Make nsga2 tests optional + docs update (#351)

* update docs for NSGA2

* make nsga2 tests optional

* add an nsga2 test to rosenbrock

* fix tests

* remove nsga2 from rosenbrock

doc/install.rst

@@ -35,12 +35,7 @@ pyOptSparse has the following dependencies:
 
 Please make sure these are installed and available for use.
 In order to use NSGA2, SWIG (v1.3+) is also required, which can be installed via the package manager.
-If those optimizers are not needed, then you do not need to install SWIG.
-Simply comment out the corresponding lines in ``pyoptsparse/pyoptsparse/meson.build`` so that they are not compiled.
-The corresponding lines in ``pyoptsparse/pyoptsparse/__init__.py`` must be commented out as well.
-
 Python dependencies are automatically handled by ``pip``, so they do not need to be installed separately.
-The only exception is ``numpy``, which is required as part of the build process and therefore must be present before installing.
 
 .. note::
   * In Linux, the python header files (``python-dev``) are also required.

tests/test_nsga2_multi_objective.py

@@ -3,7 +3,6 @@
 # Standard Python modules
 import sys
 import unittest
-import warnings
 
 # External modules
 from numpy.testing import assert_allclose
@@ -19,6 +18,14 @@
 class TestNSGA2(OptTest):
     name = "quadratic"
     optName = "NSGA2"
+    histFileName = None
+
+    def setUp(self):
+        try:
+            # First party modules
+            from pyoptsparse.pyNSGA2 import nsga2  # noqa: F401
+        except ImportError:
+            raise unittest.SkipTest("Optimizer not available: NSGA2")
 
     def objfunc(self, xdict):
         x = xdict["x"]
@@ -43,6 +50,9 @@ def setup_optProb(self, n_obj):
         if n_obj == 2:
             self.optProb.addObj("obj2")
 
+    @unittest.skipIf(
+        sys.platform == "win32", "test_nsga2_multi_objective.py fails on windows with two objectives! Skipping for now."
+    )
     @parameterized.expand([(1,), (2,)])
     def test_opt(self, n_obj):
         if n_obj == 2:
@@ -51,11 +61,7 @@ def test_opt(self, n_obj):
 
         # 300 generations will find x=(0,0), 200 or less will find x=(1,1)
         optOptions = {"maxGen": 200}
-        if sys.platform == "win32":
-            warnings.warn(
-                "test_nsga2_multi_objective.py fails on windows with two objectives! Skipping for now.", stacklevel=2
-            )
-            return
+
         sol = self.optimize(optOptions=optOptions)
         tol = 1e-2
         if n_obj == 1:

tests/testing_utils.py

@@ -61,7 +61,7 @@ def get_dict_distance(d, d2):
 }
 
 # these are optimizers which are installed by default
-DEFAULT_OPTIMIZERS = {"SLSQP", "PSQP", "CONMIN", "ALPSO", "NSGA2"}
+DEFAULT_OPTIMIZERS = {"SLSQP", "PSQP", "CONMIN", "ALPSO"}
 
 # Define gradient-based optimizers
 GRAD_BASED_OPTIMIZERS = {"CONMIN", "IPOPT", "NLPQLP", "ParOpt", "PSQP", "SLSQP", "SNOPT"}