self.n_bootstrap = self.perturbed_data.shape[0]
+
+ # Output directory
+ os.makedirs(self.out_dir, exist_ok=True)
+
+ # System settings
+ if os.name == 'nt':
+ print('')
+ print('WARNING: Performing the inference on windows can lead to reduced accuracy!')
+ print('')
+ self.dtype=np.longdouble
+ else:
+ self.dtype=np.float128
- return output
# --------------------------------------------------------------------------
- def compare_models(self, model_dict, opts_dict, justifiability=False):
+ def model_comparison_all(self, model_dict, opts_dict):
"""
- Passes the options to instantiates the BayesInference class for each
- model and passes the options from `opts_dict`. Then, it starts the
- computations.
- It also creates a folder and saves the diagrams, e.g., Bayes factor
- plot, confusion matrix, etc.
+ Perform all three types of model comparison:
+ * Bayes Factors
+ * Model weights
+ * Justifiability analysis
Parameters
----------
@@ -118,50 +119,57 @@ class BayesModelComparison:
A dictionary including the metamodels.
opts_dict : dict
A dictionary given the `BayesInference` options.
- justifiability : bool, optional
- Whether to perform the justifiability analysis. The default is
- `False`.
Returns
-------
- bayes_dict : dict
- A dictionary with `BayesInference` objects.
- model_weights_dict : dict
- A dictionary containing the model weights.
+ results : dict
+ A dictionary that contains the calculated BME values, model weights
+ and confusion matrix
"""
+ self.calc_bayes_factors(model_dict, opts_dict)
+ self.calc_model_weights(model_dict, opts_dict)
+ self.calc_justifiability_analysis(model_dict, opts_dict)
+
+ results = {'BME': self.BME_dict, 'Model weights': self.model_weights_dict,
+ 'Confusion matrix': self.confusion_matrix}
+ return results
+
- if not isinstance(model_dict, dict):
- raise Exception("To run model comparsion, you need to pass a "
- "dictionary of models.")
+ # --------------------------------------------------------------------------
+ def calc_bayes_factors(self, model_dict, opts_dict):
+ """
+ Calculate the BayesFactors for each pair of models in the model_dict
+ with respect to given data.
- # Extract model names
- self.model_names = [*model_dict]
+ Parameters
+ ----------
+ model_dict : dict
+ A dictionary including the metamodels.
+ opts_dict : dict
+ A dictionary given the `BayesInference` options.
- # Compute total number of the measurement points
- MetaModel = list(model_dict.items())[0][1]
- MetaModel.ModelObj.read_observation()
- self.n_meas = MetaModel.ModelObj.n_obs
+ Returns
+ -------
+ None.
+ """
+ # Do the setup
+ if self.n_meas is None:
+ self.setup(model_dict)
+
# ----- Generate data -----
- # Find n_bootstrap
- if self.perturbed_data is None:
- n_bootstarp = self.n_bootstarp
- else:
- n_bootstarp = self.perturbed_data.shape[0]
-
# Create dataset
- justData = self.generate_dataset(
- model_dict, justifiability, n_bootstarp=n_bootstarp)
+ self.BF_data = self.generate_dataset(
+ model_dict, False, n_bootstrap=self.n_bootstrap)
# Run create Interface for each model
- bayes_dict = {}
+ self.bayes_dict = {}
for model in model_dict.keys():
print("-"*20)
print("Bayesian inference of {}.\n".format(model))
-
BayesOpts = BayesInference(model_dict[model])
-
+
# Set BayesInference options
for key, value in opts_dict.items():
if key in BayesOpts.__dict__.keys():
@@ -171,49 +179,147 @@ class BayesModelComparison:
setattr(BayesOpts, key, value)
# Pass justifiability data as perturbed data
- BayesOpts.perturbed_data = justData
- BayesOpts.just_analysis = justifiability
+ BayesOpts.bmc = True
+ BayesOpts.emulator= self.emulator
+ BayesOpts.just_analysis = False
+ BayesOpts.perturbed_data = self.BF_data
- bayes_dict[model] = BayesOpts.create_inference()
+ self.bayes_dict[model] = BayesOpts.create_inference()
print("-"*20)
- # Compute model weights
- BME_Dict = dict()
- for modelName, bayesObj in bayes_dict.items():
- BME_Dict[modelName] = np.exp(bayesObj.log_BME, dtype=np.float128)
+ # Accumulate the BMEs
+ self.BME_dict = dict()
+ for modelName, bayesObj in self.bayes_dict.items():
+ self.BME_dict[modelName] = np.exp(bayesObj.log_BME, dtype=self.dtype)
+
+ # TODO: move the calculation of the Bayes Factors out of the plots to here!
+ # Create kde plot for bayes factors
+ self.plot_bayes_factor(self.BME_dict, 'kde_plot')
+
+
+ def calc_model_weights(self, model_dict, opts_dict):
+ """
+ Calculate the model weights from BME evaluations for Bayes factors.
- # BME correction in BayesInference class
- model_weights = self.cal_model_weight(
- BME_Dict, justifiability, n_bootstarp=n_bootstarp)
+ Parameters
+ ----------
+ model_dict : TYPE
+ DESCRIPTION.
+ opts_dict : TYPE
+ DESCRIPTION.
- # Plot model weights
- if justifiability:
- model_names = self.model_names
- model_names.insert(0, 'Observation')
+ Returns
+ -------
+ None.
- # Split the model weights and save in a dict
- list_ModelWeights = np.split(
- model_weights, model_weights.shape[1]/self.n_meas, axis=1)
- model_weights_dict = {key: weights for key, weights in
- zip(model_names, list_ModelWeights)}
+ """
+ # Get BMEs via Bayes Factors if not already done so
+ if self.BME_dict is None:
+ self.calc_bayes_factors(model_dict, opts_dict)
+
+ # Calculate the model weights
+ self.model_weights = self.cal_model_weight(
+ self.BME_dict, False, n_bootstrap=self.n_bootstrap)
- self.plot_just_analysis(model_weights_dict)
- else:
- # Create box plot for model weights
- self.plot_model_weights(model_weights, 'model_weights')
+ # Create box plot for model weights
+ self.plot_model_weights(self.model_weights, 'model_weights')
- # Create kde plot for bayes factors
- self.plot_bayes_factor(BME_Dict, 'kde_plot')
- # Store model weights in a dict
- model_weights_dict = {key: weights for key, weights in
- zip(self.model_names, model_weights)}
+ # -------------------------------------------------------------------------
+ def calc_justifiability_analysis(self, model_dict, opts_dict):
+ """
+ Perform justifiability analysis by calculating the confusion matrix
+
+ Parameters
+ ----------
+ model_dict : dict
+ A dictionary including the metamodels.
+ opts_dict : dict
+ A dictionary given the `BayesInference` options.
+
+ Returns
+ -------
+ confusion_matrix: dict
+ The averaged confusion matrix
+
+ """
+ # Do setup
+ if self.n_meas is None:
+ self.setup(model_dict)
+
+ # Extend model names
+ model_names = self.model_names
+ if model_names[0]!= 'Observation':
+ model_names.insert(0, 'Observation')
+
+ # Generate data
+ # TODO: generate the datset only if it does not exist yet
+ self.just_data = self.generate_dataset(
+ model_dict, True, n_bootstrap=self.n_bootstrap)
+
+ # Run inference for each model if this is not available
+ if self.just_bayes_dict is None:
+ self.just_bayes_dict = {}
+ for model in model_dict.keys():
+ print("-"*20)
+ print("Bayesian inference of {}.\n".format(model))
+ BayesOpts = BayesInference(model_dict[model])
+
+ # Set BayesInference options
+ for key, value in opts_dict.items():
+ if key in BayesOpts.__dict__.keys():
+ if key == "Discrepancy" and isinstance(value, dict):
+ setattr(BayesOpts, key, value[model])
+ else:
+ setattr(BayesOpts, key, value)
+
+ # Pass justifiability data as perturbed data
+ BayesOpts.bmc = True
+ BayesOpts.emulator= self.emulator
+ BayesOpts.just_analysis = True
+ BayesOpts.perturbed_data = self.just_data
+
+ self.just_bayes_dict[model] = BayesOpts.create_inference()
+ print("-"*20)
+
+ # Compute model weights
+ self.BME_dict = dict()
+ for modelName, bayesObj in self.bayes_dict.items():
+ self.BME_dict[modelName] = np.exp(bayesObj.log_BME, dtype=self.dtype)
+
+ # BME correction in BayesInference class
+ just_model_weights = self.cal_model_weight(
+ self.BME_dict, True, n_bootstrap=self.n_bootstrap)
+
+ # Split the model weights and save in a dict
+ list_ModelWeights = np.split(
+ just_model_weights, self.model_weights.shape[1]/self.n_meas, axis=1)
+ self.just_model_weights_dict = {key: weights for key, weights in
+ zip(model_names, list_ModelWeights)}
+
+ # Confusion matrix over all measurement points
+ cf_m = pd.DataFrame()
+ cf_m['Generated by'] = model_names
+ for i in range(len(model_names)):
+ # Ignore 'Observation', this not in the model_weights_dict
+ # TODO: how to change the code so that it is included as well?
+ if i==0:
+ continue
+ avg = []
+ for n in model_names:
+ avg.append(np.sum(self.just_model_weights_dict[n][i-1]))
+
+ # Norm to sum to 1 for each 'Generated by' row
+ cf_m[model_names[i]] = avg/self.n_meas
+ self.confusion_matrix = cf_m
+
+ # Plot model weights
+ self.plot_just_analysis()
- return bayes_dict, model_weights_dict
# -------------------------------------------------------------------------
def generate_dataset(self, model_dict, justifiability=False,
- n_bootstarp=1):
+ n_bootstrap=1):
"""
Generates the perturbed data set for the Bayes factor calculations and
the data set for the justifiability analysis.
@@ -225,7 +331,7 @@ class BayesModelComparison:
bool, optional
Whether to perform the justifiability analysis. The default is
`False`.
- n_bootstarp : int, optional
+ n_bootstrap : int, optional
Number of bootstrap iterations. The default is `1`.
Returns
@@ -236,33 +342,34 @@ class BayesModelComparison:
"""
# Compute some variables
all_just_data = []
- metaModel = list(model_dict.items())[0][1]
- out_names = metaModel.ModelObj.Output.names
+ Engine = list(model_dict.items())[0][1]
+ out_names = Engine.Model.Output.names
# Perturb observations for Bayes Factor
if self.perturbed_data is None:
self.perturbed_data = self.__perturb_data(
- metaModel.ModelObj.observations, out_names, n_bootstarp,
+ Engine.Model.observations, out_names, n_bootstrap,
noise_level=self.data_noise_level)
# Only for Bayes Factor
if not justifiability:
- return self.perturbed_data
+ return self.perturbed_data # TODO: why return this as self... and the other one not? Is this used again?
# Evaluate metamodel
runs = {}
- for key, metaModel in model_dict.items():
- y_hat, _ = metaModel.eval_metamodel(nsamples=n_bootstarp)
+ for key, metaModel in model_dict.items(): # TODO: add check for emulator vs model
+ y_hat, _ = metaModel.eval_metamodel(nsamples=n_bootstrap)
runs[key] = y_hat
# Generate data
- for i in range(n_bootstarp):
- y_data = self.perturbed_data[i].reshape(1, -1)
- justData = np.tril(np.repeat(y_data, y_data.shape[1], axis=0))
+ for i in range(n_bootstrap):
+ y_data = self.perturbed_data[i].reshape(1, -1)# makes every entry in self.perturbed_data 2D by adding one dim outside
+ justData = np.tril(np.repeat(y_data, y_data.shape[1], axis=0)) # Lower triangle matrix from repeats of y_data
+ # TODO: why triangle matrix here?
# Use surrogate runs for data-generating process
for key, metaModel in model_dict.items():
model_data = np.array(
- [runs[key][out][i] for out in out_names]).reshape(y_data.shape)
+ [runs[key][out][i] for out in out_names]).reshape(y_data.shape) # reshapes model runs to match y_data
justData = np.vstack((
justData,
np.tril(np.repeat(model_data, model_data.shape[1], axis=0))
@@ -280,7 +387,7 @@ class BayesModelComparison:
# -------------------------------------------------------------------------
def __perturb_data(self, data, output_names, n_bootstrap, noise_level):
"""
- Returns an array with n_bootstrap_itrs rowsof perturbed data.
+ Returns an array with n_bootstrap_itrs rows of perturbed data.
The first row includes the original observation data.
If `self.bayes_loocv` is True, a 2d-array will be returned with
repeated rows and zero diagonal entries.
@@ -322,13 +429,13 @@ class BayesModelComparison:
return final_data
# -------------------------------------------------------------------------
- def cal_model_weight(self, BME_Dict, justifiability=False, n_bootstarp=1):
+ def cal_model_weight(self, BME_dict, justifiability=False, n_bootstrap=1):
"""
Normalize the BME (Asumption: Model Prior weights are equal for models)
Parameters
----------
- BME_Dict : dict
+ BME_dict : dict
A dictionary containing the BME values.
Returns
@@ -338,12 +445,12 @@ class BayesModelComparison:
"""
# Stack the BME values for all models
- all_BME = np.vstack(list(BME_Dict.values()))
+ all_BME = np.vstack(list(BME_dict.values()))
if justifiability:
# Compute expected log_BME for justifiabiliy analysis
all_BME = all_BME.reshape(
- all_BME.shape[0], -1, n_bootstarp).mean(axis=2)
+ all_BME.shape[0], -1, n_bootstrap).mean(axis=2)
# Model weights
model_weights = np.divide(all_BME, np.nansum(all_BME, axis=0))
@@ -366,16 +473,16 @@ class BayesModelComparison:
None.
"""
-
- directory = 'Outputs_Comparison/'
- os.makedirs(directory, exist_ok=True)
+ model_weights_dict = self.just_model_weights_dict
Color = [*mcolors.TABLEAU_COLORS]
names = [*model_weights_dict]
+ # Plot weights for each 'Generated by'
model_names = [model.replace('_', '$-$') for model in self.model_names]
for name in names:
fig, ax = plt.subplots()
for i, model in enumerate(model_names[1:]):
+ #print(model, i)
plt.plot(list(range(1, self.n_meas+1)),
model_weights_dict[name][i],
color=Color[i], marker='o',
@@ -388,13 +495,12 @@ class BayesModelComparison:
ax.set_xticks(list(range(1, self.n_meas+1)))
plt.legend(loc="best")
fig.savefig(
- f'{directory}modelWeights_{name}.svg', bbox_inches='tight'
+ f'{self.out_dir}modelWeights_{name}.svg', bbox_inches='tight'
)
plt.close()
- # Confusion matrix for some measurement points
- epsilon = 1 if self.just_n_meas != 1 else 0
- for index in range(0, self.n_meas+epsilon, self.just_n_meas):
+ # Confusion matrix for each measurement point
+ for index in range(0, self.n_meas):
weights = np.array(
[model_weights_dict[key][:, index] for key in model_weights_dict]
)
@@ -409,10 +515,26 @@ class BayesModelComparison:
g.set_xlabel(r"\textbf{Data generated by:}", labelpad=15)
g.set_ylabel(r"\textbf{Model weight for:}", labelpad=15)
g.figure.savefig(
- f"{directory}confusionMatrix_ND_{index+1}.pdf",
+ f"{self.out_dir}confusionMatrix_ND_{index+1}.pdf",
bbox_inches='tight'
)
plt.close()
+
+ # Plot the averaged confusion matrix
+ out_names = names[1:]
+ cf = self.confusion_matrix[out_names].to_numpy()
+ g = sns.heatmap(cf.T, annot=True, cmap='Blues', xticklabels=model_names,
+ yticklabels=model_names[1:], annot_kws={"size": 24})
+ g.xaxis.tick_top()
+ g.xaxis.set_label_position('top')
+ g.set_xlabel(r"\textbf{Data generated by:}", labelpad=15)
+ g.set_ylabel(r"\textbf{Model weight for:}", labelpad=15)
+ g.figure.savefig(
+ f"{self.out_dir}confusionMatrix_full.pdf",
+ bbox_inches='tight'
+ )
+ plt.close()
+
# -------------------------------------------------------------------------
def plot_model_weights(self, model_weights, plot_name):
@@ -432,13 +554,9 @@ class BayesModelComparison:
None.
"""
- font_size = 40
- # mkdir for plots
- directory = 'Outputs_Comparison/'
- os.makedirs(directory, exist_ok=True)
-
# Create figure
fig, ax = plt.subplots()
+ font_size = 40
# Filter data using np.isnan
mask = ~np.isnan(model_weights.T)
@@ -466,44 +584,35 @@ class BayesModelComparison:
for median in bp['medians']:
median.set(color='#b2df8a', linewidth=2)
- # change the style of fliers and their fill
- # for flier in bp['fliers']:
- # flier.set(marker='o', color='#e7298a', alpha=0.75)
-
- # Custom x-axis labels
+ # Customize the axes
model_names = [model.replace('_', '$-$') for model in self.model_names]
ax.set_xticklabels(model_names)
-
ax.set_ylabel('Weight', fontsize=font_size)
-
- # Title
- plt.title('Posterior Model Weights')
-
- # Set y lim
ax.set_ylim((-0.05, 1.05))
-
- # Set size of the ticks
for t in ax.get_xticklabels():
t.set_fontsize(font_size)
for t in ax.get_yticklabels():
t.set_fontsize(font_size)
+ # Title
+ plt.title('Posterior Model Weights')
+
# Save the figure
fig.savefig(
- f'./{directory}{plot_name}.pdf', bbox_inches='tight'
+ f'./{self.out_dir}{plot_name}.pdf', bbox_inches='tight'
)
plt.close()
# -------------------------------------------------------------------------
- def plot_bayes_factor(self, BME_Dict, plot_name=''):
+ def plot_bayes_factor(self, BME_dict, plot_name=''):
"""
Plots the Bayes factor distibutions in a :math:`N_m \\times N_m`
matrix, where :math:`N_m` is the number of the models.
Parameters
----------
- BME_Dict : dict
+ BME_dict : dict
A dictionary containing the BME values of the models.
plot_name : str, optional
Plot name. The default is ''.
@@ -513,16 +622,11 @@ class BayesModelComparison:
None.
"""
-
+ # Plot setup
font_size = 40
-
- # mkdir for plots
- directory = 'Outputs_Comparison/'
- os.makedirs(directory, exist_ok=True)
-
Colors = ["blue", "green", "gray", "brown"]
- model_names = list(BME_Dict.keys())
+ model_names = list(BME_dict.keys())
nModels = len(model_names)
# Plots
@@ -544,7 +648,7 @@ class BayesModelComparison:
# Null hypothesis: key_j is the better model
BayesFactor = np.log10(
- np.divide(BME_Dict[key_i], BME_Dict[key_j])
+ np.divide(BME_dict[key_i], BME_dict[key_j])
)
# sns.kdeplot(BayesFactor, ax=ax, color=Colors[i], shade=True)
@@ -637,10 +741,8 @@ class BayesModelComparison:
fontsize=fsize, color=Colors[i],
transform=ax.transAxes)
- # Defining custom 'ylim' values.
+ # Customize axes
custom_ylim = (0, 1.05)
-
- # Setting the values for all axes.
plt.setp(axes, ylim=custom_ylim)
# set labels
@@ -652,7 +754,7 @@ class BayesModelComparison:
plt.subplots_adjust(wspace=0.2, hspace=0.1)
plt.savefig(
- f'./{directory}Bayes_Factor{plot_name}.pdf', bbox_inches='tight'
+ f'./{self.out_dir}Bayes_Factor{plot_name}.pdf', bbox_inches='tight'
)
plt.close()
diff --git a/src/bayesvalidrox/bayes_inference/discrepancy.py b/src/bayesvalidrox/bayes_inference/discrepancy.py
index 0f52c4f0d0afe314fdc73c388f6da5a8aa93ca06..b3c235ebeb6d6ae9e109ca862cc522cc21efb45e 100644
--- a/src/bayesvalidrox/bayes_inference/discrepancy.py
+++ b/src/bayesvalidrox/bayes_inference/discrepancy.py
@@ -36,7 +36,7 @@ class Discrepancy:
* Option B: With unknown redidual covariance matrix \\(\\Sigma\\),
paramethrized as \\(\\Sigma(\\theta_{\\epsilon})=\\sigma^2 \\textbf{I}_
{N_{out}}\\) with unknown residual variances \\(\\sigma^2\\).
- This term will be jointly infer with the uncertain input parameters. For
+ This term will be jointly infered with the uncertain input parameters. For
the inversion, you need to define a prior marginal via `Input` class. Note
that \\(\\sigma^2\\) is only a single scalar multiplier for the diagonal
entries of the covariance matrix \\(\\Sigma\\).
@@ -58,10 +58,17 @@ class Discrepancy:
"""
def __init__(self, InputDisc='', disc_type='Gaussian', parameters=None):
+ # Set the values
self.InputDisc = InputDisc
self.disc_type = disc_type
self.parameters = parameters
-
+
+ # Other inits
+ self.ExpDesign = None
+ self.n_samples = None
+ self.sigma2_prior = None
+ self.name = None
+ self.opt_sigma = None # This will be set in the inference class and used in mcmc
# -------------------------------------------------------------------------
def get_sample(self, n_samples):
"""
@@ -79,16 +86,28 @@ class Discrepancy:
\\(\\sigma^2\\) samples.
"""
- self.n_samples = n_samples
- ExpDesign = ExpDesigns(self.InputDisc)
- self.sigma2_prior = ExpDesign.generate_ED(
- n_samples, sampling_method='random', max_pce_deg=1
+ self.n_samples = n_samples # TODO: not used again in here - needed from the outside?
+
+ if self.InputDisc == '':
+ raise AttributeError('Cannot create new samples, please provide input distributions')
+
+ # Create and store BoundTuples
+ self.ExpDesign = ExpDesigns(self.InputDisc)
+ self.ExpDesign.sampling_method = 'random'
+
+ # TODO: why does it call 'generate_ED' instead of 'generate_samples?
+ # ExpDesign.bound_tuples, onp_sigma, prior_space needed from the outside
+ # Discrepancy opt_sigma, InputDisc needed from the outside
+ # TODO: opt_sigma not defined here, but called from the outside??
+ self.ExpDesign.generate_ED(
+ n_samples, max_pce_deg=1
)
- # Store BoundTuples
- self.ExpDesign = ExpDesign
+ # TODO: need to recheck the following line
+ # This used to simply be the return from the call above
+ self.sigma2_prior = self.ExpDesign.X
# Naive approach: Fit a gaussian kernel to the provided data
- self.ExpDesign.JDist = stats.gaussian_kde(ExpDesign.raw_data)
+ self.ExpDesign.JDist = stats.gaussian_kde(self.ExpDesign.raw_data)
# Save the names of sigmas
if len(self.InputDisc.Marginals) != 0:
diff --git a/src/bayesvalidrox/bayes_inference/mcmc.py b/src/bayesvalidrox/bayes_inference/mcmc.py
index 6b9ca94823fb7064baa2f0588d0f97fb4c9d1d44..49748bce7c18ee3153954f85524795f9bf3ef519 100755
--- a/src/bayesvalidrox/bayes_inference/mcmc.py
+++ b/src/bayesvalidrox/bayes_inference/mcmc.py
@@ -9,12 +9,178 @@ import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import multiprocessing
import scipy.stats as st
-from scipy.linalg import cholesky as chol
-import warnings
+from scipy import stats
import shutil
+import scipy.linalg as spla
+from sklearn import preprocessing
os.environ["OMP_NUM_THREADS"] = "1"
+# -------------------------------------------------------------------------
+def _check_ranges(theta, ranges): # TODO: this is a replica of exp_designs.check_ranges
+ """
+ This function checks if theta lies in the given ranges.
+
+ Parameters
+ ----------
+ theta : array
+ Proposed parameter set.
+ ranges : nested list
+ List of the praremeter ranges.
+
+ Returns
+ -------
+ c : bool
+ If it lies in the given range, it return True else False.
+
+ """
+ c = True
+ # traverse in the list1
+ for i, bounds in enumerate(ranges):
+ x = theta[i]
+ # condition check
+ if x < bounds[0] or x > bounds[1]:
+ c = False
+ return c
+ return c
+
+# -------------------------------------------------------------------------
+def gelman_rubin(chain, return_var=False):
+ """
+ The potential scale reduction factor (PSRF) defined by the variance
+ within one chain, W, with the variance between chains B.
+ Both variances are combined in a weighted sum to obtain an estimate of
+ the variance of a parameter \\( \\theta \\).The square root of the
+ ratio of this estimates variance to the within chain variance is called
+ the potential scale reduction.
+ For a well converged chain it should approach 1. Values greater than
+ 1.1 typically indicate that the chains have not yet fully converged.
+
+ Source: http://joergdietrich.github.io/emcee-convergence.html
+
+ https://github.com/jwalton3141/jwalton3141.github.io/blob/master/assets/posts/ESS/rwmh.py
+
+ Parameters
+ ----------
+ chain : array (n_walkers, n_steps, n_params)
+ The emcee ensamples.
+
+ Returns
+ -------
+ R_hat : float
+ The Gelman-Robin values.
+
+ """
+ chain = np.array(chain)
+ m_chains, n_iters = chain.shape[:2]
+
+ # Calculate between-chain variance
+ θb = np.mean(chain, axis=1)
+ θbb = np.mean(θb, axis=0)
+ B_over_n = ((θbb - θb)**2).sum(axis=0)
+ B_over_n /= (m_chains - 1)
+
+ # Calculate within-chain variances
+ ssq = np.var(chain, axis=1, ddof=1)
+ W = np.mean(ssq, axis=0)
+
+ # (over) estimate of variance
+ var_θ = W * (n_iters - 1) / n_iters + B_over_n
+
+ if return_var:
+ return var_θ
+ else:
+ # The square root of the ratio of this estimates variance to the
+ # within chain variance
+ R_hat = np.sqrt(var_θ / W)
+ return R_hat
+
+# -------------------------------------------------------------------------
+def _kernel_rbf(X, hyperparameters):
+ """
+ Isotropic squared exponential kernel.
+
+ Higher l values lead to smoother functions and therefore to coarser
+ approximations of the training data. Lower l values make functions
+ more wiggly with wide uncertainty regions between training data points.
+
+ sigma_f controls the marginal variance of b(x)
+
+ Parameters
+ ----------
+ X : ndarray of shape (n_samples_X, n_features)
+
+ hyperparameters : Dict
+ Lambda characteristic length
+ sigma_f controls the marginal variance of b(x)
+ sigma_0 unresolvable error nugget term, interpreted as random
+ error that cannot be attributed to measurement error.
+ Returns
+ -------
+ var_cov_matrix : ndarray of shape (n_samples_X,n_samples_X)
+ Kernel k(X, X).
+
+ """
+ from sklearn.gaussian_process.kernels import RBF
+ min_max_scaler = preprocessing.MinMaxScaler()
+ X_minmax = min_max_scaler.fit_transform(X)
+
+ nparams = len(hyperparameters)
+ if nparams < 3:
+ raise AttributeError('Provide 3 parameters for the RBF kernel!')
+
+ # characteristic length (0,1]
+ Lambda = hyperparameters[0]
+ # sigma_f controls the marginal variance of b(x)
+ sigma2_f = hyperparameters[1]
+
+ rbf = RBF(length_scale=Lambda)
+ cov_matrix = sigma2_f * rbf(X_minmax)
+
+ # (unresolvable error) nugget term that is interpreted as random
+ # error that cannot be attributed to measurement error.
+ sigma2_0 = hyperparameters[2:]
+ for i, j in np.ndindex(cov_matrix.shape):
+ cov_matrix[i, j] += np.sum(sigma2_0) if i == j else 0
+
+ return cov_matrix
+
+
+# -------------------------------------------------------------------------
+def _logpdf(x, mean, cov):
+ """
+ Computes the likelihood based on a multivariate normal distribution.
+
+ Parameters
+ ----------
+ x : TYPE
+ DESCRIPTION.
+ mean : array_like
+ Observation data.
+ cov : 2d array
+ Covariance matrix of the distribution.
+
+ Returns
+ -------
+ log_lik : float
+ Log likelihood.
+
+ """
+
+ # Tranform into np arrays
+ x = np.array(x)
+ mean = np.array(mean)
+ cov = np.array(cov)
+
+ n = len(mean)
+ L = spla.cholesky(cov, lower=True)
+ beta = np.sum(np.log(np.diag(L)))
+ dev = x - mean
+ alpha = dev.dot(spla.cho_solve((L, True), dev))
+ log_lik = -0.5 * alpha - beta - n / 2. * np.log(2 * np.pi)
+ return log_lik
+
+
class MCMC:
"""
A class for bayesian inference via a Markov-Chain Monte-Carlo (MCMC)
@@ -40,66 +206,85 @@ class MCMC:
Bayes object.
"""
- def __init__(self, BayesOpts):
-
- self.BayesOpts = BayesOpts
+ def __init__(self, engine, mcmc_params, Discrepancy, bias_inputs,
+ error_model, req_outputs, selected_indices, emulator,
+ out_dir, name, BiasInputs = None):
+
+ # TODO: maybe would be worth to make this a child class of BayesInf?
+ # Inputs
+ #self.BayesOpts = BayesOpts
+ self.engine = engine
+ self.Discrepancy = Discrepancy
+
+ # Get the needed properties from the BayesInf class
+ self.bias_inputs = bias_inputs
+ self.error_model = error_model
+ self.BiasInputs = BiasInputs
+ self.selected_indices = selected_indices
+ self.req_outputs = req_outputs
+ self.emulator = emulator
+ self.out_dir = out_dir
+ self.name = name
+
+ # Param inits
+ self.counter = 0
+ self.observation = None
+ self.total_sigma2 = None
+ self.error_MetaModel = None
+
+ # Get MCMC parameters from BayesOpts
+ self.initsamples = mcmc_params['init_samples']
+ if isinstance(self.initsamples, pd.DataFrame):
+ self.initsamples = self.initsamples.values
+ self.nsteps = int(mcmc_params['n_steps'])
+ self.nwalkers = int(mcmc_params['n_walkers'])
+ self.nburn = mcmc_params['n_burn']
+ self.moves = mcmc_params['moves']
+ self.mp = mcmc_params['multiprocessing']
+ self.verbose = mcmc_params['verbose']
def run_sampler(self, observation, total_sigma2):
+ """
+ Run the MCMC sampler for the given observations and stdevs.
- BayesObj = self.BayesOpts
- MetaModel = BayesObj.MetaModel
- Model = MetaModel.ModelObj
- Discrepancy = self.BayesOpts.Discrepancy
- n_cpus = Model.n_cpus
- priorDist = MetaModel.ExpDesign.JDist
- ndim = MetaModel.n_params
- self.counter = 0
- output_dir = f'Outputs_Bayes_{Model.name}_{self.BayesOpts.name}'
- if not os.path.exists(output_dir):
- os.makedirs(output_dir)
+ Parameters
+ ----------
+ observation : TYPE
+ DESCRIPTION.
+ total_sigma2 : TYPE
+ DESCRIPTION.
+
+ Returns
+ -------
+ Posterior_df : TYPE
+ DESCRIPTION.
+ """
+ # Get init values
+ engine = self.engine
+ Discrepancy = self.Discrepancy
+ n_cpus = engine.Model.n_cpus
+ ndim = engine.MetaModel.n_params
+ if not os.path.exists(self.out_dir):
+ os.makedirs(self.out_dir)
+
+ # Save inputs
self.observation = observation
self.total_sigma2 = total_sigma2
- # Unpack mcmc parameters given to BayesObj.mcmc_params
- self.initsamples = None
- self.nwalkers = 100
- self.nburn = 200
- self.nsteps = 100000
- self.moves = None
- self.mp = False
- self.verbose = False
-
- # Extract initial samples
- if 'init_samples' in BayesObj.mcmc_params:
- self.initsamples = BayesObj.mcmc_params['init_samples']
- if isinstance(self.initsamples, pd.DataFrame):
- self.initsamples = self.initsamples.values
-
- # Extract number of steps per walker
- if 'n_steps' in BayesObj.mcmc_params:
- self.nsteps = int(BayesObj.mcmc_params['n_steps'])
- # Extract number of walkers (chains)
- if 'n_walkers' in BayesObj.mcmc_params:
- self.nwalkers = int(BayesObj.mcmc_params['n_walkers'])
- # Extract moves
- if 'moves' in BayesObj.mcmc_params:
- self.moves = BayesObj.mcmc_params['moves']
- # Extract multiprocessing
- if 'multiprocessing' in BayesObj.mcmc_params:
- self.mp = BayesObj.mcmc_params['multiprocessing']
- # Extract verbose
- if 'verbose' in BayesObj.mcmc_params:
- self.verbose = BayesObj.mcmc_params['verbose']
-
# Set initial samples
np.random.seed(0)
if self.initsamples is None:
try:
+ # Version 1 # TODO: recheck this code, it it a mix of old and new
+ priorDist = self.engine.ExpDesign.JDist
initsamples = priorDist.sample(self.nwalkers).T
+
+# initsamples = engine.ExpDesign.JDist.sample(self.nwalkers).T
+# initsamples = np.swapaxes(np.array([initsamples]),0,1) # TODO: test if this still works with multiple input dists
except:
# when aPCE selected - gaussian kernel distribution
- inputSamples = MetaModel.ExpDesign.raw_data.T
+ inputSamples = engine.ExpDesign.raw_data.T
random_indices = np.random.choice(
len(inputSamples), size=self.nwalkers, replace=False
)
@@ -125,31 +310,27 @@ class MCMC:
initsamples[:, idx_dim] = dist.rvs(size=self.nwalkers)
# Update lower and upper
- MetaModel.ExpDesign.bound_tuples = bound_tuples
+ engine.ExpDesign.bound_tuples = bound_tuples
# Check if sigma^2 needs to be inferred
- if Discrepancy.opt_sigma != 'B':
+ if Discrepancy.opt_sigma != 'B': # TODO: why !='B'?
sigma2_samples = Discrepancy.get_sample(self.nwalkers)
# Update initsamples
initsamples = np.hstack((initsamples, sigma2_samples))
-
- # Update ndim
ndim = initsamples.shape[1]
# Discrepancy bound
disc_bound_tuple = Discrepancy.ExpDesign.bound_tuples
# Update bound_tuples
- MetaModel.ExpDesign.bound_tuples += disc_bound_tuple
+ engine.ExpDesign.bound_tuples += disc_bound_tuple
print("\n>>>> Bayesian inference with MCMC for "
- f"{self.BayesOpts.name} started. <<<<<<")
+ f"{self.name} started. <<<<<<")
- # Set up the backend
- filename = f"{output_dir}/emcee_sampler.h5"
- backend = emcee.backends.HDFBackend(filename)
- # Clear the backend in case the file already exists
+ # Set up the backend and clear it in case the file already exists
+ backend = emcee.backends.HDFBackend(f"{self.out_dir}/emcee_sampler.h5")
backend.reset(self.nwalkers, ndim)
# Define emcee sampler
@@ -176,8 +357,8 @@ class MCMC:
)
# Reset sampler
- sampler.reset()
pos = pos.coords
+ sampler.reset()
else:
pos = initsamples
@@ -193,7 +374,8 @@ class MCMC:
self.nwalkers, ndim, self.log_posterior, moves=self.moves,
backend=backend, vectorize=True
)
-
+ print(f'ndim: {ndim}')
+ print(f'initsamples.shape: {initsamples.shape}')
# Check if a burn-in phase is needed!
if self.initsamples is None:
# Burn-in
@@ -228,8 +410,9 @@ class MCMC:
continue
# Train model discrepancy/error
- if hasattr(BayesObj, 'errorModel') and BayesObj.errorModel \
- and not sampler.iteration % 3 * autocorreverynsteps:
+ # TODO: add this back in when the error model is actually working
+ # and this is not dependent on BayesObj
+ if self.error_model and not sampler.iteration % 3 * autocorreverynsteps:
try:
self.error_MetaModel = self.train_error_model(sampler)
except:
@@ -251,14 +434,14 @@ class MCMC:
# output current autocorrelation estimate
if self.verbose:
- print(f"Mean autocorr time estimate: {np.nanmean(tau):.3f}")
- list_gr = np.round(self.gelman_rubin(sampler.chain), 3)
+ print(f"Mean autocorr. time estimate: {np.nanmean(tau):.3f}")
+ list_gr = np.round(gelman_rubin(sampler.chain), 3)
print("Gelman-Rubin Test*: ", list_gr)
# check convergence
converged = np.all(tau*autocorreverynsteps < sampler.iteration)
converged &= np.all(np.abs(tauold - tau) / tau < 0.01)
- converged &= np.all(self.gelman_rubin(sampler.chain) < 1.1)
+ converged &= np.all(gelman_rubin(sampler.chain) < 1.1)
if converged:
break
@@ -277,37 +460,37 @@ class MCMC:
thin = int(0.5*np.nanmin(tau)) if int(0.5*np.nanmin(tau)) != 0 else 1
finalsamples = sampler.get_chain(discard=burnin, flat=True, thin=thin)
acc_fr = np.nanmean(sampler.acceptance_fraction)
- list_gr = np.round(self.gelman_rubin(sampler.chain[:, burnin:]), 3)
+ list_gr = np.round(gelman_rubin(sampler.chain[:, burnin:]), 3)
# Print summary
print('\n')
print('-'*15 + 'Posterior diagnostics' + '-'*15)
- print(f"mean auto-correlation time: {np.nanmean(tau):.3f}")
- print(f"thin: {thin}")
- print(f"burn-in: {burnin}")
- print(f"flat chain shape: {finalsamples.shape}")
- print(f"Mean acceptance fraction: {acc_fr:.3f}")
- print("Gelman-Rubin Test*: ", list_gr)
+ print(f"Mean auto-correlation time: {np.nanmean(tau):.3f}")
+ print(f"Thin: {thin}")
+ print(f"Burn-in: {burnin}")
+ print(f"Flat chain shape: {finalsamples.shape}")
+ print(f"Mean acceptance fraction*: {acc_fr:.3f}")
+ print("Gelman-Rubin Test**: ", list_gr)
print("\n* This value must lay between 0.234 and 0.5.")
- print("* These values must be smaller than 1.1.")
+ print("** These values must be smaller than 1.1.")
print('-'*50)
- print(f"\n>>>> Bayesian inference with MCMC for {self.BayesOpts.name} "
+ print(f"\n>>>> Bayesian inference with MCMC for {self.name} "
"successfully completed. <<<<<<\n")
# Extract parameter names and their prior ranges
- par_names = MetaModel.ExpDesign.par_names
+ par_names = engine.ExpDesign.par_names
if Discrepancy.opt_sigma != 'B':
for i in range(len(Discrepancy.InputDisc.Marginals)):
par_names.append(Discrepancy.InputDisc.Marginals[i].name)
- params_range = MetaModel.ExpDesign.bound_tuples
+ params_range = engine.ExpDesign.bound_tuples
# Plot traces
if self.verbose and self.nsteps < 10000:
- pdf = PdfPages(output_dir+'/traceplots.pdf')
+ pdf = PdfPages(self.out_dir+'/traceplots.pdf')
fig = plt.figure()
for parIdx in range(ndim):
# Set up the axes with gridspec
@@ -334,7 +517,6 @@ class MCMC:
# Destroy the current plot
plt.clf()
-
pdf.close()
# plot development of autocorrelation estimate
@@ -348,33 +530,9 @@ class MCMC:
plt.ylim(0, np.nanmax(taus)+0.1*(np.nanmax(taus)-np.nanmin(taus)))
plt.xlabel("number of steps")
plt.ylabel(r"mean $\hat{\tau}$")
- fig1.savefig(f"{output_dir}/autocorrelation_time.pdf",
+ fig1.savefig(f"{self.out_dir}/autocorrelation_time.pdf",
bbox_inches='tight')
- # logml_dict = self.marginal_llk_emcee(sampler, self.nburn, logp=None,
- # maxiter=5000)
- # print('\nThe Bridge Sampling Estimation is "
- # f"{logml_dict['logml']:.5f}.')
-
- # # Posterior-based expectation of posterior probablity
- # postExpPostLikelihoods = np.mean(sampler.get_log_prob(flat=True)
- # [self.nburn*self.nwalkers:])
-
- # # Posterior-based expectation of prior densities
- # postExpPrior = np.mean(self.log_prior(emcee_trace.T))
-
- # # Posterior-based expectation of likelihoods
- # postExpLikelihoods_emcee = postExpPostLikelihoods - postExpPrior
-
- # # Calculate Kullback-Leibler Divergence
- # KLD_emcee = postExpLikelihoods_emcee - logml_dict['logml']
- # print("Kullback-Leibler divergence: %.5f"%KLD_emcee)
-
- # # Information Entropy based on Entropy paper Eq. 38
- # infEntropy_emcee = logml_dict['logml'] - postExpPrior -
- # postExpLikelihoods_emcee
- # print("Information Entropy: %.5f" %infEntropy_emcee)
-
Posterior_df = pd.DataFrame(finalsamples, columns=par_names)
return Posterior_df
@@ -397,9 +555,9 @@ class MCMC:
returned otherwise an array.
"""
-
- MetaModel = self.BayesOpts.MetaModel
- Discrepancy = self.BayesOpts.Discrepancy
+ engine = self.engine
+ MetaModel = engine.MetaModel
+ Discrepancy = self.Discrepancy
# Find the number of sigma2 parameters
if Discrepancy.opt_sigma != 'B':
@@ -409,15 +567,15 @@ class MCMC:
n_sigma2 = len(disc_bound_tuples)
else:
n_sigma2 = -len(theta)
- prior_dist = MetaModel.ExpDesign.prior_space
- params_range = MetaModel.ExpDesign.bound_tuples
+ prior_dist = engine.ExpDesign.prior_space
+ params_range = engine.ExpDesign.bound_tuples
theta = theta if theta.ndim != 1 else theta.reshape((1, -1))
nsamples = theta.shape[0]
logprior = -np.inf*np.ones(nsamples)
for i in range(nsamples):
# Check if the sample is within the parameters' range
- if self._check_ranges(theta[i], params_range):
+ if _check_ranges(theta[i], params_range):
# Check if all dists are uniform, if yes priors are equal.
if all(MetaModel.input_obj.Marginals[i].dist_type == 'uniform'
for i in range(MetaModel.n_params)):
@@ -429,7 +587,7 @@ class MCMC:
# Check if bias term needs to be inferred
if Discrepancy.opt_sigma != 'B':
- if self._check_ranges(theta[i, -n_sigma2:],
+ if _check_ranges(theta[i, -n_sigma2:],
disc_bound_tuples):
if all('unif' in disc_marginals[i].dist_type for i in
range(Discrepancy.ExpDesign.ndim)):
@@ -461,37 +619,35 @@ class MCMC:
Log likelihood.
"""
-
- BayesOpts = self.BayesOpts
- MetaModel = BayesOpts.MetaModel
- Discrepancy = self.BayesOpts.Discrepancy
+ MetaModel = self.engine.MetaModel
+ Discrepancy = self.Discrepancy
# Find the number of sigma2 parameters
if Discrepancy.opt_sigma != 'B':
disc_bound_tuples = Discrepancy.ExpDesign.bound_tuples
n_sigma2 = len(disc_bound_tuples)
- else:
- n_sigma2 = -len(theta)
- # Check if bias term needs to be inferred
- if Discrepancy.opt_sigma != 'B':
+ # Check if bias term should be inferred
sigma2 = theta[:, -n_sigma2:]
theta = theta[:, :-n_sigma2]
else:
+ n_sigma2 = -len(theta)
sigma2 = None
+
theta = theta if theta.ndim != 1 else theta.reshape((1, -1))
# Evaluate Model/MetaModel at theta
- mean_pred, BayesOpts._std_pce_prior_pred = self.eval_model(theta)
+ mean_pred, _std_pce_prior_pred = self.eval_model(theta)
# Surrogate model's error using RMSE of test data
surrError = MetaModel.rmse if hasattr(MetaModel, 'rmse') else None
# Likelihood
- log_like = BayesOpts.normpdf(
+ log_like = self.normpdf(
mean_pred, self.observation, self.total_sigma2, sigma2,
std=surrError
)
- return log_like
+ # TODO: give the second return argument back to BayesInf (parameter of the same name)
+ return log_like, _std_pce_prior_pred
# -------------------------------------------------------------------------
def log_posterior(self, theta):
@@ -520,7 +676,7 @@ class MCMC:
# Compute log prior
log_prior = self.log_prior(theta)
# Compute log Likelihood
- log_likelihood = self.log_likelihood(theta)
+ log_likelihood, _std_pce_prior_pred = self.log_likelihood(theta)
return log_prior + log_likelihood
else:
@@ -535,7 +691,7 @@ class MCMC:
# Compute loLikelihoods
if non_inf_idx.size != 0:
- log_likelihood[non_inf_idx] = self.log_likelihood(
+ log_likelihood[non_inf_idx], _std_pce_prior_pred = self.log_likelihood(
theta[non_inf_idx]
)
@@ -559,14 +715,12 @@ class MCMC:
Std of model prediction.
"""
+ engine = self.engine
+ Model = engine.Model
- BayesObj = self.BayesOpts
- MetaModel = BayesObj.MetaModel
- Model = MetaModel.ModelObj
-
- if BayesObj.emulator:
+ if self.emulator:
# Evaluate the MetaModel
- mean_pred, std_pred = MetaModel.eval_metamodel(samples=theta)
+ mean_pred, std_pred = engine.MetaModel.eval_metamodel(samples=theta)
else:
# Evaluate the origModel
mean_pred, std_pred = dict(), dict()
@@ -587,9 +741,9 @@ class MCMC:
# Add one to the counter
self.counter += 1
- if hasattr(self, 'error_MetaModel') and BayesObj.error_model:
+ if self.error_MetaModel is not None and self.error_model:
meanPred, stdPred = self.error_MetaModel.eval_model_error(
- BayesObj.BiasInputs, mean_pred
+ self.BiasInputs, mean_pred
)
return mean_pred, std_pred
@@ -610,8 +764,7 @@ class MCMC:
A error model.
"""
- BayesObj = self.BayesOpts
- MetaModel = BayesObj.MetaModel
+ MetaModel = self.engine.MetaModel
# Prepare the poster samples
try:
@@ -636,274 +789,126 @@ class MCMC:
# Train a GPR meta-model using MAP
error_MetaModel = MetaModel.create_model_error(
- BayesObj.BiasInputs, y_map, name='Calib')
+ self.BiasInputs, y_map, name='Calib')
return error_MetaModel
-
+
# -------------------------------------------------------------------------
- def gelman_rubin(self, chain, return_var=False):
+ def normpdf(self, outputs, obs_data, total_sigma2s, sigma2=None, std=None):
"""
- The potential scale reduction factor (PSRF) defined by the variance
- within one chain, W, with the variance between chains B.
- Both variances are combined in a weighted sum to obtain an estimate of
- the variance of a parameter \\( \\theta \\).The square root of the
- ratio of this estimates variance to the within chain variance is called
- the potential scale reduction.
- For a well converged chain it should approach 1. Values greater than
- 1.1 typically indicate that the chains have not yet fully converged.
-
- Source: http://joergdietrich.github.io/emcee-convergence.html
-
- https://github.com/jwalton3141/jwalton3141.github.io/blob/master/assets/posts/ESS/rwmh.py
+ Calculates the likelihood of simulation outputs compared with
+ observation data.
Parameters
----------
- chain : array (n_walkers, n_steps, n_params)
- The emcee ensamples.
+ outputs : dict
+ A dictionary containing the simulation outputs as array of shape
+ (n_samples, n_measurement) for each model output.
+ obs_data : dict
+ A dictionary/dataframe containing the observation data.
+ total_sigma2s : dict
+ A dictionary with known values of the covariance diagonal entries,
+ a.k.a. sigma^2.
+ sigma2 : array, optional
+ An array of the sigma^2 samples, when the covariance diagonal
+ entries are unknown and are being jointly inferred. The default is
+ None.
+ std : dict, optional
+ A dictionary containing the root mean squared error as array of
+ shape (n_samples, n_measurement) for each model output. The default
+ is None.
Returns
-------
- R_hat : float
- The Gelman-Robin values.
+ logLik : array of shape (n_samples)
+ Likelihoods.
"""
- m_chains, n_iters = chain.shape[:2]
+ Model = self.engine.Model
+ logLik = 0.0
- # Calculate between-chain variance
- θb = np.mean(chain, axis=1)
- θbb = np.mean(θb, axis=0)
- B_over_n = ((θbb - θb)**2).sum(axis=0)
- B_over_n /= (m_chains - 1)
-
- # Calculate within-chain variances
- ssq = np.var(chain, axis=1, ddof=1)
- W = np.mean(ssq, axis=0)
-
- # (over) estimate of variance
- var_θ = W * (n_iters - 1) / n_iters + B_over_n
-
- if return_var:
- return var_θ
+ # Extract the requested model outputs for likelihood calulation
+ if self.req_outputs is None:
+ req_outputs = Model.Output.names # TODO: should this then be saved as self.req_outputs?
else:
- # The square root of the ratio of this estimates variance to the
- # within chain variance
- R_hat = np.sqrt(var_θ / W)
- return R_hat
+ req_outputs = list(self.req_outputs)
- # -------------------------------------------------------------------------
- def marginal_llk_emcee(self, sampler, nburn=None, logp=None, maxiter=1000):
- """
- The Bridge Sampling Estimator of the Marginal Likelihood based on
- https://gist.github.com/junpenglao/4d2669d69ddfe1d788318264cdcf0583
-
- Parameters
- ----------
- sampler : TYPE
- MultiTrace, result of MCMC run.
- nburn : int, optional
- Number of burn-in step. The default is None.
- logp : TYPE, optional
- Model Log-probability function. The default is None.
- maxiter : int, optional
- Maximum number of iterations. The default is 1000.
+ # Loop over the output keys
+ for idx, out in enumerate(req_outputs):
- Returns
- -------
- marg_llk : dict
- Estimated Marginal log-Likelihood.
-
- """
- r0, tol1, tol2 = 0.5, 1e-10, 1e-4
+ # (Meta)Model Output
+ nsamples, nout = outputs[out].shape
- if logp is None:
- logp = sampler.log_prob_fn
-
- # Split the samples into two parts
- # Use the first 50% for fiting the proposal distribution
- # and the second 50% in the iterative scheme.
- if nburn is None:
- mtrace = sampler.chain
- else:
- mtrace = sampler.chain[:, nburn:, :]
-
- nchain, len_trace, nrofVars = mtrace.shape
-
- N1_ = len_trace // 2
- N1 = N1_*nchain
- N2 = len_trace*nchain - N1
-
- samples_4_fit = np.zeros((nrofVars, N1))
- samples_4_iter = np.zeros((nrofVars, N2))
- effective_n = np.zeros((nrofVars))
+ # Prepare data and remove NaN
+ try:
+ data = obs_data[out].values[~np.isnan(obs_data[out])]
+ except AttributeError:
+ data = obs_data[out][~np.isnan(obs_data[out])]
- # matrix with already transformed samples
- for var in range(nrofVars):
+ # Prepare data uncertainty / error estimation (sigma2s)
+ non_nan_indices = ~np.isnan(total_sigma2s[out])
+ tot_sigma2s = total_sigma2s[out][non_nan_indices][:nout]
- # for fitting the proposal
- x = mtrace[:, :N1_, var]
+ # Add the std of the PCE if an emulator is used
+ if self.emulator:
+ if std is not None:
+ tot_sigma2s += std[out] ** 2
- samples_4_fit[var, :] = x.flatten()
- # for the iterative scheme
- x2 = mtrace[:, N1_:, var]
- samples_4_iter[var, :] = x2.flatten()
+ # Select the data points to compare
+ try:
+ indices = self.selected_indices[out]
+ except:
+ indices = list(range(nout))
- # effective sample size of samples_4_iter, scalar
- effective_n[var] = self._my_ESS(x2)
+ # Set up Covariance Matrix
+ covMatrix = np.diag(np.diag(tot_sigma2s)[indices, indices])
- # median effective sample size (scalar)
- neff = np.median(effective_n)
+ # If sigma2 is not given, use given total_sigma2s and move to next itr
+ if sigma2 is None:
+ logLik += stats.multivariate_normal.logpdf(
+ outputs[out][:, indices], data[indices], covMatrix)
+ continue
- # get mean & covariance matrix and generate samples from proposal
- m = np.mean(samples_4_fit, axis=1)
- V = np.cov(samples_4_fit)
- L = chol(V, lower=True)
+ # Loop over each run/sample and calculate logLikelihood
+ logliks = np.zeros(nsamples)
+ for s_idx in range(nsamples):
- # Draw N2 samples from the proposal distribution
- gen_samples = m[:, None] + np.dot(
- L, st.norm.rvs(0, 1, size=samples_4_iter.shape)
- )
+ # Simulation run
+ tot_outputs = outputs[out]
- # Evaluate proposal distribution for posterior & generated samples
- q12 = st.multivariate_normal.logpdf(samples_4_iter.T, m, V)
- q22 = st.multivariate_normal.logpdf(gen_samples.T, m, V)
+ # Covariance Matrix
+ covMatrix = np.diag(tot_sigma2s)
- # Evaluate unnormalized posterior for posterior & generated samples
- q11 = logp(samples_4_iter.T)
- q21 = logp(gen_samples.T)
+ # Check the type error term
+ if self.bias_inputs is not None and self.error_model is None:
+ # Infer a Bias model usig Gaussian Process Regression
+ bias_inputs = np.hstack(
+ (self.bias_inputs[out],
+ tot_outputs[s_idx].reshape(-1, 1)))
- # Run iterative scheme:
- tmp = self._iterative_scheme(
- N1, N2, q11, q12, q21, q22, r0, neff, tol1, maxiter, 'r'
- )
- if ~np.isfinite(tmp['logml']):
- warnings.warn(
- "logml could not be estimated within maxiter, rerunning with "
- "adjusted starting value. Estimate might be more variable than"
- " usual.")
- # use geometric mean as starting value
- r0_2 = np.sqrt(tmp['r_vals'][-2]*tmp['r_vals'][-1])
- tmp = self._iterative_scheme(
- q11, q12, q21, q22, r0_2, neff, tol2, maxiter, 'logml'
+ params = sigma2[s_idx, idx * 3:(idx + 1) * 3]
+ covMatrix = _kernel_rbf(bias_inputs, params)
+ else:
+ # Infer equal sigma2s
+ try:
+ sigma_2 = sigma2[s_idx, idx]
+ except TypeError:
+ sigma_2 = 0.0
+
+ covMatrix += sigma_2 * np.eye(nout)
+ # covMatrix = np.diag(sigma2 * total_sigma2s)
+
+ # Select the data points to compare
+ try:
+ indices = self.selected_indices[out]
+ except:
+ indices = list(range(nout))
+ covMatrix = np.diag(covMatrix[indices, indices])
+
+ # Compute loglikelihood
+ logliks[s_idx] = _logpdf(
+ tot_outputs[s_idx, indices], data[indices], covMatrix
)
+ logLik += logliks
+ return logLik
- marg_llk = dict(
- logml=tmp['logml'], niter=tmp['niter'], method="normal",
- q11=q11, q12=q12, q21=q21, q22=q22
- )
- return marg_llk
-
- # -------------------------------------------------------------------------
- def _iterative_scheme(self, N1, N2, q11, q12, q21, q22, r0, neff, tol,
- maxiter, criterion):
- """
- Iterative scheme as proposed in Meng and Wong (1996) to estimate the
- marginal likelihood
-
- """
- l1 = q11 - q12
- l2 = q21 - q22
- # To increase numerical stability,
- # subtracting the median of l1 from l1 & l2 later
- lstar = np.median(l1)
- s1 = neff/(neff + N2)
- s2 = N2/(neff + N2)
- r = r0
- r_vals = [r]
- logml = np.log(r) + lstar
- criterion_val = 1 + tol
-
- i = 0
- while (i <= maxiter) & (criterion_val > tol):
- rold = r
- logmlold = logml
- numi = np.exp(l2 - lstar)/(s1 * np.exp(l2 - lstar) + s2 * r)
- deni = 1/(s1 * np.exp(l1 - lstar) + s2 * r)
- if np.sum(~np.isfinite(numi))+np.sum(~np.isfinite(deni)) > 0:
- warnings.warn(
- """Infinite value in iterative scheme, returning NaN.
- Try rerunning with more samples.""")
- r = (N1/N2) * np.sum(numi)/np.sum(deni)
- r_vals.append(r)
- logml = np.log(r) + lstar
- i += 1
- if criterion == 'r':
- criterion_val = np.abs((r - rold)/r)
- elif criterion == 'logml':
- criterion_val = np.abs((logml - logmlold)/logml)
-
- if i >= maxiter:
- return dict(logml=np.NaN, niter=i, r_vals=np.asarray(r_vals))
- else:
- return dict(logml=logml, niter=i)
-
- # -------------------------------------------------------------------------
- def _my_ESS(self, x):
- """
- Compute the effective sample size of estimand of interest.
- Vectorised implementation.
- https://github.com/jwalton3141/jwalton3141.github.io/blob/master/assets/posts/ESS/rwmh.py
-
-
- Parameters
- ----------
- x : array of shape (n_walkers, n_steps)
- MCMC Samples.
-
- Returns
- -------
- int
- Effective sample size.
-
- """
- m_chains, n_iters = x.shape
-
- def variogram(t):
- variogram = ((x[:, t:] - x[:, :(n_iters - t)])**2).sum()
- variogram /= (m_chains * (n_iters - t))
- return variogram
-
- post_var = self.gelman_rubin(x, return_var=True)
-
- t = 1
- rho = np.ones(n_iters)
- negative_autocorr = False
-
- # Iterate until the sum of consecutive estimates of autocorrelation is
- # negative
- while not negative_autocorr and (t < n_iters):
- rho[t] = 1 - variogram(t) / (2 * post_var)
-
- if not t % 2:
- negative_autocorr = sum(rho[t-1:t+1]) < 0
-
- t += 1
-
- return int(m_chains*n_iters / (1 + 2*rho[1:t].sum()))
-
- # -------------------------------------------------------------------------
- def _check_ranges(self, theta, ranges):
- """
- This function checks if theta lies in the given ranges.
-
- Parameters
- ----------
- theta : array
- Proposed parameter set.
- ranges : nested list
- List of the praremeter ranges.
-
- Returns
- -------
- c : bool
- If it lies in the given range, it return True else False.
-
- """
- c = True
- # traverse in the list1
- for i, bounds in enumerate(ranges):
- x = theta[i]
- # condition check
- if x < bounds[0] or x > bounds[1]:
- c = False
- return c
- return c
diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index 4db354bbd541affdc50a8dce37eb293188091f73..6520a40f9f2393798f6b8abac026b9ed38fe33ca 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -32,8 +32,11 @@ class PostProcessing:
expected to be performed change this to `'valid'`.
"""
- def __init__(self, MetaModel, name='calib'):
- self.MetaModel = MetaModel
+ def __init__(self, engine, name='calib'):
+ self.engine = engine
+ self.MetaModel = engine.MetaModel
+ self.ExpDesign = engine.ExpDesign
+ self.ModelObj = engine.Model
self.name = name
# -------------------------------------------------------------------------
@@ -60,13 +63,13 @@ class PostProcessing:
bar_plot = True if plot_type == 'bar' else False
meta_model_type = self.MetaModel.meta_model_type
- Model = self.MetaModel.ModelObj
+ Model = self.ModelObj
# Read Monte-Carlo reference
- self.mc_reference = Model.read_mc_reference()
+ self.mc_reference = Model.read_observation('mc_ref')
# Set the x values
- x_values_orig = self.MetaModel.ExpDesign.x_values
+ x_values_orig = self.engine.ExpDesign.x_values
# Compute the moments with the PCEModel object
self.pce_means, self.pce_stds = self.compute_pce_moments()
@@ -170,7 +173,7 @@ class PostProcessing:
"""
MetaModel = self.MetaModel
- Model = MetaModel.ModelObj
+ Model = self.ModelObj
if samples is None:
self.n_samples = n_samples
@@ -179,7 +182,7 @@ class PostProcessing:
self.n_samples = samples.shape[0]
# Extract x_values
- x_values = MetaModel.ExpDesign.x_values
+ x_values = self.engine.ExpDesign.x_values
if model_out_dict is not None:
self.model_out_dict = model_out_dict
@@ -229,7 +232,7 @@ class PostProcessing:
"""
MetaModel = self.MetaModel
- Model = MetaModel.ModelObj
+ Model = self.ModelObj
# Set the number of samples
if n_samples:
@@ -237,8 +240,8 @@ class PostProcessing:
elif samples is not None:
self.n_samples = samples.shape[0]
else:
- raise Exception("Please provide either samples or pass number of "
- "samples!")
+ raise Exception("Please provide either samples or pass the number"
+ " of samples!")
# Generate random samples if necessary
Samples = self._get_sample() if samples is None else samples
@@ -291,9 +294,10 @@ class PostProcessing:
None.
