[options_parameters] ignore keys starting with "_" when processing op…

…tions and

add _pheno_integer_variables to options when we have fixed_variables

cma/options_parameters.py

@@ -570,6 +570,8 @@ def corrected_key(self, key):
         starting sequence to identify the valid key, ``else None``
 
         """
+        if key.startswith('_'):
+            return key
         matching_keys = []
         key = key.lower()  # this was somewhat slow, so it is speed optimized now
         if key in cma_allowed_options_keys:
@@ -615,6 +617,8 @@ def evalall(self, loc=None, defaults=None):
             popsize = self('popsize', defaults['popsize'], loc)
             for k in list(self.keys()):
                 k = self.corrected_key(k)
+                if k.startswith('_'):
+                    continue
                 self.eval(k, defaults[k],
                           {'N':loc['N'], 'popsize':popsize})
         self._lock_setting = True
@@ -693,9 +697,11 @@ def amend_integer_variables(self, dimension):
         # CAVEAT: this has not be thoroughly tested
         # transform integer indices to genotype
         popped = []  # just for the record
+        self['_pheno_integer_variables'] = list(self['integer_variables'])
         for i in reversed(range(dimension)):
             if i in self['fixed_variables']:
                 self['integer_variables'].remove(i)
+                self['_pheno_integer_variables'].remove(i)
                 if 1 < 3:  # just for catching errors
                     popped.append(i)
                     if i in self['integer_variables']:

cma/test.py

@@ -271,9 +271,12 @@ def various_doctests():
     ...     es = cma.CMAEvolutionStrategy(4 * [5], 10, opts).optimize(f)
     ...     assert 'ftarget' in es.stop() and es.result[3] < 1800
     >>> # mixing integer and fixed variables
-    >>> es = cma.CMA(5 * [1], 1, {'verbose':-9, 'integer_variables':[1,2,4],
-    ...                           'fixed_variables':{1:0}})
-    >>> assert es.opts['integer_variables'] == [1, 3]
+    >>> with warnings.catch_warnings():
+    ...     warnings.simplefilter("ignore", category=UserWarning)
+    ...     es = cma.CMA(5 * [1], 1, {'verbose':-9, 'integer_variables':[1,2,4],
+    ...                               'fixed_variables':{1:0}})
+    >>> assert es.opts['integer_variables'] == [1, 3], es.opts['integer_variables']
+    >>> assert es.opts['_pheno_integer_variables'] == [2, 4], es.opts['_pheno_integer_variables']
     >>> # TODO: do more testing here or in the class
 
     Parallel objective: