Merge pull request #245 from SURGroup/Development

Development

Author: dimtsap

README.rst

@@ -13,27 +13,31 @@
    :alt: bear-ified
 
 
-*******************************************
+**********************************************
 Uncertainty Quantification with python (UQpy)
-*******************************************
+**********************************************
 
 |logo|
 
-====
-
-+-----------------------+------------------------------------------------------------------+
-| **Product Owner:**    | Michael D. Shields                                               |
-+-----------------------+------------------------------------------------------------------+
-| **Lead Developers:**  | Dimitris Giovanis, Audrey Olivier, Dimitris Tsapetis             |
-+-----------------------+------------------------------------------------------------------+
-| **Development Team:** | Aakash Bangalore Satish, Mohit Singh Chauhan, Lohit Vandanapu,   |
-+                       +                                                                  +
-|                       | Ketson RM dos Santos, Katiana Kontolati, Dimitris Loukrezis,     |
-+                       +                                                                  +
-|                       | Promit Chakroborty, Lukáš Novák, Andrew Solanto, Connor Krill    |
-+-----------------------+------------------------------------------------------------------+
-| **Contributors:**     | Michael Gardner, Prateek Bhustali, Julius Schultz, Ulrich Römer  |
-+-----------------------+------------------------------------------------------------------+
+================================================================================================
+
+.. list-table:: 
+   :widths: 100, 200 
+   :header-rows: 0
+
+   * - **Product Owner:**
+     - Michael D. Shields
+
+   * - **Lead Developers:**
+     - Dimitris Giovanis, Audrey Olivier, Dimitris Tsapetis
+
+   * - **Development Team:**
+     - Aakash Bangalore Satish, Mohit Singh Chauhan, Lohit Vandanapu, Ketson RM dos Santos, Katiana Kontolati, Dimitris Loukrezis, Promit Chakroborty, Lukáš Novák, Andrew Solanto, Connor Krill
+
+   * - **Contributors:**
+     - Michael Gardner, Prateek Bhustali, Julius Schultz, Ulrich Römer
+     
+
 
 Contact
 ===========
@@ -54,7 +58,7 @@ Website:
            https://uqpyproject.readthedocs.io
 
 Dependencies
-===========
+===============
 
             * ::
             
@@ -65,7 +69,7 @@ License
 ===========
 UQpy is distributed under the MIT license
 
-Copyright (C) <2018> <Michael D. Shields>
+Copyright (C) <2024> <Michael D. Shields>
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 
@@ -75,7 +79,7 @@ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLI
 
 
 Installation
-===========
+===============
 
 From PyPI
 
@@ -109,13 +113,13 @@ Olivier, A., Giovanis, D.G., Aakash, B.S., Chauhan, M., Vandanapu, L., and Shiel
 
 
 Help and Support
-===========
+==================
 
 For assistance with the UQpy software package, please raise an issue on the Github Issues page. Please use the appropriate labels to indicate which module you are specifically inquiring about.
 
 .. |logo| image:: logo.jpg
-    :scale: 25 %
-    :target: https://gihub.com/SURGroup/UQpy
+    :height: 250px
+    :target: https://github.com/SURGroup/UQpy
 
 
 

requirements.txt

@@ -1,15 +1,15 @@
-numpy == 1.22.0
-scipy == 1.8.0
-matplotlib == 3.5.2
-scikit-learn == 1.0.2
-fire == 0.4.0
-pytest == 6.1.2
-coverage == 5.3
-pytest-cov == 2.10.1
-pylint == 2.6.0
-wheel == 0.38.1
-pytest-azurepipelines == 0.8.0
-twine == 3.4.1
+numpy == 1.26.4
+scipy == 1.6.0
+matplotlib == 3.8.4
+scikit-learn == 1.4.2
+fire == 0.6.0
+pytest == 8.2.0
+coverage == 7.5.0
+pytest-cov == 5.0.0
+pylint == 3.1.0
+wheel == 0.43.0
+pytest-azurepipelines == 1.0.5
+twine == 5.0.0
 pathlib~=1.0.1
-beartype ==0.9.1
+beartype == 0.18.5
 setuptools~=65.5.1

setup.py

@@ -4,23 +4,44 @@
 del sys.argv[1]
 from setuptools import setup, find_packages
 
+from pathlib import Path
+this_directory = Path(__file__).parent
+long_description = (this_directory / "README.rst").read_text()
+
 setup(
     name='UQpy',
     version=version,
     url='https://github.com/SURGroup/UQpy',
     description="UQpy is a general purpose toolbox for Uncertainty Quantification",
+    long_description=long_description,
     author="Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Aakash Bangalore-Satish, Mohit Chauhan, "
            "Lohit Vandanapu, Ketson R.M. dos Santos",
-    author_email="[email protected]",
     license='MIT',
     platforms=["OSX", "Windows", "Linux"],
     packages=find_packages("src"),
     package_dir={"": "src"},
     package_data={"": ["*.pdf"]},
+    python_requires='>3.9.0',
     install_requires=[
-        "numpy", "scipy", "matplotlib", "scikit-learn", 'fire',
-        "beartype==0.9.1",
+        "numpy", "scipy>=1.6.0", "matplotlib", "scikit-learn", 'fire',
+        "beartype==0.18.5",
     ],
+    extras_require={
+        'dev': [
+            'pytest == 8.2.0',
+            'pytest-cov == 5.0.0',
+            'pylint == 3.1.0',
+            'pytest-azurepipelines == 1.0.5',
+            'pytest-cov == 5.0.0',
+            'wheel == 0.43.0',
+            'twine == 5.0.0',
+            'sphinx_autodoc_typehints == 1.23.0',
+            'sphinx_rtd_theme == 1.2.0',
+            'sphinx_gallery == 0.13.0',
+            'sphinxcontrib_bibtex == 2.5.0',
+            'Sphinx==6.1.3',
+        ]
+    },
     classifiers=[
         'Programming Language :: Python :: 3',
         'Intended Audience :: Science/Research',

src/UQpy/dimension_reduction/diffusion_maps/DiffusionMaps.py

@@ -133,7 +133,7 @@ def _fit(self):
         transition_matrix = d_star_inv_d.dot(l_star)
 
         if self.is_sparse:
-            is_symmetric = sp.sparse.linalg.norm(transition_matrix - transition_matrix.T, sp.inf) < 1e-08
+            is_symmetric = sp.sparse.linalg.norm(transition_matrix - transition_matrix.T, np.inf) < 1e-08
         else:
             is_symmetric = np.allclose(transition_matrix, transition_matrix.T, rtol=1e-5, atol=1e-08)
 

src/UQpy/sampling/mcmc/tempering_mcmc/ParallelTemperingMCMC.py

@@ -1,5 +1,5 @@
 from scipy.special import logsumexp
-from scipy.integrate import trapz
+from scipy.integrate import trapezoid
 
 from UQpy.sampling.mcmc import MetropolisHastings
 from UQpy.sampling.mcmc.baseclass.MCMC import *
@@ -216,7 +216,7 @@ def evaluate_normalization_constant(self, compute_potential, log_Z0: float = Non
         # use quadrature to integrate between 0 and 1
         temper_param_list_for_integration = np.copy(np.array(self.tempering_parameters))
         log_pdf_averages = np.array(log_pdf_averages)
-        int_value = trapz(x=temper_param_list_for_integration, y=log_pdf_averages)
+        int_value = trapezoid(x=temper_param_list_for_integration, y=log_pdf_averages)
         if log_Z0 is None:
             samples_p0 = self.distribution_reference.rvs(nsamples=nsamples_from_p0)
             log_Z0 = np.log(1. / nsamples_from_p0) + logsumexp(

src/UQpy/sampling/stratified_sampling/strata/VoronoiStrata.py

@@ -175,18 +175,18 @@ def sample_strata(self, nsamples_per_stratum, random_state):
 
             # Compute volume of each delaunay
             volume = list()
-            for i in range(len(delaunay_obj.vertices)):
-                vert = delaunay_obj.vertices[i]
+            for i in range(len(delaunay_obj.simplices)):
+                vert = delaunay_obj.simplices[i]
                 ch = ConvexHull(seed_and_vertices[vert])
                 volume.append(ch.volume)
 
             temp_prob = np.array(volume) / sum(volume)
-            a = list(range(len(delaunay_obj.vertices)))
+            a = list(range(len(delaunay_obj.simplices)))
             for k in range(int(nsamples_per_stratum[j])):
                 simplex = random_state.choice(a, p=temp_prob)
 
                 new_samples = SimplexSampling(
-                    nodes=seed_and_vertices[delaunay_obj.vertices[simplex]],
+                    nodes=seed_and_vertices[delaunay_obj.simplices[simplex]],
                     nsamples=1,
                     random_state=self.random_state,
                 ).samples
@@ -205,15 +205,15 @@ def compute_centroids(self):
 
