Updated to Python 3.6

.travis.yml

@@ -3,7 +3,10 @@ sudo: false
 
 language: python
 python:
-  - "2.7"
+  - 2.7
+  - 3.6
+  - 3.7
+  - 3.8
 
 addons:
   apt:
@@ -13,17 +16,18 @@ addons:
     - liblapack-dev
 
 before_install:
- - travis_retry wget http://repo.continuum.io/miniconda/Miniconda-3.8.3-Linux-x86_64.sh -O miniconda.sh
- - chmod +x miniconda.sh
- - bash miniconda.sh -b -p $HOME/miniconda
- - export PATH=/home/travis/miniconda/bin:$PATH
- - conda update --yes conda
+ - #travis_retry wget http://repo.continuum.io/miniconda/Miniconda-3.8.3-Linux-x86_64.sh -O miniconda.sh
+ - #chmod +x miniconda.sh
+ - #bash miniconda.sh -b -p $HOME/miniconda
+ - #export PATH=/home/travis/miniconda/bin:$PATH
+ - #conda update --yes conda
 
 install:
- - conda create --yes -n test python=$TRAVIS_PYTHON_VERSION
- - source activate test
- - conda install --yes numpy scipy matplotlib pip nose
- - pip install setuptools
- - python setup.py install
+ #- conda create --yes -n test python=$TRAVIS_PYTHON_VERSION
+ #- source activate test
+ #- conda install --yes numpy scipy matplotlib pip nose
+#  - pip install setuptools
+ - pip install .
+ - export MPLBACKEND="Agg"
 
-script: python test.py
+script: nosetests

README.md

@@ -49,6 +49,8 @@ You should now be able to import the active subspaces library in Python scripts
 
 This method was tested on Windows 7 Professional, and Ubuntu 14.04 LTS, and Mac OSX El Capitan with the [Enthought](https://www.enthought.com/) Python distribution (Python 2.7.11, NumPy 1.10.4, SciPy 0.17.0, and matplotlib 1.5.1).
 
+This method was also tested on Ubuntu 18.04 with Python 3.6 and Python 2.7 using the [Conda](https://anaconda.org) Python distribution.
+
 # Usage
 
 For detailed examples of usage and results, see the Jupyter notebooks contained in the `tutorials` directory, the [active subspaces website]
@@ -80,4 +82,4 @@ If you have questions or feedback, contact [*Paul Constantine*](http://inside.mi
 
 # Acknowledgments
 
-This material is based upon work supported by the U.S. Department of Energy Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program under Award Number DE-SC-0011077.
+This material is based upon work supported by the U.S. Department of Energy Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program under Award Number DE-SC-0011077.

active_subspaces/__init__.py

@@ -1,2 +1,2 @@
 ''' This is the init file.'''
-import utils, domains, gradients, integrals, optimizers, response_surfaces, subspaces
+from . import utils, domains, gradients, integrals, optimizers, response_surfaces, subspaces

active_subspaces/domains.py

@@ -1,10 +1,10 @@
 """Utilities for building the domains and maps for active variables."""
 import numpy as np
-from utils.misc import process_inputs, BoundedNormalizer
+from .utils.misc import process_inputs, BoundedNormalizer
 from scipy.spatial import ConvexHull
 from scipy.misc import comb
-from utils.qp_solver import QPSolver
-from subspaces import Subspaces
+from .utils.qp_solver import QPSolver
+from .subspaces import Subspaces
 
 class ActiveVariableDomain():
     """A base class for the domain of functions of active variables.
@@ -116,7 +116,7 @@ def compute_boundary(self):
             convhull = None
             constraints = None
         else:
-	    Y, X = zonotope_vertices(W1)
+            Y, X = zonotope_vertices(W1)
             convhull = ConvexHull(Y)
             A = convhull.equations[:,:n]
             b = convhull.equations[:,n]
@@ -468,7 +468,7 @@ def zonotope_vertices(W1, Nsamples=10000, maxcount=100000):
 
     numverts = X.shape[0]
     if totalverts > numverts:
-        print 'Warning: {} of {} vertices found.'.format(numverts, totalverts)
+        print('Warning: {} of {} vertices found.'.format(numverts, totalverts))
 
