Switch to using BaseSolver (#193)

* switched to using BaseSolver * updated setup.py * updated init * added checkDefaultOptions * fixed default option type * updated default SNOPT options * renamed def_options * more overhaul of pySNOPT * removed very old numpy warning * added back on set/get options since those are mandatory * updated init API * made IPOPT also not use default options list * bump baseclasses version * renamed optimizer pages due to sphinx being weird * added autodoc mock import for baseclasses * updated capitalization in SNOPT * fixed bug in ALPSO * updated _on_setOption etc. * updated SNOPT doc * bump baseclasses version * version bump * addressed comments
mdolab · Jan 11, 2021 · 1c2bc25 · 1c2bc25
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diff --git a/doc/conf.py b/doc/conf.py
@@ -22,3 +22,6 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions.extend(["numpydoc", "ext.optimizertable"])
+
+# mock import for autodoc
+autodoc_mock_imports = ["baseclasses"]
diff --git a/doc/index.rst b/doc/index.rst
@@ -52,12 +52,12 @@ To get started, please see the :ref:`install` and the :ref:`quickstart`.
    :caption: Optimizers
    :hidden:
 
-   optimizers/pysnopt
-   optimizers/pyipopt
-   optimizers/pyslsqp
-   optimizers/pynlpqlp
-   optimizers/pynsga2
-   optimizers/pypsqp
-   optimizers/pyparopt
-   optimizers/pyconmin
-   optimizers/pyalpso
+   optimizers/SNOPT
+   optimizers/IPOPT
+   optimizers/SLSQP
+   optimizers/NLPQLP
+   optimizers/NSGA2
+   optimizers/PSQP
+   optimizers/ParOpt
+   optimizers/CONMIN
+   optimizers/ALPSO
diff --git a/doc/optimizers/pyalpso.rst → doc/optimizers/ALPSO.rst b/doc/optimizers/pyalpso.rst → doc/optimizers/ALPSO.rst
diff --git a/doc/optimizers/pyconmin.rst → doc/optimizers/CONMIN.rst b/doc/optimizers/pyconmin.rst → doc/optimizers/CONMIN.rst
diff --git a/doc/optimizers/pyipopt.rst → doc/optimizers/IPOPT.rst b/doc/optimizers/pyipopt.rst → doc/optimizers/IPOPT.rst
@@ -78,6 +78,10 @@ Here we explain a basic setup using MUMPS as the linear solver, together with ME
 
 Options
 -------
+Please refer to the `IPOPT website <https://coin-or.github.io/Ipopt/OPTIONS.html>`__ for complete listing of options.
+The following are the options which are set by default within pyOptSparse.
+All other options take the default value with IPOPT unless specified by the user.
+
 .. optimizertable:: pyoptsparse.pyIPOPT.pyIPOPT.IPOPT
    :type: options
 

diff --git a/doc/optimizers/pynlpqlp.rst → doc/optimizers/NLPQLP.rst b/doc/optimizers/pynlpqlp.rst → doc/optimizers/NLPQLP.rst
diff --git a/doc/optimizers/pynsga2.rst → doc/optimizers/NSGA2.rst b/doc/optimizers/pynsga2.rst → doc/optimizers/NSGA2.rst
diff --git a/doc/optimizers/pypsqp.rst → doc/optimizers/PSQP.rst b/doc/optimizers/pypsqp.rst → doc/optimizers/PSQP.rst
diff --git a/doc/optimizers/pyparopt.rst → doc/optimizers/ParOpt.rst b/doc/optimizers/pyparopt.rst → doc/optimizers/ParOpt.rst
diff --git a/doc/optimizers/pyslsqp.rst → doc/optimizers/SLSQP.rst b/doc/optimizers/pyslsqp.rst → doc/optimizers/SLSQP.rst
diff --git a/doc/optimizers/pysnopt.rst → doc/optimizers/SNOPT.rst b/doc/optimizers/pysnopt.rst → doc/optimizers/SNOPT.rst
@@ -23,9 +23,8 @@ The current version of SNOPT being tested is v7.7.5, although v7.7.1 is also exp
 
 Options
 -------
-The default values come directly from SNOPT, which is why they are not displayed in the table below.
-Please refer to the user manual for those options.
-Note that there are two key differences:
+Please refer to the SNOPT user manual for a complete listing of options and their default values.
+The following are the options set in Python for the wrapper.
 
 - The SNOPT option ``Proximal iterations limit`` has its default value changed to 10000, in order to fully solve the proximal point problem to optimality
 - The option ``Save major iteration variables`` is unique to the Python wrapper, and takes a list of values which can be saved at each iteration to the History file.
@@ -40,10 +39,10 @@ Note that there are two key differences:
   - ``slack``
   - ``lambda``
   - ``condZHZ``
+- The option ``Start`` corresponds to the value directly passed to the SNOPT kernel, and will be overwritten if another option, e.g. ``Cold start`` is supplied.
 
