Add unconstrained tests for all optimizers (#402)

* add tests for all optimizers

* add timeout

* fix stupid gradient bug

* adjust tolerances

* Update population size for NSGA2 test

* adjust options

* skip name check for NSGA2

* add missing metadata section

* skip ParOpt test

* need to cast to list

* also skip NSGA2 in uncon test

* added comment

---------

Co-authored-by: Shugo Kaneko <[email protected]>

Author: ewu63

.github/azure-pipelines.yaml

@@ -18,3 +18,4 @@ extends:
     IMAGE: auto
     ISORT: true
     COVERAGE: true
+    TIMEOUT_BUILD: 30

pyoptsparse/pyNSGA2/pyNSGA2.py

@@ -4,6 +4,7 @@
 """
 
 # Standard Python modules
+import datetime
 import os
 import time
 
@@ -179,6 +180,12 @@ def objconfunc(nreal, nobj, ncon, x, f, g):
             # fmt: on
             optTime = time.time() - t0
 
+            if self.storeHistory:
+                self.metadata["endTime"] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+                self.metadata["optTime"] = optTime
+                self.hist.writeData("metadata", self.metadata)
+                self.hist.close()
+
             # Broadcast a -1 to indcate NSGA2 has finished
             self.optProb.comm.bcast(-1, root=0)
 

pyoptsparse/testing/pyOpt_testing.py

@@ -274,7 +274,9 @@ def check_hist_file(self, tol):
         hist = History(self.histFileName, flag="r")
         # Metadata checks
         metadata = hist.getMetadata()
-        self.assertEqual(metadata["optimizer"], self.optName)
+        # NSGA2's official name is NSGA-II
+        if self.optName != "NSGA2":
+            self.assertEqual(metadata["optimizer"], self.optName)
         metadata_def_keys = [
             "optName",
             "optOptions",

tests/test_sphere.py

@@ -9,8 +9,11 @@
 
 # First party modules
 from pyoptsparse import Optimization
+from pyoptsparse.pyOpt_optimizer import Optimizers
 from pyoptsparse.testing import OptTest
 
+ALL_OPTIMIZERS = sorted({e.name for e in Optimizers} - {"ParOpt", "NSGA2"})
+
 
 class TestSphere(OptTest):
     ## Solve unconstrained Sphere problem.
@@ -38,7 +41,7 @@ class TestSphere(OptTest):
     xStar = {"xvars": np.zeros(N)}
 
     # Tolerances
-    tol = {"ALPSO": 1e-3}
+    tol = {k: 5e-2 if k in ["CONMIN", "ALPSO", "NSGA2"] else 1e-6 for k in ALL_OPTIMIZERS}
 
     optOptions = {
         "ALPSO": {  # sphere
@@ -48,7 +51,15 @@ class TestSphere(OptTest):
             "c2": 1.25,  # Social Parameter
             "stopCriteria": 0,  # 0: maxOuterIter, 1: convergence
             "seed": 1235,
-        }
+        },
+        "NSGA2": {  # not tested due to NSGA2 testing issues but option is kept here for reference
+            "PopSize": 32,
+            "maxGen": 100,
+            "seed": 123,
+        },
+        "SNOPT": {
+            "Major iterations limit": 10,
+        },
     }
 
     def objfunc(self, xdict):
@@ -63,8 +74,7 @@ def objfunc(self, xdict):
     def sens(self, xdict, _funcs):
         self.ng += 1
         x = xdict["xvars"]
-
-        funcsSens = {"obj": {"xvars": 2 * np.ones(len(x))}}
+        funcsSens = {"obj": {"xvars": 2 * x}}
 
         fail = False
         return funcsSens, fail
@@ -83,7 +93,7 @@ def setup_optProb(self):
         # Objective
         self.optProb.addObj("obj")
 
-    @parameterized.expand(["ALPSO"])
+    @parameterized.expand(ALL_OPTIMIZERS)
     def test_optimization(self, optName):
         self.optName = optName
         self.setup_optProb()