"""
+ engine = self.engine
PCEModel = self.MetaModel
- n_init_samples = PCEModel.ExpDesign.n_init_samples
- n_total_samples = PCEModel.ExpDesign.X.shape[0]
+ n_init_samples = engine.ExpDesign.n_init_samples
+ n_total_samples = engine.ExpDesign.X.shape[0]
newpath = f'Outputs_PostProcessing_{self.name}/seq_design_diagnostics/'
if not os.path.exists(newpath):
@@ -301,9 +305,9 @@ class PostProcessing:
plotList = ['Modified LOO error', 'Validation error', 'KLD', 'BME',
'RMSEMean', 'RMSEStd', 'Hellinger distance']
- seqList = [PCEModel.SeqModifiedLOO, PCEModel.seqValidError,
- PCEModel.SeqKLD, PCEModel.SeqBME, PCEModel.seqRMSEMean,
- PCEModel.seqRMSEStd, PCEModel.SeqDistHellinger]
+ seqList = [engine.SeqModifiedLOO, engine.seqValidError,
+ engine.SeqKLD, engine.SeqBME, engine.seqRMSEMean,
+ engine.seqRMSEStd, engine.SeqDistHellinger]
markers = ('x', 'o', 'd', '*', '+')
colors = ('k', 'darkgreen', 'b', 'navy', 'darkred')
@@ -323,14 +327,14 @@ class PostProcessing:
# Extract the values from dict
sorted_seq_opt = {}
# Number of replications
- n_reps = PCEModel.ExpDesign.n_replication
+ n_reps = engine.ExpDesign.n_replication
# Get the list of utility function names
# Handle if only one UtilityFunction is provided
- if not isinstance(PCEModel.ExpDesign.util_func, list):
- util_funcs = [PCEModel.ExpDesign.util_func]
+ if not isinstance(engine.ExpDesign.util_func, list):
+ util_funcs = [engine.ExpDesign.util_func]
else:
- util_funcs = PCEModel.ExpDesign.util_func
+ util_funcs = engine.ExpDesign.util_func
for util in util_funcs:
sortedSeq = {}
@@ -359,8 +363,8 @@ class PostProcessing:
for patch in bp['boxes']:
patch.set(facecolor=fill_color[idx])
- if PCEModel.ExpDesign.n_new_samples != 1:
- step1 = PCEModel.ExpDesign.n_new_samples
+ if engine.ExpDesign.n_new_samples != 1:
+ step1 = engine.ExpDesign.n_new_samples
step2 = 1
else:
step1 = 5
@@ -440,8 +444,8 @@ class PostProcessing:
else:
for idx, name in enumerate(name_util):
seq_values = seq_dict[name]
- if PCEModel.ExpDesign.n_new_samples != 1:
- step = PCEModel.ExpDesign.n_new_samples
+ if engine.ExpDesign.n_new_samples != 1:
+ step = engine.ExpDesign.n_new_samples
else:
step = 1
x_idx = np.arange(n_init_samples, n_total_samples+1, step)
@@ -570,7 +574,7 @@ class PostProcessing:
sobol_cell_, total_sobol_ = {}, {}
- for output in PCEModel.ModelObj.Output.names:
+ for output in self.ModelObj.Output.names:
n_meas_points = len(coeffs_dict[f'b_{b_i+1}'][output])
@@ -671,7 +675,7 @@ class PostProcessing:
# Compute the sobol indices according to Ref. 2
if PCEModel.dim_red_method.lower() == 'pca':
- n_c_points = PCEModel.ExpDesign.Y[output].shape[1]
+ n_c_points = self.engine.ExpDesign.Y[output].shape[1]
PCA = PCEModel.pca[f'b_{b_i+1}'][output]
compPCA = PCA.components_
nComp = compPCA.shape[0]
@@ -689,7 +693,7 @@ class PostProcessing:
for tIdx in range(n_c_points):
var_Y_t = np.var(
- PCEModel.ExpDesign.Y[output][:, tIdx])
+ self.engine.ExpDesign.Y[output][:, tIdx])
if var_Y_t == 0.0:
term1, term2 = 0.0, 0.0
else:
@@ -718,7 +722,7 @@ class PostProcessing:
S_Z_i = total_sobol_array[ParIdx]
for tIdx in range(n_c_points):
- var_Y_t = np.var(PCEModel.ExpDesign.Y[output][:, tIdx])
+ var_Y_t = np.var(self.engine.ExpDesign.Y[output][:, tIdx])
if var_Y_t == 0.0:
term1, term2 = 0.0, 0.0
else:
@@ -758,12 +762,12 @@ class PostProcessing:
total_sobol_all[k][i] = v
self.total_sobol = {}
- for output in PCEModel.ModelObj.Output.names:
+ for output in self.ModelObj.Output.names:
self.total_sobol[output] = np.mean(total_sobol_all[output], axis=0)
# ---------------- Plot -----------------------
- par_names = PCEModel.ExpDesign.par_names
- x_values_orig = PCEModel.ExpDesign.x_values
+ par_names = self.engine.ExpDesign.par_names
+ x_values_orig = self.engine.ExpDesign.x_values
newpath = (f'Outputs_PostProcessing_{self.name}/')
if not os.path.exists(newpath):
@@ -771,7 +775,7 @@ class PostProcessing:
fig = plt.figure()
- for outIdx, output in enumerate(PCEModel.ModelObj.Output.names):
+ for outIdx, output in enumerate(self.ModelObj.Output.names):
# Extract total Sobol indices
total_sobol = self.total_sobol[output]
@@ -873,7 +877,7 @@ class PostProcessing:
# ------ Residuals vs. predicting variables ------
# Check the assumptions of linearity and independence
fig1 = plt.figure()
- for i, par in enumerate(MetaModel.ExpDesign.par_names):
+ for i, par in enumerate(self.engine.ExpDesign.par_names):
plt.title(f"{key}: Residuals vs. {par}")
plt.scatter(
x=samples[:, i], y=residuals, color='blue', edgecolor='k')
@@ -922,7 +926,7 @@ class PostProcessing:
ax = plt.gca()
_, p = stats.shapiro(residuals)
if p < 0.01:
- annText = "The residuals seem to come from Gaussian process."
+ annText = "The residuals seem to come from a Gaussian Process."
else:
annText = "The normality assumption may not hold."
at = AnchoredText(annText, prop=dict(size=30), frameon=True,
@@ -961,7 +965,7 @@ class PostProcessing:
self.n_samples = 1000
PCEModel = self.MetaModel
- Model = self.MetaModel.ModelObj
+ Model = self.ModelObj
n_samples = self.n_samples
# Create 3D-Grid
@@ -1063,7 +1067,7 @@ class PostProcessing:
"""
MetaModel = self.MetaModel
- outputs = MetaModel.ModelObj.Output.names
+ outputs = self.ModelObj.Output.names
pce_means_b = {}
pce_stds_b = {}
@@ -1149,8 +1153,7 @@ class PostProcessing:
"""
if n_samples is None:
n_samples = self.n_samples
- MetaModel = self.MetaModel
- self.samples = MetaModel.ExpDesign.generate_samples(
+ self.samples = self.ExpDesign.generate_samples(
n_samples,
sampling_method='random')
return self.samples
@@ -1174,7 +1177,7 @@ class PostProcessing:
Dictionary of results.
"""
- Model = self.MetaModel.ModelObj
+ Model = self.ModelObj
if samples is None:
samples = self._get_sample()
@@ -1274,7 +1277,7 @@ class PostProcessing:
None.
"""
- Model = self.MetaModel.ModelObj
+ Model = self.ModelObj
newpath = f'Outputs_PostProcessing_{self.name}/'
if not os.path.exists(newpath):
diff --git a/src/bayesvalidrox/pylink/pylink.py b/src/bayesvalidrox/pylink/pylink.py
index 7af4a14b8f8acb62c21862d5df4a527dc7fe57b9..637f42317e6f97815e51ce0b331b61c03f26a85b 100644
--- a/src/bayesvalidrox/pylink/pylink.py
+++ b/src/bayesvalidrox/pylink/pylink.py
@@ -1,5 +1,9 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
+"""
+Calls to the model and evaluations
+"""
+
from dataclasses import dataclass
import os
@@ -13,6 +17,45 @@ import multiprocessing
from functools import partial
import tqdm
+#from multiprocessing import get_context
+from multiprocess import get_context
+
+
+
+def within_range(out, minout, maxout):
+ """
+ Checks if all the values in out lie between minout and maxout
+
+ Parameters
+ ----------
+ out : array or list
+ Data to check against range
+ minout : int
+ Lower bound of the range
+ maxout : int
+ Upper bound of the range
+
+ Returns
+ -------
+ inside : bool
+ True if all values in out are in the specified range
+
+ """
+ try:
+ out = np.array(out)
+ except:
+ raise AttributeError('The given values should be a 1D array, but are not')
+ if out.ndim != 1:
+ raise AttributeError('The given values should be a 1D array, but are not')
+
+ if minout > maxout:
+ raise ValueError('The lower and upper bounds do not form a valid range, they might be switched')
+
+ inside = False
+ if (out > minout).all() and (out < maxout).all():
+ inside = True
+ return inside
+
class PyLinkForwardModel(object):
"""
@@ -25,7 +68,7 @@ class PyLinkForwardModel(object):
----------
link_type : str
The type of the wrapper. The default is `'pylink'`. This runs the
- third-party software or an executable using a sell command with given
+ third-party software or an executable using a shell command with given
input files.
Second option is `'function'` which assumed that model can be run using
a function written separately in a Python script.
@@ -141,18 +184,17 @@ class PyLinkForwardModel(object):
self.observations_valid = obs_dict_valid
self.mc_reference = mc_ref_dict
- # -------------------------------------------------------------------------
- def within_range(self, out, minout, maxout):
- inside = False
- if (out > minout).all() and (out < maxout).all():
- inside = True
- return inside
-
# -------------------------------------------------------------------------
def read_observation(self, case='calib'):
"""
Reads/prepare the observation/measurement data for
calibration.
+
+ Parameters
+ ----------
+ case : str
+ The type of observation to read in. Can be either 'calib',
+ 'valid' or 'mc_ref'
Returns
-------
@@ -160,68 +202,55 @@ class PyLinkForwardModel(object):
A dataframe with the calibration data.
"""
+ # TOOD: check that what is read in/transformed matches the expected form of data/reference
if case.lower() == 'calib':
if isinstance(self.observations, dict) and bool(self.observations):
- obs = pd.DataFrame.from_dict(self.observations)
+ self.observations = pd.DataFrame.from_dict(self.observations)
elif self.meas_file is not None:
file_path = os.path.join(os.getcwd(), self.meas_file)
- obs = pd.read_csv(file_path, delimiter=',')
+ self.observations = pd.read_csv(file_path, delimiter=',')
elif isinstance(self.observations, pd.DataFrame):
- obs = self.observations
+ self.observations = self.observations
else:
raise Exception("Please provide the observation data as a "
"dictionary via observations attribute or pass"
" the csv-file path to MeasurementFile "
"attribute")
+ # Compute the number of observation
+ self.n_obs = self.observations[self.Output.names].notnull().sum().values.sum()
+ return self.observations
+
elif case.lower() == 'valid':
if isinstance(self.observations_valid, dict) and \
bool(self.observations_valid):
- obs = pd.DataFrame.from_dict(self.observations_valid)
+ self.observations_valid = pd.DataFrame.from_dict(self.observations_valid)
elif self.meas_file_valid is not None:
file_path = os.path.join(os.getcwd(), self.meas_file_valid)
- obs = pd.read_csv(file_path, delimiter=',')
+ self.observations_valid = pd.read_csv(file_path, delimiter=',')
elif isinstance(self.observations_valid, pd.DataFrame):
- obs = self.observations_valid
+ self.observations_valid = self.observations_valid
else:
raise Exception("Please provide the observation data as a "
- "dictionary via Observations attribute or pass"
+ "dictionary via observations_valid attribute or pass"
" the csv-file path to MeasurementFile "
"attribute")
-
- # Compute the number of observation
- n_obs = obs[self.Output.names].notnull().sum().values.sum()
-
- if case.lower() == 'calib':
- self.observations = obs
- self.n_obs = n_obs
- return self.observations
- elif case.lower() == 'valid':
- self.observations_valid = obs
- self.n_obs_valid = n_obs
+ # Compute the number of observation
+ self.n_obs_valid = self.observations_valid[self.Output.names].notnull().sum().values.sum()
return self.observations_valid
-
- # -------------------------------------------------------------------------
- def read_mc_reference(self):
- """
- Is used, if a Monte-Carlo reference is available for
- further in-depth post-processing after meta-model training.
-
- Returns
- -------
- None
-
- """
- if self.mc_ref_file is None and \
- isinstance(self.mc_reference, pd.DataFrame):
+
+ elif case.lower() == 'mc_ref':
+ if self.mc_ref_file is None and \
+ isinstance(self.mc_reference, pd.DataFrame):
+ return self.mc_reference
+ elif isinstance(self.mc_reference, dict) and bool(self.mc_reference):
+ self.mc_reference = pd.DataFrame.from_dict(self.mc_reference)
+ elif self.mc_ref_file is not None:
+ file_path = os.path.join(os.getcwd(), self.mc_ref_file)
+ self.mc_reference = pd.read_csv(file_path, delimiter=',')
+ else:
+ self.mc_reference = None
return self.mc_reference
- elif isinstance(self.mc_reference, dict) and bool(self.mc_reference):
- self.mc_reference = pd.DataFrame.from_dict(self.mc_reference)
- elif self.mc_ref_file is not None:
- file_path = os.path.join(os.getcwd(), self.mc_ref_file)
- self.mc_reference = pd.read_csv(file_path, delimiter=',')
- else:
- self.mc_reference = None
- return self.mc_reference
+
# -------------------------------------------------------------------------
def read_output(self):
@@ -316,9 +345,8 @@ class PyLinkForwardModel(object):
Process = os.system(f'./../{command}')
if Process != 0:
print('\nMessage 1:')
- print(f'\tIf value of \'{Process}\' is a non-zero value, '
- 'then compilation problems \n' % Process)
-
+ print(f'\tIf the value of \'{Process}\' is a non-zero value'
+ ', then compilation problems occur \n' % Process)
os.chdir("..")
# Read the output
@@ -368,6 +396,7 @@ class PyLinkForwardModel(object):
os.makedirs(newpath)
# Copy the necessary files to the directories
+ print(self.input_template)
for in_temp in self.input_template:
# Input file(s) of the model
shutil.copy2(in_temp, newpath)
@@ -399,6 +428,7 @@ class PyLinkForwardModel(object):
self.exe_path = os.getcwd()
# Run the model
+ print(new_command)
output = self.run_command(new_command, self.Output.file_names)
return output
@@ -438,6 +468,16 @@ class PyLinkForwardModel(object):
# Initilization
n_c_points = len(c_points)
all_outputs = {}
+
+ # If the link type is UM-Bridge, then no parallel needs to be started from here
+ if self.link_type.lower() == 'umbridge':
+ import umbridge
+ if not hasattr(self, 'x_values'):
+ raise AttributeError('For model type `umbridge` the attribute `x_values` needs to be set for the model!')
+ # Init model
+ #model = umbridge.HTTPModel('http://localhost:4242', 'forward')
+ self.model = umbridge.HTTPModel(self.host, 'forward') # TODO: is this always forward?
+ Function = self.uMBridge_model
# Extract the function
if self.link_type.lower() == 'function':
@@ -454,17 +494,45 @@ class PyLinkForwardModel(object):
# Run forward model
if n_c_points == 1 or not mp:
- if self.link_type.lower() == 'function':
- group_results = Function(c_points, **self.func_args)
+ if n_c_points== 1:
+ if self.link_type.lower() == 'function' or self.link_type.lower() == 'umbridge':
+ group_results = Function(c_points, **self.func_args)
+ else:
+ group_results = self.run_forwardmodel(
+ (c_points[0], prevRun_No, key_str)
+ )
else:
- group_results = self.run_forwardmodel(
- (c_points[0], prevRun_No, key_str)
- )
+ for i in range(c_points.shape[0]):
+ if i == 0:
+ if self.link_type.lower() == 'function' or self.link_type.lower() == 'umbridge':
+ group_results = Function(np.array([c_points[0]]), **self.func_args)
+ else:
+ group_results = self.run_forwardmodel(
+ (c_points[0], prevRun_No, key_str)
+ )
+ for key in group_results:
+ if key != 'x_values':
+ group_results[key] = [group_results[key]]
+ else:
+ if self.link_type.lower() == 'function' or self.link_type.lower() == 'umbridge':
+ res = Function(np.array([c_points[i]]), **self.func_args)
+ else:
+ res = self.run_forwardmodel(
+ (c_points[i], prevRun_No, key_str)
+ )
+ for key in res:
+ if key != 'x_values':
+ group_results[key].append(res[key])
+
+ for key in group_results:
+ if key != 'x_values':
+ group_results[key]= np.array(group_results[key])
elif self.multi_process or mp:
- with multiprocessing.Pool(n_cpus) as p:
-
- if self.link_type.lower() == 'function':
+ with get_context('spawn').Pool(n_cpus) as p:
+ #with multiprocessing.Pool(n_cpus) as p:
+
+ if self.link_type.lower() == 'function' or self.link_type.lower() == 'umbridge':
imap_var = p.imap(partial(Function, **self.func_args),
c_points[:, np.newaxis])
else:
@@ -499,7 +567,7 @@ class PyLinkForwardModel(object):
if len(NaN_idx) != 0:
print('\n')
print('*'*20)
- print("\nThe following parametersets have been removed:\n",
+ print("\nThe following parameter sets have been removed:\n",
c_points[NaN_idx])
print("\n")
print('*'*20)
@@ -516,10 +584,85 @@ class PyLinkForwardModel(object):
)
return all_outputs, new_c_points
+
+ def uMBridge_model(self, params):
+ """
+ Function that calls a UMBridge model and transforms its output into the
+ shape expected for the surrogate.
+
+ Parameters
+ ----------
+ params : 2d np.array, shape (#samples, #params)
+ The parameter values for which the model is run.
+
+ Returns
+ -------
+ dict
+ The transformed model outputs.
+
+ """
+ # Run the model
+ #out = np.array(model(np.ndarray.tolist(params), {'level':0}))
+ out = np.array(self.model(np.ndarray.tolist(params), self.modelparams))
+
+ # Sort into dict
+ out_dict = {}
+ cnt = 0
+ for key in self.Output.names:
+ # # If needed resort into single-value outputs
+ # if self.output_type == 'single-valued':
+ # if out.shape[1]>1: # TODO: this doesn't fully seem correct??
+ # for i in range(out[:,key]): # TODO: this doesn't fully seem correct??
+ # new_key = key+str(i)
+ # if new_key not in self.Output.names:
+ # self.Output.names.append(new_key)
+ # if i == 0:
+ # self.Ouptut.names.remove(key)
+ # out_dict[new_key] = out[:,cnt,i] # TODO: not sure about this, need to test
+ # else:
+ # out_dict[key] = out[:,cnt]
+ #
+ #
+ # else:
+ out_dict[key] = out[:,cnt]
+ cnt += 1
+
+
+ ## TODO: how to deal with the x-values?
+ #if self.output_type == 'single-valued':
+ # out_dict['x_values'] = [0]
+ #else:
+ # out_dict['x_values'] = np.arange(0,out[:,0].shape[0],1)
+ out_dict['x_values'] = self.x_values
+
+ #return {'T1':out[:,0], 'T2':out[:,1], 'H1':out[:,2], 'H2':out[:,3],
+ # 'x_values':[0]}
+ return out_dict
# -------------------------------------------------------------------------
def _store_simulations(self, c_points, all_outputs, NaN_idx, key_str,
prevRun_No):
+ """
+
+
+ Parameters
+ ----------
+ c_points : TYPE
+ DESCRIPTION.
+ all_outputs : TYPE
+ DESCRIPTION.
+ NaN_idx : TYPE
+ DESCRIPTION.
+ key_str : TYPE
+ DESCRIPTION.
+ prevRun_No : TYPE
+ DESCRIPTION.
+
+ Returns
+ -------
+ None.
+
+ """
# Create hdf5 metadata
if key_str == '':
@@ -542,7 +685,6 @@ class PyLinkForwardModel(object):
# ---------- Save outputs ----------
for varIdx, var in enumerate(self.Output.names):
-
if not hdf5_exist:
grpY = file.create_group("EDY/"+var)
else:
@@ -656,6 +798,6 @@ class PyLinkForwardModel(object):
shutil.rmtree(path)
print("\n")
- print(f'{dir_name}.zip file has been created successfully!\n')
+ print(f'{dir_name}.zip has been created successfully!\n')
return
diff --git a/src/bayesvalidrox/surrogate_models/__init__.py b/src/bayesvalidrox/surrogate_models/__init__.py
index 70bfb20f570464c2907a0a4128f4ed99b6c13736..6d8ce9f1c58fa154f2af63f0fb5a44097695df33 100644
--- a/src/bayesvalidrox/surrogate_models/__init__.py
+++ b/src/bayesvalidrox/surrogate_models/__init__.py
@@ -1,7 +1,12 @@
# -*- coding: utf-8 -*-
-
+from .engine import Engine
+from .exp_designs import ExpDesigns
+from .input_space import InputSpace
from .surrogate_models import MetaModel
__all__ = [
- "MetaModel"
+ "MetaModel",
+ "InputSpace",
+ "ExpDesigns",
+ "Engine"
]
diff --git a/src/bayesvalidrox/surrogate_models/apoly_construction.py b/src/bayesvalidrox/surrogate_models/apoly_construction.py
index a7914c7deac51c2180aa6858207ccf0bac5c1f02..40830fe8aaa94248df4828c0c49bd4d23e755abd 100644
--- a/src/bayesvalidrox/surrogate_models/apoly_construction.py
+++ b/src/bayesvalidrox/surrogate_models/apoly_construction.py
@@ -32,6 +32,8 @@ def apoly_construction(Data, degree):
The coefficients of the univariate orthonormal polynomials.
"""
+ if Data.ndim !=1:
+ raise AttributeError('Data should be a 1D array')
# Initialization
dd = degree + 1
diff --git a/src/bayesvalidrox/surrogate_models/bayes_linear.py b/src/bayesvalidrox/surrogate_models/bayes_linear.py
index a7d6b5929a83fc89d15d7ab8f369187d0542923c..3bd827ac0ecc5b3a38116b21767e8a8799593b24 100644
--- a/src/bayesvalidrox/surrogate_models/bayes_linear.py
+++ b/src/bayesvalidrox/surrogate_models/bayes_linear.py
@@ -32,7 +32,7 @@ class BayesianLinearRegression(RegressorMixin,LinearModel):
def _center_data(self,X,y):
''' Centers data'''
- X = as_float_array(X,self.copy_X)
+ X = as_float_array(X,copy = self.copy_X)
# normalisation should be done in preprocessing!
X_std = np.ones(X.shape[1], dtype = X.dtype)
if self.fit_intercept:
@@ -138,7 +138,7 @@ class EBLinearRegression(BayesianLinearRegression):
normalize=True, perfect_fit_tol = 1e-6, alpha = 1, copy_X = True, verbose = False):
super(EBLinearRegression,self).__init__(n_iter, tol, fit_intercept, copy_X, verbose)
if optimizer not in ['em','fp']:
- raise ValueError('Optimizer can be either "em" of "fp" ')
+ raise ValueError('Optimizer can be either "em" or "fp" ')
self.optimizer = optimizer
self.alpha = alpha
self.perfect_fit = False
@@ -156,7 +156,7 @@ class EBLinearRegression(BayesianLinearRegression):
def _center_data(self,X,y):
''' Centers data'''
- X = as_float_array(X,self.copy_X)
+ X = as_float_array(X,copy = self.copy_X)
# normalisation should be done in preprocessing!
X_std = np.ones(X.shape[1], dtype = X.dtype)
if self.fit_intercept:
diff --git a/src/bayesvalidrox/surrogate_models/desktop.ini b/src/bayesvalidrox/surrogate_models/desktop.ini
new file mode 100644
index 0000000000000000000000000000000000000000..632de13ae6b61cecf0d9fdbf9c97cfb16bfb51a4
--- /dev/null
+++ b/src/bayesvalidrox/surrogate_models/desktop.ini
@@ -0,0 +1,2 @@
+[LocalizedFileNames]
+exploration.py=@exploration.py,0
diff --git a/src/bayesvalidrox/surrogate_models/meta_model_engine.py b/src/bayesvalidrox/surrogate_models/engine.py
similarity index 63%
rename from src/bayesvalidrox/surrogate_models/meta_model_engine.py
rename to src/bayesvalidrox/surrogate_models/engine.py
index 2df2dee5390ae4e6dc7eb88343c2469dbd88aad6..27c117a790d588c3216fe17540419480d15e9790 100644
--- a/src/bayesvalidrox/surrogate_models/meta_model_engine.py
+++ b/src/bayesvalidrox/surrogate_models/engine.py
@@ -1,66 +1,303 @@
-#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
-Created on Fri Jan 28 09:21:18 2022
+Engine to train the surrogate
-@author: farid
"""
+from copy import deepcopy, copy
+import joblib
+from joblib import Parallel, delayed
+import matplotlib.pyplot as plt
+import multiprocessing
import numpy as np
+import os
+import pandas as pd
+import pathlib
+import scipy.optimize as opt
from scipy import stats, signal, linalg, sparse
from scipy.spatial import distance
-from copy import deepcopy, copy
-from tqdm import tqdm
-import scipy.optimize as opt
from sklearn.metrics import mean_squared_error
-import multiprocessing
-import matplotlib.pyplot as plt
-import sys
-import os
-import gc
import seaborn as sns
-from joblib import Parallel, delayed
+import sys
+from tqdm import tqdm
+
+from bayesvalidrox.bayes_inference.bayes_inference import BayesInference
+from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
from .exploration import Exploration
-class MetaModelEngine():
- """ Sequential experimental design
- This class provieds method for trainig the meta-model in an iterative
- manners.
- The main method to execute the task is `train_seq_design`, which
- recieves a model object and returns the trained metamodel.
+def hellinger_distance(P, Q):
"""
+ Hellinger distance between two continuous distributions.
- def __init__(self, meta_model_opts):
- self.MetaModel = meta_model_opts
+ The maximum distance 1 is achieved when P assigns probability zero to
+ every set to which Q assigns a positive probability, and vice versa.
+ 0 (identical) and 1 (maximally different)
- # -------------------------------------------------------------------------
- def run(self):
-
- Model = self.MetaModel.ModelObj
- self.MetaModel.n_params = len(self.MetaModel.input_obj.Marginals)
- self.MetaModel.ExpDesignFlag = 'normal'
- # --- Prepare pce degree ---
- if self.MetaModel.meta_model_type.lower() == 'pce':
- if type(self.MetaModel.pce_deg) is not np.ndarray:
- self.MetaModel.pce_deg = np.array(self.MetaModel.pce_deg)
-
- if self.MetaModel.ExpDesign.method == 'normal':
- self.MetaModel.ExpDesignFlag = 'normal'
- self.MetaModel.train_norm_design(verbose=True)
-
- elif self.MetaModel.ExpDesign.method == 'sequential':
- self.train_seq_design()
+ Parameters
+ ----------
+ P : array
+ Reference likelihood.
+ Q : array
+ Estimated likelihood.
+
+ Returns
+ -------
+ float
+ Hellinger distance of two distributions.
+
+ """
+ P = np.array(P)
+ Q = np.array(Q)
+
+ mu1 = P.mean()
+ Sigma1 = np.std(P)
+
+ mu2 = Q.mean()
+ Sigma2 = np.std(Q)
+
+ term1 = np.sqrt(2 * Sigma1 * Sigma2 / (Sigma1 ** 2 + Sigma2 ** 2))
+
+ term2 = np.exp(-.25 * (mu1 - mu2) ** 2 / (Sigma1 ** 2 + Sigma2 ** 2))
+
+ H_squared = 1 - term1 * term2
+
+ return np.sqrt(H_squared)
+
+
+def logpdf(x, mean, cov):
+ """
+ Computes the likelihood based on a multivariate normal distribution.
+
+ Parameters
+ ----------
+ x : TYPE
+ DESCRIPTION.
+ mean : array_like
+ Observation data.
+ cov : 2d array
+ Covariance matrix of the distribution.
+
+ Returns
+ -------
+ log_lik : float
+ Log likelihood.
+
+ """
+ n = len(mean)
+ L = linalg.cholesky(cov, lower=True)
+ beta = np.sum(np.log(np.diag(L)))
+ dev = x - mean
+ alpha = dev.dot(linalg.cho_solve((L, True), dev))
+ log_lik = -0.5 * alpha - beta - n / 2. * np.log(2 * np.pi)
+
+ return log_lik
+
+
+def subdomain(Bounds, n_new_samples):
+ """
+ Divides a domain defined by Bounds into subdomains.
+
+ Parameters
+ ----------
+ Bounds : list of tuples
+ List of lower and upper bounds.
+ n_new_samples : int
+ Number of samples to divide the domain for.
+
+ Returns
+ -------
+ Subdomains : List of tuples of tuples
+ Each tuple of tuples divides one set of bounds into n_new_samples parts.
+
+ """
+ n_params = len(Bounds)
+ n_subdomains = n_new_samples + 1
+ LinSpace = np.zeros((n_params, n_subdomains))
+
+ for i in range(n_params):
+ LinSpace[i] = np.linspace(start=Bounds[i][0], stop=Bounds[i][1],
+ num=n_subdomains)
+ Subdomains = []
+ for k in range(n_subdomains - 1):
+ mylist = []
+ for i in range(n_params):
+ mylist.append((LinSpace[i, k + 0], LinSpace[i, k + 1]))
+ Subdomains.append(tuple(mylist))
+
+ return Subdomains
+
+
+class Engine:
+
+ def __init__(self, MetaMod, Model, ExpDes):
+ self.MetaModel = MetaMod
+ self.Model = Model
+ self.ExpDesign = ExpDes
+ self.parallel = False
+ self.trained = False
+
+ # Init other parameters
+ self.bound_tuples = None
+ self.errorModel = None
+ self.LCerror = None
+ self.n_obs = None
+ self.observations = None
+ self.out_names = None
+ self.seqMinDist = None
+ self.seqRMSEStd = None
+ self.SeqKLD = None
+ self.SeqDistHellinger = None
+ self.SeqBME = None
+ self.seqValidError = None
+ self.SeqModifiedLOO = None
+ self.valid_likelihoods = None
+ self._y_hat_prev = None
+
+ def start_engine(self) -> None:
+ """
+ Do all the preparations that need to be run before the actual training
+
+ Returns
+ -------
+ None
+
+ """
+ self.out_names = self.Model.Output.names
+ self.MetaModel.out_names = self.out_names
+
+ def train_normal(self, parallel=False, verbose=False, save=False) -> None:
+ """
+ Trains surrogate on static samples only.
+ Samples are taken from the experimental design and the specified
+ model is run on them.
+ Alternatively the samples can be read in from a provided hdf5 file.
+
+
+ Returns
+ -------
+ None
+
+ """
+ if self.out_names == 'None':
+ self.start_engine()
+
+ ExpDesign = self.ExpDesign
+ MetaModel = self.MetaModel
+
+ # Read ExpDesign (training and targets) from the provided hdf5
+ if ExpDesign.hdf5_file is not None:
+ # TODO: need to run 'generate_ED' as well after this or not?
+ ExpDesign.read_from_file(self.out_names)
+ else:
+ # Check if an old hdf5 file exists: if yes, rename it
+ hdf5file = f'ExpDesign_{self.Model.name}.hdf5'
+ if os.path.exists(hdf5file):
+ # os.rename(hdf5file, 'old_'+hdf5file)
+ file = pathlib.Path(hdf5file)
+ file.unlink()
+
+ # 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,
+ max_pce_deg=np.max(MetaModel.pce_deg))
+
+ # Run simulations at X
+ if not hasattr(ExpDesign, 'Y') or ExpDesign.Y is None:
+ print('\n Now the forward model needs to be run!\n')
+ ED_Y, up_ED_X = self.Model.run_model_parallel(ExpDesign.X, mp=parallel)
+ ExpDesign.Y = ED_Y
else:
- raise Exception("The method for experimental design you requested"
- " has not been implemented yet.")
+ # Check if a dict has been passed.
+ if not type(ExpDesign.Y) is dict:
+ raise Exception('Please provide either a dictionary or a hdf5'
+ 'file to ExpDesign.hdf5_file argument.')
+
+ # Separate output dict and x-values
+ if 'x_values' in ExpDesign.Y:
+ ExpDesign.x_values = ExpDesign.Y['x_values']
+ del ExpDesign.Y['x_values']
+ else:
+ print('No x_values are given, this might lead to issues during PostProcessing')
+
+ # Fit the surrogate
+ MetaModel.fit(ExpDesign.X, ExpDesign.Y, parallel, verbose)
+
+ # Save what there is to save
+ if save:
+ # Save surrogate
+ with open(f'surrogates/surrogate_{self.Model.name}.pk1', 'wb') as output:
+ joblib.dump(MetaModel, output, 2)
+
+ # Zip the model run directories
+ if self.Model.link_type.lower() == 'pylink' and \
+ self.ExpDesign.sampling_method.lower() != 'user':
+ self.Model.zip_subdirs(self.Model.name, f'{self.Model.name}_')
+
+ # Set that training was done
+ self.trained = True
+
+ def train_sequential(self, parallel=False, verbose=False) -> None:
+ """
+ Train the surrogate in a sequential manner.
+ First build and train evereything on the static samples, then iterate
+ choosing more samples and refitting the surrogate on them.
- # Zip the model run directories
- if self.MetaModel.ModelObj.link_type.lower() == 'pylink' and\
- self.MetaModel.ExpDesign.sampling_method.lower() != 'user':
- Model.zip_subdirs(Model.name, f'{Model.name}_')
+ Returns
+ -------
+ None
+
+ """
+ # self.train_normal(parallel, verbose)
+ self.parallel = parallel
+ self.train_seq_design(parallel, verbose)
# -------------------------------------------------------------------------
- def train_seq_design(self):
+ def eval_metamodel(self, samples=None, nsamples=None,
+ sampling_method='random', return_samples=False):
+ """
+ Evaluates metamodel at the requested samples. One can also generate
+ nsamples.
+
+ Parameters
+ ----------
+ samples : array of shape (n_samples, n_params), optional
+ Samples to evaluate metamodel at. The default is None.
+ nsamples : int, optional
+ Number of samples to generate, if no `samples` is provided. The
+ default is None.
+ sampling_method : str, optional
+ Type of sampling, if no `samples` is provided. The default is
+ 'random'.
+ return_samples : bool, optional
+ Retun samples, if no `samples` is provided. The default is False.
+
+ Returns
+ -------
+ mean_pred : dict
+ Mean of the predictions.
+ std_pred : dict
+ Standard deviatioon of the predictions.
+ """
+ # Generate or transform (if need be) samples
+ if samples is None:
+ # Generate
+ samples = self.ExpDesign.generate_samples(
+ nsamples,
+ sampling_method
+ )
+
+ # Transformation to other space is to be done in the MetaModel
+ # TODO: sort the transformations better
+ mean_pred, std_pred = self.MetaModel.eval_metamodel(samples)
+
+ if return_samples:
+ return mean_pred, std_pred, samples
+ else:
+ return mean_pred, std_pred
+
+ # -------------------------------------------------------------------------
+ def train_seq_design(self, parallel=False, verbose=False):
"""
Starts the adaptive sequential design for refining the surrogate model
by selecting training points in a sequential manner.
@@ -71,62 +308,68 @@ class MetaModelEngine():
Meta model object.
"""
- Model = self.MetaModel.ModelObj
- # MetaModel = self.MetaModel
- self.Model = Model
+ self.parallel = parallel
# Initialization
- self.MetaModel.SeqModifiedLOO = {}
- self.MetaModel.seqValidError = {}
- self.MetaModel.SeqBME = {}
- self.MetaModel.SeqKLD = {}
- self.MetaModel.SeqDistHellinger = {}
- self.MetaModel.seqRMSEMean = {}
- self.MetaModel.seqRMSEStd = {}
- self.MetaModel.seqMinDist = []
+ self.SeqModifiedLOO = {}
+ self.seqValidError = {}
+ self.SeqBME = {}
+ self.SeqKLD = {}
+ self.SeqDistHellinger = {}
+ self.seqRMSEMean = {}
+ self.seqRMSEStd = {}
+ self.seqMinDist = []
+
+ if not hasattr(self.MetaModel, 'valid_samples') or self.MetaModel.valid_samples is None:
+ self.ExpDesign.valid_samples = []
+ self.ExpDesign.valid_model_runs = []
+ self.valid_likelihoods = []
+
+ # validError = None
# Determine the metamodel type
if self.MetaModel.meta_model_type.lower() != 'gpe':
pce = True
else:
pce = False
- mc_ref = True if bool(Model.mc_reference) else False
+ mc_ref = True if bool(self.Model.mc_reference) else False
if mc_ref:
- Model.read_mc_reference()
-
- if not hasattr(self.MetaModel, 'valid_samples'):
- self.MetaModel.valid_samples = []
- self.MetaModel.valid_model_runs = []
- self.MetaModel.valid_likelihoods = []
+ self.Model.read_observation('mc_ref')
# Get the parameters
- max_n_samples = self.MetaModel.ExpDesign.n_max_samples
- mod_LOO_threshold = self.MetaModel.ExpDesign.mod_LOO_threshold
- n_canddidate = self.MetaModel.ExpDesign.n_canddidate
- post_snapshot = self.MetaModel.ExpDesign.post_snapshot
- n_replication = self.MetaModel.ExpDesign.n_replication
- util_func = self.MetaModel.ExpDesign.util_func
- output_name = Model.Output.names
- validError = None
+ max_n_samples = self.ExpDesign.n_max_samples
+ mod_LOO_threshold = self.ExpDesign.mod_LOO_threshold
+ n_canddidate = self.ExpDesign.n_canddidate
+ post_snapshot = self.ExpDesign.post_snapshot
+ n_replication = self.ExpDesign.n_replication
+ util_func = self.ExpDesign.util_func
+ output_name = self.out_names
+
# Handle if only one UtilityFunctions is provided
if not isinstance(util_func, list):
- util_func = [self.MetaModel.ExpDesign.util_func]
+ util_func = [self.ExpDesign.util_func]
# Read observations or MCReference
- if len(Model.observations) != 0 or Model.meas_file is not None:
- self.observations = Model.read_observation()
+ # TODO: recheck the logic in this if statement
+ 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 = {}
- # TODO: ---------- Initial self.MetaModel ----------
- # self.MetaModel.train_norm_design(parallel=False, verbose=True)
- self.MetaModel.train_norm_design(verbose=True)
+
+ # ---------- Initial self.MetaModel ----------
+ if not self.trained:
+ self.train_normal(parallel=parallel, verbose=verbose)
+
initMetaModel = deepcopy(self.MetaModel)
# Validation error if validation set is provided.
- if self.MetaModel.valid_model_runs:
- init_rmse, init_valid_error = self.__validError(initMetaModel)
+ if self.ExpDesign.valid_model_runs:
+ init_rmse, init_valid_error = self._validError() # initMetaModel)
init_valid_error = list(init_valid_error.values())
else:
init_rmse = None
@@ -136,36 +379,39 @@ class MetaModelEngine():
TotalSigma2 = self.MetaModel.Discrepancy.parameters
# Calculate the initial BME
- out = self.__BME_Calculator(
- initMetaModel, obs_data, TotalSigma2, init_rmse)
+ out = self._BME_Calculator(
+ obs_data, TotalSigma2, init_rmse)
init_BME, init_KLD, init_post, init_likes, init_dist_hellinger = out
print(f"\nInitial BME: {init_BME:.2f}")
print(f"Initial KLD: {init_KLD:.2f}")
# Posterior snapshot (initial)
if post_snapshot:
- parNames = self.MetaModel.ExpDesign.par_names
+ parNames = self.ExpDesign.par_names
print('Posterior snapshot (initial) is being plotted...')
- self.__posteriorPlot(init_post, parNames, 'SeqPosterior_init')
+ self._posteriorPlot(init_post, parNames, 'SeqPosterior_init')
# Check the convergence of the Mean & Std
if mc_ref and pce:
- init_rmse_mean, init_rmse_std = self.__error_Mean_Std()
+ init_rmse_mean, init_rmse_std = self._error_Mean_Std()
print(f"Initial Mean and Std error: {init_rmse_mean:.2f},"
f" {init_rmse_std:.2f}")
# Read the initial experimental design
- Xinit = initMetaModel.ExpDesign.X
- init_n_samples = len(initMetaModel.ExpDesign.X)
- initYprev = initMetaModel.ModelOutputDict
+ Xinit = self.ExpDesign.X
+ init_n_samples = len(self.ExpDesign.X)
+ initYprev = self.ExpDesign.Y # initMetaModel.ModelOutputDict
+ # self.MetaModel.ModelOutputDict = self.ExpDesign.Y
initLCerror = initMetaModel.LCerror
n_itrs = max_n_samples - init_n_samples
+ # Get some initial statistics
# Read the initial ModifiedLOO
+ init_mod_LOO = []
if pce:
Scores_all, varExpDesignY = [], []
for out_name in output_name:
- y = self.MetaModel.ExpDesign.Y[out_name]
+ y = self.ExpDesign.Y[out_name]
Scores_all.append(list(
self.MetaModel.score_dict['b_1'][out_name].values()))
if self.MetaModel.dim_red_method.lower() == 'pca':
@@ -177,25 +423,22 @@ class MetaModelEngine():
Scores = [item for sublist in Scores_all for item in sublist]
weights = [item for sublist in varExpDesignY for item in sublist]
- init_mod_LOO = [np.average([1-score for score in Scores],
+ init_mod_LOO = [np.average([1 - score for score in Scores],
weights=weights)]
prevMetaModel_dict = {}
- # Replicate the sequential design
+ # prevExpDesign_dict = {}
+ # Can run sequential design multiple times for comparison
for repIdx in range(n_replication):
- print(f'\n>>>> Replication: {repIdx+1}<<<<')
-
- # To avoid changes ub original aPCE object
- self.MetaModel.ExpDesign.X = Xinit
- self.MetaModel.ExpDesign.Y = initYprev
- self.MetaModel.LCerror = initLCerror
+ print(f'\n>>>> Replication: {repIdx + 1}<<<<')
+ # util_func: the function to use inside the type of exploitation
for util_f in util_func:
print(f'\n>>>> Utility Function: {util_f} <<<<')
# To avoid changes ub original aPCE object
- self.MetaModel.ExpDesign.X = Xinit
- self.MetaModel.ExpDesign.Y = initYprev
- self.MetaModel.LCerror = initLCerror
+ self.ExpDesign.X = Xinit
+ self.ExpDesign.Y = initYprev
+ self.ExpDesign.LCerror = initLCerror
# Set the experimental design
Xprev = Xinit
@@ -203,18 +446,17 @@ class MetaModelEngine():
Yprev = initYprev
Xfull = []
- Yfull = []
# Store the initial ModifiedLOO
if pce:
print("\nInitial ModifiedLOO:", init_mod_LOO)
SeqModifiedLOO = np.array(init_mod_LOO)
- if len(self.MetaModel.valid_model_runs) != 0:
+ if len(self.ExpDesign.valid_model_runs) != 0:
SeqValidError = np.array(init_valid_error)
# Check if data is provided
- if len(obs_data) != 0:
+ if len(obs_data) != 0 and hasattr(self.MetaModel, 'Discrepancy'):
SeqBME = np.array([init_BME])
SeqKLD = np.array([init_KLD])
SeqDistHellinger = np.array([init_dist_hellinger])
@@ -225,44 +467,44 @@ class MetaModelEngine():
# ------- Start Sequential Experimental Design -------
postcnt = 1
- for itr_no in range(1, n_itrs+1):
+ for itr_no in range(1, n_itrs + 1):
print(f'\n>>>> Iteration number {itr_no} <<<<')
# Save the metamodel prediction before updating
prevMetaModel_dict[itr_no] = deepcopy(self.MetaModel)
+ # prevExpDesign_dict[itr_no] = deepcopy(self.ExpDesign)
if itr_no > 1:
- pc_model = prevMetaModel_dict[itr_no-1]
+ pc_model = prevMetaModel_dict[itr_no - 1]
self._y_hat_prev, _ = pc_model.eval_metamodel(
samples=Xfull[-1].reshape(1, -1))
- del prevMetaModel_dict[itr_no-1]
+ del prevMetaModel_dict[itr_no - 1]
# Optimal Bayesian Design
- self.MetaModel.ExpDesignFlag = 'sequential'
- Xnew, updatedPrior = self.opt_SeqDesign(TotalSigma2,
- n_canddidate,
- util_f)
+ # self.MetaModel.ExpDesignFlag = 'sequential'
+ Xnew, updatedPrior = self.choose_next_sample(TotalSigma2,
+ n_canddidate,
+ util_f)
S = np.min(distance.cdist(Xinit, Xnew, 'euclidean'))
- self.MetaModel.seqMinDist.append(S)
+ self.seqMinDist.append(S)
print(f"\nmin Dist from OldExpDesign: {S:2f}")
print("\n")
# Evaluate the full model response at the new sample
- Ynew, _ = Model.run_model_parallel(
+ Ynew, _ = self.Model.run_model_parallel(
Xnew, prevRun_No=total_n_samples
- )
+ )
total_n_samples += Xnew.shape[0]
# ------ Plot the surrogate model vs Origninal Model ------
- if hasattr(self.MetaModel, 'adapt_verbose') and \
- self.MetaModel.adapt_verbose:
+ if self.ExpDesign.adapt_verbose:
from .adaptPlot import adaptPlot
y_hat, std_hat = self.MetaModel.eval_metamodel(
samples=Xnew
- )
+ )
adaptPlot(
self.MetaModel, Ynew, y_hat, std_hat,
plotED=False
- )
+ )
# -------- Retrain the surrogate model -------
# Extend new experimental design
@@ -271,18 +513,19 @@ class MetaModelEngine():
# Updating experimental design Y
for out_name in output_name:
Yfull = np.vstack((Yprev[out_name], Ynew[out_name]))
- self.MetaModel.ModelOutputDict[out_name] = Yfull
+ self.ExpDesign.Y[out_name] = Yfull
# Pass new design to the metamodel object
- self.MetaModel.ExpDesign.sampling_method = 'user'
- self.MetaModel.ExpDesign.X = Xfull
- self.MetaModel.ExpDesign.Y = self.MetaModel.ModelOutputDict
+ self.ExpDesign.sampling_method = 'user'
+ self.ExpDesign.X = Xfull
+ # self.ExpDesign.Y = self.MetaModel.ModelOutputDict
# Save the Experimental Design for next iteration
Xprev = Xfull
- Yprev = self.MetaModel.ModelOutputDict
+ Yprev = self.ExpDesign.Y
# Pass the new prior as the input
+ # TODO: another look at this - no difference apc to pce to gpe?
self.MetaModel.input_obj.poly_coeffs_flag = False
if updatedPrior is not None:
self.MetaModel.input_obj.poly_coeffs_flag = True
@@ -294,14 +537,14 @@ class MetaModelEngine():
self.MetaModel.input_obj.Marginals[i].raw_data = x
# Train the surrogate model for new ExpDesign
- self.MetaModel.train_norm_design(parallel=False)
+ self.train_normal(parallel=False)
# -------- Evaluate the retrained surrogate model -------
# Extract Modified LOO from Output
if pce:
Scores_all, varExpDesignY = [], []
for out_name in output_name:
- y = self.MetaModel.ExpDesign.Y[out_name]
+ y = self.ExpDesign.Y[out_name]
Scores_all.append(list(
self.MetaModel.score_dict['b_1'][out_name].values()))
if self.MetaModel.dim_red_method.lower() == 'pca':
@@ -316,15 +559,15 @@ class MetaModelEngine():
weights = [item for sublist in varExpDesignY for item
in sublist]
ModifiedLOO = [np.average(
- [1-score for score in Scores], weights=weights)]
+ [1 - score for score in Scores], weights=weights)]
print('\n')
print(f"Updated ModifiedLOO {util_f}:\n", ModifiedLOO)
print('\n')
# Compute the validation error
- if self.MetaModel.valid_model_runs:
- rmse, validError = self.__validError(self.MetaModel)
+ if self.ExpDesign.valid_model_runs:
+ rmse, validError = self._validError() # self.MetaModel)
ValidError = list(validError.values())
else:
rmse = None
@@ -333,15 +576,14 @@ class MetaModelEngine():
if pce:
SeqModifiedLOO = np.vstack(
(SeqModifiedLOO, ModifiedLOO))
- if len(self.MetaModel.valid_model_runs) != 0:
+ if len(self.ExpDesign.valid_model_runs) != 0:
SeqValidError = np.vstack(
(SeqValidError, ValidError))
# -------- Caclulation of BME as accuracy metric -------
# Check if data is provided
if len(obs_data) != 0:
# Calculate the initial BME
- out = self.__BME_Calculator(self.MetaModel, obs_data,
- TotalSigma2, rmse)
+ out = self._BME_Calculator(obs_data, TotalSigma2, rmse)
BME, KLD, Posterior, likes, DistHellinger = out
print('\n')
print(f"Updated BME: {BME:.2f}")
@@ -349,18 +591,18 @@ class MetaModelEngine():
print('\n')
# Plot some snapshots of the posterior
- step_snapshot = self.MetaModel.ExpDesign.step_snapshot
+ step_snapshot = self.ExpDesign.step_snapshot
if post_snapshot and postcnt % step_snapshot == 0:
- parNames = self.MetaModel.ExpDesign.par_names
+ parNames = self.ExpDesign.par_names
print('Posterior snapshot is being plotted...')
- self.__posteriorPlot(Posterior, parNames,
- f'SeqPosterior_{postcnt}')
+ self._posteriorPlot(Posterior, parNames,
+ f'SeqPosterior_{postcnt}')
postcnt += 1
# Check the convergence of the Mean&Std
if mc_ref and pce:
print('\n')
- RMSE_Mean, RMSE_std = self.__error_Mean_Std()
+ RMSE_Mean, RMSE_std = self._error_Mean_Std()
print(f"Updated Mean and Std error: {RMSE_Mean:.2f}, "
f"{RMSE_std:.2f}")
print('\n')
@@ -384,26 +626,26 @@ class MetaModelEngine():
if len(obs_data) != 0:
del out
print()
- print('-'*50)
+ print('-' * 50)
print()
# Store updated ModifiedLOO and BME in dictonary
- strKey = f'{util_f}_rep_{repIdx+1}'
+ strKey = f'{util_f}_rep_{repIdx + 1}'
if pce:
- self.MetaModel.SeqModifiedLOO[strKey] = SeqModifiedLOO
- if len(self.MetaModel.valid_model_runs) != 0:
- self.MetaModel.seqValidError[strKey] = SeqValidError
+ self.SeqModifiedLOO[strKey] = SeqModifiedLOO
+ if len(self.ExpDesign.valid_model_runs) != 0:
+ self.seqValidError[strKey] = SeqValidError
# Check if data is provided
if len(obs_data) != 0:
- self.MetaModel.SeqBME[strKey] = SeqBME
- self.MetaModel.SeqKLD[strKey] = SeqKLD
+ self.SeqBME[strKey] = SeqBME
+ self.SeqKLD[strKey] = SeqKLD
if hasattr(self.MetaModel, 'valid_likelihoods') and \
- self.MetaModel.valid_likelihoods:
- self.MetaModel.SeqDistHellinger[strKey] = SeqDistHellinger
+ self.valid_likelihoods:
+ self.SeqDistHellinger[strKey] = SeqDistHellinger
if mc_ref and pce:
- self.MetaModel.seqRMSEMean[strKey] = seqRMSEMean
- self.MetaModel.seqRMSEStd[strKey] = seqRMSEStd
+ self.seqRMSEMean[strKey] = seqRMSEMean
+ self.seqRMSEStd[strKey] = seqRMSEStd
# return self.MetaModel
@@ -422,7 +664,7 @@ class MetaModelEngine():
Candidate samples.
index : int
Model output index.
- UtilMethod : string, optional
+ util_func : string, optional
Exploitation utility function. The default is 'Entropy'.
Returns
@@ -432,18 +674,19 @@ class MetaModelEngine():
"""
MetaModel = self.MetaModel
- ED_X = MetaModel.ExpDesign.X
- out_dict_y = MetaModel.ExpDesign.Y
- out_names = MetaModel.ModelObj.Output.names
+ ED_X = self.ExpDesign.X
+ out_dict_y = self.ExpDesign.Y
+ out_names = self.out_names
# Run the Metamodel for the candidate
X_can = X_can.reshape(1, -1)
Y_PC_can, std_PC_can = MetaModel.eval_metamodel(samples=X_can)
+ score = None
if util_func.lower() == 'alm':
# ----- Entropy/MMSE/active learning MacKay(ALM) -----
# Compute perdiction variance of the old model
- canPredVar = {key: std_PC_can[key]**2 for key in out_names}
+ canPredVar = {key: std_PC_can[key] ** 2 for key in out_names}
varPCE = np.zeros((len(out_names), X_can.shape[0]))
for KeyIdx, key in enumerate(out_names):
@@ -458,7 +701,7 @@ class MetaModelEngine():
# Compute perdiction error and variance of the old model
predError = {key: Y_PC_can[key] for key in out_names}
- canPredVar = {key: std_PC_can[key]**2 for key in out_names}
+ canPredVar = {key: std_PC_can[key] ** 2 for key in out_names}
# Compute perdiction error and variance of the old model
# Eq (5) from Liu et al.(2018)
@@ -466,10 +709,10 @@ class MetaModelEngine():
for KeyIdx, key in enumerate(out_names):
residual = predError[key] - out_dict_y[key][int(index)]
var = canPredVar[key]
- EIGF_PCE[KeyIdx] = np.max(residual**2 + var, axis=1)
+ EIGF_PCE[KeyIdx] = np.max(residual ** 2 + var, axis=1)
score = np.max(EIGF_PCE, axis=0)
- return -1 * score # -1 is for minimization instead of maximization
+ return -1 * score # -1 is for minimization instead of maximization
# -------------------------------------------------------------------------
def util_BayesianActiveDesign(self, y_hat, std, sigma2Dict, var='DKL'):
@@ -483,8 +726,8 @@ class MetaModelEngine():
Parameters
----------
- X_can : array of shape (n_samples, n_params)
- Candidate samples.
+ y_hat : unknown
+ std : unknown
sigma2Dict : dict
A dictionary containing the measurement errors (sigma^2).
var : string, optional
@@ -499,33 +742,39 @@ class MetaModelEngine():
# Get the data
obs_data = self.observations
- n_obs = self.Model.n_obs
+ # TODO: this should be optimizable to be calculated explicitly
+ if hasattr(self.Model, 'n_obs'):
+ n_obs = self.Model.n_obs
+ else:
+ n_obs = self.n_obs
mc_size = 10000
# Sample a distribution for a normal dist
# with Y_mean_can as the mean and Y_std_can as std.
Y_MC, std_MC = {}, {}
- logPriorLikelihoods = np.zeros((mc_size))
+ logPriorLikelihoods = np.zeros(mc_size)
for key in list(y_hat):
- cov = np.diag(std[key]**2)
- rv = stats.multivariate_normal(mean=y_hat[key], cov=cov)
+ cov = np.diag(std[key] ** 2)
+ print(key, y_hat[key], std[key])
+ # TODO: added the allow_singular = True here
+ rv = stats.multivariate_normal(mean=y_hat[key], cov=cov, allow_singular=True)
Y_MC[key] = rv.rvs(size=mc_size)
logPriorLikelihoods += rv.logpdf(Y_MC[key])
std_MC[key] = np.zeros((mc_size, y_hat[key].shape[0]))
# Likelihood computation (Comparison of data and simulation
# results via PCE with candidate design)
- likelihoods = self.__normpdf(Y_MC, std_MC, obs_data, sigma2Dict)
+ likelihoods = self._normpdf(Y_MC, std_MC, obs_data, sigma2Dict)
# Rejection Step
# Random numbers between 0 and 1
unif = np.random.rand(1, mc_size)[0]
# Reject the poorly performed prior
- accepted = (likelihoods/np.max(likelihoods)) >= unif
+ accepted = (likelihoods / np.max(likelihoods)) >= unif
# Prior-based estimation of BME
- logBME = np.log(np.nanmean(likelihoods), dtype=np.float128)
+ logBME = np.log(np.nanmean(likelihoods), dtype=np.longdouble) # float128)
# Posterior-based expectation of likelihoods
postLikelihoods = likelihoods[accepted]
@@ -537,6 +786,7 @@ class MetaModelEngine():
# Utility function Eq.2 in Ref. (2)
# Posterior covariance matrix after observing data y
# Kullback-Leibler Divergence (Sergey's paper)
+ U_J_d = None
if var == 'DKL':
# TODO: Calculate the correction factor for BME
@@ -572,15 +822,15 @@ class MetaModelEngine():
AIC = -2 * maxlogL + 2 * nModelParams
# 2 * nModelParams * (nModelParams+1) / (n_obs-nModelParams-1)
penTerm = 0
- U_J_d = 1*(AIC + penTerm)
+ U_J_d = 1 * (AIC + penTerm)
# Deviance information criterion
elif var == 'DIC':
# D_theta_bar = np.mean(-2 * Likelihoods)
N_star_p = 0.5 * np.var(np.log(likelihoods[likelihoods != 0]))
- Likelihoods_theta_mean = self.__normpdf(
+ Likelihoods_theta_mean = self._normpdf(
y_hat, std, obs_data, sigma2Dict
- )
+ )
DIC = -2 * np.log(Likelihoods_theta_mean) + 2 * N_star_p
U_J_d = DIC
@@ -597,129 +847,7 @@ class MetaModelEngine():
del Y_MC
del std_MC
- return -1 * U_J_d # -1 is for minimization instead of maximization
-
- # -------------------------------------------------------------------------
- def update_metamodel(self, MetaModel, output, y_hat_can, univ_p_val, index,
- new_pca=False):
- BasisIndices = MetaModel.basis_dict[output]["y_"+str(index+1)]
- clf_poly = MetaModel.clf_poly[output]["y_"+str(index+1)]
- Mn = clf_poly.coef_
- Sn = clf_poly.sigma_
- beta = clf_poly.alpha_
- active = clf_poly.active_
- Psi = self.MetaModel.create_psi(BasisIndices, univ_p_val)
-
- Sn_new_inv = np.linalg.inv(Sn)
- Sn_new_inv += beta * np.dot(Psi[:, active].T, Psi[:, active])
- Sn_new = np.linalg.inv(Sn_new_inv)
-
- Mn_new = np.dot(Sn_new_inv, Mn[active]).reshape(-1, 1)
- Mn_new += beta * np.dot(Psi[:, active].T, y_hat_can)
- Mn_new = np.dot(Sn_new, Mn_new).flatten()
-
- # Compute the old and new moments of PCEs
- mean_old = Mn[0]
- mean_new = Mn_new[0]
- std_old = np.sqrt(np.sum(np.square(Mn[1:])))
- std_new = np.sqrt(np.sum(np.square(Mn_new[1:])))
-
- # Back transformation if PCA is selected.
- if MetaModel.dim_red_method.lower() == 'pca':
- old_pca = MetaModel.pca[output]
- mean_old = old_pca.mean_[index]
- mean_old += np.sum(mean_old * old_pca.components_[:, index])
- std_old = np.sqrt(np.sum(std_old**2 *
- old_pca.components_[:, index]**2))
- mean_new = new_pca.mean_[index]
- mean_new += np.sum(mean_new * new_pca.components_[:, index])
- std_new = np.sqrt(np.sum(std_new**2 *
- new_pca.components_[:, index]**2))
- # print(f"mean_old: {mean_old:.2f} mean_new: {mean_new:.2f}")
- # print(f"std_old: {std_old:.2f} std_new: {std_new:.2f}")
- # Store the old and new moments of PCEs
- results = {
- 'mean_old': mean_old,
- 'mean_new': mean_new,
- 'std_old': std_old,
- 'std_new': std_new
- }
- return results
-
- # -------------------------------------------------------------------------
- def util_BayesianDesign_old(self, X_can, X_MC, sigma2Dict, var='DKL'):
- """
- Computes scores based on Bayesian sequential design criterion (var).
-
- Parameters
- ----------
- X_can : array of shape (n_samples, n_params)
- Candidate samples.
- sigma2Dict : dict
- A dictionary containing the measurement errors (sigma^2).
- var : string, optional
- Bayesian design criterion. The default is 'DKL'.
-
- Returns
- -------
- float
- Score.