     Y = np.dot(X, W1)
     return Y.reshape((numverts, n)), X.reshape((numverts, m))

active_subspaces/gradients.py

@@ -1,7 +1,7 @@
 """Utilities for approximating gradients."""
 import numpy as np
-from utils.misc import process_inputs
-from utils.simrunners import SimulationRunner
+from .utils.misc import process_inputs
+from .utils.simrunners import SimulationRunner
 
 def local_linear_gradients(X, f, p=None, weights=None):
     """Estimate a collection of gradients from input/output pairs.
@@ -58,7 +58,7 @@ def local_linear_gradients(X, f, p=None, weights=None):
         ind = ind[D2 != 0]
         A = np.hstack((np.ones((p,1)), X[ind[:p],:])) * np.sqrt(weights[ii])
         b = f[ind[:p]] * np.sqrt(weights[ii])
-        u = np.linalg.lstsq(A, b)[0]
+        u = np.linalg.lstsq(A, b, rcond=None)[0]
         df[i,:] = u[1:].T
     return df
 

active_subspaces/integrals.py

@@ -1,13 +1,13 @@
 """Utilities for exploiting active subspaces when estimating integrals."""
 
 import numpy as np
-import utils.quadrature as gq
-from utils.misc import conditional_expectations
-from utils.designs import maximin_design
-from utils.simrunners import SimulationRunner
-from domains import UnboundedActiveVariableDomain, BoundedActiveVariableDomain, \
+from active_subspaces.utils import quadrature as gq
+from active_subspaces.utils.misc import conditional_expectations
+from active_subspaces.utils.designs import maximin_design
+from active_subspaces.utils.simrunners import SimulationRunner
+from active_subspaces.domains import UnboundedActiveVariableDomain, BoundedActiveVariableDomain, \
                     ActiveVariableMap
-from response_surfaces import ActiveSubspaceResponseSurface
+from active_subspaces.response_surfaces import ActiveSubspaceResponseSurface
 from scipy.spatial import Delaunay
 
 def integrate(fun, avmap, N, NMC=10):

active_subspaces/optimizers.py

@@ -1,11 +1,11 @@
 """Utilities for exploiting active subspaces when optimizing."""
 import numpy as np
-from domains import UnboundedActiveVariableDomain, BoundedActiveVariableDomain, \
+from .domains import UnboundedActiveVariableDomain, BoundedActiveVariableDomain, \
                 ActiveVariableMap
 import scipy.optimize as scopt
-from utils.response_surfaces import PolynomialApproximation
-from utils.qp_solver import QPSolver
-from utils.misc import process_inputs_outputs
+from .utils.response_surfaces import PolynomialApproximation
+from .utils.qp_solver import QPSolver
+from .utils.misc import process_inputs_outputs
 
 class MinVariableMap(ActiveVariableMap):
     """ActiveVariableMap for optimization

active_subspaces/response_surfaces.py

@@ -1,10 +1,10 @@
 """Utilities for exploiting active subspaces in response surfaces."""
 import numpy as np
-import utils.designs as dn
-from utils.simrunners import SimulationRunner
-from utils.misc import conditional_expectations
-from utils.response_surfaces import RadialBasisApproximation
-from domains import UnboundedActiveVariableDomain, BoundedActiveVariableDomain, \
+from active_subspaces.utils import designs as dn
+from active_subspaces.utils.simrunners import SimulationRunner
+from active_subspaces.utils.misc import conditional_expectations
+from active_subspaces.utils.response_surfaces import RadialBasisApproximation
+from active_subspaces.domains import UnboundedActiveVariableDomain, BoundedActiveVariableDomain, \
                     ActiveVariableMap
 
 class ActiveSubspaceResponseSurface():

active_subspaces/subspaces.py

@@ -1,9 +1,9 @@
 """Utilities for computing active and inactive subspaces."""
-from __future__ import division
+
 import numpy as np
-from utils.misc import process_inputs, process_inputs_outputs
-from utils.response_surfaces import PolynomialApproximation
-from gradients import local_linear_gradients
+from .utils.misc import process_inputs, process_inputs_outputs
+from .utils.response_surfaces import PolynomialApproximation
+from .gradients import local_linear_gradients
 