         for j in range(self.mesh.nsimplex):
             try:
-                ConvexHull(self.points[self.mesh.vertices[j]])
+                ConvexHull(self.points[self.mesh.simplices[j]])
                 self.mesh.centroids[j, :], self.mesh.volumes[j] = \
-                    DelaunayStrata.compute_delaunay_centroid_volume(self.points[self.mesh.vertices[j]])
+                    DelaunayStrata.compute_delaunay_centroid_volume(self.points[self.mesh.simplices[j]])
             except qhull.QhullError:
                 self.mesh.centroids[j, :], self.mesh.volumes[j] = (np.mean(self.points[self.mesh.vertices[j]]), 0,)
 
     def initialize(self, samples_number, training_points):
         self.add_boundary_points_and_construct_delaunay(samples_number, training_points)
-        self.mesh.old_vertices = self.mesh.vertices.copy()
+        self.mesh.old_vertices = self.mesh.simplices.copy()
 
     def add_boundary_points_and_construct_delaunay(
             self, samples_number, training_points
@@ -377,7 +377,7 @@ def _update_strata(self, new_point, samples_u01):
         p_ = new_point.shape[0]
         # Update the matrices to have recognize the new point
         self.points_to_samplesU01 = np.hstack([self.points_to_samplesU01, np.arange(i_, i_ + p_)])
-        self.mesh.old_vertices = self.mesh.vertices
+        self.mesh.old_vertices = self.mesh.simplices
 
         # Update the Delaunay triangulation mesh to include the new point.
         self.mesh.add_points(new_point)

src/UQpy/surrogates/gaussian_process/GaussianProcessRegression.py

@@ -297,8 +297,9 @@ def predict(self, points, return_std: bool = False, hyperparameters: list = None
             else:
                 mu1 = np.einsum("ij,jk->ik", fx, self.beta)
 
-        self.kernel.kernel_parameter = kernelparameters[:-1]
-        sigma = kernelparameters[-1]
+        if kernelparameters is not None:
+            self.kernel.kernel_parameter = kernelparameters[:-1]
+            sigma = kernelparameters[-1]
 
         k = sigma**2*self.kernel.calculate_kernel_matrix(x=x_, s=s_)
         y = mu1 + k @ alpha_

src/UQpy/surrogates/polynomial_chaos/regressions/LeastAngleRegression.py

@@ -45,7 +45,7 @@ def run(self, x: np.ndarray, y: np.ndarray, design_matrix: np.ndarray):
         n_samples, inputs_number = x.shape
 
         reg = regresion.Lars(fit_intercept=self.fit_intercept, verbose=self.verbose,
-                             n_nonzero_coefs=self.n_nonzero_coefs, normalize=self.normalize)
+                             n_nonzero_coefs=self.n_nonzero_coefs)
         reg.fit(design_matrix, y)
 
         # LarsBeta = reg.coef_path_

src/UQpy/utilities/Utilities.py

@@ -316,7 +316,7 @@ def correlation_distortion(dist_object, rho):
     :rtype: float
     """
 
-    if rho == 1.0:
+    if np.isclose(rho, 1.0):
         rho = 0.999
     n = 512
     zmax = 8

src/UQpy/utilities/kernels/euclidean_kernels/Matern.py

@@ -21,11 +21,11 @@ def __init__(self, kernel_parameter: Union[int, float] = 1, nu=1.5):
     def calculate_kernel_matrix(self, x, s):
         l = self.kernel_parameter
         stack = cdist(x / l, s / l, metric='euclidean')
-        if self.nu == 0.5:
+        if np.isclose(self.nu, 0.5):
             self.kernel_matrix = np.exp(-np.abs(stack))
-        elif self.nu == 1.5:
+        elif np.isclose(self.nu,1.5):
             self.kernel_matrix = (1 + np.sqrt(3) * stack) * np.exp(-np.sqrt(3) * stack)
-        elif self.nu == 2.5:
+        elif np.isclose(self.nu, 2.5):
             self.kernel_matrix = (1 + np.sqrt(5) * stack + 5 * (stack ** 2) / 3) * np.exp(-np.sqrt(5) * stack)
         elif self.nu == np.inf:
             self.kernel_matrix = np.exp(-(stack ** 2) / 2)