Merge branch 'master' into master-2

SMTorg · Dec 3, 2024 · 41d3fc1 · 41d3fc1
2 parents 3cdb649 + 8cbd4b3
commit 41d3fc1
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Showing 9 changed files with 2,686 additions and 2,528 deletions.
diff --git a/.github/workflows/tests_coverage.yml b/.github/workflows/tests_coverage.yml
@@ -35,6 +35,7 @@ jobs:
       run: |
         ruff --version
         ruff check .
+        ruff format --check
 
     - name: Test with pytest and coverage
       run: |

diff --git a/AUTHORS.md b/AUTHORS.md
@@ -28,6 +28,7 @@ SMT has been developed thanks to contributions from:
 * Maël Tremouille
 * Mauricio Castano Aguirre
 * Mostafa Meliani
+* Neal Kesterton
 * Nick Thompson
 * Nicolas Gonel
 * Nina Moëllo

diff --git a/smt/applications/__init__.py b/smt/applications/__init__.py
@@ -7,7 +7,6 @@
 from .vfm import VFM
 from .podi import PODI, SubspacesInterpolation
 from .cckrg import CoopCompKRG
-from .tests.test_mixed_integer import TestMixedInteger
 
 __all__ = [
     "VFM",
@@ -23,5 +22,4 @@
     "PODI",
     "SubspacesInterpolation",
     "CoopCompKRG",
-    "TestMixedInteger",
 ]
diff --git a/smt/applications/mfck.py b/smt/applications/mfck.py
diff --git a/smt/applications/tests/test_ego.py b/smt/applications/tests/test_ego.py
@@ -1119,7 +1119,7 @@ def f_obj(X):
             LHS, design_space, criterion="ese", random_state=random_state
         )
         Xt = sampling(n_doe)
-        self.assertAlmostEqual(np.sum(Xt), 28.568852027679586, delta=1e-4)
+        self.assertAlmostEqual(np.sum(Xt), 33.56885202767958, delta=1e-4)
         Xt = np.array(
             [
                 [0.37454012, 1.0],
@@ -1151,8 +1151,8 @@ def f_obj(X):
             n_start=25,
         )
         x_opt, y_opt, dnk, x_data, y_data = ego.optimize(fun=f_obj)
-        self.assertAlmostEqual(np.sum(y_data), 7.8471910288712, delta=1e-4)
-        self.assertAlmostEqual(np.sum(x_data), 34.81192549, delta=1e-4)
+        self.assertAlmostEqual(np.sum(y_data), 8.846225742003778, delta=1e-4)
+        self.assertAlmostEqual(np.sum(x_data), 41.81192549000013, delta=1e-4)
 
     def test_ego_gek(self):
         ego, fun = self.initialize_ego_gek()

diff --git a/smt/applications/tests/test_mfck.py b/smt/applications/tests/test_mfck.py
@@ -4,6 +4,7 @@
 
 @author: mcastano
 """
+
 import unittest
 
 import numpy as np
@@ -50,7 +51,7 @@ def test_mfck(self):
             x_lf = deepcopy(xt)
             np.random.seed(1)
 
-            sm = MFCK(hyper_opt='Cobyla')
+            sm = MFCK(hyper_opt="Cobyla")
             if sm.options.is_declared("xlimits"):
                 sm.options["xlimits"] = prob.xlimits
             sm.options["print_global"] = False
@@ -63,14 +64,12 @@ def test_mfck(self):
 
             m, c = sm._predict(xt)
 
-
-            num = np.linalg.norm( m[:,0] - yt[:,0])
-            den = np.linalg.norm(yt[:,0])
+            num = np.linalg.norm(m[:, 0] - yt[:, 0])
+            den = np.linalg.norm(yt[:, 0])
 
             t_error = num / den
 
-            self.assert_error(t_error, 0.0, 1e-6,1e-6)
-
+            self.assert_error(t_error, 0.0, 1e-6, 1e-6)
 
     @staticmethod
     def run_mfck_example():
@@ -117,7 +116,7 @@ def hf_function(x):
 
         x = np.linspace(0, 1, 101, endpoint=True).reshape(-1, 1)
 
-        m,c = sm.predict_all_levels(x)
+        m, c = sm.predict_all_levels(x)
 
         plt.figure()
 

diff --git a/smt/applications/tests/test_mfck_1fidelity.py b/smt/applications/tests/test_mfck_1fidelity.py
@@ -12,8 +12,9 @@
 
 from smt.problems import TensorProduct
 from smt.sampling_methods import LHS
-#from smt.utils.misc import compute_rms_error
-#from smt.utils.silence import Silence
+
+# from smt.utils.misc import compute_rms_error
+# from smt.utils.silence import Silence
 from smt.utils.sm_test_case import SMTestCase
 from smt.applications.mfck import MFCK
 
@@ -41,19 +42,19 @@ def test_mfk_1fidelity(self):
 
             sampling = LHS(xlimits=prob.xlimits, random_state=0)
 
-            sm1 = MFCK(hyper_opt='Cobyla')
+            sm1 = MFCK(hyper_opt="Cobyla")
 
-            sm1.set_training_values(xt, yt[:,0])
+            sm1.set_training_values(xt, yt[:, 0])
             sm1.train()
 
             mean, cov = sm1._predict(xt)
 
-            num = np.linalg.norm( mean[:,0] - yt[:,0])
-            den = np.linalg.norm(yt[:,0])
+            num = np.linalg.norm(mean[:, 0] - yt[:, 0])
+            den = np.linalg.norm(yt[:, 0])
 
             t_error = num / den
 
-            self.assert_error(t_error, 0.0, 1e-4,1e-4)
+            self.assert_error(t_error, 0.0, 1e-4, 1e-4)
 
     @staticmethod
     def run_mfck_example_1fidelity():
@@ -78,7 +79,7 @@ def hf_function(x):
         # Evaluate the HF function
         yt_e = hf_function(xt_e)
 
-        sm1 = MFCK(theta0=[1.0], hyper_opt='Cobyla')
+        sm1 = MFCK(theta0=[1.0], hyper_opt="Cobyla")
 
         # High-fidelity dataset without name
         sm1.set_training_values(xt_e, yt_e)
@@ -90,8 +91,8 @@ def hf_function(x):
 
         # Query the outputs
         y, cov = sm1._predict(x)
-        #_mse = sm.predict_variances(x)
-        #_derivs = sm.predict_derivatives(x, kx=0)
+        # _mse = sm.predict_variances(x)
+        # _derivs = sm.predict_derivatives(x, kx=0)
 
         plt.figure()
 

diff --git a/smt/sampling_methods/lhs.py b/smt/sampling_methods/lhs.py
@@ -211,7 +211,7 @@ def _maximinESE(
             hist_T.extend(inner_loop * [T])
             hist_proba.extend(inner_loop * [p_accpt])
 
-            if PhiP_best - PhiP_oldbest < tol:
+            if PhiP_oldbest - PhiP_best > tol:
                 # flag_imp = 1
                 if p_accpt >= 0.1 and p_imp < p_accpt:
                     T = 0.8 * T