cleaned up many benchmark functions.

updated example documentations.

Author: ksargsyan

README.md

@@ -23,6 +23,7 @@ Optional dependencies include:
 * pytorch (NN surrogates)
 * QUiNN (Quantification of Uncertainties in Neural Networks)
 * pyswarms (Particle Swarm Optimization)
+* dill (for saving python objects)
 
 ## Installation
 
@@ -71,6 +72,13 @@ $ pip install 'pytuq[dev]'
     $ pip install .
 ```
 
+## Contributors
+Habib N. Najm (Sandia National Laboratories)
+Javier Murgoitio-Esandi (Google)
+Cosmin Safta (Sandia National Laboratories)
+Joy Bahr-Mueller (Sandia National Laboratories)
+Vahan Sargsyan (Stuyvesant High School)
+
 ## License
 Distributed under BSD 3-Clause License. See `LICENSE.txt` for more information.
 

apps/create_data.py

@@ -5,9 +5,16 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from pytuq.func import toy, genz, chem, benchmark, poly, oper, func
+from pytuq.func import toy, genz, chem, bench, poly, oper, func
+from pytuq.utils.xutils import instantiate_classes_from_module
 
 
+fcn_dict = {}
+for submod in ['bench', 'bench1d', 'bench2d', 'benchNd', 'chem', 'genz', 'poly', 'toy']:
+    this_objects = instantiate_classes_from_module(f"pytuq.func.{submod}")
+    for j in this_objects:
+        if j.name not in ['GenzBase', 'Poly']:
+            fcn_dict[j.name] = j
 
 
 usage_str = 'Script to create input-output data for benchmark functions.'
@@ -16,29 +23,53 @@
 #                    help="indices of requested parameters (count from 0)")
 
 parser.add_argument("-n", "--npts", dest="npts", type=int, default=100, help="Number of points")
-parser.add_argument("-f", "--func", dest="func", type=str, default='lj', help="Function name", choices=['lj', 'mb'])
-
+parser.add_argument("-f", "--func", dest="func", type=str, default='Muller-Brown', help="Function name", choices=list(fcn_dict.keys()))
+parser.add_argument("-x", "--xtrain", dest="xtrain_file", type=str, default=None, help="Optionally, provide x-sample file")
+parser.add_argument("-s", "--sigma", dest="sig", type=float, default=0.0, help="Noise size")
+parser.add_argument('-g', dest="grad", action='store_true',
+                    help='Whether to compute gradients (default: False)')
 
 args = parser.parse_args()
 
 fname = args.func
 nsam = args.npts
+grad = args.grad
+sig = args.sig
+xtrain_file = args.xtrain_file
 
-if fname == 'lj':
-    fcn = chem.LennardJones()
-elif fname == 'mb':
-    fcn = chem.MullerBrown()
-else:
-    print(f'Function {fcn} is unknown. Please use -h to see the options. Exiting.')
+try:
+    fcn = fcn_dict[fname]
+    print(f"{nsam} samples of function {fname} requested.")
+except KeyError:
+    print(f'Function {fname} is unknown. Please use -h to see the options. Exiting.')
     sys.exit()
 
-xx = fcn.sample_uniform(nsam)
+if xtrain_file is None:
+    xx = fcn.sample_uniform(nsam)
+else:
+    xx = np.loadtxt(xtrain_file)
+    if len(xx.shape)==1:
+        xx = xx.reshape(-1, 1)
+    print(f"Input data file {xtrain_file} has {xx.shape[0]} samples, ignoring the -n flag.")
+
+nsam, ndim = xx.shape
+assert(ndim==fcn.dim)
 
 yy = fcn(xx)
-gg = fcn.grad(xx) #nsam, nout, ndim
-nsam_, nout, ndim = gg.shape
-gg = gg.reshape(nsam, nout*ndim)
+yy += sig * np.random.randn(*yy.shape)
+print("Data noise sigma =", sig)
+_, nout = yy.shape
+assert(nout==fcn.outdim)
+
 
 np.savetxt('xtrain.txt', xx)
 np.savetxt('ytrain.txt', yy)
-np.savetxt('gtrain.txt', gg)
+print("Input    data saved to xtrain.txt with shape ", xx.shape)
+print("Output   data saved to ytrain.txt with shape ", yy.shape)
+if grad:
+    gg = fcn.grad(xx) #nsam, nout, ndim
+    gg = gg.reshape(nsam, nout*ndim)
+    np.savetxt('gtrain.txt', gg)
+    print(f"Gradient data saved to gtrain.txt with shape ({nsam}, {nout}x{ndim})")
+
+

apps/uqpc/uq_pc.py

@@ -10,7 +10,7 @@
 import numpy as np
 
 from pytuq.rv.pcrv import PCRV
-from pytuq.func.benchmark import Ishigami
+from pytuq.func.bench import Ishigami
 from pytuq.utils.xutils import savepk
 from pytuq.utils.mindex import get_mi, get_npc
 from pytuq.workflows.fits import pc_fit

docs/auto_examples/auto_examples_jupyter.zip

@@ -12,7 +12,7 @@ The Python Toolkit for Uncertainty Quantification (PyTUQ) is a Python-only set o
 - Methods for Gaussian process regression
 - Global sensitivity analysis methods
 - SVD-based dimensionality reduction techniques
-- Karhunen-Loeve expansions
+- Karhunen-Loève expansions
 - Various methods for linear regression
 - Bayesian compressive sensing techniques
 - MCMC classes for calibration and parameter inference
@@ -22,9 +22,18 @@ The Python Toolkit for Uncertainty Quantification (PyTUQ) is a Python-only set o
 
 Authors
 --------
-- Khachik Sargsyan
-- Bert Debusschere
-- Emilie Grace Baillo
+- Khachik Sargsyan (Sandia National Laboratories)
+- Bert Debusschere (Sandia National Laboratories)
+- Emilie Grace Baillo (Sandia National Laboratories)
+
+Contributors
+-------------
+- Habib N. Najm (Sandia National Laboratories)
+- Javier Murgoitio-Esandi (Google)
+- Cosmin Safta (Sandia National Laboratories)
+- Joy Bahr-Mueller (Sandia National Laboratories)
+- Vahan Sargsyan (Stuyvesant High School)
+
 
 Acknowledgements
 -----------------

docs/auto_examples/ex_nn.zip

@@ -1,8 +1,8 @@
 #!/usr/bin/env python
+"""Example demonstrating Bayesian Compressive Sensing (BCS) for sparse polynomial chaos regression.
 
-"""[summary]
-
-[description]
+This script shows how to use BCS to construct a sparse polynomial chaos surrogate model
+with specified multiindex and model data, comparing predictions with true function values.
 """
 import numpy as np
 import matplotlib.pyplot as plt

docs/auto_examples/ex_pce.zip

@@ -1,15 +1,18 @@
 #!/usr/bin/env python
-"""An example demonstrating bcs and multiindex growth.
+"""Example demonstrating adaptive multiindex growth with Bayesian Compressive Sensing.
+
+This script shows how to iteratively grow a polynomial chaos surrogate using
+adaptive multiindex selection and BCS regression for sparse approximation.
 """
 import numpy as np
 
 
 from pytuq.rv.pcrv import PCRV
 from pytuq.utils.mindex import get_mi
-from pytuq.utils.plotting import myrc, lighten_color, plot_dm, plot_jsens
+from pytuq.utils.plotting import myrc, plot_dm
 from pytuq.utils.maps import scale01ToDom
 from pytuq.lreg.bcs import bcs
-from pytuq.utils.mindex import mi_addfront_cons, mi_addfront
+from pytuq.utils.mindex import mi_addfront
 
 import pytuq.utils.funcbank as fcb
 

docs/misc/about.rst

@@ -1,5 +1,9 @@
 #!/usr/bin/env python
-"""Example that creates RGB triples."""
+"""Example demonstrating color palette generation and visualization.
+
+This script creates and displays a set of RGB color triples using PyTUQ's
+plotting utilities, useful for creating consistent color schemes in plots.
+"""
 
 from matplotlib import pyplot as plt