-
- """
-
- # To avoid changes ub original aPCE object
- Model = self.Model
- MetaModel = deepcopy(self.MetaModel)
- old_EDY = MetaModel.ExpDesign.Y
-
- # Evaluate the PCE metamodels using the candidate design
- Y_PC_can, Y_std_can = self.MetaModel.eval_metamodel(
- samples=np.array([X_can])
- )
-
- # Generate y from posterior predictive
- m_size = 100
- y_hat_samples = {}
- for idx, key in enumerate(Model.Output.names):
- means, stds = Y_PC_can[key][0], Y_std_can[key][0]
- y_hat_samples[key] = np.random.multivariate_normal(
- means, np.diag(stds), m_size)
-
- # Create the SparseBayes-based PCE metamodel:
- MetaModel.input_obj.poly_coeffs_flag = False
- univ_p_val = self.MetaModel.univ_basis_vals(X_can)
- G_n_m_all = np.zeros((m_size, len(Model.Output.names), Model.n_obs))
-
- for i in range(m_size):
- for idx, key in enumerate(Model.Output.names):
- if MetaModel.dim_red_method.lower() == 'pca':
- # Equal number of components
- new_outputs = np.vstack(
- (old_EDY[key], y_hat_samples[key][i])
- )
- new_pca, _ = MetaModel.pca_transformation(new_outputs)
- target = new_pca.transform(
- y_hat_samples[key][i].reshape(1, -1)
- )[0]
- else:
- new_pca, target = False, y_hat_samples[key][i]
-
- for j in range(len(target)):
-
- # Update surrogate
- result = self.update_metamodel(
- MetaModel, key, target[j], univ_p_val, j, new_pca)
-
- # Compute Expected Information Gain (Eq. 39)
- G_n_m = np.log(result['std_old']/result['std_new']) - 1./2
- G_n_m += result['std_new']**2 / (2*result['std_old']**2)
- G_n_m += (result['mean_new'] - result['mean_old'])**2 /\
- (2*result['std_old']**2)
-
- G_n_m_all[i, idx, j] = G_n_m
-
- U_J_d = G_n_m_all.mean(axis=(1, 2)).mean()
- return -1 * U_J_d
+ return -1 * U_J_d # -1 is for minimization instead of maximization
# -------------------------------------------------------------------------
def util_BayesianDesign(self, X_can, X_MC, sigma2Dict, var='DKL'):
@@ -730,6 +858,7 @@ class MetaModelEngine():
----------
X_can : array of shape (n_samples, n_params)
Candidate samples.
+ X_MC : unknown
sigma2Dict : dict
A dictionary containing the measurement errors (sigma^2).
var : string, optional
@@ -744,7 +873,7 @@ class MetaModelEngine():
# To avoid changes ub original aPCE object
MetaModel = self.MetaModel
- out_names = MetaModel.ModelObj.Output.names
+ out_names = self.out_names
if X_can.ndim == 1:
X_can = X_can.reshape(1, -1)
@@ -754,12 +883,13 @@ class MetaModelEngine():
Y_MC, Y_MC_std = MetaModel.eval_metamodel(samples=X_MC)
# Old Experimental design
- oldExpDesignX = MetaModel.ExpDesign.X
- oldExpDesignY = MetaModel.ExpDesign.Y
+ oldExpDesignX = self.ExpDesign.X
+ oldExpDesignY = self.ExpDesign.Y
# Evaluate the PCE metamodels at that location ???
Y_PC_can, Y_std_can = MetaModel.eval_metamodel(samples=X_can)
PCE_Model_can = deepcopy(MetaModel)
+ engine_can = deepcopy(self)
# Add the candidate to the ExpDesign
NewExpDesignX = np.vstack((oldExpDesignX, X_can))
@@ -767,33 +897,36 @@ class MetaModelEngine():
for key in oldExpDesignY.keys():
NewExpDesignY[key] = np.vstack(
(oldExpDesignY[key], Y_PC_can[key])
- )
+ )
- PCE_Model_can.ExpDesign.sampling_method = 'user'
- PCE_Model_can.ExpDesign.X = NewExpDesignX
- PCE_Model_can.ModelOutputDict = NewExpDesignY
- PCE_Model_can.ExpDesign.Y = NewExpDesignY
+ engine_can.ExpDesign.sampling_method = 'user'
+ engine_can.ExpDesign.X = NewExpDesignX
+ # engine_can.ModelOutputDict = NewExpDesignY
+ engine_can.ExpDesign.Y = NewExpDesignY
# Train the model for the observed data using x_can
- PCE_Model_can.input_obj.poly_coeffs_flag = False
- PCE_Model_can.train_norm_design(parallel=False)
+ engine_can.MetaModel.input_obj.poly_coeffs_flag = False
+ engine_can.start_engine()
+ engine_can.train_normal(parallel=False)
+ engine_can.MetaModel.fit(NewExpDesignX, NewExpDesignY)
+ # engine_can.train_norm_design(parallel=False)
# Set the ExpDesign to its original values
- PCE_Model_can.ExpDesign.X = oldExpDesignX
- PCE_Model_can.ModelOutputDict = oldExpDesignY
- PCE_Model_can.ExpDesign.Y = oldExpDesignY
+ engine_can.ExpDesign.X = oldExpDesignX
+ engine_can.ModelOutputDict = oldExpDesignY
+ engine_can.ExpDesign.Y = oldExpDesignY
if var.lower() == 'mi':
- # Mutual information based on Krause et al
+ # Mutual information based on Krause et al.
# Adapted from Beck & Guillas (MICE) paper
- _, std_PC_can = PCE_Model_can.eval_metamodel(samples=X_can)
+ _, std_PC_can = engine_can.MetaModel.eval_metamodel(samples=X_can)
std_can = {key: std_PC_can[key] for key in out_names}
std_old = {key: Y_std_can[key] for key in out_names}
varPCE = np.zeros((len(out_names)))
for i, key in enumerate(out_names):
- varPCE[i] = np.mean(std_old[key]**2/std_can[key]**2)
+ varPCE[i] = np.mean(std_old[key] ** 2 / std_can[key] ** 2)
score = np.mean(varPCE)
return -1 * score
@@ -804,14 +937,14 @@ class MetaModelEngine():
# metrics, 51 (2009), pp. 130–145.
# Evaluate the MetaModel at the given samples
- Y_MC_can, Y_MC_std_can = PCE_Model_can.eval_metamodel(samples=X_MC)
+ Y_MC_can, Y_MC_std_can = engine_can.MetaModel.eval_metamodel(samples=X_MC)
# Compute the score
score = []
for i, key in enumerate(out_names):
- pce_var = Y_MC_std_can[key]**2
- pce_var_can = Y_MC_std[key]**2
- score.append(np.mean(pce_var-pce_var_can, axis=0))
+ pce_var = Y_MC_std_can[key] ** 2
+ pce_var_can = Y_MC_std[key] ** 2
+ score.append(np.mean(pce_var - pce_var_can, axis=0))
score = np.mean(score)
return -1 * score
@@ -821,28 +954,29 @@ class MetaModelEngine():
MCsize = X_MC.shape[0]
ESS = 0
- while ((ESS > MCsize) or (ESS < 1)):
+ likelihoods = None
+ while (ESS > MCsize) or (ESS < 1):
# Enriching Monte Carlo samples if need be
if ESS != 0:
- X_MC = self.MetaModel.ExpDesign.generate_samples(
+ X_MC = self.ExpDesign.generate_samples(
MCsize, 'random'
- )
+ )
# Evaluate the MetaModel at the given samples
Y_MC, std_MC = PCE_Model_can.eval_metamodel(samples=X_MC)
# Likelihood computation (Comparison of data and simulation
# results via PCE with candidate design)
- likelihoods = self.__normpdf(
+ likelihoods = self._normpdf(
Y_MC, std_MC, self.observations, sigma2Dict
- )
+ )
# Check the Effective Sample Size (1<ESS<MCsize)
- ESS = 1 / np.sum(np.square(likelihoods/np.sum(likelihoods)))
+ ESS = 1 / np.sum(np.square(likelihoods / np.sum(likelihoods)))
# Enlarge sample size if it doesn't fulfill the criteria
- if ((ESS > MCsize) or (ESS < 1)):
+ if (ESS > MCsize) or (ESS < 1):
print("--- increasing MC size---")
MCsize *= 10
ESS = 0
@@ -852,19 +986,20 @@ class MetaModelEngine():
unif = np.random.rand(1, MCsize)[0]
# Reject the poorly performed prior
- accepted = (likelihoods/np.max(likelihoods)) >= unif
+ accepted = (likelihoods / np.max(likelihoods)) >= unif
# -------------------- Utility functions --------------------
# Utility function Eq.2 in Ref. (2)
# Kullback-Leibler Divergence (Sergey's paper)
+ U_J_d = None
if var == 'DKL':
# Prior-based estimation of BME
- logBME = np.log(np.nanmean(likelihoods, dtype=np.float128))
+ logBME = np.log(np.nanmean(likelihoods, dtype=np.longdouble)) # float128))
# Posterior-based expectation of likelihoods
- postLikelihoods = likelihoods[accepted]
- postExpLikelihoods = np.mean(np.log(postLikelihoods))
+ # postLikelihoods = likelihoods[accepted]
+ # postExpLikelihoods = np.mean(np.log(postLikelihoods))
# Haun et al implementation
U_J_d = np.mean(np.log(likelihoods[likelihoods != 0]) - logBME)
@@ -899,6 +1034,8 @@ class MetaModelEngine():
postExpLikelihoods = np.mean(np.log(postLikelihoods))
# Posterior-based expectation of prior densities
+ logPriorLikelihoods = []
+ logPriorLikelihoods[accepted] = None # TODO: this is not defined here, just a fix
postExpPrior = np.mean(logPriorLikelihoods[accepted])
infEntropy = logBME - postExpPrior - postExpLikelihoods
@@ -925,41 +1062,7 @@ class MetaModelEngine():
del Y_MC
del std_MC
- return -1 * U_J_d # -1 is for minimization instead of maximization
-
- # -------------------------------------------------------------------------
- def subdomain(self, Bounds, n_new_samples):
- """
- Divides a domain defined by Bounds into sub domains.
-
- Parameters
- ----------
- Bounds : list of tuples
- List of lower and upper bounds.
- n_new_samples : TYPE
- DESCRIPTION.
-
- Returns
- -------
- Subdomains : TYPE
- DESCRIPTION.
-
- """
- n_params = self.MetaModel.n_params
- n_subdomains = n_new_samples + 1
- LinSpace = np.zeros((n_params, n_subdomains))
-
- for i in range(n_params):
- LinSpace[i] = np.linspace(start=Bounds[i][0], stop=Bounds[i][1],
- num=n_subdomains)
- Subdomains = []
- for k in range(n_subdomains-1):
- mylist = []
- for i in range(n_params):
- mylist.append((LinSpace[i, k+0], LinSpace[i, k+1]))
- Subdomains.append(tuple(mylist))
-
- return Subdomains
+ return -1 * U_J_d # -1 is for minimization instead of maximization
# -------------------------------------------------------------------------
def run_util_func(self, method, candidates, index, sigma2Dict=None,
@@ -1002,7 +1105,7 @@ class MetaModelEngine():
elif method.lower() == 'bayesactdesign':
NCandidate = candidates.shape[0]
- U_J_d = np.zeros((NCandidate))
+ U_J_d = np.zeros(NCandidate)
# Evaluate all candidates
y_can, std_can = self.MetaModel.eval_metamodel(samples=candidates)
# loop through candidates
@@ -1010,23 +1113,26 @@ class MetaModelEngine():
desc="BAL Design"):
y_hat = {key: items[idx] for key, items in y_can.items()}
std = {key: items[idx] for key, items in std_can.items()}
+
+ # print(y_hat)
+ # print(std)
U_J_d[idx] = self.util_BayesianActiveDesign(
y_hat, std, sigma2Dict, var)
elif method.lower() == 'bayesoptdesign':
NCandidate = candidates.shape[0]
- U_J_d = np.zeros((NCandidate))
+ U_J_d = np.zeros(NCandidate)
for idx, X_can in tqdm(enumerate(candidates), ascii=True,
desc="OptBayesianDesign"):
U_J_d[idx] = self.util_BayesianDesign(X_can, X_MC, sigma2Dict,
var)
- return (index, -1 * U_J_d)
+ return index, -1 * U_J_d
# -------------------------------------------------------------------------
def dual_annealing(self, method, Bounds, sigma2Dict, var, Run_No,
verbose=False):
"""
- Exploration algorithim to find the optimum parameter space.
+ Exploration algorithm to find the optimum parameter space.
Parameters
----------
@@ -1037,6 +1143,7 @@ class MetaModelEngine():
List of lower and upper boundaries of parameters.
sigma2Dict : dict
A dictionary containing the measurement errors (sigma^2).
+ var : unknown
Run_No : int
Run number.
verbose : bool, optional
@@ -1052,28 +1159,29 @@ class MetaModelEngine():
"""
Model = self.Model
- max_func_itr = self.MetaModel.ExpDesign.max_func_itr
+ max_func_itr = self.ExpDesign.max_func_itr
- if method == 'VarOptDesign':
+ Res_Global = None
+ if method.lower() == 'varoptdesign':
Res_Global = opt.dual_annealing(self.util_VarBasedDesign,
bounds=Bounds,
args=(Model, var),
maxfun=max_func_itr)
- elif method == 'BayesOptDesign':
+ elif method.lower() == 'bayesoptdesign':
Res_Global = opt.dual_annealing(self.util_BayesianDesign,
bounds=Bounds,
args=(Model, sigma2Dict, var),
maxfun=max_func_itr)
if verbose:
- print(f"global minimum: xmin = {Res_Global.x}, "
+ print(f"Global minimum: xmin = {Res_Global.x}, "
f"f(xmin) = {Res_Global.fun:.6f}, nfev = {Res_Global.nfev}")
- return (Run_No, Res_Global.x)
+ return Run_No, Res_Global.x
# -------------------------------------------------------------------------
- def tradoff_weights(self, tradeoff_scheme, old_EDX, old_EDY):
+ def tradeoff_weights(self, tradeoff_scheme, old_EDX, old_EDY):
"""
Calculates weights for exploration scores based on the requested
scheme: `None`, `equal`, `epsilon-decreasing` and `adaptive`.
@@ -1081,7 +1189,7 @@ class MetaModelEngine():
`None`: No exploration.
`equal`: Same weights for exploration and exploitation scores.
`epsilon-decreasing`: Start with more exploration and increase the
- influence of exploitation along the way with a exponential decay
+ influence of exploitation along the way with an exponential decay
function
`adaptive`: An adaptive method based on:
Liu, Haitao, Jianfei Cai, and Yew-Soon Ong. "An adaptive sampling
@@ -1105,6 +1213,8 @@ class MetaModelEngine():
Exploitation weight.
"""
+ exploration_weight = None
+
if tradeoff_scheme is None:
exploration_weight = 0
@@ -1114,24 +1224,24 @@ class MetaModelEngine():
elif tradeoff_scheme == 'epsilon-decreasing':
# epsilon-decreasing scheme
# Start with more exploration and increase the influence of
- # exploitation along the way with a exponential decay function
- initNSamples = self.MetaModel.ExpDesign.n_init_samples
- n_max_samples = self.MetaModel.ExpDesign.n_max_samples
+ # exploitation along the way with an exponential decay function
+ initNSamples = self.ExpDesign.n_init_samples
+ n_max_samples = self.ExpDesign.n_max_samples
- itrNumber = (self.MetaModel.ExpDesign.X.shape[0] - initNSamples)
- itrNumber //= self.MetaModel.ExpDesign.n_new_samples
+ itrNumber = (self.ExpDesign.X.shape[0] - initNSamples)
+ itrNumber //= self.ExpDesign.n_new_samples
- tau2 = -(n_max_samples-initNSamples-1) / np.log(1e-8)
- exploration_weight = signal.exponential(n_max_samples-initNSamples,
+ tau2 = -(n_max_samples - initNSamples - 1) / np.log(1e-8)
+ exploration_weight = signal.exponential(n_max_samples - initNSamples,
0, tau2, False)[itrNumber]
elif tradeoff_scheme == 'adaptive':
# Extract itrNumber
- initNSamples = self.MetaModel.ExpDesign.n_init_samples
- n_max_samples = self.MetaModel.ExpDesign.n_max_samples
- itrNumber = (self.MetaModel.ExpDesign.X.shape[0] - initNSamples)
- itrNumber //= self.MetaModel.ExpDesign.n_new_samples
+ initNSamples = self.ExpDesign.n_init_samples
+ # n_max_samples = self.ExpDesign.n_max_samples
+ itrNumber = (self.ExpDesign.X.shape[0] - initNSamples)
+ itrNumber //= self.ExpDesign.n_new_samples
if itrNumber == 0:
exploration_weight = 0.5
@@ -1148,7 +1258,7 @@ class MetaModelEngine():
pce_y_prev = np.array(list(self._y_hat_prev.values()))[:, 0]
mseCVError = mean_squared_error(pce_y_prev, y)
- exploration_weight = min([0.5*mseError/mseCVError, 1])
+ exploration_weight = min([0.5 * mseError / mseCVError, 1])
# Exploitation weight
exploitation_weight = 1 - exploration_weight
@@ -1156,7 +1266,7 @@ class MetaModelEngine():
return exploration_weight, exploitation_weight
# -------------------------------------------------------------------------
- def opt_SeqDesign(self, sigma2, n_candidates=5, var='DKL'):
+ def choose_next_sample(self, sigma2=None, n_candidates=5, var='DKL'):
"""
Runs optimal sequential design.
@@ -1168,7 +1278,7 @@ class MetaModelEngine():
n_candidates : int, optional
Number of candidate samples. The default is 5.
var : string, optional
- Utility function. The default is None.
+ Utility function. The default is None. # TODO: default is set to DKL, not none
Raises
------
@@ -1182,76 +1292,110 @@ class MetaModelEngine():
"""
# Initialization
- MetaModel = self.MetaModel
- Bounds = MetaModel.bound_tuples
- n_new_samples = MetaModel.ExpDesign.n_new_samples
- explore_method = MetaModel.ExpDesign.explore_method
- exploit_method = MetaModel.ExpDesign.exploit_method
- n_cand_groups = MetaModel.ExpDesign.n_cand_groups
- tradeoff_scheme = MetaModel.ExpDesign.tradeoff_scheme
-
- old_EDX = MetaModel.ExpDesign.X
- old_EDY = MetaModel.ExpDesign.Y.copy()
- ndim = MetaModel.ExpDesign.X.shape[1]
- OutputNames = MetaModel.ModelObj.Output.names
+ Bounds = self.ExpDesign.bound_tuples
+ n_new_samples = self.ExpDesign.n_new_samples
+ explore_method = self.ExpDesign.explore_method
+ exploit_method = self.ExpDesign.exploit_method
+ n_cand_groups = self.ExpDesign.n_cand_groups
+ tradeoff_scheme = self.ExpDesign.tradeoff_scheme
+
+ old_EDX = self.ExpDesign.X
+ old_EDY = self.ExpDesign.Y.copy()
+ ndim = self.ExpDesign.X.shape[1]
+ OutputNames = self.out_names
# -----------------------------------------
# ----------- CUSTOMIZED METHODS ----------
# -----------------------------------------
# Utility function exploit_method provided by user
if exploit_method.lower() == 'user':
-
- Xnew, filteredSamples = MetaModel.ExpDesign.ExploitFunction(self)
+ # TODO: is the exploit_method meant here?
+ if not hasattr(self.ExpDesign, 'ExploitFunction') or self.ExpDesign.ExploitFunction is None:
+ raise AttributeError(
+ 'Function `ExploitFunction` not given to the ExpDesign, thus cannor run user-defined sequential'
+ 'scheme')
+ # TODO: syntax does not fully match the rest - can test this??
+ Xnew, filteredSamples = self.ExpDesign.ExploitFunction(self)
print("\n")
print("\nXnew:\n", Xnew)
return Xnew, filteredSamples
- # -----------------------------------------
- # ---------- EXPLORATION METHODS ----------
- # -----------------------------------------
+ # Dual-Annealing works differently from the rest, so deal with this first
+ # Here exploration and exploitation are performed simulataneously
if explore_method == 'dual annealing':
# ------- EXPLORATION: OPTIMIZATION -------
import time
start_time = time.time()
# Divide the domain to subdomains
- args = []
- subdomains = self.subdomain(Bounds, n_new_samples)
- for i in range(n_new_samples):
- args.append((exploit_method, subdomains[i], sigma2, var, i))
+ subdomains = subdomain(Bounds, n_new_samples)
# Multiprocessing
- pool = multiprocessing.Pool(multiprocessing.cpu_count())
+ if self.parallel:
+ args = []
+ for i in range(n_new_samples):
+ args.append((exploit_method, subdomains[i], sigma2, var, i))
+ pool = multiprocessing.Pool(multiprocessing.cpu_count())
- # With Pool.starmap_async()
- results = pool.starmap_async(self.dual_annealing, args).get()
+ # With Pool.starmap_async()
+ results = pool.starmap_async(self.dual_annealing, args).get()
- # Close the pool
- pool.close()
+ # Close the pool
+ pool.close()
+ # Without multiprocessing
+ else:
+ results = []
+ for i in range(n_new_samples):
+ results.append(self.dual_annealing(exploit_method, subdomains[i], sigma2, var, i))
+ # New sample
Xnew = np.array([results[i][1] for i in range(n_new_samples)])
-
print("\nXnew:\n", Xnew)
+ # Computational cost
elapsed_time = time.time() - start_time
print("\n")
- print(f"elapsed_time: {round(elapsed_time,2)} sec.")
- print('-'*20)
+ print(f"Elapsed_time: {round(elapsed_time, 2)} sec.")
+ print('-' * 20)
+
+ return Xnew, None
+
+ # Generate needed Exploration class
+ explore = Exploration(self.ExpDesign, n_candidates)
+ explore.w = 100 # * ndim #500 # TODO: where does this value come from?
+
+ # Select criterion (mc-intersite-proj-th, mc-intersite-proj)
+ explore.mc_criterion = 'mc-intersite-proj'
- elif explore_method == 'LOOCV':
+ # Generate the candidate samples
+ # TODO: here use the sampling method provided by the expdesign?
+ # sampling_method = self.ExpDesign.sampling_method
+
+ # TODO: changed this from 'random' for LOOCV
+ # TODO: these are commented out as they are not used !?
+ # if explore_method == 'LOOCV':
+ # allCandidates = self.ExpDesign.generate_samples(n_candidates,
+ # sampling_method)
+ # else:
+ # allCandidates, scoreExploration = explore.get_exploration_samples()
+
+ # -----------------------------------------
+ # ---------- EXPLORATION METHODS ----------
+ # -----------------------------------------
+ if explore_method == 'LOOCV':
# -----------------------------------------------------------------
# TODO: LOOCV model construnction based on Feng et al. (2020)
# 'LOOCV':
# Initilize the ExploitScore array
# Generate random samples
- allCandidates = MetaModel.ExpDesign.generate_samples(n_candidates,
- 'random')
+ allCandidates = self.ExpDesign.generate_samples(n_candidates,
+ 'random')
# Construct error model based on LCerror
- errorModel = MetaModel.create_ModelError(old_EDX, self.LCerror)
+ errorModel = self.MetaModel.create_ModelError(old_EDX, self.LCerror)
self.errorModel.append(copy(errorModel))
# Evaluate the error models for allCandidates
@@ -1266,7 +1410,7 @@ class MetaModelEngine():
else:
# ------- EXPLORATION: SPACE-FILLING DESIGN -------
# Generate candidate samples from Exploration class
- explore = Exploration(MetaModel, n_candidates)
+ explore = Exploration(self.ExpDesign, n_candidates)
explore.w = 100 # * ndim #500
# Select criterion (mc-intersite-proj-th, mc-intersite-proj)
explore.mc_criterion = 'mc-intersite-proj'
@@ -1276,6 +1420,20 @@ class MetaModelEngine():
if ndim == 2:
def plotter(points, allCandidates, Method,
scoreExploration=None):
+ """
+ unknown
+
+ Parameters
+ ----------
+ points
+ allCandidates
+ Method
+ scoreExploration
+
+ Returns
+ -------
+
+ """
if Method == 'Voronoi':
from scipy.spatial import Voronoi, voronoi_plot_2d
vor = Voronoi(points)
@@ -1289,7 +1447,7 @@ class MetaModelEngine():
ax1.scatter(allCandidates[:, 0], allCandidates[:, 1], s=10,
c='b', marker="o", label='Design candidates')
for i in range(points.shape[0]):
- txt = 'p'+str(i+1)
+ txt = 'p' + str(i + 1)
ax1.annotate(txt, (points[i, 0], points[i, 1]))
if scoreExploration is not None:
for i in range(allCandidates.shape[0]):
@@ -1305,44 +1463,44 @@ class MetaModelEngine():
# -----------------------------------------
# --------- EXPLOITATION METHODS ----------
# -----------------------------------------
- if exploit_method == 'BayesOptDesign' or\
- exploit_method == 'BayesActDesign':
+ if exploit_method.lower() == 'bayesoptdesign' or \
+ exploit_method.lower() == 'bayesactdesign':
# ------- Calculate Exoploration weight -------
# Compute exploration weight based on trade off scheme
- explore_w, exploit_w = self.tradoff_weights(tradeoff_scheme,
- old_EDX,
- old_EDY)
+ explore_w, exploit_w = self.tradeoff_weights(tradeoff_scheme,
+ old_EDX,
+ old_EDY)
print(f"\n Exploration weight={explore_w:0.3f} "
f"Exploitation weight={exploit_w:0.3f}\n")
# ------- EXPLOITATION: BayesOptDesign & ActiveLearning -------
if explore_w != 1.0:
+ # Check if all needed properties are set
+ 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
MCsize = 15000
- X_MC = MetaModel.ExpDesign.generate_samples(MCsize, 'random')
- candidates = MetaModel.ExpDesign.generate_samples(
- MetaModel.ExpDesign.max_func_itr, 'latin_hypercube')
+ X_MC = self.ExpDesign.generate_samples(MCsize, 'random')
+ candidates = self.ExpDesign.generate_samples(
+ n_candidates, 'latin_hypercube')
# Split the candidates in groups for multiprocessing
split_cand = np.array_split(
candidates, n_cand_groups, axis=0
- )
-
- results = Parallel(n_jobs=-1, backend='multiprocessing')(
+ )
+ # print(candidates)
+ # print(split_cand)
+ if self.parallel:
+ results = Parallel(n_jobs=-1, backend='multiprocessing')(
delayed(self.run_util_func)(
exploit_method, split_cand[i], i, sigma2, var, X_MC)
for i in range(n_cand_groups))
- # out = map(self.run_util_func,
- # [exploit_method]*n_cand_groups,
- # split_cand,
- # range(n_cand_groups),
- # [sigma2] * n_cand_groups,
- # [var] * n_cand_groups,
- # [X_MC] * n_cand_groups
- # )
- # results = list(out)
+ else:
+ results = []
+ for i in range(n_cand_groups):
+ results.append(self.run_util_func(exploit_method, split_cand[i], i, sigma2, var, X_MC))
# Retrieve the results and append them
U_J_d = np.concatenate([results[NofE][1] for NofE in
@@ -1357,18 +1515,6 @@ class MetaModelEngine():
if explore_method == 'Voronoi':
U_J_d = np.mean(U_J_d.reshape(-1, n_candidates), axis=1)
- # create surrogate model for U_J_d
- # from sklearn.preprocessing import MinMaxScaler
- # # Take care of inf entries
- # good_indices = [i for i, arr in enumerate(U_J_d)
- # if np.isfinite(arr).all()]
- # scaler = MinMaxScaler()
- # X_S = scaler.fit_transform(candidates[good_indices])
- # gp = MetaModel.gaussian_process_emulator(
- # X_S, U_J_d[good_indices], autoSelect=False
- # )
- # U_J_d = gp.predict(scaler.transform(allCandidates))
-
# Normalize U_J_d
norm_U_J_d = U_J_d / np.sum(U_J_d)
else:
@@ -1376,6 +1522,30 @@ class MetaModelEngine():
# ------- Calculate Total score -------
# ------- Trade off between EXPLORATION & EXPLOITATION -------
+ # Accumulate the samples
+ finalCandidates = np.concatenate((allCandidates, candidates), axis=0)
+ finalCandidates = np.unique(finalCandidates, axis=0)
+
+ # Calculations take into account both exploration and exploitation
+ # samples without duplicates
+ totalScore = np.zeros(finalCandidates.shape[0])
+ # self.totalScore = totalScore
+
+ for cand_idx in range(finalCandidates.shape[0]):
+ # find candidate indices
+ idx1 = np.where(allCandidates == finalCandidates[cand_idx])[0]
+ idx2 = np.where(candidates == finalCandidates[cand_idx])[0]
+
+ # exploration
+ if idx1.shape[0] > 0:
+ idx1 = idx1[0]
+ totalScore[cand_idx] += explore_w * scoreExploration[idx1]
+
+ # exploitation
+ if idx2.shape[0] > 0:
+ idx2 = idx2[0]
+ totalScore[cand_idx] += exploit_w * norm_U_J_d[idx2]
+
# Total score
totalScore = exploit_w * norm_U_J_d
totalScore += explore_w * scoreExploration
@@ -1405,26 +1575,31 @@ class MetaModelEngine():
# select the requested number of samples
Xnew[i] = newSamples[np.argmax(maxminScore)]
else:
- Xnew = allCandidates[sorted_idxtotalScore[:n_new_samples]]
+ # Changed this from allCandiates to full set of candidates
+ # TODO: still not changed for e.g. 'Voronoi'
+ Xnew = finalCandidates[sorted_idxtotalScore[:n_new_samples]]
+
- elif exploit_method == 'VarOptDesign':
+ elif exploit_method.lower() == 'varoptdesign':
# ------- EXPLOITATION: VarOptDesign -------
UtilMethod = var
# ------- Calculate Exoploration weight -------
# Compute exploration weight based on trade off scheme
- explore_w, exploit_w = self.tradoff_weights(tradeoff_scheme,
- old_EDX,
- old_EDY)
+ explore_w, exploit_w = self.tradeoff_weights(tradeoff_scheme,
+ old_EDX,
+ old_EDY)
print(f"\nweightExploration={explore_w:0.3f} "
f"weightExploitation={exploit_w:0.3f}")
# Generate candidate samples from Exploration class
nMeasurement = old_EDY[OutputNames[0]].shape[1]
+ # print(UtilMethod)
+
# Find sensitive region
if UtilMethod == 'LOOCV':
- LCerror = MetaModel.LCerror
+ LCerror = self.MetaModel.LCerror
allModifiedLOO = np.zeros((len(old_EDX), len(OutputNames),
nMeasurement))
for y_idx, y_key in enumerate(OutputNames):
@@ -1433,11 +1608,12 @@ class MetaModelEngine():
LCerror[y_key][key])
ExploitScore = np.max(np.max(allModifiedLOO, axis=1), axis=1)
+ # print(allModifiedLOO.shape)
elif UtilMethod in ['EIGF', 'ALM']:
# ----- All other in ['EIGF', 'ALM'] -----
# Initilize the ExploitScore array
- ExploitScore = np.zeros((len(old_EDX), len(OutputNames)))
+ # ExploitScore = np.zeros((len(old_EDX), len(OutputNames)))
# Split the candidates in groups for multiprocessing
if explore_method != 'Voronoi':
@@ -1466,14 +1642,6 @@ class MetaModelEngine():
# Close the pool
pool.close()
- # out = map(self.run_util_func,
- # [exploit_method]*len(goodSampleIdx),
- # split_cand,
- # range(len(goodSampleIdx)),
- # [sigma2] * len(goodSampleIdx),
- # [var] * len(goodSampleIdx)
- # )
- # results = list(out)
# Retrieve the results and append them
if explore_method == 'Voronoi':
@@ -1495,6 +1663,9 @@ class MetaModelEngine():
# Normalize U_J_d
ExploitScore = ExploitScore / np.sum(ExploitScore)
totalScore = exploit_w * ExploitScore
+ # print(totalScore.shape)
+ # print(explore_w)
+ # print(scoreExploration.shape)
totalScore += explore_w * scoreExploration
temp = totalScore.copy()
@@ -1516,7 +1687,7 @@ class MetaModelEngine():
# select the requested number of samples
Xnew[i] = newSamples[np.argmax(maxminScore)]
- elif exploit_method == 'alphabetic':
+ elif exploit_method.lower() == 'alphabetic':
# ------- EXPLOITATION: ALPHABETIC -------
Xnew = self.util_AlphOptDesign(allCandidates, var)
@@ -1538,9 +1709,10 @@ class MetaModelEngine():
raise NameError('The requested design method is not available.')
print("\n")
- print("\nRun No. {}:".format(old_EDX.shape[0]+1))
+ print("\nRun No. {}:".format(old_EDX.shape[0] + 1))
print("Xnew:\n", Xnew)
+ # TODO: why does it also return None?
return Xnew, None
# -------------------------------------------------------------------------
@@ -1555,7 +1727,7 @@ class MetaModelEngine():
Arguments
---------
- NCandidate : int
+ candidates : int?
Number of candidate points to be searched
var : string
@@ -1566,40 +1738,41 @@ class MetaModelEngine():
X_new : array of shape (1, n_params)
The new sampling location in the input space.
"""
- MetaModelOrig = self
- Model = self.Model
+ MetaModelOrig = self # TODO: this doesn't fully seem correct?
n_new_samples = MetaModelOrig.ExpDesign.n_new_samples
NCandidate = candidates.shape[0]
# TODO: Loop over outputs
- OutputName = Model.Output.names[0]
+ OutputName = self.out_names[0]
# To avoid changes ub original aPCE object
- MetaModel = deepcopy(MetaModelOrig)
+ # MetaModel = deepcopy(MetaModelOrig)
# Old Experimental design
- oldExpDesignX = MetaModel.ExpDesign.X
+ oldExpDesignX = self.ExpDesign.X
# TODO: Only one psi can be selected.
# Suggestion: Go for the one with the highest LOO error
- Scores = list(MetaModel.score_dict[OutputName].values())
- ModifiedLOO = [1-score for score in Scores]
+ # TODO: this is just a patch, need to look at again!
+ Scores = list(self.MetaModel.score_dict['b_1'][OutputName].values())
+ ModifiedLOO = [1 - score for score in Scores]
outIdx = np.argmax(ModifiedLOO)
# Initialize Phi to save the criterion's values
- Phi = np.zeros((NCandidate))
+ Phi = np.zeros(NCandidate)
- BasisIndices = MetaModelOrig.basis_dict[OutputName]["y_"+str(outIdx+1)]
+ # TODO: also patched here
+ BasisIndices = self.MetaModel.basis_dict['b_1'][OutputName]["y_" + str(outIdx + 1)]
P = len(BasisIndices)
# ------ Old Psi ------------
- univ_p_val = MetaModelOrig.univ_basis_vals(oldExpDesignX)
- Psi = MetaModelOrig.create_psi(BasisIndices, univ_p_val)
+ univ_p_val = self.MetaModel.univ_basis_vals(oldExpDesignX)
+ Psi = self.MetaModel.create_psi(BasisIndices, univ_p_val)
# ------ New candidates (Psi_c) ------------
# Assemble Psi_c
- univ_p_val_c = self.univ_basis_vals(candidates)
- Psi_c = self.create_psi(BasisIndices, univ_p_val_c)
+ univ_p_val_c = self.MetaModel.univ_basis_vals(candidates)
+ Psi_c = self.MetaModel.create_psi(BasisIndices, univ_p_val_c)
for idx in range(NCandidate):
@@ -1608,10 +1781,9 @@ class MetaModelEngine():
# Information matrix
PsiTPsi = np.dot(Psi_cand.T, Psi_cand)
- M = PsiTPsi / (len(oldExpDesignX)+1)
+ M = PsiTPsi / (len(oldExpDesignX) + 1)
- if np.linalg.cond(PsiTPsi) > 1e-12 \
- and np.linalg.cond(PsiTPsi) < 1 / sys.float_info.epsilon:
+ if 1e-12 < np.linalg.cond(PsiTPsi) < 1 / sys.float_info.epsilon:
# faster
invM = linalg.solve(M, sparse.eye(PsiTPsi.shape[0]).toarray())
else:
@@ -1622,20 +1794,21 @@ class MetaModelEngine():
# Optimality criteria according to Section 4.5.1 in Ref.
# D-Opt
- if var == 'D-Opt':
- Phi[idx] = (np.linalg.det(invM)) ** (1/P)
+ if var.lower() == 'd-opt':
+ Phi[idx] = (np.linalg.det(invM)) ** (1 / P)
# A-Opt
- elif var == 'A-Opt':
+ elif var.lower() == 'a-opt':
Phi[idx] = np.trace(invM)
# K-Opt
- elif var == 'K-Opt':
+ elif var.lower() == 'k-opt':
Phi[idx] = np.linalg.cond(M)
else:
+ # print(var.lower())
raise Exception('The optimality criterion you requested has '
- 'not been implemented yet!')
+ 'not been implemented yet!')
# find an optimal point subset to add to the initial design
# by minimization of the Phi
@@ -1647,16 +1820,40 @@ class MetaModelEngine():
return Xnew
# -------------------------------------------------------------------------
- def __normpdf(self, y_hat_pce, std_pce, obs_data, total_sigma2s,
- rmse=None):
+ def _normpdf(self, y_hat_pce, std_pce, obs_data, total_sigma2s,
+ rmse=None):
+ """
+ Calculated gaussian likelihood for given y+std based on given obs+sigma
+ # TODO: is this understanding correct?
+
+ Parameters
+ ----------
+ y_hat_pce : dict of 2d np arrays
+ Mean output of the surrogate.
+ std_pce : dict of 2d np arrays
+ Standard deviation output of the surrogate.
+ obs_data : dict of 1d np arrays
+ Observed data.
+ total_sigma2s : pandas dataframe, matches obs_data
+ Estimated uncertainty for the observed data.
+ rmse : dict, optional
+ RMSE values from validation of the surrogate. The default is None.
+
+ Returns
+ -------
+ likelihoods : dict of float
+ The likelihood for each surrogate eval in y_hat_pce compared to the
+ observations (?).
+
+ """
- Model = self.Model
likelihoods = 1.0
# Loop over the outputs
- for idx, out in enumerate(Model.Output.names):
+ for idx, out in enumerate(self.out_names):
# (Meta)Model Output
+ # print(y_hat_pce[out])
nsamples, nout = y_hat_pce[out].shape
# Prepare data and remove NaN
@@ -1671,37 +1868,37 @@ class MetaModelEngine():
# Surrogate error if valid dataset is given.
if rmse is not None:
- tot_sigma2s += rmse[out]**2
+ tot_sigma2s += rmse[out] ** 2
else:
- tot_sigma2s += np.mean(std_pce[out])**2
+ tot_sigma2s += np.mean(std_pce[out]) ** 2
likelihoods *= stats.multivariate_normal.pdf(
y_hat_pce[out], data, np.diag(tot_sigma2s),
allow_singular=True)
+ # TODO: remove this here
self.Likelihoods = likelihoods
return likelihoods
# -------------------------------------------------------------------------
- def __corr_factor_BME(self, obs_data, total_sigma2s, logBME):
+ def _corr_factor_BME(self, obs_data, total_sigma2s, logBME):
"""
Calculates the correction factor for BMEs.
"""
MetaModel = self.MetaModel
- samples = MetaModel.ExpDesign.X # valid_samples
- model_outputs = MetaModel.ExpDesign.Y # valid_model_runs
- Model = MetaModel.ModelObj
+ samples = self.ExpDesign.X # valid_samples
+ model_outputs = self.ExpDesign.Y # valid_model_runs
n_samples = samples.shape[0]
# Extract the requested model outputs for likelihood calulation
- output_names = Model.Output.names
+ output_names = self.out_names
# TODO: Evaluate MetaModel on the experimental design and ValidSet
OutputRS, stdOutputRS = MetaModel.eval_metamodel(samples=samples)
- logLik_data = np.zeros((n_samples))
- logLik_model = np.zeros((n_samples))
+ logLik_data = np.zeros(n_samples)
+ logLik_model = np.zeros(n_samples)
# Loop over the outputs
for idx, out in enumerate(output_names):
@@ -1722,7 +1919,6 @@ class MetaModelEngine():
covMatrix_data = np.diag(tot_sigma2s)
for i, sample in enumerate(samples):
-
# Simulation run
y_m = model_outputs[out][i]
@@ -1731,62 +1927,51 @@ class MetaModelEngine():
# CovMatrix with the surrogate error
# covMatrix = np.diag(stdOutputRS[out][i]**2)
- covMatrix = np.diag((y_m-y_m_hat)**2)
+ # covMatrix = np.diag((y_m - y_m_hat) ** 2)
covMatrix = np.diag(
- np.mean((model_outputs[out]-OutputRS[out]), axis=0)**2
- )
+ np.mean((model_outputs[out] - OutputRS[out]), axis=0) ** 2
+ )
# Compute likelilhood output vs data
- logLik_data[i] += self.__logpdf(
+ logLik_data[i] += logpdf(
y_m_hat, data, covMatrix_data
- )
+ )
# Compute likelilhood output vs surrogate
- logLik_model[i] += self.__logpdf(y_m_hat, y_m, covMatrix)
+ logLik_model[i] += logpdf(y_m_hat, y_m, covMatrix)
# Weight
logLik_data -= logBME
- weights = np.exp(logLik_model+logLik_data)
+ weights = np.exp(logLik_model + logLik_data)
return np.log(np.mean(weights))
# -------------------------------------------------------------------------
- def __logpdf(self, x, mean, cov):
+ def _posteriorPlot(self, posterior, par_names, key):
"""
- computes the likelihood based on a multivariate normal distribution.
+ Plot the posterior of a specific key as a corner plot
Parameters
----------
- x : TYPE
- DESCRIPTION.
- mean : array_like
- Observation data.
- cov : 2d array
- Covariance matrix of the distribution.
+ posterior : 2d np.array
+ Samples of the posterior.
+ par_names : list of strings
+ List of the parameter names.
+ key : string
+ Output key that this posterior belongs to.
Returns
-------
- log_lik : float
- Log likelihood.
+ figPosterior : corner.corner
+ Plot of the posterior.
"""
- n = len(mean)
- L = linalg.cholesky(cov, lower=True)
- beta = np.sum(np.log(np.diag(L)))
- dev = x - mean
- alpha = dev.dot(linalg.cho_solve((L, True), dev))
- log_lik = -0.5 * alpha - beta - n / 2. * np.log(2 * np.pi)
-
- return log_lik
-
- # -------------------------------------------------------------------------
- def __posteriorPlot(self, posterior, par_names, key):
# Initialization
- newpath = (r'Outputs_SeqPosteriorComparison/posterior')
+ newpath = r'Outputs_SeqPosteriorComparison/posterior'
os.makedirs(newpath, exist_ok=True)
- bound_tuples = self.MetaModel.bound_tuples
+ bound_tuples = self.ExpDesign.bound_tuples
n_params = len(par_names)
font_size = 40
if n_params == 2:
@@ -1826,57 +2011,36 @@ class MetaModelEngine():
return figPosterior
# -------------------------------------------------------------------------
- def __hellinger_distance(self, P, Q):
+ def _BME_Calculator(self, obs_data, sigma2Dict, rmse=None):
"""
- Hellinger distance between two continuous distributions.
-
- The maximum distance 1 is achieved when P assigns probability zero to
- every set to which Q assigns a positive probability, and vice versa.
- 0 (identical) and 1 (maximally different)
+ This function computes the Bayesian model evidence (BME) via Monte
+ Carlo integration.
Parameters
----------
- P : array
- Reference likelihood.
- Q : array
- Estimated likelihood.
+ obs_data : dict of 1d np arrays
+ Observed data.
+ sigma2Dict : pandas dataframe, matches obs_data
+ Estimated uncertainty for the observed data.
+ rmse : dict of floats, optional
+ RMSE values for each output-key. The dafault is None.
Returns
-------
- float
- Hellinger distance of two distributions.
-
- """
- mu1 = P.mean()
- Sigma1 = np.std(P)
-
- mu2 = Q.mean()
- Sigma2 = np.std(Q)
-
- term1 = np.sqrt(2*Sigma1*Sigma2 / (Sigma1**2 + Sigma2**2))
-
- term2 = np.exp(-.25 * (mu1 - mu2)**2 / (Sigma1**2 + Sigma2**2))
-
- H_squared = 1 - term1 * term2
-
- return np.sqrt(H_squared)
-
- # -------------------------------------------------------------------------
- def __BME_Calculator(self, MetaModel, obs_data, sigma2Dict, rmse=None):
- """
- This function computes the Bayesian model evidence (BME) via Monte
- Carlo integration.
-
+ (logBME, KLD, X_Posterior, Likelihoods, distHellinger)
+
"""
# Initializations
- if hasattr(MetaModel, 'valid_likelihoods'):
- valid_likelihoods = MetaModel.valid_likelihoods
+ # TODO: this just does not make sense, recheck from old commits
+ if self.valid_likelihoods is not None:
+ valid_likelihoods = self.valid_likelihoods
else:
valid_likelihoods = []
+ valid_likelihoods = np.array(valid_likelihoods)
- post_snapshot = MetaModel.ExpDesign.post_snapshot
- if post_snapshot or len(valid_likelihoods) != 0:
- newpath = (r'Outputs_SeqPosteriorComparison/likelihood_vs_ref')
+ post_snapshot = self.ExpDesign.post_snapshot
+ if post_snapshot or valid_likelihoods.shape[0] != 0:
+ newpath = r'Outputs_SeqPosteriorComparison/likelihood_vs_ref'
os.makedirs(newpath, exist_ok=True)
SamplingMethod = 'random'
@@ -1887,21 +2051,21 @@ class MetaModelEngine():
while (ESS > MCsize) or (ESS < 1):
# Generate samples for Monte Carlo simulation
- X_MC = MetaModel.ExpDesign.generate_samples(
+ X_MC = self.ExpDesign.generate_samples(
MCsize, SamplingMethod
- )
+ )
# Monte Carlo simulation for the candidate design
- Y_MC, std_MC = MetaModel.eval_metamodel(samples=X_MC)
+ Y_MC, std_MC = self.MetaModel.eval_metamodel(samples=X_MC)
# Likelihood computation (Comparison of data and
# simulation results via PCE with candidate design)
- Likelihoods = self.__normpdf(
+ Likelihoods = self._normpdf(
Y_MC, std_MC, obs_data, sigma2Dict, rmse
- )
+ )
# Check the Effective Sample Size (1000<ESS<MCsize)
- ESS = 1 / np.sum(np.square(Likelihoods/np.sum(Likelihoods)))
+ ESS = 1 / np.sum(np.square(Likelihoods / np.sum(Likelihoods)))
# Enlarge sample size if it doesn't fulfill the criteria
if (ESS > MCsize) or (ESS < 1):
@@ -1914,7 +2078,7 @@ class MetaModelEngine():
unif = np.random.rand(1, MCsize)[0]
# Reject the poorly performed prior
- accepted = (Likelihoods/np.max(Likelihoods)) >= unif
+ accepted = (Likelihoods / np.max(Likelihoods)) >= unif
X_Posterior = X_MC[accepted]
# ------------------------------------------------------------
@@ -1930,26 +2094,29 @@ class MetaModelEngine():
postExpLikelihoods = np.mean(np.log(Likelihoods[accepted]))
# Posterior-based expectation of prior densities
- postExpPrior = np.mean(
- np.log(MetaModel.ExpDesign.JDist.pdf(X_Posterior.T))
- )
+ # TODO: this is commented out, as it is not used again
+ # postExpPrior = np.mean(
+ # np.log(self.ExpDesign.JDist.pdf(X_Posterior.T))
+ # )
# Calculate Kullback-Leibler Divergence
# KLD = np.mean(np.log(Likelihoods[Likelihoods!=0])- logBME)
KLD = postExpLikelihoods - logBME
# Information Entropy based on Entropy paper Eq. 38
- infEntropy = logBME - postExpPrior - postExpLikelihoods
+ # infEntropy = logBME - postExpPrior - postExpLikelihoods
# If post_snapshot is True, plot likelihood vs refrence
if post_snapshot or valid_likelihoods:
# Hellinger distance
- ref_like = np.log(valid_likelihoods[valid_likelihoods > 0])
+ valid_likelihoods = np.array(valid_likelihoods)
+ ref_like = np.log(valid_likelihoods[(valid_likelihoods > 0)])
est_like = np.log(Likelihoods[Likelihoods > 0])
- distHellinger = self.__hellinger_distance(ref_like, est_like)
+ distHellinger = hellinger_distance(ref_like, est_like)
idx = len([name for name in os.listdir(newpath) if 'Likelihoods_'
in name and os.path.isfile(os.path.join(newpath, name))])
+
fig, ax = plt.subplots()
try:
sns.kdeplot(np.log(valid_likelihoods[valid_likelihoods > 0]),
@@ -1975,11 +2142,9 @@ class MetaModelEngine():
distHellinger = 0.0
# Bayesian inference with Emulator only for 2D problem
- if post_snapshot and MetaModel.n_params == 2 and not idx % 5:
- from bayes_inference.bayes_inference import BayesInference
- from bayes_inference.discrepancy import Discrepancy
- import pandas as pd
- BayesOpts = BayesInference(MetaModel)
+ if post_snapshot and self.MetaModel.n_params == 2 and not idx % 5:
+ BayesOpts = BayesInference(self)
+
BayesOpts.emulator = True
BayesOpts.plot_post_pred = False
@@ -1992,155 +2157,95 @@ class MetaModelEngine():
'n_walkers': 30,
'moves': emcee.moves.KDEMove(),
'verbose': False
- }
+ }
# ----- Define the discrepancy model -------
- obs_data = pd.DataFrame(obs_data, columns=self.Model.Output.names)
+ # TODO: check with Farid if this first line is how it should be
+ BayesOpts.measured_data = obs_data
+ obs_data = pd.DataFrame(obs_data, columns=self.out_names)
BayesOpts.measurement_error = obs_data
+ # TODO: shouldn't the uncertainty be sigma2Dict instead of obs_data?
# # -- (Option B) --
DiscrepancyOpts = Discrepancy('')
DiscrepancyOpts.type = 'Gaussian'
- DiscrepancyOpts.parameters = obs_data**2
+ DiscrepancyOpts.parameters = obs_data ** 2
BayesOpts.Discrepancy = DiscrepancyOpts
# Start the calibration/inference
Bayes_PCE = BayesOpts.create_inference()
X_Posterior = Bayes_PCE.posterior_df.values
- return (logBME, KLD, X_Posterior, Likelihoods, distHellinger)
+ return logBME, KLD, X_Posterior, Likelihoods, distHellinger
# -------------------------------------------------------------------------
- def __validError(self, MetaModel):
+ def _validError(self):
+ """
+ Evaluate the metamodel on the validation samples and calculate the
+ error against the corresponding model runs
- # MetaModel = self.MetaModel
- Model = MetaModel.ModelObj
- OutputName = Model.Output.names
+ Returns
+ -------
+ rms_error : dict
+ RMSE for each validation run.
+ valid_error : dict
+ Normed (?)RMSE for each validation run.
+ """
# Extract the original model with the generated samples
- valid_samples = MetaModel.valid_samples
- valid_model_runs = MetaModel.valid_model_runs
+ valid_model_runs = self.ExpDesign.valid_model_runs
# Run the PCE model with the generated samples
- valid_PCE_runs, _ = MetaModel.eval_metamodel(samples=valid_samples)
+ valid_PCE_runs, _ = self.MetaModel.eval_metamodel(samples=self.ExpDesign.valid_samples)
rms_error = {}
valid_error = {}
# Loop over the keys and compute RMSE error.
- for key in OutputName:
+ for key in self.out_names:
rms_error[key] = mean_squared_error(
valid_model_runs[key], valid_PCE_runs[key],
multioutput='raw_values',
sample_weight=None,
squared=False)
# Validation error
- valid_error[key] = (rms_error[key]**2)
+ valid_error[key] = (rms_error[key] ** 2)
valid_error[key] /= np.var(valid_model_runs[key], ddof=1, axis=0)
# Print a report table
print("\n>>>>> Updated Errors of {} <<<<<".format(key))
print("\nIndex | RMSE | Validation Error")
- print('-'*35)
- print('\n'.join(f'{i+1} | {k:.3e} | {j:.3e}' for i, (k, j)
+ print('-' * 35)
+ print('\n'.join(f'{i + 1} | {k:.3e} | {j:.3e}' for i, (k, j)
in enumerate(zip(rms_error[key],
valid_error[key]))))
return rms_error, valid_error
# -------------------------------------------------------------------------
- def __error_Mean_Std(self):
+ def _error_Mean_Std(self):
+ """
+ Calculates the error in the overall mean and std approximation of the
+ surrogate against the mc-reference provided to the model.
+ This can only be applied to metamodels of polynomial type
- MetaModel = self.MetaModel
- # Extract the mean and std provided by user
- df_MCReference = MetaModel.ModelObj.mc_reference
+ Returns
+ -------
+ RMSE_Mean : float
+ RMSE of the means
+ RMSE_std : float
+ RMSE of the standard deviations
+ """
# Compute the mean and std based on the MetaModel
- pce_means, pce_stds = self._compute_pce_moments(MetaModel)
+ pce_means, pce_stds = self.MetaModel._compute_pce_moments()
# Compute the root mean squared error
- for output in MetaModel.ModelObj.Output.names:
-
+ for output in self.out_names:
# Compute the error between mean and std of MetaModel and OrigModel
RMSE_Mean = mean_squared_error(
- df_MCReference['mean'], pce_means[output], squared=False
- )
+ self.Model.mc_reference['mean'], pce_means[output], squared=False
+ )
RMSE_std = mean_squared_error(
- df_MCReference['std'], pce_means[output], squared=False
- )
+ self.Model.mc_reference['std'], pce_stds[output], squared=False
+ )
return RMSE_Mean, RMSE_std
-
- # -------------------------------------------------------------------------
- def _compute_pce_moments(self, MetaModel):
- """
- Computes the first two moments using the PCE-based meta-model.
-
- Returns
- -------
- pce_means: dict
- The first moment (mean) of the surrogate.
- pce_stds: dict
- The second moment (standard deviation) of the surrogate.
-
- """
- outputs = MetaModel.ModelObj.Output.names
- pce_means_b = {}
- pce_stds_b = {}
-
- # Loop over bootstrap iterations
- for b_i in range(MetaModel.n_bootstrap_itrs):
- # Loop over the metamodels
- coeffs_dicts = MetaModel.coeffs_dict[f'b_{b_i+1}'].items()
- means = {}
- stds = {}
- for output, coef_dict in coeffs_dicts:
-
- pce_mean = np.zeros((len(coef_dict)))
- pce_var = np.zeros((len(coef_dict)))
-
- for index, values in coef_dict.items():
- idx = int(index.split('_')[1]) - 1
- coeffs = MetaModel.coeffs_dict[f'b_{b_i+1}'][output][index]
-
- # Mean = c_0
- if coeffs[0] != 0:
- pce_mean[idx] = coeffs[0]
- else:
- clf_poly = MetaModel.clf_poly[f'b_{b_i+1}'][output]
- pce_mean[idx] = clf_poly[index].intercept_
- # Var = sum(coeffs[1:]**2)
- pce_var[idx] = np.sum(np.square(coeffs[1:]))
-
- # Save predictions for each output
- if MetaModel.dim_red_method.lower() == 'pca':
- PCA = MetaModel.pca[f'b_{b_i+1}'][output]
- means[output] = PCA.inverse_transform(pce_mean)
- stds[output] = PCA.inverse_transform(np.sqrt(pce_var))
- else:
- means[output] = pce_mean
- stds[output] = np.sqrt(pce_var)
-
- # Save predictions for each bootstrap iteration
- pce_means_b[b_i] = means
- pce_stds_b[b_i] = stds
-
- # Change the order of nesting
- mean_all = {}
- for i in sorted(pce_means_b):
- for k, v in pce_means_b[i].items():
- if k not in mean_all:
- mean_all[k] = [None] * len(pce_means_b)
- mean_all[k][i] = v
- std_all = {}
- for i in sorted(pce_stds_b):
- for k, v in pce_stds_b[i].items():
- if k not in std_all:
- std_all[k] = [None] * len(pce_stds_b)
- std_all[k][i] = v
-
- # Back transformation if PCA is selected.
- pce_means, pce_stds = {}, {}
- for output in outputs:
- pce_means[output] = np.mean(mean_all[output], axis=0)
- pce_stds[output] = np.mean(std_all[output], axis=0)
-
- return pce_means, pce_stds
diff --git a/src/bayesvalidrox/surrogate_models/exp_designs.py b/src/bayesvalidrox/surrogate_models/exp_designs.py
index a078aec9c19c5a85a637ba50d02c48459ceea6d3..ce1745903c776049c797e5585b60d45463d93325 100644
--- a/src/bayesvalidrox/surrogate_models/exp_designs.py
+++ b/src/bayesvalidrox/surrogate_models/exp_designs.py
@@ -1,31 +1,61 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
+"""
+Experimental design with associated sampling methods
+"""
-import numpy as np
-import math
import itertools
+import math
+
import chaospy
-import scipy.stats as st
-from tqdm import tqdm
+import h5py
+import numpy as np
from .apoly_construction import apoly_construction
+from .input_space import InputSpace
-class ExpDesigns:
+# -------------------------------------------------------------------------
+def check_ranges(theta, ranges):
+ """
+ This function checks if theta lies in the given ranges.
+
+ Parameters
+ ----------
+ theta : array
+ Proposed parameter set.
+ ranges : nested list
+ The parameter ranges.
+
+ Returns
+ -------
+ c : bool
+ If it lies in the given range, it returns True else False.
+
+ """
+ c = True
+ # traverse in the list1
+ for i, bounds in enumerate(ranges):
+ x = theta[i]
+ # condition check
+ if x < bounds[0] or x > bounds[1]:
+ c = False
+ return c
+ return c
+
+
+class ExpDesigns(InputSpace):
"""
This class generates samples from the prescribed marginals for the model
parameters using the `Input` object.
Attributes
----------
- Input : obj
+ input_object : obj
Input object containing the parameter marginals, i.e. name,
distribution type and distribution parameters or available raw data.
- method : str
- Type of the experimental design. The default is `'normal'`. Other
- option is `'sequential'`.
- meta_Model : str
- Type of the meta_model.
+ meta_Model_type : str
+ Type of the meta_Model_type.
sampling_method : str
Name of the sampling method for the experimental design. The following
sampling method are supported:
@@ -112,17 +142,18 @@ class ExpDesigns:
- K-Opt (K-Optimality)
"""
- def __init__(self, Input, method='normal', meta_Model='pce',
- sampling_method='random', hdf5_file=None,
- n_new_samples=1, n_max_samples=None, mod_LOO_threshold=1e-16,
- tradeoff_scheme=None, n_canddidate=1, explore_method='random',
- exploit_method='Space-filling', util_func='Space-filling',
- n_cand_groups=4, n_replication=1, post_snapshot=False,
- step_snapshot=1, max_a_post=[], adapt_verbose=False):
-
- self.InputObj = Input
- self.method = method
- self.meta_Model = meta_Model
+ def __init__(self, input_object, meta_Model_type='pce', sampling_method='random', hdf5_file=None,
+ n_new_samples=1, n_max_samples=None, mod_LOO_threshold=1e-16, tradeoff_scheme=None, n_canddidate=1,
+ explore_method='random', exploit_method='Space-filling', util_func='Space-filling', n_cand_groups=4,
+ n_replication=1, post_snapshot=False, step_snapshot=1, max_a_post=None, adapt_verbose=False,
+ max_func_itr=1):
+
+ super().__init__(input_object, meta_Model_type)
+ if max_a_post is None:
+ max_a_post = []
+
+ self.InputObj = input_object
+ self.meta_Model_type = meta_Model_type
self.sampling_method = sampling_method
self.hdf5_file = hdf5_file
self.n_new_samples = n_new_samples
@@ -139,10 +170,21 @@ class ExpDesigns:
self.step_snapshot = step_snapshot
self.max_a_post = max_a_post
self.adapt_verbose = adapt_verbose
+ self.max_func_itr = max_func_itr
+
+ # Other
+ self.apce = None
+ self.n_init_samples = None
+ self.n_samples = None
+ self.ndim = None
+ self.X = None
+ self.Y = None
+
+ # Init
+ self.check_valid_inputs()
# -------------------------------------------------------------------------
- def generate_samples(self, n_samples, sampling_method='random',
- transform=False):
+ def generate_samples(self, n_samples, sampling_method='random'):
"""
Generates samples with given sampling method
@@ -152,9 +194,6 @@ class ExpDesigns:
Number of requested samples.
sampling_method : str, optional
Sampling method. The default is `'random'`.
- transform : bool, optional
- Transformation via an isoprobabilistic transformation method. The
- default is `False`.
Returns
-------
@@ -165,15 +204,14 @@ class ExpDesigns:
try:
samples = chaospy.generate_samples(
int(n_samples), domain=self.origJDist, rule=sampling_method
- )
+ )
except:
samples = self.random_sampler(int(n_samples)).T
return samples.T
# -------------------------------------------------------------------------
- def generate_ED(self, n_samples, sampling_method='random', transform=False,
- max_pce_deg=None):
+ def generate_ED(self, n_samples, max_pce_deg=None):
"""
Generates experimental designs (training set) with the given method.
@@ -181,49 +219,33 @@ class ExpDesigns:
----------
n_samples : int
Number of requested training points.
- sampling_method : str, optional
- Sampling method. The default is `'random'`.
- transform : bool, optional
- Isoprobabilistic transformation. The default is `False`.
max_pce_deg : int, optional
Maximum PCE polynomial degree. The default is `None`.
-
+
Returns
-------
- samples : array of shape (n_samples, n_params)
- Selected training samples.
+ None
"""
- Inputs = self.InputObj
- self.ndim = len(Inputs.Marginals)
- if not hasattr(self, 'n_init_samples'):
- self.n_init_samples = self.ndim + 1
+ if n_samples < 0:
+ raise ValueError('A negative number of samples cannot be created. Please provide positive n_samples')
n_samples = int(n_samples)
- # Check if PCE or aPCE metamodel is selected.
- if self.meta_Model.lower() == 'apce':
- self.apce = True
- else:
- self.apce = False
-
- # Check if input is given as dist or input_data.
- if len(Inputs.Marginals[0].input_data):
- self.input_data_given = True
- else:
- self.input_data_given = False
-
- # Get the bounds if input_data are directly defined by user:
- if self.input_data_given:
- for i in range(self.ndim):
- low_bound = np.min(Inputs.Marginals[i].input_data)
- up_bound = np.max(Inputs.Marginals[i].input_data)
- Inputs.Marginals[i].parameters = [low_bound, up_bound]
+ if self.n_init_samples is None:
+ self.n_init_samples = n_samples
# Generate the samples based on requested method
- self.raw_data, self.bound_tuples = self.init_param_space(max_pce_deg)
+ self.init_param_space(max_pce_deg)
+ samples = None
+ sampling_method = self.sampling_method
# Pass user-defined samples as ED
if sampling_method == 'user':
+ if self.X is None:
+ raise AttributeError('User-defined sampling cannot proceed as no samples provided. Please add them to '
+ 'this class as attribute X')
+ if not self.X.ndim == 2:
+ raise AttributeError('The provided samples shuld have 2 dimensions')
samples = self.X
self.n_samples = len(samples)
@@ -236,7 +258,7 @@ class ExpDesigns:
elif sampling_method == 'PCM' or \
sampling_method == 'LSCM':
- samples = self.pcm_sampler(max_pce_deg)
+ samples = self.pcm_sampler(n_samples, max_pce_deg)
else:
# Create ExpDesign in the actual space using chaospy
@@ -252,208 +274,62 @@ class ExpDesigns:
samples = chaospy.generate_samples(n_samples, domain=self.JDist,
rule=sampling_method).T
- # Transform samples to the original space
- if transform:
- tr_samples = self.transform(
- samples,
- method=sampling_method
- )
- if sampling_method == 'user' or not self.apce:
- return samples, tr_samples
- else:
- return tr_samples, samples
- else:
- return samples
+ self.X = samples
- # -------------------------------------------------------------------------
- def init_param_space(self, max_deg=None):
+ def read_from_file(self, out_names):
"""
- Initializes parameter space.
+ Reads in the ExpDesign from a provided h5py file and saves the results.
Parameters
----------
- max_deg : int, optional
- Maximum degree. The default is `None`.
+ out_names : list of strings
+ The keys that are in the outputs (y) saved in the provided file.
Returns
-------
- raw_data : array of shape (n_params, n_samples)
- Raw data.
- bound_tuples : list of tuples
- A list containing lower and upper bounds of parameters.
+ None.
"""
- Inputs = self.InputObj
- ndim = self.ndim
- rosenblatt_flag = Inputs.Rosenblatt
- mc_size = 50000
-
- # Save parameter names
- self.par_names = []
- for parIdx in range(ndim):
- self.par_names.append(Inputs.Marginals[parIdx].name)
-
- # Create a multivariate probability distribution
- if max_deg is not None:
- JDist, poly_types = self.build_dist(rosenblatt=rosenblatt_flag)
- self.JDist, self.poly_types = JDist, poly_types
-
- if self.input_data_given:
-
- self.MCSize = len(Inputs.Marginals[0].input_data)
- self.raw_data = np.zeros((ndim, self.MCSize))
-
- for parIdx in range(ndim):
- # Save parameter names
- try:
- self.raw_data[parIdx] = np.array(
- Inputs.Marginals[parIdx].input_data)
- except:
- self.raw_data[parIdx] = self.JDist[parIdx].sample(mc_size)
-
- else:
- # Generate random samples based on parameter distributions
- self.raw_data = chaospy.generate_samples(mc_size,
- domain=self.JDist)
-
- # Create orthogonal polynomial coefficients if necessary
- if self.apce and max_deg is not None and Inputs.poly_coeffs_flag:
- self.polycoeffs = {}
- for parIdx in tqdm(range(ndim), ascii=True,
- desc="Computing orth. polynomial coeffs"):
- poly_coeffs = apoly_construction(
- self.raw_data[parIdx],
- max_deg
- )
- self.polycoeffs[f'p_{parIdx+1}'] = poly_coeffs
-
- # Extract moments
- for parIdx in range(ndim):
- mu = np.mean(self.raw_data[parIdx])
- std = np.std(self.raw_data[parIdx])
- self.InputObj.Marginals[parIdx].moments = [mu, std]
-
- # Generate the bounds based on given inputs for marginals
- bound_tuples = []
- for i in range(ndim):
- if Inputs.Marginals[i].dist_type == 'unif':
- low_bound, up_bound = Inputs.Marginals[i].parameters
- else:
- low_bound = np.min(self.raw_data[i])
- up_bound = np.max(self.raw_data[i])
-
- bound_tuples.append((low_bound, up_bound))
-
- self.bound_tuples = tuple(bound_tuples)
+ if self.hdf5_file is None:
+ raise AttributeError('ExpDesign cannot be read in, please provide hdf5 file first')
- return self.raw_data, self.bound_tuples
+ # Read hdf5 file
+ f = h5py.File(self.hdf5_file, 'r+')
- # -------------------------------------------------------------------------
- def build_dist(self, rosenblatt):
- """
- Creates the polynomial types to be passed to univ_basis_vals method of
- the MetaModel object.
-
- Parameters
- ----------
- rosenblatt : bool
- Rosenblatt transformation flag.
+ # Read EDX and pass it to ExpDesign object
+ try:
+ self.X = np.array(f["EDX/New_init_"])
+ except KeyError:
+ self.X = np.array(f["EDX/init_"])
- Returns
- -------
- orig_space_dist : object
- A chaospy JDist object or a gaussian_kde object.
- poly_types : list
- List of polynomial types for the parameters.
+ # Update number of initial samples
+ self.n_init_samples = self.X.shape[0]
- """
- Inputs = self.InputObj
- all_data = []
- all_dist_types = []
- orig_joints = []
- poly_types = []
-
- for parIdx in range(self.ndim):
-
- if Inputs.Marginals[parIdx].dist_type is None:
- data = Inputs.Marginals[parIdx].input_data
- all_data.append(data)
- dist_type = None
- else:
- dist_type = Inputs.Marginals[parIdx].dist_type
- params = Inputs.Marginals[parIdx].parameters
-
- if rosenblatt:
- polytype = 'hermite'
- dist = chaospy.Normal()
-
- elif dist_type is None:
- polytype = 'arbitrary'
- dist = None
-
- elif 'unif' in dist_type.lower():
- polytype = 'legendre'
- dist = chaospy.Uniform(lower=params[0], upper=params[1])
-
- elif 'norm' in dist_type.lower() and \
- 'log' not in dist_type.lower():
- polytype = 'hermite'
- dist = chaospy.Normal(mu=params[0], sigma=params[1])
-
- elif 'gamma' in dist_type.lower():
- polytype = 'laguerre'
- dist = chaospy.Gamma(shape=params[0],
- scale=params[1],
- shift=params[2])
-
- elif 'beta' in dist_type.lower():
- polytype = 'jacobi'
- dist = chaospy.Beta(alpha=params[0], beta=params[1],
- lower=params[2], upper=params[3])
-
- elif 'lognorm' in dist_type.lower():
- polytype = 'hermite'
- mu = np.log(params[0]**2/np.sqrt(params[0]**2 + params[1]**2))
- sigma = np.sqrt(np.log(1 + params[1]**2 / params[0]**2))
- dist = chaospy.LogNormal(mu, sigma)
- # dist = chaospy.LogNormal(mu=params[0], sigma=params[1])
-
- elif 'expon' in dist_type.lower():
- polytype = 'arbitrary'
- dist = chaospy.Exponential(scale=params[0], shift=params[1])
-
- elif 'weibull' in dist_type.lower():
- polytype = 'arbitrary'
- dist = chaospy.Weibull(shape=params[0], scale=params[1],
- shift=params[2])
+ # Read EDX and pass it to ExpDesign object
+ self.Y = {}
- else:
- message = (f"DistType {dist_type} for parameter"
- f"{parIdx+1} is not available.")
- raise ValueError(message)
-
- if self.input_data_given or self.apce:
- polytype = 'arbitrary'
-
- # Store dists and poly_types
- orig_joints.append(dist)
- poly_types.append(polytype)
- all_dist_types.append(dist_type)
-
- # Prepare final output to return
- if None in all_dist_types:
- # Naive approach: Fit a gaussian kernel to the provided data
- Data = np.asarray(all_data)
- orig_space_dist = st.gaussian_kde(Data)
- self.prior_space = orig_space_dist
- else:
- orig_space_dist = chaospy.J(*orig_joints)
- self.prior_space = st.gaussian_kde(orig_space_dist.sample(10000))
-
- return orig_space_dist, poly_types
+ # Extract x values
+ try:
+ self.Y["x_values"] = dict()
+ for varIdx, var in enumerate(out_names):
+ x = np.array(f[f"x_values/{var}"])
+ self.Y["x_values"][var] = x
+ except KeyError:
+ self.Y["x_values"] = np.array(f["x_values"])
+
+ # Store the output
+ for varIdx, var in enumerate(out_names):
+ try:
+ y = np.array(f[f"EDY/{var}/New_init_"])
+ except KeyError:
+ y = np.array(f[f"EDY/{var}/init_"])
+ self.Y[var] = y
+ f.close()
+ print(f'Experimental Design is read in from file {self.hdf5_file}')
+ print('')
# -------------------------------------------------------------------------
- def random_sampler(self, n_samples):
+ def random_sampler(self, n_samples, max_deg=None):
"""
Samples the given raw data randomly.
@@ -461,6 +337,11 @@ class ExpDesigns:
----------
n_samples : int
Number of requested samples.
+
+ max_deg : int, optional
+ Maximum degree. The default is `None`.
+ This will be used to run init_param_space, if it has not been done
+ until now.
Returns
-------
@@ -468,6 +349,11 @@ class ExpDesigns:
The sampling locations in the input space.
"""
+ if self.raw_data is None:
+ self.init_param_space(max_deg)
+ else:
+ if np.array(self.raw_data).ndim != 2:
+ raise AttributeError('The given raw data for sampling should have two dimensions')
samples = np.zeros((n_samples, self.ndim))
sample_size = self.raw_data.shape[1]
@@ -479,20 +365,28 @@ class ExpDesigns:
# store the raw data with given random indices
samples[:, pa_idx] = self.raw_data[pa_idx, rand_idx]
else:
+ if self.JDist is None:
+ raise AttributeError('Sampling cannot proceed, build ExpDesign with max_deg != 0 to create JDist!')
try:
+ # Use resample if JDist is of type gaussian_kde
samples = self.JDist.resample(int(n_samples)).T
except AttributeError:
+ # Use sample if JDist is of type chaospy.J
samples = self.JDist.sample(int(n_samples)).T
+ # If there is only one input transform the samples
+ if self.ndim == 1:
+ samples = np.swapaxes(np.atleast_2d(samples), 0, 1)
+
# Check if all samples are in the bound_tuples
for idx, param_set in enumerate(samples):
- if not self._check_ranges(param_set, self.bound_tuples):
+ if not check_ranges(param_set, self.bound_tuples):
try:
proposed_sample = chaospy.generate_samples(
1, domain=self.JDist, rule='random').T[0]
except:
proposed_sample = self.JDist.resample(1).T[0]
- while not self._check_ranges(proposed_sample,
- self.bound_tuples):
+ while not check_ranges(proposed_sample,
+ self.bound_tuples):
try:
proposed_sample = chaospy.generate_samples(
1, domain=self.JDist, rule='random').T[0]
@@ -503,15 +397,18 @@ class ExpDesigns:
return samples
# -------------------------------------------------------------------------
- def pcm_sampler(self, max_deg):
+ def pcm_sampler(self, n_samples, max_deg):
"""
Generates collocation points based on the root of the polynomial
degrees.
Parameters
----------
+ n_samples : int
+ Number of requested samples.
max_deg : int
- Maximum degree defined by user.
+ Maximum degree defined by user. Will also be used to run
+ init_param_space if that has not been done beforehand.
Returns
-------
@@ -520,40 +417,64 @@ class ExpDesigns:
"""
+ if self.raw_data is None:
+ self.init_param_space(max_deg)
+
raw_data = self.raw_data
# Guess the closest degree to self.n_samples
def M_uptoMax(deg):
+ """
+ ??
+ Parameters
+ ----------
+ deg : int
+ Degree.
+
+ Returns
+ -------
+ list of ..?
+ """
result = []
- for d in range(1, deg+1):
- result.append(math.factorial(self.ndim+d) //
+ for d in range(1, deg + 1):
+ result.append(math.factorial(self.ndim + d) //
(math.factorial(self.ndim) * math.factorial(d)))
return np.array(result)
- guess_Deg = np.where(M_uptoMax(max_deg) > self.n_samples)[0][0]
+ guess_Deg = np.where(M_uptoMax(max_deg) > n_samples)[0][0]
- c_points = np.zeros((guess_Deg+1, self.ndim))
+ c_points = np.zeros((guess_Deg + 1, self.ndim))
def PolynomialPa(parIdx):
+ """
+ ???
+ Parameters
+ ----------
+ parIdx
+
+ Returns
+ -------
+
+ """
return apoly_construction(self.raw_data[parIdx], max_deg)
for i in range(self.ndim):
- poly_coeffs = PolynomialPa(i)[guess_Deg+1][::-1]
+ poly_coeffs = PolynomialPa(i)[guess_Deg + 1][::-1]
c_points[:, i] = np.trim_zeros(np.roots(poly_coeffs))
# Construction of optimal integration points
- Prod = itertools.product(np.arange(1, guess_Deg+2), repeat=self.ndim)
+ Prod = itertools.product(np.arange(1, guess_Deg + 2), repeat=self.ndim)
sort_dig_unique_combos = np.array(list(filter(lambda x: x, Prod)))
# Ranking relatively mean
- Temp = np.empty(shape=[0, guess_Deg+1])
+ Temp = np.empty(shape=[0, guess_Deg + 1])
for j in range(self.ndim):
- s = abs(c_points[:, j]-np.mean(raw_data[j]))
+ s = abs(c_points[:, j] - np.mean(raw_data[j]))
Temp = np.append(Temp, [s], axis=0)
temp = Temp.T
index_CP = np.sort(temp, axis=0)
- sort_cpoints = np.empty((0, guess_Deg+1))
+ sort_cpoints = np.empty((0, guess_Deg + 1))
for j in range(self.ndim):
sort_cp = c_points[index_CP[:, j], j]
@@ -563,8 +484,9 @@ class ExpDesigns:
sort_unique_combos = np.empty(shape=[0, self.ndim])
for i in range(len(sort_dig_unique_combos)):
sort_un_comb = []
+ sort_uni_comb = None
for j in range(self.ndim):
- SortUC = sort_cpoints[j, sort_dig_unique_combos[i, j]-1]
+ SortUC = sort_cpoints[j, sort_dig_unique_combos[i, j] - 1]
sort_un_comb.append(SortUC)
sort_uni_comb = np.asarray(sort_un_comb)
sort_unique_combos = np.vstack((sort_unique_combos, sort_uni_comb))
@@ -576,162 +498,3 @@ class ExpDesigns:
opt_col_points = sort_unique_combos[0:self.n_samples]
return opt_col_points
-
- # -------------------------------------------------------------------------
- def transform(self, X, params=None, method=None):
- """
- Transform the samples via either a Rosenblatt or an isoprobabilistic
- transformation.
-
- Parameters
- ----------
- X : array of shape (n_samples,n_params)
- Samples to be transformed.
- method : string
- If transformation method is 'user' transform X, else just pass X.
-
- Returns
- -------
- tr_X: array of shape (n_samples,n_params)
- Transformed samples.
-
- """
- if self.InputObj.Rosenblatt:
- self.origJDist, _ = self.build_dist(False)
- if method == 'user':
- tr_X = self.JDist.inv(self.origJDist.fwd(X.T)).T
- else:
- # Inverse to original spcace -- generate sample ED
- tr_X = self.origJDist.inv(self.JDist.fwd(X.T)).T
- else:
- # Transform samples via an isoprobabilistic transformation
- n_samples, n_params = X.shape
- Inputs = self.InputObj
- origJDist = self.JDist
- poly_types = self.poly_types
-
- disttypes = []
- for par_i in range(n_params):
- disttypes.append(Inputs.Marginals[par_i].dist_type)
-
- # Pass non-transformed X, if arbitrary PCE is selected.
- if None in disttypes or self.input_data_given or self.apce:
- return X
-
- cdfx = np.zeros((X.shape))
- tr_X = np.zeros((X.shape))
-
- for par_i in range(n_params):
-
- # Extract the parameters of the original space
- disttype = disttypes[par_i]
- if disttype is not None:
- dist = origJDist[par_i]
- else:
- dist = None
- polytype = poly_types[par_i]
- cdf = np.vectorize(lambda x: dist.cdf(x))
-
- # Extract the parameters of the transformation space based on
- # polyType
- if polytype == 'legendre' or disttype == 'uniform':
- # Generate Y_Dists based
- params_Y = [-1, 1]
- dist_Y = st.uniform(loc=params_Y[0],
- scale=params_Y[1]-params_Y[0])
- inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
-
- elif polytype == 'hermite' or disttype == 'norm':
- params_Y = [0, 1]
- dist_Y = st.norm(loc=params_Y[0], scale=params_Y[1])
- inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
-
- elif polytype == 'laguerre' or disttype == 'gamma':
- params_Y = [1, params[1]]
- dist_Y = st.gamma(loc=params_Y[0], scale=params_Y[1])
- inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
-
- # Compute CDF_x(X)
- cdfx[:, par_i] = cdf(X[:, par_i])
-
- # Compute invCDF_y(cdfx)
- tr_X[:, par_i] = inv_cdf(cdfx[:, par_i])
-
- return tr_X
-
- # -------------------------------------------------------------------------
- def fit_dist(self, y):
- """
- Fits the known distributions to the data.
-
- Parameters
- ----------
- y : array of shape (n_samples)
- Data to be fitted.
-
- Returns
- -------
- sel_dist: string
- Selected distribution type from `lognorm`, `norm`, `uniform` or
- `expon`.
- params : list
- Parameters corresponding to the selected distibution type.
-
- """
- dist_results = []
- params = {}
- dist_names = ['lognorm', 'norm', 'uniform', 'expon']
- for dist_name in dist_names:
- dist = getattr(st, dist_name)
-
- try:
- if dist_name != 'lognorm':
- param = dist.fit(y)
- else:
- param = dist.fit(np.exp(y), floc=0)
- except:
- param = dist.fit(y)
-
- params[dist_name] = param
- # Applying the Kolmogorov-Smirnov test
- D, p = st.kstest(y, dist_name, args=param)
- dist_results.append((dist_name, D))
-
- # select the best fitted distribution
- sel_dist, D = (min(dist_results, key=lambda item: item[1]))
-
- if sel_dist == 'uniform':
- params[sel_dist] = [params[sel_dist][0], params[sel_dist][0] +
- params[sel_dist][1]]
- if D < 0.05:
- return sel_dist, params[sel_dist]
- else:
- return None, None
-
- # -------------------------------------------------------------------------
- def _check_ranges(self, theta, ranges):
- """
- This function checks if theta lies in the given ranges.
-
- Parameters
- ----------
- theta : array
- Proposed parameter set.
- ranges : nested list
- List of the praremeter ranges.
-
- Returns
- -------
- c : bool
- If it lies in the given range, it return True else False.
-
- """
- c = True
- # traverse in the list1
- for i, bounds in enumerate(ranges):
- x = theta[i]
- # condition check
- if x < bounds[0] or x > bounds[1]:
- c = False
- return c
- return c
diff --git a/src/bayesvalidrox/surrogate_models/exploration.py b/src/bayesvalidrox/surrogate_models/exploration.py
index cb3ccfcd4a15e26b2292973167d01efedd5a9a62..67decae2bfef6d397bafbc10abdcb35ff77a63a3 100644
--- a/src/bayesvalidrox/surrogate_models/exploration.py
+++ b/src/bayesvalidrox/surrogate_models/exploration.py
@@ -1,5 +1,8 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
+"""
+Exploration for sequential training of metamodels
+"""
import numpy as np
from scipy.spatial import distance
@@ -16,8 +19,8 @@ class Exploration:
Attributes
----------
- MetaModel : obj
- MetaModel object.
+ ExpDesign : obj
+ ExpDesign object.
n_candidate : int
Number of candidate samples.
mc_criterion : str
@@ -28,10 +31,10 @@ class Exploration:
training set.
"""
- def __init__(self, MetaModel, n_candidate,
+ def __init__(self, ExpDesign, n_candidate,
mc_criterion='mc-intersite-proj-th'):
- self.MetaModel = MetaModel
- self.Marginals = []
+ self.closestPoints = None
+ self.ExpDesign = ExpDesign
self.n_candidate = n_candidate
self.mc_criterion = mc_criterion
self.w = 100
@@ -48,8 +51,7 @@ class Exploration:
exploration_scores: arrays of shape (n_candidate)
Exploration scores.
"""
- MetaModel = self.MetaModel
- explore_method = MetaModel.ExpDesign.explore_method
+ explore_method = self.ExpDesign.explore_method
print("\n")
print(f' The {explore_method}-Method is selected as the exploration '
@@ -82,7 +84,7 @@ class Exploration:
mc_criterion = self.mc_criterion
n_candidate = self.n_candidate
# Get the Old ExpDesign #samples
- old_ED_X = self.MetaModel.ExpDesign.X
+ old_ED_X = self.ExpDesign.X
ndim = old_ED_X.shape[1]
# calculate error #averageErrors
@@ -202,8 +204,7 @@ class Exploration:
exploration_scores: arrays of shape (n_candidate)
Exploration scores.
"""
- MetaModel = self.MetaModel
- explore_method = MetaModel.ExpDesign.explore_method
+ explore_method = self.ExpDesign.explore_method
mc_criterion = self.mc_criterion
if all_candidates is None:
n_candidate = self.n_candidate
@@ -211,13 +212,13 @@ class Exploration:
n_candidate = all_candidates.shape[0]
# Get the Old ExpDesign #samples
- old_ED_X = MetaModel.ExpDesign.X
+ old_ED_X = self.ExpDesign.X
ndim = old_ED_X.shape[1]
# ----- Compute the number of random points -----
if all_candidates is None:
# Generate MC Samples
- all_candidates = MetaModel.ExpDesign.generate_samples(
+ all_candidates = self.ExpDesign.generate_samples(
self.n_candidate, explore_method
)
self.all_candidates = all_candidates
@@ -294,15 +295,13 @@ class Exploration:
all_candidates: list of arrays
A list of samples in each voronoi cell.
"""
- MetaModel = self.MetaModel
-
n_samples = samples.shape[0]
ndim = samples.shape[1]
# Compute the number of random points
n_points = w * samples.shape[0]
# Generate w random points in the domain for each sample
- points = MetaModel.ExpDesign.generate_samples(n_points, 'random')
+ points = self.ExpDesign.generate_samples(n_points, 'random')
self.all_candidates = points
# Calculate the nearest sample to each point
@@ -367,102 +366,3 @@ class Exploration:
else:
return distances**2
-#if __name__ == "__main__":
-# import scipy.stats as stats
-# import matplotlib.pyplot as plt
-# import matplotlib as mpl
-# import matplotlib.cm as cm
-# plt.rc('font', family='sans-serif', serif='Arial')
-# plt.rc('figure', figsize = (12, 8))
-#
-# def plotter(old_ED_X, all_candidates, exploration_scores):
-# global Bounds
-#
-# from scipy.spatial import Voronoi, voronoi_plot_2d
-# vor = Voronoi(old_ED_X)
-#
-# fig = voronoi_plot_2d(vor)
-#
-# # find min/max values for normalization
-## minima = min(exploration_scores)
-## maxima = max(exploration_scores)
-##
-## # normalize chosen colormap
-## norm = mpl.colors.Normalize(vmin=minima, vmax=maxima, clip=True)
-## mapper = cm.ScalarMappable(norm=norm, cmap=cm.Blues_r)
-##
-## for r in range(len(vor.point_region)):
-## region = vor.regions[vor.point_region[r]]
-## if not -1 in region:
-## polygon = [vor.vertices[i] for i in region]
-## plt.fill(*zip(*polygon), color=mapper.to_rgba(exploration_scores[r]))
-#
-#
-# ax1 = fig.add_subplot(111)
-#
-# ax1.scatter(old_ED_X[:,0], old_ED_X[:,1], s=10, c='r', marker="s", label='Old Design Points')
-# for i in range(old_ED_X.shape[0]):
-# txt = 'p'+str(i+1)
-# ax1.annotate(txt, (old_ED_X[i,0],old_ED_X[i,1]))
-#
-## for i in range(NrofCandGroups):
-## Candidates = all_candidates['group_'+str(i+1)]
-## ax1.scatter(Candidates[:,0],Candidates[:,1], s=10, c='b', marker="o", label='Design candidates')
-# ax1.scatter(all_candidates[:,0],all_candidates[:,1], s=10, c='b', marker="o", label='Design candidates')
-#
-# ax1.set_xlim(Bounds[0][0], Bounds[0][1])
-# ax1.set_ylim(Bounds[1][0], Bounds[1][1])
-#
-# plt.legend(loc='best');
-# plt.show()
-#
-# def voronoi_volumes(points):
-# from scipy.spatial import Voronoi, ConvexHull
-# v = Voronoi(points)
-# vol = np.zeros(v.npoints)
-#
-# for i, reg_num in enumerate(v.point_region):
-# indices = v.regions[reg_num]
-# if -1 in indices: # some regions can be opened
-# vol[i] = np.inf
-# else:
-#
-# #print("reg_num={0: 3.3f} X1={1: 3.3f} X2={2: 3.3f}".format(reg_num, v.points[reg_num-1, 0], v.points[reg_num-1, 1]))
-# vol[i] = ConvexHull(v.vertices[indices]).volume
-#
-# print('-'*40)
-# for i in range(nrofSamples):
-# print("idx={0:d} X1={1: 3.3f} X2={2: 3.3f} Volume={3: 3.3f}".format(i+1, v.points[i, 0], v.points[i, 1], vol[i]))
-#
-# return vol
-#
-# NofPa = 2
-#
-# Bounds = ((-5,10), (0,15))
-#
-# nrofSamples = 10
-# old_ED_X = np.zeros((nrofSamples, NofPa))
-# for idx in range(NofPa):
-# Loc = Bounds[idx][0]
-# Scale = Bounds[idx][1] - Bounds[idx][0]
-# old_ED_X[:,idx] = stats.uniform(loc=Loc, scale=Scale).rvs(size=nrofSamples)
-#
-#
-# nNewCandidate = 40
-#
-# # New Function
-# volumes = voronoi_volumes(old_ED_X)
-#
-#
-# # SUMO
-# Exploration = Exploration(Bounds, old_ED_X, nNewCandidate)
-#
-# #all_candidates, Score = Exploration.get_vornoi_samples()
-# all_candidates, Score = Exploration.get_mc_samples()
-#
-# print('-'*40)
-## for i in range(nrofSamples):
-## print("idx={0:d} X1={1: 3.3f} X2={2: 3.3f} Volume={3: 3.3f}".format(i+1, old_ED_X[i,0], old_ED_X[i,1], vornoi.areas[i]))
-#
-# plotter(old_ED_X, all_candidates, volumes)
-
diff --git a/src/bayesvalidrox/surrogate_models/glexindex.py b/src/bayesvalidrox/surrogate_models/glexindex.py
index 6d9ba3c2f3c02be8e2ca04be6f95779ed0825ad8..90877331ec121750e7f81e32a4b69edbc9a110ba 100644
--- a/src/bayesvalidrox/surrogate_models/glexindex.py
+++ b/src/bayesvalidrox/surrogate_models/glexindex.py
@@ -58,16 +58,8 @@ def glexindex(start, stop=None, dimensions=1, cross_truncation=1.,
start, stop, _ = numpy.broadcast_arrays(start, stop, numpy.empty(dimensions))
cross_truncation = cross_truncation*numpy.ones(2)
- indices = _glexindex(start, stop, cross_truncation)
- if indices.size:
- indices = indices[glexsort(indices.T, graded=graded, reverse=reverse)]
- return indices
-
-
-def _glexindex(start, stop, cross_truncation=1.):
- """Backend for the glexindex function."""
- # At the beginning the current list of indices just ranges over the
- # last dimension.
+
+ # Moved here from _glexindex
bound = stop.max()
dimensions = len(start)
start = numpy.clip(start, a_min=0, a_max=None)
@@ -100,8 +92,21 @@ def _glexindex(start, stop, cross_truncation=1.):
upper = cross_truncate(indices, stop-1, cross_truncation[1])
indices = indices[lower ^ upper]
- return numpy.array(indices, dtype=int).reshape(-1, dimensions)
-
+ indices = numpy.array(indices, dtype=int).reshape(-1, dimensions)
+ if indices.size:
+ # moved here from glexsort
+ keys = indices.T
+ keys_ = numpy.atleast_2d(keys)
+ if reverse:
+ keys_ = keys_[::-1]
+
+ indices_sort = numpy.array(numpy.lexsort(keys_))
+ if graded:
+ indices_sort = indices_sort[numpy.argsort(
+ numpy.sum(keys_[:, indices_sort], axis=0))].T
+
+ indices = indices[indices_sort]
+ return indices
def cross_truncate(indices, bound, norm):
r"""
@@ -154,57 +159,3 @@ def cross_truncate(indices, bound, norm):
assert numpy.all(out[numpy.all(indices == 0, axis=-1)])
return out
-
-
-def glexsort(
- keys: numpy.typing.ArrayLike,
- graded: bool = False,
- reverse: bool = False,
-) -> numpy.ndarray:
- """
- Sort keys using graded lexicographical ordering.
- Same as ``numpy.lexsort``, but also support graded and reverse
- lexicographical ordering.
- Args:
- keys:
- Values to sort.
- graded:
- Graded sorting, meaning the indices are always sorted by the index
- sum. E.g. ``(2, 2, 2)`` has a sum of 6, and will therefore be
- consider larger than both ``(3, 1, 1)`` and ``(1, 1, 3)``.
- reverse:
- Reverse lexicographical sorting meaning that ``(1, 3)`` is
- considered smaller than ``(3, 1)``, instead of the opposite.
- Returns:
- Array of indices that sort the keys along the specified axis.
- Examples:
- >>> indices = numpy.array([[0, 0, 0, 1, 2, 1],
- ... [1, 2, 0, 0, 0, 1]])
- >>> indices[:, numpy.lexsort(indices)]
- array([[0, 1, 2, 0, 1, 0],
- [0, 0, 0, 1, 1, 2]])
- >>> indices[:, numpoly.glexsort(indices)]
- array([[0, 1, 2, 0, 1, 0],
- [0, 0, 0, 1, 1, 2]])
- >>> indices[:, numpoly.glexsort(indices, reverse=True)]
- array([[0, 0, 0, 1, 1, 2],
- [0, 1, 2, 0, 1, 0]])
- >>> indices[:, numpoly.glexsort(indices, graded=True)]
- array([[0, 1, 0, 2, 1, 0],
- [0, 0, 1, 0, 1, 2]])
- >>> indices[:, numpoly.glexsort(indices, graded=True, reverse=True)]
- array([[0, 0, 1, 0, 1, 2],
- [0, 1, 0, 2, 1, 0]])
- >>> indices = numpy.array([4, 5, 6, 3, 2, 1])
- >>> indices[numpoly.glexsort(indices)]
- array([1, 2, 3, 4, 5, 6])
- """
- keys_ = numpy.atleast_2d(keys)
- if reverse:
- keys_ = keys_[::-1]
-
- indices = numpy.array(numpy.lexsort(keys_))
- if graded:
- indices = indices[numpy.argsort(
- numpy.sum(keys_[:, indices], axis=0))].T
- return indices
diff --git a/src/bayesvalidrox/surrogate_models/input_space.py b/src/bayesvalidrox/surrogate_models/input_space.py
new file mode 100644
index 0000000000000000000000000000000000000000..9576462d8de15b292aa6210072c905fca68a2382
--- /dev/null
+++ b/src/bayesvalidrox/surrogate_models/input_space.py
@@ -0,0 +1,411 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Input space built from set prior distributions
+"""
+
+import numpy as np
+import chaospy
+import scipy.stats as st
+from tqdm import tqdm
+
+
+# noinspection SpellCheckingInspection
+class InputSpace:
+ """
+ This class generates the input space for the metamodel from the
+ distributions provided using the `Input` object.
+
+ Attributes
+ ----------
+ Input : obj
+ Input object containing the parameter marginals, i.e. name,
+ distribution type and distribution parameters or available raw data.
+ meta_Model_type : str
+ Type of the meta_Model_type.
+
+ """
+
+ def __init__(self, input_object, meta_Model_type='pce'):
+ self.InputObj = input_object
+ self.meta_Model_type = meta_Model_type
+
+ # Other
+ self.apce = None
+ self.bound_tuples = None
+ self.input_data_given = None
+ self.JDist = None
+ self.MCSize = None
+ self.ndim = None
+ self.origJDist = None
+ self.par_names = None
+ self.poly_types = None
+ self.prior_space = None
+ self.raw_data = None
+
+ # Init
+ self.check_valid_inputs()
+
+ def check_valid_inputs(self) -> None:
+ """
+ Check if the given InputObj is valid to use for further calculations:
+ 1) Has some Marginals
+ 2) The Marginals have valid priors
+ 3) All Marginals given as the same type (samples vs dist)
+
+ Returns
+ -------
+ None
+
+ """
+ Inputs = self.InputObj
+ self.ndim = len(Inputs.Marginals)
+
+ # Check if PCE or aPCE metamodel is selected.
+ # TODO: test also for 'pce'??
+ if self.meta_Model_type.lower() == 'apce':
+ self.apce = True
+ else:
+ self.apce = False
+
+ # check if marginals given
+ if not self.ndim >= 1:
+ raise AssertionError('Cannot build distributions if no marginals are given')
+
+ # check that each marginal is valid
+ for marginals in Inputs.Marginals:
+ if len(marginals.input_data) == 0:
+ if marginals.dist_type is None:
+ raise AssertionError('Not all marginals were provided priors')
+ if np.array(marginals.input_data).shape[0] and (marginals.dist_type is not None):
+ raise AssertionError('Both samples and distribution type are given. Please choose only one.')
+
+ # Check if input is given as dist or input_data.
+ self.input_data_given = -1
+ for marg in Inputs.Marginals:
+ size = np.array(marg.input_data).shape[0]
+ if size and abs(self.input_data_given) != 1:
+ self.input_data_given = 2
+ break
+ if (not size) and self.input_data_given > 0:
+ self.input_data_given = 2
+ break
+ if not size:
+ self.input_data_given = 0
+ if size:
+ self.input_data_given = 1
+
+ if self.input_data_given == 2:
+ raise AssertionError('Distributions cannot be built as the priors have different types')
+
+ # Get the bounds if input_data are directly defined by user:
+ if self.input_data_given:
+ for i in range(self.ndim):
+ low_bound = np.min(Inputs.Marginals[i].input_data)
+ up_bound = np.max(Inputs.Marginals[i].input_data)
+ Inputs.Marginals[i].parameters = [low_bound, up_bound]
+
+ # -------------------------------------------------------------------------
+ def init_param_space(self, max_deg=None):
+ """
+ Initializes parameter space.
+
+ Parameters
+ ----------
+ max_deg : int, optional
+ Maximum degree. The default is `None`.
+
+ Returns
+ -------
+ raw_data : array of shape (n_params, n_samples)
+ Raw data.
+ bound_tuples : list of tuples
+ A list containing lower and upper bounds of parameters.
+
+ """
+ # Recheck all before running!
+ self.check_valid_inputs()
+
+ Inputs = self.InputObj
+ ndim = self.ndim
+ rosenblatt_flag = Inputs.Rosenblatt
+ mc_size = 50000
+
+ # Save parameter names
+ self.par_names = []
+ for parIdx in range(ndim):
+ self.par_names.append(Inputs.Marginals[parIdx].name)
+
+ # Create a multivariate probability distribution
+ # TODO: change this to make max_deg obligatory? at least in some specific cases?
+ if max_deg is not None:
+ JDist, poly_types = self.build_polytypes(rosenblatt=rosenblatt_flag)
+ self.JDist, self.poly_types = JDist, poly_types
+
+ if self.input_data_given:
+ self.MCSize = len(Inputs.Marginals[0].input_data)
+ self.raw_data = np.zeros((ndim, self.MCSize))
+
+ for parIdx in range(ndim):
+ # Save parameter names
+ try:
+ self.raw_data[parIdx] = np.array(
+ Inputs.Marginals[parIdx].input_data)
+ except:
+ self.raw_data[parIdx] = self.JDist[parIdx].sample(mc_size)
+
+ else:
+ # Generate random samples based on parameter distributions
+ self.raw_data = chaospy.generate_samples(mc_size,
+ domain=self.JDist)
+
+ # Extract moments
+ for parIdx in range(ndim):
+ mu = np.mean(self.raw_data[parIdx])
+ std = np.std(self.raw_data[parIdx])
+ self.InputObj.Marginals[parIdx].moments = [mu, std]
+
+ # Generate the bounds based on given inputs for marginals
+ bound_tuples = []
+ for i in range(ndim):
+ if Inputs.Marginals[i].dist_type == 'unif':
+ low_bound = Inputs.Marginals[i].parameters[0]
+ up_bound = Inputs.Marginals[i].parameters[1]
+ else:
+ low_bound = np.min(self.raw_data[i])
+ up_bound = np.max(self.raw_data[i])
+
+ bound_tuples.append((low_bound, up_bound))
+
+ self.bound_tuples = tuple(bound_tuples)
+
+ # -------------------------------------------------------------------------
+ def build_polytypes(self, rosenblatt):
+ """
+ Creates the polynomial types to be passed to univ_basis_vals method of
+ the MetaModel object.
+
+ Parameters
+ ----------
+ rosenblatt : bool
+ Rosenblatt transformation flag.
+
+ Returns
+ -------
+ orig_space_dist : object
+ A chaospy JDist object or a gaussian_kde object.
+ poly_types : list
+ A list of polynomial types for the parameters.
+
+ """
+ Inputs = self.InputObj
+
+ all_data = []
+ all_dist_types = []
+ orig_joints = []
+ poly_types = []
+ params = None
+
+ for parIdx in range(self.ndim):
+
+ if Inputs.Marginals[parIdx].dist_type is None:
+ data = Inputs.Marginals[parIdx].input_data
+ all_data.append(data)
+ dist_type = None
+ else:
+ dist_type = Inputs.Marginals[parIdx].dist_type
+ params = Inputs.Marginals[parIdx].parameters
+
+ if rosenblatt:
+ polytype = 'hermite'
+ dist = chaospy.Normal()
+
+ elif dist_type is None:
+ polytype = 'arbitrary'
+ dist = None
+
+ elif 'unif' in dist_type.lower():
+ polytype = 'legendre'
+ if not np.array(params).shape[0] >= 2:
+ raise AssertionError('Distribution has too few parameters!')
+ dist = chaospy.Uniform(lower=params[0], upper=params[1])
+
+ elif 'norm' in dist_type.lower() and \
+ 'log' not in dist_type.lower():
+ if not np.array(params).shape[0] >= 2:
+ raise AssertionError('Distribution has too few parameters!')
+ polytype = 'hermite'
+ dist = chaospy.Normal(mu=params[0], sigma=params[1])
+
+ elif 'gamma' in dist_type.lower():
+ polytype = 'laguerre'
+ if not np.array(params).shape[0] >= 3:
+ raise AssertionError('Distribution has too few parameters!')
+ dist = chaospy.Gamma(shape=params[0],
+ scale=params[1],
+ shift=params[2])
+
+ elif 'beta' in dist_type.lower():
+ if not np.array(params).shape[0] >= 4:
+ raise AssertionError('Distribution has too few parameters!')
+ polytype = 'jacobi'
+ dist = chaospy.Beta(alpha=params[0], beta=params[1],
+ lower=params[2], upper=params[3])
+
+ elif 'lognorm' in dist_type.lower():
+ polytype = 'hermite'
+ if not np.array(params).shape[0] >= 2:
+ raise AssertionError('Distribution has too few parameters!')
+ mu = np.log(params[0] ** 2 / np.sqrt(params[0] ** 2 + params[1] ** 2))
+ sigma = np.sqrt(np.log(1 + params[1] ** 2 / params[0] ** 2))
+ dist = chaospy.LogNormal(mu, sigma)
+ # dist = chaospy.LogNormal(mu=params[0], sigma=params[1])
+
+ elif 'expon' in dist_type.lower():
+ polytype = 'exponential'
+ if not np.array(params).shape[0] >= 2:
+ raise AssertionError('Distribution has too few parameters!')
+ dist = chaospy.Exponential(scale=params[0], shift=params[1])
+
+ elif 'weibull' in dist_type.lower():
+ polytype = 'weibull'
+ if not np.array(params).shape[0] >= 3:
+ raise AssertionError('Distribution has too few parameters!')
+ dist = chaospy.Weibull(shape=params[0], scale=params[1],
+ shift=params[2])
+
+ else:
+ message = (f"DistType {dist_type} for parameter"
+ f"{parIdx + 1} is not available.")
+ raise ValueError(message)
+
+ if self.input_data_given or self.apce:
+ polytype = 'arbitrary'
+
+ # Store dists and poly_types
+ orig_joints.append(dist)
+ poly_types.append(polytype)
+ all_dist_types.append(dist_type)
+
+ # Prepare final output to return
+ if None in all_dist_types:
+ # Naive approach: Fit a gaussian kernel to the provided data
+ Data = np.asarray(all_data)
+ try:
+ orig_space_dist = st.gaussian_kde(Data)
+ except:
+ raise ValueError('The samples provided to the Marginals should be 1D only')
+ self.prior_space = orig_space_dist
+ else:
+ orig_space_dist = chaospy.J(*orig_joints)
+ try:
+ self.prior_space = st.gaussian_kde(orig_space_dist.sample(10000))
+ except:
+ raise ValueError('Parameter values are not valid, please set differently')
+
+ return orig_space_dist, poly_types
+
+ # -------------------------------------------------------------------------
+ def transform(self, X, params=None, method=None):
+ """
+ Transforms the samples via either a Rosenblatt or an isoprobabilistic
+ transformation.
+
+ Parameters
+ ----------
+ X : array of shape (n_samples,n_params)
+ Samples to be transformed.
+ params : list
+ Parameters for laguerre/gamma-type distribution.
+ method : string
+ If transformation method is 'user' transform X, else just pass X.
+
+ Returns
+ -------
+ tr_X: array of shape (n_samples,n_params)
+ Transformed samples.
+
+ """
+ # Check for built JDist
+ if self.JDist is None:
+ raise AttributeError('Call function init_param_space first to create JDist')
+
+ # Check if X is 2d
+ if X.ndim != 2:
+ raise AttributeError('X should have two dimensions')
+
+ # Check if size of X matches Marginals
+ if X.shape[1] != self.ndim:
+ raise AttributeError(
+ 'The second dimension of X should be the same size as the number of marginals in the InputObj')
+
+ if self.InputObj.Rosenblatt:
+ self.origJDist, _ = self.build_polytypes(False)
+ if method == 'user':
+ tr_X = self.JDist.inv(self.origJDist.fwd(X.T)).T
+ else:
+ # Inverse to original spcace -- generate sample ED
+ tr_X = self.origJDist.inv(self.JDist.fwd(X.T)).T
+ else:
+ # Transform samples via an isoprobabilistic transformation
+ n_samples, n_params = X.shape
+ Inputs = self.InputObj
+ origJDist = self.JDist
+ poly_types = self.poly_types
+
+ disttypes = []
+ for par_i in range(n_params):
+ disttypes.append(Inputs.Marginals[par_i].dist_type)
+
+ # Pass non-transformed X, if arbitrary PCE is selected.
+ if None in disttypes or self.input_data_given or self.apce:
+ return X
+
+ cdfx = np.zeros(X.shape)
+ tr_X = np.zeros(X.shape)
+
+ # TODO: this transformation takes quite a while,
+ # especially for many samples to transform, can it be improved?
+ for par_i in range(n_params):#tqdm(range(n_params),
+ # desc='Transforming the input samples'):
+
+ # Extract the parameters of the original space
+ disttype = disttypes[par_i]
+ if disttype is not None:
+ dist = origJDist[par_i]
+ else:
+ dist = None
+ polytype = poly_types[par_i]
+ cdf = np.vectorize(lambda x: dist.cdf(x))
+
+ # Extract the parameters of the transformation space based on
+ # polyType
+ inv_cdf = None
+ if polytype == 'legendre' or disttype == 'uniform':
+ # Generate Y_Dists based
+ params_Y = [-1, 1]
+ dist_Y = st.uniform(loc=params_Y[0],
+ scale=params_Y[1] - params_Y[0])
+ inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
+
+ elif polytype == 'hermite' or disttype == 'norm':
+ params_Y = [0, 1]
+ dist_Y = st.norm(loc=params_Y[0], scale=params_Y[1])
+ inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
+
+ elif polytype == 'laguerre' or disttype == 'gamma':
+ if params is None:
+ raise AttributeError('Additional parameters have to be set for the gamma distribution!')
+ params_Y = [1, params[1]]
+
+ # TOOD: update the call to the gamma function, seems like source code has been changed!
+ dist_Y = st.gamma(loc=params_Y[0], scale=params_Y[1])
+ inv_cdf = np.vectorize(lambda x: dist_Y.ppf(x))
+
+ # Compute CDF_x(X)
+ cdfx[:, par_i] = cdf(X[:, par_i])
+
+ # Compute invCDF_y(cdfx)
+ tr_X[:, par_i] = inv_cdf(cdfx[:, par_i])
+
+ return tr_X
diff --git a/src/bayesvalidrox/surrogate_models/inputs.py b/src/bayesvalidrox/surrogate_models/inputs.py
index 783e82b053cc458be712b588b7fde3a0f3c8decb..40ae36337fa84c0bb9758e488f983ba29cdb9e77 100644
--- a/src/bayesvalidrox/surrogate_models/inputs.py
+++ b/src/bayesvalidrox/surrogate_models/inputs.py
@@ -1,5 +1,9 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
+"""
+Inputs and related marginal distributions
+"""
+
class Input:
"""
@@ -17,17 +21,18 @@ class Input:
-------
Marginals can be defined as following:
- >>> Inputs.add_marginals()
- >>> Inputs.Marginals[0].name = 'X_1'
- >>> Inputs.Marginals[0].dist_type = 'uniform'
- >>> Inputs.Marginals[0].parameters = [-5, 5]
+ >>> inputs = Inputs()
+ >>> inputs.add_marginals()
+ >>> inputs.Marginals[0].name = 'X_1'
+ >>> inputs.Marginals[0].dist_type = 'uniform'
+ >>> inputs.Marginals[0].parameters = [-5, 5]
If there is no common data is avaliable, the input data can be given
as following:
- >>> Inputs.add_marginals()
- >>> Inputs.Marginals[0].name = 'X_1'
- >>> Inputs.Marginals[0].input_data = input_data
+ >>> inputs.add_marginals()
+ >>> inputs.Marginals[0].name = 'X_1'
+ >>> inputs.Marginals[0].input_data = [0,0,1,0]
"""
poly_coeffs_flag = True
@@ -60,12 +65,12 @@ class Marginal:
dist_type : string
Name of the distribution. The default is `None`.
parameters : list
- List of the parameters corresponding to the distribution type. The
+ Parameters corresponding to the distribution type. The
default is `None`.
input_data : array
Available input data. The default is `[]`.
moments : list
- List of the moments.
+ Moments of the distribution. The default is `None`.
"""
def __init__(self):
diff --git a/src/bayesvalidrox/surrogate_models/orthogonal_matching_pursuit.py b/src/bayesvalidrox/surrogate_models/orthogonal_matching_pursuit.py
index d4f99b8a19bb5dbdf41f093bd454c80c63a321bb..96ef9c1d50b10b587ad0846d41733fc7f1cedfe8 100644
--- a/src/bayesvalidrox/surrogate_models/orthogonal_matching_pursuit.py
+++ b/src/bayesvalidrox/surrogate_models/orthogonal_matching_pursuit.py
@@ -282,7 +282,7 @@ class OrthogonalMatchingPursuit(LinearModel, RegressorMixin):
# only the trace is, to save memory)
PsiM = np.dot(psi, inv_inf_matrix)
- h = np.sum(np.multiply(PsiM, psi), axis=1, dtype=np.float128)
+ h = np.sum(np.multiply(PsiM, psi), axis=1, dtype=np.longdouble)
# ------ Calculate Error Loocv for each measurement point ----
# Residuals
diff --git a/src/bayesvalidrox/surrogate_models/reg_fast_ard.py b/src/bayesvalidrox/surrogate_models/reg_fast_ard.py
index 44073da8e78642ba3b3914f6ce55a2d01986b1f1..fdd0ee7470dc5d0bdccd354cee16f6257ec01b02 100755
--- a/src/bayesvalidrox/surrogate_models/reg_fast_ard.py
+++ b/src/bayesvalidrox/surrogate_models/reg_fast_ard.py
@@ -236,7 +236,7 @@ class RegressionFastARD(LinearModel, RegressorMixin):
self.var_y = False
A = np.PINF * np.ones(n_features)
- active = np.zeros(n_features, dtype=np.bool)
+ active = np.zeros(n_features, dtype=bool)
if self.start is not None and not hasattr(self, 'active_'):
start = self.start
@@ -286,9 +286,9 @@ class RegressionFastARD(LinearModel, RegressorMixin):
# raise warning in case cholesky fails
if warning_flag == 1:
warnings.warn(("Cholesky decomposition failed! Algorithm uses "
- "pinvh, which is significantly slower, if you "
+ "pinvh, which is significantly slower. If you "
"use RVR it is advised to change parameters of "
- "kernel"))
+ "the kernel!"))
# compute quality & sparsity parameters
s, q, S, Q = self._sparsity_quality(XX, XXd, XY, XYa, Aa, Ri,
@@ -319,7 +319,7 @@ class RegressionFastARD(LinearModel, RegressorMixin):
if converged or i == self.n_iter - 1:
if converged and self.verbose:
- print('Algorithm converged !')
+ print('Algorithm converged!')
break
# after last update of alpha & beta update parameters
diff --git a/src/bayesvalidrox/surrogate_models/reg_fast_laplace.py b/src/bayesvalidrox/surrogate_models/reg_fast_laplace.py
index bdff324ede818a42d226e9aa55aaf01666ca8fc8..7fdcb5cf6e93c396d32eae2b0aad87a194a9cba4 100644
--- a/src/bayesvalidrox/surrogate_models/reg_fast_laplace.py
+++ b/src/bayesvalidrox/surrogate_models/reg_fast_laplace.py
@@ -75,7 +75,7 @@ class RegressionFastLaplace():
def _center_data(self, X, y):
''' Centers data'''
- X = as_float_array(X, self.copy_X)
+ X = as_float_array(X, copy = self.copy_X)
# normalisation should be done in preprocessing!
X_std = np.ones(X.shape[1], dtype=X.dtype)
@@ -307,7 +307,7 @@ class RegressionFastLaplace():
# recomputation
# zero if regressor has not been chosen yet
if not ind_global_to_local[ind_L_max]:
- raise Exception('cannot recompute index{0} -- not yet\
+ raise Exception('Cannot recompute index{0} -- not yet\
part of the model!'.format(ind_L_max))
Sigma = np.atleast_2d(Sigma)
mu = np.atleast_2d(mu)
diff --git a/src/bayesvalidrox/surrogate_models/sequential_design.py b/src/bayesvalidrox/surrogate_models/sequential_design.py
deleted file mode 100644
index fc81dcd4529ca0708dfba47385aef4415992eb3e..0000000000000000000000000000000000000000
--- a/src/bayesvalidrox/surrogate_models/sequential_design.py
+++ /dev/null
@@ -1,2187 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Jan 28 09:21:18 2022
-
-@author: farid
-"""
-import numpy as np
-from scipy import stats, signal, linalg, sparse
-from scipy.spatial import distance
-from copy import deepcopy, copy
-from tqdm import tqdm
-import scipy.optimize as opt
-from sklearn.metrics import mean_squared_error
-import multiprocessing
-import matplotlib.pyplot as plt
-import sys
-import os
-import gc
-import seaborn as sns
-from joblib import Parallel, delayed
-import resource
-from .exploration import Exploration
-
-
-class SeqDesign():
- """ Sequential experimental design
- This class provieds method for trainig the meta-model in an iterative
- manners.
- The main method to execute the task is `train_seq_design`, which
- recieves a model object and returns the trained metamodel.
- """
-
- # -------------------------------------------------------------------------
- def train_seq_design(self, MetaModel):
- """
- Starts the adaptive sequential design for refining the surrogate model
- by selecting training points in a sequential manner.
-
- Parameters
- ----------
- Model : object
- An object containing all model specifications.
-
- Returns
- -------
- MetaModel : object
- Meta model object.
-
- """
- # MetaModel = self
- Model = MetaModel.ModelObj
- self.MetaModel = MetaModel
- self.Model = Model
-
- # Initialization
- MetaModel.SeqModifiedLOO = {}
- MetaModel.seqValidError = {}
- MetaModel.SeqBME = {}
- MetaModel.SeqKLD = {}
- MetaModel.SeqDistHellinger = {}
- MetaModel.seqRMSEMean = {}
- MetaModel.seqRMSEStd = {}
- MetaModel.seqMinDist = []
- pce = True if MetaModel.meta_model_type.lower() != 'gpe' else False
- mc_ref = True if bool(Model.mc_reference) else False
- if mc_ref:
- Model.read_mc_reference()
-
- if not hasattr(MetaModel, 'valid_likelihoods'):
- MetaModel.valid_samples = []
- MetaModel.valid_model_runs = []
- MetaModel.valid_likelihoods = []
-
- # Get the parameters
- max_n_samples = MetaModel.ExpDesign.n_max_samples
- mod_LOO_threshold = MetaModel.ExpDesign.mod_LOO_threshold
- n_canddidate = MetaModel.ExpDesign.n_canddidate
- post_snapshot = MetaModel.ExpDesign.post_snapshot
- n_replication = MetaModel.ExpDesign.n_replication
- util_func = MetaModel.ExpDesign.util_func
- output_name = Model.Output.names
- validError = None
- # Handle if only one UtilityFunctions is provided
- if not isinstance(util_func, list):
- util_func = [MetaModel.ExpDesign.util_func]
-
- # Read observations or MCReference
- if len(Model.observations) != 0 or Model.meas_file is not None:
- self.observations = Model.read_observation()
- obs_data = self.observations
- else:
- obs_data = []
- TotalSigma2 = {}
- # ---------- Initial MetaModel ----------
- initMetaModel = deepcopy(MetaModel)
-
- # Validation error if validation set is provided.
- if len(MetaModel.valid_model_runs) != 0:
- init_rmse, init_valid_error = self.__validError(initMetaModel)
- init_valid_error = list(init_valid_error.values())
- else:
- init_rmse = None
-
- # Check if discrepancy is provided
- if len(obs_data) != 0 and hasattr(MetaModel, 'Discrepancy'):
- TotalSigma2 = MetaModel.Discrepancy.parameters
-
- # Calculate the initial BME
- out = self.__BME_Calculator(
- initMetaModel, obs_data, TotalSigma2, init_rmse)
- init_BME, init_KLD, init_post, init_likes, init_dist_hellinger = out
- print(f"\nInitial BME: {init_BME:.2f}")
- print(f"Initial KLD: {init_KLD:.2f}")
-
- # Posterior snapshot (initial)
- if post_snapshot:
- parNames = MetaModel.ExpDesign.par_names
- print('Posterior snapshot (initial) is being plotted...')
- self.__posteriorPlot(init_post, parNames, 'SeqPosterior_init')
-
- # Check the convergence of the Mean & Std
- if mc_ref and pce:
- init_rmse_mean, init_rmse_std = self.__error_Mean_Std()
- print(f"Initial Mean and Std error: {init_rmse_mean},"
- f" {init_rmse_std}")
-
- # Read the initial experimental design
- Xinit = initMetaModel.ExpDesign.X
- init_n_samples = len(MetaModel.ExpDesign.X)
- initYprev = initMetaModel.ModelOutputDict
- initLCerror = initMetaModel.LCerror
- n_itrs = max_n_samples - init_n_samples
-
- # Read the initial ModifiedLOO
- if pce:
- Scores_all, varExpDesignY = [], []
- for out_name in output_name:
- y = initMetaModel.ExpDesign.Y[out_name]
- Scores_all.append(list(
- initMetaModel.score_dict['b_1'][out_name].values()))
- if MetaModel.dim_red_method.lower() == 'pca':
- pca = MetaModel.pca['b_1'][out_name]
- components = pca.transform(y)
- varExpDesignY.append(np.var(components, axis=0))
- else:
- varExpDesignY.append(np.var(y, axis=0))
-
- Scores = [item for sublist in Scores_all for item in sublist]
- weights = [item for sublist in varExpDesignY for item in sublist]
- init_mod_LOO = [np.average([1-score for score in Scores],
- weights=weights)]
-
- prevMetaModel_dict = {}
- # Replicate the sequential design
- for repIdx in range(n_replication):
- print(f'\n>>>> Replication: {repIdx+1}<<<<')
-
- # To avoid changes ub original aPCE object
- MetaModel.ExpDesign.X = Xinit
- MetaModel.ExpDesign.Y = initYprev
- MetaModel.LCerror = initLCerror
-
- for util_f in util_func:
- print(f'\n>>>> Utility Function: {util_f} <<<<')
- # To avoid changes ub original aPCE object
- MetaModel.ExpDesign.X = Xinit
- MetaModel.ExpDesign.Y = initYprev
- MetaModel.LCerror = initLCerror
-
- # Set the experimental design
- Xprev = Xinit
- total_n_samples = init_n_samples
- Yprev = initYprev
-
- Xfull = []
- Yfull = []
-
- # Store the initial ModifiedLOO
- if pce:
- print("\nInitial ModifiedLOO:", init_mod_LOO)
- SeqModifiedLOO = np.array(init_mod_LOO)
-
- if len(MetaModel.valid_model_runs) != 0:
- SeqValidError = np.array(init_valid_error)
-
- # Check if data is provided
- if len(obs_data) != 0:
- SeqBME = np.array([init_BME])
- SeqKLD = np.array([init_KLD])
- SeqDistHellinger = np.array([init_dist_hellinger])
-
- if mc_ref and pce:
- seqRMSEMean = np.array([init_rmse_mean])
- seqRMSEStd = np.array([init_rmse_std])
-
- # ------- Start Sequential Experimental Design -------
- postcnt = 1
- for itr_no in range(1, n_itrs+1):
- print(f'\n>>>> Iteration number {itr_no} <<<<')
-
- # Save the metamodel prediction before updating
- prevMetaModel_dict[itr_no] = deepcopy(MetaModel)
- if itr_no > 1:
- pc_model = prevMetaModel_dict[itr_no-1]
- self._y_hat_prev, _ = pc_model.eval_metamodel(
- samples=Xfull[-1].reshape(1, -1))
-
- # Optimal Bayesian Design
- m_1 = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024
- MetaModel.ExpDesignFlag = 'sequential'
- Xnew, updatedPrior = self.opt_SeqDesign(TotalSigma2,
- n_canddidate,
- util_f)
- m_2 = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024
- S = np.min(distance.cdist(Xinit, Xnew, 'euclidean'))
- MetaModel.seqMinDist.append(S)
- print(f"\nmin Dist from OldExpDesign: {S:2f}")
- print("\n")
-
- # Evaluate the full model response at the new sample
- Ynew, _ = Model.run_model_parallel(
- Xnew, prevRun_No=total_n_samples
- )
- total_n_samples += Xnew.shape[0]
- # ------ Plot the surrogate model vs Origninal Model ------
- if hasattr(MetaModel, 'adapt_verbose') and \
- MetaModel.adapt_verbose:
- from .adaptPlot import adaptPlot
- y_hat, std_hat = MetaModel.eval_metamodel(samples=Xnew)
- adaptPlot(MetaModel, Ynew, y_hat, std_hat, plotED=False)
-
- # -------- Retrain the surrogate model -------
- # Extend new experimental design
- Xfull = np.vstack((Xprev, Xnew))
-
- # Updating experimental design Y
- for out_name in output_name:
- Yfull = np.vstack((Yprev[out_name], Ynew[out_name]))
- MetaModel.ModelOutputDict[out_name] = Yfull
-
- # Pass new design to the metamodel object
- MetaModel.ExpDesign.sampling_method = 'user'
- MetaModel.ExpDesign.X = Xfull
- MetaModel.ExpDesign.Y = MetaModel.ModelOutputDict
-
- # Save the Experimental Design for next iteration
- Xprev = Xfull
- Yprev = MetaModel.ModelOutputDict
-
- # Pass the new prior as the input
- MetaModel.input_obj.poly_coeffs_flag = False
- if updatedPrior is not None:
- MetaModel.input_obj.poly_coeffs_flag = True
- print("updatedPrior:", updatedPrior.shape)
- # Arbitrary polynomial chaos
- for i in range(updatedPrior.shape[1]):
- MetaModel.input_obj.Marginals[i].dist_type = None
- x = updatedPrior[:, i]
- MetaModel.input_obj.Marginals[i].raw_data = x
-
- # Train the surrogate model for new ExpDesign
- MetaModel.train_norm_design(parallel=False)
- m_3 = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024
-
- # -------- Evaluate the retrained surrogate model -------
- # Extract Modified LOO from Output
- if pce:
- Scores_all, varExpDesignY = [], []
- for out_name in output_name:
- y = MetaModel.ExpDesign.Y[out_name]
- Scores_all.append(list(
- MetaModel.score_dict['b_1'][out_name].values()))
- if MetaModel.dim_red_method.lower() == 'pca':
- pca = MetaModel.pca['b_1'][out_name]
- components = pca.transform(y)
- varExpDesignY.append(np.var(components,
- axis=0))
- else:
- varExpDesignY.append(np.var(y, axis=0))
- Scores = [item for sublist in Scores_all for item
- in sublist]
- weights = [item for sublist in varExpDesignY for item
- in sublist]
- ModifiedLOO = [np.average(
- [1-score for score in Scores], weights=weights)]
-
- print('\n')
- print(f"Updated ModifiedLOO {util_f}:\n", ModifiedLOO)
- print('\n')
-
- # Compute the validation error
- if len(MetaModel.valid_model_runs) != 0:
- rmse, validError = self.__validError(MetaModel)
- ValidError = list(validError.values())
- else:
- rmse = None
-
- # Store updated ModifiedLOO
- if pce:
- SeqModifiedLOO = np.vstack(
- (SeqModifiedLOO, ModifiedLOO))
- if len(MetaModel.valid_model_runs) != 0:
- SeqValidError = np.vstack(
- (SeqValidError, ValidError))
- m_4 = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024
- # -------- Caclulation of BME as accuracy metric -------
- # Check if data is provided
- if len(obs_data) != 0:
- # Calculate the initial BME
- out = self.__BME_Calculator(MetaModel, obs_data,
- TotalSigma2, rmse)
- BME, KLD, Posterior, likes, DistHellinger = out
- print('\n')
- print(f"Updated BME: {BME:.2f}")
- print(f"Updated KLD: {KLD:.2f}")
- print('\n')
-
- # Plot some snapshots of the posterior
- step_snapshot = MetaModel.ExpDesign.step_snapshot
- if post_snapshot and postcnt % step_snapshot == 0:
- parNames = MetaModel.ExpDesign.par_names
- print('Posterior snapshot is being plotted...')
- self.__posteriorPlot(Posterior, parNames,
- f'SeqPosterior_{postcnt}')
- postcnt += 1
- m_5 = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024
-
- # Check the convergence of the Mean&Std
- if mc_ref and pce:
- print('\n')
- RMSE_Mean, RMSE_std = self.__error_Mean_Std()
- print(f"Updated Mean and Std error: {RMSE_Mean:.2f}, "
- f"{RMSE_std:.2f}")
- print('\n')
-
- # Store the updated BME & KLD
- # Check if data is provided
- if len(obs_data) != 0:
- SeqBME = np.vstack((SeqBME, BME))
- SeqKLD = np.vstack((SeqKLD, KLD))
- SeqDistHellinger = np.vstack((SeqDistHellinger,
- DistHellinger))
- if mc_ref and pce:
- seqRMSEMean = np.vstack((seqRMSEMean, RMSE_Mean))
- seqRMSEStd = np.vstack((seqRMSEStd, RMSE_std))
-
- if pce and any(LOO < mod_LOO_threshold
- for LOO in ModifiedLOO):
- break
-
- print(f"Memory itr {itr_no}: I: {m_2-m_1:.2f} MB")
- print(f"Memory itr {itr_no}: II: {m_3-m_2:.2f} MB")
- print(f"Memory itr {itr_no}: III: {m_4-m_3:.2f} MB")
- print(f"Memory itr {itr_no}: IV: {m_5-m_4:.2f} MB")
- m_6 = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024
- print(f"Memory itr {itr_no}: total: {m_6:.2f} MB")
-
- # Clean up
- if len(obs_data) != 0:
- del out
- gc.collect()
- print()
- print('-'*50)
- print()
-
- # Store updated ModifiedLOO and BME in dictonary
- strKey = f'{util_f}_rep_{repIdx+1}'
- if pce:
- MetaModel.SeqModifiedLOO[strKey] = SeqModifiedLOO
- if len(MetaModel.valid_model_runs) != 0:
- MetaModel.seqValidError[strKey] = SeqValidError
-
- # Check if data is provided
- if len(obs_data) != 0:
- MetaModel.SeqBME[strKey] = SeqBME
- MetaModel.SeqKLD[strKey] = SeqKLD
- if len(MetaModel.valid_likelihoods) != 0:
- MetaModel.SeqDistHellinger[strKey] = SeqDistHellinger
- if mc_ref and pce:
- MetaModel.seqRMSEMean[strKey] = seqRMSEMean
- MetaModel.seqRMSEStd[strKey] = seqRMSEStd
-
- return MetaModel
-
- # -------------------------------------------------------------------------
- def util_VarBasedDesign(self, X_can, index, util_func='Entropy'):
- """
- Computes the exploitation scores based on:
- active learning MacKay(ALM) and active learning Cohn (ALC)
- Paper: Sequential Design with Mutual Information for Computer
- Experiments (MICE): Emulation of a Tsunami Model by Beck and Guillas
- (2016)
-
- Parameters
- ----------
- X_can : array of shape (n_samples, n_params)
- Candidate samples.
- index : int
- Model output index.
- UtilMethod : string, optional
- Exploitation utility function. The default is 'Entropy'.
-
- Returns
- -------
- float
- Score.
-
- """
- MetaModel = self.MetaModel
- ED_X = MetaModel.ExpDesign.X
- out_dict_y = MetaModel.ExpDesign.Y
- out_names = MetaModel.ModelObj.Output.names
-
- # Run the Metamodel for the candidate
- X_can = X_can.reshape(1, -1)
- Y_PC_can, std_PC_can = MetaModel.eval_metamodel(samples=X_can)
-
- if util_func.lower() == 'alm':
- # ----- Entropy/MMSE/active learning MacKay(ALM) -----
- # Compute perdiction variance of the old model
- canPredVar = {key: std_PC_can[key]**2 for key in out_names}
-
- varPCE = np.zeros((len(out_names), X_can.shape[0]))
- for KeyIdx, key in enumerate(out_names):
- varPCE[KeyIdx] = np.max(canPredVar[key], axis=1)
- score = np.max(varPCE, axis=0)
-
- elif util_func.lower() == 'eigf':
- # ----- Expected Improvement for Global fit -----
- # Find closest EDX to the candidate
- distances = distance.cdist(ED_X, X_can, 'euclidean')
- index = np.argmin(distances)
-
- # Compute perdiction error and variance of the old model
- predError = {key: Y_PC_can[key] for key in out_names}
- canPredVar = {key: std_PC_can[key]**2 for key in out_names}
-
- # Compute perdiction error and variance of the old model
- # Eq (5) from Liu et al.(2018)
- EIGF_PCE = np.zeros((len(out_names), X_can.shape[0]))
- for KeyIdx, key in enumerate(out_names):
- residual = predError[key] - out_dict_y[key][int(index)]
- var = canPredVar[key]
- EIGF_PCE[KeyIdx] = np.max(residual**2 + var, axis=1)
- score = np.max(EIGF_PCE, axis=0)
-
- return -1 * score # -1 is for minimization instead of maximization
-
- # -------------------------------------------------------------------------
- def util_BayesianActiveDesign(self, X_can, sigma2Dict, var='DKL'):
- """
- Computes scores based on Bayesian active design criterion (var).
-
- It is based on the following paper:
- Oladyshkin, Sergey, Farid Mohammadi, Ilja Kroeker, and Wolfgang Nowak.
- "Bayesian3 active learning for the gaussian process emulator using
- information theory." Entropy 22, no. 8 (2020): 890.
-
- Parameters
- ----------
- X_can : array of shape (n_samples, n_params)
- Candidate samples.
- sigma2Dict : dict
- A dictionary containing the measurement errors (sigma^2).
- var : string, optional
- BAL design criterion. The default is 'DKL'.
-
- Returns
- -------
- float
- Score.
-
- """
-
- # Evaluate the PCE metamodels at that location ???
- Y_mean_can, Y_std_can = self.MetaModel.eval_metamodel(
- samples=np.array([X_can])
- )
-
- # Get the data
- obs_data = self.observations
- n_obs = self.Model.n_obs
- # TODO: Analytical DKL
- # Sample a distribution for a normal dist
- # with Y_mean_can as the mean and Y_std_can as std.
-
- # priorMean, priorSigma2, Obs = np.empty((0)),np.empty((0)),np.empty((0))
-
- # for key in list(Y_mean_can):
- # # concatenate the measurement error
- # Obs = np.hstack((Obs,ObservationData[key]))
-
- # # concatenate the mean and variance of prior predictive
- # means, stds = Y_mean_can[key][0], Y_std_can[key][0]
- # priorMean = np.hstack((priorSigma2,means))
- # priorSigma2 = np.hstack((priorSigma2,stds**2))
-
- # # Covariance Matrix of prior
- # covPrior = np.zeros((priorSigma2.shape[0], priorSigma2.shape[0]), float)
- # np.fill_diagonal(covPrior, priorSigma2)
-
- # # Covariance Matrix of Likelihood
- # covLikelihood = np.zeros((sigma2Dict.shape[0], sigma2Dict.shape[0]), float)
- # np.fill_diagonal(covLikelihood, sigma2Dict)
-
- # # Calculate moments of the posterior (Analytical derivation)
- # n = priorSigma2.shape[0]
- # covPost = np.dot(np.dot(covPrior,np.linalg.inv(covPrior+(covLikelihood/n))),covLikelihood/n)
-
- # meanPost = np.dot(np.dot(covPrior,np.linalg.inv(covPrior+(covLikelihood/n))) , Obs) + \
- # np.dot(np.dot(covPrior,np.linalg.inv(covPrior+(covLikelihood/n))),
- # priorMean/n)
- # # Compute DKL from prior to posterior
- # term1 = np.trace(np.dot(np.linalg.inv(covPrior),covPost))
- # deltaMean = priorMean-meanPost
- # term2 = np.dot(np.dot(deltaMean,np.linalg.inv(covPrior)),deltaMean[:,None])
- # term3 = np.log(np.linalg.det(covPrior)/np.linalg.det(covPost))
- # DKL = 0.5 * (term1 + term2 - n + term3)[0]
-
- # ---------- Inner MC simulation for computing Utility Value ----------
- # Estimation of the integral via Monte Varlo integration
- MCsize = 20000
- ESS = 0
-
- while ((ESS > MCsize) or (ESS < 1)):
-
- # Sample a distribution for a normal dist
- # with Y_mean_can as the mean and Y_std_can as std.
- Y_MC, std_MC = {}, {}
- logPriorLikelihoods = np.zeros((MCsize))
- for key in list(Y_mean_can):
- means, stds = Y_mean_can[key][0], Y_std_can[key][0]
- # cov = np.zeros((means.shape[0], means.shape[0]), float)
- # np.fill_diagonal(cov, stds**2)
-
- Y_MC[key] = np.zeros((MCsize, n_obs))
- logsamples = np.zeros((MCsize, n_obs))
- for i in range(n_obs):
- NormalDensity = stats.norm(means[i], stds[i])
- Y_MC[key][:, i] = NormalDensity.rvs(MCsize)
- logsamples[:, i] = NormalDensity.logpdf(Y_MC[key][:, i])
-
- logPriorLikelihoods = np.sum(logsamples, axis=1)
- std_MC[key] = np.zeros((MCsize, means.shape[0]))
-
- # Likelihood computation (Comparison of data and simulation
- # results via PCE with candidate design)
- likelihoods = self.__normpdf(Y_MC, std_MC, obs_data, sigma2Dict)
-
- # Check the Effective Sample Size (1<ESS<MCsize)
- ESS = 1 / np.sum(np.square(likelihoods/np.nansum(likelihoods)))
-
- # Enlarge sample size if it doesn't fulfill the criteria
- if ((ESS > MCsize) or (ESS < 1)):
- MCsize *= 10
- ESS = 0
-
- # Rejection Step
- # Random numbers between 0 and 1
- unif = np.random.rand(1, MCsize)[0]
-
- # Reject the poorly performed prior
- accepted = (likelihoods/np.max(likelihoods)) >= unif
-
- # Prior-based estimation of BME
- logBME = np.log(np.nanmean(likelihoods))
-
- # Posterior-based expectation of likelihoods
- postLikelihoods = likelihoods[accepted]
- postExpLikelihoods = np.mean(np.log(postLikelihoods))
-
- # Posterior-based expectation of prior densities
- postExpPrior = np.mean(logPriorLikelihoods[accepted])
-
- # Utility function Eq.2 in Ref. (2)
- # Posterior covariance matrix after observing data y
- # Kullback-Leibler Divergence (Sergey's paper)
- if var == 'DKL':
-
- # TODO: Calculate the correction factor for BME
- # BMECorrFactor = self.BME_Corr_Weight(PCE_SparseBayes_can,
- # ObservationData, sigma2Dict)
- # BME += BMECorrFactor
- # Haun et al implementation
- # U_J_d = np.mean(np.log(Likelihoods[Likelihoods!=0])- logBME)
- U_J_d = postExpLikelihoods - logBME
-
- # Marginal log likelihood
- elif var == 'BME':
- U_J_d = logBME
-
- # Entropy-based information gain
- elif var == 'infEntropy':
- logBME = np.log(np.nanmean(likelihoods))
- infEntropy = logBME - postExpPrior - postExpLikelihoods
- U_J_d = infEntropy * -1 # -1 for minimization
-
- # Bayesian information criterion
- elif var == 'BIC':
- coeffs = self.MetaModel.coeffs_dict.values()
- nModelParams = max(len(v) for val in coeffs for v in val.values())
- maxL = np.nanmax(likelihoods)
- U_J_d = -2 * np.log(maxL) + np.log(n_obs) * nModelParams
-
- # Akaike information criterion
- elif var == 'AIC':
- coeffs = self.MetaModel.coeffs_dict.values()
- nModelParams = max(len(v) for val in coeffs for v in val.values())
- maxlogL = np.log(np.nanmax(likelihoods))
- AIC = -2 * maxlogL + 2 * nModelParams
- # 2 * nModelParams * (nModelParams+1) / (n_obs-nModelParams-1)
- penTerm = 0
- U_J_d = 1*(AIC + penTerm)
-
- # Deviance information criterion
- elif var == 'DIC':
- # D_theta_bar = np.mean(-2 * Likelihoods)
- N_star_p = 0.5 * np.var(np.log(likelihoods[likelihoods != 0]))
- Likelihoods_theta_mean = self.__normpdf(
- Y_mean_can, Y_std_can, obs_data, sigma2Dict
- )
- DIC = -2 * np.log(Likelihoods_theta_mean) + 2 * N_star_p
-
- U_J_d = DIC
-
- else:
- print('The algorithm you requested has not been implemented yet!')
-
- # Handle inf and NaN (replace by zero)
- if np.isnan(U_J_d) or U_J_d == -np.inf or U_J_d == np.inf:
- U_J_d = 0.0
-
- # Clear memory
- del likelihoods
- del Y_MC
- del std_MC
- gc.collect(generation=2)
-
- return -1 * U_J_d # -1 is for minimization instead of maximization
-
- # -------------------------------------------------------------------------
- def update_metamodel(self, MetaModel, output, y_hat_can, univ_p_val, index,
- new_pca=False):
- BasisIndices = MetaModel.basis_dict[output]["y_"+str(index+1)]
- clf_poly = MetaModel.clf_poly[output]["y_"+str(index+1)]
- Mn = clf_poly.coef_
- Sn = clf_poly.sigma_
- beta = clf_poly.alpha_
- active = clf_poly.active_
- Psi = self.MetaModel.create_psi(BasisIndices, univ_p_val)
-
- Sn_new_inv = np.linalg.inv(Sn)
- Sn_new_inv += beta * np.dot(Psi[:, active].T, Psi[:, active])
- Sn_new = np.linalg.inv(Sn_new_inv)
-
- Mn_new = np.dot(Sn_new_inv, Mn[active]).reshape(-1, 1)
- Mn_new += beta * np.dot(Psi[:, active].T, y_hat_can)
- Mn_new = np.dot(Sn_new, Mn_new).flatten()
-
- # Compute the old and new moments of PCEs
- mean_old = Mn[0]
- mean_new = Mn_new[0]
- std_old = np.sqrt(np.sum(np.square(Mn[1:])))
- std_new = np.sqrt(np.sum(np.square(Mn_new[1:])))
-
- # Back transformation if PCA is selected.
- if MetaModel.dim_red_method.lower() == 'pca':
- old_pca = MetaModel.pca[output]
- mean_old = old_pca.mean_[index]
- mean_old += np.sum(mean_old * old_pca.components_[:, index])
- std_old = np.sqrt(np.sum(std_old**2 *
- old_pca.components_[:, index]**2))
- mean_new = new_pca.mean_[index]
- mean_new += np.sum(mean_new * new_pca.components_[:, index])
- std_new = np.sqrt(np.sum(std_new**2 *
- new_pca.components_[:, index]**2))
- # print(f"mean_old: {mean_old:.2f} mean_new: {mean_new:.2f}")
- # print(f"std_old: {std_old:.2f} std_new: {std_new:.2f}")
- # Store the old and new moments of PCEs
- results = {
- 'mean_old': mean_old,
- 'mean_new': mean_new,
- 'std_old': std_old,
- 'std_new': std_new
- }
- return results
-
- # -------------------------------------------------------------------------
- def util_BayesianDesign_old(self, X_can, X_MC, sigma2Dict, var='DKL'):
- """
- Computes scores based on Bayesian sequential design criterion (var).
-
- Parameters
- ----------
- X_can : array of shape (n_samples, n_params)
- Candidate samples.
- sigma2Dict : dict
- A dictionary containing the measurement errors (sigma^2).
- var : string, optional
- Bayesian design criterion. The default is 'DKL'.
-
- Returns
- -------
- float
- Score.
-
- """
-
- # To avoid changes ub original aPCE object
- Model = self.Model
- MetaModel = deepcopy(self.MetaModel)
- old_EDY = MetaModel.ExpDesign.Y
-
- # Evaluate the PCE metamodels using the candidate design
- Y_PC_can, Y_std_can = self.MetaModel.eval_metamodel(
- samples=np.array([X_can])
- )
-
- # Generate y from posterior predictive
- m_size = 100
- y_hat_samples = {}
- for idx, key in enumerate(Model.Output.names):
- means, stds = Y_PC_can[key][0], Y_std_can[key][0]
- y_hat_samples[key] = np.random.multivariate_normal(
- means, np.diag(stds), m_size)
-
- # Create the SparseBayes-based PCE metamodel:
- MetaModel.input_obj.poly_coeffs_flag = False
- univ_p_val = self.MetaModel.univ_basis_vals(X_can)
- G_n_m_all = np.zeros((m_size, len(Model.Output.names), Model.n_obs))
-
- for i in range(m_size):
- for idx, key in enumerate(Model.Output.names):
- if MetaModel.dim_red_method.lower() == 'pca':
- # Equal number of components
- new_outputs = np.vstack(
- (old_EDY[key], y_hat_samples[key][i])
- )
- new_pca, _ = MetaModel.pca_transformation(new_outputs)
- target = new_pca.transform(
- y_hat_samples[key][i].reshape(1, -1)
- )[0]
- else:
- new_pca, target = False, y_hat_samples[key][i]
-
- for j in range(len(target)):
-
- # Update surrogate
- result = self.update_metamodel(
- MetaModel, key, target[j], univ_p_val, j, new_pca)
-
- # Compute Expected Information Gain (Eq. 39)
- G_n_m = np.log(result['std_old']/result['std_new']) - 1./2
- G_n_m += result['std_new']**2 / (2*result['std_old']**2)
- G_n_m += (result['mean_new'] - result['mean_old'])**2 /\
- (2*result['std_old']**2)
-
- G_n_m_all[i, idx, j] = G_n_m
-
- U_J_d = G_n_m_all.mean(axis=(1, 2)).mean()
- return -1 * U_J_d
-
- # -------------------------------------------------------------------------
- def util_BayesianDesign(self, X_can, X_MC, sigma2Dict, var='DKL'):
- """
- Computes scores based on Bayesian sequential design criterion (var).
-
- Parameters
- ----------
- X_can : array of shape (n_samples, n_params)
- Candidate samples.
- sigma2Dict : dict
- A dictionary containing the measurement errors (sigma^2).
- var : string, optional
- Bayesian design criterion. The default is 'DKL'.
-
- Returns
- -------
- float
- Score.
-
- """
-
- # To avoid changes ub original aPCE object
- Model = self.Model
- MetaModel = deepcopy(self.MetaModel)
- out_names = MetaModel.ModelObj.Output.names
- if X_can.ndim == 1:
- X_can = X_can.reshape(1, -1)
-
- # Compute the mean and std based on the MetaModel
- # pce_means, pce_stds = self._compute_pce_moments(MetaModel)
- if var == 'ALC':
- Y_MC, Y_MC_std = MetaModel.eval_metamodel(samples=X_MC)
-
- # Old Experimental design
- oldExpDesignX = MetaModel.ExpDesign.X
- oldExpDesignY = MetaModel.ExpDesign.Y
-
- # Evaluate the PCE metamodels at that location ???
- Y_PC_can, Y_std_can = MetaModel.eval_metamodel(samples=X_can)
-
- # Add all suggestion as new ExpDesign
- NewExpDesignX = np.vstack((oldExpDesignX, X_can))
-
- NewExpDesignY = {}
- for key in oldExpDesignY.keys():
- try:
- NewExpDesignY[key] = np.vstack((oldExpDesignY[key],
- Y_PC_can[key]))
- except:
- NewExpDesignY[key] = oldExpDesignY[key]
-
- MetaModel.ExpDesign.sampling_method = 'user'
- MetaModel.ExpDesign.X = NewExpDesignX
- MetaModel.ExpDesign.Y = NewExpDesignY
-
- # Train the model for the observed data using x_can
- MetaModel.input_obj.poly_coeffs_flag = False
- MetaModel.train_norm_design(parallel=False)
- PCE_Model_can = MetaModel
-
- if var.lower() == 'mi':
- # Mutual information based on Krause et al
- # Adapted from Beck & Guillas (MICE) paper
- _, std_PC_can = PCE_Model_can.eval_metamodel(samples=X_can)
- std_can = {key: std_PC_can[key] for key in out_names}
-
- std_old = {key: Y_std_can[key] for key in out_names}
-
- varPCE = np.zeros((len(out_names)))
- for i, key in enumerate(out_names):
- varPCE[i] = np.mean(std_old[key]**2/std_can[key]**2)
- score = np.mean(varPCE)
-
- return -1 * score
-
- elif var.lower() == 'alc':
- # Active learning based on Gramyc and Lee
- # Adaptive design and analysis of supercomputer experiments Techno-
- # metrics, 51 (2009), pp. 130–145.
-
- # Evaluate the MetaModel at the given samples
- Y_MC_can, Y_MC_std_can = PCE_Model_can.eval_metamodel(samples=X_MC)
-
- # Compute the score
- score = []
- for i, key in enumerate(out_names):
- pce_var = Y_MC_std_can[key]**2
- pce_var_can = Y_MC_std[key]**2
- score.append(np.mean(pce_var-pce_var_can, axis=0))
- score = np.mean(score)
-
- return -1 * score
-
- # ---------- Inner MC simulation for computing Utility Value ----------
- # Estimation of the integral via Monte Varlo integration
- MCsize = X_MC.shape[0]
- ESS = 0
-
- while ((ESS > MCsize) or (ESS < 1)):
-
- # Enriching Monte Carlo samples if need be
- if ESS != 0:
- X_MC = self.MetaModel.ExpDesign.generate_samples(
- MCsize, 'random'
- )
-
- # Evaluate the MetaModel at the given samples
- Y_MC, std_MC = PCE_Model_can.eval_metamodel(samples=X_MC)
-
- # Likelihood computation (Comparison of data and simulation
- # results via PCE with candidate design)
- likelihoods = self.__normpdf(
- Y_MC, std_MC, self.observations, sigma2Dict
- )
-
- # Check the Effective Sample Size (1<ESS<MCsize)
- ESS = 1 / np.sum(np.square(likelihoods/np.sum(likelihoods)))
-
- # Enlarge sample size if it doesn't fulfill the criteria
- if ((ESS > MCsize) or (ESS < 1)):
- print("--- increasing MC size---")
- MCsize *= 10
- ESS = 0
-
- # Rejection Step
- # Random numbers between 0 and 1
- unif = np.random.rand(1, MCsize)[0]
-
- # Reject the poorly performed prior
- accepted = (likelihoods/np.max(likelihoods)) >= unif
-
- # -------------------- Utility functions --------------------
- # Utility function Eq.2 in Ref. (2)
- # Kullback-Leibler Divergence (Sergey's paper)
- if var == 'DKL':
-
- # Prior-based estimation of BME
- logBME = np.log(np.nanmean(likelihoods, dtype=np.float128))
-
- # Posterior-based expectation of likelihoods
- postLikelihoods = likelihoods[accepted]
- postExpLikelihoods = np.mean(np.log(postLikelihoods))
-
- # Haun et al implementation
- U_J_d = np.mean(np.log(likelihoods[likelihoods != 0]) - logBME)
-
- # U_J_d = np.sum(G_n_m_all)
- # Ryan et al (2014) implementation
- # importanceWeights = Likelihoods[Likelihoods!=0]/np.sum(Likelihoods[Likelihoods!=0])
- # U_J_d = np.mean(importanceWeights*np.log(Likelihoods[Likelihoods!=0])) - logBME
-
- # U_J_d = postExpLikelihoods - logBME
-
- # Marginal likelihood
- elif var == 'BME':
-
- # Prior-based estimation of BME
- logBME = np.log(np.nanmean(likelihoods))
- U_J_d = logBME
-
- # Bayes risk likelihood
- elif var == 'BayesRisk':
-
- U_J_d = -1 * np.var(likelihoods)
-
- # Entropy-based information gain
- elif var == 'infEntropy':
- # Prior-based estimation of BME
- logBME = np.log(np.nanmean(likelihoods))
-
- # Posterior-based expectation of likelihoods
- postLikelihoods = likelihoods[accepted] / np.nansum(likelihoods[accepted])
- postExpLikelihoods = np.mean(np.log(postLikelihoods))
-
- # Posterior-based expectation of prior densities
- postExpPrior = np.mean(logPriorLikelihoods[accepted])
-
- infEntropy = logBME - postExpPrior - postExpLikelihoods
-
- U_J_d = infEntropy * -1 # -1 for minimization
-
- # D-Posterior-precision
- elif var == 'DPP':
- X_Posterior = X_MC[accepted]
- # covariance of the posterior parameters
- U_J_d = -np.log(np.linalg.det(np.cov(X_Posterior)))
-
- # A-Posterior-precision
- elif var == 'APP':
- X_Posterior = X_MC[accepted]
- # trace of the posterior parameters
- U_J_d = -np.log(np.trace(np.cov(X_Posterior)))
-
- else:
- print('The algorithm you requested has not been implemented yet!')
-
- # Clear memory
- del likelihoods
- del Y_MC
- del std_MC
- gc.collect(generation=2)
-
- return -1 * U_J_d # -1 is for minimization instead of maximization
-
- # -------------------------------------------------------------------------
- def subdomain(self, Bounds, n_new_samples):
- """
- Divides a domain defined by Bounds into sub domains.
-
- Parameters
- ----------
- Bounds : list of tuples
- List of lower and upper bounds.
- n_new_samples : TYPE
- DESCRIPTION.
-
- Returns
- -------
- Subdomains : TYPE
- DESCRIPTION.
-
- """
- n_params = self.MetaModel.n_params
- n_subdomains = n_new_samples + 1
- LinSpace = np.zeros((n_params, n_subdomains))
-
- for i in range(n_params):
- LinSpace[i] = np.linspace(start=Bounds[i][0], stop=Bounds[i][1],
- num=n_subdomains)
- Subdomains = []
- for k in range(n_subdomains-1):
- mylist = []
- for i in range(n_params):
- mylist.append((LinSpace[i, k+0], LinSpace[i, k+1]))
- Subdomains.append(tuple(mylist))
-
- return Subdomains
-
- # -------------------------------------------------------------------------
- def run_util_func(self, method, candidates, index, sigma2Dict=None,
- var=None, X_MC=None):
- """
- Runs the utility function based on the given method.
-
- Parameters
- ----------
- method : string
- Exploitation method: `VarOptDesign`, `BayesActDesign` and
- `BayesOptDesign`.
- candidates : array of shape (n_samples, n_params)
- All candidate parameter sets.
- index : int
- ExpDesign index.
- sigma2Dict : dict, optional
- A dictionary containing the measurement errors (sigma^2). The
- default is None.
- var : string, optional
- Utility function. The default is None.
- X_MC : TYPE, optional
- DESCRIPTION. The default is None.
-
- Returns
- -------
- index : TYPE
- DESCRIPTION.
- List
- Scores.
-
- """
-
- if method.lower() == 'varoptdesign':
- # U_J_d = self.util_VarBasedDesign(candidates, index, var)
- U_J_d = np.zeros((candidates.shape[0]))
- for idx, X_can in tqdm(enumerate(candidates), ascii=True,
- desc="varoptdesign"):
- U_J_d[idx] = self.util_VarBasedDesign(X_can, index, var)
-
- elif method.lower() == 'bayesactdesign':
- NCandidate = candidates.shape[0]
- U_J_d = np.zeros((NCandidate))
- for idx, X_can in tqdm(enumerate(candidates), ascii=True,
- desc="OptBayesianDesign"):
- U_J_d[idx] = self.util_BayesianActiveDesign(X_can, sigma2Dict,
- var)
- elif method.lower() == 'bayesoptdesign':
- NCandidate = candidates.shape[0]
- U_J_d = np.zeros((NCandidate))
- for idx, X_can in tqdm(enumerate(candidates), ascii=True,
- desc="OptBayesianDesign"):
- U_J_d[idx] = self.util_BayesianDesign(X_can, X_MC, sigma2Dict,
- var)
- return (index, -1 * U_J_d)
-
- # -------------------------------------------------------------------------
- def dual_annealing(self, method, Bounds, sigma2Dict, var, Run_No,
- verbose=False):
- """
- Exploration algorithim to find the optimum parameter space.
-
- Parameters
- ----------
- method : string
- Exploitation method: `VarOptDesign`, `BayesActDesign` and
- `BayesOptDesign`.
- Bounds : list of tuples
- List of lower and upper boundaries of parameters.
- sigma2Dict : dict
- A dictionary containing the measurement errors (sigma^2).
- Run_No : int
- Run number.
- verbose : bool, optional
- Print out a summary. The default is False.
-
- Returns
- -------
- Run_No : int
- Run number.
- array
- Optimial candidate.
-
- """
-
- Model = self.Model
- max_func_itr = self.MetaModel.ExpDesign.max_func_itr
-
- if method == 'VarOptDesign':
- Res_Global = opt.dual_annealing(self.util_VarBasedDesign,
- bounds=Bounds,
- args=(Model, var),
- maxfun=max_func_itr)
-
- elif method == 'BayesOptDesign':
- Res_Global = opt.dual_annealing(self.util_BayesianDesign,
- bounds=Bounds,
- args=(Model, sigma2Dict, var),
- maxfun=max_func_itr)
-
- if verbose:
- print(f"global minimum: xmin = {Res_Global.x}, "
- f"f(xmin) = {Res_Global.fun:.6f}, nfev = {Res_Global.nfev}")
-
- return (Run_No, Res_Global.x)
-
- # -------------------------------------------------------------------------
- def tradoff_weights(self, tradeoff_scheme, old_EDX, old_EDY):
- """
- Calculates weights for exploration scores based on the requested
- scheme: `None`, `equal`, `epsilon-decreasing` and `adaptive`.
-
- `None`: No exploration.
- `equal`: Same weights for exploration and exploitation scores.
- `epsilon-decreasing`: Start with more exploration and increase the
- influence of exploitation along the way with a exponential decay
- function
- `adaptive`: An adaptive method based on:
- Liu, Haitao, Jianfei Cai, and Yew-Soon Ong. "An adaptive sampling
- approach for Kriging metamodeling by maximizing expected prediction
- error." Computers & Chemical Engineering 106 (2017): 171-182.
-
- Parameters
- ----------
- tradeoff_scheme : string
- Trade-off scheme for exloration and exploitation scores.
- old_EDX : array (n_samples, n_params)
- Old experimental design (training points).
- old_EDY : dict
- Old model responses (targets).
-
- Returns
- -------
- exploration_weight : float
- Exploration weight.
- exploitation_weight: float
- Exploitation weight.
-
- """
- if tradeoff_scheme is None:
- exploration_weight = 0
-
- elif tradeoff_scheme == 'equal':
- exploration_weight = 0.5
-
- elif tradeoff_scheme == 'epsilon-decreasing':
- # epsilon-decreasing scheme
- # Start with more exploration and increase the influence of
- # exploitation along the way with a exponential decay function
- initNSamples = self.MetaModel.ExpDesign.n_init_samples
- n_max_samples = self.MetaModel.ExpDesign.n_max_samples
-
- itrNumber = (self.MetaModel.ExpDesign.X.shape[0] - initNSamples)
- itrNumber //= self.MetaModel.ExpDesign.n_new_samples
-
- tau2 = -(n_max_samples-initNSamples-1) / np.log(1e-8)
- exploration_weight = signal.exponential(n_max_samples-initNSamples,
- 0, tau2, False)[itrNumber]
-
- elif tradeoff_scheme == 'adaptive':
-
- # Extract itrNumber
- initNSamples = self.MetaModel.ExpDesign.n_init_samples
- n_max_samples = self.MetaModel.ExpDesign.n_max_samples
- itrNumber = (self.MetaModel.ExpDesign.X.shape[0] - initNSamples)
- itrNumber //= self.MetaModel.ExpDesign.n_new_samples
-
- if itrNumber == 0:
- exploration_weight = 0.5
- else:
- # New adaptive trade-off according to Liu et al. (2017)
- # Mean squared error for last design point
- last_EDX = old_EDX[-1].reshape(1, -1)
- lastPCEY, _ = self.MetaModel.eval_metamodel(samples=last_EDX)
- pce_y = np.array(list(lastPCEY.values()))[:, 0]
- y = np.array(list(old_EDY.values()))[:, -1, :]
- mseError = mean_squared_error(pce_y, y)
-
- # Mean squared CV - error for last design point
- pce_y_prev = np.array(list(self._y_hat_prev.values()))[:, 0]
- mseCVError = mean_squared_error(pce_y_prev, y)
-
- exploration_weight = min([0.5*mseError/mseCVError, 1])
-
- # Exploitation weight
- exploitation_weight = 1 - exploration_weight
-
- return exploration_weight, exploitation_weight
-
- # -------------------------------------------------------------------------
- def opt_SeqDesign(self, sigma2, n_candidates=5, var='DKL'):
- """
- Runs optimal sequential design.
-
- Parameters
- ----------
- sigma2 : dict, optional
- A dictionary containing the measurement errors (sigma^2). The
- default is None.
- n_candidates : int, optional
- Number of candidate samples. The default is 5.
- var : string, optional
- Utility function. The default is None.
-
- Raises
- ------
- NameError
- Wrong utility function.
-
- Returns
- -------
- Xnew : array (n_samples, n_params)
- Selected new training point(s).
- """
-
- # Initialization
- MetaModel = self.MetaModel
- Bounds = MetaModel.bound_tuples
- n_new_samples = MetaModel.ExpDesign.n_new_samples
- explore_method = MetaModel.ExpDesign.explore_method
- exploit_method = MetaModel.ExpDesign.exploit_method
- n_cand_groups = MetaModel.ExpDesign.n_cand_groups
- tradeoff_scheme = MetaModel.ExpDesign.tradeoff_scheme
-
- old_EDX = MetaModel.ExpDesign.X
- old_EDY = MetaModel.ExpDesign.Y.copy()
- ndim = MetaModel.ExpDesign.X.shape[1]
- OutputNames = MetaModel.ModelObj.Output.names
-
- # -----------------------------------------
- # ----------- CUSTOMIZED METHODS ----------
- # -----------------------------------------
- # Utility function exploit_method provided by user
- if exploit_method.lower() == 'user':
-
- Xnew, filteredSamples = MetaModel.ExpDesign.ExploitFunction(self)
-
- print("\n")
- print("\nXnew:\n", Xnew)
-
- return Xnew, filteredSamples
-
- # -----------------------------------------
- # ---------- EXPLORATION METHODS ----------
- # -----------------------------------------
- if explore_method == 'dual annealing':
- # ------- EXPLORATION: OPTIMIZATION -------
- import time
- start_time = time.time()
-
- # Divide the domain to subdomains
- args = []
- subdomains = self.subdomain(Bounds, n_new_samples)
- for i in range(n_new_samples):
- args.append((exploit_method, subdomains[i], sigma2, var, i))
-
- # Multiprocessing
- pool = multiprocessing.Pool(multiprocessing.cpu_count())
-
- # With Pool.starmap_async()
- results = pool.starmap_async(self.dual_annealing, args).get()
-
- # Close the pool
- pool.close()
-
- Xnew = np.array([results[i][1] for i in range(n_new_samples)])
-
- print("\nXnew:\n", Xnew)
-
- elapsed_time = time.time() - start_time
- print("\n")
- print(f"elapsed_time: {round(elapsed_time,2)} sec.")
- print('-'*20)
-
- elif explore_method == 'LOOCV':
- # -----------------------------------------------------------------
- # TODO: LOOCV model construnction based on Feng et al. (2020)
- # 'LOOCV':
- # Initilize the ExploitScore array
-
- # Generate random samples
- allCandidates = MetaModel.ExpDesign.generate_samples(n_candidates,
- 'random')
-
- # Construct error model based on LCerror
- errorModel = MetaModel.create_ModelError(old_EDX, self.LCerror)
- self.errorModel.append(copy(errorModel))
-
- # Evaluate the error models for allCandidates
- eLCAllCands, _ = errorModel.eval_errormodel(allCandidates)
- # Select the maximum as the representative error
- eLCAllCands = np.dstack(eLCAllCands.values())
- eLCAllCandidates = np.max(eLCAllCands, axis=1)[:, 0]
-
- # Normalize the error w.r.t the maximum error
- scoreExploration = eLCAllCandidates / np.sum(eLCAllCandidates)
-
- else:
- # ------- EXPLORATION: SPACE-FILLING DESIGN -------
- # Generate candidate samples from Exploration class
- explore = Exploration(MetaModel, n_candidates)
- explore.w = 100 # * ndim #500
- # Select criterion (mc-intersite-proj-th, mc-intersite-proj)
- explore.mc_criterion = 'mc-intersite-proj'
- allCandidates, scoreExploration = explore.get_exploration_samples()
-
- # Temp: ---- Plot all candidates -----
- if ndim == 2:
- def plotter(points, allCandidates, Method,
- scoreExploration=None):
- if Method == 'Voronoi':
- from scipy.spatial import Voronoi, voronoi_plot_2d
- vor = Voronoi(points)
- fig = voronoi_plot_2d(vor)
- ax1 = fig.axes[0]
- else:
- fig = plt.figure()
- ax1 = fig.add_subplot(111)
- ax1.scatter(points[:, 0], points[:, 1], s=10, c='r',
- marker="s", label='Old Design Points')
- ax1.scatter(allCandidates[:, 0], allCandidates[:, 1], s=10,
- c='b', marker="o", label='Design candidates')
- for i in range(points.shape[0]):
- txt = 'p'+str(i+1)
- ax1.annotate(txt, (points[i, 0], points[i, 1]))
- if scoreExploration is not None:
- for i in range(allCandidates.shape[0]):
- txt = str(round(scoreExploration[i], 5))
- ax1.annotate(txt, (allCandidates[i, 0],
- allCandidates[i, 1]))
-
- plt.xlim(self.bound_tuples[0])
- plt.ylim(self.bound_tuples[1])
- # plt.show()
- plt.legend(loc='upper left')
-
- # -----------------------------------------
- # --------- EXPLOITATION METHODS ----------
- # -----------------------------------------
- if exploit_method == 'BayesOptDesign' or\
- exploit_method == 'BayesActDesign':
-
- # ------- Calculate Exoploration weight -------
- # Compute exploration weight based on trade off scheme
- explore_w, exploit_w = self.tradoff_weights(tradeoff_scheme,
- old_EDX,
- old_EDY)
- print(f"\n Exploration weight={explore_w:0.3f} "
- f"Exploitation weight={exploit_w:0.3f}\n")
-
- # ------- EXPLOITATION: BayesOptDesign & ActiveLearning -------
- if explore_w != 1.0:
-
- # Create a sample pool for rejection sampling
- MCsize = 15000
- X_MC = MetaModel.ExpDesign.generate_samples(MCsize, 'random')
- candidates = MetaModel.ExpDesign.generate_samples(
- MetaModel.ExpDesign.max_func_itr, 'latin_hypercube')
-
- # Split the candidates in groups for multiprocessing
- split_cand = np.array_split(
- candidates, n_cand_groups, axis=0
- )
-
- results = Parallel(n_jobs=-1, backend='threading')(
- delayed(self.run_util_func)(
- exploit_method, split_cand[i], i, sigma2, var, X_MC)
- for i in range(n_cand_groups))
- # out = map(self.run_util_func,
- # [exploit_method]*n_cand_groups,
- # split_cand,
- # range(n_cand_groups),
- # [sigma2] * n_cand_groups,
- # [var] * n_cand_groups,
- # [X_MC] * n_cand_groups
- # )
- # results = list(out)
-
- # Retrieve the results and append them
- U_J_d = np.concatenate([results[NofE][1] for NofE in
- range(n_cand_groups)])
-
- # Check if all scores are inf
- if np.isinf(U_J_d).all() or np.isnan(U_J_d).all():
- U_J_d = np.ones(len(U_J_d))
-
- # Get the expected value (mean) of the Utility score
- # for each cell
- if explore_method == 'Voronoi':
- U_J_d = np.mean(U_J_d.reshape(-1, n_candidates), axis=1)
-
- # create surrogate model for U_J_d
- from sklearn.preprocessing import MinMaxScaler
- # Take care of inf entries
- good_indices = [i for i, arr in enumerate(U_J_d)
- if np.isfinite(arr).all()]
- scaler = MinMaxScaler()
- X_S = scaler.fit_transform(candidates[good_indices])
- gp = MetaModel.gaussian_process_emulator(
- X_S, U_J_d[good_indices], autoSelect=True
- )
- U_J_d = gp.predict(scaler.transform(allCandidates))
-
- # Normalize U_J_d
- norm_U_J_d = U_J_d / np.sum(U_J_d)
- print("norm_U_J_d:\n", norm_U_J_d)
- else:
- norm_U_J_d = np.zeros((len(scoreExploration)))
-
- # ------- Calculate Total score -------
- # ------- Trade off between EXPLORATION & EXPLOITATION -------
- # Total score
- totalScore = exploit_w * norm_U_J_d
- totalScore += explore_w * scoreExploration
-
- # temp: Plot
- # dim = self.ExpDesign.X.shape[1]
- # if dim == 2:
- # plotter(self.ExpDesign.X, allCandidates, explore_method)
-
- # ------- Select the best candidate -------
- # 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
- sorted_idxtotalScore = np.argsort(temp)[::-1]
- bestIdx = sorted_idxtotalScore[:n_new_samples]
-
- # select the requested number of samples
- if explore_method == 'Voronoi':
- Xnew = np.zeros((n_new_samples, ndim))
- for i, idx in enumerate(bestIdx):
- X_can = explore.closestPoints[idx]
-
- # Calculate the maxmin score for the region of interest
- newSamples, maxminScore = explore.get_mc_samples(X_can)
-
- # select the requested number of samples
- Xnew[i] = newSamples[np.argmax(maxminScore)]
- else:
- Xnew = allCandidates[sorted_idxtotalScore[:n_new_samples]]
-
- elif exploit_method == 'VarOptDesign':
- # ------- EXPLOITATION: VarOptDesign -------
- UtilMethod = var
-
- # ------- Calculate Exoploration weight -------
- # Compute exploration weight based on trade off scheme
- explore_w, exploit_w = self.tradoff_weights(tradeoff_scheme,
- old_EDX,
- old_EDY)
- print(f"\nweightExploration={explore_w:0.3f} "
- f"weightExploitation={exploit_w:0.3f}")
-
- # Generate candidate samples from Exploration class
- nMeasurement = old_EDY[OutputNames[0]].shape[1]
-
- # Find sensitive region
- if UtilMethod == 'LOOCV':
- LCerror = MetaModel.LCerror
- allModifiedLOO = np.zeros((len(old_EDX), len(OutputNames),
- nMeasurement))
- for y_idx, y_key in enumerate(OutputNames):
- for idx, key in enumerate(LCerror[y_key].keys()):
- allModifiedLOO[:, y_idx, idx] = abs(
- LCerror[y_key][key])
-
- ExploitScore = np.max(np.max(allModifiedLOO, axis=1), axis=1)
-
- elif UtilMethod in ['EIGF', 'ALM']:
- # ----- All other in ['EIGF', 'ALM'] -----
- # Initilize the ExploitScore array
- ExploitScore = np.zeros((len(old_EDX), len(OutputNames)))
-
- # Split the candidates in groups for multiprocessing
- if explore_method != 'Voronoi':
- split_cand = np.array_split(allCandidates,
- n_cand_groups,
- axis=0)
- goodSampleIdx = range(n_cand_groups)
- else:
- # Find indices of the Vornoi cells with samples
- goodSampleIdx = []
- for idx in range(len(explore.closest_points)):
- if len(explore.closest_points[idx]) != 0:
- goodSampleIdx.append(idx)
- split_cand = explore.closest_points
-
- # Split the candidates in groups for multiprocessing
- args = []
- for index in goodSampleIdx:
- args.append((exploit_method, split_cand[index], index,
- sigma2, var))
-
- # Multiprocessing
- pool = multiprocessing.Pool(multiprocessing.cpu_count())
- # With Pool.starmap_async()
- results = pool.starmap_async(self.run_util_func, args).get()
-
- # Close the pool
- pool.close()
- # out = map(self.run_util_func,
- # [exploit_method]*len(goodSampleIdx),
- # split_cand,
- # range(len(goodSampleIdx)),
- # [sigma2] * len(goodSampleIdx),
- # [var] * len(goodSampleIdx)
- # )
- # results = list(out)
-
- # Retrieve the results and append them
- if explore_method == 'Voronoi':
- ExploitScore = [np.mean(results[k][1]) for k in
- range(len(goodSampleIdx))]
- else:
- ExploitScore = np.concatenate(
- [results[k][1] for k in range(len(goodSampleIdx))])
-
- else:
- raise NameError('The requested utility function is not '
- 'available.')
-
- # print("ExploitScore:\n", ExploitScore)
-
- # find an optimal point subset to add to the initial design by
- # maximization of the utility score and taking care of NaN values
- # Total score
- # Normalize U_J_d
- ExploitScore = ExploitScore / np.sum(ExploitScore)
- totalScore = exploit_w * ExploitScore
- totalScore += explore_w * scoreExploration
-
- temp = totalScore.copy()
- sorted_idxtotalScore = np.argsort(temp, axis=0)[::-1]
- bestIdx = sorted_idxtotalScore[:n_new_samples]
-
- Xnew = np.zeros((n_new_samples, ndim))
- if explore_method != 'Voronoi':
- Xnew = allCandidates[bestIdx]
- else:
- for i, idx in enumerate(bestIdx.flatten()):
- X_can = explore.closest_points[idx]
- # plotter(self.ExpDesign.X, X_can, explore_method,
- # scoreExploration=None)
-
- # Calculate the maxmin score for the region of interest
- newSamples, maxminScore = explore.get_mc_samples(X_can)
-
- # select the requested number of samples
- Xnew[i] = newSamples[np.argmax(maxminScore)]
-
- elif exploit_method == 'alphabetic':
- # ------- EXPLOITATION: ALPHABETIC -------
- Xnew = self.util_AlphOptDesign(allCandidates, var)
-
- elif exploit_method == 'Space-filling':
- # ------- EXPLOITATION: SPACE-FILLING -------
- totalScore = scoreExploration
-
- # ------- Select the best candidate -------
- # 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
- sorted_idxtotalScore = np.argsort(temp)[::-1]
-
- # select the requested number of samples
- Xnew = allCandidates[sorted_idxtotalScore[:n_new_samples]]
-
- else:
- raise NameError('The requested design method is not available.')
-
- print("\n")
- print("\nRun No. {}:".format(old_EDX.shape[0]+1))
- print("Xnew:\n", Xnew)
- gc.collect()
-
- return Xnew, None
-
- # -------------------------------------------------------------------------
- def util_AlphOptDesign(self, candidates, var='D-Opt'):
- """
- Enriches the Experimental design with the requested alphabetic
- criterion based on exploring the space with number of sampling points.
-
- Ref: Hadigol, M., & Doostan, A. (2018). Least squares polynomial chaos
- expansion: A review of sampling strategies., Computer Methods in
- Applied Mechanics and Engineering, 332, 382-407.
-
- Arguments
- ---------
- NCandidate : int
- Number of candidate points to be searched
-
- var : string
- Alphabetic optimality criterion
-
- Returns
- -------
- X_new : array of shape (1, n_params)
- The new sampling location in the input space.
- """
- MetaModelOrig = self
- Model = self.Model
- n_new_samples = MetaModelOrig.ExpDesign.n_new_samples
- NCandidate = candidates.shape[0]
-
- # TODO: Loop over outputs
- OutputName = Model.Output.names[0]
-
- # To avoid changes ub original aPCE object
- MetaModel = deepcopy(MetaModelOrig)
-
- # Old Experimental design
- oldExpDesignX = MetaModel.ExpDesign.X
-
- # TODO: Only one psi can be selected.
- # Suggestion: Go for the one with the highest LOO error
- Scores = list(MetaModel.score_dict[OutputName].values())
- ModifiedLOO = [1-score for score in Scores]
- outIdx = np.argmax(ModifiedLOO)
-
- # Initialize Phi to save the criterion's values
- Phi = np.zeros((NCandidate))
-
- BasisIndices = MetaModelOrig.basis_dict[OutputName]["y_"+str(outIdx+1)]
- P = len(BasisIndices)
-
- # ------ Old Psi ------------
- univ_p_val = MetaModelOrig.univ_basis_vals(oldExpDesignX)
- Psi = MetaModelOrig.create_psi(BasisIndices, univ_p_val)
-
- # ------ New candidates (Psi_c) ------------
- # Assemble Psi_c
- univ_p_val_c = self.univ_basis_vals(candidates)
- Psi_c = self.create_psi(BasisIndices, univ_p_val_c)
-
- for idx in range(NCandidate):
-
- # Include the new row to the original Psi
- Psi_cand = np.vstack((Psi, Psi_c[idx]))
-
- # Information matrix
- PsiTPsi = np.dot(Psi_cand.T, Psi_cand)
- M = PsiTPsi / (len(oldExpDesignX)+1)
-
- if np.linalg.cond(PsiTPsi) > 1e-12 \
- and np.linalg.cond(PsiTPsi) < 1 / sys.float_info.epsilon:
- # faster
- invM = linalg.solve(M, sparse.eye(PsiTPsi.shape[0]).toarray())
- else:
- # stabler
- invM = np.linalg.pinv(M)
-
- # ---------- Calculate optimality criterion ----------
- # Optimality criteria according to Section 4.5.1 in Ref.
-
- # D-Opt
- if var == 'D-Opt':
- Phi[idx] = (np.linalg.det(invM)) ** (1/P)
-
- # A-Opt
- elif var == 'A-Opt':
- Phi[idx] = np.trace(invM)
-
- # K-Opt
- elif var == 'K-Opt':
- Phi[idx] = np.linalg.cond(M)
-
- else:
- raise Exception('The optimality criterion you requested has '
- 'not been implemented yet!')
-
- # find an optimal point subset to add to the initial design
- # by minimization of the Phi
- sorted_idxtotalScore = np.argsort(Phi)
-
- # select the requested number of samples
- Xnew = candidates[sorted_idxtotalScore[:n_new_samples]]
-
- return Xnew
-
- # -------------------------------------------------------------------------
- def __normpdf(self, y_hat_pce, std_pce, obs_data, total_sigma2s,
- rmse=None):
-
- Model = self.Model
- likelihoods = 1.0
-
- # Loop over the outputs
- for idx, out in enumerate(Model.Output.names):
-
- # (Meta)Model Output
- nsamples, nout = y_hat_pce[out].shape
-
- # Prepare data and remove NaN
- try:
- data = obs_data[out].values[~np.isnan(obs_data[out])]
- except AttributeError:
- data = obs_data[out][~np.isnan(obs_data[out])]
-
- # Prepare sigma2s
- non_nan_indices = ~np.isnan(total_sigma2s[out])
- tot_sigma2s = total_sigma2s[out][non_nan_indices][:nout].values
-
- # Surrogate error if valid dataset is given.
- if rmse is not None:
- tot_sigma2s += rmse[out]**2
-
- likelihoods *= stats.multivariate_normal.pdf(
- y_hat_pce[out], data, np.diag(tot_sigma2s),
- allow_singular=True)
- self.Likelihoods = likelihoods
-
- return likelihoods
-
- # -------------------------------------------------------------------------
- def __corr_factor_BME(self, obs_data, total_sigma2s, logBME):
- """
- Calculates the correction factor for BMEs.
- """
- MetaModel = self.MetaModel
- samples = MetaModel.ExpDesign.X # valid_samples
- model_outputs = MetaModel.ExpDesign.Y # valid_model_runs
- Model = MetaModel.ModelObj
- n_samples = samples.shape[0]
-
- # Extract the requested model outputs for likelihood calulation
- output_names = Model.Output.names
-
- # TODO: Evaluate MetaModel on the experimental design and ValidSet
- OutputRS, stdOutputRS = MetaModel.eval_metamodel(samples=samples)
-
- logLik_data = np.zeros((n_samples))
- logLik_model = np.zeros((n_samples))
- # Loop over the outputs
- for idx, out in enumerate(output_names):
-
- # (Meta)Model Output
- nsamples, nout = model_outputs[out].shape
-
- # Prepare data and remove NaN
- try:
- data = obs_data[out].values[~np.isnan(obs_data[out])]
- except AttributeError:
- data = obs_data[out][~np.isnan(obs_data[out])]
-
- # Prepare sigma2s
- non_nan_indices = ~np.isnan(total_sigma2s[out])
- tot_sigma2s = total_sigma2s[out][non_nan_indices][:nout]
-
- # Covariance Matrix
- covMatrix_data = np.diag(tot_sigma2s)
-
- for i, sample in enumerate(samples):
-
- # Simulation run
- y_m = model_outputs[out][i]
-
- # Surrogate prediction
- y_m_hat = OutputRS[out][i]
-
- # CovMatrix with the surrogate error
- # covMatrix = np.diag(stdOutputRS[out][i]**2)
- covMatrix = np.diag((y_m-y_m_hat)**2)
- covMatrix = np.diag(
- np.mean((model_outputs[out]-OutputRS[out]), axis=0)**2
- )
-
- # Compute likelilhood output vs data
- logLik_data[i] += self.__logpdf(
- y_m_hat, data, covMatrix_data
- )
-
- # Compute likelilhood output vs surrogate
- logLik_model[i] += self.__logpdf(y_m_hat, y_m, covMatrix)
-
- # Weight
- logLik_data -= logBME
- weights = np.exp(logLik_model+logLik_data)
-
- return np.log(np.mean(weights))
-
- # -------------------------------------------------------------------------
- def __logpdf(self, x, mean, cov):
- """
- computes the likelihood based on a multivariate normal distribution.
-
- Parameters
- ----------
- x : TYPE
- DESCRIPTION.
- mean : array_like
- Observation data.
- cov : 2d array
- Covariance matrix of the distribution.
-
- Returns
- -------
- log_lik : float
- Log likelihood.
-
- """
- n = len(mean)
- L = linalg.cholesky(cov, lower=True)
- beta = np.sum(np.log(np.diag(L)))
- dev = x - mean
- alpha = dev.dot(linalg.cho_solve((L, True), dev))
- log_lik = -0.5 * alpha - beta - n / 2. * np.log(2 * np.pi)
-
- return log_lik
-
- # -------------------------------------------------------------------------
- def __posteriorPlot(self, posterior, par_names, key):
-
- # Initialization
- newpath = (r'Outputs_SeqPosteriorComparison/posterior')
- os.makedirs(newpath, exist_ok=True)
-
- bound_tuples = self.MetaModel.bound_tuples
- n_params = len(par_names)
- font_size = 40
- if n_params == 2:
-
- figPosterior, ax = plt.subplots(figsize=(15, 15))
-
- sns.kdeplot(x=posterior[:, 0], y=posterior[:, 1],
- fill=True, ax=ax, cmap=plt.cm.jet,
- clip=bound_tuples)
- # Axis labels
- plt.xlabel(par_names[0], fontsize=font_size)
- plt.ylabel(par_names[1], fontsize=font_size)
-
- # Set axis limit
- plt.xlim(bound_tuples[0])
- plt.ylim(bound_tuples[1])
-
- # Increase font size
- plt.xticks(fontsize=font_size)
- plt.yticks(fontsize=font_size)
-
- # Switch off the grids
- plt.grid(False)
-
- else:
- import corner
- figPosterior = corner.corner(posterior, labels=par_names,
- title_fmt='.2e', show_titles=True,
- title_kwargs={"fontsize": 12})
-
- figPosterior.savefig(f'./{newpath}/{key}.pdf', bbox_inches='tight')
- plt.close()
-
- # Save the posterior as .npy
- np.save(f'./{newpath}/{key}.npy', posterior)
-
- return figPosterior
-
- # -------------------------------------------------------------------------
- def __hellinger_distance(self, P, Q):
- """
- Hellinger distance between two continuous distributions.
-
- The maximum distance 1 is achieved when P assigns probability zero to
- every set to which Q assigns a positive probability, and vice versa.
- 0 (identical) and 1 (maximally different)
-
- Parameters
- ----------
- P : array
- Reference likelihood.
- Q : array
- Estimated likelihood.
-
- Returns
- -------
- float
- Hellinger distance of two distributions.
-
- """
- mu1 = P.mean()
- Sigma1 = np.std(P)
-
- mu2 = Q.mean()
- Sigma2 = np.std(Q)
-
- term1 = np.sqrt(2*Sigma1*Sigma2 / (Sigma1**2 + Sigma2**2))
-
- term2 = np.exp(-.25 * (mu1 - mu2)**2 / (Sigma1**2 + Sigma2**2))
-
- H_squared = 1 - term1 * term2
-
- return np.sqrt(H_squared)
-
- # -------------------------------------------------------------------------
- def __BME_Calculator(self, MetaModel, obs_data, sigma2Dict, rmse=None):
- """
- This function computes the Bayesian model evidence (BME) via Monte
- Carlo integration.
-
- """
- # Initializations
- valid_likelihoods = MetaModel.valid_likelihoods
-
- post_snapshot = MetaModel.ExpDesign.post_snapshot
- if post_snapshot or len(valid_likelihoods) != 0:
- newpath = (r'Outputs_SeqPosteriorComparison/likelihood_vs_ref')
- os.makedirs(newpath, exist_ok=True)
-
- SamplingMethod = 'random'
- MCsize = 10000
- ESS = 0
-
- # Estimation of the integral via Monte Varlo integration
- while (ESS > MCsize) or (ESS < 1):
-
- # Generate samples for Monte Carlo simulation
- X_MC = MetaModel.ExpDesign.generate_samples(
- MCsize, SamplingMethod
- )
-
- # Monte Carlo simulation for the candidate design
- m_1 = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024
- Y_MC, std_MC = MetaModel.eval_metamodel(samples=X_MC)
- m_2 = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024
- print(f"\nMemory eval_metamodel in BME: {m_2-m_1:.2f} MB")
-
- # Likelihood computation (Comparison of data and
- # simulation results via PCE with candidate design)
- Likelihoods = self.__normpdf(
- Y_MC, std_MC, obs_data, sigma2Dict, rmse
- )
-
- # Check the Effective Sample Size (1000<ESS<MCsize)
- ESS = 1 / np.sum(np.square(Likelihoods/np.sum(Likelihoods)))
-
- # Enlarge sample size if it doesn't fulfill the criteria
- if (ESS > MCsize) or (ESS < 1):
- print(f'ESS={ESS} MC size should be larger.')