 .. optimizertable:: pyoptsparse.pySNOPT.pySNOPT.SNOPT
    :type: options
-   :nodefvalues:
 
 
 Informs

diff --git a/pyoptsparse/__init__.py b/pyoptsparse/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "2.3.0"
+__version__ = "2.4.0"
 
 from .pyOpt_history import History
 from .pyOpt_variable import Variable

diff --git a/pyoptsparse/pyALPSO/pyALPSO.py b/pyoptsparse/pyALPSO/pyALPSO.py
@@ -32,7 +32,7 @@ class ALPSO(Optimizer):
     - pll_type -> STR: ALPSO Parallel Implementation (None, SPM- Static, DPM- Dynamic, POA-Parallel Analysis), *Default* = None
     """
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, raiseError=True, options={}):
 
         from . import alpso
 
@@ -81,7 +81,7 @@ def __init__(self, *args, **kwargs):
                 str,
                 "ALPSO.out",
             ],  # We could probably remove fileout flag if filename or fileinstance is given
-            "seed": [float, 0],  # Random Number Seed (0 - Auto-Seed based on time clock)
+            "seed": [int, 0],  # Random Number Seed (0 - Auto-Seed based on time clock)
             "HoodSize": [int, 40],  # Number of Neighbours of Each Particle
             "HoodModel": [
                 str,
@@ -92,10 +92,10 @@ def __init__(self, *args, **kwargs):
                 1,
             ],  # Selfless Neighbourhood Model (0 - Include Particle i in NH i, 1 - Don't Include Particle i)
             "Scaling": [int, 1],  # Design Variables Scaling Flag (0 - no scaling, 1 - scaling between [-1, 1])
-            "parallelType": [str, ""],  # Type of parallelization ('' or 'EXT')
+            "parallelType": [str, ["", "EXT"]],  # Type of parallelization ('' or 'EXT')
         }
         self.informs = {}
-        Optimizer.__init__(self, "ALPSO", category, self.defOpts, self.informs, *args, **kwargs)
+        super().__init__("ALPSO", category, defaultOptions=self.defOpts, informs=self.informs, options=options)
 
     def __call__(self, optProb, storeHistory=None, **kwargs):
         """
@@ -172,9 +172,6 @@ def objconfunc(x):
             if opt("fileout") not in [0, 1, 2, 3]:
                 raise Error("Incorrect fileout Setting")
 
-            if opt("seed") == 0:
-                self.setOption("seed", time.time())
-
             # Run ALPSO
             t0 = time.time()
             # fmt: off
@@ -223,12 +220,6 @@ def _on_setOption(self, name, value):
                 except ImportError:
                     raise ImportError("pyALPSO: ALPSO EXT shared library failed to import.")
 
-            else:
-                raise ValueError("parallel_type must be either '' or 'EXT'.")
-
-    def _on_getOption(self, name, value):
-        pass
-
     def _communicateSolution(self, sol):
         if sol is not None:
             sol.userObjCalls = self.optProb.comm.allreduce(sol.userObjCalls)

diff --git a/pyoptsparse/pyCONMIN/pyCONMIN.py b/pyoptsparse/pyCONMIN/pyCONMIN.py
@@ -36,11 +36,11 @@ class CONMIN(Optimizer):
     CONMIN Optimizer Class - Inherited from Optimizer Abstract Class
     """
 
-    def __init__(self, raiseError=True, *args, **kwargs):
+    def __init__(self, raiseError=True, options={}):
         name = "CONMIN"
         category = "Local Optimizer"
         self.defOpts = {
-            "ITMAX": [int, 1e4],  # Maximum Number of Iterations
+            "ITMAX": [int, int(1e4)],  # Maximum Number of Iterations
             "DELFUN": [float, 1e-6],  # Objective Relative Tolerance
             "DABFUN": [float, 1e-6],  # Objective Absolute Tolerance
             "ITRM": [int, 5],
@@ -55,7 +55,7 @@ def __init__(self, raiseError=True, *args, **kwargs):
                 raise Error("There was an error importing the compiled conmin module")
 
         self.set_options = []
-        Optimizer.__init__(self, name, category, self.defOpts, self.informs, *args, **kwargs)
+        super().__init__(name, category, defaultOptions=self.defOpts, informs=self.informs, options=options)
 
         # CONMIN needs Jacobians in dense format
         self.jacType = "dense2d"
@@ -260,9 +260,3 @@ def cnmngrad(n1, n2, x, f, g, ct, df, a, ic, nac):
         sol = self._communicateSolution(sol)
 
         return sol
-
-    def _on_setOption(self, name, value):
-        pass
-
-    def _on_getOption(self, name, value):
-        pass