Fix failing tests

Author: jdjakem

docs/source/conf.py

@@ -28,7 +28,7 @@
 author = 'John D. Jakeman'
 
 # The full version, including alpha/beta/rc tags
-release = '1.0.2'
+release = '1.0.3'
 
 
 # -- General configuration ---------------------------------------------------

pyapprox/benchmarks/multifidelity_benchmarks.py

@@ -73,7 +73,8 @@ def _get_covariance_matrix(self) -> np.ndarray:
         for fi, mi in zip(self._flat_funs, means):
             jj = 0
             for fj, mj in zip(self._flat_funs, means):
-                cov[ii, jj] = ((fi(quad_xx)-mi)*(fj(quad_xx)-mj)).dot(quad_ww)
+                cov[ii, jj] = (
+                    (fi(quad_xx)-mi)*(fj(quad_xx)-mj)).dot(quad_ww).item()
                 jj += 1
             ii += 1
         return cov
@@ -151,7 +152,7 @@ def covariance_of_centered_values_kronker_product(self) -> np.ndarray:
                                 quad_cov = self._V_fun(
                                     jj1, kk1, ll1, jj2, kk2, ll2,
                                     means, flat_covs, xx).dot(ww)
-                                est_cov[cnt1, cnt2] = quad_cov
+                                est_cov[cnt1, cnt2] = quad_cov.item()
                                 cnt2 += 1
                     cnt1 += 1
         return np.array(est_cov)
@@ -177,7 +178,7 @@ def covariance_of_mean_and_variance_estimators(self) -> np.ndarray:
                     for ll in range(self.nqoi):
                         quad_cov = self._B_fun(
                             ii, jj, kk, ll, means, flat_covs, xx).dot(ww)
-                        est_cov[ii, cnt] = quad_cov
+                        est_cov[ii, cnt] = quad_cov.item()
                         cnt += 1
         return np.array(est_cov)
 

pyapprox/interface/tests/test_wrappers.py

@@ -100,7 +100,8 @@ def test_work_tracking_model(self):
             timer_model, max_eval_concurrency, base_model=base_model,
             assert_omp=False)
         # pool_model = timer_model
-        model = WorkTrackingModel(pool_model, base_model)
+        model = WorkTrackingModel(
+            pool_model, base_model, enforce_timer_model=False)
 
         samples = np.random.normal(0, 1, (nvars, nsamples))
         values = model(samples)
@@ -109,9 +110,6 @@ def test_work_tracking_model(self):
         assert np.allclose(values, exact_values)
         values, grads = model(samples, return_grad=True)
         assert np.allclose(values, exact_values)
-        print(grads)
-        print(np.vstack(grads))
-        print(exact_grads)
         assert np.allclose(np.vstack(grads), exact_grads)
 
     def test_pool_model(self):

pyapprox/multifidelity/acv.py

@@ -192,6 +192,7 @@ def __init__(self, stat, costs, opt_criteria=None):
         self._npartitions = 1
 
     def _check_cov(self, cov, costs):
+        costs = np.atleast_1d(costs)
         if costs.ndim != 1:
             raise ValueError("costs is not a 1D iterable")
         nmodels = len(costs)

pyapprox/multifidelity/tests/test_multioutput_monte_carlo.py

@@ -495,11 +495,10 @@ def test_best_model_subset_estimator(self):
         target_cost = 10
         est._save_candidate_estimators = True
         np.set_printoptions(linewidth=1000)
-        print(cov, "C###")
         est.allocate_samples(target_cost, {"verbosity": 1, "nprocs": 1})
 
         criteria = np.array(
-            [e._optimized_criteria for e in est._candidate_estimators])
+            [e[0]._optimized_criteria for e in est._candidate_estimators])
         assert np.allclose(criteria.min(), est._optimized_criteria)
 
         ntrials, max_eval_concurrency = int(1e3), 1

pyapprox/surrogates/interp/tests/test_sparse_grid.py

@@ -1576,7 +1576,8 @@ def test_online_cost_function(self):
         # function of polynomial model that just fixes cost for each level of
         # the multilevel model
         timer_model = base_model
-        model = WorkTrackingModel(timer_model, base_model, 1)
+        model = WorkTrackingModel(
+            timer_model, base_model, 1, enforce_timer_model=False)
 
         ranges = [2*(-1)**(ii+1) for ii in range(2*num_vars)]
         canonical_ranges = [(-1)**(ii+1) for ii in range(2*num_vars)]

setup.py

@@ -33,7 +33,7 @@ def no_cythonize(extensions, **_ignore):
 
 setuptools.setup(
     name="pyapprox",
-    version="1.0.2",
+    version="1.0.3",
     author="John D. Jakeman",
     author_email="[email protected]",
     description="High-dimensional function approximation and estimation",
@@ -97,7 +97,7 @@ def no_cythonize(extensions, **_ignore):
 
 # To test pyapprox on test.pypi use the following.
 # Note the use of extra-index-url
-# python -m pip install --extra-index-url=https://test.pypi.org/simple/ pyapprox==1.0.2
+# python -m pip install --extra-index-url=https://test.pypi.org/simple/ pyapprox==1.0.3
 
 # To build wheel locally use
 # python -m build