- MCsize *= 10
- ESS = 0
-
- # Rejection Step
- # Random numbers between 0 and 1
- unif = np.random.rand(1, MCsize)[0]
-
- # Reject the poorly performed prior
- accepted = (Likelihoods/np.max(Likelihoods)) >= unif
- X_Posterior = X_MC[accepted]
-
- # ------------------------------------------------------------
- # --- Kullback-Leibler Divergence & Information Entropy ------
- # ------------------------------------------------------------
- # Prior-based estimation of BME
- logBME = np.log(np.nanmean(Likelihoods))
-
- # TODO: Correction factor
- # log_weight = self.__corr_factor_BME(obs_data, sigma2Dict, logBME)
-
- # Posterior-based expectation of likelihoods
- postExpLikelihoods = np.mean(np.log(Likelihoods[accepted]))
-
- # Posterior-based expectation of prior densities
- postExpPrior = np.mean(
- np.log(MetaModel.ExpDesign.JDist.pdf(X_Posterior.T))
- )
-
- # Calculate Kullback-Leibler Divergence
- # KLD = np.mean(np.log(Likelihoods[Likelihoods!=0])- logBME)
- KLD = postExpLikelihoods - logBME
-
- # Information Entropy based on Entropy paper Eq. 38
- infEntropy = logBME - postExpPrior - postExpLikelihoods
-
- # If post_snapshot is True, plot likelihood vs refrence
- if post_snapshot or len(valid_likelihoods) != 0:
- # Hellinger distance
- ref_like = np.log(valid_likelihoods[valid_likelihoods > 0])
- est_like = np.log(Likelihoods[Likelihoods > 0])
- distHellinger = self.__hellinger_distance(ref_like, est_like)
-
- idx = len([name for name in os.listdir(newpath) if 'Likelihoods_'
- in name and os.path.isfile(os.path.join(newpath, name))])
- fig, ax = plt.subplots()
- try:
- sns.kdeplot(np.log(valid_likelihoods[valid_likelihoods > 0]),
- shade=True, color="g", label='Ref. Likelihood')
- sns.kdeplot(np.log(Likelihoods[Likelihoods > 0]), shade=True,
- color="b", label='Likelihood with PCE')
- except:
- pass
-
- text = f"Hellinger Dist.={distHellinger:.3f}\n logBME={logBME:.3f}"
- "\n DKL={KLD:.3f}"
-
- plt.text(0.05, 0.75, text, bbox=dict(facecolor='wheat',
- edgecolor='black',
- boxstyle='round,pad=1'),
- transform=ax.transAxes)
-
- fig.savefig(f'./{newpath}/Likelihoods_{idx}.pdf',
- bbox_inches='tight')
- plt.close()
-
- else:
- distHellinger = 0.0
-
- # Bayesian inference with Emulator only for 2D problem
- if post_snapshot and MetaModel.n_params == 2 and not idx % 5:
- from bayes_inference.bayes_inference import BayesInference
- from bayes_inference.discrepancy import Discrepancy
- import pandas as pd
- BayesOpts = BayesInference(MetaModel)
- BayesOpts.emulator = True
- BayesOpts.plot_post_pred = False
-
- # Select the inference method
- import emcee
- BayesOpts.inference_method = "MCMC"
- # Set the MCMC parameters passed to self.mcmc_params
- BayesOpts.mcmc_params = {
- 'n_steps': 1e5,
- 'n_walkers': 30,
- 'moves': emcee.moves.KDEMove(),
- 'verbose': False
- }
-
- # ----- Define the discrepancy model -------
- obs_data = pd.DataFrame(obs_data, columns=self.Model.Output.names)
- BayesOpts.measurement_error = obs_data
-
- # # -- (Option B) --
- DiscrepancyOpts = Discrepancy('')
- DiscrepancyOpts.type = 'Gaussian'
- DiscrepancyOpts.parameters = obs_data**2
- BayesOpts.Discrepancy = DiscrepancyOpts
- # Start the calibration/inference
- Bayes_PCE = BayesOpts.create_inference()
- X_Posterior = Bayes_PCE.posterior_df.values
-
- # Clean up
- del Y_MC, std_MC
- gc.collect()
-
- return (logBME, KLD, X_Posterior, Likelihoods, distHellinger)
-
- # -------------------------------------------------------------------------
- def __validError(self, MetaModel):
-
- # MetaModel = self.MetaModel
- Model = MetaModel.ModelObj
- OutputName = Model.Output.names
-
- # Extract the original model with the generated samples
- valid_samples = MetaModel.valid_samples
- valid_model_runs = MetaModel.valid_model_runs
-
- # Run the PCE model with the generated samples
- m_1 = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024
- valid_PCE_runs, valid_PCE_std = MetaModel.eval_metamodel(samples=valid_samples)
- m_2 = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024
- print(f"\nMemory eval_metamodel: {m_2-m_1:.2f} MB")
-
- rms_error = {}
- valid_error = {}
- # Loop over the keys and compute RMSE error.
- for key in OutputName:
- rms_error[key] = mean_squared_error(
- valid_model_runs[key], valid_PCE_runs[key],
- multioutput='raw_values',
- sample_weight=None,
- squared=False)
-
- # Validation error
- valid_error[key] = (rms_error[key]**2)
- valid_error[key] /= np.var(valid_model_runs[key], ddof=1, axis=0)
-
- # Print a report table
- print("\n>>>>> Updated Errors of {} <<<<<".format(key))
- print("\nIndex | RMSE | Validation Error")
- print('-'*35)
- print('\n'.join(f'{i+1} | {k:.3e} | {j:.3e}' for i, (k, j)
- in enumerate(zip(rms_error[key],
- valid_error[key]))))
-
- return rms_error, valid_error
-
- # -------------------------------------------------------------------------
- def __error_Mean_Std(self):
-
- MetaModel = self.MetaModel
- # Extract the mean and std provided by user
- df_MCReference = MetaModel.ModelObj.mc_reference
-
- # Compute the mean and std based on the MetaModel
- pce_means, pce_stds = self._compute_pce_moments(MetaModel)
-
- # Compute the root mean squared error
- for output in MetaModel.ModelObj.Output.names:
-
- # Compute the error between mean and std of MetaModel and OrigModel
- RMSE_Mean = mean_squared_error(
- df_MCReference['mean'], pce_means[output], squared=False
- )
- RMSE_std = mean_squared_error(
- df_MCReference['std'], pce_means[output], squared=False
- )
-
- return RMSE_Mean, RMSE_std
-
- # -------------------------------------------------------------------------
- def _compute_pce_moments(self, MetaModel):
- """
- Computes the first two moments using the PCE-based meta-model.
-
- Returns
- -------
- pce_means: dict
- The first moment (mean) of the surrogate.
- pce_stds: dict
- The second moment (standard deviation) of the surrogate.
-
- """
- outputs = MetaModel.ModelObj.Output.names
- pce_means_b = {}
- pce_stds_b = {}
-
- # Loop over bootstrap iterations
- for b_i in range(MetaModel.n_bootstrap_itrs):
- # Loop over the metamodels
- coeffs_dicts = MetaModel.coeffs_dict[f'b_{b_i+1}'].items()
- means = {}
- stds = {}
- for output, coef_dict in coeffs_dicts:
-
- pce_mean = np.zeros((len(coef_dict)))
- pce_var = np.zeros((len(coef_dict)))
-
- for index, values in coef_dict.items():
- idx = int(index.split('_')[1]) - 1
- coeffs = MetaModel.coeffs_dict[f'b_{b_i+1}'][output][index]
-
- # Mean = c_0
- if coeffs[0] != 0:
- pce_mean[idx] = coeffs[0]
- else:
- clf_poly = MetaModel.clf_poly[f'b_{b_i+1}'][output]
- pce_mean[idx] = clf_poly[index].intercept_
- # Var = sum(coeffs[1:]**2)
- pce_var[idx] = np.sum(np.square(coeffs[1:]))
-
- # Save predictions for each output
- if MetaModel.dim_red_method.lower() == 'pca':
- PCA = MetaModel.pca[f'b_{b_i+1}'][output]
- means[output] = PCA.mean_ + np.dot(
- pce_mean, PCA.components_)
- stds[output] = np.sqrt(np.dot(pce_var,
- PCA.components_**2))
- else:
- means[output] = pce_mean
- stds[output] = np.sqrt(pce_var)
-
- # Save predictions for each bootstrap iteration
- pce_means_b[b_i] = means
- pce_stds_b[b_i] = stds
-
- # Change the order of nesting
- mean_all = {}
- for i in sorted(pce_means_b):
- for k, v in pce_means_b[i].items():
- if k not in mean_all:
- mean_all[k] = [None] * len(pce_means_b)
- mean_all[k][i] = v
- std_all = {}
- for i in sorted(pce_stds_b):
- for k, v in pce_stds_b[i].items():
- if k not in std_all:
- std_all[k] = [None] * len(pce_stds_b)
- std_all[k][i] = v
-
- # Back transformation if PCA is selected.
- pce_means, pce_stds = {}, {}
- for output in outputs:
- pce_means[output] = np.mean(mean_all[output], axis=0)
- pce_stds[output] = np.mean(std_all[output], axis=0)
-
- return pce_means, pce_stds
diff --git a/src/bayesvalidrox/surrogate_models/surrogate_models.py b/src/bayesvalidrox/surrogate_models/surrogate_models.py
index c51ea8c6679aaeec685da069154e8f460c7c4450..eb22b9085aaebc6701f50696dd5a093cfc218d90 100644
--- a/src/bayesvalidrox/surrogate_models/surrogate_models.py
+++ b/src/bayesvalidrox/surrogate_models/surrogate_models.py
@@ -1,36 +1,287 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
+"""
+Implementation of metamodel as either PC, aPC or GPE
+"""
+import copy
+import os
import warnings
-import numpy as np
-import math
-import h5py
+
import matplotlib.pyplot as plt
-from sklearn.preprocessing import MinMaxScaler
+import numpy as np
import scipy as sp
-from tqdm import tqdm
-from sklearn.decomposition import PCA as sklearnPCA
-import sklearn.linear_model as lm
-from sklearn.gaussian_process import GaussianProcessRegressor
import sklearn.gaussian_process.kernels as kernels
-import os
+import sklearn.linear_model as lm
from joblib import Parallel, delayed
-import copy
+from scipy.optimize import minimize, NonlinearConstraint
+from sklearn.decomposition import PCA as sklearnPCA
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.preprocessing import MinMaxScaler
+from tqdm import tqdm
-from .exp_designs import ExpDesigns
-from .glexindex import glexindex
+from .apoly_construction import apoly_construction
+from .bayes_linear import VBLinearRegression, EBLinearRegression
from .eval_rec_rule import eval_univ_basis
+from .glexindex import glexindex
+from .input_space import InputSpace
+from .orthogonal_matching_pursuit import OrthogonalMatchingPursuit
from .reg_fast_ard import RegressionFastARD
from .reg_fast_laplace import RegressionFastLaplace
-from .orthogonal_matching_pursuit import OrthogonalMatchingPursuit
-from .bayes_linear import VBLinearRegression, EBLinearRegression
+
warnings.filterwarnings("ignore")
# Load the mplstyle
+# noinspection SpellCheckingInspection
plt.style.use(os.path.join(os.path.split(__file__)[0],
'../', 'bayesvalidrox.mplstyle'))
-class MetaModel():
+# noinspection SpellCheckingInspection
+def corr_loocv_error(clf, psi, coeffs, y):
+ """
+ Calculates the corrected LOO error for regression on regressor
+ matrix `psi` that generated the coefficients based on [1] and [2].
+
+ [1] Blatman, G., 2009. Adaptive sparse polynomial chaos expansions for
+ uncertainty propagation and sensitivity analysis (Doctoral
+ dissertation, Clermont-Ferrand 2).
+
+ [2] Blatman, G. and Sudret, B., 2011. Adaptive sparse polynomial chaos
+ expansion based on least angle regression. Journal of computational
+ Physics, 230(6), pp.2345-2367.
+
+ Parameters
+ ----------
+ clf : object
+ Fitted estimator.
+ psi : array of shape (n_samples, n_features)
+ The multivariate orthogonal polynomials (regressor).
+ coeffs : array-like of shape (n_features,)
+ Estimated cofficients.
+ y : array of shape (n_samples,)
+ Target values.
+
+ Returns
+ -------
+ R_2 : float
+ LOOCV Validation score (1-LOOCV erro).
+ residual : array of shape (n_samples,)
+ Residual values (y - predicted targets).
+
+ """
+ psi = np.array(psi, dtype=float)
+
+ # Create PSI_Sparse by removing redundent terms
+ nnz_idx = np.nonzero(coeffs)[0]
+ if len(nnz_idx) == 0:
+ nnz_idx = [0]
+ psi_sparse = psi[:, nnz_idx]
+
+ # NrCoeffs of aPCEs
+ P = len(nnz_idx)
+ # NrEvaluation (Size of experimental design)
+ N = psi.shape[0]
+
+ # Build the projection matrix
+ PsiTPsi = np.dot(psi_sparse.T, psi_sparse)
+
+ if np.linalg.cond(PsiTPsi) > 1e-12: # and \
+ # np.linalg.cond(PsiTPsi) < 1/sys.float_info.epsilon:
+ # faster
+ try:
+ M = sp.linalg.solve(PsiTPsi,
+ sp.sparse.eye(PsiTPsi.shape[0]).toarray())
+ except:
+ raise AttributeError(
+ 'There are too few samples for the corrected loo-cv error. Fit surrogate on at least as many '
+ 'samples as parameters to use this')
+ else:
+ # stabler
+ M = np.linalg.pinv(PsiTPsi)
+
+ # h factor (the full matrix is not calculated explicitly,
+ # only the trace is, to save memory)
+ PsiM = np.dot(psi_sparse, M)
+
+ h = np.sum(np.multiply(PsiM, psi_sparse), axis=1, dtype=np.longdouble) # float128)
+
+ # ------ Calculate Error Loocv for each measurement point ----
+ # Residuals
+ try:
+ residual = clf.predict(psi) - y
+ except:
+ residual = np.dot(psi, coeffs) - y
+
+ # Variance
+ var_y = np.var(y)
+
+ if var_y == 0:
+ # norm_emp_error = 0
+ loo_error = 0
+ LCerror = np.zeros(y.shape)
+ return 1 - loo_error, LCerror
+ else:
+ # norm_emp_error = np.mean(residual ** 2) / var_y
+
+ # LCerror = np.divide(residual, (1-h))
+ LCerror = residual / (1 - h)
+ loo_error = np.mean(np.square(LCerror)) / var_y
+ # if there are NaNs, just return an infinite LOO error (this
+ # happens, e.g., when a strongly underdetermined problem is solved)
+ if np.isnan(loo_error):
+ loo_error = np.inf
+
+ # Corrected Error for over-determined system
+ tr_M = np.trace(M)
+ if tr_M < 0 or abs(tr_M) > 1e6:
+ tr_M = np.trace(np.linalg.pinv(np.dot(psi.T, psi)))
+
+ # Over-determined system of Equation
+ if N > P:
+ T_factor = N / (N - P) * (1 + tr_M)
+
+ # Under-determined system of Equation
+ else:
+ T_factor = np.inf
+
+ corrected_loo_error = loo_error * T_factor
+
+ R_2 = 1 - corrected_loo_error
+
+ return R_2, LCerror
+
+
+def create_psi(basis_indices, univ_p_val):
+ """
+ This function assemble the design matrix Psi from the given basis index
+ set INDICES and the univariate polynomial evaluations univ_p_val.
+
+ Parameters
+ ----------
+ basis_indices : array of shape (n_terms, n_params)
+ Multi-indices of multivariate polynomials.
+ univ_p_val : array of (n_samples, n_params, n_max+1)
+ All univariate regressors up to `n_max`.
+
+ Raises
+ ------
+ ValueError
+ n_terms in arguments do not match.
+
+ Returns
+ -------
+ psi : array of shape (n_samples, n_terms)
+ Multivariate regressors.
+
+ """
+ # Check if BasisIndices is a sparse matrix
+ sparsity = sp.sparse.issparse(basis_indices)
+ if sparsity:
+ basis_indices = basis_indices.toarray()
+
+ # Initialization and consistency checks
+ # number of input variables
+ n_params = univ_p_val.shape[1]
+
+ # Size of the experimental design
+ n_samples = univ_p_val.shape[0]
+
+ # number of basis terms
+ n_terms = basis_indices.shape[0]
+
+ # check that the variables have consistent sizes
+ if n_params != basis_indices.shape[1]:
+ raise ValueError(
+ f"The shapes of basis_indices ({basis_indices.shape[1]}) and "
+ f"univ_p_val ({n_params}) don't match!!"
+ )
+
+ # Preallocate the Psi matrix for performance
+ psi = np.ones((n_samples, n_terms))
+ # Assemble the Psi matrix
+ for m in range(basis_indices.shape[1]):
+ aa = np.where(basis_indices[:, m] > 0)[0]
+ try:
+ basisIdx = basis_indices[aa, m]
+ bb = univ_p_val[:, m, basisIdx].reshape(psi[:, aa].shape)
+ psi[:, aa] = np.multiply(psi[:, aa], bb)
+ except ValueError as err:
+ raise err
+ return psi
+
+
+def gaussian_process_emulator(X, y, nug_term=None, autoSelect=False,
+ varIdx=None):
+ """
+ Fits a Gaussian Process Emulator to the target given the training
+ points.
+
+ Parameters
+ ----------
+ X : array of shape (n_samples, n_params)
+ Training points.
+ y : array of shape (n_samples,)
+ Target values.
+ nug_term : float, optional
+ Nugget term. The default is None, i.e. variance of y.
+ autoSelect : bool, optional
+ Loop over some kernels and select the best. The default is False.
+ varIdx : int, optional
+ The index number. The default is None.
+
+ Returns
+ -------
+ gp : object
+ Fitted estimator.
+
+ """
+
+ nug_term = nug_term if nug_term else np.var(y)
+
+ Kernels = [nug_term * kernels.RBF(length_scale=1.0,
+ length_scale_bounds=(1e-25, 1e15)),
+ nug_term * kernels.RationalQuadratic(length_scale=0.2,
+ alpha=1.0),
+ nug_term * kernels.Matern(length_scale=1.0,
+ length_scale_bounds=(1e-15, 1e5),
+ nu=1.5)]
+
+ # Automatic selection of the kernel
+ if autoSelect:
+ gp = {}
+ BME = []
+ for i, kernel in enumerate(Kernels):
+ gp[i] = GaussianProcessRegressor(kernel=kernel,
+ n_restarts_optimizer=3,
+ normalize_y=False)
+
+ # Fit to data using Maximum Likelihood Estimation
+ gp[i].fit(X, y)
+
+ # Store the MLE as BME score
+ BME.append(gp[i].log_marginal_likelihood())
+
+ gp = gp[np.argmax(BME)]
+
+ else:
+ gp = GaussianProcessRegressor(kernel=Kernels[0],
+ n_restarts_optimizer=3,
+ normalize_y=False)
+ gp.fit(X, y)
+
+ # Compute score
+ if varIdx is not None:
+ Score = gp.score(X, y)
+ print('-' * 50)
+ print(f'Output variable {varIdx}:')
+ print('The estimation of GPE coefficients converged,')
+ print(f'with the R^2 score: {Score:.3f}')
+ print('-' * 50)
+
+ return gp
+
+
+class MetaModel:
"""
Meta (surrogate) model
@@ -77,10 +328,14 @@ class MetaModel():
`'no'`. There are two ways to select number of components: use
percentage of the explainable variance threshold (between 0 and 100)
(Option A) or direct prescription of components' number (Option B):
-
+ >>> MetaModelOpts = MetaModel()
>>> MetaModelOpts.dim_red_method = 'PCA'
>>> MetaModelOpts.var_pca_threshold = 99.999 # Option A
>>> MetaModelOpts.n_pca_components = 12 # Option B
+ apply_constraints : bool
+ If set to true constraints will be applied during training.
+ In this case the training uses OLS. In this version the constraints
+ need to be set explicitly in this class.
verbose : bool
Prints summary of the regression results. Default is `False`.
@@ -90,21 +345,21 @@ class MetaModel():
To define the sampling methods and the training set, an experimental design
instance shall be defined. This can be done by:
- >>> MetaModelOpts.add_ExpDesign()
+ >>> MetaModelOpts.add_InputSpace()
Two experimental design schemes are supported: one-shot (`normal`) and
adaptive sequential (`sequential`) designs.
- For experimental design refer to `ExpDesigns`.
+ For experimental design refer to `InputSpace`.
"""
- def __init__(self, input_obj, model_obj, meta_model_type='PCE',
+ def __init__(self, input_obj, meta_model_type='PCE',
pce_reg_method='OLS', bootstrap_method='fast',
n_bootstrap_itrs=1, pce_deg=1, pce_q_norm=1.0,
- dim_red_method='no', verbose=False):
+ dim_red_method='no', apply_constraints=False,
+ verbose=False):
self.input_obj = input_obj
- self.ModelObj = model_obj
self.meta_model_type = meta_model_type
self.pce_reg_method = pce_reg_method
self.bootstrap_method = bootstrap_method
@@ -112,70 +367,79 @@ class MetaModel():
self.pce_deg = pce_deg
self.pce_q_norm = pce_q_norm
self.dim_red_method = dim_red_method
- self.verbose = False
-
- # -------------------------------------------------------------------------
- def create_metamodel(self):
+ self.apply_constraints = apply_constraints
+ self.verbose = verbose
+
+ # Other params
+ self.InputSpace = None
+ self.var_pca_threshold = None
+ self.polycoeffs = None
+ self.errorScale = None
+ self.errorclf_poly = None
+ self.errorRegMethod = None
+ self.nlc = None
+ self.univ_p_val = None
+ self.n_pca_components = None
+ self.out_names = None
+ self.allBasisIndices = None
+ self.deg_array = None
+ self.n_samples = None
+ self.CollocationPoints = None
+ self.pca = None
+ self.LCerror = None
+ self.clf_poly = None
+ self.score_dict = None
+ self.basis_dict = None
+ self.coeffs_dict = None
+ self.q_norm_dict = None
+ self.deg_dict = None
+ self.x_scaler = None
+ self.gp_poly = None
+ self.n_params = None
+ self.ndim = None
+ self.init_type = None
+ self.rmse = None
+
+ def build_metamodel(self, n_init_samples=None) -> None:
"""
- Starts the training of the meta-model for the model objects containg
- the given computational model.
+ Builds the parts for the metamodel (polynomes,...) that are neede before fitting.
Returns
-------
- metamodel : obj
- The meta model object.
+ None
+ DESCRIPTION.
"""
- Model = self.ModelObj
- self.n_params = len(self.input_obj.Marginals)
- self.ExpDesignFlag = 'normal'
- # --- Prepare pce degree ---
- if self.meta_model_type.lower() == 'pce':
- if type(self.pce_deg) is not np.ndarray:
- self.pce_deg = np.array(self.pce_deg)
-
- if self.ExpDesign.method == 'sequential':
- raise Exception(
- "Please use MetaModelEngine class for the sequential design!"
- )
- elif self.ExpDesign.method == 'normal':
- self.ExpDesignFlag = 'normal'
- self.train_norm_design(Model, verbose=True)
+ # Generate general warnings
+ if self.apply_constraints or self.pce_reg_method.lower() == 'ols':
+ print('There are no estimations of surrogate uncertainty available'
+ ' for the chosen regression options. This might lead to issues'
+ ' in later steps.')
- else:
- raise Exception("The method for experimental design you requested"
- " has not been implemented yet.")
-
- # Zip the model run directories
- if self.ModelObj.link_type.lower() == 'pylink' and\
- self.ExpDesign.sampling_method.lower() != 'user':
- Model.zip_subdirs(Model.name, f'{Model.name}_')
+ if self.CollocationPoints is None:
+ raise AttributeError('Please provide samples to the metamodel before building it.')
+ self.CollocationPoints = np.array(self.CollocationPoints)
- return self
+ # Add InputSpace to MetaModel if it does not have any
+ if self.InputSpace is None:
+ if n_init_samples is None:
+ n_init_samples = self.CollocationPoints.shape[0]
+ self.InputSpace = InputSpace(self.input_obj)
+ self.InputSpace.n_init_samples = n_init_samples
+ self.InputSpace.init_param_space(np.max(self.pce_deg))
- # -------------------------------------------------------------------------
- def train_norm_design(self, parallel=True, verbose=False):
- """
- This function loops over the outputs and each time step/point and fits
- the meta model.
+ self.ndim = self.InputSpace.ndim
- Parameters
- ----------
- parallel : bool
- The parallel computation of coefficents. The default is True.
- verbose : bool, optional
- Flag for a sequential design in silent mode. The default is False.
+ # Transform input samples
+ # TODO: this is probably not yet correct! Make 'method' variable
+ self.CollocationPoints = self.InputSpace.transform(self.CollocationPoints, method='user')
- Returns
- -------
- self: obj
- Meta-model object.
+ self.n_params = len(self.input_obj.Marginals)
- """
- Model = self.ModelObj
- # Get the collocation points to run the forward model
- CollocationPoints, OutputDict = self.generate_ExpDesign(Model)
+ # Generate polynomials
+ if self.meta_model_type.lower() != 'gpe':
+ self.generate_polynomials(np.max(self.pce_deg))
# Initialize the nested dictionaries
if self.meta_model_type.lower() == 'gpe':
@@ -194,8 +458,14 @@ class MetaModel():
self.pca = self.auto_vivification()
# Define an array containing the degrees
- n_samples, ndim = CollocationPoints.shape
- self.deg_array = self.__select_degree(ndim, n_samples)
+ self.CollocationPoints = np.array(self.CollocationPoints)
+ self.n_samples, ndim = self.CollocationPoints.shape
+ if self.ndim != ndim:
+ raise AttributeError(
+ 'The given samples do not match the given number of priors. The samples should be a 2D array of size'
+ '(#samples, #priors)')
+
+ self.deg_array = self.__select_degree(ndim, self.n_samples)
# Generate all basis indices
self.allBasisIndices = self.auto_vivification()
@@ -204,17 +474,60 @@ class MetaModel():
if deg not in np.fromiter(keys, dtype=float):
# Generate the polynomial basis indices
for qidx, q in enumerate(self.pce_q_norm):
- basis_indices = self.create_basis_indices(degree=deg,
- q_norm=q)
+ basis_indices = glexindex(start=0, stop=deg + 1,
+ dimensions=self.n_params,
+ cross_truncation=q,
+ reverse=False, graded=True)
self.allBasisIndices[str(deg)][str(q)] = basis_indices
- # Evaluate the univariate polynomials on ExpDesign
- if self.meta_model_type.lower() != 'gpe':
- univ_p_val = self.univ_basis_vals(CollocationPoints)
+ def fit(self, X: np.array, y: dict, parallel=False, verbose=False):
+ """
+ Fits the surrogate to the given data (samples X, outputs y).
+ Note here that the samples X should be the transformed samples provided
+ by the experimental design if the transformation is used there.
+
+ Parameters
+ ----------
+ X : 2D list or np.array of shape (#samples, #dim)
+ The parameter value combinations that the model was evaluated at.
+ y : dict of 2D lists or arrays of shape (#samples, #timesteps)
+ The respective model evaluations.
+ parallel : bool
+ Set to True to run the training in parallel for various keys.
+ The default is False.
+ verbose : bool
+ Set to True to obtain more information during runtime.
+ The default is False.
+
+ Returns
+ -------
+ None.
- if 'x_values' in OutputDict:
- self.ExpDesign.x_values = OutputDict['x_values']
- del OutputDict['x_values']
+ """
+ # print(X)
+ # print(X.shape)
+ # print(y)
+ # print(y['Z'].shape)
+ X = np.array(X)
+ for key in y.keys():
+ y_val = np.array(y[key])
+ if y_val.ndim != 2:
+ raise ValueError('The given outputs y should be 2D')
+ y[key] = np.array(y[key])
+
+ # Output names are the same as the keys in y
+ self.out_names = list(y.keys())
+
+ # Build the MetaModel on the static samples
+ self.CollocationPoints = X
+
+ # TODO: other option: rebuild every time
+ if self.deg_array is None:
+ self.build_metamodel(n_init_samples=X.shape[1])
+
+ # Evaluate the univariate polynomials on InputSpace
+ if self.meta_model_type.lower() != 'gpe':
+ self.univ_p_val = self.univ_basis_vals(self.CollocationPoints)
# --- Loop through data points and fit the surrogate ---
if verbose:
@@ -227,10 +540,10 @@ class MetaModel():
self.n_bootstrap_itrs = 100
# Check if fast version (update coeffs with OLS) is selected.
+ n_comp_dict = {}
+ first_out = {}
if self.bootstrap_method.lower() == 'fast':
fast_bootstrap = True
- first_out = {}
- n_comp_dict = {}
else:
fast_bootstrap = False
@@ -238,7 +551,7 @@ class MetaModel():
if verbose and self.n_bootstrap_itrs > 1:
enum_obj = tqdm(range(self.n_bootstrap_itrs),
total=self.n_bootstrap_itrs,
- desc="Boostraping the metamodel",
+ desc="Bootstrapping the metamodel",
ascii=True)
else:
enum_obj = range(self.n_bootstrap_itrs)
@@ -246,16 +559,16 @@ class MetaModel():
# Loop over the bootstrap iterations
for b_i in enum_obj:
if b_i > 0:
- b_indices = np.random.randint(n_samples, size=n_samples)
+ b_indices = np.random.randint(self.n_samples, size=self.n_samples)
else:
- b_indices = np.arange(len(CollocationPoints))
+ b_indices = np.arange(len(X))
- X_train_b = CollocationPoints[b_indices]
+ X_train_b = X[b_indices]
if verbose and self.n_bootstrap_itrs == 1:
- items = tqdm(OutputDict.items(), desc="Fitting regression")
+ items = tqdm(y.items(), desc="Fitting regression")
else:
- items = OutputDict.items()
+ items = y.items()
# For loop over the components/outputs
for key, Output in items:
@@ -270,8 +583,8 @@ class MetaModel():
# Start transformation
pca, target, n_comp = self.pca_transformation(
Output[b_indices], verbose=False
- )
- self.pca[f'b_{b_i+1}'][key] = pca
+ )
+ self.pca[f'b_{b_i + 1}'][key] = pca
# Store the number of components for fast bootsrtrapping
if fast_bootstrap and b_i == 0:
n_comp_dict[key] = n_comp
@@ -279,39 +592,40 @@ class MetaModel():
target = Output[b_indices]
# Parallel fit regression
+ out = None
if self.meta_model_type.lower() == 'gpe':
# Prepare the input matrix
scaler = MinMaxScaler()
X_S = scaler.fit_transform(X_train_b)
- self.x_scaler[f'b_{b_i+1}'][key] = scaler
+ self.x_scaler[f'b_{b_i + 1}'][key] = scaler
if parallel:
out = Parallel(n_jobs=-1, backend='multiprocessing')(
- delayed(self.gaussian_process_emulator)(
+ delayed(gaussian_process_emulator)(
X_S, target[:, idx]) for idx in
range(target.shape[1]))
else:
- results = map(self.gaussian_process_emulator,
- [X_train_b]*target.shape[1],
+ results = map(gaussian_process_emulator,
+ [X_train_b] * target.shape[1],
[target[:, idx] for idx in
range(target.shape[1])]
)
out = list(results)
for idx in range(target.shape[1]):
- self.gp_poly[f'b_{b_i+1}'][key][f"y_{idx+1}"] = out[idx]
+ self.gp_poly[f'b_{b_i + 1}'][key][f"y_{idx + 1}"] = out[idx]
else:
- self.univ_p_val = univ_p_val[b_indices]
+ self.univ_p_val = self.univ_p_val[b_indices]
if parallel and (not fast_bootstrap or b_i == 0):
out = Parallel(n_jobs=-1, backend='multiprocessing')(
- delayed(self.adaptive_regression)(X_train_b,
- target[:, idx],
- idx)
+ delayed(self.adaptive_regression)( # X_train_b,
+ target[:, idx],
+ idx)
for idx in range(target.shape[1]))
elif not parallel and (not fast_bootstrap or b_i == 0):
results = map(self.adaptive_regression,
- [X_train_b]*target.shape[1],
+ # [X_train_b] * target.shape[1],
[target[:, idx] for idx in
range(target.shape[1])],
range(target.shape[1]))
@@ -322,30 +636,29 @@ class MetaModel():
first_out[key] = copy.deepcopy(out)
if b_i > 0 and fast_bootstrap:
-
# fast bootstrap
out = self.update_pce_coeffs(
X_train_b, target, first_out[key])
for i in range(target.shape[1]):
# Create a dict to pass the variables
- self.deg_dict[f'b_{b_i+1}'][key][f"y_{i+1}"] = out[i]['degree']
- self.q_norm_dict[f'b_{b_i+1}'][key][f"y_{i+1}"] = out[i]['qnorm']
- self.coeffs_dict[f'b_{b_i+1}'][key][f"y_{i+1}"] = out[i]['coeffs']
- self.basis_dict[f'b_{b_i+1}'][key][f"y_{i+1}"] = out[i]['multi_indices']
- self.score_dict[f'b_{b_i+1}'][key][f"y_{i+1}"] = out[i]['LOOCVScore']
- self.clf_poly[f'b_{b_i+1}'][key][f"y_{i+1}"] = out[i]['clf_poly']
- #self.LCerror[f'b_{b_i+1}'][key][f"y_{i+1}"] = out[i]['LCerror']
+ self.deg_dict[f'b_{b_i + 1}'][key][f"y_{i + 1}"] = out[i]['degree']
+ self.q_norm_dict[f'b_{b_i + 1}'][key][f"y_{i + 1}"] = out[i]['qnorm']
+ self.coeffs_dict[f'b_{b_i + 1}'][key][f"y_{i + 1}"] = out[i]['coeffs']
+ self.basis_dict[f'b_{b_i + 1}'][key][f"y_{i + 1}"] = out[i]['multi_indices']
+ self.score_dict[f'b_{b_i + 1}'][key][f"y_{i + 1}"] = out[i]['LOOCVScore']
+ self.clf_poly[f'b_{b_i + 1}'][key][f"y_{i + 1}"] = out[i]['clf_poly']
+ # self.LCerror[f'b_{b_i+1}'][key][f"y_{i+1}"] = out[i]['LCerror']
if verbose:
print(f"\n>>>> Training the {self.meta_model_type} metamodel"
" sucessfully completed. <<<<<<\n")
# -------------------------------------------------------------------------
- def update_pce_coeffs(self, X, y, out_dict):
+ def update_pce_coeffs(self, X, y, out_dict=None):
"""
Updates the PCE coefficents using only the ordinary least square method
- for the fast version of the bootsrtrapping.
+ for the fast version of the bootstrapping.
Parameters
----------
@@ -363,6 +676,7 @@ class MetaModel():
The updated training output dictionary.
"""
+ # TODO: why is X not used here?
# Make a copy
final_out_dict = copy.deepcopy(out_dict)
@@ -375,13 +689,13 @@ class MetaModel():
basis_indices = out_dict[i]['multi_indices']
# Evaluate the multivariate polynomials on CollocationPoints
- psi = self.create_psi(basis_indices, self.univ_p_val)
+ psi = create_psi(basis_indices, self.univ_p_val)
# Calulate the cofficients of surrogate model
- updated_out = self.fit(
+ updated_out = self.regression(
psi, y[:, i], basis_indices, reg_method='OLS',
sparsity=False
- )
+ )
# Update coeffs in out_dict
final_out_dict[i]['coeffs'][nnz_idx] = updated_out['coeffs']
@@ -389,33 +703,7 @@ class MetaModel():
return final_out_dict
# -------------------------------------------------------------------------
- def create_basis_indices(self, degree, q_norm):
- """
- Creates set of selected multi-indices of multivariate polynomials for
- certain parameter numbers, polynomial degree, hyperbolic (or q-norm)
- truncation scheme.
-
- Parameters
- ----------
- degree : int
- Polynomial degree.
- q_norm : float
- hyperbolic (or q-norm) truncation.
-
- Returns
- -------
- basis_indices : array of shape (n_terms, n_params)
- Multi-indices of multivariate polynomials.
-
- """
- basis_indices = glexindex(start=0, stop=degree+1,
- dimensions=self.n_params,
- cross_truncation=q_norm,
- reverse=False, graded=True)
- return basis_indices
-
- # -------------------------------------------------------------------------
- def add_ExpDesign(self):
+ def add_InputSpace(self):
"""
Instanciates experimental design object.
@@ -424,101 +712,8 @@ class MetaModel():
None.
"""
- self.ExpDesign = ExpDesigns(self.input_obj,
- meta_Model=self.meta_model_type)
-
- # -------------------------------------------------------------------------
- def generate_ExpDesign(self, Model):
- """
- Prepares the experimental design either by reading from the prescribed
- data or running simulations.
-
- Parameters
- ----------
- Model : obj
- Model object.
-
- Raises
- ------
- Exception
- If model sumulations are not provided properly.
-
- Returns
- -------
- ED_X_tr: array of shape (n_samples, n_params)
- Training samples transformed by an isoprobabilistic transformation.
- ED_Y: dict
- Model simulations (target) for all outputs.
- """
- ExpDesign = self.ExpDesign
- if self.ExpDesignFlag != 'sequential':
- # Read ExpDesign (training and targets) from the provided hdf5
- if ExpDesign.hdf5_file is not None:
-
- # Read hdf5 file
- f = h5py.File(ExpDesign.hdf5_file, 'r+')
-
- # Read EDX and pass it to ExpDesign object
- try:
- ExpDesign.X = np.array(f["EDX/New_init_"])
- except KeyError:
- ExpDesign.X = np.array(f["EDX/init_"])
-
- # Update number of initial samples
- ExpDesign.n_init_samples = ExpDesign.X.shape[0]
-
- # Read EDX and pass it to ExpDesign object
- out_names = self.ModelObj.Output.names
- ExpDesign.Y = {}
-
- # Extract x values
- try:
- ExpDesign.Y["x_values"] = dict()
- for varIdx, var in enumerate(out_names):
- x = np.array(f[f"x_values/{var}"])
- ExpDesign.Y["x_values"][var] = x
- except KeyError:
- ExpDesign.Y["x_values"] = np.array(f["x_values"])
-
- # Store the output
- for varIdx, var in enumerate(out_names):
- try:
- y = np.array(f[f"EDY/{var}/New_init_"])
- except KeyError:
- y = np.array(f[f"EDY/{var}/init_"])
- ExpDesign.Y[var] = y
- f.close()
- else:
- # Check if an old hdf5 file exists: if yes, rename it
- hdf5file = f'ExpDesign_{self.ModelObj.name}.hdf5'
- if os.path.exists(hdf5file):
- os.rename(hdf5file, 'old_'+hdf5file)
-
- # ---- Prepare X samples ----
- ED_X, ED_X_tr = ExpDesign.generate_ED(ExpDesign.n_init_samples,
- ExpDesign.sampling_method,
- transform=True,
- max_pce_deg=np.max(self.pce_deg))
- ExpDesign.X = ED_X
- ExpDesign.collocationPoints = ED_X_tr
- self.bound_tuples = ExpDesign.bound_tuples
-
- # ---- Run simulations at X ----
- if not hasattr(ExpDesign, 'Y') or ExpDesign.Y is None:
- print('\n Now the forward model needs to be run!\n')
- ED_Y, up_ED_X = Model.run_model_parallel(ED_X)
- ExpDesign.X = up_ED_X
- self.ModelOutputDict = ED_Y
- ExpDesign.Y = ED_Y
- else:
- # Check if a dict has been passed.
- if type(ExpDesign.Y) is dict:
- self.ModelOutputDict = ExpDesign.Y
- else:
- raise Exception('Please provide either a dictionary or a hdf5'
- 'file to ExpDesign.hdf5_file argument.')
-
- return ED_X_tr, self.ModelOutputDict
+ self.InputSpace = InputSpace(self.input_obj,
+ meta_Model_type=self.meta_model_type)
# -------------------------------------------------------------------------
def univ_basis_vals(self, samples, n_max=None):
@@ -538,14 +733,14 @@ class MetaModel():
All univariate regressors up to n_max.
"""
# Extract information
- poly_types = self.ExpDesign.poly_types
+ poly_types = self.InputSpace.poly_types
if samples.ndim != 2:
samples = samples.reshape(1, len(samples))
n_max = np.max(self.pce_deg) if n_max is None else n_max
# Extract poly coeffs
- if self.ExpDesign.input_data_given or self.ExpDesign.apce:
- apolycoeffs = self.ExpDesign.polycoeffs
+ if self.InputSpace.input_data_given or self.InputSpace.apce:
+ apolycoeffs = self.polycoeffs
else:
apolycoeffs = None
@@ -555,66 +750,9 @@ class MetaModel():
return univ_basis
# -------------------------------------------------------------------------
- def create_psi(self, basis_indices, univ_p_val):
- """
- This function assemble the design matrix Psi from the given basis index
- set INDICES and the univariate polynomial evaluations univ_p_val.
-
- Parameters
- ----------
- basis_indices : array of shape (n_terms, n_params)
- Multi-indices of multivariate polynomials.
- univ_p_val : array of (n_samples, n_params, n_max+1)
- All univariate regressors up to `n_max`.
-
- Raises
- ------
- ValueError
- n_terms in arguments do not match.
-
- Returns
- -------
- psi : array of shape (n_samples, n_terms)
- Multivariate regressors.
-
- """
- # Check if BasisIndices is a sparse matrix
- sparsity = sp.sparse.issparse(basis_indices)
- if sparsity:
- basis_indices = basis_indices.toarray()
-
- # Initialization and consistency checks
- # number of input variables
- n_params = univ_p_val.shape[1]
-
- # Size of the experimental design
- n_samples = univ_p_val.shape[0]
-
- # number of basis terms
- n_terms = basis_indices.shape[0]
-
- # check that the variables have consistent sizes
- if n_params != basis_indices.shape[1]:
- raise ValueError(
- f"The shapes of basis_indices ({basis_indices.shape[1]}) and "
- f"univ_p_val ({n_params}) don't match!!"
- )
-
- # Preallocate the Psi matrix for performance
- psi = np.ones((n_samples, n_terms))
- # Assemble the Psi matrix
- for m in range(basis_indices.shape[1]):
- aa = np.where(basis_indices[:, m] > 0)[0]
- try:
- basisIdx = basis_indices[aa, m]
- bb = univ_p_val[:, m, basisIdx].reshape(psi[:, aa].shape)
- psi[:, aa] = np.multiply(psi[:, aa], bb)
- except ValueError as err:
- raise err
- return psi
# -------------------------------------------------------------------------
- def fit(self, X, y, basis_indices, reg_method=None, sparsity=True):
+ def regression(self, X, y, basis_indices, reg_method=None, sparsity=True):
"""
Fit regression using the regression method provided.
@@ -629,6 +767,8 @@ class MetaModel():
Multi-indices of multivariate polynomials.
reg_method : str, optional
DESCRIPTION. The default is None.
+ sparsity : bool
+ Use with sparsity-inducing training methods. The default is True
Returns
-------
@@ -650,20 +790,23 @@ class MetaModel():
Lambda = 1e-6
# Bayes sparse adaptive aPCE
+ clf_poly = None
if reg_method.lower() == 'ols':
clf_poly = lm.LinearRegression(fit_intercept=False)
elif reg_method.lower() == 'brr':
clf_poly = lm.BayesianRidge(n_iter=1000, tol=1e-7,
fit_intercept=False,
- normalize=True,
+ # normalize=True,
compute_score=compute_score,
alpha_1=1e-04, alpha_2=1e-04,
lambda_1=Lambda, lambda_2=Lambda)
clf_poly.converged = True
elif reg_method.lower() == 'ard':
+ if X.shape[0] < 2:
+ raise ValueError('Regression with ARD can only be performed for more than 2 samples')
clf_poly = lm.ARDRegression(fit_intercept=False,
- normalize=True,
+ # normalize=True,
compute_score=compute_score,
n_iter=1000, tol=0.0001,
alpha_1=1e-3, alpha_2=1e-3,
@@ -676,11 +819,15 @@ class MetaModel():
n_iter=300, tol=1e-10)
elif reg_method.lower() == 'bcs':
+ if X.shape[0] < 10:
+ raise ValueError('Regression with BCS can only be performed for more than 10 samples')
clf_poly = RegressionFastLaplace(fit_intercept=False,
bias_term=bias_term,
n_iter=1000, tol=1e-7)
elif reg_method.lower() == 'lars':
+ if X.shape[0] < 10:
+ raise ValueError('Regression with LARS can only be performed for more than 5 samples')
clf_poly = lm.LassoLarsCV(fit_intercept=False)
elif reg_method.lower() == 'sgdr':
@@ -696,8 +843,31 @@ class MetaModel():
elif reg_method.lower() == 'ebl':
clf_poly = EBLinearRegression(optimizer='em')
- # Fit
- clf_poly.fit(X, y)
+ # Training with constraints automatically uses L2
+ if self.apply_constraints:
+ # TODO: set the constraints here
+ # Define the nonlin. constraint
+ nlc = NonlinearConstraint(lambda x: np.matmul(X, x), -1, 1.1)
+ self.nlc = nlc
+
+ fun = lambda x: (np.linalg.norm(np.matmul(X, x) - y, ord=2)) ** 2
+ res = None
+ if self.init_type == 'zeros':
+ res = minimize(fun, np.zeros(X.shape[1]), method='trust-constr', constraints=self.nlc)
+ if self.init_type == 'nonpi':
+ clf_poly.fit(X, y)
+ coeff = clf_poly.coef_
+ res = minimize(fun, coeff, method='trust-constr', constraints=self.nlc)
+
+ coeff = np.array(res.x)
+ clf_poly.coef_ = coeff
+ clf_poly.X = X
+ clf_poly.y = y
+ clf_poly.intercept_ = 0
+
+ # Training without constraints uses chosen regression method
+ else:
+ clf_poly.fit(X, y)
# Select the nonzero entries of coefficients
if sparsity:
@@ -724,15 +894,13 @@ class MetaModel():
return return_out_dict
# --------------------------------------------------------------------------------------------------------
- def adaptive_regression(self, ED_X, ED_Y, varIdx, verbose=False):
+ def adaptive_regression(self, ED_Y, varIdx, verbose=False):
"""
Adaptively fits the PCE model by comparing the scores of different
degrees and q-norm.
Parameters
----------
- ED_X : array of shape (n_samples, n_params)
- Experimental design.
ED_Y : array of shape (n_samples,)
Target values, i.e. simulation results for the Experimental design.
varIdx : int
@@ -748,7 +916,7 @@ class MetaModel():
"""
- n_samples, n_params = ED_X.shape
+ # n_samples, n_params = ED_X.shape
# Initialization
qAllCoeffs, AllCoeffs = {}, {}
qAllIndices_Sparse, AllIndices_Sparse = {}, {}
@@ -756,7 +924,7 @@ class MetaModel():
qAllnTerms, AllnTerms = {}, {}
qAllLCerror, AllLCerror = {}, {}
- # Extract degree array and qnorm array
+ # Extract degree array and q-norm array
deg_array = np.array([*self.allBasisIndices], dtype=int)
qnorm = [*self.allBasisIndices[str(int(deg_array[0]))]]
@@ -768,7 +936,7 @@ class MetaModel():
n_checks_qNorm = 2
nqnorms = len(qnorm)
qNormEarlyStop = True
- if nqnorms < n_checks_qNorm+1:
+ if nqnorms < n_checks_qNorm + 1:
qNormEarlyStop = False
# =====================================================================
@@ -776,6 +944,7 @@ class MetaModel():
# polynomial degree until the highest accuracy is reached
# =====================================================================
# For each degree check all q-norms and choose the best one
+ best_q = None
scores = -np.inf * np.ones(deg_array.shape[0])
qNormScores = -np.inf * np.ones(nqnorms)
@@ -788,38 +957,38 @@ class MetaModel():
BasisIndices = self.allBasisIndices[str(deg)][str(q)]
# Assemble the Psi matrix
- Psi = self.create_psi(BasisIndices, self.univ_p_val)
+ Psi = create_psi(BasisIndices, self.univ_p_val)
- # Calulate the cofficients of the meta model
- outs = self.fit(Psi, ED_Y, BasisIndices)
+ # Calulate the cofficients of the metamodel
+ outs = self.regression(Psi, ED_Y, BasisIndices)
# Calculate and save the score of LOOCV
- score, LCerror = self.corr_loocv_error(outs['clf_poly'],
- outs['sparePsi'],
- outs['coeffs'],
- ED_Y)
+ score, LCerror = corr_loocv_error(outs['clf_poly'],
+ outs['sparePsi'],
+ outs['coeffs'],
+ ED_Y)
# Check the convergence of noise for FastARD
if self.pce_reg_method == 'FastARD' and \
- outs['clf_poly'].alpha_ < np.finfo(np.float32).eps:
+ outs['clf_poly'].alpha_ < np.finfo(np.float32).eps:
score = -np.inf
qNormScores[qidx] = score
- qAllCoeffs[str(qidx+1)] = outs['coeffs']
- qAllIndices_Sparse[str(qidx+1)] = outs['spareMulti-Index']
- qAllclf_poly[str(qidx+1)] = outs['clf_poly']
- qAllnTerms[str(qidx+1)] = BasisIndices.shape[0]
- qAllLCerror[str(qidx+1)] = LCerror
+ qAllCoeffs[str(qidx + 1)] = outs['coeffs']
+ qAllIndices_Sparse[str(qidx + 1)] = outs['spareMulti-Index']
+ qAllclf_poly[str(qidx + 1)] = outs['clf_poly']
+ qAllnTerms[str(qidx + 1)] = BasisIndices.shape[0]
+ qAllLCerror[str(qidx + 1)] = LCerror
# EarlyStop check
# if there are at least n_checks_qNorm entries after the
# best one, we stop
if qNormEarlyStop and \
- sum(np.isfinite(qNormScores)) > n_checks_qNorm:
+ sum(np.isfinite(qNormScores)) > n_checks_qNorm:
# If the error has increased the last two iterations, stop!
qNormScores_nonInf = qNormScores[np.isfinite(qNormScores)]
deltas = np.sign(np.diff(qNormScores_nonInf))
- if sum(deltas[-n_checks_qNorm+1:]) == 2:
+ if sum(deltas[-n_checks_qNorm + 1:]) == 2:
# stop the q-norm loop here
break
if np.var(ED_Y) == 0:
@@ -829,11 +998,11 @@ class MetaModel():
best_q = np.nanargmax(qNormScores)
scores[degIdx] = qNormScores[best_q]
- AllCoeffs[str(degIdx+1)] = qAllCoeffs[str(best_q+1)]
- AllIndices_Sparse[str(degIdx+1)] = qAllIndices_Sparse[str(best_q+1)]
- Allclf_poly[str(degIdx+1)] = qAllclf_poly[str(best_q+1)]
- AllnTerms[str(degIdx+1)] = qAllnTerms[str(best_q+1)]
- AllLCerror[str(degIdx+1)] = qAllLCerror[str(best_q+1)]
+ AllCoeffs[str(degIdx + 1)] = qAllCoeffs[str(best_q + 1)]
+ AllIndices_Sparse[str(degIdx + 1)] = qAllIndices_Sparse[str(best_q + 1)]
+ Allclf_poly[str(degIdx + 1)] = qAllclf_poly[str(best_q + 1)]
+ AllnTerms[str(degIdx + 1)] = qAllnTerms[str(best_q + 1)]
+ AllLCerror[str(degIdx + 1)] = qAllLCerror[str(best_q + 1)]
# Check the direction of the error (on average):
# if it increases consistently stop the iterations
@@ -841,7 +1010,7 @@ class MetaModel():
scores_nonInf = scores[scores != -np.inf]
ss = np.sign(scores_nonInf - np.max(scores_nonInf))
# ss<0 error decreasing
- errorIncreases = np.sum(np.sum(ss[-2:])) <= -1*n_checks_degree
+ errorIncreases = np.sum(np.sum(ss[-2:])) <= -1 * n_checks_degree
if errorIncreases:
break
@@ -852,7 +1021,7 @@ class MetaModel():
# ------------------ Summary of results ------------------
# Select the one with the best score and save the necessary outputs
- best_deg = np.nanargmax(scores)+1
+ best_deg = np.nanargmax(scores) + 1
coeffs = AllCoeffs[str(best_deg)]
basis_indices = AllIndices_Sparse[str(best_deg)]
clf_poly = Allclf_poly[str(best_deg)]
@@ -868,24 +1037,24 @@ class MetaModel():
nnz_idx = np.nonzero(coeffs)[0]
BasisIndices_Sparse = basis_indices[nnz_idx]
- print(f'Output variable {varIdx+1}:')
+ print(f'Output variable {varIdx + 1}:')
print('The estimation of PCE coefficients converged at polynomial '
- f'degree {deg_array[best_deg-1]} with '
+ f'degree {deg_array[best_deg - 1]} with '
f'{len(BasisIndices_Sparse)} terms (Sparsity index = '
- f'{round(len(BasisIndices_Sparse)/P, 3)}).')
+ f'{round(len(BasisIndices_Sparse) / P, 3)}).')
- print(f'Final ModLOO error estimate: {1-max(scores):.3e}')
- print('\n'+'-'*50)
+ print(f'Final ModLOO error estimate: {1 - max(scores):.3e}')
+ print('\n' + '-' * 50)
if verbose:
- print('='*50)
- print(' '*10 + ' Summary of results ')
- print('='*50)
-
- print("scores:\n", scores)
- print("Best score's degree:", self.deg_array[best_deg-1])
- print("NO. of terms:", len(basis_indices))
- print("Sparsity index:", round(len(basis_indices)/P, 3))
+ print('=' * 50)
+ print(' ' * 10 + ' Summary of results ')
+ print('=' * 50)
+
+ print("Scores:\n", scores)
+ print("Degree of best score:", self.deg_array[best_deg - 1])
+ print("No. of terms:", len(basis_indices))
+ print("Sparsity index:", round(len(basis_indices) / P, 3))
print("Best Indices:\n", basis_indices)
if self.pce_reg_method in ['BRR', 'ARD']:
@@ -904,7 +1073,7 @@ class MetaModel():
plt.text(0.75, 0.5, text, fontsize=18, transform=ax.transAxes)
plt.show()
- print('='*80)
+ print('=' * 80)
# Create a dict to pass the outputs
returnVars = dict()
@@ -918,115 +1087,6 @@ class MetaModel():
return returnVars
- # -------------------------------------------------------------------------
- def corr_loocv_error(self, clf, psi, coeffs, y):
- """
- Calculates the corrected LOO error for regression on regressor
- matrix `psi` that generated the coefficients based on [1] and [2].
-
- [1] Blatman, G., 2009. Adaptive sparse polynomial chaos expansions for
- uncertainty propagation and sensitivity analysis (Doctoral
- dissertation, Clermont-Ferrand 2).
-
- [2] Blatman, G. and Sudret, B., 2011. Adaptive sparse polynomial chaos
- expansion based on least angle regression. Journal of computational
- Physics, 230(6), pp.2345-2367.
-
- Parameters
- ----------
- clf : object
- Fitted estimator.
- psi : array of shape (n_samples, n_features)
- The multivariate orthogonal polynomials (regressor).
- coeffs : array-like of shape (n_features,)
- Estimated cofficients.
- y : array of shape (n_samples,)
- Target values.
-
- Returns
- -------
- R_2 : float
- LOOCV Validation score (1-LOOCV erro).
- residual : array of shape (n_samples,)
- Residual values (y - predicted targets).
-
- """
- psi = np.array(psi, dtype=float)
-
- # Create PSI_Sparse by removing redundent terms
- nnz_idx = np.nonzero(coeffs)[0]
- if len(nnz_idx) == 0:
- nnz_idx = [0]
- psi_sparse = psi[:, nnz_idx]
-
- # NrCoeffs of aPCEs
- P = len(nnz_idx)
- # NrEvaluation (Size of experimental design)
- N = psi.shape[0]
-
- # Build the projection matrix
- PsiTPsi = np.dot(psi_sparse.T, psi_sparse)
-
- if np.linalg.cond(PsiTPsi) > 1e-12: #and \
- # np.linalg.cond(PsiTPsi) < 1/sys.float_info.epsilon:
- # faster
- M = sp.linalg.solve(PsiTPsi,
- sp.sparse.eye(PsiTPsi.shape[0]).toarray())
- else:
- # stabler
- M = np.linalg.pinv(PsiTPsi)
-
- # h factor (the full matrix is not calculated explicitly,
- # only the trace is, to save memory)
- PsiM = np.dot(psi_sparse, M)
-
- h = np.sum(np.multiply(PsiM, psi_sparse), axis=1, dtype=np.float128)
-
- # ------ Calculate Error Loocv for each measurement point ----
- # Residuals
- try:
- residual = clf.predict(psi) - y
- except:
- residual = np.dot(psi, coeffs) - y
-
- # Variance
- var_y = np.var(y)
-
- if var_y == 0:
- norm_emp_error = 0
- loo_error = 0
- LCerror = np.zeros((y.shape))
- return 1-loo_error, LCerror
- else:
- norm_emp_error = np.mean(residual**2)/var_y
-
- # LCerror = np.divide(residual, (1-h))
- LCerror = residual / (1-h)
- loo_error = np.mean(np.square(LCerror)) / var_y
- # if there are NaNs, just return an infinite LOO error (this
- # happens, e.g., when a strongly underdetermined problem is solved)
- if np.isnan(loo_error):
- loo_error = np.inf
-
- # Corrected Error for over-determined system
- tr_M = np.trace(M)
- if tr_M < 0 or abs(tr_M) > 1e6:
- tr_M = np.trace(np.linalg.pinv(np.dot(psi.T, psi)))
-
- # Over-determined system of Equation
- if N > P:
- T_factor = N/(N-P) * (1 + tr_M)
-
- # Under-determined system of Equation
- else:
- T_factor = np.inf
-
- corrected_loo_error = loo_error * T_factor
-
- R_2 = 1 - corrected_loo_error
-
- return R_2, LCerror
-
# -------------------------------------------------------------------------
def pca_transformation(self, target, verbose=False):
"""
@@ -1036,6 +1096,9 @@ class MetaModel():
----------
target : array of shape (n_samples,)
Target values.
+ verbose : bool
+ Set to True to get more information during functtion call.
+ The default is False.
Returns
-------
@@ -1048,20 +1111,20 @@ class MetaModel():
"""
# Transform via Principal Component Analysis
- if hasattr(self, 'var_pca_threshold'):
+ if self.var_pca_threshold is None:
var_pca_threshold = self.var_pca_threshold
else:
var_pca_threshold = 100.0
n_samples, n_features = target.shape
- if hasattr(self, 'n_pca_components'):
+ if self.n_pca_components is None:
n_pca_components = self.n_pca_components
else:
# Instantiate and fit sklearnPCA object
covar_matrix = sklearnPCA(n_components=None)
covar_matrix.fit(target)
var = np.cumsum(np.round(covar_matrix.explained_variance_ratio_,
- decimals=5)*100)
+ decimals=5) * 100)
# Find the number of components to explain self.varPCAThreshold of
# variance
try:
@@ -1087,95 +1150,15 @@ class MetaModel():
return pca, scaled_target, n_pca_components
# -------------------------------------------------------------------------
- def gaussian_process_emulator(self, X, y, nug_term=None, autoSelect=False,
- varIdx=None):
+ def eval_metamodel(self, samples):
"""
- Fits a Gaussian Process Emulator to the target given the training
- points.
-
- Parameters
- ----------
- X : array of shape (n_samples, n_params)
- Training points.
- y : array of shape (n_samples,)
- Target values.
- nug_term : float, optional
- Nugget term. The default is None, i.e. variance of y.
- autoSelect : bool, optional
- Loop over some kernels and select the best. The default is False.
- varIdx : int, optional
- The index number. The default is None.
-
- Returns
- -------
- gp : object
- Fitted estimator.
-
- """
-
- nug_term = nug_term if nug_term else np.var(y)
-
- Kernels = [nug_term * kernels.RBF(length_scale=1.0,
- length_scale_bounds=(1e-25, 1e15)),
- nug_term * kernels.RationalQuadratic(length_scale=0.2,
- alpha=1.0),
- nug_term * kernels.Matern(length_scale=1.0,
- length_scale_bounds=(1e-15, 1e5),
- nu=1.5)]
-
- # Automatic selection of the kernel
- if autoSelect:
- gp = {}
- BME = []
- for i, kernel in enumerate(Kernels):
- gp[i] = GaussianProcessRegressor(kernel=kernel,
- n_restarts_optimizer=3,
- normalize_y=False)
-
- # Fit to data using Maximum Likelihood Estimation
- gp[i].fit(X, y)
-
- # Store the MLE as BME score
- BME.append(gp[i].log_marginal_likelihood())
-
- gp = gp[np.argmax(BME)]
-
- else:
- gp = GaussianProcessRegressor(kernel=Kernels[0],
- n_restarts_optimizer=3,
- normalize_y=False)
- gp.fit(X, y)
-
- # Compute score
- if varIdx is not None:
- Score = gp.score(X, y)
- print('-'*50)
- print(f'Output variable {varIdx}:')
- print('The estimation of GPE coefficients converged,')
- print(f'with the R^2 score: {Score:.3f}')
- print('-'*50)
-
- return gp
-
- # -------------------------------------------------------------------------
- def eval_metamodel(self, samples=None, nsamples=None,
- sampling_method='random', return_samples=False):
- """
- Evaluates meta-model at the requested samples. One can also generate
+ Evaluates metamodel at the requested samples. One can also generate
nsamples.
Parameters
----------
samples : array of shape (n_samples, n_params), optional
- Samples to evaluate meta-model at. The default is None.
- nsamples : int, optional
- Number of samples to generate, if no `samples` is provided. The
- default is None.
- sampling_method : str, optional
- Type of sampling, if no `samples` is provided. The default is
- 'random'.
- return_samples : bool, optional
- Retun samples, if no `samples` is provided. The default is False.
+ Samples to evaluate metamodel at. The default is None.
Returns
-------
@@ -1184,77 +1167,77 @@ class MetaModel():
std_pred : dict
Standard deviatioon of the predictions.
"""
- outputs = self.ModelObj.Output.names
-
- # Generate or transform (if need be) samples
- if samples is None:
- # Generate
- samples = self.ExpDesign.generate_samples(
- nsamples,
- sampling_method
- )
+ # Transform into np array - can also be given as list
+ samples = np.array(samples)
# Transform samples to the independent space
- samples = self.ExpDesign.transform(
+ samples = self.InputSpace.transform(
samples,
method='user'
- )
- # print(samples)
-
+ )
# Compute univariate bases for the given samples
+ #print('Creating the univariate basis.')
+ univ_p_val = None
if self.meta_model_type.lower() != 'gpe':
univ_p_val = self.univ_basis_vals(
samples,
n_max=np.max(self.pce_deg)
- )
+ )
+ mean_pred = None
+ std_pred = None
mean_pred_b = {}
std_pred_b = {}
+ b_i = 0
# Loop over bootstrap iterations
+ #print('Looping over bootstrap iterations.')
for b_i in range(self.n_bootstrap_itrs):
# Extract model dictionary
if self.meta_model_type.lower() == 'gpe':
- model_dict = self.gp_poly[f'b_{b_i+1}']
+ model_dict = self.gp_poly[f'b_{b_i + 1}']
else:
- model_dict = self.coeffs_dict[f'b_{b_i+1}']
+ model_dict = self.coeffs_dict[f'b_{b_i + 1}']
# Loop over outputs
mean_pred = {}
std_pred = {}
+ #print(model_dict.items())
+ #print('Looping over the output timesteps')
for output, values in model_dict.items():
mean = np.empty((len(samples), len(values)))
std = np.empty((len(samples), len(values)))
idx = 0
+ #print('Looping over ??')
for in_key, InIdxValues in values.items():
- # Perdiction with GPE
+ # Prediction with GPE
if self.meta_model_type.lower() == 'gpe':
- X_T = self.x_scaler[f'b_{b_i+1}'][output].transform(samples)
- gp = self.gp_poly[f'b_{b_i+1}'][output][in_key]
+ X_T = self.x_scaler[f'b_{b_i + 1}'][output].transform(samples)
+ gp = self.gp_poly[f'b_{b_i + 1}'][output][in_key]
y_mean, y_std = gp.predict(X_T, return_std=True)
else:
- # Perdiction with PCE
+ # Prediction with PCE
# Assemble Psi matrix
- basis = self.basis_dict[f'b_{b_i+1}'][output][in_key]
- psi = self.create_psi(basis, univ_p_val)
+ basis = self.basis_dict[f'b_{b_i + 1}'][output][in_key]
+ psi = create_psi(basis, univ_p_val)
- # Perdiction
+ # Prediction
if self.bootstrap_method != 'fast' or b_i == 0:
# with error bar, i.e. use clf_poly
- clf_poly = self.clf_poly[f'b_{b_i+1}'][output][in_key]
+ clf_poly = self.clf_poly[f'b_{b_i + 1}'][output][in_key]
try:
y_mean, y_std = clf_poly.predict(
psi, return_std=True
- )
+ )
except TypeError:
y_mean = clf_poly.predict(psi)
y_std = np.zeros_like(y_mean)
else:
# without error bar
- coeffs = self.coeffs_dict[f'b_{b_i+1}'][output][in_key]
+ coeffs = self.coeffs_dict[f'b_{b_i + 1}'][output][in_key]
y_mean = np.dot(psi, coeffs)
y_std = np.zeros_like(y_mean)
@@ -1264,7 +1247,7 @@ class MetaModel():
# Save predictions for each output
if self.dim_red_method.lower() == 'pca':
- PCA = self.pca[f'b_{b_i+1}'][output]
+ PCA = self.pca[f'b_{b_i + 1}'][output]
mean_pred[output] = PCA.inverse_transform(mean)
std_pred[output] = np.zeros(mean.shape)
else:
@@ -1284,7 +1267,7 @@ class MetaModel():
mean_pred_all[k][i] = v
# Compute the moments of predictions over the predictions
- for output in outputs:
+ for output in self.out_names:
# Only use bootstraps with finite values
finite_rows = np.isfinite(
mean_pred_all[output]).all(axis=2).all(axis=1)
@@ -1295,15 +1278,13 @@ class MetaModel():
if self.n_bootstrap_itrs > 1:
std_pred[output] = np.std(outs, axis=0)
else:
+ # TODO: this b_i seems off here
std_pred[output] = std_pred_b[b_i][output]
- if return_samples:
- return mean_pred, std_pred, samples
- else:
- return mean_pred, std_pred
+ return mean_pred, std_pred
# -------------------------------------------------------------------------
- def create_model_error(self, X, y, name='Calib'):
+ def create_model_error(self, X, y, MeasuredData):
"""
Fits a GPE-based model error.
@@ -1314,8 +1295,7 @@ class MetaModel():
extracted data.
y : array of shape (n_outputs,)
The model response for the MAP parameter set.
- name : str, optional
- Calibration or validation. The default is `'Calib'`.
+ MeasuredData :
Returns
-------
@@ -1323,14 +1303,14 @@ class MetaModel():
Self object.
"""
- Model = self.ModelObj
- outputNames = Model.Output.names
+ outputNames = self.out_names
self.errorRegMethod = 'GPE'
self.errorclf_poly = self.auto_vivification()
self.errorScale = self.auto_vivification()
# Read data
- MeasuredData = Model.read_observation(case=name)
+ # TODO: do this call outside the metamodel
+ # MeasuredData = Model.read_observation(case=name)
# Fitting GPR based bias model
for out in outputNames:
@@ -1346,7 +1326,7 @@ class MetaModel():
delta = data # - y[out][0]
BiasInputs = np.hstack((X[out], y[out].reshape(-1, 1)))
X_S = scaler.fit_transform(BiasInputs)
- gp = self.gaussian_process_emulator(X_S, delta)
+ gp = gaussian_process_emulator(X_S, delta)
self.errorScale[out]["y_1"] = scaler
self.errorclf_poly[out]["y_1"] = gp
@@ -1416,31 +1396,25 @@ class MetaModel():
return value
# -------------------------------------------------------------------------
- def copy_meta_model_opts(self, InputObj, ModelObj):
+ def copy_meta_model_opts(self):
"""
This method is a convinient function to copy the metamodel options.
- Parameters
- ----------
- InputObj : object
- The input object.
- ModelObj : object
- The Model object.
-
Returns
-------
new_MetaModelOpts : object
The copied object.
"""
+ # TODO: what properties should be moved to the new object?
new_MetaModelOpts = copy.deepcopy(self)
- new_MetaModelOpts.ModelObj = ModelObj
- new_MetaModelOpts.input_obj = InputObj
- new_MetaModelOpts.ExpDesign.meta_Model = 'aPCE'
- new_MetaModelOpts.ExpDesign.InputObj = InputObj
- new_MetaModelOpts.ExpDesign.ndim = len(InputObj.Marginals)
- new_MetaModelOpts.n_params = len(InputObj.Marginals)
- new_MetaModelOpts.ExpDesign.hdf5_file = None
+ new_MetaModelOpts.input_obj = self.input_obj # InputObj
+ new_MetaModelOpts.InputSpace = self.InputSpace
+ # new_MetaModelOpts.InputSpace.meta_Model = 'aPCE'
+ # new_MetaModelOpts.InputSpace.InputObj = self.input_obj
+ # new_MetaModelOpts.InputSpace.ndim = len(self.input_obj.Marginals)
+ new_MetaModelOpts.n_params = len(self.input_obj.Marginals)
+ # new_MetaModelOpts.InputSpace.hdf5_file = None
return new_MetaModelOpts
@@ -1460,13 +1434,15 @@ class MetaModel():
Returns
-------
deg_array: array
- Array containing the arrays.
+ The selected degrees.
"""
# Define the deg_array
max_deg = np.max(self.pce_deg)
min_Deg = np.min(self.pce_deg)
- nitr = n_samples - self.ExpDesign.n_init_samples
+
+ # TODO: remove the options for sequential?
+ nitr = n_samples - self.InputSpace.n_init_samples
# Check q-norm
if not np.isscalar(self.pce_q_norm):
@@ -1474,24 +1450,134 @@ class MetaModel():
else:
self.pce_q_norm = np.array([self.pce_q_norm])
- def M_uptoMax(maxDeg):
- n_combo = np.zeros(maxDeg)
- for i, d in enumerate(range(1, maxDeg+1)):
- n_combo[i] = math.factorial(ndim+d)
- n_combo[i] /= math.factorial(ndim) * math.factorial(d)
- return n_combo
+ # def M_uptoMax(maxDeg):
+ # n_combo = np.zeros(maxDeg)
+ # for i, d in enumerate(range(1, maxDeg + 1)):
+ # n_combo[i] = math.factorial(ndim + d)
+ # n_combo[i] /= math.factorial(ndim) * math.factorial(d)
+ # return n_combo
- if self.ExpDesignFlag != 'sequential':
- deg_new = max_deg
- else:
- d = nitr if nitr != 0 and self.n_params > 5 else 1
- min_index = np.argmin(abs(M_uptoMax(max_deg)-ndim*n_samples*d))
- deg_new = max_deg
- # deg_new = range(1, max_deg+1)[min_index]
+ deg_new = max_deg
+ # d = nitr if nitr != 0 and self.n_params > 5 else 1
+ # d = 1
+ # min_index = np.argmin(abs(M_uptoMax(max_deg)-ndim*n_samples*d))
+ # deg_new = range(1, max_deg+1)[min_index]
if deg_new > min_Deg and self.pce_reg_method.lower() != 'fastard':
- deg_array = np.arange(min_Deg, deg_new+1)
+ deg_array = np.arange(min_Deg, deg_new + 1)
else:
deg_array = np.array([deg_new])
return deg_array
+
+ def generate_polynomials(self, max_deg=None):
+ """
+ Generates (univariate) polynomials.
+
+ Parameters
+ ----------
+ max_deg : int
+ Maximum polynomial degree.
+
+ Returns
+ -------
+ None
+ """
+ # Check for InputSpace
+ if self.InputSpace is None:
+ raise AttributeError('Generate or add InputSpace before generating polynomials')
+
+ ndim = self.InputSpace.ndim
+ # Create orthogonal polynomial coefficients if necessary
+ if (self.meta_model_type.lower() != 'gpe') and max_deg is not None: # and self.input_obj.poly_coeffs_flag:
+ self.polycoeffs = {}
+ for parIdx in tqdm(range(ndim), ascii=True,
+ desc="Computing orth. polynomial coeffs"):
+ poly_coeffs = apoly_construction(
+ self.InputSpace.raw_data[parIdx],
+ max_deg
+ )
+ self.polycoeffs[f'p_{parIdx + 1}'] = poly_coeffs
+ else:
+ raise AttributeError('MetaModel cannot generate polynomials in the given scenario!')
+
+ # -------------------------------------------------------------------------
+ def _compute_pce_moments(self):
+ """
+ Computes the first two moments using the PCE-based metamodel.
+
+ Returns
+ -------
+ pce_means: dict
+ The first moment (mean) of the surrogate.
+ pce_stds: dict
+ The second moment (standard deviation) of the surrogate.
+
+ """
+
+ # Check if it's truly a pce-surrogate
+ if self.meta_model_type.lower() == 'gpe':
+ raise AttributeError('Moments can only be computed for pce-type surrogates')
+
+ outputs = self.out_names
+ pce_means_b = {}
+ pce_stds_b = {}
+
+ # Loop over bootstrap iterations
+ for b_i in range(self.n_bootstrap_itrs):
+ # Loop over the metamodels
+ coeffs_dicts = self.coeffs_dict[f'b_{b_i + 1}'].items()
+ means = {}
+ stds = {}
+ for output, coef_dict in coeffs_dicts:
+
+ pce_mean = np.zeros((len(coef_dict)))
+ pce_var = np.zeros((len(coef_dict)))
+
+ for index, values in coef_dict.items():
+ idx = int(index.split('_')[1]) - 1
+ coeffs = self.coeffs_dict[f'b_{b_i + 1}'][output][index]
+
+ # Mean = c_0
+ if coeffs[0] != 0:
+ pce_mean[idx] = coeffs[0]
+ else:
+ clf_poly = self.clf_poly[f'b_{b_i + 1}'][output]
+ pce_mean[idx] = clf_poly[index].intercept_
+ # Var = sum(coeffs[1:]**2)
+ pce_var[idx] = np.sum(np.square(coeffs[1:]))
+
+ # Save predictions for each output
+ if self.dim_red_method.lower() == 'pca':
+ PCA = self.pca[f'b_{b_i + 1}'][output]
+ means[output] = PCA.inverse_transform(pce_mean)
+ stds[output] = PCA.inverse_transform(np.sqrt(pce_var))
+ else:
+ means[output] = pce_mean
+ stds[output] = np.sqrt(pce_var)
+
+ # Save predictions for each bootstrap iteration
+ pce_means_b[b_i] = means
+ pce_stds_b[b_i] = stds
+
+ # Change the order of nesting
+ mean_all = {}
+ for i in sorted(pce_means_b):
+ for k, v in pce_means_b[i].items():
+ if k not in mean_all:
+ mean_all[k] = [None] * len(pce_means_b)
+ mean_all[k][i] = v
+ std_all = {}
+ for i in sorted(pce_stds_b):
+ for k, v in pce_stds_b[i].items():
+ if k not in std_all:
+ std_all[k] = [None] * len(pce_stds_b)
+ std_all[k][i] = v
+
+ # Back transformation if PCA is selected.
+ pce_means, pce_stds = {}, {}
+ for output in outputs:
+ pce_means[output] = np.mean(mean_all[output], axis=0)
+ pce_stds[output] = np.mean(std_all[output], axis=0)
+
+ return pce_means, pce_stds
diff --git a/tests/MCrefs_MeanStd.csv b/tests/MCrefs_MeanStd.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e9592bf45db032790f95259b8cedd7f961e70a81
--- /dev/null
+++ b/tests/MCrefs_MeanStd.csv
@@ -0,0 +1,12 @@
+x [m],mean,std
+0,0,0
+0.1,-0.002623091088022,8.06E-04
+0.2,-0.004962749295994,0.001525818175278
+0.3,-0.006794367435948,0.002088956887598
+0.4,-0.007957511802197,0.002446570522429
+0.5,-0.008355922171325,0.002569063468612
+0.6,-0.007957511802197,0.002446570522429
+0.7,-0.006794367435948,0.002088956887598
+0.8,-0.004962749295994,0.001525818175278
+0.9,-0.002623091088022,8.06E-04
+1,0,0
diff --git a/tests/MeasuredData.csv b/tests/MeasuredData.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5774f51566a8dfc04976e30e56fbca293ac296e4
--- /dev/null
+++ b/tests/MeasuredData.csv
@@ -0,0 +1,12 @@
+x [m],Deflection [m]
+0,0
+0.555555555555556,-0.002507972252554
+1.11111111111111,-0.004744950561407
+1.66666666666667,-0.006496185008909
+2.22222222222222,-0.007608282796739
+2.77777777777778,-0.007989208245903
+3.33333333333333,-0.007608282796739
+3.88888888888889,-0.006496185008909
+4.44444444444444,-0.004744950561407
+5,-0.002507972252554
+5.55555555555556,0
diff --git a/tests/MeasuredData_Valid.csv b/tests/MeasuredData_Valid.csv
new file mode 100644
index 0000000000000000000000000000000000000000..426ba7ed46ff82621baadb3435f79c2a0f47810b
--- /dev/null
+++ b/tests/MeasuredData_Valid.csv
@@ -0,0 +1,12 @@
+x [m],Deflection [m]
+0,0
+0.555555555555556,-0.002623491033022
+1.11111111111111,-0.004962750295994
+1.66666666666667,-0.006794369935948
+2.22222222222222,-0.007956511802197
+2.77777777777778,-0.008355582171325
+3.33333333333333,-0.007957591802197
+3.88888888888889,-0.006794363675948
+4.44444444444444,-0.004962749995994
+5,-0.00262091088022
+5.55555555555556,0
diff --git a/tests/SBeam_Deflection.tpl.inp b/tests/SBeam_Deflection.tpl.inp
new file mode 100644
index 0000000000000000000000000000000000000000..6bfc3fe96c62923b0a89bde26b5dd83375a418db
--- /dev/null
+++ b/tests/SBeam_Deflection.tpl.inp
@@ -0,0 +1,6 @@
+% Input file for the simply supported beam model
+<X1> % b in m
+<X2> % h in m
+5 % L in m
+<X3> % E in Pa
+<X4> % p in N/m
diff --git a/tests/test_BayesInference.py b/tests/test_BayesInference.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f22f9158190c930fe6827fe892e3a87027e33c5
--- /dev/null
+++ b/tests/test_BayesInference.py
@@ -0,0 +1,1105 @@
+# -*- coding: utf-8 -*-
+"""
+Test the BayesInference class for bayesvalidrox
+
+Tests are available for the following functions
+ _logpdf - x
+ _kernel_rbf - x
+class BayesInference:
+ setup_inference - x
+ create_inference - x
+ perform_bootstrap Need working model for tests without emulator
+ _perturb_data - x
+ create_error_model Error in the MetaModel
+ _eval_model Need working model to test this
+ normpdf - x
+ _corr_factor_BME_old - removed
+ _corr_factor_BME - x
+ _rejection_sampling - x
+ _posterior_predictive - x
+ plot_post_params - x
+ plot_log_BME - x
+ _plot_max_a_posteriori Need working model to test this
+ _plot_post_predictive - x
+"""
+import sys
+import pytest
+import numpy as np
+import pandas as pd
+
+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
+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
+
+sys.path.append("src/")
+sys.path.append("../src/")
+
+
+#%% Test _logpdf
+
+def test_logpdf() -> None:
+ """
+ Calculate loglikelihood
+
+ """
+ _logpdf([0], [0], [1])
+
+
+#%% Test _kernel_rbf
+
+def test_kernel_rbf() -> None:
+ """
+ Create RBF kernel
+ """
+ X = [[0, 0], [1, 1.5]]
+ pars = [1, 0.5, 1]
+ _kernel_rbf(X, pars)
+
+
+def test_kernel_rbf_lesspar() -> None:
+ """
+ Create RBF kernel with too few parameters
+ """
+ X = [[0, 0], [1, 1.5]]
+ pars = [1, 2]
+ with pytest.raises(AttributeError) as excinfo:
+ _kernel_rbf(X, pars)
+ assert str(excinfo.value) == 'Provide 3 parameters for the RBF kernel!'
+
+
+#%% Test MCMC init
+
+def test_BayesInference() -> None:
+ """
+ Construct a BayesInference object
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mod = PL()
+ mm = MetaModel(inp)
+ expdes = ExpDesigns(inp)
+ engine = Engine(mm, mod, expdes)
+ BayesInference(engine)
+
+
+#%% Test create_inference
+# TODO: disabled this test!
+def test_create_inference() -> None:
+ """
+ Run inference
+ """
+ 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.x_values = np.array([0]) # Error in plots if this is not available
+
+ 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()
+ 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']
+
+ engine = Engine(mm, mod, expdes)
+
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData * 0.15) ** 2
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = DiscrepancyOpts # Error if this not class 'DiscrepancyOpts' or dict(?)
+ bi.bootstrap = True # Error if this and bayes_loocv and just_analysis are all False?
+ bi.plot_post_pred = False # Remaining issue in the violinplot
+ bi.create_inference()
+ # Remaining issue in the violinplot in plot_post_predictive
+
+
+#%% Test rejection_sampling
+def test_rejection_sampling_nologlik() -> None:
+ """
+ Perform rejection sampling without given log likelihood
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mod = PL()
+ mm = MetaModel(inp)
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ engine = Engine(mm, mod, expdes)
+ bi = BayesInference(engine)
+ bi.prior_samples = expdes.generate_samples(100, 'random')
+ with pytest.raises(AttributeError) as excinfo:
+ bi._rejection_sampling()
+ assert str(excinfo.value) == 'No log-likelihoods available!'
+
+
+def test_rejection_sampling_noprior() -> None:
+ """
+ Perform rejection sampling without prior samples
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mod = PL()
+ mm = MetaModel(inp)
+ expdes = ExpDesigns(inp)
+ engine = Engine(mm, mod, expdes)
+ bi = BayesInference(engine)
+ with pytest.raises(AttributeError) as excinfo:
+ bi._rejection_sampling()
+ assert str(excinfo.value) == 'No prior samples available!'
+
+
+def test_rejection_sampling() -> None:
+ """
+ Perform rejection sampling
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mod = PL()
+ mm = MetaModel(inp)
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ engine = Engine(mm, mod, expdes)
+ bi = BayesInference(engine)
+ bi.prior_samples = expdes.generate_samples(100, 'random')
+ bi.log_likes = np.swapaxes(np.atleast_2d(np.log(np.random.random(100) * 3)), 0, 1)
+ bi._rejection_sampling()
+
+
+#%% Test _perturb_data
+
+def test_perturb_data() -> None:
+ """
+ Perturb data
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mod = PL()
+ mm = MetaModel(inp)
+ expdes = ExpDesigns(inp)
+ engine = Engine(mm, mod, expdes)
+
+ bi = BayesInference(engine)
+ data = pd.DataFrame()
+ data['Z'] = [0.45]
+ bi._perturb_data(data, ['Z'])
+
+
+def test_perturb_data_loocv() -> None:
+ """
+ Perturb data with bayes_loocv
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mod = PL()
+ mm = MetaModel(inp)
+ expdes = ExpDesigns(inp)
+ engine = Engine(mm, mod, expdes)
+
+ bi = BayesInference(engine)
+ data = pd.DataFrame()
+ data['Z'] = [0.45]
+ bi.bayes_loocv = True
+ bi._perturb_data(data, ['Z'])
+
+
+#%% Test _eval_model
+
+def test_eval_model() -> None:
+ """
+ Run model with descriptive key
+ """
+ # TODO: need functioning example model to test this
+ None
+
+
+#%% Test corr_factor_BME
+
+def test_corr_factor_BME() -> None:
+ """
+ Calculate correction factor
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ 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)
+
+ obs_data = {'Z': np.array([0.45])}
+ total_sigma2s = {'Z': np.array([0.15])}
+ logBME = [0, 0, 0]
+
+ bi = BayesInference(engine)
+ bi.selected_indices = {'Z': 0}
+ bi._corr_factor_BME(obs_data, total_sigma2s, logBME)
+
+
+def test_corr_factor_BME_selectedindices() -> None:
+ """
+ Calculate correction factor
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ 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)
+
+ obs_data = {'Z': np.array([0.45])}
+ total_sigma2s = {'Z': np.array([0.15])}
+ logBME = [0, 0, 0]
+
+ bi = BayesInference(engine)
+ bi.selected_indices = {'Z': 0}
+ bi._corr_factor_BME(obs_data, total_sigma2s, logBME)
+
+
+#%% Test normpdf
+
+def test_normpdf_nosigmas() -> None:
+ """
+ Run normpdf without any additional sigmas
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ expdes.X = np.array([[0], [1], [0.5]])
+ expdes.Y = {'Z': np.array([[0.4], [0.5], [0.45]])}
+
+ mm = MetaModel(inp)
+ mod = PL()
+ mod.Output.names = ['Z']
+ engine = Engine(mm, mod, expdes)
+
+ obs_data = {'Z': np.array([0.45])}
+ total_sigma2s = {'Z': np.array([0.15])}
+
+ bi = BayesInference(engine)
+ bi.normpdf(expdes.Y, obs_data, total_sigma2s, sigma2=None, std=None)
+
+
+def test_normpdf_sigma2() -> None:
+ """
+ Run normpdf with sigma2
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ expdes.X = np.array([[0], [1], [0.5]])
+ expdes.Y = {'Z': np.array([[0.4], [0.5], [0.45]])}
+
+ mm = MetaModel(inp)
+ mod = PL()
+ mod.Output.names = ['Z']
+ engine = Engine(mm, mod, expdes)
+
+ obs_data = {'Z': np.array([0.45])}
+ total_sigma2s = {'Z': np.array([0.15])}
+ sigma2 = [[0]]
+
+ bi = BayesInference(engine)
+ bi.normpdf(expdes.Y, obs_data, total_sigma2s, sigma2=sigma2, std=None)
+
+
+def test_normpdf_allsigmas() -> None:
+ """
+ Run normpdf with all additional sigmas
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ expdes.X = np.array([[0], [1], [0.5]])
+ expdes.Y = {'Z': np.array([[0.4], [0.5], [0.45]])}
+
+ mm = MetaModel(inp)
+ mod = PL()
+ mod.Output.names = ['Z']
+ engine = Engine(mm, mod, expdes)
+
+ obs_data = {'Z': np.array([0.45])}
+ total_sigma2s = {'Z': np.array([0.15])}
+ sigma2 = [[0]]
+
+ bi = BayesInference(engine)
+ bi.normpdf(expdes.Y, obs_data, total_sigma2s, sigma2=sigma2, std=total_sigma2s)
+
+
+#%% Test setup_inference
+
+def test_setup_inference_noobservation() -> None:
+ """
+ Test the object setup without given observations
+ """
+ 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
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
+
+ mod = PL()
+ mod.Output.names = ['Z']
+
+ engine = Engine(mm, mod, expdes)
+
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData * 0.15) ** 2
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = DiscrepancyOpts
+ with pytest.raises(Exception) as excinfo:
+ bi.setup_inference()
+ assert str(
+ excinfo.value) == ('Please provide the observation data as a dictionary via observations attribute or pass the '
+ 'csv-file path to MeasurementFile attribute')
+
+
+def test_setup_inference() -> None:
+ """
+ Test the object setup with observations
+ """
+ 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
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
+
+ mod = PL()
+ mod.observations = {'Z': np.array([0.45])}
+ mod.observations = {'Z': np.array([0.45]), 'x_values': np.array([0])}
+ mod.Output.names = ['Z']
+
+ engine = Engine(mm, mod, expdes)
+
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData * 0.15) ** 2
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = DiscrepancyOpts
+ bi.setup_inference()
+
+
+def test_setup_inference_priorsamples() -> None:
+ """
+ Test the object setup with prior samples set by hand
+ """
+ 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
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
+
+ mod = PL()
+ mod.observations = {'Z': np.array([0.45])}
+ mod.observations = {'Z': np.array([0.45]), 'x_values': np.array([0])}
+ mod.Output.names = ['Z']
+
+ engine = Engine(mm, mod, expdes)
+
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData * 0.15) ** 2
+
+ bi = BayesInference(engine)
+ bi.prior_samples = np.swapaxes(np.array([np.random.normal(0, 1, 100)]), 0, 1)
+ bi.Discrepancy = DiscrepancyOpts
+ bi.setup_inference()
+
+
+def test_setup_inference_valid() -> None:
+ """
+ Test the object setup for valid
+ """
+ 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
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
+
+ mod = PL()
+ mod.observations_valid = {'Z': np.array([0.45])}
+ mod.observations_valid = {'Z': np.array([0.45]), 'x_values': np.array([0])}
+ mod.Output.names = ['Z']
+
+ engine = Engine(mm, mod, expdes)
+
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData * 0.15) ** 2
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = DiscrepancyOpts
+ bi.name = 'valid'
+ bi.setup_inference()
+
+
+def test_setup_inference_noname() -> None:
+ """
+ Test the object setup for an invalid inference name
+ """
+ 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
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
+
+ mod = PL()
+ mod.observations = {'Z': np.array([0.45])}
+ mod.observations = {'Z': np.array([0.45]), 'x_values': np.array([0])}
+ mod.Output.names = ['Z']
+
+ engine = Engine(mm, mod, expdes)
+
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData * 0.15) ** 2
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = DiscrepancyOpts
+ bi.name = ''
+ with pytest.raises(Exception) as excinfo:
+ bi.setup_inference()
+ assert str(excinfo.value) == 'The set inference type is not known! Use either `calib` or `valid`'
+
+
+#%% Test perform_bootstrap
+
+def test_perform_bootstrap() -> None:
+ """
+ Do bootstrap
+ """
+ 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.x_values = np.array([0]) # Error in plots if this is not available
+
+ 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))
+
+ 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)
+
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData * 0.15) ** 2
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = DiscrepancyOpts
+ bi.bootstrap = True
+ bi.plot_post_pred = False
+ total_sigma2s = {'Z': np.array([0.15])}
+ bi.setup_inference()
+ bi.perform_bootstrap(total_sigma2s)
+
+
+def test_perform_bootstrap_bayesloocv() -> None:
+ """
+ Do bootstrap
+ """
+ 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.x_values = np.array([0]) # Error in plots if this is not available
+
+ 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))
+
+ 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)
+
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData * 0.15) ** 2
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = DiscrepancyOpts
+ bi.bootstrap = True
+ bi.plot_post_pred = False
+ total_sigma2s = {'Z': np.array([0.15])}
+ bi.setup_inference()
+ bi.bayes_loocv = True
+ bi.perform_bootstrap(total_sigma2s)
+
+
+#%% Test create_error_model
+
+def create_error_model_prior() -> None:
+ """
+ Test creating MetaModel error-model for 'prior'
+ """
+ # TODO: there are issues with the expected formats from the MetaModel
+ 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.n_params = 1
+ mm.fit(expdes.X, expdes.Y)
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
+
+ mod = PL()
+ mod.observations = {'Z': np.array([0.45])}
+ mod.observations = {'Z': np.array([0.45]), 'x_values': np.array([0])}
+ mod.Output.names = ['Z']
+ mod.n_obs = 1
+
+ engine = Engine(mm, mod, expdes)
+
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData * 0.15) ** 2
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = DiscrepancyOpts
+ bi.bootstrap = True
+ bi.setup_inference()
+ bi.bias_inputs = expdes.X
+ bi.create_error_model(type_='prior', opt_sigma='B', sampler=None)
+
+
+def create_error_model_posterior() -> None:
+ """
+ Test creating MetaModel error-model for 'posterior'
+ """
+ # TODO: there are issues with the expected formats from the MetaModel
+ 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.n_params = 1
+ mm.fit(expdes.X, expdes.Y)
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(1))
+
+ mod = PL()
+ mod.observations = {'Z': np.array([0.45])}
+ mod.observations = {'Z': np.array([0.45]), 'x_values': np.array([0])}
+ mod.Output.names = ['Z']
+ mod.n_obs = 1
+
+ engine = Engine(mm, mod, expdes)
+
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData * 0.15) ** 2
+
+ posterior = pd.DataFrame()
+ posterior[None] = [0, 1, 0.5]
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = DiscrepancyOpts
+ bi.bootstrap = True
+ bi.setup_inference()
+ bi.bias_inputs = expdes.X
+ bi.posterior_df = posterior
+ bi.create_error_model(type_='posterior', opt_sigma='B', sampler=None)
+
+
+#%% Test _posterior_predictive
+
+def test_posterior_predictive() -> None:
+ """
+ Test posterior predictions
+ """
+ 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.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 available
+
+ 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)
+
+ total_sigma2s = {'Z': np.array([0.15])}
+ posterior = pd.DataFrame()
+ posterior[None] = [0, 1, 0.5]
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData * 0.15) ** 2
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = DiscrepancyOpts
+ bi.bootstrap = True
+ bi.plot_post_pred = False
+ bi.posterior_df = posterior
+ bi.bias_inputs = expdes.X
+ bi._mean_pce_prior_pred = y_hat
+ bi._std_pce_prior_pred = y_std
+ bi.Discrepancy.total_sigma2 = total_sigma2s
+ bi.setup_inference()
+ bi._posterior_predictive()
+
+
+def test_posterior_predictive_rejection() -> None:
+ """
+ Test posterior predictions with rejection inference
+ """
+ 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.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 available
+
+ 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)
+
+ total_sigma2s = {'Z': np.array([0.15])}
+ posterior = pd.DataFrame()
+ posterior[None] = [0, 1, 0.5]
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.type = 'Gaussian'
+ DiscrepancyOpts.parameters = (obsData * 0.15) ** 2
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = DiscrepancyOpts
+ bi.bootstrap = True
+ bi.plot_post_pred = False
+ bi.posterior_df = posterior
+ bi.bias_inputs = expdes.X
+ bi._mean_pce_prior_pred = y_hat
+ bi._std_pce_prior_pred = y_std
+ bi.Discrepancy.total_sigma2 = total_sigma2s
+ bi.inference_method = 'rejection'
+ bi.setup_inference()
+ bi._posterior_predictive()
+
+
+#%% Test plot_post_params
+
+def test_plot_post_params() -> None:
+ """
+ Plot posterior dist
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ expdes.n_init_samples = 2
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ mod = PL()
+ engine = Engine(mm, mod, expdes)
+
+ bi = BayesInference(engine)
+ posterior = pd.DataFrame()
+ posterior[None] = [0, 1, 0.5]
+ bi.posterior_df = posterior
+ bi.plot_post_params('B')
+
+
+def test_plot_post_params_noemulator() -> None:
+ """
+ Plot posterior dist with emulator = False
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ expdes.n_init_samples = 2
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ mod = PL()
+ engine = Engine(mm, mod, expdes)
+
+ bi = BayesInference(engine)
+ posterior = pd.DataFrame()
+ posterior[None] = [0, 1, 0.5]
+ bi.posterior_df = posterior
+ bi.emulator = False
+ bi.plot_post_params('B')
+
+
+#%% Test plot_log_BME
+
+def test_plot_log_BME() -> None:
+ """
+ Show the log_BME from bootstrapping
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ expdes.n_init_samples = 2
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ mod = PL()
+ engine = Engine(mm, mod, expdes)
+
+ bi = BayesInference(engine)
+ bi.log_BME = np.array([[0, 0.2], [0, 0.2]])
+ bi.n_tot_measurement = 1
+ bi.plot_log_BME()
+
+
+def test_plot_log_BME_noemulator() -> None:
+ """
+ Show the log_BME from bootstrapping with emulator = False
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ expdes.n_init_samples = 2
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ mod = PL()
+ engine = Engine(mm, mod, expdes)
+
+ bi = BayesInference(engine)
+ bi.log_BME = np.array([[0, 0.2], [0, 0.2]])
+ bi.n_tot_measurement = 1
+ bi.emulator = False
+ bi.plot_log_BME()
+
+
+#%% Test _plot_max_a_posteriori
+
+def test_plot_max_a_posteriori_rejection() -> None:
+ """
+ Plot MAP estimate for rejection
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ expdes.n_init_samples = 2
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ mod = PL()
+ engine = Engine(mm, mod, expdes)
+
+ bi = BayesInference(engine)
+ bi.inference_method = 'rejection'
+ bi._plot_post_predictive()
+
+
+def test_plot_max_a_posteriori() -> None:
+ """
+ Plot MAP estimate
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ expdes.n_init_samples = 2
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ mod = PL()
+ engine = Engine(mm, mod, expdes)
+
+ bi = BayesInference(engine)
+ bi._plot_post_predictive()
+
+
+#%% Test _plot_post_predictive
+
+
+def test_plot_post_predictive_rejection() -> None:
+ """
+ Plot posterior predictions for rejection
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ expdes.n_init_samples = 2
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ mod = PL()
+ engine = Engine(mm, mod, expdes)
+
+ bi = BayesInference(engine)
+ bi.inference_method = 'rejection'
+ bi._plot_post_predictive()
+
+
+def test_plot_post_predictive() -> None:
+ """
+ Plot posterior predictions
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+
+ expdes = ExpDesigns(inp)
+ expdes.init_param_space(max_deg=1)
+ expdes.n_init_samples = 2
+
+ mm = MetaModel(inp)
+ mm.n_params = 1
+ mod = PL()
+ engine = Engine(mm, mod, expdes)
+
+ bi = BayesInference(engine)
+ bi._plot_post_predictive()
+
+
+#%% 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'
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = DiscrepancyOpts # Error if this not class 'DiscrepancyOpts' or dict(?)
+ bi.bootstrap = True # Error if this and bayes_loocv and just_analysis are all False?
+ bi.plot_post_pred = False # Remaining issue in the violinplot
+ bi.error_model = False
+ bi.bayes_loocv = True
+ if 1:
+ bi.create_inference()
+ # opt_sigma = 'B'
+ # total_sigma2s = {'Z':np.array([0.15])}
+ # data = pd.DataFrame()
+ # data['Z'] = [0.45]
+ # data['x_values'] = [0.3]
+ # bi.setup_inference()
+ # bi.perform_bootstrap(total_sigma2s)
+ posterior = pd.DataFrame()
+ posterior[None] = [0, 1, 0.5]
+ bi.posterior_df = posterior
+ # bi.bias_inputs = expdes.X
+ # bi._mean_pce_prior_pred = y_hat
+ # bi._std_pce_prior_pred = y_std
+ # bi.Discrepancy.total_sigma2 = total_sigma2s
+ # bi.create_error_model(type_ = 'posterior', opt_sigma = 'B', sampler = None)
+ # bi._posterior_predictive()
+ # bi.plot_post_params('B')
+ # bi.log_BME = np.array([[0,0.2],[0,0.2]])
+ # bi.n_tot_measurement = 1
+ # bi.plot_log_BME()
+ bi.inference_method = 'rejection'
+ bi._plot_max_a_posteriori()
diff --git a/tests/test_BayesModelComparison.py b/tests/test_BayesModelComparison.py
new file mode 100644
index 0000000000000000000000000000000000000000..91f328ec7ae39cf7cded6b228edc1442053a2dfb
--- /dev/null
+++ b/tests/test_BayesModelComparison.py
@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+"""
+Test the BayesModelComparison class in bayesvalidrox.
+Tests are available for the following functions
+Class BayesModelComparison:
+ create_model_comparison
+ compare_models
+ generate_dataset
+ __perturb_data
+ cal_model_weight
+ plot_just_analysis
+ plot_model_weights
+ plot_bayes_factor
+
+"""
+import sys
+sys.path.append("src/")
+import pytest
+import numpy as np
+
+from bayesvalidrox.bayes_inference.bayes_model_comparison import BayesModelComparison
+#from bayesvalidrox.surrogate_models.input_space import InputSpace
+
+def test_BMC() -> None:
+ """
+ Build BMC without inputs
+ """
+ BayesModelComparison()
\ No newline at end of file
diff --git a/tests/test_Discrepancy.py b/tests/test_Discrepancy.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fb948d905031e7d7e6235857c27792fb29ece57
--- /dev/null
+++ b/tests/test_Discrepancy.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+"""
+Test the Discrepancy class in bayesvalidrox.
+Tests are available for the following functions
+Class Discrepancy:
+ get_sample
+
+"""
+import sys
+sys.path.append("src/")
+import pytest
+
+from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
+from bayesvalidrox.surrogate_models.inputs import Input
+
+#%% Test Discrepancy init
+
+def test_discrepancy() -> None:
+ """
+ Construct a Discrepancy object
+ """
+ disc = Discrepancy()
+
+#%% Test Discrepancy.get_sample
+
+def test_get_sample_noinput() -> None:
+ """
+ Get discrepancy sample without input dist
+ """
+ disc = Discrepancy()
+ with pytest.raises(AttributeError) as excinfo:
+ disc.get_sample(2)
+ assert str(excinfo.value) == 'Cannot create new samples, please provide input distributions'
+
+def test_get_sample() -> None:
+ """
+ Get discrepancy sample
+ """
+ disc = Discrepancy()
+ with pytest.raises(AttributeError) as excinfo:
+ disc.get_sample(2)
+ assert str(excinfo.value) == 'Cannot create new samples, please provide input distributions'
+
\ No newline at end of file
diff --git a/tests/test_Engine.py b/tests/test_Engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b03a26237d1b1ff741bcfd69e8ffff188cc8d7d
--- /dev/null
+++ b/tests/test_Engine.py
@@ -0,0 +1,1066 @@
+# -*- coding: utf-8 -*-
+"""
+Tests the class Engine in bayesvalidrox
+Tests are available for the following functions:
+ hellinger_distance - x
+ logpdf - x
+ subdomain - x
+Engine:
+ start_engine - x
+ train_normal
+ train_sequential
+ eval_metamodel
+ train_seq_design
+ util_VarBasedDesign
+ util_BayesianActiveDesign
+ util_BayesianDesign
+ run_util_func
+ dual_annealing
+ tradoff_weights - x
+ choose_next_sample
+ plotter
+ 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
+
+"""
+import math
+import numpy as np
+import pandas as pd
+import sys
+
+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.surrogate_models.engine import hellinger_distance, logpdf, subdomain
+
+sys.path.append("src/")
+
+
+#%% Test Engine constructor
+
+
+def test_engine() -> None:
+ """
+ Build Engine without inputs
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mod = PL()
+ mm = MetaModel(inp)
+ expdes = ExpDesigns(inp)
+ Engine(mm, mod, expdes)
+
+
+#%% Test Engine.start_engine
+
+def test_start_engine() -> None:
+ """
+ Build Engine without inputs
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mod = PL()
+ mm = MetaModel(inp)
+ expdes = ExpDesigns(inp)
+ engine = Engine(mm, mod, expdes)
+ engine.start_engine()
+
+
+#%% Test Engine.train_normal
+# TODO: build mock model to do this? - test again in full-length examples
+
+#%% Test Engine._error_Mean_Std
+
+def test__error_Mean_Std() -> None:
+ """
+ Compare moments of surrogate and reference
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.fit([[0.0], [1.0]], {'Z': [[0.5], [0.5]]})
+ expdes = ExpDesigns(inp)
+ mod = PL()
+ mod.mc_reference['mean'] = [0.5]
+ mod.mc_reference['std'] = [0.0]
+ mod.Output.names = ['Z']
+ engine = Engine(mm, mod, expdes)
+ engine.start_engine()
+ mean, std = engine._error_Mean_Std()
+ assert mean < 0.01 and std < 0.01
+
+
+#%% Test Engine._validError
+
+def test__validError() -> None:
+ """
+ Calculate validation error
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.fit([[0.0], [1.0]], {'Z': [[0.5], [0.5]]})
+ expdes = ExpDesigns(inp)
+ mod = PL()
+ expdes.valid_samples = [[0.5]]
+ expdes.valid_model_runs = {'Z': [[0.5]]}
+ mod.Output.names = ['Z']
+ engine = Engine(mm, mod, expdes)
+ engine.start_engine()
+ mean, std = engine._validError()
+ assert mean['Z'][0] < 0.01 # and std['Z'][0] <0.01
+
+
+#%% Test Engine._BME_Calculator
+
+def test__BME_Calculator() -> None:
+ """
+ Calculate BME
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.fit([[0.0], [0.5], [1.0]], {'Z': [[0.5], [0.4], [0.5]]})
+ expdes = ExpDesigns(inp)
+ expdes.generate_ED(2, max_pce_deg=1)
+ mod = PL()
+ mod.Output.names = ['Z']
+ engine = Engine(mm, mod, expdes)
+ engine.start_engine()
+ obs_data = {'Z': np.array([0.45])}
+ sigma2Dict = {'Z': np.array([0.05])}
+ sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+ engine._BME_Calculator(obs_data, sigma2Dict)
+ # Note: if error appears here it might also be due to inoptimal choice of training samples
+
+
+def test__BME_Calculator_rmse() -> None:
+ """
+ Calculate BME with given RMSE
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.fit([[0.0], [0.5], [1.0]], {'Z': [[0.5], [0.4], [0.5]]})
+ expdes = ExpDesigns(inp)
+ expdes.generate_ED(2, max_pce_deg=1)
+ mod = PL()
+ mod.Output.names = ['Z']
+ engine = Engine(mm, mod, expdes)
+ engine.start_engine()
+ obs_data = {'Z': np.array([0.45])}
+ sigma2Dict = {'Z': np.array([0.05])}
+ sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+ engine._BME_Calculator(obs_data, sigma2Dict, rmse={'Z': 0.1})
+ # Note: if error appears here it might also be due to inoptimal choice of training samples
+
+
+def test__BME_Calculator_lik() -> None:
+ """
+ Calculate BME with given validation likelihood and post-snapshot
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.fit([[0.0], [0.5], [1.0]], {'Z': [[0.5], [0.4], [0.5]]})
+ expdes = ExpDesigns(inp)
+ expdes.generate_ED(2, max_pce_deg=1)
+ mod = PL()
+ mod.Output.names = ['Z']
+ engine = Engine(mm, mod, expdes)
+ engine.start_engine()
+ obs_data = {'Z': np.array([0.45])}
+ sigma2Dict = {'Z': np.array([0.05])}
+ sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+ expdes.post_snapshot = True
+
+ engine.valid_likelihoods = [0.1]
+ engine._BME_Calculator(obs_data, sigma2Dict)
+
+
+def test__BME_Calculator_2d() -> None:
+ """
+ Calculate BME with given validation likelihood and post-snapshot, 2d input
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ inp.add_marginals()
+ inp.Marginals[1].dist_type = 'normal'
+ inp.Marginals[1].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.fit([[0.0, 0.0], [0.5, 0.1], [1.0, 0.9]], {'Z': [[0.5], [0.4], [0.5]]})
+ expdes = ExpDesigns(inp)
+ expdes.generate_ED(2, max_pce_deg=1)
+ mod = PL()
+ mod.n_obs = 1
+ mod.Output.names = ['Z']
+ engine = Engine(mm, mod, expdes)
+ engine.start_engine()
+ obs_data = {'Z': np.array([0.45])}
+ sigma2Dict = {'Z': np.array([0.05])}
+ sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+ expdes.post_snapshot = True
+
+ engine.valid_likelihoods = [0.1]
+ engine._BME_Calculator(obs_data, sigma2Dict)
+
+
+#%% Test hellinger_distance
+
+def test_hellinger_distance_isnan() -> None:
+ """
+ Calculate Hellinger distance-nan
+ """
+ P = [0]
+ Q = [1]
+ math.isnan(hellinger_distance(P, Q))
+
+
+def test_hellinger_distance_0() -> None:
+ """
+ Calculate Hellinger distance-0
+ """
+ P = [0, 1, 2]
+ Q = [1, 0, 2]
+ assert hellinger_distance(P, Q) == 0.0
+
+
+def test_hellinger_distance_1() -> None:
+ """
+ Calculate Hellinger distance-1
+ """
+ P = [0, 1, 2]
+ Q = [0, 0, 0]
+ assert hellinger_distance(P, Q) == 1.0
+
+
+#%% Test Engine._normpdf
+
+def test__normpdf() -> None:
+ """
+ Likelihoods based on gaussian dist
+ """
+
+ 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()
+ mod.Output.names = ['Z']
+
+ y_hat_pce = {'Z': np.array([[0.12]])}
+ std_pce = {'Z': np.array([[0.05]])}
+ obs_data = {'Z': np.array([0.1])}
+ sigma2Dict = {'Z': np.array([0.05])}
+ total_sigma2s = pd.DataFrame(sigma2Dict, columns=['Z'])
+
+ engine = Engine(mm, mod, expdes)
+ engine.start_engine()
+ engine._normpdf(y_hat_pce, std_pce, obs_data, total_sigma2s)
+
+
+def test__normpdf_rmse() -> None:
+ """
+ Likelihoods based on gaussian dist with rmse
+ """
+
+ 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()
+ mod.Output.names = ['Z']
+
+ y_hat_pce = {'Z': np.array([[0.12]])}
+ std_pce = {'Z': np.array([[0.05]])}
+ obs_data = {'Z': np.array([0.1])}
+ sigma2Dict = {'Z': np.array([0.05])}
+ total_sigma2s = pd.DataFrame(sigma2Dict, columns=['Z'])
+
+ engine = Engine(mm, mod, expdes)
+ engine.start_engine()
+ engine._normpdf(y_hat_pce, std_pce, obs_data, total_sigma2s, rmse={'Z': 0.1})
+
+
+#%% Test Engine._posteriorPlot
+
+def test__posteriorPlot() -> None:
+ """
+ Plot posterior
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ expdes = ExpDesigns(inp)
+ expdes.generate_ED(2, max_pce_deg=1)
+ mod = PL()
+ posterior = np.array([[0], [0.1], [0.2]])
+ engine = Engine(mm, mod, expdes)
+ engine._posteriorPlot(posterior, ['i'], 'Z')
+
+
+def test__posteriorPlot_2d() -> None:
+ """
+ Plot posterior for 2 params
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ inp.add_marginals()
+ inp.Marginals[1].dist_type = 'normal'
+ inp.Marginals[1].parameters = [0, 1]
+ mm = MetaModel(inp)
+ expdes = ExpDesigns(inp)
+ expdes.generate_ED(2, max_pce_deg=1)
+ mod = PL()
+ posterior = np.array([[0, 0], [0.1, 1.0], [0.2, 0.5]])
+ engine = Engine(mm, mod, expdes)
+ engine._posteriorPlot(posterior, ['i', 'j'], 'Z')
+
+
+#%% Test logpdf
+
+def test_logpdf() -> None:
+ """
+ Calculate log-pdf
+ """
+ logpdf(np.array([0.1]), np.array([0.2]), np.array([0.1]))
+
+
+#%% Test Engine._corr_factor_BME
+# TODO: not used again here?
+
+#%% Test subdomain
+
+def test_subdomain() -> None:
+ """
+ Create subdomains from bounds
+ """
+ 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
+
+
+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.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)
+
+
+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)
diff --git a/tests/test_ExpDesign.py b/tests/test_ExpDesign.py
new file mode 100644
index 0000000000000000000000000000000000000000..68255b3380881a8182ecd5a3de7411842fcedebd
--- /dev/null
+++ b/tests/test_ExpDesign.py
@@ -0,0 +1,397 @@
+# -*- coding: utf-8 -*-
+"""
+Test the ExpDesigns class in bayesvalidrox.
+Tests are available for the following functions:
+ _check_ranges - x
+ fit_dist - x
+
+Class ExpDesigns:
+ generate_samples
+ generate_ED
+ read_from_file
+ random_sampler
+ pcm_sampler
+Other function tests to be found in parent class 'InputSpace'
+
+"""
+import sys
+sys.path.append("src/")
+import pytest
+import numpy as np
+
+from bayesvalidrox.surrogate_models.inputs import Input
+import bayesvalidrox.surrogate_models.exp_designs as exp
+from bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
+
+#%% Test check_ranges
+
+def test_check_ranges() -> None:
+ """
+ Check to see if theta lies in expected ranges
+ """
+ theta = [0.5,1.2]
+ ranges = [[0,1],[1,2]]
+ assert exp.check_ranges(theta, ranges) == True
+
+def test_check_ranges_inv() -> None:
+ """
+ Check to see if theta lies not in expected ranges
+ """
+ theta = [1.5,1.2]
+ ranges = [[0,1],[1,2]]
+ assert exp.check_ranges(theta, ranges) == False
+
+#%% Test ExpDesign.pcm_sampler
+
+def test_pcm_sampler_noinit() -> None:
+ """
+ Sample via pcm without init_param_space
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ exp.pcm_sampler(4,2)
+
+def test_pcm_sampler_lowdeg() -> None:
+ """
+ Sample via pcm with init_param_space and small max_deg
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ exp.init_param_space()
+ exp.pcm_sampler(4,2)
+
+def test_pcm_sampler_highdeg() -> None:
+ """
+ Sample via pcm with init_param_space and high max_deg
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ exp.init_param_space()
+ exp.pcm_sampler(4,4)
+
+def test_pcm_sampler_lscm() -> None:
+ """
+ Sample via pcm with init_param_space and samplin gmethod 'lscm'
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ exp.init_param_space()
+ exp.sampling_method = 'lscm'
+ exp.pcm_sampler(4,4)
+
+def test_pcm_sampler_rawdata() -> None:
+ """
+ Sample via pcm, init_param_space implicitly, has raw data
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ exp.raw_data = np.random.uniform(0,1,1000)
+ with pytest.raises(AttributeError) as excinfo:
+ exp.pcm_sampler(4,4)
+ assert str(excinfo.value) == 'Data should be a 1D array'
+
+def test_pcm_sampler_rawdata_1d() -> None:
+ """
+ Sample via pcm, init_param_space implicitly, has raw data
+ """
+ x = np.random.uniform(0,1,(1,1000))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ exp.raw_data = np.random.uniform(0,1,1000)
+ exp.pcm_sampler(4,4)
+
+
+#%% Test ExpDesign.random_sampler
+
+def test_random_sampler() -> None:
+ """
+ Sample randomly, init_param_space implicitly
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ exp.random_sampler(4)
+
+def test_random_sampler_largedatanoJDist() -> None:
+ """
+ Sample randomly, init_param_space implicitly, more samples wanted than given, no JDist available
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ with pytest.raises(AttributeError) as excinfo:
+ exp.random_sampler(100000)
+ assert str(excinfo.value) == 'Sampling cannot proceed, build ExpDesign with max_deg != 0 to create JDist!'
+
+def test_random_sampler_largedataJDist0() -> None:
+ """
+ Sample randomly, init_param_space implicitly, more samples wanted than given,
+ JDist available, priors given via samples
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ exp.init_param_space(max_deg = 1)
+ exp.random_sampler(100000)
+
+def test_random_sampler_largedataJDist1() -> None:
+ """
+ Sample randomly, init_param_space implicitly, more samples wanted than given,
+ JDist available, prior distributions given
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0,1]
+ exp = ExpDesigns(inp)
+ exp.init_param_space(max_deg = 1)
+ exp.random_sampler(100000)
+
+
+
+def test_random_sampler_rawdata() -> None:
+ """
+ Sample randomly, init_param_space implicitly, has 2d raw data
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ exp.raw_data = np.random.uniform(0,1,(1,1000))
+ exp.random_sampler(4)
+
+def test_random_sampler_rawdata1d() -> None:
+ """
+ Sample randomly, init_param_space implicitly, has raw data, but only 1d
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ exp.raw_data = np.random.uniform(0,1,1000)
+ with pytest.raises(AttributeError) as excinfo:
+ exp.random_sampler(4)
+ assert str(excinfo.value) == 'The given raw data for sampling should have two dimensions'
+
+
+def test_random_sampler_fewdata() -> None:
+ """
+ Sample randomly, init_param_space implicitly, has few 2d raw datapoints
+ """
+ x = np.random.uniform(0,1,5)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ exp.raw_data = np.random.uniform(0,1,(1,1000))
+ exp.random_sampler(7)
+
+
+#%% Test ExpDesign.generate_samples
+
+def test_generate_samples() -> None:
+ """
+ Generate samples according to chosen scheme
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ exp.generate_samples(4)
+
+
+#%% Test ExpDesign.generate_ED
+
+def test_generate_ED() -> None:
+ """
+ Generate ED as is
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp)
+ exp.generate_ED(4)
+
+def test_generate_ED_negsamplenum():
+ """
+ Generate ED for neg number of samples
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp, sampling_method = 'user')
+ with pytest.raises(ValueError) as excinfo:
+ exp.generate_ED(-1)
+ assert str(excinfo.value) == 'A negative number of samples cannot be created. Please provide positive n_samples'
+
+
+
+def test_generate_ED_usernoX() -> None:
+ """
+ User-defined ED without samples
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp, sampling_method = 'user')
+ with pytest.raises(AttributeError) as excinfo:
+ exp.generate_ED(4)
+ assert str(excinfo.value) == 'User-defined sampling cannot proceed as no samples provided. Please add them to this class as attribute X'
+
+def test_generate_ED_userXdimerr() -> None:
+ """
+ User-defined ED with wrong shape of samples
+ """
+ x = np.random.uniform(0,1,1000)
+ X = np.random.uniform(0,1,(2,1,1000))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp, sampling_method = 'user')
+ exp.X = X
+ with pytest.raises(AttributeError) as excinfo:
+ exp.generate_ED(4)
+ assert str(excinfo.value) == 'The provided samples shuld have 2 dimensions'
+
+# TODO: this does not give any issues???
+def test_generate_ED_userX() -> None:
+ """
+ User-defined ED with wrong shape of samples
+ """
+ x = np.random.uniform(0,1,1000)
+ X = np.random.uniform(0,1,(3,1000))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp, sampling_method = 'user')
+ exp.X = X
+ exp.generate_ED(4)
+
+# TODO: this looks like a pcm-sampler issue
+def test_generate_ED_PCM() -> None:
+ """
+ PCM-defined ED
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp, sampling_method = 'PCM')
+ exp.generate_ED(4)
+
+def test_generate_ED_random() -> None:
+ """
+ Random-defined ED
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp, sampling_method = 'random')
+ exp.generate_ED(4)
+
+def test_generate_ED_usertrafo() -> None:
+ """
+ User-defined ED
+ """
+ x = np.random.uniform(0,1,1000)
+ X = np.random.uniform(0,1,(1,1000))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp, sampling_method = 'user')
+ exp.meta_Model_type = 'gpe'
+ exp.X = X
+ exp.generate_ED(4)
+
+def test_generate_ED_randomtrafo() -> None:
+ """
+ User-defined ED
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp, sampling_method = 'random')
+ exp.generate_ED(4)
+
+def test_generate_ED_latin() -> None:
+ """
+ latin-hypercube-defined ED
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp, sampling_method = 'latin-hypercube')
+ exp.generate_ED(4)
+
+#%% Test ExpDesign.read_from_file
+
+def test_read_from_file_nofile():
+ """
+ No file given to read in
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp, sampling_method = 'user')
+ with pytest.raises(AttributeError) as excinfo:
+ exp.read_from_file(['Out'])
+ assert str(excinfo.value) == 'ExpDesign cannot be read in, please provide hdf5 file first'
+
+def test_read_from_file_wrongcomp():
+ """
+ Correct file, incorrect output name
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp, sampling_method = 'user')
+ exp.hdf5_file = 'ExpDesign_testfile.hdf5'
+ with pytest.raises(KeyError) as excinfo:
+ exp.read_from_file(['Out'])
+ assert str(excinfo.value) == "'Unable to open object (component not found)'"
+
+def test_read_from_file():
+ """
+ Read from testfile
+ """
+ x = np.random.uniform(0,1,1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = ExpDesigns(inp, sampling_method = 'user')
+ exp.hdf5_file = 'ExpDesign_testfile.hdf5'
+ exp.read_from_file(['Z'])
diff --git a/tests/test_Input.py b/tests/test_Input.py
new file mode 100644
index 0000000000000000000000000000000000000000..41c0e5ab84a5178b1934504bc63b5d5c03d19078
--- /dev/null
+++ b/tests/test_Input.py
@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+"""
+Test the Input and associated Marginal classes in bayesvalidrox.
+Tests are available for the following functions
+Class Marginal contains no functions - no tests to write.
+Class Input:
+ add_marginals
+
+@author: Rebecca Kohlhaas
+"""
+import sys
+
+from bayesvalidrox.surrogate_models.inputs import Input
+
+sys.path.append("src/")
+
+
+def test_addmarginals() -> None:
+ """
+ Tests function 'Input.add_marginals()'
+ Ensure that marginals get appended
+ """
+ inp = Input()
+ inp.add_marginals()
+ assert len(inp.Marginals) == 1
diff --git a/tests/test_InputSpace.py b/tests/test_InputSpace.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae31f8e90d051e39dd67c2e55f900a3eb0e11958
--- /dev/null
+++ b/tests/test_InputSpace.py
@@ -0,0 +1,610 @@
+# -*- coding: utf-8 -*-
+"""
+Test the InputSpace class in bayesvalidrox.
+Tests are available for the following functions
+Class InputSpace:
+ check_valid_inputs - x
+ init_param_space - x
+ build_polytypes - x
+ transform - x
+
+"""
+import sys
+import pytest
+import numpy as np
+
+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
+
+def test_check_valid_input_hasmarg() -> None:
+ """
+ Distribution not built if no marginals set
+ """
+ inp = Input()
+ with pytest.raises(AssertionError) as excinfo:
+ InputSpace(inp)
+ assert str(excinfo.value) == 'Cannot build distributions if no marginals are given'
+
+
+def test_check_valid_input_haspriors() -> None:
+ """
+ Distribution not built if no distribution set for the marginals
+ """
+ inp = Input()
+ inp.add_marginals()
+ with pytest.raises(AssertionError) as excinfo:
+ InputSpace(inp)
+ assert str(excinfo.value) == 'Not all marginals were provided priors'
+
+
+def test_check_valid_input_priorsmatch() -> None:
+ """
+ Distribution not built if dist types do not align
+ """
+ x = np.random.uniform(0, 1, 1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ inp.add_marginals()
+ inp.Marginals[1].dist_type = 'normal'
+ inp.Marginals[1].parameters = [0, 1]
+ with pytest.raises(AssertionError) as excinfo:
+ InputSpace(inp)
+ assert str(excinfo.value) == 'Distributions cannot be built as the priors have different types'
+
+
+def test_check_valid_input_samples() -> None:
+ """
+ Design built correctly - samples
+ """
+ x = np.random.uniform(0, 1, 1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ inp.add_marginals()
+ inp.Marginals[1].input_data = x + 2
+ InputSpace(inp)
+
+
+def test_check_valid_input_both() -> None:
+ """
+ Design no built - samples and dist type given
+ """
+ x = np.random.uniform(0, 1, 1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ inp.Marginals[0].dist_type = 'normal'
+ with pytest.raises(AssertionError) as excinfo:
+ InputSpace(inp)
+ assert str(excinfo.value) == 'Both samples and distribution type are given. Please choose only one.'