 SQRTEPS = np.sqrt(np.finfo(float).eps)
 
@@ -171,7 +171,9 @@ def partition(self, n):
 
         """
         if not isinstance(n, int):
-            raise TypeError('n should be an integer')
+            #raise TypeError('n should be an integer')
+            n = int(n)
+            print(Warning("n should be an integer. Performing conversion."))
 
         m = self.eigenvecs.shape[0]
         if n<1 or n>m:
@@ -236,7 +238,7 @@ def ols_subspace(X, f, weights):
     # solve weighted least squares
     A = np.hstack((np.ones((M, 1)), X)) * np.sqrt(weights)
     b = f * np.sqrt(weights)
-    u = np.linalg.lstsq(A, b)[0]
+    u = np.linalg.lstsq(A, b, rcond=None)[0]
     w = u[1:].reshape((m, 1))
 
     # compute rank-1 C
@@ -273,7 +275,7 @@ def qphd_subspace(X, f, weights):
     X, f, M, m = process_inputs_outputs(X, f)
 
     # check if the points are uniform or Gaussian, set 2nd moment
-    if np.amax(X) > 1.0 or np.amin < -1.0:
+    if np.amax(X) > 1.0 or np.amin(X) < -1.0:
         gamma = 1.0
     else:
         gamma = 1.0 / 3.0

active_subspaces/utils/__init__.py

@@ -1 +1 @@
-import designs, misc, plotters, qp_solver, quadrature, response_surfaces, simrunners
+from . import designs, misc, plotters, qp_solver, quadrature, response_surfaces, simrunners

active_subspaces/utils/designs.py

@@ -1,7 +1,7 @@
 """Utilities for constructing design-of-experiments."""
 import numpy as np
-import misc as mi
-from quadrature import gauss_hermite
+from . import misc as mi
+from .quadrature import gauss_hermite
 from scipy.spatial import ConvexHull, distance_matrix
 from scipy.optimize import minimize
 
@@ -120,7 +120,7 @@ def _maximin_design_obj(y, vert=None):
     the design and between points and vertices.
     """
     Ny, n = vert.shape
-    N = y.size / n
+    N = y.size // n
     Y = y.reshape((N, n))
 
     # get minimum distance among points
@@ -149,7 +149,7 @@ def _maximin_design_grad(y, vert=None):
     Ny, n = vert.shape
     v = vert.reshape((Ny*n, ))
 
-    N = y.size / n
+    N = y.size // n
     Y = y.reshape((N, n))
 
     # get minimum distance among points
@@ -165,19 +165,23 @@ def _maximin_design_grad(y, vert=None):
     g = np.zeros((N*n, ))
     if d0star < d1star:
         dstar, kstar = d0star, k0star
-        istar = kstar/N
+        istar = kstar // N
         jstar = np.mod(kstar, N)
 
         for k in range(n):
-            g[istar*n + k] = 2*(y[istar*n + k] - y[jstar*n + k])
-            g[jstar*n + k] = 2*(y[jstar*n + k] - y[istar*n + k])
+            i = int(np.floor(istar*n))
+            j = int(np.floor(jstar*n))
+            g[i + k] = 2*(y[i + k] - y[j + k])
+            g[j + k] = 2*(y[j + k] - y[i + k])
 
     else:
         dstar, kstar = d1star, k1star
-        istar = kstar/Ny
+        istar = kstar // Ny
         jstar = np.mod(kstar, Ny)
 
         for k in range(n):
-            g[istar*n + k] = 2*(y[istar*n + k] - v[jstar*n + k])
+            i = int(np.floor(istar*n))
+            j = int(np.floor(jstar*n))
+            g[i + k] = 2*(y[i + k] - v[j + k])
 
     return -g

active_subspaces/utils/misc.py

@@ -348,7 +348,7 @@ def atleast_2d(A, oned_as='row'):
         elif oned_as == 'col':
             A = A[:,None]
         else:
-            raise Exception , "oned_as must be 'row' or 'col' "
+            raise Exception("oned_as must be 'row' or 'col' ")
 
     return A