Small fixes, start on ModelComp without MetaModel

examples/model-comparison/example_model_comparison.py

@@ -22,7 +22,12 @@ import scipy.io as io
 import pandas as pd
 import joblib
 import sys
+<<<<<<< HEAD
 sys.path.append("../../src/bayesvalidrox/")
+=======
+#sys.path.append("../../src/bayesvalidrox/")
+sys.path.append("../../src/")
+>>>>>>> 99013313 (Small fixes, start on ModelComp without MetaModel)
 
 from bayesvalidrox.pylink.pylink import PyLinkForwardModel
 from bayesvalidrox.surrogate_models.inputs import Input

examples/only-model/L2_model.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This is a simple linear model.
+
+The code for this numerical experiments is available at
+https://github.com/MichaelSinsbeck/paper_sequential-design-model-selection.
+
+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
+
+Created on Fri Oct 8 2021
+
+"""
+import numpy as np
+
+
+def L2_model(xx):
+    """
+    Linear model y = a*x+b
+
+    Models adapted from Anneli Guthke's paper:
+        ch€oniger, A., T. W€ohling, L. Samaniego,and W. Nowak (2014), Model
+        selection on solid ground: Rigorous comparison ofnine ways to evaluate
+        Bayesian modelevidence,Water Resour. Res.,50,9484–9513,
+        doi:10.1002/2014WR016062
+
+    Parameters
+    ----------
+    xx : array
+        Parameters a and b.
+
+    Returns
+    -------
+    2D-array
+        The first row contains the measurement locations.
+        The second row contains the model outputs.
+
+    """
+    n_output = 15
+    meas_loc = np.linspace(0.25, 4.75, n_output)
+
+    # L2_model
+    L2_model = xx[:, 0] * meas_loc + xx[:, 1]
+
+    # Output
+    output = {
+        'x_values': meas_loc,
+        'Z': L2_model
+        }
+
+    return output

examples/only-model/NL2_model.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This is a nonlinear cosine model.
+
+The code for this numerical experiments is available at
+https://github.com/MichaelSinsbeck/paper_sequential-design-model-selection.
+
+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
+
+Created on Fri Oct 8 2021
+
+"""
+import numpy as np
+
+
+def NL2_model(xx):
+    """
+    Nonlinear model y = exp(a*x) + b
+
+    Models adapted from Anneli Guthke's paper:
+        ch€oniger, A., T. W€ohling, L. Samaniego,and W. Nowak (2014), Model
+        selection on solid ground: Rigorous comparison ofnine ways to evaluate
+        Bayesian modelevidence,Water Resour. Res.,50,9484–9513,
+        doi:10.1002/2014WR016062
+
+    Parameters
+    ----------
+    xx : array
+        Parameters a and b.
+
+    Returns
+    -------
+    2D-array
+        The first row contains the measurement locations.
+        The second row contains the model outputs.
+
+    """
+    n_output = 15
+    meas_loc = np.linspace(0.25, 4.75, n_output)
+
+    # NL2_model
+    NL2_model = np.exp(xx[:, 0] * meas_loc) + xx[:, 1]
+
+    # Output
+    output = {
+        'x_values': meas_loc,
+        'Z': NL2_model
+        }
+
+    return output

examples/only-model/NL4_model.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This is a nonlinear cosine model.
+
+The code for this numerical experiments is available at
+https://github.com/MichaelSinsbeck/paper_sequential-design-model-selection.
+
+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
+
+Created on Fri Oct 8 2021
+
+"""
+import numpy as np
+
+
+def NL4_model(xx):
+    """
+    Nonlinear model y = a*cos(b*x+c)+d
+
+    Models adapted from Anneli Guthke's paper:
+        ch€oniger, A., T. W€ohling, L. Samaniego,and W. Nowak (2014), Model
+        selection on solid ground: Rigorous comparison ofnine ways to evaluate
+        Bayesian modelevidence,Water Resour. Res.,50,9484–9513,
+        doi:10.1002/2014WR016062
+
+    Parameters
+    ----------
+    xx : array
+        Parameters a and b.
+
+    Returns
+    -------
+    2D-array
+        The first row contains the measurement locations.
+        The second row contains the model outputs.
+
+    """
+    n_output = 15
+    meas_loc = np.linspace(0.25, 4.75, n_output)
+
+    # NL4_model
+    NL4_model = xx[:, 0] * np.cos(xx[:, 1] * meas_loc + xx[:, 2]) + xx[:, 3]
+
+    # Output
+    output = {
+        'x_values': meas_loc,
+        'Z': NL4_model
+        }
+
+    return output

examples/only-model/bayesvalidrox/__init__.py

-# -*- coding: utf-8 -*-
-__version__ = "0.0.5"
-
-from .pylink.pylink import PyLinkForwardModel
-from .surrogate_models.surrogate_models import MetaModel
-from .surrogate_models.meta_model_engine import MetaModelEngine
-from .surrogate_models.inputs import Input
-from .post_processing.post_processing import PostProcessing
-from .bayes_inference.bayes_inference import BayesInference
-from .bayes_inference.bayes_model_comparison import BayesModelComparison
-from .bayes_inference.discrepancy import Discrepancy
-
-__all__ = [
-    "__version__",
-    "PyLinkForwardModel",
-    "Input",
-    "Discrepancy",
-    "MetaModel",
-    "MetaModelEngine",
-    "PostProcessing",
-    "BayesInference",
-    "BayesModelComparison"
-    ]

examples/only-model/bayesvalidrox/bayes_inference/__init__.py

-# -*- coding: utf-8 -*-
-
-from .bayes_inference import BayesInference
-from .mcmc import MCMC
-
-__all__ = [
-    "BayesInference",
-    "MCMC"
-    ]

examples/only-model/bayesvalidrox/bayes_inference/discrepancy.py

-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import scipy.stats as stats
-from bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
-
-
-class Discrepancy:
-    """
-    Discrepancy class for Bayesian inference method.
-    We define the reference or reality to be equal to what we can model and a
-    descripancy term \\( \\epsilon \\). We consider the followin format:
-
-    $$\\textbf{y}_{\\text{reality}} = \\mathcal{M}(\\theta) + \\epsilon,$$
-
-    where \\( \\epsilon \\in R^{N_{out}} \\) represents the the effects of
-    measurement error and model inaccuracy. For simplicity, it can be defined
-    as an additive Gaussian disrepancy with zeromean and given covariance
-    matrix \\( \\Sigma \\):
-
-    $$\\epsilon \\sim \\mathcal{N}(\\epsilon|0, \\Sigma). $$
-
-    In the context of model inversion or calibration, an observation point
-    \\( \\textbf{y}_i \\in \\mathcal{y} \\) is a realization of a Gaussian
-    distribution with mean value of \\(\\mathcal{M}(\\theta) \\) and covariance
-    matrix of \\( \\Sigma \\).
-
-    $$ p(\\textbf{y}|\\theta) = \\mathcal{N}(\\textbf{y}|\\mathcal{M}
-                                             (\\theta))$$
-
-    The following options are available:
-
-    * Option A: With known redidual covariance matrix \\(\\Sigma\\) for
-    independent measurements.
-
-    * 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
-    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\\).
-
-    Attributes
-    ----------
-    InputDisc : obj
-        Input object. When the \\(\\sigma^2\\) is expected to be inferred
-        jointly with the parameters (`Option B`).If multiple output groups are
-        defined by `Model.Output.names`, each model output needs to have.
-        a prior marginal using the `Input` class. The default is `''`.
-    disc_type : str
-        Type of the noise definition. `'Gaussian'` is only supported so far.
-    parameters : dict or pandas.DataFrame
-        Known residual variance \\(\\sigma^2\\), i.e. diagonal entry of the
-        covariance matrix of the multivariate normal likelihood in case of
-        `Option A`.
-
-    """
-
-    def __init__(self, InputDisc='', disc_type='Gaussian', parameters=None):
-        self.InputDisc = InputDisc
-        self.disc_type = disc_type
-        self.parameters = parameters
-
-    # -------------------------------------------------------------------------
-    def get_sample(self, n_samples):
-        """
-        Generate samples for the \\(\\sigma^2\\), i.e. the diagonal entries of
-        the variance-covariance matrix in the multivariate normal distribution.
-
-        Parameters
-        ----------
-        n_samples : int
-            Number of samples (parameter sets).
-
-        Returns
-        -------
-        sigma2_prior: array of shape (n_samples, n_params)
-            \\(\\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
-            )
-        # Store BoundTuples
-        self.ExpDesign = ExpDesign
-
-        # Naive approach: Fit a gaussian kernel to the provided data
-        self.ExpDesign.JDist = stats.gaussian_kde(ExpDesign.raw_data)
-
-        # Save the names of sigmas
-        if len(self.InputDisc.Marginals) != 0:
-            self.name = []
-            for Marginalidx in range(len(self.InputDisc.Marginals)):
-                self.name.append(self.InputDisc.Marginals[Marginalidx].name)
-
-        return self.sigma2_prior

examples/only-model/bayesvalidrox/bayesvalidrox.mplstyle

-figure.titlesize : 30
-axes.titlesize : 30
-axes.labelsize : 30
-axes.linewidth : 3
-axes.grid : True
-lines.linewidth : 3
-lines.markersize : 10
-xtick.labelsize : 30
-ytick.labelsize : 30
-legend.fontsize : 30
-font.family : serif
-font.serif : Arial
-font.size : 30
-text.usetex : True
-grid.linestyle : -
-figure.figsize : 24, 16