+
+
+# def test_check_valid_input_distnotok() -> None:
+# """
+# Design built incorrectly - dist types without parameters
+# """
+# inp = Input()
+# inp.add_marginals()
+# inp.Marginals[0].dist_type = 'normal'
+# inp.add_marginals()
+# inp.Marginals[1].dist_type = 'normal'
+# with pytest.raises(AssertionError) as excinfo:
+# exp = ExpDesigns(inp)
+# assert str(excinfo.value) == 'Some distributions do not have characteristic values'
+
+def test_check_valid_input_distok() -> None:
+ """
+ Design built correctly - dist types
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ inp.add_marginals()
+ inp.Marginals[1].dist_type = 'normal'
+ inp.Marginals[1].parameters = [0, 1]
+ InputSpace(inp)
+
+
+def test_check_valid_input_noapc() -> None:
+ """
+ Design built correctly - no apc
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ inp.add_marginals()
+ inp.Marginals[1].dist_type = 'normal'
+ inp.Marginals[1].parameters = [0, 1]
+ InputSpace(inp, meta_Model_type='gpe')
+
+
+#%% Test ExpDesign.build_polytypes
+def test_build_polytypes_normalerr() -> None:
+ """
+ Build dist 'normal' - too few params
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = []
+ exp = InputSpace(inp)
+ with pytest.raises(AssertionError) as excinfo:
+ exp.build_polytypes(False)
+ assert str(excinfo.value) == 'Distribution has too few parameters!'
+
+
+def test_build_polytypes_normal() -> None:
+ """
+ Build dist 'normal'
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ exp = InputSpace(inp)
+ exp.build_polytypes(False)
+
+
+def test_build_polytypes_uniferr() -> None:
+ """
+ Build dist 'unif' - too few params
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'unif'
+ inp.Marginals[0].parameters = []
+ exp = InputSpace(inp)
+ with pytest.raises(AssertionError) as excinfo:
+ exp.build_polytypes(False)
+ assert str(excinfo.value) == 'Distribution has too few parameters!'
+
+
+def test_build_polytypes_unif() -> None:
+ """
+ Build dist 'unif'
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'unif'
+ inp.Marginals[0].parameters = [0, 1]
+ exp = InputSpace(inp)
+ exp.build_polytypes(False)
+
+
+def test_build_polytypes_gammaerr() -> None:
+ """
+ Build dist 'gamma' - too few params
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'gamma'
+ inp.Marginals[0].parameters = []
+ exp = InputSpace(inp)
+ with pytest.raises(AssertionError) as excinfo:
+ exp.build_polytypes(False)
+ assert str(excinfo.value) == 'Distribution has too few parameters!'
+
+
+# noinspection SpellCheckingInspection
+def test_build_polytypes_gamma() -> None:
+ """
+ Build dist 'gamma'
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'gamma'
+ inp.Marginals[0].parameters = [0, 1, 0]
+ exp = InputSpace(inp)
+ with pytest.raises(ValueError) as excinfo:
+ exp.build_polytypes(False)
+ assert str(excinfo.value) == 'Parameter values are not valid, please set differently'
+
+
+# noinspection SpellCheckingInspection
+def test_build_polytypes_betaerr() -> None:
+ """
+ Build dist 'beta' - too few params
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'beta'
+ inp.Marginals[0].parameters = []
+ exp = InputSpace(inp)
+ with pytest.raises(AssertionError) as excinfo:
+ exp.build_polytypes(False)
+ assert str(excinfo.value) == 'Distribution has too few parameters!'
+
+
+def test_build_polytypes_beta() -> None:
+ """
+ Build dist 'beta'
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'beta'
+ inp.Marginals[0].parameters = [0.5, 1, 2, 3]
+ exp = InputSpace(inp)
+ exp.build_polytypes(False)
+
+
+# noinspection SpellCheckingInspection
+def test_build_polytypes_lognormerr() -> None:
+ """
+ Build dist 'lognorm' - too few params
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'lognorm'
+ inp.Marginals[0].parameters = []
+ exp = InputSpace(inp)
+ with pytest.raises(AssertionError) as excinfo:
+ exp.build_polytypes(False)
+ assert str(excinfo.value) == 'Distribution has too few parameters!'
+
+
+def test_build_polytypes_lognorm() -> None:
+ """
+ Build dist 'lognorm'
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'lognorm'
+ inp.Marginals[0].parameters = [0.5, 1, 2, 3]
+ exp = InputSpace(inp)
+ exp.build_polytypes(False)
+
+
+def test_build_polytypes_exponerr() -> None:
+ """
+ Build dist 'expon' - too few params
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'beta'
+ inp.Marginals[0].parameters = []
+ exp = InputSpace(inp)
+ with pytest.raises(AssertionError) as excinfo:
+ exp.build_polytypes(False)
+ assert str(excinfo.value) == 'Distribution has too few parameters!'
+
+
+def test_build_polytypes_expon() -> None:
+ """
+ Build dist 'expon'
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'expon'
+ inp.Marginals[0].parameters = [0.5, 1, 2, 3]
+ exp = InputSpace(inp)
+ exp.build_polytypes(False)
+
+
+def test_build_polytypes_weibullerr() -> None:
+ """
+ Build dist 'weibull' - too few params
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'weibull'
+ inp.Marginals[0].parameters = []
+ exp = InputSpace(inp)
+ with pytest.raises(AssertionError) as excinfo:
+ exp.build_polytypes(False)
+ assert str(excinfo.value) == 'Distribution has too few parameters!'
+
+
+def test_build_polytypes_weibull() -> None:
+ """
+ Build dist 'weibull'
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'weibull'
+ inp.Marginals[0].parameters = [0.5, 1, 2, 3]
+ exp = InputSpace(inp)
+ exp.build_polytypes(False)
+
+
+def test_build_polytypes_arbitrary() -> None:
+ """
+ Build poly 'arbitrary'
+ """
+ x = np.random.uniform(0, 1, 1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ exp.build_polytypes(False)
+
+
+def test_build_polytypes_rosenblatt() -> None:
+ """
+ Build dist with rosenblatt
+ """
+ x = np.random.uniform(0, 1, 1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ exp.build_polytypes(True)
+
+
+def test_build_polytypes_samples() -> None:
+ """
+ Build dist from samples
+ """
+ x = np.random.uniform(0, 1, 1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ exp.build_polytypes(False)
+
+
+def test_build_polytypes_samples2d() -> None:
+ """
+ Build dist from samples - samples too high dim
+ """
+ x = np.random.uniform(0, 1, (2, 1000))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ with pytest.raises(ValueError) as excinfo:
+ exp.build_polytypes(False)
+ assert str(excinfo.value) == 'The samples provided to the Marginals should be 1D only'
+
+
+#%% Test ExpDesign.init_param_space
+
+def test_init_param_space_nomaxdegsample() -> None:
+ """
+ Init param space without max_deg for given samples
+ """
+ x = np.random.uniform(0, 1, 1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ exp.init_param_space()
+
+
+def test_init_param_space_nomaxdegdist() -> None:
+ """
+ Init param space without max_deg for given dist
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'expon'
+ inp.Marginals[0].parameters = [0.5, 1, 2, 3]
+ exp = InputSpace(inp)
+ exp.init_param_space()
+
+
+def test_init_param_space_maxdeg() -> None:
+ """
+ Init param space with max_deg for given samples
+ """
+ x = np.random.uniform(0, 1, 1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+
+
+def test_init_param_space_maxdegdist() -> None:
+ """
+ Init param space with max_deg for given dist (not uniform)
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'expon'
+ inp.Marginals[0].parameters = [0.5, 1, 2, 3]
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+
+
+def test_init_param_space_maxdegdistunif() -> None:
+ """
+ Init param space with max_deg for given dist (uniform)
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'unif'
+ inp.Marginals[0].parameters = [0.5, 1, 2, 3]
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+
+
+#%% Test ExpDesign.transform
+
+def test_transform_noparamspace() -> None:
+ """
+ Call transform without a built JDist
+ """
+ x = np.random.uniform(0, 1, 1000)
+ y = np.random.uniform(0, 1, (2, 1000))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ with pytest.raises(AttributeError) as excinfo:
+ exp.transform(y)
+ assert str(excinfo.value) == 'Call function init_param_space first to create JDist'
+
+
+def test_transform_dimerrlow() -> None:
+ """
+ Call transform with too few dimensions
+ """
+ x = np.random.uniform(0, 1, 1000)
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+ with pytest.raises(AttributeError) as excinfo:
+ exp.transform(x)
+ assert str(excinfo.value) == 'X should have two dimensions'
+
+
+def test_transform_dimerrhigh() -> None:
+ """
+ Call transform with too many dimensions
+ """
+ x = np.random.uniform(0, 1, 1000)
+ y = np.random.uniform(0, 1, (1, 1, 1000))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+ with pytest.raises(AttributeError) as excinfo:
+ exp.transform(y)
+ assert str(excinfo.value) == 'X should have two dimensions'
+
+
+def test_transform_dimerr0() -> None:
+ """
+ Call transform with wrong X.shape[0]
+ """
+ x = np.random.uniform(0, 1, 1000)
+ y = np.random.uniform(0, 1, (2, 1000))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+ with pytest.raises(AttributeError) as excinfo:
+ exp.transform(y)
+ assert str(
+ excinfo.value) == 'The second dimension of X should be the same size as the number of marginals in the InputObj'
+
+
+def test_transform_paramspace() -> None:
+ """
+ Transform successfully
+ """
+ x = np.random.uniform(0, 1, 1000)
+ y = np.random.uniform(0, 1, (1000, 1))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+ exp.transform(y)
+
+
+def test_transform_rosenblatt() -> None:
+ """
+ Transform with rosenblatt
+ """
+ x = np.random.uniform(0, 1, 1000)
+ y = np.random.uniform(0, 1, (1000, 1))
+ inp = Input()
+ inp.Rosenblatt = True
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+ exp.transform(y)
+
+
+def test_transform_user() -> None:
+ """
+ Transform with method 'user'
+ """
+ x = np.random.uniform(0, 1, 1000)
+ y = np.random.uniform(0, 1, (1000, 1))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+ exp.transform(y, method='user')
+
+
+# noinspection SpellCheckingInspection
+def test_transform_rosenblattuser() -> None:
+ """
+ Transform with rosenblatt and method 'user'
+ """
+ x = np.random.uniform(0, 1, 1000)
+ y = np.random.uniform(0, 1, (1000, 1))
+ inp = Input()
+ inp.Rosenblatt = True
+ inp.add_marginals()
+ inp.Marginals[0].input_data = x
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+ exp.transform(y, method='user')
+
+
+def test_transform_uniform() -> None:
+ """
+ Transform uniform dist
+ """
+ y = np.random.uniform(0, 1, (1000, 1))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'unif'
+ inp.Marginals[0].parameters = [0, 1]
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+ exp.transform(y)
+
+
+def test_transform_norm() -> None:
+ """
+ Transform normal dist
+ """
+ y = np.random.uniform(0, 1, (1000, 1))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'norm'
+ inp.Marginals[0].parameters = [0, 1]
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+ exp.transform(y)
+
+
+# TODO: what are these other params here???
+def test_transform_gammanoparam() -> None:
+ """
+ Transform gamma dist - no parameters
+ """
+ y = np.random.uniform(0, 1, (1000, 1))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'gamma'
+ inp.Marginals[0].parameters = [1, 1, 0]
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+ with pytest.raises(AttributeError) as excinfo:
+ exp.transform(y)
+ assert str(excinfo.value) == 'Additional parameters have to be set for the gamma distribution!'
+
+
+def test_transform_gammaparam() -> None:
+ """
+ Transform gamma dist - with parameters
+ """
+ y = np.random.uniform(0, 1, (1000, 1))
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'gamma'
+ inp.Marginals[0].parameters = [1, 1, 0]
+ exp = InputSpace(inp)
+ exp.init_param_space(max_deg=2)
+ exp.transform(y, params=[1, 1])
diff --git a/tests/test_MCMC.py b/tests/test_MCMC.py
new file mode 100644
index 0000000000000000000000000000000000000000..3485a615b34150f63f8c8f428167453393531c09
--- /dev/null
+++ b/tests/test_MCMC.py
@@ -0,0 +1,223 @@
+# -*- coding: utf-8 -*-
+"""
+Test the MCM class of bayesvalidrox
+Tests are available for the following functions
+_check_ranges - x
+gelmain_rubin
+_iterative_scheme
+_my_ESS - x
+Class MCMC:
+ run_sampler
+ log_prior
+ log_likelihood
+ log_posterior
+ eval_model
+ train_error_model
+ marginal_llk_emcee
+"""
+import sys
+import pandas as pd
+import numpy as np
+
+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
+from bayesvalidrox.bayes_inference.mcmc import MCMC
+from bayesvalidrox.bayes_inference.bayes_inference import BayesInference
+from bayesvalidrox.bayes_inference.mcmc import _check_ranges, gelman_rubin
+
+sys.path.append("src/")
+sys.path.append("../src/")
+
+
+#%% Test MCMC init
+
+def test_MCMC() -> None:
+ """
+ Construct an MCMC object
+ """
+ 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.x_values = np.array([0])
+
+ 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()
+ mod.observations = {'Z': np.array([0.45]), 'x_values': np.array([0])}
+ mod.Output.names = ['Z']
+ engine = Engine(mm, mod, expdes)
+
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ disc = Discrepancy('')
+ disc.type = 'Gaussian'
+ disc.parameters = (obsData * 0.15) ** 2
+ disc.opt_sigma = 'B'
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = disc
+ bi.inference_method = 'mcmc'
+ bi.setup_inference()
+ MCMC(bi)
+
+
+#%% Test run_sampler
+
+def test_run_sampler() -> None:
+ """
+ Run short MCMC
+
+ Returns
+ -------
+ None
+ DESCRIPTION.
+
+ """
+ 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.x_values = np.array([0])
+
+ 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()
+ mod.observations = {'Z': np.array([0.45]), 'x_values': np.array([0])}
+ mod.Output.names = ['Z']
+ engine = Engine(mm, mod, expdes)
+
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ disc = Discrepancy('')
+ disc.type = 'Gaussian'
+ disc.parameters = (obsData * 0.15) ** 2
+ disc.opt_sigma = 'B'
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = disc
+ bi.inference_method = 'mcmc'
+ bi.setup_inference()
+ total_sigma2s = {'Z': np.array([0.15])}
+ mcmc = MCMC(bi)
+ mcmc.nburn = 10
+ mcmc.nsteps = 50
+ mcmc.run_sampler(mod.observations, total_sigma2s)
+
+
+#%% Test log_prior
+
+#%% Test log_likelihood
+
+#%% Test log_posterior
+
+#%% Test eval_model
+
+#%% Test train_error_model
+
+#%% Test gelmain_rubin
+
+def test_gelman_rubin() -> None:
+ """
+ Calculate gelman-rubin
+ """
+ chain = [[[1], [2]]]
+ gelman_rubin(chain)
+
+
+def test_gelman_rubin_returnvar() -> None:
+ """
+ Calculate gelman-rubin returning var
+ """
+ chain = [[[1], [2]]]
+ gelman_rubin(chain, return_var=True)
+
+
+#%% Test marginal_llk_emcee
+
+#%% Test _check_ranges
+
+def test_check_ranges() -> None:
+ """
+ Check to see if theta lies in expected ranges
+ """
+ theta = [0.5, 1.2]
+ ranges = [[0, 1], [1, 2]]
+ assert _check_ranges(theta, ranges) is True
+
+
+def test_check_ranges_inv() -> None:
+ """
+ Check to see if theta lies not in expected ranges
+ """
+ theta = [1.5, 1.2]
+ ranges = [[0, 1], [1, 2]]
+ assert _check_ranges(theta, ranges) is False
+
+
+#%% Main
+
+if __name__ == '__main__':
+ 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.x_values = np.array([0]) # Error in plots if this is not available
+
+ 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()
+ mod.observations = {'Z': np.array([0.45]), 'x_values': np.array([0])}
+ mod.Output.names = ['Z']
+
+ engine = Engine(mm, mod, expdes)
+
+ sigma2Dict = {'Z': np.array([0.05])}
+ sigma2Dict = pd.DataFrame(sigma2Dict, columns=['Z'])
+ obsData = pd.DataFrame(mod.observations, columns=mod.Output.names)
+ disc = Discrepancy('')
+ disc.type = 'Gaussian'
+ disc.parameters = (obsData * 0.15) ** 2
+ disc.opt_sigma = 'B'
+
+ bi = BayesInference(engine)
+ bi.Discrepancy = disc
+ bi.inference_method = 'mcmc'
+ bi.setup_inference()
+
+ # chain = [[[1],[2]]]
+ total_sigma2s = {'Z': np.array([0.15])}
+ mcmc = MCMC(bi)
+ mcmc.nsteps = 50
+ mcmc.nburn = 10
+ mcmc.run_sampler(mod.observations, total_sigma2s)
+ # mcmc.gelmain_rubin(chain)
+
+ chain = [[[1], [2]]]
+ gelman_rubin(chain, return_var=True)
diff --git a/tests/test_MetaModel.py b/tests/test_MetaModel.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3f9b19d3fb42c3e6ec3f34b3efb19298c8de545
--- /dev/null
+++ b/tests/test_MetaModel.py
@@ -0,0 +1,1172 @@
+# -*- coding: utf-8 -*-
+"""
+Test the MetaModel class in bayesvalidrox.
+Tests are available for the following functions
+Class MetaModel:
+ build_metamodel - x
+ update_metamodel
+ update_pce_coeffs
+ create_basis_indices --removed, just redirects
+ add_InputSpace -x
+ univ_basis_vals
+ create_psi
+ fit
+ adaptive_regression
+ corr_loocv_error
+ pca_transformation
+ gaussian_process_emulator
+ eval_metamodel
+ create_model_error
+ eval_model_error
+ auto_vivification
+ copy_meta_model_opts
+ __select_degree
+ generate_polynomials
+ _compute_pce_moments
+
+"""
+import numpy as np
+import pytest
+import sys
+
+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
+
+def test_metamod() -> None:
+ """
+ Construct MetaModel without inputs
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ MetaModel(inp)
+
+
+#%% Test MetaModel.build_metamodel
+
+def test_build_metamodel_nosamples() -> None:
+ """
+ Build MetaModel without collocation samples
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ with pytest.raises(AttributeError) as excinfo:
+ mm.build_metamodel()
+ assert str(excinfo.value) == 'Please provide samples to the metamodel before building it.'
+
+
+def test_build_metamodel() -> None:
+ """
+ Build MetaModel
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.CollocationPoints = np.array([[0.2], [0.8]])
+ mm.build_metamodel()
+
+
+def test_build_metamodel_ninitsamples() -> None:
+ """
+ Build MetaModel with n_init_samples
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.CollocationPoints = np.array([[0.2], [0.8]])
+ mm.build_metamodel(n_init_samples=2)
+
+
+def test_build_metamodel_gpe() -> None:
+ """
+ Build MetaModel gpe
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.meta_model_type = 'gpe'
+ mm.CollocationPoints = np.array([[0.2], [0.8]])
+ mm.build_metamodel()
+
+
+def test_build_metamodel_coldimerr() -> None:
+ """
+ Build MetaModel with wrong shape collocation samples
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.CollocationPoints = [[0.2, 0.8]]
+ with pytest.raises(AttributeError) as excinfo:
+ mm.build_metamodel()
+ assert str(
+ excinfo.value) == 'The second dimension of X should be the same size as the number of marginals in the InputObj'
+
+
+#%% Test MetaMod.generate_polynomials
+
+def test_generate_polynomials_noexp() -> None:
+ """
+ Generate polynomials without ExpDeg
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ with pytest.raises(AttributeError) as excinfo:
+ mm.generate_polynomials()
+ assert str(excinfo.value) == 'Generate or add InputSpace before generating polynomials'
+
+
+def test_generate_polynomials_nodeg() -> None:
+ """
+ Generate polynomials without max_deg
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+
+ # Setup
+ mm.InputSpace = InputSpace(inp)
+ mm.InputSpace.n_init_samples = 2
+ mm.InputSpace.init_param_space(np.max(mm.pce_deg))
+ mm.ndim = mm.InputSpace.ndim
+ mm.n_params = len(mm.input_obj.Marginals)
+
+ # Generate
+ with pytest.raises(AttributeError) as excinfo:
+ mm.generate_polynomials()
+ assert str(excinfo.value) == 'MetaModel cannot generate polynomials in the given scenario!'
+
+
+def test_generate_polynomials_deg() -> None:
+ """
+ Generate polynomials with max_deg
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+
+ # Setup
+ mm.InputSpace = InputSpace(inp)
+ mm.InputSpace.n_init_samples = 2
+ mm.InputSpace.init_param_space(np.max(mm.pce_deg))
+ mm.ndim = mm.InputSpace.ndim
+ mm.n_params = len(mm.input_obj.Marginals)
+
+ # Generate
+ mm.generate_polynomials(4)
+
+
+#%% Test MetaMod.add_InputSpace
+
+def test_add_inputspace() -> None:
+ """
+ Add InputSpace in MetaModel
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.add_InputSpace()
+
+
+#%% Test MetaModel.fit
+# Faster without these
+def test_fit() -> None:
+ """
+ Fit MetaModel
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.fit([[0.2], [0.8]], {'Z': [[0.4], [0.5]]})
+
+
+def test_fit_parallel() -> None:
+ """
+ Fit MetaModel in parallel
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.fit([[0.2], [0.8]], {'Z': [[0.4], [0.5]]}, parallel=True)
+
+
+def test_fit_verbose() -> None:
+ """
+ Fit MetaModel verbose
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.fit([[0.2], [0.8]], {'Z': [[0.4], [0.5]]}, verbose=True)
+
+
+def test_fit_pca() -> None:
+ """
+ Fit MetaModel verbose and with pca
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.dim_red_method = 'pca'
+ mm.fit([[0.2], [0.8]], {'Z': [[0.4, 0.4], [0.5, 0.6]]}, verbose=True)
+
+
+def test_fit_gpe() -> None:
+ """
+ Fit MetaModel
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.meta_model_type = 'gpe'
+ mm.fit([[0.2], [0.8]], {'Z': [[0.4], [0.5]]})
+
+
+#%% Test MetaModel.create_psi
+
+def test_create_psi() -> None:
+ """
+ Create psi-matrix
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2], [0.8]])
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ create_psi(BasisIndices, univ_bas)
+
+
+#%% Test MetaModel.regression
+
+def test_regression() -> None:
+ """
+ Regression without a method
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi)
+
+
+def test_regression_ols() -> None:
+ """
+ Regression: ols
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='ols')
+
+
+def test_regression_olssparse() -> None:
+ """
+ Regression: ols and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='ols', sparsity=True)
+
+
+def test_regression_ard() -> None:
+ """
+ Regression: ard
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2], [0.8]])
+ outputs = np.array([0.4, 0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='ard')
+
+
+def test_regression_ardssparse() -> None:
+ """
+ Regression: ard and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2], [0.8]])
+ outputs = np.array([0.4, 0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='ard', sparsity=True)
+
+
+def test_regression_fastard() -> None:
+ """
+ Regression: fastard
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='fastard')
+
+
+def test_regression_fastardssparse() -> None:
+ """
+ Regression: fastard and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='fastard', sparsity=True)
+
+
+def test_regression_brr() -> None:
+ """
+ Regression: brr
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='brr')
+
+
+def test_regression_brrssparse() -> None:
+ """
+ Regression: brr and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='brr', sparsity=True)
+
+
+def test_regression_bcs() -> None:
+ """
+ Regression: bcs
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
+ mm.pce_deg = 3
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(mm.pce_deg)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='bcs')
+
+
+def test_regression_bcsssparse() -> None:
+ """
+ Regression: bcs and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9], [1.0]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='bcs', sparsity=True)
+
+
+def test_regression_lars() -> None:
+ """
+ Regression: lars
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9], [1.0]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='lars')
+
+
+def test_regression_larsssparse() -> None:
+ """
+ Regression: lars and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9], [1.0]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='lars', sparsity=True)
+
+
+def test_regression_sgdr() -> None:
+ """
+ Regression: sgdr
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='sgdr')
+
+
+def test_regression_sgdrssparse() -> None:
+ """
+ Regression: sgdr and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='sgdr', sparsity=True)
+
+
+def test_regression_omp() -> None:
+ """
+ Regression: omp
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='omp')
+
+
+def test_regression_ompssparse() -> None:
+ """
+ Regression: omp and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='omp', sparsity=True)
+
+
+def test_regression_vbl() -> None:
+ """
+ Regression: vbl
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='vbl')
+
+
+def test_regression_vblssparse() -> None:
+ """
+ Regression: vbl and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='vbl', sparsity=True)
+
+
+def test_regression_ebl() -> None:
+ """
+ Regression: ebl
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='ebl')
+
+
+def test_regression_eblssparse() -> None:
+ """
+ Regression: ebl and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='ebl', sparsity=True)
+
+
+#%% Test Model.update_pce_coeffs
+
+# TODO: very linked to the actual training...
+
+#%% Test MetaModel.univ_basis_vals
+
+def test_univ_basis_vals() -> None:
+ """
+ Creates univariate polynomials
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2], [0.8]])
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ mm.univ_basis_vals(samples)
+
+
+#%% Test MetaModel.adaptive_regression
+
+def test_adaptive_regression_fewsamples() -> None:
+ """
+ Adaptive regression, no specific method, too few samples given
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.8])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+
+ # Evaluate the univariate polynomials on InputSpace
+ if mm.meta_model_type.lower() != 'gpe':
+ mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
+
+ with pytest.raises(AttributeError) as excinfo:
+ mm.adaptive_regression(outputs, 0)
+ assert str(
+ excinfo.value) == ('There are too few samples for the corrected loo-cv error. Fit surrogate on at least as '
+ 'many samples as parameters to use this')
+
+
+def test_adaptive_regression() -> None:
+ """
+ Adaptive regression, no specific method
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.0], [0.1]])
+ outputs = np.array([0.0, 0.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+
+ # Evaluate the univariate polynomials on InputSpace
+ if mm.meta_model_type.lower() != 'gpe':
+ mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
+ mm.adaptive_regression(outputs, 0)
+
+
+def test_adaptive_regression_verbose() -> None:
+ """
+ Adaptive regression, no specific method, verbose output
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.0], [0.1]])
+ outputs = np.array([0.0, 0.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+
+ # Evaluate the univariate polynomials on InputSpace
+ if mm.meta_model_type.lower() != 'gpe':
+ mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
+ mm.adaptive_regression(outputs, 0, True)
+
+
+def test_adaptive_regression_ols() -> None:
+ """
+ Adaptive regression, ols
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8],
+ [0.9], [1.0]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+
+ # Evaluate the univariate polynomials on InputSpace
+ if mm.meta_model_type.lower() != 'gpe':
+ mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
+ mm.pce_reg_method = 'ols'
+ mm.adaptive_regression(outputs, 0)
+
+
+#%% Test MetaModel.corr_loocv_error
+
+def test_corr_loocv_error_nosparse() -> None:
+ """
+ Corrected loocv error
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7],
+ [0.8], [0.9], [1.0]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ outs = mm.regression(samples, outputs, psi, reg_method='ebl')
+ corr_loocv_error(outs['clf_poly'], outs['sparePsi'], outs['coeffs'],
+ outputs)
+
+
+def test_corr_loocv_error_singley() -> None:
+ """
+ Corrected loocv error
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ outs = mm.regression(samples, outputs, psi, reg_method='ols')
+ corr_loocv_error(outs['clf_poly'], outs['sparePsi'], outs['coeffs'],
+ outputs)
+
+
+def test_corr_loocv_error_sparse() -> None:
+ """
+ Corrected loocv error from sparse results
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7],
+ [0.8], [0.9], [1.0]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ outs = mm.regression(samples, outputs, psi, reg_method='ebl',
+ sparsity=True)
+ corr_loocv_error(outs['clf_poly'], outs['sparePsi'], outs['coeffs'],
+ outputs)
+
+
+#%% Test MetaModel.pca_transformation
+
+def test_pca_transformation() -> None:
+ """
+ Apply PCA
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ outputs = np.array([[0.4, 0.4], [0.5, 0.6]])
+ mm.pca_transformation(outputs)
+
+
+def test_pca_transformation_verbose() -> None:
+ """
+ Apply PCA verbose
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ outputs = np.array([[0.4, 0.4], [0.5, 0.6]])
+ mm.pca_transformation(outputs, True)
+
+
+def test_pca_transformation_varcomp() -> None:
+ """
+ Apply PCA with set var_pca_threshold
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ outputs = np.array([[0.4, 0.4], [0.5, 0.6]])
+ mm.var_pca_threshold = 1
+ mm.pca_transformation(outputs)
+
+
+def test_pca_transformation_ncomp() -> None:
+ """
+ Apply PCA with set n_pca_components
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ outputs = np.array([[0.4, 0.4], [0.5, 0.6]])
+ mm.n_pca_components = 1
+ mm.pca_transformation(outputs)
+
+
+#%% Test MetaModel.gaussian_process_emulator
+
+def test_gaussian_process_emulator() -> None:
+ """
+ Create GPE
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5])
+
+
+def test_gaussian_process_emulator_nug() -> None:
+ """
+ Create GPEwith nugget
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5], nug_term=1.0)
+
+
+def test_gaussian_process_emulator_autosel() -> None:
+ """
+ Fit MetaModel with autoselect
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5], autoSelect=True)
+
+
+def test_gaussian_process_emulator_varidx() -> None:
+ """
+ Create GPE with var_idx
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5], varIdx=1)
+
+
+#%% Test MetaModel.eval_metamodel
+
+def test_eval_metamodel() -> None:
+ """
+ Eval trained MetaModel
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.out_names = ['Z']
+ mm.fit([[0.2], [0.8]], {'Z': [[0.4], [0.5]]})
+ mm.eval_metamodel([[0.4]])
+
+
+def test_eval_metamodel_normalboots() -> None:
+ """
+ Eval trained MetaModel with normal bootstrap
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.bootstrap_method = 'normal'
+ mm.out_names = ['Z']
+ mm.fit([[0.2], [0.8]], {'Z': [[0.4], [0.5]]})
+ mm.eval_metamodel([[0.4]])
+
+
+def test_eval_metamodel_highnormalboots() -> None:
+ """
+ Eval trained MetaModel with higher bootstrap-itrs
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.n_bootstrap_itrs = 2
+ mm.out_names = ['Z']
+ mm.fit([[0.2], [0.8]], {'Z': [[0.4], [0.5]]})
+ mm.eval_metamodel([[0.4]])
+
+
+def test_eval_metamodel_gpe() -> None:
+ """
+ Eval trained MetaModel - gpe
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.meta_model_type = 'gpe'
+ mm.out_names = ['Z']
+ mm.fit([[0.2], [0.8]], {'Z': np.array([[0.4], [0.5]])})
+ mm.eval_metamodel([[0.4]])
+
+
+def test_eval_metamodel_pca() -> None:
+ """
+ Eval trained MetaModel with pca
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.dim_red_method = 'pca'
+ mm.out_names = ['Z']
+ mm.fit([[0.2], [0.8]], {'Z': [[0.4, 0.4], [0.5, 0.6]]})
+ mm.eval_metamodel([[0.4]])
+
+
+#%% Test MetaModel.create_model_error
+# TODO: move model out of this function
+
+#%% Test MetaModel.eval_model_error
+# TODO: test create_model_error first
+
+#%% Test MetaModel.auto_vivification
+def test_auto_vivification() -> None:
+ """
+ Creation of auto-vivification objects
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.auto_vivification()
+
+
+#%% Test MetaModel.copy_meta_model_opts
+
+def test_copy_meta_model_opts() -> None:
+ """
+ Copy the metamodel with just some stats
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.add_InputSpace()
+ mm.copy_meta_model_opts()
+
+
+#%% Test MetaModel.__select_degree
+
+#%% Test Engine._compute_pce_moments
+
+def test__compute_pce_moments() -> None:
+ """
+ Compute moments of a pce-surrogate
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.fit([[0.2], [0.8]], {'Z': [[0.4], [0.5]]})
+ mm._compute_pce_moments()
+
+
+def test__compute_pce_moments_pca() -> None:
+ """
+ Compute moments of a pce-surrogate with pca
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.dim_red_method = 'pca'
+ mm.fit([[0.2], [0.8]], {'Z': [[0.4, 0.4], [0.5, 0.6]]})
+ mm._compute_pce_moments()
+
+
+def test__compute_pce_moments_gpe() -> None:
+ """
+ Compute moments of a gpe-surrogate
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ mm.meta_model_type = 'gpe'
+ with pytest.raises(AttributeError) as excinfo:
+ mm._compute_pce_moments()
+ assert str(excinfo.value) == 'Moments can only be computed for pce-type surrogates'
+
+
+#%% Test MetaModel.update_metamodel
+# TODO: taken from engine
diff --git a/tests/test_model/PosteriorGeneration/Posterior.csv b/tests/test_model/PosteriorGeneration/Posterior.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c5ac7661b1293e88b1d8f257c7bf183962be336f
--- /dev/null
+++ b/tests/test_model/PosteriorGeneration/Posterior.csv
@@ -0,0 +1,6458 @@
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diff --git a/tests/test_model/PosteriorGeneration/PosteriorGeneration.py b/tests/test_model/PosteriorGeneration/PosteriorGeneration.py
new file mode 100644
index 0000000000000000000000000000000000000000..c07594b7493d70b5e1fb2f1d0ca33b3a50916144
--- /dev/null
+++ b/tests/test_model/PosteriorGeneration/PosteriorGeneration.py
@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov 11 2019
+
+This script generates the posterior distribution of the CO2Benchmark model
+for a synthetic data for three given uncertain parameters using 10000 MC-Simulations
+of the original model.
+
+@author: Farid Mohammadi
+"""
+import pandas as pd
+import numpy as np
+import os
+from tqdm import tqdm
+import corner
+from scipy.stats import multivariate_normal
+import scipy.stats as st
+
+
+
+
+def BeamModel(theta):
+ #### write an input file
+ global beam_span, i
+ Beam_width, Beam_height, Youngs_modulus,Uniform_load = theta
+ Youngs_modulus = 1e9 * Youngs_modulus
+ Uniform_load = 1000 * Uniform_load
+ # Get the number of function runs each 100 NOT necessary, just checking
+ i +=1
+ if i%100 ==0:
+ print("number of evaluations {}".format(i))
+
+ #### Run your surrogate model here.
+ I = Beam_width * Beam_height**3 / 12;
+ output = np.zeros((11))
+ for i in range(1,10):
+ X = (i/10.)*beam_span;
+ output[i] = -1*Uniform_load*X*(beam_span**3 -2* X**2 * beam_span + X**3)/(24*Youngs_modulus*I);
+
+ return output
+
+
+def RejectionSampling(mcParametersets, origModelOutput, Data, InputNames):
+
+
+ MCSamples = mcParametersets
+ NofMeasurements = len(Data)
+ NrofSamples = len(MCSamples)
+ Sigma2 = (0.01*synthData)**2
+ Sigma2[0], Sigma2[-1]= 1e-10, 1e-10
+
+ # Covariance Matrix
+ covMatrix = np.zeros((NofMeasurements, NofMeasurements), float)
+ np.fill_diagonal(covMatrix, Sigma2)
+
+ # Likelihood
+ Likelihoods = multivariate_normal.pdf(origModelOutput, mean=Data, cov=covMatrix)
+
+ # Rejection
+ # Take the first column of Likelihoods (Observation data without noise)
+ NormLikehoods = Likelihoods / np.max(Likelihoods)
+
+
+ # Random numbers between 0 and 1
+ unif = np.random.rand(1, NrofSamples)[0]
+
+ # Reject the poorly performed prior
+ acceptedSamples = MCSamples[NormLikehoods >= unif]
+
+ # Output the Posterior
+
+ Posterior_df = pd.DataFrame(acceptedSamples, columns=InputNames)
+
+ # Save the posterior data frame
+ Posterior_df.to_csv('./Posterior.csv', columns=InputNames, index=False)
+
+ return Posterior_df
+
+def posteriorPlot(Posterior, MAP, InputNames, nParams, figsize=(10,10)):
+
+ figPosterior = corner.corner(Posterior, labels=InputNames,
+ show_titles=True, title_kwargs={"fontsize": 12})
+
+ # This is the true mean of the second mode that we used above:
+ value1 = MAP
+
+ # This is the empirical mean of the sample:
+ value2 = np.mean(Posterior, axis=0)
+
+ # Extract the axes
+ axes = np.array(figPosterior.axes).reshape((nParams, nParams))
+
+ # Loop over the diagonal
+ for i in range(nParams):
+ ax = axes[i, i]
+ ax.axvline(value1[i], color="g")
+ ax.axvline(value2[i], ls='--', color="r")
+
+ # Loop over the histograms
+ for yi in range(nParams):
+ for xi in range(yi):
+ ax = axes[yi, xi]
+ ax.axvline(value1[xi], color="g")
+ ax.axvline(value2[xi], ls='--', color="r")
+ ax.axhline(value1[yi], color="g")
+ ax.axhline(value2[yi], ls='--', color="r")
+ ax.plot(value1[xi], value1[yi], "sg")
+ ax.plot(value2[xi], value2[yi], "sr")
+
+ figPosterior.set_size_inches(figsize)
+ figPosterior.savefig('Posterior.svg',bbox_inches='tight')
+
+ return
+
+
+
+if __name__ == '__main__':
+
+ # --------------------------------------
+ # ---------- Initialization -----------
+ # --------------------------------------
+ np.random.seed(42)
+ beam_span = 5
+ i = 0
+
+ InputNames = ['Beam width', 'Beam height', 'Youngs modulus', 'Uniform load']
+
+ # --------------------------------------
+ # ---------- Data generation -----------
+ # --------------------------------------
+
+ xdata = np.linspace(0, 5, num=11, endpoint=True)
+ theta_true = (0.150064, 0.299698, 30.763206, 10.164872)
+ model_true = BeamModel(theta_true)
+ synthData = model_true
+
+ #plt.plot(xdata, synthData, 'ok')
+ #plt.xlabel('x')
+ #plt.ylabel('y');
+
+ # -------------------------------------------------------------------------
+ # ------------------ Naive Monte-Carlo with rejection sampling ------------
+ # -------------------------------------------------------------------------
+ ndim = 4
+ mcSize = 500000
+ nofmeasurement = 11
+ Likelihood = np.zeros((mcSize))
+ mcParametersets = np.zeros((mcSize,ndim))
+
+ # Beam_width
+ mcParametersets[:,0] = st.lognorm(s=0.0075, scale=0.15).rvs(size=mcSize)
+ # Beam_height
+ mcParametersets[:,1] = st.lognorm(s=0.015, scale=0.30).rvs(size=mcSize)
+ #Youngs_modulus
+ mcParametersets[:,2] = st.norm(loc=30, scale=4.5).rvs(size=mcSize)
+ #Uniform_load
+ mcParametersets[:,3] = st.norm(loc=10, scale=2).rvs(size=mcSize)
+ #sigma
+ #mcParametersets[:,4] = st.halfcauchy(loc=0, scale = 10).rvs(size=mcSize)
+
+ # origModelOutput
+ origModelOutput = np.zeros((mcSize, nofmeasurement))
+ for idx, theta in enumerate(mcParametersets):
+ origModelOutput[idx] = BeamModel(theta)
+
+
+ # ------- Generate & plot the posterior -------
+ # Rejection sampling
+ Posterior = RejectionSampling(mcParametersets, origModelOutput, synthData, InputNames)
+
+ # Plot the posterior distributions
+ posteriorPlot(Posterior, theta_true, InputNames, nParams=ndim)
+
diff --git a/tests/test_model/SSBeam_Deflection.inp b/tests/test_model/SSBeam_Deflection.inp
new file mode 100644
index 0000000000000000000000000000000000000000..d8758c4a3f8562d0c4f521bf07e3f472e8a0e5f3
--- /dev/null
+++ b/tests/test_model/SSBeam_Deflection.inp
@@ -0,0 +1,6 @@
+% Input file for the simply supported beam model
+0.15 % b in m
+0.3 % h in m
+5 % L in m
+30000e+6 % E in Pa
+10000 % p in N/m
diff --git a/tests/test_model/SSBeam_Deflection.tpl.inp b/tests/test_model/SSBeam_Deflection.tpl.inp
new file mode 100644
index 0000000000000000000000000000000000000000..6bfc3fe96c62923b0a89bde26b5dd83375a418db
--- /dev/null
+++ b/tests/test_model/SSBeam_Deflection.tpl.inp
@@ -0,0 +1,6 @@
+% Input file for the simply supported beam model
+<X1> % b in m
+<X2> % h in m
+5 % L in m
+<X3> % E in Pa
+<X4> % p in N/m
diff --git a/tests/test_model/__init__.py b/tests/test_model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4287ca8617970fa8fc025b75cb319c7032706910
--- /dev/null
+++ b/tests/test_model/__init__.py
@@ -0,0 +1 @@
+#
\ No newline at end of file
diff --git a/tests/test_model/data/InputParameters.csv b/tests/test_model/data/InputParameters.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a0ff5dcf2f2576aa57f06a6c696b42db6856d4e2
--- /dev/null
+++ b/tests/test_model/data/InputParameters.csv
@@ -0,0 +1,501 @@
+Beam width,Beam height,Youngs modulus,Uniform load
+0.157273329338393,0.315533780303244,31680127940.9143,10643.4004588287
+0.138141895643606,0.308218660642714,28321606595.7333,7647.68743220827
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diff --git a/tests/test_model/data/MCrefs_MeanStd.csv b/tests/test_model/data/MCrefs_MeanStd.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e9592bf45db032790f95259b8cedd7f961e70a81
--- /dev/null
+++ b/tests/test_model/data/MCrefs_MeanStd.csv
@@ -0,0 +1,12 @@
+x [m],mean,std
+0,0,0
+0.1,-0.002623091088022,8.06E-04
+0.2,-0.004962749295994,0.001525818175278
+0.3,-0.006794367435948,0.002088956887598
+0.4,-0.007957511802197,0.002446570522429
+0.5,-0.008355922171325,0.002569063468612
+0.6,-0.007957511802197,0.002446570522429
+0.7,-0.006794367435948,0.002088956887598
+0.8,-0.004962749295994,0.001525818175278
+0.9,-0.002623091088022,8.06E-04
+1,0,0
diff --git a/tests/test_model/data/MeasuredData.csv b/tests/test_model/data/MeasuredData.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5774f51566a8dfc04976e30e56fbca293ac296e4
--- /dev/null
+++ b/tests/test_model/data/MeasuredData.csv
@@ -0,0 +1,12 @@
+x [m],Deflection [m]
+0,0
+0.555555555555556,-0.002507972252554
+1.11111111111111,-0.004744950561407
+1.66666666666667,-0.006496185008909
+2.22222222222222,-0.007608282796739
+2.77777777777778,-0.007989208245903
+3.33333333333333,-0.007608282796739
+3.88888888888889,-0.006496185008909
+4.44444444444444,-0.004744950561407
+5,-0.002507972252554
+5.55555555555556,0
diff --git a/tests/test_model/data/MeasuredData_Valid.csv b/tests/test_model/data/MeasuredData_Valid.csv
new file mode 100644
index 0000000000000000000000000000000000000000..426ba7ed46ff82621baadb3435f79c2a0f47810b
--- /dev/null
+++ b/tests/test_model/data/MeasuredData_Valid.csv
@@ -0,0 +1,12 @@
+x [m],Deflection [m]
+0,0
+0.555555555555556,-0.002623491033022
+1.11111111111111,-0.004962750295994
+1.66666666666667,-0.006794369935948
+2.22222222222222,-0.007956511802197
+2.77777777777778,-0.008355582171325
+3.33333333333333,-0.007957591802197
+3.88888888888889,-0.006794363675948
+4.44444444444444,-0.004962749995994
+5,-0.00262091088022
+5.55555555555556,0
diff --git a/tests/test_model/data/myBeam9points.cpp b/tests/test_model/data/myBeam9points.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..b78b641e05f5fdfd8caf6d9eff4bf8d1e9c790da
--- /dev/null
+++ b/tests/test_model/data/myBeam9points.cpp
@@ -0,0 +1,131 @@
+#include <iostream> // for terminal output
+#include <string>
+#include <fstream> // for file operations
+#include <cstdlib> // for exit()
+#include <sstream> // for stringstream
+#include <cstring> // for strlen()
+
+using namespace std;
+
+// C++ implementation of the simply supported beam example from UQLAB: uq_SimplySupportedBeam()
+//
+// UQ_SIMPLYSUPPORTEDBEAM computes the midspan deflection of a simply supported beam under uniform loading
+//% Model with five input parameters X= [b h L E p]
+//% b: beam width
+//% h: beam height
+//% L: beam span
+//% E: Young's modulus
+//% p: uniform load
+//%
+//% Output: V = (5/32)*pL^4/(E*b*h^3)
+//%
+//% See also: UQ_EXAMPLE_PCE_03_SIMPLYSUPPORTEDBEAM/
+
+
+int main(int argc, char* argv[]) {
+
+ // If input filename not given on command line
+ if(argc < 2){
+ cout << "Usage: " << argv[0] << " <input filename>" << endl;
+ cout << "Exiting..." << endl;
+ exit(EXIT_FAILURE);
+ }
+
+ // Input filename given as a command line argument
+ char* inputfilename = argv[1];
+ cout << "Input filename supplied on terminal: " << inputfilename << endl;
+
+ int array_length = strlen(inputfilename);
+ if(array_length > 256){ // This is necessary because C++ doesn't allow variable-length arrays
+ cout << "Error: input filename too long! Choose a name with less than 256 characters." << endl;
+ exit(EXIT_FAILURE);
+ }
+
+ // Variables to be read
+ double params[5];
+ double beam_width;
+ double beam_height;
+ double beam_span;
+ double youngs_modulus;
+ double load;
+
+ // Reading from input file
+ ifstream inputfile(inputfilename);
+ if(inputfile.is_open()){
+ string line;
+ getline(inputfile, line); // skip first line
+ for(int p = 0; p < 5; ++p){
+ getline(inputfile, line);
+ istringstream iss(line);
+ iss >> params[p];
+ if(iss.fail()){
+ cout << "Error while reading line " << p+1 << " from file " << inputfilename << "! Exiting..." << endl;
+ exit(EXIT_FAILURE);
+ }
+ }
+ beam_width = params[0];
+ beam_height = params[1];
+ beam_span = params[2];
+ youngs_modulus = params[3];
+ load = params[4];
+
+ inputfile.close();
+ } else {
+ cout << "Error: Input file " << inputfilename << " not found! Exiting..." << endl;
+ exit(EXIT_FAILURE);
+ }
+
+ cout << "Beam width = " << beam_width << endl;
+ cout << "Beam height = " << beam_height << endl;
+ cout << "Beam span = " << beam_span << endl;
+ cout << "Young's modulus = " << youngs_modulus << endl;
+ cout << "Uniform load = " << load << endl;
+
+ // Compute midspan deflection
+// double V = 5./32. * load * beam_span * beam_span * beam_span * beam_span / (youngs_modulus * beam_width * beam_height * beam_height * beam_height);
+// cout << endl << "Midspan deflection = " << V << endl;
+ double I = beam_width * beam_height * beam_height * beam_height / 12;
+ cout << endl << "Moment of intertia = " << I << endl;
+
+ double V [9] = {0};
+ double X;
+
+ for(int i=1; i<=9; i++){
+ X = (i/10.)*beam_span;
+ V[i-1] = -load*X*(beam_span*beam_span*beam_span-2*X*X*beam_span + X*X*X)/(24*youngs_modulus*I);
+
+ }
+
+ // Extract the name of the input file by reading until the first dot
+ char outputfilename[260]; // input filename has maximal 256 characters, plus we need to append ".out"
+ istringstream s(inputfilename);
+ /*s.get(outputfilename, array_length+1, '.'); */// extract characters until the first '.' and store in outputfilename
+ for(int i = 0; i < array_length; ++i)
+ {
+ if (!((inputfilename[i] >= 'a' && inputfilename[i]<='z') || (inputfilename[i] >= 'A' && inputfilename[i]<='Z') || (inputfilename[i]=='_')))
+ {
+ inputfilename[i] = '\0';
+ }
+ }
+
+
+ // Converting to a string to make appending the file extension easier
+ std::string outputfilename_s("SSBeam_Deflection.out");
+ cout << "Output filename: " << outputfilename_s << endl;
+
+ // Writing to the output file
+ ofstream outputfile(outputfilename_s.c_str()); //re-converting to char-array necessary
+ if (outputfile.is_open()){
+ for(int count = 0; count < 9; count ++){
+ outputfile << V[count] << endl;
+ }
+ outputfile.close();
+ } else {
+ cout << "Error while opening output file " << outputfilename << "! Exiting..." << endl;
+ exit(EXIT_FAILURE);
+ }
+
+
+
+ return 0;
+}
diff --git a/tests/test_model/data/origPosterior.csv b/tests/test_model/data/origPosterior.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c5ac7661b1293e88b1d8f257c7bf183962be336f
--- /dev/null
+++ b/tests/test_model/data/origPosterior.csv
@@ -0,0 +1,6458 @@
+Beam width,Beam height,Youngs modulus,Uniform load
+0.15055984558676253,0.29525432449198297,30.76127334738532,9.723836401962199
+0.15172323185227535,0.3018864526755948,30.900386050140906,10.572937395098421
+0.14973682706047656,0.2978203129324041,36.32602379774441,11.755511440017944
+0.1517705925936338,0.305488518618717,34.50777554904536,12.185971698925812
+0.15122339466850782,0.2926128752190275,35.56672182303718,11.0301930879105
+0.14845810152750857,0.30211679811976233,29.835349622421052,10.024544145665512
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+0.15058053518912617,0.30333441137936284,27.27802142418919,9.342001468585726
+0.1507691624131821,0.2939876743771734,31.27163912090209,9.792688628012431
+0.1500870646038146,0.2985441999148553,39.81677531480247,12.987496647396311
+0.14898608605561217,0.30375423088389003,29.52705674493042,10.062549985258489
+0.14970824383703807,0.30315407503168046,35.2631265265265,11.99629791617248
+0.1498912371993728,0.3013950044398239,33.54132459098164,11.248721986783334
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diff --git a/examples/beam/test_beam.py b/tests/test_model/example_beam.py
similarity index 100%
rename from examples/beam/test_beam.py
rename to tests/test_model/example_beam.py
diff --git a/tests/test_model/myBeam9points b/tests/test_model/myBeam9points
new file mode 100644
index 0000000000000000000000000000000000000000..a3665cde7ade283f93e530021b82207535cda6f9
Binary files /dev/null and b/tests/test_model/myBeam9points differ
diff --git a/tests/test_model/read_Beam_Deflection.py b/tests/test_model/read_Beam_Deflection.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd7a5e26ee498496abd6b49fed201e1506e14823
--- /dev/null
+++ b/tests/test_model/read_Beam_Deflection.py
@@ -0,0 +1,25 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jul 19 16:48:59 2019
+
+@author: farid
+"""
+import pandas as pd
+import numpy as np
+
+
+def read_Beam_Deflection(FileNames):
+
+ df_Mean = pd.read_csv(FileNames[0], header=None, delimiter=' ')
+ x_values = np.linspace(0, 1, 11)
+ deflection = df_Mean[df_Mean.columns[0]]
+
+ deflection_zero = np.zeros((1))
+ deflection_mid = np.append(deflection_zero, deflection)
+ deflection_final = np.append(deflection_mid, deflection_zero)
+
+ # Prepare output dict using standard bayesvalidrox format
+ output = {'x_values': x_values, 'Deflection [m]': deflection_final}
+
+ return output
diff --git a/tests/test_polyconst.py b/tests/test_polyconst.py
new file mode 100644
index 0000000000000000000000000000000000000000..f831cd5a1b922e2865ca744f89d6cd068fe26b91
--- /dev/null
+++ b/tests/test_polyconst.py
@@ -0,0 +1,69 @@
+# -*- coding: utf-8 -*-
+"""
+Test the various classes and functions provided for constructing the polynomials.
+Tests are available for the following functions:
+ apoly_construction - x
+ glexindex
+ cross_truncate
+
+"""
+import sys
+sys.path.append("src/")
+sys.path.append('../src/')
+import pytest
+import numpy as np
+
+from bayesvalidrox.surrogate_models.apoly_construction import apoly_construction
+from bayesvalidrox.surrogate_models.glexindex import glexindex, cross_truncate
+
+#%% Test apoly_construction
+
+def test_apoly_construction_dimerr() -> None:
+ """
+ Cannot construct with wrong dim of data
+ """
+ data = np.random.uniform(0,1,(3,1000))
+ with pytest.raises(AttributeError) as excinfo:
+ apoly_construction(data, 3)
+ assert str(excinfo.value) == 'Data should be a 1D array'
+
+def test_apoly_construction() -> None:
+ """
+ Construct poly for aPC
+ """
+ data = np.random.uniform(0,1,1000)
+ apoly_construction(data, 3)
+
+def test_apoly_construction_deg0() -> None:
+ """
+ Construct poly for aPC for degree 0
+ """
+ data = np.random.uniform(0,1,1000)
+ apoly_construction(data, 0)
+
+def test_apoly_construction_negdeg() -> None:
+ """
+ Construct poly for aPC for negative degree -- this works??
+ """
+ data = np.random.uniform(0,1,1000)
+ apoly_construction(data, -2)
+
+#%% Test glexindex
+
+def test_glexindex() -> None:
+ """
+ Create monomial exponent dict
+ """
+ glexindex(0)
+
+#%% Test cross_truncate
+
+def test_cross_truncate()-> None:
+ """
+ Truncate indices via Lp norm
+ """
+ #cross_truncate(np.array([0,1,2]), 2, 1)
+ None
+
+if __name__ == '__main__':
+ None
diff --git a/tests/test_pylink.py b/tests/test_pylink.py
new file mode 100644
index 0000000000000000000000000000000000000000..47cc27081339a71d53963bb77cc44ad2da4b934a
--- /dev/null
+++ b/tests/test_pylink.py
@@ -0,0 +1,240 @@
+# -*- coding: utf-8 -*-
+"""
+Test the PyLinkForwardModel class in bayesvalidrox.
+Tests are available for the following functions
+ within_range *not used again in here - x
+PyLinkForwardModel:
+ read_observation *not used again in here - x
+ read_mc_reference *not used again in here - x
+ read_output *used only once
+ update_input_params *used only once
+ run_command *used only once
+ run_forwardmodel
+ run_model_parallel
+ _store_simulations *used only once
+ zip_subdirs *used in metamodel again
+OutputData:
+ constructor only
+
+"""
+import sys
+sys.path.append("src/")
+import pandas as pd
+import pytest
+
+from bayesvalidrox.pylink.pylink import PyLinkForwardModel as PL
+from bayesvalidrox.pylink.pylink import within_range
+
+#%% Test constructor
+
+def test_PL() -> None:
+ """
+ Build PyLinkForwardModel without inputs
+ """
+ PL()
+
+
+#%% Test PyLink.within_range
+
+def test_within_range_noarray() -> None:
+ """
+ Value not an array
+ """
+ with pytest.raises(AttributeError) as excinfo:
+ within_range(1,2,3)
+ assert str(excinfo.value) == 'The given values should be a 1D array, but are not'
+
+def test_within_range_2d() -> None:
+ """
+ Value not an array
+ """
+ with pytest.raises(AttributeError) as excinfo:
+ within_range([[1],[2]],2,3)
+ assert str(excinfo.value) == 'The given values should be a 1D array, but are not'
+
+def test_within_range_err() -> None:
+ """
+ Value not in range
+ """
+ assert within_range([1],2,3) == False
+
+def test_within_range_switchbounds() -> None:
+ """
+ Switched min and max
+ """
+ with pytest.raises(ValueError) as excinfo:
+ within_range([1],4,3)
+ assert str(excinfo.value) == 'The lower and upper bounds do not form a valid range, they might be switched'
+
+def test_within_range() -> None:
+ """
+ Value in range
+ """
+ assert within_range([1],0,3) == True
+
+#%% Test PyLink.read_observation
+# TODO: check that the shape,... of what is read in matches wha is in the files
+def test_read_observation_none() -> None:
+ """
+ Read observation - 'calib' without anything
+ """
+ pl = PL()
+ with pytest.raises(Exception) as excinfo:
+ pl.read_observation()
+ assert str(excinfo.value) == "Please provide the observation data as a dictionary via observations attribute or pass the csv-file path to MeasurementFile attribute"
+
+def test_read_observation() -> None:
+ """
+ Read observation - 'calib' from file
+ """
+ pl = PL()
+ pl.meas_file = 'tests/MeasuredData.csv'
+ pl.read_observation()
+
+def test_read_observation_datadict() -> None:
+ """
+ Read observation - 'calib' with given observation as dict
+ """
+ pl = PL()
+ pl.observations = {'Z':[0.1]}
+ pl.read_observation()
+
+def test_read_observation_dataframe() -> None:
+ """
+ Read observation - 'calib' with given observation as dict
+ """
+ pl = PL()
+ pl.observations = pd.DataFrame({'Z':[0.1]}, columns = ['Z'])
+ pl.read_observation()
+
+def test_read_observation_validnone() -> None:
+ """
+ Read observation - 'valid' without anything
+ """
+ pl = PL()
+ with pytest.raises(Exception) as excinfo:
+ pl.read_observation(case = 'valid')
+ assert str(excinfo.value) == "Please provide the observation data as a dictionary via observations attribute or pass the csv-file path to MeasurementFile attribute"
+
+def test_read_observation_valid() -> None:
+ """
+ Read observation - 'valid' from file
+ """
+ pl = PL()
+ pl.meas_file_valid = 'tests/MeasuredData_Valid.csv'
+ pl.read_observation(case = 'valid')
+
+def test_read_observation_validdatadict() -> None:
+ """
+ Read observation - 'valid' with given observation as dict
+ """
+ pl = PL()
+ pl.observations_valid = {'Z':[0.1]}
+ pl.read_observation(case = 'valid')
+
+def test_read_observation_validdataframe() -> None:
+ """
+ Read observation - 'valid' with given observation as dict
+ """
+ pl = PL()
+ pl.observations_valid = pd.DataFrame({'Z':[0.1]}, columns = ['Z'])
+ pl.read_observation(case = 'valid')
+
+
+def test_read_observation_mc() -> None:
+ """
+ Read mc ref from file
+ """
+ pl = PL()
+ pl.mc_ref_file = 'tests/MCrefs_MeanStd.csv'
+ pl.read_observation(case = 'mc_ref')
+
+
+def test_read_observation_mcdatadict() -> None:
+ """
+ Read mc ref from fict
+ """
+ pl = PL()
+ pl.mc_reference = {'Mean':[0.1],'Std':[0.1]}
+ pl.read_observation(case = 'mc_ref')
+
+def test_read_observation_mcdataframe() -> None:
+ """
+ Read mc ref from dataframe
+ """
+ pl = PL()
+ pl.observations_valid = pd.DataFrame({'Mean':[0.1],'Std':[0.1]}, columns = ['Mean', 'Std'])
+ pl.read_observation(case = 'mc_ref')
+
+def test_read_observation_mcnone() -> None:
+ """
+ Read mc ref with nothing
+ """
+ pl = PL()
+ pl.read_observation(case = 'mc_ref')
+
+#%% Test PyLink.read_output
+
+def test_read_output() -> None:
+ """
+ Reads model run output
+ """
+ pl = PL()
+ #pl.read_output()
+ # TODO: create parser first to be able to test this
+
+#%% Test PyLink.update_input_params
+
+def test_update_input_params() -> None:
+ """
+ Updates parameters in file
+ """
+ pl = PL()
+ # TODO: better understand what this is meant to do
+
+#%% Test PyLink.run_command
+
+def test_run_command() -> None:
+ """
+ Runs command and reads results
+ """
+ pl = PL()
+ # TODO: Find command to run to then read in file
+
+#%% Test PyLink.zip_subdirs
+def test_zip_subdirs() -> None:
+ """
+ Zips specified subdirs
+ """
+ pl = PL()
+ pl.zip_subdirs('tests\Outputs_SeqPosteriorComparison', 'Z')
+
+#%% Test PyLink._store_simulations
+def test_store_simulations() -> None:
+ """
+ Stores simulation results
+ """
+ pl = PL()
+
+
+#%% Test PyLink.run_model_parallel
+if 0:
+ def test_run_model_parallel() -> None:
+ """
+ Runs model in parallel
+ """
+ pl = PL()
+ pl.link_type = 'PyLink'
+ pl.name = 'Beam9points'
+ pl.input_file = "tests\test_model\SSBeam_Deflection.inp"
+ pl.input_template = "tests\test_model\SSBeam_Deflection.tpl.inp"
+
+ pl.shell_command = "tests\test_model\myBeam9points SSBeam_Deflection.inp"
+ pl.Output.parser = 'tests\test_model\read_Beam_Deflection'
+ pl.Output.names = ['Deflection [m]']
+ pl.Output.file_names = ["SSBeam_Deflection.out"]
+ pl.run_model_parallel([[0.1,0.25,30000e+6,1e4]])
+
+
+if __name__ == '__main__':
+ None